From 7e452ffc3ec5fcd35574f3f3858f4fc31b2b1408 Mon Sep 17 00:00:00 2001 From: kumar-a Date: Tue, 9 Apr 2024 16:50:44 +0530 Subject: [PATCH] updated output --- docs/about.html | 5 +- docs/index.html | 5 +- docs/listings.json | 19 +- docs/posts/index.html | 5 +- docs/posts/mid-domain-effect/index.html | 5 +- docs/posts/post-with-code/index.html | 5 +- docs/posts/sdm-himalaya/index.html | 5 +- docs/posts/siwalik-alien-flora/index.html | 5 +- docs/posts/sukhna-wls/index.html | 5 +- docs/posts/welcome/index.html | 5 +- docs/publications.html | 38 +-- docs/publications/2020-kumar-jsr/index.html | 5 +- docs/publications/2021-kumar-tdf/index.html | 10 +- docs/publications/2022-kumar-troe/index.html | 5 +- docs/publications/2022-singh-ee/index.html | 5 +- docs/publications/2022-singh-ldd/index.html | 5 +- docs/publications/2023-kumar/index.html | 5 +- docs/publications/2023-patil/index.html | 5 +- docs/search.json | 288 +++++++------------ 19 files changed, 168 insertions(+), 262 deletions(-) diff --git a/docs/about.html b/docs/about.html index cf50f08..f45c36e 100644 --- a/docs/about.html +++ b/docs/about.html @@ -2,7 +2,7 @@ - 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"search-submit-button-title": "Submit" + "search-submit-button-title": "Submit", + "search-label": "Search" } } diff --git a/docs/posts/post-with-code/index.html b/docs/posts/post-with-code/index.html index 0123e1d..ca73fd3 100644 --- a/docs/posts/post-with-code/index.html +++ b/docs/posts/post-with-code/index.html @@ -2,7 +2,7 @@ - + @@ -91,7 +91,8 @@ "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", "search-detached-cancel-button-title": "Cancel", - "search-submit-button-title": "Submit" + "search-submit-button-title": "Submit", + "search-label": "Search" } } diff --git a/docs/posts/sdm-himalaya/index.html b/docs/posts/sdm-himalaya/index.html index e80a9ec..a01e9c2 100644 --- a/docs/posts/sdm-himalaya/index.html +++ b/docs/posts/sdm-himalaya/index.html @@ -2,7 +2,7 @@ - + @@ -75,7 +75,8 @@ "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", "search-detached-cancel-button-title": "Cancel", - "search-submit-button-title": "Submit" + "search-submit-button-title": "Submit", + "search-label": "Search" } } diff --git a/docs/posts/siwalik-alien-flora/index.html b/docs/posts/siwalik-alien-flora/index.html index 9c25c3b..06ad0de 100644 --- a/docs/posts/siwalik-alien-flora/index.html +++ b/docs/posts/siwalik-alien-flora/index.html @@ -2,7 +2,7 @@ - + @@ -56,7 +56,8 @@ "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", "search-detached-cancel-button-title": "Cancel", - "search-submit-button-title": "Submit" + "search-submit-button-title": "Submit", + "search-label": "Search" } } diff --git a/docs/posts/sukhna-wls/index.html b/docs/posts/sukhna-wls/index.html index d7fd561..580caff 100644 --- a/docs/posts/sukhna-wls/index.html +++ b/docs/posts/sukhna-wls/index.html @@ -2,7 +2,7 @@ - + @@ -76,7 +76,8 @@ "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", "search-detached-cancel-button-title": "Cancel", - 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Publications

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Categories
All (4)
Coal mine (2)
Ecological Engineering (1)
Journal Article (3)
Journal of Scientific Research (1)
Land Degradation & Development (1)
Restoration (2)
Siwalik (1)
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-Mycorrhizal fungi accelerates litter decomposition rates in forest ecosystems -

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-The role of mycorrhizal fungi in modulating litter decomposition rates is complex, with potential outcomes of both facilitation and competition. However, the magnitude and… -
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-Meenu Patil, Abhishek Kumar, Pardeep Kumar, Anand Narain Singh -
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diff --git a/docs/publications/2020-kumar-jsr/index.html b/docs/publications/2020-kumar-jsr/index.html index 58cc8a2..50458d3 100644 --- a/docs/publications/2020-kumar-jsr/index.html +++ b/docs/publications/2020-kumar-jsr/index.html @@ -2,7 +2,7 @@ - + @@ -79,7 +79,8 @@ "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", "search-detached-cancel-button-title": "Cancel", - "search-submit-button-title": "Submit" + "search-submit-button-title": "Submit", + "search-label": "Search" } } diff --git a/docs/publications/2021-kumar-tdf/index.html b/docs/publications/2021-kumar-tdf/index.html index f210cc9..d412bfe 100644 --- a/docs/publications/2021-kumar-tdf/index.html +++ b/docs/publications/2021-kumar-tdf/index.html @@ -121,6 +121,7 @@ + @@ -363,7 +364,7 @@

Effects of

The litter decomposition rates in terrestrial ecosystems are highly inclined with nutrient availability. For example, phosphorus is an essential nutrient that can potentially regulate the litter decomposition rates since the growth and activity of many organisms are limited by phosphorus availability (?@fig-paddition). Thus, soil phosphorus concentration can substantially impact the decay patterns of litter in tropical ecosystems (Parsons2014?). Plenty of available evidence suggests that external addition of phosphorus tends to accelerate the litter decomposition rates (Barantal et al. 2012; Camenzind2018?; Mori2018?), though may also either retard or did not affect the litter decomposition rates. Moreover, the results of phosphorus fertilization experiments have indicated that litter decomposition rates in tropical forests are constrained by the phosphorus limitation (Vitousek2010?).

The litter decomposition rates are accelerated when the litter is supplied with external phosphorus sources (Barantal et al. 2012). This enhanced decomposition rate may be due to the increased activity of decomposer organisms because their growth and activity are also limited by phosphorus availability (Camenzind2018?; Mori2018?). The phosphorus limitation of microbial growth has been evaluated by experiments where the external supply of phosphorus significantly increased microbial growth and their activity (Fanin2015?; Camenzind2018?).

Figure 8. Graphical representation of Phosphorus Addition effects on Phosphorus Cycling in the Tropical Forests. The abbreviations are P = Phosphorus, Po = Organic Phosphorus, Pi = Inorganic Phosphorus.

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Therefore, we can assume that the external addition of phosphorus might be responsible for increased litter decomposition rate by enhancing soil respiration (Feng2019?), microbial biomass (Liu2012?; Liu2013?), microbial abundance, and microbial activity (Camenzind2018?). This enhanced decomposition rate can be attributed to the increased activity of phosphorus releasing enzymes such as phosphatases (Yokoyama2017?). However, another study reported that the external addition of phosphorus decreased the activity of phosphatases (Dietrich2016?). These contrasting observations suggest that effects on the activity of phosphorus releasing enzymes are not the sole mechanism. Also, this mechanism is challenged by reports where litter decomposition rates are either slowed down (H. Chen et al. 2013) or not affected after the addition of phosphorus (Powers2011?). The slower decomposition rates after phosphorus addition are suggested to be related to augmentation of phosphorus immobilization by microbial organisms because they accessed sufficient phosphorus from external supply only (H. Chen et al. 2013).

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Therefore, we can assume that the external addition of phosphorus might be responsible for increased litter decomposition rate by enhancing soil respiration (Feng2019?), microbial biomass (Liu2012?; Liu2013?), microbial abundance, and microbial activity (Camenzind2018?). This enhanced decomposition rate can be attributed to the increased activity of phosphorus releasing enzymes such as phosphatases (Yokoyama2017?). However, another study reported that the external addition of phosphorus decreased the activity of phosphatases (Dietrich, Spoeri, and Oelmann 2016). These contrasting observations suggest that effects on the activity of phosphorus releasing enzymes are not the sole mechanism. Also, this mechanism is challenged by reports where litter decomposition rates are either slowed down (H. Chen et al. 2013) or not affected after the addition of phosphorus (Powers2011?). The slower decomposition rates after phosphorus addition are suggested to be related to augmentation of phosphorus immobilization by microbial organisms because they accessed sufficient phosphorus from external supply only (H. Chen et al. 2013).

Phosphorus Availability in Tropical Forests

Soils are major and perhaps the only source of nutrients for plants in tropical ecosystems (Janssens2015?). In soils, phosphorus occurs in several forms, and all forms are not available to plants for uptake and metabolism. The organic phosphorus comprises phosphate monoesters, phosphate diesters, organic polyphosphates, phosphonates, and phytates (Turner2011?), whereas the inorganic form mainly comprises orthophosphates. Phosphorus is poorly mobile in the soil; therefore, the soil solution contains a limited amount of inorganic phosphorus, which is available for uptake to the plants in the form of phosphate-pi (PO4-Pi) (Vitousek2010?).

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Response

Nutrient cycling in terrestrial ecosystems is highly controlled by climatic factors, especially temperature and precipitation (Afreen, Singh, and Singh 2019). The available data suggests that litter decomposition rate is positively associated with temperature and precipitation ((Figure-10?)). Since litter decomposition rate is associated with mean annual temperature and precipitation, we can expect potential shifts in nutrient cycling patterns due to changing environmental conditions.

Figure 11. Impacts of Climate Change and Disturbance on Soil Phosphorus Availability in Tropical Forest Ecosystems. Green Arrows represent the Positive Effects, and Red Arrows represent the Negative Effects. LD refers to Litter decomposition.

The ongoing climate change is projected to alter phosphorus cycling in tropical forest ecosystems (Friedlingstein2010?). These effects may cascade through changes in litter quality, nutrient availability, and litter decomposition rates in tropical forests (Gavito2018?; Afreen, Singh, and Singh 2019). Specifically, litter decomposition rates are expected to face major alterations because they are highly influenced by abiotic factors such as temperature and precipitation (Waring2012?; Zhang2015?), as well as biotic factors like soil fauna (Hattenschwiler2010?).

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Among various abiotic factors, the effects of warming and moisture availability have been exclusively investigated, and most of the studies have revealed consistent direct or indirect effects of warming on litter decomposition (Patil2020?). The climatic control of litter decomposition suggests that decomposition rates are expected to be higher with an increase in average surface temperatures (Pandey2014?). This theoretical prediction is supported by several studies conducted in tropical forest ecosystems, as these ecosystems are very sensitive to warming (Bai et al. 2013). The effects of warming on litter decomposition might be modulated by changes in leaf nutrients (Wu2019?). For example, it has been shown that warming leads to the production of leaves and litter with higher phosphorus concentrations (Campo and Merino 2019; Lie2019?), which eventually decompose at faster rates. Consequently, warming-induced higher decomposition rates lead to higher nutrient release in tropical soils (Liu2017?). This is supported by a few studies where warming significantly increased the available soil phosphorus (Lie2019?) and microbial phosphorus (Li2014?) in tropical forests ((Figure-11?)). The increased phosphorus availability may lead to reduced production and activities of enzymes associated with phosphorus mineralization in tropical forests. A recent study found that the activity of the acid phospho-mono-esterase enzyme, an enzyme associated with phosphorus mineralization, was reduced due to warming (Lie2019?). However, the activity of this enzyme increased during the wet season (Lie2019?) due to increased moisture availability (Dietrich2016?), suggesting that soil moisture availability is more important for the activity of this enzyme. Thus, warming can alter nutrient cycling in tropical forests by modifying litter decomposition, nutrient release, litter quality, and soil moisture availability. Moreover, the warming induced-effects on the nutrient release have been reported greater compared to litter quality and soil moisture (Liu2017?).

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The effects of precipitation are linked to soil moisture availability because higher precipitation often results in high soil moisture availability. The soil moisture availability is a dominant controller of litter decomposition rates, and it is positively related to leaf litter weight loss (Pandey2014?). Since moisture availability varies during different seasons, the decomposition rates also show seasonal variations. Many tropical forests experience a rainy season of varying lengths, and it was found that litter decomposition rates are higher during the rainy season compared to the dry season (Pandey2014?). Further, it was shown that a reduction in precipitation also leads to suppression in the litter decomposition rates (Zhou2018?). These effects on litter decomposition are also driven by changes in leaf nutrient concentration, enzymes, and microbial activities in tropical forest ecosystems. The leaf and litter phosphorus concentrations are positively related to precipitation and soil moisture availability (Mani2019?). For example, the leaf phosphorus concentration increases in the wet season compared to the relatively dry season in tropical plants (Renteria2011?). Further, litter decomposition rates were found to be higher in the warm-rainy season compared to the dry-winter seasons in tropical forests (Pandey2014?). This faster rate of decomposition in the warm-rainy season can be attributed to higher microbial growth and activities in the rainy-warm season compared to a dry-winter season (Liu2012?). Higher activities of micro-organisms may have increased the production and activity of decomposition- associated enzymes because it was observed that enzymatic activities tend to increase in the wet season (Lie2019?). In particular, the activity of acid phosphatase enzymes increased in the wet season in tropical forests (Waring2014?). Similarly, the activities of the phospho-mono-esterase have also been observed to be positively correlated to soil moisture content (Dietrich2016?). Higher soil enzyme activities in wet seasons may have resulted in greater phosphorus availability in tropical soils because wet soils usually have higher phosphorus availability than dry soils (Lie2019?). Further, soil moisture is an important driver to regulate soil phosphorus, which has also been found to be positively associated with concentrations of available phosphorus in the tropical soils of Puerto Rico (Wood2016?).

+

Among various abiotic factors, the effects of warming and moisture availability have been exclusively investigated, and most of the studies have revealed consistent direct or indirect effects of warming on litter decomposition (Patil2020?). The climatic control of litter decomposition suggests that decomposition rates are expected to be higher with an increase in average surface temperatures (Pandey2014?). This theoretical prediction is supported by several studies conducted in tropical forest ecosystems, as these ecosystems are very sensitive to warming (Bai et al. 2013). The effects of warming on litter decomposition might be modulated by changes in leaf nutrients (Wu2019?). For example, it has been shown that warming leads to the production of leaves and litter with higher phosphorus concentrations (Campo and Merino 2019; Lie2019?), which eventually decompose at faster rates. Consequently, warming-induced higher decomposition rates lead to higher nutrient release in tropical soils (Liu2017?). This is supported by a few studies where warming significantly increased the available soil phosphorus (Lie2019?) and microbial phosphorus (Li2014?) in tropical forests ((Figure-11?)). The increased phosphorus availability may lead to reduced production and activities of enzymes associated with phosphorus mineralization in tropical forests. A recent study found that the activity of the acid phospho-mono-esterase enzyme, an enzyme associated with phosphorus mineralization, was reduced due to warming (Lie2019?). However, the activity of this enzyme increased during the wet season (Lie2019?) due to increased moisture availability (Dietrich, Spoeri, and Oelmann 2016), suggesting that soil moisture availability is more important for the activity of this enzyme. Thus, warming can alter nutrient cycling in tropical forests by modifying litter decomposition, nutrient release, litter quality, and soil moisture availability. Moreover, the warming induced-effects on the nutrient release have been reported greater compared to litter quality and soil moisture (Liu2017?).

+

The effects of precipitation are linked to soil moisture availability because higher precipitation often results in high soil moisture availability. The soil moisture availability is a dominant controller of litter decomposition rates, and it is positively related to leaf litter weight loss (Pandey2014?). Since moisture availability varies during different seasons, the decomposition rates also show seasonal variations. Many tropical forests experience a rainy season of varying lengths, and it was found that litter decomposition rates are higher during the rainy season compared to the dry season (Pandey2014?). Further, it was shown that a reduction in precipitation also leads to suppression in the litter decomposition rates (Zhou2018?). These effects on litter decomposition are also driven by changes in leaf nutrient concentration, enzymes, and microbial activities in tropical forest ecosystems. The leaf and litter phosphorus concentrations are positively related to precipitation and soil moisture availability (Mani2019?). For example, the leaf phosphorus concentration increases in the wet season compared to the relatively dry season in tropical plants (Renteria2011?). Further, litter decomposition rates were found to be higher in the warm-rainy season compared to the dry-winter seasons in tropical forests (Pandey2014?). This faster rate of decomposition in the warm-rainy season can be attributed to higher microbial growth and activities in the rainy-warm season compared to a dry-winter season (Liu2012?). Higher activities of micro-organisms may have increased the production and activity of decomposition- associated enzymes because it was observed that enzymatic activities tend to increase in the wet season (Lie2019?). In particular, the activity of acid phosphatase enzymes increased in the wet season in tropical forests (Waring2014?). Similarly, the activities of the phospho-mono-esterase have also been observed to be positively correlated to soil moisture content (Dietrich, Spoeri, and Oelmann 2016). Higher soil enzyme activities in wet seasons may have resulted in greater phosphorus availability in tropical soils because wet soils usually have higher phosphorus availability than dry soils (Lie2019?). Further, soil moisture is an important driver to regulate soil phosphorus, which has also been found to be positively associated with concentrations of available phosphorus in the tropical soils of Puerto Rico (Wood2016?).

Apart from climatic factors, the disturbance is also an important factor that affects litter decomposition and nutrient availability in tropical forests. Logging, wildfires, and hurricanes are probably major disturbing factors across the global tropical forest ecosystems. In general, disturbance tends to slow down the litter decomposition, possibly due to alterations in biotic components of ecosystems. For example, litter decay rates are found to be slower in logged forest fragments than in the unlogged forest fragments (Ewers2015?; Yeong2016?). However, exclusion of invertebrates (termites, ants, beetles, and earthworms) from leaf litter has not affected the litter decomposition rates in logged forests suggesting that invertebrates had less contribution to litter decomposition in the logged forest as compared to the unlogged forest (Ewers2015?). Wildfires are another disturbing factor that acts to slow down the litter decomposition rates in tropical forests. Wildfires have been suggested to slow down the decomposition rates by imposing environments of high temperature and low humidity (Brando2012?), which retards decomposition. Further, wildfires may have caused high mortality of soil decomposers for a short time. The effects of hurricanes are variable on litter decomposition and nutrient cycling in tropical forests (Gavito2018?). For instance, the decomposition rates were found to decrease by hurricane Jova but increased by hurricane Patricia (Gavito2018?).

However, hurricanes are supposed to affect nutrient cycling through modifications in leaf nutrients, litter quality, and litter decomposition (Gavito2018?; Jaramillo2018?). For example, hurricane Jova has been suggested to induce the increment in phosphorus concentration in leaf litterfall (Jaramillo2018?). Thus, phosphorus availability in tropical soils is regulated by climatic and disturbance factors ((Figure?) 11).

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Conclusion

Dalling, James W., Katherine Heineman, Omar R. Lopez, S. Joseph Wright, and Benjamin L. Turner. 2016. “Nutrient Availability in Tropical Rain Forests: The Paradigm of Phosphorus Limitation.” In Tropical Tree Physiology, edited by Guillermo Goldstein and Louis S. Santiago, 261–73. Cham: Springer. https://doi.org/10.1007/978-3-319-27422-5_12.
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+Dietrich, Karla, Elena Spoeri, and Yvonne Oelmann. 2016. “Nutrient Addition Modifies Phosphatase Activities Along an Altitudinal Gradient in a Tropical Montane Forest in Southern Ecuador.” Frontiers in Earth Science 4 (February): 12. https://doi.org/10.3389/feart.2016.00012. +
Krishna, M. P., and Mahesh Mohan. 2017. “Litter Decomposition in Forest Ecosystems: A Review.” Energy, Ecology and Environment 2 (4): 236–49. https://doi.org/10.1007/s40974-017-0064-9.
diff --git a/docs/publications/2022-kumar-troe/index.html b/docs/publications/2022-kumar-troe/index.html index 6f39463..d5f3e28 100644 --- a/docs/publications/2022-kumar-troe/index.html +++ b/docs/publications/2022-kumar-troe/index.html @@ -2,7 +2,7 @@ - + @@ -81,7 +81,8 @@ "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", "search-detached-cancel-button-title": "Cancel", - "search-submit-button-title": "Submit" + "search-submit-button-title": "Submit", + "search-label": "Search" } } diff --git a/docs/publications/2022-singh-ee/index.html b/docs/publications/2022-singh-ee/index.html index 7a60a4e..d635850 100644 --- a/docs/publications/2022-singh-ee/index.html +++ b/docs/publications/2022-singh-ee/index.html @@ -2,7 +2,7 @@ - + @@ -78,7 +78,8 @@ "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", "search-detached-cancel-button-title": "Cancel", - "search-submit-button-title": "Submit" + "search-submit-button-title": "Submit", + "search-label": "Search" } } diff --git a/docs/publications/2022-singh-ldd/index.html b/docs/publications/2022-singh-ldd/index.html index b305ef7..271f1c1 100644 --- a/docs/publications/2022-singh-ldd/index.html +++ b/docs/publications/2022-singh-ldd/index.html @@ -2,7 +2,7 @@ - + @@ -78,7 +78,8 @@ "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", "search-detached-cancel-button-title": "Cancel", - "search-submit-button-title": "Submit" + "search-submit-button-title": "Submit", + "search-label": "Search" } } diff --git a/docs/publications/2023-kumar/index.html b/docs/publications/2023-kumar/index.html index bec7caa..28bf1a7 100644 --- a/docs/publications/2023-kumar/index.html +++ b/docs/publications/2023-kumar/index.html @@ -2,7 +2,7 @@ - + @@ -81,7 +81,8 @@ "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", "search-detached-cancel-button-title": "Cancel", - "search-submit-button-title": "Submit" + "search-submit-button-title": "Submit", + "search-label": "Search" } } diff --git a/docs/publications/2023-patil/index.html b/docs/publications/2023-patil/index.html index 67cc6b9..6f0cd3c 100644 --- a/docs/publications/2023-patil/index.html +++ b/docs/publications/2023-patil/index.html @@ -2,7 +2,7 @@ - + @@ -79,7 +79,8 @@ "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", "search-detached-cancel-button-title": "Cancel", - "search-submit-button-title": "Submit" + "search-submit-button-title": "Submit", + "search-label": "Search" } } diff --git a/docs/search.json b/docs/search.json index 85dc486..23840d6 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1,4 +1,102 @@ [ + { + "objectID": "publications.html", + "href": "publications.html", + "title": "Publications", + "section": "", + "text": "Order By\n Default\n \n Title\n \n \n Date - Oldest\n \n \n Date - Newest\n \n \n Author\n \n \n \n \n \n \n \n\n\n\n\n \n\n\n\n\nPlant ecology in Indian Siwalik range: a systematic map and its bibliometric analysis\n\n\n\n\n\n\n\n\n\n\n\n\nFeb 14, 2022\n\n\nAbhishek Kumar, Meenu Patil, Pardeep Kumar, Manoj Kumar, Anand Narain Singh\n\n\n\n\n\n\n \n\n\n\n\nComparative soil restoration potential of exotic and native woody plantations on coal mine spoil in a dry tropical environment of India: A case study\n\n\n\n\n\n\n\nCoal mine\n\n\nRestoration\n\n\nJournal Article\n\n\nLand Degradation & Development\n\n\n\n\nWe compared the soil restoration potential of exotic and native plant species on coal mine. Our results suggested that native species are more beneficial for soil restoration than the exotic species.\n\n\n\n\n\n\nFeb 11, 2022\n\n\nAnand Narain Singh, Abhishek Kumar\n\n\n\n\n\n\n \n\n\n\n\nEcological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region\n\n\n\n\n\n\n\nCoal mine\n\n\nRestoration\n\n\nJournal Article\n\n\nEcological Engineering\n\n\n\n\nIn this article we analysed the growth and biomass production of exotic and native woody species on Indian coal mine spoils. We observed higher survival of native species on coal mine spoil, though exotic species exhibited faster growth rates than the native species on coal mine spoil. However, biomass production was higher for native species on coal mine overburden. Overall, we showed that native species performed better than exotic species in rehabilitation of coal mine.\n\n\n\n\n\n\nApr 11, 2021\n\n\nAnand Narain Singh, Abhishek Kumar\n\n\n\n\n\n\n \n\n\n\n\nShivalik, Siwalik, Shiwalik or Sivalik: Which one is an appropriate term for the foothills of Himalayas?\n\n\n\n\n\n\n\nSiwalik\n\n\nJournal Article\n\n\nJournal of Scientific Research\n\n\n\n\nThe present study has applied bibliometric analysis to resolve the inconsistency about the usage of these terms. Here, we have shown that the term ‘Siwalik’ was most dominant in the available literature.\n\n\n\n\n\n\nJan 9, 2020\n\n\nAbhishek Kumar, Meenu Patil, Anand Narain Singh\n\n\n\n\n\n\nNo matching items" + }, + { + "objectID": "publications/2022-singh-ee/index.html", + "href": "publications/2022-singh-ee/index.html", + "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", + "section": "", + "text": "Note\n\n\n\nThis article is originally written by authors and may differ from published version. Please refer to https://doi.org/10.1016/j.ecoleng.2021.106470" + }, + { + "objectID": "publications/2022-singh-ee/index.html#abstract", + "href": "publications/2022-singh-ee/index.html#abstract", + "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", + "section": "Abstract", + "text": "Abstract\nCoal extraction by opencast mining involves the dumping of overburden or mine spoil as large heaps. These large heaps of overburdened materials can act as a serious threat to ecological integrity and, therefore, overall societal well-being. Plantations are often employed to establish revegetation and management of mine spoil, thus mitigating the effects of mining on the environment. However, the performance of plant species can be highly variable due to environmental and species-specific effects. Therefore, the present paper’s primary objective is to compare exotic (Acacia auriculiformis, Cassia siamea, Casuarina equisetifolia and Grevillea pteridifolia) and native (Albizia lebbeck, Albizia procera, Dendrocalamus strictus and Tectona grandis) species’ performance on the coal mine spoils. Previous studies on the Singrauli coalfields allowed us to compare the growth performance, standing biomass, and net primary production (NPP) of four exotic and four native species plantations. Our results showed that native species have significantly higher survival, stem diameter, biomass, and NPP than exotic woody plantations. Thus, exotic species might not be useful in mine spoil rehabilitation than the native species. Overall, this study suggests that native species are useful for mine spoil rehabilitation despite the faster growth of exotic species.\nKeywords: Ecological restoration; Soil redevelopment; Exotic species; Native species; Coal mine spoil" + }, + { + "objectID": "publications/2022-singh-ee/index.html#introduction", + "href": "publications/2022-singh-ee/index.html#introduction", + "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", + "section": "Introduction", + "text": "Introduction\nIndia is one of the significant coal producers worldwide; however, the demand for coal for electricity generation and industrial production is so high that it needs to import substantial coal quantities (IEA 2019). Most of the coal in India is extracted by surface mining, which involves removing the earth’s surface in the form of sheets resulting in a large amount of waste material, usually referred to as overburden or mine spoil (A. N. Singh and Singh 2006). This overburden is piled up to form new landforms looking like large stacks of mine spoil until refilling. These piles of mine spoils are characterised by a high concentration of metals and toxic chemical compounds (Novianti et al. 2018), which cascades into the ecosystem and reaches humans through various sources like contaminated food and water. Further, removal of topsoil and alteration in soil profile causes unavoidable loss to biodiversity, which disrupts the ecosystem structure and functions (Adibee, Osanloo, and Rahmanpour 2013; Feng et al. 2019).\nGrowing concerns about the environmental impacts of coal mining, together with the slow natural recovery of mine spoils, urge technical solutions to restore these degraded ecosystems into their original states (Macdonald et al. 2015). A successful restoration programme accelerates the natural recovery processes to check soil erosion, restore soil fertility, and enhance biological diversity (A. N. Singh, Raghubanshi, and Singh 2002). Therefore, the first step in any restoration programme, of course, is to protect the disturbed habitat and communities from being further wasted. Then follow attempts to accelerate the revegetation process for increasing biodiversity and stabilising nutrient cycling (A. N. Singh and Singh 2006; A. N. Singh, Zeng, and Chen 2006).\nPlantations have been contemporarily used to restore degraded lands worldwide effectively (Badı́a et al. 2007; Bohre and Chaubey 2016; Erskine, Lamb, and Borschmann 2005; Jeżowski et al. 2017; A. Singh 2001; A. N. Singh and Singh 1999; A. N. Singh, Raghubanshi, and Singh 2004b). However, the suitability of species and their performance on coal mine spoil have remained a challenging task as the characteristics of coal mine spoils are highly heterogeneous and lack soil organic matter (SOM), so that it is regarded as a recalcitrant medium for plant growth (Adibee, Osanloo, and Rahmanpour 2013; Feng et al. 2019; K. Singh, Singh, and Tewari 2021).\nSome of the earlier studies have evaluated several plant species’ growth and biomass production on coal mine spoil (Badı́a et al. 2007; Bohre and Chaubey 2016; Erskine, Lamb, and Borschmann 2005; Jeżowski et al. 2017; A. Singh 2001; A. N. Singh and Singh 1999; A. N. Singh, Raghubanshi, and Singh 2004b). Although exotic woody species are often suggested to restore coal mine spoil due to their fast growth and high economic or livelihood benefits, it often results in low biodiversity development (DAntonio and Meyerson 2002; Dutta and Agrawal 2003; Lamb, Erskine, and Parrotta 2005). Many previous studies have shown that exotic species can positively or negatively impact soil fertility and native flora while restoring degraded lands (Berger 1993; DAntonio and Meyerson 2002; Yan et al. 2020). Although exotic species may have higher survival (Citadini-Zanette et al. 2017) and improve soil properties (Yan et al. 2020), they often result in low carbon development compared to native species (Citadini-Zanette et al. 2017).\nNet primary production is considered a critical functional parameter that helps evaluate species’ quality. Biomass is a crucial parameter of structural attributes. They directly contribute to organic matter, energy transformation, and nutrient cycling between vegetation and soil. Exotic species show successful establishment, and their fast growth often outcompetes the native species during the restoration (Huxtable, Koen, and Waterhouse 2005). Another study showed slight differences in biomass production among the exotic and native plants established on degraded lands (Islam et al. 1999). The biomass allocation to different plant parts can be controlled by environmental and biological (species-specific) factors (Boonman et al. 2020; Freschet, Swart, and Cornelissen 2015; Poorter and Sack 2012). However, the biomass allocation to different plant parts can vary between exotic and native species because species may have adapted to their native habitats and exhibit differential allocation strategies. Thus, there is a need for an increased understanding of the biology and impacts of exotic and native species on degraded lands. Therefore, comparing survival, growth and biomass production among exotic and native species becomes essential to assess the suitability of plant species for the reclamation process.\nThe present study compares the survival, growth performance, biomass accumulation, net primary productivity of 5-year-old native and exotic woody plantations established on coal mine spoils. We expect exotic species to have higher survival, growth, and biomass production on coal mine spoils because they usually exhibit higher competitive abilities. Therefore, they can sustain themselves on nutrient-poor and degraded lands. Specifically, we address the following four questions from our study:\n\nCan native species have higher survival and growth performance on coal mine spoils?\nWhat are the biomass and net primary production level among native and exotic plantations at earlier stages?\nWhether biomass production and net primary production (NPP) are species-specific?" + }, + { + "objectID": "publications/2022-singh-ee/index.html#material-and-methods", + "href": "publications/2022-singh-ee/index.html#material-and-methods", + "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", + "section": "Material and methods", + "text": "Material and methods\n\nStudy site and climate\nThe plantations under present study were located in the west section of Jayant block of Singrauli Coalfields in Singrauli district of Madhya Pradesh, India, which lies between latitudes 24° 6′ 45″ – 24° 11′ 15″ N and longitude 82° 36′ 40″ – 82° 41′ 15″ E. The study area is situated on a plateau above the plain (around 500 m above mean sea level) on its southwest side. In contrast, the plateau’s foot’s average elevation is approximately 300 m above mean sea level. The climate of the area is tropical monsoon, and the year is divisible into a mild winter (November–February), a hot summer (March–June), and a warm rainy season (July–October). The mean monthly minimum temperature within the annual cycle ranges from 6 to 28 °C and the mean monthly maximum from 20 to 40 °C. The rainfall annually averages 1069 mm, of which about 90% occurs from late June to early September. The rainfall is characterised by a high degree of inter-annual variation, as during the study period 1990–1996, it ranges from 700 to 1450 mm yr−1 (A. N. Singh, Raghubanshi, and Singh 2004b, 2004a).\n\n\nPlantations and experimental design\nPlantations of native species were raised in July–August of 1990–91 by planting nursery-raised seedlings in previously dug pits of 40 cm × 40 cm × 40 cm size at a spacing of 2 m × 2 m. The plantations of Albizia lebbeck (L.) Benth., Albizia procera (Roxb.) Benth. and Tectona grandis L.f. were raised in 1990, whereas Dendrocalamus strictus Nees plantation was raised in 1991 by planting 7 to 8 months old nursery raised seedlings. The total planted area for A. lebbeck and A. procera was 1.5 ha, whereas the same for T. grandis and D. strictus was about 0.5 ha each. For sampling, three permanent plots were established for each species. The sample plots’ size was 25 m × 25 m for A. lebbeck and A. procera whereas 15 m × 15 m plot size for T. grandis and D. strictus.\nInitially a total of 2500 seedlings per hectare were planted for each species. After five years, survival is estimated as the number of individuals (clumps in D. strictus) in each plot, which was inventoried in February–March during 1995–1996.\n\n\nBiomass and net primary production\nAllometric equations relating tree dimensions to the plant parts’ biomass were developed to measure tree biomass. Twelve individuals of each species, representing a gradient of diameter, were felled from an area adjoining the permanent plots, and their diameter (D) and height (H) were measured. The felled individuals were separated into stem and foliage. The root systems of the felled plants were excavated to a depth of 1 m. Each component’s fresh weight (stem, foliage, and coarse roots with a diameter greater than 5 mm) was recorded in the field. Sub-samples were brought to the laboratory to determine dry weights. The data were subjected to regression analysis to relate the dry weight of stem, foliage, rhizome, and root with D or D2H or their natural log values. The highest R2 (correlation coefficient) equations were selected, which were also used in earlier studies (Dutta and Agrawal 2003; A. N. Singh, Raghubanshi, and Singh 2004b; A. N. Singh and Singh 1999). The standing biomass of different components (stem, foliage, and root) was calculated using the biomass estimation equations. These values were then multiplied by the density of tree species. Per hectare biomass estimations were obtained separately for each plot and averaged across the plots to get the mean estimates at different ages.\nFine root (less than 5 mm in diameter) biomass was quantified by digging out 20 cm × 20 cm × 20 cm monoliths at 20 cm intervals from the plant base to 1-m distance. Monoliths were washed with a fine jet of water, and fine roots were collected, dried, and weighed. Tree roots were separated from roots of herbaceous plants based on colour and appearance.\nThe net primary production was estimated using diameter increments and biomass data described by earlier studies on the study site (Dutta and Agrawal 2003; A. N. Singh, Raghubanshi, and Singh 2004a; L. Singh and Singh 1991).\n\n\nData for exotic species\nPrevious studies have investigated the restoration potential of some exotic species on the same study site (Dutta and Agrawal 2003, 2001; J. S. Singh, Singh, and Jha 1995). These studies provided an opportunity to compare exotic and native species’ restoration potential because they followed a similar experimental design. These studies considered four exotic species (Casuarina equisetifolia L., Cassia siamea Lam., Grevillea pteridifolia Knight, and Acacia auriculiformis A. Cunn. ex Benth.). The total planted area for C. equisetifolia and G. pteridifolia was 1.5 ha each, whereas the same for A. auriculiformis and C. siamea was about 0.5 ha each. For sampling, three permanent plots were established for each species. The sample plots’ size was 25 m × 25 m for C. equisetifolia and G. pteridifolia and; 10 m × 10 m for A. auriculiformis and C. siamea (Dutta and Agrawal 2003, 2001; J. S. Singh, Singh, and Jha 1995).\n\n\nStatistical analyses\nSPSS-PC statistical software was used for all statistical analyses, except wherever specifically mentioned. The data were subjected to the General Linear Model (GLM) for analysis of variance (ANOVA) to observe the species’ effect. Mean values were tested for difference among plantation species with Tukey’s honestly significant difference (HSD) mean separation test (SPSS, 2003, version 10.0). Regression equations were developed through the same statistical package. To observe the effect of origin (exotic vs. native), student’s t-test was conducted using the package rstatix (Kassambara 2021) in the R language and environment for statistical computation (R Core Team 2020)." + }, + { + "objectID": "publications/2022-singh-ee/index.html#results", + "href": "publications/2022-singh-ee/index.html#results", + "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", + "section": "Results", + "text": "Results\n\nSurvival\nPlantations’ survival has been estimated as the stocking density (Individual stems ha-1) for each species (three plots) under exotic and native plantations, and the results are tabulated in Table 1.\n\n\n\n\n\n\n\n\nTable 1: Stocking density (Individuals / ha) of 5-yr old planted exotic and native woody species on coal mine spoil\n\n\n\n\n\n\n\n\n\nPlanted species\nSurviving individuals / ha (%)\nMean ± 1 SE\n\n\nPlot 1\nPlot 2\nPlot 3\n\n\n\n\nExotic\n\n\n\nAcacia auriculiformis\n\n1600 (64)\n1760 (70.4)\n1670 (66.8)\n\n1677 ± 46cd (67)\n\n\n\n\nCasuarina equisetifolia\n\n1450 (58)\n1600 (64)\n1650 (66)\n\n1566 ± 61d (63)\n\n\n\n\nCassia siamea\n\n1900 (76)\n1778 (71)\n1790 (72)\n\n1822 ± 39bc (73)\n\n\n\n\nGrevillea pteridifolia\n\n1789 (72)\n2000 (80)\n1786 (71)\n\n1858 ± 71bc (74)\n\n\n\nNative\n\n\n\nAlbizia lebbeck\n\n2192 (89)\n2160 (86)\n2208 (88)\n\n2187 ± 14a (87)\n\n\n\n\nAlbizia procera\n\n2224 (89)\n2192 (88)\n2208 (88)\n\n2208 ± 9a (88)\n\n\n\n\nTectona grandis\n\n1645 (66)\n1822 (73)\n1867 (75)\n\n1778 ± 68cd (71)\n\n\n\n\nDendrocalamus strictus\n\n2000 (80)\n2000 (80)\n2088 (84)\n\n2029 ± 29ab (81)\n\n\n\n\nData for exotic species obtained from Dutta and Agrawal (2003) and Singh et al. (1995)\n\n\nValues given in parenthesis represent the percent of survival of individuals\n\n\nWithin the Mean ± 1 SE column, values followed by the same letter are not significantly different at p < 0.05, using the Tukey’s HSD test\n\n\n\n\n\n\n\n\n\nThe stocking density (individual stem ha-1) at the time of plantation was 2,500 in both types of plantations. After five years of plantation establishment, about 71-88% of individuals were survived in native and 63-74% in exotic plantations. Among all plantations, the highest survival rate was observed in the native species (A. procera) and lowest in the exotic species (C. equisetifolia); therefore, ANOVA indicated significant differences in stocking density due to species (Table 2).\n\n\n\n\n\n\n\n\nTable 2: Summary of ANOVA for plantation species’ effect on growth parameters, biomass, and net primary production components\n\n\nComponents\nF7,16\np-value\n\n\n\n\nHeight\n82.051\n0.0000\n\n\nDiameter\n56.333\n0.0000\n\n\nHeight / Diameter (H/D)\n97.601\n0.0000\n\n\nTree volume (D2H)\n13.71\n0.0000\n\n\nFoliage biomass\n86.147\n0.0000\n\n\nStem biomass\n220.673\n0.0000\n\n\nCoarse root biomass\n266.112\n0.0000\n\n\nFine root biomass\n31.883\n0.0000\n\n\nTotal biomass\n304.449\n0.0000\n\n\nFoliage production\n556.164\n0.0000\n\n\nStem production\n44.873\n0.0000\n\n\nCoarse root production\n61.241\n0.0000\n\n\nFine root production\n98.281\n0.0000\n\n\nTotal tree layer production\n71.518\n0.0000\n\n\n\n\n\n\n\n\nHowever, the survival rates were significantly higher in native plantations than in exotic species (Figure 1).\n\n#> [1] FALSE\n\n\n\n\nFigure 1: Violin plots with mean and standard deviation for survival, growth, biomass, and net primary productivity among the exotic and native woody species plantations on coal mine spoil. The statistical significance was determined by using student’s t-test and the p-values <0.0001, <0.001, <0.01, <0.05 and 0.1 corresponds to ****, ***, **, * and ns, respectively.\n\n\n\n\n\n\nGrowth performance\nThe growth performance of exotic and native species was determined in terms of height and diameter. Height and diameter (growth parameter) were significantly varied among all plantations of exotic and native species (Table 2). Among all plantation species (native and exotics), the maximum height was attained by G. pteridifolia, whereas maximum diameter was observed for A. lebbeck after 5-years of their establishment. The height and diameter values varied from 2.19 to 5.18 m and 4.32 to 7.58 cm, respectively, in native and 2.75 to 5.88 m and 2.99 to 4.90 cm, respectively, in exotic plantations (Table 3). However, the height was significantly higher, and the diameter was significantly smaller in exotic species plantations than native species plantations (Figure 1).\n\n\n\n\n\n\n\n\nTable 3: Growth performance of 5-yr-old planted exotic and native woody species on coal mine spoil\n\n\n\n\n\n\n\n\n\n\n\n\n\nParameters\nExotic\nNative\n\n\nAA\nCE\nCS\nGP\nAL\nAP\nTG\nDS\n\n\n\n\n\nHeight (m)\n\n\n5.00d\n\n\n5.29ab\n\n\n2.75cd\n\n\n5.88a\n\n\n3.38c\n\n\n2.97c\n\n\n2.19d\n\n\n5.18ab\n\n\n\n\nDiameter (cm)\n\n\n3.95cd\n\n\n4.90bc\n\n\n2.99d\n\n\n3.87cd\n\n\n7.58a\n\n\n7.32a\n\n\n5.22b\n\n\n4.32bc\n\n\n\n\nH/D ratio\n\n\n126.59b\n\n\n107.97bc\n\n\n91.97c\n\n\n151.94a\n\n\n44.59d\n\n\n40.57d\n\n\n41.95d\n\n\n119.91b\n\n\n\n\nD2H (cm3)\n\n\n7801cd\n\n\n12701abc\n\n\n2458d\n\n\n8806cd\n\n\n19420a\n\n\n15913ab\n\n\n5967cd\n\n\n9667bc\n\n\n\n\nData for exotic species obtained from Dutta and Agrawal (2003) and Singh et al. (1995)\n\n\nValues are means of three replicates\n\n\nWithin the columns, values followed by the same letter are not significantly different at p < 0.05, using the Tukey’s HSD test\n\n\nAA, Acacia auriculiformis; CE, Casuarina equisetifolia; CS, Cassia siamea; GP, Grevillea pteridifolia; AL, Albizia lebbeck; AP, Albizia procera; TG, Tectona grandis; DS, Dendrocalamus strictus.\n\n\n\n\n\n\n\n\n\nConsequently, the height to diameter ratio was significantly smaller in native species. A significant positive correlation is observed for height and diameter in exotic species, whereas selected native species did not exhibit any significant correlation (Figure 2 a). Further, non-legumes showed a significant negative correlation (Figure 2 b).\n\n\n\n\n\nFigure 2: Relationship between Height and Diameter for exotic and native woody species (a), and legume and non-leguminous species plantations (b). The regression line was fitted using the linear model, and Pearson’s correlation coefficient values are represented by letter ‘R’ with corresponding probability p-values.\n\n\n\n\n\n\nBiomass production\nThe observed values for the biomass of different plant components are summarised in Table 4. It was noted that D. strictus had shown the highest total biomass production among all the species, whereas A. auriculiformis exhibited the highest total biomass production among the exotic species. The biomass of different plant components was significantly varied due to species among all the plantations (Table 2). Therefore, values in native plantations significantly varied from 7.68 to 74.68 t ha-1, minimum for T. grandis and maximum for D. strictus plantation and 8.49-31.03 t ha-1 exotic plantations, being maximum in A. auriculiformis and minimum in C. siamea (Table 4).\n\n\n\n\n\n\nTable 4: Biomass production (t / ha) under 5-yr old planted exotic and native\nwoody species on coal mine spoil \n \n \n \n Parameters\n \n Exotic\n \n \n Native\n \n \n \n AA\n CE\n CS\n GP\n AL\n AP\n TG\n DS1\n \n \n \n Foliage\n\n6.68bc\n\n2.74d\n\n0.72e\n\n5.06c\n\n6.59bc\n\n7.26b\n\n2.39de\n\n10.68a\n\n Stem\n\n16.77c\n\n15.69c\n\n4.91e\n\n11.46d\n\n32.32b\n\n14.21cd\n\n2.98e\n\n38.5a\n\n Coarse root\n\n4.53d\n\n2.98e\n\n2.56e\n\n5.84c\n\n12.05a\n\n10.65b\n\n1.94e\n\n5.27cd\n\n Fine root\n\n3.05a\n\n0.40c\n\n0.30c\n\n0.57c\n\n0.85bc\n\n0.74bc\n\n0.37c\n\n1.40b\n\n Total\n\n31.03c\n\n21.81d\n\n8.49e\n\n22.90d\n\n51.81b\n\n32.86c\n\n7.68e\n\n74.68a\n\n \n \n \n Data for exotic species obtained from Dutta and Agrawal (2003) and Singh et al. (1995)\n \n \n Values are means of three replicates\n \n \n Within the columns, values followed by the same letter are not significantly different at p < 0.05, using the Tukey’s HSD test\n \n \n AA, Acacia auriculiformis; CE, Casuarina equisetifolia; CS, Cassia siamea; GP, Grevillea pteridifolia; AL, Albizia lebbeck; AP, Albizia procera; TG, Tectona grandis; DS, Dendrocalamus strictus.\n \n \n \n \n 1 Values of rhizome component included in the total biomass\n \n \n\n\n\n\n\nAmong plant parts, stem contributed more than 50% to the total biomass for both exotic and native species (Figure 3 a) and leguminous and non-leguminous species (Figure 3 b). The share of aboveground components in the total biomass in the present study was 65.3-91.1% in native and 66.3-84.5% in exotic and belowground contribution was in the range of 8.9-34.7% in native and 15.5-33.7% in exotic plantations, respectively (Figure 3 c). However, the belowground biomass was higher for leguminous species than the non-leguminous species (Figure 3 d). Moreover, relative contributions of short-lived components (foliage and fine root < 5 mm) were small as compared to long-lived tree component (stem) to the tree layer biomass for both types of plantations calculated at 5-yr age (Figure 3 e), and a similar pattern was observed for the leguminous and non-leguminous species (Figure 3 f). Further, all components’ biomass production except fine root biomass was significantly higher in native species plantations compared to exotic species plantations (Figure 1).\n\n\n\n\n\nFigure 3: Biomass partitioning among different plant parts (a, b), aboveground and below ground (c, d), and short-lived and long-lived components (e, f) for exotic vs. native and legume vs. non-legume species plantation, respectively.\n\n\n\n\n\n\nNet primary production\nThe net primary productivity of different components of plant species is given in Table 5. Total net production (above + below ground) among these plantations on mine spoil varied from 4.76 to 32.04 t ha-1 yr-1 in native and 3.72-18.24 t ha-1 yr-1 exotic species (Table 5). The differences in net production of all plant values components were significantly varied due to species, as indicated by ANOVA (Table 2). The aboveground net production of present planted species ranged from 3.75-24.28 t ha-1 yr-1 in native plantations, maximum in D. strictus, and minimum in T. grandis where 2.55-12.55 t ha-1 yr-1 in exotic plantations, being maximum in A. auriculiformis and minimum in C. siamea plantation. Similar to biomass, relative contributions of short-lived (foliage and fine root <5 mm) were lower long-lived tree components (stem) to the tree layer biomass and NPP for both types of plantations calculated at 5-yr age. The foliage contribution and fine root (<5 mm diameter) to the biomass were much smaller than that of NPP in all four native species, while it was the opposite of exotic species. For example, foliage component contributed in exotic plantations were in the range of 8.5-22.1%, being maximum by G. pteridifolia and minimum by C. siamea. Fine roots were in the range of 1.8-9.8%, being maximum by A. auriculiformis and minimum by C. equisetifolia.\n\n\n\n\n\n\nTable 5: Net primary production (t ha-1 yr-1) under 5-yr\nold planted exotic and native woody species on coal mine spoil. \n \n \n \n Parameters\n \n Exotic\n \n \n Native\n \n \n \n AA\n CE\n CS\n GP\n AL\n AP\n TG\n DS1\n \n \n \n Foliage\n\n1.38d\n\n1.07de\n\n0.31e\n\n1.67cd\n\n6.59b\n\n7.26b\n\n2.39c\n\n10.68a\n\n Stem\n\n11.17ab\n\n7.78c\n\n2.19d\n\n6.75c\n\n11.26a\n\n8.07bc\n\n1.36d\n\n13.60a\n\n Coarse root\n\n2.64bc\n\n1.62d\n\n0.92de\n\n3.31ab\n\n3.61a\n\n2.54c\n\n0.63e\n\n1.12de\n\n Fine root\n\n3.05a\n\n0.40cd\n\n0.30d\n\n0.54cd\n\n0.85c\n\n0.74cd\n\n0.37d\n\n1.40b\n\n Total\n\n18.24c\n\n10.86d\n\n3.72e\n\n12.27d\n\n23.86b\n\n19.30bc\n\n4.76e\n\n32.04a\n\n \n \n \n Data for exotic species obtained from Dutta and Agrawal (2003) and Singh et al. (1995)\n \n \n Values are means of three replicates\n \n \n Within the columns, values followed by the same letter are not significantly different at p < 0.05, using the Tukey’s HSD test\n \n \n AA, Acacia auriculiformis; CE, Casuarina equisetifolia; CS, Cassia siamea; GP, Grevillea pteridifolia; AL, Albizia lebbeck; AP, Albizia procera; TG, Tectona grandis; DS, Dendrocalamus strictus.\n \n \n \n \n 1 Values of rhizome component included in the total biomass\n \n \n\n\n\n\n\n\n\n\n\n\nFigure 4: Net primary productivity (NPP) partitioning among different plant parts (a, b), aboveground and below ground (c, d), and short-lived and long-lived components (e, f) for exotic vs. native and legume vs. non-legume species plantation, respectively.\n\n\n\n\nSimilarly, in native species, foliage contributed 12.7-32.1% being maximum by T. grandis and minimum by A. lebbeck, whereas fine root was in the range of 1.6-5.1%, respectively. Interestingly, the foliage and total net primary production of exotic species were significantly higher in native species than the exotic species (Figure 1). However, stem and roots’ net primary production did not significantly differ among the exotic and native species (Figure 1). A significant positive correlation was observed for total and stem net primary production with the foliage biomass (Figure 5).\n\n\n\n\n\nFigure 5: Relationships between net primary production and foliage biomass for 5-yr-old plantations of all exotic and all native woody species on coal mine spoil. The linear regression equation (y = a + bx), Pearson’s correlation coefficient (r), and corresponding probability values (p) are shown in the top-left corner of each subplot." + }, + { + "objectID": "publications/2022-singh-ee/index.html#discussion", + "href": "publications/2022-singh-ee/index.html#discussion", + "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", + "section": "Discussion", + "text": "Discussion\n\nNative species plantations had a higher survival\nThe present study indicated that native species better survive on degraded lands than exotic species, supported by earlier studies (Islam et al. 1999). Although some previous studies have investigated survivability of native species on coal mine spoil (Mosseler, Major, and Labrecque 2014; A. N. Singh, Raghubanshi, and Singh 2004b, 2004a), very few studies compared survivability with exotic species (Huxtable, Koen, and Waterhouse 2005; Islam et al. 1999). Another study conducted on saline soils of northern Australia also suggested the high survival of native species (D. Sun and Dickinson 1995). Evidently, in this study, more remarkable survival was observed in all native species indicating better adaptability of species for mine spoil restoration. Thus, the higher survival of native species may be due to their pre-adaptation to the environmental conditions. However, survival may not be directly linked to species’ origin; rather, it can be a legacy of function and life-history traits. Therefore, survival cannot be considered as the only indicator of ecological restoration.\n\n\nGrowth performance\nThe height and diameter of woody plant species are critical structural parameters involved in measuring growth performance, which is affected by environmental conditions (Lestari et al. 2019; Sumida, Miyaura, and Torii 2013). Our results suggested that exotic plants tend to invest more photosynthates in height growth, whereas native plants invested more photosynthates in stem growth. A higher diameter in native species indicates the possibility of their adaptation to windbreak and endorses a longer establishment. Further, these observations indicate that exotic plants might be suffering from a limitation of light. In contrast, native plants may be pre-adapted to environmental conditions and therefore invested more photosynthates in diameter for an efficient supply of resources to the shoot (Boonman et al. 2020). However, this finding contrasts the reports of an earlier study in similar environmental conditions (Islam et al. 1999).\nHeight growth is usually associated with the production of newer leaves and more significant resource acquisition. In contrast, the increase in stem diameter ensures tissues’ development supports the leaves (Sumida, Miyaura, and Torii 2013). Thus, the increase in height should be accompanied by an increase in the stem’s diameter, as suggested by biomechanical models (Henry and Aarssen 1999; J. Sun et al. 2019). Our results also seemed to support this hypothesis at least in exotic species but contrasted by the non-leguminous species, suggesting a trade-off between height and diameter in non-leguminous plants, possibly due to limited resources.\nIn contrast to height growth, diameter growth depends primarily on current photosynthesis, although some reserve carbohydrates may be used for diameter growth very early in the season (Kozlowski 1962). Thus, the greater height in case of exotic species can be attributed to their higher photosynthetic rate 12.1 (A. auriculiformis) to 30.14 µmol CO2 m-2 s-1 (C. equisetifolia) as compared to native plantations in the present research site might be a promoting point to faster growth behaviour of exotic species (V. Singh and Toky 1995). The smaller height to diameter ratio of exotic species indicates their higher competitive ability and rapid growth on degraded sites. This view is supported by a study conducted on the degraded tropical pasture of southern Costa Rica, where it was shown that exotic species outperformed the native plantations (Carpenter, Nichols, and Sandi 2004). Nevertheless, native species can also display growth rates similar to exotic species depending on the site environments (Bare and Ashton 2016).\n\n\nBiomass and net primary production\nThe present study indicated that native species produced higher biomass as compared to exotic species. This suggests a better adaptation and higher resource use efficiency of native species than the exotic species on the coal mine spoil. In contrast to our finding, a previous study reported little differences in biomass production among the exotic and native species (Islam et al. 1999).\nFurther, biomass partitioning into different plant parts revealed that native species produced significantly higher biomass for foliage, stem, and coarse roots (diameter greater than 5 mm) compared to exotic species. In contrast, fine root biomass was higher in the case of exotic species, though not significant. Higher biomass production of fine roots in exotic species suggests that these plants responded to the stressed environment of coal mine spoils to get available nutrient and water sources to maintain their growth performance, especially height instead of diameter (Boonman et al. 2020).\nSince the present study focused on woody tree species, the higher contribution of aboveground biomass to the total biomass is expectable and supported by many previous studies; exotic species invested more in belowground components. In contrast, native species invested more in aboveground components while responding to the same coal mine spoils. It is believed that plants tend to invest more in belowground components during the disturbance or stressful conditions such as the nutrient-poor coal mine spoils (Poorter and Sack 2012; Priest et al. 2015). Further, both species invested more in long-lived components than in short-lived components; however, exotic species invested more in short-lived components, especially the fine roots. The long-lived component (stem) contribution was much higher than to NPP in all four native species but the opposite in the exotic plantations. Thus, the short-lived components might be associated with ecosystem functions whereas long-lived components account for structural attributes in the native plantations.\nMoreover, foliage accounted for a lower proportion of ecosystem function in all plantations of exotic species. In contrast, native species showed considerably very high that more foliage biomass production in such a stressed environment (degraded mine spoil) may provide more soil organic matter to regulate the cycling of nutrients. The native and exotic species have different allocational strategies.\nAccording to the functional equilibrium hypothesis, this suggests that exotic species may experience belowground resource (water and nutrients) limitation, whereas native species experience an aboveground resource (sunlight and CO2) limitation (Boonman et al. 2020; Brouwer 1983). These changes may be attributed to morphological adaptations or phenotypic plasticity rather than biomass allocation (Freschet, Swart, and Cornelissen 2015; Poorter and Sack 2012). However, if none of the resources is limiting or equally limiting, plants tend to allocate the resources optimally. This is referred to as the ‘optimal partitioning hypothesis’ (Gedroc, McConnaughay, and Coleman 1996) or ‘balanced growth hypothesis’ (Shipley and Meziane 2002). Thus, if both types of species faced similar environmental conditions (probably that was the case in the present study), those species which produce greater belowground biomass during the initial stages may be better suited for reclamation of coal mine overburden. Following this, our results suggest that exotic and leguminous plants may be better suited for coal mine restoration, though these effects can be highly species-specific.\nThe net primary production or NPP is associated with photosynthesis and biomass production, as indicated by a strong positive relation between biomass and NPP. The present study suggested an overall NPP ranging from 3 to 32 t ha-1 yr-1, comparable to earlier studies (Dutta and Agrawal 2003; A. N. Singh and Singh 1999; V. Singh and Toky 1995). The early successional species are reported to exhibit net production of 8-21 t ha-1 yr-1 in natural dry tropical forests (Murphy and Lugo 1986), whereas the aboveground net production of plantations on coal mine spoil and natural forests in the tropical zone ranged between 1.5 and 32.62 t ha-1 yr-1 (Dutta and Agrawal 2003; P. K. Singh and Singh 1998; V. Singh and Toky 1995; L. Singh and Singh 1991). However, our comparison suggested that native species had significantly higher total and foliage NPP than the exotic species on coal mine spoils. This indicated that native species were much more efficient in resource utilisation, possibly due to their pre-adaptation to the tropical environments. Thus, achieving an early vegetation cover and high biomass production on mine spoil can be approached through proper selection and planting of pioneer native tree species. Such species can exist under harsh soil conditions and require less long-term maintenance (A. N. Singh, Raghubanshi, and Singh 2004b; A. N. Singh and Singh 2006)." + }, + { + "objectID": "publications/2022-singh-ee/index.html#conclusions", + "href": "publications/2022-singh-ee/index.html#conclusions", + "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", + "section": "Conclusions", + "text": "Conclusions\nThe present study points out that native species performs well than the exotic species in the rehabilitation and restoration of coal mine spoils. This conclusion is supported by the higher biomass and NPP for native species compared to exotic species, though exotic species exhibited more remarkable height growth. However, consideration of species’ leguminous nature did not affect the biomass and NPP in the present study, though it may affect the redevelopment of soils in degraded habitats. Further, the effect of exotic species seemed to be highly variable and species-specific. Therefore, more comparative knowledge on the species-specific effects on ecosystem restoration, biodiversity reconstruction, and its possible effects on their services towards the ecosystem and local people is still required. Therefore, more future investigations on various ecological restoration scales are warranted with a more significant number of species while inferring effects of exotic and native species for comparative restoration potential." + }, + { + "objectID": "publications/2022-singh-ee/index.html#acknowledgments", + "href": "publications/2022-singh-ee/index.html#acknowledgments", + "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", + "section": "Acknowledgments", + "text": "Acknowledgments\n\nAuthors are grateful to the Chairperson, Department of Botany, Panjab University, Chandigarh, and the Chairperson, Department of Botany, Banaras Hindu University, Varanasi, to provide all necessary facilities required for the work. The authors are also profoundly thankful to Prof. J. S. Singh for his constructive guidelines and supervision during the study." + }, + { + "objectID": "publications/2022-singh-ee/index.html#funding", + "href": "publications/2022-singh-ee/index.html#funding", + "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", + "section": "Funding", + "text": "Funding\n\nThis work was supported by the University Grants Commission, Government of India as GATE fellowship and MRP to ANS, and Junior Research Fellowship to AK [507/(OBC) (CSIR-UGC NET DEC. 2016)]." + }, + { + "objectID": "publications/2022-singh-ee/index.html#authors-contributions", + "href": "publications/2022-singh-ee/index.html#authors-contributions", + "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", + "section": "Author’s contributions", + "text": "Author’s contributions\n\nAnand Narain Singh: Conceptualisation, Methodology, Validation, Formal analyses, Investigation, Data curation, Writing - original draft, Writing - review & editing, Supervision. Abhishek Kumar: Formal analyses, Writing - review & editing, Visualization, Funding acquisition." + }, + { + "objectID": "posts/welcome/index.html", + "href": "posts/welcome/index.html", + "title": "Welcome To My Blog", + "section": "", + "text": "This is the first post in a Quarto blog. Welcome!\n\nSince this post doesn’t specify an explicit image, the first image in the post will be used in the listing page of posts." + }, + { + "objectID": "posts/post-with-code/index.html", + "href": "posts/post-with-code/index.html", + "title": "Post With Code", + "section": "", + "text": "This is a post with executable code.\n\n1 + 1\n\n[1] 2" + }, + { + "objectID": "posts/index.html", + "href": "posts/index.html", + "title": "Species distribution modelling studies in Himalayas", + "section": "", + "text": "library(tidyverse)\n\n── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──\n✔ dplyr 1.1.2 ✔ readr 2.1.4\n✔ forcats 1.0.0 ✔ stringr 1.5.0\n✔ ggplot2 3.4.3 ✔ tibble 3.2.1\n✔ lubridate 1.9.2 ✔ tidyr 1.3.0\n✔ purrr 1.0.2 \n── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag() masks stats::lag()\nℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors\n\ntribble(\n ~Species, ~Latitude, ~Longitude, ~Reference,\n \n)\n\n# A tibble: 0 × 4\n# ℹ 4 variables: Species <???>, Latitude <???>, Longitude <???>,\n# Reference <???>" + }, { "objectID": "about.html", "href": "about.html", @@ -14,18 +112,11 @@ "text": "Species distribution modelling studies in Himalayas\n\n\n\n\n\n\n\n\n\n\n\n\nAbhishek Kumar, Abhishek Kumar\n\n\n\n\n\n\n \n\n\n\n\nSpecies distribution modelling studies for Plants in Western Himalayas\n\n\n\n\n\n\n\n\n\n\n\n\nAbhishek Kumar\n\n\n\n\n\n\n \n\n\n\n\nAlien Flora of Indian Siwaliks\n\n\n\n\n\n\n\n\n\n\n\n\nAbhishek Kumar, Abhishek Kumar\n\n\n\n\n\n\n \n\n\n\n\nPost With Code\n\n\n\n\n\n\n\nnews\n\n\ncode\n\n\nanalysis\n\n\n\n\n\n\n\n\n\n\n\nAug 25, 2022\n\n\nAbhishek Kumar, Harlow Malloc\n\n\n\n\n\n\n \n\n\n\n\nWelcome To My Blog\n\n\n\n\n\n\n\nnews\n\n\n\n\n\n\n\n\n\n\n\nAug 22, 2022\n\n\nAbhishek Kumar, Tristan O’Malley\n\n\n\n\n\n\nNo matching items" }, { - "objectID": "posts/post-with-code/index.html", - "href": "posts/post-with-code/index.html", - "title": "Post With Code", - "section": "", - "text": "This is a post with executable code.\n\n1 + 1\n\n[1] 2" - }, - { - "objectID": "posts/welcome/index.html", - "href": "posts/welcome/index.html", - "title": "Welcome To My Blog", + "objectID": "posts/sdm-himalaya/index.html", + "href": "posts/sdm-himalaya/index.html", + "title": "Species distribution modelling studies for Plants in Western Himalayas", "section": "", - "text": "This is the first post in a Quarto blog. Welcome!\n\nSince this post doesn’t specify an explicit image, the first image in the post will be used in the listing page of posts." + "text": "Species\nReference\n\n\n\n\nAbies densa\nMalik et al. (2022)\n\n\nAbies pindrow\nMalik et al. (2022)\n\n\nAbies spectabilis\nMalik et al. (2022)\n\n\nAconitum heterophyllum\nZ. A. Wani et al. (2022)\n\n\nBetula utilis\nMohapatra et al. (2019)\n\n\nBetula utilis\nSingh, Samant, and Naithani (2021b)\n\n\nBoehmeria clidemioides\nGupta et al. (2023)\n\n\nBuxus wallichiana\nZ. A. Wani et al. (2023)\n\n\nDactylorhiza hatagirea\nChandra et al. (2022)\n\n\nDactylorhiza hatagirea\nSharma, Ram, and Chawla (2023)\n\n\nDactylorhiza hatagirea\nThakur et al. (2021)\n\n\nDrepanostachyum falcatum\nMeena et al. (2023)\n\n\nFritillaria roylei\nChandora et al. (2023)\n\n\nIncarvillea altissima\nRana et al. (2021)\n\n\nIncarvillea arguta\nRana et al. (2021)\n\n\nIncarvillea beresowskii\nRana et al. (2021)\n\n\nIncarvillea compacta\nRana et al. (2021)\n\n\nIncarvillea delavayi\nRana et al. (2021)\n\n\nIncarvillea emodi\nRana et al. (2021)\n\n\nIncarvillea forrestii\nRana et al. (2021)\n\n\nIncarvillea lutea\nRana et al. (2021)\n\n\nIncarvillea mairei\nRana et al. (2021)\n\n\nIncarvillea olgae\nRana et al. (2021)\n\n\nIncarvillea potaninii\nRana et al. (2021)\n\n\nIncarvillea sinensis\nRana et al. (2021)\n\n\nIncarvillea younghusbandii\nRana et al. (2021)\n\n\nIncarvillea zhongdianensis\nRana et al. (2021)\n\n\nLagotis cashmeriana\nSalam, Reshi, and Shah (2022)\n\n\nPicrorhiza kurroa\nRawat et al. (2022)\n\n\nPinus gerardiana\nPaul, Lata, and Barman (2023)\n\n\nPittosporum eriocarpum\nPaul and Samant (2023)\n\n\nQuercus oblongata\nBarman et al. (2023)\n\n\nQuercus semecarpifolia\nSaran et al. (2010)\n\n\nQuercus semecarpifolia\nSingh, Samant, and Naithani (2021a)\n\n\nRheum webbianum\nI. A. Wani et al. (2021)\n\n\nShorea robusta\nKaur et al. (2023)\n\n\nTaxus contorta\nChauhan et al. (2022)\n\n\nTrillium govanianum\nRather et al. (2022)\n\n\nValeriana wallichii\nKumari et al. (2022)\n\n\n\n\n\n\n\n\n\nReferences\n\nBarman, Tanay, S. S. Samant, L. M. Tewari, Nidhi Kanwar, Amit Singh, Shiv Paul, and Swaran Lata. 2023. “Ecological Assessment and Suitability Ranges of Ban Oak (Quercus Oblongata d. Don) in Chamba District, Himalayas: Implications for Present and Future Conservation.” Brazilian Journal of Botany 46 (2): 477–97. https://doi.org/10.1007/s40415-023-00885-w.\n\n\nChandora, Rahul, Shiv Paul, Kanishka RC, Pankaj Kumar, Badal Singh, Pradeep Kumar, Abhay Sharma, et al. 2023. “Ecological Survey, Population Assessment and Habitat Distribution Modelling for Conserving Fritillaria Roylei – a Critically Endangered Himalayan Medicinal Herb.” South African Journal of Botany 160 (September): 75–87. https://doi.org/10.1016/j.sajb.2023.06.057.\n\n\nChandra, Naveen, Gajendra Singh, Shashank Lingwal, J. S Jalal, M. S Bisht, Vineet Pal, M. P. S Bisht, Balwant Rawat, and L. M Tiwari. 2022. “Ecological Niche Modeling and Status of Threatened Alpine Medicinal Plant Dactylorhiza Hatagirea d.don in Western Himalaya.” Journal of Sustainable Forestry 41 (10): 1029–45. https://doi.org/10.1080/10549811.2021.1923530.\n\n\nChauhan, Saurav, Shankharoop Ghoshal, K. S. Kanwal, Vikas Sharma, and G. Ravikanth. 2022. “Ecological Niche Modelling for Predicting the Habitat Suitability of Endangered Tree Species Taxus Contorta Griff. In Himachal Pradesh (Western Himalayas, India).” Tropical Ecology 63 (2): 300–313. https://doi.org/10.1007/s42965-021-00200-2.\n\n\nGupta, A., D. Adhikari, I. A. Hurrah, and V. V. Wagh. 2023. “Extended Distribution, Typification and Modelling of Potential Areas of Boehmeria Clidemioides (Urticaceae) in the Western Himalaya, India.” Rheedea 33 (1): 8–16. https://doi.org/10.22244/rheedea.2023.33.01.02.\n\n\nKaur, Sharanjeet, Siddhartha Kaushal, Dibyendu Adhikari, Krishna Raj, K. S. Rao, Rajesh Tandon, Shailendra Goel, Saroj K. Barik, and Ratul Baishya. 2023. “Different GCMs yet Similar Outcome: Predicting the Habitat Distribution of Shorea Robusta c.f. Gaertn. In the Indian Himalayas Using CMIP5 and CMIP6 Climate Models.” Environmental Monitoring and Assessment 195 (6). https://doi.org/10.1007/s10661-023-11317-3.\n\n\nKumari, Priyanka, Ishfaq Ahmad Wani, Sajid Khan, Susheel Verma, Shazia Mushtaq, Aneela Gulnaz, and Bilal Ahamad Paray. 2022. “Modeling of Valeriana Wallichii Habitat Suitability and Niche Dynamics in the Himalayan Region Under Anticipated Climate Change.” Biology 11 (4): 498. https://doi.org/10.3390/biology11040498.\n\n\nMalik, Rayees A., Zafar A. Reshi, Iflah Rafiq, and S. P. Singh. 2022. “Decline in the Suitable Habitat of Dominant Abies Species in Response to Climate Change in the Hindu Kush Himalayan Region: Insights from Species Distribution Modelling.” Environmental Monitoring and Assessment 194 (9). https://doi.org/10.1007/s10661-022-10245-y.\n\n\nMeena, Rajendra K., Nitika Negi, Rajeev Shankhwar, Maneesh S. Bhandari, Rama Kant, Shailesh Pandey, Narinder Kumar, Rajesh Sharma, and Harish S. Ginwal. 2023. “Ecological Niche Modelling and Population Genetic Analysis of Indian Temperate Bamboo Drepanostachyum Falcatum in the Western Himalayas.” Journal of Plant Research 136 (4): 483–99. https://doi.org/10.1007/s10265-023-01465-5.\n\n\nMohapatra, Jakesh, Chandra Prakash Singh, Maroof Hamid, Anirudh Verma, Sudeep Chandra Semwal, Bandan Gajmer, Anzar A. Khuroo, et al. 2019. “Modelling Betula Utilis Distribution in Response to Climate-Warming Scenarios in Hindu-Kush Himalaya Using Random Forest.” Biodiversity and Conservation 28 (8-9): 2295–2317. https://doi.org/10.1007/s10531-019-01731-w.\n\n\nPaul, Shiv, Swaran Lata, and Tanay Barman. 2023. “Habitat Distribution Modeling of the Pinus Gerardiana Under Projected Climate Change in the North-Western Himalaya, India.” Landscape and Ecological Engineering, July. https://doi.org/10.1007/s11355-023-00570-w.\n\n\nPaul, Shiv, and S. S. Samant. 2023. “Population Biology, Ecological Niche Modelling of Endangered and Endemic Pittosporum Eriocarpum Royle in Western Himalaya, India.” Journal for Nature Conservation 72 (April): 126356. https://doi.org/10.1016/j.jnc.2023.126356.\n\n\nRana, Santosh Kumar, Hum Kala Rana, Dong Luo, and Hang Sun. 2021. “Estimating Climate-Induced ’Nowhere to Go’ Range Shifts of the Himalayan Incarvillea Juss. Using Multi-Model Median Ensemble Species Distribution Models.” Ecological Indicators 121 (February): 107127. https://doi.org/10.1016/j.ecolind.2020.107127.\n\n\nRather, Zubair Ahmad, Rameez Ahmad, Tanvir-Ul-Hassan Dar, and Anzar Ahmad Khuroo. 2022. “Ensemble Modelling Enables Identification of Suitable Sites for Habitat Restoration of Threatened Biodiversity Under Climate Change: A Case Study of Himalayan Trillium.” Ecological Engineering 176 (March): 106534. https://doi.org/10.1016/j.ecoleng.2021.106534.\n\n\nRawat, Neelam, Saurabh Purohit, Vikas Painuly, Govind Singh Negi, and Mahendra Pratap Singh Bisht. 2022. “Habitat Distribution Modeling of Endangered Medicinal Plant Picrorhiza Kurroa (Royle Ex Benth) Under Climate Change Scenarios in Uttarakhand Himalaya, India.” Ecological Informatics 68 (May): 101550. https://doi.org/10.1016/j.ecoinf.2021.101550.\n\n\nSalam, Nadeem, Zafar A. Reshi, and Manzoor A. Shah. 2022. “Habitat Suitability Modelling for Lagotis cashmeriana (Royle) Rupr., A Threatened Species Endemic to Kashmir Himalayan Alpines.” Geology, Ecology, and Landscapes 6 (4): 241–51. https://doi.org/10.1080/24749508.2020.1816871.\n\n\nSaran, S., R. Joshi, S. Sharma, H. Padalia, and V. K. Dadhwal. 2010. “Geospatial Modeling of Brown Oak (Quercus Semecarpifolia) Habitats in the Kumaun Himalaya Under Climate Change Scenario.” Journal of the Indian Society of Remote Sensing 38 (3): 535–47. https://doi.org/10.1007/s12524-010-0038-2.\n\n\nSharma, Manish K., Bittu Ram, and Amit Chawla. 2023. “Ensemble Modelling Under Multiple Climate Change Scenarios Predicts Reduction in Highly Suitable Range of Habitats of Dactylorhiza Hatagirea (d.don) Soo in Himachal Pradesh, Western Himalaya.” South African Journal of Botany 154 (March): 203–18. https://doi.org/10.1016/j.sajb.2022.12.026.\n\n\nSingh, Amit, S. S. Samant, and Suneet Naithani. 2021a. “Population Ecology and Habitat Suitability Modelling of Quercus Semecarpifolia Sm. In the Sub-Alpine Ecosystem of Great Himalayan National Park, North-Western Himalaya, India.” South African Journal of Botany 141 (September): 158–70. https://doi.org/10.1016/j.sajb.2021.04.022.\n\n\n———. 2021b. “Population Ecology and Habitat Suitability Modelling of Betula Utilis d. Don in the Sub-Alpine Ecosystem of Great Himalayan National Park, North-Western Indian Himalaya: A UNESCO World Heritage Site.” Proceedings of the Indian National Science Academy 87 (4): 640–56. https://doi.org/10.1007/s43538-021-00055-0.\n\n\nThakur, Dinesh, Nikita Rathore, Manish Kumar Sharma, Om Parkash, and Amit Chawla. 2021. “Identification of Ecological Factors Affecting the Occurrence and Abundance of Dactylorhiza Hatagirea (d.don) Soo in the Himalaya.” Journal of Applied Research on Medicinal and Aromatic Plants 20 (February): 100286. https://doi.org/10.1016/j.jarmap.2020.100286.\n\n\nWani, Ishfaq Ahmad, Susheel Verma, Priyanka Kumari, Bipin Charles, Maha J. Hashim, and Hamed A. El-Serehy. 2021. “Ecological Assessment and Environmental Niche Modelling of Himalayan Rhubarb (Rheum Webbianum Royle) in Northwest Himalaya.” PLOS ONE 16 (11): e0259345. https://doi.org/10.1371/journal.pone.0259345.\n\n\nWani, Zishan Ahmad, Qamer Ridwan, Sajid Khan, Shreekar Pant, Sazada Siddiqui, Mahmoud Moustafa, Ahmed Ezzat Ahmad, and Habab M. Yassin. 2022. “Changing Climatic Scenarios Anticipate Dwindling of Suitable Habitats for Endemic Species of Himalaya – Predictions of Ensemble Modelling Using Aconitum Heterophyllum as a Model Plant.” Sustainability 14 (14): 8491. https://doi.org/10.3390/su14148491.\n\n\nWani, Zishan Ahmad, K. V. Satish, Tajamul Islam, Shalini Dhyani, and Shreekar Pant. 2023. “Habitat Suitability Modelling of Buxus Wallichiana Bail.: An Endemic Tree Species of Himalaya.” Vegetos 36 (2): 583–90. https://doi.org/10.1007/s42535-022-00428-w." }, { "objectID": "publications/2020-kumar-jsr/index.html", @@ -125,76 +216,6 @@ "section": "Conclusion", "text": "Conclusion\nThe present systematic map assessed the state of plant ecology in Indian Siwaliks and provided a methodological framework for transparent and reproducible reviews. The proportion of studies focusing on plant ecology is disproportionately more petite than the demand for ecological understanding about the region. Despite the proliferation of ecological studies during the past decade, increased understanding is required about the ecological structure and functions of the Siwalik landscape. The evidence base is often very scarce and scattered that too suffers from geographic and discipline-specific bias. Therefore, we urge researchers to carry out scientific investigations focusing on the ecological problems of the region. These investigations will help make evidence-based decisions and policies for future research lines to sustain this fragile ecosystem." }, - { - "objectID": "publications/2022-singh-ee/index.html", - "href": "publications/2022-singh-ee/index.html", - "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", - "section": "", - "text": "Note\n\n\n\nThis article is originally written by authors and may differ from published version. Please refer to https://doi.org/10.1016/j.ecoleng.2021.106470" - }, - { - "objectID": "publications/2022-singh-ee/index.html#abstract", - "href": "publications/2022-singh-ee/index.html#abstract", - "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", - "section": "Abstract", - "text": "Abstract\nCoal extraction by opencast mining involves the dumping of overburden or mine spoil as large heaps. These large heaps of overburdened materials can act as a serious threat to ecological integrity and, therefore, overall societal well-being. Plantations are often employed to establish revegetation and management of mine spoil, thus mitigating the effects of mining on the environment. However, the performance of plant species can be highly variable due to environmental and species-specific effects. Therefore, the present paper’s primary objective is to compare exotic (Acacia auriculiformis, Cassia siamea, Casuarina equisetifolia and Grevillea pteridifolia) and native (Albizia lebbeck, Albizia procera, Dendrocalamus strictus and Tectona grandis) species’ performance on the coal mine spoils. Previous studies on the Singrauli coalfields allowed us to compare the growth performance, standing biomass, and net primary production (NPP) of four exotic and four native species plantations. Our results showed that native species have significantly higher survival, stem diameter, biomass, and NPP than exotic woody plantations. Thus, exotic species might not be useful in mine spoil rehabilitation than the native species. Overall, this study suggests that native species are useful for mine spoil rehabilitation despite the faster growth of exotic species.\nKeywords: Ecological restoration; Soil redevelopment; Exotic species; Native species; Coal mine spoil" - }, - { - "objectID": "publications/2022-singh-ee/index.html#introduction", - "href": "publications/2022-singh-ee/index.html#introduction", - "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", - "section": "Introduction", - "text": "Introduction\nIndia is one of the significant coal producers worldwide; however, the demand for coal for electricity generation and industrial production is so high that it needs to import substantial coal quantities (IEA 2019). Most of the coal in India is extracted by surface mining, which involves removing the earth’s surface in the form of sheets resulting in a large amount of waste material, usually referred to as overburden or mine spoil (A. N. Singh and Singh 2006). This overburden is piled up to form new landforms looking like large stacks of mine spoil until refilling. These piles of mine spoils are characterised by a high concentration of metals and toxic chemical compounds (Novianti et al. 2018), which cascades into the ecosystem and reaches humans through various sources like contaminated food and water. Further, removal of topsoil and alteration in soil profile causes unavoidable loss to biodiversity, which disrupts the ecosystem structure and functions (Adibee, Osanloo, and Rahmanpour 2013; Feng et al. 2019).\nGrowing concerns about the environmental impacts of coal mining, together with the slow natural recovery of mine spoils, urge technical solutions to restore these degraded ecosystems into their original states (Macdonald et al. 2015). A successful restoration programme accelerates the natural recovery processes to check soil erosion, restore soil fertility, and enhance biological diversity (A. N. Singh, Raghubanshi, and Singh 2002). Therefore, the first step in any restoration programme, of course, is to protect the disturbed habitat and communities from being further wasted. Then follow attempts to accelerate the revegetation process for increasing biodiversity and stabilising nutrient cycling (A. N. Singh and Singh 2006; A. N. Singh, Zeng, and Chen 2006).\nPlantations have been contemporarily used to restore degraded lands worldwide effectively (Badı́a et al. 2007; Bohre and Chaubey 2016; Erskine, Lamb, and Borschmann 2005; Jeżowski et al. 2017; A. Singh 2001; A. N. Singh and Singh 1999; A. N. Singh, Raghubanshi, and Singh 2004b). However, the suitability of species and their performance on coal mine spoil have remained a challenging task as the characteristics of coal mine spoils are highly heterogeneous and lack soil organic matter (SOM), so that it is regarded as a recalcitrant medium for plant growth (Adibee, Osanloo, and Rahmanpour 2013; Feng et al. 2019; K. Singh, Singh, and Tewari 2021).\nSome of the earlier studies have evaluated several plant species’ growth and biomass production on coal mine spoil (Badı́a et al. 2007; Bohre and Chaubey 2016; Erskine, Lamb, and Borschmann 2005; Jeżowski et al. 2017; A. Singh 2001; A. N. Singh and Singh 1999; A. N. Singh, Raghubanshi, and Singh 2004b). Although exotic woody species are often suggested to restore coal mine spoil due to their fast growth and high economic or livelihood benefits, it often results in low biodiversity development (DAntonio and Meyerson 2002; Dutta and Agrawal 2003; Lamb, Erskine, and Parrotta 2005). Many previous studies have shown that exotic species can positively or negatively impact soil fertility and native flora while restoring degraded lands (Berger 1993; DAntonio and Meyerson 2002; Yan et al. 2020). Although exotic species may have higher survival (Citadini-Zanette et al. 2017) and improve soil properties (Yan et al. 2020), they often result in low carbon development compared to native species (Citadini-Zanette et al. 2017).\nNet primary production is considered a critical functional parameter that helps evaluate species’ quality. Biomass is a crucial parameter of structural attributes. They directly contribute to organic matter, energy transformation, and nutrient cycling between vegetation and soil. Exotic species show successful establishment, and their fast growth often outcompetes the native species during the restoration (Huxtable, Koen, and Waterhouse 2005). Another study showed slight differences in biomass production among the exotic and native plants established on degraded lands (Islam et al. 1999). The biomass allocation to different plant parts can be controlled by environmental and biological (species-specific) factors (Boonman et al. 2020; Freschet, Swart, and Cornelissen 2015; Poorter and Sack 2012). However, the biomass allocation to different plant parts can vary between exotic and native species because species may have adapted to their native habitats and exhibit differential allocation strategies. Thus, there is a need for an increased understanding of the biology and impacts of exotic and native species on degraded lands. Therefore, comparing survival, growth and biomass production among exotic and native species becomes essential to assess the suitability of plant species for the reclamation process.\nThe present study compares the survival, growth performance, biomass accumulation, net primary productivity of 5-year-old native and exotic woody plantations established on coal mine spoils. We expect exotic species to have higher survival, growth, and biomass production on coal mine spoils because they usually exhibit higher competitive abilities. Therefore, they can sustain themselves on nutrient-poor and degraded lands. Specifically, we address the following four questions from our study:\n\nCan native species have higher survival and growth performance on coal mine spoils?\nWhat are the biomass and net primary production level among native and exotic plantations at earlier stages?\nWhether biomass production and net primary production (NPP) are species-specific?" - }, - { - "objectID": "publications/2022-singh-ee/index.html#material-and-methods", - "href": "publications/2022-singh-ee/index.html#material-and-methods", - "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", - "section": "Material and methods", - "text": "Material and methods\n\nStudy site and climate\nThe plantations under present study were located in the west section of Jayant block of Singrauli Coalfields in Singrauli district of Madhya Pradesh, India, which lies between latitudes 24° 6′ 45″ – 24° 11′ 15″ N and longitude 82° 36′ 40″ – 82° 41′ 15″ E. The study area is situated on a plateau above the plain (around 500 m above mean sea level) on its southwest side. In contrast, the plateau’s foot’s average elevation is approximately 300 m above mean sea level. The climate of the area is tropical monsoon, and the year is divisible into a mild winter (November–February), a hot summer (March–June), and a warm rainy season (July–October). The mean monthly minimum temperature within the annual cycle ranges from 6 to 28 °C and the mean monthly maximum from 20 to 40 °C. The rainfall annually averages 1069 mm, of which about 90% occurs from late June to early September. The rainfall is characterised by a high degree of inter-annual variation, as during the study period 1990–1996, it ranges from 700 to 1450 mm yr−1 (A. N. Singh, Raghubanshi, and Singh 2004b, 2004a).\n\n\nPlantations and experimental design\nPlantations of native species were raised in July–August of 1990–91 by planting nursery-raised seedlings in previously dug pits of 40 cm × 40 cm × 40 cm size at a spacing of 2 m × 2 m. The plantations of Albizia lebbeck (L.) Benth., Albizia procera (Roxb.) Benth. and Tectona grandis L.f. were raised in 1990, whereas Dendrocalamus strictus Nees plantation was raised in 1991 by planting 7 to 8 months old nursery raised seedlings. The total planted area for A. lebbeck and A. procera was 1.5 ha, whereas the same for T. grandis and D. strictus was about 0.5 ha each. For sampling, three permanent plots were established for each species. The sample plots’ size was 25 m × 25 m for A. lebbeck and A. procera whereas 15 m × 15 m plot size for T. grandis and D. strictus.\nInitially a total of 2500 seedlings per hectare were planted for each species. After five years, survival is estimated as the number of individuals (clumps in D. strictus) in each plot, which was inventoried in February–March during 1995–1996.\n\n\nBiomass and net primary production\nAllometric equations relating tree dimensions to the plant parts’ biomass were developed to measure tree biomass. Twelve individuals of each species, representing a gradient of diameter, were felled from an area adjoining the permanent plots, and their diameter (D) and height (H) were measured. The felled individuals were separated into stem and foliage. The root systems of the felled plants were excavated to a depth of 1 m. Each component’s fresh weight (stem, foliage, and coarse roots with a diameter greater than 5 mm) was recorded in the field. Sub-samples were brought to the laboratory to determine dry weights. The data were subjected to regression analysis to relate the dry weight of stem, foliage, rhizome, and root with D or D2H or their natural log values. The highest R2 (correlation coefficient) equations were selected, which were also used in earlier studies (Dutta and Agrawal 2003; A. N. Singh, Raghubanshi, and Singh 2004b; A. N. Singh and Singh 1999). The standing biomass of different components (stem, foliage, and root) was calculated using the biomass estimation equations. These values were then multiplied by the density of tree species. Per hectare biomass estimations were obtained separately for each plot and averaged across the plots to get the mean estimates at different ages.\nFine root (less than 5 mm in diameter) biomass was quantified by digging out 20 cm × 20 cm × 20 cm monoliths at 20 cm intervals from the plant base to 1-m distance. Monoliths were washed with a fine jet of water, and fine roots were collected, dried, and weighed. Tree roots were separated from roots of herbaceous plants based on colour and appearance.\nThe net primary production was estimated using diameter increments and biomass data described by earlier studies on the study site (Dutta and Agrawal 2003; A. N. Singh, Raghubanshi, and Singh 2004a; L. Singh and Singh 1991).\n\n\nData for exotic species\nPrevious studies have investigated the restoration potential of some exotic species on the same study site (Dutta and Agrawal 2003, 2001; J. S. Singh, Singh, and Jha 1995). These studies provided an opportunity to compare exotic and native species’ restoration potential because they followed a similar experimental design. These studies considered four exotic species (Casuarina equisetifolia L., Cassia siamea Lam., Grevillea pteridifolia Knight, and Acacia auriculiformis A. Cunn. ex Benth.). The total planted area for C. equisetifolia and G. pteridifolia was 1.5 ha each, whereas the same for A. auriculiformis and C. siamea was about 0.5 ha each. For sampling, three permanent plots were established for each species. The sample plots’ size was 25 m × 25 m for C. equisetifolia and G. pteridifolia and; 10 m × 10 m for A. auriculiformis and C. siamea (Dutta and Agrawal 2003, 2001; J. S. Singh, Singh, and Jha 1995).\n\n\nStatistical analyses\nSPSS-PC statistical software was used for all statistical analyses, except wherever specifically mentioned. The data were subjected to the General Linear Model (GLM) for analysis of variance (ANOVA) to observe the species’ effect. Mean values were tested for difference among plantation species with Tukey’s honestly significant difference (HSD) mean separation test (SPSS, 2003, version 10.0). Regression equations were developed through the same statistical package. To observe the effect of origin (exotic vs. native), student’s t-test was conducted using the package rstatix (Kassambara 2021) in the R language and environment for statistical computation (R Core Team 2020)." - }, - { - "objectID": "publications/2022-singh-ee/index.html#results", - "href": "publications/2022-singh-ee/index.html#results", - "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", - "section": "Results", - "text": "Results\n\nSurvival\nPlantations’ survival has been estimated as the stocking density (Individual stems ha-1) for each species (three plots) under exotic and native plantations, and the results are tabulated in Table 1.\n\n\n\n\n\n\n\n\nTable 1: Stocking density (Individuals / ha) of 5-yr old planted exotic and native woody species on coal mine spoil\n\n\n\n\n\n\n\n\n\nPlanted species\nSurviving individuals / ha (%)\nMean ± 1 SE\n\n\nPlot 1\nPlot 2\nPlot 3\n\n\n\n\nExotic\n\n\n\nAcacia auriculiformis\n\n1600 (64)\n1760 (70.4)\n1670 (66.8)\n\n1677 ± 46cd (67)\n\n\n\n\nCasuarina equisetifolia\n\n1450 (58)\n1600 (64)\n1650 (66)\n\n1566 ± 61d (63)\n\n\n\n\nCassia siamea\n\n1900 (76)\n1778 (71)\n1790 (72)\n\n1822 ± 39bc (73)\n\n\n\n\nGrevillea pteridifolia\n\n1789 (72)\n2000 (80)\n1786 (71)\n\n1858 ± 71bc (74)\n\n\n\nNative\n\n\n\nAlbizia lebbeck\n\n2192 (89)\n2160 (86)\n2208 (88)\n\n2187 ± 14a (87)\n\n\n\n\nAlbizia procera\n\n2224 (89)\n2192 (88)\n2208 (88)\n\n2208 ± 9a (88)\n\n\n\n\nTectona grandis\n\n1645 (66)\n1822 (73)\n1867 (75)\n\n1778 ± 68cd (71)\n\n\n\n\nDendrocalamus strictus\n\n2000 (80)\n2000 (80)\n2088 (84)\n\n2029 ± 29ab (81)\n\n\n\n\nData for exotic species obtained from Dutta and Agrawal (2003) and Singh et al. (1995)\n\n\nValues given in parenthesis represent the percent of survival of individuals\n\n\nWithin the Mean ± 1 SE column, values followed by the same letter are not significantly different at p < 0.05, using the Tukey’s HSD test\n\n\n\n\n\n\n\n\n\nThe stocking density (individual stem ha-1) at the time of plantation was 2,500 in both types of plantations. After five years of plantation establishment, about 71-88% of individuals were survived in native and 63-74% in exotic plantations. Among all plantations, the highest survival rate was observed in the native species (A. procera) and lowest in the exotic species (C. equisetifolia); therefore, ANOVA indicated significant differences in stocking density due to species (Table 2).\n\n\n\n\n\n\n\n\nTable 2: Summary of ANOVA for plantation species’ effect on growth parameters, biomass, and net primary production components\n\n\nComponents\nF7,16\np-value\n\n\n\n\nHeight\n82.051\n0.0000\n\n\nDiameter\n56.333\n0.0000\n\n\nHeight / Diameter (H/D)\n97.601\n0.0000\n\n\nTree volume (D2H)\n13.71\n0.0000\n\n\nFoliage biomass\n86.147\n0.0000\n\n\nStem biomass\n220.673\n0.0000\n\n\nCoarse root biomass\n266.112\n0.0000\n\n\nFine root biomass\n31.883\n0.0000\n\n\nTotal biomass\n304.449\n0.0000\n\n\nFoliage production\n556.164\n0.0000\n\n\nStem production\n44.873\n0.0000\n\n\nCoarse root production\n61.241\n0.0000\n\n\nFine root production\n98.281\n0.0000\n\n\nTotal tree layer production\n71.518\n0.0000\n\n\n\n\n\n\n\n\nHowever, the survival rates were significantly higher in native plantations than in exotic species (Figure 1).\n\n#> [1] FALSE\n\n\n\n\nFigure 1: Violin plots with mean and standard deviation for survival, growth, biomass, and net primary productivity among the exotic and native woody species plantations on coal mine spoil. The statistical significance was determined by using student’s t-test and the p-values <0.0001, <0.001, <0.01, <0.05 and 0.1 corresponds to ****, ***, **, * and ns, respectively.\n\n\n\n\n\n\nGrowth performance\nThe growth performance of exotic and native species was determined in terms of height and diameter. Height and diameter (growth parameter) were significantly varied among all plantations of exotic and native species (Table 2). Among all plantation species (native and exotics), the maximum height was attained by G. pteridifolia, whereas maximum diameter was observed for A. lebbeck after 5-years of their establishment. The height and diameter values varied from 2.19 to 5.18 m and 4.32 to 7.58 cm, respectively, in native and 2.75 to 5.88 m and 2.99 to 4.90 cm, respectively, in exotic plantations (Table 3). However, the height was significantly higher, and the diameter was significantly smaller in exotic species plantations than native species plantations (Figure 1).\n\n\n\n\n\n\n\n\nTable 3: Growth performance of 5-yr-old planted exotic and native woody species on coal mine spoil\n\n\n\n\n\n\n\n\n\n\n\n\n\nParameters\nExotic\nNative\n\n\nAA\nCE\nCS\nGP\nAL\nAP\nTG\nDS\n\n\n\n\n\nHeight (m)\n\n\n5.00d\n\n\n5.29ab\n\n\n2.75cd\n\n\n5.88a\n\n\n3.38c\n\n\n2.97c\n\n\n2.19d\n\n\n5.18ab\n\n\n\n\nDiameter (cm)\n\n\n3.95cd\n\n\n4.90bc\n\n\n2.99d\n\n\n3.87cd\n\n\n7.58a\n\n\n7.32a\n\n\n5.22b\n\n\n4.32bc\n\n\n\n\nH/D ratio\n\n\n126.59b\n\n\n107.97bc\n\n\n91.97c\n\n\n151.94a\n\n\n44.59d\n\n\n40.57d\n\n\n41.95d\n\n\n119.91b\n\n\n\n\nD2H (cm3)\n\n\n7801cd\n\n\n12701abc\n\n\n2458d\n\n\n8806cd\n\n\n19420a\n\n\n15913ab\n\n\n5967cd\n\n\n9667bc\n\n\n\n\nData for exotic species obtained from Dutta and Agrawal (2003) and Singh et al. (1995)\n\n\nValues are means of three replicates\n\n\nWithin the columns, values followed by the same letter are not significantly different at p < 0.05, using the Tukey’s HSD test\n\n\nAA, Acacia auriculiformis; CE, Casuarina equisetifolia; CS, Cassia siamea; GP, Grevillea pteridifolia; AL, Albizia lebbeck; AP, Albizia procera; TG, Tectona grandis; DS, Dendrocalamus strictus.\n\n\n\n\n\n\n\n\n\nConsequently, the height to diameter ratio was significantly smaller in native species. A significant positive correlation is observed for height and diameter in exotic species, whereas selected native species did not exhibit any significant correlation (Figure 2 a). Further, non-legumes showed a significant negative correlation (Figure 2 b).\n\n\n\n\n\nFigure 2: Relationship between Height and Diameter for exotic and native woody species (a), and legume and non-leguminous species plantations (b). The regression line was fitted using the linear model, and Pearson’s correlation coefficient values are represented by letter ‘R’ with corresponding probability p-values.\n\n\n\n\n\n\nBiomass production\nThe observed values for the biomass of different plant components are summarised in Table 4. It was noted that D. strictus had shown the highest total biomass production among all the species, whereas A. auriculiformis exhibited the highest total biomass production among the exotic species. The biomass of different plant components was significantly varied due to species among all the plantations (Table 2). Therefore, values in native plantations significantly varied from 7.68 to 74.68 t ha-1, minimum for T. grandis and maximum for D. strictus plantation and 8.49-31.03 t ha-1 exotic plantations, being maximum in A. auriculiformis and minimum in C. siamea (Table 4).\n\n\n\n\n\n\nTable 4: Biomass production (t / ha) under 5-yr old planted exotic and native\nwoody species on coal mine spoil \n \n \n \n Parameters\n \n Exotic\n \n \n Native\n \n \n \n AA\n CE\n CS\n GP\n AL\n AP\n TG\n DS1\n \n \n \n Foliage\n\n6.68bc\n\n2.74d\n\n0.72e\n\n5.06c\n\n6.59bc\n\n7.26b\n\n2.39de\n\n10.68a\n\n Stem\n\n16.77c\n\n15.69c\n\n4.91e\n\n11.46d\n\n32.32b\n\n14.21cd\n\n2.98e\n\n38.5a\n\n Coarse root\n\n4.53d\n\n2.98e\n\n2.56e\n\n5.84c\n\n12.05a\n\n10.65b\n\n1.94e\n\n5.27cd\n\n Fine root\n\n3.05a\n\n0.40c\n\n0.30c\n\n0.57c\n\n0.85bc\n\n0.74bc\n\n0.37c\n\n1.40b\n\n Total\n\n31.03c\n\n21.81d\n\n8.49e\n\n22.90d\n\n51.81b\n\n32.86c\n\n7.68e\n\n74.68a\n\n \n \n \n Data for exotic species obtained from Dutta and Agrawal (2003) and Singh et al. (1995)\n \n \n Values are means of three replicates\n \n \n Within the columns, values followed by the same letter are not significantly different at p < 0.05, using the Tukey’s HSD test\n \n \n AA, Acacia auriculiformis; CE, Casuarina equisetifolia; CS, Cassia siamea; GP, Grevillea pteridifolia; AL, Albizia lebbeck; AP, Albizia procera; TG, Tectona grandis; DS, Dendrocalamus strictus.\n \n \n \n \n 1 Values of rhizome component included in the total biomass\n \n \n\n\n\n\n\nAmong plant parts, stem contributed more than 50% to the total biomass for both exotic and native species (Figure 3 a) and leguminous and non-leguminous species (Figure 3 b). The share of aboveground components in the total biomass in the present study was 65.3-91.1% in native and 66.3-84.5% in exotic and belowground contribution was in the range of 8.9-34.7% in native and 15.5-33.7% in exotic plantations, respectively (Figure 3 c). However, the belowground biomass was higher for leguminous species than the non-leguminous species (Figure 3 d). Moreover, relative contributions of short-lived components (foliage and fine root < 5 mm) were small as compared to long-lived tree component (stem) to the tree layer biomass for both types of plantations calculated at 5-yr age (Figure 3 e), and a similar pattern was observed for the leguminous and non-leguminous species (Figure 3 f). Further, all components’ biomass production except fine root biomass was significantly higher in native species plantations compared to exotic species plantations (Figure 1).\n\n\n\n\n\nFigure 3: Biomass partitioning among different plant parts (a, b), aboveground and below ground (c, d), and short-lived and long-lived components (e, f) for exotic vs. native and legume vs. non-legume species plantation, respectively.\n\n\n\n\n\n\nNet primary production\nThe net primary productivity of different components of plant species is given in Table 5. Total net production (above + below ground) among these plantations on mine spoil varied from 4.76 to 32.04 t ha-1 yr-1 in native and 3.72-18.24 t ha-1 yr-1 exotic species (Table 5). The differences in net production of all plant values components were significantly varied due to species, as indicated by ANOVA (Table 2). The aboveground net production of present planted species ranged from 3.75-24.28 t ha-1 yr-1 in native plantations, maximum in D. strictus, and minimum in T. grandis where 2.55-12.55 t ha-1 yr-1 in exotic plantations, being maximum in A. auriculiformis and minimum in C. siamea plantation. Similar to biomass, relative contributions of short-lived (foliage and fine root <5 mm) were lower long-lived tree components (stem) to the tree layer biomass and NPP for both types of plantations calculated at 5-yr age. The foliage contribution and fine root (<5 mm diameter) to the biomass were much smaller than that of NPP in all four native species, while it was the opposite of exotic species. For example, foliage component contributed in exotic plantations were in the range of 8.5-22.1%, being maximum by G. pteridifolia and minimum by C. siamea. Fine roots were in the range of 1.8-9.8%, being maximum by A. auriculiformis and minimum by C. equisetifolia.\n\n\n\n\n\n\nTable 5: Net primary production (t ha-1 yr-1) under 5-yr\nold planted exotic and native woody species on coal mine spoil. \n \n \n \n Parameters\n \n Exotic\n \n \n Native\n \n \n \n AA\n CE\n CS\n GP\n AL\n AP\n TG\n DS1\n \n \n \n Foliage\n\n1.38d\n\n1.07de\n\n0.31e\n\n1.67cd\n\n6.59b\n\n7.26b\n\n2.39c\n\n10.68a\n\n Stem\n\n11.17ab\n\n7.78c\n\n2.19d\n\n6.75c\n\n11.26a\n\n8.07bc\n\n1.36d\n\n13.60a\n\n Coarse root\n\n2.64bc\n\n1.62d\n\n0.92de\n\n3.31ab\n\n3.61a\n\n2.54c\n\n0.63e\n\n1.12de\n\n Fine root\n\n3.05a\n\n0.40cd\n\n0.30d\n\n0.54cd\n\n0.85c\n\n0.74cd\n\n0.37d\n\n1.40b\n\n Total\n\n18.24c\n\n10.86d\n\n3.72e\n\n12.27d\n\n23.86b\n\n19.30bc\n\n4.76e\n\n32.04a\n\n \n \n \n Data for exotic species obtained from Dutta and Agrawal (2003) and Singh et al. (1995)\n \n \n Values are means of three replicates\n \n \n Within the columns, values followed by the same letter are not significantly different at p < 0.05, using the Tukey’s HSD test\n \n \n AA, Acacia auriculiformis; CE, Casuarina equisetifolia; CS, Cassia siamea; GP, Grevillea pteridifolia; AL, Albizia lebbeck; AP, Albizia procera; TG, Tectona grandis; DS, Dendrocalamus strictus.\n \n \n \n \n 1 Values of rhizome component included in the total biomass\n \n \n\n\n\n\n\n\n\n\n\n\nFigure 4: Net primary productivity (NPP) partitioning among different plant parts (a, b), aboveground and below ground (c, d), and short-lived and long-lived components (e, f) for exotic vs. native and legume vs. non-legume species plantation, respectively.\n\n\n\n\nSimilarly, in native species, foliage contributed 12.7-32.1% being maximum by T. grandis and minimum by A. lebbeck, whereas fine root was in the range of 1.6-5.1%, respectively. Interestingly, the foliage and total net primary production of exotic species were significantly higher in native species than the exotic species (Figure 1). However, stem and roots’ net primary production did not significantly differ among the exotic and native species (Figure 1). A significant positive correlation was observed for total and stem net primary production with the foliage biomass (Figure 5).\n\n\n\n\n\nFigure 5: Relationships between net primary production and foliage biomass for 5-yr-old plantations of all exotic and all native woody species on coal mine spoil. The linear regression equation (y = a + bx), Pearson’s correlation coefficient (r), and corresponding probability values (p) are shown in the top-left corner of each subplot." - }, - { - "objectID": "publications/2022-singh-ee/index.html#discussion", - "href": "publications/2022-singh-ee/index.html#discussion", - "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", - "section": "Discussion", - "text": "Discussion\n\nNative species plantations had a higher survival\nThe present study indicated that native species better survive on degraded lands than exotic species, supported by earlier studies (Islam et al. 1999). Although some previous studies have investigated survivability of native species on coal mine spoil (Mosseler, Major, and Labrecque 2014; A. N. Singh, Raghubanshi, and Singh 2004b, 2004a), very few studies compared survivability with exotic species (Huxtable, Koen, and Waterhouse 2005; Islam et al. 1999). Another study conducted on saline soils of northern Australia also suggested the high survival of native species (D. Sun and Dickinson 1995). Evidently, in this study, more remarkable survival was observed in all native species indicating better adaptability of species for mine spoil restoration. Thus, the higher survival of native species may be due to their pre-adaptation to the environmental conditions. However, survival may not be directly linked to species’ origin; rather, it can be a legacy of function and life-history traits. Therefore, survival cannot be considered as the only indicator of ecological restoration.\n\n\nGrowth performance\nThe height and diameter of woody plant species are critical structural parameters involved in measuring growth performance, which is affected by environmental conditions (Lestari et al. 2019; Sumida, Miyaura, and Torii 2013). Our results suggested that exotic plants tend to invest more photosynthates in height growth, whereas native plants invested more photosynthates in stem growth. A higher diameter in native species indicates the possibility of their adaptation to windbreak and endorses a longer establishment. Further, these observations indicate that exotic plants might be suffering from a limitation of light. In contrast, native plants may be pre-adapted to environmental conditions and therefore invested more photosynthates in diameter for an efficient supply of resources to the shoot (Boonman et al. 2020). However, this finding contrasts the reports of an earlier study in similar environmental conditions (Islam et al. 1999).\nHeight growth is usually associated with the production of newer leaves and more significant resource acquisition. In contrast, the increase in stem diameter ensures tissues’ development supports the leaves (Sumida, Miyaura, and Torii 2013). Thus, the increase in height should be accompanied by an increase in the stem’s diameter, as suggested by biomechanical models (Henry and Aarssen 1999; J. Sun et al. 2019). Our results also seemed to support this hypothesis at least in exotic species but contrasted by the non-leguminous species, suggesting a trade-off between height and diameter in non-leguminous plants, possibly due to limited resources.\nIn contrast to height growth, diameter growth depends primarily on current photosynthesis, although some reserve carbohydrates may be used for diameter growth very early in the season (Kozlowski 1962). Thus, the greater height in case of exotic species can be attributed to their higher photosynthetic rate 12.1 (A. auriculiformis) to 30.14 µmol CO2 m-2 s-1 (C. equisetifolia) as compared to native plantations in the present research site might be a promoting point to faster growth behaviour of exotic species (V. Singh and Toky 1995). The smaller height to diameter ratio of exotic species indicates their higher competitive ability and rapid growth on degraded sites. This view is supported by a study conducted on the degraded tropical pasture of southern Costa Rica, where it was shown that exotic species outperformed the native plantations (Carpenter, Nichols, and Sandi 2004). Nevertheless, native species can also display growth rates similar to exotic species depending on the site environments (Bare and Ashton 2016).\n\n\nBiomass and net primary production\nThe present study indicated that native species produced higher biomass as compared to exotic species. This suggests a better adaptation and higher resource use efficiency of native species than the exotic species on the coal mine spoil. In contrast to our finding, a previous study reported little differences in biomass production among the exotic and native species (Islam et al. 1999).\nFurther, biomass partitioning into different plant parts revealed that native species produced significantly higher biomass for foliage, stem, and coarse roots (diameter greater than 5 mm) compared to exotic species. In contrast, fine root biomass was higher in the case of exotic species, though not significant. Higher biomass production of fine roots in exotic species suggests that these plants responded to the stressed environment of coal mine spoils to get available nutrient and water sources to maintain their growth performance, especially height instead of diameter (Boonman et al. 2020).\nSince the present study focused on woody tree species, the higher contribution of aboveground biomass to the total biomass is expectable and supported by many previous studies; exotic species invested more in belowground components. In contrast, native species invested more in aboveground components while responding to the same coal mine spoils. It is believed that plants tend to invest more in belowground components during the disturbance or stressful conditions such as the nutrient-poor coal mine spoils (Poorter and Sack 2012; Priest et al. 2015). Further, both species invested more in long-lived components than in short-lived components; however, exotic species invested more in short-lived components, especially the fine roots. The long-lived component (stem) contribution was much higher than to NPP in all four native species but the opposite in the exotic plantations. Thus, the short-lived components might be associated with ecosystem functions whereas long-lived components account for structural attributes in the native plantations.\nMoreover, foliage accounted for a lower proportion of ecosystem function in all plantations of exotic species. In contrast, native species showed considerably very high that more foliage biomass production in such a stressed environment (degraded mine spoil) may provide more soil organic matter to regulate the cycling of nutrients. The native and exotic species have different allocational strategies.\nAccording to the functional equilibrium hypothesis, this suggests that exotic species may experience belowground resource (water and nutrients) limitation, whereas native species experience an aboveground resource (sunlight and CO2) limitation (Boonman et al. 2020; Brouwer 1983). These changes may be attributed to morphological adaptations or phenotypic plasticity rather than biomass allocation (Freschet, Swart, and Cornelissen 2015; Poorter and Sack 2012). However, if none of the resources is limiting or equally limiting, plants tend to allocate the resources optimally. This is referred to as the ‘optimal partitioning hypothesis’ (Gedroc, McConnaughay, and Coleman 1996) or ‘balanced growth hypothesis’ (Shipley and Meziane 2002). Thus, if both types of species faced similar environmental conditions (probably that was the case in the present study), those species which produce greater belowground biomass during the initial stages may be better suited for reclamation of coal mine overburden. Following this, our results suggest that exotic and leguminous plants may be better suited for coal mine restoration, though these effects can be highly species-specific.\nThe net primary production or NPP is associated with photosynthesis and biomass production, as indicated by a strong positive relation between biomass and NPP. The present study suggested an overall NPP ranging from 3 to 32 t ha-1 yr-1, comparable to earlier studies (Dutta and Agrawal 2003; A. N. Singh and Singh 1999; V. Singh and Toky 1995). The early successional species are reported to exhibit net production of 8-21 t ha-1 yr-1 in natural dry tropical forests (Murphy and Lugo 1986), whereas the aboveground net production of plantations on coal mine spoil and natural forests in the tropical zone ranged between 1.5 and 32.62 t ha-1 yr-1 (Dutta and Agrawal 2003; P. K. Singh and Singh 1998; V. Singh and Toky 1995; L. Singh and Singh 1991). However, our comparison suggested that native species had significantly higher total and foliage NPP than the exotic species on coal mine spoils. This indicated that native species were much more efficient in resource utilisation, possibly due to their pre-adaptation to the tropical environments. Thus, achieving an early vegetation cover and high biomass production on mine spoil can be approached through proper selection and planting of pioneer native tree species. Such species can exist under harsh soil conditions and require less long-term maintenance (A. N. Singh, Raghubanshi, and Singh 2004b; A. N. Singh and Singh 2006)." - }, - { - "objectID": "publications/2022-singh-ee/index.html#conclusions", - "href": "publications/2022-singh-ee/index.html#conclusions", - "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", - "section": "Conclusions", - "text": "Conclusions\nThe present study points out that native species performs well than the exotic species in the rehabilitation and restoration of coal mine spoils. This conclusion is supported by the higher biomass and NPP for native species compared to exotic species, though exotic species exhibited more remarkable height growth. However, consideration of species’ leguminous nature did not affect the biomass and NPP in the present study, though it may affect the redevelopment of soils in degraded habitats. Further, the effect of exotic species seemed to be highly variable and species-specific. Therefore, more comparative knowledge on the species-specific effects on ecosystem restoration, biodiversity reconstruction, and its possible effects on their services towards the ecosystem and local people is still required. Therefore, more future investigations on various ecological restoration scales are warranted with a more significant number of species while inferring effects of exotic and native species for comparative restoration potential." - }, - { - "objectID": "publications/2022-singh-ee/index.html#acknowledgments", - "href": "publications/2022-singh-ee/index.html#acknowledgments", - "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", - "section": "Acknowledgments", - "text": "Acknowledgments\n\nAuthors are grateful to the Chairperson, Department of Botany, Panjab University, Chandigarh, and the Chairperson, Department of Botany, Banaras Hindu University, Varanasi, to provide all necessary facilities required for the work. The authors are also profoundly thankful to Prof. J. S. Singh for his constructive guidelines and supervision during the study." - }, - { - "objectID": "publications/2022-singh-ee/index.html#funding", - "href": "publications/2022-singh-ee/index.html#funding", - "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", - "section": "Funding", - "text": "Funding\n\nThis work was supported by the University Grants Commission, Government of India as GATE fellowship and MRP to ANS, and Junior Research Fellowship to AK [507/(OBC) (CSIR-UGC NET DEC. 2016)]." - }, - { - "objectID": "publications/2022-singh-ee/index.html#authors-contributions", - "href": "publications/2022-singh-ee/index.html#authors-contributions", - "title": "Ecological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region", - "section": "Author’s contributions", - "text": "Author’s contributions\n\nAnand Narain Singh: Conceptualisation, Methodology, Validation, Formal analyses, Investigation, Data curation, Writing - original draft, Writing - review & editing, Supervision. Abhishek Kumar: Formal analyses, Writing - review & editing, Visualization, Funding acquisition." - }, { "objectID": "publications/2022-singh-ldd/index.html", "href": "publications/2022-singh-ldd/index.html", @@ -257,110 +278,5 @@ "title": "Comparative soil restoration potential of exotic and native woody plantations on coal mine spoil in a dry tropical environment of India: A case study", "section": "Author’s contribution", "text": "Author’s contribution\n\nAnand Narain Singh: Conceptualization, Data curation (lead), Formal Analysis (equal), Investigation (lead), Methodology (lead), Project administration, Resources, Supervision, Validation (lead), Writing – original draft (lead), Writing – review & editing (equal). Abhishek Kumar: Data curation (supporting), Formal Analysis (equal), Funding acquisition, Methodology (supporting), Software, Validation (supporting), Visualization (lead), Writing – original draft (supporting), Writing – review & editing (equal)." - }, - { - "objectID": "publications/2023-patil/index.html", - "href": "publications/2023-patil/index.html", - "title": "Mycorrhizal fungi accelerates litter decomposition rates in forest ecosystems", - "section": "", - "text": "The role of mycorrhizal fungi in modulating litter decomposition rates is complex, with potential outcomes of both facilitation and competition. However, the magnitude and direction of these effects have remained uncertain due to limited empirical evidence. We conducted a meticulous meta-analysis of published peer-reviewed literature to address this knowledge gap. Surprisingly, our systematic literature search, guided by pre-defined inclusion criteria, yielded only five comparable studies. Despite this constraint, our dataset comprised 14 estimates across nine species to evaluate the effects of mycorrhizal fungi on litter decomposition rates. Applying a random-effect meta-analytic model, we discovered compelling evidence (RR = 1.07, Z = 3.58, p < 0.001) supporting accelerated litter decomposition rates in the presence of mycorrhizal fungi. However, the influence of mycorrhizal fungi on decomposition varied significantly with species identity, while no discernible effects were observed based on phylogeny. These findings challenge the generality of the widely recognised “Gadgil effect” and underscore the importance of species-specific considerations. Our results highlight the potential of mycorrhizal-mediated mechanisms to enhance litter decomposition, prompting a re-evaluation of nutrient cycling dynamics. Incorporating mycorrhizal effects into ecological models and management strategies could significantly advance our understanding of ecosystem responses to global environmental change. Overall, this work contributes to the broader understanding of the ecological implications of mycorrhizal fungi and calls for additional research efforts to elucidate the mechanisms and generalizability of these effects comprehensively.\nKeywords: gadgil effect; ectomycorrhizal fungi; arbuscular mycorrhizal fungi; forest ecosystem; litter decomposition; meta-analysis" - }, - { - "objectID": "publications/2023-patil/index.html#abstract", - "href": "publications/2023-patil/index.html#abstract", - "title": "Mycorrhizal fungi accelerates litter decomposition rates in forest ecosystems", - "section": "", - "text": "The role of mycorrhizal fungi in modulating litter decomposition rates is complex, with potential outcomes of both facilitation and competition. However, the magnitude and direction of these effects have remained uncertain due to limited empirical evidence. We conducted a meticulous meta-analysis of published peer-reviewed literature to address this knowledge gap. Surprisingly, our systematic literature search, guided by pre-defined inclusion criteria, yielded only five comparable studies. Despite this constraint, our dataset comprised 14 estimates across nine species to evaluate the effects of mycorrhizal fungi on litter decomposition rates. Applying a random-effect meta-analytic model, we discovered compelling evidence (RR = 1.07, Z = 3.58, p < 0.001) supporting accelerated litter decomposition rates in the presence of mycorrhizal fungi. However, the influence of mycorrhizal fungi on decomposition varied significantly with species identity, while no discernible effects were observed based on phylogeny. These findings challenge the generality of the widely recognised “Gadgil effect” and underscore the importance of species-specific considerations. Our results highlight the potential of mycorrhizal-mediated mechanisms to enhance litter decomposition, prompting a re-evaluation of nutrient cycling dynamics. Incorporating mycorrhizal effects into ecological models and management strategies could significantly advance our understanding of ecosystem responses to global environmental change. Overall, this work contributes to the broader understanding of the ecological implications of mycorrhizal fungi and calls for additional research efforts to elucidate the mechanisms and generalizability of these effects comprehensively.\nKeywords: gadgil effect; ectomycorrhizal fungi; arbuscular mycorrhizal fungi; forest ecosystem; litter decomposition; meta-analysis" - }, - { - "objectID": "publications/2023-patil/index.html#graphical-abstract", - "href": "publications/2023-patil/index.html#graphical-abstract", - "title": "Mycorrhizal fungi accelerates litter decomposition rates in forest ecosystems", - "section": "Graphical abstract", - "text": "Graphical abstract" - }, - { - "objectID": "publications/2023-patil/index.html#introduction", - "href": "publications/2023-patil/index.html#introduction", - "title": "Mycorrhizal fungi accelerates litter decomposition rates in forest ecosystems", - "section": "Introduction", - "text": "Introduction\nGlobal environmental change is expected to substantially alter the biogeochemistry and nutrient cycling in terrestrial ecosystems (Shu et al. 2019). Among terrestrial ecosystems, forest ecosystems acts as significant sink for soil organic matter and therefore, substantially influence the global nutrient cycling (Pan et al. 2011). The cycling of nutrients is primarily regulated by the process of litter decomposition, which releases nutrients from plant biomass to the soils (Shu et al. 2019; Patil, Kumar, Kumar, and Singh 2020). Much of the variations in the litter decomposition rates are explained by climate and litter quality (Zhang et al. 2008; Cornwell et al. 2008; Patil, Kumar, Kumar, Cheema, et al. 2020). However, the remaining proportion of variation is often attributed to the composition and activity of decomposer organisms (McGuire and Treseder 2010; Sulman et al. 2017). Among decomposer organisms, bacteria and fungi are two major group organisms involved in litter decomposition (Schneider et al. 2012; Heijden et al. 2015). Although the role of saprotrophic fungi is appreciated in litter decomposition, mycorrhizal fungi can also substantially influence litter decomposition because they often act as mediators of nutrients between plants and soil (Finlay 2005; Frey 2019; Kumar et al. 2021). Among the mycorrhizal associations, arbuscular mycorrhizal fungi (AMF) and ectomycorrhizal fungi (EMF) are commonly associated with vascular plants (Brundrett and Tedersoo 2018). While the AMF dominates the tropical forests, EMF associations are frequently observed in extra-tropical forests (Steidinger et al. 2019). Thus, the effects of mycorrhizal fungi are expected to vary not only among mycorrhizal groups, but also across the plant species, ecosystems and environmental conditions.\nMycorrhizal fungi have been observed to decrease the litter decomposition rates (Ruth L. Gadgil and Gadgil 1971; Ruth L. Gadgil and Gadgil 1975), which is frequently referred as ‘Gadgil effect’ in the literature (Ruth L. Gadgil and Gadgil 1971; Fernandez and Kennedy 2016). The ‘Gadgil effect’ has been observed for both ectomycorrhizal (Ruth L. Gadgil and Gadgil 1971) and arbuscular mycorrhizal fungi (Leifheit, Verbruggen, and Rillig 2015). The resource competition between saprotrophs (free living microbes) and symbionts (mycorrhizal fungi) have been suggested to retard the decomposition rates in presence of mycorrhizal fungi (Ruth L. Gadgil and Gadgil 1971; Brzostek et al. 2015). The specialized enzymatic suite and foraging behaviour of these mycorrhizal fungi have been attributed to their ability to outcompete the saprotrophic fungi or other detritivore community (Averill, Turner, and Finzi 2014; Talbot et al. 2015). Further, a literature review (Fernandez and Kennedy 2016) has also ascribed Gadgil effect to nitrogen competition (Orwin et al. 2011; Averill, Turner, and Finzi 2014), chemical inhibition or allelopathy (Krywolap, Grand, and Casida Jr. 1964; Rasanayagam and Jeffries 1992), myco-parasitism (Kubicek et al. 2011), and altered moisture-regimes (Koide and Wu 2003).\nIn contrast to Gadgil effect, mycorrhizal fungi also reported to enhance the litter decomposition rates (Frey 2019) through extracellular enzymatic degradation (Talbot et al. 2015), oxidation via Fenton chemistry (Beeck et al. 2018), and priming effects, i.e., stimulation of growth and activities of saprotrophic decomposers by providing plant-derived carbon sources (Herman et al. 2012; Gorka et al. 2019). The priming effects appear to be more widespread as mycorrhizal fungi have limited genetic capabilities to produce organic matter degrading enzymes (Frey 2019). The availability of labile exudates together with mycorrhizal necromass stimulates the activity of free-living bacteria and saprotrophic fungi, which leads to higher decomposition rates (Fernandez and Kennedy 2016). Further, the the differences in ecophysiology of mycorrhizal groups are expected to modulate their effects on litter decomposition rates (Tedersoo and Bahram 2019). For instance, the capacities for enzymatic degradation of organic matter widely vary among mycorrhizal taxa and it appears that EMF taxa have more degrading capacities than the AMF taxa (Frey 2019). Therefore, considering the type of mycorrhizal association can be important in understanding the role of mycorrhizal fungi in nutrient cycling.\nThus, available literature suggest that mycorrhizal fungi can either enhance the litter decomposition by providing plant-derived carbon-source (Zhu and Ehrenfeld 1996; Shah et al. 2016; Gui et al. 2017; Sterkenburg et al. 2018) or suppress it by competing for the same resources with saprotrophic fungi, also known as ‘Gadgil effect’ (Ruth L. Gadgil and Gadgil 1971; Ruth L. Gadgil and Gadgil 1975; Brzostek et al. 2015). Nevertheless, some studies found little or no effects of mycorrhizal association on litter decomposition (Staaf 1988). Therefore, the effects of mycorrhizal association on litter decomposition remained equivocal and highly variable in magnitude and direction, suggesting more complex mechanisms and contexts than previously thought (Fernandez and Kennedy 2016). Although few exemplary syntheses about the effects of mycorrhizal association on litter decomposition exist (Averill, Turner, and Finzi 2014; Fernandez and Kennedy 2016), quantitative estimates and direction of such effects remained ambiguous. Further, none of the earlier syntheses conducted a formal meta-analysis and their general conclusions might be confounded by sampling error or other sources of variations. Therefore, our primary objective is to quantitatively synthesise the magnitude and direction of mycorrhizal effects on litter decomposition rates in forest ecosystems. Further, we can expect substantial differences in effect due to species identity and phylogenetic relatedness among them because each species may have different quality of litter which can influence the decomposition rates. In addition to this, we aim to identify the knowledge gaps and suggestions for future investigations. Specifically, we aim to address the following questions:\n\nDo mycorrhizal presence affect rates of litter decomposition in forest ecosystems?\nWhat is the magnitude and direction of mycorrhizal effects on litter decomposition rates?\nAre these effects influenced by species or their phylogenetic relatedness?\nDo these effects differ between angiosperms and gymnosperms?\n\nTo accomplish this, we carried out a formal meta-analysis of available data from systematically identified comparable studies. In this meta-analysis, we included a forest plot showing the overall effect mycorrhizal fungi on litter decomposition, between-study heterogeneity, and a funnel plot for showing publication bias of studies. We explored different meta-analytic models and assessed the robustness of our estimates. Then, we discussed the results of all studies briefly. In the end, we draw conclusions of the mycorrhizal effect on litter decomposition based on the results of our meta-analysis." - }, - { - "objectID": "publications/2023-patil/index.html#methodology", - "href": "publications/2023-patil/index.html#methodology", - "title": "Mycorrhizal fungi accelerates litter decomposition rates in forest ecosystems", - "section": "Methodology", - "text": "Methodology\nOur methodology broadly consisted of five phases, i.e., searching, screening, data extraction, statistical meta-analysis and sensitivity analysis. We followed the guidelines developed by Collaboration on Environmental Evidence (CEE 2018) to identify eligible studies using a systematic literature search and screening protocol. The protocol was developed by organising meetings and implemented in a similar manner as described earlier (Kumar et al. 2022). Further, the quality and reproducibility of meta-analysis was ensured by adhering to previously recommended guidelines (Koricheva and Gurevitch 2014; Nakagawa et al. 2017).\n\nLiterature search\nWe systematically searched Scopus (https://scopus.com/) and Web of Science Core Collection (https://webofknowledge.com/) on 17-18 February 2021 with the following search string:\n\n(mycorrhiza* OR ectomycorrhiza* OR “saprotrophic fungi” OR plant-fung*) AND (litter OR “litter decay” OR (litter AND decompos*) OR decomposition OR “nutrient acquisition”) AND (forest).\n\nThis search string was developed by combining previously identified groups of keywords (see Supplementary Information). Additionally, Google Scholar (https://scholar.google.com/) was searched on 18-Feb-2021 using “Mycorrhiza decomposition” and “Mycorrhiza nutrient acquisition”. Thus, we obtained 789 records from Scopus, 1,293 records from Web of Science Core Collection, and 14 records from Google Scholar. All these 2,096 records were exported as BibTex files for further processing (see details in Supplementary Information).\n\n\nArticle screening\nOur article screening involved de-duplication, title and abstract screening, and full-text screening. We left 1,455 records after de-duplication, and 641 duplicates were removed (Figure 1). All these records were imported to freely available Mendeley reference management software for further screening. We manually screened titles and abstracts of each document following a pre-defined criterion (see Supplementary information). Based on the title and abstract screening, we excluded 1,265 articles and left with 190 articles (Figure 1). We could not retrieve the full-text of six articles (see Supplementary information Table S2) and, therefore, excluded these six articles. Next, the full text of each article was screened based on PICO inclusion and exclusion criteria (Supplementary information Table S3). Full-text screening resulted in the exclusion of 181 articles based on PICO criteria (Figure 1). Thus, we were finally left with only three eligible studies (Entry, Rose, and Cromack 1991; Zhu and Ehrenfeld 1996; Lang et al. 2021). We further employed snowballing method to identify related eligible studies. We could not identify any suitable related study based on references of selected studies (backward snowballing). However, two more eligible articles (Mayor and Henkel 2006; Fernandez, See, and Kennedy 2020) were identified through forward snowballing (citation tracking). Thus, five studies were included after article screening and snowballing.\n\n\n\nFigure 1: Schematic flow diagram for literature search, screening and inclusion process (modified from Haddaway et al. 2018)\n\n\n\n\nData extraction\nWe needed data on litter decomposition rates in the presence and absence of mycorrhizal fungi to test our hypothesis. The litter decomposition rate is often expressed as annual decay constant usually estimated from Olson’s single exponential model (Olson 1963). Therefore, we aimed to extract the decay constant (\\(\\kappa\\)) and associated standard error (SE) or standard deviation (SD) for each study to establish consistency in our analysis. Unfortunately, only a single study (Entry, Rose, and Cromack 1991) reported the data in required form. All other studies have represented litter decomposition rates as mass remaining at different time intervals (Zhu and Ehrenfeld 1996; Mayor and Henkel 2006; Fernandez, See, and Kennedy 2020; Lang et al. 2021). In such cases, we extracted each remaining mass value in triplicate (one mean value and two standard error of mean values) at each given time interval using the WebPlotDigitizer (https://automeris.io/WebPlotDigitizer/). Thus, the actual number of replicates of litterbags at each sampling time was not taken into account; instead, we considered each data point as a sample to obtain a more uniform and comparable measure of litter mass remaining. In case of any ambiguity in timings, we considered the time stated in the methodology section of the published article (Lang et al. 2021).\nWhen the mass remaining data was available, the decay constant (\\(\\kappa\\)) and its associated standard error (SE) was estimated by fitting a linearised form of Olson’s single exponential decay model (Olson 1963) as represented in (Equation 1).\n\\[\n\\log(M_t) = -\\kappa~t + \\log(M_0)\n\\tag{1}\\]\nwhere \\(M_0\\) is the initial litter mass, \\(M_t\\) is the mass remaining at time \\(t\\), and \\(\\kappa\\) is the decay constant. When a study reported data for the same species at different sub-sites (Fernandez, See, and Kennedy 2020) or different seasons (Entry, Rose, and Cromack 1991), we estimated or recorded the decay constant (\\(\\kappa\\)) and its associated standard error (SE) or standard deviation (SD) separately. In addition to this, we recorded the plant species, mycorrhizal types, forest ecosystem types, litter types, and geographic coordinates of study sites. Further, we also noted technical information about methodologies, including the number of study experiments, duration of the investigation, exclusion method for mycorrhizal fungi, and the characteristics of litter and litter bags.\n\n\nPhylogenetic data\nTo test the influence of phylogenetic relatedness, we used the recently published GBOTB backbone of phylogeny for seed plants, which is based on data from GenBank and Open Tree of Life (Smith and Brown 2018). It is considered as the largest dated phylogeny for 79,881 taxa of seed plants resolved at species level. We used the package V.PhyloMaker2 (version 0.1.0) to prepare a phylogenetic tree for the selected plant species (Jin and Qian 2022). The phylogeny was generated from the GBOTB.extended.LCVP mega-tree, which is a combination of two published phylogenies for plants (Smith and Brown 2018; Zanne et al. 2014). Plant names in this phylogenetic tree are standardised according to the Leipzig Catalogue of Vascular Plants (LCVP) (Freiberg et al. 2020). The nodes were built using the build.nodes.1.LCVP function, which returns the most recent common ancestor of all the tips in the largest cluster of the genus, and define it as the basal node of the genus. Further, the phylogeny was generated under the Scenario 3 for binding of new genus tip to the family branch as defined in earlier study (Qian and Jin 2016).\n\n\nEffect size and variance\nVarious effect sizes are used to summarize the magnitude and direction of the relationship between two variables (Cooper, Hedges, and Valentine 2019; Harrer et al. 2021). Among available effect sizes, response ratios (also called as Ratio of Means) and standardised mean differences have been frequently used to compare the means between two groups (i.e., experimental and control) in ecology and evolution (Nakagawa and Santos 2012; Koricheva and Gurevitch 2014). The response ratio or ratio of means is recommended when the mean of control group is not very small as compared to experimental group and both groups have estimates of same sign either positive or negative (Hedges, Gurevitch, and Curtis 1999; Lajeunesse 2015). The log transformation of response ratio (\\(\\text{lnRR}\\)) makes the metric equally sensitive to changes in both numerator and denominator, apart from making the metric nearly normal. Thus, it measures the actual difference in responses scaled to mean in control groups. However, \\(\\text{lnRR}\\) is considered to be slightly biased towards null effects and usually estimate high heterogeneity than other effect size measures (Lajeunesse 2015).\nSince the rate of litter decomposition is influenced by time (i.e., greater weight loss during initial phase than the later phases), we decided to compare decay constant rather than the weight loss. The log transformed response ratio (\\(\\text{lnRR}\\)) and associated variance was calculated using the (Equation 2).\n\\[\n\\text{lnRR} = \\ln \\left( \\frac{\\bar X_E}{\\bar X_C} \\right); \\quad\n\\text{Var(lnRR)} = \\frac {SD_E^2}{n_E \\bar X_E^2} + \\frac {SD_C^2}{n_C \\bar X_C^2}\n\\tag{2}\\]\nwhere, \\(\\bar X_E\\) and \\(\\bar X_C\\) represents the absolute litter decay constant (\\(\\kappa\\)) for experimental (presence of mycorrhizal fungi) and control (absence of mycorrhizal fungi) groups, respectively. The large-sample approximation was used to compute the sampling variances (Equation 2) associated with the effect size \\(\\text{lnRR}\\) (Hedges, Gurevitch, and Curtis 1999). The accuracy of \\(\\text{lnRR}\\) was ensured with with Geary’s test (Equation 3) for both control and experimental group (Lajeunesse 2015).\n\\[\n\\frac{\\bar X}{SD} \\sqrt{N} \\geq 3\n\\tag{3}\\]\nA positive value of response ratio (\\(\\text{lnRR} > 0\\)) indicates faster whereas a negative value (\\(\\text{lnRR} < 0\\)) means slower decomposition rates in presence of mycorrhizal fungi, and a value close to zero (\\(\\text{lnRR} \\approx 0\\)) indicates little or no effect. Both effect size and associated sampling variances were calculated using the function escalc() from the metafor package (Viechtbauer 2010).\n\n\nMeta-analytic model\nThe overall aim of meta-analyses is to combine the effects or observations from different studies. There are three main types of meta-analytic models i.e., fixed-effect model (FEM), random-effects model (REM), and multilevel model (MLM), which are commonly used to compute the overall effect estimate in meta-analysis. The most basic fixed-effect or common-effect model assumes a common overall mean for all studies. Since it assumes homogeneity between studies, it is far from the reality, at least for ecological studies. In contrary to the fixed effect model, the assumptions of random-effects model are more realistic as it considers between-study heterogeneity. This model assumes that there is not only one actual effect size but a distribution of true effect sizes (Borenstein et al. 2010; Harrer et al. 2021). According to this model, the observed effect size \\(y_i\\) with variance \\(v_i\\) deviates from the mean true effect size \\(\\mu\\) for a single study \\(i\\) (with \\(i = 1, \\ldots , N_{studies}\\)) by two error terms \\(u_i\\) (between-study heterogeneity) and \\(e_i\\) (sampling error). Thus, the random effects model can be expressed as (Equation 4)\n\\[\n\\begin{align}\ny_i &= \\mu + u_i + e_i\\\\\n\\textbf{u} &\\sim \\mathcal{N}(0, \\sigma_u^2 \\textbf{I}_u) \\\\\n\\textbf{e} &\\sim \\mathcal{N}(0, \\textbf{V})\n\\end{align}\n\\tag{4}\\]\nwhere, \\(u_i\\) is the random effect corresponding to the \\(i^{th}\\) study, \\(\\textbf{u}\\) is a \\(1 \\times N_{studies}\\) column vector with the \\(u_i\\) values (which are assumed to be normally distributed with mean \\(0\\) and variance \\(\\sigma_u^2\\)) and \\(\\textbf{I}_u\\) is an \\(N_{studies} \\times N_{studies}\\) identity matrix, \\(\\textbf{e}\\) is a \\(1 \\times N_{studies}\\) column vector with the \\(e_{i}\\) values (which are assumed to be normally distributed with mean \\(0\\) and variance \\(v_i\\)), \\(0\\) is a column vector of zeros and \\(\\textbf{V}\\) is an \\(N_{studies} \\times N_{studies}\\) matrix with the \\(v_i\\) values along the diagonal.\nAlthough the random-effects model is commonly used in ecology, it did not take into consideration the non-independence among effect sizes (Nakagawa and Santos 2012). The non-independence can arise when the effect sizes are shared by studies (i.e., multiple effect sizes from the same study). Further, phylogenetic relatedness among species can also introduce non-independence among the effect size estimates (Cinar, Nakagawa, and Viechtbauer 2021). This issue of non-independence can be addressed by using a multilevel (hierarchical) meta-analytic model, which considers a random effect at each level of variability in effect sizes. Among the several factors for non-independence of effect sizes, we specifically assessed the dependence due to studies, species and phylogenetic relatedness among species as described recently (Cinar, Nakagawa, and Viechtbauer 2021). We used a complex multilevel meta-analytic model mathematically expressed in (Equation 5)\n\\[\n\\begin{align}\ny_{ijk} &= \\mu + u_{ij} + s_i + n_k + p_k + e_{ij} \\\\\n\\textbf{u} &\\sim \\mathcal{N}(0, \\sigma_u^2 \\textbf{I}_u) \\\\\n\\textbf{s} &\\sim \\mathcal{N}(0, \\sigma_s^2 \\textbf{I}_s) \\\\\n\\textbf{n} &\\sim \\mathcal{N}(0, \\sigma_n^2 \\textbf{I}_n) \\\\\n\\textbf{p} &\\sim \\mathcal{N}(0, \\sigma_p^2 \\textbf{A}) \\\\\n\\textbf{e} &\\sim \\mathcal{N}(0, \\textbf{V})\n\\end{align}\n\\tag{5}\\]\nwhere\n\n\\(y_{ijk}\\) is the \\(j^{th}\\) effect (with \\(j = 1, \\ldots, N_i\\), where \\(N_i\\) is the number of effect sizes reported in the \\(i^{th}\\) study) in the \\(i^{th}\\) study (with \\(i = 1, \\ldots, N_{studies}\\)) for the \\(k^{th}\\) species (with \\(k = 1, \\ldots, N_{species}\\)), and \\(v_{ijk}\\) are corresponding sampling variances,\n\\(\\mu\\) is the overall meta-analytic mean,\n\\(u_{ij}\\) is a random effect corresponding to the \\(j^{th}\\) effect size in the \\(i^{th}\\) study,\n\\(s_i\\) is a study-specific random effect for \\(i^{th}\\) study,\n\\(n_k\\) is a species-specific random effect for \\(k^{th}\\) species,\n\\(p_k\\) denotes the phylogenetic random effect for the \\(k^{th}\\) species,\n\\(e_{ij}\\) is the sampling error or residual corresponding to the \\(j^{th}\\) effect size in the \\(i^{th}\\) study,\n\\(\\textbf{u}\\) is a \\(1 \\times N_{total}\\) column vector with the \\(u_{ij}\\) values (which are assumed to be normally distributed with mean \\(0\\) and within-study variance \\(\\sigma_u^2\\)), \\(N_{total} = \\sum_{i = 1}^{N_{studies}} N_i\\) represents the total number of the effect sizes, \\(\\textbf{I}_u\\) is an \\(N_{total} \\times N_{total}\\) identity matrix,\n\\(\\textbf{s}\\) is a \\(1 \\times N_{studies}\\) column vector with the \\(s_i\\) values (which are assumed to be normally distributed with mean \\(0\\) and between-study variance \\(\\sigma_s^2\\)), \\(\\textbf{I}_s\\) is an \\(N_{studies} \\times N_{studies}\\) identity matrix,\n\\(\\textbf{n}\\) is a \\(1 \\times N_{species}\\) column vector with the \\(n_k\\) values (which are assumed to be normally distributed with mean \\(0\\) and between-species variance \\(\\sigma_n^2\\)) and \\(\\textbf{I}_n\\) has dimensions \\(N_{species} × N_{species}\\),\n\\(\\textbf{p}\\) is a \\(1 \\times N_{species}\\) column vector with the \\(p_k\\) values (which are assumed to follow a multivariate normal distribution with mean \\(0\\) and variance–covariance matrix \\(\\sigma_p^2 \\textbf{A}\\), where \\(\\sigma_p^2\\) denotes between-species variance due to the phylogeny, and \\(\\textbf{A}\\) is the \\(N_{species} \\times N_{species}\\) phylogenetic correlation matrix), and\n\\(\\textbf{e}\\) is a \\(1 \\times N_{total}\\) column vector with the \\(e_{ij}\\) values and \\(\\textbf{V}\\) is the corresponding (diagonal) variance–covariance matrix with dimensions \\(N_{total} \\times N_{total}\\).\n\nThe random-effects model was fitted using the rma() function whereas the complex multilevel meta-analytic model was fitted using the rma.mv() function from the metafor package (Viechtbauer 2010). The amount of heterogeneity under the both models were estimated using the less biased restricted maximum likelihood (REML) estimator (Viechtbauer 2005). The phylogenetic correlation matrix, denoted as \\(\\textbf{A}\\) in (Equation 5), was computed under the Brownian model using the function vcv() from the ape package version 5.7.1 (Paradis and Schliep 2019). The branch lengths for each species was computed with the Grafen’s method (Grafen 1989) using the function compute.brlen() from the same package (Paradis and Schliep 2019).\n\n\nHeterogeneity\nThe heterogeneity reflects the variations in true effect sizes, which is not accounted by the sampling error variance within the meta-analysis. Cochran’s \\(Q\\) is traditionally used in meta-analysis to assess the heterogeneity in meta-analysis (Cochran 1954). It is the inverse variance weighted sum of squares, which can be mathematically represented by (Equation 6)\n\\[\nQ = \\sum_{i=1}^{N_{studies}} w_i (y_i - \\mu)^2;\n\\quad w_i = \\frac{1}{v_i}\n\\tag{6}\\]\nwhere \\(y_i\\) is the observed effect for each study \\(i^{th}\\); \\(\\mu\\) is the overall summary effect, \\(w_i\\) is the weight defined as inverse of variance (\\(1/v_i\\)) for \\(i^{th}\\) study.\nAlthough Cochran’s \\(Q\\) has been widely used to test heterogeneity in meta-analyses (Nakagawa and Santos 2012; Koricheva and Gurevitch 2014), it is sensitive to size and precision of studies included meta-analysis (Borenstein et al. 2009; Harrer et al. 2021). Higgins and Thompson’s \\(I^2\\) statistic presents the more standardized form to estimate the between-study heterogeneity. It is defined as the percentage of variability in the effect size that is not caused by sampling error (Higgins and Thompson 2002). Similarly, the \\(H^2\\) statistic is used to describe the variance in observed effect sizes due to sampling error (Higgins and Thompson 2002). It is estimated as the ratio of observed variation to the expected variance due to sampling error. Although both \\(I^2\\) and \\(H^2\\) are measured based on the Cochran’s \\(Q\\), the Higgins and Thompson’s \\(I^2\\) and \\(H^2\\) were calculated based on more general definitions using the (Equation 7).\n\\[\nI^2 = \\frac{\\sigma_u^2}{\\sigma_u^2 + \\sigma_e^2} \\times 100; \\quad\nH^2 = \\frac{\\sigma_u^2 + \\sigma_e^2}{\\sigma_e^2}; \\quad\n\\sigma_e^2 =\\frac{(N_{studies}-1)\\sum w_i}{(\\sum w_i)^2 - \\sum w_i^2}\n\\tag{7}\\]\nwhere \\(\\sigma_u^2\\) is the between-study variance (also referred as \\(\\tau^2\\)), \\(\\sigma_e^2\\) is the typical within-study variance, \\(N_{studies}\\) is the total number of studies, \\(w_i\\) is the weight defined as inverse of variance (\\(1/v_i\\)) for \\(i^{th}\\) study. We also included a prediction interval for the true overall summary effect (\\(\\mu\\)) because high heterogeneity is expected for ecological studies (Senior et al. 2016).\nSince \\(Q\\) and \\(I^2\\) are not designed to assess between-study heterogeneity for multilevel models (Nakagawa and Santos 2012), we computed heterogeneity \\(I^2\\) for our multilevel model as suggested earlier (Nakagawa and Santos 2012). Following the previous mathematical notations, the within-study heterogeneity \\(I_u^2\\) can be represented as (Equation 8) whereas \\(H^2\\) or phylogenetic heritability can be represented by (Equation 9). The values of \\(H^2\\) can range from 0 to 1, which corresponds to no phylogenetic relatedness to exact proportional to phylogenetic relatedness among effect sizes.\n\\[\nI_u^2 = \\frac {\\sigma_u^2} {\\sigma_u^2 + \\sigma_s^2 + \\sigma_n^2 + \\sigma_p^2 + \\sigma_e^2}\n\\tag{8}\\]\n\\[\nH^2 = \\frac{\\sigma_p^2}{\\sigma_u^2 + \\sigma_s^2 + \\sigma_n^2 + \\sigma_p^2}\n\\tag{9}\\]\nMeta-regression: The heterogeneity in the observed outcomes can be explained using a set of predictors or moderators in meta-analysis. Such effect size partitioning among various variables is sometimes known as subgroup analysis. In absence of potentially better predictors, we explored species identity and clade (angiosperm or gymnosperm) as predictors since they can act as a surrogate for species level traits including the litter quality. We fitted mixed-effects models to assess the clade-specific or species-specific variations in the effects.\nOutlier and influential analysis: The overall pooled effect size can be heavily influenced by one or more outcomes (effect size), so that our estimated effect size is not robust. We assessed the outliers and influential cases using the studentised residuals and Cook’s distances for our random effects model (Viechtbauer and Cheung 2010). Outcomes with a studentised residual larger than the \\(100 \\times (1 − 0.05 / (2 \\times N_{total}))^{th}\\) percentile of a standard normal distribution are considered potential outliers (i.e., using a Bonferroni correction with two-sided \\(\\alpha = 0.05\\) for \\(N_{total}\\) outcomes included in the meta-analysis). Similarly, outcomes with a Cook’s distance larger than the median plus six times the interquartile range of the Cook’s distances are considered to be influential.\n\n\nPublication bias\nThe publication bias in the average effect size was assessed using the classical funnel plot. It is a scatter plot of the observed effect size for each study on the x-axis against a measure of the standard error on the y-axis of each study. In the absence of any publication bias, effect size distribution should roughly follow the funnel shape in the plot. We conducted the rank correlation test (Begg and Mazumdar 1994) and the regression test (Sterne and Egger 2006) to assess the asymmetry in the funnel plot. The regression was carried using the standard error of the observed outcomes as predictor.\nFurther, we computed the fail-safe numbers to assess the robustness of estimated average effect. Fail-safe numbers estimate the number of non-significant and or unpublished studies which would reduce the significance of overall average estimate if included in the meta-analysis. We used the unweighted Rosenthal’s (Rosenthal 1979) and weighted Rosenberg’s (Rosenberg 2005) approaches to estimate the fail-safe numbers.\nAll analyses were performed in R programming language and statistical environment version 4.3.0 (R Core Team 2023). The R package metafor version 4.2.0 (Viechtbauer 2010) for statistical meta-analysis, package ggtree version 3.7.2 used for visualisation of phylogenetic tree (Yu et al. 2016) and package tidyverse version 2.0.0 (Wickham et al. 2019) for general data manipulation and visualization." - }, - { - "objectID": "publications/2023-patil/index.html#results", - "href": "publications/2023-patil/index.html#results", - "title": "Mycorrhizal fungi accelerates litter decomposition rates in forest ecosystems", - "section": "Results", - "text": "Results\n\nOverview of dataset\nOur systematic literature search and screening found five eligible studies for the present analysis (Figure 1). Studies in our dataset differed in numerous ways, including experimental duration, sampling frequency, initial litter mass, litter species, mycorrhizal type, and forest type (Table 1).\n\n\n\n\nTable 1: Overview of selected studies included in the meta-analysis\n\n\n\n\n\n\n\n\n\n\n\n\nStudy\nExperiments\nSpecies\nSampling frequency\nInitial weight (g)\nMesh size (mm)\nMycorrhizal type\nForest type\n\n\n\n\nEntry, Rose, and Cromack (1991)\n4\n1\n1\n5.00\n1\nEMF\nTemperate\n\n\nZhu and Ehrenfeld (1996)\n1\n1\n4\n1.63\n1\nEMF\nTemperate\n\n\nMayor and Henkel (2006)\n1\n1\n3\n17.00\n1\nEMF\nTropical\n\n\nFernandez, See, and Kennedy (2020)\n4\n2\n3\n2.00\n2\nEMF & AMF\nTemperate\n\n\nLang et al. (2021)\n4\n4\n3\n1.00\n2\nEMF\nTemperate\n\n\n\n\n\n\nThese studies conducted total 14 experiments for nine different species. The leaf litter of nine plant species (Acer saccharum Marshall, Betula alleghaniensis Britton, Dicymbe corymbosa Benth., Fagus grandifolia Ehrh., Fraxinus americana L., Pinus rigida Mill., Pinus strobus L., Pseudotsuga menziesii (Mirbel) Franco Quercus ellipsoidalis E. J. Hill) was used during the experiments (Figure 2 a). The duration of experiments is also differed among studies and ranged from one year to 2.4 years. The initial leaf litter mass varied from 1.0 g to 17.0 g, and three studies used up to 2.0 g of leaf litter, whereas two studies used more than 2.0 g of leaf litter (Entry, Rose, and Cromack 1991; Mayor and Henkel 2006). Further, most of the studies involved ectomycorrhizal fungi (Entry, Rose, and Cromack 1991; Zhu and Ehrenfeld 1996; Mayor and Henkel 2006; Fernandez, See, and Kennedy 2020), while one study showed the involvement of both ectomycorrhizal and arbuscular mycorrhizal fungi (Lang et al. 2021). All the studies assured mycorrhizal exclusion by trenching to 30 cm soil depth.\nFurther, our dataset revealed that most studies were conducted in the temperate deciduous forest of North America (n = 4), and only one study was conducted in the tropical forest of Guyana of South America’s North Atlantic Coast (Figure 2 b). The earliest study included was from 1991 (Entry, Rose, and Cromack 1991), whereas the latest study was published in 2021 (Lang et al. 2021).\n\n\n\n\n\nFigure 2: (a) Phylogenetic relationship among the species used for litter decomposition. The symbols represents the studies to which a species belong. (b) Geographical distribution of studies included in our dataset\n\n\n\n\n\n\nOverall effect estimate\nA total of 14 estimates of litter decomposition (\\(\\kappa\\)) were included in the meta-analysis (Figure 3). The observed response ratios (\\(\\text{RR}\\)) ranged from 0.74 to 1.58, with the majority of estimates being positive (71%). Thus, the decomposition rate is also expected to be higher in the presence of mycorrhizal fungi on average (Figure 3). Further, a strong positive relationship was observed between annual decay constants for the presence and absence of mycorrhizal fungi (R2 = 0.94, p < 0.001), indicating that our dataset does not include extreme effect estimates.\n\n\n\n\n\nFigure 3: Effects of mycorrhizal fungi on annual litter decay constant (\\(\\kappa\\)). Each point represents a single comparison of \\(\\kappa\\). The color of each point denotes the change in \\(\\kappa\\) due to the presence of mycorrhizal fungi. Values above and below the 1:1 line represents the stimulation and inhibition of litter decomposition rate in presence of mycorrhizal fungi, respectively. The values falling on the 1:1 line represent the neutral or no effect\n\n\n\n\nConsistent with our expectations, both meta-analytic models also suggested an overall positive effect of mycorrhizal fungi on litter decomposition rates. The overall average effect estimate (\\(\\hat \\mu\\)) was 1.07 (95% CI: 1.03, 1.11) for the random effects model whereas the same was 1.08 (95% CI: 1, 1.18) for the multilevel model. The wider confidence intervals are expected in the latter model as it included additional sources of variations (number of levels, i.e., id, study, species). In ecological sense, these models indicated that on average the annual decay constant for leaf litter was 7-8% higher in presence of mycorrhizal fungi (Figure 4). The random-effects model found strong statistical evidence (Z = 3.58, p < 0.001) whereas the multilevel model showed moderate statistical evidence (Z = 1.96, p = 0.05) for the accelerated decomposition rates. This positive effect is also evident in the forest plot, where the right-side position of the diamond demonstrates that litter decomposition rate increased in the presence of mycorrhizal fungi (Figure 4).\n\n\n\n\n\nFigure 4: Forest plot showing the observed effects of mycorrhizal fungi on litter decomposition rates for each experiment (n = 14). The size of each square is proportional to the weight of each study, and the associated bars represent the 95% confidence interval associated with individual effect size. The diamonds at the bottom represent the overall average effect estimated from meta-analytic models. The length of diamond denotes the 95% confidence intervals whereas associated dotted lines indicate the 95% prediction intervals\n\n\n\n\nThe Cochran’s \\(Q\\) test for heterogeneity revealed significant variations (\\(Q\\) = 259.45, df = 13, p < 0.001) in true outcomes. Further, very high value of Higgins & Thompson’s statistic (\\(I^2\\) = 97.85%; 95% CI: 97.77, 99.94) suggest that most of the observed variations captured the real differences in true effects (\\(\\tau^2\\) = 0.002). Similarly, the within-study variations in observed estimates were larger than the sampling error as indicated by \\(H^2\\) statistic (\\(H^2\\) = 46.43).\nConsistent with random-effects model, the multilevel model also indicated very high heterogeneity (\\(I_t^2\\) = 99.5%) in true outcomes. The main advantage of multilevel model is that it allows us to estimate the heterogeneity in true outcomes at different levels of meta-analytic model. This model suggested that the heterogeneity due to species-specific effects was very large (\\(\\sigma_n^2\\) = 0.007; \\(I_n^2\\) = 84.32%) followed by the within-study heterogeneity (\\(\\sigma_u^2\\) = 0.001; \\(I_u^2\\) = 15.22%). Interestingly, neither between-study heterogeneity (\\(\\sigma_s^2\\) = 0; \\(I_s^2\\) = 0) nor phylogeny-specific heterogeneity (\\(\\sigma_p^2\\) = 0; \\(I_p^2\\) = 0) was detected by our multilevel model. Since no phylogenetic effects were observed, the phylogenetic heritability (\\(H^2\\) = 0) was also absent.\nGiven the high heterogeneity in true outcomes, the 95% prediction interval ranged from 0.97 to 1.17 as computed by the random effects model. However, the multilevel model suggested a much wider 95% prediction interval ranging from as low as 0.89 to as high as 1.33. Since the predicted values of average estimate included values both lesser and greater than one, we can expect slower decomposition rates in some experiments, though the presence of mycorrhizal fungi enhanced the litter decomposition on average (Figure 4).\nThe meta-regression model with clade (Angiosperm vs Gymnosperm) as categorical moderator suggested that experiments using litter of gymnosperm species obtained on average smaller effect (\\(\\beta_1\\) = -0.03, SE = 0.05) as compared to angiosperms, though this difference was not statistically significant (\\(Q_M\\) = 0.31, df = 1, p = 0.578). Thus, inclusion of clade as moderator variable did not explained any variation in true effects. While the mixed-effects model with species as moderator reflected the variations of effect estimates among species (Figure 5). This meta-regression model indicated that at least one species had significantly different (higher or lower) response ratio than the response ratio for the reference level (Acer saccharum in our case). Thus, this model provided moderate statistical evidence that some amount of variations in true effects are indeed due to species (\\(Q_M\\) = 16.96, df = 8, p = 0.031). It appeared that about 29% of the total variations in the true outcomes were due to species. Although species explained some proportion of the total variations, there still exist variations in true effects that are not attributable to species. These unexplained variations are evident from the test for residual heterogeneity (\\(Q\\) = 239.52, df = 5, p < 0.001), which suggested large amount of unexplained variations in true effects. Further, this unexplained heterogeneity has large variance (\\(\\tau^2\\) = 0.001) and it reflects the real variations that are not explained by species as suggested by high value of \\(I^2\\) = 98.8%.\n\n\n\n\n\nFigure 5: The average effect of mycorrhizal association on litter decomposition rate for each species included in the meta-analysis. The values represent the average difference in response ratios compared to the reference species (Acer saccharum) and the associated bars denote the 95% confidence intervals\n\n\n\n\nFurther, the examination of the studentised residuals revealed that none of the studies had a value larger than ± 2.91 and hence there was no indication of outliers in the context of this model. Similarly, the inspection of Cook’s distances indicated that none of the studies could be considered overly influential.\n\n\nPublication bias\nThe visual inspection of the funnel plot did not indicate the publication bias as the outcomes appeared to be symmetrically distributed around the average effect estimate (Figure 6). The outcomes were well-fitted within the funnel as larger outcomes (with higher precision) were closely clustered at top whereas smaller outcomes (with lower precision) were dispersed around the average effect at bottom. Further, the funnel plot included nearly half outcomes in the non-significant region again suggesting absence of publication bias due to null findings of studies.\n\n\n\n\n\nFigure 6: The contour-enhanced funnel plot shows the effect size of each study (expressed as the response ratio) on the x-axis against a measure of their standard error (from large to small) on the y-axis. The vertical lines in the middle of the funnel show the average effect estimate and associated 95% confidence interval. The shaded region indicates the different significance levels for the estimated effect size for each outcome\n\n\n\n\nThe intercept estimated from the regression test (\\(\\beta_0\\) = 0.06; 95% CI: 0.016, 0.104) was not significantly higher than zero (Z = 0.51, p = 0.609). Also, there was non-significant low rank correlation (Kendall’s \\(\\tau\\) = -0.27, p = 0.193) between standard error and response ratio of outcomes. Thus, both the statistical tests for funnel asymmetry also corroborated our initial visual observations of funnel plot symmetry, which can be considered as indication of negligible or no evidence of publication bias for the present meta-analysis. (Figure 6).\nThe Rosenthal’s fail-safe N of 2107 suggested that over 2,000 outcomes with average response ratio of 1.0 need to be added to make the average effect non-significant (p = 0.05). Similarly, the Rosenberg’s fail-safe N was 4019, indicating that there would need to be over 4,000 outcomes with a mean response ratio of 1.0 added to the analysis, before the cumulative effect would become statistically non-significant (p = 0.05). Considering that we were able to identify only 14 relevant outcomes, it is unlikely that we missed over 4,000 or over 2,000 outcomes. Thus, although it may be possible that we might have estimated biased effect, it is unlikely that the actual overall effect is zero. However, we acknowledge that it is just one form of publication bias among the several other sources of bias in the estimate." - }, - { - "objectID": "publications/2023-patil/index.html#discussion", - "href": "publications/2023-patil/index.html#discussion", - "title": "Mycorrhizal fungi accelerates litter decomposition rates in forest ecosystems", - "section": "Discussion", - "text": "Discussion\nConsidering the initially posed questions, our analysis found moderate to strong evidence that mycorrhizal presence indeed affects the rate of litter decomposition in forest ecosystems (Question 1). The overall average estimate suggested that mycorrhizal fungi tends to enhance the litter decomposition rate by 7-8% on average (Question 2), though it can vary from a decrease of about 10% to an increase of over 30% in presence of mycorrhizal fungi. As with other ecological meta-analyses, large variations in effects were observed as indicated by potentially large heterogeneity. A small proportion (~30%) of these variations can be attributed to differences in species specific traits, possibly through litter quality (Question 3). However, no statistical evidence was found for the differences between angiosperm and gymnosperm species, though slightly higher enhancement was observed for angiosperms than the gymnosperms (Question 4). Further, these effects did not vary due to phylogenetic relationship among the included species (Question 3).\nIn contrast to expectations of Gadgil effect, our findings suggested an overall increase in the litter decomposition rates in presence of mycorrhizal fungi, which is consistent with previous findings (Shah et al. 2016; Gui et al. 2017; Frey 2019; Lang et al. 2021). The enhanced litter decomposition rates have been observed in the presence of both ectomycorrhizal (Shah et al. 2016) and arbuscular mycorrhizal fungi (Gui et al. 2017). The ectomycorrhizal-mediated enhancement was suggested to be associated with the secretion of enzymes to acquire nutrients from organic matter (Jackson et al. 2019). These enzymes degrade the complex compounds and make them available to other decomposer organisms. However, the increased litter decomposition in the presence of arbuscular mycorrhizal fungi was attributed to their saprotrophic capabilities, which tend to acquire nutrients directly from organic matter (Hodge, Campbell, and Fitter 2001; Herman et al. 2012). Further, both ectomycorrhizal (Brzostek et al. 2015) and arbuscular mycorrhizal fungi (Herman et al. 2012) are observed to increase the activity of microbial communities by providing plant-based carbon sources, i.e., priming effects. Since microbial communities are associated with the breakdown of complex compounds and the release of nutrients, their increased activity enhances the litter decomposition rate (Schneider et al. 2012). Thus, our findings question the generality of ‘Gadgil effect’ and suggest that the magnitude and direction of ‘Gadgil effect’ can be highly inconsistent and variable (Fernandez and Kennedy 2016).\nThe much wider range of average effect (-10% to 30%) in our analysis suggest that multiple variables are involved in deriving the mycorrhizal effects on litter decomposition. Such a wider range is expected because the rate of litter decomposition is not controlled by mycorrhizal effects alone and several factors including climate, litter quality and soil decomposer organisms are involved in determining the decomposition rates (Zhang et al. 2008; Cornwell et al. 2008; Kumar et al. 2021). Thus, the mycorrhizal-mediated litter decomposition rates may either hamper or stimulate depending upon the stage of decomposition, soil profile, climatic condition, plant species, and fungal species (Fernandez and Kennedy 2016; Sterkenburg et al. 2018). The species-specific effects are observed in our analysis, as species identity appeared to explain a small part of total variations in the mycorrhizal effects. This species-specific variation in mycorrhizal effects can be attributed to the resource allocation strategy of plant species. When mycorrhizal fungi receive more carbon from the host plant, they can invest more in enzymes and secondary metabolites (Rineau et al. 2013). This higher investment can directly increase the enzymatic degradation capacity of mycorrhizal fungi leading to faster decomposition rates. Further, high resource allocation can result in higher production of labile exudates leading to stimulation of growth and activity of saprotrophic microbes (priming effects).\nFurther, the species identity can indirectly affect mycorrhizal effects through the quality of litter produced by a species (Cornwell et al. 2008; Patil, Kumar, Kumar, Cheema, et al. 2020). It is observed that ‘high quality’ litter (low lignin and high nitrogen) decomposes more rapidly than the ‘low quality’ litter (high lignin, low nitrogen) (Patil, Kumar, Kumar, Cheema, et al. 2020). Suppose a species will produce ‘high quality’ litter (low lignin and high nitrogen). In that case, mycorrhizal effects will be weaker due to more activities of saprotrophic fungi and decomposer micro-organisms whereas ‘low quality’ litter (high lignin, low nitrogen and high carbon content) will favour strong mycorrhizal effects as it will become accessible to the mycorrhizal fungi. Thus, it seems that various factors modulate the effects of mycorrhizal fungi on litter decomposition rate; therefore, these effects can be highly context-dependent (Fernandez and Kennedy 2016).\nAlthough our meta-analysis supports the mycorrhizal-mediated faster decomposition rates, it suffers from some limitations and caveats. We expect that these limitations and caveats will inspire future investigations to advance our understanding about the role of mycorrhizal fungi in nutrient cycling. One of the major limitation of the present analysis is that the systematic literature search and screening resulted in a small number of comparable studies. Although the practice of systematic literature search and screening substantially improve the reproducibility of overall analysis, it may hamper us to identify all the evidence from the literature. Further, our strict eligibility criteria to identify comparable studies appeared to be overly narrower. Therefore, we suggest future research synthesis to include all possible studies across wider range of plant species and ecosystem types. Further, litter decomposition in natural ecosystems is regulated by a complex interaction of several factors (e.g., climate, soil, plant, and mycorrhizal fungi). However, our limited database did not allowed us to test the effects of these variables due to insufficient sample size. Furthermore, tropical forests are under-represented in our analysis, it may be possible that observed positive effects of mycorrhizal fungi are localised to temperate forests (Keller and Phillips 2019). Similarly, all the eligible studies considered only leaf litter for decomposition, the inclusion of litter from other sources might lead to variation in the results. Moreover, we have not considered the actual sample size, mean, and variation of measurements; instead, we considered the number of data points as the sample size to ensure the consistency among the studies. Finally, as with any systematic exercise, deviation from the systematic protocol (searching keywords, eligibility criteria) may lead to different results.\nWe encourage researchers to consider the known limitations of the present study. We observed that most studies considered leaf litter and litter from other sources (root, shoot, and wood) is lacking. Thus, there is a need to experiment with other litter along with leaf litter. Further, we suggest the future investigations to study the mycorrhizal effects on litter decomposition rates across biomes (grassland, heathland, temperate forest and tropical forest), plant species (angiosperm vs gymnosperm, exotic vs native species, leguminous vs non leguminous species, evergreen vs deciduous), litter type (fine roots vs leaf litter, high quality litter vs low quality litter), and mycorrhizal groups (arbuscular mycorrhizal fungi, ectomycorrhizal fungi, ericoid mycorrhizal fungi, orchid mycorrhizal fungi). Moreover, we need to investigate these effects along the gradients of climate, elevation and abundance of mycorrhizal fungi (i.e., trenching depth)." - }, - { - "objectID": "publications/2023-patil/index.html#conclusion", - "href": "publications/2023-patil/index.html#conclusion", - "title": "Mycorrhizal fungi accelerates litter decomposition rates in forest ecosystems", - "section": "Conclusion", - "text": "Conclusion\nOur analysis provides valuable insights into the influence of mycorrhizal fungi on litter decomposition rates in forest ecosystems, challenging the generality of the widely recognised ‘Gadgil effect.’ The overall findings suggest a tendency for increased litter decomposition rates in the presence of mycorrhizal fungi. However, the considerable variability observed across species highlights the involvement of multiple underlying mechanisms. While our results shed light on the potential importance of mycorrhizal fungi in regulating litter decomposition, caution must be exercised regarding the generalisability and interpretability of these effects and processes. The complex nature of mycorrhizal associations and their interactions with other factors in ecosystems necessitates a deeper understanding of the specific contexts in which mycorrhizal fungi regulate litter decomposition and the mechanisms involved. To advance our knowledge and modelling capabilities regarding nutrient cycling responses to changing environments, improving our understanding of the mechanisms by which mycorrhizal fungi exert their influence on litter decomposition is crucial. However, the limited availability of evidence on this topic underscores the need for additional data and further research to comprehensively elucidate the role of mycorrhizal fungi in the mechanisms driving litter decomposition. In conclusion, our meta-analysis serves as a catalyst for future research endeavours, stimulating further investigations that will enhance our understanding of the roles of mycorrhizal fungi in nutrient cycling regulation across various scales. By expanding our knowledge in this area, we can ultimately improve the accuracy and effectiveness of ecosystem models and management strategies." - }, - { - "objectID": "publications/2023-patil/index.html#acknowledgements", - "href": "publications/2023-patil/index.html#acknowledgements", - "title": "Mycorrhizal fungi accelerates litter decomposition rates in forest ecosystems", - "section": "Acknowledgements", - "text": "Acknowledgements\n\nThe authors are grateful to the Chairperson, Department of Botany, Panjab University, Chandigarh, to provide all the necessary facilities for the work. University Grants Commission, Government of India, New Delhi is acknowledged for financial support in the form of Junior Research Fellowships to Meenu Patil [(492/ (CSIR-UGC NET JUNE 2017)], Abhishek Kumar [507/ (OBC) (CSIR-UGC NET DEC. 2016)], and Pardeep Kumar [443/ (CSIR-UGC NET DEC. 2017)]." - }, - { - "objectID": "publications/2023-patil/index.html#author-contributions", - "href": "publications/2023-patil/index.html#author-contributions", - "title": "Mycorrhizal fungi accelerates litter decomposition rates in forest ecosystems", - "section": "Author contributions", - "text": "Author contributions\n\nMeenu Patil: Conceptualisation (equal), Methodology (equal), Resources (equal), Investigation (equal), Data curation (equal), Formal analysis (equal), Visualisation (equal), Software (equal), Validation (equal), Writing – original draft (lead), Writing – review & editing (equal), Funding acquisition (equal). Abhishek Kumar: Conceptualisation (equal), Methodology (equal), Resources (equal), Investigation (equal), Data curation (equal), Formal analysis (equal), Visualisation (equal), Software (equal), Validation (equal), Writing – original draft (equal), Writing – review & editing (equal), Funding acquisition (equal). Pardeep Kumar: Conceptualisation (supporting), Methodology (supporting), Resources (supporting), Investigation (supporting), Validation (supporting), Writing – original draft (equal), Writing – review & editing (equal), Funding acquisition (equal). Anand Narain Singh: Conceptualisation (equal), Methodology (supporting), Resources (supporting), Investigation (supporting), Data curation (equal), Formal analysis (supporting), Validation (equal), Writing – original draft (equal), Writing – review & editing (lead), Supervision (lead), Project administration (lead)." - }, - { - "objectID": "publications/2023-patil/index.html#data-availability", - "href": "publications/2023-patil/index.html#data-availability", - "title": "Mycorrhizal fungi accelerates litter decomposition rates in forest ecosystems", - "section": "Data availability", - "text": "Data availability\n\nAll data and codes are currently available from https://github.com/kumar-a/kumar-a.github.io/tree/main/publications/2023-patil. After acceptance, we will publish all datasets and R codes with a DOI in Zenodo or FigShare repository." - }, - { - "objectID": "publications/2023-patil/index.html#funding", - "href": "publications/2023-patil/index.html#funding", - "title": "Mycorrhizal fungi accelerates litter decomposition rates in forest ecosystems", - "section": "Funding", - "text": "Funding\n\nUniversity Grants Commission, Government of India, New Delhi is acknowledged for financial support in the form of Senior Research Fellowships to Meenu Patil [(492/ (CSIR-UGC NET JUNE 2017)], Abhishek Kumar [507/ (OBC) (CSIR-UGC NET DEC. 2016)], and Pardeep Kumar [443/ (CSIR-UGC NET DEC. 2017)]." - }, - { - "objectID": "publications.html", - "href": "publications.html", - "title": "Publications", - "section": "", - "text": "Order By\n Default\n \n Title\n \n \n Date - Oldest\n \n \n Date - Newest\n \n \n Author\n \n \n \n \n \n \n \n\n\n\n\n \n\n\n\n\nMycorrhizal fungi accelerates litter decomposition rates in forest ecosystems\n\n\n\n\n\n\n\n\n\n\n\n\nJul 17, 2023\n\n\nMeenu Patil, Abhishek Kumar, Pardeep Kumar, Anand Narain Singh\n\n\n\n\n\n\n \n\n\n\n\nPlant ecology in Indian Siwalik range: a systematic map and its bibliometric analysis\n\n\n\n\n\n\n\n\n\n\n\n\nFeb 14, 2022\n\n\nAbhishek Kumar, Meenu Patil, Pardeep Kumar, Manoj Kumar, Anand Narain Singh\n\n\n\n\n\n\n \n\n\n\n\nComparative soil restoration potential of exotic and native woody plantations on coal mine spoil in a dry tropical environment of India: A case study\n\n\n\n\n\n\n\nCoal mine\n\n\nRestoration\n\n\nJournal Article\n\n\nLand Degradation & Development\n\n\n\n\nWe compared the soil restoration potential of exotic and native plant species on coal mine. Our results suggested that native species are more beneficial for soil restoration than the exotic species.\n\n\n\n\n\n\nFeb 11, 2022\n\n\nAnand Narain Singh, Abhishek Kumar\n\n\n\n\n\n\n \n\n\n\n\nEcological performances of exotic and native woody species on coal mine spoil in Indian dry tropical region\n\n\n\n\n\n\n\nCoal mine\n\n\nRestoration\n\n\nJournal Article\n\n\nEcological Engineering\n\n\n\n\nIn this article we analysed the growth and biomass production of exotic and native woody species on Indian coal mine spoils. We observed higher survival of native species on coal mine spoil, though exotic species exhibited faster growth rates than the native species on coal mine spoil. However, biomass production was higher for native species on coal mine overburden. Overall, we showed that native species performed better than exotic species in rehabilitation of coal mine.\n\n\n\n\n\n\nApr 11, 2021\n\n\nAnand Narain Singh, Abhishek Kumar\n\n\n\n\n\n\n \n\n\n\n\nShivalik, Siwalik, Shiwalik or Sivalik: Which one is an appropriate term for the foothills of Himalayas?\n\n\n\n\n\n\n\nSiwalik\n\n\nJournal Article\n\n\nJournal of Scientific Research\n\n\n\n\nThe present study has applied bibliometric analysis to resolve the inconsistency about the usage of these terms. Here, we have shown that the term ‘Siwalik’ was most dominant in the available literature.\n\n\n\n\n\n\nJan 9, 2020\n\n\nAbhishek Kumar, Meenu Patil, Anand Narain Singh\n\n\n\n\n\n\nNo matching items" - }, - { - "objectID": "posts/sdm-himalaya/index.html", - "href": "posts/sdm-himalaya/index.html", - "title": "Species distribution modelling studies for Plants in Western Himalayas", - "section": "", - "text": "Species\nReference\n\n\n\n\nAbies densa\nMalik et al. (2022)\n\n\nAbies pindrow\nMalik et al. (2022)\n\n\nAbies spectabilis\nMalik et al. (2022)\n\n\nAconitum heterophyllum\nZ. A. Wani et al. (2022)\n\n\nBetula utilis\nMohapatra et al. (2019)\n\n\nBetula utilis\nSingh, Samant, and Naithani (2021b)\n\n\nBoehmeria clidemioides\nGupta et al. (2023)\n\n\nBuxus wallichiana\nZ. A. Wani et al. (2023)\n\n\nDactylorhiza hatagirea\nChandra et al. (2022)\n\n\nDactylorhiza hatagirea\nSharma, Ram, and Chawla (2023)\n\n\nDactylorhiza hatagirea\nThakur et al. (2021)\n\n\nDrepanostachyum falcatum\nMeena et al. (2023)\n\n\nFritillaria roylei\nChandora et al. (2023)\n\n\nIncarvillea altissima\nRana et al. (2021)\n\n\nIncarvillea arguta\nRana et al. (2021)\n\n\nIncarvillea beresowskii\nRana et al. (2021)\n\n\nIncarvillea compacta\nRana et al. (2021)\n\n\nIncarvillea delavayi\nRana et al. (2021)\n\n\nIncarvillea emodi\nRana et al. (2021)\n\n\nIncarvillea forrestii\nRana et al. (2021)\n\n\nIncarvillea lutea\nRana et al. (2021)\n\n\nIncarvillea mairei\nRana et al. (2021)\n\n\nIncarvillea olgae\nRana et al. (2021)\n\n\nIncarvillea potaninii\nRana et al. (2021)\n\n\nIncarvillea sinensis\nRana et al. (2021)\n\n\nIncarvillea younghusbandii\nRana et al. (2021)\n\n\nIncarvillea zhongdianensis\nRana et al. (2021)\n\n\nLagotis cashmeriana\nSalam, Reshi, and Shah (2022)\n\n\nPicrorhiza kurroa\nRawat et al. (2022)\n\n\nPinus gerardiana\nPaul, Lata, and Barman (2023)\n\n\nPittosporum eriocarpum\nPaul and Samant (2023)\n\n\nQuercus oblongata\nBarman et al. (2023)\n\n\nQuercus semecarpifolia\nSaran et al. (2010)\n\n\nQuercus semecarpifolia\nSingh, Samant, and Naithani (2021a)\n\n\nRheum webbianum\nI. A. Wani et al. (2021)\n\n\nShorea robusta\nKaur et al. (2023)\n\n\nTaxus contorta\nChauhan et al. (2022)\n\n\nTrillium govanianum\nRather et al. (2022)\n\n\nValeriana wallichii\nKumari et al. (2022)\n\n\n\n\n\n\n\n\n\nReferences\n\nBarman, Tanay, S. S. Samant, L. M. Tewari, Nidhi Kanwar, Amit Singh, Shiv Paul, and Swaran Lata. 2023. “Ecological Assessment and Suitability Ranges of Ban Oak (Quercus Oblongata d. Don) in Chamba District, Himalayas: Implications for Present and Future Conservation.” Brazilian Journal of Botany 46 (2): 477–97. https://doi.org/10.1007/s40415-023-00885-w.\n\n\nChandora, Rahul, Shiv Paul, Kanishka RC, Pankaj Kumar, Badal Singh, Pradeep Kumar, Abhay Sharma, et al. 2023. “Ecological Survey, Population Assessment and Habitat Distribution Modelling for Conserving Fritillaria Roylei – a Critically Endangered Himalayan Medicinal Herb.” South African Journal of Botany 160 (September): 75–87. https://doi.org/10.1016/j.sajb.2023.06.057.\n\n\nChandra, Naveen, Gajendra Singh, Shashank Lingwal, J. S Jalal, M. S Bisht, Vineet Pal, M. P. S Bisht, Balwant Rawat, and L. M Tiwari. 2022. “Ecological Niche Modeling and Status of Threatened Alpine Medicinal Plant Dactylorhiza Hatagirea d.don in Western Himalaya.” Journal of Sustainable Forestry 41 (10): 1029–45. https://doi.org/10.1080/10549811.2021.1923530.\n\n\nChauhan, Saurav, Shankharoop Ghoshal, K. S. Kanwal, Vikas Sharma, and G. Ravikanth. 2022. “Ecological Niche Modelling for Predicting the Habitat Suitability of Endangered Tree Species Taxus Contorta Griff. In Himachal Pradesh (Western Himalayas, India).” Tropical Ecology 63 (2): 300–313. https://doi.org/10.1007/s42965-021-00200-2.\n\n\nGupta, A., D. Adhikari, I. A. Hurrah, and V. V. Wagh. 2023. “Extended Distribution, Typification and Modelling of Potential Areas of Boehmeria Clidemioides (Urticaceae) in the Western Himalaya, India.” Rheedea 33 (1): 8–16. https://doi.org/10.22244/rheedea.2023.33.01.02.\n\n\nKaur, Sharanjeet, Siddhartha Kaushal, Dibyendu Adhikari, Krishna Raj, K. S. Rao, Rajesh Tandon, Shailendra Goel, Saroj K. Barik, and Ratul Baishya. 2023. “Different GCMs yet Similar Outcome: Predicting the Habitat Distribution of Shorea Robusta c.f. Gaertn. In the Indian Himalayas Using CMIP5 and CMIP6 Climate Models.” Environmental Monitoring and Assessment 195 (6). https://doi.org/10.1007/s10661-023-11317-3.\n\n\nKumari, Priyanka, Ishfaq Ahmad Wani, Sajid Khan, Susheel Verma, Shazia Mushtaq, Aneela Gulnaz, and Bilal Ahamad Paray. 2022. “Modeling of Valeriana Wallichii Habitat Suitability and Niche Dynamics in the Himalayan Region Under Anticipated Climate Change.” Biology 11 (4): 498. https://doi.org/10.3390/biology11040498.\n\n\nMalik, Rayees A., Zafar A. Reshi, Iflah Rafiq, and S. P. Singh. 2022. “Decline in the Suitable Habitat of Dominant Abies Species in Response to Climate Change in the Hindu Kush Himalayan Region: Insights from Species Distribution Modelling.” Environmental Monitoring and Assessment 194 (9). https://doi.org/10.1007/s10661-022-10245-y.\n\n\nMeena, Rajendra K., Nitika Negi, Rajeev Shankhwar, Maneesh S. Bhandari, Rama Kant, Shailesh Pandey, Narinder Kumar, Rajesh Sharma, and Harish S. Ginwal. 2023. “Ecological Niche Modelling and Population Genetic Analysis of Indian Temperate Bamboo Drepanostachyum Falcatum in the Western Himalayas.” Journal of Plant Research 136 (4): 483–99. https://doi.org/10.1007/s10265-023-01465-5.\n\n\nMohapatra, Jakesh, Chandra Prakash Singh, Maroof Hamid, Anirudh Verma, Sudeep Chandra Semwal, Bandan Gajmer, Anzar A. Khuroo, et al. 2019. “Modelling Betula Utilis Distribution in Response to Climate-Warming Scenarios in Hindu-Kush Himalaya Using Random Forest.” Biodiversity and Conservation 28 (8-9): 2295–2317. https://doi.org/10.1007/s10531-019-01731-w.\n\n\nPaul, Shiv, Swaran Lata, and Tanay Barman. 2023. “Habitat Distribution Modeling of the Pinus Gerardiana Under Projected Climate Change in the North-Western Himalaya, India.” Landscape and Ecological Engineering, July. https://doi.org/10.1007/s11355-023-00570-w.\n\n\nPaul, Shiv, and S. S. Samant. 2023. “Population Biology, Ecological Niche Modelling of Endangered and Endemic Pittosporum Eriocarpum Royle in Western Himalaya, India.” Journal for Nature Conservation 72 (April): 126356. https://doi.org/10.1016/j.jnc.2023.126356.\n\n\nRana, Santosh Kumar, Hum Kala Rana, Dong Luo, and Hang Sun. 2021. “Estimating Climate-Induced ’Nowhere to Go’ Range Shifts of the Himalayan Incarvillea Juss. Using Multi-Model Median Ensemble Species Distribution Models.” Ecological Indicators 121 (February): 107127. https://doi.org/10.1016/j.ecolind.2020.107127.\n\n\nRather, Zubair Ahmad, Rameez Ahmad, Tanvir-Ul-Hassan Dar, and Anzar Ahmad Khuroo. 2022. “Ensemble Modelling Enables Identification of Suitable Sites for Habitat Restoration of Threatened Biodiversity Under Climate Change: A Case Study of Himalayan Trillium.” Ecological Engineering 176 (March): 106534. https://doi.org/10.1016/j.ecoleng.2021.106534.\n\n\nRawat, Neelam, Saurabh Purohit, Vikas Painuly, Govind Singh Negi, and Mahendra Pratap Singh Bisht. 2022. “Habitat Distribution Modeling of Endangered Medicinal Plant Picrorhiza Kurroa (Royle Ex Benth) Under Climate Change Scenarios in Uttarakhand Himalaya, India.” Ecological Informatics 68 (May): 101550. https://doi.org/10.1016/j.ecoinf.2021.101550.\n\n\nSalam, Nadeem, Zafar A. Reshi, and Manzoor A. Shah. 2022. “Habitat Suitability Modelling for Lagotis cashmeriana (Royle) Rupr., A Threatened Species Endemic to Kashmir Himalayan Alpines.” Geology, Ecology, and Landscapes 6 (4): 241–51. https://doi.org/10.1080/24749508.2020.1816871.\n\n\nSaran, S., R. Joshi, S. Sharma, H. Padalia, and V. K. Dadhwal. 2010. “Geospatial Modeling of Brown Oak (Quercus Semecarpifolia) Habitats in the Kumaun Himalaya Under Climate Change Scenario.” Journal of the Indian Society of Remote Sensing 38 (3): 535–47. https://doi.org/10.1007/s12524-010-0038-2.\n\n\nSharma, Manish K., Bittu Ram, and Amit Chawla. 2023. “Ensemble Modelling Under Multiple Climate Change Scenarios Predicts Reduction in Highly Suitable Range of Habitats of Dactylorhiza Hatagirea (d.don) Soo in Himachal Pradesh, Western Himalaya.” South African Journal of Botany 154 (March): 203–18. https://doi.org/10.1016/j.sajb.2022.12.026.\n\n\nSingh, Amit, S. S. Samant, and Suneet Naithani. 2021a. “Population Ecology and Habitat Suitability Modelling of Quercus Semecarpifolia Sm. In the Sub-Alpine Ecosystem of Great Himalayan National Park, North-Western Himalaya, India.” South African Journal of Botany 141 (September): 158–70. https://doi.org/10.1016/j.sajb.2021.04.022.\n\n\n———. 2021b. “Population Ecology and Habitat Suitability Modelling of Betula Utilis d. Don in the Sub-Alpine Ecosystem of Great Himalayan National Park, North-Western Indian Himalaya: A UNESCO World Heritage Site.” Proceedings of the Indian National Science Academy 87 (4): 640–56. https://doi.org/10.1007/s43538-021-00055-0.\n\n\nThakur, Dinesh, Nikita Rathore, Manish Kumar Sharma, Om Parkash, and Amit Chawla. 2021. “Identification of Ecological Factors Affecting the Occurrence and Abundance of Dactylorhiza Hatagirea (d.don) Soo in the Himalaya.” Journal of Applied Research on Medicinal and Aromatic Plants 20 (February): 100286. https://doi.org/10.1016/j.jarmap.2020.100286.\n\n\nWani, Ishfaq Ahmad, Susheel Verma, Priyanka Kumari, Bipin Charles, Maha J. Hashim, and Hamed A. El-Serehy. 2021. “Ecological Assessment and Environmental Niche Modelling of Himalayan Rhubarb (Rheum Webbianum Royle) in Northwest Himalaya.” PLOS ONE 16 (11): e0259345. https://doi.org/10.1371/journal.pone.0259345.\n\n\nWani, Zishan Ahmad, Qamer Ridwan, Sajid Khan, Shreekar Pant, Sazada Siddiqui, Mahmoud Moustafa, Ahmed Ezzat Ahmad, and Habab M. Yassin. 2022. “Changing Climatic Scenarios Anticipate Dwindling of Suitable Habitats for Endemic Species of Himalaya – Predictions of Ensemble Modelling Using Aconitum Heterophyllum as a Model Plant.” Sustainability 14 (14): 8491. https://doi.org/10.3390/su14148491.\n\n\nWani, Zishan Ahmad, K. V. Satish, Tajamul Islam, Shalini Dhyani, and Shreekar Pant. 2023. “Habitat Suitability Modelling of Buxus Wallichiana Bail.: An Endemic Tree Species of Himalaya.” Vegetos 36 (2): 583–90. https://doi.org/10.1007/s42535-022-00428-w." - }, - { - "objectID": "posts/index.html", - "href": "posts/index.html", - "title": "Species distribution modelling studies in Himalayas", - "section": "", - "text": "library(tidyverse)\n\n── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──\n✔ dplyr 1.1.2 ✔ readr 2.1.4\n✔ forcats 1.0.0 ✔ stringr 1.5.0\n✔ ggplot2 3.4.3 ✔ tibble 3.2.1\n✔ lubridate 1.9.2 ✔ tidyr 1.3.0\n✔ purrr 1.0.2 \n── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag() masks stats::lag()\nℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors\n\ntribble(\n ~Species, ~Latitude, ~Longitude, ~Reference,\n \n)\n\n# A tibble: 0 × 4\n# ℹ 4 variables: Species <???>, Latitude <???>, Longitude <???>,\n# Reference <???>" } ] \ No newline at end of file