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formulas to calculate bloomers #9
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Interesting paper @onadeulofeu ! While you keep reviewing the literature out there, I think we could start with a simple metric based on the definition of bloomer you gave me: a taxon that can increase abundance fast and become dominant in the community for a certain period of time. We could easily translate that into code: detecting fast rates of change in abundance (in %) and also detect taxa that reach certain threshold in abundance (e.g. 20%, 40%, no idea, should be based on the ecology of these systems). And then decide for how long that dominant abundance should be kept to be considered a bloomer (3 days? 4 days? 1 week? Again based on the ecology of these systems) Then we could relate this to temporal variation in environmental factors (temperature, nutrients, etc) |
Hi! I find this idea of Paco but not so easy to implement, because you also need to define a time period for the increase. In the case of daily censuses, maybe is easier, but not for monthly censuses. A basic reference for this is here: https://link.springer.com/article/10.1007/s00442-008-0993-2 Here they also use this method for a blooming species: https://link.springer.com/chapter/10.1007/978-90-481-9541-1_7 There is an R package for working with wavelets (although I have never used it before): https://cran.r-project.org/web/packages/WaveletComp/WaveletComp.pdf I can try to use these functions if you find this interesting. |
Hi! Thanks Irene! I'll take a look at it! I was trying to define functions that did not depend on the time-scale of the sampling to make it more flexible, but we could try something like this :) |
https://ami-journals.onlinelibrary.wiley.com/doi/full/10.1111/1462-2920.16086 similar to what they do here. |
I believe that both methods (wavelets or dissimilarity analyses among correlative censuses) are equivalent in terms of time-scale, but we could discuss this further. In any case, my advice is to do it the simplest as possible. I am sure that @ibartomeus thinks something similar ;-) |
Hi Ona! Could you advance with this? Let us know if you need help. |
Gibbons_2017.pdf
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