Hi guys,
for example. I'm interesting to add a temperature layer to my model and I think to add a layer of average Temperature of the period of my data. Is it correct? if not, How could I done?
Hi Ricardo,
You can add temperature into your model as well as any other variables. May be some of these questions could help you to find an answer: why to use a layer? Have you data enough to do projections? Temperature data are in the right scale or, in other way, are they useful (or potential) to explain your questions?
Hope this may help you.
Yes, you can use a layer of the current temperature (as said above by Fabiana) or you can use future projections as well (depending of your question), as you can see in this recently published paper (see below). It is a nice approach and you can answer questions aimed to the conservation of your species in the long-term, based in the projections of climate change made by IPCC researchers. Please, see details in the paper. Good luck in your research! I hope to have helped you!
Ortega-Andrade HM, Prieto-Torres DA, Gómez-Lora I, Lizcano DJ (2015) Ecological and Geographical Analysis of the Distribution of the Mountain Tapir (Tapirus pinchaque) in Ecuador: Importance of Protected Areas in Future Scenarios of Global Warming. PLoS ONE 10(3): e0121137. doi:10.1371/journal.pone.0121137
Hi Ryan,
Yes, I'm traying to predict past distribution. My period of time are ten years and my I'd like to include environmental variables such as average temperature. Do you think that is posible to use Average, max and min T? or is it technically not possible?
Thanks Fabiano and Italo,
I guess that this type of environmental layer (T) could explain my question. I'll review the papers and your advice. Cheers!!
I would have tried both average, min, max and the 10 % lowest and highest temperatures. Then you would get an idea of which part of the temperature that is creating a response in your data. Using the 10 % lowest and highest will give you something other than the min and max, as it is not just the one min/max value.
If your species has some critical temperature that you know that it responds to (e.g. it stops reproducing below 2 degrees), then you can add the number og days with temperature lower that that limit as a predictor.
Addition then standard deviation of temperature will give an indication on whether or not you species responds to differences in variation in temperature, not the temperature itself. Some species are adapted to high variation, others to more stable situations.
Remember to test the covariance between you predictors, as different versions of temperature tend to be highly correlated, and then should not be used in the same modell.
Remember also that temperature can covary with other variables, variables that you do not hav included in your model. So if you find and effect of temperature, this can be caused by e.g. an effect of fresh water influence (these two often covary), but if you to not have salinity in your model, then temperature will capture the effect of this variable as well. Sremember that we often work with correlations, not necessarily causal connections, and that even though you find and effect of a predictor, this predictor might be a proxy for something else.
Yes, Trine is rigth about covariance. Nevertheless, even the use of several temperatures measures may be not useful because sometimes temperatures are also correlates and finally all of them may contribute to explain some of the deviance! I prefer to run a correlation test before to run de model with all variables and after that, to use those that are not correlated. You know your system and data, you have to take the decision.
You may consider to generate seasonal SDMs for climate variables (see attached).
Article Where the bears roam in Majella National Park, Italy
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