Hi
I did a mark recapture program for 2 butterfly species for one year on a very seasonal habitat. I have data for the rainfall, max daily temperature and min daily temperature. Checking the graphs of the butterfly "abundance" (min number of individuals present on the population) it seems that butterflies are more abundant when it rained a lot on the previous months. Which makes sense because the larval cycle of these butterflies is 30 days. Thus I am trying to verify if the recruitment of butterflies is determined by the precipitation on the previous month. Also I believe that the adult survival will dependent on the accumulated precipitation of their adult life (7 days). So I basically I am testing how the population behaves according to a delayed response (accumulated precipitation on the last 30 days) and a non-delayed precipitation (rainfall over the last 7 days). Is there a way to do this using MARK or do I rely on linear regression. I talked with David Anderson about it and he suggested me to do something similar to the Durbam Storm Data (Burham and Anderson 2002) but the more I read about it the less sense it makes. Does anyone have any idea on how to tackle this problem?
Thank you very much
PS: I attended the MARK workshop in June I read chapters 1 thru 8 (Gentle introduction) , but I did not came across with an example that matched my question.