I've come across a statistical mystery. Any insight would be much appreciated.
I attempted to attach a figure to illustrate the problem, but the listserv rejected it. I can send it to anyone who thinks they may help. The data concerns presence/absence data for aquatic insects across several thousand streams. We're looking to find the effect of land use change (i.e. urban, agriculture, etc) on individual taxa. In particular, we want to determine if a specific land use has the potential to eliminate a taxa once development reaches a certain point. To do this, my first approach went as follows: 1) Narrow down the data to the appropriate subset of physiographic provinces where the specific insect is found 2) Compare the cumulative frequency distribution of all watersheds (expected CDF) to the actual CDF where the insects were collected (observed CDF) based on different levels of land cover change 3) Run a Kolmogorov-Smirnov test between the 2 distributions This worked fine until I came across the scenario of the insect Baetis in relation to the variable agricultural development. The the distribution where the insect Baetis ocurred was very close to the expected. In fact, in the extreme right end of the distribution (where the stressor is highest), observed vs. expected distributions were almost identical. We visually interpreted this to mean agriculture had little affect on whether or not you'd see Baetis in a stream. However... The KS test stated otherwise; the distributions were highly significantly different. I attributed this to the high power of the KS test and the sensitivity of the test near the middle of the curve. Other similar tests behaved similarly. But as you would see in the figure (if you'd like me to send it), Baetis survives at high levels of agriculture, even if variation between the curves ocurrs earlier on (maybe ~40% agricultural development). Does anyone know of a better approach? I've tried a number of other tests/approaches, but nothing gets me anywhere. Many thanks to anyone who could possibly help. -Ryan Utz PhD Student University of Maryland Appalachian Laboratory
