
Hi to all,


This is my first post on r-sig-ecology mailing list and it concerns the difficulties I have in fomulating a certain ecological model for my data. 



My data consist of 567 individual roe deer crania, with 50 linear characters measured across 12 populations. I want to test the influence of habitat structure on cranial variability in this sample. Habitat structure is expressed as the ratio of forest to meadowland to plowland and all ratios add up to one (0.10 forest - 0.20 meadow - 0.70 plowland, etc...). 


I know that individual approach is not good since all individuals from the same populations have the same values of this habitat ratio. 


The idea is to use PCA or DA to reduce the dimensionality and to extract population scores that can be used in the model. My question is how to design such a model, that will include population scores and habitat ratios (maybe even two more independent variables, population density and the mean body weight)? Simple lm models are either insignificant or run out of degrees of freedom since only 12 variables are present. 


I tried summarizing habitat structure with the diversity index (thanks to Seth from r-sig-mixed effect list), but all such indices score ecotonal habitats the most (the ones with the most even ratio) and lm`s are again insignificant. I know that variability is constrained by the extreme habitats (the ones with the most plowland or forest, while meadowland contributes to the area of favorable foraging sites present in both extreme habitat types, but is never the most dominant habitat since it has the lowest ratios.
   

Best regards,
Milos Blagojevic
paulidealiste@aol.com


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