Do habitat association models have any generality? Predictingskylark Alauda ar
ensis abundance in different regions of southernEngland

Much of conservation science is based upon determining the use by organisms ofdifferent resources. However, the field data used to construct habitat associationmodels generally come from a small number of sites covering a fraction of the areaof interest. It is important therefore to assess the generality of those models forspecies occurring over large geographical areas. In this paper we test the generality ofmodels describing skylark Alauda arensis abundance across farmland in southernEngland in relation to crop type, crop structure and field structure (i.e. height ofsurrounding boundaries). Skylarks responded to most predictors we measured insimilar ways across three regions of differing farming practices (arable-dominated,pasture-dominated and a mixture of the two). Most of the regional differences inhabitat associations could be related to differences in the speed of crop development.For example, the sowing of cereals in spring, a much lauded strategy to increaseskylark populations, is likely to have less of an effect in regions where cerealdevelopment is slow than in regions where it is fast.Most studies that explicitly test the performance of a model developed in one placeelsewhere use presence/absence models. We adopt a more sensitive and novelapproach by using counts. We found regression equations developed in one regionperformed poorly when tested as a direct predictor (i.e. a 1:1 relationship) on datafrom other regions. However, the skylark territories observed in any one region werepositively correlated with the territory numbers predicted by models built using datafrom other regions, so models were good predictors of relative abundance.The results suggest that, for this species at least, conservationists should haveconfidence when advocating management strategies based upon habitat-associationmodels and extrapolating their generality to other areas. However, our results warnagainst using regression equations developed in one place to make absolute quantitativepredictions elsewhere. Decision-makers should beware of using models in thisway.

Mark J. Whittingham, Jeremy D. Wilson and Paul F. Donald, ECOGRAPHY 26: 521-531, 2003