Foraging patterns of pileated woodpeckers in a managed Acadian forest: a resource selection function

We analyzed the relative influence of foraging substrate characteristics as predictors of the probability of use by the pileated woodpecker (Dryocopus pileatus L.) and determined threshold values for significant predictors. We sampled used and available substrates around 126 stations distributed in an intensively managed forest in northwestern New Brunswick, Canada. We developed a resource selection function (RSF), validated by a resampling procedure, and compared selection ratios for significant predictors. Diameter at breast height (DBH) of trees and snags was the most significant predictor, probably reflecting nesting selection by its main prey, carpenter ants (Camponotus spp.). The pileated woodpecker preferred deciduous substrates with DBH >35 cm and coniferous substrates with DBH >30 cm. Among deciduous substrates, it preferred snags over living trees, but there was no such preference for coniferous substrates. American beech (Fagus grandifolia Ehrh.) was clearly preferred over all other species. The RSF we developed and the thresholds we obtained should help forest managers and conservation planners assess habitat quality for this keystone species.

Estimating a resource selection function with line transect sampling

A resource selection probability function is a function that gives the prob- ability that a resource unit (e.g., a plot of land) that is described by a set of habitat variables X1 to Xp will be used by an animal or group of animals in a certain period of time. The estimation of a resource selection function is usually based on the comparison of a sample of resource units used by an animal with a sample of the resource units that were available for use, with both samples being assumed to be effectively randomly selected from the relevant populations. In this paper the possibility of using a modified sampling scheme is examined, with the used units obtained by line transect sampling. A logistic regression type of model is proposed, with estimation by conditional maximum likelihood. A simulation study indicates that the proposed method should be useful in practice.