Describing avifaunal richness with functional and structural bioindicators derived from advanced airborne remotely sensed data

Abstract

We investigate whether the richness of distinct avian guilds (species grouped together based on similar exploitation of environmental resources) can be described using indicators of ecosystem function and tree species diversity derived from hyperspectral data and/or aspects of vegetation structure derived from lidar. Bird surveys facilitated discriminant analyses to establish which variables best differentiated between guilds. Akaike's Information Criterion (AIC) and generalized linear models (GLMs) were then utilized to develop predictive models. Bioindicators representing foliar water content and tree species diversity were the most useful hyperspectrally derived variables for differentiating between guilds (p < 0.01) and were most often selected for describing richness. Using ecosystem function bioindicators alone, the adjusted coefficient of determination (R 2 adj) of GLMs ranged from 0.32 (generalist) to 0.58 (forest). In contrast, mean under-, mid-, and overstorey cover and mean surface elevation were the most useful structural bioindicators for guild differentiation (p < 0.05) and were most often selected for describing richness. R 2 adj of GLMs built from structural bioindicators alone ranged from 0.19 (generalist) to 0.64 (forest). Overall, structural bioindicators described more variance for open country and forest guilds, whereas functional bioindicators explained more variance for generalist bird species and all guilds considered concurrently. Simultaneously considering functional and structural bioindicators accounted for the most variance in richness (59%) for open country birds; however, combining bioindicator types did not improve upon the best models for generalist and/or forest guilds.