Factors affecting duck nesting in the aspen parklands: A spatial analysis

Habitat fragmentation often has been cited as a cause for reduced reproductive success of grassland-nesting birds, including ducks, though results of many studies have been equivocal. As remotely sensed habitat data become increasingly available, an increased understanding of how habitat configurations affect demographic parameters will allow wildlife managers to make better decisions about habitat preservation and restoration. We used duck (Anas spp.) nesting data from 15 65-km2 study areas (n ≈ 6300 nests) dispersed throughout the aspen (Populus tremuloides) parklands of south-central Canada, to test hypotheses and build models that predict hatching rates and nest-site distributions in relation to landscape features. We constructed separate models using landscape features generated at 3 different spatial extents and using 3 different habitat classification schemes. Generalized linear mixed-modeling techniques were used to model hatching rates, and logistic regression was used to discriminate between nest location and random points. Information-theoretic techniques were used to select the best models. Hatching rates generally increased with habitat patch size, and with distance from habitat edge and nearest wetland though relationships were complex. Several interactions improved the fit of our models. We used life-history theory and models of hatching rates to construct hypotheses about how birds should choose nest sites. The same covariates that were useful for predicting hatching rates also were useful for discriminating between nest sites and random points; however, birds did not always choose the safest habitats as nest locations. Therefore, fitness may not be maximized by nest choice. In each case, models built from landscape features generated at the smallest spatial extent had the greatest discriminatory ability; however, inclusion of variables from >1 spatial extent significantly improved our models. Finally, we demonstrate how our models can be incorporated into spatially explicit decision support tools to help guide management. Based on our results, it is clearly important to consider spatial configurations of habitats when planning habitat management.