Terrain and vegetation structural influences on local avian species richness in two mixed-conifer forests

- LiDAR and Landsat metrics predicted total and nesting guild species richness.
- The relative importance of metrics differed between study areas and guilds.
- Model R2s ranged from 0.05 to 0.42.
- Total species richness was positively associated with increased understory density.
- Predicted richness patterns were mapped across 50,000 ha of actively managed forest.

Using remotely-sensed metrics to identify regions containing high animal diversity and/or specific animal species or guilds can help prioritize forest management and conservation objectives across actively managed landscapes. We predicted avian species richness in two mixed conifer forests, Moscow Mountain and Slate Creek, containing different management contexts and located in north-central Idaho. We utilized general linear models and an AIC model selection approach to examine the relative importance of a wide range of remotely-sensed ecological variables, including LiDAR-derived metrics of vertical and horizontal structural heterogeneities of both vegetation and terrain, and Landsat-derived vegetation reflectance indices. We also examined the relative importance of these remotely sensed variables in predicting nesting guild distributions of ground/understory nesters, mid-upper canopy nesters, and cavity nesters. All top models were statistically significant, with adjusted R2s ranging from 0.05 to 0.42. Regardless of study area, the density of the understory was positively associated with total species richness and the ground/understory nesting guild. However, the relative importance of ecological predictors generally differed between the study areas and among the nesting guilds. For example, for mid-upper canopy nester richness, the best predictors at Moscow Mountain included height variability and canopy density whereas at Slate Creek they included slope, elevation, patch diversity and height variability. Topographic variables were not found to influence species richness at Moscow Mountain but were strong predictors of avian species richness at the higher elevation Slate Creek, where species richness decreased with increasing slope and elevation. A variance in responses between focal areas suggests that we expand such studies to determine the relative importance of different factors in determining species richness. It is also important to note that managers using predictive maps should realize that models from one region may not adequately represent communities in other areas.