Effects of spatial scale and heterogeneity on avian occupancy dynamics and population trends in forested mountain landscapes

Graduation date: 2015Population trends and patterns in species distributions are the major currencies used to examine responses by biodiversity to changing environments. Effective conservation recommendations require that models of both distribution dynamics and population trends accurately reflect reality. However, identification of the appropriate temporal and spatial scales of animal response, and then obtaining data at these scales present two major challenges to developing predictive models. In heterogeneous forested mountain landscapes I examined: A) the relative drivers of climatic variability at fine spatial scales under the forest canopy ('microclimate'), B) the influence of microclimate on local-scale occupancy dynamics of bird communities, and C) the effects of spatial scale and imperfect bird detection on long-term avian population trends.\ud \ud Climate change has been predicted to cause widespread biodiversity declines. However, the capacity of climate envelope models for predicting the future of biodiversity has been questioned due to the mismatch between the scale of available data (i.e., global climate models) and the scales at which organisms experience their environment. Local-scale variation in microclimate is hypothesized to provide potential ‘microrefugia’ for biodiversity, but the relative role of elevation, microtopography, and vegetation structure in driving microclimate is not well known. If the microrefugia hypothesis is true, I expected to see areas on the landscape that remained relatively cooler (i.e., buffered sites). To test this, I collected temperature data at 183 sites across elevation and forest structure gradients in complex terrain of the H. J. Andrews Experimental Forest in the Cascade Mountains of Oregon, USA (Chapter 2). I used boosted regression trees, a novel machine learning approach, to determine the relative influence of vegetation structure, microtopography, and elevation as drivers of microclimate and mapped fine-scale distributions of temperature across the landscape. Models performed extremely well on independent data - cross-validation correlations between testing and training data were 0.69 - 0.98 for ten selected climate variables. Elevation was the dominant driver in fine-scale microclimate patterns, although vegetation and microtopography also showed substantial relative influences. For instance, during the spring-summer transition, maximum monthly temperatures observed in old-growth sites were 2.6°C (95% CI: 1.8 - 3.3°C) cooler than plantation sites and minimum temperatures during winter months were 0.6°C (95% CI: 0.4 - 0.8°C) warmer. This suggests that older forest stands mediate changes in temperature by buffering against warming during summer months and moderating cold temperatures during the winter. \ud \ud Climate is generally considered most influential on species distributions at large spatial scales; however much microclimate variability exists within regional patterns. I tested whether this high degree of microclimate variability has relevance for predicting species distributions and occupancy dynamics of the Andrews Forest bird community. I collected bird occurrence data in 2012 and 2013 at all 183 sites with fine-scale temperature measurements. I used dynamic occupancy models to test the effects of temperature on occupancy and apparent within-season bird movement while statistically accounting for vegetation effects and imperfect bird detection (Chapter 3). Most species (87%) exhibited within-season shifts in response to local-scale temperature metrics. Effects of temperature on within-season occupancy dynamics were as large or larger (1 to 1.7 times) than vegetation. However, individual species were almost as likely to shift toward warmer sites as toward cooler sites, suggesting that microclimate preferences are species-specific. My results emphasize that high-resolution temperature data provide valuable insight into avian distribution dynamics in montane forest environments and that microclimate is an important variable in breeding season habitat selection by forest birds. I hypothesize that microclimate-associated distribution shifts may reflect species' potential for behavioral buffering from climate change in complex terrain.\ud \ud Factors influencing population trends often differ depending on the spatial scale under consideration. Further, accurate estimation of trends requires accounting for biases caused by imperfect detection. To test the degree to which population trends are consistent across scales, I estimated landscape-scale bird population trends from 1999-2012 for 38 species at the Hubbard Brook Experimental Forest (HBEF) in the White Mountains of New Hampshire, USA and compared them to regional and local trends (Chapter 4). I used a new method - open-population binomial mixture models - to test the hypothesis that imperfect detection in bird sampling has the potential to bias trend estimates. I also tested for generalities in species responses by predicting population trends as a function of life history and ecological traits. Landscape-scale trends were correlated with regional and local trends, but generally these correlations were weak (r = 0.12 - 0.4). Further, more species were declining at the regional scale compared to within the relatively undisturbed HBEF. Life history and ecological traits did not explain any of the variability in the HBEF trends. However, at the regional scale, species that occurred at higher elevations were more likely to be declining and species associated with older forests have increased. I hypothesize that these differences could be attributed to both elevated rates of land-use change in the broader region and the fact that the structure of regional data did not permit modeling of imperfect detection. Indeed, accounting for imperfect detection resulted in more accurate population trend estimates at the landscape scale; without accounting for detection we would have both missed trends and falsely identified trends where none existed. These results highlight two important cautions for trend analysis: 1) population trends estimated at fine spatial scales may not be extrapolated to broader scales and 2) accurate trends require accounting for imperfect detection