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Human activities alter ecosystems everywhere, causing rapid biodiversity loss and biotic homogenization. These losses necessitate coordinated conservation actions guided by biodiversity and species distribution spatial data that cover large areas yet have fine-enough resolution to be management-relevant (i.e., ≤5 km). However, most biodiversity products are too coarse for management or are only available for small areas. Furthermore, many maps generated for biodiversity assessment and conservation do not explicitly quantify the inherent tradeoff between resolution and accuracy when predicting biodiversity patterns. Our goals were to generate predictive models of overall breeding bird species richness and species richness of different guilds based on nine functional or life-history-based traits across the conterminous United States at three resolutions (0.5, 2.5, and 5 km) and quantify the tradeoff between resolution and accuracy and, hence, relevance for management of the resulting biodiversity maps. We summarized 18 years of North American Breeding Bird Survey data (1992-2019) and modeled species richness using random forests, including 66 predictor variables (describing climate, vegetation, geomorphology, and anthropogenic conditions), 20 of which we newly derived. Among the three spatial resolutions, the percentage variance explained ranged from 27% to 60% (median = 54%; mean = 57%) for overall species richness and 12% to 87% (median = 61%; mean = 58%) for our different guilds. Overall species richness and guild-specific species richness were best explained at 5-km resolution using ~24 predictor variables based on percentage variance explained, symmetric mean absolute percentage error, and root mean square error values. However, our 2.5-km-resolution maps were almost as accurate and provided more spatially detailed information, which is why we recommend them for most management applications. Our results represent the first consistent, occurrence-based, and nationwide maps of breeding bird richness with a thorough accuracy assessment that are also spatially detailed enough to inform local management decisions. More broadly, our findings highlight the importance of explicitly considering tradeoffs between resolution and accuracy to create management-relevant biodiversity products for large areas.

The fields of ecology and conservation have evolved rapidly over the past century. Synthesizing larger trends in these disciplines remains a challenge yet is critical to bridging subdisciplines, guiding research, and informing educational frameworks. Here, we provide what we believe is the largest full‐text culturomic analysis of ecology and conservation journals, covering 80 years, 52 journals, and half a billion words. Our analysis illuminates the boom‐and‐bust of ecological hypotheses and theories; the adoption of statistical, genetic, and social‐science approaches; and the domination of terms that have emerged in recent decades (eg climate change, invasive species, ecosystem services, meta‐analysis, and supplementary material, which largely replaced unpublished data). We track the evolution of ecology from a largely descriptive field focused on natural history and observational studies to a more data‐driven, multidisciplinary field focused on applied environmental issues. Overall, our analysis highlights the increasing breadth of the field, illustrating that there is room for more diversity of ecologists and conservationists today than ever before.

Species loss is occurring globally at unprecedented rates, and effective conservation planning requires an understanding of landscape characteristics that determine biodiversity patterns. Habitat heterogeneity is an important determinant of species diversity, but is difficult to measure across large areas using field-based methods that are costly and logistically challenging. Satellite image texture analysis offers a cost-effective alternative for quantifying habitat heterogeneity across broad spatial scales. We tested the ability of texture measures derived from 30-m resolution Enhanced Vegetation Index (EVI) data to capture habitat heterogeneity and predict bird species richness across the conterminous United States. We used Landsat 8 satellite imagery from 2013-2017 to derive a suite of texture measures characterizing vegetation heterogeneity. Individual texture measures explained up to 21% of the variance in bird richness patterns in North American Breeding Bird Survey (BBS) data during the same time period. Texture measures were positively related to total breeding bird richness, but this relationship varied among forest, grassland, and shrubland habitat specialists. Multiple texture measures combined with mean EVI explained up to 41% of the variance in total bird richness, and models including EVI-based texture measures explained up to 10% more variance than those that included only EVI. Models that also incorporated topographic and land cover metrics further improved predictive performance, explaining up to 51% of the variance in total bird richness. A texture measure contributed predictive power and characterized landscape features that EVI and forest cover alone could not, even though the latter two were overall more important variables. Our results highlight the potential of texture measures for mapping habitat heterogeneity and species richness patterns across broad spatial extents, especially when used in conjunction with vegetation indices or land cover data. By generating 30-m resolution texture maps and modeling bird richness at a near-continental scale, we expand on previous applications of image texture measures for modeling biodiversity that were either limited in spatial extent or based on coarse-resolution imagery. Incorporating texture measures into broad-scale biodiversity models may advance our understanding of mechanisms underlying species richness patterns and improve predictions of species responses to rapid global change.

There is clear evidence that species' ranges along environmental gradients are constrained by both biotic and abiotic factors, yet their relative importance in structuring realized distributions remains uncertain. We surveyed breeding bird communities while collecting in situ temperature and vegetation data along five elevational transects in the Himalayas differing in temperature variability, habitat zonation, and bird richness in order to disentangle temperature, habitat, and congeneric competition as mechanisms structuring elevational ranges. Our results from species' abundance models representing these three mechanisms differed markedly from previous, foundational research in the tropics. Contrary to general expectations, we found little evidence for competition as a major determinant of range boundaries, with congeneric species limiting only 12% of ranges. Instead, temperature and habitat were found to structure the majority of species' distributions, limiting 48 and 40% of ranges, respectively. Our results suggest that different mechanisms may structure species ranges in the temperate Himalayas compared to tropical systems. Despite recent evidence suggesting temperate species have broader thermal tolerances than tropical species, our findings reinforce the notion that the abiotic environment has significant control over the distributions of temperate species.