Biodiversity

Environmental heterogeneity enhances species richness by creating niches and providing refugia. Spatial variation in climate has a particularly strong positive correlation with richness, but is often indirectly inferred from proxy variables, such as elevation and related topographic heterogeneity indices, or derived from interpolated coarsegrain weather station data. Our aim was to develop new remotely sensed metrics of relative temperature and thermal heterogeneity, compare them with proxy measures, and evaluate their performance in predicting species richness patterns. We analyzed Landsat 8’s Thermal Infrared Sensor data, calculated two thermal metrics during summer and winter, and compared their seasonal spatial patterns with those of elevation and topographic heterogeneity. We fit generalized least squares models to evaluate each variable’s effect in predicting seasonal bird richness using data from the North American Breeding Bird Survey. Generally speaking, neither elevation nor topographic heterogeneity were good proxies for temperature or thermal heterogeneity, respectively. Relative temperature had a non-linear relationship with elevation that was negatively quadratic in summer, but slightly positively quadratic in winter. Topographic heterogeneity had a stronger positive relationship with thermal heterogeneity in winter than in summer. The magnitude and direction of elevation–temperature and topographic heterogeneity–thermal heterogeneity relationships in each season also varied substantially across ecoregions. Remotely sensed metrics of relative temperature and thermal heterogeneity improved the predictive performance of species richness models, and both thermal variables had significant effects on bird richness that were independent of elevation and topographic heterogeneity. Thermal heterogeneity was positively related to total breeding bird richness, migrant breeding bird richness and resident bird richness, whereas topographic heterogeneity was negatively related to total breeding richness and unrelated to migrant or resident bird richness. Because thermal and topographic heterogeneity had contrasting seasonal patterns and effects on richness, they must be carefully contextualized when guiding conservation priorities.

The seasonal dynamics of snow cover strongly affect ecosystem processes and winter habitat, making them an important driver of terrestrial biodiversity patterns. Snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra satellites can capture these dynamics over large spatiotemporal scales, allowing for the development of indices with specific application in ecological research and predicting biodiversity. Here, our primary objective was to derive winter habitat indices (WHIs) from MODIS that quantify snow season length, snow cover variability, and the prevalence of frozen ground without snow as a proxy for subnivium conditions. We calculated the WHIs for the full snow year (Aug-Jul) and winter months (Dec-Feb) across the contiguous US from 2003/04 to 2017/18 and validated them with ground-based data from 797 meteorological stations. To demonstrate the potential of the WHIs for biodiversity assessments, we modeled their relationships with winter bird species richness derived from eBird observations. The WHIs had clear spatial patterns reflecting both altitudinal and latitudinal gradients in snow cover. Snow season length was generally longer at higher latitudes and elevations, while snow cover variability and frozen ground without snow were highest across low elevations of the mid latitudes. Variability in the WHIs was largely driven by elevation in the West and by latitude in the East. Snow season length and frozen ground without snow were most accurately mapped, and had correlations with station data across all years of 0.91 and 0.85, respectively. Snow cover variability was accurately mapped for winter (r = 0.79), but not for the full snow year (r = 􀀀 0.21). The model containing all three WHIs used to predict winter bird species richness patterns across the contiguous US was by far the best, demonstrating the individual value of each index. Regions with longer snow seasons generally supported fewer species. Species richness increased steadily up to moderate levels of snow cover variability and frozen ground without snow, after which it steeply declined. Our results show that the MODIS WHIs accurately characterized unique gradients of snow cover dynamics and provided important information on winter habitat conditions for birds, highlighting their potential for ecological research and conservation planning.

Addressing global declines in biodiversity requires accurate assessments of key environmental attributes determining patterns of species diversity. Spatial heterogeneity of vegetation strongly affects species diversity patterns, and measures of vegetation structure derived from lidar and satellite image texture analysis correlate well with species richness. Our goal here was to gain a better understanding of why image texture explains bird richness, by linking field-based measures of vegetation structure directly with both image texture and bird richness. In addition, we asked how image texture compares with lidar-based canopy height variability, and how sensor resolution affects the explanatory power of image texture. We generated texture metrics from 30 m (Landsat 8) and 10 m (Sentinel-2) resolution Enhanced Vegetation Index (EVI) imagery from 2017 to 2019. We compared textures with vegetation metrics and bird richness data from 27 National Ecological Observatory Network (NEON) terrestrial field sites across the continental US. Both 30 and 10 m resolution texture metrics were strongly correlated with lidar-based canopy height variability (|r| = 0.64 and 0.80, respectively). Texture was moderately correlated with field-based metrics, including variability of vegetation height and tree stem diameter, and foliage height diversity (range |r| = 0.31–0.52). Generally, 10 m resolution texture had stronger correlations with lidar and field-based metrics than 30 m resolution texture. In univariate linear models of total bird richness, 10 m resolution texture metrics also had higher explanatory power (up to R2adj = 0.45), than 30 m texture metrics (up to R2adj = 0.31). Among all metrics evaluated, the 10 m homogeneity texture was the best univariate predictor of total bird richness. In multivariate bird richness models that combined texture with lidar-based canopy height variability and field-based metrics, both 30 m and 10 m resolution texture metrics were selected in top-ranked models and independently contributed explanatory power (up to R2adj = 46%). Lidar-based canopy height variability was also selected in a top-ranked model of total bird richness, but independently contributed only 15% of the variance explained. Our results show satellite image texture characterized multiple features of structural and compositional vegetation heterogeneity, complemented more commonly used metrics in models of bird richness and for some guilds outperformed both lidar-based canopy height variability and field-based vegetation measurements. Ours is the first study to directly link image texture both to specific components of vegetation heterogeneity and to bird richness across multiple ecoregions and spatial resolutions, thereby shedding light on habitat features underlying the strong correlation between image texture and biodiversity.

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.