Search

Species distribution models are vital to management decisions that require understanding habitat use patterns, particularly for species of conservation concern. However, the production of distribution maps for individual species is often hampered by data scarcity, and existing species maps are rarely spatially validated due to limited occurrence data. Furthermore, communitylevel maps based on stacked species distribution models lack important community assemblage information (e.g., competitive exclusion) relevant to conservation. Thus, multispecies, guild, or community models are often used in conservation practice instead. To address these limitations, we aimed to generate fine-scale, spatially continuous, nationwide maps for species represented in the North American Breeding Bird Survey (BBS) between 1992 and 2019. We developed ensemble models for each species at three spatial resolutions--0.5, 2.5, and 5 km--across the conterminous United States. We also compared species richness patterns from stacked single-species models with those of 19 functional guilds developed using the same data to assess the similarity between predictions. We successfully modeled 192 bird species at 5-km resolution, 160 species at 2.5-km resolution, and 80 species at 0.5-km resolution. However, the species we could model represent only 28%-56% of species found in the conterminous US BBSs across resolutions owing to data limitations. We found that stacked maps and guild maps generally had high correlations across resolutions (median = 84%), but spatial agreement varied regionally by resolution and was most pronounced between the East and West at the 5-km resolution. The spatial differences between our stacked maps and guild maps illustrate the importance of spatial validation in conservation planning. Overall, our species maps are useful for single-species conservation and can support fine-scale decision-making across the United States and support community-level conservation when used in tandem with guild maps. However, there remain data scarcity issues for many species of conservation concern when using the BBS for single-species models. [ABSTRACT FROM AUTHOR]

Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long-term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modeling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002-2020 time series, and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non-photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote sensing-based monitoring of trends in grasslands so that underlying processes can be discerned. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 coarse‐grain 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. [ABSTRACT FROM AUTHOR]

The thermal environment limits species ranges through its influence on physiology and resource distributions and thus affects species richness patterns over broad spatial scales. Understanding how temperature drives species richness patterns is particularly important in the context of global change and for effective conservation planning. Landsat 8's Thermal Infrared Sensor (TIRS) allows direct mapping of temperature at moderate spatial resolutions (100 m, downscaled by the USGS to 30 m), overcoming limitations inherent in coarse interpolated weather station data that poorly capture fine-scale temperature patterns over broad areas. TIRS data thus offer the unique opportunity to understand how the thermal environment influences species richness patterns. Our aim was to develop and assess the ability of TIRS-based temperature metrics to predict patterns of winter bird richness across the conterminous United States during winter, a period of marked temperature stress for birds. We used TIRS data from 2013-2018 to derive metrics of relative temperature and intra-seasonal thermal heterogeneity. To quantify winter bird richness across the conterminous US, we tabulated the richness only for resident bird species, i.e., those species that do not move between the winter and breeding seasons, from the North American Breeding Bird Survey, the most extensive survey of birds in the US. We expected that relative temperature and thermal heterogeneity would have strong positive associations with winter bird richness because colder temperatures heighten temperature stress for birds, and thermal heterogeneity is a proxy for thermal niches and potential thermal refugia that can support more species. We further expected that both the strength of the effects and the relative importance of these variables would be greater for species with greater climate sensitivity, such as small-bodied species and climate-threatened species (i.e., those with large discrepancies between their current and future distributions following projected climate change). Consistent with our predictions, relative temperature and thermal heterogeneity strongly positively influenced winter bird richness patterns, with statistical models explaining 37.3% of the variance in resident bird richness. Thermal heterogeneity was the strongest predictor of small-bodied and climate-threatened species in our models, whereas relative temperature was the strongest predictor of large-bodied and climate-stable species. Our results demonstrate the important role that the thermal environment plays in governing winter bird richness patterns and highlight the previously underappreciated role that intra-seasonal thermal heterogeneity may have in supporting high winter bird species richness. Our findings thus illustrate the exciting potential for TIRS data to guide conservation planning in an era of global change. • Landsat 8 TIRS data are used to develop two new metrics of the thermal environment. • Relative temperature and thermal heterogeneity predict winter bird richness. • Thermal variables are more important for thermally sensitive species across the US. • Thermal remote sensing data from TIRS have promise to guide conservation planning. [ABSTRACT FROM AUTHOR]