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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.

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.

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.

ContextSince 2005, unconventional gas development has rapidly altered forests across the Marcellus-Utica shale basin in the central Appalachian region of the eastern United States, an area of high conservation value for biodiversity. Much is still unknown about ecological impacts of associated land cover change.ObjectivesOur goal was to identify threshold responses among bird species and habitat guilds to (1) overall forest loss and fragmentation in affected landscapes, and (2) distance from anthropogenic disturbance, both related and unrelated to shale gas.MethodsWe conducted 2589 bird surveys at 190 sites across this region, and quantified community-level and species-specific thresholds relating to forest cover and distance to anthropogenic disturbance, using Threshold Indicator Taxa Analysis (TITAN).ResultsForest interior species decreased abruptly in abundance and frequency of occurrence above a threshold of 17.0% overall forest loss, while early successional and synanthropic species increased abruptly above 30.5–36.5% forest loss, respectively. Broad quantile intervals around responses to distance from anthropogenic disturbance suggest these were not sharp threshold responses, but more gradual or linear responses. Among forest interior species evaluated, 48.1% increased in abundance farther from shale gas development, while 55.6% of early successional and synanthropic species decreased.ConclusionsWe found evidence of avian threshold responses to overall forest loss and fragmentation in affected landscapes across the Marcellus-Utica shale region. Our results suggest that efforts to avoid shale gas development in regional core forests—particularly those still retaining ≥ 83% forest cover—can reduce negative effects on area-sensitive, forest interior dependent species.

Unconventional shale gas development is a rapidly expanding driver of forest loss and fragmentation in the central Appalachian region. We evaluated the relationship between breeding passerine abundances and distance from shale gas development at a long-term (2008–2017) study site in northern West Virginia, USA. We examined responses of 27 species within 3 habitat guilds: forest interior, early successional, and synanthropic. More than half of the species evaluated showed sensitivity to distance from unconventional shale gas infrastructure (e.g., well pads, access roads, pipelines). Five forest interior species occurred in greater abundances farther from shale gas development, whereas 3 forest interior gap specialists increased in abundance closer to shale gas. Early successional and synanthropic species, including the nest-parasitic Brown-headed Cowbird (Molothrus ater), generally occurred in greater abundances closer to shale gas infrastructure. We used interpolated distributions of 4 focal species to assess their spatial response to unconventional shale gas development over time. Our results indicate that breeding passerine distributions and community composition are changing with forest disturbance driven by unconventional shale gas energy development.

Unprecedented rates of climate change and biodiversity loss have galvanized efforts to expand protected areas (PAs) globally. However, limited spatial overlap between the most important landscapes for mitigating climate change and those with the highest value for biodiversity may impede efforts to simultaneously address both issues through new protections. At the same time, there is a need to understand how lands with high conservation value align with existing patterns of land management, both public and private, which will inform strategies for developing new conservation areas. To address these challenges, we developed three composite indices to identify the highest conservation value lands across the conterminous United States (CONUS) and Alaska, drawing on a suite of key ecological and environmental indicators. Two indices characterize the most important conservation lands for addressing climate change (based on climate accessibility, climate stability, and total carbon storage) and biodiversity (based on species richness, ecological integrity, and ecological connectivity), while a third, combined index simultaneously addresses both conservation challenges. We found that existing PAs in the United States have relatively low overlap with the highest conservation value lands, regardless of the index used (10%–13% in CONUS, 27%–34% in Alaska), suggesting limited effectiveness of current protections but substantial opportunity for expanding conservation into high‐value, unprotected areas. In unprotected landscapes, the highest value lands for addressing climate change generally diverged from those identified as most important for protecting biodiversity (22%–38% overlap, depending on index and geography). Our combined index reconciled these spatial trade‐offs through high overlap with both the climate and biodiversity indices (66%–72%). Of the unprotected high conservation value lands identified by each of our three indices, we found ≥70% are privately managed in CONUS, while 16%–27% are privately managed in Alaska, underscoring the need to engage private landowners and land trusts in efforts to substantially increase the total footprint of conservation lands in the United States. Our findings highlight the importance of balancing climate and biodiversity objectives when identifying new lands for conservation and provide guidance on where to target new protections to simultaneously address both goals. To facilitate planning using the indices, we developed an interactive web application.

Aim On‐the‐ground conservation efforts require managers to balance various and sometimes conflicting conservation goals. For instance, areas important for conserving threatened and endangered species may have little spatial agreement with high functional redundancy. Using prioritisation tools can further complicate conservation prioritisations if conflicting diversity metrics identify different high‐priority areas. We compared five community‐level diversity metrics for birds across the conterminous US to identify how much agreement existed between each before and after using a prioritisation framework. Location Contiguous US. Methods We examined spatial agreement among metrics before (a priori) and after (a posteriori) prioritisation using integer linear programming. We compared a posteriori outputs for 10% and 30% conservation goals. We also assessed data layer correlation and agreement (i.e., overlap) a priori and a posteriori. Results As expected, the a priori diversity metrics were poorly to moderately correlated (median = 0.31, range = 0.11–0.71), but all a posteriori solutions had areas of agreement. Accordingly, our a posteriori metrics identified different areas as high priority for conservation, none aligning well with the current protected areas (mean = 13%–15% agreement). However, the a posteriori approach allowed us to include a continuity constraint (identify adjacent important pixels) and easily find areas of high‐priority agreement. Main Conclusions Metric agreement depended on a priori or a posteriori evaluation, highlighting managers' challenges when deciding where and how to enact conservation. Given these challenges, a posteriori solutions best support multiple‐objective, complex and large planning conservation problems. Importantly, all of our a posteriori maps agreed in areas, suggesting aggregates of several metrics could instill certainty in decision‐making if prioritisation solutions were obtained at different times. Overall, our results underscore the critical importance of generating maps and metrics useful for on‐the‐ground management, carefully selecting biodiversity metrics that best reflect conservation goals and employing prioritisation software for generating conservation solutions.

Birds select habitat characteristics, such as variability in habitat structure, across multiple spatial scales (grain and extent). Measuring habitat variability at multiple scales can better capture factors that influence avifauna communities than focusing on one scale only. One valuable tool in assessing habitat heterogeneity is the cumulative dynamic habitat index (DHI), which is derived from satellite data and captures temporal variability in vegetation productivity. Our goals were to develop new habitat measures from the cumulative DHI at multiple scales based on scalograms, and to test their performance in models of bird abundance. We counted birds at 188 plots during three breeding seasons (2007–2009) at Fort McCoy military installation, USA, to assess the abundance of forest (ovenbird), shrubland (indigo bunting), and grassland (grasshopper sparrow) bird specialists. We then calculated NDVI based on PlanetScope (3 m), Sentinel‐2 (10 m), Landsat‐8 (30 m), and MODIS (250 m) data to quantify cumulative DHI. We summarized the averaged NDVI cumulative DHI within multiple extents around each bird survey and developed 11 new habitat measures to test their predictive power in models of bird abundance. We found positive relationships between cumulative DHI at different extents and the abundances of both ovenbirds and indigo buntings, a forest and a shrubland specialist, respectively; and a negative relationship with grasshopper sparrows, a grassland specialist. In multiple linear regression models that incorporated single‐ and multi‐grain predictors, the scalogram habitat measures explained moderate to high levels of variability in bird abundance, with R2 = 0.77, 0.37, and 0.75 for our forest, shrubland, and grassland specialists, respectively. Our results show that scalograms are an effective tool for capturing multiscale habitat configuration, because they capture the variability of habitat conditions in forests, shrublands, and grasslands. The scalogram habitat measures that we developed can be computed using our new R package ‘scalogram'.

In this study, I evaluated impacts of unconventional shale gas development, or the combined use of horizontal drilling and hydraulic fracturing (i.e., fracking), on forest land cover and breeding songbirds in the Marcellus-Utica shale region. Since 2005, extraction of natural gas from the Marcellus-Utica shale has increased exponentially in the central Appalachians, an area of high conservation value for global biodiversity. Although there has been an increase in research on the effects of shale gas development in the region, the industry is still relatively new in the eastern U.S. and much is still unknown about biological and environmental impacts. In Chapter 1, I summarize potential effects of unconventional shale gas development on regional forests, and review the literature on songbird responses to forest loss and fragmentation. I also outline the overall research objectives of this dissertation and provide a summary of chapter topics. In Chapter 2, I evaluated the effects of shale gas development on a heavily forested, long-term study site in northern West Virginia, from 2008–2015. Construction of gas well pads and linear infrastructure contributed to an overall 4.5% loss in forest cover at the site, a 12.4% loss in core forest, and a 51.7% increase in forest edge density. I evaluated the relationship between land-cover metrics and species richness within three avian guilds: forest-interior, early-successional, and synanthropic, in addition to abundances of 21 focal species. Land-cover impacts were evaluated at two spatial extents: a point-level within 100-m and 500-m buffers of each avian survey station, and a landscape-level across the study area (4,326 ha). Although I observed variability in species-specific responses, I found distinct trends in long-term response among the three avian guilds. Forest-interior species richness declined at all points across the site and at points impacted within 100 m by shale gas but did not change at unimpacted points. Early-successional and synanthropic species richness increased at all points and at impacted points but did not change at unimpacted points. In Chapter 3, I focused on spatial responses of focal songbird species to distance from shale gas development, at the same long-term study site in northern West Virginia (2008–2017). I found that more than half of the focal species evaluated showed sensitivity to distance from shale gas infrastructure. Several forest interior species occurred in higher abundances with increasing distance from shale gas, while a few forest interior gap specialists increased in abundance closer to shale gas. Early successional and synanthropic species generally occurred in higher abundances closer to shale gas. Interpolated distribution maps for four focal species helped visualize patterns of spatial response to shale gas development, over time. In Chapter 4, I conducted a region-wide assessment of impacts of Marcellus-Utica shale gas development on forests and breeding songbirds in the central Appalachian region. I evaluated land cover and bird count data from 190 forested sites across the Marcellus-Utica region: 120 sites affected by shale gas development and 70 sites affected only by human development unrelated to shale gas. First, I quantified the footprint of shale gas infrastructure in forested landscapes and found that shale gas generally occupied a relatively small footprint on the landscape (x? = 2.7%), and that linear shale gas infrastructure accounted for greater forest loss and fragmentation than well pad development region. I then compared bird community assemblages at sites with and without shale gas development and found they did not differ, suggesting that at a broad scale, bird communities generally responded similarly to shale gas development and other types of human-caused forest disturbance. However, finer-resolution analyses of bird counts across gradients of forest cover and human development revealed some distinct patterns of songbird response relative to shale gas development: I observed lower species richness and abundance among forest interior birds relative to shale gas metrics, while early successional and synanthropic birds showed higher richness and abundance relative to shale gas development. Lastly, in Chapter 5, I evaluated potential region-wide threshold responses in species abundance to distance from human development and percent forest/core forest cover, across the Marcellus-Utica shale region. My results supported the occurrence of some threshold avian responses to distance from shale gas development, and narrower threshold responses to the proportion of forest cover and core forest cover in landscapes altered by shale gas development. These findings are consistent with other localized studies from the region, documenting decreasing abundance and diversity of forest interior birds and increasing numbers and diversity of disturbance-dependent and human adapted species, in landscapes altered by shale gas development. (Abstract shortened by ProQuest.)

Early American Fiction 1789-1875 is an expansion of the Chadwyck-Healey database Early American Fiction 1774-1850. The first version, released in 2000, was a collaboration between the University of Virginia Library (with funding from the Andrew W. Mellon Foundation) and Chadwyck-Healey. It brought more than 500 volumes of American novels and short fiction to the web in searchable text and in facsimile page images.