Landsat 8 TIRS-derived relative temperature and thermal heterogeneity predict winter bird species richness patterns across the conterminous United States

Elsen, Paul R.; Farwell, Laura S.; Pidgeon, Anna M.; Radeloff, Volker C.

doi:10.1016/j.rse.2019.111514

Cited by 22 publications

(23 citation statements)

References 70 publications

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“…This finding is generally in line with conclusions reached in a global study using coarser-resolution data that found that topographic heterogeneity and temperature were the most important predictors of bird richness patterns (Davies et al 2007). It is also consistent with recent research assessing the predictive performance of winter thermal heterogeneity on richness patterns of climate sensitive species across the conterminous US (Elsen et al 2020a). Our results highlight the value of including variables related to both relative temperature and thermal heterogeneity in models of broad scale richness patterns.…”

Section: Discussionsupporting

confidence: 92%

“…Many of these regions closely align with priority regions for other terrestrial vertebrates, freshwater fish and trees (Jenkins et al 2015). Protecting and restoring habitat in these areas would likely provide particularly large benefits for biodiversity, especially for species threatened by climate change (Heller and Zavaleta 2009, Elsen et al 2020a).…”

Section: Discussionmentioning

confidence: 99%

“…We build on our previous work developing metrics of relative temperature and thermal heterogeneity for winter using data from the Landsat 8 Thermal Infrared Sensor (TIRS) by developing these two metrics for distinct seasons (Elsen et al 2020a). Doing so allowed us to compare seasonal patterns and better understand variability in their relationships to commonly used proxy measures of elevation and topographic heterogeneity.…”

Section: Methodsmentioning

confidence: 99%

“…We chose a 19.7 km radius because it encompasses an entire BBS route and because georeferenced maps of BBS routes were unavailable for all of our routes, which prohibited us from accurately circumscribing smaller areas from which to summarize environmental variables with confidence. Our approach is thus conservative by not making additional assumptions about route locations, and follows convention used in route‐level analyses of BBS data (Flather and Sauer 1996, Pidgeon et al 2007, Rittenhouse et al 2012, Elsen et al 2020a).…”

Section: Methodsmentioning

confidence: 99%

“…additional assumptions about route locations, and follows convention used in route-level analyses of BBS data (Flather and Sauer 1996, Pidgeon et al 2007, Rittenhouse et al 2012, Elsen et al 2020a).…”

Section: Remotely Sensed Thermal Elevation and Land Cover Datamentioning

confidence: 99%

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Contrasting seasonal patterns of relative temperature and thermal heterogeneity and their influence on breeding and winter bird richness patterns across the conterminous United States

et al. 2021

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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.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Remotely Sensed Thermal Elevation and Land Cover Datamentioning

confidence: 99%

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Contrasting seasonal patterns of relative temperature and thermal heterogeneity and their influence on breeding and winter bird richness patterns across the conterminous United States

et al. 2021

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Habitat heterogeneity captured by 30‐m resolution satellite image texture predicts bird richness across the United States

Farwell

Elsen

Razenkova

et al. 2020

Ecological Applications

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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.

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Mapping breeding bird species richness at management‐relevant resolutions across the United States

Carroll

Farwell

Pidgeon

et al. 2022

Ecological Applications

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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.

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Landsat 8 TIRS-derived relative temperature and thermal heterogeneity predict winter bird species richness patterns across the conterminous United States

Cited by 22 publications

References 70 publications

Contrasting seasonal patterns of relative temperature and thermal heterogeneity and their influence on breeding and winter bird richness patterns across the conterminous United States

Contrasting seasonal patterns of relative temperature and thermal heterogeneity and their influence on breeding and winter bird richness patterns across the conterminous United States

Habitat heterogeneity captured by 30‐m resolution satellite image texture predicts bird richness across the United States

Mapping breeding bird species richness at management‐relevant resolutions across the United States

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