Winter Habitat Indices (WHIs) for the contiguous US and their relationship with winter bird diversity

Gudex‐Cross, David; Keyser, Spencer R.; Zuckerberg, Benjamin; Fink, Daniel; Zhu, Likai; Pauli, Jonathan N.; Radeloff, Volker C.

doi:10.1016/j.rse.2021.112309

Cited by 16 publications

(40 citation statements)

References 61 publications

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“…Dnsc is moderately correlated with the minimum temperatures of the driest and coldest period, which well represent the harshness of winter season. This is consistent with previous findings that Dnsc is one of effective indicators of subnivium, and high values suggest the longer exposure of organisms to extremely low air temperatures due to lack of snow cover [19,30,32]. Snow variability is only strongly and negatively correlated with temperature annual range, but it can provide unique information about the variability of winter temperatures, which is important for many over-wintering species [19].…”

Section: Discussionsupporting

confidence: 91%

“…Our research highlights the use of solely remote sensing observations from multiple sources to generate a dataset, which is able to update annually given the rapidly increasing optical and microwave sensors retrieving snow cover and ground freeze/thaw status [41,44]. This could overcome the limitations of gridded datasets interpolated from station measurements or estimated by data assimilation models (e.g., SNODAS), which are not always available and have higher uncertainties [19]. Our derived winter habitat indices identify distinct patterns of winter conditions across China.…”

Section: Discussionmentioning

confidence: 99%

“…SnowVar was calculated as the times of transitional events (from snow to no snow or from no snow to snow) divided by winter season length. Further detail on our calculations of winter habitat indices is available in Zhu et al (2017) for the frozen ground-based indices [32] and Gudex-Cross et al (2021) for snow cover variability [19].…”

Section: Calculating Winter Habitat Indicesmentioning

confidence: 99%

“…The extensive applications suggest that the WorldClim dataset can well capture the water-energy dynamics relevant to habitat and display a high prediction power of species richness [18]. These merits make WorldClim data a perfect reference to gauge environmental information within remote sensing-based indices, although they are biased at some regions due to the lack of weather stations for interpolation [8,19]. In contrast, remote sensing is the most cost-effective and comprehensive approach to retrieve climate, water and energy-relevant variables of the Earth's surface [6,15,20] and offers the opportunity for broad-scale characterizations of species richness in a systematic, repeatable, and spatially exhaustive manner [21,22].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, remote sensing is the most cost-effective and comprehensive approach to retrieve climate, water and energy-relevant variables of the Earth's surface [6,15,20] and offers the opportunity for broad-scale characterizations of species richness in a systematic, repeatable, and spatially exhaustive manner [21,22]. Although many indicators have been developed with remote sensing data, the essential variables for explaining and mapping species richness are still not fully considered, e.g., variables for cryospheric properties [19,22]. With increasingly available remote sensing collections and more dialogues between remote sensing and ecology, characterizing species richness with remote sensing observations is promising, but great efforts should be made to develop and test remote sensing-based variables.…”

Section: Introductionmentioning

confidence: 99%

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Remotely Sensed Winter Habitat Indices Improve the Explanation of Broad-Scale Patterns of Mammal and Bird Species Richness in China

Zhu

Guo

2022

Remote Sensing

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Climate change is transforming winter environmental conditions rapidly. Shifts in snow regimes and freeze/thaw cycles that are unique to the harsh winter season can strongly influence ecological processes and biodiversity patterns of mammals and birds. However, the role of the winter environment in structuring a species richness pattern is generally downplayed, especially in temperate regions. Here we developed a suite of winter habitat indices at 500 m spatial resolution by fusing MODIS snow products and NASA MEaSUREs daily freeze/thaw records from passive microwave sensors and tested how these indices could improve the explanation of species richness patterns across China. We found that the winter habitat indices provided unique and mutually complementary environmental information compared to the commonly used Dynamic Habitat Indices (DHIs). Winter habitat indices significantly increased the explanatory power for species richness of all mammal and bird groups. Particularly, winter habitat indices contributed more to the explanation of bird species than mammals. Regarding the independent contribution, winter season length made the largest contributions to the explained variance of winter birds (30%), resident birds (27%), and mammals (18%), while the frequency of snow-free frozen ground contributed the most to the explanation of species richness for summer birds (23%). Our research provides new insights into the interpretation of broad-scale species diversity, which has great implications for biodiversity assessment and conservation.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Calculating Winter Habitat Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Remotely Sensed Winter Habitat Indices Improve the Explanation of Broad-Scale Patterns of Mammal and Bird Species Richness in China

Zhu

Guo

2022

Remote Sensing

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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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Mapping multiscale breeding bird species distributions across the United States and evaluating their conservation applications

Carroll,

Pidgeon,

Elsen

et al. 2023

Ecological Applications

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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, community‐level 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.

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Winter Habitat Indices (WHIs) for the contiguous US and their relationship with winter bird diversity

Cited by 16 publications

References 61 publications

Remotely Sensed Winter Habitat Indices Improve the Explanation of Broad-Scale Patterns of Mammal and Bird Species Richness in China

Remotely Sensed Winter Habitat Indices Improve the Explanation of Broad-Scale Patterns of Mammal and Bird Species Richness in China

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

Mapping multiscale breeding bird species distributions across the United States and evaluating their conservation applications

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