Spatial scale selection for informing species conservation in a changing landscape

Monroe, Adrian P.; Heinrichs, Julie A.; Whipple, A.; O’Donnell, Michael S.; Edmunds, David R.; Aldridge, Cameron L.

doi:10.1002/ecs2.4320

Cited by 9 publications

(4 citation statements)

References 104 publications

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“…These models share information within species and amongst neighboring regions, creating fine-scale models (Figure 6). Many biotic and abiotic factors act at small scales (Thogmartin et al 2004, Paton et al 2019, Monroe et al 2022, requiring fine-scale modeling.…”

Section: Discussionmentioning

confidence: 99%

Spatially explicit Bayesian hierarchical models for avian population status and trends

Smith¹,

Binley²,

Daly³

et al. 2023

Preprint

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Population trend estimates form the core of avian conservation assessments in North America and indicate important changes in the state of the natural world. The models used to estimate these trends would be more efficient and informative for conservation if they explicitly considered the spatial locations of the monitoring data. We created spatially explicit versions of some standard status and trend models applied to long-term monitoring data for birds across North America. We compared the spatial models to simpler non-spatial versions of the same models, fitting them to simulated data and real data from three broad-scale monitoring programs: the North American Breeding Bird Survey (BBS), the Christmas Bird Count (CBC), and a collection of programs we refer to as Migrating Shorebird Surveys (MSS). All the models generally reproduced the true simulated trends and population trajectories when there were many data, but the spatial models outperformed the non-spatial models when there were fewer data and in locations where the local trends differed from the range-wide means. When fit to real data, the spatial models revealed interesting spatial patterns in trends that were much less apparent in results from the non-spatial versions. The spatially explicit sharing of information also means we can fit the models with much smaller strata, allowing for finer-grained patterns in trends. Spatially informed trends will facilitate more locally relevant conservation, highlight areas of conservation successes and challenges, and help generate and test hypotheses about the spatially dependent drivers of population change.

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Section: Discussionmentioning

confidence: 99%

Spatially explicit Bayesian hierarchical models for avian population status and trends

Smith¹,

Binley²,

Daly³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…or states/provinces (Thogmartin et al 2004, Paton et al 2019, Monroe et al 2022). Route-level patterns are useful in guiding conservation and/or further monitoring efforts, such as identifying small areas for conservation purposes or diverging population trends within management areas (i.e., strata or BCR).…”

Section: Discussionmentioning

confidence: 99%

Patterns and drivers of population trends on individual Breeding Bird Survey routes using spatially explicit models and route-level covariates

Aponte,

Hudson,

English

et al. 2023

Preprint

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Spatial patterns in population trends, particularly those at finer geographic scales, can help us better understand the factors driving population change in North American birds. The standard status and trend models for the North American Breeding Bird Survey (BBS) were designed to estimate trends within broad geographic strata, such as Bird Conservation Regions, U.S. states, and Canadian territories or provinces. Calculating trend estimates at the level of individual survey transects (“routes”) from the BBS allows us to explore finer spatial patterns and simultaneously estimate the effects of covariates, such as habitat-loss or annual weather, on both relative abundance and trend (changes in relative abundance through time). Here, we describe four related hierarchical Bayesian models that estimate trends for individual BBS routes, implemented in the probabilistic programing language Stan. All four models estimate route-level trends and relative abundances using a hierarchical structure that shares information among routes, and three of the models share information in a spatially explicit way. The spatial models use either an intrinsic Conditional Autoregressive structure or a distance-based Gaussian process to estimate the spatial components. We fit all four models to data for 71 species and then fit only two of the models (one spatial and one non-spatial) for an additional 216 species due to computational limitations. Leave-future-out cross-validation showed the spatial models outperformed the non-spatial model for 284 out of 287 species. For the species tested here, the best approach to modeling the spatial components depended on the species; the Gaussian Process had the highest predictive accuracy for 2/3 of the species tested here and the iCAR was better for the remaining 1/3. We also present two examples of route-level covariate analyses focused on spatial and temporal variation in habitat for Rufous Hummingbird (Selasphorus rufus) and Horned Grebe (Podiceps auritus). Covariates explain or affect patterns in the rate of population change for both species. Route-level models for BBS data are useful for visualizing spatial patterns of population change, generating hypotheses on the causes of change, comparing patterns of change among regions and species, and testing hypotheses on causes of change with relevant covariates.

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“…While bobwhites likely respond to vegetation composition and structure at micro and macro scales (Taylor et al 1999 a , Yeiser et al 2021, Sinnott et al 2023 b ), we restricted our scale of interest to a radius in which vegetation composition and structure could vary daily with movements of individual bobwhites and that captured proximity of vegetation features associated with predator risk (Peterson et al 2022, Sinnott et al 2022). We selected a scale of interest based on prior knowledge of bobwhite and predator movements and vegetation associations, but we did not identify the optimal scale to evaluate demographic–environmental relationships (Monroe et al 2022).…”

Section: Methodsmentioning

confidence: 99%

Northern bobwhite breeding season and nest survival are greater on native grasslands

Sinnott,

Thompson,

Hedges

et al. 2023

J Wildl Manag

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Northern bobwhites (Colinus virginianus) are declining across their range. Population recovery requires a better understanding of the influence of management strategies and land cover on specific demographic components of full annual cycle population dynamics. We used known‐fate daily survival models to relate adult breeding season survival and nest survival to land cover composition, woody cover, and management actions on 5 study sites in southwest Missouri, USA. We collected 3,516 observations of daily nest survival from 364 bobwhite nests for 7,509 exposure days and 34,386 observations of adult survival from 955 bobwhites for 85,225 exposure days during breeding seasons in 2014–2018. The most supported model for nest and adult breeding season survival included environmental covariates for percent native grass and agricultural cover, distance to shrub, and distance to tree. Both nest and adult breeding season survival were positively related to distance to trees. Nest survival was negatively related to percent agricultural crop cover. Areal nest and adult breeding season survival were greater on native grassland sites compared to conservation areas that maintained small units of strip crop and wooded cover. Management to sustain or expand native grass cover and reduce tree cover should benefit bobwhite nest survival and adult breeding season survival in the landscapes we studied.

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Spatial scale selection for informing species conservation in a changing landscape

Cited by 9 publications

References 104 publications

Spatially explicit Bayesian hierarchical models for avian population status and trends

Spatially explicit Bayesian hierarchical models for avian population status and trends

Patterns and drivers of population trends on individual Breeding Bird Survey routes using spatially explicit models and route-level covariates

Northern bobwhite breeding season and nest survival are greater on native grasslands

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