The use of range size to assess risks to biodiversity from stochastic threats

Murray, Nicholas J.; Keith, David A.; Bland, Lucie M.; Nicholson, Emily; Regan, Tracey J.; Rodrı́guez, Jon Paul; Bedward, Michael

doi:10.1111/ddi.12533

Cited by 39 publications

(46 citation statements)

References 47 publications

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“…There are several aspects that determine the risk of extinction of a species. The IUCN (IUCN 2012) considers the following criteria to assess the risk of extinction of a given species: the number of individuals, the generation length, the population trend and the range size and its spatial aggregation (IUCN 2012, Joppa et al 2016, Murray et al 2017, Keith et al 2018. While a useful measure of conservation status, a species range size can be difficult to measure (Gaston 1991, 2003, Gaston and Fuller 2009).…”

Section: Introductionmentioning

confidence: 99%

Range area matters, and so does spatial configuration: predicting conservation status in vertebrates

2019

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The current rapid loss of biodiversity globally calls for improved tools to predict conservation status. Conservation status varies among taxa and is influenced by intrinsic species’ traits and extrinsic factors. Among these predictors, the most consistently recognized and widely available is geographic range area. However, ranges of equal area can have diverse spatial configurations that reflect variation in threatening processes and species’ characteristics (e.g. dispersal ability), and can affect local and regional population dynamics. The aim of this study is to assess if and how the spatial configuration of a species’ range relates to its conservation status. We obtained range maps and two descriptors of conservation status: extinction risk and population trend, from the IUCN for 11 052 species of amphibians, non‐marine birds and terrestrial mammals distributed across the World. We characterized spatial configuration using descriptors of shape and fragmentation (fragment number and size heterogeneity) and used regression analysis to evaluate their role in explaining current extinction risk and population trend. The most important predictor of conservation status was range area, but our analyses also identified shape and fragmentation as valuable predictors. We detected complex relationships, revealed by multiple interaction terms, e.g. more circular shapes were negatively correlated with population trend, and heterogeneity was positively correlated with extinction risk for small range areas but negatively for bigger ranges. Considering descriptors of spatial configuration beyond size improves our understanding of conservation status among vertebrates. The metrics we propose are relatively easy to define (although values can be sensitive to data quality), and unlike other correlates of status, like species’ traits, are readily available for many species (all of those with range maps). We argue that considering spatial configuration predictors is a straightforward way to improve our capacity to predict conservation status and thus, can be useful to promote more effective conservation.

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Section: Introductionmentioning

confidence: 99%

Range area matters, and so does spatial configuration: predicting conservation status in vertebrates

2019

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“…Examples include land‐use change driven by regional socioeconomic factors, chemical spills, disease outbreaks, exploitation of biota or habitats driven by regional markets, disturbances such as wildland fires, or tropical storms that may affect areas of a few square kilometers up to thousands of square kilometers in a small number of events (Murray et al. ).…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, recent research has shown that relationships between AOO and extinction risk are relatively robust to types of threat footprints and to random versus clustered patterns of occurrence, although edge effects produce different responses (Murray et al. ). We therefore expect our general conclusions about the scale‐sensitivity of AOO as a predictor of risk and the importance of threats in defining the optimal scales for measuring AOO to be robust when more complex distribution patterns and threats are analyzed.…”

Section: Discussionmentioning

confidence: 99%

“…We used a spatially explicit simulation model (Murray et al. ), applicable to both species and ecosystems, to explore the performance of AOO as a predictor of risks to biodiversity with varied distribution patterns subject to a range of threat regimes across varied spatial scales. We generated a suite of exemplar biological distribution patterns and characterized their scale–area relationships to provide context for interpreting simulation outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Scaling range sizes to threats for robust predictions of risks to biodiversity

Keith

Akçakaya

Murray

2018

Conservation Biology

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Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range-size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in red-list assessments for decades, appropriate spatial scales of AOO for predicting risks of species' extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scale-sensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81<|r|<0.98) and performed optimally when measured with grid cells 0.1-1.0 times the largest plausible area threatened by an event. Contrary to previous assertions, estimates of AOO at these relatively coarse scales were better predictors of risk than finer-scale estimates of AOO (e.g., when measurement cells are <1% of the area of the largest threat). The optimal scale depended on the spatial scales of threats more than the shape or size of biotic distributions. Although we found appreciable potential for grid-measurement errors, current IUCN guidelines for estimating AOO neutralize geometric uncertainty and incorporate effective scaling procedures for assessing risks posed by landscape-scale threats to species and ecosystems.

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“…To assess Criterion B of the IUCN Red List of Ecosystems, Remap applies a minimum convex polygon to a class of interest and reports its area, representing the Extent of Occurrence (EOO) of the map class. Finally, the Area of Occupancy (AOO) of a map class is calculated by applying a 10 × 10 km grid and counting the number of grid cells occupied by the map class (Bland et al., ; Murray et al., ).…”

Section: Remap: Remote Ecosystem Monitoring and Assessment Pipelinementioning

confidence: 99%

Remap: An online remote sensing application for land cover classification and monitoring

et al. 2018

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Recent assessments of progress towards global conservation targets have revealed a paucity of indicators suitable for assessing the changing state of ecosystems. Moreover, land managers and planners are often unable to gain timely access to the maps they need to support their routine decision‐making. This deficiency is partly due to a lack of suitable data on ecosystem change, driven mostly by the considerable technical expertise needed to develop ecosystem maps from remote sensing data. We have developed a free and open‐access online remote sensing and environmental modelling application, the Remote Ecosystem Monitoring and Assessment Pipeline (Remap; https://remap-app.org), that enables volunteers, managers and scientists with little or no experience in remote sensing to generate classifications (maps) of land cover and land use change over time. Remap utilizes the geospatial data storage and analysis capacity of Google Earth Engine and requires only spatially resolved training data that define map classes of interest (e.g. ecosystem types). The training data, which can be uploaded or annotated interactively within Remap, are used in a random forest classification of up to 13 publicly available predictor datasets to assign all pixels in a focal region to map classes. Predictor datasets available in Remap represent topographic (e.g. slope, elevation), spectral (archival Landsat image composites) and climatic variables (precipitation, temperature) that are relevant to the distribution of ecosystems and land cover classes. The ability of Remap to develop and export high‐quality classified maps in a very short (<10 min) time frame represents a considerable advance towards globally accessible and free application of remote sensing technology. By enabling access to data and simplifying remote sensing classifications, Remap can catalyse the monitoring of land use and change to support environmental conservation, including developing inventories of biodiversity, identifying hotspots of ecosystem diversity, ecosystem‐based spatial conservation planning, mapping ecosystem loss at local scales and supporting environmental education initiatives.

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The use of range size to assess risks to biodiversity from stochastic threats

Cited by 39 publications

References 47 publications

Range area matters, and so does spatial configuration: predicting conservation status in vertebrates

Range area matters, and so does spatial configuration: predicting conservation status in vertebrates

Scaling range sizes to threats for robust predictions of risks to biodiversity

Remap: An online remote sensing application for land cover classification and monitoring

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