Species' range maps based on expert opinion are a critical resource for conservation planning. Expert maps are usually accompanied by species descriptions that specify sources of internal range heterogeneity, such as habitat associations, but these are rarely considered when using expert maps for analyses. We developed a quantitative metric (expert score) to evaluate the agreement between an expert map and a habitat probability surface obtained from a species distribution model. This method rewards both the avoidance of unsuitable sites and the inclusion of suitable sites in the expert map. We obtained expert maps of 330 butterfly species from each of 2 widely used North American sources (Glassberg [1999, 2001] and Scott [1986]) and computed species-wise expert scores for each. Overall, the Glassberg maps secured higher expert scores than Scott (0.61 and 0.41, respectively) due to the specific rules (e.g., Glassberg only included regions where the species was known to reproduce whereas Scott included all areas a species expanded to each year) they used to include or exclude areas from ranges. The predictive performance of expert maps was almost always hampered by the inclusion of unsuitable sites, rather than by exclusion of suitable sites (deviance outside of expert maps was extremely low). Map topology was the primary predictor of expert performance rather than any factor related to species characteristics such as mobility. Given the heterogeneity and discontinuity of suitable landscapes, expert maps drawn with more detail are more likely to agree with species distribution models and thus minimize both commission and omission errors.
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http://dx.doi.org/10.1111/cobi.13492 | DOI Listing |
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January 2025
The Bradley Department of Electrical and Computer Engineering, College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
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View Article and Find Full Text PDFLancet Planet Health
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Washington State University, Pullman, WA, USA.
Background: An increase in pandemics of zoonotic origin has led to a growing interest in using statistical prediction to identify hotspots of zoonotic emergence. However, the rare nature of pathogen emergence requires modellers to impose simplifying assumptions, which limit the model's validity. We present a novel approach to hotspot mapping that aims to improve validity by combining model-based insights with expert knowledge.
View Article and Find Full Text PDFParasit Vectors
January 2025
Faculty of Geography, Environmental Informatics, University of Marburg, Deutschhausstraße 12, 35032, Marburg, Hessen, Germany.
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View Article and Find Full Text PDFBrain
January 2025
Department of Neurology, University of South Carolina, Columbia, SC 29203, USA.
Despite decades of advancements in diagnostic MRI, 30-50% of temporal lobe epilepsy (TLE) patients remain categorized as "non-lesional" (i.e., MRI negative or MRI-) based on visual assessment by human experts.
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January 2025
Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN. Electronic address:
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