Matching expert range maps with species distribution model predictions

© 2020 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.

Bibliographische Detailangaben
Veröffentlicht in:Conservation biology : the journal of the Society for Conservation Biology. - 1999. - 34(2020), 5 vom: 02. Okt., Seite 1292-1304
1. Verfasser: Mainali, Kumar (VerfasserIn)
Weitere Verfasser: Hefley, Trevor, Ries, Leslie, Fagan, William F
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2020
Zugriff auf das übergeordnete Werk:Conservation biology : the journal of the Society for Conservation Biology
Schlagworte:Journal Article Research Support, U.S. Gov't, Non-P.H.S. Glassberg Glassberg 地图 Scott Scott 地图 acuerdo entre expertos borde detallado concavidad concavity mehr... detailed edge distribution models expert agreement expert score inhomogeneous Poisson point process map porosity modelos de distribución de especies porosidad de mapa proceso de punto de Poisson no homogéneo puntaje de expertos species 专家协议 专家评分 函数凹性 地图孔隙度 物种分布模型 精细边缘 非均匀泊松点过程
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100 1 |a Mainali, Kumar  |e verfasserin  |4 aut 
245 1 0 |a Matching expert range maps with species distribution model predictions 
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520 |a 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 
650 4 |a Journal Article 
650 4 |a Research Support, U.S. Gov't, Non-P.H.S. 
650 4 |a Glassberg 
650 4 |a Glassberg 地图 
650 4 |a Scott 
650 4 |a Scott 地图 
650 4 |a acuerdo entre expertos 
650 4 |a borde detallado 
650 4 |a concavidad 
650 4 |a concavity 
650 4 |a detailed edge 
650 4 |a distribution models 
650 4 |a expert agreement 
650 4 |a expert score 
650 4 |a inhomogeneous Poisson point process 
650 4 |a map porosity 
650 4 |a modelos de distribución de especies 
650 4 |a porosidad de mapa 
650 4 |a proceso de punto de Poisson no homogéneo 
650 4 |a puntaje de expertos 
650 4 |a species 
650 4 |a 专家协议 
650 4 |a 专家评分 
650 4 |a 函数凹性 
650 4 |a 地图孔隙度 
650 4 |a 物种分布模型 
650 4 |a 精细边缘 
650 4 |a 非均匀泊松点过程 
700 1 |a Hefley, Trevor  |e verfasserin  |4 aut 
700 1 |a Ries, Leslie  |e verfasserin  |4 aut 
700 1 |a Fagan, William F  |e verfasserin  |4 aut 
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