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- Publisher Website: 10.1111/1749-4877.12736
- Scopus: eid_2-s2.0-85161384115
- PMID: 37259699
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Article: Data error propagation in stacked bioclimatic envelope models
Title | Data error propagation in stacked bioclimatic envelope models |
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Authors | |
Keywords | richness patterns species distribution stacked bioclimatic envelope models uncertainty |
Issue Date | 1-Mar-2024 |
Publisher | Wiley |
Citation | Integrative Zoology, 2024, v. 19, n. 2, p. 262-276 How to Cite? |
Abstract | Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models (BEMs). The approach can be used to investigate patterns and processes of species richness. If data limitations on individual species distributions are inevitable, but how do they affect inferences of patterns and processes of species richness? We investigate the influence of different data sources on estimated species richness gradients in China. We fitted BEMs using species distributions data for 334 bird species obtained from (1) global range maps, (2) regional checklists, (3) museum records and surveys, and (4) citizen science data using presence-only (Mahalanobis distance), presence-background (MAXENT), and presence–absence (GAM and BRT) BEMs. Individual species predictions were stacked to generate species richness gradients. Here, we show that different data sources and BEMs can generate spatially varying gradients of species richness. The environmental predictors that best explained species distributions also differed between data sources. Models using citizen-based data had the highest accuracy, whereas those using range data had the lowest accuracy. Potential richness patterns estimated by GAM and BRT models were robust to data uncertainty. When multiple data sets exist for the same region and taxa, we advise that explicit treatments of uncertainty, such as sensitivity analyses of the input data, should be conducted during the process of modeling. |
Persistent Identifier | http://hdl.handle.net/10722/348089 |
ISSN | 2023 Impact Factor: 3.5 |
DC Field | Value | Language |
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dc.contributor.author | Li, Xueyan | - |
dc.contributor.author | Naimi, Babak | - |
dc.contributor.author | Gong, Peng | - |
dc.contributor.author | Araújo, Miguel B | - |
dc.date.accessioned | 2024-10-05T00:30:27Z | - |
dc.date.available | 2024-10-05T00:30:27Z | - |
dc.date.issued | 2024-03-01 | - |
dc.identifier.citation | Integrative Zoology, 2024, v. 19, n. 2, p. 262-276 | - |
dc.identifier.issn | 1749-4869 | - |
dc.identifier.uri | http://hdl.handle.net/10722/348089 | - |
dc.description.abstract | Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models (BEMs). The approach can be used to investigate patterns and processes of species richness. If data limitations on individual species distributions are inevitable, but how do they affect inferences of patterns and processes of species richness? We investigate the influence of different data sources on estimated species richness gradients in China. We fitted BEMs using species distributions data for 334 bird species obtained from (1) global range maps, (2) regional checklists, (3) museum records and surveys, and (4) citizen science data using presence-only (Mahalanobis distance), presence-background (MAXENT), and presence–absence (GAM and BRT) BEMs. Individual species predictions were stacked to generate species richness gradients. Here, we show that different data sources and BEMs can generate spatially varying gradients of species richness. The environmental predictors that best explained species distributions also differed between data sources. Models using citizen-based data had the highest accuracy, whereas those using range data had the lowest accuracy. Potential richness patterns estimated by GAM and BRT models were robust to data uncertainty. When multiple data sets exist for the same region and taxa, we advise that explicit treatments of uncertainty, such as sensitivity analyses of the input data, should be conducted during the process of modeling. | - |
dc.language | eng | - |
dc.publisher | Wiley | - |
dc.relation.ispartof | Integrative Zoology | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | richness patterns | - |
dc.subject | species distribution | - |
dc.subject | stacked bioclimatic envelope models | - |
dc.subject | uncertainty | - |
dc.title | Data error propagation in stacked bioclimatic envelope models | - |
dc.type | Article | - |
dc.identifier.doi | 10.1111/1749-4877.12736 | - |
dc.identifier.pmid | 37259699 | - |
dc.identifier.scopus | eid_2-s2.0-85161384115 | - |
dc.identifier.volume | 19 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 262 | - |
dc.identifier.epage | 276 | - |
dc.identifier.eissn | 1749-4877 | - |
dc.identifier.issnl | 1749-4869 | - |