Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?
Background Shrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distr...
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| Language: | English |
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PeerJ Inc.
2017-06-01
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| Online Access: | https://peerj.com/articles/3446.pdf |
| _version_ | 1797419666269798400 |
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| author | Yasmin Hageer Manuel Esperón-Rodríguez John B. Baumgartner Linda J. Beaumont |
| author_facet | Yasmin Hageer Manuel Esperón-Rodríguez John B. Baumgartner Linda J. Beaumont |
| author_sort | Yasmin Hageer |
| collection | DOAJ |
| description | Background Shrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear. We evaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent. Methods This study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only. Results The predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables. Conclusions Our study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants. |
| first_indexed | 2024-03-09T06:51:32Z |
| format | Article |
| id | doaj.art-ca6579a2a20d41a3970630110db446db |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:51:32Z |
| publishDate | 2017-06-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
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| spelling | doaj.art-ca6579a2a20d41a3970630110db446db2023-12-03T10:27:19ZengPeerJ Inc.PeerJ2167-83592017-06-015e344610.7717/peerj.3446Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?Yasmin Hageer0Manuel Esperón-Rodríguez1John B. Baumgartner2Linda J. Beaumont3Department of Biological Sciences, Macquarie University, Sydney, New South Wales, AustraliaDepartment of Biological Sciences, Macquarie University, Sydney, New South Wales, AustraliaDepartment of Biological Sciences, Macquarie University, Sydney, New South Wales, AustraliaDepartment of Biological Sciences, Macquarie University, Sydney, New South Wales, AustraliaBackground Shrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear. We evaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent. Methods This study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only. Results The predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables. Conclusions Our study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants.https://peerj.com/articles/3446.pdfAustraliaClimateGrowth formHabitat suitabilityMaxentPredictor choice |
| spellingShingle | Yasmin Hageer Manuel Esperón-Rodríguez John B. Baumgartner Linda J. Beaumont Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species? PeerJ Australia Climate Growth form Habitat suitability Maxent Predictor choice |
| title | Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species? |
| title_full | Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species? |
| title_fullStr | Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species? |
| title_full_unstemmed | Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species? |
| title_short | Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species? |
| title_sort | climate soil or both which variables are better predictors of the distributions of australian shrub species |
| topic | Australia Climate Growth form Habitat suitability Maxent Predictor choice |
| url | https://peerj.com/articles/3446.pdf |
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