Cannot see the random forest for the decision trees: selecting predictive models for restoration ecology
Improving predictions of restoration outcomes is increasingly important to resource managers for accountability and adaptive management, yet there is limited guidance for selecting a predictive model from the multitude available. The goal of this article was to identify an optimal predictive framewo...
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| Veröffentlicht in: | Restoration ecology Jg. 27; H. 5; S. 1053 - 1063 |
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| Hauptverfasser: | , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Malden, USA
Wiley Periodicals, Inc
01.09.2019
Blackwell Publishing Ltd |
| Schlagworte: | |
| ISSN: | 1061-2971, 1526-100X |
| Online-Zugang: | Volltext |
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| Abstract | Improving predictions of restoration outcomes is increasingly important to resource managers for accountability and adaptive management, yet there is limited guidance for selecting a predictive model from the multitude available. The goal of this article was to identify an optimal predictive framework for restoration ecology using 11 modeling frameworks (including machine learning, inferential, and ensemble approaches) and three data groups (field data, geographic data [GIS], and a combination thereof). We test this approach with a dataset from a large postfire sagebrush reestablishment project in the Great Basin, U.S.A. Predictive power varied among models and data groups, ranging from 58% to 79% accuracy. Finer‐scale field data generally had the greatest predictive power, although GIS data were present in the best models overall. An ensemble prediction computed from the 10 models parameterized to field data was well above average for accuracy but was outperformed by others that prioritized model parsimony by selecting predictor variables based on rankings of their importance among all candidate models. The variation in predictive power among a suite of modeling frameworks underscores the importance of a model comparison and refinement approach that evaluates multiple models and data groups, and selects variables based on their contribution to predictive power. The enhanced understanding of factors influencing restoration outcomes accomplished by this framework has the potential to aid the adaptive management process for improving future restoration outcomes. |
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| AbstractList | Improving predictions of restoration outcomes is increasingly important to resource managers for accountability and adaptive management, yet there is limited guidance for selecting a predictive model from the multitude available. The goal of this article was to identify an optimal predictive framework for restoration ecology using 11 modeling frameworks (including machine learning, inferential, and ensemble approaches) and three data groups (field data, geographic data [GIS], and a combination thereof). We test this approach with a dataset from a large postfire sagebrush reestablishment project in the Great Basin, U.S.A. Predictive power varied among models and data groups, ranging from 58% to 79% accuracy. Finer‐scale field data generally had the greatest predictive power, although GIS data were present in the best models overall. An ensemble prediction computed from the 10 models parameterized to field data was well above average for accuracy but was outperformed by others that prioritized model parsimony by selecting predictor variables based on rankings of their importance among all candidate models. The variation in predictive power among a suite of modeling frameworks underscores the importance of a model comparison and refinement approach that evaluates multiple models and data groups, and selects variables based on their contribution to predictive power. The enhanced understanding of factors influencing restoration outcomes accomplished by this framework has the potential to aid the adaptive management process for improving future restoration outcomes. |
| Author | Pilliod, David S. Applestein, Cara Arkle, Robert S. Germino, Matthew J. Davidson, Bill E. Fisk, Matthew R. Barnard, David M. |
| Author_xml | – sequence: 1 givenname: David M. orcidid: 0000-0003-1877-3151 surname: Barnard fullname: Barnard, David M. organization: Forest and Rangeland Ecosystem Science Center – sequence: 2 givenname: Matthew J. orcidid: 0000-0001-6326-7579 surname: Germino fullname: Germino, Matthew J. email: mgermino@usgs.gov organization: Forest and Rangeland Ecosystem Science Center – sequence: 3 givenname: David S. orcidid: 0000-0003-4207-3518 surname: Pilliod fullname: Pilliod, David S. organization: Forest and Rangeland Ecosystem Science Center – sequence: 4 givenname: Robert S. orcidid: 0000-0003-3021-1389 surname: Arkle fullname: Arkle, Robert S. organization: Forest and Rangeland Ecosystem Science Center – sequence: 5 givenname: Cara surname: Applestein fullname: Applestein, Cara organization: Forest and Rangeland Ecosystem Science Center – sequence: 6 givenname: Bill E. surname: Davidson fullname: Davidson, Bill E. organization: Forest and Rangeland Ecosystem Science Center – sequence: 7 givenname: Matthew R. surname: Fisk fullname: Fisk, Matthew R. organization: Forest and Rangeland Ecosystem Science Center |
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| Cites_doi | 10.1111/j.1365-2656.2008.01390.x 10.1111/1365-2664.12938 10.1890/ES13-00295.1 10.1007/978-3-540-39804-2_12 10.1007/978-1-4614-6849-3 10.1111/j.1365-2486.2009.02000.x 10.1080/02626667909491834 10.1007/978-1-4419-7390-0_8 10.1016/j.ecoinf.2010.06.003 10.3732/ajb.1000285 10.1016/j.rala.2016.02.002 10.1002/eap.1589 10.1007/s10980-018-0662-8 10.18637/jss.v017.i02 10.1214/09-SS054 10.1111/j.1472-4642.2010.00725.x 10.18637/jss.v028.i05 10.1111/1365-2664.12935 10.1007/s11004-008-9156-6 10.1071/WF08088 10.1080/01431160110040323 10.1111/j.1654-1103.2002.tb02087.x 10.1890/14-0661.1 10.1177/001316446002000104 10.1016/j.rama.2018.05.003 10.1111/oik.03726 10.1890/07-0539.1 10.1890/0012-9658(2000)081[3178:CARTAP]2.0.CO;2 10.1007/s10021-005-0054-1 10.2111/REM-D-11-00026.1 10.2478/v10208-011-0016-2 10.2478/v10208-011-0015-3 10.1086/587826 |
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| Copyright | Published 2019. This article is a U.S. Government work and is in the public domain in the USA. 2019 Society for Ecological Restoration |
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| SubjectTerms | Accuracy Adaptive management Artemisia artificial intelligence basins Data data collection decision support systems Decision trees ecological prediction ecological restoration Ecology ensemble modeling Geographic information systems Geographical information systems Learning algorithms Machine learning model comparison Modelling postfire restoration prediction Prediction models predictive framework Resource management Restoration United States |
| Title | Cannot see the random forest for the decision trees: selecting predictive models for restoration ecology |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1111%2Frec.12938 https://www.proquest.com/docview/2287011323 https://www.proquest.com/docview/2315287615 |
| Volume | 27 |
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