Making predictive modelling ART: accurate, reliable, and transparent
Abstract Models are increasingly being used for prediction in ecological research. The ability to generate accurate and robust predictions is necessary to help respond to ecosystem change and to further scientific research. Successful predictive models are typically accurate, reliable, and transpare...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-06-01
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| Series: | Ecosphere |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ecs2.3160 |
| _version_ | 1818231776773931008 |
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| author | Korryn Bodner Marie‐Josée Fortin Péter K. Molnár |
| author_facet | Korryn Bodner Marie‐Josée Fortin Péter K. Molnár |
| author_sort | Korryn Bodner |
| collection | DOAJ |
| description | Abstract Models are increasingly being used for prediction in ecological research. The ability to generate accurate and robust predictions is necessary to help respond to ecosystem change and to further scientific research. Successful predictive models are typically accurate, reliable, and transparent regarding their assumptions and expectations, indicating high predictive capacity, robustness, and clarity in their objectives and standards. Research on improving these properties is becoming more common, but often individual research projects are focused on a single aspect of the modelling process and are typically disseminated only within the field where the research originated. The goal of this review is to synthesize research from various disciplines and topics to provide a coherent framework for developing efficient predictive models. Our framework summarizes the process of creating predictive models into three main stages: (1) Framing the Question; (2) Model‐Building and Testing; and (3) Uncertainty Evaluation with proposed strategies associated with each stage to help produce more successful predictive models. The key strategies identified within our framework form specific guidelines, providing a new perspective to help researchers make predictive modelling more accurate, reliable, and transparent. |
| first_indexed | 2024-12-12T10:55:46Z |
| format | Article |
| id | doaj.art-252ff6de567d4fbcb460e1f6015222a3 |
| institution | Directory Open Access Journal |
| issn | 2150-8925 |
| language | English |
| last_indexed | 2024-12-12T10:55:46Z |
| publishDate | 2020-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecosphere |
| spelling | doaj.art-252ff6de567d4fbcb460e1f6015222a32022-12-22T00:26:39ZengWileyEcosphere2150-89252020-06-01116n/an/a10.1002/ecs2.3160Making predictive modelling ART: accurate, reliable, and transparentKorryn Bodner0Marie‐Josée Fortin1Péter K. Molnár2Department of Ecology & Evolutionary Biology University of Toronto Toronto Ontario CanadaDepartment of Ecology & Evolutionary Biology University of Toronto Toronto Ontario CanadaDepartment of Ecology & Evolutionary Biology University of Toronto Toronto Ontario CanadaAbstract Models are increasingly being used for prediction in ecological research. The ability to generate accurate and robust predictions is necessary to help respond to ecosystem change and to further scientific research. Successful predictive models are typically accurate, reliable, and transparent regarding their assumptions and expectations, indicating high predictive capacity, robustness, and clarity in their objectives and standards. Research on improving these properties is becoming more common, but often individual research projects are focused on a single aspect of the modelling process and are typically disseminated only within the field where the research originated. The goal of this review is to synthesize research from various disciplines and topics to provide a coherent framework for developing efficient predictive models. Our framework summarizes the process of creating predictive models into three main stages: (1) Framing the Question; (2) Model‐Building and Testing; and (3) Uncertainty Evaluation with proposed strategies associated with each stage to help produce more successful predictive models. The key strategies identified within our framework form specific guidelines, providing a new perspective to help researchers make predictive modelling more accurate, reliable, and transparent.https://doi.org/10.1002/ecs2.3160adaptive modellingecological forecastingecological predictionmodel selectionuncertainty quantificationuncertainty reduction |
| spellingShingle | Korryn Bodner Marie‐Josée Fortin Péter K. Molnár Making predictive modelling ART: accurate, reliable, and transparent Ecosphere adaptive modelling ecological forecasting ecological prediction model selection uncertainty quantification uncertainty reduction |
| title | Making predictive modelling ART: accurate, reliable, and transparent |
| title_full | Making predictive modelling ART: accurate, reliable, and transparent |
| title_fullStr | Making predictive modelling ART: accurate, reliable, and transparent |
| title_full_unstemmed | Making predictive modelling ART: accurate, reliable, and transparent |
| title_short | Making predictive modelling ART: accurate, reliable, and transparent |
| title_sort | making predictive modelling art accurate reliable and transparent |
| topic | adaptive modelling ecological forecasting ecological prediction model selection uncertainty quantification uncertainty reduction |
| url | https://doi.org/10.1002/ecs2.3160 |
| work_keys_str_mv | AT korrynbodner makingpredictivemodellingartaccuratereliableandtransparent AT mariejoseefortin makingpredictivemodellingartaccuratereliableandtransparent AT peterkmolnar makingpredictivemodellingartaccuratereliableandtransparent |