Emily G Simmonds, Kwaku P Adjei, Benjamin Cretois, Lisa Dickel, Ricardo González-Gil, Jack H Laverick, Caitlin P Mandeville, Elizabeth G Mandeville, Otso Ovaskainen, Jorge Sicacha-Parada, Emma S Skarstein, Bob O'Hara
{"title":"Recommendations for quantitative uncertainty consideration in ecology and evolution.","authors":"Emily G Simmonds, Kwaku P Adjei, Benjamin Cretois, Lisa Dickel, Ricardo González-Gil, Jack H Laverick, Caitlin P Mandeville, Elizabeth G Mandeville, Otso Ovaskainen, Jorge Sicacha-Parada, Emma S Skarstein, Bob O'Hara","doi":"10.1016/j.tree.2023.10.012","DOIUrl":null,"url":null,"abstract":"<p><p>Ecological and evolutionary studies are currently failing to achieve complete and consistent reporting of model-related uncertainty. We identify three key barriers - a focus on parameter-related uncertainty, obscure uncertainty metrics, and limited recognition of uncertainty propagation - which have led to gaps in uncertainty consideration. However, these gaps can be closed. We propose that uncertainty reporting in ecology and evolution can be improved through wider application of existing statistical solutions and by adopting good practice from other scientific fields. Our recommendations include greater consideration of input data and model structure uncertainties, field-specific uncertainty standards for methods and reporting, and increased uncertainty propagation through the use of hierarchical models.</p>","PeriodicalId":23274,"journal":{"name":"Trends in ecology & evolution","volume":null,"pages":null},"PeriodicalIF":16.7000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in ecology & evolution","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.tree.2023.10.012","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/11/28 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Ecological and evolutionary studies are currently failing to achieve complete and consistent reporting of model-related uncertainty. We identify three key barriers - a focus on parameter-related uncertainty, obscure uncertainty metrics, and limited recognition of uncertainty propagation - which have led to gaps in uncertainty consideration. However, these gaps can be closed. We propose that uncertainty reporting in ecology and evolution can be improved through wider application of existing statistical solutions and by adopting good practice from other scientific fields. Our recommendations include greater consideration of input data and model structure uncertainties, field-specific uncertainty standards for methods and reporting, and increased uncertainty propagation through the use of hierarchical models.
期刊介绍:
Trends in Ecology & Evolution (TREE) is a comprehensive journal featuring polished, concise, and readable reviews, opinions, and letters in all areas of ecology and evolutionary science. Catering to researchers, lecturers, teachers, field workers, and students, it serves as a valuable source of information. The journal keeps scientists informed about new developments and ideas across the spectrum of ecology and evolutionary biology, spanning from pure to applied and molecular to global perspectives. In the face of global environmental change, Trends in Ecology & Evolution plays a crucial role in covering all significant issues concerning organisms and their environments, making it a major forum for life scientists.