Revealing hidden sources of uncertainty in biodiversity trend assessments
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| Gepubliceerd in: | Ecography vol. 2025, no. 5 (May 1, 2025) |
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| Andere auteurs: | , , , , , |
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John Wiley & Sons, Inc.
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| Online toegang: | Citation/Abstract Full Text Full Text - PDF |
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3199081917 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0906-7590 | ||
| 022 | |a 1600-0587 | ||
| 022 | |a 0105-9327 | ||
| 024 | 7 | |a 10.1111/ecog.07441 |2 doi | |
| 035 | |a 3199081917 | ||
| 045 | 0 | |b d20250501 | |
| 084 | |a 166638 |2 nlm | ||
| 100 | 1 | |a Wilkes, Martin A. |u School of Life Sciences, University of Essex, Colchester, UK | |
| 245 | 1 | |a Revealing hidden sources of uncertainty in biodiversity trend assessments | |
| 260 | |b John Wiley & Sons, Inc. |c May 1, 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Idiosyncratic decisions during the biodiversity trend assessment process may limit reproducibility, whilst ‘hidden' uncertainty due to collection bias, taxonomic incompleteness, and variable taxonomic resolution may limit the reliability of reported trends. We model alternative decisions made during assessment of taxon‐level abundance and distribution trends using an 18‐year time series covering freshwater fish, invertebrates, and primary producers in England. Through three case studies, we test for collection bias and quantify uncertainty stemming from data preparation and model specification decisions, assess the risk of conflating trends for individual species when aggregating data to higher taxonomic ranks, and evaluate the potential uncertainty stemming from taxonomic incompleteness. Choice of optimizer algorithm and data filtering to obtain more complete time series explained 52.5% of the variation in trend estimates, obscuring the signal from taxon‐specific trends. The use of penalized iteratively reweighted least squares, a simplified approach to model optimization, was the most important source of uncertainty. Application of increasingly harsh data filters exacerbated collection bias in the modelled dataset. Aggregation to higher taxonomic ranks was a significant source of uncertainty, leading to conflation of trends among protected and invasive species. We also found potential for substantial positive bias in trend estimation across six fish populations which were not consistently recorded in all operational areas. We complement analyses of observational data with in silico experiments in which monitoring and trend assessment processes were simulated to enable comparison of trend estimates with known underlying trends, confirming that collection bias, data filtering and taxonomic incompleteness have significant negative impacts on the accuracy of trend estimates. Identifying and managing uncertainty in biodiversity trend assessment is crucial for informing effective conservation policy and practice. We highlight several serious sources of uncertainty affecting biodiversity trend analyses and present tools to improve the transparency of decisions made during the trend assessment process. | |
| 651 | 4 | |a United Kingdom--UK | |
| 651 | 4 | |a England | |
| 653 | |a Plankton | ||
| 653 | |a Freshwater fish | ||
| 653 | |a Invasive species | ||
| 653 | |a Bias | ||
| 653 | |a Datasets | ||
| 653 | |a Trends | ||
| 653 | |a Estimates | ||
| 653 | |a Taxonomy | ||
| 653 | |a Biodiversity | ||
| 653 | |a Data processing | ||
| 653 | |a Approximation | ||
| 653 | |a Data analysis | ||
| 653 | |a Protected species | ||
| 653 | |a Risk assessment | ||
| 653 | |a Time series | ||
| 653 | |a Fish | ||
| 653 | |a Uncertainty | ||
| 653 | |a Fish populations | ||
| 653 | |a Case studies | ||
| 653 | |a Aquatic plants | ||
| 653 | |a Invertebrates | ||
| 653 | |a Introduced species | ||
| 653 | |a Algorithms | ||
| 653 | |a Environmental policy | ||
| 653 | |a Rivers | ||
| 653 | |a Filtration | ||
| 653 | |a Decisions | ||
| 653 | |a Taxa | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Mckenzie, Morwenna |u Geography and Environment, Loughborough University, Loughborough, UK | |
| 700 | 1 | |a Johnson, Andrew |u School of Geography & water@leeds, University of Leeds, Leeds, UK | |
| 700 | 1 | |a Hassall, Christopher |u School of Biology, University of Leeds, Leeds, UK | |
| 700 | 1 | |a Kelly, Martyn |u Bowburn Consultancy, Durham, UK | |
| 700 | 1 | |a Willby, Nigel |u Department of Biological and Environmental Sciences, University of Stirling, Stirling, UK | |
| 700 | 1 | |a Brown, Lee E. |u School of Geography & water@leeds, University of Leeds, Leeds, UK | |
| 773 | 0 | |t Ecography |g vol. 2025, no. 5 (May 1, 2025) | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3199081917/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3199081917/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3199081917/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |