Evaluating impact using time-series data
dc.contributor.author | Wauchope, HS | |
dc.contributor.author | Amano, T | |
dc.contributor.author | Geldmann, J | |
dc.contributor.author | Johnston, A | |
dc.contributor.author | Simmons, BI | |
dc.contributor.author | Sutherland, WJ | |
dc.contributor.author | Jones, JPG | |
dc.date.accessioned | 2020-12-03T12:56:06Z | |
dc.date.issued | 2020-12-10 | |
dc.description.abstract | Humanity’s impact on the environment is increasing, as are strategies to conserve biodiversity, but a lack of understanding about how interventions affect ecological and conservation outcomes hampers decision-making. Time-series are often used to assess impacts, but ecologists tend to compare average values from before to after an impact; overlooking the potential for the intervention to elicit a change in trend. Without methods that allow for a range of responses, erroneous conclusions can be drawn. This is especially so for large, multi-time-series datasets which are increasingly available. Drawing on literature in other disciplines and pioneering work in ecology, we present a standardised framework to robustly assesses how interventions, like natural disasters or conservation policies, affect ecological time series. | en_GB |
dc.description.sponsorship | Royal Commission 1851 | en_GB |
dc.description.sponsorship | Cambridge Trust Poynton Scholarship | en_GB |
dc.description.sponsorship | Cambridge Department of Zoology J.S. Gardiner Studentship | en_GB |
dc.description.sponsorship | Cambridge Philosophical Society | en_GB |
dc.description.sponsorship | Australian Research Council (ARC) | en_GB |
dc.description.sponsorship | University of Queensland | en_GB |
dc.description.sponsorship | European Union Horizon 2020 | en_GB |
dc.description.sponsorship | Villum Fonden | en_GB |
dc.description.sponsorship | Arcadia | en_GB |
dc.description.sponsorship | Leverhulme Trust | en_GB |
dc.identifier.citation | Published online 10 December 2020 | en_GB |
dc.identifier.doi | 10.1016/j.tree.2020.11.001 | |
dc.identifier.grantnumber | RF511/2019 | en_GB |
dc.identifier.grantnumber | FT180100354 | en_GB |
dc.identifier.grantnumber | 706784 | en_GB |
dc.identifier.grantnumber | VKR023371 | en_GB |
dc.identifier.grantnumber | RPG-2014-056 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/123894 | |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier (Cell Press) | en_GB |
dc.rights | © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | |
dc.subject | Before-After-Control-Intervention | en_GB |
dc.subject | Longitudinal data | en_GB |
dc.subject | Counterfactual | en_GB |
dc.subject | Interrupted Time Series | en_GB |
dc.subject | Causal Inference | en_GB |
dc.subject | Difference in Differences | en_GB |
dc.title | Evaluating impact using time-series data | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-12-03T12:56:06Z | |
dc.identifier.issn | 0169-5347 | |
dc.description | This is the final version. Available on open access from Elsevier via the DOI in this record | en_GB |
dc.identifier.journal | Trends in Ecology and Evolution | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2020-11-05 | |
exeter.funder | ::Royal Commission 1851 | en_GB |
exeter.funder | ::Royal Commission 1851 | en_GB |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2020-11-05 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-12-03T12:15:13Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2020-12-18T10:56:21Z | |
refterms.panel | A | en_GB |
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Except where otherwise noted, this item's licence is described as © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).