TY - JOUR
T1 - Evaluating impact using time-series data
AU - Wauchope, Hannah S.
AU - Amano, Tatsuya
AU - Geldmann, Jonas
AU - Johnston, Alison
AU - Simmons, Benno
AU - Sutherland, William J.
AU - Jones, Julia P. G.
N1 - H.S.W. was supported by the Cambridge Trust Poynton Scholarship, Cambridge Department of Zoology J.S. Gardiner Studentship, and Cambridge Philosophical Society; T.A. was supported by the Australian Research Council Future Fellowship (FT180100354), and the University of Queensland strategic funding; J.G. was supported by European Union’s Horizon 2020 Marie Skłodowska-Curie program (No. 706784), and VILLUM FONDEN (VKR023371); B.I.S. was supported by a Royal Commission for the Exhibition of 1851 Research Fellowship; W.J.S. is funded by Arcadia and J.P.G.J. was supported by the Leverhulme Trust: RPG-2014-056.
PY - 2021/3
Y1 - 2021/3
N2 - 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, especially 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.
AB - 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, especially 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.
KW - Before-after-control-intervention
KW - Longitudinal data
KW - Counterfactual
KW - Interrupted time series
KW - Causal inference
KW - Difference in differences
U2 - 10.1016/j.tree.2020.11.001
DO - 10.1016/j.tree.2020.11.001
M3 - Review article
SN - 0169-5347
VL - 36
SP - 196
EP - 205
JO - Trends in Ecology & Evolution
JF - Trends in Ecology & Evolution
IS - 3
ER -