Ecologists have called for more robust studies on the impact of conservation interventions, or environmental shocks, on outcomes of interest, such as populations, habitat loss, or pressures.
Time-series data are increasingly available and can, if appropriately analysed, allow such causal inferences.
However, there are important pitfalls that make large-scale analyses involving multiple time series problematic.
There has been progress in a range of fields, but the literature is fragmented and not all is easily accessible to ecologists.
A framework is presented, with clear and consistent terminology, to support ecologists to conduct effective impact evaluation with time-series data. This will allow them to contribute to better-informed environmental management decisions.
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.