Wet grassland populations of wading birds in the United Kingdom have declined severely since 1990. To help mitigate these declines, the Royal Society for the Protection of Birds has restored and managed lowland wet grassland nature reserves to benefit these and other species. However, the impact of these reserves on bird population trends has not been evaluated experimentally due to a lack of control populations. We compared population trends from 1994 to 2018 among 5 bird species of conservation concern that breed on these nature reserves with counterfactual trends created from matched breeding bird survey observations. We compared reserve trends with 3 different counterfactuals based on different scenarios of how reserve populations could have developed in the absence of conservation. Effects of conservation interventions were positive for all 4 targeted wading bird species: Lapwing (Vanellus vanellus), Redshank (Tringa totanus), Curlew (Numenius arquata), and Snipe (Gallinago gallinago). There was no positive effect of conservation interventions on reserves for the passerine, Yellow Wagtail (Motacilla flava). Our approach using monitoring data to produce valid counterfactual controls is a broadly applicable method allowing large‐scale evaluation of conservation impact.

In recent years, vertebrate population abundance has declined at unprecedented rates. In response, targeted conservation measures such as breeding programs or species-specific habitat management have been applied to halt population declines, aid population recovery, and reduce and reverse the loss of biodiversity. Until now, assessments of conservation actions have focused on the extent to which they reduce extinction risk, impact populations within protected areas, or increase the global area of land under protection. Here, we record and analyze conservation actions for 26,904 vertebrate populations from 4,629 species, to measure the impact of targeted conservation on vertebrate abundance. Using a counterfactual approach to represent population trends in the absence of conservation, we demonstrate that targeted actions have delivered substantial positive effects on the abundance of recipient vertebrate populations worldwide. We show that, in the absence of conservation, a global indicator of vertebrate abundance would have declined even more. Positive population trends were associated with vertebrate populations subject to species or habitat management. We demonstrate that targeted conservation actions can help to reverse global biodiversity loss and show the value of counterfactual analysis for impact evaluation, an important step towards reversing biodiversity declines.

Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification, clustering, prediction, and anomaly detection. These common tasks in ecological research rely on the notion of (dis-) similarity which can be determined by using distance measures. A plethora of distance measures have been described in the scientific literature, but many of them have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures and the properties they focus on for time-series related tasks.Here we describe 16 potentially desirable properties of distance measures, test 42 distance measures for each property, and present an objective method to select appropriate distance measures for any task and ecological dataset. We then demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK. We also discuss ways to overcome some of the difficulties involved in using distance measures to compare time series.Our real-world population trends exhibit a common challenge for time series comparison: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well-suited to comparing noisy time series, and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends.The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing time series within ecology, and allow for the answering of new ecological questions.