Aim Trait data are widely used in ecological and evolutionary phylogenetic comparative studies, but often values are not available for all species of interest. Traditionally, researchers have excluded species without data from analyses, but estimation of missing values using imputation has been proposed as a better approach. However, imputation methods have largely been designed for randomly missing data, whereas trait data are often not missing at random (e.g., more data for bigger species). Here, we evaluate the performance of approaches for handling missing values when considering biased datasets. Location Any. Time period Any. Major taxa studied Any. Methods We simulated continuous traits and separate response variables to test the performance of nine imputation methods and complete‐case analysis (excluding missing values from the dataset) under biased missing data scenarios. We characterized performance by estimating the error in imputed trait values (deviation from the true value) and inferred trait–response relationships (deviation from the true relationship between a trait and response). Results Generally, Rphylopars imputation produced the most accurate estimate of missing values and best preserved the response–trait slope. However, estimates of missing data were still inaccurate, even with only 5% of values missing. Under severe biases, errors were high with every approach. Imputation was not always the best option, with complete‐case analysis frequently outperforming Mice imputation and, to a lesser degree, BHPMF imputation. Mice, a popular approach, performed poorly when the response variable was excluded from the imputation model. Main conclusions Imputation can handle missing data effectively in some conditions but is not always the best solution. None of the methods we tested could deal effectively with severe biases, which can be common in trait datasets. We recommend rigorous data checking for biases before and after imputation and propose variables that can assist researchers working with incomplete datasets to detect data biases and minimize errors.

Translocations are a valuable tool within conservation, and when performed successfully can rescue species from extinction. However, to label a translocation a success, extensive post-translocation monitoring is required, ensuring the population is growing at the expected rate. In 2011, a habitat assessment identified Frégate Island as a suitable island to host a Seychelles Warbler (Acrocephalus sechellensis) population. Later that year, 59 birds were translocated from Cousin Island to Frégate Island. Here, we determine Seychelles Warbler habitat use and population growth on Frégate Island, assessing the status of the translocation and identifying any interventions that may be required. We found that territory quality, an important predictor of fledgling production on Cousin Island, was a poor predictor of bird presence on Frégate Island. Instead, tree diversity, middle-storey vegetation density, and broad-leafed vegetation density all predicted bird presence positively. A habitat suitability map based on these results suggests most of Frégate Island contains either a suitable or a moderately suitable habitat, with patches of unsuitable overgrown coconut plantation. To achieve the maximum potential Seychelles Warbler population size on Frégate Island, we recommend habitat regeneration, such that the highly diverse subset of broad-leafed trees and a dense middle storey should be protected and replace the unsuitable coconut. Frégate Island's Seychelles Warbler population has grown to 141 birds since the release, the slowest growth rate of all Seychelles Warbler translocations; the cause of this is unclear. This study highlights the value of post-translocation monitoring, identifying habitat use and areas requiring restoration, and ultimately ensuring that the population is growing.

Land-use and climate change have been linked to changes in wildlife populations, but the role of socioeconomic factors in driving declines, and promoting population recoveries, remains relatively unexplored. Here, we evaluate potential drivers of population changes observed in 50 species of some of the world’s most charismatic and functionally important fauna—large mammalian carnivores. Our results reveal that human socioeconomic development is more associated with carnivore population declines than habitat loss or climate change. Rapid increases in socioeconomic development are linked to sharp population declines, but, importantly, once development slows, carnivore populations have the potential to recover. The context- and threshold-dependent links between human development and wildlife population health are challenges to the achievement of the UN Sustainable development goals.

Methods in Ecology and Evolution This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

In the face of rapid global change and an uncertain fate for biodiversity, it is vital to quantify trends in wild populations. These trends are typically estimated from abundance time series for suites of species across large geographic and temporal scales. Such data implicitly contain phylogenetic, spatial, and temporal structure which, if not properly accounted for, may obscure the true magnitude and direction of biodiversity change. Here, using a novel statistical framework to simultaneously account for all three of these structures, we show that the majority of current abundance trends estimates among 10 high-profile datasets, representing millions of abundance observations, are likely unreliable or incorrect. Our new approach suggests that previous models are too simplistic, incorrectly estimating global abundance trends and often dramatically underestimating uncertainty, an aspect that is critical when translating global assessments into policy outcomes. Further, our approach also results in substantial improvements in abundance forecasting accuracy. Whilst our results do not improve the outlook for biodiversity, our framework does allow us to make more robust estimates of global wildlife abundance trends, which is critical for developing policy to protect our biosphere.

The COVID-19 pandemic has had severe impacts on global public health. In the UK, social distancing measures and a nationwide lockdown were introduced to reduce the spread of the virus. Green space accessibility may have been particularly important during this lockdown, as it could have provided benefits for physical and mental wellbeing, while also limiting the risk of transmission. However, the effects of public green space use on the rate of COVID-19 transmission are yet to be quantified, and as the size and accessibility of green spaces vary within local authorities, the risks and benefits to the public of using green space may well be context-dependent. To evaluate how green space affected COVID-19 transmission across 98 local authorities in England, we first split case rates into two periods, the pre-peak rise and the post-peak decline in cases, and assessed how baseline health and mobility variables influenced these rates. Next, looking at the residual case rates, we investigated how landscape structure (e.g. area and patchiness of green space) and park use influenced transmission. We first show that pre- and post-peak case rates were significantly reduced when overall mobility was low, especially in areas with high population clustering, and high population density during the post-peak period only. After accounting for known mechanisms behind transmission rates, we found that park use (showing a preference for park mobility) decreased residual pre-peak case rates, especially when green space was low and contiguous (not patchy). Whilst in the post-peak period, park use and green landscape structure had no effect on residual case rates. Our results show that utilising green spaces rather than other activities (e.g. visiting shops and workplaces) can reduce the transmission rate of COVID-19, especially during an exponential phase of transmission.

Motivation: Population trend information is an 'essential biodiversity variable' for monitoring change in biodiversity over time. Here, we present a database of 1,122 population trends from around the world, describing changes in abundance over time in large mammal species (n = 50) from four families in the order Carnivora. For this subset of taxa, we provide approximately 21 times more trends than BioTIME and three times more trends than the Living Planet database.Main types of variables included: Key data fields for each trend: species, coordinates, trend time-frame, methods of data collection and analysis, and population time series or summarized trend value. Population trend values are reported using quantitative metrics in 75% of records that collectively represent more than 6,500 population estimates.The remaining records qualitatively describe population change (e.g., increase). Spatial location and grain:Trends represent 621 unique locations across the globe

As the impact of anthropogenic activity on the environment has grown, research into biodiversity change and associated threats has also accelerated. Synthesising this vast literature is important for understanding the drivers of biodiversity change and identifying those actions that will mitigate further ecological losses. However, keeping pace with an ever‐increasing publication rate presents a substantial challenge to efficient syntheses, an issue which could be partly addressed by increasing levels of automation in the synthesis pipeline.Here, we evaluate the potential for automated tools to extract ecologically important information from the abstracts of articles compiled in the Living Planet Database. Specifically, we focused on extracting key information on taxonomy (studied species names), geographic location and estimated population trend, assessing the accuracy of automated versus manual information extraction, the potential for automated tools to introduce biases into syntheses, and evaluating if synthesising abstracts was enough to capture the key information from the full article.Taxonomic and geographic extraction tools performed reasonably well, although information on studied species was sometimes limited in the abstract (compared to the main text) preventing fast extraction. In contrast, extraction of trends was less successful, highlighting the challenges involved in automating information extraction from abstracts, such as deficiencies in the algorithms, linguistic complexity associated with ecological findings, and limited information when compared to the main text.In light of these results, we cautiously advocate for a wider use of automated taxonomic and geographic parsing tools for ecological synthesis. Additionally, to further the use of automated synthesis within ecology, we recommend a dual approach: development of improved computational tools to reduce biases; and enhanced protocols for abstracts (and associated metadata) to ensure key information is included in a format that facilitates machine‐readability.