Explore the vast range of titles available.

High numbers of threatened species might be expected to occur where overall species richness is also high; however, this explains only a proportion of the global variation in threatened species richness. Understanding why many areas have more or fewer threatened species than would be expected given their species richness, and whether that is consistent across taxa, is essential for identifying global conservation priorities. Here, we show that, after controlling for species richness, environmental factors, such as temperature and insularity, are typically more important than human impacts for explaining spatial variation in global threatened species richness. Human impacts, nevertheless, have an important role, with relationships varying between vertebrate groups and zoogeographic regions. Understanding this variation provides a framework for establishing global conservation priorities, identifying those regions where species are inherently more vulnerable to the effects of threatening human processes, and forecasting how threatened species might be distributed in a changing world. Understanding to what extent geographic patterns in threatened species diversity are driven by environmental features or human activities could aid conservation. Here, Howard et al. investigate broad scale patterns in species richness of threatened vertebrates and test the role of environmental and anthropogenic drivers.

1. Rewilding, here defined as \"the reorganisation of biota and ecosystem processes to set an identified social-ecological system on a preferred trajectory, leading to the self-sustaining provision of ecosystem services with minimal ongoing management,\" is increasingly considered as an environmental management option, with potential for enhancing both biodiversity and ecosystem services. 2. Despite burgeoning interest in the concept, there are uncertainties and difficulties associated with the practical implementation of rewilding projects, while the evidence available for facilitating sound decision-making for rewilding initiatives remains elusive. 3. We identify five key research areas to inform the implementation of future rewilding initiatives: increased understanding of the links between actions and impacts; improved risk assessment processes, through, for example, better definition and quantification of ecological risks; improved predictions of spatio-temporal variation in potential economic costs and associated benefits; better identification and characterisation of the likely social impacts of a given rewilding project; and facilitated emergence of a comprehensive and practical framework for the monitoring and evaluation of rewilding projects. 4. Policy implications. Environmental legislation is commonly based on a \"compositionalist\" paradigm itself predicated on the preservation of historical conditions characterised by the presence of particular species assemblages and habitat types. However, global environmental change is driving some ecosystems beyond their limits so that restoration to historical benchmarks or modern likely equivalents may no longer be an option. This means that the current environmental policy context could present barriers to the broad implementation of rewilding projects. To progress the global rewilding agenda, a better appreciation of current policy opportunities and constraints is required. This, together with a clear definition of rewilding and a scientifically robust rationale for its local implementation, is a prerequisite to engage governments in revising legislation where required to facilitate the operationalisation of rewilding.

Birds are important indicators for monitoring both biodiversity and habitat health; they also play a crucial role in ecosystem management. Declines in bird populations can result in reduced ecosystem services, including seed dispersal, pollination and pest control. Accurate and long-term monitoring of birds to identify species of concern while measuring the success of conservation interventions is essential for ecologists. However, monitoring is time-consuming, costly and often difficult to manage over long durations and at meaningfully large spatial scales. Technology such as camera traps, acoustic monitors and drones provide methods for non-invasive monitoring. There are two main problems with using camera traps for monitoring: (a) cameras generate many images, making it difficult to process and analyse the data in a timely manner; and (b) the high proportion of false positives hinders the processing and analysis for reporting. In this paper, we outline an approach for overcoming these issues by utilising deep learning for real-time classification of bird species and automated removal of false positives in camera trap data. Images are classified in real-time using a Faster-RCNN architecture. Images are transmitted over 3/4G cameras and processed using Graphical Processing Units (GPUs) to provide conservationists with key detection metrics, thereby removing the requirement for manual observations. Our models achieved an average sensitivity of 88.79%, a specificity of 98.16% and accuracy of 96.71%. This demonstrates the effectiveness of using deep learning for automatic bird monitoring.

Aim Global declines in the populations of migratory species have been attributed largely to climate change and anthropogenic habitat change. However, the relative contribution of these factors on species’ breeding and non‐breeding ranges is unclear. Here, we present the first large‐scale assessment of the relative importance of climatic conditions and land cover on both the breeding and non‐breeding grounds in driving the long‐term population trends of migratory species. Location Europe and Africa. Methods We use data on the long‐term population trends of 61 short‐ and 39 long‐distance migratory species of European breeding birds. We analyse these population trends in relation to changes in climate and land cover across species’ breeding and non‐breeding ranges over a 36‐year period, along with species’ migratory behaviour. Results The population trends of European migratory birds appear to be more closely related to changes in climate than changes in land cover on their breeding grounds, but the converse is true on their non‐breeding grounds. While improvements in climate suitability across the breeding ranges of short‐distance migrants led to increasing population trends, the same was not true for long‐distance migrants. The combined effects of changes in climate and land cover account for approximately 40% of the variation in migratory species’ population trends, suggesting that factors other than climate and land cover as we have measured them, such as habitat quality, also affect the population trends of migrant birds. Main Conclusions Over recent decades, population trends of most migrant species are most strongly related to climatic conditions on the breeding grounds but land cover change on the non‐breeding grounds. This suggests that management to stem the declines of migrant birds requires an integrated approach that considers all processes affecting migrant birds across their dynamic distributions throughout the year.

Camera trapping has become an increasingly reliable and mainstream tool for surveying a diversity of wildlife species. Concurrent with this has been an increasing effort to involve the wider public in the research process, in an approach known as ‘citizen science’. To date, millions of people have contributed to research across a wide variety of disciplines as a result. Although their value for public engagement was recognised early on, camera traps were initially ill-suited for citizen science. As camera trap technology has evolved, cameras have become more user-friendly and the enormous quantities of data they now collect has led researchers to seek assistance in classifying footage. This has now made camera trap research a prime candidate for citizen science, as reflected by the large number of camera trap projects now integrating public participation. Researchers are also turning to Artificial Intelligence (AI) to assist with classification of footage. Although this rapidly-advancing field is already proving a useful tool, accuracy is variable and AI does not provide the social and engagement benefits associated with citizen science approaches. We propose, as a solution, more efforts to combine citizen science with AI to improve classification accuracy and efficiency while maintaining public involvement.

An aspect of life history that has seen increasing attention in recent years is that of strategies for financing the costs of offspring production. These strategies are often described by a continuum ranging from capital breeding, in which costs are met purely from endogenous reserves, to income breeding, in which costs are met purely from concurrent intake. A variety of factors that might drive strategies toward a given point on the capital-income continuum has been reviewed, and assessed using analytical models. However, aspects of food supply, including seasonality and unpredictability, have often been cited as important drivers of capital and income breeding, but are difficult to assess using analytical models. Consequently, we used dynamic programming to assess the role of the food supply in shaping offspring provisioning strategies. Our model is parameterized for a pinniped (one taxon remarkable for the range of offspring-provisioning strategies that it illustrates). We show that increased food availability, increased seasonality, and, to a lesser extent, increased unpredictability can all favor the emergence of capital breeding. In terms of the conversion of energy into offspring growth, the shorter periods of care associated with capital breeding are considerably more energetically efficient than income breeding, because shorter periods of care are associated with a higher ratio of energy put into offspring growth to energy spent on parent and offspring maintenance metabolism. Moreover, no clear costs are currently associated with capital accumulation in pinnipeds. This contrasts with general assumptions about endotherms, which suggest that income breeding will usually be preferred. Our model emphasizes the role of seasonally high abundances of food in enabling mothers to pursue an energetically efficient capital-breeding strategy. We discuss the importance of offspring development for dictating strategies for financing offspring production.

Populations of many migratory taxa have been declining over recent decades. Although protected areas are a cornerstone for conservation, their role in protecting migratory species can be incomplete due to the dynamic distributions of these species. Here, we use a pan-European citizen science bird occurrence dataset (EurobirdPortal) with Spatiotemporal Exploratory Modelling to assess how the weekly distributions of 30 passerine and near passerine species overlap with protected areas in Europe and compare this to range adjusted policy protection targets. Thirteen of our 30 species were inadequately covered by protected areas for some, or all, of the European part of their annual cycle under a target based on the 2020 Convention on Biodiversity framework and none were adequately covered under a target based on the 2030 Convention on Biodiversity framework. Species associated with farmland had the lowest percentage of their weekly distribution protected. The percentage of a species’ distribution within protected areas was positively correlated with its long-term population trend, even after accounting for confounding factors, suggesting a positive influence of protected areas on long-term trends. This emphasises the positive contribution that an informed expansion of the European protected area system could play for the future conservation of migratory land birds. Populations of many migratory taxa have been declining over recent decades. This study examines how well protected areas in Europe cover the dynamic distributions of migratory birds throughout their annual cycles and finds that many species are inadequately protected, especially farmland birds, and that higher protected area coverage correlates with more positive long-term population trends.