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The potential for statistical complexity in species distribution models (SDMs) has greatly increased with advances in computational power. Structurally complex models provide the flexibility to analyse intricate ecological systems and realistically messy data, but can be difficult to interpret, reducing their practical impact. Founding model complexity in ecological theory can improve insight gained from SDMs. Here, we evaluate a marked point process approach, which uses multiple Gaussian random fields to represent population dynamics of the Eurasian crane Grus grus in a spatio‐temporal species distribution model. We discuss the role of model components and their impacts on predictions, in comparison with a simpler binomial presence/absence approach. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with inlabru, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used in SDMs. Using the marked point process approach, crane distribution was predicted to be dependent on the density of suitable habitat patches, as well as close to observations of the existing population. This demonstrates the advantage of complex model components in accounting for spatio‐temporal population dynamics (such as habitat preferences and dispersal limitations) that are not explained by environmental variables. However, including an AR1 temporal correlation structure in the models resulted in unrealistic predictions of species distribution; highlighting the need for careful consideration when determining the level of model complexity. Increasing model complexity, with careful evaluation of the effects of additional model components, can provide a more realistic representation of a system, which is of particular importance for a practical and impact‐focused discipline such as ecology (though these methods extend to applications for a wide range of systems). Founding complexity in contextual theory is not only fundamental to maintaining model interpretability but can be a useful approach to improving insight gained from model outputs.

Understanding and predicting a species’ distribution across a landscape is of central importance in ecology, biogeography and conservation biology. However, it presents daunting challenges when populations are highly dynamic (i.e. increasing or decreasing their ranges), particularly for small populations where information about ecology and life history traits is lacking. Currently, many modelling approaches fail to distinguish whether a site is unoccupied because the available habitat is unsuitable or because a species expanding its range has not arrived at the site yet. As a result, habitat that is indeed suitable may appear unsuitable. To overcome some of these limitations, we use a statistical modelling approach based on spatio‐temporal log‐Gaussian Cox processes. These model the spatial distribution of the species across available habitat and how this distribution changes over time, relative to covariates. In addition, the model explicitly accounts for spatio‐temporal dynamics that are unaccounted for by covariates through a spatio‐temporal stochastic process. We illustrate the approach by predicting the distribution of a recently established population of Eurasian cranes Grus grus in England, UK, and estimate the effect of a reintroduction in the range expansion of the population. Our models show that wetland extent and perimeter‐to‐area ratio have a positive and negative effect, respectively, in crane colonisation probability. Moreover, we find that cranes are more likely to colonise areas near already occupied wetlands and that the colonisation process is progressing at a low rate. Finally, the reintroduction of cranes in SW England can be considered a human‐assisted long‐distance dispersal event that has increased the dispersal potential of the species along a longitudinal axis in S England. Spatio‐temporal log‐Gaussian Cox process models offer an excellent opportunity for the study of species where information on life history traits is lacking, since these are represented through the spatio‐temporal dynamics reflected in the model.

Online wildlife trade is widespread and affects thousands of species. Yet, attempts to quantify online wildlife trade have mainly focused on a few platforms and taxonomic groups. Here, we study the prevalence of wildlife trade using automated data collection and filtering methods. We analyze trade across five digital platforms and 156 animal and plant species of conservation concern from a global biodiversity hotspot, the Philippines. We identified approximately 5000 highly relevant instances of trade in 1.47 million posts, focusing on 108 species, 79 of which are classified as threatened. Trade mainly occurred on webpages indexed in Google and on Twitter. We found that manual validation is essential, as animals obtained a higher proportion of hits prior to validation. Following manual validation, we observed a shift toward plant‐related trade hits. Scaling up these approaches to a global level is key to understanding the extent of digital wildlife trade across the globe.

Billions of vertebrates migrate to and from their breeding grounds annually, exhibiting astonishing feats of endurance. Many such movements are energetically costly yet there is little consensus on whether or how such costs might influence schedules of survival and reproduction in migratory animals. Here we provide a global analysis of associations between migratory behaviour and vertebrate life histories. After controlling for latitudinal and evolutionary patterns, we find that migratory birds and mammals have faster paces of life than their non-migratory relatives. Among swimming and walking species, migrants tend to have larger body size, while among flying species, migrants are smaller. We discuss whether pace of life is a determinant, consequence, or adaptive outcome, of migration. Our findings have important implications for the understanding of the migratory phenomenon and will help predict the responses of bird and mammal species to environmental change. Migration is costly. In the first global analysis of migratory vertebrates, authors report that migratory birds and mammals have faster paces of life than their non-migratory relatives, and that among swimming and walking species, migrants tend to be larger, while among flying species, migrants are smaller.

Aim Online reptile trade poses new challenges to species conservation and requires automated monitoring. Range‐restricted and endemic reptile species are especially vulnerable to wildlife trade and unsustainable exploitation. In this study, we investigated the magnitude and geographic distribution of online trade of 96 endemic and range‐restricted reptile species from the Lesser Antilles. Location Global. Methods We developed methods for automated collection, filtering and processing of wildlife trade content for the targeted species from publicly accessible online platforms. Results We identified 599 relevant advertisements originating from 231 different advertisers and 41 websites focusing on 43 species. Species advertised included threatened species according to the International Union for the Conservation of Nature (IUCN) Red List and species listed in the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendices. Among threatened species, five are Critically Endangered, three are Endangered and two are Vulnerable according to the IUCN Red List. Moreover, three of the six most advertised species were classified as Near Threatened. Germany was the country with the highest number of advertisements (N = 124), followed by the United States (N = 55), the Netherlands (N = 15) and United Kingdom (N = 15). Based on data from sale advertisements that included price and currency data, prices ranged from one to over a thousand Euros. Main Conclusions We present a framework for automated analysis of online trade in reptiles that can be extended to other taxonomic groups. Our results highlight countries, such as Germany and the United States, where enhanced monitoring actions would be important to assess the origin (i.e. captive bred or wild sourced individuals) and the legality of the trade. Immediate conservation actions, such as population monitoring, are also needed to ensure wildlife trade is not threatening the persistence of endemic reptile populations in the wild.

The ongoing digital revolution in the age of big data is opening new research opportunities. Culturomics and iEcology, two emerging research areas based on the analysis of online data resources, can provide novel scientific insights and inform conservation and management efforts. To date, culturomics and iEcology have been applied primarily in the terrestrial realm. Here, we advocate for expanding such applications to the aquatic realm by providing a brief overview of these new approaches and outlining key areas in which culturomics and iEcology are likely to have the highest impact, including the management of protected areas; fisheries; flagship species identification; detection and distribution of threatened, rare, and alien species; assessment of ecosystem status and anthropogenic impacts; and social impact assessment. When deployed in the right context with awareness of potential biases, culturomics and iEcology are ripe for rapid development as low-cost research approaches based on data available from digital sources, with increasingly diverse applications for aquatic ecosystems.

Recent studies have suggested that protected areas often fail to conserve target species. However, the efficacy of terrestrial protected areas is difficult to measure, especially for highly vagile species like migratory birds that may move between protected and unprotected areas throughout their lives. Here, we use a 30-y dataset of detailed demographic data from a migratory waterbird, the Whooper swan (Cygnus cygnus), to assess the value of nature reserves (NRs). We assess how demographic rates vary at sites with varying levels of protection and how they are influenced by movements between sites. Swans had a lower breeding probability when wintering inside NRs than outside but better survival for all age classes, generating a 30-fold higher annual growth rate within NRs. There was also a net movement of individuals from NRs to non-NRs. By combining these demographic rates and estimates of movement (into and out of NRs) into population projection models, we show that the NRs should help to double the population of swans wintering in the United Kingdom by 2030. These results highlight the major effect that spatial management can have on species conservation, even when the areas protected are relatively small and only used during short periods of the life cycle.

Social media has become a popular stage for people’s views over climate change. Monitoring how climate change is perceived on social media is relevant for informed decision-making. This work advances the way social media users’ perceptions and reactions towards climate change can be understood over time, by implementing a scalable methodological framework grounded on natural language processing. The framework was tested in over 1771 thousand X/Twitter posts of Spanish, Portuguese, and English discourses from Southwestern Europe. The employed models were successful (i.e., > 84% success rate) in detecting relevant climate change posts. The methodology detected specific climate phenomena in users’ discourse, coinciding with the occurrence of major climatic events in the test area (e.g., wildfires, storms). The classification of sentiments, emotions, and irony was also efficient, with evaluation metrics ranging from 71 to 92%. Most users’ reactions were neutral (> 35%) or negative (> 39%), mostly associated to sentiments of anger and sadness over climate impacts. Almost a quarter of posts showed ironic content, reflecting the common use of irony in social media communication. Our exploratory study holds potential to support climate decisions based on deep learning tools from monitoring people’s perceptions towards climate issues in the online space.

Online digital data from media platforms have the potential to complement biodiversity monitoring efforts. We propose a strategy for integrating these data into current biodiversity datasets in light of the Kunming-Montreal Global Biodiversity Framework.