Mapping wild boar density across Europe: combining spatial models and density estimates - Graham C. Smith; Joaquin Vicente; Simon Croft; Daniel Warren; José A. Blanco-Aguiar; Pelayo Acevedo; Tancredi Guerrasio; Massimo Scandura; Marco Apollonio; João Carvalho; Rita Tinoco Torres; Nuno Pinto; et al. (European Journal of Wildlife Research, 2026)
The wild boar population in Europe has been growing in recent decades prior to the arrival of African swine fever (ASF), which has now spread across much of eastern Europe. We obtained two independent sources of wild boar data: occurrence sightings and hunting harvest. We combined these with environmental predictors and used them in two species distribution modelling approaches, a Maxent approach for occurrence data and a GLMM for the hunting harvest, to produce output at the European level. The output of these models was then combined with robust and comparable density estimates from 77 sites across Europe to produce a density estimate and total population size for each country prior to documenting ASF in that country (from 2007 for Georgia to 2022 for most EU countries that are still free from disease). The output indicates a total population of wild boar in Europe between 13.5 and 19.6 million individuals prior to the hunting season each year in the core wild boar range. Population estimates of wild boar in Europe based on occurrence sightings and hunting harvest are highly similar yet vary substantially among countries. Although the output may need to be adjusted where local factors affect the population (e.g., areas of range spread) the output can be used for assessing risk of disease spread and effect of management. We propose that the availability of density estimates from the European Observatory of Wildlife will permit robust population estimates for other species of interest since the methodology is consistent between species and habitats.

A methodological approach for assessing cross-border supply chain exposure to climate change impacts on crop production and transport - Emilie Stokeld; Chris West; Simon Croft; Lindsay C. Stringer; Cornelia Auer (Climatic Change, 2026)

Satellite-based winter wheat yield estimation with a newly parameterized LUE model based on crop water status and leaf chlorophyll content - Ali Mokhtari; Haibo Yang; Holly Croft; Simon Vlad Luca; Fei Li; Mirjana Minceva; Urs Schmidthalter; Kang Yu (Field Crops Research, 2025)

Selection of movement rules to simulate species dispersal in a mosaic landscape model - Susannah Gold; Simon Croft; Richard Budgey; James Aegerter (Ecological Complexity, 2024)

Stakeholder perspectives on cross-border climate risks in the Brazil-Europe soy supply chain - Emilie Stokeld; Simon Croft; Tiago N.P. dos Reis; Lindsay C. Stringer; Chris West (Journal of Cleaner Production, 2023)

Potential for redistribution of post‐moult habitat for Eudyptes penguins in the Southern Ocean under future climate conditions - Cara‐Paige Green; David B. Green; Norman Ratcliffe; David Thompson; Mary‐Anne Lea; Alastair M. M. Baylis; Alexander L. Bond; Charles‐André Bost; Sarah Crofts; Richard J. Cuthbert; Jacob González‐Solís; Kyle W. Morrison; et al. (Global Change Biology, 2023)
Anthropogenic climate change is resulting in spatial redistributions of many species. We assessed the potential effects of climate change on an abundant and widely distributed group of diving birds, Eudyptes penguins, which are the main avian consumers in the Southern Ocean in terms of biomass consumption. Despite their abundance, several of these species have undergone population declines over the past century, potentially due to changing oceanography and prey availability over the important winter months. We used light‐based geolocation tracking data for 485 individuals deployed between 2006 and 2020 across 10 of the major breeding locations for five taxa of Eudyptes penguins. We used boosted regression tree modelling to quantify post‐moult habitat preference for southern rockhopper ( E. chrysocome ), eastern rockhopper ( E. filholi ), northern rockhopper ( E. moseleyi ) and macaroni/royal ( E. chrysolophus and E. schlegeli ) penguins. We then modelled their redistribution under two climate change scenarios, representative concentration pathways RCP4.5 and RCP8.5 (for the end of the century, 2071–2100). As climate forcings differ regionally, we quantified redistribution in the Atlantic, Central Indian, East Indian, West Pacific and East Pacific regions. We found sea surface temperature and sea surface height to be the most important predictors of current habitat for these penguins; physical features that are changing rapidly in the Southern Ocean. Our results indicated that the less severe RCP4.5 would lead to less habitat loss than the more severe RCP8.5. The five taxa of penguin may experience a general poleward redistribution of their preferred habitat, but with contrasting effects in the (i) change in total area of preferred habitat under climate change (ii) according to geographic region and (iii) the species (macaroni/royal vs. rockhopper populations). Our results provide further understanding on the regional impacts and vulnerability of species to climate change.

A spatially explicit approach to assessing commodity-driven fertilizer use and its impact on biodiversity - Karin Eliasson; Christopher D. West; Simon A. Croft; Jonathan M.H. Green (Journal of Cleaner Production, 2023)

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Climate change, crops and commodity traders: subnational trade analysis highlights differentiated risk exposure - Emilie Stokeld; Simon A. Croft; Jonathan M. H. Green; Christopher D. West (Climatic Change, 2020)
The global food system is increasingly interconnected and under pressure to support growing demand. At the same time, crop production is facing new and uncertain impacts from climate change. To date, understanding how downstream supply chain actors, such as commodity traders, are exposed to climate change risks has been difficult due to a lack of high-resolution climate and trade data. However, the recent availability of supply chain data linking subnational production to downstream actors, and gridded projections of crop yield under climate change, allows us to assess individual commodity trader exposure to long-term climate change risk. We apply such an analysis to soy production in Brazil, the world’s largest soy exporter. Whilst uncertainty across crop models’ yield projections means it remains difficult to accurately predict how production across the region will be affected by climate change, we demonstrate that the risk exposure of trading actors differs substantially due to the heterogeneity in their sourcing. Our study offers a first attempt to analyze subnational climate risk to individual trading actors operating across an entire production landscape, leading to more precise risk exposure analysis. With sufficient subnational data, this method can be applied to any crop and country combination, and in the context of wider food security issues, it will be pertinent to apply these methods across other production systems and downstream actors in the food system.

Leishmaniasis immunopathology—impact on design and use of vaccines, diagnostics and drugs - Paul M. Kaye; Israel Cruz; Albert Picado; Katrien Van Bocxlaer; Simon L. Croft (Seminars in Immunopathology, 2020)

Predicting population trends using citizen science data: do subsampling methods produce reliable estimates for mammals? - Katie Callcutt; Simon Croft; Graham C. Smith (European Journal of Wildlife Research, 2018)

Risk management to prioritise the eradication of new and emerging invasive non-native species - Olaf Booy; Aileen C. Mill; Helen E. Roy; Alice Hiley; Niall Moore; Pete Robertson; Simon Baker; Matt Brazier; Mathilde Bue; Richard Bullock; Steve Campbell; Dominic Eyre; et al. (Biological Invasions, 2017)

Sexual response of male Drosophila to honey bee queen mandibular pheromone: implications for genetic studies of social insects - Justin R. Croft; Tom Liu; Alison L. Camiletti; Anne F. Simon; Graham J. Thompson (Journal of Comparative Physiology A, 2017)

Optimal root proliferation strategies: the roles of nutrient heterogeneity, competition and mycorrhizal networks - Simon Antony Croft; Angela Hodge; Jonathan W. Pitchford (Plant and Soil, 2012)