Simon Croft

Agricultural land-use change is the dominant driver of terrestrial biodiversity loss and is increasingly attributed to international agrifood trade. This study investigates the political-economic structures underpinning asymmetric flows of embedded biodiversity loss by integrating ecological unequal exchange (EUE) and post-Keynesian theoretical frameworks. We combine the ‘land-cover impacts on future extinction’ (LIFE) metric from the global environmental indicators of consumption (GEIC) dataset, GEIC’s underlying multi-regional input-output model GTAP 11, and a novel fiscal policy space index (FPSI), which captures national monetary sovereignty and position within the currency hierarchy, to analyse a global country-year panel. Results demonstrate that countries with greater fiscal policy space receive significantly higher monetary compensation for each land-use-driven extinction embodied in their agrifood exports. This higher compensation is associated with larger net imports of embodied land-use-driven extinctions, confirming a key mechanism of EUE. Mediation analysis reveals that monetary compensation partially transmits the effect of fiscal policy space on extinction outcomes, yet a strong direct effect remains, indicating additional structural transmission channels. These findings show that asymmetries in biodiversity loss are not merely a product of consumption patterns but are structured by global monetary and financial power relations. Consequently, territorially bounded conservation policies risk exacerbating leakage and inequity unless they engage with the international monetary and financial architectures that enable the externalisation of environmental burdens. This study advances EUE theory by explicitly linking fiscal policy space to land-use-driven species extinction outcomes and underscores the need for transformative governance of global food systems to address the nature crisis.

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.

Knowledge of where wildlife species are and in what number is critical to support robust contingency planning for diseases affecting animal and human health. For common widespread species in particular, comprehensive surveillance is impractical and challenging to coordinate. This is where models can be useful. The ENETWILD project was commissioned by the European Food Safety Authority (EFSA) in 2017 to review available data and modelling methods to predict distributions for key species involved in diseases of concern. Here, we outline our latest methodology based on available occurrence data and apply it to predict distributions for twelve common European mammal species. We use the outputs of model evaluation to highlight areas where further data collection would be most helpful. Model performance was acceptable (Area-Under-Curve statistic above 0.7) within the limits of the available training data across eleven of the twelve species. However, beyond these limits the reliability of prediction was substantially reduced. Assessment of the available data suggested large areas of Eastern and Southern (particularly mountainous) parts of Europe have a distinct environmental signature not sufficiently captured by the existing sampling. Within Europe there remain environmental conditions which are not well represented by existing surveillance, predominantly in Eastern and Southern regions. To reliably apply a robust modelling approach to a Europe-wide context targeted data collection is required.

In an interconnected world, climate change impacts can cascade across sectors and regions, creating systemic risks. Here we analyse cascading climate change impacts on the EU, originating from outside the region, and identify critical intervention points for adaptation. Using network analysis, we integrate stakeholder-co-produced impact chains with quantitative data for 102 countries across foreign policy, human security, trade and finance. Our archetypal impact cascade model reveals critical intervention points related to water, livelihoods, agriculture, infrastructure and economy, and violent conflict. Livelihood instability, with violence exacerbating conditions in conflict-prone regions, tends to amplify risks of cascading impacts emerging from low-income countries. High-income countries can trigger cascading impacts through, for example, reduced crop exports. Our findings highlight the importance of policy coherence in addressing interconnected vulnerabilities rather than isolated risks. Thus, agricultural intensification without integrated water management may exacerbate scarcity, whereas safeguarding livelihoods alleviates cascading risks related to forced migration, violent conflict and instability.

In an interconnected world, climate change impacts can cascade across sectors and regions, creating systemic risks. We analyze cascading climate change impacts on the EU27, originating from outside the EU27, and identify critical intervention points for adaptation. Using network analysis, we develop an archetypal impact cascade model synthesizing stakeholder-co-produced impact chains and quantitative data across diverse sectors, integrating insights from foreign policy, trade, human security, and finance for 102 non-EU countries. Key network nodes – water, agriculture, infrastructure, and livelihoods – emerge as critical intervention points. Particularly, low-income countries' vulnerability to cascading impacts is driven by livelihood instability, with violence amplifying risks in conflict-prone regions. High-income countries can induce cascading impacts through reduced crop exports. Our findings highlight risks from isolated adaptation measures: EU27 nearshoring could destabilize livelihoods elsewhere, while agricultural intensification without integrated water-management may exacerbate scarcity – both potentially triggering cascading impacts. Effective adaptation requires strategies addressing interconnected vulnerabilities, not isolated risks.

Vector-borne diseases have significant impacts on animal and human health globally, and these impacts are likely to increase in the future due to environmental and climate change. Understanding where to target surveillance and control measures to mitigate the impacts of emerging vector-borne diseases can be challenging when pathogens or disease is absent. In this study, we utilise a species distribution modelling approach previously applied to the UK to predict areas at higher risk of mosquito-borne disease across Europe, using West Nile Virus (WNV) as a case study. WNV is an Orthoflavivirus that is naturally transmitted between Culex mosquitoes and a range of avian species. However, it can spread to hosts such as humans and horses where it has the potential to lead to severe illness and mortality. Suitability predictions for Culex ( Cx. pipiens and Cx. modestus ) and avian hosts (mainly Passerine species) are made across Europe to determine potential risk of WNV circulation and establishment. These maps are then combined with information on human and horse density to determine risk to human and equine health. The resulting risk maps reveal that across Europe, there are areas of higher and lower risk that are predominantly driven by vector suitability as avian hosts are widespread. These predictions are projected into the future in 2100 using best- and worst-case Shared Socioeconomic Pathways (SSP1 and SSP5 respectively) to determine how risk may change over time, revealing that some areas see an increase in suitability for both vectors and hosts leading to higher risk (e.g., central England and northern Belgium) whilst other areas see a decline in suitability and consequently lower WNV risk (e.g., northern Italy and western Germany). Overall, this work will improve understanding of mosquito-borne disease risk in changing environments and demonstrates how species distribution modelling can be used to aid contingency preparedness by highlighting areas at higher risk of emerging disease.

Context Knowledge of where wildlife species are and in what number is critical to support robust contingency planning for diseases affecting animal and human health. For common widespread species in particular, comprehensive surveillance is impractical and challenging to coordinate. This is where models can be useful, to fill in the gaps and direct survey efforts to maximise understanding. The ENETWILD project was commissioned by EFSA in 2017 to review available data and modelling methods to predict distributions for key species involved in diseases of concern (e.g., wild boar and African swine fever). Here, we outline the latest methodology to predict species distributions based on occurrence data and outline the areas where further data would be most helpful. Method We present a generic framework to model the distribution of common mammal species in Europe, using global data. Based on occurrence records from large scale repositories, our method first attempts to address known biases to produce a presence-absence dataset and then applies random forest modelling to predict habitat suitability. We apply this approach to twelve species spanning distinct survey methodologies within mammal recording (visual observation, trapping, and bats/audio monitoring). Results Model performance was acceptable across all species within the limits of the available training data (AUC close to or above 0.7). However, beyond these limits the reliability of prediction was substantially reduced. Assessment of the available data suggested large areas of Eastern and Southern (particularly mountainous) parts of Europe have a distinct environmental signature not sufficiently captured by the existing sampling. Conclusions Within Europe there remain environmental conditions which are not well represented by existing surveillance, predominantly in Eastern and Southern regions. To reliably apply a robust modelling approach to a Europe-wide context targeted data collection is required.

Dispersal is an ecological process central to population dynamics, representing an important driver of movement between populations and across landscapes. In spatial population models for terrestrial vertebrates, capturing plausible dispersal behaviour is of particular importance when considering the spread of disease or invasive species. The distribution of distances travelled by dispersers, or the dispersal kernel, is typically highly skewed, with most individuals remaining close to their origin but some travelling substantially further. Using mechanistic models to simulate individual dispersal behaviour, the dispersal kernel can be generated as an emergent property. Through stepwise simulation of the entire movement path, models can also account for the influence of the local environment, and contacts during the dispersal event which may spread disease. In this study, we explore a range of simple rules to emulate individual dispersal behaviour within a mosaic model generated using irregular geometry. Movement rules illustrate a limited range of behavioural assumptions and when applied across these simple synthetic landscapes generated a wide range of emergent kernels. Given the variability in dispersal distances observed within species, our results highlight the importance of considering landscape heterogeneity and individual-level variation in movement, with simpler rules approximating random walks providing less plausible emergent kernels. As a case study, we demonstrate how rule sets can be selected by comparison to an empirical kernel for a study species (red fox; Vulpes vulpes ). These results provide a foundation for the selection of movement rules to represent dispersal in spatial agent-based models, however, we also emphasise the need to corroborate rules against the behaviour of specific species and within chosen landscapes to avoid the potential for these rules to bias predictions.

Context: Invasive non-native species are on the rise worldwide, exacerbating already significant environmental and economic impacts. Concurrently, public attitudes towards methods of controlling these species are changing, with greater demand for non-lethal solutions. This has fostered research into developing new, effective technologies and strategies for wildlife management. Aims: On the basis of a case study focusing on the eastern grey squirrel (Sciurus carolinensis) in the UK, this study aimed to understand the potential for population management, using either live trapping or oral contraception. First, assuming completely coordinated landscape-scale control, and then, coordinated control only on parts of the landscape, accounting for the potential that landowners’ attitudes towards alternative methods may differ and reflect those of the wider public. Methods: We used an existing spatially explicit individual-based modelling approach applying various parameterisations to define management. We varied the density of traps or hoppers delivering contraceptives, the duration of deployment, contraceptive efficacy, initial population size, and the probability of landowner participation. The latter was based on a previous public survey, suggesting that 40% of the respondents were open to management using trapping and 64% to using contraception. Key results: With complete coordinated control, trapping was generally faster and more cost-effective than was contraception. However, when differences in social acceptance were considered, reducing participation and, consequently, the spatial coverage of management, contraception was found to maintain greater population reductions than was trapping with similar effort, assuming a contraceptive efficacy of 75% or higher. Conclusions: This study added another layer of complexity to managing invasive non-native species, namely the potential effect that landowners’ attitudes to different methods of population management might have on the level of coordinated control at landscape scale. In a situation such as the one modelled by this study, where management is not a legal requirement, this human dimension must be considered alongside cost-effectiveness, to develop successful control in line with management goals. Implications: Further work is required to establish the actual attitudes of landowners, and in different contexts (e.g. urban, rural areas), how this may change as new approaches become available, and then how spatial variation (clustering) in the resulting wildlife population control may affect outcomes.

Countries’ reliance on global food trade networks implies that regionally different climate change impacts on crop yields will be transmitted across borders. This redistribution constitutes a significant challenge for climate adaptation planning and may affect how countries engage in cooperative action. This paper investigates the long-term (2070–2099) potential impacts of climate change on global food trade networks of three key crops: wheat, rice and maize. We propose a simple network model to project how climate change impacts on crop yields may be translated into changes in trade. Combining trade and climate impact data, our analysis proceeds in three steps. First, we use network community detection to analyse how the concentration of global production in present-day trade communities may become disrupted with climate change impacts. Second, we study how countries may change their network position following climate change impacts. Third, we study the total climate-induced change in production plus import within trade communities. Results indicate that the stability of food trade network structures compared to today differs between crops, and that countries’ maize trade is least stable under climate change impacts. Results also project that threats to global food security may depend on production change in a few major global producers, and whether trade communities can balance production and import loss in some vulnerable countries. Overall, our model contributes a baseline analysis of cross-border climate impacts on food trade networks.