Boris Leroy

In response to escalating environmental pressures, many sectors have turned to sustainability-focused solutions, but these can backfire when ecological complexity is overlooked. Insect farming has recently emerged as a promising alternative to mitigate environmental challenges, yet it carries the risk of facilitating biological invasions, one of the most severe threats to global biodiversity. In this piece, we apply key concepts and hypotheses from invasion biology to the context of insect farming to better understand and anticipate this risk. We also highlight the crucial importance of biosecurity and outline practical measures to minimize the likelihood of invasive events. This perspective is particularly urgent for policymakers and international organizations aiming to advance sustainable food systems while preventing further biodiversity loss.

Aim: This study investigates a poorly known component of coral reefs, the cryptobiome, species that live hidden within the reef matrix, essential for reef functioning and productivity. It focuses on patterns of diversity and rarity of sessile cryptobenthic communities across Mascarene islands, and across shallow and mesophotic habitats (in deep low‐light conditions). It aims to: (i) assess community uniqueness and local extinction risks for conservation, (ii) evaluate taxonomic overlap between mesophotic and shallow communities to explore mesophotic habitats as refugia for degrading shallow reefs. Location: Reunion and Rodrigues islands, Mascarene Archipelago, south‐west Indian Ocean. Method: Forty‐four Autonomous Reef Monitoring Structures (ARMS) were deployed on outer reef slopes at Reunion (~11 m and ~51 m depths) and at Rodrigues (~11 m depth). Seven hundred four ARMS plate photographs were analysed using a point counting method, and 138 morphospecies (MSP) were distinguished. Analyses included alpha, beta and gamma diversity, rarity and taxonomic overlap. Results: Sessile cryptobenthic communities from Rodrigues and mesophotic habitats were characterized by unique MSP compositions, higher spatial heterogeneity and elevated rarity indices. A strong species turnover was observed between shallow and mesophotic habitats and taxonomic overlap assessment revealed that two‐thirds of the MSP were habitat‐exclusive, while only one‐quarter were common and shared across habitats. Main Conclusions: We highlight the uniqueness and rarity of sessile cryptobenthic communities in mesophotic habitats and in shallow habitats on the southwest coast of Reunion and in Rodrigues, and the need to consider broad geographical coverage in conservation strategies to account for their spatial heterogeneity and uniqueness. The low proportion of shallow‐water MSP found in deeper habitats argues against the deep reef refugia hypothesis for sessile cryptobenthic communities in Reunion. Each habitat holds a distinct conservation value that should be recognized independently.

Insect farming is promoted as a sustainable alternative to traditional livestock, offering substantial reductions in land, water, and feed use while generating fewer greenhouse gas emissions. However, the escape or release of farmed insects into non-native environments may create significant biological invasion risks, with consequences for biodiversity, human well-being, and economies. This study provides the first systematic assessment of the ecological characteristics influencing the likelihood of edible insects becoming invasive, and identifies species not yet reported as invasive that could present risks if farmed outside their native ranges. We evaluated 1,030 species (943 native and 88 introduced) spanning six insect orders (Coleoptera, Lepidoptera, Hymenoptera, Hemiptera, Orthoptera, and Blattodea) in a multidimensional functional space. Our analyses highlight the importance of both larval and adult diet breadth, macroecological factors such as temperature and humidity, and native-range distribution size as predictors of invasion potential. Using machine learning, we identified species with high invasive risk, with Orthoptera, Lepidoptera, Hemiptera, and Hymenoptera being the groups most likely to become invasive. By combining ecological variables with predictive modeling, this work provides a framework for invasion risk assessment and delivers practical guidance to support sustainable insect farming while minimizing ecological threats.

Insect research remains hindered by limited data availability and fragmented knowledge compared to other, better-documented taxonomic groups. Yet, both the macroecological and the insect research communities highlight the need to integrate large-scale ecological trait datasets for insects. Insects and humans are interconnected through diverse relationships, ranging from beneficial interactions, such as the use of insects as nutritional resources, to adverse impacts, including their role in to the emergence and spread of biological invasions. Understanding the traits of insects associated with human use, movement and impact is therefore important for linking insects to ecosystem function and global change. We present AnthropInsect , one of the largest database on insect traits to date, which uniquely includes variables describing human-insect associations. AnthropInsect describes species through 35 variables grouped into five categories: (i) taxonomic descriptors; (ii) ecological descriptors (native bioregions and habitat); (iii) human-insect associations (edibility and invasive status); (iv) functional traits (behavior, morphology, life history and feeding); (v) and macroecological descriptors of native-range geography and climate. AnthropInsect currently includes 5867 species across six major orders: Coleoptera, Lepidoptera, Hemiptera, Hymenoptera, Orthoptera and Blattodea. Data extracted from peer-reviewed, grey literature and from existing databases were standardized and validated with expert knowledge to ensure accuracy. By providing traits data with information on insect–human interactions, this rigorously curated resource supports global research in entomology, ecology, conservation, and global change.

Insect farming is recognized as a sustainable alternative to traditional livestock, offering significant reductions in land, water, and feed use, along with lower greenhouse gas emissions. However, the potential for farmed insects to escape or be released poses a significant biological invasion risk, threatening biodiversity, human well-being, and economies. This study is the first to quantify the global spatial invasion risk of seven of the most farmed insect species. We employ state-of-the-art Species Distribution Models integrating climatic and socio-economic variables to predict habitat suitability and identify high-risk regions. Our analysis highlights substantial invasion risks globally, with key hotspots in the Caribbean, South America, Western and Central Europe, and Oceania. While high-risk areas are species-dependent, the banded cricket, common housefly, and black soldier fly pose the greatest risks. Notably, nearly all surveyed insect farms are located in areas with intermediate to high invasion risks, underscoring the urgent need for stringent biosecurity measures in existing farms and strategic species selection for future farms. By providing a robust methodology and guidelines, our work offers a foundation for policymakers and industry stakeholders to ensure sustainable insect farming and critical insights into this understudied field.

Sampling bias is an inherent problem in widely available biodiversity data, undermining the robustness of correlative species distribution models (SDMs). To some extent, subsampling occurrence data can account for uneven sampling efforts; yet, conventional approaches subsample in geographical space, while subsampling in environmental space remains underexplored. Here, we compared the effectiveness of subsampling methods that correct sampling bias either in geographical space (spatial gridding, spatial distance thinning) or directly in environmental space (environmental gridding), including two novel approaches introduced here: environmental clustering and environmental distance thinning. We hypothesised that environmental subsampling methods would be more effective in improving SDM performance across its three primary uses: explaining, predicting, and projecting. Using a virtual ecologist framework, we assessed SDM performance against four evaluation tests: replicating true species–environment response curves, predicting within the sampling region via internal cross‐validation and evaluation against independent data, and projecting outside the sampling region. Our findings demonstrate that environmental subsampling methods, especially environmental clustering and environmental distance thinning, outperformed other methods in yielding robust SDMs in almost all evaluation tests. Interestingly, cross‐validation favoured SDMs with no sampling bias correction, highlighting the inability of cross‐validation to identify unbiased models. Our findings emphasise a critical conceptual disconnect: SDMs appearing to perform well in predicting species' distributions may not reliably estimate species–environment relationships, nor transfer predictions onto novel environments. Environmental subsampling methods are reliable approaches for all uses, but are particularly suited for explaining species' niches and transferring predictions across space and/or time, such as when anticipating species' responses to climate change or assessing the risk of biological invasions. Conversely, geographic subsampling methods may suffice for predicting species' distributions within their current environmental context, as required in conservation planning. Our study firmly establishes the critical importance of correcting environmental sampling bias, while also providing reliable solutions for supporting biodiversity conservation in an ever‐changing world.

To predict the spatial responses of biodiversity to climate change, studies typically rely on species‐specific approaches, such as species distribution models. In this study, we propose an alternative methodology that investigates the collective response of species groups by modelling biogeographical regions. Biogeographical regions are areas defined by homogeneous species compositions and separated by barriers to dispersal. When climate acts as such a barrier, species within the same region are expected to respond similar to changing climatic conditions, enabling the prediction of entire region shifts in response to future climate scenarios. We applied this approach to the Southern Ocean, which exhibits sharp climatic transitions known as oceanic fronts, focusing on the mesopelagic lanternfishes (family Myctophidae). We compiled occurrence data for 115 lanternfish species from 1950 onwards and employed a network‐based analysis to identify two major biogeographical regions: a southern and a subtropical region. These regions were found to be distinct, with minimal overlap in species distributions along the temperature gradient and a separation around 8°C, indicating that temperature likely acts as a climatic barrier. Using an ensemble modelling approach, we projected the response of these regions to future temperature changes under various climate scenarios. Our results suggest a circumpolar expansion of the subtropical region and a contraction of the southern region, with the Southern Ocean becoming a cul‐de‐sac for southern species. Ultimately, our results suggest that when support is found for the climatic barrier hypothesis, community‐level models from a ‘group first, then predict’ strategy may effectively predict future shifts in species assemblages.

Biological invasions threaten global biodiversity, human well-being and economies. Many regional and taxonomic syntheses of monetary costs have been produced recently but with important knowledge gaps owing to uneven geographic and taxonomic research intensity. Here we combine species distribution models, macroeconomic data and the InvaCost database to produce the highest resolution spatio-temporal cost estimates currently available to bridge these gaps. From a subset of 162 invasive species with ‘highly reliable’ documented costs at the national level, our interpolation focuses on countries that have not reported any costs despite the known presence of invasive species. This analysis demonstrates a substantial underestimation, with global costs potentially estimated to be 1,646% higher for these species than previously recorded. This discrepancy was uneven geographically and taxonomically, respectively peaking in Europe and for plants. Our results showed that damage costs were primarily driven by gross domestic product, human population size, agricultural area and environmental suitability, whereas management expenditure correlated with gross domestic product and agriculture areas. We also found a lag time for damage costs of 46 years, but management spending was not delayed. The methodological predictive approach of this study provides a more complete view of the economic dimensions of biological invasions and narrows the global disparity in invasion cost reporting.