Biology subjects

Reino, L.

Publications and source records attributed to Reino, L..

4 recordsLinked to original sources

bioRxiv2026

Two centuries of change in the traits and origins of non-native vertebrates

Globalization is redistributing species worldwide, yet whether the traits and origins of non-native fauna have changed through time remains unclear. We combined global first-record data for non-native species from 1800-2019 with harmonized information on body size, diet, habitat use, native range characteristics, and climatic niche characteristics for 1,910 non-native mammals, birds, reptiles, amphibians, and freshwater fishes. Across most groups, species recorded earlier originated disproportionately from higher latitudes, occupied broader native ranges, and had wider thermal niches. More recent first records increasingly involve species from warmer, lower-latitude regions with smaller and more restricted native distributions. Body size also declined through time in several groups, whereas shifts in diet and habitat use were more taxon-specific. Temporal changes in introduction pathways partly explained these patterns: declines in deliberate release and production-related pathways, together with increases in pet and ornamental pathways, were associated with shifts toward smaller-bodied, lower-latitude, and more range-restricted species. These results indicate that the functional and biogeographic composition of non-native vertebrates has been progressively reshaped over the past two centuries, weakening the historical dominance of widespread temperate species and increasingly incorporating tropical and range-restricted fauna into global redistribution. Anticipating future biological invasions will therefore require attention not only to the number of species being transported, but also to how the characteristics and pathways of transported species are changing through time.

ecology↗

bioRxiv2026

Data-driven forecasts of regional arrivals of non-native vertebrates worldwide

AimTo forecast near-future arrivals of non-native terrestrial and freshwater vertebrates at the regional level. LocationGlobal (geopolitical regions worldwide, including countries and main administrative divisions). MethodsWe compiled first regional record data and assembled functional and macroecological variables for 1,931 non-native vertebrate species. For each region, we identified recently arrived non-native species using retrospective windows of thirty and twenty years ending in 2015 (1986-2015; 1996-2015). We then fitted region-specific random-forest models classifying recently arrived species versus those not yet arrived using as predictors: (i) harmonised species traits (e.g., habitat, diet, body size and native-range attributes) and (ii) spread history, capturing time since first record elsewhere. Predictive performance was evaluated using leave-one-out cross-validation, comparing full models with trait-only and spread-only variants. We also assessed relationships between predictive accuracy, predictor importance, and the geographic positioning and trade connectedness of regions. Finally, we predicted region-specific probabilities of arrival for species not yet recorded. ResultsForecasting accuracy was consistently high across regions and taxa, with AUC values above 0.9 in more than half of the focal regions. Full models substantially outperformed models using either predictor set alone, and spread-history-only models typically exceeded trait-only models. Relative importance of spread-history predictors declined with geographic distance to the focal region, whereas predictability was lower in highly trade-connected regions. Predicted near-future high-risk arrivals were dominated by birds and freshwater fishes and showed strong regional structuring. A small set of species ranked highly across many regions (e.g., birds: Phasianus colchicus, Acridotheres tristis, Amandava amandava, Colinus virginianus, Corvus splendens and Lonchura malacca; fishes: Coregonus peled and Oreochromis mossambicus; mammal: Oryctolagus cuniculus), suggesting substantial unrealised spread potential. Main conclusionsNear-future regional arrivals of non-native vertebrates are predictable from spread history and species traits. This enables scalable, updateable regional watchlists to support prevention, early detection and horizon scanning.

ecology↗

bioRxiv2025

Predicting current and future distribution of the common waxbill using a mechanistic modelling approach

Biological invasions and climate change are two of the most pressing drivers of biodiversity loss worldwide. Anticipating where invasive species are likely to establish, as well as the potential impact of climate change on their range expansion, is essential for early detection and targeted management. In this study, we use a mechanistic species distribution model (SDM) to evaluate the current and future areas at risk of invasion by the common waxbill (Estrilda astrild), a widespread avian invader. Our model accurately predicts the species current range in Iberia and identifies additional climatically suitable areas, particularly in southern and western Europe. Under warming scenarios of +2 {degrees}C and +4 {degrees}C, suitable areas expand northwards, with over two-thirds of Europe classified as suitable under the most extreme scenario. These results suggest that the species is already operating near the cold limits of its thermal niche in parts of its invasive range, and that rising temperatures may remove these constraints, allowing expansion into previously unsuitable areas. Our findings demonstrate the power of mechanistic models in identifying regions at risk of colonisation and underscore the importance of early intervention and targeted monitoring in areas projected to become suitable.

ecology↗

bioRxiv2024

Large language models overcome the challenges of unstructured text data in ecology

The vast volume of currently available unstructured text data, such as research papers, news, and technical report data, shows great potential for ecological research. However, manual processing of such data is labour-intensive, posing a significant challenge. In this study, we aimed to assess the application of three state-of-the-art prompt-based large language models (LLMs), GPT 3.5, GPT 4, and LLaMA-2-70B, to automate the identification, interpretation, extraction, and structuring of relevant ecological information from unstructured textual sources. We focused on species distribution data from two sources: news outlets and research papers. We assessed the LLMs for four key tasks: classification of documents with species distribution data, identification of regions where species are recorded, generation of geographical coordinates for these regions, and supply of results in a structured format. GPT 4 consistently outperformed the other models, demonstrating a high capacity to interpret textual data and extract relevant information, with the percentage of correct outputs often exceeding 90% (average accuracy across tasks: 87-100%). Its performance also depended on the data source type and task, with better results achieved with news reports, in the identification of regions with species reports and presentation of structured output. Its predecessor, GPT 3.5, exhibited reasonably low accuracy across all tasks and data sources (average accuracy across tasks: 81-97%), whereas LLaMA-2-70B showed the worst performance (37- 73%). These results demonstrate the potential benefit of integrating prompt-based LLMs into ecological data assimilation workflows as essential tools to efficiently process large volumes of textual data.

ecology↗