Ignasi Bartomeus

Anthropogenic global change presents a growing threat to insect pollinators. Yet, long-term, continental-scale assessments of their population trends remain limited. Here, we leveraged a curated database of over four million occurrence records, covering 88% of extant bee and hoverfly species in Europe, to assess persistence, richness, and occupancy trends over the past century. Species persistence at the European level was relatively high, with modern records available for 87% of bee and 89% of hoverfly species. Regional richness changes were modest, with declines in the Atlantic and Continental regions for both pollinator groups. Recent increases were observed in the Alpine region for bees, but species richness remained largely stable elsewhere. At the species level, however, a more concerning picture emerged: Most bee species and about half of hoverfly species declined in occupancy. Species traits explained little variation in these trends, although there was a weak tendency for cold-adapted bees to show steeper declines than warm-adapted ones. Genera showing some of the steepest declines include parasitic or dietary specialists, though neither of these traits significantly predicted occupancy trends. Overall, we highlight clear warning signs of species population declines, but with potential recovery opportunities if we take urgent actions to reverse those trends.

Monitoring biodiversity through time is critical for understanding the impacts of global change, yet the reliability of available time-series for estimating population trends remains poorly studied. We simulated population dynamics to study how monitoring duration, inter-annual variability, and temporal autocorrelation influence trend reliability. We then inferred the expected reliability of 16,450 empirical time-series of terrestrial animals based on those simulations. Longer, less variable, and negatively autocorrelated time-series produced more reliable trend estimates. Time-series shorter than 20 years led to large magnitude errors and low power in estimating population trends, despite generally low sign uncertainty. Overall, 80% of the empirical time-series were too short and/or noisy to identify meaningful temporal trends, yet reliability varies among taxa. Our results show important limitations in the available biodiversity data, stressing the need to sustain long-term monitoring to better estimate population trends, a top priority in the current biodiversity crisis.

Forecasting species population dynamics is a central challenge in computational ecology, yet existing approaches rarely combine flexible nonlinear modelling, support for count-based ecological data, and systematic uncertainty quantification within a single, scalable framework. Here we introduce reserBUGS, an open-source Python framework for ecological forecasting based on reservoir computing, a recurrent neural network architecture in which only a simple readout layer is trained while a fixed high-dimensional dynamical system encodes temporal memory and nonlinear dependencies. reserBUGS integrates species abundance time series with environmental covariates retrieved automatically from global climate products, generates probabilistic ensemble forecasts, and provides tools for forecast evaluation and reliability assessment. We evaluated reserBUGS using insect abundance time series from available biodiversity monitoring datasets, comparing its performance against seven statistical and machine-learning baselines over one- to five-year forecast horizons. Reservoir-based models consistently outperformed alternatives in both predicting future abundance and capturing forecast uncertainty, with environmental predictors increasing the proportion of stable forecasts and contributing additional predictive value beyond historical abundance dynamics alone, particularly at 3-4-year forecast horizons. Probabilistic forecasts further enabled the identification of conditions associated with reduced predictive skill, providing a practical basis for communicating forecast confidence to end users. While default configurations already achieved competitive performance across a taxonomically and geographically diverse set of time series, hyperparameter optimisation revealed substantial room for performance gains through series-specific tuning. reserBUGS offers a computationally efficient and extensible framework for ecological forecasting that is well suited to the short, heterogeneous time series typical of biodiversity monitoring programmes. Its combination of flexible nonlinear modelling, probabilistic uncertainty quantification, and automated environmental data integration addresses key practical barriers to the adoption of modern forecasting methods in conservation and ecological research.

Mutualistic interactions between species form complex networks that shape evolutionary and ecological processes. Traditionally, these networks have been studied at the species level, with species abundance and traits proposed as key structuring mechanisms. However, such approaches often fail to explain commonly observed network structural patterns, which have consequences for community stability and functioning. This limitation may stem from overlooking fine‐scale interactions between individuals, the actual network building blocks. By shifting the focus from species to individuals, here we aim to better understand the mechanisms shaping mutualistic networks using plant–pollinator assemblages. To do so, we combined field data on pollinator visitation to individual plants and agent‐based models (ABMs) within a Bayesian framework. We found that explaining a key network structural pattern, the pollinator community degree distributions, required accounting for species abundances, distinct pollinator behaviours, and plant spatial configurations. These factors determined the probability of interactions between individuals and altogether explained a substantial portion of the degree distribution variation even without including specific trait‐matching mechanisms. Nevertheless, our predictive ability varied across different flowering stages, highlighting that distinct mechanisms operate across time. By integrating empirical data with individual‐based modelling, we were able to capture details of interactions occurring in the real world, revealing how fine‐scale processes shape community‐wide network structure and offering new insights into the emergence of complex ecological networks.

Despite encompassing one of the richest bee faunas in Europe, the Iberian Peninsula lacks distributional data that often hampers effective conservation. This gap is particularly critical within protected areas such as the Doñana Natural Area, where biodiversity is expected to be increasingly vulnerable to environmental pressures. Here, we present the first checklist of the wild bees of Doñana. A total of 387 species belonging to 47 genera were recorded, including recently described species and a newly recorded species for continental Europe, Andrena purpurascens . The exceptional diversity of the area's bee fauna accounts for approximately one‐third of the known Iberian apifauna and supports about 15% of the European apifauna, highlighting its conservation importance at a continental scale. We provide information on habitat and floral use, offering key ecological insights for conservation planning. This knowledge is essential to support habitat management decisions, particularly in the face of intensifying climate change and water scarcity across the protected area. In addition, to evaluate long‐term ecological changes, we compared recent field data (2020–2021) with historical surveys conducted at the same site between 1984 and 1985. While a similar number of species were recorded in both periods (49 vs. 43 species), only 15 species were shared between the two. This large‐scale species observed turnover indicates the dynamic nature of a region undergoing significant environmental shifts. These findings underscore the importance of baseline biodiversity assessments and long‐term monitoring for understanding and mitigating pollinator dynamics in protected ecosystems, which are increasingly shaped by climate instability and anthropogenic pressures.

The decline in biodiversity, particularly in agricultural landscapes, is a pressing global challenge. Monitoring is vital to assess biodiversity trends and conservation effectiveness, yet farmland remains underinvestigated. Engaging farmers and encouraging naturalists to take on new roles could help expand monitoring efforts and strengthen connections to biodiversity. Framing biodiversity monitoring as a social practice rooted in naturalist communities, we consider how this practice is reconfigured when it enters a farming context. Drawing on case studies from Spain, Sweden and the United Kingdom, we examine processes aimed at involving farmers in pollinator monitoring on farmland, through three modes of participant involvement: farmer‐led, collaborative and naturalist‐led, focusing on motivations, experiences and perceived impacts of participation. A joint survey, capturing experiences, ensured comparability of social data across studies. While farmers were motivated to contribute to conservation and deepen their understanding of biodiversity, those engaging in the farmer‐led mode struggled with the skills and technologies required for monitoring. Farmers who instead received feedback from naturalists valued this but expressed that they lacked time and knowledge to carry out independent monitoring. This suggests that although farmers are eager to learn about biodiversity on their land, they are unlikely to adopt naturalist roles at scale. Naturalists were motivated by a desire to share knowledge, support farmland biodiversity and learn more about farming systems. In one new initiative, naturalists emphasised knowledge‐sharing but found building relationships with farmers difficult; in another more established monitoring network, they focused on expanding data coverage, acknowledging the time and effort required to engage meaningfully with farmers. These findings point to the importance of supportive structures to sustain farmer–naturalist collaboration. Our study shows that participation in biodiversity monitoring fosters understanding between farmers and naturalists and strengthens pro‐conservation attitudes among farmers. Monitoring becomes a learning process that reinforces farmers' openness to science and nature conservation and arms the collaboration with valuable local knowledge regarding land management. However, time constraints and steep learning curves remain key barriers for both groups. Unlocking the full potential of collaborative monitoring requires long‐term investment in locally embedded facilitation, adaptive technologies and relationship‐building. Read the free Plain Language Summary for this article on the Journal blog.

In a context where scientific communication is caught between the for-profit motives of large publishers and the need for democratized knowledge, the journal Ecosistemas reaffirms its commitment to a publication model that is, above all, by the scientific community and for the scientific community. We are not only consolidating our vision, but also taking firm steps toward a new stage of modernization and internationalization. In this edi-torial, we review the main new developments, analyze the peer review process, and announce the award for the best reviewer in Ecosistemas, the list of reviewers of the years 2024 and 2025, and we acknowledge the work of the guest editors who have led the monographs published in the journal during this period.

Understanding plant competitive outcomes, driven by niche and fitness differences, requires exploring these differences across simultaneous changes in abiotic and biotic conditions, such as soil properties, mutualisms and natural enemies. Here, we combined coexistence theory with detailed field observations in an annual grassland over two consecutive years and in silico simulations to assess changes in competitive outcomes, from coexistence to competitive exclusion, and from exclusion to priority effects. Our analysis shows that competitive exclusion is the most common outcome but with marked changes in the identity of the superior competitor under abiotic and biotic variation, highlighting the potential for regional coexistence. Herbivores were identified as the factor with the largest effect on the sensitivity of species pairs to shifts between coexistence and exclusion, although the effect size reflects sensitivity rather than the direction of change. Additionally, herbivores strongly influence the identity of the superior competitor, which can promote coexistence at larger spatial scales. Local coexistence was rarely predicted in our system. Finally, priority effects were mostly observed in the absence of a deterministic driver, as expected. Overall, our results suggest that while local coexistence is limited, species diversity can be maintained across larger spatial scales through shifts in competitive dominance mediated by multiple abiotic and biotic drivers.

Previous research has revealed how species traits determine species interactions, and how species interactions influence species coexistence. However, this hierarchical view ignores the coevolutionary feedback from species interactions to species traits and its consequences for species coexistence. Here, we developed a theoretical model of quantitative genetics to explore how the coevolution of morphological and phenological traits shapes the structure and stability of mutualistic interaction networks. We found that, in comparison to morphological traits, phenological traits led to distinctive species evolutionary trajectories, resulting in different emergent properties at the community level. This is because phenological traits promoted facilitation over competition. While morphological coevolution gave rise to modular and specialized interaction networks with a negative diversity-stability trade-off, phenological coevolution was required for the emergence of nested interaction networks that exhibited a positive relationship between diversity and structural stability. Empirical observations from 17 pollination networks were consistent with the theoretical results: we found many phenological motifs promoting facilitation over competition, suggesting an important role of phenological coevolution in community assembly. The seasonal organization of empirical interactions enhanced the community stability and dampened the diversity-stability trade-off that was observed when aggregating interactions across time. Our results highlight the importance of phenological coevolution in the emergence of diverse and stable communities.

Arthropods dominate global biodiversity and provide essential ecosystem services, yet they remain largely overlooked in conservation research and policy. The identification of areas of high biological diversity is key to guiding efficient conservation actions. We aimed to identify arthropod biodiversity hotspots and conservation priority areas in the Iberian Peninsula and Balearic Islands (a major European biodiversity hotspot under increasing anthropogenic pressures) using the first integrative, large‐scale assessment of arthropod distribution in the region, while also evaluating the effectiveness of the Natura 2000 network (N2000) in protecting these areas. We compiled over 416,000 occurrence records representing 6231 arthropod species from 13 complete taxonomic groups. We mapped species richness patterns and identified hotspots for each group, then delineated 40 Priority Areas for Iberobalear Arthropods (PAIA), defined as areas that were hotspots for at least three groups. Hotspot congruence across groups was low, with only one grid cell shared by seven taxa (Sierra Nevada), highlighting the importance of multi‐taxon approaches for conservation planning. PAIA were concentrated in major mountain systems (Central and Baetic Systems, Cantabrian Mountains and Pyrenees), reflecting their role as Mediterranean biodiversity centres and Pleistocene refugia. Collectively, the 40 PAIA (0.6% of the territory) hosted 3416 species (55% of the total). Most PAIA overlapped substantially with the N2000 (>90% for multi‐taxon hotspots), although some key areas remained marginally protected or unprotected. Our findings support N2000 as an effective foundation for arthropod conservation but emphasize the urgent need to explicitly integrate arthropods into its management plans. The PAIA identified here provide a robust baseline for safeguarding arthropod diversity and guiding the post‐2020 Global Biodiversity Framework.