Biology subjects

Bartomeus, I.

Publications and source records attributed to Bartomeus, I..

At least 19 recordsLinked to original sources

bioRxiv2026

Available time-series of terrestrial animals provide low reliability in population trend estimates

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.

ecology↗

bioRxiv2026

reserBUGS: A reservoir computing framework for probabilistic forecasting of ecological abundance time series

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.

bioinformatics↗

bioRxiv2025

Time-varying interactions drive species coexistence via rainfall variation

Species interactions are fundamental to maintaining diversity. However, it remains unclear how environmental variability modifies the structure, sign, and strength of species interactions that ultimately affect coexistence mechanisms. Here, we combined structural stability theory with time-varying population models to study the temporal variation of niche and fitness differences over nine years of annual plants in Mediterranean grasslands. Rainfall variation modulated species-specific responses in performance, and changed the magnitude of self-limitation and the variability in the strength of interspecific interactions for both competition and facilitation. These changes implied substantial variation in yearly niche and fitness differences, which ultimately impacted community stability. Additional simulations show that this full biological reorganization due to rainfall variation can maintain diversity by promoting temporal shifts in winners and losers compared to a single climatic year. Our results highlight the need to incorporate time-varying interactions to understand species coexistence under contrasting environmental conditions.

ecology↗

bioRxiv2025

Mapping wild bee diversity and flower use for an effective conservation of the Donana biodiversity hotspot

Despite the high bee species richness found in the Iberian Peninsula, the lack of distributional data often hampers effective conservation. This gap is particularly critical within protected areas such as the Donana Natural Area, where biodiversity is expected to be increasingly vulnerable to environmental pressures. Here, we present the first checklist of the wild bees of Donana. A total of 385 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 areas bee fauna accounts for approximately one-third of the known Iberian apifauna. 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 periods. This large-scale species 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 loss in protected ecosystems, which are increasingly shaped by climate instability and anthropogenic pressures.

ecology↗

bioRxiv2024

Integrating intraspecific variation into population dynamics reveals how interacting species persist in mutualistic communities

Mutualistic interactions among organisms are fundamental to the origin and maintenance of bio-diversity. Yet, the study of community dynamics often relies on species-level values, ignoring the effects of intraspecific variation. We propose a theoretical framework for evaluating the extent to which variation within populations can influence species persistence in mutualistic systems. Next, drawing from detailed empirical data on plant-pollinator interactions and plant fitness, we quantify intraspecific variation in the mutualistic benefits received by plants and incorporate this variation into estimations of structural stability, a robust theoretical measure of species likelihood of persistence. Through explicit consideration of intraspecific variation, we demonstrate that, in the absence of heterospecific plant competitors, plant populations composed of specialized individuals promote the persistence of the plant species they belong to and their associated pollinator community. However, these positive effects do not hold when plant species compete with a broader plant community. In this case, having more heterogeneous mixtures of plant individuals (i.e., both specialized and generalized) is more beneficial for a populations persistence. By integrating the proposed framework with empirical data, we are able to explicitly account for individual-level variation, opening the door to a better understanding of the mechanisms promoting biodiversity in mutualistic communities.

ecology↗

bioRxiv2024

Coevolution and temporal dynamics of species interactions shape species coexistence

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 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.

ecology↗

bioRxiv2023

Structural asymmetry in biotic interactions as a tool to understand and forecast ecological persistence

A universal feature of ecological systems is that species do not interact with others with the same sign and strength. Yet, the consequences of this asymmetry in biotic interactions for the short- and long-term persistence of individual species and entire communities remains unclear. Here, we develop a set of metrics to evaluate how asymmetric interactions among species translate to asymmetries in their individual vulnerability to extinction under changing environmental conditions. These metrics, which solve previous limitations of how to independently quantify the size from the shape of the so-called feasibility domain, provide rigorous advances to understand simultaneously why some species and communities present more opportunities to persist than others. We further demonstrate that our shape-related metrics are useful to predict short-term changes in species relative abundances during seven years in a Mediterranean grassland. Our approach is designed to be applied to any ecological system regardless of the number of species and type of interactions. With it, we show that is possible to obtain both mechanistic and predictive information on ecological persistence for individual species and entire communities, paving the way for a stronger integration of theoretical and empirical research.

ecology↗

bioRxiv2022

Plant spatial aggregation modulates the interplay between plant competition and pollinator attraction with contrasting outcomes of plant fitness

Ecosystem functions such as seed production are the result of a complex interplay between competitive plant-plant interactions and mutualistic pollinator-plant interactions. In this interplay, spatial plant aggregation could work in two different directions: it could increase intra- and interspecific competition, thus reducing seed production; but it could also attract pollinators increasing plant fitness. To shed light on how plant spatial arrangement modulates this balance, we conducted a field study in a Mediterranean annual grassland with three focal plant species with different phenology (Chamaemelum fuscatum (early phenology), Leontodon maroccanus (middle phenology) and Pulicaria paludosa (late phenology)) and a diverse guild of pollinators (flies, bees, beetles, and butterflies). All three species showed spatial aggregation of conspecific individuals. Additionally, we found that the two mechanisms were working simultaneously: crowded neighborhoods reduced individual seed production via plant-plant competition, but they also made individual plants more attractive for some pollinator guilds, increasing visitation rates and plant fitness. The balance between these two forces varied depending on the focal species and the spatial scale considered. Therefore, our results indicate that mutualistic interactions not always effectively compensate for competitive interactions in situations of spatial aggregation of flowering plants, at least in our study system. We highlight the importance of explicitly considering the spatial structure at different spatial scales of multitrophic interactions to better understand individual plant fitness and community dynamics.

ecology↗

bioRxiv2022

The non-random assembly of functional motifs in plant-pollinator networks

Ecological processes leave distinct structural imprints on the species interactions that shape the topology of mutualistic networks. Detecting those relationships is not trivial since they go beyond pairwise interactions, but may get blurred when considering full network descriptors. Recent work has shown that the network meso-scale can capture this important information. The meso-scale describes network subgraphs representing patterns of interactions between a small number of species (i.e., motifs) that constitute the building blocks of the whole network. Despite the possible implications of network motifs to better capture species interactions, they remain overlooked in natural plant-pollinator networks. By exploring 60 empirical plant-pollinator networks from 18 different studies with wide geographical coverage we show that some motifs are consistently under- or over-represented worldwide, suggesting that the building blocks of plant-pollinator networks are not random. Furthermore, we find that distinct motif positions describing species ecological roles (e.g., generalisation and number of indirect interactions) are occupied by different plant and floral visitor groups on both trophic levels. Bees appear less frequently in specialised motif positions with high number of indirect interactions, while the rest of floral visitor groups are infrequent in generalised motif positions with low number of indirect interactions. All plant groups tend to be over-represented on specialised motif positions, except tall plant species with separated sexes (e.g., woody dioecious or monoecious species), which are more frequent on motif positions with low number of indirect interactions. Interestingly, the realized combinations of different species groups within a motif can not be retrieved from their joint probability distributions, indicating that group combinations are not random either. Our result highlights the non-random structure of the meso-scale on plant-pollinators networks and the association of different plant and floral visitor groups with certain motifs that involve different ecological roles at a macro-ecological scale.

ecology↗

bioRxiv2021

Trade-offs among plant reproductive traits determine interactions with floral visitors

Plant life-history strategies are constrained by cost-benefit trade-offs that determine plant form and function. However, despite recent advances in the understanding of trade-offs for vegetative and physiological traits, little is known about plant reproductive economics and how they constrain plant life-history strategies and shape interactions with floral visitors. Here, we investigate plant reproductive trade-offs and how these drive interactions with floral visitors using a dataset of 17 reproductive traits for 1,506 plant species from 28 plant-pollinator studies across 18 countries. We tested whether a plants reproductive strategy predicts its interactions with floral visitors and if the different reproductive traits predict the plants role within the pollination network. We found that over half of all plant reproductive trait variation was explained by two independent axes that encompassed plant form and function. Specifically, the first axis indicated the presence of a trade-off between flower number and flower size, while the second axis indicated a pollinator dependency trade-off. Plant reproductive trade-offs helped explain partly the presence or absence of interactions with floral visitors, but not differences in visitation rate. However, we did find important differences in the interaction level among floral visitor guilds on the different axes of trait variation. Finally, we found that plant size and floral rewards were the most important traits in the understanding of the plant species network role. Our results highlight the importance of plant reproductive trade-offs in determining plant life-history strategies and plant-pollinator interactions in a global context.

ecology↗

bioRxiv2021

The non-random structure of multi-trophic ecological interactions maximizes species coexistence within ecologically realistic constraints

Theory posits that the persistence of species in ecological communities is shaped by their interactions within and across trophic guilds. However, we lack empirical evaluations of how the structure, strength, and sign of biotic interactions drive the potential to coexist in diverse multi-trophic communities. Here we model community feasibility domains, a theoretically-informed measure of multi-species coexistence probability, from grassland communities comprising more than 45 species on average from three trophic guilds (plants, pollinators, and herbivores). Contrary to our hypothesis, increasing community complexity, measured either as the number of guilds or community richness, did not decrease community feasibility. Rather, we observed that high degrees of species self-regulation and niche partitioning allow maintaining larger levels of community feasibility and higher species persistence in more diverse communities. Our results show that biotic interactions within and across guilds are not random in nature and both structures significantly contribute to maintaining multi-trophic diversity.

ecology↗

bioRxiv2021

Multitrophic higher-order interactions modulate species persistence

There is growing recognition that interactions between species pairs are modified in a multispecies context by the density of a third species. However, how these higher-order interactions (HOIs) affect species persistence remains poorly understood. To explore the effect of HOIs steaming from multiple trophic layers on plant persistence, we experimentally built a mutualistic system containing three plants and three pollinators species with two contrasting network structures. For both structures, we first estimated the statistically supported HOIs on plant species, in addition to the pairwise interactions among plants and plant-pollinators. Following a structuralist approach, we then assessed the effects of the supported HOIs on the persistence probability of each of the three competing plant species and their combinations. HOIs produced substantial effects on the strength and sign of per capita interactions between plant species to such an extent that predictions of species persistence differ from a non-HOIs scenario. Changes in network structure due to removing a plant-pollinator link further modulated the species persistence probabilities by reorganizing per capita interaction strengths of both pairwise interactions and HOIs. Our study provides empirical evidence of the joint importance of HOIs and network structure for determining the probability of species to persist within diverse communities.

ecology↗

bioRxiv2021

Towards a system-level causative knowledge of pollinator communities

Pollination plays a central role both in the maintenance of biodiversity and in crop production. However, habitat loss, pesticides, invasive species, and larger environmental fluctuations are contributing to a dramatic decline of numerous pollinators world-wide. This has increased the need for interventions to protect the composition, functioning, and dynamics of pollinator communities. Yet, how to make these interventions successful at the system level remains extremely challenging due to the complex nature of species interactions and the various unknown or unmeasured confounding ecological factors. Here, we propose that this knowledge can be derived by following a probabilistic causal analysis of pollinator communities. This analysis implies the inference of interventional expectations from the integration of observational and synthetic data. We propose that such synthetic data can be generated using theoretical models that can enable the tractability and scalability of unseen confounding ecological factors affecting the behavior of pollinator communities. We discuss a road map for how this probabilistic causal analysis can be accomplished to increase our system-level causative knowledge of natural communities.

ecology↗

bioRxiv2021

Community level individual-based plant-pollinator networks unveil pollen flow dynamics and plant reproductive success

Interaction networks are a widely used tool to understand the dynamics of plant-pollinator ecological communities. However, while most mutualistic networks have been defined at the species level, ecological processes such as pollination take place at different scales, including the individual or patch levels. Yet, current approaches studying fine-grain sub-specific plant-pollinator networks only account for interactions among nodes belonging to a single plant species due to the conceptual and mathematical limitations of modeling simultaneously several plant species each composed of several nodes. Here, we introduce a multilayer diffusion network framework that allows modeling simple diffusion processes between nodes pertaining to the same or different layers (i.e. species). It is designed to depict from the network structure the potential conspecific and heterospecific pollen flows among plant individuals or patches. This potential pollen flow is modeled as a transport-like system, in which pollen grain movements are represented as random-walkers that diffuse on an ensemble of bipartite layers of conspecific plants and their shared pollinators. We exemplify this physical conceptualization using a dataset of nine fine-grain sub-specific plant-pollinator networks from a Mediterranean grassland of annual plants, where plant nodes represent groups of conspecifics within patches of 1m2. The diffusion networks show pollinators effectively connecting sets of patches of the same and different plant species, forming a modular structure. Interestingly, different properties of the network structure, such as the conspecific pollen arrival probability and the number of conspecific subgraphs in which plants are embedded, are critical for the seed production of different plant species. We provide a simple but robust set of metrics to calculate potential pollen flow and scale down network ecology to functioning properties at the individual or patch level, where most ecological processes take place, hence moving forward the description and interpretation of species-rich communities across scales.

ecology↗

bioRxiv2021

Species-area relationships emerge from multiple coexistence mechanisms

The increase of species richness with area is a universal phenomenon on Earth. How-ever, this observation contrasts with our poor understanding of how these species-area relationships (SARs) emerge from the collective effects of area, spatial heterogeneity, and local interactions. By combining a structuralist approach with five years of empirical observations in a highly-diverse Mediterranean grassland, we show that spatial hetero-geneity plays a little role in the accumulation of species richness with area in our system. Instead, as we increase the sampled area more species combinations are realized, and they coexist mainly due to direct pairwise interactions rather than by changes in single-species dominance or by indirect interactions. We also identify a small set of transient species with small population sizes that are consistently found across spatial scales. These findings empirically support the importance of the architecture of species interactions together with stochastic events for driving coexistence- and species-area relationships.

ecology↗

bioRxiv2021

Fine scale prediction of ecological community composition using a two-step sequential machine learning ensemble

Prediction is one the last frontiers in ecology. Indeed, predicting fine scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models informed by ecological deterministic processes to predict species abundances using reasonably easy to obtain data. To overcome the classical procedure in ecology of parameterizing complex population models of multiple species interactions and poor predictive power, we followed instead a sequential data-driven modeling approach. We use this framework to predict species abundances over 5 years in a highly diverse annual plant community. Our models show a surprisingly high spatial predictive accuracy (RSE ~ 0.13) using only easy to measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggest that predicting the temporal dimension of our system requires longer time series data. Such data would likely capture additional sources of variability that determine temporal patterns of species abundances. In addition, we show that these data-driven models can also inform back mechanistic models of important missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Being able to gain predictive power at fine-scale species composition while maintaining a mechanistic understanding of the underlying processes can be a pivotal tool for conservation, specially given the human induced rapid environmental changes we are experiencing. Here, we document how this objective can be achieved by promoting the interplay between classic modelling approaches in ecology and recently developed data-driven models. Author summaryPrediction is challenging but recently developed machine learning techniques allow to dramatically improve prediction accuracy in several domains. However, these tools are often of little application in ecology due to the complexity of gathering information on the needed explanatory variables, which often comprise not only physical variables such as temperature or soil nutrients, but also information about the complex network of species interactions regulating species abundances. Here we present a two-step sequential modelling framework that overcomes these constraints. We first infer potential species abundances training models just with easily obtained abiotic variables, and then use this outcome to fine-tune the prediction of the realized species abundances when taking into account the rest of the predicted species in the community. Overall, our results show a promising way forward for fine scale prediction in ecology.

ecology↗

bioRxiv2020

Phenotypic and microsite plant individual variation determine the pollination network structure and its functional consequences

The biotic and abiotic context of individual plants in animal-pollinated plant populations may influence pollinator foraging behaviour and therefore how the pollen flow occurs. Thus, this variation among conspecifics within a given plant population can ultimately influence the plant reproductive success. Here we used a fine-scale, well characterized population of Halimium halimifolium in combination with exponential random graph models (ERGMs) to assess how the intrapopulation variation in intrinsic (i.e. phenotype and phenology) and extrinsic (i.e. microsite) plant attributes configures individual plant-pollinator networks and its functional consequences. We found that pollinator visitation patterns and the emerging network configuration were associated with both intrinsic and extrinsic plant attributes, such as the number of flowers, the flowering synchrony and the cover of intraspecific and interspecific neighbours. Both intrinsic and extrinsic plant attributes also affected the plant female reproductive success directly and indirectly - through its effects on the probability of conspecifics plants to share pollinators. Our study opens up new approaches to assess and predict the functional consequences of context-dependency in plant-pollinator interactions, especially under global change scenarios, where the ecological context of individual plants is likely to change.

ecology↗

bioRxiv2020

Brain size predicts learning abilities in bees

A large brain is widely considered a distinctive feature of intelligence, a notion that mostly derives from studies in mammals. However, studies in insects demonstrates that cognitively sophisticated processes, such as social learning and tool use, are still possible with very small brains. Even after accounting for the allometric effect of body size, substantial variation in brain size still remains unexplained. A plausible advantage of a disproportionately larger brain might be an enhanced ability to learn new behaviors to cope with novel or complex challenges. While this hypothesis has received ample support from studies in birds and mammals, similar evidence is not available for small-brained animals like insects. Our objective is to compare the learning abilities of different bee species with their brain size investment. We conducted an experiment in which field-collected individuals had to associate an unconditioned stimulus (sucrose), with a conditioned stimulus (colored strip). We show that the probability of learning the reward-colour association was related to both absolute and relative brain size. This study shows that other bee species aside from the long studied honeybees and bumblebees, can be used in cognitive experiments and opens the door to explore the importance of relative brain sizes in cognitive tasks for insects and its consequences for species survival in a changing world.

animal behavior and cognition↗