Ecological Modelling

Given the accumulation of evidence that evolution can affect ecological dynamics, especially under global change scenarios, a key question is how such ecoevolutionary dynamics may change the coexistence of species and biodiversity in general. In the present article, we propose a graphical approach allowing to simultaneously discuss ecological coexistence and phenotype evolution. Our graphical approach allows tackling the two aspects in the same parameter space, allowing direct links between ecological and evolutionary perspectives. While evolution is often thought positive for the resilience of ecological systems, we first highlight it does not usually allow for better coexistence for the system as a whole. Even when focusing on the fate of the species that is evolving, evolution often leads to greater vulnerability. The graphical approach we propose is flexible and can be applied to all interaction types and covers variations in trade-off structures. Using this flexibility, we highlight how evolutionary effects can be positive or negative for coexistence, depending on these two components. Finally, we illustrate how the approach can be applied, using empirical examples derived from the literature. We thereby highlight the critical ingredients needed to inform the graphical approach, its potential use for proposing testable scenarios, but also clarify its limits.

Successful seagrass restoration will provide habitat for a variety of species. Here, ecological community assembly in a newly planted seagrass meadow has been modelled mathematically using a combination of numerical integration and a permanence-based method, and using real data to parametrise the models. We have studied the transient dynamics of the system: how the ecological communities assemble and change over a 100-year period. Using a trophic structure and a range of species pool sizes, we investigated how much variability there was in community size for a given sized species pool, whether it is possible to use early monitoring to predict the final community size, and to what extent monitoring gives an indication of final vs transient species. For the majority of cases modelled, the community either reached or was headed towards an endpoint community which was uniquely determined by the species pool. However, for 1.4% of cases, no unique endpoint community could be calculated. The simulated communities began to assemble within the first ten years, but 13% had still not reached their endpoint community even after 100 years. In 62% of our models, no consumer species colonised in the first two years, suggesting that monitoring should certainly be continued beyond a two-year period. We counted how many of the species that were present at any observation point in the 100 years would also be present in the endpoint community, and found that this proportion generally decreased with increasing species pool size, to an average of 86% when the species pool had 49-56 consumer species. By monitoring the community over the first ten years, it is not possible to deduce what the final community will be; however a very small number of fauna species present over the first ten years might be used to predict very small endpoint communities.

Habitat fragmentation, driven by human activities, disrupts habitat connectivity and alters ecological processes through geometric and demographic fragmentation effects. Dispersal plays a fundamental role in shaping the distribution, abundance, and persistence of species in modified landscapes. While previous research looked at the evolution of dispersal strategies at the species level, community-level dynamics remain underexplored. Species exhibit diverse dispersal strategies to persist in modified landscapes, yet predicting how these strategies interact at the community level requires a more integrated approach. This study employed an individual-based simulation model to explore how fragmentation and other landscape characteristics influence community-level dispersal strategies. We tested the effects of varying fragmentation levels, environmental autocorrelation, habitat amount, and disturbance levels on the emerging distribution of dispersal distances within a community in modified and continuous landscapes. We hypothesised that fragmentation and other spatial patterns would significantly shape community composition, favouring particular dispersal strategies under specific environmental conditions. The findings reveal that higher disturbance levels and greater habitat amount increased the community-weighted mean of dispersal distance, while fragmentation showed only minor variation. Additionally, low autocorrelation was associated with the highest community-weighted mean of dispersal distance. These results highlight the importance of considering community-level dynamics when predicting ecosystem responses to landscape modification. By clarifying how landscape structure and disturbance shape community-level dispersal strategies, this study advances our understanding of the mechanisms underlying species persistence and community structure in modified landscapes.

Establishing protected areas is a promising tool to address the accelerating loss of biodiversity. However, protection levels are often low, and there is an ongoing debate over the most effective spatial configuration of reserves. This debate rarely considers trophic structure and ignores biodiversity outside protected areas. In this study, we investigate which reserve configurations best support species diversity and the persistence of high trophic levels, across systems and spatial scales, both inside and outside protected areas. Using a spatially explicit stochastic model, we assess how reserve architecture influences multiple conservation objectives across 27 empirical terrestrial, freshwater, and marine food webs. Specifically, we explore reserve architecture along three dimensions: the aggregation of protected areas, their proportion at the landscape scale, and the effectiveness level of protection measures. Our results show that having few but larger protected areas enhances all conservation metrics within reserves, while diversity within and outside reserves is relatively insensitive to reserve aggregation. Smaller and more dispersed reserves improve the overall abundance of species off-reserves through spillover effects. Reconciling all objectives inside and outside reserves becomes feasible when protection effectiveness is sufficiently high. Increasing the efficiency of protection allows for a reduction in the total amount of protected land without compromising conservation outcomes. Moreover, higher species dispersal facilitates the achievement of multiple conservation goals, supporting the implementation of architectures that enhance connectivity among reserves. These findings highlight the importance of an integrated approach combining spatial ecology and trophic functioning to optimize protected area planning under multiple objectives.

Using linear inverse ecosystem modeling as a data assimilation tool, we compare spawning grounds of Atlantic and Southern Bluefin Tuna (ABT and SBT, respectively) based on results from field campaigns in the Gulf of Mexico (GoM) and eastern Indian Ocean off northwest Australia (Argo Basin). Both regions are warm, stratified, low-nutrient waters dominated by cyanobacteria (Prochlorococcus). Despite these similarities, the Argo Basin is more productive, with [~]1.5X higher net primary production and nearly 2X higher production of top trophic levels in the model (tuna larvae, planktivorous fish, and predatory gelatinous zooplankton). Higher primary production in the Argo Basin is mainly driven by higher N2 fixation and storm mixing of new nutrients in the upper and lower euphotic zone, respectively. Increased ecosystem efficiency (secondary production of top trophic levels / primary production) results from differences in plankton food web organization. In the GoM, protistan zooplankton are the direct consumers of nearly all phytoplankton production. In contrast, higher rates of herbivory by crustaceans feeding on nanophytoplankton combines with a higher impact of appendicularians on cyanobacteria to convert plankton production into larval tuna prey more efficiently in the Argo Basin. Despite similarities in the proportions of phytoplankton production mediated by cyanobacteria and other picoplankton in both systems, food web pathways to larval tuna and other planktivorous fish are substantially shorter in the Argo Basin. Our results highlight the impact of distinct zooplankton ecological niches on ecosystem efficiency and suggest a need for better inclusion of plankton food-web structure in models simulating climate impacts on fisheries production. HIGHLIGHTSO_LIDeveloped food web models of tuna spawning habitat (Indian Ocean & Gulf of Mexico) C_LIO_LISpawning habitats in the Argo Basin and Gulf of Mexico (GoM) are both oligotrophic C_LIO_LIArgo Basin had higher net primary production in part as a result of nitrogen fixation C_LIO_LIArgo Basin had higher rates of direct herbivory by metazoan zooplankton C_LIO_LIThis resulted in greater ecosystem efficiency in the Argo Basin. C_LI

Climate change is dramatically affecting species distribution and phenology worldwide. Its impact on arthropod vectors, such as the Aedes albopictus mosquito, has important consequences for biting nuisance and arbovirus transmission risk. Here, we assess the impact of climate change on the presence and abundance of Ae. albopictus, as well as the risk of dengue transmission over France during the 21st century. We use a mechanistic model that we adjusted against records of recent autochthonous cases of dengue in France. We simulate climatic suitability indicators, such as the adult abundance during the activity period, epidemic risk and secondary cases of dengue under different climatic and demographic scenarios at different periods up to 2085. Future simulations are based on a high-pressure scenario (high greenhouse gas emissions, high demographic growth) and a median-pressure scenario (median greenhouse gas emissions, demographic stagnation). To account for climate model uncertainty, we repeat the simulations for three different regional climate models. By 2085, in the high-pressure scenario, most of France (89-96%) will be climatically suitable for the establishment of Ae. albopictus, with the exception of mountain ranges. Similarly, autochthonous transmission of dengue will be theoretically possible in all colonized areas except over northern lowlands (71-95%). In the median pressure scenario, both climatic suitability for establishment (49-89%) and autochthonous dengue transmission risk (31-82%) exhibit large variation. Low population density areas show moderate suitability for vector establishment but exhibit the highest potential for dengue transmission. Overwintering mechanisms, such as egg diapause, indispensable for survival in temperate climates, may not be necessary along the Mediterranean and Basque coasts, allowing activity of the vector all year-round in the future.

Phase synchronized population dynamics of various species constituting a complex ecosystem elevates the risk of their extinction due to both environmental stochasticity and simulateneous low density fluctuations. Therefore, an extremely vital approach to measure the extinction risk of an ecosystem as a whole is to quantify the phase synchrony among the species populations co-habiting and interacting with each other in an ecosystem. Generally, in models describing population dynamics of ecosystems, both trophic and non-trophic inter-species interactions are modelled as interactions between two species. This approach contradicts the fact with such a large number of species living in close proximity, more than two species must partake in the same interaction influencing the population dynamics of each other. To address this, higher-order interactions need to be incorporated in the models describing population dynamics of an ecosystem. Consequently, their effect on phase synchronization of populations also need to be investigated. In this study, we model a species-rich ecosystem as a complex phase oscillator network and examine the phase dynamics of the total population. Each node of this network represents a constituent species, modelled as a Sakugachi-Kuramoto phase oscillator coupled non-linearly to the other nodes through both first-order and higher-order inter-species interactions. These interactions can be both mutualistic (positive) and antagonistic (negative) in nature. Along with the higher-order interactions, we also incorporate inherent asymmetry among the nodes to account for habitat heterogeneity. Further, we investigate the effects of both higher-order coupling and asymmetry on the phase synchronization of the total population. Our findings demonstrate that higher-order interactions above a threshold amplitude enforces a transition from synchronous to asynchronous dynamics of the ecosystem. Further, we find that increase in the size and diversity of the ecosystem leads to an increase in the threshold value of higher order coupling required to reach asynchronous dynamics. We also demonstrate that this higher-order induced asynchrony is further promoted by high asymmetry among the individual nodes. Importantly, negative inter-species interactions, if existing to a high degree also induce asynchrony in the system. Moreover, the size of the network also plays a role in deciding the threshold value of higher order coupling required to induce asynchrony.

The influence of environmental factors on the dynamics of living organisms can imply non-linear relationships. Some of them exhibit threshold effects. Hyperbolic functions effectively represent ecological processes that display threshold behaviours, such as those described by the law of the minimum, or law of the limiting factor. However, the mathematical formulation of the hyperbola is complex, which makes its use challenging and its parameters difficult to interpret. In this article, we propose an efficient mathematical formulation for the hyperbola, one in which all the parameters are independent and easily interpretable. We also provide an R script and a Python script to facilitate the implementation of this hyperbolic formulation in modelling studies. We then used this new hyperbolic function to model the influence of edaphic and climatic factors on the growth of 18 forest tree species widely distributed across Europe based on a dataset of 8,330 plots from the French National Forest Inventory. Our hyperbolic function allowed us to identify the threshold effects of summer climatic constraints on forest growth for several species. In particular, we found negative effects for soil water deficit and maximum summer temperature, although for several species these effects only appear beyond a certain level of constraint. Accounting for such threshold effects is crucial to improve our ability to understand and predict forest ecosystem responses in the context of climate change.

Wind has a strong influence on the flight characteristics, movements, energetics, demography, life-history traits and biogeography of flying animals. With climate change affecting atmospheric circulation patterns at different time scales, understanding the links between wind and animal movements is crucial for predicting its impact on flying biodiversity. Most studies on the relationship between wind and seabird movements have, however, focused on local scales, exploring birds perceptive sensitivity to local wind. In this study, we examine low-level wind pattern oscillations in the Southern Indian Ocean at multiple time scales to explain the local- to large-scale movements of the Amsterdam albatross. Adult individuals exhibited smooth trajectories, strongly correlated with seasonal, intra-seasonal or interannual wind oscillations. Conversely, younger individuals displayed more erratic and exploratory movements, often being swept away by eastward moving low-pressure systems at a synoptic time scale. Our results suggest that Amsterdam albatrosses can learn and adapt to the annual and monthly low-level wind climatology and interannual variability of the Southern Indian Ocean. This also highlights the importance of investigating seabird movements in relation to broader-scale wind patterns to support their conservation in a changing climate due to human activities. A robust assessment of regional circulation response to climate change for upcoming decades could help project the impact of climate change on seabird movements and mitigate its effects.

We developed an agent-based model (ABM) to assess how area-based controls on fishing methods can reduce fishing mortality and population declines. The model incorporates the behavior and distributions of dolphins and fishing vessels, and realistic displacement of fishing effort when protection is extended. Our case study is New Zealand dolphin - Hectors and Maui dolphins. The model was designed and calibrated using pattern-oriented modeling. Our results show that mortality due to entanglement in fishing gears has been reduced thanks to a gradual increase in dolphin protection. However, current protection is not as effective as previously thought, and scarce populations are negatively affected by Allee effects. Neither national nor international goals for reducing bycatch are met by current dolphin protection. The IUCN has recommended banning gillnet and trawl fisheries in New Zealand waters < 100m deep. For most New Zealand dolphin populations, this would be effective in achieving national and international goals for reducing bycatch. Only two populations would require additional protection. This modelling approach is also suitable for assessing impacts of bycatch and ship strikes for other marine species, making it suitable for informing management decisions in many regions.

O_LIMaking informed conservation decisions under climate change is a challenging task for practitioners, since decisions depend on changing environmental conditions and uncertain ecosystem responses to climate change. Given such uncertainties, the best practice to manage natural systems is adaptive management, where decisions dynamically adapt to the response of the ecosystem to previous conservation actions. Although adaptive approaches are optimal, they are also difficult to implement, have high computational costs, and recommend strategies that can be complex to interpret. These factors can hinder their on-ground application. On the other hand, simpler but suboptimal decision models can result in more interpretable recommendations, and might still yield good outcomes for ecosystems. Exploring trade-offs between complex optimal solutions and simpler sub-optimal solutions is essential for maximising conservation impact. C_LIO_LIIn this manuscript, we use value of information theory to help managers simplify their decision-models, while balancing optimality of strategies. Our approach provides modelling recommendations by determining the benefits of modelling non-stationary ecosystem dynamics and the uncertain ecosystem response to climate change. We illustrate our approach on four scenarios inspired from the management of the Great Barrier Reef, Australia, under different climate change trajectories. C_LIO_LIWe find that the two main drivers of the recommended reduction in model complexity are the strength of non-stationarity (e.g. climate change trajectory) and the degree of uncertainty in ecosystem responses to climate change (e.g. uncertainty in the thermal resistance of a coral reef). When non-stationarity is weak, the decision problem can be reduced from a non-stationary to a stationary formulation. Similarly, when uncertainty in the response to climate change is low, this uncertainty can be safely ignored in the decision-making process. Conversely, when non-stationarity is strong and/or uncertainty is high, our approach justifies the need to account for these complexities when making decisions, as simpler approaches would yield poor outcomes. C_LIO_LIThis manuscript guides managers in simplifying a modelling approach to manage ecosystems in the face of climate change. Our protocol can help simplify complex decision problems, allowing to reduce computational costs and enhance interpretability. By finding the balance between simplicity and optimality of models, this work contributes to bridging the gap between complex modelling and on-ground applications. C_LI

The hockey-stick (HS) stock recruitment relationship (SRR) has been widely used as an empirical alternative to conventional SRRs such as the Beverton-Holt (BH) and Ricker (RI) models. However, the management performance and risks associated with estimating maximum-sustainable-yield (MSY) reference points (RPs) based on HS remain insufficiently understood. This study first defines deterministic and stochastic MSY RPs under the HS model and provides an overview of their properties. We then conduct simulation experiments to investigate the bias and management consequences that arise when MSY RPs are estimated from the HS model (HS-derived MSY RPs) rather than from the true SRR (e.g., BH) across a range of biological and stochastic parameters, with particular focus on scenarios with insufficient data contrast. Our results show that HS-derived MSY RPs tend to exhibit higher bias but lower variance than MSY RPs derived from the true SRR. Management strategy evaluation simulations further reveal that management procedures combining HS-derived MSY RPs with adaptive model learning and some precautionary measures gradually reduce this bias and achieve average spawning biomass and yield that are comparable to those obtained under management based on the true BH SRR. We also show that the management effectiveness of the precautionary measures depends on life-history traits and recruitment variability. These findings indicate that although HS-derived MSY RPs may be biased and require cautious use, combining them with appropriate precautionary measures allows management to remain robust while limiting variability and yield losses. This broadens the range of management options that are available for supporting sustainable fisheries management.

Fuel availability and fuel dryness are consistently shown to be the primary drivers of wildfire intensity and burnt area. Here we hypothesise that differences in the timing of fuel build up and drying determine the optimal time for wildfire occurrence. We use gross primary production (GPP) as a measure of biomass production and hence fuel availability, and vapour pressure deficit (VPD) as a measure of fuel drying. We use the phase difference in the seasonal time course and magnitude of GPP and VPD to cluster regions that should therefore have distinct wildfire behaviour. We then show that each of the resultant clusters is distinctive in terms of one or more fire properties, specifically number of ignitions, burnt area, size, speed, duration, intensity, and length of the wildfire season. The emergence of distinct regimes as a function of two biophysical drivers reflects the fact that both vegetation and wildfire properties are a consequence of eco-evolutionary adaptions to environmental conditions. We then examine the degree to which human activities or vegetation properties modify these fire regimes within each of these clusters. Variability in GPP and VPD largely explains the within-cluster variation in fire properties. The type of vegetation cover has an influence on burnt area and carbon emissions in particular, while human activities are more important for fire properties such as size, rate of spread and duration largely through their influence of landscape fragmentation. Although both human activities and vegetation properties modify wildfire regimes, the ability to distinguish wildfire regimes using GPP and VPD alone emphasizes that land management, fire use and fire suppression are constrained by environmental conditions. This eco-evolutionary optimality approach to characterising wildfire regimes provides a basis for designing a simple fire model for Earth System modelling.

Mediterranean Basin, one of the most important hot spots for reptiles, is also expected to experience significant impacts with climate change, posing a severe risk for the herpetofauna of the region. This study uses the snake-eyed lizard Ophisops elegans as a model organism to investigate the potential impacts of past and future climate change on reptile distributions in the region. An ecological niche model (ENM) was developed with the Maxent algorithm, with location points from GBIF and bioclimatic variables from the CHELSA dataset, then projected onto past LGM ([~]21 kya) and future (2071-2100 SSP3-7.0 and SSP5-8.5) scenarios. Results show that the present-day distribution of O. elegans is primarily driven by temperature seasonality and precipitation, indicating a preference for coastal Mediterranean climates with dry summers. The LGM projection suggests a fragmented and contracted range, confined to coastal refugia around the Mediterranean and Caspian Seas. Future projections for 2071-2100 show consistent and alarming contraction of suitable habitats under both SSP scenarios. In conclusion these findings indicate that O. elegans is vulnerable to significant habitat loss under projected climate change. This severe impact on a wide-spread species implies that the herpetofauna of the Mediterranean Basin may face a significant threat in future.

Bat populations experience inter-annual variation in demographic rates in response to environmental conditions. This variation has the potential to impact population sizes and structures, in addition to population-level processes such as disease spread. To establish the influence of variation in demography on these processes, we develop a spatial, individual based model of a serotine bat (Eptesicus serotinus) population, within which we introduce a synthetic lyssavirus-like disease. Model results show that increasing demographic variation, particularly in survival rates, may drive substantial population decline in bat populations. Increasing environmental fluctuations driven by climate change may therefore be problematic for population persistence. The likelihood of disease persistence was also reduced by increasing variation. These findings highlight the limitations of only considering mean demographic rates for prediction of population size change and disease dynamics from models.

O_LIClimate change is reshaping agriculture through both gradual shifts and increasingly unpredictable extremes. Plants cope using developmental plasticity and bet-hedging, but it is unclear how these biological strategies align with the ways farmers perceive and respond to climate risks. This study investigates: (1) whether farmers understand climate change as incremental trends or recurrent shocks, (2) how their adaptations parallel plant plasticity and bet-hedging, and (3) under which climate scenarios these adaptations best support yield stability. C_LIO_LIWe combined qualitative research and modelling by conducting fifty semi-structured interviews with farmers, agricultural associations and public administrators across three climatically distinct Italian regions, and by developing an agent-based stochastic simulation that represents farmer-like plasticity (delayed sowing) and bet-hedging (staggered sowing) under drought and flood scenarios. C_LIO_LIFarmers described climate change as both gradual transformation and intensifying volatility. Their adaptive responses - adjusting calendars, switching crops and diversifying production - closely aligned with plant strategies, though articulated in practical rather than scientific terms. Simulation results showed that plasticity enhanced yields under systematic shifts in conditions, whereas bet-hedging reduced losses in highly variable climates characterised by frequent transitions between extremes. C_LIO_LITogether, the qualitative and modelling findings demonstrate that plant and farmer adaptation logics converge in complementary ways. Plasticity supports performance under gradual change, while bet-hedging buffers unpredictability. These insights highlight the potential for co-designed tools that link plant traits, farmer decision-making and ecological risk, strengthening climate-resilient agricultural planning and improving communication between farmers, breeders and plant scientists. C_LI Societal Impact StatementClimate change is transforming agriculture through both gradual shifts and increasingly unpredictable extremes, challenging farmers ability to protect crops and livelihoods. This study brings together farmer experiences and plant adaptation strategies to explore how people and plants respond to similar climate pressures. By showing that farmers practices mirror plant plasticity and bet-hedging, our findings highlight opportunities to design climate-resilient agriculture that aligns biological traits with real-world decision-making. This work can inform plant breeders, extension services and policymakers seeking to support farmers through clearer communication, better risk-management tools and more adaptable crop varieties, ultimately strengthening resilience in food systems.

Making robust and generalizable predictions within ecological systems such as forests remains challenging due to limited data availability and the slow pace of environmental change. To address this, we integrate a semi-empirical environmental process model (PRELES) to support deep learning approaches, specifically artificial neural networks (ANNs). We replicate and extend previous work on process-guided neural networks (PGNN) by introducing new model types and conducting a comprehensive hyperparameter optimisation within systematic nested cross-validation analyses in both data-thinning and extrapolative scenarios. Results show that both data-driven ANNs and PGNNs consistently outperform the stand-alone process model, while PGNNs provide additional advantages over ANNs in data-sparse settings and under transfer scenarios to unseen, changing climatic conditions. We further estimate the generalisation error for data-driven models as a function of the amount of training data, allowing for guidance on model suitability under different data availability. A variable importance analysis using accumulated local effects reveals that both PGNNs and ANNs learn simple, physically plausible relationships, whereas PRELES exhibits a strong bias toward boreal conditions and limited ability to predict unseen, climatically divergent sites. HighlightsO_LIProcess-guided, and plain neural networks outperform a calibrated process-based model (PRELES) in predicting forest ecosystem carbon fluxes. C_LIO_LIProcess-guided neural networks provide advantages over naive neural networks in sparse-data settings and show greater robustness under transferable scenarios with unseen changing climatic conditions. C_LIO_LIVariable-importance analyses using accumulated local effects show that both process-guided and naive neural networks learn simple yet physically plausible relationships between meteorological drivers and target responses, whereas the process model (PRELES) exhibits a better fit toward boreal conditions and limited ability to predict unseen, climatically divergent sites. C_LI

Ecological regime shifts are potentially a common property of ecosystems, describing transitions between alternative stable states that can represent healthy or unhealthy conditions under the same environmental drivers. Once a tipping point, defined as a critical threshold separating alternative stable states, is crossed, the system may degrade and recovery can be difficult, making early detection essential for effective ecosystem management. Predicting these tipping points requires models that exhibit bistability, representing systems that can exist in two alternative stable states under identical environmental conditions. A key question is whether standard ecological monitoring data can be used to identify bistability and accurately estimate tipping points. Using the Carpenter model of lake eutrophication, which expresses bistability between clear and polluted water states, we generate synthetic data under known stability regimes. Profile likelihood analysis is then applied to assess parameter identifiability and detect system stability and tipping points. Our results show that standard monitoring data do not always provide sufficient information to distinguish bistable from stable regimes. Importantly, bistability and tipping points become practically identifiable only when data are collected very close to the tipping point.

O_LISpecies diversity potentially has a dual effect on communities: a generally positive effect on overall community biomass, reflecting the expression of species response and interaction traits, and a poorly characterised effect on mass-specific species contribution to ecosystem functions, reflecting the expression of their effect traits. Disentangling the effects of biodiversity on total biomass from those on effect trait expression would help settle a long-standing debate by clarifying how biodiversity relates to both facets of species effects on ecosystem functioning. C_LIO_LIFollowing the classical BEF approach, we calculate expected ecosystem function based on observed functioning in monoculture. We then derive a net biodiversity effect (NBE) and decompose it into four components: the classical complementarity and selection effects on total community biomass, and complementarity and selection effects on effect trait expression. The latter two reflect, respectively, a complementarity or facilitation in how effect traits influence the function, and how species with the highest potential for increasing the function become dominant in the community. C_LIO_LIWe illustrate this NBE decomposition with three ecosystem functions (nitrogen retention capacity, soil hydraulic conductivity improvement, and forage digestibility) measured in assembled communities under controlled experimental conditions of perennial grassland plants. Regarding nitrogen retention, we find a positive complementary effect via total biomass, but a negative biodiversity effect via effect trait expression. For hydraulic conductivity improvement, biodiversity effects are mostly mediated by total biomass. As for forage digestibility, we found a positive complementarity effect on trait expression, outweighed however by a negative selection effect. This analysis reveals how biodiversity may have contrasting effects on ecosystem functions via its impact on biomass and effect trait expression. C_LI SynthesisSeparating between the effect of biodiversity on plant community biomass and on effect trait expression at the community level is one important step towards understanding the pathways by which diverse plant communities drive ecosystem functioning.

The Allee effect is a phenomenon where individual fitness is reduced in small populations, for example because of mate-finding difficulties or increased predation. Allee effects matter in conservation biology because they can drive small populations to extinction. The severity of Allee effects can depend on traits such as mate-search rate and defense against predators. Many natural populations exhibit considerable intraspecific trait variation (ITV) in such traits, but most studies so far assume these traits to be constant. Thus the impact of ITV on populations with Allee effect is largely unknown. Here we create two individual-based stochastic models that simulate a small population experiencing either a mate-finding Allee effect or a predator-driven Allee effect. We analyze how ITV, trait inheritance, and mutation affect the proportion of surviving populations. Under the mate-finding Allee effect, higher ITV hindered population survival and increased Allee thresholds. This can be explained by Jensens inequality and the negative curvature of the mate-finding function. Under the predator-driven Allee effect, ITV effects were weak, but higher mutation standard deviations were beneficial, likely because they provided more substrate for selection to act on. We thus recommend to take into account ITV when dealing with threatened populations with an Allee effect.