Biology subjects

Pellissier, L.

Publications and source records attributed to Pellissier, L..

At least 19 recordsLinked to original sources

bioRxiv2026

Large river eDNA sampling designs with remote-sensing-based clustering stratification

Environmental DNA (eDNA) provides a powerful tool for biodiversity monitoring in large river ecosystems. However, current studies often rely on subjective site selection and lack systematic sampling designs. This can limit the ability to capture the full spectrum of environmental conditions that species depend on, thereby compromising sampling efficiency. To address this challenge, we propose utilizing remote sensing-based clustering for environmental stratification of sampling designs, thereby enhancing detection capabilities and increasing the objectivity of eDNA sampling. Using GBIF-based fish species distribution models and simulated distributions along the Danube, we demonstrate that this approach enhances detection efficiency compared to conventional random and regular sampling methods. To facilitate practical implementation, we developed a tool to help fieldwork planners of river sampling campaigns automatically apply this method and select stratified sampling sites without the need for extensive data processing. Finally, we demonstrate that eDNA detection occurred most frequently within the range of 0-20km downstream of the expected modeled distribution of species, suggesting that the diffusion of the signal should be further considered in the sampling design process. Our findings highlight the potential of remote sensing-based stratification to create more efficient and objective sampling designs but suggest that sampling design should be further combined with hydrological information to optimize cost-efficient sampling. The development of standard and robust sampling protocols will help advance more cost-effective eDNA-based biodiversity monitoring in riverine ecosystems.

ecology↗

bioRxiv2026

Assessing the impact of renewable energy installations on biodiversity and identifying sustainable trade-offs

Renewable energy is crucial to achieve climate neutrality, but its rapid expansion can threaten biodiversity through habitat loss or fragmentation and ecological disruption. We present a spatially explicit assessment framework that quantifies biodiversity impacts from land use change associated with renewable energy infrastructure across a broad range of species groups, and identifies siting configurations that balance energy provision and conservation goals. Drawing on metrics from life cycle assessment, combined with species distribution models and siting strategies, we evaluate alternative deployment strategies. Using Switzerland as a case study, we compare three siting strategies (maximizing energy output, minimizing biodiversity impact, and a trade-off approach) for photovoltaic systems, run-of-river hydropower, and wind turbines. For solar and hydropower installations, prioritizing energy efficiency yields the highest cumulative biodiversity losses. However, these impacts can be substantially reduced with only a slight increase in land use by favouring biodiversity protection. For wind installations, strict avoidance of sensitive ecosystems may increase total impacts, as less efficient and therefore additional sites are required to achieve the same annual energy yield. Overall, our results show that trade-off-based siting strategies can effectively balance performance and biodiversity protection, highlighting that renewable energy can be provided without sacrificing sensitive ecosystems.

ecology↗

bioRxiv2026

Dynamic sedimentary ancient DNA models of Fennoscandic Holocene plant communities reveal the role of temperature and competition

Ecosystems are constantly responding to shifting environmental conditions, and the observed structure of ecological communities at any given discrete observation timepoint is part of a larger dynamic response. However, we lack long-term time series of ecosystem responses allowing understanding of the processes driving community dynamics. Sedimentary ancient DNA (sed aDNA) offers novel opportunities to link empirical data with ecological processes, providing near continuous records of plant and animal community changes over millennia. Here, we analyzed metabarcoding data from 10 lakes from Northern Fennoscandia characterizing plant and mammal communities at temporal resolution of 100 years over several millenia. We used sed aDNA data to test how biotic processes, including temperature-dependent plant growth, competition, shading, and herbivory, may have driven the dynamics of Fennoscandic Holocene plant communities since the Last Glacial Maximum. We compared the explanatory power of ordinary differential equation-based models simulating community changes and including these processes. The fit of the models improved with the consideration of temperature dependent densification and biotic interactions, most notably shading. When considering individual taxa, the importance of shading and herbivory was greatest for the dwarf shrubs, while tree and shrub taxa were more often best predicted by models without competition. Our study demonstrates how using sed aDNA time series together with process-based inverse modelling can uncover the mechanisms of species response to climate change, offering potential for more realistic predictions.

ecology↗

bioRxiv2025

Multi-scale neural networks enhance species distribution modelling across predictors and taxonomic groups

Ecological processes and patterns are scale-dependent, as reflected in how relationships between species occurrence and the environment vary across scales. In species distribution models (SDMs), spatial scale can strongly affect model predictions and performance. Despite this, the choice of scale is often overlooked. The development of SDMs using deep learning models has enabled spatially structured data, such as patches of environmental raster data, to be considered as model inputs, making the question of scale even more crucial. Here, we evaluate convolutional neural network-based multi-species SDMs considering different scales on a dataset from Switzerland, including more than 8 million observations for 2390 plant and 1006 animal species comprising amphibians, butterflies, beetles, and mammals. We investigate how scale affects model performance and compare single- and multi-scale models. Our results reveal stronger scale effects for remote sensing indices than for bioclimatic and edaphic variables. In single-scale models, we find that plant species perform better with smaller spatial scales, while the opposite is true for animal species. Multi-scale models consistently improve predictive performance and reduce sensitivity to arbitrary scale selection across all modalities and taxonomic groups. The effect of scale is nonetheless smaller than the increase in performance achieved by considering multiple predictor groups. To improve the interpretability of such complex models, we use attribution methods to compute the relative contribution of different scales and predictor groups. Our study demonstrates the importance of accounting for scale and the potential of deep learning methods to integrate ecological complexity with spatial data across scales. HighlightsO_LIDeep learning SDMs can use spatial environmental data as model predictors C_LIO_LIThe spatial extent of the predictors affects model performance C_LIO_LIMulti-scale models improve performance across predictors and taxonomic groups C_LIO_LIMulti-scale modelling reduces sensitivity to arbitrary scale selection C_LIO_LIExplainable AI methods provide contribution scores for scales and predictor groups C_LI

ecology↗

bioRxiv2025

Spatial Biodiversity Indicators and a Composite Index for Conservation Prioritization in Switzerland

Spatially explicit indicators that quantify to which extent landscapes support biodiversity are essential for guiding evidence-based conservation planning. Here, we present a 25-meter resolution dataset for Switzerland, encompassing three key biodiversity indicators-- Complementarity, Extinction Risk, and Ecological Connectivity--developed across 17 major taxonomic groups, using habitat suitability maps for approximately 7,500 individual species. These three indicators capture (1) the contribution of landscapes to taxonomic, functional, and phylogenetic diversity, (2) species vulnerability to extinction and (3) ecological connectivity. Outputs are provided for both terrestrial and aquatic realms, including versions adjusted for species richness, and integrated into composite indices. In total, 272 spatial layers were produced and made openly accessible. Relationships among the three indicators were analyzed to assess their distinct contributions, and expert-based validations were performed to evaluate their ecological plausibility and relevance for conservation applications. This dataset provides a robust foundation to support spatial planning and conservation decision-making in Switzerland and can be used as a blueprint for analogue integration in other countries.

ecology↗

bioRxiv2024

Acute, chronic and conditioned effects of intranasal oxytocin in the mu opioid receptor knockout mouse model of autism: social context matters

Autism Spectrum Disorders (ASD) are neurodevelopmental disorders whose diagnosis relies on deficient social interaction and communication together with repetitive behaviours. Multiple studies have highlighted the potential of oxytocin (OT) to ameliorate behavioural abnormalities in animal models and subjects with ASD. Clinical trials, however, yielded disappointing results. Our study aimed at challenging hypotheses accounting for such negative results by assessing the behavioural effects of different regimens of OT administration in the Oprm1 null mouse model of ASD. We assessed the effects of intranasal OT injected once at different doses and time points following administration, or chronically, on ASD-related behaviours in Oprm1+/+and Oprm1-/- mice. We then tested whether pairing intranasal OT injection with social experience would influence its outcome on ASD-like core symptoms, and measured gene expression in several regions of the reward/social circuit. Acute intranasal OT improved social behaviour in Oprm1-/- mice at a moderate dose (0.3 IU) shortly after administration (5 min). Effects on non-social behaviours were limited. Chronic OT at this dose maintained beneficial effects in Oprm1 null mice but was deleterious in wild-type mice. Finally, improvements in the social behaviour of Oprm1-/- mice were greater and longer lasting when OT was administered in a social context, while the expression of OT and vasopressin receptor genes, as well as marker genes of striatal projection neurons, was suppressed. Our results highlight the importance of considering dosage and social context when evaluating the effects of OT treatment in ASD. HighlightsO_LIAcute intranasal oxytocin improved social behaviour shortly after administration C_LIO_LIEffects of oxytocin on non-social behaviours were limited C_LIO_LIChronic oxytocin maintained beneficial effects in mutant mice but was deleterious in wild-type mice C_LIO_LIOxytocin triggers greater and longer-lasting improvements in social behaviour when administered within a social context C_LIO_LIChronic oxytocin suppresses the expression of its own receptor gene and key striatal neuron gene markers C_LI

animal behavior and cognition↗

bioRxiv2023

The evolutionary age-range size relationship is modulated by insularity and dispersal in plants and animals

Earth is home to millions of plant and animal species, with more than 40 thousand species facing extinction worldwide (Diaz et al. 2019). Species range size is particularly important in this context because it influences extinction risk (Purvis et al. 2000, Gaston & Fuller 2009), but the causes underlying the wide natural variation in range size remain poorly known. Here, we investigate how evolutionary age is related to present-day range size for over 25,000 species of mammals, birds, reptiles, amphibians, reef fishes, and plants. We show that, on average, older species have significantly larger ranges, but the effect of age on range size is modulated by clade, geographical context and dispersal ability. Specifically, age does not affect range size for island species, because islands limit dispersal and hence range size, regardless of species age. Furthermore, species from clades with high dispersal capabilities obtain large ranges faster, thereby further neutralizing the relationship between age and range size. Our results can help supporting global conservation priorities, by showing that species that are young, occupy islands, and/or are dispersal limited often have small ranges and therefore increased extinction risk.

ecology↗

bioRxiv2023

Terrestrial land cover shapes fish diversity in major subtropical rivers

Freshwater biodiversity is critically affected by human modifications of terrestrial land use and land cover (LULC)1,2. Yet, knowledge of the spatial extent and magnitude of LULC-aquatic biodiversity linkages is still surprisingly limited, impeding the implementation of optimal management strategies3. Here, we compiled fish diversity data across a 160,000-km2 subtropical river catchment in Thailand characterized by exceptional biodiversity4 yet intense anthropogenic alterations5, and attributed fish species richness and community composition to contemporary terrestrial LULC across the catchment. We estimated a spatial range of LULC effects extending up to about 20 km upstream from sampling sites, and explained nearly 60 % of the variance in the observed species richness, associated with major LULC categories including croplands, forest, and urban areas. We find that integrating both spatial range and magnitudes of LULC effects is needed to accurately predict fish species richness. Further, projected LULC changes showcase future gains and losses of fish species richness across the river network and offer a scalable basis for riverine biodiversity conservation and land management, allowing for potential mitigation of biodiversity loss in highly diverse yet data-deficient tropical to sub-tropical riverine habitats.

ecology↗

bioRxiv2023

Recurrent gene flow events shaped the diversification of the clownfish skunk complex

Clownfish (subfamily Amphiprioninae) are an iconic group of coral reef fish that evolved a mutualistic interaction with sea anemones, which was shown to have triggered the adaptive radiation of the group. Within clownfishes, the skunk complex is particularly interesting as, besides ecological speciation, gene flow between species and hybrid speciation are suggested to have shaped the diversification of the group. We investigated, for the first time, the mechanisms underlying the diversification of this complex. By taking advantage of their disjunct geographical distribution, we obtained whole-genome data of sympatric and allopatric populations of the three main species of the complex (Amphiprion akallopisos, A. perideraion and A. sandaracinos). We examined the population structure, genomic divergence patterns and introgression signals, and performed demographic modeling to identify the most realistic diversification scenario. We excluded scenarios of strict isolation, of hybrid origin of A. sandaracinos, and ruled out the presence of extensive gene flow in sympatry. We discovered moderate gene flow from A. perideraion to the ancestor of A. akallopisos + A. sandaracinos and weak gene flow between the species in the Indo-Australian Archipelago throughout the diversification process of the group. We identified introgressed regions in A. sandaracinos and detected two large regions of high divergence in A. perideraion, likely maintained by the disruption of recombination. Altogether, our results show that ancestral hybridization events shaped the groups diversification. However, more recent gene flow is less pervasive than initially thought and suggests a role of host repartition or behavioral barriers in maintaining the genetic identity of the species in sympatry.

evolutionary biology↗

bioRxiv2023

Leaf metabolic traits reveal hidden dimensions of plant form and function

The plant metabolome encompasses the biochemical mechanisms through which evolutionary and ecological processes shape plant form and function1,2. However, while the metabolome should thus be an important component of plant life-history variation3, we know little about how it varies across the plant kingdom. Here, we use the plant functional trait concept4 - a powerful framework for describing plant form and function5-7 - to interpret leaf metabolome variation among 457 tropical and 339 temperate plant species. Distilling metabolite chemistry into five discriminant metabolic functional traits reveals that plants vary along two major axes of leaf metabolic specialization - a leaf chemical defense spectrum and an expression of leaf longevity. These axes are qualitatively consistent for tropical and temperate species, with many trait combinations being viable. However, axes of leaf metabolic specialization vary orthogonally to life-history strategies described by widely used functional traits5-7, while being at least equally important to them. Our findings question classical trait6 and plant defense8 theory that predicts relationships between the leaf chemical phenotype, plant productivity, and pace of life. Moreover, we show that metabolic functional traits describe unique dimensions of plant life-history variation that are complementary to, and independent from, those captured by existing plant functional traits.

ecology↗

bioRxiv2023

Habitat amount, temperature and biotic interactions drive community structure, life-history traits, and performance traits of cavity-nesting bees and wasps and their natural enemies in cities

O_LIUrban ecosystems are associated with socio-ecological conditions that can filter and promote taxa. However, the strength of the effect of ecological filtering on biodiversity could vary among biotic and abiotic factors. Here, we investigate the effects of habitat amount, temperature, and host-enemy biotic interactions in shaping communities of cavity-nesting bees and wasps (CNBW) and their natural enemies. C_LIO_LIWe installed trap-nests in 80 sites distributed along urban intensity gradients in 5 European cities (Antwerp, Paris, Poznan, Tartu and Zurich). We quantified the species richness and abundance of CNBW hosts and their natural enemies, as well as two performance traits (survival and parasitism) and two life-history traits (sex ratio and number of offspring per nest for the hosts). We analysed the importance of the abiotic and biotic variables using generalized linear models and multi-model inference. C_LIO_LIWe found that habitat amount and temperature were the main drivers of CNBW host responses, with larger habitat amounts resulting in higher species richness and abundance, and a larger total number of brood cells per nest for both bees and wasps, as well as a larger probability of survival for bees. Conversely, higher local temperatures decreased species richness, abundance, survival rate, number of brood cells per nest, and proportion of females in CNBW hosts. C_LIO_LIBiotic interactions with natural enemies shaped wasp species richness, with higher levels of parasitism resulting in more wasp species. Similarly, our results showed direct density-dependence between CNBW hosts and their natural enemies. C_LIO_LIOverall, our study highlights the importance of habitat amount and temperature in shaping urban food webs, through direct effects on hosts responses and the subsequent consequences for their natural enemies. As cities prepare to tackle the future consequences of global change, strategies that make it possible to maintain available habitat and mitigate urban overheating emerge as a key urban adaptation for biodiversity conservation. C_LI

ecology↗

bioRxiv2022

Mini-batching ecological data to improve ecosystem models with machine learning

Process-based, dynamic models are essential for extrapolating beyond current trends and anticipating biodiversity responses to global change. However, their practical adoption for forecasting purposes remains limited due to difficulties in calibrating them against data and structural inaccuracies in their mathematical formulations. While ecological time series could, in principle, be used to directly estimate model parameters and refine model structures, the large noise levels in ecological datasets and the strong nonlinearity of ecological dynamics challenge conventional calibration methods. Here, we present a robust and scalable calibration framework that addresses these challenges by integrating techniques from scientific computing and deep learning. Our approach combines a segmentation strategy where state variables are estimated independently, differentiable programming for efficient gradient computation, parameter transformations to ensure the feasibility and stability of the model simulations, and mini-batching to accomodate large datasets. Through comprehensive benchmarks using simulated food web dynamics of increasing complexity, we demonstrate that the framework substantially improves the convergence of gradient descent algorithms and Monte Carlo sampling methods, accommodating for realistic scenarios with noisy and partial observations. This yields improved parameter estimation and forecasts within both mode estimation and full posterior distribution contexts. Crucially, we show that the calibration framework scales effectively with both the number of parameters and state variables. The improved convergence and scalability of the calibration framework enables hybrid modeling approaches, where neural networks parameterize complex processes within the dynamic model. In particular, we demonstrate that neural networks can effectively capture environmental dependencies in demographic rates and recover functional responses governing trophic interactions. Neural network-based parameterizations have the capability to improve the structural inaccuracies of models while maintaining ecological interpretability through post-hoc analysis of learned representations. We provide an implementation of the calibration framework and other key utilities as the open-source Julia package HybridDynamicModels.jl, with the hope that the package will facilitate the development of hybrid modeling approaches in quantitative ecology and related fields.

ecology↗

bioRxiv2022

Climatic niche conservatism and ecological diversification in the Holarctic cold-dwelling butterfly genus Erebia

The diversification of alpine species has been modulated by their climatic niches interacting with changing climatic conditions. The relative roles of climatic niche conservatism promoting geographical speciation and of climatic niche diversification are poorly understood in diverse temperate groups. Here, we investigate the climatic niche evolution in a species rich butterfly genus, Erebia. This Holarctic cold-dwelling genus reaches the highest diversity in European mountains. We generated a nearly complete molecular phylogeny and modelled the climatic niche evolution using geo-referenced occurrence records. We reconstructed the evolution of the climatic niche and tested how the species climatic niche width changes across the occupied climate gradient and compared two main Erebia clades, the European and the Asian clade. We further explored climatic niche overlaps among species. Our analyses revealed that the evolution of Erebia has been shaped by climatic niche conservatism, supported by a strong phylogenetic signal and niche overlap in sister species, likely promoting allopatric speciation. The European and the Asian clades evolved their climatic niches toward different local optima. In addition, species in the European clade have narrower niches compared to the Asian clade. Contrasts among the clades may be related to regional climate differences, with lower climate seasonality in Europe compared to Central Asia favouring the evolution of narrower niches. Further, adaptive divergence could appear in other traits, such as habitat use, which can be reflected by narrower climatic niches detected in the European clade. In conclusion, our study extends knowledge about the complexity of evolutionary drivers in temperate insects.

evolutionary biology↗

bioRxiv2021

Blue and green food webs respond differently to elevation and land use

While aquatic (blue) and terrestrial (green) food webs are parts of the same landscape, it remains unclear whether they respond similarly to shared environmental gradients. We use empirical community data from hundreds of sites across Switzerland, and show that blue and green food webs have different structural and ecological properties along elevation as a temperature proxy, and among various land-use types. Specifically, in green food webs, their modular structure increases with elevation and the overlap of consumers diet niche decreases, while the opposite pattern is observed in blue food webs. Such differences between blue and green food webs are particularly pronounced in farmland-dominated habitats, indicating that anthropogenic habitat modification moderates the climatic effects on food webs but differently in blue versus green systems. These findings indicate general structural differences between blue and green food webs and suggest their potential divergent future alterations through land use or climatic changes.

ecology↗

bioRxiv2021

The interaction of climate history and evolution impacts alpine biodiversity assembly differently in freshwater and on land

Quaternary climate fluctuations can affect biodiversity assembly through speciation in two non-mutually-exclusive ways: a glacial species pump, where isolation in glacial refugia accelerates allopatric speciation, and adaptive radiation during ice-free periods. Here we detected biogeographic and genetic signatures associated with both mechanisms in the generation of the European Alps biodiversity. Age distributions of endemic and widespread species within aquatic and terrestrial taxa (amphipods, fishes, amphibians, butterflies and flowering plants) revealed that endemic fish evolved only in lakes, are highly sympatric and mainly of Holocene age, consistent with adaptive radiation. Endemic amphipods are ancient, suggesting preglacial radiation with limited range expansion and local Pleistocene survival, perhaps facilitated by a groundwater-dwelling lifestyle. Terrestrial endemics are mostly of Pleistocene age, and are thus more consistent with the glacial species pump. The lack of evidence for Holocene adaptive radiation in the terrestrial biome may be attributable to a faster range expansion of these taxa after glacial retreats, though fewer stable environments may also have contributed to differences between terrestrial areas and lakes. The high proportion of young, endemic species make the Alps vulnerable to climate change, but the mechanisms and consequences of species loss will likely differ between biomes because of their distinct histories.

evolutionary biology↗

bioRxiv2021

Global plant-frugivore trait matching is shaped by climate and biogeographic history

Species interactions are influenced by the trait structure of local multi-trophic communities. However, it remains unclear whether mutualistic interactions in particular can drive trait patterns at the global scale, where climatic constraints and biogeographic processes gain importance. Here we evaluate global relationships between traits of frugivorous birds and palms (Arecaceae), and how these relationships are affected, directly or indirectly, by assemblage richness, climate and biogeographic history. We leverage a new and expanded gape size dataset for nearly all avian frugivores, and find a positive relationship between gape size and fruit size, i.e., trait matching, which is influenced indirectly by palm richness and climate. We also uncover a latitudinal gradient in trait matching strength, which increases towards the tropics and varies among zoogeographic realms. Taken together, our results suggest trophic interactions have consistent influences on trait structure, but that abiotic, biogeographic and richness effects also play important, though sometimes indirect, roles in shaping the functional biogeography of mutualisms.

ecology↗

bioRxiv2021

Topology and habitat assortativity drive neutral and adaptive diversification in spatial graphs

Biodiversity results from differentiation mechanisms developing within biological populations. Such mechanisms are influenced by the properties of the landscape over which individuals interact, disperse and evolve. Notably, landscape connectivity and habitat heterogeneity constrain the movement and survival of individuals, thereby promoting differentiation through drift and local adaptation. Nevertheless, the complexity of landscape features can blur our understanding of how they drive differentiation. Here, we formulate a stochastic, eco-evolutionary model where individuals are structured over a graph that captures complex connectivity patterns and accounts for habitat heterogeneity. Individuals possess neutral and adaptive traits, whose divergence results in differentiation at the population level. The modelling framework enables an analytical underpinning of emerging macroscopic properties, which we complement with numerical simulations to investigate how the graph topology and the spatial habitat distribution affect differentiation. We show that in the absence of selection, graphs with high characteristic length and high heterogeneity in degree promote neutral differentiation. Habitat assortativity, a metric that captures habitat spatial autocorrelation in graphs, additionally drives differentiation patterns under habitat-dependent selection. While assortativity systematically amplifies adaptive differentiation, it can foster or depress neutral differentiation depending on the migration regime. By formalising the eco-evolutionary and spatial dynamics of biological populations in complex landscapes, our study establishes the link between landscape features and the emergence of diversification, contributing to a fundamental understanding of the origin of biodiversity gradients. Significance statementIt is not clear how landscape connectivity and habitat heterogeneity influence differentiation in biological populations. To obtain a mechanistic understanding of underlying processes, we construct an individualbased model that accounts for eco-evolutionary and spatial dynamics over graphs. Individuals possess both neutral and adaptive traits, whose co-evolution results in differentiation at the population level. In agreement with empirical studies, we show that characteristic length, heterogeneity in degree and habitat assortativity drive differentiation. By using analytical tools that permit a macroscopic description of the dynamics, we further link differentiation patterns to the mechanisms that generate them. Our study provides support for a mechanistic understanding of how landscape features affect diversification.

evolutionary biology↗

bioRxiv2021

Mapping tree species for restoration potential resilient to climate change

The restoration of forest ecosystems is associated with key benefits for biodiversity and ecosystem services. Where possible, ecosystem restoration efforts should be guided by a detailed knowledge of the native flora to regenerate ecosystems in a way that benefits natural biodiversity, ecosystem services, and natures contribution to people. Machine learning can map the ecological suitability of tree species globally, which then can guide restoration efforts, especially in regions where knowledge about the native tree flora is still insufficient. We developed an algorithm that combines ecological niche modelling and geographic distributions that allows for the high resolution (1km) global mapping of the native range and suitability of 3,987 tree species under current and future climatic conditions. We show that in most regions where forest cover could be potentially increased, heterogeneity in ecological conditions and narrow species niche width limit species occupancy, so that in several areas with reforestation potential, a large amount of potentially suitable species would be required for successful reforestation. Local tree planting efforts should consider a wide variety of species to ensure that the equally large variety of ecological conditions can be covered. Under climate change, a large fraction of the surface for restoration will suffer significant turnover in suitability, so that areas that are suitable for many species under current conditions will not be suitable in the future anymore. Such a turnover due to shifting climate is less pronounced in regions containing species with broader geographical distributions. This indicates that if restoration decisions are solely based on current climatic conditions, a large fraction of the restored area will become unsuitable in the future. Decisions on forest restoration should therefore take the niche width of a tree species into account to mitigate the risk of climate-driven ecosystem degradation.

ecology↗