Predicting future distributions of mountain plants under climate change: does dispersal capacity matter?

Many studies have investigated the potential impacts of climate change on the distribution of plant species, but few have attempted to constrain projections through plant dispersal limitations. Instead, most studies published so far have simplified dispersal as either unlimited or null. However, depending on the dispersal capacity of a species, landscape fragmentation, and the rate of climatic change, these assumptions can lead to serious over- or underestimation of the future distribution of plant species.
To quantify the discrepancies between simulations accounting for dispersal or not, we carried out projections of future distribution over the 21st century for 287 mountain plant species in a study area of the western Swiss Alps. For each species, simulations were run for four dispersal scenarios (unlimited dispersal, no dispersal, realistic dispersal, and realistic dispersal with long-distance dispersal events) and under four climate change scenarios.
Although simulations accounting for realistic dispersal limitations did significantly differ from those considering dispersal as unlimited or null in terms of projected future distribution, the unlimited dispersal simplification did nevertheless provide good approximations for species extinctions under more moderate climate change scenarios. Overall, simulations accounting for dispersal limitations produced, for our mountainous study area, results that were significantly closer to unlimited dispersal than to no dispersal. Finally, analysis of the temporal pattern of species extinctions over the entire 21st century revealed that important species extinctions for our study area might not occur before the 2080–2100 period, due to the possibility of a large number of species shifting their distribution to higher elevation.     

The MIGCLIM R package – seamless integration of dispersal constraints into projections of species distribution models

The MIGCLIM R package is a function library for the open source R software that enables the implementation of species‐specific dispersal constraints into projections of species distribution models under environmental change and/or landscape fragmentation scenarios. The model is based on a cellular automaton and the basic modeling unit is a cell that is inhabited or not. Model parameters include dispersal distance and kernel, long distance dispersal, barriers to dispersal, propagule production potential and habitat invasibility. The MIGCLIM R package has been designed to be highly flexible in the parameter values it accepts, and to offer good compatibility with existing species distribution modeling software. Possible applications include the projection of future species distributions under environmental change conditions and modeling the spread of invasive species.     

Climate warming and the decline of amphibians and reptiles in Europe

Aim We explore the relationship between current European distributions of amphibian and reptile species and observed climate, and project species potential distributions into the future. Potential impacts of climate warming are assessed by quantifying the magnitude and direction of modelled distributional shifts for every species. In particular we ask, first, what proportion of amphibian and reptile species are projected to lose and gain suitable climate space in the future? Secondly, do species projections vary according to taxonomic, spatial or environmental properties? And thirdly, what climate factors might be driving projections of loss or gain in suitable environments for species? Location Europe. Methods Distributions of species are modelled with four species–climate envelope techniques (artificial neural networks, generalized linear models, generalized additive models, and classification tree analyses) and distributions are projected into the future using five climate‐change scenarios for 2050. Future projections are made considering two extreme assumptions: species have unlimited dispersal ability and species have no dispersal ability. A novel hybrid approach for combining ensembles of forecasts is then used to group linearly covarying projections into clusters with reduced inter‐model variability. Results We show that a great proportion of amphibian and reptile species are projected to expand distributions if dispersal is unlimited. This is because warming in the cooler northern ranges of species creates new opportunities for colonization. If species are unable to disperse, then most species are projected to lose range. Loss of suitable climate space for species is projected to occur mainly in the south‐west of Europe, including the Iberian Peninsula, whilst species in the south‐east are projected to gain suitable climate. This is because dry conditions in the south‐west are projected to increase, approaching the levels found in North Africa, where few amphibian species are able to persist. Main conclusions The impact of increasing temperatures on amphibian and reptile species may be less deleterious than previously postulated; indeed, climate cooling would be more deleterious for the persistence of amphibian and reptile species than warming. The ability of species to cope with climate warming may, however, be offset by projected decreases in the availability of water. This should be particularly true for amphibians. Limited dispersal ability may further increase the vulnerability of amphibians and reptiles to changes in climate.     

Prediction of climate warming impacts on plant species could be more complex than expected. Evidence from a case study in the Himalaya

Many studies have investigated the possible impact of climate change on the distributions of plant species. In the present study, we test whether the concept of potential distribution is able to effectively predict the impact of climate warming on plant species.Using spatial simulation models, we related the actual (current species distribution), potential (modelled distribution assuming unlimited dispersal) and predicted (modelled distribution accounting for wind-limited seed dispersal) distributions of two plant species under several warming scenarios in the Sagarmatha National Park (Nepal). We found that the two predicted distributions were, respectively, seven and nine times smaller than the potential ones. Under a +3 °C scenario, both species would likely lose their actual and predicted distributions, while their potential distributions would remain partially safe. Our results emphasize that the predicted distributions of plant species may diverge to a great extent from their potential distributions, particularly in mountain areas, and predictions of species preservation in the face of climate warming based on the potential distributions of plant species are at risk of producing overoptimistic projections.We conclude that the concept of potential distribution is likely to lead to limited or inefficacious conservation of plant species due to its excessively optimistic projections of species preservation. More robust strategies should utilize concepts such as “optimal reintroduction”, which maximizes the benefit–cost ratio of conservation activities by limiting reintroduction efforts to suitable areas that could not otherwise be reached by a species; moreover, such strategies maximize the probability of species establishment by excluding areas that will be endangered under future climate scenarios.     

Does dispersal capacity matter for freshwater biodiversity under climate change?

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Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change

Correlative species distribution models are frequently used to predict species’ range shifts under climate change. However, climate variables often show high collinearity and most statistical approaches require the selection of one among strongly correlated variables. When causal relationships between species presence and climate parameters are unknown, variable selection is often arbitrary, or based on predictive performance under current conditions. While this should only marginally affect current range predictions, future distributions may vary considerably when climate parameters do not change in concert. We investigated this source of uncertainty using four highly correlated climate variables together with a constant set of landscape variables in order to predict current (2010) and future (2050) distributions of four mountain bird species in central Europe. Simulating different parameterization decisions, we generated a) four models including each of the climate variables singly, b) a model taking advantage of all variables simultaneously and c) an un‐weighted average of the predictions of a). We compared model accuracy under current conditions, predicted distributions under four scenarios of climate change, and – for one species – evaluated back‐projections using historical occurrence data. Although current and future variable‐correlations remained constant, and the models’ accuracy under contemporary conditions did not differ, future range predictions varied considerably in all climate change scenarios. Averaged models and models containing all climate variables simultaneously produced intermediate predictions; the latter, however, performed best in back‐projections. This pattern, consistent across different modelling methods, indicates a benefit from including multiple climate predictors in ambiguous situations. Variable selection proved to be an important source of uncertainty for future range predictions, difficult to control using contemporary information. Small, but diverging changes of climate variables, masked by constant overall correlation patterns, can cause substantial differences between future range predictions which need to be accounted for, particularly when outcomes are intended for conservation decisions.     

Potential impacts of climate and landscape fragmentation changes on plant distributions: Coupling multi-temporal satellite imagery with GIS-based cellular automata model

Climate change and landscape fragmentation are considered to be the main treats to biodiversity. In this study, probable alteration of future species distribution was tested based on the association of landscape fragmentation and climate change scenarios compared to the classical approach that assumed an unchanged landscape. Also, projected range shifts including realistic dispersal scenarios were compared with classical models, in which no or full dispersal has been supposed.A GIS-based cellular automata model, MigClim, was implemented to projection of future distribution over the 21st century for three plant species in a study area of the central Germany. For each species, simulations were run for four dispersal scenarios (full dispersal, no dispersal, realistic dispersal, and realistic dispersal with long-distance dispersal events), two landscape fragmentation (static and dynamic change) and two climate change (RCP4.5 and RCP8.5) scenarios. In this research, temporal satellite data were utilized to simulate landscape changes by the use of a hybrid (CA-Markov) model for the years 2020, 2040, 2060 and 2080.A significant difference appears to be between the simulations of realistic dispersal limitations and those considering full or no dispersal for projected future distributions. Although simulations accounting for dispersal limitations produced, for our study area, results that were closer to no dispersal than to full dispersal. Additionally, our results revealed that change in landscape fragmentation is more effective than the climate change impacts on species distributions in this study.     

Population connectivity and genetic diversity of American marten (Martes americana) in the United States northern Rocky Mountains in a climate change context

Climate change is likely to alter population connectivity, particularly for species associated with higher elevation environments. The goal of this study is to predict the potential effects of future climate change on population connectivity and genetic diversity of American marten populations across a 30.2 million hectare region of the in the US northern Rocky Mountains. We use a landscape resistance model validated from empirical landscape genetics modeling to predict the current and expected future extent and fragmentation of American marten dispersal habitat under five climate change scenarios, corresponding to climatic warming of between 0.7 and 3.3 °C, consistent with expected climate change by year 2080. We predict the regions of the current and future landscapes where gene flow is expected to be governed by isolation by distance and the regions where population fragmentation is expected to limit gene flow. Finally, we predict changes in the strength and location of predicted movement corridors, fracture zones and the location of dispersal barriers across the study area in each scenario. We found that under the current climate, gene flow is predicted to be limited primarily by distance (isolation), and landscape structure does not significantly limit gene flow, resulting in very high genetic diversity over most of the study area. Projected climatic warming substantially reduces the extent and increases the fragmentation of marten populations in the western and northwestern parts of the study area. In contrast, climate change is not predicted to fragment the extensive higher elevation mountain massifs in central Idaho, the northern U.S. continental divide, and Greater Yellowstone Ecosystem. In addition, we show locations in the study area that are important corridors in the current landscape that remain intact across the climate change scenarios.     

Disregarding the edaphic dimension in species distribution models leads to the omission of crucial spatial information under climate change: the case of Quercus pubescens in France

Species distribution modelling is an easy, persuasive and useful tool for anticipating species distribution shifts under global change. Numerous studies have used only climate variables to predict future potential species range shifts and have omitted environmental factors important for determining species distribution. Here, we assessed the importance of the edaphic dimension in the niche‐space definition of Quercus pubescens and in future spatial projections under global change over the metropolitan French forest territory. We fitted two species distribution models (SDM) based on presence/absence data (111 013 plots), one calibrated from climate variables only (mean temperature of January and climatic water balance of July) and the other one from both climate and edaphic (soil pH inferred from plants) variables. Future predictions were conducted under two climate scenarios (PCM B2 and HadCM3 A2) and based on 100 simulations using a cellular automaton that accounted for seed dispersal distance, landscape barriers preventing migration and unsuitable land cover. Adding the edaphic dimension to the climate‐only SDM substantially improved the niche‐space definition of Q. pubescens, highlighting an increase in species tolerance in confronting climate constraints as the soil pH increased. Future predictions over the 21st century showed that disregarding the edaphic dimension in SDM led to an overestimation of the potential distribution area, an underestimation of the spatial fragmentation of this area, and prevented the identification of local refugia, leading to an underestimation of the northward shift capacity of Q. pubescens and its persistence in its current distribution area. Spatial discrepancies between climate‐only and climate‐plus‐edaphic models are strengthened when seed dispersal and forest fragmentation are accounted for in predicting a future species distribution area. These discrepancies highlight some imprecision in spatial predictions of potential distribution area of species under climate change scenarios and possibly wrong conclusions for conservation and management perspectives when climate‐only models are used.     

The fate of European breeding birds under climate,land‐use and dispersal scenarios

Many species have already shifted their distributions in response to recent climate change. Here, we aimed at predicting the future breeding distributions of European birds under climate, land‐use, and dispersal scenarios. We predicted current and future distributions of 409 species within an ensemble forecast framework using seven species distribution models (SDMs), five climate scenarios and three emission and land‐use scenarios. We then compared results from SDMs using climate‐only variables, habitat‐only variables or both climate and habitat variables. In order to account for a species’ dispersal abilities, we used natal dispersal estimates and developed a probabilistic method that produced a dispersal scenario intermediate between the null and full dispersal scenarios generally considered in such studies. We then compared results from all scenarios in terms of future predicted range changes, range shifts, and variations in species richness. Modeling accuracy was better with climate‐only variables than with habitat‐only variables, and better with both climate and habitat variables. Habitat models predicted smaller range shifts and smaller variations in range size and species richness than climate models. Using both climate and habitat variables, it was predicted that the range of 71% of the species would decrease by 2050, with a 335 km median shift. Predicted variations in species richness showed large decreases in the southern regions of Europe, as well as increases, mainly in Scandinavia and northern Russia. The partial dispersal scenario was significantly different from the full dispersal scenario for 25% of the species, resulting in the local reduction of the future predicted species richness of up to 10%. We concluded that the breeding range of most European birds will decrease in spite of dispersal abilities close to a full dispersal hypothesis, and that given the contrasted predictions obtained when modeling climate change only and land‐use change only, both scenarios must be taken into consideration.     

Warming increases the spread of an invasive thistle

Background

Global warming and shifted precipitation regimes increasingly affect species abundances and distributions worldwide. Despite a large literature on species'' physiological, phenological, growth, and reproductive responses to such climate change, dispersal is rarely examined. Our study aims to test whether the dispersal ability of a non-native, wind-dispersed plant species is affected by climate change, and to quantify the ramifications for future invasion spread rates.

Methodology/Principal Findings

We experimentally increased temperature and precipitation in a two-cohort, factorial field study (n = 80). We found an overwhelming warming effect on plant life history: warming not only improved emergence, survival, and reproduction of the thistle Carduus nutans, but also elevated plant height, which increased seed dispersal distances. Using spatial population models, we demonstrate that these empirical warming effects on demographic vital rates, and dispersal parameters, greatly exacerbate spatial spread. Predicted levels of elevated winter precipitation decreased seed production per capitulum, but this only slightly offset the warming effect on spread. Using a spread rate decomposition technique (c*-LTRE), we also found that plant height-mediated changes in dispersal contribute most to increased spread rate under climate change.

Conclusions/Significance

We found that both dispersal and spread of this wind-dispersed plant species were strongly impacted by climate change. Dispersal responses to climate change can improve, or diminish, a species'' ability to track climate change spatially, and should not be overlooked. Methods that combine both demographic and dispersal responses thus will be an invaluable complement to projections of suitable habitat under climate change.     

Land Use Compounds Habitat Losses under Projected Climate Change in a Threatened California Ecosystem

Given the rapidly growing human population in mediterranean-climate systems, land use may pose a more immediate threat to biodiversity than climate change this century, yet few studies address the relative future impacts of both drivers. We assess spatial and temporal patterns of projected 21^st century land use and climate change on California sage scrub (CSS), a plant association of considerable diversity and threatened status in the mediterranean-climate California Floristic Province. Using a species distribution modeling approach combined with spatially-explicit land use projections, we model habitat loss for 20 dominant shrub species under unlimited and no dispersal scenarios at two time intervals (early and late century) in two ecoregions in California (Central Coast and South Coast). Overall, projected climate change impacts were highly variable across CSS species and heavily dependent on dispersal assumptions. Projected anthropogenic land use drove greater relative habitat losses compared to projected climate change in many species. This pattern was only significant under assumptions of unlimited dispersal, however, where considerable climate-driven habitat gains offset some concurrent climate-driven habitat losses. Additionally, some of the habitat gained with projected climate change overlapped with projected land use. Most species showed potential northern habitat expansion and southern habitat contraction due to projected climate change, resulting in sharply contrasting patterns of impact between Central and South Coast Ecoregions. In the Central Coast, dispersal could play an important role moderating losses from both climate change and land use. In contrast, high geographic overlap in habitat losses driven by projected climate change and projected land use in the South Coast underscores the potential for compounding negative impacts of both drivers. Limiting habitat conversion may be a broadly beneficial strategy under climate change. We emphasize the importance of addressing both drivers in conservation and resource management planning.     

Dispersal through stream networks: modelling climate‐driven range expansions of fishes

Aim To incorporate dispersal through stream networks into models predicting the future distribution of a native, freshwater fish given climate change scenarios. Location Sweden. Methods We used logistic regression to fit climate and habitat data to observed pike (Esox lucius Linnaeus) distributions in 13,476 lakes. We used GIS to map dispersal pathways through streams. Lakes either (1) contained pike or were downstream from pike lakes, (2) were upstream from pike lakes, but downstream from natural dispersal barriers, or (3) were isolated from streams or were upstream from natural dispersal barriers. We then used climate projections to model future distributions of pike and compared our results with and without including dispersal. Results Given climate and habitat, pike were predicted present in all of 99,249 Swedish lakes by 2100. After accounting for dispersal barriers, we only predicted pike presence in 31,538 lakes. Dispersal barriers most strongly limited pike invasion in mountainous regions, but low connectivity also characterized some relatively flat regions. Main conclusions The dendritic network structure of streams and interconnected lakes makes a two‐dimensional representation of the landscape unsuitable for predicting range shifts of many freshwater organisms. If dispersal through stream networks is not accounted for, predictions of future fish distributions in a warmer climate might grossly overestimate range expansions of warm and cool‐water fishes and underestimate range contractions of cold‐water fishes. Dispersal through stream networks can be modelled in any region for which a digital elevation model and species occurrence data are available.     

Simulating forest ecosystem response to climate warming incorporating spatial effects in north-eastern China

Aim Predictions of ecosystem responses to climate warming are often made using gap models, which are among the most effective tools for assessing the effects of climate change on forest composition and structure. Gap models do not generally account for broad‐scale effects such as the spatial configuration of the simulated forest ecosystems, disturbance, and seed dispersal, which extend beyond the simulation plots and are important under changing climates. In this study we incorporate the broad‐scale spatial effects (spatial configurations of the simulated forest ecosystems, seed dispersal and fire disturbance) in simulating forest responses to climate warming. We chose the Changbai Natural Reserve in China as our study area. Our aim is to reveal the spatial effects in simulating forest responses to climate warming and make new predictions by incorporating these effects in the Changbai Natural Reserve. Location Changbai Natural Reserve, north‐eastern China. Method We used a coupled modelling approach that links a gap model with a spatially explicit landscape model. In our approach, the responses (establishment) of individual species to climate warming are simulated using a gap model (linkages ) that has been utilized previously for making predictions in this region; and the spatial effects are simulated using a landscape model (LANDIS) that incorporates spatial configurations of the simulated forest ecosystems, seed dispersal and fire disturbance. We used the recent predictions of the Canadian Global Coupled Model (CGCM2) for the Changbai Mountain area (4.6 °C average annual temperature increase and little precipitation change). For the area encompassed by the simulation, we examined four major ecosystems distributed continuously from low to high elevations along the northern slope: hardwood forest, mixed Korean pine hardwood forest, spruce‐fir forest, and sub‐alpine forest. Results The dominant effects of climate warming were evident on forest ecosystems in the low and high elevation areas, but not in the mid‐elevation areas. This suggests that the forest ecosystems near the southern and northern ranges of their distributions will have the strongest response to climate warming. In the mid‐elevation areas, environmental controls exerted the dominant influence on the dynamics of these forests (e.g. spruce‐fir) and their resilience to climate warming was suggested by the fact that the fluctuations of species trajectories for these forests under the warming scenario paralleled those under the current climate scenario. Main conclusions With the spatial effects incorporated, the disappearance of tree species in this region due to the climate warming would not be expected within the 300‐year period covered by the simulation. Neither Korean pine nor spruce‐fir was completely replaced by broadleaf species during the simulation period. Even for the sub‐alpine forest, mountain birch did not become extinct under the climate warming scenario, although its occurrence was greatly reduced. However, the decreasing trends characterizing Korean pine, spruce, and fir indicate that in simulations beyond 300 years these species could eventually be replaced by broadleaf tree species. A complete forest transition would take much longer than the time periods predicted by the gap models.     

An Objective Approach to Select Climate Scenarios when Projecting Species Distribution under Climate Change

An impressive number of new climate change scenarios have recently become available to assess the ecological impacts of climate change. Among these impacts, shifts in species range analyzed with species distribution models are the most widely studied. Whereas it is widely recognized that the uncertainty in future climatic conditions must be taken into account in impact studies, many assessments of species range shifts still rely on just a few climate change scenarios, often selected arbitrarily. We describe a method to select objectively a subset of climate change scenarios among a large ensemble of available ones. Our k-means clustering approach reduces the number of climate change scenarios needed to project species distributions, while retaining the coverage of uncertainty in future climate conditions. We first show, for three biologically-relevant climatic variables, that a reduced number of six climate change scenarios generates average climatic conditions very close to those obtained from a set of 27 scenarios available before reduction. A case study on potential gains and losses of habitat by three northeastern American tree species shows that potential future species distributions projected from the selected six climate change scenarios are very similar to those obtained from the full set of 27, although with some spatial discrepancies at the edges of species distributions. In contrast, projections based on just a few climate models vary strongly according to the initial choice of climate models. We give clear guidance on how to reduce the number of climate change scenarios while retaining the central tendencies and coverage of uncertainty in future climatic conditions. This should be particularly useful during future climate change impact studies as more than twice as many climate models were reported in the fifth assessment report of IPCC compared to the previous one.     

Recalculating route: dispersal constraints will drive the redistribution of Amazon primates in the Anthropocene

Climate change will redistribute the global biodiversity in the Anthropocene. As climates change, species might move from one place to another, due to local extinctions and colonization of new environments. However, the existence of permeable migratory routes precedes faunal migrations in fragmented landscapes. Here, we investigate how dispersal will affect the outcome of climate change on the distribution of Amazon's primate species. We modeled the distribution of 80 Amazon primate species, using ecological niche models, and projected their potential distribution on scenarios of climate change. Then, we imposed landscape restrictions to primate dispersal, derived from a natural biogeographical barrier to primates (the main tributaries of the Amazon river) and an anthropogenic constraint to the migration of many canopy‐dependent animals (deforested areas). We also highlighted potential conflict zones, i.e. regions of high migration potential but predicted to be deforested. Species response to climate change varied across dispersal limitation scenarios. If species could occupy all newly suitable climate, almost 70% of species could expand ranges. Including dispersal barriers (natural and anthropogenic), however, led to range expansion in only less than 20% of the studied species. When species were not allowed to migrate, all of them lost an average of 90% of the suitable area, suggesting that climate may become unsuitable within their present distributions. All Amazon primate species may need to move as climate changes to avoid deleterious effects of exposure to non‐analog climates. The effect of climate change on the distribution of Amazon primates will ultimately depend on whether landscape permeability will allow climate‐driven faunal migrations. The network of protected areas in the Amazon could work as ‘stepping stones’ but most are outside important migratory routes. Therefore, protecting important dispersal corridors is foremost to allow effective migrations of the Amazon fauna in face of climate change and deforestation.     

Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here, we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species‐specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured interannual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including aboveground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model‐data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate.     

Matching habitat choice promotes species persistence under climate change

Species may survive under contemporary climate change by either shifting their range or adapting locally to the warmer conditions. Theoretical and empirical studies recently underlined that dispersal, the central mechanism behind these responses, may depend on the match between an individuals’ phenotype and local environment. Such matching habitat choice is expected to induce an adaptive gene flow, but it now remains to be studied whether this local process could promote species’ responses to climate change. Here, we investigate this by developing an individual‐based model including either random dispersal or temperature‐dependent matching habitat choice. We monitored population composition and distribution through space and time under climate change. Relative to random dispersal, matching habitat choice induced an adaptive gene flow that lessened spatial range loss during climate warming by improving populations’ viability within the range (i.e. limiting range fragmentation) and by facilitating colonization of new habitats at the cold margin. The model even predicted range contraction under random dispersal but range expansion under optimal matching habitat choice. These benefits of matching habitat choice for population persistence mostly resulted from adaptive immigration decision and were greater for populations with larger dispersal distance and higher emigration probability. We also found that environmental stochasticity resulted in suboptimal matching habitat choice, decreasing the benefits of this dispersal mode under climate change. However population persistence was still better under suboptimal matching habitat choice than under random dispersal. Our results highlight the urgent need to implement more realistic mechanisms of dispersal such as matching habitat choice into models predicting the impacts of ongoing climate change on biodiversity.     

Climate,competition and connectivity affect future migration and ranges of European trees

Aim Species ranges have adapted during the Holocene to altering climate conditions, but it remains unclear if species will be able to keep pace with recent and future climate change. The goal of our study is to assess the influence of changing macroclimate, competition and habitat connectivity on the migration rates of 14 tree species. We also compare the projections of range shifts from species distribution models (SDMs) that incorporate realistic migration rates with classical models that assume no or unlimited migration. Location Europe. Methods We calibrated SDMs with species abundance data from 5768 forest plots from ICP Forest Level 1 in relation to climate, topography, soil and land‐use data to predict current and future tree distributions. To predict future species ranges from these models, we applied three migration scenarios: no migration, unlimited migration and realistic migration. The migration rates for the SDMs incorporating realistic migration were estimated according to macroclimate, inter‐specific competition and habitat connectivity from simulation experiments with a spatially explicit process model (TreeMig). From these relationships, we then developed a migration cost surface to constrain the predicted distributions of the SDMs. Results The distributions of early‐successional species during the 21st century predicted by SDMs that incorporate realistic migration matched quite well with the unlimited migration assumption (mean migration rate over Europe for A1fi/GRAS climate and land‐use change scenario 156.7 ± 79.1 m year^?1 and for B1/SEDG 164.3 ± 84.2 m year^?1). The predicted distributions of mid‐ to late‐successional species matched better with the no migration assumption (A1fi/GRAS, 15.2 ± 24.5 m year^?1 and B1/SEDG, 16.0 ± 25.6 m year^?1). Inter‐specific competition, which is higher under favourable growing conditions, reduced range shift velocity more than did adverse macroclimatic conditions (i.e. very cold or dry climate). Habitat fragmentation also led to considerable time lags in range shifts. Main conclusions Migration rates depend on species traits, competition, spatial habitat configuration and climatic conditions. As a result, re‐adjustments of species ranges to climate and land‐use change are complex and very individualistic, yet still quite predictable. Early‐successional species track climate change almost instantaneously while mid‐ to late‐ successional species were predicted to migrate very slowly.