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Aim Local richness is a result of both regional richness and local site‐specific factors. We quantify the effects of different regional (dispersal, environmental filtering) and local (habitat selection) community assembly processes in the Cape reeds (Restionaceae) of the Cape flora and test if community assembly processes vary spatially. Location Southern Africa, Cape Floristic Region. Taxon Angiosperms, African Restionaceae (restios). Methods We calculate local probabilistic species pools, based on local species richness, dispersal rates and environmental filtering at a 4 km2 resolution. We then compare the relative contribution of the processes shaping the species pool spatially. Finally, we correlate observed species richness (based on 916 circular, 10 m diameter releves) with the local species pools to test the explanatory power of additional local edaphic variables that could act as filters at very small scales. Results There is substantial spatial variation in the local probabilistic species pools. Within the central part of the Cape, the local pools are mostly influenced by environmental filtering, while along the periphery dispersal limitation dominates. Observed species richness is lower than the predicted species pool size, which forms an upper boundary of the potential species richness for a local site. Local, site‐specific edaphic factors help to explain the remaining differences between local species pool size and α‐richness. Main conclusion The analytical framework for inferring local probabilistic species pools provides a powerful tool for including the influence of regional species richness on variations in releve α‐richness. This allows us to disentangle the various processes that operate at different spatial scales, and ultimately determine local restio species richness in the Cape Floristic Region.

International audience; Aim: Statistical species distribution models (SDMs) are the most common tool to predict the impact of climate change on biodiversity. They can be tuned to fit relationships at various levels of complexity (defined here as parameterization complexity, number of predictors, and multicollinearity) that may co-determine whether projections to novel climatic conditions are useful or misleading. Here, we assessed how model complexity affects the performance of model extrapolations and influences projections of species ranges under future climate change. Location: Europe. Taxon: 34 European tree species. Methods: We sampled three replicates of predictor sets for all combinations of 10 levels (n = 3-12) of environmental variables (climate, terrain, soil) and 10 levels of multicollinearity. We used these sets for each species to fit four SDM algorithms at three levels of parameterization complexity. The >100,000 resulting SDM fits were then evaluated under environmental block cross-validation and projected to environmental conditions for 2061-2080 considering four climate models and two emission scenarios. Finally, we investigated the relationships of model design with model performance and projected distributional changes. Results: Model complexity affected both model performance and projections of species distributional change. Fits of intermediate parameterization complexity performed best, and more complex parameterizations were associated with higher projected loss of current ranges. Model performance peaked at 10-11 variables but increasing number of variables had no consistent effect on distributional change projections. Multicollinearity had a low impact on model performance but distinctly increased projected loss of current ranges. Main conclusions: SDM-based climate change impact assessments should be based on ensembles of projections, varying SDM algorithms as well as parameterization complexity, besides emission scenarios and climate models. The number of predictor variables should be kept reasonably small and the classical threshold of maximum absolute Pearson correlation of 0.7 restricts collinearity-driven effects in projections of species ranges.

Recent years have seen an exponential increase in the amount of data available in all sciences and application domains. Macroecology is part of this "Big Data" trend, with a strong rise in the volume of data that we are using for our research. Here, we summarize the most recent developments in macroecology in the age of Big Data that were presented at the 2018 annual meeting of the Specialist Group Macroecology of the Ecological Society of Germany, Austria and Switzerland (Gfo). Supported by computational advances, macroecology has been a rapidly developing field over recent years. Our meeting highlighted important avenues for further progress in terms of standardized data collection, data integration, method development and process integration. In particular, we focus on (a) important data gaps and new initiatives to close them, for example through space- and airborne sensors, (b) how various data sources and types can be integrated, (c) how uncertainty can be assessed in data-driven analyses and (d) how Big Data and machine learning approaches have opened new ways of investigating processes rather than simply describing patterns. We discuss how Big Data opens up new opportunities, but also poses new challenges to macroecological research. In the future, it will be essential to carefully assess data quality, the reproducibility of data compilation and analytical methods, and the communication of uncertainties. Major progress in the field will depend on the definition of data standards and workflows for macroecology, such that scientific quality and integrity are guaranteed, and collaboration in research projects is made easier.

Background Under ongoing climate and land-use change, biodiversity is continuously decreasing and monitoring biodiversity is becoming increasingly important. National Forest Inventory (NFI) programmes provide valuable time-series data on biodiversity and thus contribute to assessments of the state and trends in biodiversity, as well as ecosystem functioning. Data quality in this context is of paramount relevance, particularly for ensuring a meaningful interpretation of changes. The Swiss NFI revisits about 8%–10% of its sample plots regularly in repeat surveys to supervise the quality of fieldwork. Methods We analysed the relevance of observer bias with equivalence tests, examined data quality objectives defined by the Swiss NFI instructors, and calculated the pseudo-turnover (PT) of species composition, that is, the percentage of species not observed by both teams. Three attributes of woody species richness from the latest Swiss NFI cycles (3 and 4) were analysed: occurrence of small tree and shrub species (1) on the sample plot and (2) at the forest edge, and (3) main shrub and trees species in the upper storey. Results We found equivalent results between regular and repeat surveys for all attributes. Data quality, however, was significantly below expectations in all cases, that is, as much as 20%–30% below the expected data quality limit of 70%–80% (proportion of observations that should not deviate from a predefined threshold). PT values were about 10%–20%, and the PT of two out of three attributes decreased significantly in NFI4. This type of uncertainty – typically caused by a mixture of overlooking and misidentifying species – should be considered carefully when interpreting change figures on species richness estimates from NFI data. Conclusions Our results provide important information on the data quality achieved in Swiss NFIs in terms of the reproducibility of the collected data. The three applied approaches proved to be effective for evaluating the quality of plot-level species richness and composition data in forest inventories and other biodiversity monitoring programmes. As such, they could also be recommended for assessing the quality of biodiversity indices derived from monitoring data.

It is generally accepted that the spatial distribution of neutral genetic diversity within a species' native range mostly depends on effective population size, demographic history, and geographic position. However, it is unclear how genetic diversity at adaptive loci correlates with geographic peripherality or with habitat suitability within the ecological niche. Using exome-wide genomic data and distribution maps of the Alpine range, we first tested whether geographic peripherality correlates with four measures of population genetic diversity at > 17,000 SNP loci in 24 Alpine populations (480 individuals) of Swiss stone pine (Pinus cembra) from Switzerland. To distinguish between neutral and adaptive SNP sets, we used four approaches (two gene diversity estimates, FST outlier test, and environmental association analysis) that search for signatures of selection. Second, we established ecological niche models for P. cembra in the study range and investigated how habitat suitability correlates with genetic diversity at neutral and adaptive loci. All estimates of neutral genetic diversity decreased with geographic peripherality, but were uncorrelated with habitat suitability. However, heterozygosity (He ) at adaptive loci based on Tajima's D declined significantly with increasingly suitable conditions. No other diversity estimates at adaptive loci were correlated with habitat suitability. Our findings suggest that populations at the edge of a species' geographic distribution harbour limited neutral genetic diversity due to demographic properties. Moreover, we argue that populations from suitable habitats went through strong selection processes, are thus well adapted to local conditions, and therefore exhibit reduced genetic diversity at adaptive loci compared to populations at niche margins.

Aim We aimed to assess the relative influence of the historical and contemporary processes determining global patterns of current β-diversity. Specifically, we quantified the relative effects of contemporary climate and historical plate tectonics on β-diversity at different phylogenetic scales. Location Global. Time Period Contemporaneous. Major taxa studied Mammals and birds. Methods We analysed the current β-diversity patterns of birds and mammal assemblages at sequential depths in the phylogeny, that is, from the tips to deeper branches. This was done by slicing bird and mammal phylogenetic trees into 66 time slices of 1 Ma (from 0 to 65 Ma) and recording the branches within each slice. Using global distribution data, we defined the branches’ geographical distribution as the union of the corresponding downstream species distributions. For each time slice, we (a) computed pairwise β-diversity across all the grid cells for the whole world and (b) estimated the correlation between this β-diversity matrix and contemporary climatic and geographical distances, and past geological distances, a proxy for plate tectonics. Results Contemporary climate best explained the β-diversity of shallow branches (i.e., species). For mammals, the geographical isolation of landmasses generated by plate tectonics best explained the β-diversity of deeper branches, whereas the effect of past isolation was weaker for birds. Main conclusions Our study shows that the relative influence of contemporary climate and plate tectonics on the β-diversity of bird and mammal assemblages varies along the phylogenetic time-scale. Our phylogenetic time-scale approach is general and flexible enough to be applied to a broad spectrum of study systems and spatial scales.

International audience; In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as well as better reflect model selection uncertainty. These claims, however, are often demonstrated by isolated examples. Analysts must better understand under which conditions model averaging can improve predictions and their uncertainty estimates. Moreover, a large range of different model averaging methods exists, raising the question of how they differ in their behaviour and performance. Here, we review the mathematical foundations of model averaging along with the diversity of approaches available. We explain that the error in model‐averaged predictions depends on each model's predictive bias and variance, as well as the covariance in predictions between models, and uncertainty about model weights. We show that model averaging is particularly useful if the predictive error of contributing model predictions is dominated by variance, and if the covariance between models is low. For noisy data, which predominate in ecology, these conditions will often be met. Many different methods to derive averaging weights exist, from Bayesian over information‐theoretical to cross‐validation optimized and resampling approaches. A general recommendation is difficult, because the performance of methods is often context dependent. Importantly, estimating weights creates some additional uncertainty. As a result, estimated model weights may not always outperform arbitrary fixed weights, such as equal weights for all models. When averaging a set of models with many inadequate models, however, estimating model weights will typically be superior to equal weights. We also investigate the quality of the confidence intervals calculated for model‐averaged predictions, showing that they differ greatly in behaviour and seldom manage to achieve nominal coverage. Our overall recommendations stress the importance of non‐parametric methods such as cross‐validation for a reliable uncertainty quantification of model‐averaged predictions.

Summary 1. While fire-induced changes in biodiversity are well documented, less is known about how fire impacts life-history variation and diversity of functional traits that represent distinct strategies for persistence in fire-driven ecosystems. One example is the dichotomy in which ‘resprouter’ species usually survive fires to produce new growth, while ‘reseeder’ species perish and re-establish from seed. 2. Variablerelativenumbers of reseederand resprouter speciesin local assemblagesof Restionaceae (Poales) of the Cape Floristic Region (CFR), and their high species richness and endemism, make this family representative of the region’s functional, taxonomic and ecological diversity. 3. We investigate how the proportion of resprouter species (resprouter fraction) changes among edaphic conditions and along a climate moisture (i.e. aridity) gradient that integrates annual precipitation (range 130‐1300 mm) and potential evapotranspiration (range 3‐ 8 mm m ! 2 day ! 1 ). We sampled local assemblages using a stratified-random design that fully spanned the environmental conditions of the CFR. 4. As climate moisture across sites with infertile soils increased, so did resprouter fraction. Resprouters may increasingly dominate reseeders as moisture increases under conditions in which productivity is generally limited. This suggests the explanatory hypothesis that in these conditions, greater moisture accelerates both biomass accumulation (fuel) and fire frequency, providing an advantage to resprouters. However, we found no evidence for increasing resprouter fraction with increasing climate moisture on more fertile soils. 5. This statistical interaction suggests the possibility that moisture affects resprouter fraction dependent on soil fertility, potentially through: (i) impacts on species composition, if soil type acts as an environmental filter or promotes biotic interactions, and/or (ii) impacts on fire dynamics, potentially through fuel load drying rates. Future research should include a comprehensive fire model for the CFR, to enable direct examination of relationships of life-history and functional traits across variation in fire dynamics. Additionally, our results indicate that we need both (i) demographic studies on plant establishment, growth rates and survival, and (ii) field experiments that address how environmental and biotic filters affect species composition across variable fire regimes. These together should facilitate mechanistic interpretation of how fire mediates life-history variation and diversity of functional traits in the CFR.

Modern multifunctional forest management can profit from high-quality information on the potential distribution of woody species generated by species distribution models (SDMs). Forest structure is an important factor in determining the distribution of woody species in forests, for example because it affects light conditions within forest stands. Remotely sensed data from light detection and ranging (LiDAR) can capture this three-dimensional structure of forests, leading to the expectation that LiDAR-derived data should enhance the predictive performance of SDMs. We test if and how LiDAR-derived data increases the predictive performance of SDMs for light-demanding and shade-tolerant shrub and tree species in Swiss forests. Our analyses suggest that LiDAR-derived data generally increases predictive performance of models. However, the response to including LiDAR-derived data varies depending on plant functional type: the increase in predictive performance is largest for light-demanding shrubs, reduced for light-demanding trees, and is lost for shade-tolerant species. We further find that shade-tolerant and light-demanding species show opposing responses along the LiDAR-derived predictors. Our results suggest that LiDAR-derived data indeed capture some aspects of light availability in forests, and that including LiDAR-derived predictors in SDMs should be considered for light-demanding shrubs, but may be of less use for trees (especially if shade-tolerant). We conclude that improving SDMs and resulting maps by including LiDAR-derived predictors potentially helps to ameliorate multifunctional, biodiversity-friendly forest stand management.

Summary Convergent adaptive evolution of species' ecological niches—i.e., the appearance of similar niches in independent lineages—is the result of natural selection acting on niche-related species traits ("traits" hereafter) and contrasts with neutral evolution [1–4]. Although trait convergences are recognized as being of importance at the species scale, we still know little about the impact of species convergence on the overall trait and niche structure of entire biotas at large spatial scales [5]. Here, we map the convergent evolution of four traits (diet, body mass, activity cycle, and foraging strata) for mammal species and assemblages (defined at 200 × 200 km resolution) at a global scale. Using data on the geographic distributions, traits, and phylogenetic relationships of species and by comparing observed patterns of trait β-diversity to evolutionary neutral expectations, we show that trait convergence is not restricted to particular lineages but scales up to entire assemblages (i.e., whole species communities). We find region-wide biota convergence in traits between regions with similar climates, particularly between Australia and other continents. Pairs of assemblages that show trait divergence often involves Arctic regions where rapid evolutionary changes occurred in response to extreme climatic constraints. By integrating both macroecological and macroevolutionary approaches into a single framework, our study quantifies the crucial role of evolutionary processes such as natural selection in the spatial distribution and structure of large-scale species assemblages.

One of the most common trends in plant evolution, loss of self-incompatibility and ensuing increases in selfing, is generally assumed to be associated with a suite of phenotypic changes, notably a reduction of floral size, termed the selfing syndrome. We investigate whether floral morphological traits indeed decrease in a deterministic fashion after losses of self-incompatibility, as traditionally expected, using a phylogeny of 124 primrose species containing nine independent transitions from heterostyly (heteromorphic incompatibility) to homostyly (monomorphic self-compatibility), a classic system for evolution of selfing. We find similar overall variability of homostylous and heterostylous species, except for diminished herkogamy in homostyles. Bayesian mixed models demonstrate differences between homostylous and heterostylous species in all traits, but net effects across species are small (except herkogamy) and directionality differs among traits. Strongly drift-like evolutionary trajectories of corolla tube length and corolla diameter inferred by Ornstein-Uhlenbeck models contrast with expected deterministic trajectories toward small floral size. Lineage-specific population genetic effects associated with evolution of selfing may explain that reductions of floral size represent one of several possible outcomes of floral evolution after loss of heterostyly in primroses. Contrary to the traditional paradigm, selfing syndromes may, but do not necessarily evolve in response to increased selfing.

Fire has been proposed as a factor explaining the exceptional plant species richness found in Mediterranean regions. A fire response trait that allows plants to cope with frequent fire by either reseeding or resprouting could differentially affect rates of species diversification. However, little is known about the generality of the effects of differing fire response on species evolution. We study this question in the Restionaceae, a family that radiated in Southern Africa and Australia. These radiations occurred independently and represent evolutionary replicates. We apply Bayesian approaches to estimate trait-specific diversification rates and patterns of climatic niche evolution. We also compare the climatic heterogeneity of South Africa and Australia. Reseeders diversify faster than resprouters in South Africa, but not in Australia. We show that climatic preferences evolve more rapidly in reseeder lineages than in resprouters and that the optima of these climatic preferences differ between the two strategies. We find that South Africa is more climatically heterogeneous than Australia, independent of the spatial scale we consider. We propose that rapid shifts between states of the fire response trait promote speciation by separating species ecologically, but this only happens when the landscape is sufficiently heterogeneous.

Aim We sought to understand the variables that limit the distribution range of a clade (here the danthonioid grasses). We tested time, area of origin, habitat suitability, disjunction width and nature, and wind direction as possible range determinants. Location Global, but predominantly the Southern Hemisphere. Methods We mapped the range of the subfamily Danthonioideae, and used 39,000 locality records and an ensemble modelling approach to define areas with suitable danthonioid habitat. We used a well-sampled, dated phylogeny to estimate the number and direction of historical dispersal events, based on parsimony optimization. We tested for the impact of wind direction on dispersal rate using a likelihood approach, and for the effects of barrier width with a regression approach. Results We found 17 geographically isolated areas with suitable habitats for danthonioids. All currently suitable Southern Hemisphere areas have been occupied, but three apparently suitable areas in the Northern Hemisphere have not. We infer that southern Africa was first occupied in the Oligocene and that dispersal to the other areas was initiated in the middle Miocene. Inferred dispersal rate was correlated with the width of the disjunctions, up to a distance of 5000km. There was no support for wind direction having influenced differences in dispersal rate. Main conclusions The current range of the Danthonioideae can be predicted ecologically (areas with suitable habitat) and historically (the width of the disjunctions separating the areas with suitable habitat and the area of origin). The direction of dispersal is dictated by the area of origin and by serendipity: there is no evidence for general patterns of dispersal, for example for dispersal occurring more frequently over land than over sea or in an easterly versus a westerly direction around the Southern Hemisphere. Thus the range and range-filling of Danthonioideae can be accounted for by surprisingly few variables: habitat suitability, distance between suitable areas, and area of origin.

Aim Our aim was to seek explanations for the differences in the diversity among the austral continents by comparing the diversification rates and patterns in the grass subfamily Danthonioideae. We asked specifically whether diversification is density dependent, whether it is different for each continent, and whether immigration rates impact on diversification rates. We attempted to account for intercontinental differences by comparing the Pleistocene climatic and Neogene geomorphological histories with the inferred diversification rates. Location Mainly the Southern Hemisphere, treated as four areas for the analyses: Africa, Australia, New Zealand and South America. Methods We based our analyses on a densely sampled, dated phylogeny for the grass subfamily Danthonioideae. We compared 24 diversification models for these continental radiations, taking into account speciation models, and extinction and dispersal rates. We used available distribution data to infer the climates under which danthonioids are found, and used these to estimate the change in area and location of suitable habitats between contemporary and Last Glacial Maximum climates. We inferred the geomorphological history from the literature. Results We show that long-distance dispersal led to parallel radiations, which more than doubled the final standing diversity in the subfamily. Diversification models with the strongest support included separate time-varying diversification processes for each major geographical region. Pleistocene climatic fluctuation did not explain the intercontinental differences in diversification patterns. Main conclusions Although our results are consistent with density-dependent diversification, this explanation is not consistent with the time of arrival of danthonioids on each continent. The diversification patterns on the four major Southern Hemisphere landmasses are idiosyncratic. The two most important predictors of diversity may be the lineage-specific effect of time, and the general effect of topographical complexity and orogenesis.

Aim The origins of ecological diversity in continental species assemblages have long intrigued biogeographers. We apply phylogenetic comparative analyses to disentangle the evolutionary patterns of ecological niches in an assemblage of European birds. We compare phylogenetic patterns in trophic, habitat and climatic niche components. Location Europe. Methods From polygon range maps and handbook data we inferred the realized climatic, habitat and trophic niches of 405 species of breeding birds in Europe. We fitted Pagel's lambda and kappa statistics, and conducted analyses of disparity through time to compare temporal patterns of ecological diversification on all niche axes together. All observed patterns were compared with expectations based on neutral (Brownian) models of niche divergence. Results In this assemblage, patterns of phylogenetic signal (lambda) suggest that related species resemble each other less in regard to their climatic and habitat niches than they do in their trophic niche. Kappa estimates show that ecological divergence does not gradually increase with divergence time, and that this punctualism is stronger in climatic niches than in habitat and trophic niches. Observed niche disparity markedly exceeds levels expected from a Brownian model of ecological diversification, thus providing no evidence for past phylogenetic niche conservatism in these multivariate niches. Levels of multivariate disparity are greatest for the climatic niche, followed by disparity of the habitat and the trophic niches. Main conclusions Phylogenetic patterns in the three niche components differ within this avian assemblage. Variation in evolutionary rates (degree of gradualism, constancy through the tree) and/or non-random macroecological sampling probably lead here to differences in the phylogenetic structure of niche components. Testing hypotheses on the origin of these patterns requires more complete phylogenetic trees of the birds, and extended ecological data on different niche components for all bird species., Global Ecology and Biogeography, 23 (4), ISSN:1466-822X, ISSN:1466-8238

Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence–environment relationships using statistical and machine‐learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree‐based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence–environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building ‘under fit’ models, having insufficient flexibility to describe observed occurrence–environment relationships, we risk misunderstanding the factors shaping species distributions. By building ‘over fit’ models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges., Ecography, 37 (12), ISSN:0906-7590, ISSN:1600-0587

Understanding the drivers of population divergence, speciation and species persistence is of great interest to molecular ecology, especially for species-rich radiations inhabiting the world's biodiversity hotspots. The toolbox of population genomics holds great promise for addressing these key issues, especially if genomic data are analysed within a spatially and ecologically explicit context. We have studied the earliest stages of the divergence continuum in the Restionaceae, a species-rich and ecologically important plant family of the Cape Floristic Region (CFR) of South Africa, using the widespread CFR endemic Restio capensis (L.) H.P. Linder & C.R. Hardy as an example. We studied diverging populations of this morphotaxon for plastid DNA sequences and >14 400 nuclear DNA polymorphisms from Restriction site Associated DNA (RAD) sequencing and analysed the results jointly with spatial, climatic and phytogeographic data, using a Bayesian generalized linear mixed modelling (GLMM) approach. The results indicate that population divergence across the extreme environmental mosaic of the CFR is mostly driven by isolation by environment (IBE) rather than isolation by distance (IBD) for both neutral and non-neutral markers, consistent with genome hitchhiking or coupling effects during early stages of divergence. Mixed modelling of plastid DNA and single divergent outlier loci from a Bayesian genome scan confirmed the predominant role of climate and pointed to additional drivers of divergence, such as drift and ecological agents of selection captured by phytogeographic zones. Our study demonstrates the usefulness of population genomics for disentangling the effects of IBD and IBE along the divergence continuum often found in species radiations across heterogeneous ecological landscapes.

International audience; Evolutionary adaptation is a key driver of species’ range dynamics. Understanding the factors that affect rates of adaptation at range margins is thus crucial for interpreting and predicting changes in species’ ranges. The spatial structure of environmental conditions is one of the determinants of whether and how quickly adaptations occur. However, while landscape structures at range edges are typically complex, most theoretical work has so far focused on relatively simple environmental geometries. Using an individual-based allelic model, we explore the effects of different landscape structures on the rate of adaptation to novel environments and investigate how these structures interact with the genetic architecture of the trait governing adaptation and the dispersal capacity of the considered species. Generally, we find that rapid adaptation is favored by a good match between the coarseness of the trait's genetic architecture (many loci of small effects versus few loci of large effects) and the coarseness of the landscape (abruptness of transitions in environmental conditions). For example, in rugged landscapes, adaptation is quicker for genetic architectures with few loci of large effects, while for shallow gradients the opposite is true. Moreover, dispersal capacities affect the rate of adaptation by modulating the ‘apparent coarseness’ of the landscape: a gradient perceived as smooth by species with limited dispersal capacities appears rather steep for highly dispersive ones. We also find that the distribution of evolving phenotypes strongly depends on the interplay of landscape structure and dispersal capacities, ranging from two distinct phenotypes for most rugged landscapes, over the co-occurrence of an additional third phenotype for highly dispersive species, to the whole range of phenotypes on smooth gradients. By identifying basic factors that drive the fixation probability of newly arising beneficial mutations, we hope to further broaden the understanding of evolutionary adaptation at range margins and, hence, species’ range dynamics.

Biodiversity and Conservation, 22 (6-7), ISSN:1572-9710, ISSN:0960-3115