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Climate change is leading to species redistributions. In the tundra biome, shrubs are generally expanding, but not all tundra shrub species will benefit from warming. Winner and loser species, and the characteristics that may determine success or failure, have not yet been fully identified. Here, we investigate whether past abundance changes, current range sizes and projected range shifts derived from species distribution models are related to plant trait values and intraspecific trait variation. We combined 17,921 trait records with observed past and modelled future distributions from 62 tundra shrub species across three continents. We found that species with greater variation in seed mass and specific leaf area had larger projected range shifts, and projected winner species had greater seed mass values. However, trait values and variation were not consistently related to current and projected ranges, nor to past abundance change. Overall, our findings indicate that abundance change and range shifts will not lead to directional modifications in shrub trait composition, since winner and loser species share relatively similar trait spaces. Functional trait data could guide predictions of species responses to environmental change. Here, the authors show that winner and loser shrub species in the warming tundra biome overlap in trait space and may therefore be difficult to predict based on commonly measured traits. [ABSTRACT FROM AUTHOR]

Aim: Improving species distribution models (SDMs) and species abundance models (SAMs) of woody shrubs is critical for predicting biodiversity changes in the Arctic, which is experiencing especially high warming rates. Yet, it remains relatively unexplored if SDMs and SAMs can explain local scale patterns. We aim to identify predictor differences for the distribution versus abundance of two widespread Arctic shrub species with high resolution models and to compare validation approaches to assess the models' predictive abilities. Location: Nuup Kangerlua (NK) and Kangerluarsunnguaq (K), two adjacent fjords in Southwest Greenland. Methods: We conducted two separate field surveys in either fjord to construct high resolution (~90 m) SDMs and SAMs for Betula nana and Salix glauca, analysing the predictive influences of local scale climate, topography and soil moisture indicators. We then alternatively trained and validated models in either NK or K fjord and compared these results with the common split‐sample validation approach. Finally, we assessed if including local scale biotic predictors improves SAM performance. Results: Temperature extremes and precipitation best predicted the distributions of both species, whereas insolation and soil moisture indicators best predicted abundances. Compared to split‐sample validation, both SDM and SAM performance was substantially reduced with separate survey validation. Regardless of validation approach, models performed poor to moderately well, and including local scale biotic parameters improved SAM performance. Main conclusions: Substantial differences in model performance between validation approaches highlight the usefulness of using a separate survey for validating model predictive performance. We discuss various factors that might have caused poor model performance, such as not capturing all relevant predictors or enough local scale heterogeneity in predictor or response variables. We emphasise the need to include predictors relevant at the spatial scale of study, such as local scale biotic interactions, for improved predictions at high spatial resolution. [ABSTRACT FROM AUTHOR]

Whether non-arctic species persisted in northern Europe during the Last Glacial Maximum (LGM) is highly debated. Until now, the debate has mostly focused on plants, with little consideration for other groups of organisms, e.g. the numerous plant-dependent insect species. Here, we study the late-Quaternary evolution of the European range of a boreo-montane leaf beetle, Gonioctena intermedia, which feeds exclusively on the boreal and temperate trees Prunus padus and Sorbus aucuparia. Using species distribution models, we estimated the congruence between areas of past and present suitable climate for this beetle and its host plants. Then we derived historical hypotheses from the congruent range estimates and evaluated their compatibility with observed DNA sequence variation at five independent loci. We investigated compatibility using computer simulations of population evolution under various coalescence models. We find strong evidence for range modifications in response to late-Quaternary climate changes, and support for the presence of populations of G. intermedia in northern Europe since the beginning of the last glaciation. The presence of a co-dependent insect in the region through the LGM provides new evidence supporting the glacial survival of cold-tolerant tree species in northern Europe. [ABSTRACT FROM AUTHOR]

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species' range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species' response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species. [ABSTRACT FROM AUTHOR]

Detecting the legacy of time-lagged migration in species ranges is an urgent matter for understanding range dynamics. KISSMig is a simple migration model which generates maps of accessibility from areas of origin and allows the generation and testing of hypotheses about the influence of specific spread patterns on species distributions. KISSMig has important applications: 1) uncovering the influence of limited migration relative to other drivers, 2) detecting areas of origin and their importance as sources of migration, and 3) accounting for limited migration in modeling species distributions. Here we introduce KISSMig and use the oak species Quercus cerris to illustrate these applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km2 to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: ( i) integrating pairwise dependencies, ( ii) using integrative predictors, and ( iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim To identify potential source and sink locations for climate-driven species range shifts in Europe since the Last Glacial Maximum (LGM). Location Europe. Methods We developed a new approach combining past-climate simulations with the concept of analogous climate space. Our index gives a continuous measure of the potential of a location to have acted as a source or a sink for species that have shifted their ranges since the LGM. High glacial source potential is indicated by LGM climatic conditions that are widespread now; high post-glacial sink potential is indicated by current climatic conditions that were widespread at the LGM. The degree of isolation of source and sink areas was calculated as the median distance to areas with analogous climate conditions. Results We identified areas of high glacial source potential in the previously recognized refugial areas in the southern European peninsulas, but also in large areas in central-western Europe. The most climatically isolated source areas were located in northern Spain, in north-western Europe and in eastern Turkey. From here species would have had to cover substantial distances to find current climate conditions analogous to LGM conditions of these areas. Areas with high post-glacial sink potential were mainly located in Fennoscandia and in central and south-eastern Europe. Some of the most isolated sink areas were located in the Spanish highlands and around the Baltic Sea. Main conclusions Our species-independent approach successfully identified previously recognized glacial refugial areas with high source potential for species range shifts in southern Europe and in addition highlighted other potential source areas in central Europe. This study offers new insights into how the distribution of past and current climatic conditions may have influenced past species range shifts and current large-scale biodiversity patterns. [ABSTRACT FROM AUTHOR]

Mountain ecosystems will likely be affected by global warming during the 21st century, with substantial biodiversity loss predicted by species distribution models (SDMs). Depending on the geographic extent, elevation range, and spatial resolution of data used in making these models, different rates of habitat loss have been predicted, with associated risk of species extinction. Few coordinated across-scale comparisons have been made using data of different resolutions and geographic extents. Here, we assess whether climate change-induced habitat losses predicted at the European scale (10 × 10' grid cells) are also predicted from local-scale data and modeling (25 m × 25 m grid cells) in two regions of the Swiss Alps. We show that local-scale models predict persistence of suitable habitats in up to 100% of species that were predicted by a European-scale model to lose all their suitable habitats in the area. Proportion of habitat loss depends on climate change scenario and study area. We find good agreement between the mismatch in predictions between scales and the fine-grain elevation range within 10 × 10' cells. The greatest prediction discrepancy for alpine species occurs in the area with the largest nival zone. Our results suggest elevation range as the main driver for the observed prediction discrepancies. Local-scale projections may better reflect the possibility for species to track their climatic requirement toward higher elevations. [ABSTRACT FROM AUTHOR]

1. The Pleistocene is an important period for assessing the impact of climate change on biodiversity. During the Last Glacial Maximum (LGM; 21 000 years ago), large glaciers and permafrost reached far south in Europe. Trees are traditionally thought to have survived only in scattered Mediterranean refugia (southern refugia hypothesis), but a recent proposal suggests that trees may have been much more widely and northerly distributed (northern refugia hypothesis). 2. In this study, the southern vs. northern refugia hypotheses were investigated by estimating the potential LGM distributions of 7 boreal and 15 nemoral widespread European tree species using species distribution modelling. The models were calibrated using data for modern species distributions and climate and projected onto two LGM climate simulations for Europe. Five modelling variants were implemented. 3. Models with moderate to good predictive ability for current species range limits and species richness patterns were developed. 4. Broadly consistent results were obtained irrespective of the climate simulation and modelling variant used. Our results indicate that LGM climatic conditions suitable for boreal species existed across Central and Eastern Europe and into the Russian Plain. In contrast, suitable climatic conditions for nemoral tree species were largely restricted to the Mediterranean and Black Sea regions. Large proportions of these northern and southern regions would have been suitable for a number of boreal or boreal plus nemoral tree species, respectively. 5. These findings are consistent with recent palaeoecological and phylogeographic data regarding LGM distributions of trees and other boreal and nemoral taxa. 6. Synthesis. It is clear that the view of the LGM landscape in Europe as largely treeless, especially north of the Alps, needs to be revised. Trees were probably much more widespread during the LGM than hitherto thought, although patchily distributed at low densities due to low atmospheric CO2 concentrations and high wind-speeds. The findings presented here help explain the occurrence of mammal assemblages with mixtures of forest, tundra and steppe species at many localities in southern Central and Eastern Europe during the LGM, as well as the phylogeographic evidence for the extra-Mediterranean persistence of many boreal species. [ABSTRACT FROM AUTHOR]

Abstract: Species distribution modeling (SDM: statistical and/or mechanistic approaches to the assessment of range determinants and prediction of species occurrence) offers new possibilities for estimating and studying past organism distributions. SDM complements fossil and genetic evidence by providing (i) quantitative and potentially high-resolution predictions of the past organism distributions, (ii) statistically formulated, testable ecological hypotheses regarding past distributions and communities, and (iii) statistical assessment of range determinants. In this article, we provide an overview of applications of SDM to paleobiology, outlining the methodology, reviewing SDM-based studies to paleobiology or at the interface of paleo- and neobiology, discussing assumptions and uncertainties as well as how to handle them, and providing a synthesis and outlook. Key methodological issues for SDM applications to paleobiology include predictor variables (types and properties; special emphasis is given to paleoclimate), model validation (particularly important given the emphasis on cross-temporal predictions in paleobiological applications), and the integration of SDM and genetics approaches. Over the last few years the number of studies using SDM to address paleobiology-related questions has increased considerably. While some of these studies only use SDM (23%), most combine them with genetically inferred patterns (49%), paleoecological records (22%), or both (6%). A large number of SDM-based studies have addressed the role of Pleistocene glacial refugia in biogeography and evolution, especially in Europe, but also in many other regions. SDM-based approaches are also beginning to contribute to a suite of other research questions, such as historical constraints on current distributions and diversity patterns, the end-Pleistocene megafaunal extinctions, past community assembly, human paleobiogeography, Holocene paleoecology, and even deep-time biogeography (notably, providing insights into biogeographic dynamics >400 million years ago). We discuss important assumptions and uncertainties that affect the SDM approach to paleobiology – the equilibrium postulate, niche stability, changing atmospheric CO2 concentrations – as well as ways to address these (ensemble, functional SDM, and non-SDM ecoinformatics approaches). We conclude that the SDM approach offers important opportunities for advances in paleobiology by providing a quantitative ecological perspective, and hereby also offers the potential for an enhanced contribution of paleobiology to ecology and conservation biology, e.g., for estimating climate change impacts and for informing ecological restoration. [Copyright &y& Elsevier]