Modelling range dynamics under global change: which framework and why?

Simulating implications of fish behavioral response for managing hypoxia in estuaries with spatial dissolved oxygen variability

Hypoxia, or low dissolved oxygen (DO), is a widespread water quality problem affecting estuaries and coastal waters around the world. Water quality criteria for DO have been established for every estuary in the US and are an important part of the regulatory response to nutrient pollution and associated anthropogenic eutrophication. Experimental studies examining effects of low DO exposure have been to quantify outcomes based on hypoxia effects observed in individuals, such as increased mortality or growth impairment. Although laboratory exposure tests provide useful benchmarks for policy development, most of those considered in policy development did not consider behavioral responses to low DO. However, experimental research has shown that behavioral responses occur, and that behavior modifies exposure to low DO conditions. Here we begin development of a spatially explicit individual based model (SEIBM) intended to project behavioral outcomes of exposure to spatially variable hypoxia in estuaries. Our goal is to consider the responsiveness of an SEIBM to both different behavioral hypotheses, as well as realistic spatial patterns in hypoxia. A sensitivity analysis was used to explore responsiveness based on two movement strategies: avoidance and behavioral switching. We tested the sensitivity of a suite of movement parameters to changes in spatial patterns representative of an index estuary. The sensitivity analysis demonstrated that model responses to changes in movement strategies include biologically meaningful changes in site occupancy and movement distance centered on individual behavior near a normoxic-hypoxic boundary. Further, the model demonstrated important sensitivity to realistic changes in movement parameters, including the size and shape of the individual neighborhood describing knowledge useful for movement decisions. These results support the utility of the developed SEIBM for exploring behavioral responses of fish to hypoxia in estuaries. The sensitivity analysis also demonstrates parameter values that must be set based on empirical data and are sensitive to data quality. These results will be used to further develop the model and to plan field and laboratory studies to support model parametrization. The end goal is a model framework that can inform policy decisions regarding hypoxia resulting from anthropogenic nutrient loading in estuaries.

Temperature-dependent dispersal and ectotherm species' distributions in a warming world

Dispersal is a crucial component of species' responses to climate warming. Warming-induced changes in species' distributions are the outcome of how temperature affects dispersal at the individual level. Yet, there is little or no theory that considers the temperature dependence of dispersal when investigating the impacts of warming on species' distributions. Here I take a first step towards filling this key gap in our knowledge. I focus on ectotherms, species whose body temperature depends on the environmental temperature, not least because they constitute the majority of biodiversity on the planet. I develop a mathematical model of spatial population dynamics that explicitly incorporates mechanistic descriptions of ectotherm life history trait responses to temperature. A novel feature of this framework is the explicit temperature dependence of all phases of dispersal: emigration, transfer and settlement. I report three key findings. First, dispersal, regardless of whether it is random or temperature-dependent, allows both tropical and temperate ectotherms to track warming-induced changes in their thermal environments and to expand their distributions beyond the lower and upper thermal limits of their respective climate envelopes. In the absence of dispersal mortality, warming does not alter these new distributional limits. Second, an analysis based solely on trait response data predicts that tropical ectotherms should be able to expand their distributions polewards to a greater degree than temperate ectotherms. Analysis of the dynamical model confirms this prediction. Tropical ectotherms have an advantage when moving to cooler climates because they experience lower within-patch and dispersal mortality, and their higher thermal optima and maximal birth rates allow them to take advantage of the warmer parts of the year. Previous theory has shown that tropical ectotherms are more successful in invading and adapting the temperate climates than vice versa. This study provides the key missing piece, by showing how temperature-dependent dispersal could facilitate both invasion and adaptation. Third, dispersal mortality does not affect the poleward expansion of ectotherm distributions. But, it prevents both tropical and temperate ectotherms from maintaining sink populations in localities that are too warm to be viable in the absence of dispersal. Dispersal mortality also affects species' abundance patterns, causing a larger decline in abundance throughout the range when species disperse randomly rather than in response to thermal habitat suitability. In this way, dispersal mortality can facilitate the evolution of dispersal modes that maximize fitness in warmer thermal environments.

Predicting the dispersal and invasion dynamics of ambrosia beetles through demographic reconstruction and process-explicit modeling

Evaluating potential routes of invasion of pathogens and vectors of sanitary importance is essential for planning and decision-making at multiple scales. An effective tool are process-explicit models that allow coupling environmental, demographic and dispersal information to evaluate population growth and range dynamics as a function of the abiotic conditions in a region. In this work we simulate multiple dispersal/invasion routes in Mexico that could be taken by ambrosia beetles and a specific symbiont, Harringtonia lauricola, responsible for a severe epiphytic of Lauraceae in North America. We used Xyleborus bispinatus Eichhoff 1868 as a study subject and estimated its demography in the laboratory in a temperature gradient (17, 20, 26, 29, 35 °C), which we then used to parameterize a process-based model to estimate its metapopulation dynamics. The maximum intrinsic growth rate of X. bispinatus is 0.13 with a thermal optimum of 26.2 °C. The models suggest important regions for the establishment and dispersal the states of Veracruz, Chiapas and Oaxaca (high host and secondary vectors diversity), the Isthmus of Tehuantepec (connectivity region), and Michoacán and Jalisco (important avocado plantations). The use of hybrid process-based models is a promising tool to refine the predictions applied to the study of biological invasions and species distributions.

Schrödinger's Range-Shifting Cat: How Skewed Temperature Dependence Impacts Persistence with Climate Change

AbstractThe majority of species display strongly asymmetric responses to climatic variables, yet most analytic models used to investigate how species will respond to climate change assume symmetric responses, with largely unknown consequences. Applying a known mapping of population dynamical equations onto corresponding well-studied problems from quantum mechanics, we extend analytical results to incorporate this asymmetry. We derive expressions in terms of parameters representing climate velocity, dispersal rate, maximum growth rate, niche width, high-frequency climate variability, and environmental performance curve skew for three key responses: (1) population persistence, (2) lag between range displacement and climate displacement, and (3) location of maximum population sensitivity. We find that asymmetry impacts these climate change responses, but surprisingly, under our model assumptions, the direction (i.e., warm skewed or cool skewed) of performance curve asymmetry does not strongly contribute to either persistence or lags. Conservation measures to support range-shifting populations may have most benefit near their environmental optimum or where the environmental dependence is shallow, irrespective of whether this is the leading or trailing edge. A metapopulation simulation corroborates our results. Our results shed fresh light on how key features of a species' environmental performance curve can impact its response to climate change.

Mechanistic models project bird invasions with accuracy

Invasive species pose a major threat to biodiversity and inflict massive economic costs. Effective management of bio-invasions depends on reliable predictions of areas at risk of invasion, as they allow early invader detection and rapid responses. Yet, considerable uncertainty remains as to how to predict best potential invasive distribution ranges. Using a set of mainly (sub)tropical birds introduced to Europe, we show that the true extent of the geographical area at risk of invasion can accurately be determined by using ecophysiological mechanistic models that quantify species' fundamental thermal niches. Potential invasive ranges are primarily constrained by functional traits related to body allometry and body temperature, metabolic rates, and feather insulation. Given their capacity to identify tolerable climates outside of contemporary realized species niches, mechanistic predictions are well suited for informing effective policy and management aimed at preventing the escalating impacts of invasive species.

Coding for Life: Designing a Platform for Projecting and Protecting Global Biodiversity

Time is running out to limit further devastating losses of biodiversity and nature's contributions to humans. Addressing this crisis requires accurate predictions about which species and ecosystems are most at risk to ensure efficient use of limited conservation and management resources. We review existing biodiversity projection models and discover problematic gaps. Current models usually cannot easily be reconfigured for other species or systems, omit key biological processes, and cannot accommodate feedbacks with Earth system dynamics. To fill these gaps, we envision an adaptable, accessible, and universal biodiversity modeling platform that can project essential biodiversity variables, explore the implications of divergent socioeconomic scenarios, and compare conservation and management strategies. We design a roadmap for implementing this vision and demonstrate that building this biodiversity forecasting platform is possible and practical.

A socio-ecological model for predicting impacts of land-use and climate change on regional plant diversity in the Austrian Alps

Climate and land-use change jointly affect the future of biodiversity. Yet, biodiversity scenarios have so far concentrated on climatic effects because forecasts of land use are rarely available at appropriate spatial and thematic scales. Agent-based models (ABMs) represent a potentially powerful but little explored tool for establishing thematically and spatially fine-grained land-use scenarios. Here, we use an ABM parameterized for 1,329 agents, mostly farmers, in a Central European model region, and simulate the changes to land-use patterns resulting from their response to three scenarios of changing socio-economic conditions and three scenarios of climate change until the mid of the century. Subsequently, we use species distribution models to, first, analyse relationships between the realized niches of 832 plant species and climatic gradients or land-use types, respectively, and, second, to project consequent changes in potential regional ranges of these species as triggered by changes in both the altered land-use patterns and the changing climate. We find that both drivers determine the realized niches of the studied plants, with land use having a stronger effect than any single climatic variable in the model. Nevertheless, the plants' future distributions appear much more responsive to climate than to land-use changes because alternative future socio-economic backgrounds have only modest impact on land-use decisions in the model region. However, relative effects of climate and land-use changes on biodiversity may differ drastically in other regions, especially where landscapes are still dominated by natural or semi-natural habitat. We conclude that agent-based modelling of land use is able to provide scenarios at scales relevant to individual species distribution and suggest that coupling ABMs with models of species' range change should be intensified to provide more realistic biodiversity forecasts.

Genes on the edge: A framework to detect genetic diversity imperiled by climate change

Ongoing global warming is disrupting several ecological and evolutionary processes, spanning different levels of biological organization. Species are expected to shift their ranges as a response to climate change, with relevant implications to peripheral populations at the trailing and leading edges. Several studies have analyzed the exposure of species to climate change but few have explored exposure at the intraspecific level. We introduce a framework to forecast exposure to climate change at the intraspecific level. We build on existing methods by combining correlative species distribution models, a model of species range dynamics, and a model of phylogeographic interpolation. We demonstrate the framework by applying it to 20 Iberian amphibian and reptile species. Our aims were to: (a) identify which species and intraspecific lineages will be most exposed to future climate change; (b) test if nucleotide diversity at the edges of species ranges are significantly higher or lower than on the overall range; and (c) analyze if areas of higher species gain, loss, and turnover coincide with those predicted for lineages richness and nucleotide diversity. We found that about 80% of the studied species are predicted to contract their range. Within each species, some lineages were predicted to contract their range, while others were predicted to maintain or expand it. Therefore, estimating the impacts of climate change at the species level only can underestimate losses at the intraspecific level. Some species had significant high amount of nucleotide at the trailing or leading edge, or both, but we did not find a consistent pattern across species. Spatial patterns of species richness, gain, loss, and turnover were fairly concurrent with lineages richness and nucleotide diversity. Our results support the need for increased attention to intraspecific diversity regarding monitoring and conservation strategies under climate change.

Forecasting species range dynamics with process-explicit models: matching methods to applications

Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process-explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process-explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs - regulatory planning, extinction risk, climate refugia and invasive species - we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process-explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.

A concise guide to developing and using quantitative models in conservation management

Quantitative models are powerful tools for informing conservation management and decision-making. As applied modeling is increasingly used to address conservation problems, guidelines are required to clarify the scope of modeling applications and to facilitate the impact and acceptance of models by practitioners. We identify three key roles for quantitative models in conservation management: (a) to assess the extent of a conservation problem; (b) to provide insights into the dynamics of complex social and ecological systems; and, (c) to evaluate the efficacy of proposed conservation interventions. We describe 10 recommendations to facilitate the acceptance of quantitative models in conservation management, providing a basis for good practice to guide their development and evaluation in conservation applications. We structure these recommendations within four established phases of model construction, enabling their integration within existing workflows: (a) design (two recommendations); (b) specification (two); (c) evaluation (one); and (d) inference (five). Quantitative modeling can support effective conservation management provided that both managers and modelers understand and agree on the place for models in conservation. Our concise review and recommendations will assist conservation managers and modelers to collaborate in the development of quantitative models that are fit-for-purpose, and to trust and use these models appropriately while understanding key drivers of uncertainty.

Effects of species biological traits and environmental heterogeneity on simulated tree species distribution shifts under climate change

Demographic processes (fecundity, dispersal, colonization, growth, and mortality) and their interactions with environmental changes are not well represented in current climate-distribution models (e.g., niche and biophysical process models) and constitute a large uncertainty in projections of future tree species distribution shifts. We investigate how species biological traits and environmental heterogeneity affect species distribution shifts. We used a species-specific, spatially explicit forest dynamic model LANDIS PRO, which incorporates site-scale tree species demography and competition, landscape-scale dispersal and disturbances, and regional-scale abiotic controls, to simulate the distribution shifts of four representative tree species with distinct biological traits in the central hardwood forest region of United States. Our results suggested that biological traits (e.g., dispersal capacity, maturation age) were important for determining tree species distribution shifts. Environmental heterogeneity, on average, reduced shift rates by 8% compared to perfect environmental conditions. The average distribution shift rates ranged from 24 to 200myear^-1 under climate change scenarios, implying that many tree species may not able to keep up with climate change because of limited dispersal capacity, long generation time, and environmental heterogeneity. We suggest that climate-distribution models should include species demographic processes (e.g., fecundity, dispersal, colonization), biological traits (e.g., dispersal capacity, maturation age), and environmental heterogeneity (e.g., habitat fragmentation) to improve future predictions of species distribution shifts in response to changing climates.

Population and evolutionary dynamics in spatially structured seasonally varying environments

Increasingly imperative objectives in ecology are to understand and forecast population dynamic and evolutionary responses to seasonal environmental variation and change. Such population and evolutionary dynamics result from immediate and lagged responses of all key life‐history traits, and resulting demographic rates that affect population growth rate, to seasonal environmental conditions and population density. However, existing population dynamic and eco‐evolutionary theory and models have not yet fully encompassed within‐individual and among‐individual variation, covariation, structure and heterogeneity, and ongoing evolution, in a critical life‐history trait that allows individuals to respond to seasonal environmental conditions: seasonal migration. Meanwhile, empirical studies aided by new animal‐tracking technologies are increasingly demonstrating substantial within‐population variation in the occurrence and form of migration versus year‐round residence, generating diverse forms of ‘partial migration’ spanning diverse species, habitats and spatial scales. Such partially migratory systems form a continuum between the extreme scenarios of full migration and full year‐round residence, and are commonplace in nature.

Here, we first review basic scenarios of partial migration and associated models designed to identify conditions that facilitate the maintenance of migratory polymorphism. We highlight that such models have been fundamental to the development of partial migration theory, but are spatially and demographically simplistic compared to the rich bodies of population dynamic theory and models that consider spatially structured populations with dispersal but no migration, or consider populations experiencing strong seasonality and full obligate migration. Second, to provide an overarching conceptual framework for spatio‐temporal population dynamics, we define a ‘partially migratory meta‐population’ system as a spatially structured set of locations that can be occupied by different sets of resident and migrant individuals in different seasons, and where locations that can support reproduction can also be linked by dispersal. We outline key forms of within‐individual and among‐individual variation and structure in migration that could arise within such systems and interact with variation in individual survival, reproduction and dispersal to create complex population dynamics and evolutionary responses across locations, seasons, years and generations. Third, we review approaches by which population dynamic and eco‐evolutionary models could be developed to test hypotheses regarding the dynamics and persistence of partially migratory meta‐populations given diverse forms of seasonal environmental variation and change, and to forecast system‐specific dynamics. To demonstrate one such approach, we use an evolutionary individual‐based model to illustrate that multiple forms of partial migration can readily co‐exist in a simple spatially structured landscape. Finally, we summarise recent empirical studies that demonstrate key components of demographic structure in partial migration, and demonstrate diverse associations with reproduction and survival. We thereby identify key theoretical and empirical knowledge gaps that remain, and consider multiple complementary approaches by which these gaps can be filled in order to elucidate population dynamic and eco‐evolutionary responses to spatio‐temporal seasonal environmental variation and change.

How complex should models be? Comparing correlative and mechanistic range dynamics models

Criticism has been levelled at climate-change-induced forecasts of species range shifts that do not account explicitly for complex population dynamics. The relative importance of such dynamics under climate change is, however, undetermined because direct tests comparing the performance of demographic models vs. simpler ecological niche models are still lacking owing to difficulties in evaluating forecasts using real-world data. We provide the first comparison of the skill of coupled ecological-niche-population models and ecological niche models in predicting documented shifts in the ranges of 20 British breeding bird species across a 40-year period. Forecasts from models calibrated with data centred on 1970 were evaluated using data centred on 2010. We found that more complex coupled ecological-niche-population models (that account for dispersal and metapopulation dynamics) tend to have higher predictive accuracy in forecasting species range shifts than structurally simpler models that only account for variation in climate. However, these better forecasts are achieved only if ecological responses to climate change are simulated without static snapshots of historic land use, taken at a single point in time. In contrast, including both static land use and dynamic climate variables in simpler ecological niche models improve forecasts of observed range shifts. Despite being less skilful at predicting range changes at the grid-cell level, ecological niche models do as well, or better, than more complex models at predicting the magnitude of relative change in range size. Therefore, ecological niche models can provide a reasonable first approximation of the magnitude of species' potential range shifts, especially when more detailed data are lacking on dispersal dynamics, demographic processes underpinning population performance, and change in land cover.

HexSim: a modeling environment for ecology and conservation

CONTEXT

Simulation models are increasingly used in both theoretical and applied studies to explore system responses to natural and anthropogenic forcing functions, develop defensible predictions of future conditions, challenge simplifying assumptions that facilitated past research, and to train students in scientific concepts and technology. Researcher's increased use of simulation models has created a demand for new platforms that balance performance, utility, and flexibility.

OBJECTIVES

We describe HexSim, a powerful new spatially-explicit, individual-based modeling framework that will have applications spanning diverse landscape settings, species, stressors, and disciplines (e.g. ecology, conservation, genetics, epidemiology). We begin with a model overview and follow-up with a discussion of key formative studies that influenced HexSim's development. We then describe specific model applications of relevance to readers of Landscape Ecology. Our goal is to introduce readers to this new modeling platform, and to provide examples characterizing its novelty and utility.

CONCLUSIONS

With this publication, we conclude a >10 year development effort, and assert that our HexSim model is mature, robust, extremely well tested, and ready for adoption by the research community. The HexSim model, documentation, worked examples, and other materials can be freely obtained from the website www.hexsim.net.

A modeling framework for the establishment and spread of invasive species in heterogeneous environments

Natural and human-induced events are continuously altering the structure of our landscapes and as a result impacting the spatial relationships between individual landscape elements and the species living in the area. Yet, only recently has the influence of the surrounding landscape on invasive species spread started to be considered. The scientific community increasingly recognizes the need for broader modeling framework that focuses on cross-study comparisons at different spatiotemporal scales. Using two illustrative examples, we introduce a general modeling framework that allows for a systematic investigation of the effect of habitat change on invasive species establishment and spread. The essential parts of the framework are (i) a mechanistic spatially explicit model (a modular dispersal framework-MDIG) that allows population dynamics and dispersal to be modeled in a geographical information system (GIS), (ii) a landscape generator that allows replicated landscape patterns with partially controllable spatial properties to be generated, and (iii) landscape metrics that depict the essential aspects of landscape with which dispersal and demographic processes interact. The modeling framework provides functionality for a wide variety of applications ranging from predictions of the spatiotemporal spread of real species and comparison of potential management strategies, to theoretical investigation of the effect of habitat change on population dynamics. Such a framework allows to quantify how small-grain landscape characteristics, such as habitat size and habitat connectivity, interact with life-history traits to determine the dynamics of invasive species spread in fragmented landscape. As such, it will give deeper insights into species traits and landscape features that lead to establishment and spread success and may be key to preventing new incursions and the development of efficient monitoring, surveillance, control or eradication programs.

Vulnerability of eastern US tree species to climate change

Climate change is expected to alter the distribution of tree species because of critical environmental tolerances related to growth, mortality, reproduction, disturbances, and biotic interactions. How this is realized in 21st century remains uncertain, in large part due to limitations on plant migration and the impacts of landscape fragmentation. Understanding these changes is of particular concern for forest management, which requires information at an appropriately fine spatial resolution. Here we provide a framework and application for tree species vulnerability to climate change in the eastern United States that accounts for influential drivers of future distributions. We used species distribution models to project changes in habitat suitability at 800 m for 40 tree species that vary in physiology, range, and environmental niche. We then developed layers of adaptive capacity based on migration potential, forest fragmentation, and propagule pressure. These were combined into metrics of vulnerability, including an overall index and spatially explicit categories designed to inform management. Despite overall favorable changes in suitability, the majority of species and the landscape were considered vulnerable to climate change. Vulnerability was significantly exacerbated by projections of pests and pathogens for some species. Northern and high-elevation species tended to be the most vulnerable. There were, however, some notable areas of particular resilience, including most of West Virginia. Our approach combines some of the most important considerations for species vulnerability in a straightforward framework, and can be used as a tool for managers to prioritize species, areas, and actions.

Benchmarking novel approaches for modelling species range dynamics

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.

A Landscape Approach to Invasive Species Management

Biological invasions are not only a major threat to biodiversity, they also have major impacts on local economies and agricultural production systems. Once established, the connection of local populations into metapopulation networks facilitates dispersal at landscape scales, generating spatial dynamics that can impact the outcome of pest-management actions. Much planning goes into landscape-scale invasive species management. However, effective management requires knowledge on the interplay between metapopulation network topology and management actions. We address this knowledge gap using simulation models to explore the effectiveness of two common management strategies, applied across different extents and according to different rules for selecting target localities in metapopulations with different network topologies. These management actions are: (i) general population reduction, and (ii) reduction of an obligate resource. The reduction of an obligate resource was generally more efficient than population reduction for depleting populations at landscape scales. However, the way in which local populations are selected for management is important when the topology of the metapopulation is heterogeneous in terms of the distribution of connections among local populations. We tested these broad findings using real-world scenarios of European rabbits (Oryctolagus cuniculus) infesting agricultural landscapes in Western Australia. Although management strategies targeting central populations were more effective in simulated heterogeneous metapopulation structures, no difference was observed in real-world metapopulation structures that are highly homogeneous. In large metapopulations with high proximity and connectivity of neighbouring populations, different spatial management strategies yield similar outcomes. Directly considering spatial attributes in pest-management actions will be most important for metapopulation networks with heterogeneously distributed links. Our modelling framework provides a simple approach for identifying the best possible management strategy for invasive species based on metapopulation structure and control capacity. This information can be used by managers trying to devise efficient landscape-oriented management strategies for invasive species and can also generate insights for conservation purposes.

A trait-based approach for predicting species responses to environmental change from sparse data: how well might terrestrial mammals track climate change?

Estimating population spread rates across multiple species is vital for projecting biodiversity responses to climate change. A major challenge is to parameterise spread models for many species. We introduce an approach that addresses this challenge, coupling a trait-based analysis with spatial population modelling to project spread rates for 15 000 virtual mammals with life histories that reflect those seen in the real world. Covariances among life-history traits are estimated from an extensive terrestrial mammal data set using Bayesian inference. We elucidate the relative roles of different life-history traits in driving modelled spread rates, demonstrating that any one alone will be a poor predictor. We also estimate that around 30% of mammal species have potential spread rates slower than the global mean velocity of climate change. This novel trait-space-demographic modelling approach has broad applicability for tackling many key ecological questions for which we have the models but are hindered by data availability.

Uncertainty in predicting range dynamics of endemic alpine plants under climate warming

Correlative species distribution models have long been the predominant approach to predict species' range responses to climate change. Recently, the use of dynamic models is increasingly advocated for because these models better represent the main processes involved in range shifts and also simulate transient dynamics. A well-known problem with the application of these models is the lack of data for estimating necessary parameters of demographic and dispersal processes. However, what has been hardly considered so far is the fact that simulating transient dynamics potentially implies additional uncertainty arising from our ignorance of short-term climate variability in future climatic trends. Here, we use endemic mountain plants of Austria as a case study to assess how the integration of decadal variability in future climate affects outcomes of dynamic range models as compared to projected long-term trends and uncertainty in demographic and dispersal parameters. We do so by contrasting simulations of a so-called hybrid model run under fluctuating climatic conditions with those based on a linear interpolation of climatic conditions between current values and those predicted for the end of the 21st century. We find that accounting for short-term climate variability modifies model results nearly as differences in projected long-term trends and much more than uncertainty in demographic/dispersal parameters. In particular, range loss and extinction rates are much higher when simulations are run under fluctuating conditions. These results highlight the importance of considering the appropriate temporal resolution when parameterizing and applying range-dynamic models, and hybrid models in particular. In case of our endemic mountain plants, we hypothesize that smoothed linear time series deliver more reliable results because these long-lived species are primarily responsive to long-term climate averages.

Climate change and biological invasions: evidence, expectations, and response options

A changing climate may directly or indirectly influence biological invasions by altering the likelihood of introduction or establishment, as well as modifying the geographic range, environmental impacts, economic costs or management of alien species. A comprehensive assessment of empirical and theoretical evidence identified how each of these processes is likely to be shaped by climate change for alien plants, animals and pathogens in terrestrial, freshwater and marine environments of Great Britain. The strongest contemporary evidence for the potential role of climate change in the establishment of new alien species is for terrestrial arthropods, as a result of their ectothermic physiology, often high dispersal rate and their strong association with trade as well as commensal relationships with human environments. By contrast, there is little empirical support for higher temperatures increasing the rate of alien plant establishment due to the stronger effects of residence time and propagule pressure. The magnitude of any direct climate effect on the number of new alien species will be small relative to human-assisted introductions driven by socioeconomic factors. Casual alien species (sleepers) whose population persistence is limited by climate are expected to exhibit greater rates of establishment under climate change assuming that propagule pressure remains at least at current levels. Surveillance and management targeting sleeper pests and diseases may be the most cost-effective option to reduce future impacts under climate change. Most established alien species will increase their distribution range in Great Britain over the next century. However, such range increases are very likely be the result of natural expansion of populations that have yet to reach equilibrium with their environment, rather than a direct consequence of climate change. To assess the potential realised range of alien species will require a spatially explicit approach that not only integrates bioclimatic suitability and population-level demographic rates but also simulation of landscape-level processes (e.g. dispersal, land-use change, host/habitat distribution, non-climatic edaphic constraints). In terms of invasive alien species that have known economic or biodiversity impacts, the taxa that are likely to be the most responsive are plant pathogens and insect pests of agricultural crops. However, the extent to which climate adaptation strategies lead to new crops, altered rotations, and different farming practices (e.g. irrigation, fertilization) will all shape the potential agricultural impacts of alien species. The greatest uncertainty in the effects of climate change on biological invasions exists with identifying the future character of new species introductions and predicting ecosystem impacts. Two complementary strategies may work under these conditions of high uncertainty: (i) prioritise ecosystems in terms of their perceived vulnerability to climate change and prevent ingress or expansion of alien species therein that may exacerbate problems; (ii) target those ecosystem already threatened by alien species and implement management to prevent the situation deteriorating under climate change.

Habitat availability and gene flow influence diverging local population trajectories under scenarios of climate change: a place-based approach

Ecological niche theory holds that species distributions are shaped by a large and complex suite of interacting factors. Species distribution models (SDMs) are increasingly used to describe species' niches and predict the effects of future environmental change, including climate change. Currently, SDMs often fail to capture the complexity of species' niches, resulting in predictions that are generally limited to climate-occupancy interactions. Here, we explore the potential impact of climate change on the American pika using a replicated place-based approach that incorporates climate, gene flow, habitat configuration, and microhabitat complexity into SDMs. Using contemporary presence-absence data from occupancy surveys, genetic data to infer connectivity between habitat patches, and 21 environmental niche variables, we built separate SDMs for pika populations inhabiting eight US National Park Service units representing the habitat and climatic breadth of the species across the western United States. We then predicted occurrence probability under current (1981-2010) and three future time periods (out to 2100). Occurrence probabilities and the relative importance of predictor variables varied widely among study areas, revealing important local-scale differences in the realized niche of the American pika. This variation resulted in diverse and - in some cases - highly divergent future potential occupancy patterns for pikas, ranging from complete extirpation in some study areas to stable occupancy patterns in others. Habitat composition and connectivity, which are rarely incorporated in SDM projections, were influential in predicting pika occupancy in all study areas and frequently outranked climate variables. Our findings illustrate the importance of a place-based approach to species distribution modeling that includes fine-scale factors when assessing current and future climate impacts on species' distributions, especially when predictions are intended to manage and conserve species of concern within individual protected areas.

Global change and terrestrial plant community dynamics

Anthropogenic drivers of global change include rising atmospheric concentrations of carbon dioxide and other greenhouse gasses and resulting changes in the climate, as well as nitrogen deposition, biotic invasions, altered disturbance regimes, and land-use change. Predicting the effects of global change on terrestrial plant communities is crucial because of the ecosystem services vegetation provides, from climate regulation to forest products. In this paper, we present a framework for detecting vegetation changes and attributing them to global change drivers that incorporates multiple lines of evidence from spatially extensive monitoring networks, distributed experiments, remotely sensed data, and historical records. Based on a literature review, we summarize observed changes and then describe modeling tools that can forecast the impacts of multiple drivers on plant communities in an era of rapid change. Observed responses to changes in temperature, water, nutrients, land use, and disturbance show strong sensitivity of ecosystem productivity and plant population dynamics to water balance and long-lasting effects of disturbance on plant community dynamics. Persistent effects of land-use change and human-altered fire regimes on vegetation can overshadow or interact with climate change impacts. Models forecasting plant community responses to global change incorporate shifting ecological niches, population dynamics, species interactions, spatially explicit disturbance, ecosystem processes, and plant functional responses. Monitoring, experiments, and models evaluating multiple change drivers are needed to detect and predict vegetation changes in response to 21st century global change.

Hot topics in biodiversity and climate change research

With scientific and societal interest in biodiversity impacts of climate change growing enormously over the last decade, we analysed directions and biases in the recent most highly cited data papers in this field of research (from 2012 to 2014). The majority of this work relied on leveraging large databases of already collected historical information (but not paleo- or genetic data), and coupled these to new methodologies for making forward projections of shifts in species’ geographical ranges, with a focus on temperate and montane plants. A consistent finding was that the pace of climate-driven habitat change, along with increased frequency of extreme events, is outpacing the capacity of species or ecological communities to respond and adapt.

Live Fast, Die Young: Experimental Evidence of Population Extinction Risk due to Climate Change

Evidence has accumulated in recent decades on the drastic impact of climate change on biodiversity. Warming temperatures have induced changes in species physiology, phenology, and have decreased body size. Such modifications can impact population dynamics and could lead to changes in life cycle and demography. More specifically, conceptual frameworks predict that global warming will severely threaten tropical ectotherms while temperate ectotherms should resist or even benefit from higher temperatures. However, experimental studies measuring the impacts of future warming trends on temperate ectotherms' life cycle and population persistence are lacking. Here we investigate the impacts of future climates on a model vertebrate ectotherm species using a large-scale warming experiment. We manipulated climatic conditions in 18 seminatural populations over two years to obtain a present climate treatment and a warm climate treatment matching IPCC predictions for future climate. Warmer temperatures caused a faster body growth, an earlier reproductive onset, and an increased voltinism, leading to a highly accelerated life cycle but also to a decrease in adult survival. A matrix population model predicts that warm climate populations in our experiment should go extinct in around 20 y. Comparing our experimental climatic conditions to conditions encountered by populations across Europe, we suggest that warming climates should threaten a significant number of populations at the southern range of the distribution. Our findings stress the importance of experimental approaches on the entire life cycle to more accurately predict population and species persistence in future climates.

Warmer climates accelerate the pace of life of lizards and this demographic change leads to a strong decrease in population growth rate that may ultimately result in population extinctions.

Ongoing climate change has potentially drastic impacts on biodiversity. Because their body temperature depends on their external environment, ectotherm (“cold-blooded”) species are thought to be more at risk from warming climates than endotherm (“warm-blooded”) species that regulate their temperature internally. Tropical ectotherms should be particularly threatened by climate change, while temperate ectotherms should resist or even benefit from higher temperatures. While most of the evidence on the impacts of climate change comes from long-term field studies, experimental evidence of the impact of future climatic conditions is still lacking. Here we investigate the impacts of future climates on a temperate lizard using a seminatural warming experiment. We find that warmer temperatures led to a highly accelerated life cycle and a decrease in adult survival. As a result, we postulate that populations in such warm climates would be expected to go extinct in around 20 y. Comparing our experimental conditions to climatic conditions in European populations of common lizards, we show that many populations should be threatened in the next century, particularly in Southern Europe. Our findings challenge the optimistic view that climate change is only a threat for tropical ectotherms and stress the importance of experimental approaches to predicting the consequences of future warming trends.