Predicting impacts of climate change on biodiversity: a role for semi‐mechanistic community‐level modelling

Aim Robust and reliable predictions of the effects of climate change on biodiversity are required in formulating conservation and management strategies that best retain biodiversity into the future. Significant challenges in modelling climate change impacts arise from limitations in our current knowledge of biodiversity. Community‐level modelling can complement species‐level approaches in overcoming these limitations and predicting climate change impacts on biodiversity as a whole. However, the community‐level approaches applied to date have been largely correlative, ignoring the key processes that influence change in biodiversity over space and time. Here, we suggest that the development of new ‘semi‐mechanistic’ community‐level models would substantially increase our capacity to predict climate change impacts on biodiversity. Location Global. Methods Drawing on an expansive review of biodiversity modelling approaches and recent advances in semi‐mechanistic modelling at the species level, we outline the main elements of a new semi‐mechanistic community‐level modelling approach. Results Our quantitative review revealed a sharp divide between mechanistic and non‐mechanistic biodiversity modelling approaches, with very few semi‐mechanistic models developed to date. Main conclusions We suggest that the conceptual framework presented here for combining mechanistic and non‐mechanistic community‐level approaches offers a promising means of incorporating key processes into predictions of climate change impacts on biodiversity whilst working within the limits of our current knowledge.     

Microclimate modelling of beach sand temperatures reveals high spatial and temporal variation at sea turtle rookeries

The continual development of ecological models and availability of high-resolution gridded climate surfaces have stimulated studies that link climate variables to functional traits of organisms. A primary constraint of these studies is the ability to reliably predict the microclimate that an organism experiences using macroscale climate inputs. This is particularly important in regions where access to empirical information is limited. Here, we contrast correlative models based on both ambient and sea surface temperatures to mechanistic modelling approaches to predict beach sand temperatures at depths relevant to sea turtle nesting. We show that mechanistic models are congruent with correlative models at predicting sand temperatures. We used these predictions to explore thermal variation across 46 mainland and island beaches that span the geographical range of sea turtle nesting in Western Australia. Using high resolution gridded climate surfaces and site-specific soil reflectance, we predict almost 9 °C variation in average annual temperatures between beaches, and nearly 10 °C variation in average temperatures during turtle nesting seasons. Validation of models demonstrated that predictions were typically within 2 °C of observations and, although most sites had high correlations (r² > 0.7), predictive capacity varied between sites. An advantage of the mechanistic model demonstrated here is that it can be used to explore the impacts of climate change on sea turtle nesting beach temperatures as, unlike correlative models, it can be forced with novel combinations of environmental variables.     

Trait‐based climate vulnerability assessments in data‐rich systems: An application to eastern Bering Sea fish and invertebrate stocks

Trait‐based climate vulnerability assessments based on expert evaluation have emerged as a rapid tool to assess biological vulnerability when detailed correlative or mechanistic studies are not feasible. Trait‐based assessments typically view vulnerability as a combination of sensitivity and exposure to climate change. However, in some locations, a substantial amount of information may exist on system productivity and environmental conditions (both current and projected), with potential disparities in the information available for data‐rich and data‐poor stocks. Incorporating this level of detailed information poses challenges when conducting, and communicating uncertainty from, rapid vulnerability assessments. We applied a trait‐based vulnerability assessment to 36 fish and invertebrate stocks in the eastern Bering Sea (EBS), a data‐rich ecosystem. In recent years, the living marine resources of the EBS and Aleutian Islands have supported fisheries worth more than US $1 billion of annual ex‐vessel value. Our vulnerability assessment uses projections (to 2039) from three downscaled climate models, and graphically characterizes the variation in climate projections between climate models and between seasons. Bootstrapping was used to characterize uncertainty in specific biological traits and environmental variables, and in the scores for sensitivity, exposure, and vulnerability. The sensitivity of EBS stocks to climate change ranged from “low” to “high,” but vulnerability ranged between “low” and “moderate” due to limited exposure to climate change. Comparison with more detailed studies reveals that water temperature is an important variable for projecting climate impacts on stocks such as walleye pollock (Gadus chalcogrammus), and sensitivity analyses revealed that modifying the rule for determining vulnerability increased the vulnerability scores. This study demonstrates the importance of considering several uncertainties (e.g., climate projections, biological, and model structure) when conducting climate vulnerability assessments, and can be extended in future research to consider the vulnerability of user groups dependent on these stocks.     

Matching the forecast horizon with the relevant spatial and temporal processes and data sources

Most phenomenological, statistical models used to generate ecological forecasts take either a time-series approach, based on long-term data from one location, or a space-for-time approach, based on data describing spatial patterns across environmental gradients. However, the magnitude and even the sign of environment–response relationships detected using these two approaches often differs, leading to contrasting predictions about responses to future environmental change. Here we consider how the forecast horizon determines whether more accurate predictions come from the time-series approach, the space-for-time approach or a combination of the two. As proof of concept, we use simulated case studies to show that forecasts for short and long forecast horizons need to focus on different ecological processes, which are reflected in different kinds of data. First, we simulated population or community dynamics under stationary temperature using two simple, mechanistic models. Second, we fit statistical models to the simulated data using a time-series approach, a space-for-time approach or a weighted average. We then forecast the response to a temperature increase using the statistical models, and compared these forecasts to temperature effects simulated by the mechanistic models. We found that the time-series approach made accurate short-term predictions because it captured initial conditions and effects of fast processes such as birth and death. The space-for-time approach made more accurate long-term predictions because it better captured the influence of slower processes such as evolutionary and ecological selection. The weighted average made accurate predictions at all time scales, including intermediate time-scales where the other two approaches performed poorly. A weighted average of time-series and space-for-time approaches shows promise, but making this weighted model operational will require new research to predict the rate at which slow processes begin to influence dynamics.     

Watershed‐scale climate influences productivity of Chinook salmon populations across southcentral Alaska

The ecosystems supporting Pacific salmon (Oncorhynchus spp.) are changing rapidly as a result of climate change and habitat alteration. Understanding how—and how consistently—salmon populations respond to changes at regional and watershed scales has major implications for fisheries management and habitat conservation. Chinook salmon (O. tshawytscha) populations across Alaska have declined over the past decade, resulting in fisheries closures and prolonged impacts to local communities. These declines are associated with large‐scale climate drivers, but uncertainty remains about the role of local conditions (e.g., precipitation, streamflow, and stream temperature) that vary among the watersheds where salmon spawn and rear. We estimated the effects of these and other environmental indicators on the productivity of 15 Chinook salmon populations in the Cook Inlet basin, southcentral Alaska, using a hierarchical Bayesian stock‐recruitment model. Salmon spawning during 2003–2007 produced 57% fewer recruits than the previous long‐term average, leading to declines in adult returns beginning in 2008. These declines were explained in part by density dependence, with reduced population productivity following years of high spawning abundance. Across all populations, productivity declined with increased precipitation during the fall spawning and early incubation period and increased with above‐average precipitation during juvenile rearing. Above‐average stream temperatures during spawning and rearing had variable effects, with negative relationships in many warmer streams and positive relationships in some colder streams. Productivity was also associated with regional indices of streamflow and ocean conditions, with high variability among populations. The cumulative effects of adverse conditions in freshwater, including high spawning abundance, heavy fall rains, and hot, dry summers may have contributed to the recent population declines across the region. Identifying both coherent and differential responses to environmental change underscores the importance of targeted, watershed‐specific monitoring and conservation efforts for maintaining resilient salmon runs in a warming world.     

Vanishing islands in the sky? A comparison of correlation- and mechanism-based forecasts of range dynamics for montane salamanders under climate change

Forecasting the effects of climate change on species and populations is a fundamental goal of conservation biology, especially for montane endemics which seemingly are under the greatest threat of extinction given their association with cool, high elevation habitats. Species distribution models (also known as niche models) predict where on the landscape there is suitable habitat for a species of interest. Correlative niche modeling, the most commonly employed approach to predict species' distributions, relies on correlations between species' localities and current environmental data. This type of model could spuriously forecast less future suitable habitat because species' current distributions may not adequately represent their thermal tolerance, and future climate conditions may not be analogous to current conditions. We compared the predicted distributions for three montane species of Plethodon salamanders in the southern Appalachian Mountains of North America using a correlative modeling approach and a mechanistic model. The mechanistic model incorporates species-specific physiology, morphology and behavior to predict an annual energy budget on the landscape. Both modeling approaches performed well at predicting the species' current distributions and predicted that all species could persist in habitats at higher elevation through 2085. The mechanistic model predicted more future suitable habitat than the correlative model. We attribute these differences to the mechanistic approach being able to model shifts in key range-limiting biological processes (changes in surface activity time and energy costs) that the correlative approach cannot. Choice of global circulation model (GCM) contributed significantly to distribution predictions, with a tenfold difference in future suitability based on GCM, indicating that GCM variability should be either directly included in models of species distributions or, indirectly, through the use of multi-model ensemble averages. Our results indicate that correlative models are over-predicting habitat loss for montane species, suggesting a critical need to incorporate mechanisms into forecasts of species' range dynamics.     

The Combined Use of Correlative and Mechanistic Species Distribution Models Benefits Low Conservation Status Species

Species can respond to climate change by tracking appropriate environmental conditions in space, resulting in a range shift. Species Distribution Models (SDMs) can help forecast such range shift responses. For few species, both correlative and mechanistic SDMs were built, but allis shad (Alosa alosa), an endangered anadromous fish species, is one of them. The main purpose of this study was to provide a framework for joint analyses of correlative and mechanistic SDMs projections in order to strengthen conservation measures for species of conservation concern. Guidelines for joint representation and subsequent interpretation of models outputs were defined and applied. The present joint analysis was based on the novel mechanistic model GR3D (Global Repositioning Dynamics of Diadromous fish Distribution) which was parameterized on allis shad and then used to predict its future distribution along the European Atlantic coast under different climate change scenarios (RCP 4.5 and RCP 8.5). We then used a correlative SDM for this species to forecast its distribution across the same geographic area and under the same climate change scenarios. First, projections from correlative and mechanistic models provided congruent trends in probability of habitat suitability and population dynamics. This agreement was preferentially interpreted as referring to the species vulnerability to climate change. Climate change could not be accordingly listed as a major threat for allis shad. The congruence in predicted range limits between SDMs projections was the next point of interest. The difference, when noticed, required to deepen our understanding of the niche modelled by each approach. In this respect, the relative position of the northern range limit between the two methods strongly suggested here that a key biological process related to intraspecific variability was potentially lacking in the mechanistic SDM. Based on our knowledge, we hypothesized that local adaptations to cold temperatures deserved more attention in terms of modelling, but further in conservation planning as well.     

Mechanistic modeling of climate effects on redistribution and population growth in a community of fish species

Understanding community responses to climate is critical for anticipating the future impacts of global change. However, despite increased research efforts in this field, models that explicitly include important biological mechanisms are lacking. Quantifying the potential impacts of climate change on species is complicated by the fact that the effects of climate variation may manifest at several points in the biological process. To this end, we extend a dynamic mechanistic model that combines population dynamics, such as species interactions, with species redistribution by allowing climate to affect both processes. We examine their relative contributions in an application to the changing biomass of a community of eight species in the Gulf of Maine using over 30 years of fisheries data from the Northeast Fishery Science Center. Our model suggests that the mechanisms driving biomass trends vary across space, time, and species. Phase space plots demonstrate that failing to account for the dynamic nature of the environmental and biologic system can yield theoretical estimates of population abundances that are not observed in empirical data. The stock assessments used by fisheries managers to set fishing targets and allocate quotas often ignore environmental effects. At the same time, research examining the effects of climate change on fish has largely focused on redistribution. Frameworks that combine multiple biological reactions to climate change are particularly necessary for marine researchers. This work is just one approach to modeling the complexity of natural systems and highlights the need to incorporate multiple and possibly interacting biological processes in future models.     

Spatiotemporal landscape genetics: Investigating ecology and evolution through space and time

Genetic time‐series data from historical samples greatly facilitate inference of past population dynamics and species evolution. Yet, although climate and landscape change are often touted as post‐hoc explanations of biological change, our understanding of past climate and landscape change influences on evolutionary processes is severely hindered by the limited application of methods that directly relate environmental change to species dynamics through time. Increased integration of spatiotemporal environmental and genetic data will revolutionize the interpretation of environmental influences on past population processes and the quantification of recent anthropogenic impacts on species, and vastly improve prediction of species responses under future climate change scenarios, yielding widespread revelations across evolutionary biology, landscape ecology and conservation genetics. This review encourages greater use of spatiotemporal landscape genetic analyses that explicitly link landscape, climate and genetic data through time by providing an overview of analytical approaches for integrating historical genetic and environmental data in five key research areas: population genetic structure, demography, phylogeography, metapopulation connectivity and adaptation. We also include a tabular summary of key methodological information, suggest approaches for mitigating the particular difficulties in applying these techniques to ancient DNA and palaeoclimate data, and highlight areas for future methodological development.     

Trait-based approaches to conservation physiology: forecasting environmental change risks from the bottom up

Trait-based approaches have long been a feature of physiology and of ecology. While the latter fields drifted apart in the twentieth century, they are converging owing at least partly to growing similarities in their trait-based approaches, which have much to offer conservation biology. The convergence of spatially explicit approaches to understanding trait variation and its ecological implications, such as encapsulated in community assembly and macrophysiology, provides a significant illustration of the similarity of these areas. Both adopt trait-based informatics approaches which are not only providing fundamental biological insights, but are also delivering new information on how environmental change is affecting diversity and how such change may perhaps be mitigated. Such trait-based conservation physiology is illustrated here for each of the major environmental change drivers, specifically: the consequences of overexploitation for body size and physiological variation; the impacts of vegetation change on thermal safety margins; the consequences of changing net primary productivity and human use thereof for physiological variation and ecosystem functioning; the impacts of rising temperatures on water loss in ectotherms; how hemisphere-related variation in traits may affect responses to changing rainfall regimes and pollution; and how trait-based approaches may enable interactions between climate change and biological invasions to be elucidated.     

Quantitative approaches in climate change ecology

Contemporary impacts of anthropogenic climate change on ecosystems are increasingly being recognized. Documenting the extent of these impacts requires quantitative tools for analyses of ecological observations to distinguish climate impacts in noisy data and to understand interactions between climate variability and other drivers of change. To assist the development of reliable statistical approaches, we review the marine climate change literature and provide suggestions for quantitative approaches in climate change ecology. We compiled 267 peer‐reviewed articles that examined relationships between climate change and marine ecological variables. Of the articles with time series data (n = 186), 75% used statistics to test for a dependency of ecological variables on climate variables. We identified several common weaknesses in statistical approaches, including marginalizing other important non‐climate drivers of change, ignoring temporal and spatial autocorrelation, averaging across spatial patterns and not reporting key metrics. We provide a list of issues that need to be addressed to make inferences more defensible, including the consideration of (i) data limitations and the comparability of data sets; (ii) alternative mechanisms for change; (iii) appropriate response variables; (iv) a suitable model for the process under study; (v) temporal autocorrelation; (vi) spatial autocorrelation and patterns; and (vii) the reporting of rates of change. While the focus of our review was marine studies, these suggestions are equally applicable to terrestrial studies. Consideration of these suggestions will help advance global knowledge of climate impacts and understanding of the processes driving ecological change.     

Individual to community-level faunal responses to environmental change from a marine fossil record of Early Miocene global warming

Modern climate change has a strong potential to shift earth systems and biological communities into novel states that have no present-day analog, leaving ecologists with no observational basis to predict the likely biotic effects. Fossil records contain long time-series of past environmental changes outside the range of modern observation, which are vital for predicting future ecological responses, and are capable of (a) providing detailed information on rates of ecological change, (b) illuminating the environmental drivers of those changes, and (c) recording the effects of environmental change on individual physiological rates. Outcrops of Early Miocene Newport Member of the Astoria Formation (Oregon) provide one such time series. This record of benthic foraminiferal and molluscan community change from continental shelf depths spans a past interval environmental change (≈ 20.3-16.7 mya) during which the region warmed 2.1-4.5°C, surface productivity and benthic organic carbon flux increased, and benthic oxygenation decreased, perhaps driven by intensified upwelling as on the modern Oregon coast. The Newport Member record shows that (a) ecological responses to natural environmental change can be abrupt, (b) productivity can be the primary driver of faunal change during global warming, (c) molluscs had a threshold response to productivity change while foraminifera changed gradually, and (d) changes in bivalve body size and growth rates parallel changes in taxonomic composition at the community level, indicating that, either directly or indirectly through some other biological parameter, the physiological tolerances of species do influence community change. Ecological studies in modern and fossil records that consider multiple ecological levels, environmental parameters, and taxonomic groups can provide critical information for predicting future ecological change and evaluating species vulnerability.     

Integrating mechanistic and correlative niche models to unravel range‐limiting processes in a temperate amphibian

Insights into the causal mechanisms that limit species distributions are likely to improve our ability to anticipate species range shifts in response to climate change. For species with complex life histories, a mechanistic understanding of how climate affects different lifecycle stages may be crucial for making accurate forecasts. Here, we use mechanistic niche modeling (NicheMapR) to derive “proximate” (mechanistic) variables for tadpole, juvenile, and adult Rana temporaria. We modeled the hydroperiod, and maximum and minimum temperatures of shallow (30 cm) ponds, as well as activity windows for juveniles and adults. We then used those (“proximate”) variables in correlative ecological niche models (Maxent) to assess their role in limiting the species’ current distribution, and to investigate the potential effects of climate change on R. temporaria across Europe. We further compared the results with a model based on commonly used macroclimatic (“distal”) layers (i.e., bioclimatic layers from WorldClim). The maximum temperature of the warmest month (a macroclimatic variable) and maximum pond temperatures (a mechanistic variable) were the most important range‐limiting factors, and maximum temperature thresholds were consistent with the observed upper thermal limit of R. temporaria tadpoles. We found that range shift forecasts in central Europe are far more pessimistic when using distal macroclimatic variables, compared to projections based on proximate mechanistic variables. However, both approaches predicted extensive decreases in climatic suitability in southern Europe, which harbors a significant fraction of the species’ genetic diversity. We show how mechanistic modeling provides ways to depict gridded layers that directly reflect the microenvironments experienced by organisms at continental scales, and to reconstruct those predictors without extrapolation under novel future conditions. Furthermore, incorporating those predictors in correlative ecological niche models can help shed light on range‐limiting processes, and can have substantial impacts on predictions of climate‐induced range shifts.     

Grassland responses to increased rainfall depend on the timescale of forcing

Forecasting impacts of future climate change is an important challenge to biologists, both for understanding the consequences of different emissions trajectories and for developing adaptation measures that will minimize biodiversity loss. Existing variation provides a window into the effects of climate on species and ecosystems, but in many places does not encompass the levels or timeframes of forcing expected under directional climatic change. Experiments help us to fill in these uncertainties, simulating directional shifts to examine outcomes of new levels and sustained changes in conditions. Here, we explore the translation between short‐term responses to climate variability and longer‐term trajectories that emerge under directional climatic change. In a decade‐long experiment, we compare effects of short‐term and long‐term forcings across three trophic levels in grassland plots subjected to natural and experimental variation in precipitation. For some biological responses (plant productivity), responses to long‐term extension of the rainy season were consistent with short‐term responses, while for others (plant species richness, abundance of invertebrate herbivores and predators), there was pronounced divergence of long‐term trajectories from short‐term responses. These differences between biological responses mean that sustained directional changes in climate can restructure ecological relationships characterizing a system. Importantly, a positive relationship between plant diversity and productivity turned negative under one scenario of climate change, with a similar change in the relationship between plant productivity and consumer biomass. Inferences from experiments such as this form an important part of wider efforts to understand the complexities of climate change responses.     

Loss of trophic complexity in saline prairie lakes as indicated by stable-isotope based community-metrics

Variations in climate, watershed characteristics and lake-internal processes often result in a large variability of food-web complexity in lake ecosystems. Some of the largest ranges in these environmental parameters can be found in lakes across the northern Great Plains as they are characterized by extreme gradients in respect to lake morphometry and water chemistry, with individual parameters often varying over several orders of magnitude. To evaluate the effects of environmental conditions on trophic complexity in prairie lake food-webs, we analyzed carbon and nitrogen stable isotopes of fishes, zooplankton and littoral macroinvertebrates in 20 lakes across southern Saskatchewan. Our two-year study identified very diverse patterns of trophic complexity, with was predominantly associated with among-lake differences. Small but significant temporal effects were also detected, which were predominantly associated with changes in productivity. The most influential parameters related to changes in trophic complexity among lakes were salinity, complexity of fish assemblage, and indicators of productivity (e.g. nutrients, Chl a). Generally, trophic diversity, number of trophic levels, and trophic redundancy were highest in productive freshwater lakes with diverse fish communities. Surprisingly, mesosaline lakes that were characterized by very low or no predation pressure from fishes were not colonized by invertebrate predators as it is often the case in boreal systems; instead, trophic complexity was further reduced. Together, prairie lake food-webs appear to be highly sensitive to changes in salinity and the loss of piscivorous fishes, making freshwater and mesosaline lakes most vulnerable to the impacts of climate variability. This is particularly important as global circulation models predict future climate warming to have disproportionate negative impacts on hydrologic conditions in this area.     

Predicted decrease in global climate suitability masks regional complexity of invasive fruit fly species response to climate change

Climate change affects the rate of insect invasions as well as the abundance, distribution and impacts of such invasions on a global scale. Among the principal analytical approaches to predicting and understanding future impacts of biological invasions are Species Distribution Models (SDMs), typically in the form of correlative Ecological Niche Models (ENMs). An underlying assumption of ENMs is that species–environment relationships remain preserved during extrapolations in space and time, although this is widely criticised. The semi-mechanistic modelling platform, CLIMEX, employs a top-down approach using species ecophysiological traits and is able to avoid some of the issues of extrapolation, making it highly applicable to investigating biological invasions in the context of climate change. The tephritid fruit flies (Diptera: Tephritidae) comprise some of the most successful invasive species and serious economic pests around the world. Here we project 12 tephritid species CLIMEX models into future climate scenarios to examine overall patterns of climate suitability and forecast potential distributional changes for this group. We further compare the aggregate response of the group against species-specific responses. We then consider additional drivers of biological invasions to examine how invasion potential is influenced by climate, fruit production and trade indices. Considering the group of tephritid species examined here, climate change is predicted to decrease global climate suitability and to shift the cumulative distribution poleward. However, when examining species-level patterns, the predominant directionality of range shifts for 11 of the 12 species is eastward. Most notably, management will need to consider regional changes in fruit fly species invasion potential where high fruit production, trade indices and predicted distributions of these flies overlap.     

Multi‐model comparison highlights consistency in predicted effect of warming on a semi‐arid shrub

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi‐model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi‐model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.     

Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges

Species distribution models (SDMs) use spatial environmental data to make inferences on species' range limits and habitat suitability. Conceptually, these models aim to determine and map components of a species' ecological niche through space and time, and they have become important tools in pure and applied ecology and evolutionary biology. Most approaches are correlative in that they statistically link spatial data to species distribution records. An alternative strategy is to explicitly incorporate the mechanistic links between the functional traits of organisms and their environments into SDMs. Here, we review how the principles of biophysical ecology can be used to link spatial data to the physiological responses and constraints of organisms. This provides a mechanistic view of the fundamental niche which can then be mapped to the landscape to infer range constraints. We show how physiologically based SDMs can be developed for different organisms in different environmental contexts. Mechanistic SDMs have different strengths and weaknesses to correlative approaches, and there are many exciting and unexplored prospects for integrating the two approaches. As physiological knowledge becomes better integrated into SDMs, we will make more robust predictions of range shifts in novel or non-equilibrium contexts such as invasions, translocations, climate change and evolutionary shifts.     

A field facility to simulate climate warming and increased nutrient supply in shallow aquatic ecosystems

Global warming and excess nitrogen deposition can exert strong impacts on aquatic populations, communities, and ecosystems. However, experimental data to establish clear cause-and-effect relationships in naturally complex field conditions are scarce in aquatic environments. Here, we describe the design and performance of a unique outdoor enclosure facility used to simulate warming, increased nitrogen supply, and both factors combined in a littoral freshwater wetland dominated by common reed, Phragmites australis. The experimental system effectively simulated a 2.8 °C climate warming scenario over an extended period, capturing the natural temperature variations in the wetland at diel and seasonal scales with only small deviations. Excess nitrogen supply enhanced nitrate concentrations especially in winter when it was associated with increased concentration of ammonium and dissolved organic carbon. Nitrogen also reduced dissolved oxygen concentrations, particularly in the summer. Importantly, by stimulating biological activity, warming enhanced the nitrogen uptake capacity of the wetland during the winter, emphasizing the need for multifactorial global change experiments that examine both warming and nitrogen loading in concert. Establishing similar experiments across broad environmental gradients holds great potential to provide robust assessments of the impacts of climate change on shallow aquatic ecosystems.