Forecasting climate change impacts on plant populations over large spatial extents

Tree rings reveal the transient risk of extinction hidden inside climate envelope forecasts

Given the importance of climate in shaping species' geographic distributions, climate change poses an existential threat to biodiversity. Climate envelope modeling, the predominant approach used to quantify this threat, presumes that individuals in populations respond to climate variability and change according to species-level responses inferred from spatial occurrence data-such that individuals at the cool edge of a species' distribution should benefit from warming (the "leading edge"), whereas individuals at the warm edge should suffer (the "trailing edge"). Using 1,558 tree-ring time series of an aridland pine (Pinus edulis) collected at 977 locations across the species' distribution, we found that trees everywhere grow less in warmer-than-average and drier-than-average years. Ubiquitous negative temperature sensitivity indicates that individuals across the entire distribution should suffer with warming-the entire distribution is a trailing edge. Species-level responses to spatial climate variation are opposite in sign to individual-scale responses to time-varying climate for approximately half the species' distribution with respect to temperature and the majority of the species' distribution with respect to precipitation. These findings, added to evidence from the literature for scale-dependent climate responses in hundreds of species, suggest that correlative, equilibrium-based range forecasts may fail to accurately represent how individuals in populations will be impacted by changing climate. A scale-dependent view of the impact of climate change on biodiversity highlights the transient risk of extinction hidden inside climate envelope forecasts and the importance of evolution in rescuing species from extinction whenever local climate variability and change exceeds individual-scale climate tolerances.

The ties that bind the sagebrush biome: integrating genetic connectivity into range-wide conservation of greater sage-grouse

Conserving genetic connectivity is fundamental to species persistence, yet rarely is made actionable into spatial planning for imperilled species. Climate change and habitat degradation have added urgency to embrace connectivity into networks of protected areas. Our two-step process integrates a network model with a functional connectivity model, to identify population centres important to maintaining genetic connectivity then to delineate those pathways most likely to facilitate connectivity thereamong for the greater sage-grouse (Centrocercus urophasianus), a species of conservation concern ranging across eleven western US states and into two Canadian provinces. This replicable process yielded spatial action maps, able to be prioritized by importance to maintaining range-wide genetic connectivity. We used these maps to investigate the efficacy of 3.2 million ha designated as priority areas for conservation (PACs) to encompass functional connectivity. We discovered that PACs encompassed 41.1% of cumulative functional connectivity-twice the amount of connectivity as random-and disproportionately encompassed the highest-connectivity landscapes. Comparing spatial action maps to impedances to connectivity such as cultivation and woodland expansion allows both planning for future management and tracking outcomes from past efforts.

Climate disequilibrium dominates uncertainty in long-term projections of primary productivity

Rapid climate change may exceed ecosystems' capacities to respond through processes including phenotypic plasticity, compositional turnover and evolutionary adaption. However, consequences of the resulting climate disequilibria for ecosystem functioning are rarely considered in projections of climate change impacts. Combining statistical models fit to historical climate data and remotely-sensed estimates of herbaceous net primary productivity with an ensemble of climate models, we demonstrate that assumptions concerning the magnitude of climate disequilibrium are a dominant source of uncertainty: models assuming maximum disequilibrium project widespread decreases in productivity in the western US by 2100, while models assuming minimal disequilibrium project productivity increases. Uncertainty related to climate disequilibrium is larger than uncertainties from variation among climate models or emissions pathways. A better understanding of processes that regulate climate disequilibria is essential for improving long-term projections of ecological responses and informing management to maintain ecosystem functioning at historical baselines.

Different effects of warming treatments in forests versus hedgerows on the understorey plant Geum urbanum

The effectiveness of hedgerows as functional corridors in the face of climate warming has been little researched. Here we investigated the effects of warming temperatures on plant performance and population growth of Geum urbanum in forests versus hedgerows in two European temperate regions. Adult individuals were transplanted in three forest-hedgerow pairs in each of two different latitudes, and an experimental warming treatment using open-top chambers was used in a full factorial design. Plant performance was analysed using mixed models and population performance was analysed using Integral Projection Models and elasticity analyses. Temperature increases due to open-top chamber installation were higher in forests than in hedgerows. In forests, the warming treatment had a significant negative effect on the population growth rate of G. urbanum. In contrast, no significant effect of the warming treatment on population dynamics was detected in hedgerows. Overall, the highest population growth rates were found in the forest control sites, which was driven by a higher fecundity rather than a higher survival probability. Effects of warming treatments on G. urbanum population growth rates differed between forests and hedgerows. In forests, warming treatments negatively affected population growth, but not in hedgerows. This could be a consequence of the overall lower warming achieved in hedgerows. We conclude that maintenance of cooler forest microclimates coul, at least temporarily, moderate the species response to climate warming.

Statistical considerations of nonrandom treatment applications reveal region-wide benefits of widespread post-fire restoration action

Accurate predictions of ecological restoration outcomes are needed across the increasingly large landscapes requiring treatment following disturbances. However, observational studies often fail to account for nonrandom treatment application, which can result in invalid inference. Examining a spatiotemporally extensive management treatment involving post-fire seeding of declining sagebrush shrubs across semiarid areas of the western USA over two decades, we quantify drivers and consequences of selection biases in restoration using remotely sensed data. From following more than 1,500 wildfires, we find treatments were disproportionately applied in more stressful, degraded ecological conditions. Failure to incorporate unmeasured drivers of treatment allocation led to the conclusion that costly, widespread seedings were unsuccessful; however, after considering sources of bias, restoration positively affected sagebrush recovery. Treatment effects varied with climate, indicating prioritization criteria for interventions. Our findings revise the perspective that post-fire sagebrush seedings have been broadly unsuccessful and demonstrate how selection biases can pose substantive inferential hazards in observational studies of restoration efficacy and the development of restoration theory.

Divergent climate change effects on widespread dryland plant communities driven by climatic and ecohydrological gradients

Plant community response to climate change will be influenced by individual plant responses that emerge from competition for limiting resources that fluctuate through time and vary across space. Projecting these responses requires an approach that integrates environmental conditions and species interactions that result from future climatic variability. Dryland plant communities are being substantially affected by climate change because their structure and function are closely tied to precipitation and temperature, yet impacts vary substantially due to environmental heterogeneity, especially in topographically complex regions. Here, we quantified the effects of climate change on big sagebrush (Artemisia tridentata Nutt.) plant communities that span 76 million ha in the western United States. We used an individual-based plant simulation model that represents intra- and inter-specific competition for water availability, which is represented by a process-based soil water balance model. For dominant plant functional types, we quantified changes in biomass and characterized agreement among 52 future climate scenarios. We then used a multivariate matching algorithm to generate fine-scale interpolated surfaces of functional type biomass for our study area. Results suggest geographically divergent responses of big sagebrush to climate change (changes in biomass of -20% to +27%), declines in perennial C₃ grass and perennial forb biomass in most sites, and widespread, consistent, and sometimes large increases in perennial C₄ grasses. The largest declines in big sagebrush, perennial C₃ grass and perennial forb biomass were simulated in warm, dry sites. In contrast, we simulated no change or increases in functional type biomass in cold, moist sites. There was high agreement among climate scenarios on climate change impacts to functional type biomass, except for big sagebrush. Collectively, these results suggest divergent responses to warming in moisture-limited versus temperature-limited sites and potential shifts in the relative importance of some of the dominant functional types that result from competition for limiting resources.

Ecological forecasts reveal limitations of common model selection methods: predicting changes in beaver colony densities

Over the past two decades, there have been numerous calls to make ecology a more predictive science through direct empirical assessments of ecological models and predictions. While the widespread use of model selection using information criteria has pushed ecology toward placing a higher emphasis on prediction, few attempts have been made to validate the ability of information criteria to correctly identify the most parsimonious model with the greatest predictive accuracy. Here, we used an ecological forecasting framework to test the ability of information criteria to accurately predict the relative contribution of density dependence and density-independent factors (forage availability, harvest, weather, wolf [Canis lupus] density) on inter-annual fluctuations in beaver (Castor canadensis) colony densities. We modeled changes in colony densities using a discrete-time Gompertz model, and assessed the performance of four models using information criteria values: density-independent models with (1) and without (2) environmental covariates; and density-dependent models with (3) and without (4) environmental covariates. We then evaluated the forecasting accuracy of each model by withholding the final one-third of observations from each population and compared observed vs. predicted densities. Information criteria and our forecasting accuracy metrics both provided strong evidence of compensatory density dependence in the annual dynamics of beaver colony densities. However, despite strong within-sample performance by the most complex model (density-dependent with covariates) as determined using information criteria, hindcasts of colony densities revealed that the much simpler density-dependent model without covariates performed nearly as well predicting out-of-sample colony densities. The hindcast results indicated that the complex model over-fit our data, suggesting that parameters identified by information criteria as important predictor variables are only marginally valuable for predicting landscape-scale beaver colony dynamics. Our study demonstrates the importance of evaluating ecological models and predictions with long-term data and revealed how a known limitation of information criteria (over-fitting of complex models) can affect our interpretation of ecological dynamics. While incorporating knowledge of the factors that influence animal population dynamics can improve population forecasts, we suggest that comparing forecast performance metrics can likewise improve our knowledge of the factors driving population dynamics.

Integrating anthropogenic factors into regional-scale species distribution models-A novel application in the imperiled sagebrush biome

Species distribution models (SDMs) that rely on regional-scale environmental variables will play a key role in forecasting species occurrence in the face of climate change. However, in the Anthropocene, a number of local-scale anthropogenic variables, including wildfire history, land-use change, invasive species, and ecological restoration practices can override regional-scale variables to drive patterns of species distribution. Incorporating these human-induced factors into SDMs remains a major research challenge, in part because spatial variability in these factors occurs at fine scales, rendering prediction over regional extents problematic. Here, we used big sagebrush (Artemisia tridentata Nutt.) as a model species to explore whether including human-induced factors improves the fit of the SDM. We applied a Bayesian hurdle spatial approach using 21,753 data points of field-sampled vegetation obtained from the LANDFIRE program to model sagebrush occurrence and cover by incorporating fire history metrics and restoration treatments from 1980 to 2015 throughout the Great Basin of North America. Models including fire attributes and restoration treatments performed better than those including only climate and topographic variables. Number of fires and fire occurrence had the strongest relative effects on big sagebrush occurrence and cover, respectively. The models predicted that the probability of big sagebrush occurrence decreases by 1.2% (95% CI: -6.9%, 0.6%) when one fire occurs and cover decreases by 44.7% (95% CI: -47.9%, -41.3%) if at least one fire occurred over the 36 year period of record. Restoration practices increased the probability of big sagebrush occurrence but had minimal effect on cover. Our results demonstrate the potential value of including disturbance and land management along with climate in models to predict species distributions. As an increasing number of datasets representing land-use history become available, we anticipate that our modeling framework will have broad relevance across a range of biomes and species.

Influences of potential oil and gas development and future climate on Sage-grouse declines and redistribution

Multiple environmental stressors impact wildlife populations, but we often know little about their cumulative and combined influences on population outcomes. We generally know more about past effects than potential future impacts, and direct influences such as changes of habitat footprints than indirect, long-term responses in behavior, distribution, or abundance. Yet, an understanding of all these components is needed to plan for future landscapes that include human activities and wildlife. We developed a case study to assess how spatially explicit individual-based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater Sage-grouse in southwest Wyoming, USA, we projected oil and gas development footprints and climate-induced vegetation changes 50 years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we recalculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term Sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered a useful means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. The inclusion of movement and demographic responses to oil and gas infrastructure resulted in substantive changes in distribution and abundance when cumulated over several decades and throughout the regional population. When combined, additive development and climate-induced vegetation changes reduced abundance by up to half of the original size. In our example, the consideration of only a single population stressor the final possible population size by as much as 50%. Multiple stressors and their cumulative impacts need to be broadly considered through space and time to avoid underestimating the impacts of multiple gradual changes and overestimating the ability of populations to withstand 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.

Adapting management to a changing world: Warm temperatures, dry soil, and interannual variability limit restoration success of a dominant woody shrub in temperate drylands

Restoration and rehabilitation of native vegetation in dryland ecosystems, which encompass over 40% of terrestrial ecosystems, is a common challenge that continues to grow as wildfire and biological invasions transform dryland plant communities. The difficulty in part stems from low and variable precipitation, combined with limited understanding about how weather conditions influence restoration outcomes, and increasing recognition that one-time seeding approaches can fail if they do not occur during appropriate plant establishment conditions. The sagebrush biome, which once covered over 620,000 km² of western North America, is a prime example of a pressing dryland restoration challenge for which restoration success has been variable. We analyzed field data on Artemisia tridentata (big sagebrush) restoration collected at 771 plots in 177 wildfire sites across its western range, and used process-based ecohydrological modeling to identify factors leading to its establishment. Our results indicate big sagebrush occurrence is most strongly associated with relatively cool temperatures and wet soils in the first spring after seeding. In particular, the amount of winter snowpack, but not total precipitation, helped explain the availability of spring soil moisture and restoration success. We also find considerable interannual variability in the probability of sagebrush establishment. Adaptive management strategies that target seeding during cool, wet years or mitigate effects of variability through repeated seeding may improve the likelihood of successful restoration in dryland ecosystems. Given consistent projections of increasing temperatures, declining snowpack, and increasing weather variability throughout midlatitude drylands, weather-centric adaptive management approaches to restoration will be increasingly important for dryland restoration success.

The response of big sagebrush (Artemisia tridentata) to interannual climate variation changes across its range

Understanding how annual climate variation affects population growth rates across a species' range may help us anticipate the effects of climate change on species distribution and abundance. We predict that populations in warmer or wetter parts of a species' range should respond negatively to periods of above average temperature or precipitation, respectively, whereas populations in colder or drier areas should respond positively to periods of above average temperature or precipitation. To test this, we estimated the population sensitivity of a common shrub species, big sagebrush (Artemisia tridentata), to annual climate variation across its range. Our analysis includes 8,175 observations of year-to-year change in sagebrush cover or production from 131 monitoring sites in western North America. We coupled these observations with seasonal weather data for each site and analyzed the effects of spring through fall temperatures and fall through spring accumulated precipitation on annual changes in sagebrush abundance. Sensitivity to annual temperature variation supported our hypothesis: years with above average temperatures were beneficial to sagebrush in colder locations and detrimental to sagebrush in hotter locations. In contrast, sensitivity to precipitation did not change significantly across the distribution of sagebrush. This pattern of responses suggests that regional abundance of this species may be more limited by temperature than by precipitation. We also found important differences in how the ecologically distinct subspecies of sagebrush responded to the effects of precipitation and temperature. Our model predicts that a short-term temperature increase could produce an increase in sagebrush cover at the cold edge of its range and a decrease in cover at the warm edge of its range. This prediction is qualitatively consistent with predictions from species distribution models for sagebrush based on spatial occurrence data, but it provides new mechanistic insight and helps estimate how much and how fast sagebrush cover may change within its range.

Forecasting biodiversity in breeding birds using best practices

Biodiversity forecasts are important for conservation, management, and evaluating how well current models characterize natural systems. While the number of forecasts for biodiversity is increasing, there is little information available on how well these forecasts work. Most biodiversity forecasts are not evaluated to determine how well they predict future diversity, fail to account for uncertainty, and do not use time-series data that captures the actual dynamics being studied. We addressed these limitations by using best practices to explore our ability to forecast the species richness of breeding birds in North America. We used hindcasting to evaluate six different modeling approaches for predicting richness. Hindcasts for each method were evaluated annually for a decade at 1,237 sites distributed throughout the continental United States. All models explained more than 50% of the variance in richness, but none of them consistently outperformed a baseline model that predicted constant richness at each site. The best practices implemented in this study directly influenced the forecasts and evaluations. Stacked species distribution models and "naive" forecasts produced poor estimates of uncertainty and accounting for this resulted in these models dropping in the relative performance compared to other models. Accounting for observer effects improved model performance overall, but also changed the rank ordering of models because it did not improve the accuracy of the "naive" model. Considering the forecast horizon revealed that the prediction accuracy decreased across all models as the time horizon of the forecast increased. To facilitate the rapid improvement of biodiversity forecasts, we emphasize the value of specific best practices in making forecasts and evaluating forecasting methods.

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.