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

Establishing the distribution of Carpophilus truncatus in Australia using an integrative approach for an emerging global pest

The nitidulid beetle Carpophilus truncatus is rapidly becoming a major pest of nut crops around the world. This insect first infested Australian almonds in 2013 and has since escalated to be the preeminent insect pest for the industry. Data pertaining to C. truncatus distribution are scant, but without awareness of its origin, distribution, and ecological factors that influence distribution, efforts to understand and manage the insect as a pest are stymied. Here, we employ an integrative approach to gain a multifaceted understanding of the distribution of C. truncatus in Australia. Methods employed were (1) reviewing historical records in insect collections to establish the presence of C. truncatus prior to commercial almond horticulture, (2) field trapping of insects to establish presence in regions of interest, (3) laboratory trials to determine the thermal limits of the organism, and (4) correlative species distribution modelling to describe its current distribution. We find that C. truncatus is more widespread across Australia than was previously known, with historical records preceding commercial almond production in Australia by a century. The methods developed in this study can be applied elsewhere in the world where C. truncatus is an emerging pest, or to novel pest species as they arise with increasing frequency in a globalised and warming world.

Predicting habitat suitability of the critically endangered Be'er Sheva fringe-fingered lizard

Anthropogenic changes, such as land use, are the main drivers causing climate change and biodiversity loss, with hundreds of thousands of species lacking sufficient habitats for their populations to persist and likely to go extinct within decades. Endemic species are more susceptible to habitat changes and are at the forefront of the biodiversity crisis. We used species distribution models to generate a relative habitat suitability map and identified the habitat requirements of the critically endangered and endemic Be'er Sheva fringe-fingered lizard (Acanthodactylus beershebensis). The model showed that the species' suitable habitats are associated with arid loess plains characterized by scattered, low vegetation cover, primarily on north-facing aspects, suggesting that these species-specific habitat requirements limit its distribution. The size of the potentially suitable area within the species' historical range is 1350.73 km2. However, anthropogenic changes decreased the remaining suitable habitat to 995.04 km2. Most of this area is unprotected and at risk of further adverse anthropogenic effects. Only 91.72 km2 of this area is protected by the Israel Nature and Parks Authority, and 587.11 km2 may be considered indirectly protected because it is within military firing zones. Our study is the first attempt to map the remaining suitable habitat of A. beershebensis based on the results of a species distribution model. The results of this model can assist in prioritizing the protection of areas needed for the conservation of this critically endangered and endemic lizard species.

Global climate change-driven impacts on the Asian distribution of Limassolla leafhoppers, with implications for biological and environmental conservation

Knowing the impacts of global climate change on the habitat suitability distribution of Limassolla leafhoppers contributes to understanding the feedback of organisms on climate change from a macroecological perspective, and provides important scientific basis for protecting the ecological environment and biodiversity. However, there is limited knowledge on this aspect. Thus, our study aimed to address this gap by analyzing Asian habitat suitability and centroid shifts of Limassolla based on 19 bioclimatic variables and occurrence records. Selecting five ecological niche models with the outstanding predictive performance (Maxlike, generalized linear model, generalized additive model, random forest, and maximum entropy) along with their ensemble model from 12 models, the current habitat suitability of Limassolla and its future habitat suitability under two Shared Socio-economic Pathways (SSP1-2.6 and SSP5-8.5) in the 2050s and 2090s were predicted. The results showed that the prediction results of the five models are generally consistent. Based on ensemble model, 11 potential biodiversity hotspots with high suitability were identified. With climate change, the suitable range of Limassolla will experience both expansion and contraction. In SSP5-8.52050s, the expansion area is 118.56 × 104 km2, while the contraction area is 25.40 × 104 km2; in SSP1-2.62090s, the expansion area is 91.71 × 104 km2, and the contraction area is 26.54 × 104 km2. Furthermore, the distribution core of Limassolla will shift toward higher latitudes in the northeast direction, and the precipitation of warmest quarter was found to have the greatest impact on the distribution of Limassolla. Our research results supported our four hypotheses. Finally, this research suggests establishing ecological reserves in identified contraction to prevent habitat loss, enhancing the protection of biodiversity hotspots, and pursuing a sustainable development path with reduced emissions.

Pollen metabarcoding reveals the origin and multigenerational migratory pathway of an intercontinental-scale butterfly outbreak

Migratory insects may move in large numbers, even surpassing migratory vertebrates in biomass. Long-distance migratory insects complete annual cycles through multiple generations, with each generation's reproductive success linked to the resources available at different breeding grounds. Climatic anomalies in these grounds are presumed to trigger rapid population outbreaks. Here, we infer the origin and track the multigenerational path of a remarkable outbreak of painted lady (Vanessa cardui) butterflies that took place at an intercontinental scale in Europe, the Middle East, and Africa from March 2019 to November 2019. Using metabarcoding, we identified pollen transported by 264 butterflies captured in 10 countries over 7 months and modeled the distribution of the 398 plants detected. The analysis showed that swarms collected in Eastern Europe in early spring originated in Arabia and the Middle East, coinciding with a positive anomaly in vegetation growth in the region from November 2018 to April 2019. From there, the swarms advanced to Northern Europe during late spring, followed by an early reversal toward southwestern Europe in summer. The pollen-based evidence matched spatiotemporal abundance peaks revealed by citizen science, which also suggested an echo effect of the outbreak in West Africa during September-November. Our results show that population outbreaks in a part of species' migratory ranges may disseminate demographic effects across multiple generations in a wide geographic area. This study represents an unprecedented effort to track a continuous multigenerational insect migration on an intercontinental scale.

Unveiling the resistance of native weed communities: insights for managing invasive weed species in disturbed environments

Weed communities influence the dynamics of ecosystems, particularly in disturbed environments where anthropogenic activities often result in higher pollution. Understanding the dynamics existing between native weed communities and invasive species in disturbed environments is crucial for effective management and normal ecosystem functioning. Recognising the potential resistance of native weed communities to invasion in disturbed environments can help identify suitable native plants for restoration operations. This review aims to investigate the adaptations exhibited by native and non-native weeds that may affect invasions within disturbed environments. Factors such as ecological characteristics, altered soil conditions, and adaptations of native weed communities that potentially confer a competitive advantage relative to non-native or invasive weeds in disturbed environments are analysed. Moreover, the roles of biotic interactions such as competition, mutualistic relationships, and allelopathy in shaping the invasion resistance of native weed communities are described. Emphasis is given to the consideration of the resistance of native weeds as a key factor in invasion dynamics that provides insights for conservation and restoration efforts in disturbed environments. Additionally, this review underscores the need for further research to unravel the underlying mechanisms and to devise targeted management strategies. These strategies aim to promote the resistance of native weed communities and mitigate the negative effects of invasive weed species in disturbed environments. By delving deeper into these insights, we can gain an understanding of the ecological dynamics within disturbed ecosystems and develop valuable insights for the management of invasive species, and to restore long-term ecosystem sustainability.

Fitting individual-based models of spatial population dynamics to long-term monitoring data

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial-block cross-validation scheme and assess predictive performance with a rank-correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

Projected habitat preferences of commercial fish under different scenarios of climate change

The challenges of commercial species with the threats of climate change make it necessary to predict the changes in the distributional shifts and habitat preferences of the species under possible future scenarios. We aim to demonstrate how future climatic changes will affect the habitat suitability of three species of commercial fish using the predictive technique MaxEnt. The dataset used to extract geographical records included OBIS (54%), GBIF (1%), and literature (45%). The output of the model indicated accurate projections of MaxEnt (AUC above 0.9). Temperature was the main descriptor responsible for the main effects on the distribution of commercial fish. With increasing RCP from 2.5 to 8.5, the species would prefer saltier, higher temperatures and deeper waters in the future. We observed different percentages of suitable habitats between species during RCPs showing distinct sensitivity of each fish in facing climate changes. Negative effects from climate change on the distribution patterns of commercial fish were predicted to lead to varying degrees of reduction and changes of suitable habitats and movement of species towards higher latitudes. The finding emphasizes to implement adaptive management measures to preserve the stocks of these commercial fish considering that the intensification of the effects of climate change on subtropical areas and overexploited species is predicted.

Degeneration of foundation cushion species induced by ecological constraints can cause massive changes in alpine plant communities

Foundational cushion plants can re-organize community structures and sustain a prominent proportion of alpine biodiversity, but they are sensitive to climate change. The loss of cushion species can have broad consequences for associated biota. The potential plant community changes with the population dynamics of cushion plants remain, however, unclear. Using eight plant communities along a climatic and community successional gradient, we assessed cushion population dynamics, the underlying ecological constraints and hence associated plant community changes in alpine communities dominated by the foundational cushion plant Arenaria polytrichoides. The population dynamics of Arenaria are attributed to ecological constraints at a series of life history stages. Reproductive functions are constrained by increasing associated beneficiary plants; subsequent seedling establishment is constrained by temperature, water and light availability, extreme climate events, and interspecific competition; strong competitive exclusion may accelerate mortality and degeneration of cushion populations. Along with cushion dynamics, species composition, abundance and community structure gradually change. Once cushion plants completely degenerate, previously cushion-dominated communities shift to relatively stable communities that are overwhelmingly dominated by sedges. Climate warming may accelerate the degeneration process of A. polytrichoides. Degeneration of this foundational cushion plant will possibly induce massive changes in alpine plant communities and hence ecosystem functions in alpine ecosystems. The assessment of the population dynamics of foundation species is critical for an effective conservation of alpine biodiversity.

Ecological forecasts of insect range dynamics: a broad range of taxa includes winners and losers under future climate

Species distribution models are the primary tools to project future species' distributions, but this complex task is influenced by data limitations and evolving best practices. The majority of the 53 studies we examined utilized correlative models and did not follow current best practices for validating retrospective or future environmental data layers. Despite this, a summary of results is largely unsurprising: shifts toward cooler regions, but otherwise mixed dynamics emphasizing winners and losers. Harmful insects were more likely to show positive outcomes compared with beneficial species. Our restricted ability to consider mechanisms complicates interpretation of any single study. To improve this area of modeling, more classic field and lab studies to uncover basic ecology and physiology are crucial.

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.

Thermal sensitivity of digestion in Sceloporus consobrinus, with comments on geographic variation

Individual variation in energetics, environment, and genetics can influence population-level processes. However, it is often assumed that locally measured thermal and bioenergetic responses apply among broadly related species. Even closely related taxa may differ in the thermal sensitivity of performance, which in turn influences population persistence, population vital rates, and the ability to respond to environmental changes. The objectives of this project were to quantify the thermal sensitivity of digestive physiology in an Sceloporus lizards, to compare closely related, but geographically distinct, populations. Sceloporus lizards are a model organism, as they are known to exhibit thermally dependent physiologies and are geographically widespread. Digestive passage time, food consumption, fecal and urate production, metabolizable energy intake (MEI), and assimilated energy (AE) were compared for Sceloporus consobrinus in Arkansas and S. undulatus in South Carolina and New Jersey. Published data were acquired for NJ and SC lizards, while original data were collected for S. consobrinus. Comparisons of digestion among populations were made at 30 °C, 33 °C, or 36 °C. Results suggest that digestive physiology differs among populations, with S. consobrinus being more efficient at warmer temperatures. In contrast, NJ and SC lizards had quicker passage times and lower fecal and urate production at 30 °C in comparison to AR. The results of the current study exemplify how closely related organisms can differ in thermal sensitivity of performance. Such data are important for understanding how individual-level processes can vary in response to climate, with implications for understanding variation in physiological traits across the range of Sceloporus lizards.

A species' response to spatial climatic variation does not predict its response to climate change

The dominant paradigm for assessing ecological responses to climate change assumes that future states of individuals and populations can be predicted by current, species-wide performance variation across spatial climatic gradients. However, if the fates of ecological systems are better predicted by past responses to in situ climatic variation through time, this current analytical paradigm may be severely misleading. Empirically testing whether spatial or temporal climate responses better predict how species respond to climate change has been elusive, largely due to restrictive data requirements. Here, we leverage a newly collected network of ponderosa pine tree-ring time series to test whether statistically inferred responses to spatial versus temporal climatic variation better predict how trees have responded to recent climate change. When compared to observed tree growth responses to climate change since 1980, predictions derived from spatial climatic variation were wrong in both magnitude and direction. This was not the case for predictions derived from climatic variation through time, which were able to replicate observed responses well. Future climate scenarios through the end of the 21st century exacerbated these disparities. These results suggest that the currently dominant paradigm of forecasting the ecological impacts of climate change based on spatial climatic variation may be severely misleading over decadal to centennial timescales.

Tracking long-term shifts in non-native freshwater macroinvertebrates across three European countries

Non-native species introductions have been acknowledged as one of the main drivers of freshwater biodiversity decline worldwide, compromising provided ecosystem services and functioning. Despite growing introduction numbers of non-native species, their impacts in conjunction with anthropogenic stressors remain poorly documented. To fill this gap, we studied temporal changes in α (local scale) and γ (regional scale), as well as β (ratio between γ and α) diversity of non-native freshwater macroinvertebrate species in three European countries (the Netherlands, England and Hungary) using long-term time series data of up to 17 years (2003-2019). We further calculated four ecological and four biological trait metrics to identify changes in trait occurrences over time. We found that α and γ diversities of non-native species were increasing across all countries whereas β diversity remained stable. We did not identify any significant changes in any trait metric over time, while the predictors tested (land use, climatic predictors, site-specific factor) were similar across countries (e.g., site characteristics or climatic predictors on non-native species trends). Additionally, we projected trends of α, β, and γ diversity and trait metrics until 2040, which indicated that non-native species will decline across all countries to lower levels except in England for γ diversity and the Netherlands for α diversity where an increase was observed. Thus, our findings indicate shifts in non-native freshwater macroinvertebrate diversity at both local and regional scales in response to the various growing anthropogenic pressures. Our findings underscore the continuous dynamics of non-native species distribution, with the diversity of individual communities and overall landscapes witnessing changes. However, the differentiation in species composition between communities remains unaltered. This could have profound implications for conservation strategies and ecological management in the face of continuously changing biodiversity patterns.

Forecasting insect dynamics in a changing world

Predicting how insects will respond to stressors through time is difficult because of the diversity of insects, environments, and approaches used to monitor and model. Forecasting models take correlative/statistical, mechanistic models, and integrated forms; in some cases, temporal processes can be inferred from spatial models. Because of heterogeneity associated with broad community measurements, models are often unable to identify mechanistic explanations. Many present efforts to forecast insect dynamics are restricted to single-species models, which can offer precise predictions but limited generalizability. Trait-based approaches may offer a good compromise that limits the masking of the ranges of responses while still offering insight. Regardless of the modeling approach, the data used to parameterize a forecasting model should be carefully evaluated for temporal autocorrelation, minimum data needs, and sampling biases in the data. Forecasting models can be tested using near-term predictions and revised to improve future forecasts.

Accounting for trait variability and coordination in predictions of drought-induced range shifts in woody plants

Functional traits offer a promising avenue to improve predictions of species range shifts under climate change, which will entail warmer and often drier conditions. Although the conceptual foundation linking traits with plant performance and range shifts appears solid, the predictive ability of individual traits remains generally low. In this review, we address this apparent paradox, emphasizing examples of woody plants and traits associated with drought responses at the species' rear edge. Low predictive ability reflects the fact not only that range dynamics tend to be complex and multifactorial, as well as uncertainty in the identification of relevant traits and limited data availability, but also that trait effects are scale- and context-dependent. The latter results from the complex interactions among traits (e.g. compensatory effects) and between them and the environment (e.g. exposure), which ultimately determine persistence and colonization capacity. To confront this complexity, a more balanced coverage of the main functional dimensions involved (stress tolerance, resource use, regeneration and dispersal) is needed, and modelling approaches must be developed that explicitly account for: trait coordination in a hierarchical context; trait variability in space and time and its relationship with exposure; and the effect of biotic interactions in an ecological community context.

Demography-environment relationships improve mechanistic understanding of range dynamics under climate change

Species respond to climate change with range and abundance dynamics. To better explain and predict them, we need a mechanistic understanding of how the underlying demographic processes are shaped by climatic conditions. Here, we aim to infer demography-climate relationships from distribution and abundance data. For this, we developed spatially explicit, process-based models for eight Swiss breeding bird populations. These jointly consider dispersal, population dynamics and the climate-dependence of three demographic processes-juvenile survival, adult survival and fecundity. The models were calibrated to 267 nationwide abundance time series in a Bayesian framework. The fitted models showed moderate to excellent goodness-of-fit and discriminatory power. The most influential climatic predictors for population performance were the mean breeding-season temperature and the total winter precipitation. Contemporary climate change benefitted the population trends of typical mountain birds leading to lower population losses or even slight increases, whereas lowland birds were adversely affected. Our results emphasize that generic process-based models embedded in a robust statistical framework can improve our predictions of range dynamics and may allow disentangling of the underlying processes. For future research, we advocate a stronger integration of experimental and empirical studies in order to gain more precise insights into the mechanisms by which climate affects populations. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

Which demographic processes control competitive equilibria? Bayesian calibration of a size-structured forest population model

In forest communities, light competition is a key process for community assembly. Species' differences in seedling and sapling tolerance to shade cast by overstory trees is thought to determine species composition at late-successional stages. Most forests are distant from these late-successional equilibria, impeding a formal evaluation of their potential species composition. To extrapolate competitive equilibria from short-term data, we therefore introduce the JAB model, a parsimonious dynamic model with interacting size-structured populations, which focuses on sapling demography including the tolerance to overstory competition. We apply the JAB model to a two-"species" system from temperate European forests, that is, the shade-tolerant species Fagus sylvatica L. and the group of all other competing species. Using Bayesian calibration with prior information from external Slovakian national forest inventory (NFI) data, we fit the JAB model to short time series from the German NFI. We use the posterior estimates of demographic rates to extrapolate that F. sylvatica will be the predominant species in 94% of the competitive equilibria, despite only predominating in 24% of the initial states. We further simulate counterfactual equilibria with parameters switched between species to assess the role of different demographic processes for competitive equilibria. These simulations confirm the hypothesis that the higher shade tolerance of F. sylvatica saplings is key for its long-term predominance. Our results highlight the importance of demographic differences in early life stages for tree species assembly in forest communities.

Forecast-based action for conservation

Extreme weather events pose an immediate threat to biodiversity, but existing conservation strategies have limitations. Advances in meteorological forecasting and innovation in the humanitarian sector provide a possible solution-forecast-based action (FbA). The growth of ecological forecasting demonstrates the huge potential to anticipate conservation outcomes, but a lack of operational examples suggests a new approach is needed to translate forecasts into action. FbA provides such a framework, formalizing the use of meteorological forecasts to anticipate and mitigate the impacts of extreme weather. Based on experience from the humanitarian sector, I suggest how FbA could work in conservation, demonstrating key concepts using the theoretical example of heatwave impacts on sea turtle embryo mortality, and address likely challenges in realizing FbA for conservation, including establishing a financing mechanism, allocating funds to actions, and decision-making under uncertainty. FbA will demand changes in conservation research, practice, and governance. Researchers must increase efforts to understand the impacts of extreme weather at more immediate and actionable timescales and should coproduce forecasts of such impacts with practitioners. International conservation funders should establish systems to fund anticipatory actions based on uncertain forecasts.

The establishment of plants following long-distance dispersal

Long-distance dispersal (LDD) beyond the range of a species is an important driver of ecological and evolutionary patterns, but insufficient attention has been given to postdispersal establishment. In this review, we summarize current knowledge of the post-LDD establishment phase in plant colonization, identify six key determinants of establishment success, develop a general quantitative framework for post-LDD establishment, and address the major challenges and opportunities in future research. These include improving detection and understanding of LDD using novel approaches, investigating mechanisms determining post-LDD establishment success using mechanistic modeling and inference, and comparison of establishment between past and present. By addressing current knowledge gaps, we aim to further our understanding of how LDD affects plant distributions, and the long-term consequences of LDD events.

Mechanistic forecasts of species responses to climate change: The promise of biophysical ecology

A core challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are thermodynamically connected to their environments through the exchange of heat and water at fine spatial and temporal scales and this exchange can be captured with biophysical models. Although mechanistic models based on biophysical ecology have a long history of development and application, their use in global change biology remains limited despite their enormous promise and increasingly accessible software. We contend that greater understanding and training in the theory and methods of biophysical ecology is vital to expand their application. Our review shows how biophysical models can be implemented to understand and predict climate change impacts on species' behavior, phenology, survival, distribution, and abundance. It also illustrates the types of outputs that can be generated, and the data inputs required for different implementations. Examples range from simple calculations of body temperature at a particular site and time, to more complex analyses of species' distribution limits based on projected energy and water balances, accounting for behavior and phenology. We outline challenges that currently limit the widespread application of biophysical models relating to data availability, training, and the lack of common software ecosystems. We also discuss progress and future developments that could allow these models to be applied to many species across large spatial extents and timeframes. Finally, we highlight how biophysical models are uniquely suited to solve global change biology problems that involve predicting and interpreting responses to environmental variability and extremes, multiple or shifting constraints, and novel abiotic or biotic environments.

Pathways to global-change effects on biodiversity: new opportunities for dynamically forecasting demography and species interactions

In structured populations, persistence under environmental change may be particularly threatened when abiotic factors simultaneously negatively affect survival and reproduction of several life cycle stages, as opposed to a single stage. Such effects can then be exacerbated when species interactions generate reciprocal feedbacks between the demographic rates of the different species. Despite the importance of such demographic feedbacks, forecasts that account for them are limited as individual-based data on interacting species are perceived to be essential for such mechanistic forecasting-but are rarely available. Here, we first review the current shortcomings in assessing demographic feedbacks in population and community dynamics. We then present an overview of advances in statistical tools that provide an opportunity to leverage population-level data on abundances of multiple species to infer stage-specific demography. Lastly, we showcase a state-of-the-art Bayesian method to infer and project stage-specific survival and reproduction for several interacting species in a Mediterranean shrub community. This case study shows that climate change threatens populations most strongly by changing the interaction effects of conspecific and heterospecific neighbours on both juvenile and adult survival. Thus, the repurposing of multi-species abundance data for mechanistic forecasting can substantially improve our understanding of emerging threats on biodiversity.

Choosing among correlative, mechanistic, and hybrid models of species' niche and distribution

Different models are available to estimate species' niche and distribution. Mechanistic and correlative models have different underlying conceptual bases, thus generating different estimates of a species' niche and geographic extent. Hybrid models, which combining correlative and mechanistic approaches, are considered a promising strategy; however, no synthesis in the literature assessed their applicability for terrestrial vertebrates to allow best-choice model considering their strengths and trade-offs. Here, we provide a systematic review of studies that compared or integrated correlative and mechanistic models to estimate species' niche for terrestrial vertebrates under climate change. Our goal was to understand their conceptual, methodological, and performance differences, and the applicability of each approach. The studies we reviewed directly compared mechanistic and correlative predictions in terms of accuracy or estimated suitable area, however, without any quantitative analysis to support comparisons. Contrastingly, many studies suggest that instead of comparing approaches, mechanistic and correlative methods should be integrated (hybrid models). However, we stress that the best approach is highly context-dependent. Indeed, the quality and effectiveness of the prediction depends on the study's objective, methodological design, and which type of species' niche and geographic distribution estimated are more appropriate to answer the study's issue.

Forecasting the future of life in Antarctica

Antarctic ecosystems are under increasing anthropogenic pressure, but efforts to predict the responses of Antarctic biodiversity to environmental change are hindered by considerable data challenges. Here, we illustrate how novel data capture technologies provide exciting opportunities to sample Antarctic biodiversity at wider spatiotemporal scales. Data integration frameworks, such as point process and hierarchical models, can mitigate weaknesses in individual data sets, improving confidence in their predictions. Increasing process knowledge in models is imperative to achieving improved forecasts of Antarctic biodiversity, which can be attained for data-limited species using hybrid modelling frameworks. Leveraging these state-of-the-art tools will help to overcome many of the data scarcity challenges presented by the remoteness of Antarctica, enabling more robust forecasts both near- and long-term.

Bedrock may dictate the distribution of the fire salamander in the southern border of its global range

Understanding the factors that determine the spatial distribution of species is crucial for conservation planning. In this short communication, we review previous distribution models of the fire salamander (Salamandra infraimmaculata) in northern Israel, produced by the group of the late Prof. Leon Blaustein, while suggesting a biologically-informed reinterpretation of their main predictions. We argue for the prime importance of bedrock, specifically hard limestone, because it is tightly associated with the availability of karstic formations that are key to adult survival throughout the summer. Furthermore, we suggest that the spatial distribution of limestone bedrock also determines large-scale inter-population connectivity, and may explain the observed genetic differentiation among populations, as well as the southernmost limit of the species’ global distribution.

Recommendations for quantifying and reducing uncertainty in climate projections of species distributions

Projecting the future distributions of commercially and ecologically important species has become a critical approach for ecosystem managers to strategically anticipate change, but large uncertainties in projections limit climate adaptation planning. Although distribution projections are primarily used to understand the scope of potential change-rather than accurately predict specific outcomes-it is nonetheless essential to understand where and why projections can give implausible results and to identify which processes contribute to uncertainty. Here, we use a series of simulated species distributions, an ensemble of 252 species distribution models, and an ensemble of three regional ocean climate projections, to isolate the influences of uncertainty from earth system model spread and from ecological modeling. The simulations encompass marine species with different functional traits and ecological preferences to more broadly address resource manager and fishery stakeholder needs, and provide a simulated true state with which to evaluate projections. We present our results relative to the degree of environmental extrapolation from historical conditions, which helps facilitate interpretation by ecological modelers working in diverse systems. We found uncertainty associated with species distribution models can exceed uncertainty generated from diverging earth system models (up to 70% of total uncertainty by 2100), and that this result was consistent across species traits. Species distribution model uncertainty increased through time and was primarily related to the degree to which models extrapolated into novel environmental conditions but moderated by how well models captured the underlying dynamics driving species distributions. The predictive power of simulated species distribution models remained relatively high in the first 30 years of projections, in alignment with the time period in which stakeholders make strategic decisions based on climate information. By understanding sources of uncertainty, and how they change at different forecast horizons, we provide recommendations for projecting species distribution models under global climate change.

Ecological lags govern the pace and outcome of plant community responses to 21st-century climate change

Forecasting the trajectories of species assemblages in response to ongoing climate change requires quantifying the time lags in the demographic and ecological processes through which climate impacts species' abundances. Since experimental climate manipulations are typically abrupt, the observed species responses may not match their responses to gradual climate change. We addressed this problem by transplanting alpine grassland turfs to lower elevations, recording species' demographic responses to climate and competition, and using these data to parameterise community dynamics models forced by scenarios of gradual climate change. We found that shifts in community structure following an abrupt climate manipulation were not simply accelerated versions of shifts expected under gradual warming, as the former missed the transient rise of species benefiting from moderate warming. Time lags in demography and species interactions controlled the pace and trajectory of changing species' abundances under simulated 21st-century climate change, and thereby prevented immediate diversity loss.

Near-term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability

As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near-term, iterative forecasts of phytoplankton 1-14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7-day and 14-day horizons, a trend that increased up to the 14-day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems and adds to the growing body of work regarding the predictability of ecological systems broadly.

Dynamic Energy Budget models: fertile ground for understanding resource allocation in plants in a changing world

Climate change is having dramatic effects on the diversity and distribution of species. Many of these effects are mediated by how an organism's physiological patterns of resource allocation translate into fitness through effects on growth, survival and reproduction. Empirically, resource allocation is challenging to measure directly and so has often been approached using mathematical models, such as Dynamic Energy Budget (DEB) models. The fact that all plants require a very similar set of exogenous resources, namely light, water and nutrients, integrates well with the DEB framework in which a small number of variables and processes linked through pathways represent an organism's state as it changes through time. Most DEB theory has been developed in reference to animals and microorganisms. However, terrestrial vascular plants differ from these organisms in fundamental ways that make resource allocation, and the trade-offs and feedbacks arising from it, particularly fundamental to their life histories, but also challenging to represent using existing DEB theory. Here, we describe key features of the anatomy, morphology, physiology, biochemistry, and ecology of terrestrial vascular plants that should be considered in the development of a generic DEB model for plants. We then describe possible approaches to doing so using existing DEB theory and point out features that may require significant development for DEB theory to accommodate them. We end by presenting a generic DEB model for plants that accounts for many of these key features and describing gaps that would need to be addressed for DEB theory to predict the responses of plants to climate change. DEB models offer a powerful and generalizable framework for modelling resource allocation in terrestrial vascular plants, and our review contributes a framework for expansion and development of DEB theory to address how plants respond to anthropogenic change.

Process-explicit models reveal the structure and dynamics of biodiversity patterns

With ever-growing data availability and computational power at our disposal, we now have the capacity to use process-explicit models more widely to reveal the ecological and evolutionary mechanisms responsible for spatiotemporal patterns of biodiversity. Most research questions focused on the distribution of diversity cannot be answered experimentally, because many important environmental drivers and biological constraints operate at large spatiotemporal scales. However, we can encode proposed mechanisms into models, observe the patterns they produce in virtual environments, and validate these patterns against real-world data or theoretical expectations. This approach can advance understanding of generalizable mechanisms responsible for the distributions of organisms, communities, and ecosystems in space and time, advancing basic and applied science. We review recent developments in process-explicit models and how they have improved knowledge of the distribution and dynamics of life on Earth, enabling biodiversity to be better understood and managed through a deeper recognition of the processes that shape genetic, species, and ecosystem diversity.

Paninvasion severity assessment of a U.S. grape pest to disrupt the global wine market

Economic impacts from plant pests are often felt at the regional scale, yet some impacts expand to the global scale through the alignment of a pest's invasion potentials. Such globally invasive species (i.e., paninvasives) are like the human pathogens that cause pandemics. Like pandemics, assessing paninvasion risk for an emerging regional pest is key for stakeholders to take early actions that avoid market disruption. Here, we develop the paninvasion severity assessment framework and use it to assess a rapidly spreading regional U.S. grape pest, the spotted lanternfly planthopper (Lycorma delicatula; SLF), to spread and disrupt the global wine market. We found that SLF invasion potentials are aligned globally because important viticultural regions with suitable environments for SLF establishment also heavily trade with invaded U.S. states. If the U.S. acts as an invasive bridgehead, Italy, France, Spain, and other important wine exporters are likely to experience the next SLF introductions. Risk to the global wine market is high unless stakeholders work to reduce SLF invasion potentials in the U.S. and globally.

The role of demographic compensation in stabilising marginal tree populations in North America

Demographic compensation-the opposing responses of vital rates along environmental gradients-potentially delays anticipated species' range contraction under climate change, but no consensus exists on its actual contribution. We calculated population growth rate (λ) and demographic compensation across the distributional ranges of 81 North American tree species and examined their responses to simulated warming and tree competition. We found that 43% of species showed stable population size at both northern and southern edges. Demographic compensation was detected in 25 species, yet 15 of them still showed a potential retraction from southern edges, indicating that compensation alone cannot maintain range stability. Simulated climatic warming caused larger decreases in λ for most species and weakened the effectiveness of demographic compensation in stabilising ranges. These findings suggest that climate stress may surpass the limited capacity of demographic compensation and pose a threat to the viability of North American tree populations.

Survival and growth of a high-mountain daisy transplanted outside its local range, and implications for climate-induced distribution shifts

Field transplant experiments can improve our understanding of the effects of climate on distributions of plants versus a milieu of biotic factors which may be mediated by climate. We use a transplant experiment to test how survival and growth of a mountain-top daisy (Podolepis robusta), when planted within and outside its current local range, varies as a function of individual plant size, elevation, aspect and the presence of other vegetation. We expected a home-site advantage for the species, with highest survival and growth within the species' current elevational limits, and a decline in vital rates above (due to physiological limitations) and below (due to competition with near-neighbours) these limits. Transplant survival during the beginning of the census was high (89 %), though by the third growing season, 36 % of initial transplants were remaining. Elevation had a significant negative effect on individual mortality rates; plants growing at higher elevations had a lower estimated hazard rate and thus, higher survival relative to those planted at elevations below the current lower limit of the distribution. By contrast, we detected no significant effect of elevation on growth rates. Small vegetation gaps had no effect on growth rates, though we found a negative, but non-significant, effect on mortality rates. Aspect had a very strong impact on growth. Plants transplanted to cool aspects had a significantly lower growth rate relative to transplants growing on a warm aspect. Conversely, aspect was not a significant predictor of individual mortality rates. Restrictions on the local distribution of P. robusta appear to be governed by mortality drivers at lower elevation and by growth drivers associated with aspect. We highlight that our ability to understand the drivers of distributions in current and future climates will be limited if contextual- and individual-level plant responses remain understudied.

Global implications of crop‐based bioenergy with carbon capture and storage for terrestrial vertebrate biodiversity

Bioenergy with carbon capture and storage (BECCS) based on purpose‐grown lignocellulosic crops can provide negative CO₂ emissions to mitigate climate change, but its land requirements present a threat to biodiversity. Here, we analyse the implications of crop‐based BECCS for global terrestrial vertebrate species richness, considering both the land‐use change (LUC) required for BECCS and the climate change prevented by BECCS. LUC impacts are determined using global‐equivalent, species–area relationship‐based loss factors. We find that sequestering 0.5–5 Gtonne of CO₂ per year with lignocellulosic crop‐based BECCS would require hundreds of Mha of land, and commit tens of terrestrial vertebrate species to extinction. Species loss per unit of negative emissions decreases with: (i) longer lifetimes of BECCS systems, (ii) less overall deployment of crop‐based BECCS and (iii) optimal land allocation, that is prioritizing locations with the lowest species loss per negative emission potential, rather than minimizing overall land use or prioritizing locations with the lowest biodiversity. The consequences of prevented climate change for biodiversity are based on existing climate response relationships. Our tentative comparison shows that for crop‐based BECCS considered over 30 years, LUC impacts on vertebrate species richness may outweigh the positive effects of prevented climate change. Conversely, for BECCS considered over 80 years, the positive effects of climate change mitigation on biodiversity may outweigh the negative effects of LUC. However, both effects and their interaction are highly uncertain and require further understanding, along with the analysis of additional species groups and biodiversity metrics. We conclude that factoring in biodiversity means lignocellulosic crop‐based BECCS should be used early to achieve the required mitigation over longer time periods, on optimal biomass cultivation locations, and most importantly, as little as possible where conversion of natural land is involved, looking instead to sustainably grown or residual biomass‐based feedstocks and alternative strategies for carbon dioxide removal.

The shadow model: how and why small choices in spatially explicit species distribution models affect predictions

The use of species distribution models (SDMs) has rapidly increased over the last decade, driven largely by increasing observational evidence of distributional shifts of terrestrial and aquatic populations. These models permit, for example, the quantification of range shifts, the estimation of species co-occurrence, and the association of habitat to species distribution and abundance. The increasing complexity of contemporary SDMs presents new challenges-as the choices among modeling options increase, it is essential to understand how these choices affect model outcomes. Using a combination of original analysis and literature review, we synthesize the effects of three common model choices in semi-parametric predictive process species distribution modeling: model structure, spatial extent of the data, and spatial scale of predictions. To illustrate the effects of these choices, we develop a case study centered around sablefish (Anoplopoma fimbria) distribution on the west coast of the USA. The three modeling choices represent decisions necessary in virtually all ecological applications of these methods, and are important because the consequences of these choices impact derived quantities of interest (e.g., estimates of population size and their management implications). Truncating the spatial extent of data near the observed range edge, or using a model that is misspecified in terms of covariates and spatial and spatiotemporal fields, led to bias in population biomass trends and mean distribution compared to estimates from models using the full dataset and appropriate model structure. In some cases, these suboptimal modeling decisions may be unavoidable, but understanding the tradeoffs of these choices and impacts on predictions is critical. We illustrate how seemingly small model choices, often made out of necessity or simplicity, can affect scientific advice informing management decisions-potentially leading to erroneous conclusions about changes in abundance or distribution and the precision of such estimates. For example, we show how incorrect decisions could cause overestimation of abundance, which could result in management advice resulting in overfishing. Based on these findings and literature gaps, we outline important frontiers in SDM development.

Potential distribution of invasive crop pests under climate change: incorporating mitigation responses of insects into prediction models

Climate change facilitates biological invasions globally. Predicting potential distribution shifts of invasive crop pests under climate change is essential for global food security in the context of ongoing world population increase. However, existing predictions often omit the capacity of crop pests to mitigate the impacts of climate change by using microclimates, as well as through thermoregulation, life history variation and evolutionary responses. Microclimates provide refugia buffering climate extremes. Thermoregulation and life history variation can reduce the effects of diurnal and seasonal temperature variability. Evolutionary responses allow insects to adapt to long-term climate change. Neglecting these ecological processes may lead to overestimations in the negative impacts of climate change on invasive pests whereas in turn cause underestimations in their range expansions. To improve model predictions, we need to incorporate the fine-scale microclimates experienced by invasive crop pests and the mitigation responses of insects to climate change into species distribution models.

The macroecology of plant populations from local to global scales

Population ecologists develop theoretical and pragmatic knowledge of how and why populations change or remain stable, how life histories evolve and devise management strategies for populations of concern. However, forecasting the effects of global change or recommending management strategies is often urgent, requiring ecologists to work without detailed local evidence while using data and models from outside the focal location or species. Here we explore how the comparative ecology of populations, population macroecology, can be used to develop generalisations within and between species across different scales, using available demographic, environmental, life history, occurrence and trait data. We outline the strengths and weaknesses of using broad climatic variables and suitability inferred from probability of occupancy models to represent environmental variation in comparative analyses. We evaluate the contributions of traits, environment and their interaction as drivers of life history strategy. We propose that insights from life history theory, together with the adaptive capacity of populations and individuals, can inform on 'persist in place' vs 'shift in space' responses to changing conditions. As demographic data accumulate at landscape and regional scales for single species, and throughout plant phylogenies, we will have new opportunities for testing macroecological generalities within and across species.

Individual-based eco-evolutionary models for understanding adaptation in changing seas

As climate change threatens species' persistence, predicting the potential for species to adapt to rapidly changing environments is imperative for the development of effective conservation strategies. Eco-evolutionary individual-based models (IBMs) can be useful tools for achieving this objective. We performed a literature review to identify studies that apply these tools in marine systems. Our survey suggested that this is an emerging area of research fuelled in part by developments in modelling frameworks that allow simulation of increasingly complex ecological, genetic and demographic processes. The studies we identified illustrate the promise of this approach and advance our understanding of the capacity for adaptation to outpace climate change. These studies also identify limitations of current models and opportunities for further development. We discuss three main topics that emerged across studies: (i) effects of genetic architecture and non-genetic responses on adaptive potential; (ii) capacity for gene flow to facilitate rapid adaptation; and (iii) impacts of multiple stressors on persistence. Finally, we demonstrate the approach using simple simulations and provide a framework for users to explore eco-evolutionary IBMs as tools for understanding adaptation in changing seas.

Predicting species and community responses to global change using structured expert judgement: An Australian mountain ecosystems case study

Conservation managers are under increasing pressure to make decisions about the allocation of finite resources to protect biodiversity under a changing climate. However, the impacts of climate and global change drivers on species are outpacing our capacity to collect the empirical data necessary to inform these decisions. This is particularly the case in the Australian Alps which have already undergone recent changes in climate and experienced more frequent large-scale bushfires. In lieu of empirical data, we use a structured expert elicitation method (the IDEA protocol) to estimate the change in abundance and distribution of nine vegetation groups and 89 Australian alpine and subalpine species by the year 2050. Experts predicted that most alpine vegetation communities would decline in extent by 2050; only woodlands and heathlands are predicted to increase in extent. Predicted species-level responses for alpine plants and animals were highly variable and uncertain. In general, alpine plants spanned the range of possible responses, with some expected to increase, decrease or not change in cover. By contrast, almost all animal species are predicted to decline or not change in abundance or elevation range; more species with water-centric life-cycles are expected to decline in abundance than other species. While long-term ecological data will always be the gold standard for informing the future of biodiversity, the method and outcomes outlined here provide a pragmatic and coherent basis upon which to start informing conservation policy and management in the face of rapid change and a paucity of data.

Can dynamic occupancy models improve predictions of species' range dynamics? A test using Swiss birds

Predictions of species' current and future ranges are needed to effectively manage species under environmental change. Species ranges are typically estimated using correlative species distribution models (SDMs), which have been criticized for their static nature. In contrast, dynamic occupancy models (DOMs) explicitily describe temporal changes in species' occupancy via colonization and local extinction probabilities, estimated from time series of occurrence data. Yet, tests of whether these models improve predictive accuracy under current or future conditions are rare. Using a long-term data set on 69 Swiss birds, we tested whether DOMs improve the predictions of distribution changes over time compared to SDMs. We evaluated the accuracy of spatial predictions and their ability to detect population trends. We also explored how predictions differed when we accounted for imperfect detection and parameterized models using calibration data sets of different time series lengths. All model types had high spatial predictive performance when assessed across all sites (mean AUC > 0.8), with flexible machine learning SDM algorithms outperforming parametric static and DOMs. However, none of the models performed well at identifying sites where range changes are likely to occur. In terms of estimating population trends, DOMs performed best, particularly for species with strong population changes and when fit with sufficient data, while static SDMs performed very poorly. Overall, our study highlights the importance of considering what aspects of performance matter most when selecting a modelling method for a particular application and the need for further research to improve model utility. While DOMs show promise for capturing range dynamics and inferring population trends when fitted with sufficient data, computational constraints on variable selection and model fitting can lead to reduced spatial accuracy of predictions, an area warranting more attention.