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

Relieving seawater intrusion and soil salinization in coastal sponge cities: An integrated approach for optimizing the rainfall infiltration thresholds

Coastal regions often suffer from intertwined issues of seawater intrusion and soil salinization due to various factors. Although the application of rainwater infiltration measures in sponge city construction have shown effectiveness for stormwater control and water resource management, their role in addressing coastal ecological issues and the potential to identify optimal permeation thresholds to simultaneously mitigate these coastal challenges remains unexplored. This study developed an integrated modeling approach, including a rainfall infiltration model, variable density solute transport model, and statistical soil salinization model, to assess the impacts of different rainfall infiltration levels on seawater intrusion and soil salinization in Zhuanghe, China. The models were calibrated and validated using monthly observational data and extensive field sampling measurements. Scenario simulation analyses quantified sponge city implementation increased rainwater infiltration by 2.752 × 105 m3/a and raised groundwater levels by 4 m compared to non-sponge conditions, thereby mitigating seawater intrusion extent, with intrusion area and distance reduced by 21% and 33% respectively after 20 years. However, excessive infiltration also rebounded soil salinity, reaching 0.015% higher than initial levels. A rainfall infiltration increase of approximately 2 × 105 m3/a was identified as optimal threshold for simultaneously mitigating both issues in the region. This study provides quantitative insights into designing appropriate infiltration recharge strategies through coastal sponge city construction.

Paleorecords Reveal Biological Mechanisms Crucial for Reliable Species Range Shift Projections Amid Rapid Climate Change

The recent acceleration of global climate warming has created an urgent need for reliable projections of species distributions, widely used by natural resource managers. Such projections have been mainly produced by species distribution models with little information on their performances in novel climates. Here, we hindcast the range shifts of forest tree species across Europe over the last 12,000 years to compare the reliability of three different types of models. We show that in the most climatically dissimilar conditions, process-explicit models (PEMs) tend to outperform correlative species distribution models (CSDMs), and that PEM projections are likely to be more reliable than those made with CSDMs by the end of the 21st century. These results demonstrate for the first time the often promoted albeit so far untested idea that explicit description of mechanisms confers model robustness, and highlight a new avenue to increase model projection reliability in the future.

Identifying species traits that predict vulnerability to climate change

Accurately predicting the vulnerabilities of species to climate change requires a more detailed understanding of the functional and life-history traits that make some species more susceptible to declines and extinctions in shifting climates. This is because existing trait-based correlates of extinction risk from climate and environmental disturbances vary widely, often being idiosyncratic and context dependent. A powerful solution is to analyse the growing volume of biological data on changes in species ranges and abundances using process-explicit ecological models that run at fine temporal and spatial scales and across large geographical extents. These simulation-based approaches can unpack complex interactions between species' traits and climate and other threats. This enables species-responses to climatic change to be contextualised and integrated into future biodiversity projections and to be used to formulate and assess conservation policy goals. By providing a more complete understanding of the traits and contexts that regulate different responses of species to climate change, these process-driven approaches are likely to result in more certain predictions of the species that are most vulnerable to climate change.

A systematic review of Aedes aegypti population dynamics models based on differential equations

The global spread of Aedes aegypti and the associated public health risk have stimulated the development of several mathematical models to predict population dynamics in response to biological or environmental changes in real, future, or simulated scenarios. The aim of this study is to identify published articles on differential equation-based population dynamics models of Aedes aegypti, highlight their differences and commonalities, and examine their application in surveillance and control programs. Following the PRISMA guidelines, a systematic review was conducted in seven electronic databases (Scopus, PUBMED, IEEE Xplore, Science Direct, DOAJ, Scielo, and Google Scholar), with the last update on 8 February 2023. The initial search yielded 513 studies, of which 31 were finally selected. The articles analyzed showed great variability in the equations, processes, and variables included, with temperature being the most common environmental factor. Only a few models incorporated spatial heterogeneity or validation methods. Our findings suggest that improving the generation of temporal and spatially explicit forecasts through interdisciplinary collaboration, the use of new technologies, and validation with field data is essential for these models to effectively support public health efforts. Differential equation-based population dynamics models offer valuable insights and could greatly benefit mosquito surveillance programs if standardized and tailored to relevant scales.

Climate-Smart Siting for renewable energy expansion

A massive expansion of renewable energy (RE) is underway to meet the world's climate goals. Although RE serves to reduce threats from climate change, it can also pose threats to species whose current and future ranges intersect with RE installations. Here, we propose a "Climate-Smart Siting" framework for addressing potential conflicts between RE expansion and biodiversity conservation. The framework engenders authentic consultation with affected and disadvantaged communities throughout and uses overlay and optimization routines to identify focal areas now and in the future where RE development poses promise and peril as species' ranges shift in response to climate change. We use this framework to demonstrate methods, identify decision outcomes, and discuss market-based levers for aligning RE expansion with the United Nations Global Biodiversity Framework now and as climate change progresses. In the face of the climate crisis, a Climate-Smart Siting strategy could help create solutions without causing further harm to biodiversity and human communities..

Incorporating plant phenological responses into species distribution models reduces estimates of future species loss and turnover

Anthropogenetic climate change has caused range shifts among many species. Species distribution models (SDMs) are used to predict how species ranges may change in the future. However, most SDMs rarely consider how climate-sensitive traits, such as phenology, which affect individuals' demography and fitness, may influence species' ranges. Using > 120 000 herbarium specimens representing 360 plant species distributed across the eastern United States, we developed a novel 'phenology-informed' SDM that integrates phenological responses to changing climates. We compared the ranges of each species forecast by the phenology-informed SDM with those from conventional SDMs. We further validated the modeling approach using hindcasting. When examining the range changes of all species, our phenology-informed SDMs forecast less species loss and turnover under climate change than conventional SDMs. These results suggest that dynamic phenological responses of species may help them adjust their ecological niches and persist in their habitats as the climate changes. Plant phenology can modulate species' responses to climate change, mitigating its negative effects on species persistence. Further application of our framework will contribute to a generalized understanding of how traits affect species distributions along environmental gradients and facilitate the use of trait-based SDMs across spatial and taxonomic scales.

The cumulative niche approach: A framework to assess the performance of ecological niche model projections

Ecological Niche Models (ENMs) are often used to project species distributions within alien ranges and in future climatic scenarios. However, ENMs depend on species-environment equilibrium, which may be absent for actively expanding species. We present a novel framework to estimate whether species have reached environmental equilibrium in their native and alien ranges. The method is based on the estimation of niche breadth with the accumulation of species occurrences. An asymptote will indicate exhaustive knowledge of the realised niches. We demonstrate the CNA framework for 26 species of mammals, amphibians, and birds. Possible outcomes of the framework include: (1) There is enough data to quantify the native and alien realised niches, allowing us to calculate niche expansion between the native and alien ranges, also indicating that ENMs can be reliably projected to new environmental conditions. (2) The data in the native range is not adequate but an asymptote is reached in the alien realised niche, indicating low confidence in our ability to evaluate niche expansion in the alien range but high confidence in model projections to new environmental conditions within the alien range. (3) There is enough data to quantify the native realised niche, but not enough knowledge about the alien realised niche, hindering the reliability of projections beyond sampled conditions. (4) Both the native and alien ranges do not reach an asymptote, and thus few robust conclusions about the species' niche or future projections can be made. Our framework can be used to detect species' environmental equilibrium in both the native and alien ranges, to quantify changes in the realised niche during the invasion processes, and to estimate the likely accuracy of model projections to new environmental conditions.

Space-for-time substitutions in climate change ecology and evolution

In an epoch of rapid environmental change, understanding and predicting how biodiversity will respond to a changing climate is an urgent challenge. Since we seldom have sufficient long-term biological data to use the past to anticipate the future, spatial climate-biotic relationships are often used as a proxy for predicting biotic responses to climate change over time. These 'space-for-time substitutions' (SFTS) have become near ubiquitous in global change biology, but with different subfields largely developing methods in isolation. We review how climate-focussed SFTS are used in four subfields of ecology and evolution, each focussed on a different type of biotic variable - population phenotypes, population genotypes, species' distributions, and ecological communities. We then examine the similarities and differences between subfields in terms of methods, limitations and opportunities. While SFTS are used for a wide range of applications, two main approaches are applied across the four subfields: spatial in situ gradient methods and transplant experiments. We find that SFTS methods share common limitations relating to (i) the causality of identified spatial climate-biotic relationships and (ii) the transferability of these relationships, i.e. whether climate-biotic relationships observed over space are equivalent to those occurring over time. Moreover, despite widespread application of SFTS in climate change research, key assumptions remain largely untested. We highlight opportunities to enhance the robustness of SFTS by addressing key assumptions and limitations, with a particular emphasis on where approaches could be shared between the four subfields.

Parameterizing mechanistic niche models in biophysical ecology: a review of empirical approaches

Mechanistic niche models are computational tools developed using biophysical principles to address grand challenges in ecology and evolution, such as the mechanisms that shape the fundamental niche and the adaptive significance of traits. Here, we review the empirical basis of mechanistic niche models in biophysical ecology, which are used to answer a broad array of questions in ecology, evolution and global change biology. We describe the experiments and observations that are frequently used to parameterize these models and how these empirical data are then incorporated into mechanistic niche models to predict performance, growth, survival and reproduction. We focus on the physiological, behavioral and morphological traits that are frequently measured and then integrated into these models. We also review the empirical approaches used to incorporate evolutionary processes, phenotypic plasticity and biotic interactions. We discuss the importance of validation experiments and observations in verifying underlying assumptions and complex processes. Despite the reliance of mechanistic niche models on biophysical theory, empirical data have and will continue to play an essential role in their development and implementation.

Forecasting Brassica napus production under climate change with a mechanistic species distribution model

Brassica napus, a versatile crop with significant socioeconomic importance, serves as a valuable source of nutrition for humans and animals while also being utilized in biodiesel production. The expansion potential of B. napus is profoundly influenced by climatic variations, yet there remains a scarcity of studies investigating the correlation between climatic factors and its distribution. This research employs CLIMEX to identify the current and future ecological niches of B. napus under the RCP 8.5 emission scenario, utilizing the Access 1.0 and CNRM-CM5 models for the time frame of 2040-2059. Additionally, a sensitivity analysis of parameters was conducted to determine the primary climatic factors affecting B. napus distribution and model responsiveness. The simulated outcomes demonstrate a satisfactory alignment with the known current distribution of B. napus, with 98% of occurrence records classified as having medium to high climatic suitability. However, the species displays high sensitivity to thermal parameters, thereby suggesting that temperature increases could trigger shifts in suitable and unsuitable areas for B. napus, impacting regions such as Canada, China, Brazil, and the United States.

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'.

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.

Spatiotemporal influences of climate and humans on muskox range dynamics over multiple millennia

Processes leading to range contractions and population declines of Arctic megafauna during the late Pleistocene and early Holocene are uncertain, with intense debate on the roles of human hunting, climatic change, and their synergy. Obstacles to a resolution have included an overreliance on correlative rather than process-explicit approaches for inferring drivers of distributional and demographic change. Here, we disentangle the ecological mechanisms and threats that were integral in the decline and extinction of the muskox (Ovibos moschatus) in Eurasia and in its expansion in North America using process-explicit macroecological models. The approach integrates modern and fossil occurrence records, ancient DNA, spatiotemporal reconstructions of past climatic change, species-specific population ecology, and the growth and spread of anatomically modern humans. We show that accurately reconstructing inferences of past demographic changes for muskox over the last 21,000 years require high dispersal abilities, large maximum densities, and a small Allee effect. Analyses of validated process-explicit projections indicate that climatic change was the primary driver of muskox distribution shifts and demographic changes across its previously extensive (circumpolar) range, with populations responding negatively to rapid warming events. Regional analyses show that the range collapse and extinction of the muskox in Europe (~13,000 years ago) was likely caused by humans operating in synergy with climatic warming. In Canada and Greenland, climatic change and human activities probably combined to drive recent population sizes. The impact of past climatic change on the range and extinction dynamics of muskox during the Pleistocene-Holocene transition signals a vulnerability of this species to future increased warming. By better establishing the ecological processes that shaped the distribution of the muskox through space and time, we show that process-explicit macroecological models have important applications for the future conservation and management of this iconic species in a warming Arctic.

Potential for invasion of traded birds under climate and land-cover change

Humans have moved species away from their native ranges since the Neolithic, but globalization accelerated the rate at which species are being moved. We fitted more than half million distribution models for 610 traded bird species on the CITES list to examine the separate and joint effects of global climate and land-cover change on their potential end-of-century distributions. We found that climate-induced suitability for modelled invasive species increases with latitude, because traded birds are mainly of tropical origin and much of the temperate region is 'tropicalizing.' Conversely, the tropics are becoming more arid, thus limiting the potential from cross-continental invasion by tropical species. This trend is compounded by forest loss around the tropics since most traded birds are forest dwellers. In contrast, net gains in forest area across the temperate region could compound climate change effects and increase the potential for colonization of low-latitude birds. Climate change has always led to regional redistributions of species, but the combination of human transportation, climate, and land-cover changes will likely accelerate the redistribution of species globally, increasing chances of alien species successfully invading non-native lands. Such process of biodiversity homogenization can lead to emergence of non-analogue communities with unknown environmental and socioeconomic consequences.

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.

Predicted declines in suitable habitat for greater one-horned rhinoceros (Rhinoceros unicornis) under future climate and land use change scenarios

Rapidly changing climate is likely to modify the spatial distribution of both flora and fauna. Land use change continues to alter the availability and quality of habitat and further intensifies the effects of climate change on wildlife species. We used an ensemble modeling approach to predict changes in habitat suitability for an iconic wildlife species, greater one-horned rhinoceros due to the combined effects of climate and land use changes. We compiled an extensive database on current rhinoceros distribution and selected nine ecologically meaningful environmental variables for developing ensemble models of habitat suitability using ten different species distribution modeling algorithms in the BIOMOD2 R package; and we did this under current climatic conditions and then projected them onto two possible climate change scenarios (SSP1-2.6 and SSP5-8.5) and two different time frames (2050 and 2070). Out of ten algorithms, random forest performed the best, and five environmental variables-distance from grasslands, mean temperature of driest quarter, distance from wetlands, annual precipitation, and slope, contributed the most in the model. The ensemble model estimated the current suitable habitat of rhinoceros to be 2610 km2, about 1.77% of the total area of Nepal. The future habitat suitability under the lowest and highest emission scenarios was estimated to be: (1) 2325 and 1904 km2 in 2050; and (2) 2287 and 1686 km2 in 2070, respectively. Our results suggest that over one-third of the current rhinoceros habitat would become unsuitable within a period of 50 years, with the predicted declines being influenced to a greater degree by climatic changes than land use changes. We have recommended several measures to moderate these impacts, including relocation of the proposed Nijgad International Airport given that a considerable portion of potential rhinoceros habitat will be lost if the airport is constructed on the currently proposed site.

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.

Extending coverage and thematic resolution of compositional land cover maps in a hierarchical Bayesian framework

Ecological models are constrained by the availability of high-quality data at biologically appropriate resolutions and extents. Modeling a species' affinity or aversion with a particular land cover class requires data detailing that class across the full study area. Data sets with detailed legends (i.e., high thematic resolution) and/or high accuracy often sacrifice geographic extent, while large-area data sets often compromise on the number of classes and local accuracy. Consequently, ecologists must often restrict their study extent to match that of the more precise data set, or ignore potentially key land cover associations to study a larger area. We introduce a hierarchical Bayesian model to capitalize on the thematic resolution and accuracy of a regional land cover data set, and on the geographic breadth of a large area land cover data set. For the full extent (i.e., beyond the regional data set), the model predicts systematic discrepancies of the large-area data set with the regional data set, and divides an aggregated class into two more specific classes detailed by the regional data set. We illustrate the application of our model for mapping eastern white pine (Pinus strobus) forests, an important timber species that also provides habitat for an invasive shrub in the northeastern United States. We use the National Land Cover Database (NLCD), which covers the full study area but includes only generalized forest classes, and the NH GRANIT land cover data set, which maps White Pine Forest and has high accuracy, but only exists within New Hampshire. We evaluate the model at coarse (20 km² ) and fine (2 km² ) resolutions, with and without spatial random effects. The hierarchical model produced improved maps of compositional land cover for the full extent, reducing inaccuracy relative to NLCD while partitioning a White Pine Forest class out of the Evergreen Forest class. Accuracy was higher with spatial random effects and at the coarse resolution. All models improved upon simply partitioning Evergreen Forest in NLCD based on the predicted distribution of white pine. This flexible statistical method helps ecologists leverage localized mapping efforts to expand models of species distributions, population dynamics, and management strategies beyond the political boundaries that frequently delineate land cover data sets.

Bryophytes are predicted to lag behind future climate change despite their high dispersal capacities

The extent to which species can balance out the loss of suitable habitats due to climate warming by shifting their ranges is an area of controversy. Here, we assess whether highly efficient wind-dispersed organisms like bryophytes can keep-up with projected shifts in their areas of suitable climate. Using a hybrid statistical-mechanistic approach accounting for spatial and temporal variations in both climatic and wind conditions, we simulate future migrations across Europe for 40 bryophyte species until 2050. The median ratios between predicted range loss vs expansion by 2050 across species and climate change scenarios range from 1.6 to 3.3 when only shifts in climatic suitability were considered, but increase to 34.7–96.8 when species dispersal abilities are added to our models. This highlights the importance of accounting for dispersal restrictions when projecting future distribution ranges and suggests that even highly dispersive organisms like bryophytes are not equipped to fully track the rates of ongoing climate change in the course of the next decades.

Bryophytes tend to be sensitive to warming, but their high dispersal ability could help them track climate change. Here the authors combine correlative niche models and mechanistic dispersal models for 40 European bryophyte species under RCP4.5 and RCP8.5, finding that most of these species are unlikely to track climate change over the coming decades.

Survive or swim: different relationships between migration potential and larval size in three sympatric Mediterranean octocorals

Knowledge about migration potential is key to forecasting species distributions in changing environments. For many marine benthic invertebrates, migration happens during reproduction because of larval dispersal. The present study aims to test whether larval size can be used as a surrogate for migration potential arising from larval longevity, competence, sinking, or swimming behavior. The hypothesis was tested using larvae of three sympatric gorgonian species that release brooded lecithotrophic larvae in the same season: Paramuricea clavata, Corallium rubrum and Eunicella singularis. Despite different fecundities and larval sizes, the median larval longevity was similar among the three species. Free-fall speed increased with larval size. Nevertheless, the only net sinkers were the P. clavata larvae, as swimming was more common than free fall in the other two species with larger larvae. For the other two species, swimming activity frequency decreased as larval size increased. Interestingly, maximum larval longevity was lowest for the most active but intermediately sized larvae. Larval size did not covary consistently with any larval traits of the three species when considered individually. We thus advise not using larval size as a surrogate for migration potential in distribution models. The three species exemplified that different mechanisms, i.e., swimming activity or larval longevity, resulting from a trade-off in the use of energy reserves can facilitate migration, regardless of life history strategy.

Identifying island safe havens to prevent the extinction of the World's largest lizard from global warming

The Komodo dragon (Varanus komodoensis) is an endangered, island‐endemic species with a naturally restricted distribution. Despite this, no previous studies have attempted to predict the effects of climate change on this iconic species. We used extensive Komodo dragon monitoring data, climate, and sea‐level change projections to build spatially explicit demographic models for the Komodo dragon. These models project the species’ future range and abundance under multiple climate change scenarios. We ran over one million model simulations with varying model parameters, enabling us to incorporate uncertainty introduced from three main sources: (a) structure of global climate models, (b) choice of greenhouse gas emission trajectories, and (c) estimates of Komodo dragon demographic parameters. Our models predict a reduction in range‐wide Komodo dragon habitat of 8%–87% by 2050, leading to a decrease in habitat patch occupancy of 25%–97% and declines of 27%–99% in abundance across the species' range. We show that the risk of extirpation on the two largest protected islands in Komodo National Park (Rinca and Komodo) was lower than other island populations, providing important safe havens for Komodo dragons under global warming. Given the severity and rate of the predicted changes to Komodo dragon habitat patch occupancy (a proxy for area of occupancy) and abundance, urgent conservation actions are required to avoid risk of extinction. These should, as a priority, be focused on managing habitat on the islands of Komodo and Rinca, reflecting these islands’ status as important refuges for the species in a warming world. Variability in our model projections highlights the importance of accounting for uncertainties in demographic and environmental parameters, structural assumptions of global climate models, and greenhouse gas emission scenarios when simulating species metapopulation dynamics under climate change.

Toward reliable habitat suitability and accessibility models in an era of multiple environmental stressors

Global biodiversity declines, largely driven by climate and land-use changes, urge the development of transparent guidelines for effective conservation strategies. Species distribution modeling (SDM) is a widely used approach for predicting potential shifts in species distributions, which can in turn support ecological conservation where environmental change is expected to impact population and community dynamics. Improvements in SDM accuracy through incorporating intra- and interspecific processes have boosted the SDM field forward, but simultaneously urge harmonizing the vast array of SDM approaches into an overarching, widely adoptable, and scientifically justified SDM framework. In this review, we first discuss how climate warming and land-use change interact to govern population dynamics and species' distributions, depending on species' dispersal and evolutionary abilities. We particularly emphasize that both land-use and climate change can reduce the accessibility to suitable habitat for many species, rendering the ability of species to colonize new habitat and to exchange genetic variation a crucial yet poorly implemented component of SDM. We then unite existing methodological SDM practices that aim to increase model accuracy through accounting for multiple global change stressors, dispersal, or evolution, while shifting our focus to model feasibility. We finally propose a roadmap harmonizing model accuracy and feasibility, applicable to both common and rare species, particularly those with poor dispersal abilities. This roadmap (a) paves the way for an overarching SDM framework allowing comparison and synthesis of different SDM studies and (b) could advance SDM to a level that allows systematic integration of SDM outcomes into effective conservation plans.

Community science validates climate suitability projections from ecological niche modeling

Climate change poses an intensifying threat to many bird species and projections of future climate suitability provide insight into how species may shift their distributions in response. Climate suitability is characterized using ecological niche models (ENMs), which correlate species occurrence data with current environmental covariates and project future distributions using the modeled relationships together with climate predictions. Despite their widespread adoption, ENMs rely on several assumptions that are rarely validated in situ and can be highly sensitive to modeling decisions, precluding their reliability in conservation decision-making. Using data from a novel, large-scale community science program, we developed dynamic occupancy models to validate near-term climate suitability projections for bluebirds and nuthatches in summer and winter. We estimated occupancy, colonization, and extinction dynamics across species' ranges in the United States in relation to projected climate suitability in the 2020s, and used a Gibbs variable selection approach to quantify evidence of species-climate relationships. We also included a Bird Conservation Region strata-level random effect to examine among-strata variation in occupancy that may be attributable to land-use and ecoregional differences. Across species and seasons, we found strong evidence that initial occupancy and colonization were positively related to 2020 climate suitability, illustrating an independent validation of projections from ENMs across a large geographic area. Random strata effects revealed that occupancy probabilities were generally higher than average in core areas and lower than average in peripheral areas of species' ranges, and served as a first step in identifying spatial patterns of occupancy from these community science data. Our findings lend much-needed support to the use of ENM projections for addressing questions about potential climate-induced changes in species' occupancy dynamics. More broadly, our work highlights the value of community scientist observations for ground-truthing projections from statistical models and for refining our understanding of the processes shaping species' distributions under a changing climate.

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

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

Open Science principles for accelerating trait-based science across the Tree of Life

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges.

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

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

Correlative climatic niche models predict real and virtual species distributions equally well

Climate is one of the main factors driving species distributions and global biodiversity patterns. Obtaining accurate predictions of species' range shifts in response to ongoing climate change has thus become a key issue in ecology and conservation. Correlative species distribution models (cSDMs) have become a prominent tool to this aim in the last decade and have demonstrated good predictive abilities with current conditions, irrespective of the studied taxon. However, cSDMs rely on statistical association between species' presence and environmental conditions and have rarely been challenged on their actual capacity to reflect causal relationships between species and climate. In this study, we question whether cSDMs can accurately identify if climate and species distributions are causally linked, a prerequisite for accurate prediction of range shift in relation to climate change. We compared the performance of cSDMs in predicting the distributions of 132 European terrestrial species, chosen randomly within five taxonomic groups (three vertebrate groups and two plant groups), and of 1,320 virtual species whose distribution is causally fully independent from climate. We found that (1) for real species, the performance of cSDMs varied principally with range size, rather than with taxonomic groups and (2) cSDMs did not predict the distributions of real species with a greater accuracy than the virtual ones. Our results unambiguously show that the high predictive power of cSDMs can be driven by spatial autocorrelation in climatic and distributional data and does not necessarily reflect causal relationships between climate and species distributions. Thus, high predictive performance of cSDMs does not ensure that they accurately depict the role of climate in shaping species distributions. Our findings therefore call for strong caution when using cSDMs to provide predictions on future range shifts in response to climate change.

Predicting population responses to environmental change from individual-level mechanisms: towards a standardized mechanistic approach

Animal populations will mediate the response of global biodiversity to environmental changes. Population models are thus important tools for both understanding and predicting animal responses to uncertain future conditions. Most approaches, however, are correlative and ignore the individual-level mechanisms that give rise to population dynamics. Here, we assess several existing population modelling approaches and find limitations to both 'correlative' and 'mechanistic' models. We advocate the need for a standardized mechanistic approach for linking individual mechanisms (physiology, behaviour, and evolution) to population dynamics in spatially explicit landscapes. Such an approach is potentially more flexible and informative than current population models. Key to realizing this goal, however, is overcoming current data limitations, the development and testing of eco-evolutionary theory to represent interactions between individual mechanisms, and standardized multi-dimensional environmental change scenarios which incorporate multiple stressors. Such progress is essential in supporting environmental decisions in uncertain future conditions.

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.

Future climate change likely to reduce the Australian plague locust (Chortoicetes terminifera) seasonal outbreaks

Climate is a major limiting factor for insect distributions and it is expected that a changing climate will likely alter spatial patterns of pest outbreaks. The Australian plague locust (APL) Chortoicetes terminifera, is the most economically important locust species in Australia. Invasions cause large scale economic damage to agricultural crops and pastures. Understanding the regional-scale and long-term dynamics is a prerequisite to develop effective control and preventive management strategies. In this study, we used a 32-year locust survey database to uncover the relationship between historical bioclimatic variables and spatial seasonal outbreaks by developing two machine learning species distribution models (SDMs), random forest and boosted regression trees. The explanatory variables were ranked by contribution to the generated models. The bio-climate models were then projected into a future climate change scenario (RCP8.5) using downscaled 34 global climate models (GCMs) to assess how climate change may alter APL seasonal distribution patterns in eastern Australia. Our results show that the model for the distribution of spring outbreaks performed better than those for summer and autumn, based on statistical evaluation criteria. The spatial models of seasonal outbreaks indicate that the areas subject to APL outbreaks were likely to decrease in all seasons. Multi-GCM ensemble means show the largest decrease in area was for spring outbreaks, reduced by 93-94% by 2071-2090, while the area of summer outbreaks decreased by 78-90%, and 67-74% for autumn outbreaks. The bioclimatic variables could explain 78-98% outbreak areas change. This study represents an important step toward the assessment of the effects of the changing climate on locust outbreaks and can help inform future priorities for regional mitigation efforts in the context of global climate change in eastern Australia.

ΔTraitSDMs: species distribution models that account for local adaptation and phenotypic plasticity

Improving our understanding of species ranges under rapid climate change requires application of our knowledge of the tolerance and adaptive capacity of populations to changing environmental conditions. Here, we describe an emerging modelling approach, ΔTraitSDM, which attempts to achieve this by explaining species distribution ranges based on phenotypic plasticity and local adaptation of fitness-related traits measured across large geographical gradients. The collection of intraspecific trait data measured in common gardens spanning broad environmental clines has promoted the development of these new models - first in trees but now rapidly expanding to other organisms. We review, explain and harmonize the main findings from this new generation of models that, by including trait variation over geographical scales, are able to provide new insights into future species ranges. Overall, ΔTraitSDM predictions generally deliver a less alarming message than previous models of species distribution under new climates, indicating that phenotypic plasticity should help, to a considerable degree, some plant populations to persist under climate change. The development of ΔTraitSDMs offers a new perspective to analyse intraspecific variation in single and multiple traits, with the rationale that trait (co)variation and consequently fitness can significantly change across geographical gradients and new climates.

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

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

Impacts of climate change on infestations of Dubas bug (Ommatissus lybicus Bergevin) on date palms in Oman

Climate change has determined shifts in distributions of species and is likely to affect species in the future. Our study aimed to (i) demonstrate the linkage between spatial climatic variability and the current and historical Dubas bug (Ommatissus lybicus Bergevin) distribution in Oman and (ii) model areas becoming highly suitable for the pest in the future. The Dubas bug is a pest of date palm trees that can reduce the crop yield by 50% under future climate scenarios in Oman. Projections were made in three species distribution models; generalized linear model, maximum entropy, boosted regression tree using of four global circulation models (GCMs) (a) HadGEM2, (b) CCSM4, (c) MIROC5 and (d) HadGEM2-AO, under four representative concentration pathways (2.6, 4.5, 6.0 and 8.5) for the years 2050 and 2070. We utilized the most commonly used threshold of maximum sensitivity + specificity for classifying outputs. Results indicated that northern Oman is currently at great risk of Dubas bug infestations (highly suitable climatically) and the infestations level will remain high in 2050 and 2070. Other non-climatic integrated pest management methods may be greater value than climatic parameters for monitoring infestation levels, and may provide more effective strategies to manage Dubas bug infestations in Oman. This would ensure the continuing competitiveness of Oman in the global date fruit market and preserve national yields.

How will climate novelty influence ecological forecasts? Using the Quaternary to assess future reliability

Future climates are projected to be highly novel relative to recent climates. Climate novelty challenges models that correlate ecological patterns to climate variables and then use these relationships to forecast ecological responses to future climate change. Here, we quantify the magnitude and ecological significance of future climate novelty by comparing it to novel climates over the past 21,000 years in North America. We then use relationships between model performance and climate novelty derived from the fossil pollen record from eastern North America to estimate the expected decrease in predictive skill of ecological forecasting models as future climate novelty increases. We show that, in the high emissions scenario (RCP 8.5) and by late 21st century, future climate novelty is similar to or higher than peak levels of climate novelty over the last 21,000 years. The accuracy of ecological forecasting models is projected to decline steadily over the coming decades in response to increasing climate novelty, although models that incorporate co-occurrences among species may retain somewhat higher predictive skill. In addition to quantifying future climate novelty in the context of late Quaternary climate change, this work underscores the challenges of making reliable forecasts to an increasingly novel future, while highlighting the need to assess potential avenues for improvement, such as increased reliance on geological analogs for future novel climates and improving existing models by pooling data through time and incorporating assemblage-level information.

Major challenges for correlational ecological niche model projections to future climate conditions

Species-level forecasts of distributional potential and likely distributional shifts, in the face of changing climates, have become popular in the literature in the past 20 years. Many refinements have been made to the methodology over the years, and the result has been an approach that considers multiple sources of variation in geographic predictions, and how that variation translates into both specific predictions and uncertainty in those predictions. Although numerous previous reviews and overviews of this field have pointed out a series of assumptions and caveats associated with the methodology, three aspects of the methodology have important impacts but have not been treated previously in detail. Here, we assess those three aspects: (1) effects of niche truncation on model transfers to future climate conditions, (2) effects of model selection procedures on future-climate transfers of ecological niche models, and (3) relative contributions of several factors (replicate samples of point data, general circulation models, representative concentration pathways, and alternative model parameterizations) to overall variance in model outcomes. Overall, the view is one of caution: although resulting predictions are fascinating and attractive, this paradigm has pitfalls that may bias and limit confidence in niche model outputs as regards the implications of climate change for species' geographic distributions.