Climate change projected to exacerbate the economic costs of biological invasions

Climate change projected to exacerbate the economic costs of biological invasions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Climate change projected to exacerbate the economic costs of biological invasions Shimin Gu, Shengnan Chen, Weishan Tu, Lixia Han, Qing Zhang, Yanhua Hong, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7029011/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Invasive species are causing high and increasing economic costs worldwide. However, the potential economic costs associated with range shifts of invasive species under climate change remain understudied. Here, we incorporated abundance-based species distribution modeling, management temporal dynamics, and socioeconomic factors to evaluate the effect of climate change on potential economic costs for 121 animal invaders in 67 countries. On average, the future potential economic costs associated with biological invasions in 2060 were 19.6% (SSP 126)–21.0% (SSP 585) higher than the current potential costs. On average, 87.1% of countries would experience increased future costs associated with 84.8% of animal invaders, which is driven mainly by the costliest invaders worldwide. We demonstrated that improvements in management efforts, especially preinvasion strategies, might reduce future costs by 65.4% at most. Our findings highlight the importance of proactive and early management strategies for the costliest invaders to mitigate economic losses under accelerating biological invasion and climate change. Biological sciences/Ecology/Climate-change ecology Biological sciences/Ecology/Invasive species biological invasion economic cost climate change global change potential impact Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Main Biological invasions exert pervasive impacts on global biodiversity, the economy and public health 1 – 3 . Compared with the considerable efforts to examine the loss of native species and zoonotic disease transmission due to invasive species 3 – 5 , the high economic costs of invasive species in agriculture, public and social welfare, human health, and management expenditures have just recently been estimated comprehensively 6 . Notably, the global economic cost of invasive species has reached a minimum of US $ 1.288 trillion (2017 US dollars) and has increased over the past few decades (1970–2017) 6 . However, the economic costs of biological invasions are very likely to continue to increase as a result of shifts in ranges with climate change 7 – 13 . Consequently, it is urgent to understand the effect of climate change on the potential economic costs of invasive species for identifying sensitive areas and the costliest invaders to facilitate the development of early prevention schemes under climate change. All else remaining equal, the potential economic costs of invasive species may primarily depend on their geographical ranges 14 . Species distribution models (SDMs, also referred to as ecological niche models) have been widely employed to predict potential distributions for nonnative species 15 , 16 , and has been recently applied to predict invasion costs based on their potential ranges with the macroeconomic data 14 . Despite this striking advance by linking SDMs with potential economic cost of invasive species, predicting the invasion costs under future climate change has still some challenges to account for species abundance data, management effort dynamics with time, and habitat variables beyond climate that may influence invasion costs. For example, the potential economic costs may vary with the abundance of invasive populations 17 , 18 . Policy-makers are likely to increase their management efforts with the spread to reduce impacts of invasive species. In addition, habitat factors can influence food availability, reproduction and biotic interactions, which are increasingly regarded important to SDM performance and thus affect the prediction of invasion risk prediction 19 , 20 . Furthermore, several socioeconomic factors, such as the Gross Domestic Product (GDP) and human population density, are positively associated with the economic costs of invasive species 21 . Moreover, the economic costs of invasive species have usually been assessed on the basis of certain impacted sectors 22 . However, suitable geographical ranges after controlling for sampling bias, population abundance, management dynamics, habitat predictors, socioeconomic factors, impacted sectors have never been combined to comprehensively evaluate the potential economic costs of invasive species under climate change. Here, we incorporated abundance-based species distribution modeling after accounting for species sampling effort, non-climate habitat variables, and management dynamics into a recently developed interpolation approach using socioeconomic factors and impacted sectors 23 to evaluate the effect of climate change on potential economic costs of animal invaders at the global scale (Fig. 1 ). We focus our predictions on those countries where the observed cost was reported in InvaCost Database to avoid the great differences in management capacity, infrastructure, cultural and legal conditions among countries. We first obtained animal invaders with highly reliable observed costs at the country level from the InvaCost v4.1 database 6 (details of the filtering process are shown in Supplementary Fig. 1). We compiled cost entries for 121 animal invaders, including invertebrates, mammals, birds, amphibians, reptiles, and fishes, across terrestrial and freshwater ecosystems in 67 countries for analysis. We then predicted potential habitat suitability using abundance-based SDMs for each animal invader in invaded countries under current and future climatic conditions on the basis of different shared socioeconomic pathway (SSP) forcing scenarios from 2040–2060 after correcting for sampling bias issue. In addition to climatic conditions, we identified potential geographical ranges via the use of known habitat types for each invader to obtain more precise predictions of suitable establishment areas. Finally, we integrated ecological factors, socioeconomic variables, the importance of impacted sectors, and management dynamics to weight the observed economic costs and fractionally project the future potential economic costs under climate change. The detailed assumptions for predictions in each of modeling steps were summarized (Supplementary Table 1). Finally, to validate the robustness of our predictions in the main analysis based on interpolation approach, we verified the consistency between the predicted values and historical observed economic costs (Supplementary Fig. 7). Then, we applied a benefit transfer (BT) approach regarded as a comprehensive assessment for economic costs from one studied location to another non-studied location with similar environmental conditions 24 – 26 to verify the sensitivity of our predictions using different approaches (Supplementary Fig. 7). We also conducted a series of sensitive analyses to reduce the uncertainties when evaluating the future potential economic costs using climate-habitat model and climate-only model, and calculated the isolated contribution of the changing habitat, the variations in GDP and human population density on changes in the predictions of potential economic costs. Results Increasing potential economic costs of animal invasion under climate change at the country level Compared with the current potential total economic costs, most countries (87.1 ± 0.9%, Supplementary Table 2) would experience increased potential economic costs under future climate change across the different SSP scenarios (with average values ranging from 19.6 ± 12.6% under SSP 126 to 21.0 ± 13.0% under SSP 585 across countries; Fig. 2 a, Wilcoxon paired test; refer to Supplementary Table 3) for both damage and management costs by 2060 (Fig. 2 b and c). In particular, North American (e.g., the United States and Canada), South American (e.g., Argentina), Asian (e.g., China, India, Thailand, the Philippines, Vietnam, and Indonesia), and Oceanian countries (e.g., Australia) are the top ten costliest countries under both the current (~ 84.2%) and future (~ 83.8%) climatic conditions (Fig. 2 d, g, j, and m, respectively; Supplementary Fig. 3a). We observed similar patterns in the damage (Fig. 2 e, h, k, and n) and management (Fig. 2 f, i, l, and o, respectively) costs (Supplementary Fig. 3b and c) for these countries. Notably, some countries with relatively low current economic costs are predicted to experience greatly increased costs under future climate change, especially African and European countries (Fig. 3 ). Increasing potential economic costs of invasive animals under climate change at the taxonomic level Our prediction results indicated an increase in the potential economic costs of animal invaders of 84.8 ± 1.6% under future climate change across taxa (Fig. 4 , Supplementary Table 4). Specifically, most of the costliest species were terrestrial invertebrates, such as the yellow fever mosquito ( Aedes aegypti ), and mammal invaders, such as the black rat ( Rattus rattus ) (Supplementary Fig. 5 and Data 1). Specifically, A. aegypti is currently predicted to generate the highest potential economic costs that will continue to increase by 14.7 ± 1.1% on average under future climate change globally. Further analysis revealed that the contributions of the costliest species varied among countries (Fig. 5 ; Supplementary Fig. 2). For example, A. aegypti greatly contributed to the current potential total costs in countries ranging from the lowest costs in Australia (~ 0.03%) to the highest costs in India (~ 63.5%), but the future potential costs would increase the most in the United States (~ 8.9 ± 1.7%) (Fig. 5 ). Other terrestrial invertebrate invaders contributed to economic costs in Canada (95.8% of current costs and 95.6% of future costs), China (92.6% of current costs and 94.1% of future costs), and Australia (49.0% of current costs and 49.9% of future costs) (Fig. 5 ), and terrestrial vertebrates such as mammals contributed to economic costs in Argentina (76.8% of current costs and 76.3% of future costs), Australia (50.8% of current costs and 49.9% of future costs), and the United States (30.4% of current costs and 26.4% of future costs) (Fig. 5 ). Importance of management dynamics in predicting the potential economic costs of animal invaders under climate change Considering the important role of government management efforts in mitigating the expansion and impacts of invaders, we evaluated the effect of management dynamics, quantified as the proactive response capacity, on the potential economic costs of animal invaders under different climate change scenarios (for methodological details, please refer to the Methods section). We showed that improvements in management efforts could reduce the potential economic costs of invasive animals, especially in countries in the Americas, Africa, and northern Europe (Fig. 6 a, c, and e, respectively). In contrast, we found that increases in management efforts might yield relatively limited effects on curbing the potential economic costs in most parts of Asia, Oceania, and central and southern Europe (Fig. 6 b, d, and f, respectively). Further analysis revealed that countries with preinvasion management efforts generally exhibited lower potential economic costs than those with only postinvasion management efforts (Fig. 6 g and h, respectively). Sensitivity analyses We obtained high consistent results between our predicted costs using interpolation approach and the historical observed economic costs (the average goodness of fit R 2 was 0.47 ± 0.01, P < 0.001, Supplementary Fig. 8). In addition, the trends of predictions using interpolation and BT approaches had also high consistency ( R 2 = 0.68, P < 0.001, Supplementary Fig. 9). This indicated that our predictions were less sensitivity to different approaches. Compared with predictions using climate-habitat model, the predicted potential economic costs using climate-only model had shown differences under the current and SSP585 climatic scenarios (Supplementary Fig. 12) but no differences under SSP 126 or SSP 245 scenarios. This indicated that the available habitat had covaried with climatic conditions and had significant contributions to the potential economic cost predictions. As expected, the growth of GDP and human population density indeed had significant influences on the future potential economic costs across all scenarios (Supplementary Fig. 13). By partitioning the independent contributions of habitat-filtered species population abundance, GDP, and human population density in predicting future potential costs, the results shew that human population density was dominant in the SSP126 scenario compared to the current climate condition. As the degree of future climate warming increases, the importance of species population abundance and GDP would gradually increase (Supplementary Fig. 14). Discussion In this study, we assessed the effect of future climate change on potential economic costs of animal invaders on the basis of their abundance-based predicted suitable habitats after accounting for sampling bias, socioeconomic factors, management dynamics, and importance of impacted sectors worldwide. Our predictions indicated that the future potential economic costs of most invasive animals would exceed their current potential economic costs at the global scale. Furthermore, we found that improving management efforts after invasion might impose a limited effect on reducing the potential economic costs in most countries (~ 60%) without preinvasion management measures. Our predictions can be validated by the trends of historical observed economic costs across countries and we obtained similar results when we used different methods of economic cost predictions based on the interpolation approach and BT approach (Supplementary Fig. 9). There are several possible explanations for the overall increase in the future potential economic costs of animal invaders. First, our predictions are based on successful invaders that have caused economic costs 6 , and these species exhibit high growth rates, generalist characteristics, and high phenotypic plasticity, which may help them adapt to future climatic conditions and thus occupy large suitable ranges (e.g., the geographical ranges of over 60% of animal invaders are predicted to increase under climate change according to our predictions; Supplementary Data 2) 13 , 27 . In particular, the breadth of the habitats of invasive species is important for determining their potential invasion ranges and impacts 28 . Indeed, most of the study species can occupy various habitats across forests, grasslands, farmlands and urban areas, and eight of the ten costliest invaders can occupy at least three types of habitats (Supplementary Data 3). Second, invaders with a high degree of potential cost increase generally exhibit relatively short residence times in the invaded countries (Fig. 3 , Supplementary Fig. 6 and Data 4). Unsurprisingly, these invaders might exhibit greater potential for increasing the associated economic costs under climate change, as there might be time lags in population growth and range expansion after establishment 29 . Finally, higher future economic costs were mainly concentrated in North America, Asia, and Oceania (Fig. 2 and Supplementary Fig. 4), which have been recognized as future invasion hotspots owing to their high climate compatibility with invasive species 30 , 31 . Our results confirmed that the increasing trends in economic costs impacted sector and taxonomic dependences 22 . Adopting the two costliest taxa (i.e., invasive terrestrial invertebrates and mammals) as examples, their potential economic costs are mainly incurred in the human health and public and social welfare sectors, with economic costs that are six times greater than those of other taxa (Supplementary Table 5). In addition, the number of reported impacted sectors in terms of economic costs (a maximum of 4 sectors, with an average of 1.8 ± 0.8 sectors) was greater than that reported for other taxonomic groups (a maximum of 2 sectors, with an average of 1.4 ± 0.5 sectors). However, terrestrial invertebrates and mammals are associated with the highest reported economic costs in the literature globally 6 , 32 . We thus suggest the requirement to investigate more hidden impact pathways, sectors, and economic costs associated with less-studied taxa in the future. Our results indicated that an increase in the proactive response capacity of a given country can proportionally decrease future potential economic costs, especially in several American, African, and North European countries with preinvasion management efforts. These findings support those of previous studies suggesting that early intervention is a promising way to reduce invasion-related economic costs in the future 33 , 34 . For example, in North America, there have been systematic and early assessments of invasion-related economic costs, invaded ecosystems and impacted sectors through the Multilateral Invasive Species Project Inventory 35 . In Africa, the Strategic Result Areas and Actions Framework has coordinated prevention strategies across multiple spatial scales 36 . In northern Europe, impact/risk-based management actions have been implemented through cross-sectoral strategies for invasive species 37 and the National Strategy for alien species 38 to reduce the impacts of biological invasions. Furthermore, national management efforts to prevent the establishment of invasive species may be related to the development level of the country and the rate of increase in response capabilities after suffering impacts 39 . For example, high levels of economic development in North America, Europe, and Oceania usually require numerous resources and governance to control invasive species 40 . We found that certain West African countries (such as Nigeria and Cameroon) and East African countries (such as Ethiopia, Uganda, Zimbabwe, and Mozambique) exhibited lower potential economic costs than other African countries under future scenarios did (Fig. 6 ). One alternative explanation is that these West and East African countries could respond more quickly and increase their management levels over time 40 . Our prediction results suggested that more than half (40/67) of the countries with increased response capabilities may still face no change or even an increase in future economic costs. One possible reason is that these countries primarily lack early prevention measures and typically control invaders only after impact occurrence (Fig. 6 h). This trend is consistent with that obtained in an empirical evaluation study showing that low preinvasion management expenditures can increase 25 times of more economic costs 41 . Hence, our results demonstrated that management strategies at the early stages before invasion success are key to efficiently reducing the future potential economic costs. We acknowledge that there are still some caveats to our predictions. For example, although we aimed to predict the average economic costs of each invader on the basis of socioeconomic, ecological, and management dynamic factors at the country level, there might still be differences in the potential economic costs of invaders among geographical populations due to variations in detection, research, control, medical treatment, and infrastructure costs and cultural and legal conditions over time 42 , 43 , which warrants future investigations when related data become available at the global scale. In addition, our predictions were based on the invasion potentials of species according to their climatic and habitat suitability levels as most SDM-based work did 14 . In the real world, the distributions of invasive species may also depend on other factors, such as interactions with sympatric species and land-use disturbances 12 , 44 – 46 , which requires the development of more precise prediction models in which these processes can be incorporated in the future. Finally, our main analyses provided a conserved prediction by focusing on those countries where the economic cost of studied species has been reported. However, these invaders may further spread to other out-of-sample countries with the aid of human-assisted dispersal through different pathways such as trade circulation and transportation networks 47 – 49 , which unfortunately are not available under future scenarios at the global scale. Even so, we have tried to conduct supplementary analysis to interpolate the economic costs of species in those out-of-sample countries, and find that future economic costs may be higher than our conserved predictions by hundreds of times on average (Supplementary Fig. 10 and Fig. 11). Despite these potential caveats, this study represents the first step toward the development of early and targeted management plans for national costliest invaders in sensitive areas under accelerating biological invasion and climate change. Methods Economic cost data and processing Historical observed economic costs of invasive species based on the most up-to-date version of the InvaCost database ( InvaCost v.4.1; accessible at https://doi.org/10.6084/m9.figshare.12668570 ) 50 were employed to predict the current and future potential economic costs under climate change. This database provides monetary costs associated with invasive species worldwide, thereby standardizing the different currencies of countries, which may influence the cost estimation results. The original dataset contained 13,553 cost entries from 1,973 peer-reviewed publications, reports, and gray literature. We excluded historical observed costs without specific information on animal invaders (‘ Kingdom ’ column in InvaCost ), those that are not specific to the species level (‘ Species ’ column in InvaCost ), those that are not specific to the country level (‘ Spatial_scale ’ column in InvaCost ), those without the name of the official country (‘ Official_country ’ column in InvaCost ), those without raw cost data (‘ Raw_cost_estimate_2017_USD_exchange_rate ’ column in InvaCost ), and those without cost estimates (‘ Cost_estimate_per_year_2017_USD_exchange_rate ’ column in InvaCost ). We also excluded entries with only potential costs (‘ Implementation ’ column in InvaCost ), entries with low reliability (in both the ‘ Method_reliability ’ and ‘ Method_reliability_refined ’ columns in InvaCost ), and entries for marine species from further analysis. We had also removed the cost entries associated with multiple species. These steps resulted in the retention of a total of 698 cost entries across 129 invasive animal species in 74 countries for further analysis (Supplementary Fig. 1 and Data 5). Abundance-based species distribution modeling Species occurrence data. We obtained occurrence data of the 129 animal invaders with precise geographical coordinates from various references (Supplementary Data 6) and online databases, including the Global Biodiversity Information Facility (GBIF; http://www.gbif.org/ ), Biodiversity Information Serving Our Nation (BISON; https://bison.usgs.gov/ ), iNaturalist (iNat; https://www.inaturalist.org/ ), Integrated Digitized Biocollections (iDigBio; https://www.idigbio.org/ ) and Atlas of Living Australia (ALA; http://www.ala.org.au/ ). Notably, record data were downloaded from these databases using the spocc (v.1.2.0) 51 and rgbif (v.1.2.0) 52 , 53 packages. We then employed the CoordinateCleaner package in R to clear invalid (duplicated geographic coordinates and occurrences without or with erroneous coordinates falling outside terrestrial and freshwater borders) records 54 and retained one record per 5-arcmin (~ 9.2 km at the equator) grid cell to avoid adverse effects on the model fitting results 55 . Occurrence data of both native and nonnative species were used to train and establish SDMs to eliminate biases in evaluating species niches because many nonnative species may experience climatic niche shifts when invading new ranges 56 , 57 . Species population abundance data. We collected population abundance data for invasive species from peer-reviewed publications, reports, and gray literature. Data collection was conducted until July 2024. The following search terms were applied in Google Scholar for paper collection: (density OR abundance) AND (species scientific name). We excluded papers pertaining to (1) laboratory or greenhouse experiments and (2) field experiments in which release and count values were manipulated, as well as papers with (3) no original recorded abundance data or (4) no abundance data for specific species. From the papers, we recorded the species name, taxonomic information, coordinates and name of the locality, country name, life stage, density estimate, and source citations (Supplementary Data 7). GetData graph Digitizer (v.2.24) was applied to extract abundance data from figures in the papers. All coordinates were transformed to longitudinal and latitudinal coordinates in decimal degrees. Owing to differences in the field population survey methods among taxonomic groups (e.g., counting juvenile abundance per m 2 leaf area for insect species; capturing adult individuals per km 2 for mammal and herpetofaunal species; or determining the catch per unit effort for fish species), we unified the units for each species to represent variations in population abundance across different studies and satisfy the requirements for further abundance-based modeling predictions. The population abundance data for each species were thinned at 5-arcmin resolution via the thin function of the spThin (v.0.2.0) package 58 . Environmental predictor variables . We obtained data for different bioclimatic variables at a spatial resolution of 5 arcmin from the WorldClim Global Climate Database version 2.1 ( https://worldclim.org/ ) 59 . The chosen spatial resolution is widely employed in global studies for practical invasion biosecurity decisions 31 . On the basis of the WorldClim database, current climatic conditions were produced with monthly averages from 1970–2000. Future climatic conditions were downscaled to monthly predicted climate data from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and a total of four general circulation models (GCMs), namely, ACCESS-CM2, MPI-ESM1-2-HR, IPSL-CM6A-LR and MIROC6, were adopted to obtain predicted climate data for the 2041–2060 period. We employed three SSP forcing scenarios: (1) SSP 126, a broadly sustainable pathway consisting of limiting warming to 2°C; (2) SSP 245, a middle of the road pathway encompassing the best warming estimate of approximately 2.7°C by the end of the 21st century 60 ; and (3) SSP 585, a fossil-fueled development pathway consisting of warming by approximately 4.4°C by the end of this century 60 , 61 . To model realized niches and predict potential distributions of taxa, we not only selected an appropriate number of variables to mitigate overfitting problems in the training data but also accounted for climatic physiological constraints that differed across taxonomic groups. To achieve this goal, we followed the approaches in previous studies 62 , 63 and employed taxonomically dependent bioclimatic variables to predict the potential distributions of species. For amphibians and reptiles, temperature and precipitation are key environmental variables that can independently influence their population distributions 64 . For example, many amphibians and reptiles exhibit specific temperature thresholds that determine their metabolism, activity level, and reproductive success 65 . In contrast, other herpetofauna may rely on specific microhabitat preferences (moisture conditions) for breeding and larval development 66 . As a result, we selected the annual average temperature, seasonal temperature, extreme temperatures in the warmest and coldest months, annual precipitation, and precipitation in the wettest and driest quarters as bioclimatic predictors 67 . Notably, birds exhibit relatively high dispersal abilities as they must cover large distances quickly during migration; moreover, they relocate and adjust their breeding and migratory patterns in response to changing environments 68 – 70 . Therefore, the seasonal temperature and precipitation, extreme temperatures in the warmest and coldest months, and extreme precipitation in the wettest and driest quarters were included in bird species distribution modeling. In mammals, various physiological mechanisms, such as insulation (fur, fat, etc.) and metabolic adjustments, have evolved to regulate the body temperature. These adaptations help them cope with extreme temperatures more effectively than ectothermic species 71 , 72 . However, water availability is important to mammals because of their need for hydration, while vegetation, food and other habitat qualities are important for survival and reproduction. For example, precipitation influences plant growth and the availability of food resources for herbivorous mammals. Changes in precipitation can directly affect the abundance and quality of vegetation, thus impacting foraging success, nutritional intake, and shelter availability 73 . In particular, the interaction of temperature and precipitation variables could directly or indirectly limit mammal distributions even when these variables alone do not exceed species tolerance levels 74 . Thus, the annual average temperature, average temperatures in the wettest and driest quarters, average temperatures in the warmest and coldest quarters, annual precipitation, precipitation in the wettest and driest quarters, and precipitation in the warmest and coldest quarters were included in mammal species distribution modeling. Many terrestrial invertebrates are r-strategy species that quickly regenerate, and in addition to the average temperature and precipitation, short periods of extremely high or low temperature and humidity levels are key to determining their performance 75 . Furthermore, the diurnal temperature range (maximum temperature–minimum temperature) plays an important role in determining their physiological and life histories, such as development time, metamorphosis, and generations 57 , 76 . Therefore, the mean diurnal range, extreme temperatures in the warmest and coldest quarters, annual precipitation, and precipitation in the wettest and driest quarters were included for terrestrial invertebrate species distribution modeling. For freshwater fishes, the diurnal range is considered a key variable in the prediction of shifts in the ranges of fish species under climate change 77 . Furthermore, fish depend on water bodies, and precipitation-related bioclimatic variables were included in their distribution modeling 78 . For freshwater invertebrates, we followed the methods in previous studies and included the extreme temperatures in the warmest and coldest months and precipitation in the wettest and direst quarters 79 . To better predict the distributions of invasive fishes and invertebrates in aquatic environments, we also included the water layer from the Global Lakes and Wetlands Database (GLWD; https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database ), which contains features of lakes, reservoirs, and rivers with surface areas ≥ 0.1 km 2 . The distributions of saltwater lakes were obtained from the Saline Lakes Database ( http://lakes.chebucto.org/saline1.html ), but they were not included as predictors of species potential distributions (more detailed lists of the selected bioclimatic variables for each taxonomic group are provided in Supplementary Table 6). Two-stage modeling for potential population abundance prediction. The two-stage modeling approach is commonly employed to predict the potential population abundance across taxonomic groups 80 – 82 . In this approach, traditional SDMs are first applied to predict potentially suitable geographical areas, after which species abundance data from field surveys are employed to predict the abundance of the target species in different areas by fitting empirical models 82 . At the first stage, we developed ensemble SDMs for the 129 invasive species using the maximum entropy algorithm, the random forest algorithm and generalized additive models, which have been widely used in conservation, invasion and biogeography studies to account for the uncertainties in single models 83 . To prevent spatial bias from skewing environmental gradients in less-surveyed regions, we applied the target group background approach by using the randomPoints function in the dismo (v.1.3-8) package to randomly sample 10,000 background points without replacement 84 , 85 . We adopted previous methods to generate a target group background on the basis of the distribution points of all studied species belonging to the same taxon 86 . Prior to model fitting, we partitioned the occurrence data of each species into three spatially structured folds of equal size by using the part_random function in the flexsdm (v.1.3.3) package 87 . On the basis of the partitioned occurrence data, each model was subjected to threefold cross-validation, thereby evaluating spatial transferability and fitting performance. The default metric of the true skill statistic (TSS) was chosen to select the best combination of hyperparameter values 88 . Then, we established and fitted multiple candidate models using different feature classes and regularization multiplication. We evaluated the performance of the established models via different metrics, including the area under the receiver operating characteristic curve (AUC, ranging from 0 to 1, with a value above 0.9 indicating excellent performance), TSS (ranging from − 1 to 1, with a value above 0.8 indicating excellent performance) and the Boyce index (ranging from − 1 to 1, with a higher value indicating greater model performance) 89 – 91 . Finally, six species were excluded from further analysis because they failed to meet the performance threshold in predictions under the current and future climatic conditions (Supplementary Data 8 and Data 9). All the ensemble SDMs were calibrated and evaluated using the flexsdm (v.1.3.3) package 87 . Afterward, we employed random forest regression analysis to fit the relationship between species population abundance and predictor variables, including the probability of species occurrence predicted by the ensemble SDMs and bioclimatic and habitat covariates 92 . We followed the approaches in previous work by incorporating the probability of species occurrence into the fitting process, as species occurrence and abundance are not always affected by exactly the same environmental variables 80 , 93 . The random forest regression method was chosen owing to (1) its lower sensitivity to data distributions with a high percentage of zero values and (2) its robustness to overfitting with potential collinear variables 94 . We fitted random forest models via the train function to obtain empirical regression relationships, on the basis of which we then predicted the potential abundance of each species at a 5-acrmin resolution via the predict function in the caret (v.6.0–93) package 95 . The goodness of fit of the prediction models (Supplementary Data 9) was assessed via the root mean square-error (RMSE) and the mean absolute error (MAE), which are commonly used to identify the optimal model 96 . To determine occupied number of potential girds in following analysis, we transformed the generated continuous habitat suitability outputs into binary maps of species presence (1) or absence (0) by using the maximizing TSS as a threshold 70 . The optimal threshold was determined to maximize sensitivity (true predictions of high suitability for establishment) plus specificity (true predictions of low suitability for establishment) using PresenceAbsence (v.1.1.11) package 97 . Projections of the current and future potential economic costs of invasive animals We predicted the potential economic cost of invaders in each country on the basis of historical observed economic costs and weighted variables, including ecological factors, socioeconomic factors, importance of the impacted sectors, and management efforts under current and future climate change conditions under the different SSP scenarios. Ecological factors. Because the size of the ranges of invasive species is positively related to their impact risk 28 and a larger number of potential distribution grids could indicate that a given invasive species exhibits higher adaptive and ecologically competitive abilities to occupy the niches of native biota and thus increase their impacts on native ecosystems 98 , we chose the number of grids with predicted suitable habitats and the potential population abundance in each grid to quantify the ecological effects on the predicted economic costs. For example, the population abundance and impact severity of invaders have been reported to follow linear, threshold-dependent, or S-shaped relationships 99 , 100 . After species distribution modelling, we used the available habitat types of each of the invasive animals to generate more conservative and robust predictions of their potential distributions 101 . We searched for habitat types for each invasive animal on the basis of descriptions in the IUCN Red List (iucnredlist.org), CAB International (cabi.org), Animal Diversity Web (animaldiversity.org), and published studies (Supplementary Data 3). We followed the methods in previous work 102 by classifying habitat types into ten categories, including forests (forested primary land, nonforested primary land, potentially forested secondary land, and potentially nonforested second land), grasslands (savannas, managed pastures and rangeland), farmlands (C3/C4 annual and perennial crops), shrublands, wetlands, rocky areas, deserts, artificial habitats, marine coastal/supratidal land, and urban land. Habitat cover data were sourced from the Land-Use Harmonization dataset (LUH2, https://luh.umd.edu/ ) for the current and future periods. On the basis of this habitat-filtered process, we calculated the average population abundance across all predicted distributed grids and counted the number of grids where the population abundance exceeded zero 81 . Socioeconomic factors. The GDP, human population density, and importance of the impacted sector (as a percentage of the GDP) are considered important socioeconomic factors influencing the economic costs of invasive species 23 . Global spatially explicit GDP data for the historical (hereafter referred to as current) and future periods were estimated by Murakami et al. (2021) on a 5-arcmin grid scale 103 . The GDP data for the current (1970, 1980, 1990, and 2000) and future (2040, 2050, and 2060) periods were averaged. Global spatially explicit human population density data (per km 2 ) for the current and future periods were sourced from the Gridded Population of the World (GPW) v3 database, which was established by the Socioeconomic Data and Applications Center in NASA’s Earth Observing System Data and Information System (EOSDIS) at Columbia University ( https://sedac.ciesin.columbia.edu/data/collection/gpac-v3 ; accessed on May 7, 2024). Compared with the latest released version of GPWv4.11, GPWv3 provides earlier estimated population density data. In addition, GPWv3 exhibits a coarse spatial resolution (30-acrmin for the current period and 7.5-arcmin for the future period) that has been commonly adopted in previous studies 103 – 105 but is suitable for our analysis purposes. Then, the human population densities during the current (1990, 1995, 2000, 2010 and 2020) and future (2040, 2050, and 2060) periods were averaged. We standardized the current and future gridded human population densities to a 5-arcmin resolution via the resample function in the raster (v.3.5–29) package 106 . Finally, the economic cost of invasive species was associated with the importance of the impacted sector, expressed as a percentage of the GDP 23 . For example, the agriculture and health sectors are much more important than the environmental sectors in African, American, and Asian countries, which explains why the economic costs of invasive species in these regions are related mainly to agricultural and health impacts rather than to environmental impacts 107 . We then considered six categories of impacted sectors, including agriculture–forestry–fishery, industry, manufacturing, services, forest rents, and health expenditures, which cover the reported types of sectors in the InvaCost database. We collected importance values of the different sectors in each country from the World Development Indicators—Economy on May 11, 2024 ( https://datatopics.worldbank.org/world-development-indicators/themes/economy.html ). We determined the importance of the impacted sectors by averaging the available values in 2000, 2010, and 2020, considering their potential temporal dynamics 108 , and we assumed a constant value under climate change, as future data are unavailable. Management dynamics. The economic costs of invasive species could be negatively affected by adequate management efforts 100 . The proactive response capacity is a commonly employed metric to represent management efforts in preventing invasions at the country level, as evaluated by Faulkner et al. (2020) 39 . We used the matrix to quantify high or low county management efforts in response to the economic costs of invaders. A higher proactive response capacity indicates a country with a greater possibility of intervention or early containment of emerging invasive species in new regions 31 . Because the future proactive response capacity of a given country is related to the potential spatial ranges invaded under climate change 13 , we calculated the ratio of future to current potential ranges of species and multiplied this ratio by the historical response capacity in each country. Finally, the sum of the product value and the country’s basic historical response capacity was used to represent its future potential response capacity under the different SSP scenarios. Projection of potential economic costs. We applied a recently developed interpolation approach to project the potential economic cost of invasive species 23 . We included the total GDP of the country, average GDP across the potential species distribution area, human population density, importance of the impacted sector expressed as a percentage of the GDP, number of grids containing potential suitable habitats, and average species population abundance as predictors. All possible combinations of the predictor variables were fitted via the following interpolation model: $$\:{cost}_{i,potential}=\frac{1}{n}{\sum\:}_{k=1}^{n}{cost}_{k,observed}{\prod\:}_{q=1}^{p}{\left(\frac{{f}_{q,k,potential}}{{f}_{q,k,ovserved}}\right)}^{{\gamma\:}_{q}}$$ where cost k,observed denotes the historical observed economic cost, k is the number of same species-impacted sector-type cost (damage, management, mixed or unspecified) combinations in country i , q is the number of predictors used in the interpolation model, f q,k,observed is a predictor based on historical conditions, f q,k,potential is a predictor based on the current or future potential conditions, and γ q is a fitting coefficient that ensures fractional but not proportional changes in weighted predictions with historical conditions 23 . We followed previous work by applying truncated scalar ratios to avoid overinterpolation during model fitting 23 . The maximum likelihood method was applied using the optim function (a built-in function in R) to determine the goodness of fit between the predicted and training-stage costs. On the basis of different combinations of predictor variables, we calculate predicted average values of the current or future potential economic costs. All data analyses were conducted in R (4.2.1) 109 . Declarations Acknowledgments We thank three anonymous reviewers for constructive comments that have greatly improved this manuscript. We also thank Dr. Zhixin Zhang for the assistance provided with SDM construction. This work was supported by the Third Xinjiang Scientific Expedition Program (2022xjkk0800, 2021xjkk0600), the National Natural Sciences Foundation of China (31970393, 32171657, 32301459), the grant of high quality economic and social development in southern Xinjiang (NFS2101), the grant from Youth Innovation Promotion Association of Chinese Academy of Sciences (Y201920), the grant from the Institute of Zoology, Chinese Academy of Sciences (2023IOZ0104), the Key Project of Science and Technology of Sichuan Province (22NSFSC2743), and the State Key Laboratory of Animal Biodiversity Conservation and Integrated Pest Management (Grant No. SKLA2504). Author contributions X.L. conceived the study; X.L. and W.L. supervised the project; X.L., S.G., and S.C. designed the study; S.G., S.C., W.T., L.H., Q.Z., Y.H., Z.L., Y.D., and X.L. collected and analyzed the data; S.G., X.L., and S.C. wrote the manuscript. Competing interests The authors declare that they have no competing interests. Data availability All the data supporting the results are available in https://doi.org/10.6084/m9.figshare.29425643.v1 Code availability All the codes used to conduct the main analyses are available here: https://doi.org/10.6084/m9.figshare.29425643.v1 References Iwamura T, Guzman-Holst A, Murray KA (2020) Accelerating invasion potential of disease vector Aedes aegypti under climate change. Nat Commun 11:2130. https://doi.org:10.1038/s41467-020-16010-4 Paini DR et al (2016) Global threat to agriculture from invasive species. Proc Natl Acad Sci USA 113:7575–7579. https://doi.org:10.1073/pnas.1602205113 Zhang L et al (2022) Biological invasions facilitate zoonotic disease emergences. Nat Commun 13:1762. https://doi.org:10.1038/s41467-022-29378-2 Blackburn TM, Bellard C, Ricciardi A (2019) Alien versus native species as drivers of recent extinctions. Front Ecol Environ 17:203–207. https://doi.org:10.1002/fee.2020 Chinchio E et al (2020) Invasive alien species and disease risk: An open challenge in public and animal health. PLoS Pathog 16:e1008922. https://doi.org:10.1371/journal.ppat.1008922 Diagne C et al (2021) High and rising economic costs of biological invasions worldwide. Nature 592:571–576. https://doi.org:10.1038/s41586-021-03405-6 Haubrock PJ et al (2021) Economic costs of invasive species in Germany. Neobiota 67:225–246. https://doi.org:10.3897/neobiota.67.59502 Kirichenko N et al (2021) Economic costs of biological invasions in terrestrial ecosystems in Russia. Neobiota 67:103–130. https://doi.org:10.3897/neobiota.67.58529 Kourantidou M et al (2021) Economic costs of invasive alien species in the Mediterranean basin. Neobiota 67:427–458. https://doi.org:10.3897/neobiota.67.58926 Liu CL et al (2021) Economic costs of biological invasions in Asia. Neobiota 67:53–78. https://doi.org:10.3897/neobiota.67.58147 Renault D et al (2021) Biological invasions in France: Alarming costs and even more alarming knowledge gaps. Neobiota 67:191–224. https://doi.org:10.3897/neobiota.67.59134 Bellard C et al (2013) Will climate change promote future invasions? Glob Change Biol 19:3740–3748. https://doi.org:10.1111/gcb.12344 Hulme PE (2017) Climate change and biological invasions: evidence, expectations, and response options. Biol Rev Camb Philos Soc 92:1297–1313. https://doi.org:10.1111/brv.12282 Soto I et al (2025) Using species ranges and macroeconomic data to fill the gap in costs of biological invasions. Nat Ecol Evol 9:1021–1030. https://doi.org:10.1038/s41559-025-02697-5 Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods Ecol Evol 1:330–342. https://doi.org:10.1111/j.2041-210X.2010.00036.x Waldock C et al (2022) (2022) A quantitative review of abundance-based species distribution models. Ecography https://doi.org: https://doi.org/10.1111/ecog.05694 Strayer DL (2020) Non-native species have multiple abundance–impact curves. Ecol Evol 10:6833–6843. https://doi.org/10.1002/ece3.6364 . https://doi.org: Ahmed DA et al Predicting future damage costs of non-native species using combined dynamical and cost-density equations. Preprint https://doi.org: https://doi.org/10.32942/X2P631 Troia MJ (2019) e. a. Species traits and reduced habitat suitability limit efficacy of climate change refugia in streams. Nat Ecol Evol Rodriguez LF (2006) Can Invasive Species Facilitate Native Species? Evidence of How, When, and Why These Impacts Occur. Biol Invasions 8:927–939. https://doi.org:10.1007/s10530-005-5103-3 Haubrock PJ et al (2021) Economic costs of invasive alien species across Europe. Neobiota 67:153–190. https://doi.org:10.3897/neobiota.67.58196 Turbelin AJ et al (2024) Biological invasions as burdens to primary economic sectors. Glob Environ Change 87:102858. https://doi.org/10.1016/j.gloenvcha.2024.102858 . https://doi.org: Henry M et al (2023) Unveiling the hidden economic toll of biological invasions in the European Union. Environ Sci Europe 35:43. https://doi.org:10.1186/s12302-023-00750-3 Hanley N, Barbier EB, Barbier E (2009) Pricing nature: cost-benefit analysis and environmental policy. Edward Elgar Publishing Greiner R, Kancans R, Nelson R (2023) Methods for non-market valuation of alien invasive species, Australian Bureau of Agricultural Resource Economics and Sciences ABARES, Department of Agriculture, Fisheries and Forestry, Canberra, July. CC BY 4.0. https://doi.org:https://doi.org/10.25814/m4rt-6h95 Rosenberger RS, Loomis JB (2001) Benefit transfer of outdoor recreation use values: A technical document supporting the Forest Service Strategic Plan (2000 revision). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station Pyšek P et al (2020) Scientists' warning on invasive alien species. Biol Rev 95:1511–1534. https://doi.org: https://doi.org/10.1111/brv.12627 Wang S, Li W, Zhang J, Luo Z, Li Y (2024) Alien range size, habitat breadth, origin location, and domestication of alien species matter to their impact risks. Integr Zool 00:1–15. https://doi.org:https://doi.org/10.1111/1749-4877.12837 Crooks JA (2005) Lag times and exotic species: The ecology and management of biological invasions in slow-motion11. Écoscience 12, 316–329 https://doi.org:10.2980/i1195-6860-12-3-316.1 Hubbard JAG, Drake DAR, Mandrak NE (2024) Climate change alters global invasion vulnerability among ecoregions. Divers Distrib 30:26–40. https://doi.org:https://doi.org/10.1111/ddi.13778 Early R et al (2016) Global threats from invasive alien species in the twenty-first century and national response capacities. Nat Commun 7:12485. https://doi.org:10.1038/ncomms12485 Fantle-Lepczyk JE et al (2022) Economic costs of biological invasions in the United States. Sci Total Environ 806:151318. https://doi.org:10.1016/j.scitotenv.2021.151318 Simberloff D et al (2013) Impacts of biological invasions: what's what and the way forward. Trends Ecol Evol 28:58–66. https://doi.org/10.1016/j.tree.2012.07.013 . https://doi.org: Ahmed DA et al (2022) Managing biological invasions: the cost of inaction. Biol Invasions 24:1927–1946. https://doi.org:10.1007/s10530-022-02755-0 Crystal-Ornelas R et al (2021) Economic costs of biological invasions within North America. Neobiota 67:485–510. https://doi.org:10.3897/neobiota.67.58038 Nampala P et al (2020) Strategy for Managing Invasive Species in Africa 2021–2030. (International Centre of Insect Physiology and Ecology (icipe); CAB International (CABI); International Institute of Tropical Agriculture (IITA) and African Union (AU) Sandvik H, Olsen SL, Töpper JP, Hilmo O (2022) Pathways of introduction of alien species in Norway: Analyses of an exhaustive dataset to prioritise management efforts. J Appl Ecol 59:2959–2970. https://doi.org:https://doi.org/10.1111/1365-2664.14287 Kourantidou M et al (2022) The economic costs, management and regulation of biological invasions in the Nordic countries. J Environ Manage 324:116374. https://doi.org/10.1016/j.jenvman.2022.116374 . https://doi.org: Faulkner KT, Robertson MP, Wilson JR (2020) U. Stronger regional biosecurity is essential to prevent hundreds of harmful biological invasions. Glob Change Biol 26:2449–2462. https://doi.org:https://doi.org/10.1111/gcb.15006 Latombe G et al (2023) Capacity of countries to reduce biological invasions. Sustain Sci 18:771–789. https://doi.org:10.1007/s11625-022-01166-3 Cuthbert RN et al (2022) Biological invasion costs reveal insufficient proactive management worldwide. Sci Total Environ 819:153404. https://doi.org/10.1016/j.scitotenv.2022.153404 . https://doi.org: Colautti RI, Bailey SA, van Overdijk CDA, Amundsen K, MacIsaac HJ (2006) Characterised and projected costs of nonindigenous species in Canada. Biol Invasions 8:45–59. https://doi.org:10.1007/s10530-005-0236-y Williams F et al (2010) The economic cost of invasive non-native species on Great Britain. CABI, Egham Liu X et al (2014) Congener diversity, topographic heterogeneity and human-assisted dispersal predict spread rates of alien herpetofauna at a global scale. Ecol Lett 17:821–829. https://doi.org:10.1111/ele.12286 Polaina E, Soultan A, Part T, Recio MR (2021) The future of invasive terrestrial vertebrates in Europe under climate and land-use change. Environ Res Lett 16:044004. https://doi.org:10.1088/1748-9326/abe95e Wisz MS et al (2013) The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biol Rev 88:15–30. https://doi.org:10.1111/j.1469-185X.2012.00235.x Hubbard JAG, Drake DAR, Mandrak NE (2023) Estimating potential global sources and secondary spread of freshwater invasions under historical and future climates. Divers Distrib 29:986–996. https://doi.org:https://doi.org/10.1111/ddi.13695 Capinha C, Essl F, Porto M, Seebens H (2023) The worldwide networks of spread of recorded alien species. Proceedings of the National Academy of Sciences 120, e2201911120 https://doi.org:doi:10.1073/pnas.2201911120 IPBES (2023) (eds H. E. Roy (IPBES Secretariat Diagne C et al (2020) InvaCost, a public database of the economic costs of biological invasions worldwide. Sci Data 7:277. https://doi.org:10.1038/s41597-020-00586-z Chamberlain S (2021) spocc: Interface to Species Occurrence Data Sources , < https://CRAN.R-project.org/package=spocc Chamberlain S (2022) rgbif: Interface to the Global Biodiversity Information Facility API , < https://CRAN.R-project.org/package=rgbif Chamberlain S, Boettiger CR (2017) Python, and Ruby clients for GBIF species occurrence data. PeerJ Preprints. https://doi.org:10.7287/peerj.preprints.3304v1 Zizka A et al (2019) Standardized cleaning of occurrence records from biological collection databases. Methods Ecol Evol 10:744–751. https://doi.org:10.1111/2041-210x.13152 Kramer-Schadt S et al (2013) The importance of correcting for sampling bias in MaxEnt species distribution models. Divers Distrib 19:1366–1379. https://doi.org:10.1111/ddi.12096 Li Y, Liu X, Li X, Petitpierre B, Guisan A (2014) Residence time, expansion toward the equator in the invaded range and native range size matter to climatic niche shifts in non-native species. Glob Ecol Biogeogr 23:1094–1104. https://doi.org:https://doi.org/10.1111/geb.12191 Hill MP, Gallardo B, Terblanche JS (2017) A global assessment of climatic niche shifts and human influence in insect invasions. Glob Ecol Biogeogr 26:679–689. https://doi.org:10.1111/geb.12578 Aiello-Lammens ME, Boria RA, Radosavljevic A, Vilela B, Anderson RP (2015) spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38:541–545. https://doi.org:https://doi.org/10.1111/ecog.01132 Fick SE, Hijmans RJ (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37:4302–4315. https://doi.org:https://doi.org/10.1002/joc.5086 IPCC. Climate Change (2021) : The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. (2021). https://doi.org:10.1017/9781009157896 Lenton TM et al (2023) Quantifying the human cost of global warming. Nat Sustain 6:1237–1247. https://doi.org:10.1038/s41893-023-01132-6 Gu S, Qi T, Rohr JR, Liu X (2023) Meta-analysis reveals less sensitivity of non-native animals than natives to extreme weather worldwide. Nat Ecol Evol 7. https://doi.org:10.1038/s41559-023-02235-1 Liu X et al (2019) Risks of Biological Invasion on the Belt and Road. Curr Biol 29:499–505e494. https://doi.org/10.1016/j.cub.2018.12.036 . https://doi.org: Araújo MB et al (2008) Quaternary climate changes explain diversity among reptiles and amphibians. Ecography 31:8–15. https://doi.org:10.1111/j.2007.0906-7590.05318.x Angilletta MJ Jr (2009) Thermal Adaptation: A Theoretical and Empirical Synthesis. Oxford University Press Vitt LJ, Caldwell JP (2013) Herpetology: an introductory biology of amphibians and reptiles. Academic Pineda E, Lobo JM (2009) Assessing the accuracy of species distribution models to predict amphibian species richness patterns. J Anim Ecol 78:182–190. https://doi.org/10.1111/j.1365-2656.2008.01471.x . https://doi.org: Barbet-Massin M, Jetz W (2015) The effect of range changes on the functional turnover, structure and diversity of bird assemblages under future climate scenarios. Glob Change Biol 21:2917–2928. https://doi.org:10.1111/gcb.12905 Ferger SW, Schleuning M, Hemp A, Howell KM, Bohning-Gaese K (2014) Food resources and vegetation structure mediate climatic effects on species richness of birds. Glob Ecol Biogeogr 23:541–549. https://doi.org:10.1111/geb.12151 Thuiller W et al (2014) The European functional tree of bird life in the face of global change. Nat Commun 5:3118. https://doi.org:10.1038/ncomms4118 Li Y et al (2016) Climate and topography explain range sizes of terrestrial vertebrates. Nat Clim Change 6:498–502. https://doi.org:10.1038/nclimate2895 Visconti P et al (2016) Projecting global biodiversity indicators under future development scenarios. Conserv Lett 9:5–13. https://doi.org:10.1111/conl.12159 Letnic M, Dickman CR (2010) Resource pulses and mammalian dynamics: conceptual models for hummock grasslands and other Australian desert habitats. Biol Rev 85:501–521. https://doi.org/10.1111/j.1469-185X.2009.00113.x . https://doi.org: Smith AB (2013) The relative influence of temperature, moisture and their interaction on range limits of mammals over the past century. Glob Ecol Biogeogr 22:334–343. https://doi.org/10.1111/j.1466-8238.2012.00785.x . https://doi.org: Weaving H, Terblanche JS, Pottier P, English S (2022) Meta-analysis reveals weak but pervasive plasticity in insect thermal limits. Nat Commun 13:5292. https://doi.org:10.1038/s41467-022-32953-2 Fournier A, Penone C, Pennino MG, Courchamp F (2019) Predicting future invaders and future invasions. Proc Natl Acad Sci USA 116:7905–7910. https://doi.org:10.1073/pnas.1803456116 Ruiz-Navarro A, Gillingham PK, Britton JR (2016) Predicting shifts in the climate space of freshwater fishes in Great Britain due to climate change. Biol Conserv 203:33–42. https://doi.org: https://doi.org/10.1016/j.biocon.2016.08.021 Kärcher O, Frank K, Walz A, Markovic D (2019) Scale effects on the performance of niche-based models of freshwater fish distributions. Ecol Model 405:33–42. https://doi.org:10.1016/j.ecolmodel.2019.05.006 Zhang ZX et al (2021) Lineage-level distribution models lead to more realistic climate change predictions for a threatened crayfish. Divers Distrib 27:684–695. https://doi.org:10.1111/ddi.13225 Hill L et al (2017) Abundance distributions for tree species in Great Britain: A two-stage approach to modeling abundance using species distribution modeling and random forest. Ecol Evol 7:1043–1056. https://doi.org:https://doi.org/10.1002/ece3.2661 Barras AG, Braunisch V, Arlettaz R (2021) Predictive models of distribution and abundance of a threatened mountain species show that impacts of climate change overrule those of land use change. Divers Distrib 27:989–1004. https://doi.org:https://doi.org/10.1111/ddi.13247 Lee-Yaw A, McCune JL, Pironon J, S., Sheth N (2022) S. Species distribution models rarely predict the biology of real populations. Ecography e05877 (2022). https://doi.org:https://doi.org/10.1111/ecog.05877 Araújo MB, New M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22:42–47. https://doi.org/10.1016/j.tree.2006.09.010 . https://doi.org: Hijmans RJ, Phillips S, Leathwick J, Elith J, Hijmans MR (2017) J Package ‘dismo’ Circles 9:1–68 Phillips SJ et al (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl 19:181–197. https://doi.org: https://doi.org/10.1890/07-2153.1 Barber RA, Ball SG, Morris RKA, Gilbert F (2022) Target-group backgrounds prove effective at correcting sampling bias in Maxent models. Divers Distrib 28:128–141. https://doi.org:https://doi.org/10.1111/ddi.13442 Velazco SJE, Rose MB, de Andrade AFA, Minoli I, Franklin J (2022) flexsdm: An r package for supporting a comprehensive and flexible species distribution modelling workflow. Methods Ecol Evol 13:1661–1669. https://doi.org:https://doi.org/10.1111/2041-210X.13874 Rose MB, Velazco SJE, Regan HM, Franklin J (2023) Rarity, geography, and plant exposure to global change in the California Floristic Province. Glob Ecol Biogeogr 32:218–232. https://doi.org:https://doi.org/10.1111/geb.13618 Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43:1223–1232. https://doi.org:10.1111/j.1365-2664.2006.01214.x Hirzel AH, Le Lay G, Helfer V, Randin C, Guisan A (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecol Model 199:142–152. https://doi.org:10.1016/j.ecolmodel.2006.05.017 Wisz MS et al (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14:763–773. https://doi.org:10.1111/j.1472-4642.2008.00482.x de la Fuente A, Hirsch BT, Cernusak LA, Williams SE (2021) Predicting species abundance by implementing the ecological niche theory. Ecography 44:1723–1730. https://doi.org:https://doi.org/10.1111/ecog.05776 Ehrlén J, Morris WF (2015) Predicting changes in the distribution and abundance of species under environmental change. Ecol Lett 18:303–314. https://doi.org: https://doi.org/10.1111/ele.12410 Prasad AM, Iverson LR, Liaw A (2006) Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction. Ecosystems 9:181–199. https://doi.org:10.1007/s10021-005-0054-1 Kuhn M (2008) Building Predictive Models in R Using the caret Package. J Stat Softw 28:1–26. https://doi.org:10.18637/jss.v028.i05 Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250. https://doi.org:10.5194/gmd-7-1247-2014 Freeman EA, Moisen G, PresenceAbsence (2008) An R Package for Presence Absence Analysis. J Stat Softw 23:1–31. https://doi.org:10.18637/jss.v023.i11 Lockwood JL, Hoopes MF, Marchetti MP (2013) Invasion ecology. Wiley Yokomizo H, Possingham HP, Thomas MB, Buckley YM (2009) Managing the impact of invasive species: the value of knowing the density–impact curve. Ecol Appl 19:376–386. https://doi.org: https://doi.org/10.1890/08-0442.1 Ahmed DA et al (2022) Modelling the damage costs of invasive alien species. Biol Invasions 24:1949–1972. https://doi.org:10.1007/s10530-021-02586-5 Powers RP, Jetz W (2019) Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat Clim Change 9:323–329. https://doi.org:10.1038/s41558-019-0406-z Hurtt GC et al (2020) Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. Geosci Model Dev 13:5425–5464. https://doi.org:10.5194/gmd-13-5425-2020 Murakami D, Yoshida T, Yamagata Y, Gridded (2021) GDP Projections Compatible With the Five SSPs (Shared Socioeconomic Pathways). Front Built Environ 7. https://doi.org:10.3389/fbuil.2021.760306 Gao J, O'Neill B (2021) Different Spatiotemporal Patterns in Global Human Population and Built-Up Land. Earths Future 9. https://doi.org: https://doi.org/10.1029/2020EF001920 . e2020EF001920 Bengtsson M, Shen Y, Oki T (2006) A SRES-based gridded global population dataset for 1990–2100. Popul Environ 28:113–131. https://doi.org:10.1007/s11111-007-0035-8 Hijmans RJ, Raster (2022) Geographic data analysis and modeling. R Package Version 3:5–29. https://doi.org:https://CRAN.R-project.org/package=raster Zenni RD, Essl F, García-Berthou E, McDermott SM (2021) The economic costs of biological invasions around the world. NeoBiota 67. https://doi.org:10.3897/neobiota.67.69971 Foerster AT, Hornstein A, Sarte P-DG, Watson MW (2022) Aggregate Implications of Changing Sectoral Trends. J Polit Econ 130:3286–3333. https://doi.org:10.1086/720763 R Core Team (2022) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ Additional Declarations There is NO Competing Interest. Supplementary Files NEESI.docx Supplementary Information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7029011","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":483068831,"identity":"8eead533-0aeb-44f4-9716-78169f0d6435","order_by":0,"name":"Shimin Gu","email":"","orcid":"https://orcid.org/0000-0002-2044-5897","institution":"Institute of Zoology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shimin","middleName":"","lastName":"Gu","suffix":""},{"id":483068832,"identity":"b9144eec-4dea-4277-bf2b-023e4806f458","order_by":1,"name":"Shengnan Chen","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Shengnan","middleName":"","lastName":"Chen","suffix":""},{"id":483068833,"identity":"d86cf028-0728-4787-9798-8c1deafa0024","order_by":2,"name":"Weishan Tu","email":"","orcid":"","institution":"Institute of Zoology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Weishan","middleName":"","lastName":"Tu","suffix":""},{"id":483068834,"identity":"98d2b7ca-fa1e-4093-8548-df315bd4fedf","order_by":3,"name":"Lixia Han","email":"","orcid":"https://orcid.org/0009-0000-2530-7875","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lixia","middleName":"","lastName":"Han","suffix":""},{"id":483068835,"identity":"5e7870c1-8904-46fc-a966-8d5da7e75881","order_by":4,"name":"Qing Zhang","email":"","orcid":"","institution":"Institute of Zoology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Zhang","suffix":""},{"id":483068836,"identity":"dd414d2a-a623-4901-b751-293ac22fc7f9","order_by":5,"name":"Yanhua Hong","email":"","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Yanhua","middleName":"","lastName":"Hong","suffix":""},{"id":483068837,"identity":"fe5ec43b-1452-4975-adc2-958dfc411b7d","order_by":6,"name":"Zhiqiang Lin","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Zhiqiang","middleName":"","lastName":"Lin","suffix":""},{"id":483068838,"identity":"a5e8a90b-8b7d-4e6c-b13c-09aed0923fba","order_by":7,"name":"Yuanbao Du","email":"","orcid":"https://orcid.org/0000-0003-1345-4127","institution":"Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yuanbao","middleName":"","lastName":"Du","suffix":""},{"id":483068839,"identity":"7e2ae48e-d390-4fd0-b5ed-6f29df4731bf","order_by":8,"name":"Wenbo Liao","email":"","orcid":"https://orcid.org/0000-0001-5303-4114","institution":"College of Life Science, China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Wenbo","middleName":"","lastName":"Liao","suffix":""},{"id":483068830,"identity":"49545549-6fc6-4bc9-b9c6-92efc0f70521","order_by":9,"name":"Xuan Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACPmYILcdGtBY2qBZjErRA6cQG4rWw85hJ89TcSe9jb3/A8KOGQd6csMN4jI15jj3LbeM5kMDYc4zBcCch+4BaDB/zsB3ObZNIOMDA28CQYHCAsBaDwzz/DqezSSQ2MP4lUovhY962wwlsEskMzETawlZsOLfvsGEbzzGGwzLHJAw3ENLCz394m8Sbb4fl5dvbHz58U2MjT9AWFABULEGK+lEwCkbBKBgFuAAAs2EzLSGTy5YAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1572-1268","institution":"Institute of Zoology, Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-07-02 11:50:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7029011/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7029011/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87490349,"identity":"4b7c0049-625b-4251-b98d-d97a838a38d1","added_by":"auto","created_at":"2025-07-24 11:42:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":357220,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFramework to evaluate the effect of climate change on potential economic costs of invasive animals.\u003c/strong\u003e The three components include (a) information on economic cost entries used in this study; (b) weighted variables, including socioeconomic factors, management efforts, and ecological factors under current and future climate scenarios; and (c) potential economic costs predicted using an interpolation approach.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7029011/v1/4d4ad6dbcabac340f0d2bd7d.png"},{"id":87490350,"identity":"e152461b-5ce6-4db3-9333-dd846b891538","added_by":"auto","created_at":"2025-07-24 11:42:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":355859,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends of the potential economic costs of invasive animals and geographical patterns of the total, damage, and management potential costs under climate change with different SSP scenarios.\u003c/strong\u003e Comparisons between the predicted current and future potential economic costs (in millions of 2017 US dollars) for (a) the total, (b) damage, and (c) management costs using the Wilcoxon paired test (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; Supplementary Table 3). In a–c, the red lines indicate an increase in the potential economic costs in the future under the various SSP scenarios; the green lines indicate decreasing trends. On the basis of the analysis framework shown in Fig. 1, the potential economic costs were projected under (d-f) current climatic and socioeconomic conditions and the (g-i) SSP 126, (j-l) SSP 245, and (m-o) SSP 585 scenarios.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7029011/v1/7ec4583a61971f29626e9a28.jpeg"},{"id":87490351,"identity":"2d587b95-b23e-4b75-96a6-bf030ef28dc0","added_by":"auto","created_at":"2025-07-24 11:42:13","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":453562,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePercentage changes in the potential economic costs under climate change with different SSP scenarios among countries. \u003c/strong\u003e(a) Total economic costs, (b) damage costs, and (c) management costs were calculated on the basis of the percentage changes in costs between future (SSP 126, SSP 245, and SS P585) and current climatic conditions. The error bars of varying lengths in the subplots denote the means and standardized deviations of the percentage changes in each country.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7029011/v1/a613e601a35043cadf4c6f30.jpeg"},{"id":87490354,"identity":"66e40ca3-3664-40e3-b073-1baefdfddd1e","added_by":"auto","created_at":"2025-07-24 11:42:13","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":241490,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparisons of the potential economic costs across taxonomic groups under future climatic and socioeconomic conditions. \u003c/strong\u003eThe Wilcoxon paired test was performed to assess the significance of the differences in the potential economic costs between future (SSP 126, SSP245, and SSP 585) and current climatic conditions. In the subplots of (a) all taxa, (b) terrestrial invertebrates, (c) terrestrial mammals, (d) freshwater invertebrates, (e) birds, (f) amphibians, (g) reptiles, and (h) freshwater fishes, the scatter points denote the predicted economic cost values. ‘ns’ denotes nonsignificance according to the Wilcoxon paired test (Supplementary Table 7), with *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, and ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001. Animal silhouettes were sourced from the PhyloPic database (www.phylopic.org).\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7029011/v1/d9ede8baabfa293126b6f5ab.jpeg"},{"id":87490355,"identity":"0bc7b66d-7ff2-4416-9b2e-960c981fa574","added_by":"auto","created_at":"2025-07-24 11:42:13","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":477524,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTop 10 costliest countries and their associations with the top 10 costliest animal invaders and country-specific costly animal invaders under future climate change conditions.\u003c/strong\u003e The distributed squares denote the associations between costly invasive species and the invaded countries. The squares filled with colors indicate the potential economic costs of invasive species, and darker colors indicate higher potential economic costs. Animal silhouettes were sourced from the PhyloPic database (www.phylopic.org).\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7029011/v1/d65f43b10f76c0640c61cb6e.jpeg"},{"id":87490353,"identity":"1b9733a8-6555-4917-8790-e9e5863b4f6b","added_by":"auto","created_at":"2025-07-24 11:42:13","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":218572,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparisons of the potential economic costs with and without increasing proactive activity levels to manage invasive animals under future climate change conditions. \u003c/strong\u003eDifferences between the potential economic costs under current and future increased proactive response capacity levels in countries under (a) SSP 126, (c) SSP 245, and (e) SSP 585. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. The Wilcoxon paired test was performed to obtain subplots a, c, and e, and the Wilcoxon independent test was performed to obtain subplot h (SSP 126: n = 67, \u003cem\u003eV\u003c/em\u003e= 1314, and \u003cem\u003eP\u003c/em\u003e = 1.22E-3; SSP245: n = 67, \u003cem\u003eV\u003c/em\u003e= 1353, and \u003cem\u003eP\u003c/em\u003e = 1.28E-3; SSP 585: n = 67, \u003cem\u003eV\u003c/em\u003e = 1387, and \u003cem\u003eP\u003c/em\u003e= 1.54E-3). In a, c and e, the red, green, and gray lines indicate that the potential economic costs increased, decreased, and did not change, respectively, after improving management efforts. b, d, and f show the spatial distributions of the percentage changes in the potential economic costs under SSP 126, SSP 245, and SSP 585, respectively. (g) A generalized additive model was employed to fit the relationship between the future improved proactive response capacity and the percentage changes in economic costs in a given country. The red regression line denotes future increased or unchanged potential economic costs; the green regression line denotes future decreased potential economic costs.\u003cstrong\u003e \u003c/strong\u003e(h) Comparison of the recorded postinvasion management practices between countries with decreasing and unchanged or increasing future costs. ‘Decrease’ indicates countries with lower future potential economic costs after the proactive response capacity is enhanced; ‘maintain or increase’ indicates countries with no change or higher future potential economic costs after the proactive response capacity is enhanced. Moreover, n denotes the number of countries.\u003c/p\u003e","description":"","filename":"image6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7029011/v1/f501619a3c684b1b8ab449f9.jpg"},{"id":87609958,"identity":"f7880c4b-7c8f-4831-9414-02040ec4d09b","added_by":"auto","created_at":"2025-07-25 20:11:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3294315,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7029011/v1/33e84a95-3861-4895-a9d7-4d4eedcd0bbe.pdf"},{"id":87490352,"identity":"ead0352f-c838-4697-b2c7-9d14fe3586a6","added_by":"auto","created_at":"2025-07-24 11:42:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3096269,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"NEESI.docx","url":"https://assets-eu.researchsquare.com/files/rs-7029011/v1/a3eea21141eeb938faa2a0dd.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Climate change projected to exacerbate the economic costs of biological invasions","fulltext":[{"header":"Main","content":"\u003cp\u003eBiological invasions exert pervasive impacts on global biodiversity, the economy and public health \u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Compared with the considerable efforts to examine the loss of native species and zoonotic disease transmission due to invasive species \u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, the high economic costs of invasive species in agriculture, public and social welfare, human health, and management expenditures have just recently been estimated comprehensively \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Notably, the global economic cost of invasive species has reached a minimum of US\u003cspan\u003e$\u003c/span\u003e1.288 trillion (2017 US dollars) and has increased over the past few decades (1970–2017) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, the economic costs of biological invasions are very likely to continue to increase as a result of shifts in ranges with climate change \u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Consequently, it is urgent to understand the effect of climate change on the potential economic costs of invasive species for identifying sensitive areas and the costliest invaders to facilitate the development of early prevention schemes under climate change.\u003c/p\u003e\u003cp\u003eAll else remaining equal, the potential economic costs of invasive species may primarily depend on their geographical ranges \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Species distribution models (SDMs, also referred to as ecological niche models) have been widely employed to predict potential distributions for nonnative species \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and has been recently applied to predict invasion costs based on their potential ranges with the macroeconomic data \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Despite this striking advance by linking SDMs with potential economic cost of invasive species, predicting the invasion costs under future climate change has still some challenges to account for species abundance data, management effort dynamics with time, and habitat variables beyond climate that may influence invasion costs. For example, the potential economic costs may vary with the abundance of invasive populations \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Policy-makers are likely to increase their management efforts with the spread to reduce impacts of invasive species. In addition, habitat factors can influence food availability, reproduction and biotic interactions, which are increasingly regarded important to SDM performance and thus affect the prediction of invasion risk prediction \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Furthermore, several socioeconomic factors, such as the Gross Domestic Product (GDP) and human population density, are positively associated with the economic costs of invasive species \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Moreover, the economic costs of invasive species have usually been assessed on the basis of certain impacted sectors \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, suitable geographical ranges after controlling for sampling bias, population abundance, management dynamics, habitat predictors, socioeconomic factors, impacted sectors have never been combined to comprehensively evaluate the potential economic costs of invasive species under climate change.\u003c/p\u003e\u003cp\u003eHere, we incorporated abundance-based species distribution modeling after accounting for species sampling effort, non-climate habitat variables, and management dynamics into a recently developed interpolation approach using socioeconomic factors and impacted sectors \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e to evaluate the effect of climate change on potential economic costs of animal invaders at the global scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We focus our predictions on those countries where the observed cost was reported in \u003cem\u003eInvaCost\u003c/em\u003e Database to avoid the great differences in management capacity, infrastructure, cultural and legal conditions among countries. We first obtained animal invaders with highly reliable observed costs at the country level from the \u003cem\u003eInvaCost\u003c/em\u003e v4.1 database \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e (details of the filtering process are shown in Supplementary Fig.\u0026nbsp;1). We compiled cost entries for 121 animal invaders, including invertebrates, mammals, birds, amphibians, reptiles, and fishes, across terrestrial and freshwater ecosystems in 67 countries for analysis. We then predicted potential habitat suitability using abundance-based SDMs for each animal invader in invaded countries under current and future climatic conditions on the basis of different shared socioeconomic pathway (SSP) forcing scenarios from 2040–2060 after correcting for sampling bias issue. In addition to climatic conditions, we identified potential geographical ranges via the use of known habitat types for each invader to obtain more precise predictions of suitable establishment areas. Finally, we integrated ecological factors, socioeconomic variables, the importance of impacted sectors, and management dynamics to weight the observed economic costs and fractionally project the future potential economic costs under climate change. The detailed assumptions for predictions in each of modeling steps were summarized (Supplementary Table\u0026nbsp;1). Finally, to validate the robustness of our predictions in the main analysis based on interpolation approach, we verified the consistency between the predicted values and historical observed economic costs (Supplementary Fig.\u0026nbsp;7). Then, we applied a benefit transfer (BT) approach regarded as a comprehensive assessment for economic costs from one studied location to another non-studied location with similar environmental conditions \u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e–\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e to verify the sensitivity of our predictions using different approaches (Supplementary Fig.\u0026nbsp;7). We also conducted a series of sensitive analyses to reduce the uncertainties when evaluating the future potential economic costs using climate-habitat model and climate-only model, and calculated the isolated contribution of the changing habitat, the variations in GDP and human population density on changes in the predictions of potential economic costs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eIncreasing potential economic costs of animal invasion under climate change at the country level\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCompared with the current potential total economic costs, most countries (87.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9%, Supplementary Table\u0026nbsp;2) would experience increased potential economic costs under future climate change across the different SSP scenarios (with average values ranging from 19.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6% under SSP 126 to 21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0% under SSP 585 across countries; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Wilcoxon paired test; refer to Supplementary Table\u0026nbsp;3) for both damage and management costs by 2060 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and c). In particular, North American (e.g., the United States and Canada), South American (e.g., Argentina), Asian (e.g., China, India, Thailand, the Philippines, Vietnam, and Indonesia), and Oceanian countries (e.g., Australia) are the top ten costliest countries under both the current (~\u0026thinsp;84.2%) and future (~\u0026thinsp;83.8%) climatic conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, g, j, and m, respectively; Supplementary Fig.\u0026nbsp;3a). We observed similar patterns in the damage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee, h, k, and n) and management (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef, i, l, and o, respectively) costs (Supplementary Fig.\u0026nbsp;3b and c) for these countries. Notably, some countries with relatively low current economic costs are predicted to experience greatly increased costs under future climate change, especially African and European countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIncreasing potential economic costs of invasive animals under climate change at the taxonomic level\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur prediction results indicated an increase in the potential economic costs of animal invaders of 84.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6% under future climate change across taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplementary Table\u0026nbsp;4). Specifically, most of the costliest species were terrestrial invertebrates, such as the yellow fever mosquito (\u003cem\u003eAedes aegypti\u003c/em\u003e), and mammal invaders, such as the black rat (\u003cem\u003eRattus rattus\u003c/em\u003e) (Supplementary Fig.\u0026nbsp;5 and Data 1). Specifically, \u003cem\u003eA. aegypti\u003c/em\u003e is currently predicted to generate the highest potential economic costs that will continue to increase by 14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1% on average under future climate change globally. Further analysis revealed that the contributions of the costliest species varied among countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Supplementary Fig.\u0026nbsp;2). For example, \u003cem\u003eA. aegypti\u003c/em\u003e greatly contributed to the current potential total costs in countries ranging from the lowest costs in Australia (~\u0026thinsp;0.03%) to the highest costs in India (~\u0026thinsp;63.5%), but the future potential costs would increase the most in the United States (~\u0026thinsp;8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Other terrestrial invertebrate invaders contributed to economic costs in Canada (95.8% of current costs and 95.6% of future costs), China (92.6% of current costs and 94.1% of future costs), and Australia (49.0% of current costs and 49.9% of future costs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), and terrestrial vertebrates such as mammals contributed to economic costs in Argentina (76.8% of current costs and 76.3% of future costs), Australia (50.8% of current costs and 49.9% of future costs), and the United States (30.4% of current costs and 26.4% of future costs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eImportance of management dynamics in predicting the potential economic costs of animal invaders under climate change\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConsidering the important role of government management efforts in mitigating the expansion and impacts of invaders, we evaluated the effect of management dynamics, quantified as the proactive response capacity, on the potential economic costs of animal invaders under different climate change scenarios (for methodological details, please refer to the Methods section). We showed that improvements in management efforts could reduce the potential economic costs of invasive animals, especially in countries in the Americas, Africa, and northern Europe (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, c, and e, respectively). In contrast, we found that increases in management efforts might yield relatively limited effects on curbing the potential economic costs in most parts of Asia, Oceania, and central and southern Europe (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, d, and f, respectively). Further analysis revealed that countries with preinvasion management efforts generally exhibited lower potential economic costs than those with only postinvasion management efforts (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eg and h, respectively).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSensitivity analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe obtained high consistent results between our predicted costs using interpolation approach and the historical observed economic costs (the average goodness of fit \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e was 0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Supplementary Fig.\u0026nbsp;8). In addition, the trends of predictions using interpolation and BT approaches had also high consistency (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.68, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Supplementary Fig.\u0026nbsp;9). This indicated that our predictions were less sensitivity to different approaches. Compared with predictions using climate-habitat model, the predicted potential economic costs using climate-only model had shown differences under the current and SSP585 climatic scenarios (Supplementary Fig.\u0026nbsp;12) but no differences under SSP 126 or SSP 245 scenarios. This indicated that the available habitat had covaried with climatic conditions and had significant contributions to the potential economic cost predictions. As expected, the growth of GDP and human population density indeed had significant influences on the future potential economic costs across all scenarios (Supplementary Fig.\u0026nbsp;13). By partitioning the independent contributions of habitat-filtered species population abundance, GDP, and human population density in predicting future potential costs, the results shew that human population density was dominant in the SSP126 scenario compared to the current climate condition. As the degree of future climate warming increases, the importance of species population abundance and GDP would gradually increase (Supplementary Fig.\u0026nbsp;14).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we assessed the effect of future climate change on potential economic costs of animal invaders on the basis of their abundance-based predicted suitable habitats after accounting for sampling bias, socioeconomic factors, management dynamics, and importance of impacted sectors worldwide. Our predictions indicated that the future potential economic costs of most invasive animals would exceed their current potential economic costs at the global scale. Furthermore, we found that improving management efforts after invasion might impose a limited effect on reducing the potential economic costs in most countries (~ 60%) without preinvasion management measures. Our predictions can be validated by the trends of historical observed economic costs across countries and we obtained similar results when we used different methods of economic cost predictions based on the interpolation approach and BT approach (Supplementary Fig.\u0026nbsp;9).\u003c/p\u003e\u003cp\u003eThere are several possible explanations for the overall increase in the future potential economic costs of animal invaders. First, our predictions are based on successful invaders that have caused economic costs \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, and these species exhibit high growth rates, generalist characteristics, and high phenotypic plasticity, which may help them adapt to future climatic conditions and thus occupy large suitable ranges (e.g., the geographical ranges of over 60% of animal invaders are predicted to increase under climate change according to our predictions; Supplementary Data 2) \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In particular, the breadth of the habitats of invasive species is important for determining their potential invasion ranges and impacts \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Indeed, most of the study species can occupy various habitats across forests, grasslands, farmlands and urban areas, and eight of the ten costliest invaders can occupy at least three types of habitats (Supplementary Data 3). Second, invaders with a high degree of potential cost increase generally exhibit relatively short residence times in the invaded countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Fig.\u0026nbsp;6 and Data 4). Unsurprisingly, these invaders might exhibit greater potential for increasing the associated economic costs under climate change, as there might be time lags in population growth and range expansion after establishment \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Finally, higher future economic costs were mainly concentrated in North America, Asia, and Oceania (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Fig.\u0026nbsp;4), which have been recognized as future invasion hotspots owing to their high climate compatibility with invasive species \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur results confirmed that the increasing trends in economic costs impacted sector and taxonomic dependences \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Adopting the two costliest taxa (i.e., invasive terrestrial invertebrates and mammals) as examples, their potential economic costs are mainly incurred in the human health and public and social welfare sectors, with economic costs that are six times greater than those of other taxa (Supplementary Table\u0026nbsp;5). In addition, the number of reported impacted sectors in terms of economic costs (a maximum of 4 sectors, with an average of 1.8 ± 0.8 sectors) was greater than that reported for other taxonomic groups (a maximum of 2 sectors, with an average of 1.4 ± 0.5 sectors). However, terrestrial invertebrates and mammals are associated with the highest reported economic costs in the literature globally \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. We thus suggest the requirement to investigate more hidden impact pathways, sectors, and economic costs associated with less-studied taxa in the future.\u003c/p\u003e\u003cp\u003eOur results indicated that an increase in the proactive response capacity of a given country can proportionally decrease future potential economic costs, especially in several American, African, and North European countries with preinvasion management efforts. These findings support those of previous studies suggesting that early intervention is a promising way to reduce invasion-related economic costs in the future \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. For example, in North America, there have been systematic and early assessments of invasion-related economic costs, invaded ecosystems and impacted sectors through the Multilateral Invasive Species Project Inventory \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In Africa, the Strategic Result Areas and Actions Framework has coordinated prevention strategies across multiple spatial scales \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In northern Europe, impact/risk-based management actions have been implemented through cross-sectoral strategies for invasive species \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e and the National Strategy for alien species \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e to reduce the impacts of biological invasions. Furthermore, national management efforts to prevent the establishment of invasive species may be related to the development level of the country and the rate of increase in response capabilities after suffering impacts \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. For example, high levels of economic development in North America, Europe, and Oceania usually require numerous resources and governance to control invasive species \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. We found that certain West African countries (such as Nigeria and Cameroon) and East African countries (such as Ethiopia, Uganda, Zimbabwe, and Mozambique) exhibited lower potential economic costs than other African countries under future scenarios did (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). One alternative explanation is that these West and East African countries could respond more quickly and increase their management levels over time \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Our prediction results suggested that more than half (40/67) of the countries with increased response capabilities may still face no change or even an increase in future economic costs. One possible reason is that these countries primarily lack early prevention measures and typically control invaders only after impact occurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eh). This trend is consistent with that obtained in an empirical evaluation study showing that low preinvasion management expenditures can increase 25 times of more economic costs \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Hence, our results demonstrated that management strategies at the early stages before invasion success are key to efficiently reducing the future potential economic costs.\u003c/p\u003e\u003cp\u003eWe acknowledge that there are still some caveats to our predictions. For example, although we aimed to predict the average economic costs of each invader on the basis of socioeconomic, ecological, and management dynamic factors at the country level, there might still be differences in the potential economic costs of invaders among geographical populations due to variations in detection, research, control, medical treatment, and infrastructure costs and cultural and legal conditions over time \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, which warrants future investigations when related data become available at the global scale. In addition, our predictions were based on the invasion potentials of species according to their climatic and habitat suitability levels as most SDM-based work did \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In the real world, the distributions of invasive species may also depend on other factors, such as interactions with sympatric species and land-use disturbances \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e–\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, which requires the development of more precise prediction models in which these processes can be incorporated in the future. Finally, our main analyses provided a conserved prediction by focusing on those countries where the economic cost of studied species has been reported. However, these invaders may further spread to other out-of-sample countries with the aid of human-assisted dispersal through different pathways such as trade circulation and transportation networks \u003csup\u003e\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e–\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, which unfortunately are not available under future scenarios at the global scale. Even so, we have tried to conduct supplementary analysis to interpolate the economic costs of species in those out-of-sample countries, and find that future economic costs may be higher than our conserved predictions by hundreds of times on average (Supplementary Fig.\u0026nbsp;10 and Fig.\u0026nbsp;11). Despite these potential caveats, this study represents the first step toward the development of early and targeted management plans for national costliest invaders in sensitive areas under accelerating biological invasion and climate change.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eEconomic cost data and processing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHistorical observed economic costs of invasive species based on the most up-to-date version of the \u003cem\u003eInvaCost\u003c/em\u003e database (\u003cem\u003eInvaCost\u003c/em\u003e v.4.1; accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6084/m9.figshare.12668570\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.12668570\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e were employed to predict the current and future potential economic costs under climate change. This database provides monetary costs associated with invasive species worldwide, thereby standardizing the different currencies of countries, which may influence the cost estimation results. The original dataset contained 13,553 cost entries from 1,973 peer-reviewed publications, reports, and gray literature. We excluded historical observed costs without specific information on animal invaders (‘\u003cem\u003eKingdom\u003c/em\u003e’ column in \u003cem\u003eInvaCost\u003c/em\u003e), those that are not specific to the species level (‘\u003cem\u003eSpecies\u003c/em\u003e’ column in \u003cem\u003eInvaCost\u003c/em\u003e), those that are not specific to the country level (‘\u003cem\u003eSpatial_scale\u003c/em\u003e’ column in \u003cem\u003eInvaCost\u003c/em\u003e), those without the name of the official country (‘\u003cem\u003eOfficial_country\u003c/em\u003e’ column in \u003cem\u003eInvaCost\u003c/em\u003e), those without raw cost data (‘\u003cem\u003eRaw_cost_estimate_2017_USD_exchange_rate\u003c/em\u003e’ column in \u003cem\u003eInvaCost\u003c/em\u003e), and those without cost estimates (‘\u003cem\u003eCost_estimate_per_year_2017_USD_exchange_rate\u003c/em\u003e’ column in \u003cem\u003eInvaCost\u003c/em\u003e). We also excluded entries with only potential costs (‘\u003cem\u003eImplementation\u003c/em\u003e’ column in \u003cem\u003eInvaCost\u003c/em\u003e), entries with low reliability (in both the ‘\u003cem\u003eMethod_reliability\u003c/em\u003e’ and ‘\u003cem\u003eMethod_reliability_refined\u003c/em\u003e’ columns in \u003cem\u003eInvaCost\u003c/em\u003e), and entries for marine species from further analysis. We had also removed the cost entries associated with multiple species. These steps resulted in the retention of a total of 698 cost entries across 129 invasive animal species in 74 countries for further analysis (Supplementary Fig.\u0026nbsp;1 and Data 5).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAbundance-based species distribution modeling\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpecies occurrence data.\u003c/b\u003e We obtained occurrence data of the 129 animal invaders with precise geographical coordinates from various references (Supplementary Data 6) and online databases, including the Global Biodiversity Information Facility (GBIF; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gbif.org/\u003c/span\u003e\u003cspan address=\"http://www.gbif.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Biodiversity Information Serving Our Nation (BISON; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bison.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://bison.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), iNaturalist (iNat; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.inaturalist.org/\u003c/span\u003e\u003cspan address=\"https://www.inaturalist.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Integrated Digitized Biocollections (iDigBio; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.idigbio.org/\u003c/span\u003e\u003cspan address=\"https://www.idigbio.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Atlas of Living Australia (ALA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ala.org.au/\u003c/span\u003e\u003cspan address=\"http://www.ala.org.au/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Notably, record data were downloaded from these databases using the \u003cem\u003espocc\u003c/em\u003e (v.1.2.0) \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003ergbif\u003c/em\u003e (v.1.2.0) \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e packages. We then employed the \u003cem\u003eCoordinateCleaner\u003c/em\u003e package in R to clear invalid (duplicated geographic coordinates and occurrences without or with erroneous coordinates falling outside terrestrial and freshwater borders) records \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e and retained one record per 5-arcmin (~ 9.2 km at the equator) grid cell to avoid adverse effects on the model fitting results \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Occurrence data of both native and nonnative species were used to train and establish SDMs to eliminate biases in evaluating species niches because many nonnative species may experience climatic niche shifts when invading new ranges \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpecies population abundance data.\u003c/b\u003e We collected population abundance data for invasive species from peer-reviewed publications, reports, and gray literature. Data collection was conducted until July 2024. The following search terms were applied in Google Scholar for paper collection: (density OR abundance) AND (species scientific name). We excluded papers pertaining to (1) laboratory or greenhouse experiments and (2) field experiments in which release and count values were manipulated, as well as papers with (3) no original recorded abundance data or (4) no abundance data for specific species. From the papers, we recorded the species name, taxonomic information, coordinates and name of the locality, country name, life stage, density estimate, and source citations (Supplementary Data 7). GetData graph Digitizer (v.2.24) was applied to extract abundance data from figures in the papers. All coordinates were transformed to longitudinal and latitudinal coordinates in decimal degrees. Owing to differences in the field population survey methods among taxonomic groups (e.g., counting juvenile abundance per m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e leaf area for insect species; capturing adult individuals per km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e for mammal and herpetofaunal species; or determining the catch per unit effort for fish species), we unified the units for each species to represent variations in population abundance across different studies and satisfy the requirements for further abundance-based modeling predictions. The population abundance data for each species were thinned at 5-arcmin resolution via the \u003cem\u003ethin\u003c/em\u003e function of the \u003cem\u003espThin\u003c/em\u003e (v.0.2.0) package \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEnvironmental predictor variables\u003c/b\u003e. We obtained data for different bioclimatic variables at a spatial resolution of 5 arcmin from the WorldClim Global Climate Database version 2.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://worldclim.org/\u003c/span\u003e\u003cspan address=\"https://worldclim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. The chosen spatial resolution is widely employed in global studies for practical invasion biosecurity decisions \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. On the basis of the WorldClim database, current climatic conditions were produced with monthly averages from 1970–2000. Future climatic conditions were downscaled to monthly predicted climate data from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and a total of four general circulation models (GCMs), namely, ACCESS-CM2, MPI-ESM1-2-HR, IPSL-CM6A-LR and MIROC6, were adopted to obtain predicted climate data for the 2041–2060 period. We employed three SSP forcing scenarios: (1) SSP 126, a broadly sustainable pathway consisting of limiting warming to 2°C; (2) SSP 245, a middle of the road pathway encompassing the best warming estimate of approximately 2.7°C by the end of the 21st century \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e; and (3) SSP 585, a fossil-fueled development pathway consisting of warming by approximately 4.4°C by the end of this century \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. To model realized niches and predict potential distributions of taxa, we not only selected an appropriate number of variables to mitigate overfitting problems in the training data but also accounted for climatic physiological constraints that differed across taxonomic groups. To achieve this goal, we followed the approaches in previous studies \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e and employed taxonomically dependent bioclimatic variables to predict the potential distributions of species. For amphibians and reptiles, temperature and precipitation are key environmental variables that can independently influence their population distributions \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. For example, many amphibians and reptiles exhibit specific temperature thresholds that determine their metabolism, activity level, and reproductive success \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. In contrast, other herpetofauna may rely on specific microhabitat preferences (moisture conditions) for breeding and larval development \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. As a result, we selected the annual average temperature, seasonal temperature, extreme temperatures in the warmest and coldest months, annual precipitation, and precipitation in the wettest and driest quarters as bioclimatic predictors \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Notably, birds exhibit relatively high dispersal abilities as they must cover large distances quickly during migration; moreover, they relocate and adjust their breeding and migratory patterns in response to changing environments \u003csup\u003e\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e–\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Therefore, the seasonal temperature and precipitation, extreme temperatures in the warmest and coldest months, and extreme precipitation in the wettest and driest quarters were included in bird species distribution modeling. In mammals, various physiological mechanisms, such as insulation (fur, fat, etc.) and metabolic adjustments, have evolved to regulate the body temperature. These adaptations help them cope with extreme temperatures more effectively than ectothermic species \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. However, water availability is important to mammals because of their need for hydration, while vegetation, food and other habitat qualities are important for survival and reproduction. For example, precipitation influences plant growth and the availability of food resources for herbivorous mammals. Changes in precipitation can directly affect the abundance and quality of vegetation, thus impacting foraging success, nutritional intake, and shelter availability \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. In particular, the interaction of temperature and precipitation variables could directly or indirectly limit mammal distributions even when these variables alone do not exceed species tolerance levels \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Thus, the annual average temperature, average temperatures in the wettest and driest quarters, average temperatures in the warmest and coldest quarters, annual precipitation, precipitation in the wettest and driest quarters, and precipitation in the warmest and coldest quarters were included in mammal species distribution modeling. Many terrestrial invertebrates are r-strategy species that quickly regenerate, and in addition to the average temperature and precipitation, short periods of extremely high or low temperature and humidity levels are key to determining their performance \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Furthermore, the diurnal temperature range (maximum temperature–minimum temperature) plays an important role in determining their physiological and life histories, such as development time, metamorphosis, and generations \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Therefore, the mean diurnal range, extreme temperatures in the warmest and coldest quarters, annual precipitation, and precipitation in the wettest and driest quarters were included for terrestrial invertebrate species distribution modeling. For freshwater fishes, the diurnal range is considered a key variable in the prediction of shifts in the ranges of fish species under climate change \u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Furthermore, fish depend on water bodies, and precipitation-related bioclimatic variables were included in their distribution modeling \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. For freshwater invertebrates, we followed the methods in previous studies and included the extreme temperatures in the warmest and coldest months and precipitation in the wettest and direst quarters \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. To better predict the distributions of invasive fishes and invertebrates in aquatic environments, we also included the water layer from the Global Lakes and Wetlands Database (GLWD; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldwildlife.org/pages/global-lakes-and-wetlands-database\u003c/span\u003e\u003cspan address=\"https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which contains features of lakes, reservoirs, and rivers with surface areas ≥ 0.1 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The distributions of saltwater lakes were obtained from the Saline Lakes Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://lakes.chebucto.org/saline1.html\u003c/span\u003e\u003cspan address=\"http://lakes.chebucto.org/saline1.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), but they were not included as predictors of species potential distributions (more detailed lists of the selected bioclimatic variables for each taxonomic group are provided in Supplementary Table\u0026nbsp;6).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTwo-stage modeling for potential population abundance prediction.\u003c/b\u003e The two-stage modeling approach is commonly employed to predict the potential population abundance across taxonomic groups \u003csup\u003e\u003cspan additionalcitationids=\"CR81\" citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e–\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. In this approach, traditional SDMs are first applied to predict potentially suitable geographical areas, after which species abundance data from field surveys are employed to predict the abundance of the target species in different areas by fitting empirical models \u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. At the first stage, we developed ensemble SDMs for the 129 invasive species using the maximum entropy algorithm, the random forest algorithm and generalized additive models, which have been widely used in conservation, invasion and biogeography studies to account for the uncertainties in single models \u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. To prevent spatial bias from skewing environmental gradients in less-surveyed regions, we applied the target group background approach by using the \u003cem\u003erandomPoints\u003c/em\u003e function in the \u003cem\u003edismo\u003c/em\u003e (v.1.3-8) package to randomly sample 10,000 background points without replacement \u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e,\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. We adopted previous methods to generate a target group background on the basis of the distribution points of all studied species belonging to the same taxon \u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. Prior to model fitting, we partitioned the occurrence data of each species into three spatially structured folds of equal size by using the \u003cem\u003epart_random\u003c/em\u003e function in the \u003cem\u003eflexsdm\u003c/em\u003e (v.1.3.3) package \u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. On the basis of the partitioned occurrence data, each model was subjected to threefold cross-validation, thereby evaluating spatial transferability and fitting performance. The default metric of the true skill statistic (TSS) was chosen to select the best combination of hyperparameter values \u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. Then, we established and fitted multiple candidate models using different feature classes and regularization multiplication. We evaluated the performance of the established models via different metrics, including the area under the receiver operating characteristic curve (AUC, ranging from 0 to 1, with a value above 0.9 indicating excellent performance), TSS (ranging from − 1 to 1, with a value above 0.8 indicating excellent performance) and the Boyce index (ranging from − 1 to 1, with a higher value indicating greater model performance) \u003csup\u003e\u003cspan additionalcitationids=\"CR90\" citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e–\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. Finally, six species were excluded from further analysis because they failed to meet the performance threshold in predictions under the current and future climatic conditions (Supplementary Data 8 and Data 9). All the ensemble SDMs were calibrated and evaluated using the \u003cem\u003eflexsdm\u003c/em\u003e (v.1.3.3) package \u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. Afterward, we employed random forest regression analysis to fit the relationship between species population abundance and predictor variables, including the probability of species occurrence predicted by the ensemble SDMs and bioclimatic and habitat covariates \u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. We followed the approaches in previous work by incorporating the probability of species occurrence into the fitting process, as species occurrence and abundance are not always affected by exactly the same environmental variables \u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e,\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e. The random forest regression method was chosen owing to (1) its lower sensitivity to data distributions with a high percentage of zero values and (2) its robustness to overfitting with potential collinear variables \u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e. We fitted random forest models via the \u003cem\u003etrain\u003c/em\u003e function to obtain empirical regression relationships, on the basis of which we then predicted the potential abundance of each species at a 5-acrmin resolution via the \u003cem\u003epredict\u003c/em\u003e function in the caret (v.6.0–93) package \u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. The goodness of fit of the prediction models (Supplementary Data 9) was assessed via the root mean square-error (RMSE) and the mean absolute error (MAE), which are commonly used to identify the optimal model \u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. To determine occupied number of potential girds in following analysis, we transformed the generated continuous habitat suitability outputs into binary maps of species presence (1) or absence (0) by using the maximizing TSS as a threshold \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. The optimal threshold was determined to maximize sensitivity (true predictions of high suitability for establishment) plus specificity (true predictions of low suitability for establishment) using \u003cem\u003ePresenceAbsence\u003c/em\u003e (v.1.1.11) package \u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eProjections of the current and future potential economic costs of invasive animals\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe predicted the potential economic cost of invaders in each country on the basis of historical observed economic costs and weighted variables, including ecological factors, socioeconomic factors, importance of the impacted sectors, and management efforts under current and future climate change conditions under the different SSP scenarios.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEcological factors.\u003c/b\u003e Because the size of the ranges of invasive species is positively related to their impact risk \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and a larger number of potential distribution grids could indicate that a given invasive species exhibits higher adaptive and ecologically competitive abilities to occupy the niches of native biota and thus increase their impacts on native ecosystems \u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e, we chose the number of grids with predicted suitable habitats and the potential population abundance in each grid to quantify the ecological effects on the predicted economic costs. For example, the population abundance and impact severity of invaders have been reported to follow linear, threshold-dependent, or S-shaped relationships \u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e,\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e. After species distribution modelling, we used the available habitat types of each of the invasive animals to generate more conservative and robust predictions of their potential distributions \u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e. We searched for habitat types for each invasive animal on the basis of descriptions in the IUCN Red List (iucnredlist.org), CAB International (cabi.org), Animal Diversity Web (animaldiversity.org), and published studies (Supplementary Data 3). We followed the methods in previous work \u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e by classifying habitat types into ten categories, including forests (forested primary land, nonforested primary land, potentially forested secondary land, and potentially nonforested second land), grasslands (savannas, managed pastures and rangeland), farmlands (C3/C4 annual and perennial crops), shrublands, wetlands, rocky areas, deserts, artificial habitats, marine coastal/supratidal land, and urban land. Habitat cover data were sourced from the Land-Use Harmonization dataset (LUH2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://luh.umd.edu/\u003c/span\u003e\u003cspan address=\"https://luh.umd.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for the current and future periods. On the basis of this habitat-filtered process, we calculated the average population abundance across all predicted distributed grids and counted the number of grids where the population abundance exceeded zero \u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSocioeconomic factors.\u003c/b\u003e The GDP, human population density, and importance of the impacted sector (as a percentage of the GDP) are considered important socioeconomic factors influencing the economic costs of invasive species \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Global spatially explicit GDP data for the historical (hereafter referred to as current) and future periods were estimated by Murakami et al. (2021) on a 5-arcmin grid scale \u003csup\u003e\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e. The GDP data for the current (1970, 1980, 1990, and 2000) and future (2040, 2050, and 2060) periods were averaged. Global spatially explicit human population density data (per km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) for the current and future periods were sourced from the Gridded Population of the World (GPW) v3 database, which was established by the Socioeconomic Data and Applications Center in NASA’s Earth Observing System Data and Information System (EOSDIS) at Columbia University (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sedac.ciesin.columbia.edu/data/collection/gpac-v3\u003c/span\u003e\u003cspan address=\"https://sedac.ciesin.columbia.edu/data/collection/gpac-v3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on May 7, 2024). Compared with the latest released version of GPWv4.11, GPWv3 provides earlier estimated population density data. In addition, GPWv3 exhibits a coarse spatial resolution (30-acrmin for the current period and 7.5-arcmin for the future period) that has been commonly adopted in previous studies \u003csup\u003e\u003cspan additionalcitationids=\"CR104\" citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e–\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e but is suitable for our analysis purposes. Then, the human population densities during the current (1990, 1995, 2000, 2010 and 2020) and future (2040, 2050, and 2060) periods were averaged. We standardized the current and future gridded human population densities to a 5-arcmin resolution via the \u003cem\u003eresample\u003c/em\u003e function in the \u003cem\u003eraster\u003c/em\u003e (v.3.5–29) package \u003csup\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e. Finally, the economic cost of invasive species was associated with the importance of the impacted sector, expressed as a percentage of the GDP \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. For example, the agriculture and health sectors are much more important than the environmental sectors in African, American, and Asian countries, which explains why the economic costs of invasive species in these regions are related mainly to agricultural and health impacts rather than to environmental impacts \u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e. We then considered six categories of impacted sectors, including agriculture–forestry–fishery, industry, manufacturing, services, forest rents, and health expenditures, which cover the reported types of sectors in the \u003cem\u003eInvaCost\u003c/em\u003e database. We collected importance values of the different sectors in each country from the World Development Indicators—Economy on May 11, 2024 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://datatopics.worldbank.org/world-development-indicators/themes/economy.html\u003c/span\u003e\u003cspan address=\"https://datatopics.worldbank.org/world-development-indicators/themes/economy.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We determined the importance of the impacted sectors by averaging the available values in 2000, 2010, and 2020, considering their potential temporal dynamics \u003csup\u003e\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e, and we assumed a constant value under climate change, as future data are unavailable.\u003c/p\u003e\u003cp\u003e\u003cb\u003eManagement dynamics.\u003c/b\u003e The economic costs of invasive species could be negatively affected by adequate management efforts \u003csup\u003e\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e. The proactive response capacity is a commonly employed metric to represent management efforts in preventing invasions at the country level, as evaluated by Faulkner et al. (2020) \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. We used the matrix to quantify high or low county management efforts in response to the economic costs of invaders. A higher proactive response capacity indicates a country with a greater possibility of intervention or early containment of emerging invasive species in new regions \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Because the future proactive response capacity of a given country is related to the potential spatial ranges invaded under climate change \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, we calculated the ratio of future to current potential ranges of species and multiplied this ratio by the historical response capacity in each country. Finally, the sum of the product value and the country’s basic historical response capacity was used to represent its future potential response capacity under the different SSP scenarios.\u003c/p\u003e\u003cp\u003e\u003cb\u003eProjection of potential economic costs.\u003c/b\u003e We applied a recently developed interpolation approach to project the potential economic cost of invasive species \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. We included the total GDP of the country, average GDP across the potential species distribution area, human population density, importance of the impacted sector expressed as a percentage of the GDP, number of grids containing potential suitable habitats, and average species population abundance as predictors. All possible combinations of the predictor variables were fitted via the following interpolation model:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{cost}_{i,potential}=\\frac{1}{n}{\\sum\\:}_{k=1}^{n}{cost}_{k,observed}{\\prod\\:}_{q=1}^{p}{\\left(\\frac{{f}_{q,k,potential}}{{f}_{q,k,ovserved}}\\right)}^{{\\gamma\\:}_{q}}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere \u003cem\u003ecost\u003c/em\u003e\u003csub\u003e\u003cem\u003ek,observed\u003c/em\u003e\u003c/sub\u003e denotes the historical observed economic cost, \u003cem\u003ek\u003c/em\u003e is the number of same species-impacted sector-type cost (damage, management, mixed or unspecified) combinations in country \u003cem\u003ei\u003c/em\u003e, \u003cem\u003eq\u003c/em\u003e is the number of predictors used in the interpolation model, \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003eq,k,observed\u003c/em\u003e\u003c/sub\u003e is a predictor based on historical conditions, \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003eq,k,potential\u003c/em\u003e\u003c/sub\u003e is a predictor based on the current or future potential conditions, and \u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003eq\u003c/em\u003e\u003c/sub\u003e is a fitting coefficient that ensures fractional but not proportional changes in weighted predictions with historical conditions \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. We followed previous work by applying truncated scalar ratios to avoid overinterpolation during model fitting \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The maximum likelihood method was applied using the \u003cem\u003eoptim\u003c/em\u003e function (a built-in function in R) to determine the goodness of fit between the predicted and training-stage costs. On the basis of different combinations of predictor variables, we calculate predicted average values of the current or future potential economic costs. All data analyses were conducted in R (4.2.1) \u003csup\u003e\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank three anonymous reviewers for constructive comments that have greatly improved this manuscript. We also thank Dr. Zhixin Zhang for the assistance provided with SDM construction. This work was supported by the Third Xinjiang Scientific Expedition Program (2022xjkk0800, 2021xjkk0600), the National Natural Sciences Foundation of China (31970393, 32171657, 32301459), the grant of high quality economic and social development in southern Xinjiang (NFS2101), the grant from Youth Innovation Promotion Association of Chinese Academy of Sciences (Y201920), the grant from the Institute of Zoology, Chinese Academy of Sciences (2023IOZ0104), the Key Project of Science and Technology of Sichuan Province (22NSFSC2743), and the State Key Laboratory of Animal Biodiversity Conservation and Integrated Pest Management (Grant No. SKLA2504).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.L. conceived the study; X.L. and W.L. supervised the project; X.L., S.G., and S.C. designed the study; S.G., S.C., W.T., L.H., Q.Z., Y.H., Z.L., Y.D., and X.L. collected and analyzed the data; S.G., X.L., and S.C. wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data supporting the results are available in https://doi.org/10.6084/m9.figshare.29425643.v1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the codes used to conduct the main analyses are available here: https://doi.org/10.6084/m9.figshare.29425643.v1\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIwamura T, Guzman-Holst A, Murray KA (2020) Accelerating invasion potential of disease vector Aedes aegypti under climate change. Nat Commun 11:2130. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1038/s41467-020-16010-4\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1038/s41467-020-16010-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaini DR et al (2016) Global threat to agriculture from invasive species. Proc Natl Acad Sci USA 113:7575\u0026ndash;7579. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1073/pnas.1602205113\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1073/pnas.1602205113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang L et al (2022) Biological invasions facilitate zoonotic disease emergences. Nat Commun 13:1762. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1038/s41467-022-29378-2\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1038/s41467-022-29378-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlackburn TM, Bellard C, Ricciardi A (2019) Alien versus native species as drivers of recent extinctions. Front Ecol Environ 17:203\u0026ndash;207. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1002/fee.2020\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1002/fee.2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChinchio E et al (2020) Invasive alien species and disease risk: An open challenge in public and animal health. PLoS Pathog 16:e1008922. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1371/journal.ppat.1008922\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1371/journal.ppat.1008922\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiagne C et al (2021) High and rising economic costs of biological invasions worldwide. Nature 592:571\u0026ndash;576. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1038/s41586-021-03405-6\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1038/s41586-021-03405-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaubrock PJ et al (2021) Economic costs of invasive species in Germany. Neobiota 67:225\u0026ndash;246. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3897/neobiota.67.59502\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3897/neobiota.67.59502\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKirichenko N et al (2021) Economic costs of biological invasions in terrestrial ecosystems in Russia. Neobiota 67:103\u0026ndash;130. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3897/neobiota.67.58529\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3897/neobiota.67.58529\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKourantidou M et al (2021) Economic costs of invasive alien species in the Mediterranean basin. Neobiota 67:427\u0026ndash;458. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3897/neobiota.67.58926\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3897/neobiota.67.58926\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu CL et al (2021) Economic costs of biological invasions in Asia. Neobiota 67:53\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3897/neobiota.67.58147\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3897/neobiota.67.58147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRenault D et al (2021) Biological invasions in France: Alarming costs and even more alarming knowledge gaps. Neobiota 67:191\u0026ndash;224. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3897/neobiota.67.59134\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3897/neobiota.67.59134\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBellard C et al (2013) Will climate change promote future invasions? Glob Change Biol 19:3740\u0026ndash;3748. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1111/gcb.12344\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/gcb.12344\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHulme PE (2017) Climate change and biological invasions: evidence, expectations, and response options. Biol Rev Camb Philos Soc 92:1297\u0026ndash;1313. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1111/brv.12282\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/brv.12282\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoto I et al (2025) Using species ranges and macroeconomic data to fill the gap in costs of biological invasions. Nat Ecol Evol 9:1021\u0026ndash;1030. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1038/s41559-025-02697-5\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1038/s41559-025-02697-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods Ecol Evol 1:330\u0026ndash;342. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1111/j.2041-210X.2010.00036.x\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/j.2041-210X.2010.00036.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWaldock C et al (2022) (2022) A quantitative review of abundance-based species distribution models. \u003cem\u003eEcography\u003c/em\u003e https://doi.org:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ecog.05694\u003c/span\u003e\u003cspan address=\"10.1111/ecog.05694\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStrayer DL (2020) Non-native species have multiple abundance\u0026ndash;impact curves. Ecol Evol 10:6833\u0026ndash;6843. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.6364\u003c/span\u003e\u003cspan address=\"10.1002/ece3.6364\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. https://doi.org:\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhmed DA et al Predicting future damage costs of non-native species using combined dynamical and cost-density equations. \u003cem\u003ePreprint\u003c/em\u003e https://doi.org:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.32942/X2P631\u003c/span\u003e\u003cspan address=\"10.32942/X2P631\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTroia MJ (2019) e. a. Species traits and reduced habitat suitability limit efficacy of climate change refugia in streams. Nat Ecol Evol\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodriguez LF (2006) Can Invasive Species Facilitate Native Species? Evidence of How, When, and Why These Impacts Occur. Biol Invasions 8:927\u0026ndash;939. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1007/s10530-005-5103-3\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1007/s10530-005-5103-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaubrock PJ et al (2021) Economic costs of invasive alien species across Europe. Neobiota 67:153\u0026ndash;190. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3897/neobiota.67.58196\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3897/neobiota.67.58196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTurbelin AJ et al (2024) Biological invasions as burdens to primary economic sectors. Glob Environ Change 87:102858. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gloenvcha.2024.102858\u003c/span\u003e\u003cspan address=\"10.1016/j.gloenvcha.2024.102858\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. https://doi.org:\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHenry M et al (2023) Unveiling the hidden economic toll of biological invasions in the European Union. Environ Sci Europe 35:43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1186/s12302-023-00750-3\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1186/s12302-023-00750-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHanley N, Barbier EB, Barbier E (2009) Pricing nature: cost-benefit analysis and environmental policy. Edward Elgar Publishing\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreiner R, Kancans R, Nelson R (2023) Methods for non-market valuation of alien invasive species, Australian Bureau of Agricultural Resource Economics and Sciences ABARES, Department of Agriculture, Fisheries and Forestry, Canberra, July. CC BY 4.0. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.25814/m4rt-6h95\u003c/span\u003e\u003cspan address=\"https://doi.org:10.25814/m4rt-6h95\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosenberger RS, Loomis JB (2001) Benefit transfer of outdoor recreation use values: A technical document supporting the Forest Service Strategic Plan (2000 revision). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePyšek P et al (2020) Scientists' warning on invasive alien species. Biol Rev 95:1511\u0026ndash;1534. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:\u003c/span\u003e\u003cspan address=\"https://doi.org:\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/brv.12627\u003c/span\u003e\u003cspan address=\"10.1111/brv.12627\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang S, Li W, Zhang J, Luo Z, Li Y (2024) Alien range size, habitat breadth, origin location, and domestication of alien species matter to their impact risks. Integr Zool 00:1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.1111/1749-4877.12837\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/1749-4877.12837\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrooks JA (2005) Lag times and exotic species: The ecology and management of biological invasions in slow-motion11. \u003cem\u003e\u0026Eacute;coscience\u003c/em\u003e 12, 316\u0026ndash;329 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.2980/i1195-6860-12-3-316.1\u003c/span\u003e\u003cspan address=\"https://doi.org:10.2980/i1195-6860-12-3-316.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHubbard JAG, Drake DAR, Mandrak NE (2024) Climate change alters global invasion vulnerability among ecoregions. Divers Distrib 30:26\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.1111/ddi.13778\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/ddi.13778\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEarly R et al (2016) Global threats from invasive alien species in the twenty-first century and national response capacities. Nat Commun 7:12485. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1038/ncomms12485\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1038/ncomms12485\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFantle-Lepczyk JE et al (2022) Economic costs of biological invasions in the United States. Sci Total Environ 806:151318. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.scitotenv.2021.151318\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.scitotenv.2021.151318\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimberloff D et al (2013) Impacts of biological invasions: what's what and the way forward. Trends Ecol Evol 28:58\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tree.2012.07.013\u003c/span\u003e\u003cspan address=\"10.1016/j.tree.2012.07.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. https://doi.org:\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhmed DA et al (2022) Managing biological invasions: the cost of inaction. Biol Invasions 24:1927\u0026ndash;1946. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1007/s10530-022-02755-0\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1007/s10530-022-02755-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrystal-Ornelas R et al (2021) Economic costs of biological invasions within North America. Neobiota 67:485\u0026ndash;510. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3897/neobiota.67.58038\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3897/neobiota.67.58038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNampala P et al (2020) Strategy for Managing Invasive Species in Africa 2021\u0026ndash;2030. (International Centre of Insect Physiology and Ecology (icipe); CAB International (CABI); International Institute of Tropical Agriculture (IITA) and African Union (AU)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSandvik H, Olsen SL, T\u0026ouml;pper JP, Hilmo O (2022) Pathways of introduction of alien species in Norway: Analyses of an exhaustive dataset to prioritise management efforts. J Appl Ecol 59:2959\u0026ndash;2970. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.1111/1365-2664.14287\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/1365-2664.14287\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKourantidou M et al (2022) The economic costs, management and regulation of biological invasions in the Nordic countries. J Environ Manage 324:116374. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jenvman.2022.116374\u003c/span\u003e\u003cspan address=\"10.1016/j.jenvman.2022.116374\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. https://doi.org:\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFaulkner KT, Robertson MP, Wilson JR (2020) U. Stronger regional biosecurity is essential to prevent hundreds of harmful biological invasions. Glob Change Biol 26:2449\u0026ndash;2462. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.1111/gcb.15006\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/gcb.15006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLatombe G et al (2023) Capacity of countries to reduce biological invasions. Sustain Sci 18:771\u0026ndash;789. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1007/s11625-022-01166-3\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1007/s11625-022-01166-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCuthbert RN et al (2022) Biological invasion costs reveal insufficient proactive management worldwide. Sci Total Environ 819:153404. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2022.153404\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2022.153404\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. https://doi.org:\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eColautti RI, Bailey SA, van Overdijk CDA, Amundsen K, MacIsaac HJ (2006) Characterised and projected costs of nonindigenous species in Canada. Biol Invasions 8:45\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1007/s10530-005-0236-y\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1007/s10530-005-0236-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilliams F et al (2010) The economic cost of invasive non-native species on Great Britain. CABI, Egham\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu X et al (2014) Congener diversity, topographic heterogeneity and human-assisted dispersal predict spread rates of alien herpetofauna at a global scale. Ecol Lett 17:821\u0026ndash;829. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1111/ele.12286\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/ele.12286\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePolaina E, Soultan A, Part T, Recio MR (2021) The future of invasive terrestrial vertebrates in Europe under climate and land-use change. Environ Res Lett 16:044004. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1088/1748-9326/abe95e\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1088/1748-9326/abe95e\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWisz MS et al (2013) The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biol Rev 88:15\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1111/j.1469-185X.2012.00235.x\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/j.1469-185X.2012.00235.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHubbard JAG, Drake DAR, Mandrak NE (2023) Estimating potential global sources and secondary spread of freshwater invasions under historical and future climates. Divers Distrib 29:986\u0026ndash;996. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.1111/ddi.13695\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/ddi.13695\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCapinha C, Essl F, Porto M, Seebens H (2023) The worldwide networks of spread of recorded alien species. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e 120, e2201911120 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:doi:10.1073/pnas.2201911120\u003c/span\u003e\u003cspan address=\"https://doi.org:doi:10.1073/pnas.2201911120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIPBES (2023) (eds H. E. Roy (IPBES Secretariat\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiagne C et al (2020) InvaCost, a public database of the economic costs of biological invasions worldwide. Sci Data 7:277. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1038/s41597-020-00586-z\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1038/s41597-020-00586-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChamberlain S (2021) \u003cem\u003espocc: Interface to Species Occurrence Data Sources\u003c/em\u003e, \u0026lt;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=spocc\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=spocc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChamberlain S (2022) \u003cem\u003ergbif: Interface to the Global Biodiversity Information Facility API\u003c/em\u003e, \u0026lt;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=rgbif\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=rgbif\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChamberlain S, Boettiger CR (2017) Python, and Ruby clients for GBIF species occurrence data. PeerJ Preprints. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.7287/peerj.preprints.3304v1\u003c/span\u003e\u003cspan address=\"https://doi.org:10.7287/peerj.preprints.3304v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZizka A et al (2019) Standardized cleaning of occurrence records from biological collection databases. Methods Ecol Evol 10:744\u0026ndash;751. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1111/2041-210x.13152\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/2041-210x.13152\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKramer-Schadt S et al (2013) The importance of correcting for sampling bias in MaxEnt species distribution models. Divers Distrib 19:1366\u0026ndash;1379. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1111/ddi.12096\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/ddi.12096\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Liu X, Li X, Petitpierre B, Guisan A (2014) Residence time, expansion toward the equator in the invaded range and native range size matter to climatic niche shifts in non-native species. Glob Ecol Biogeogr 23:1094\u0026ndash;1104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.1111/geb.12191\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/geb.12191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHill MP, Gallardo B, Terblanche JS (2017) A global assessment of climatic niche shifts and human influence in insect invasions. Glob Ecol Biogeogr 26:679\u0026ndash;689. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1111/geb.12578\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/geb.12578\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAiello-Lammens ME, Boria RA, Radosavljevic A, Vilela B, Anderson RP (2015) spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38:541\u0026ndash;545. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.1111/ecog.01132\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/ecog.01132\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFick SE, Hijmans RJ (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37:4302\u0026ndash;4315. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.1002/joc.5086\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1002/joc.5086\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIPCC. Climate Change (2021) : The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. P\u0026eacute;an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T. K. Maycock, T. Waterfield, O. Yelek\u0026ccedil;i, R. Yu and B. Zhou (eds.)]. Cambridge University Press. (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1017/9781009157896\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1017/9781009157896\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLenton TM et al (2023) Quantifying the human cost of global warming. Nat Sustain 6:1237\u0026ndash;1247. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1038/s41893-023-01132-6\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1038/s41893-023-01132-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGu S, Qi T, Rohr JR, Liu X (2023) Meta-analysis reveals less sensitivity of non-native animals than natives to extreme weather worldwide. Nat Ecol Evol 7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1038/s41559-023-02235-1\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1038/s41559-023-02235-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu X et al (2019) Risks of Biological Invasion on the Belt and Road. Curr Biol 29:499\u0026ndash;505e494. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cub.2018.12.036\u003c/span\u003e\u003cspan address=\"10.1016/j.cub.2018.12.036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. https://doi.org:\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAra\u0026uacute;jo MB et al (2008) Quaternary climate changes explain diversity among reptiles and amphibians. Ecography 31:8\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1111/j.2007.0906-7590.05318.x\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/j.2007.0906-7590.05318.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAngilletta MJ Jr (2009) Thermal Adaptation: A Theoretical and Empirical Synthesis. Oxford University Press\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVitt LJ, Caldwell JP (2013) Herpetology: an introductory biology of amphibians and reptiles. Academic\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePineda E, Lobo JM (2009) Assessing the accuracy of species distribution models to predict amphibian species richness patterns. J Anim Ecol 78:182\u0026ndash;190. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2656.2008.01471.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2656.2008.01471.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. https://doi.org:\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarbet-Massin M, Jetz W (2015) The effect of range changes on the functional turnover, structure and diversity of bird assemblages under future climate scenarios. Glob Change Biol 21:2917\u0026ndash;2928. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1111/gcb.12905\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/gcb.12905\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerger SW, Schleuning M, Hemp A, Howell KM, Bohning-Gaese K (2014) Food resources and vegetation structure mediate climatic effects on species richness of birds. Glob Ecol Biogeogr 23:541\u0026ndash;549. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1111/geb.12151\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/geb.12151\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThuiller W et al (2014) The European functional tree of bird life in the face of global change. Nat Commun 5:3118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1038/ncomms4118\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1038/ncomms4118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y et al (2016) Climate and topography explain range sizes of terrestrial vertebrates. Nat Clim Change 6:498\u0026ndash;502. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1038/nclimate2895\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1038/nclimate2895\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVisconti P et al (2016) Projecting global biodiversity indicators under future development scenarios. Conserv Lett 9:5\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1111/conl.12159\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/conl.12159\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLetnic M, Dickman CR (2010) Resource pulses and mammalian dynamics: conceptual models for hummock grasslands and other Australian desert habitats. Biol Rev 85:501\u0026ndash;521. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1469-185X.2009.00113.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469-185X.2009.00113.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. https://doi.org:\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith AB (2013) The relative influence of temperature, moisture and their interaction on range limits of mammals over the past century. Glob Ecol Biogeogr 22:334\u0026ndash;343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1466-8238.2012.00785.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1466-8238.2012.00785.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. https://doi.org:\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeaving H, Terblanche JS, Pottier P, English S (2022) Meta-analysis reveals weak but pervasive plasticity in insect thermal limits. Nat Commun 13:5292. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1038/s41467-022-32953-2\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1038/s41467-022-32953-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFournier A, Penone C, Pennino MG, Courchamp F (2019) Predicting future invaders and future invasions. Proc Natl Acad Sci USA 116:7905\u0026ndash;7910. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1073/pnas.1803456116\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1073/pnas.1803456116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuiz-Navarro A, Gillingham PK, Britton JR (2016) Predicting shifts in the climate space of freshwater fishes in Great Britain due to climate change. Biol Conserv 203:33\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:\u003c/span\u003e\u003cspan address=\"https://doi.org:\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocon.2016.08.021\u003c/span\u003e\u003cspan address=\"10.1016/j.biocon.2016.08.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eK\u0026auml;rcher O, Frank K, Walz A, Markovic D (2019) Scale effects on the performance of niche-based models of freshwater fish distributions. Ecol Model 405:33\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.ecolmodel.2019.05.006\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.ecolmodel.2019.05.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang ZX et al (2021) Lineage-level distribution models lead to more realistic climate change predictions for a threatened crayfish. Divers Distrib 27:684\u0026ndash;695. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1111/ddi.13225\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/ddi.13225\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHill L et al (2017) Abundance distributions for tree species in Great Britain: A two-stage approach to modeling abundance using species distribution modeling and random forest. Ecol Evol 7:1043\u0026ndash;1056. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.1002/ece3.2661\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1002/ece3.2661\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarras AG, Braunisch V, Arlettaz R (2021) Predictive models of distribution and abundance of a threatened mountain species show that impacts of climate change overrule those of land use change. Divers Distrib 27:989\u0026ndash;1004. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.1111/ddi.13247\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/ddi.13247\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee-Yaw A, McCune JL, Pironon J, S., Sheth N (2022) S. Species distribution models rarely predict the biology of real populations. \u003cem\u003eEcography\u003c/em\u003e e05877 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.1111/ecog.05877\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/ecog.05877\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAra\u0026uacute;jo MB, New M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22:42\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tree.2006.09.010\u003c/span\u003e\u003cspan address=\"10.1016/j.tree.2006.09.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. https://doi.org:\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHijmans RJ, Phillips S, Leathwick J, Elith J, Hijmans MR (2017) J Package \u0026lsquo;dismo\u0026rsquo; Circles 9:1\u0026ndash;68\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePhillips SJ et al (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl 19:181\u0026ndash;197. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:\u003c/span\u003e\u003cspan address=\"https://doi.org:\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/07-2153.1\u003c/span\u003e\u003cspan address=\"10.1890/07-2153.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarber RA, Ball SG, Morris RKA, Gilbert F (2022) Target-group backgrounds prove effective at correcting sampling bias in Maxent models. Divers Distrib 28:128\u0026ndash;141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.1111/ddi.13442\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/ddi.13442\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVelazco SJE, Rose MB, de Andrade AFA, Minoli I, Franklin J (2022) flexsdm: An r package for supporting a comprehensive and flexible species distribution modelling workflow. Methods Ecol Evol 13:1661\u0026ndash;1669. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.1111/2041-210X.13874\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/2041-210X.13874\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRose MB, Velazco SJE, Regan HM, Franklin J (2023) Rarity, geography, and plant exposure to global change in the California Floristic Province. Glob Ecol Biogeogr 32:218\u0026ndash;232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.1111/geb.13618\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/geb.13618\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43:1223\u0026ndash;1232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1111/j.1365-2664.2006.01214.x\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/j.1365-2664.2006.01214.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHirzel AH, Le Lay G, Helfer V, Randin C, Guisan A (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecol Model 199:142\u0026ndash;152. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.ecolmodel.2006.05.017\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.ecolmodel.2006.05.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWisz MS et al (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14:763\u0026ndash;773. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1111/j.1472-4642.2008.00482.x\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/j.1472-4642.2008.00482.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede la Fuente A, Hirsch BT, Cernusak LA, Williams SE (2021) Predicting species abundance by implementing the ecological niche theory. Ecography 44:1723\u0026ndash;1730. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://doi.org/10.1111/ecog.05776\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1111/ecog.05776\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEhrl\u0026eacute;n J, Morris WF (2015) Predicting changes in the distribution and abundance of species under environmental change. Ecol Lett 18:303\u0026ndash;314. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:\u003c/span\u003e\u003cspan address=\"https://doi.org:\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ele.12410\u003c/span\u003e\u003cspan address=\"10.1111/ele.12410\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrasad AM, Iverson LR, Liaw A (2006) Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction. Ecosystems 9:181\u0026ndash;199. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1007/s10021-005-0054-1\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1007/s10021-005-0054-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuhn M (2008) Building Predictive Models in R Using the caret Package. J Stat Softw 28:1\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.18637/jss.v028.i05\u003c/span\u003e\u003cspan address=\"https://doi.org:10.18637/jss.v028.i05\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? \u0026ndash; Arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247\u0026ndash;1250. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.5194/gmd-7-1247-2014\u003c/span\u003e\u003cspan address=\"https://doi.org:10.5194/gmd-7-1247-2014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFreeman EA, Moisen G, PresenceAbsence (2008) An R Package for Presence Absence Analysis. J Stat Softw 23:1\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.18637/jss.v023.i11\u003c/span\u003e\u003cspan address=\"https://doi.org:10.18637/jss.v023.i11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLockwood JL, Hoopes MF, Marchetti MP (2013) Invasion ecology. Wiley\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYokomizo H, Possingham HP, Thomas MB, Buckley YM (2009) Managing the impact of invasive species: the value of knowing the density\u0026ndash;impact curve. Ecol Appl 19:376\u0026ndash;386. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:\u003c/span\u003e\u003cspan address=\"https://doi.org:\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/08-0442.1\u003c/span\u003e\u003cspan address=\"10.1890/08-0442.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhmed DA et al (2022) Modelling the damage costs of invasive alien species. Biol Invasions 24:1949\u0026ndash;1972. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1007/s10530-021-02586-5\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1007/s10530-021-02586-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePowers RP, Jetz W (2019) Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat Clim Change 9:323\u0026ndash;329. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1038/s41558-019-0406-z\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1038/s41558-019-0406-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHurtt GC et al (2020) Harmonization of global land use change and management for the period 850\u0026ndash;2100 (LUH2) for CMIP6. Geosci Model Dev 13:5425\u0026ndash;5464. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.5194/gmd-13-5425-2020\u003c/span\u003e\u003cspan address=\"https://doi.org:10.5194/gmd-13-5425-2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurakami D, Yoshida T, Yamagata Y, Gridded (2021) GDP Projections Compatible With the Five SSPs (Shared Socioeconomic Pathways). Front Built Environ 7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3389/fbuil.2021.760306\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3389/fbuil.2021.760306\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao J, O'Neill B (2021) Different Spatiotemporal Patterns in Global Human Population and Built-Up Land. Earths Future 9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:\u003c/span\u003e\u003cspan address=\"https://doi.org:\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2020EF001920\u003c/span\u003e\u003cspan address=\"10.1029/2020EF001920\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. e2020EF001920\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBengtsson M, Shen Y, Oki T (2006) A SRES-based gridded global population dataset for 1990\u0026ndash;2100. Popul Environ 28:113\u0026ndash;131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1007/s11111-007-0035-8\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1007/s11111-007-0035-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHijmans RJ, Raster (2022) Geographic data analysis and modeling. R Package Version 3:5\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:https://CRAN.R-project.org/package=raster\u003c/span\u003e\u003cspan address=\"https://doi.org:https://CRAN.R-project.org/package=raster\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZenni RD, Essl F, Garc\u0026iacute;a-Berthou E, McDermott SM (2021) The economic costs of biological invasions around the world. NeoBiota 67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3897/neobiota.67.69971\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3897/neobiota.67.69971\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFoerster AT, Hornstein A, Sarte P-DG, Watson MW (2022) Aggregate Implications of Changing Sectoral Trends. J Polit Econ 130:3286\u0026ndash;3333. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1086/720763\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1086/720763\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eR Core Team (2022) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"biological invasion, economic cost, climate change, global change, potential impact","lastPublishedDoi":"10.21203/rs.3.rs-7029011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7029011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInvasive species are causing high and increasing economic costs worldwide. However, the potential economic costs associated with range shifts of invasive species under climate change remain understudied. Here, we incorporated abundance-based species distribution modeling, management temporal dynamics, and socioeconomic factors to evaluate the effect of climate change on potential economic costs for 121 animal invaders in 67 countries. On average, the future potential economic costs associated with biological invasions in 2060 were 19.6% (SSP 126)\u0026ndash;21.0% (SSP 585) higher than the current potential costs. On average, 87.1% of countries would experience increased future costs associated with 84.8% of animal invaders, which is driven mainly by the costliest invaders worldwide. We demonstrated that improvements in management efforts, especially preinvasion strategies, might reduce future costs by 65.4% at most. Our findings highlight the importance of proactive and early management strategies for the costliest invaders to mitigate economic losses under accelerating biological invasion and climate change.\u003c/p\u003e","manuscriptTitle":"Climate change projected to exacerbate the economic costs of biological invasions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-24 11:42:09","doi":"10.21203/rs.3.rs-7029011/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d6ac49f7-5377-4fd1-ae09-23fba5815ec1","owner":[],"postedDate":"July 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":51293641,"name":"Biological sciences/Ecology/Climate-change ecology"},{"id":51293642,"name":"Biological sciences/Ecology/Invasive species"}],"tags":[],"updatedAt":"2025-07-25T20:03:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-24 11:42:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7029011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7029011","identity":"rs-7029011","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}