Invasions of alien plants pose unprecedented challenges to China's nature reserves under climate change

Invasions of alien plants pose unprecedented challenges to China's nature reserves under climate change | 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 Research Article Invasions of alien plants pose unprecedented challenges to China's nature reserves under climate change Jia-Xin Wang, Rainer W. Bussmann, Fei Qin, Yun-Fen Liang, Bao-Cai Han, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6783801/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Biological invasions are considered the second-greatest threat to global biodiversity. In China, nature reserves (NRs) are crucial in terms of biodiversity conservation, but many are at high risk of biological invasion. However, as climate change progresses the NR invasion risk posed by invasive alien plants (IAPs) remains unclear. Here, we compiled an inventory of 402 IAPs with over 120,000 occurrences to investigate IAP distribution patterns and the potential invasion risks in China’s NRs under current and future climate scenarios. We also analyzed the key environmental and socioeconomic factors influencing IAP distribution. Our results indicate that approximately 63% of national nature reserves (NNRs) and 38% of provincial nature reserves (PNRs) contain IAPs. Most NRs with high numbers of IAPs are located in South, East, Southwest, and Central China. In addition, up to 73% of PNRs and 80% of NNRs are highly vulnerable, which have IAP records within NRs or outer 5 km buffer areas. Under current and future climate scenarios, approximately 85% of China’s NRs contain suitable habitats for IAPs, representing a 38% increase compared to the collected distribution. The predicted IAP distribution pattern generally shows a decreasing trend from southeast to northwest. Population density, elevation, area, year of establishment, and temperature annual range (BIO7) significantly affect IAP richness in NRs. Under future climate scenarios, China’s NRs will be confronted with a greater risk of IAP invasion. Our findings can work as fundamental material when managing IAPs in NRs, providing valuable insights for targeted strategies and improving the protective effectiveness of NRs in China. biological invasions climate change conservation distribution pattern ensemble model nature reserves Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Protected areas (PAs), as cornerstones of biodiversity conservation, are designed to protect representative natural landscapes, ensure the persistence of biodiversity and key ecosystem processes, and provide refuges for native species (Rodrigues et al. 2004 ; Foxcroft et al. 2010 , 2013 ). However, in recent decades, factors such as the growing international and domestic trade driven by global economic integration, advanced modern transportation, the booming tourism industry, and climate change have enhanced the ability of alien species to overcome geographical barriers, making biological invasions a major global issue for ecological and biological security (Butchart et al. 2010 ; Wei et al. 2023 ). Invasive alien plants (IAPs) have become a major threat when seeking to manage PAs (Pyšek et al. 2013 ; Foxcroft et al. 2017 ; Moodley et al. 2020 ). According to a report by the Global Invasive Species Programme (GISP), 487 PAs worldwide face biodiversity challenges posed by IAPs (De Poorter et al. 2007). Even at high-altitude and in arid regions, the problem still exists (Milton and Dean 2010 ; Alexander et al. 2016 ). One of the primary objectives of establishing PAs is to conserve biodiversity. However, if large-scale biological invasions occur within PAs, the resulting biodiversity loss could be more severe than in non-PAs. This outcome would directly contradict our original purpose of establishing PAs (Foxcroft et al. 2013 ). Since 1980, global records of alien species have increased by 40%, with nearly one-fifth of the earth's surface now at risk of biological invasion (IPBES 2019 ). About 10% of species on the IUCN Red List of Threatened Species are affected by alien invasions (IUCN 2022). Once noxious IAPs have successfully established dominance in a new habitat, the result may be the breakdown of ecosystem function and increased biodiversity loss (Ju et al. 2012 ). Climate is considered a major environmental factor influencing species distribution (Xiong et al. 2019 ; Li et al. 2020 ). Under the background of climate change, invasions by alien plants may intensify (Robinson et al. 2020 ). Research suggests that climate change, for example the intensity and duration of extreme events (heat waves, heavy rainfall, wildfires, droughts, and hurricanes), may actively facilitate invasions by alien plants and ultimately compromise biodiversity protection within PAs (White et al. 2001 ; Fargione et al. 2003 ; Hannah et al. 2007 ; Pereira et al. 2010 ; Diez et al. 2012 ; Hou et al. 2014 ; Early et al. 2016 ; Grenz and Clements 2023 ), potentially accelerating the risk of extinction for up to one in six species (Bellard et al. 2012 , 2016 ; Urban 2015 ). It has been claimed that future climate change will both increase and reduce invasions (Seebens et al. 2015 ; Ziska et al. 2019 ; Clements et al. 2022 ; Qin et al. 2024 ). These contrasting responses imply that some invasive alien plants may expand their ranges under future climate scenarios, while others may contract. Given the variation in geographic location and species composition among NRs, the changes in invasion risks they face under future climate scenarios remain uncertain. Therefore, when discussing the issue of invasion in NRs, it is necessarily essential to consider the impact of climate change. Global species and ecosystem diversity are declining at an unprecedented rate (Butchart et al. 2010 ). Biological invasions and climate change are two major driving forces (Walther et al. 2009 ; Jaureguiberry et al. 2022 ). Particularly in China, rapid economic development has promoted the international exchange of goods and people, and IAPs have increased rapidly (Li and Ma 2010 ). Currently, China may face an even more severe situation in terms of biological damage than most other countries (Yang 2008 ; Paini et al. 2016 ). Historically, China has introduced nonnative species that afford significant economic benefits (Li et al. 2015 ; Lin et al. 2022 ; Hao and Ma 2023 ). Geographically, the terrain and climate of China are very diverse (Mittermeier and Goettsch Mittermeier 2005 ). Thus, the country contains many habitats suitable for IAPs if invasion is successful (Yu et al. 2020 ; Yu and Chen 2020 ; Qin et al. 2024 ). Rapid economic development, accompanied by explosive growth in international trade and transportation, has heightened the risk of alien species invasions (Li and Ma 2010 ; Ju et al. 2012 ; Yu et al. 2020 ; Yu and Chen 2020 ). What’s more, the damage caused by an invasion lags the invasion per se (Mooney and Cleland 2001 ; Gallardo et al. 2017 ). Thus, the situation in China is likely to become even more challenging in the future (Kelly et al. 2021 ; Robeck et al. 2024 ). According to an available data, the direct economic loss caused by 283 invasive species in China has exceeded USD 2397.39 million annually, of which 66.4% is attributable to IAPs (Xu et al. 2006 ; Ding et al. 2015 ). To date, more than 400 IAPs have been recorded in China (Yan et al. 2020b ; Lin et al. 2022 ; Qin et al. 2024 ). Many studies have described IAP compositions, distributions, and potential areas of invasion (Yan et al. 2020b ; Lin et al. 2022 ; Qin et al. 2024 ). In terms of researches on distribution pattern, previous studies have focused primarily on between-province differences in IAP distribution patterns or only assessed the risks to a few NNRs (Pan et al. 2015 ; Gong et al. 2017 ; Chen et al. 2021a , b , 2022 ; Zhao et al. 2022 ). Thus, invasive risks to the whole nature reserve (NR) network still remain unknown (Gong et al. 2017 ; Wang et al. 2020b ; Zhao et al. 2022 ). The regional specificities of NRs in terms of both species composition and population dynamics have been overlooked, so the information available is too general to aid the practical management of NRs (Chen et al. 2021b , 2022 ; Hao and Ma 2023 ; Qin et al. 2024 ). Nonetheless, they have been valuable in terms of the research frameworks chosen and the data collected, shedding new light on the risk that IAPs pose to NRs. It is estimated that the minimum economic cost of biological invasions between 1970 and 2017 amounted to USD 1.288 trillion (Diagne et al. 2021 ). Among all measures for managing alien species invasions, early monitoring and rapid response are the most cost-effective approaches. Specifically, every dollar spent on prevention can save USD 17 in long-term costs (MISAC, 2016 ). Therefore, assessing the current status and distribution patterns of IAPs in China's NRs, as well as predicting future invasion dynamics under climate change, is of paramount importance for the management and construction of NRs and for biodiversity conservation in China. In this study, we focused on the risks posed by IAPs to NNRs and PNRs. We documented > 120,000 IAP occurrences and used several algorithms to analyze the current and possible future distribution patterns. We present the IAP distribution patterns in the NR network and the key factors driving these patterns, the potential IAP distribution patterns in the NR network under multiple climate scenarios, the invasive risks posed by IAPs to the NR network, and countermeasures that may prevent IAP invasion and mitigate NR biodiversity loss. 2. Materials and Methods 2.1 IAP inventory and occurrence database We followed previous studies (Jin et al. 2020 ; Liu et al. 2020a ; Wang et al. 2020a ; Yan et al. 2020a , b ; Yu and Chen 2020 ), particularly that of Qin et al. ( 2024 ), when compiling a checklist of IAPs in China. This checklist contains 402 IAPs (including four varieties) of 234 genera and 63 families. The IAP geographic distributions were primarily sourced from the Chinese Virtual Herbarium ( http://www.cvh.ac.cn ), the Global Biodiversity Information Facility ( https://www.gbif.org ), as well as field survey data and distribution records documented in other books and literature. After downloading the data, we updated the species names according to the Catalogue of Life China. We then compiled a database of all regions in China and their coordinates on the basis of the Gazetteer of China (Institute of Geographical Names, State Bureau of Surveying and Mapping, 1997). To avoid identification or registration errors, the data were cross-checked and filtered against the provincial distribution ranges of IAPs listed in the Alien Invasive Flora of China ( http://www.iplant.cn/ias ). Any record of an IAP outside the known distribution was considered misidentified or incorrectly documented and thus excluded. After careful verification, our occurrence database contained 120,071 records with latitude and longitude of 402 IAP species. 2.2 Nature reserves in China By 2017, China had established more than 2,750 NRs at national, provincial, municipal, and county levels, covering a total area of 1.47 million square kilometers, equivalent to 15% of the country's land area (Ma et al. 2019 ). Among them, NNRs and PNRs cover nearly 90% of the total protected areas and are strictly managed. However, most municipal and county nature reserves only cover a small portion and often lack proper maintenance as well as comprehensive, accurate, and reliable data because of insufficient funds (Zhang et al. 2015 , 2017 ; Ma et al. 2019 ; Yang et al. 2019 ). Thus, we only focused on NNRs and PNRs in this study. The geographic database of China’s NRs was initially built using the 2017 version of the National List of Nature Reserves released by the Ministry of Ecology and Environment ( https://www.mee.gov.cn ) and then supplemented with field survey reports and the latest planning maps when digitizing the reserve boundaries. In total, we obtained geographic data for 463 NNRs and 805 PNRs (Table S1 ). 2.3 Variable screening for prediction of IAP distribution In line with previous studies, we used 26 variables to predict the potential distribution of IAPs (Table S2), including 20 environmental variables (BIO1-BIO19, elevation) and 6 socioeconomic variables (population density, gross domestic product, distance to the nearest railway, expressway, national highway, and provincial capital) (Yang et al. 2013 ; Wang et al. 2016 , 2017 ; Qin et al. 2024 ). The environmental variables were obtained from the WorldClim database ( https://www.worldclim.org ) at a resolution of 10 min, encompassing one current climate scenario (1970–2000) and four future climate scenarios (2070, mean for 2061–2080). In terms of socioeconomic variables, gross domestic product (GDP) data were sourced from the spatial distribution dataset of China's GDP for 1990 to 2015 released by the Resource and Environmental Science Data Centre of the Chinese Academy of Sciences. The spatial files that covered population density, railways, expressways, national highways, and provincial capitals were retrieved from the DIVA-GIS database ( http://www.diva-gis.org ) at a resolution of 10 min. The distances from grid centroids to the nearest transportation routes or provincial capitals were calculated using ArcGIS v.10.8 (Environmental Systems Research Institute, Redlands, CA, USA) to represent the influence of transportation. The future climate scenarios were derived from the Beijing Climate Centre Climate System Model ver. 1.1 (BCC-CSM1–1) (Van Vuuren et al. 2011 ). This circulation model considers four Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) that identified in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC5, 2015). This model is highly recommended for its stable performance and robust capability in climate change studies in China, particularly in terms of simulating the summer monsoon precipitation and the circulation patterns in East Asia (Wu et al. 2014 ). In brief, during predictions, seven variables (elevation, population density, gross domestic product, distance to the nearest railway, expressway, national highway, and provincial capital) were held constant, but the 19 climate variables (BIO1-BIO19) were allowed to vary in the five climate scenarios, one of which was current and the others future. We estimated correlations among the 26 variables using the cor function of R to reduce the risk of model overfitting because of multicollinearity. Variables with high correlations (r ≥ 0.75) were excluded (Liu et al. 2019 ; El-Barougy et al. 2021 ). Ultimately, 14 variables were used to predict the potential distribution of different IAPs. These were the annual mean temperature (BIO1), mean diurnal range (BIO2), isothermality (BIO3), temperature seasonality (BIO4), annual precipitation (BIO12), precipitation of driest month (BIO14), precipitation seasonality (BIO15), elevation, population density, GDP, distance to the nearest railway, distance to the nearest expressway, distance to the nearest national highway, and distance to the nearest provincial capital. 2.4 Driving factors of IAP distribution IAP distribution patterns are influenced by both biotic and abiotic factors (Kelly et al. 2014 ). Previous studies on invasive species distributions in NRs were inconsistent in terms of the principal driving factors identified (Alston and Richardson 2006 ; Spear et al. 2013 ; Gantchoff et al. 2018 ; Essl et al. 2019 ; Gulzar et al. 2024 ). We used both environmental and socioeconomic factors when seeking drivers of IAP distribution in China’s NRs. The environmental factors included 19 bioclimatic variables (1970–2000) and an elevation variable, as mentioned above. The socioeconomic factors included population density, the year of establishment of the NR, and the area of the NR. Data on population density were obtained from the DIVA-GIS database at a resolution of 10 min. The year of establishment and the area of each NR were retrieved and compiled from the List of Nature Reserves in China (Table S1 ). 2.5 Statistical analyses 2.5.1 IAP distribution patterns in NRs We used ArcGIS to link the point data of detected IAP distributions to the NNR and PNR layers. We retained only matching records, thus 5,389 for NNRs and 3,462 for PNRs. As a given species may be recorded more than once within a single NR, we retained only one record per species when calculating the total number of IAP species in each reserve. Then the data were organized and the IAP distribution patterns in NNRs and PNRs were mapped. Areas surrounding NRs, particularly those with high levels of human activity, may significantly influence protected areas by increasing the inward IAP spreading pressure (Liu et al. 2020b ). Therefore, we carried out a buffer analysis in ArcGIS to detect IAP records within 5, 10, and 50 km outside the NR boundaries, and quantified the IAP richness within these buffer zones. The results were visualized in the form of stacked bar charts prepared using Sigmaplot v.14 (Systat Software, San Jose, CA, USA). 2.5.2 Predicted IAP distribution patterns in NRs Before the prediction of IAP distribution patterns, we used ArcGIS to divide the map of China into 34,666 grid cells with a resolution of 10 min. Then these cells were connected to the NNR and PNR layers, yielding 4,715 matching cells for NNRs and 2,492 for PNRs. We used the “sdm” package of R to predict species distributions under current and future climate scenarios. This ensemble model employs five algorithms, including a generalized linear model (GLM), a general additive model (GAM), a generalized boosted model (GBM), a random forest (RF), and a maximum entropy model (MaxEnt). All have been widely used to predict species distribution trends under climate change and have shown excellent performance (Phillips et al. 2006 ; Elith et al. 2010 ; Dullinger et al. 2017 ; Tang et al. 2018 ; Li et al. 2021 ). We randomly selected 70% of the IAP distribution data as the training set and used the remaining 30% as the test set when evaluating the model (Williams et al. 2009 ; Convertino et al. 2014 ; Gholamy et al. 2018 ). Next, we evaluated the accuracy of model predictions using two commonly employed metrics: the area under the receiver operating characteristic curve (AUC) and the true skill statistic (TSS) (Phillips et al. 2006 ; Phillips 2017 ; Mi et al. 2023 ). Models with TSS ≥ 0.5 and AUC ≥ 0.7 were considered valid and thus retained (Pearce and Ferrier 2000 ; Coetzee et al. 2009 ). Then these models were integrated into a new ensemble model with each valid model weighted by reference to its TSS score (Thuiller et al. 2009 ). To generate predicted IAP richness maps, we transformed each continuous species probability into binary data (presence/absence) using the threshold that maximized the TSS (Freeman and Moisen 2008 ; Barbet-Massin et al. 2012 ; Fourcade et al. 2018 ; Steen et al. 2021 ). We joined the binary (presence/absence) distribution maps of all IAPs in grid cells with the NNR and PNR layers and kept only the matching data in ArcGIS. Finally, we summed the distinct species numbers in every reserve to obtain the predicted IAP distribution patterns under one current and four future climate scenarios. 2.5.3 Driving factors Before conducting further analysis, the mean values of the 19 bioclimatic variables (BIO-BIO19), the population density, and the elevation of each NR were calculated using ArcGIS. A generalized linear model (GLM) was constructed using the detected IAP richness as the dependent variable and the 23 variables, including the 19 bioclimatic variables, the population density, elevation, the year of establishment, and the area of NR, as independent variables. Prior to model construction, all independent variables were log-transformed to address data skewness and thus improve analytical stability (Kalusová et al. 2019 ). As more than half (669) of the 1,268 protected areas lacked records of IAPs, we employed a zero-inflated negative binomial (ZINB) regression model, which is a variant of GLM, to test the significance of different driving factors in terms of explaining IAP richness (Liu et al. 2020b ). The ZINB model well handles over-dispersed data because it assumes that excess zero counts are modelled using a logit or probit model, but the remaining counts modelled employing a negative binomial distribution (Moghimbeigi et al. 2008 ). Given that the relationship between IAP richness and NR area might be influenced by geographic location, we categorized all invaded NRs into seven groups (Northeast China, North China, East China, South China, Central China, Northwest China, and Southwest China) according to Cao et al. ( 2018 ) based on the administrative regions they belong to (Figure S1 ). Then we explored whether the ratio of IAP richness to NR area varied across different geographic locations. The type of NR and the vegetation regionalization might also influence the IAP spatial distribution patterns (Liu et al. 2020b ; Mungi et al. 2021 ). Therefore, we retrieved the category of each NR from the “List of Nature Reserves in China” and the vegetation type from the vegetation regionalization map of the “Vegetation Atlas of China (1:1,000,000)” (Figure S2) (Hou, 2001 ). Then we calculated the maximum and mean IAP richness for NRs in various categories and for those with different vegetation types. The data are presented using the violin plot of Origin v. 2021 (Origin Lab Corporation, Northampton, MA, USA). 3. Results 3.1 IAP distribution patterns in NRs IAPs were more common in NNRs than PNRs (Fig. 1 a-b). In summary, 294 of 463 NNRs (63%) and 305 of 805 PNRs (38%) contained IAPs. Although almost half of all NRs harbored IAPs, most exhibited low levels of invasion. Specifically, about 31% of PNRs and 45% of NNRs hosted only 1 to 10 IAPs. NRs with more IAPs were mainly located in South, East, Southwest, and Central China (Fig. 1 c). In addition, certain reserves in Gansu, Heilongjiang, Jilin, Liaoning, Hebei, Shandong, Tibet, and Xinjiang contained significantly more IAPs than surrounding reserves (Fig. 1 c). Analysis of IAP richness in NR buffer areas revealed that 35% of PNRs that were not yet invaded had IAPs within 5 km of the boundaries, 46% IAPs within 10 km, and 60% IAPs within 50 km (Fig. 2 ). By contrast, 17% of NNRs that were not yet invaded had IAPs within 5 km, 22% IAPs within 10 km, and 35% IAPs within 50 km. The proportions of PNRs and NNRs threatened by IAP invasion, including those currently invaded and not invaded but with IAPs within 5 km buffer, were 73% and 80%, respectively. 3.2 Predicted IAP distribution patterns in NRs under current and future climate scenarios We ultimately selected 14 variables to predict IAP distributions after analyzing correlations among different variables. The prediction analysis showed that under all the current and future (RCP2.6, RCP4.5, RCP6.0, RCP8.5) climate scenarios, approximately 85% of China’s NRs could host IAPs (Figs. 1 f, 3 ). NRs with extremely high predicted IAP richness were principally in southern Yunnan, Guangxi, Guangdong, Fujian, Hainan, and Taiwan (Figs. 1 f, 3 ). Some were in Hunan, Hubei, Jiangxi, Anhui, and Jiangsu, mainly near the middle and lower reaches of the Yangtze River (Figs. 1 f, 3 ). The NRs with a relatively high predicted number of IAPs were principally in Central and East China, southern Shanxi, eastern Sichuan, and most areas of Chongqing, Yunnan, and Guizhou (Figs. 1 f, 3 ). NRs with moderate predicted IAP richness were mainly in Northeast China, Northwest China, North China, western Sichuan, southern Tibet, western Hubei and western Henan (Figs. 1 f, 3 ). Those with low predicted richness were mainly in certain regions of Northeast China, North China, Northwest China, and Southwest China (Figs. 1 f, 3 ). Overall, the predicted IAP richness generally decreased from southeast to northwest (Figs. 1 , 3 , Figures S3, S4). Comparison of predicted IAP distribution patterns in NRs under current and future climate scenarios revealed that IAP habitat suitability fluctuated in different climate scenarios (Figures S3, S4). Under the RCP 2.6 climate scenario, habitat suitability increased in 63% of NNRs and 54% of PNRs (Figure S3). Under the RCP 4.5 climate scenario, habitat suitability rose in 25% of NNRs and 23% of PNRs (Figure S3). Under the RCP 6.0 climate scenario, habitat suitability climbed in 35% of NNRs and 32% of PNRs (Figure S4). Under the RCP 8.5 climate scenario, habitat suitability grew in 39% of NNRs and 33% of PNRs (Figure S4). When comparing changes in IAP richness across different RCP scenarios, 221 NRs exhibited the highest IAP richness under current climate conditions, 533 NRs under RCP 2.6, 68 NRs under RCP 4.5, 110 NRs under RCP 6.0, and 136 NRs under RCP 8.5, while the remaining 200 NRs was unchanged across all five climate scenarios. Overall, most NRs (42%) exhibited the highest IAP richness under RCP 2.6 (Fig. 3 , Table S1 ). 3.3 Driving factors of IAP richness in NRs Initially, all variables were subjected to Pearson correlation testing (Figure S5) and those with correlation coefficients > 0.75 were excluded (Pino et al. 2005 ; Gassó et al. 2009 ). The selection process adhered to the following principles: priority was given to retention of the annual mean temperature (BIO1), annual precipitation (BIO12), and other non-climatic factors; as many explanatory variables as possible were retained. Ultimately, 10 explanatory variables were selected and doublechecked by Pearson correlation testing (Figure S6). These were the population density, elevation, year of NR establishment, area of NR, annual mean temperature (BIO1), isothermality (BIO3), maximum temperature of the warmest month (BIO5), temperature annual range (BIO7), annual precipitation (BIO12), and precipitation seasonality (BIO15). The ZINB regression model was used to analyze the relationships between these variables and IAP richness. Of the ten explanatory variables, five significantly affected the outcome (Table 1 ). Specifically, population density (β = 0.938), elevation (β = 0.796), and NR area (β = 0.962) demonstrated a significant positive association with the outcome (all p < 0.001). By contrast, year of NR establishment (β = − 259.870, p < 0.001) and BIO7 (β = − 3.915, p = 0.001) had significant negative effects. In addition, BIO1, BIO5, and BIO12 were positively correlated with IAP richness, and BIO3 and BIO15 were negatively correlated but no such effect was statistically significant. The overdispersion parameter (α) was 2.563, confirming that the data exhibits significant overdispersion and justifying the use of a negative binomial model. Table 1 Analysis result of Zero-inflated Negative Binomial Variable Regression coefficient (β) Standard error Z value P value Population density 0.938 0.137 6.863 < 0.001 Elevation 0.796 0.133 7.043 < 0.001 Year of establishment -259.870 23.681 -10.974 < 0.001 Area 0.962 0.090 10.689 < 0.001 Annual mean temperature (BIO1) 0.413 0.744 0.555 0.579 Isothermality (BIO3) -1.114 1.235 -0.902 0.367 Maximum temperature of the warmest month (BIO5) 2.257 1.346 1.676 0.094 Temperature annual range (BIO7) -3.915 1.145 -3.419 0.001 Annual precipitation (BIO12) 0.089 0.345 0.258 0.797 Precipitation seasonality variation coefficient (BIO15) -0.019 0.583 -0.033 0.974 McFadden R 2 : 0.077. Bold values were significant. We identified three main categories of NRs out of nine: VII forest ecosystem (46% of NRs), VIII wild animal (21% of NRs), and VI inland wetland (17% of NRs). Among different categories of NRs, the highest peak IAP richness was found in NRs of forest ecosystem (87 species), followed by wild plant (71 species), and wild animal (62 species) (Fig. 4 a). Furthermore, the highest average IAP richness was found in NRs of forest ecosystem (5.60 species), followed by ocean and seacoast (4.37 species), and wild plant (3.79 species) (Fig. 4 a). We also identified three major vegetation types of NRs out of eight: IV subtropical evergreen broadleaf forest zone (48% of NRs), III warm-temperate deciduous broadleaf forest zone (16% of NRs), and VI temperate steppe zone (12% of NRs). NRs of subtropical evergreen broadleaf forest zone had the highest peak IAP richness at 87 species, followed by tropical rain forest and monsoon forest zone at 68 species, and warm-temperate deciduous broadleaf forest zone at 62 species (Fig. 4 b). NRs of tropical rain forest and monsoon forest zone had the highest average number of IAPs at 8.33 species, followed by subtropical evergreen broadleaf forest zone at 5.03 species, and warm-temperate deciduous broadleaf forest zone at 3.24 species (Fig. 4 b). NRs were divided into seven regions (Northeast China, North China, East China, South China, Central China, Northwest China, and Southwest China) to study the relationship between IAP richness and NR area within each region (Fig. 5 ). According to the relationship between IAP richness and NR area, we divided NRs into three classes (Fig. 5 ): (I) NRs in North, Northeast, and Northwest China (generally large area with low IAPs richness); (II) NRs in Central, East, and South China (moderate area with relatively high IAP richness); (III) NRs in Southwest China (generally large area and some with high IAP richness). 4. Discussion Our study primarily investigated the distribution patterns of 402 IAPs in NNRs and PNRs in China. Compared to previous studies, our research covered a greater number of species, and subsequently offers higher resolution data, and encompasses a more comprehensive range of nature reserves (Guo et al. 2017 ; Tu et al. 2021 ; Zhao et al. 2022 ; Yang et al. 2023 ). This may shed new light on the IAP invasion risk and will aid the formulation of reliable countermeasures and identify areas in need of priority management. 4.1 Detected distribution of IAPs The situation regarding the IAPs in China's nature reserves is concerning, with approximately 38% of PNRs and 63% of NNRs containing IAPs. Gong et al. ( 2017 ) studied 53 NNRs and found that most were invaded by alien plants and animals. Zhao et al. ( 2022 ) found that 35 IAPs occurred in 72 studied NNRs with an average of 7.78 ± 0.47 species per NR. It is difficult to compare our work to previous studies because the scales and sampling methods differ. Liu et al. ( 2020a ) reported that more than 90% of global NRs had not yet been invaded by alien animals. We found that both NNRs and PNRs were at much higher IAP invasion risks. This discrepancy may due to differences in the scale of plants and animals, both globally and regionally (Keller et al. 2011 ; Luque et al. 2014 ), and invasive alien animals are mobile and often elusive, thus more difficult to monitor than plants. Moreover, China’s situation is quite unique compared to other countries. Due to inadequate site planning during the early establishment of NNRs, many densely populated areas were included, with much of the land being collectively or privately owned, which hindered unified management. Farmland and the edges of artificial forests within NRs harbor the highest densities of IAPs, as local residents depended on plant and land resources, thereby increasing the risk of plant invasions (Liu et al. 2001 , 2003 ; Xu et al. 2012 ; Zhao et al. 2019 ; Liu et al. 2020c ). Approximately 34.78% of PNRs and 16.85% of NNRs had IAPs in their 5 km buffer zones, while no IAPs were found within the reserves themselves. If this is not addressed, IAPs may easily invade such reserves in the future. To advance the Beautiful China Initiative, a plan for the sustainable development of the Chinese nation, the 19th National Congress proposed the establishment of a nature reserve system with national parks as the main body (Fang et al. 2020 ). The implementation of this policy will inevitably involve the integration of nature reserve areas (Tang et al. 2020). Thus, to maximize conservation effectiveness, we suggest considering IAP richness dynamics during the adjustment of reserve boundaries, along with other crucial factors such as biodiversity hotspots, conservation gaps, minimum viable population, and the fragmentation and isolation of PAs (Leader-Williams et al. 1990 ). Overall, the spatial distribution of IAPs in China’s nature reserves shows a preference for the southeast over the northwest. Nature reserves with a higher number of IAPs are mainly located in South, East, Southwest, and Central China. This distribution pattern is consistent with previous studies conducted at the provincial or county level (Bai et al. 2013 ; Pan et al. 2015 ; Chen et al. 2023 ; Yang et al. 2023 ). Results of the analysis also indicates that most NRs with high IAP richness spontaneously fall in the southeast side of the 0°C isotherm of the coldest month and the 800mm isohyet, which is not a coincidence. A global observation of 220 tree species revealed that minimum annual temperature effectively limits the distribution of different vegetation types (Woodward and Williams 1987 ). Relevant studies on IAPs have also found a strong correlation between the distribution of IAPs and the mean temperature of the coldest month (Beerling et al. 1995 ; Jones et al. 2010 ). This is because low temperatures in spring increase the likelihood of seedling mortality, and precipitation is also a crucial factor influencing the distribution of IAPs (Ibáñez et al. 2009 ; Inderjit et al. 2018 ), further emphasizing the role of climate as a filter for IAP distribution (Kraft et al. 2015 ; Young et al. 2017 ). A global-scale study found that hotspots of IAP richness are often located on islands or in coastal areas (Dawson et al. 2017 ). Our finding that some NRs in Taiwan Island, Hainan Island, the Shandong Peninsula, the Liaodong Peninsula, and coastal areas of eastern and southern China have higher IAP richness further supports this conclusion. High population density, developed transportation, and frequent trade all contribute to high propagule pressure, which ultimately facilitates the invasion of IAPs in these coastal regions (Hobbs and Huenneke 1992 ; Lockwood et al. 2005 ; Hulme 2009 ; Zimmermann et al. 2014 ; Gioria et al. 2023 ). And islands, due to their unique geographic location and natural resources, often serve as tourist destinations or major trade ports, thus facing significant human disturbance (Cheng and Lu 2013 ; Zhang and Ju 2021 ). Moreover, the warm and humid climate in eastern and southern coastal regions makes these areas hotbeds for IAPs. A research had compiled on the first found locations of 90 top invasive plant species in China and found that Hong Kong and Taiwan are the most critical stepping-stones for IAPs entering mainland China, with nearly 40% of these species invading mainland China through these regions (Lu et al. 2018 ). The result suggests that islands and coastal cities might work as important defense lines against IAPs, and their importance in preventing invasions warrants greater attention. NRs with high IAP richness were also found in Central and Southwest China. This is closely related to favorable climates, convenient transportation, and concentrated populations. For example, NRs in Xinjiang with high IAP richness are located along the middle and lower reaches of the Niyang River urban belt, while those in Central China are distributed near the middle section of the "two vertical and three horizontal" Jing-Guang and Jing-Ha economic belt (Fang et al. 2016). Our analysis also reveals that some NRs near the border of China may face severe invasive challenges, such as those in Heilongjiang, Jilin, Liaoning, Tibet, Xinjiang, Yunnan, and Guangxi. For example, Ageratina adenophora initially spread along the China-Myanmar border, and then became one of the most serious IAPs in China (Yan et al. 2001 ; Wang and Wang 2006 ). Biological invasions often transcend political boundaries, and once a species is successfully established in one country, the invasion risk to its neighboring countries increases significantly (Stoett 2007 ; Hurley et al. 2017 ). Therefore, to more effectively tackle new challenges and mitigate the risks posed by IAPs while avoiding potential conflicts of interest, biosecurity cooperation organizations may serve as a viable solution (Faulkner et al. 2020 ; Figuera et al. 2022 ). 4.2 Potential distribution of IAPs The prediction of 402 IAPs’ potential distribution revealed that the distribution pattern of species richness within NRs generally exhibited a decreasing trend from southeast to northwest under all five climate scenarios (1 present, 4 future). This pattern is consistent with previous studies on the distribution of IAPs at the provincial level in China (Liu et al. 2005 ; Xu et al. 2012 ). Notably, some NRs with high species richness of IAPs overlap with biodiversity hotspots in China (Yang et al. 2022 ). This overlap is particularly concerning, as without effective monitoring and timely control measures, widespread invasions could lead to severe biodiversity losses. When comparing the predicted distributions of IAPs with the actual detected distribution, the predicted ones appear to be significantly more severe. Specifically, 84% of NRs are suitable for at least one IAP, but detected distribution data shows only 47% currently harbor them. Moreover, in invaded reserves, predicted IAP richness generally exceeds detected levels. The gap between the predicted and the detected distributions has many possible reasons. On one hand, the distribution of IAPs is determined by both biotic and abiotic factors (Foxcroft et al. 2011 ; Gurevitch et al. 2011 ; Young et al. 2017 ). Given the fact that it is impossible to establish a perfect and universally applicable model that considers all potential influencing factors (Elliott-Graves 2016 ; Srivastava et al. 2019 ), our model primarily considered factors related to climate and human activity. However, factors not included in our model, such as soil properties, land use, biotic interactions, and adaptive evolution, have been identified in previous studies as influencing the distribution and spread of IAPs (Mooney and Cleland 2001 ; Kulmatiski et al. 2006 ; Reinhart and Callaway 2006 ; Araújo and Luoto 2007 ; Niu et al. 2007 ). On the other hand, the detected and recorded data may be insufficient, failing to fully reflect the real invasion situation. To some extent, the gap between predicted and detected distributions suggests that IAPs have not yet reached saturation, leaving significant potential for future invasions. Climate change is profoundly altering global environments, potentially expanding the distribution range of IAPs. Regions previously unsuitable for these species due to physiological constraints may face colonization risks in the future. High-altitude and high-latitude areas, in particular, warrant increased attention, as IAPs could overcome previous temperature barriers and migrate into these regions under changing climatic conditions (Smith et al. 2012 ; Shrestha and Shrestha 2019 ). 4.3 Factors driving IAP invasion At the national scale the key natural factors influencing the distribution of IAPs within NRs were always mean annual temperature and mean annual precipitation in previous studies. IAPs tend to thrive in environments that are abundant in water and heat resources, as these conditions provide the necessary support for their growth, reproduction, and spread (Li et al. 2020 ; Chen et al. 2021a ; Zhao et al. 2022 ). However, in our study, although BIO1 and BIO12 were positively related to IAP richness, neither of these relationships was statistically significant. Instead, BIO7 had a large effect on the distribution patterns of IAPs, which aligns with previous research (Wan and Wang 2018 ; Heringer et al. 2022 ). Compared to more stable and moderate climatic conditions, extreme fluctuations in temperature could limit the survival and spread of IAPs, resulting in a negative correlation between BIO7 and IAP richness. In other studies, the relationship between BIO7 and IAP richness showed changes into positive when choosing certain species as research objects or carrying the research at small scales (Park et al. 2021 ; Teklegiorgis et al. 2024 ). In our study, altitude was positively correlated with the richness of IAPs within NRs, which contradicts findings from other international studies that reported a negative correlation between altitude and IAP richness (Dark 2004 ; Steyn et al. 2017 ). Such a special distribution pattern may have several possible explanations: (1) some IAPs in China have a high upper altitude limit, with some records showing their presence at elevations of 2,500 meters or even higher (Weber et al. 2008 ); (2) IAPs exhibit phenotypic plasticity, which increases their tolerance and ecological amplitude. Studies have also shown that plants with a wide geographical distribution can adapt to the altitude in their habitats, displaying clinal patterns on a geographical scale in some trait variations (Jonas and Geber 1999 ; Alexander et al. 2016 ). For example, Ageratina adenophora , a member of the Asteraceae family, exhibits a clear altitudinal cline in seed weight, width, germination rate, and germination speed after invading China. Seeds from high-altitude populations are larger, with higher germination rates and faster germination speeds, indicating that phenotypic plasticity enhances the adaptability of invasive alien plants to harsh environments (Li and Feng 2009 ). 4.4 Recommendations and countermeasures 4.4.1 Strengthen fundamental research It is crucial to enhance fundamental research on IAPs in China, focusing on their pollination, dispersal, toxicity, life history, ecological strategies, and suitable habitats, which informs the introduction of new species and the development of eradication strategies (Herron et al. 2007 ; Sol et al. 2012 ; Tripathi et al. 2012 ). For instance, reducing growth and fecundity transitions might be an effective way to curb plant invasions for short-lived invaders, whereas reducing survival might be crucial for long-lived invaders (Ramula et al. 2008 ). The strategy of "one policy for one species" for precise management and effective eradication is heavily dependent on robust foundational research data and information (Du et al. 2023). Besides, investigations of IAPs within China's NRs remain insufficient, with baseline information on these species largely lacking. As reported by Wang et al. ( 2020b ), only 2.5% of all NRs nationwide have undertaken dedicated surveys on IAPs, which is far from adequate to support evidence-based management and conservation planning within NRs. 4.4.2 Enhance public participation Managing IAPs requires substantial human and material resources, and increasing public involvement through education, community cooperation, and volunteer efforts can help (Bryce et al. 2010 ; Shackleton et al. 2019 ). Numerous successful international cases can offer us valuable lessons. In the marine protected areas of the Azores, scuba divers actively participated in the detection of Caulerpa webbiana , an invasive seaweed, with a dedicated webpage established for recording observations (Amat et al. 2008 ). In New York, the Nature Conservancy organized volunteers to monitor invasive species in the Adirondack State Park, mapping the distribution of 13 IAPs along major roads (Brown et al. 2001 ). Additionally, volunteer-led initiatives, such as the "Balsam Blitz" to control Impatiens glandulifera in the Pembrokeshire Coast National Park and the removal of Lysichiton americanus in the Taunus Nature Park in Germany, highlight the effectiveness of local NGOs in combating invasive species (Pyšek et al. 2013 ). Such collaborative efforts can reduce management costs and raise public awareness of IAPs' impacts. 4.4.3 Establish strict quarantine systems and standardized quarantine procedures Customs serve as the first line of defense in safeguarding national biosecurity, preventing IAPs from crossing borders. Strict quarantine systems and standardized quarantine procedures play a vital role in preventing invasions. Countries like New Zealand use rigorous quarantine measures, including X-ray machines and detector dogs, to detect risks (Sikes et al. 2018 ). Quarantine systems should target both domestic and foreign invasive species, especially those with similar environments to China. Effective quarantine and prevention measures can control the entry of IAPs at the source. New monitoring tools and technologies, including image recognition, machine learning, and remote sensing, can enhance the efficiency of invasive species monitoring and encourage the participation rate of citizens (August et al. 2015 ; Terry et al. 2020 ). 4.4.4 Manage nature reserves in a more efficient way Preventing and intercepting IAPs should be prioritized. If invaders breach defenses, early detection and rapid response are the most cost-effective strategies, as demonstrated in New Zealand. The cost of intervening after invasive species have become widely established is approximately 40 times higher than the cost of early management (Harris and Timmins 2009 ). Moreover, early intervention poses fewer risks to ecosystem stability, helping to avoid the creation of ecological niches that could be exploited by new alien species during large-scale removal efforts (Caut et al. 2009 ). According to our research, NRs that were forest ecosystems were at higher invasion risk than others. In particular, NRs with subtropical evergreen broadleaf forest were at the highest risk. Most such NRs lie in south and southeast China and experience intense human disturbance and contain high-quality IAP habitat (Liu et al. 2020c ). NRs that protect wild animals and inland wetlands were also high in IAPs. Most such NRs are subtropical evergreen broadleaf forests of southern China. Such areas require priority management to prevent further invasion. In addition, some smaller NRs in Central China, East China, and South China also contained high levels of IAPs. Thus, considering limited resources, prioritizing the protection, monitoring, and eradication of invasive species in the NRs mentioned above may yield greater conservation returns. Training for personnel is also crucial to prevent further spread of IAPs. A survey of Belgian horticultural workers and nature reserve managers revealed that, despite frequent contact with IAPs, their knowledge of these species was still limited (Vanderhoeven et al. 2011 ). So, it is necessary and important to provide staff and related professionals with specialized training so as to standardize their work processes. Declarations Acknowledgements We thank the Chinese Virtual Herbarium (CVH) and Global Biodiversity Information Facility (GBIF) for permission to access species distribution data. Author contributions All authors contributed intellectual input and assistance to the manuscript preparation. Jia-Xin Wang: Conceptualization, Methodology, Software, Visualization, Writing - review & editing. Rainer W. Bussmann: Writing - review & editing. Fei Qin: Data collation, Methodology, Software. Yun-Fen Liang: Writing - review & editing. Bao-Cai Han: Writing - review & editing. Hai-Yan Bi: Writing - review & editing. Tian-Tian Xue: Supervision, Methodology, Writing - review & editing. Sheng-Xiang Yu: Supervision, Conceptualization, Methodology, Writing - review & editing, Resources, Data curation, Funding acquisition. Funding This work was supported by National Key R&D Program of China (Grant numbers 2024YFF1307602) and National Natural Science Foundation of China (Grant numbers 32372565). Data availability Factor layers are sourced from public databases. Data are available from the Dryad Digital Repository: https:// doi.org/10.5061/dryad.xksn02vng (Qin et al. 2024) Competing interests The authors have no relevant financial or non-financial interests to disclose. 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Bussmann","email":"","orcid":"","institution":"Ilia State University","correspondingAuthor":false,"prefix":"","firstName":"Rainer","middleName":"W.","lastName":"Bussmann","suffix":""},{"id":512763827,"identity":"49480521-6af7-4c85-8f07-e0f0b2d36623","order_by":2,"name":"Fei Qin","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Qin","suffix":""},{"id":512763829,"identity":"d6fc8553-50cc-40ac-a75c-450ec45037d1","order_by":3,"name":"Yun-Fen Liang","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yun-Fen","middleName":"","lastName":"Liang","suffix":""},{"id":512763831,"identity":"d4e434fe-0322-439c-b780-2f13c94c184b","order_by":4,"name":"Bao-Cai Han","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bao-Cai","middleName":"","lastName":"Han","suffix":""},{"id":512763832,"identity":"f586d7a2-dbb2-48fc-884f-cd6973ccdbe9","order_by":5,"name":"Hai-Yan Bi","email":"","orcid":"","institution":"National Natural History Museum of China","correspondingAuthor":false,"prefix":"","firstName":"Hai-Yan","middleName":"","lastName":"Bi","suffix":""},{"id":512763833,"identity":"5cb832ce-1362-4b28-8fff-385c37756c20","order_by":6,"name":"Tian-Tian Xue","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tian-Tian","middleName":"","lastName":"Xue","suffix":""},{"id":512763834,"identity":"cacd7fd8-6036-4e50-9eb6-b3ff1e13cecc","order_by":7,"name":"Sheng-Xiang Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYHACNgT5weAAiVoYZ5CkBQSYeRiI0GJwI/3Zg487ahP72M8efm1TcEeev/8A44cfDHZ5uLXkmBvOPHPcmI0nL806x+CZ4YwbCcySPQzJxXi0sEnzth2TY2PIMTPOMTicwHCDgUGageFAYgMeh0n/bTvGw8b/xszYAqhF/vwB5t/4tSSYSTO21cixSeQYP2YAajE4kMCG1xbJM2/MJHvbDhizSbwxY+wB+mXjjcQ2yx6DZJxa+I6nP5P42VaXOL8/x/jDjz935OXOHz5840eFHU4tCgfA1GEQwSYBEWMEKjbAoR4I5CFm1YEI5g+41Y2CUTAKRsFIBgAAA1q9UYZVoAAAAABJRU5ErkJggg==","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Sheng-Xiang","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-05-30 10:23:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6783801/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6783801/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91511393,"identity":"33de7061-77e1-4ea7-a2e6-c72c1024bf9f","added_by":"auto","created_at":"2025-09-17 08:49:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3671058,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIAP distribution patterns in NRs of China based on point data and predicted data.\u003c/strong\u003e (a) IAP richness in PNRs based on point data; (b) IAP richness in NNRs based on point data; (c) IAP richness in NRs based on point data; (d) IAP richness in PNRs based on predicted data under current climate scenario; (e) IAP richness in NNRs based on predicted data under current climate scenario; (f) IAP richness in NRs based on predicted data under current climate scenario.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6783801/v1/382e975d24808e40d59547c2.jpg"},{"id":91513112,"identity":"5114b1f0-c17b-46f3-8fdc-4ada7a673e57","added_by":"auto","created_at":"2025-09-17 08:57:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":191091,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProportions of nature reserves and their surrounding areas (within 5, 10, and 50 km from the NR boundaries) colonized by IAPs. \u003c/strong\u003eWe designated an NR as invaded when at least one alien plant species was documented therein, and we also quantified the proportionsof uninvaded NRs with at least one alien plant species record within 5, 10, and 50 km of the NR boundary.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6783801/v1/2f97ca536799c444ec146361.jpg"},{"id":91513114,"identity":"da4df64c-20f2-47af-85c2-6c7394a5e131","added_by":"auto","created_at":"2025-09-17 08:57:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3004715,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted IAP distribution patterns in NRs of China under future climate scenarios.\u003c/strong\u003e (a) IAP richness in NRs under RCP 2.6; (b) IAP richness in NRs under RCP 4.5; (c) IAP richness in NRs under RCP 6.0; (d) IAP richness in NRs under RCP 8.5.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6783801/v1/658f75d3c73667ef6888cbe7.jpg"},{"id":91511391,"identity":"a41bd9ae-02e5-4491-b1b1-48a29ca12593","added_by":"auto","created_at":"2025-09-17 08:49:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":650241,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eViolin plots showing IAP richness across nature reserve types and vegetation regionalization.\u003c/strong\u003e (a) From left to right, the groups are: Ⅰ Steppe and meadow, Ⅱ Geological formation, Ⅲ Ancient organism remains, Ⅳ Ocean and seacoast, Ⅴ Desert ecosystem, Ⅵ Inland wetland, Ⅶ Forest ecosystem, Ⅷ Wild animal, Ⅸ Wild plant; (b) From left to right, the groups are: Ⅰ Cold-temperature (boreal) coniferous forest zone, Ⅱ Temperate mixed coniferous-broadleaved forest zone, Ⅲ Warm-temperate deciduous broadleaved forest zone, Ⅳ Subtropical evergreen broadleaved forest zone, Ⅴ Tropical rain forest and monsoon forest zone, Ⅵ Temperate steppe zone, Ⅶ Temperate desert zone, Ⅷ Tibetan high-cold plateau zone. The black rectangles represent the interquartile range (IQR), which covers the middle 50% of the data. It shows where most of the values lie within each group. 1.5 IQR indicates the range within 1.5 times the interquartile range from the lower and upper quartiles. The white dots mark the average value of IAP richness for each group.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6783801/v1/44b71039d1ab322a8886edbb.jpg"},{"id":91513117,"identity":"c61d9764-9c99-4eb7-b1c7-316b7a6a3609","added_by":"auto","created_at":"2025-09-17 08:57:14","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2071918,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between IAP richness and the areas of nature reserves in different administrative regions.\u003c/strong\u003e Administrative regions are defined following Cao et al. (2018), a commonly adopted classification. The scatter plots depict the relationship between IAP richness and the area of NRs across these regions. Linear regression lines are fitted in each panel to illustrate the trends.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6783801/v1/ab5927506665a54889053d39.jpg"},{"id":91516010,"identity":"33267fc5-6543-48e1-b0dc-88a88d27e01b","added_by":"auto","created_at":"2025-09-17 09:21:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8288751,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6783801/v1/f262bc33-92f3-4628-a27c-09aa639f3aa5.pdf"},{"id":91511418,"identity":"0e79e398-2a49-439b-9c25-f1d7cf37ba24","added_by":"auto","created_at":"2025-09-17 08:49:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16566208,"visible":true,"origin":"","legend":"","description":"","filename":"Supportinginformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6783801/v1/2528e25d6cc78ce9abb292d6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Invasions of alien plants pose unprecedented challenges to China's nature reserves under climate change","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eProtected areas (PAs), as cornerstones of biodiversity conservation, are designed to protect representative natural landscapes, ensure the persistence of biodiversity and key ecosystem processes, and provide refuges for native species (Rodrigues et al. \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Foxcroft et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, in recent decades, factors such as the growing international and domestic trade driven by global economic integration, advanced modern transportation, the booming tourism industry, and climate change have enhanced the ability of alien species to overcome geographical barriers, making biological invasions a major global issue for ecological and biological security (Butchart et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Invasive alien plants (IAPs) have become a major threat when seeking to manage PAs (Pyšek et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Foxcroft et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Moodley et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to a report by the Global Invasive Species Programme (GISP), 487 PAs worldwide face biodiversity challenges posed by IAPs (De Poorter et al. 2007). Even at high-altitude and in arid regions, the problem still exists (Milton and Dean \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Alexander et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). One of the primary objectives of establishing PAs is to conserve biodiversity. However, if large-scale biological invasions occur within PAs, the resulting biodiversity loss could be more severe than in non-PAs. This outcome would directly contradict our original purpose of establishing PAs (Foxcroft et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSince 1980, global records of alien species have increased by 40%, with nearly one-fifth of the earth's surface now at risk of biological invasion (IPBES \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). About 10% of species on the IUCN Red List of Threatened Species are affected by alien invasions (IUCN 2022). Once noxious IAPs have successfully established dominance in a new habitat, the result may be the breakdown of ecosystem function and increased biodiversity loss (Ju et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Climate is considered a major environmental factor influencing species distribution (Xiong et al. \u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Under the background of climate change, invasions by alien plants may intensify (Robinson et al. \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Research suggests that climate change, for example the intensity and duration of extreme events (heat waves, heavy rainfall, wildfires, droughts, and hurricanes), may actively facilitate invasions by alien plants and ultimately compromise biodiversity protection within PAs (White et al. \u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Fargione et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Hannah et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Pereira et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Diez et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hou et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Early et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Grenz and Clements \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), potentially accelerating the risk of extinction for up to one in six species (Bellard et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Urban \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It has been claimed that future climate change will both increase and reduce invasions (Seebens et al. \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ziska et al. \u003cspan citationid=\"CR179\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Clements et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Qin et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These contrasting responses imply that some invasive alien plants may expand their ranges under future climate scenarios, while others may contract. Given the variation in geographic location and species composition among NRs, the changes in invasion risks they face under future climate scenarios remain uncertain. Therefore, when discussing the issue of invasion in NRs, it is necessarily essential to consider the impact of climate change.\u003c/p\u003e\u003cp\u003eGlobal species and ecosystem diversity are declining at an unprecedented rate (Butchart et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Biological invasions and climate change are two major driving forces (Walther et al. \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Jaureguiberry et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Particularly in China, rapid economic development has promoted the international exchange of goods and people, and IAPs have increased rapidly (Li and Ma \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Currently, China may face an even more severe situation in terms of biological damage than most other countries (Yang \u003cspan citationid=\"CR165\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Paini et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Historically, China has introduced nonnative species that afford significant economic benefits (Li et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hao and Ma \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Geographically, the terrain and climate of China are very diverse (Mittermeier and Goettsch Mittermeier \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Thus, the country contains many habitats suitable for IAPs if invasion is successful (Yu et al. \u003cspan citationid=\"CR172\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yu and Chen \u003cspan citationid=\"CR171\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Qin et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Rapid economic development, accompanied by explosive growth in international trade and transportation, has heightened the risk of alien species invasions (Li and Ma \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ju et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR172\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yu and Chen \u003cspan citationid=\"CR171\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). What\u0026rsquo;s more, the damage caused by an invasion lags the invasion per se (Mooney and Cleland \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Gallardo et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Thus, the situation in China is likely to become even more challenging in the future (Kelly et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Robeck et al. \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAccording to an available data, the direct economic loss caused by 283 invasive species in China has exceeded USD 2397.39\u0026nbsp;million annually, of which 66.4% is attributable to IAPs (Xu et al. \u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ding et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To date, more than 400 IAPs have been recorded in China (Yan et al. \u003cspan citationid=\"CR164\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Qin et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Many studies have described IAP compositions, distributions, and potential areas of invasion (Yan et al. \u003cspan citationid=\"CR164\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Qin et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In terms of researches on distribution pattern, previous studies have focused primarily on between-province differences in IAP distribution patterns or only assessed the risks to a few NNRs (Pan et al. \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gong et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003eb\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR177\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, invasive risks to the whole nature reserve (NR) network still remain unknown (Gong et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR177\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The regional specificities of NRs in terms of both species composition and population dynamics have been overlooked, so the information available is too general to aid the practical management of NRs (Chen et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hao and Ma \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Qin et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nonetheless, they have been valuable in terms of the research frameworks chosen and the data collected, shedding new light on the risk that IAPs pose to NRs.\u003c/p\u003e\u003cp\u003eIt is estimated that the minimum economic cost of biological invasions between 1970 and 2017 amounted to USD 1.288 trillion (Diagne et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Among all measures for managing alien species invasions, early monitoring and rapid response are the most cost-effective approaches. Specifically, every dollar spent on prevention can save USD 17 in long-term costs (MISAC, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, assessing the current status and distribution patterns of IAPs in China's NRs, as well as predicting future invasion dynamics under climate change, is of paramount importance for the management and construction of NRs and for biodiversity conservation in China. In this study, we focused on the risks posed by IAPs to NNRs and PNRs. We documented\u0026thinsp;\u0026gt;\u0026thinsp;120,000 IAP occurrences and used several algorithms to analyze the current and possible future distribution patterns. We present the IAP distribution patterns in the NR network and the key factors driving these patterns, the potential IAP distribution patterns in the NR network under multiple climate scenarios, the invasive risks posed by IAPs to the NR network, and countermeasures that may prevent IAP invasion and mitigate NR biodiversity loss.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 IAP inventory and occurrence database\u003c/h2\u003e\u003cp\u003eWe followed previous studies (Jin et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Yan et al. \u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan citationid=\"CR164\" class=\"CitationRef\"\u003eb\u003c/span\u003e; Yu and Chen \u003cspan citationid=\"CR171\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), particularly that of Qin et al. (\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), when compiling a checklist of IAPs in China. This checklist contains 402 IAPs (including four varieties) of 234 genera and 63 families.\u003c/p\u003e\u003cp\u003eThe IAP geographic distributions were primarily sourced from the Chinese Virtual Herbarium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cvh.ac.cn\u003c/span\u003e\u003cspan address=\"http://www.cvh.ac.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the Global Biodiversity Information Facility (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gbif.org\u003c/span\u003e\u003cspan address=\"https://www.gbif.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), as well as field survey data and distribution records documented in other books and literature. After downloading the data, we updated the species names according to the Catalogue of Life China. We then compiled a database of all regions in China and their coordinates on the basis of the Gazetteer of China (Institute of Geographical Names, State Bureau of Surveying and Mapping, 1997). To avoid identification or registration errors, the data were cross-checked and filtered against the provincial distribution ranges of IAPs listed in the Alien Invasive Flora of China (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.iplant.cn/ias\u003c/span\u003e\u003cspan address=\"http://www.iplant.cn/ias\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Any record of an IAP outside the known distribution was considered misidentified or incorrectly documented and thus excluded. After careful verification, our occurrence database contained 120,071 records with latitude and longitude of 402 IAP species.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Nature reserves in China\u003c/h2\u003e\u003cp\u003eBy 2017, China had established more than 2,750 NRs at national, provincial, municipal, and county levels, covering a total area of 1.47\u0026nbsp;million square kilometers, equivalent to 15% of the country's land area (Ma et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Among them, NNRs and PNRs cover nearly 90% of the total protected areas and are strictly managed. However, most municipal and county nature reserves only cover a small portion and often lack proper maintenance as well as comprehensive, accurate, and reliable data because of insufficient funds (Zhang et al. \u003cspan citationid=\"CR175\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR173\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR166\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, we only focused on NNRs and PNRs in this study. The geographic database of China\u0026rsquo;s NRs was initially built using the 2017 version of the National List of Nature Reserves released by the Ministry of Ecology and Environment (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mee.gov.cn\u003c/span\u003e\u003cspan address=\"https://www.mee.gov.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and then supplemented with field survey reports and the latest planning maps when digitizing the reserve boundaries. In total, we obtained geographic data for 463 NNRs and 805 PNRs (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Variable screening for prediction of IAP distribution\u003c/h2\u003e\u003cp\u003eIn line with previous studies, we used 26 variables to predict the potential distribution of IAPs (Table S2), including 20 environmental variables (BIO1-BIO19, elevation) and 6 socioeconomic variables (population density, gross domestic product, distance to the nearest railway, expressway, national highway, and provincial capital) (Yang et al. \u003cspan citationid=\"CR168\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Qin et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The environmental variables were obtained from the WorldClim database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldclim.org\u003c/span\u003e\u003cspan address=\"https://www.worldclim.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) at a resolution of 10 min, encompassing one current climate scenario (1970\u0026ndash;2000) and four future climate scenarios (2070, mean for 2061\u0026ndash;2080). In terms of socioeconomic variables, gross domestic product (GDP) data were sourced from the spatial distribution dataset of China's GDP for 1990 to 2015 released by the Resource and Environmental Science Data Centre of the Chinese Academy of Sciences. The spatial files that covered population density, railways, expressways, national highways, and provincial capitals were retrieved from the DIVA-GIS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.diva-gis.org\u003c/span\u003e\u003cspan address=\"http://www.diva-gis.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) at a resolution of 10 min. The distances from grid centroids to the nearest transportation routes or provincial capitals were calculated using ArcGIS v.10.8 (Environmental Systems Research Institute, Redlands, CA, USA) to represent the influence of transportation.\u003c/p\u003e\u003cp\u003eThe future climate scenarios were derived from the Beijing Climate Centre Climate System Model ver. 1.1 (BCC-CSM1\u0026ndash;1) (Van Vuuren et al. \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This circulation model considers four Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) that identified in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC5, 2015). This model is highly recommended for its stable performance and robust capability in climate change studies in China, particularly in terms of simulating the summer monsoon precipitation and the circulation patterns in East Asia (Wu et al. \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In brief, during predictions, seven variables (elevation, population density, gross domestic product, distance to the nearest railway, expressway, national highway, and provincial capital) were held constant, but the 19 climate variables (BIO1-BIO19) were allowed to vary in the five climate scenarios, one of which was current and the others future.\u003c/p\u003e\u003cp\u003eWe estimated correlations among the 26 variables using the \u003cem\u003ecor\u003c/em\u003e function of R to reduce the risk of model overfitting because of multicollinearity. Variables with high correlations (r\u0026thinsp;\u0026ge;\u0026thinsp;0.75) were excluded (Liu et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; El-Barougy et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Ultimately, 14 variables were used to predict the potential distribution of different IAPs. These were the annual mean temperature (BIO1), mean diurnal range (BIO2), isothermality (BIO3), temperature seasonality (BIO4), annual precipitation (BIO12), precipitation of driest month (BIO14), precipitation seasonality (BIO15), elevation, population density, GDP, distance to the nearest railway, distance to the nearest expressway, distance to the nearest national highway, and distance to the nearest provincial capital.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Driving factors of IAP distribution\u003c/h2\u003e\u003cp\u003eIAP distribution patterns are influenced by both biotic and abiotic factors (Kelly et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Previous studies on invasive species distributions in NRs were inconsistent in terms of the principal driving factors identified (Alston and Richardson \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Spear et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gantchoff et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Essl et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gulzar et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We used both environmental and socioeconomic factors when seeking drivers of IAP distribution in China\u0026rsquo;s NRs. The environmental factors included 19 bioclimatic variables (1970\u0026ndash;2000) and an elevation variable, as mentioned above. The socioeconomic factors included population density, the year of establishment of the NR, and the area of the NR. Data on population density were obtained from the DIVA-GIS database at a resolution of 10 min. The year of establishment and the area of each NR were retrieved and compiled from the List of Nature Reserves in China (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analyses\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.5.1 IAP distribution patterns in NRs\u003c/h2\u003e\u003cp\u003eWe used ArcGIS to link the point data of detected IAP distributions to the NNR and PNR layers. We retained only matching records, thus 5,389 for NNRs and 3,462 for PNRs. As a given species may be recorded more than once within a single NR, we retained only one record per species when calculating the total number of IAP species in each reserve. Then the data were organized and the IAP distribution patterns in NNRs and PNRs were mapped.\u003c/p\u003e\u003cp\u003eAreas surrounding NRs, particularly those with high levels of human activity, may significantly influence protected areas by increasing the inward IAP spreading pressure (Liu et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). Therefore, we carried out a buffer analysis in ArcGIS to detect IAP records within 5, 10, and 50 km outside the NR boundaries, and quantified the IAP richness within these buffer zones. The results were visualized in the form of stacked bar charts prepared using Sigmaplot v.14 (Systat Software, San Jose, CA, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.5.2 Predicted IAP distribution patterns in NRs\u003c/h2\u003e\u003cp\u003eBefore the prediction of IAP distribution patterns, we used ArcGIS to divide the map of China into 34,666 grid cells with a resolution of 10 min. Then these cells were connected to the NNR and PNR layers, yielding 4,715 matching cells for NNRs and 2,492 for PNRs.\u003c/p\u003e\u003cp\u003eWe used the \u0026ldquo;sdm\u0026rdquo; package of R to predict species distributions under current and future climate scenarios. This ensemble model employs five algorithms, including a generalized linear model (GLM), a general additive model (GAM), a generalized boosted model (GBM), a random forest (RF), and a maximum entropy model (MaxEnt). All have been widely used to predict species distribution trends under climate change and have shown excellent performance (Phillips et al. \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Elith et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Dullinger et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tang et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We randomly selected 70% of the IAP distribution data as the training set and used the remaining 30% as the test set when evaluating the model (Williams et al. \u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Convertino et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gholamy et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Next, we evaluated the accuracy of model predictions using two commonly employed metrics: the area under the receiver operating characteristic curve (AUC) and the true skill statistic (TSS) (Phillips et al. \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Phillips \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mi et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Models with TSS\u0026thinsp;\u0026ge;\u0026thinsp;0.5 and AUC\u0026thinsp;\u0026ge;\u0026thinsp;0.7 were considered valid and thus retained (Pearce and Ferrier \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Coetzee et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Then these models were integrated into a new ensemble model with each valid model weighted by reference to its TSS score (Thuiller et al. \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo generate predicted IAP richness maps, we transformed each continuous species probability into binary data (presence/absence) using the threshold that maximized the TSS (Freeman and Moisen \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Barbet-Massin et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fourcade et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Steen et al. \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We joined the binary (presence/absence) distribution maps of all IAPs in grid cells with the NNR and PNR layers and kept only the matching data in ArcGIS. Finally, we summed the distinct species numbers in every reserve to obtain the predicted IAP distribution patterns under one current and four future climate scenarios.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.5.3 Driving factors\u003c/h2\u003e\u003cp\u003eBefore conducting further analysis, the mean values of the 19 bioclimatic variables (BIO-BIO19), the population density, and the elevation of each NR were calculated using ArcGIS. A generalized linear model (GLM) was constructed using the detected IAP richness as the dependent variable and the 23 variables, including the 19 bioclimatic variables, the population density, elevation, the year of establishment, and the area of NR, as independent variables. Prior to model construction, all independent variables were log-transformed to address data skewness and thus improve analytical stability (Kalusov\u0026aacute; et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As more than half (669) of the 1,268 protected areas lacked records of IAPs, we employed a zero-inflated negative binomial (ZINB) regression model, which is a variant of GLM, to test the significance of different driving factors in terms of explaining IAP richness (Liu et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). The ZINB model well handles over-dispersed data because it assumes that excess zero counts are modelled using a logit or probit model, but the remaining counts modelled employing a negative binomial distribution (Moghimbeigi et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven that the relationship between IAP richness and NR area might be influenced by geographic location, we categorized all invaded NRs into seven groups (Northeast China, North China, East China, South China, Central China, Northwest China, and Southwest China) according to Cao et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) based on the administrative regions they belong to (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Then we explored whether the ratio of IAP richness to NR area varied across different geographic locations. The type of NR and the vegetation regionalization might also influence the IAP spatial distribution patterns (Liu et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Mungi et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, we retrieved the category of each NR from the \u0026ldquo;List of Nature Reserves in China\u0026rdquo; and the vegetation type from the vegetation regionalization map of the \u0026ldquo;Vegetation Atlas of China (1:1,000,000)\u0026rdquo; (Figure S2) (Hou, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Then we calculated the maximum and mean IAP richness for NRs in various categories and for those with different vegetation types. The data are presented using the violin plot of Origin v. 2021 (Origin Lab Corporation, Northampton, MA, USA).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 IAP distribution patterns in NRs\u003c/h2\u003e\u003cp\u003eIAPs were more common in NNRs than PNRs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-b). In summary, 294 of 463 NNRs (63%) and 305 of 805 PNRs (38%) contained IAPs. Although almost half of all NRs harbored IAPs, most exhibited low levels of invasion. Specifically, about 31% of PNRs and 45% of NNRs hosted only 1 to 10 IAPs. NRs with more IAPs were mainly located in South, East, Southwest, and Central China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). In addition, certain reserves in Gansu, Heilongjiang, Jilin, Liaoning, Hebei, Shandong, Tibet, and Xinjiang contained significantly more IAPs than surrounding reserves (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnalysis of IAP richness in NR buffer areas revealed that 35% of PNRs that were not yet invaded had IAPs within 5 km of the boundaries, 46% IAPs within 10 km, and 60% IAPs within 50 km (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). By contrast, 17% of NNRs that were not yet invaded had IAPs within 5 km, 22% IAPs within 10 km, and 35% IAPs within 50 km. The proportions of PNRs and NNRs threatened by IAP invasion, including those currently invaded and not invaded but with IAPs within 5 km buffer, were 73% and 80%, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Predicted IAP distribution patterns in NRs under current and future climate scenarios\u003c/h2\u003e\u003cp\u003eWe ultimately selected 14 variables to predict IAP distributions after analyzing correlations among different variables. The prediction analysis showed that under all the current and future (RCP2.6, RCP4.5, RCP6.0, RCP8.5) climate scenarios, approximately 85% of China\u0026rsquo;s NRs could host IAPs (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). NRs with extremely high predicted IAP richness were principally in southern Yunnan, Guangxi, Guangdong, Fujian, Hainan, and Taiwan (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Some were in Hunan, Hubei, Jiangxi, Anhui, and Jiangsu, mainly near the middle and lower reaches of the Yangtze River (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The NRs with a relatively high predicted number of IAPs were principally in Central and East China, southern Shanxi, eastern Sichuan, and most areas of Chongqing, Yunnan, and Guizhou (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). NRs with moderate predicted IAP richness were mainly in Northeast China, Northwest China, North China, western Sichuan, southern Tibet, western Hubei and western Henan (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Those with low predicted richness were mainly in certain regions of Northeast China, North China, Northwest China, and Southwest China (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Overall, the predicted IAP richness generally decreased from southeast to northwest (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Figures S3, S4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eComparison of predicted IAP distribution patterns in NRs under current and future climate scenarios revealed that IAP habitat suitability fluctuated in different climate scenarios (Figures S3, S4). Under the RCP 2.6 climate scenario, habitat suitability increased in 63% of NNRs and 54% of PNRs (Figure S3). Under the RCP 4.5 climate scenario, habitat suitability rose in 25% of NNRs and 23% of PNRs (Figure S3). Under the RCP 6.0 climate scenario, habitat suitability climbed in 35% of NNRs and 32% of PNRs (Figure S4). Under the RCP 8.5 climate scenario, habitat suitability grew in 39% of NNRs and 33% of PNRs (Figure S4). When comparing changes in IAP richness across different RCP scenarios, 221 NRs exhibited the highest IAP richness under current climate conditions, 533 NRs under RCP 2.6, 68 NRs under RCP 4.5, 110 NRs under RCP 6.0, and 136 NRs under RCP 8.5, while the remaining 200 NRs was unchanged across all five climate scenarios. Overall, most NRs (42%) exhibited the highest IAP richness under RCP 2.6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Driving factors of IAP richness in NRs\u003c/h2\u003e\u003cp\u003eInitially, all variables were subjected to Pearson correlation testing (Figure S5) and those with correlation coefficients\u0026thinsp;\u0026gt;\u0026thinsp;0.75 were excluded (Pino et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Gass\u0026oacute; et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The selection process adhered to the following principles: priority was given to retention of the annual mean temperature (BIO1), annual precipitation (BIO12), and other non-climatic factors; as many explanatory variables as possible were retained. Ultimately, 10 explanatory variables were selected and doublechecked by Pearson correlation testing (Figure S6). These were the population density, elevation, year of NR establishment, area of NR, annual mean temperature (BIO1), isothermality (BIO3), maximum temperature of the warmest month (BIO5), temperature annual range (BIO7), annual precipitation (BIO12), and precipitation seasonality (BIO15). The ZINB regression model was used to analyze the relationships between these variables and IAP richness. Of the ten explanatory variables, five significantly affected the outcome (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Specifically, population density (β\u0026thinsp;=\u0026thinsp;0.938), elevation (β\u0026thinsp;=\u0026thinsp;0.796), and NR area (β\u0026thinsp;=\u0026thinsp;0.962) demonstrated a significant positive association with the outcome (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). By contrast, year of NR establishment (β = \u0026minus;\u0026thinsp;259.870, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and BIO7 (β = \u0026minus;\u0026thinsp;3.915, p\u0026thinsp;=\u0026thinsp;0.001) had significant negative effects. In addition, BIO1, BIO5, and BIO12 were positively correlated with IAP richness, and BIO3 and BIO15 were negatively correlated but no such effect was statistically significant. The overdispersion parameter (α) was 2.563, confirming that the data exhibits significant overdispersion and justifying the use of a negative binomial model.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis result of Zero-inflated Negative Binomial\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegression\u003c/p\u003e\u003cp\u003ecoefficient (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear of establishment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-259.870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-10.974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual mean temperature (BIO1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.579\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIsothermality (BIO3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum temperature of the warmest month (BIO5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature annual range (BIO7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual precipitation (BIO12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.797\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrecipitation seasonality variation coefficient (BIO15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.974\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMcFadden \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e: 0.077.\u003c/p\u003e\u003cp\u003eBold values were significant.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe identified three main categories of NRs out of nine: VII forest ecosystem (46% of NRs), VIII wild animal (21% of NRs), and VI inland wetland (17% of NRs). Among different categories of NRs, the highest peak IAP richness was found in NRs of forest ecosystem (87 species), followed by wild plant (71 species), and wild animal (62 species) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Furthermore, the highest average IAP richness was found in NRs of forest ecosystem (5.60 species), followed by ocean and seacoast (4.37 species), and wild plant (3.79 species) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). We also identified three major vegetation types of NRs out of eight: IV subtropical evergreen broadleaf forest zone (48% of NRs), III warm-temperate deciduous broadleaf forest zone (16% of NRs), and VI temperate steppe zone (12% of NRs). NRs of subtropical evergreen broadleaf forest zone had the highest peak IAP richness at 87 species, followed by tropical rain forest and monsoon forest zone at 68 species, and warm-temperate deciduous broadleaf forest zone at 62 species (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). NRs of tropical rain forest and monsoon forest zone had the highest average number of IAPs at 8.33 species, followed by subtropical evergreen broadleaf forest zone at 5.03 species, and warm-temperate deciduous broadleaf forest zone at 3.24 species (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNRs were divided into seven regions (Northeast China, North China, East China, South China, Central China, Northwest China, and Southwest China) to study the relationship between IAP richness and NR area within each region (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). According to the relationship between IAP richness and NR area, we divided NRs into three classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e): (I) NRs in North, Northeast, and Northwest China (generally large area with low IAPs richness); (II) NRs in Central, East, and South China (moderate area with relatively high IAP richness); (III) NRs in Southwest China (generally large area and some with high IAP richness).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study primarily investigated the distribution patterns of 402 IAPs in NNRs and PNRs in China. Compared to previous studies, our research covered a greater number of species, and subsequently offers higher resolution data, and encompasses a more comprehensive range of nature reserves (Guo et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tu et al. \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR177\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR169\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This may shed new light on the IAP invasion risk and will aid the formulation of reliable countermeasures and identify areas in need of priority management.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Detected distribution of IAPs\u003c/h2\u003e\u003cp\u003eThe situation regarding the IAPs in China's nature reserves is concerning, with approximately 38% of PNRs and 63% of NNRs containing IAPs. Gong et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) studied 53 NNRs and found that most were invaded by alien plants and animals. Zhao et al. (\u003cspan citationid=\"CR177\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that 35 IAPs occurred in 72 studied NNRs with an average of 7.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 species per NR. It is difficult to compare our work to previous studies because the scales and sampling methods differ. Liu et al. (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e) reported that more than 90% of global NRs had not yet been invaded by alien animals. We found that both NNRs and PNRs were at much higher IAP invasion risks. This discrepancy may due to differences in the scale of plants and animals, both globally and regionally (Keller et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Luque et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and invasive alien animals are mobile and often elusive, thus more difficult to monitor than plants. Moreover, China\u0026rsquo;s situation is quite unique compared to other countries. Due to inadequate site planning during the early establishment of NNRs, many densely populated areas were included, with much of the land being collectively or privately owned, which hindered unified management. Farmland and the edges of artificial forests within NRs harbor the highest densities of IAPs, as local residents depended on plant and land resources, thereby increasing the risk of plant invasions (Liu et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR176\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020c\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eApproximately 34.78% of PNRs and 16.85% of NNRs had IAPs in their 5 km buffer zones, while no IAPs were found within the reserves themselves. If this is not addressed, IAPs may easily invade such reserves in the future. To advance the Beautiful China Initiative, a plan for the sustainable development of the Chinese nation, the 19th National Congress proposed the establishment of a nature reserve system with national parks as the main body (Fang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The implementation of this policy will inevitably involve the integration of nature reserve areas (Tang et al. 2020). Thus, to maximize conservation effectiveness, we suggest considering IAP richness dynamics during the adjustment of reserve boundaries, along with other crucial factors such as biodiversity hotspots, conservation gaps, minimum viable population, and the fragmentation and isolation of PAs (Leader-Williams et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOverall, the spatial distribution of IAPs in China\u0026rsquo;s nature reserves shows a preference for the southeast over the northwest. Nature reserves with a higher number of IAPs are mainly located in South, East, Southwest, and Central China. This distribution pattern is consistent with previous studies conducted at the provincial or county level (Bai et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pan et al. \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR169\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Results of the analysis also indicates that most NRs with high IAP richness spontaneously fall in the southeast side of the 0\u0026deg;C isotherm of the coldest month and the 800mm isohyet, which is not a coincidence. A global observation of 220 tree species revealed that minimum annual temperature effectively limits the distribution of different vegetation types (Woodward and Williams \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Relevant studies on IAPs have also found a strong correlation between the distribution of IAPs and the mean temperature of the coldest month (Beerling et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Jones et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This is because low temperatures in spring increase the likelihood of seedling mortality, and precipitation is also a crucial factor influencing the distribution of IAPs (Ib\u0026aacute;\u0026ntilde;ez et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Inderjit et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), further emphasizing the role of climate as a filter for IAP distribution (Kraft et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Young et al. \u003cspan citationid=\"CR170\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA global-scale study found that hotspots of IAP richness are often located on islands or in coastal areas (Dawson et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our finding that some NRs in Taiwan Island, Hainan Island, the Shandong Peninsula, the Liaodong Peninsula, and coastal areas of eastern and southern China have higher IAP richness further supports this conclusion. High population density, developed transportation, and frequent trade all contribute to high propagule pressure, which ultimately facilitates the invasion of IAPs in these coastal regions (Hobbs and Huenneke \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Lockwood et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Hulme \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zimmermann et al. \u003cspan citationid=\"CR178\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gioria et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). And islands, due to their unique geographic location and natural resources, often serve as tourist destinations or major trade ports, thus facing significant human disturbance (Cheng and Lu \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zhang and Ju \u003cspan citationid=\"CR174\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, the warm and humid climate in eastern and southern coastal regions makes these areas hotbeds for IAPs. A research had compiled on the first found locations of 90 top invasive plant species in China and found that Hong Kong and Taiwan are the most critical stepping-stones for IAPs entering mainland China, with nearly 40% of these species invading mainland China through these regions (Lu et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The result suggests that islands and coastal cities might work as important defense lines against IAPs, and their importance in preventing invasions warrants greater attention.\u003c/p\u003e\u003cp\u003eNRs with high IAP richness were also found in Central and Southwest China. This is closely related to favorable climates, convenient transportation, and concentrated populations. For example, NRs in Xinjiang with high IAP richness are located along the middle and lower reaches of the Niyang River urban belt, while those in Central China are distributed near the middle section of the \"two vertical and three horizontal\" Jing-Guang and Jing-Ha economic belt (Fang et al. 2016). Our analysis also reveals that some NRs near the border of China may face severe invasive challenges, such as those in Heilongjiang, Jilin, Liaoning, Tibet, Xinjiang, Yunnan, and Guangxi. For example, \u003cem\u003eAgeratina adenophora\u003c/em\u003e initially spread along the China-Myanmar border, and then became one of the most serious IAPs in China (Yan et al. \u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Wang and Wang \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Biological invasions often transcend political boundaries, and once a species is successfully established in one country, the invasion risk to its neighboring countries increases significantly (Stoett \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hurley et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, to more effectively tackle new challenges and mitigate the risks posed by IAPs while avoiding potential conflicts of interest, biosecurity cooperation organizations may serve as a viable solution (Faulkner et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Figuera et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Potential distribution of IAPs\u003c/h2\u003e\u003cp\u003eThe prediction of 402 IAPs\u0026rsquo; potential distribution revealed that the distribution pattern of species richness within NRs generally exhibited a decreasing trend from southeast to northwest under all five climate scenarios (1 present, 4 future). This pattern is consistent with previous studies on the distribution of IAPs at the provincial level in China (Liu et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Notably, some NRs with high species richness of IAPs overlap with biodiversity hotspots in China (Yang et al. \u003cspan citationid=\"CR167\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This overlap is particularly concerning, as without effective monitoring and timely control measures, widespread invasions could lead to severe biodiversity losses. When comparing the predicted distributions of IAPs with the actual detected distribution, the predicted ones appear to be significantly more severe. Specifically, 84% of NRs are suitable for at least one IAP, but detected distribution data shows only 47% currently harbor them. Moreover, in invaded reserves, predicted IAP richness generally exceeds detected levels.\u003c/p\u003e\u003cp\u003eThe gap between the predicted and the detected distributions has many possible reasons. On one hand, the distribution of IAPs is determined by both biotic and abiotic factors (Foxcroft et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gurevitch et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Young et al. \u003cspan citationid=\"CR170\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Given the fact that it is impossible to establish a perfect and universally applicable model that considers all potential influencing factors (Elliott-Graves \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Srivastava et al. \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), our model primarily considered factors related to climate and human activity. However, factors not included in our model, such as soil properties, land use, biotic interactions, and adaptive evolution, have been identified in previous studies as influencing the distribution and spread of IAPs (Mooney and Cleland \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Kulmatiski et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Reinhart and Callaway \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ara\u0026uacute;jo and Luoto \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Niu et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). On the other hand, the detected and recorded data may be insufficient, failing to fully reflect the real invasion situation. To some extent, the gap between predicted and detected distributions suggests that IAPs have not yet reached saturation, leaving significant potential for future invasions.\u003c/p\u003e\u003cp\u003eClimate change is profoundly altering global environments, potentially expanding the distribution range of IAPs. Regions previously unsuitable for these species due to physiological constraints may face colonization risks in the future. High-altitude and high-latitude areas, in particular, warrant increased attention, as IAPs could overcome previous temperature barriers and migrate into these regions under changing climatic conditions (Smith et al. \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Shrestha and Shrestha \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Factors driving IAP invasion\u003c/h2\u003e\u003cp\u003eAt the national scale the key natural factors influencing the distribution of IAPs within NRs were always mean annual temperature and mean annual precipitation in previous studies. IAPs tend to thrive in environments that are abundant in water and heat resources, as these conditions provide the necessary support for their growth, reproduction, and spread (Li et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR177\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, in our study, although BIO1 and BIO12 were positively related to IAP richness, neither of these relationships was statistically significant. Instead, BIO7 had a large effect on the distribution patterns of IAPs, which aligns with previous research (Wan and Wang \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Heringer et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Compared to more stable and moderate climatic conditions, extreme fluctuations in temperature could limit the survival and spread of IAPs, resulting in a negative correlation between BIO7 and IAP richness. In other studies, the relationship between BIO7 and IAP richness showed changes into positive when choosing certain species as research objects or carrying the research at small scales (Park et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Teklegiorgis et al. \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our study, altitude was positively correlated with the richness of IAPs within NRs, which contradicts findings from other international studies that reported a negative correlation between altitude and IAP richness (Dark \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Steyn et al. \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Such a special distribution pattern may have several possible explanations: (1) some IAPs in China have a high upper altitude limit, with some records showing their presence at elevations of 2,500 meters or even higher (Weber et al. \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2008\u003c/span\u003e); (2) IAPs exhibit phenotypic plasticity, which increases their tolerance and ecological amplitude. Studies have also shown that plants with a wide geographical distribution can adapt to the altitude in their habitats, displaying clinal patterns on a geographical scale in some trait variations (Jonas and Geber \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Alexander et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For example, \u003cem\u003eAgeratina adenophora\u003c/em\u003e, a member of the Asteraceae family, exhibits a clear altitudinal cline in seed weight, width, germination rate, and germination speed after invading China. Seeds from high-altitude populations are larger, with higher germination rates and faster germination speeds, indicating that phenotypic plasticity enhances the adaptability of invasive alien plants to harsh environments (Li and Feng \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Recommendations and countermeasures\u003c/h2\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e4.4.1 Strengthen fundamental research\u003c/h2\u003e\u003cp\u003eIt is crucial to enhance fundamental research on IAPs in China, focusing on their pollination, dispersal, toxicity, life history, ecological strategies, and suitable habitats, which informs the introduction of new species and the development of eradication strategies (Herron et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sol et al. \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tripathi et al. \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For instance, reducing growth and fecundity transitions might be an effective way to curb plant invasions for short-lived invaders, whereas reducing survival might be crucial for long-lived invaders (Ramula et al. \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The strategy of \"one policy for one species\" for precise management and effective eradication is heavily dependent on robust foundational research data and information (Du et al. 2023). Besides, investigations of IAPs within China's NRs remain insufficient, with baseline information on these species largely lacking. As reported by Wang et al. (\u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e), only 2.5% of all NRs nationwide have undertaken dedicated surveys on IAPs, which is far from adequate to support evidence-based management and conservation planning within NRs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e4.4.2 Enhance public participation\u003c/h2\u003e\u003cp\u003eManaging IAPs requires substantial human and material resources, and increasing public involvement through education, community cooperation, and volunteer efforts can help (Bryce et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Shackleton et al. \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Numerous successful international cases can offer us valuable lessons. In the marine protected areas of the Azores, scuba divers actively participated in the detection of \u003cem\u003eCaulerpa webbiana\u003c/em\u003e, an invasive seaweed, with a dedicated webpage established for recording observations (Amat et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In New York, the Nature Conservancy organized volunteers to monitor invasive species in the Adirondack State Park, mapping the distribution of 13 IAPs along major roads (Brown et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Additionally, volunteer-led initiatives, such as the \"Balsam Blitz\" to control \u003cem\u003eImpatiens glandulifera\u003c/em\u003e in the Pembrokeshire Coast National Park and the removal of \u003cem\u003eLysichiton americanus\u003c/em\u003e in the Taunus Nature Park in Germany, highlight the effectiveness of local NGOs in combating invasive species (Pyšek et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Such collaborative efforts can reduce management costs and raise public awareness of IAPs' impacts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e4.4.3 Establish strict quarantine systems and standardized quarantine procedures\u003c/h2\u003e\u003cp\u003eCustoms serve as the first line of defense in safeguarding national biosecurity, preventing IAPs from crossing borders. Strict quarantine systems and standardized quarantine procedures play a vital role in preventing invasions. Countries like New Zealand use rigorous quarantine measures, including X-ray machines and detector dogs, to detect risks (Sikes et al. \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Quarantine systems should target both domestic and foreign invasive species, especially those with similar environments to China. Effective quarantine and prevention measures can control the entry of IAPs at the source. New monitoring tools and technologies, including image recognition, machine learning, and remote sensing, can enhance the efficiency of invasive species monitoring and encourage the participation rate of citizens (August et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Terry et al. \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e4.4.4 Manage nature reserves in a more efficient way\u003c/h2\u003e\u003cp\u003ePreventing and intercepting IAPs should be prioritized. If invaders breach defenses, early detection and rapid response are the most cost-effective strategies, as demonstrated in New Zealand. The cost of intervening after invasive species have become widely established is approximately 40 times higher than the cost of early management (Harris and Timmins \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Moreover, early intervention poses fewer risks to ecosystem stability, helping to avoid the creation of ecological niches that could be exploited by new alien species during large-scale removal efforts (Caut et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). According to our research, NRs that were forest ecosystems were at higher invasion risk than others. In particular, NRs with subtropical evergreen broadleaf forest were at the highest risk. Most such NRs lie in south and southeast China and experience intense human disturbance and contain high-quality IAP habitat (Liu et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020c\u003c/span\u003e). NRs that protect wild animals and inland wetlands were also high in IAPs. Most such NRs are subtropical evergreen broadleaf forests of southern China. Such areas require priority management to prevent further invasion. In addition, some smaller NRs in Central China, East China, and South China also contained high levels of IAPs. Thus, considering limited resources, prioritizing the protection, monitoring, and eradication of invasive species in the NRs mentioned above may yield greater conservation returns. Training for personnel is also crucial to prevent further spread of IAPs. A survey of Belgian horticultural workers and nature reserve managers revealed that, despite frequent contact with IAPs, their knowledge of these species was still limited (Vanderhoeven et al. \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). So, it is necessary and important to provide staff and related professionals with specialized training so as to standardize their work processes.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eWe thank the Chinese Virtual Herbarium (CVH) and Global Biodiversity Information Facility (GBIF) for permission to access species distribution data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp; All authors contributed intellectual input and assistance to the manuscript preparation. Jia-Xin Wang: Conceptualization, Methodology, Software, Visualization, Writing - review \u0026amp; editing. Rainer W. Bussmann: Writing - review \u0026amp; editing. Fei Qin: Data collation, Methodology, Software. Yun-Fen Liang: Writing - review \u0026amp; editing. Bao-Cai Han: Writing - review \u0026amp; editing. Hai-Yan Bi: Writing - review \u0026amp; editing. Tian-Tian Xue: Supervision, Methodology, Writing - review \u0026amp; editing. Sheng-Xiang Yu: Supervision, Conceptualization, Methodology, Writing - review \u0026amp; editing, Resources, Data curation, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis work was supported by National Key R\u0026amp;D Program of China (Grant numbers 2024YFF1307602) and National Natural Science Foundation of China (Grant numbers 32372565).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eFactor layers are sourced from public databases. Data are available from the Dryad Digital Repository: https:// doi.org/10.5061/dryad.xksn02vng (Qin et al. 2024)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOpen Access\u0026nbsp;\u003c/strong\u003eThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u0026rsquo;s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u0026rsquo;s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlexander JM, Lembrechts JJ, Cavieres LA, et al (2016) Plant invasions into mountains and alpine ecosystems: current status and future challenges. Alp Bot 126:89\u0026ndash;103. https://doi.org/10.1007/s00035-016-0172-8\u003c/li\u003e\n\u003cli\u003eAlston KP, Richardson DM (2006) The roles of habitat features, disturbance, and distance from putative source populations in structuring alien plant invasions at the urban/wildland interface on the Cape Peninsula, South Africa. 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Invasive Plant Sci Manag 12: 79\u0026ndash;88.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"biological invasions, climate change, conservation, distribution pattern, ensemble model, nature reserves","lastPublishedDoi":"10.21203/rs.3.rs-6783801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6783801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBiological invasions are considered the second-greatest threat to global biodiversity. In China, nature reserves (NRs) are crucial in terms of biodiversity conservation, but many are at high risk of biological invasion. However, as climate change progresses the NR invasion risk posed by invasive alien plants (IAPs) remains unclear. Here, we compiled an inventory of 402 IAPs with over 120,000 occurrences to investigate IAP distribution patterns and the potential invasion risks in China\u0026rsquo;s NRs under current and future climate scenarios. We also analyzed the key environmental and socioeconomic factors influencing IAP distribution. Our results indicate that approximately 63% of national nature reserves (NNRs) and 38% of provincial nature reserves (PNRs) contain IAPs. Most NRs with high numbers of IAPs are located in South, East, Southwest, and Central China. In addition, up to 73% of PNRs and 80% of NNRs are highly vulnerable, which have IAP records within NRs or outer 5 km buffer areas. Under current and future climate scenarios, approximately 85% of China\u0026rsquo;s NRs contain suitable habitats for IAPs, representing a 38% increase compared to the collected distribution. The predicted IAP distribution pattern generally shows a decreasing trend from southeast to northwest. Population density, elevation, area, year of establishment, and temperature annual range (BIO7) significantly affect IAP richness in NRs. Under future climate scenarios, China\u0026rsquo;s NRs will be confronted with a greater risk of IAP invasion. Our findings can work as fundamental material when managing IAPs in NRs, providing valuable insights for targeted strategies and improving the protective effectiveness of NRs in China.\u003c/p\u003e","manuscriptTitle":"Invasions of alien plants pose unprecedented challenges to China's nature reserves under climate change","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 08:49:09","doi":"10.21203/rs.3.rs-6783801/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-09T11:55:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T21:38:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338522848163015809093776655105071616717","date":"2025-11-03T15:48:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-09T19:27:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-09T14:11:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-31T04:00:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biodiversity and Conservation","date":"2025-05-30T10:21:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2fe902b2-446f-4ead-853c-400bf4c93f93","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T06:54:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 08:49:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6783801","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6783801","identity":"rs-6783801","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}