Vulnerability of Global Afforestation Projects to a Polyphagous Invasive Fall Webworm

Vulnerability of Global Afforestation Projects to a Polyphagous Invasive Fall Webworm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Vulnerability of Global Afforestation Projects to a Polyphagous Invasive Fall Webworm Lilin Zhao, Jing Ning, Jingjing Du, Deliang Lu, Jiquan Chen, Hui Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5939468/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Global plantations, crucial for restoring degraded landscapes, face rising invasive pest threats, in recent decades. This study highlights that the invasion and proliferation of the fall webworm have been facilitated by the global afforestation boom in the last 80 years and could pose a threat to all global ecological projects until 2050. Since 1940, this polyphagous pest has spread to 40 countries, following an S- curve pattern. The distribution of the fall webworm is positively correlated with the expansion rates of afforestation and host tree plantation areas. China is now the most affected country by the fall webworm, with the highest comprehensive threat index (CTI). The number of host species has risen from 121 in the U.S. to 400 in China, and the host range has expanded from hardwoods to include coniferous trees. Notably, two-thirds of the total 600 host plants are tree species utilized for afforestation purposes. The preferred host species, Acer , Quercus , and Populus , are predominant in eight major global ecological projects. Additionally, hydroclimate extremes are projected to increase threats to 65.8% of afforestation zones by 2070, highlighting the need for strategic tree species selection to achieve sustainable ecological goals of global ecological projects, and protect against pests. Biological sciences/Ecology/Invasive species Earth and environmental sciences/Ecology/Forestry Biological sciences/Ecology/Ecosystem services Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The restoration of degraded landscapes has predominantly been achieved through global afforestation initiatives. In addition to their socioeconomic advantages, these plantations offer vital ecosystem services 1-3 . By the end of the century, it is projected that forest plantations will constitute 20% of the total global forest area 4 . China is at the forefront of this endeavor, having completed afforestation on over 800,310 km 2 5,6 . The ecological and economic benefits of this achievement are of considerable interest 7 . However, the potential connection between afforestation activities and biological disasters has not been fully assessed 8 . It is essential to elucidate the relationship between the dispersal of invasive pests and plantation activities to refine traditional afforestation policies. Invasive pests have been identified as a significant and pervasive threat to forest ecosystems 9 . Particularly, temperate forests in the Northern Hemisphere have experienced substantial damage due to invasive pests 10,11 . Over 450 species of tree-feeding pests have invaded forests in the United States 12 . In extreme cases, invasive pests can lead to the extinction of entire tree species within specific geographic regions 13,14 . Specific factors, such as global warming, alterations in precipitation patterns, increased frequency of extreme climate events, and extended growing seasons, are recognized as key drivers of invasive species outbreaks 15,16 . However, the impact of human-driven global afforestation efforts has largely been neglected. The fall webworm ( Hyphantria cunea ) is a highly destructive invasive pest, renowned for its exceptional reproductive capacity and its tendency to form dense larval populations 17,18 . The larvae are notorious for constructing extensive web-like nests, which destroy urban greenery and disrupt the daily lives of residents worldwide. The larvae consume tree leaves, causing extensive defoliation and even devastating forests 16,19 . This study examines the risks associated with fall webworm outbreaks in global afforestation projects, particularly in the context of rapid plantation establishment. We analyze the global distribution of the fall webworm, its spatial correlation with host species, and the potential threats it poses to global ecological projects. The study highlights the importance of adjusting plant species to enhance the success and sustainability of forest plantations. Results The surge in afforestation projects has coincided with the invasion of the fall webworm The fall webworm is a widespread invasive pest with a broad global distribution. It originated in North America and was first recorded in the United States in 1770, in Mexico by 1871, and in Canada by 1922 (Fig. 1 A). We conducted a logistic regression analysis to examine the spread of the fall webworm in invaded countries using data from 1940 to 2019. The fitted logistic curve is characterized by a maximum value of L = 40 (***, p < 0.001), a growth rate of k = 0.05551 (***, p < 0.001), and an inflection point in the year x 0 = 1975 (***, p < 0.001), indicating that 1975 marked the peak in the number of newly invaded countries. This model suggests that the global spread of the fall webworm experienced a period of rapid expansion before stabilizing. Subsequently, with economic development and increased trade, the pest has invaded 35 countries in Europe and Asia. The number of invaded countries increased slowly from 1940 to 1960, accelerated during 1960–1990 with 1975 as the peak year, and then slowed down. Based on this model, it is projected that the number of invaded countries will reach saturation by 2050 ( x = 2050, y ≈ 39.39) (Fig. 1 D), assuming no significant changes in global forest plantations. Notably, the rapid spread of the fall webworm coincided with the period of globalization and the rapid expansion of afforestation activities (1960–1990) (Fig. 1 E). Spatially, eight of the ten countries with the largest afforestation areas fall within the webworm's distribution range, and 64% of the world’s forest plantations are concentrated in these countries (Fig. 1 B). A more detailed analysis, involving overlays of the global artificial forest map with afforestation data from first-level administrative units and specific fall webworm distribution points, reveals a clear pattern: the areas affected by the fall webworm strongly coincide with regions of intensive afforestation activities, such as the northwestern United States, the southeastern coast of the United States, much of Japan, Korea, North Korea, the North China Plain, and most of Europe (Fig. 1 C, S1 ). These findings suggest a potential correlation between the spread of the fall webworm and regional forestry practices, particularly in areas with concentrated artificial forestation. Positive correlation between fall webworm infestation and the expansion of afforestation areas and host tree plantations A phylogenetic analysis was conducted on the 606 identified host plants at both the familial and generic levels. The impact of the fall webworm is not limited to the well-documented angiosperms (flowering plants) but also extends to a broader range of plant taxa, including gymnosperms (non-flowering plants) and ferns, which are less frequently recognized in this context. The analysis encompassed 109 families, with a significant predominance of species within the Rosaceae (65 species), Fabaceae (36 species), and Asteraceae (29 species) families. This highlights a pronounced familial diversity, as these families are characterized by their broad geographic distributions and high species diversity, providing a rich habitat mosaic for the fall webworm (Fig. 2 A). At the genus level, the fall webworm’s highest host species counts were found in Prunus (20 species), Quercus (19 species), Acer (16 species), Salix (15 species), and Populus (12 species), indicating a preference for broad-leaved deciduous trees (Fig. 2 A). These genera are ecologically significant, widely distributed across temperate and subtropical zones, and are vital components of forest ecosystems. They contribute to the forest canopy, exhibit rapid growth, and are commonly found in natural forests, urban green spaces, landscape design, and ecological restoration initiatives 20 . Subsequently, these 606 host plants were categorized into various forest types, revealing that 421 hosts are arboreal species within forest ecosystems. This classification included non-wood product forests (305 species), special-purpose forests (288 species), windbreak forests (176 species), timber forests (155 species), and fuelwood forests (66 species) (Fig. 2 D). These forests serve diverse afforestation objectives: windbreak forests combat soil erosion and desertification; timber forests supply wood for industries such as construction and paper production; non-wood product forests yield fruits, nuts, and medicinal materials; fuelwood forests provide firewood and charcoal; and special-purpose forests cultivate medicinal and ornamental plants (Fig. 2 B) 6 . To quantify the impact of the fall webworm on afforestation, we analyzed the spatial coherence between affected zones in North America, Central and Eastern Europe, and East Asia, and the intensity of afforestation efforts. For each observation point, a 2.1 km buffer zone was established to calculate the total affected area within each province. This was then compared with afforestation ratios, which were calculated as the area of afforestation divided by the total administrative area. We found that regions with larger potential impact zones, such as the central and eastern United States, Eastern Europe, and the North China Plain, exhibited higher afforestation rates. This spatial correlation suggests that increased tree cover is associated with higher prevalence rates of the fall webworm (Fig. 2 C). A linear regression model was applied to the fall webworm damage area (y) from 2000 to 2020 and the corresponding afforestation area ( x ) ( R² = 0.878), revealing a positive correlation between afforestation expansion and increased fall webworm damage (Fig. 2 E, left). As a relatively independent spatial unit, the urban ecological environment and forestry development within it exhibit a certain degree of unity and continuity. Another linear regression analysis was conducted on the fall webworm infestation area as a percentage of total forest area ( y ) in 50 Chinese cities for 2020 versus net forest change ( x ), resulting in a reliable model ( R² = 0.75). This indicates that an increase in net forest change corresponds to a higher percentage of fall webworm infestation (Fig. 2 E, right). To further verify the spatial interdependency, data were collected from 40 cities and 95 counties in China in 2020. The results indicate for every 1% increase in the proportion of planted forests to total forests, the proportion of forest area affected by the fall webworm increases by an average of 0.49%, and for every 1% increase in the ratio of planted to natural forests, the proportion of forest area affected increases by an average of 0.27%. These results suggest that afforestation practices may contribute to the exacerbation of fall webworm damage (Fig. 2 F). In fact, there is a simultaneous global trend of decreasing natural forest area and increasing plantation forest area (Fig. S2 , S3). The impact of invasion and the species of host trees in affected areas were further investigated. The association coefficient (AC) was calculated, and significance tests were conducted using Spearman's rank correlation coefficient for host plants at both the family and genus levels. Host plants with an AC value greater than 0.67 were selected and visually represented using a Sankey diagram to illustrate their phylogenetic relationships. At the family level, Asteraceae, Fabaceae, and Rosaceae demonstrated the strongest interspecies associations with the fall webworm. Other families exhibiting significant associations included Salicaceae, Betulaceae, Ulmaceae, Fagaceae, Cupressaceae, Juglandaceae, and Moraceae (Fig. 2 D). A high interspecies association was observed with Cupressaceae (AC = 0.71), with recent studies identifying Taxodium and Metasequoia as key hosts in newly invaded areas of the fall webworm 21 . At the genus level, Acer , Prunus , and Populus exhibited the strongest associations, all of which are commonly utilized in afforestation initiatives. Other genera with robust associations included Salix , Ulmus , Quercus , Robinia , Equisetum , Betula , Fraxinus , and Juglans . Herbaceous plants such as Trifolium , Plantago , and Rumex , known for their diverse distribution and habitats, also showed strong correlations with the fall webworm. These species may serve as significant intermediate hosts, complicating management strategies. Asteraceae was the family with the most pronounced association with the fall webworm, due to the cumulative influence of multiple genera rather than a single genus. In contrast, Sapindaceae, with only one highly correlated genus, Acer , exhibited the strongest genus-level association, indicating a significant adaptability of Acer to the pest (Fig. 2 D). These results suggest that the fall webworm is widely associated with various afforestation tree species, influencing its invasion patterns. A Pearson correlation analysis was conducted to validate the findings, determining the correlation between fall webworm damage areas, five forest types, and nine significant host species across seven Chinese provinces. The results indicated positive correlations with non-wood product forests (0.95), windbreak forests (0.84), fuelwood forests (0.65), timber forests (0.48), and special-purpose forests (0.11). These included Ulmus (0.76), Betula (0.73), Castanea (0.75), Robinia (0.71), Juglans (0.65), Fraxinus & Philodendron & Juglans (0.56), Quercus (0.65), Populus (0.58), and Salix (0.34) (Fig. 2 G). These findings suggest that larger areas of specific forest types and tree species used in afforestation are associated with increased fall webworm damage. Host and climatic adaptations during the invasion process of the fall webworm Further statistical analysis of host species diversity in both native and invaded regions revealed that Canada and the United States, which are native regions for the fall webworm, host 39 and 121 host species, respectively. In contrast, invaded regions such as Europe, South Korea, Japan, and China exhibit higher counts of host species, with 165, 171, 286, and 400 species, respectively. This disparity suggests a potential interdependency between the diversity of host species in invaded regions and the likelihood of fall webworm outbreaks (Fig. 3 A). Notably, increased relative abundance was also observed along the eastern coast of Australia, the eastern coast of Africa, the Gulf of Guinea, as well as in Central and South America, indicating the potential for these areas to serve as suitable habitats for the fall webworm (Fig. 3 A). The fall webworm has two larval types: the red-headed type with a narrower host range and the black-headed type with a broader host range. Notably, only the black-headed type has spread to Asia and Europe. Statistical analysis reveals that the black-headed type is adapted to higher latitudes compared to the red-headed type, with average latitudes of 39.05°N and 37.80°N, respectively (****, p < 0.0001). Moreover, the invasive black-headed type have further extended their latitudinal range, with their northernmost distribution shifting from 52.13°N to 65.9°N and their average latitude increasing from 38.75°N to 40.74°N (****, p < 0.0001) (Fig. 3 B). In addition to the availability of host species, the fall webworm has shown significant changes in its climatic adaptability during its invasion. Latitudinal distribution patterns reveal that univoltine populations (one generation per year) are restricted to the native range, while bivoltine and trivoltine populations (two and three generations per year) are found in both native and invaded areas, indicating greater adaptability in the latter. Specifically, in invasive region, bivoltine populations have expanded their range from an average latitude of 38.86°N to 43.28°N (****, p < 0.0001), while trivoltine populations have shifted from 35.78°N to 36.04°N (****, p < 0.0001). The tetravoltine population (four generations per year), which evolved from the trivoltine population, has a more limited distribution, with records only found in China (Fig. 3 C). A principal component analysis (PCA) of 19 climate variables from the distribution data revealed that invasive populations have a significantly larger PCA3 distribution range compared to native populations. This suggests that invasive populations are better able to adapt to diverse environmental conditions, especially in variables like precipitation seasonality and precipitation of the warmest quarter. Besides, the invaded populations have expanded into drier climates in winter, such as Dwb and Dwd (Fig. 3 D, S4 , S7 ). The potential threat of fall webworm to key global afforestation projects Currently, numerous large-scale ecological projects are being implemented globally with the aim of increasing forest cover and promoting ecological restoration. These initiatives are vital for the recovery of global ecosystems; however, they also present potential risks from pests such as the fall webworm. Therefore, it is essential to assess the interrelationships between these ecological projects, the distribution of the fall webworm, and the preferred host species of the fall webworm to inform future forest management and pest control strategies. The 30 main tree species used in twelve key projects have been compiled. The results showed that, except for the National Green Program in the Philippines, the other eleven projects frequently used 19 host plants of the fall webworm as afforestation tree species. The species Acer , Prunus , Populus , Ulmus , Quercus , Robinia , Betula , and Fraxinus are high-risk species (Fig. 2 D, 4 A). A detailed breakdown of the proportion of preferred host species in each project is as follows: Prairie States Forestry Project : 3 host species/3 total species Canada’s Green Plan : 2 host species/4 total species Stalin’s Rebuilding – Nature Plan : 7 host species/8 total species France’s Forestry Ecological Project : 3 host species/5 total species The Green Dam Project : 2 host species/4 total species Japan’s Mountain Renovating Plan : 2 host species/2 total species India’s Social Forestry Plan : 2 host species/3 total species Mountain Renovating and Country Green Plan : 6 host species/7 total species Important Forestry Ecological Engineering : 6 host species/8 total species Bonn Challenge : 3 host species/ 5 total species The National Greening Program in Philippines : 0 host species/ 3 total species The Great Green Wall : 2 host species/ 3 total species This study introduced a novel variable, the Comprehensive Threat Index (CTI), which is based on the proportions of host species in key projects globally, the weighted host spatial correlation index, and the area adjustment factor. Utilizing data from afforestation projects across various administrative regions, the CTI for each region was calculated and evaluated across multiple dimensions, including the proportion of host tree species, the weighted spatial correlation index, and the area adjustment factor (Fig. 4 B). The findings suggest that afforestation projects in Asia and North America generally face higher threat indices, with East Asia exhibiting significantly higher CTI compared to other regions. Specifically, regions with a higher proportion of host tree species, such as the United States, China, South Korea, and Russia, demonstrate higher threat indices due to the strong spatial correlation between the fall webworm and the primary host tree species. These areas also display generally high weighted spatial correlation indices, indicating that the spatial distribution of the fall webworm is closely linked to the distribution of host tree species, potentially accelerating the pest's spread. Additionally, regions such as France and Canada, despite having lower proportions of host tree species, still exhibit moderate threat levels due to their large-scale afforestation projects and the high correlation between host tree species and the fall webworm. Meanwhile, areas where the fall webworm has not yet invaded, such as South America and Africa, are also at a certain level of risk. Proactive adjustments in the selection of tree species for afforestation are required to reduce the risks of invasion and infestation (Fig. 4 C). By analyzing the comprehensive threat indices across different regions, this study provides a global threat distribution map for afforestation projects facing the fall webworm, offering scientific support for relevant departments in developing regional control measures and optimizing planting strategies. Risk assessment of fall webworm in future afforestation zones The jackknife test revealed that the environmental variable with the highest predictive gain was annual average temperature (Bio1, 10–18℃), followed by average temperature of the wettest quarter (Bio8, 22°C–28°C) and precipitation of the wettest month (Bio13, 140–270 mm; Figure S9-S10). Considering the effect of historical climatic factors and the distribution of hosts, the suitable habitats for fall webworm were estimated as 45.72% of the plantation forests and will expand to 52.69% by 2030, 60.15% by 2050, and 65.80% by 2070 under the future climate change (Fig. 5 a). It is expected that in Asia, the distribution of the fall webworm will further spread southward, while in Europe and North America, there is a trend of spreading northward. In addition, the availability of suitable climate and hosts in Africa, South America, and Australia will also become potential invasion areas for the fall webworm. Among these, the areas under threat within the management of major ecological projects are estimated to be 2.60 × 10⁶, 2.79 × 10⁶, 3.57 × 10⁶, and 4.47 × 10⁶ hectares for the current period, 2030, 2050, and 2070, respectively. These areas represent 73.60%, 73.23%, 72.90%, and 74.25% of the total threatened areas, indicating a relatively stable proportion over time (Fig. 5 B). Discussion Our research indicates that global afforestation initiatives have inadvertently facilitated the spread of the invasive fall webworm, exacerbating the damage it causes and threatening eleven of the twelve major global ecological projects. With the initiation of the United Nations Decade on Ecosystem Restoration, efforts to restore forests have been intensified worldwide 22 , 23 . However, many tree species commonly utilized in afforestation, such as Acer , Prunus , Populus , Ulmus , Quercus , Robinia , Beutla , Fraxinus , are preferred hosts for the fall webworm. The distribution of these species closely aligns with areas heavily impacted by the pest, and their large-scale planting may have further expanded the fall webworm's range. Additionally, the increasing proportion of plantation forests and the decreasing proportion of natural forests are expected to amplify the damage caused by the pest. These findings highlight a significant challenge in the current global restoration paradigm. While afforestation efforts are essential for mitigating climate change and restoring biodiversity 24 , the potential damage caused by invasive pests must be carefully considered. Countries with higher levels of development are more likely to engage in afforestation. Consequently, nations that rank among the top ten in global afforestation efforts, such as China, the United States, Russia, Canada, Sweden, Japan, Finland, and Germany, must remain vigilant against the threat of invasive pests and diseases to newly planted forests. Furthermore, an increasing number of developing countries, particularly those in Africa and South America, have joined afforestation initiatives 25 . For these countries, maintaining the outcomes of afforestation presents greater challenges 26 . This underscores the importance of carefully selecting tree species, prioritizing pest-resistant varieties, and emphasizing the ecological regulation of management practices 27 – 29 . For instance, reducing pesticide use while promoting insect and soil microbial diversity could significantly enhance the sustainability and resilience of afforestation projects 30 . By analyzing the comprehensive threat indices across different regions, this study provides a global threat distribution map for afforestation projects facing the fall webworm, offering scientific support for relevant departments in developing regional control measures and optimizing planting strategies. The fall webworm's adaptation during its invasion process is expected to accelerate its spread from localized hotspots to a global scale. With the increasing hydroclimate volatility (55% higher than the current) caused by global warming, the fall webworm may further invade coastal regions of the Southern Hemisphere and high-latitude areas of the Northern Hemisphere 31 , 32 . Coupled with its adaptation to new hosts, 65.80% of future afforestation areas are likely to be threatened by this species 33 . In recent years, the "insect crisis" has garnered significant attention from scientists, the public, and policymakers, particularly in the context of global climate change 34 – 36 . While most insect species face challenges such as declining abundance, habitat loss, and reduced ecosystem services, invasive insect species present a stark contrast 37 , 38 . These invasive species may continue to expand their habitats in the future, thriving in new regions due to their adaptability to changing climates and the availability of novel ecological niches, thereby posing significant threats to native ecosystems and biodiversity. Materials and Methods Data Source Fall webworm data collection : To document initial encounters with the fall webworm across invaded regions, an exhaustive investigation was undertaken, encompassing scholarly literature, media reports, species databases, and other relevant resources. This facilitated the chronicling of initial detection dates in different nations and the reconstruction of historical distribution profiles (for detailed data, refer to Zenodo: https://doi.org/10.5281/zenodo.14011611 ). Data on areas impacted by fall webworm defoliation in China from 2000 to 2020, as well as affected areas in seven provinces, fifty cities, and ninety-five counties in 2020, were obtained from the National Forestry and Grassland Administration of China. To collect information on the fall webworm, we conducted a thorough literature search using the Web of Science and Scopus databases. Additionally, data were gathered from publicly available resources such as GBIF, EPPO, CABI, and the National Forestry and Grassland Administration of China, as well as from news reports and other online platforms (for detailed data, refer to Zenodo: https://doi.org/10.5281/zenodo.14011611 ). After removing duplicate records, we compiled 26,023 data points for each fall webworm occurrence, including information on latitude and longitude, continent, country, province, year of discovery, and data source. We also classified each occurrence as either native or invasive, noting the red/black head type, the number of generations per year, and the source of the data. Host information acquisition : We assembled a comprehensive list of all fall webworm host plants from the literature and then revised the genus and species names for the 606 host plant species to correct outdated nomenclature. This involved removing variants, hybrids, and subspecies, as well as updating naming conventions, resulting in a final list of 606 host species. The list includes species names, taxonomic information, and Forest category classification information (Zenodo: https://doi.org/10.5281/zenodo.14011611 ). Host distribution data was sourced from GBIF ( https://www.gbif.org/ ) and includes precise geographical information (i.e., latitude and longitude). Subsequently, due to the lack of distribution data for China in GBIF, we supplemented with several important databases, including: including the 2023 Contributions of Plant Specimen Data in China ( https://doi.org/10.15468/9us6fb ), plant specimens from the Chengdu Institute of Botany, Chinese Academy of Sciences (CDBI) ( https://doi.org/10.15468/pui83d ), plant specimens from the Wuhan Botanical Garden, Chinese Academy of Sciences (HIB) ( https://doi.org/10.15468/iuxeth ), plant specimens from the Guangxi Institute of Botany, Chinese Academy of Sciences (IBK) ( https://doi.org/10.15468/dk5gko ), plant specimens from the Lushan Botanical Garden, Jiangxi, and the Chinese Academy of Sciences (LBG) ( https://doi.org/10.15468/kvfygp ), plant specimens from the Institute of Botany, Jiangsu Province and the Chinese Academy of Sciences (NAS) ( https://doi.org/10.15468/r2la8h ), some plant specimens from KUN, IBSC, and NAS herbaria from 1900 to 1950 ( https://doi.org/10.15468/irnwew ), some plant specimens from PE herbarium from 1900 to 1950 ( https://doi.org/10.15468/liiipc ), and 500,000 plant specimens from the PE herbarium from 1950 to 1999 ( https://doi.org/10.15468/44r5e4 ). We then merged all datasets, removing outliers and duplicates to minimize sampling bias. The cleaned dataset includes 16,138,802 geographic information records, stored in Zenodo: https://doi.org/10.5281/zenodo.14011611 . These data were used to create a global map of host plant distribution using Tableau and ArcGIS software 39 . Global forest data : The natural regeneration forest and plantation area from 1990–2020 was sourced from the Global Forest Resources Assessment ( https://fra-data.fao.org/assessments/fra/2020/WO/home/overview ). Data on the area of different forest types and major tree species in various provinces of China were obtained from the 2018 China Forest Resources Report. Our global reforestation opportunity spatial dataset from Griscom et al. (2017) uses modified 1 km resolution maps from the Forest Landscape Restoration Opportunity Atlas, excluding northern ecological zones, grass-dominated ecosystems, and areas with unsuitable tree cover percentages to estimate potential afforestation scope 8. Data on global key ecological engineering projects were obtained from the book World's Important Ecological Engineerings (ISBN 9787030206770) and the Bonn Challenge ( https://www.bonnchallenge.org/ ) 40 . For key projects we retained information on the project name, project duration, project area, project cost, project distribution, and the main tree species involved (Table S7). World forest distribution maps are sourced from High-Resolution Global Maps of 21st-Century Forest Cover Change ( https://glad.umd.edu/dataset/global-2010-tree-cover-30-m ). Global climate data : The climate zone data is sourced from "Present and future Köppen-Geiger climate classification maps at 1-km resolution" ( https://figshare.com/ndownloader/files/12407516 ). Extract the climate zone of each distribution point using spatial interpolation. We obtained bioclimatic variables from WorldClim ( http://www.worldclim.org/ ). The WorldClim dataset provides bioclimatic data (Version 2.0, http://www.worldclim.org/ ) for 1970–2000 and future climate data (i.e., the 2050s: 2041–2060; 2070s: 2061–2080) with a 2.5′ spatial resolution. To overcome the problem of over-fitting caused by the multicollinearity of 19 climatic factors in the model results, we screened variables using the Pearson's correlation coefficient. If the value was > .8, it was omitted and deemed to be a highly correlated climate factor (Fig. S5, S6) 41 . Finally, for the climatic differentiation between native and invasive distribution points, we selected nine climate variables for PCA analysis. For the species distribution model, we chose six climate variables to predict its potential range. The importance of environmental factors was assessed using the Jackknife method (Fig. S9). Data Analysis Logistic regression analysis of the annual total number of countries invaded by fall webworm We employed logistic regression to analyze the annual total number of countries invaded by the fall webworm. The dependent variable was the total number of countries invaded each year, while the independent variable was the year of observation. Logistic regression analysis was performed using R software. This approach allowed us to assess how the invasion dynamics changed over time. Reconstruction of the phylogenetic tree of major host plant families and genera of the fall webworm and enrichment analysis of included species : We constructed a phylogenetic tree of the major host plants for the fall webworm using the commontree feature of the NCBI taxonomy database ( https://www.ncbi.nlm.nih.gov/Taxonomy/CommonTree/wwwcmt.cgi ). Subsequently, we conducted a family and genus enrichment analysis by counting the number of species under each family and genus to assess their abundance among the host plants (Table S3,4). To enhance the visual appeal of the phylogenetic tree, we employed evolview3 ( https://www.evolgenius.info/evolview/ ) to make the generated tree more intuitive and easier to read 42 . Analysis of the correlation between fall webworm and host plants We used R to spatially grid the species distribution data and calculated the frequency of species occurrence at a 1° × 1° grid scale. The distribution of the fall webworm and its potential host plants was converted into a species distribution matrix, with rows representing sample plots, columns representing species, and values indicating species occurrence frequency. For the distribution of host plants, we integrated the data at the family and genus levels, resulting in a distribution matrix for 109 families and a distribution matrix for 190 genera within families exhibiting an Association Coefficient (AC) > 0.67, which was subsequently used for correlation analyses. Interspecific correlation was measured using Spearman's rank correlation and association coefficient (AC) tests. Spearman's rank correlation assesses the monotonic relationship between species, while the AC test quantifies the degree of association between species. The Spearman's correlation coefficient \(\:{r}_{S}(i,k)\) is calculated to measure the rank correlation between variable i and variable k , providing insight into their monotonic relationship: $$\:{r}_{S}(i,k)=1-\frac{6\sum\:{d}_{j}^{2}}{{N}^{3}-N}$$ where \(\:{r}_{S}(i,k)\) is the Spearman's correlation coefficient between species i and k in sample plot j , \(\:N\) is the total number of sample plots, and \(\:{d}_{j}\) is the rank difference between species i and k in sample plot j . The calculation of the AC follows the method described in references as outlined below 43 , 44 : Assuming species A and B occur in sample plots, let a , b , c and d represent the following: a: number of sample plots where both species A and B occur b: number of sample plots where only species A occurs c: number of sample plots where only species B occurs d: number of sample plots where neither species A nor B occurs n: total number of sample plots, n = a + b + c + d The correlation between the fall webworm and its hosts is calculated as follows: If ad ≥ bc : \(\:AC=\frac{ad-bc}{\left(a+b\right)\left(b+d\right)}\) If bc > ad and d ≥ a : \(\:AC=\frac{ad-bc}{\left(a+b\right)\left(a+c\right)}\) If bc > ad and d < a : \(\:AC=\frac{ad-bc}{\left(b+d\right)\left(d+c\right)}\) The AC value ranges from − 1 to 1. An AC closer to -1 indicates a strong negative association, while an AC closer to 1 indicates a strong positive association. An AC of 0 means the species are completely independent. Specifically: AC = 0.67 indicates a strong positive association 0.67 > AC > 0 indicates a weak positive association AC = 0 indicates no association −0.67 < AC < 0 indicates a weak negative association AC ≤ − 0.67 indicates a strong negative association Correlation analysis of afforestation area and fall webworm occurrence : Simple linear regression was employed to analyze the correlation between afforestation proportion, natural forest proportion and the occurrence area of the fall webworm from both temporal and spatial perspectives. The R-squared ( R² ) value was used to assess model fit: values of 0.7 ≤ R² < 0.9 indicate strong explanatory power and a good fit, while R² ≥ 0.9 signifies very strong explanatory power and an excellent fit. Pearson correlation analysis of forest types, tree species, and fall webworm occurrence : Pearson correlation analysis was performed to evaluate the relationship between the areas of different forest types and the tree species used in afforestation, as well as the occurrence area of the fall webworm. An r value greater than 0 indicates a positive correlation, categorized as follows: 0.7 ≤ r < 1 signifies a strong correlation, 0.5 ≤ r < 0.7 indicates a moderate correlation, and 0 < r < 0.5 reflects a weak correlation. Ecological niche differentiation of fall webworm during its invasion process In this study, we extracted relevant climate variable data from the WorldClim database for distribution points of the fall webworm in both its native and invaded regions. Using PCA analysis, we compared the differences in climate niches between the two regions and explored the species' climatic adaptability across different areas. The analysis of latitudinal adaptability differences in the fall webworm An independent sample t test was used for all other assays. GraphPad Prism 5 (GraphPad Software Inc., San Diego, CA, USA) and IBM SPSS 18.0 software (SPSS, Inc., Chicago, IL, USA) were used for statistical analyses. A value of p < 0.05 was considered statistically different. The Comprehensive Threat Index of the Fall Webworm to Major Global Afforestation Projects To assess the threat posed by the fall webworm to global afforestation projects, we employed a method that calculates the variation in impact based on host diversity, abundance, and the strength of interaction with the fall webworm. For calculating the diversity of host tree species within the project, we used the following formula: $$\:HTLP=\frac{{N}_{host}}{{N}_{total}}$$ 1 Where HTLP represents the Host Tree Species Proportion, \(\:{N}_{total}\:\) denotes the total number of tree species commonly used in each ecological project, and \(\:{N}_{host}\) refers to the number of species within the project that are host tree species of the fall webworm. For calculating the interaction strength between host plants and the fall webworm, we used the following formula: $$\:WSCI=\sum\:_{i=1}^{{N}_{host}}\left(\frac{{S}_{i}}{{\sum\:}_{i=1}^{{N}_{host}}{S}_{i}}\right)\times\:{S}_{i}$$ 2 WSCI represents the Weighted Spatial Correlation Index, where \(\:{S}_{i}\) is the spatial correlation index (AC value) of the 𝑖-th host tree species, and \(\:{\sum\:}_{{N}_{i=1}}^{{N}_{host}}{S}_{i}\) is the sum of the spatial correlation indices of all host tree species, used to normalize the spatial correlation of each species. The purpose of the Weighted Spatial Correlation Index is to measure the spatial proximity and intensity of potential threats posed by host tree species within each ecological project, taking into account the contribution of each host tree species to the overall threat. For the relative abundance of hosts, the following formula is used: $$\:AAF=\frac{{A}_{program}}{{A}_{max}}$$ 3 AAF represents the Area Adjustment Factor. Here, \(\:{A}_{project}\) is the area of each project; larger areas may indicate more host tree species and a wider habitat, potentially leading to greater threats. Area can also influence the spatial distribution density of host tree species, and thus needs to be accounted for in the calculation of the threat index. \(\:{A}_{max}\) is the maximum area among all projects, used to standardize the effect of area. The Comprehensive Threat Index for each major project is calculated using the following formula: $$\:CTI=HTLP\times\:WSCI\times\:AAF$$ 4 Based on this model, different ecological projects can be compared to assess the level of threat posed by the fall webworm to each project. Species Distribution Modeling We used ensemble methods to predict the potential distribution of the species at a 2.5' × 2.5' grid resolution. Specifically, we applied Maximum Entropy (MaxEnt), which is a distinct modeling algorithm. This was accomplished by inputting the occurrence data with climate and human activity factors to generate pseudoabsence records using the “gRandom” method 45 . We then used a 75% random sample of initial data (presence–absence) as training data and evaluated this against the remaining 25% of the samples. We repeated the split sampling 10 times to account for the uncertainty associated with data partitioning 46 . One key metric is the Area Under the Receiver Operating Characteristic Curve (AUC), which measures the rank correlation of predicted values. The AUC of the test data is 0.786 (Fig. S8). The final map of each species' potential suitable habitat under the current climate had a range of values from 0 to 1, with values less than 0.2 representing unsuitable habitats. After using the current climatic data to model the spatial extent of suitable habitat for the fall webworm, modeling projections were performed for future climate scenarios SSP5-8.5-2030s/2050s/2070s to predict the extent of suitable habitat. We assessed the spatial overlap between areas highly suitable for the fall webworm, based on major global ecological projects and the global dataset of reforestation opportunities. Using the approach of Early et al. 47 , we converted these areas into a 1 km resolution grid and evaluated the proportion of overlap. Declarations Code availability Data analysis and plotting were processed with the “tidyverse”, “ggtext”, “camcorder”, “scales”, “ggsci”, “ggdist”, “gghalves”, “dplyr”, “reader”, “devtools”, “spaa”, “jsonlite”, “dismo”, “ggplot2”, “sf”, “raster”, “ggcorrplot”, “CoordinateCleaner”, “rgbif”, “maptools”, “readxl”, “geodata”, “ggpubr”, “maps”, “patchwork”, “plotly” and “reshape2” packages in R v.4.1.176. Acknowledgments This study was supported by the National Natural Science Foundation of China (3240030502), the Postdoctoral Fellowship Program of CPSF (GZC20230652). the China Scholarship Council Innovative Talent Program (No.2022–2260). References Xu, H., Yue, C., Zhang, Y., Liu, D. & Piao, S. Forestation at the right time with the right species can generate persistent carbon benefits in China. Proc. Natl. Acad. Sci. U.S.A 120, e2304988120 (2023). Feng, Y. et al. Multispecies forest plantations outyield monocultures across a broad range of conditions. Science 376, 865–868 (2022). 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Global forest restoration and the importance of prioritizing local communities. Nat. Ecol. Evol 4, 1472–1476 (2020). Knoke, T. et al. Afforestation or intense pasturing improve the ecological and economic value of abandoned tropical farmlands. Nat. Commun 5, 1–12 (2014). Dave, R., Maginnis, S. & Crouzeilles, R. Forests: many benefits of the Bonn Challenge. Nature 570, 164–165 (2019). Roebroek, C. T., Duveiller, G., Seneviratne, S. I., Davin, E. L. & Cescatti, A. Releasing global forests from human management: How much more carbon could be stored? Science 380, 749–753 (2023). Hasegawa, T., Fujimori, S., Ito, A. & Takahashi, K. Careful selection of forest types in afforestation can increase carbon sequestration by 25% without compromising sustainability. Communications Earth & Environment 5, 171 (2024). Seddon, N., Turner, B., Berry, P., Chausson, A. & Girardin, C. A. Grounding nature-based climate solutions in sound biodiversity science. Nat. Clim. Change 9, 84–87 (2019). 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Müller, J. et al. Weather explains the decline and rise of insect biomass over 34 years. Nature 628, 349–354 (2024). van Klink, R. et al. Disproportionate declines of formerly abundant species underlie insect loss. Nature 628, 359–364 (2024). Wessely, J. et al. Habitat-based conservation strategies cannot compensate for climate-change-induced range loss. Nat. Clim. Change 7, 823–827 (2017). Murray, D. G. Tableau your data!: fast and easy visual analysis with tableau software . (John Wiley & Sons, 2013). Li, S., Li, Y., Jiang, N. & Xu, W. Development of key ecological conservation and restoration projects in the past century. Ecological Frontiers (2024). Carvalho, J. S. et al. A global risk assessment of primates under climate and land use/cover scenarios. Glob Chang Biol 25, 3163–3178 (2019). Subramanian, B., Gao, S., Lercher, M. J., Hu, S. & Chen, W.-H. Evolview v3: a webserver for visualization, annotation, and management of phylogenetic trees. Nucleic Acids Res. 47, W270-W275 (2019). Gu, L., Gong, Z.-w. & Li, W.-z. Niches and interspecific associations of dominant populations in three changed stages of natural secondary forests on Loess Plateau, PR China. Sci. Rep. 7, 6604 (2017). Ma, Y. et al. Niche and interspecific associations of Pseudoanabaena limnetica–Exploring the influencing factors of its succession stage. Ecol. Indic. 138, 108806 (2022). Voldoire, A. et al. The CNRM-CM5. 1 global climate model: description and basic evaluation. Climate dynamics 40, 2091–2121 (2013). Mi, C. et al. Global protected areas as refuges for amphibians and reptiles under climate change. Nat. Commun 14, 1389 (2023). Early, R. et al. Global threats from invasive alien species in the twenty-first century and national response capacities. Nat. Commun 7, 12485 (2016). Additional Declarations There is NO Competing Interest. Supplementary Files Supplementarytables.docx Supplementary tables Supplementaryfigures.docx Supplementary figures Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5939468","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":414313542,"identity":"40863a5d-d819-48e2-91b7-dd9f5f3a6181","order_by":0,"name":"Lilin Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYDACCSB+UGDDQ6KWBIM00rUcJkGH/Ozmhw8SDM7L8PMfYPzwg8Euj6AWxjnHjA0SDG7zSDYcYJbsYUguJqiFWSLBTAKkxeBgA4M0A8OBxAZCWtgk0r8BtZzjAXqH+TdRWngkckC2HOAxOMbARpwtEhI5xUC/JPNI9jC2WfYYJBPWIj8jfeODDxV29vz8hw/f+FFhR1gLEmAEKjYgQf0oGAWjYBSMAtwAAF74MxceP0ijAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3706-1267","institution":"Institute of Zoology, Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Lilin","middleName":"","lastName":"Zhao","suffix":""},{"id":414313543,"identity":"39c2a5f9-23b7-4ccc-827f-629cd8bb3565","order_by":1,"name":"Jing Ning","email":"","orcid":"","institution":"Institute of Zoology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Ning","suffix":""},{"id":414313544,"identity":"e5cc6308-4b5a-4e5e-8650-c8419c1efb15","order_by":2,"name":"Jingjing Du","email":"","orcid":"","institution":"Institute of Zoology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Du","suffix":""},{"id":414313545,"identity":"9081a94e-9410-4f39-8945-36104290a657","order_by":3,"name":"Deliang Lu","email":"","orcid":"","institution":"Institute of Applied Ecology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Deliang","middleName":"","lastName":"Lu","suffix":""},{"id":414313546,"identity":"ff18473d-9b6e-4639-aba4-dd49238d1fa8","order_by":4,"name":"Jiquan Chen","email":"","orcid":"https://orcid.org/0000-0003-0761-9458","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"Jiquan","middleName":"","lastName":"Chen","suffix":""},{"id":414313547,"identity":"e28f1358-f811-4f9d-b7cd-398e6271f807","order_by":5,"name":"Hui Wang","email":"","orcid":"","institution":"Chinese Academy of Forestry","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Wang","suffix":""},{"id":414313548,"identity":"c9e537e2-2b03-4487-909b-b8c7bbdf0948","order_by":6,"name":"Dejun Hao","email":"","orcid":"","institution":"Co-Innovation Center for the Sustainable Forestry in Southern China, Nanjing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Dejun","middleName":"","lastName":"Hao","suffix":""},{"id":414313549,"identity":"dd128e13-391f-4e11-b362-28e84895614e","order_by":7,"name":"Jianting Fan","email":"","orcid":"","institution":"School of Forestry \u0026 Biotechnology, Zhejiang Agricultural and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Jianting","middleName":"","lastName":"Fan","suffix":""},{"id":414313550,"identity":"cae8b911-8b29-4752-8155-3f9383d4e93f","order_by":8,"name":"Lei Guo","email":"","orcid":"","institution":"Beijing Municipal Bureau of Landscape and Greening Approval Service Center","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Guo","suffix":""},{"id":414313551,"identity":"6340661d-3eaa-4f60-82c2-bde354918ad0","order_by":9,"name":"Wei Song","email":"","orcid":"","institution":"College of Plant Protection, Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2025-02-01 02:55:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5939468/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5939468/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76184925,"identity":"bb82a3d0-5554-4d9e-80ff-fe43e238ed05","added_by":"auto","created_at":"2025-02-13 08:13:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22539539,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatiotemporal patterns of the association between the fall webworm and afforestation.\u003c/strong\u003e (A) The afforestation area of various countries and the first record time of the fall webworm. The colorful dots represent the first reports of the fall webworm in various countries from 1770 to 2019. The green shaded areas represent global distribution of planted forests. Deep green represents larger areas, while white indicates negligible planted forests. (B) Top ten countries with planted forests ranked by the total plantation area. (C) Distribution of the fall webworm (orange dots) across forest plantations (green) in three regions, with widespread outbreaks in North America, Europe and East Asia. The green color represents the afforestation area, with darker green indicating larger afforestation areas. (D) Logistic regression curve fitting for the spread of the fall webworm to new countries. The residual standard error of the fit is 2.23, and the pseudo \u003cem\u003eR²\u003c/em\u003e is 0.959, demonstrating a high explanatory power of the model. (E) Development phases and duration of 15 world-wide key ecological projects.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5939468/v1/7840991eb664ef54f05cef4e.png"},{"id":76184920,"identity":"2d43a1e2-8073-4891-bf59-c19957b1ee49","added_by":"auto","created_at":"2025-02-13 08:13:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78756715,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe correlation between the area of fall webworm damage and the area of planted forests and the choice of afforestation species. \u003c/strong\u003e(A) Phylogenetic relationships of the 606 host plants by family (total = 109), genus (total = 319, top 40 displayed) and seed production (i.e., Ferns, Angiosperms and Gymnosperms). (B) The number of host species by forest type (NWPF – non-wood product forests; SPF – special-purpose forests; WF – windbreak forests; TPF – timber forests; and FF – fuelwood forests). (C) Map overlay of relative affected areas by the fall webworm in the three major affected regions of North America, Europe, and East Asia (2.1 km buffer zone) with local afforestation areas. The orange dots represent the relative size of affected area, with larger dots indicating larger relative areas. Deep green represents regions with a higher afforestation rates (i.e., the ratio of planted forest area to administrative area), while white indicates regions with a very low afforestation rates. (D) Inter-species correlation between the fall webworm and its host plants at the family and genus levels. The size of each dot represents the association coefficient index, and the color represents the Spearman’s rank correlation coefficient, with red and yellow indicating the lowest and the highest values, respectively. The Sankey diagram illustrates the phylogenetic relationships between families and genera. (E) Spatiotemporal correlations of fall webworm infection and afforestation in China. The left panel shows the correlation between affected area (1000 ha) and afforestation area (1000 ha) from 2000 to 2020. The right panel displays the correlations between the percentage of affected area in forests of each city of China in 2020 and the net change rate of forests from 2000 to 2020. (F) Correlation between the area infested by fall webworm and different forest types at the city and county levels in China. (G) Correlation analysis of forested areas (top) and common afforestation tree species areas (bottom) in affected regions (FW – the fall webworm; NWPF – non-wood product forests; SPF – special-purpose forests; WF – windbreak forests; TPF – timber forests; and FF – fuelwood forests). The color of the grid cells represents the Pearson correlation coefficient, with purple and orange indicating negative positive correlations, respectively.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5939468/v1/abd51d77e5e6915c6511c262.png"},{"id":76184939,"identity":"c7b09c49-58c6-493c-b72f-11aedbfd336a","added_by":"auto","created_at":"2025-02-13 08:13:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":78583168,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdaptive changes of the fall webworm during its invasion process. \u003c/strong\u003e(A) World distribution heatmap of 606 fall webworm host plant species, with a bar chart showing the number of host species in five hotspot regions. (B) Reconstruction of the global distribution and latitudinal range of the red-headed and black-headed types of the fall webworm in both native and invasive ranges. **** stands for significant difference under \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 [Student's \u003cem\u003et\u003c/em\u003e test]. (C) Analysis of the reconstruction of the global distribution and latitudinal range differences of the red-headed and black-headed types of the fall webworm in both native and invasive ranges. **** stands for significant difference under \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.0001 [Student's \u003cem\u003et\u003c/em\u003e test]. (D) Reconstruction of the distribution of the fall webworm across different climatic zones and analysis of climate adaptive differentiation between native and invasive populations.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5939468/v1/247078b6490d78f1f09589fa.png"},{"id":76184922,"identity":"e693224d-3704-45ca-b560-6cf2977bace4","added_by":"auto","created_at":"2025-02-13 08:13:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":12169242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Comprehensive threat index of the fall webworm to global key ecological projects.\u003c/strong\u003e (A) Summary of the primary tree species utilized in major global ecological projects, with a total of 30 species identified, 19 of which serve as host plants for the fall webworm. (B) Analysis diagram to estimate the comprehensive threat index of the fall webworm to global key ecological projects at the global scale. It is determined by considering host tree species proportion, weighted spatial correlation index, and area adjustment factor. (C) The global map shows the threat level of the fall webworm to key ecological projects in various administrative regions worldwide.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5939468/v1/4b1963bcb270301a719a87c6.png"},{"id":76184916,"identity":"7f739f1b-651a-48be-855a-67aba5c3b871","added_by":"auto","created_at":"2025-02-13 08:13:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":24498179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRisk assessment of fall webworm in future afforestation zones.\u003c/strong\u003e (A) Impact of future climate change on the potential distribution of the fall webworm. The map illustrates the projected distribution and proportion of the fall webworm in global afforestation zones (green) across different timeframes: current (red), 2030 (dark orange), 2050 (light orange), and 2070 (yellow). (B) Threatened areas by the fall webworm under future climate change, categorized by countries with major afforestation projects and those without. The threatened areas in countries with major afforestation projects are shown in solid colors, while those in countries without such projects are displayed in transparent colors. Timeframes are represented as follows: current (red), 2030 (dark orange), 2050 (light orange), and 2070 (yellow).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5939468/v1/b9a31d11f34ca34c027d88f1.png"},{"id":76184910,"identity":"e2249b85-3499-480c-b402-27eed109804e","added_by":"auto","created_at":"2025-02-13 08:13:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1171798,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5939468/v1/e18ea131-77f4-4329-b506-59e0fa20b1fc.pdf"},{"id":76185286,"identity":"019762b8-9d81-443a-8744-a68f66d3474e","added_by":"auto","created_at":"2025-02-13 08:21:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":210048,"visible":true,"origin":"","legend":"Supplementary tables","description":"","filename":"Supplementarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5939468/v1/abffc80384e210ce010d44a9.docx"},{"id":76184921,"identity":"99d31225-0d61-48e9-8dd3-c06f408dd3fa","added_by":"auto","created_at":"2025-02-13 08:13:02","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3054417,"visible":true,"origin":"","legend":"Supplementary figures","description":"","filename":"Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5939468/v1/bf688d4ba3295fd455a61817.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Vulnerability of Global Afforestation Projects to a Polyphagous Invasive Fall Webworm","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe restoration of degraded landscapes has predominantly been achieved through global afforestation initiatives. In addition to their socioeconomic advantages, these plantations offer vital ecosystem services\u003csup\u003e1-3\u003c/sup\u003e. By the end of the century, it is projected that forest plantations will constitute 20% of the total global forest area\u003csup\u003e4\u003c/sup\u003e. China is at the forefront of this endeavor, having completed afforestation on over 800,310 km\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e5,6\u003c/sup\u003e. The ecological and economic benefits of this achievement are of considerable interest\u0026nbsp;\u003csup\u003e7\u003c/sup\u003e. However, the potential connection between afforestation activities and biological disasters has not been fully assessed\u003csup\u003e8\u003c/sup\u003e. It is essential to elucidate the relationship between the dispersal of invasive pests and plantation activities to refine traditional afforestation policies.\u003c/p\u003e\n\u003cp\u003eInvasive pests have been identified as a significant and pervasive threat to forest ecosystems\u003csup\u003e9\u003c/sup\u003e. Particularly, temperate forests in the Northern Hemisphere have experienced substantial damage due to invasive pests \u003csup\u003e10,11\u003c/sup\u003e. Over 450 species of tree-feeding pests have invaded forests in the United States \u003csup\u003e12\u003c/sup\u003e. In extreme cases, invasive pests can lead to the extinction of entire tree species within specific geographic regions \u003csup\u003e13,14\u003c/sup\u003e. Specific factors, such as global warming, alterations in precipitation patterns, increased frequency of extreme climate events, and extended growing seasons, are recognized as key drivers of invasive species outbreaks\u003csup\u003e15,16\u003c/sup\u003e. However, the impact of human-driven global afforestation efforts has largely been neglected.\u003c/p\u003e\n\u003cp\u003eThe fall webworm (\u003cem\u003eHyphantria cunea\u003c/em\u003e) is a highly destructive invasive pest, renowned for its exceptional reproductive capacity and its tendency to form dense larval populations\u003csup\u003e17,18\u003c/sup\u003e. The larvae are notorious for constructing extensive web-like nests, which destroy urban greenery and disrupt the daily lives of residents worldwide. The larvae consume tree leaves, causing extensive defoliation and even devastating forests\u003csup\u003e16,19\u003c/sup\u003e. This study examines the risks associated with fall webworm outbreaks in global afforestation projects, particularly in the context of rapid plantation establishment. We analyze the global distribution of the fall webworm, its spatial correlation with host species, and the potential threats it poses to global ecological projects. The study highlights the importance of adjusting plant species to enhance the success and sustainability of forest plantations.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eThe surge in afforestation projects has coincided with the invasion of the fall webworm\u003c/h2\u003e \u003cp\u003eThe fall webworm is a widespread invasive pest with a broad global distribution. It originated in North America and was first recorded in the United States in 1770, in Mexico by 1871, and in Canada by 1922 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). We conducted a logistic regression analysis to examine the spread of the fall webworm in invaded countries using data from 1940 to 2019. The fitted logistic curve is characterized by a maximum value of L\u0026thinsp;=\u0026thinsp;40 (***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a growth rate of \u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05551 (***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and an inflection point in the year \u003cem\u003ex\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1975 (***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that 1975 marked the peak in the number of newly invaded countries. This model suggests that the global spread of the fall webworm experienced a period of rapid expansion before stabilizing. Subsequently, with economic development and increased trade, the pest has invaded 35 countries in Europe and Asia. The number of invaded countries increased slowly from 1940 to 1960, accelerated during 1960\u0026ndash;1990 with 1975 as the peak year, and then slowed down. Based on this model, it is projected that the number of invaded countries will reach saturation by 2050 (\u003cem\u003ex\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2050, \u003cem\u003ey\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;39.39) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), assuming no significant changes in global forest plantations. Notably, the rapid spread of the fall webworm coincided with the period of globalization and the rapid expansion of afforestation activities (1960\u0026ndash;1990) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Spatially, eight of the ten countries with the largest afforestation areas fall within the webworm's distribution range, and 64% of the world\u0026rsquo;s forest plantations are concentrated in these countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). A more detailed analysis, involving overlays of the global artificial forest map with afforestation data from first-level administrative units and specific fall webworm distribution points, reveals a clear pattern: the areas affected by the fall webworm strongly coincide with regions of intensive afforestation activities, such as the northwestern United States, the southeastern coast of the United States, much of Japan, Korea, North Korea, the North China Plain, and most of Europe (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These findings suggest a potential correlation between the spread of the fall webworm and regional forestry practices, particularly in areas with concentrated artificial forestation.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePositive correlation between fall webworm infestation and the expansion of afforestation areas and host tree plantations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA phylogenetic analysis was conducted on the 606 identified host plants at both the familial and generic levels. The impact of the fall webworm is not limited to the well-documented angiosperms (flowering plants) but also extends to a broader range of plant taxa, including gymnosperms (non-flowering plants) and ferns, which are less frequently recognized in this context. The analysis encompassed 109 families, with a significant predominance of species within the Rosaceae (65 species), Fabaceae (36 species), and Asteraceae (29 species) families. This highlights a pronounced familial diversity, as these families are characterized by their broad geographic distributions and high species diversity, providing a rich habitat mosaic for the fall webworm (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). At the genus level, the fall webworm\u0026rsquo;s highest host species counts were found in \u003cem\u003ePrunus\u003c/em\u003e (20 species), \u003cem\u003eQuercus\u003c/em\u003e (19 species), \u003cem\u003eAcer\u003c/em\u003e (16 species), \u003cem\u003eSalix\u003c/em\u003e (15 species), and \u003cem\u003ePopulus\u003c/em\u003e (12 species), indicating a preference for broad-leaved deciduous trees (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). These genera are ecologically significant, widely distributed across temperate and subtropical zones, and are vital components of forest ecosystems. They contribute to the forest canopy, exhibit rapid growth, and are commonly found in natural forests, urban green spaces, landscape design, and ecological restoration initiatives\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Subsequently, these 606 host plants were categorized into various forest types, revealing that 421 hosts are arboreal species within forest ecosystems. This classification included non-wood product forests (305 species), special-purpose forests (288 species), windbreak forests (176 species), timber forests (155 species), and fuelwood forests (66 species) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). These forests serve diverse afforestation objectives: windbreak forests combat soil erosion and desertification; timber forests supply wood for industries such as construction and paper production; non-wood product forests yield fruits, nuts, and medicinal materials; fuelwood forests provide firewood and charcoal; and special-purpose forests cultivate medicinal and ornamental plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e2\u003c/span\u003eB)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo quantify the impact of the fall webworm on afforestation, we analyzed the spatial coherence between affected zones in North America, Central and Eastern Europe, and East Asia, and the intensity of afforestation efforts. For each observation point, a 2.1 km buffer zone was established to calculate the total affected area within each province. This was then compared with afforestation ratios, which were calculated as the area of afforestation divided by the total administrative area. We found that regions with larger potential impact zones, such as the central and eastern United States, Eastern Europe, and the North China Plain, exhibited higher afforestation rates. This spatial correlation suggests that increased tree cover is associated with higher prevalence rates of the fall webworm (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). A linear regression model was applied to the fall webworm damage area (y) from 2000 to 2020 and the corresponding afforestation area (\u003cem\u003ex\u003c/em\u003e) (\u003cem\u003eR\u0026sup2;\u003c/em\u003e= 0.878), revealing a positive correlation between afforestation expansion and increased fall webworm damage (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, left). As a relatively independent spatial unit, the urban ecological environment and forestry development within it exhibit a certain degree of unity and continuity. Another linear regression analysis was conducted on the fall webworm infestation area as a percentage of total forest area (\u003cem\u003ey\u003c/em\u003e) in 50 Chinese cities for 2020 versus net forest change (\u003cem\u003ex\u003c/em\u003e), resulting in a reliable model (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.75). This indicates that an increase in net forest change corresponds to a higher percentage of fall webworm infestation (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, right). To further verify the spatial interdependency, data were collected from 40 cities and 95 counties in China in 2020. The results indicate for every 1% increase in the proportion of planted forests to total forests, the proportion of forest area affected by the fall webworm increases by an average of 0.49%, and for every 1% increase in the ratio of planted to natural forests, the proportion of forest area affected increases by an average of 0.27%. These results suggest that afforestation practices may contribute to the exacerbation of fall webworm damage (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). In fact, there is a simultaneous global trend of decreasing natural forest area and increasing plantation forest area (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, S3).\u003c/p\u003e \u003cp\u003eThe impact of invasion and the species of host trees in affected areas were further investigated. The association coefficient (AC) was calculated, and significance tests were conducted using Spearman's rank correlation coefficient for host plants at both the family and genus levels. Host plants with an AC value greater than 0.67 were selected and visually represented using a Sankey diagram to illustrate their phylogenetic relationships. At the family level, Asteraceae, Fabaceae, and Rosaceae demonstrated the strongest interspecies associations with the fall webworm. Other families exhibiting significant associations included Salicaceae, Betulaceae, Ulmaceae, Fagaceae, Cupressaceae, Juglandaceae, and Moraceae (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). A high interspecies association was observed with Cupressaceae (AC\u0026thinsp;=\u0026thinsp;0.71), with recent studies identifying Taxodium and Metasequoia as key hosts in newly invaded areas of the fall webworm\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. At the genus level, \u003cem\u003eAcer\u003c/em\u003e, \u003cem\u003ePrunus\u003c/em\u003e, and \u003cem\u003ePopulus\u003c/em\u003e exhibited the strongest associations, all of which are commonly utilized in afforestation initiatives. Other genera with robust associations included \u003cem\u003eSalix\u003c/em\u003e, \u003cem\u003eUlmus\u003c/em\u003e, \u003cem\u003eQuercus\u003c/em\u003e, \u003cem\u003eRobinia\u003c/em\u003e, \u003cem\u003eEquisetum\u003c/em\u003e, \u003cem\u003eBetula\u003c/em\u003e, \u003cem\u003eFraxinus\u003c/em\u003e, and \u003cem\u003eJuglans\u003c/em\u003e. Herbaceous plants such as \u003cem\u003eTrifolium\u003c/em\u003e, \u003cem\u003ePlantago\u003c/em\u003e, and \u003cem\u003eRumex\u003c/em\u003e, known for their diverse distribution and habitats, also showed strong correlations with the fall webworm. These species may serve as significant intermediate hosts, complicating management strategies. Asteraceae was the family with the most pronounced association with the fall webworm, due to the cumulative influence of multiple genera rather than a single genus. In contrast, Sapindaceae, with only one highly correlated genus, \u003cem\u003eAcer\u003c/em\u003e, exhibited the strongest genus-level association, indicating a significant adaptability of Acer to the pest (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). These results suggest that the fall webworm is widely associated with various afforestation tree species, influencing its invasion patterns.\u003c/p\u003e \u003cp\u003eA Pearson correlation analysis was conducted to validate the findings, determining the correlation between fall webworm damage areas, five forest types, and nine significant host species across seven Chinese provinces. The results indicated positive correlations with non-wood product forests (0.95), windbreak forests (0.84), fuelwood forests (0.65), timber forests (0.48), and special-purpose forests (0.11). These included \u003cem\u003eUlmus\u003c/em\u003e (0.76), \u003cem\u003eBetula\u003c/em\u003e (0.73), \u003cem\u003eCastanea\u003c/em\u003e (0.75), \u003cem\u003eRobinia\u003c/em\u003e (0.71), \u003cem\u003eJuglans\u003c/em\u003e (0.65), \u003cem\u003eFraxinus\u003c/em\u003e \u0026amp; \u003cem\u003ePhilodendron\u003c/em\u003e \u0026amp; \u003cem\u003eJuglans\u003c/em\u003e (0.56), \u003cem\u003eQuercus\u003c/em\u003e (0.65), \u003cem\u003ePopulus\u003c/em\u003e (0.58), and \u003cem\u003eSalix\u003c/em\u003e (0.34) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). These findings suggest that larger areas of specific forest types and tree species used in afforestation are associated with increased fall webworm damage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHost and climatic adaptations during the invasion process of the fall webworm\u003c/h2\u003e \u003cp\u003eFurther statistical analysis of host species diversity in both native and invaded regions revealed that Canada and the United States, which are native regions for the fall webworm, host 39 and 121 host species, respectively. In contrast, invaded regions such as Europe, South Korea, Japan, and China exhibit higher counts of host species, with 165, 171, 286, and 400 species, respectively. This disparity suggests a potential interdependency between the diversity of host species in invaded regions and the likelihood of fall webworm outbreaks (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Notably, increased relative abundance was also observed along the eastern coast of Australia, the eastern coast of Africa, the Gulf of Guinea, as well as in Central and South America, indicating the potential for these areas to serve as suitable habitats for the fall webworm (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The fall webworm has two larval types: the red-headed type with a narrower host range and the black-headed type with a broader host range. Notably, only the black-headed type has spread to Asia and Europe. Statistical analysis reveals that the black-headed type is adapted to higher latitudes compared to the red-headed type, with average latitudes of 39.05\u0026deg;N and 37.80\u0026deg;N, respectively (****, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Moreover, the invasive black-headed type have further extended their latitudinal range, with their northernmost distribution shifting from 52.13\u0026deg;N to 65.9\u0026deg;N and their average latitude increasing from 38.75\u0026deg;N to 40.74\u0026deg;N (****, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eIn addition to the availability of host species, the fall webworm has shown significant changes in its climatic adaptability during its invasion. Latitudinal distribution patterns reveal that univoltine populations (one generation per year) are restricted to the native range, while bivoltine and trivoltine populations (two and three generations per year) are found in both native and invaded areas, indicating greater adaptability in the latter. Specifically, in invasive region, bivoltine populations have expanded their range from an average latitude of 38.86\u0026deg;N to 43.28\u0026deg;N (****, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), while trivoltine populations have shifted from 35.78\u0026deg;N to 36.04\u0026deg;N (****, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The tetravoltine population (four generations per year), which evolved from the trivoltine population, has a more limited distribution, with records only found in China (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). A principal component analysis (PCA) of 19 climate variables from the distribution data revealed that invasive populations have a significantly larger PCA3 distribution range compared to native populations. This suggests that invasive populations are better able to adapt to diverse environmental conditions, especially in variables like precipitation seasonality and precipitation of the warmest quarter. Besides, the invaded populations have expanded into drier climates in winter, such as Dwb and Dwd (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe potential threat of fall webworm to key global afforestation projects\u003c/h3\u003e\n\u003cp\u003eCurrently, numerous large-scale ecological projects are being implemented globally with the aim of increasing forest cover and promoting ecological restoration. These initiatives are vital for the recovery of global ecosystems; however, they also present potential risks from pests such as the fall webworm. Therefore, it is essential to assess the interrelationships between these ecological projects, the distribution of the fall webworm, and the preferred host species of the fall webworm to inform future forest management and pest control strategies. The 30 main tree species used in twelve key projects have been compiled. The results showed that, except for the National Green Program in the Philippines, the other eleven projects frequently used 19 host plants of the fall webworm as afforestation tree species. The species \u003cem\u003eAcer\u003c/em\u003e, \u003cem\u003ePrunus\u003c/em\u003e, \u003cem\u003ePopulus\u003c/em\u003e, \u003cem\u003eUlmus\u003c/em\u003e, \u003cem\u003eQuercus\u003c/em\u003e, \u003cem\u003eRobinia\u003c/em\u003e, \u003cem\u003eBetula\u003c/em\u003e, and \u003cem\u003eFraxinus\u003c/em\u003e are high-risk species (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). A detailed breakdown of the proportion of preferred host species in each project is as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePrairie States Forestry Project\u003c/b\u003e: 3 host species/3 total species\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCanada\u0026rsquo;s Green Plan\u003c/b\u003e: 2 host species/4 total species\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStalin\u0026rsquo;s Rebuilding \u0026ndash; Nature Plan\u003c/b\u003e: 7 host species/8 total species\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFrance\u0026rsquo;s Forestry Ecological Project\u003c/b\u003e: 3 host species/5 total species\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe Green Dam Project\u003c/b\u003e: 2 host species/4 total species\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eJapan\u0026rsquo;s Mountain Renovating Plan\u003c/b\u003e: 2 host species/2 total species\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIndia\u0026rsquo;s Social Forestry Plan\u003c/b\u003e: 2 host species/3 total species\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMountain Renovating and Country Green Plan\u003c/b\u003e: 6 host species/7 total species\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImportant Forestry Ecological Engineering\u003c/b\u003e: 6 host species/8 total species\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBonn Challenge\u003c/b\u003e: 3 host species/ 5 total species\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe National Greening Program in Philippines\u003c/b\u003e: 0 host species/ 3 total species\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe Great Green Wall\u003c/b\u003e: 2 host species/ 3 total species\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis study introduced a novel variable, the Comprehensive Threat Index (CTI), which is based on the proportions of host species in key projects globally, the weighted host spatial correlation index, and the area adjustment factor. Utilizing data from afforestation projects across various administrative regions, the CTI for each region was calculated and evaluated across multiple dimensions, including the proportion of host tree species, the weighted spatial correlation index, and the area adjustment factor (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The findings suggest that afforestation projects in Asia and North America generally face higher threat indices, with East Asia exhibiting significantly higher CTI compared to other regions. Specifically, regions with a higher proportion of host tree species, such as the United States, China, South Korea, and Russia, demonstrate higher threat indices due to the strong spatial correlation between the fall webworm and the primary host tree species. These areas also display generally high weighted spatial correlation indices, indicating that the spatial distribution of the fall webworm is closely linked to the distribution of host tree species, potentially accelerating the pest's spread. Additionally, regions such as France and Canada, despite having lower proportions of host tree species, still exhibit moderate threat levels due to their large-scale afforestation projects and the high correlation between host tree species and the fall webworm. Meanwhile, areas where the fall webworm has not yet invaded, such as South America and Africa, are also at a certain level of risk. Proactive adjustments in the selection of tree species for afforestation are required to reduce the risks of invasion and infestation (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). By analyzing the comprehensive threat indices across different regions, this study provides a global threat distribution map for afforestation projects facing the fall webworm, offering scientific support for relevant departments in developing regional control measures and optimizing planting strategies.\u003c/p\u003e\n\u003ch3\u003eRisk assessment of fall webworm in future afforestation zones\u003c/h3\u003e\n\u003cp\u003eThe jackknife test revealed that the environmental variable with the highest predictive gain was annual average temperature (Bio1, 10\u0026ndash;18℃), followed by average temperature of the wettest quarter (Bio8, 22\u0026deg;C\u0026ndash;28\u0026deg;C) and precipitation of the wettest month (Bio13, 140\u0026ndash;270 mm; Figure S9-S10). Considering the effect of historical climatic factors and the distribution of hosts, the suitable habitats for fall webworm were estimated as 45.72% of the plantation forests and will expand to 52.69% by 2030, 60.15% by 2050, and 65.80% by 2070 under the future climate change (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). It is expected that in Asia, the distribution of the fall webworm will further spread southward, while in Europe and North America, there is a trend of spreading northward. In addition, the availability of suitable climate and hosts in Africa, South America, and Australia will also become potential invasion areas for the fall webworm. Among these, the areas under threat within the management of major ecological projects are estimated to be 2.60 \u0026times; 10⁶, 2.79 \u0026times; 10⁶, 3.57 \u0026times; 10⁶, and 4.47 \u0026times; 10⁶ hectares for the current period, 2030, 2050, and 2070, respectively. These areas represent 73.60%, 73.23%, 72.90%, and 74.25% of the total threatened areas, indicating a relatively stable proportion over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur research indicates that global afforestation initiatives have inadvertently facilitated the spread of the invasive fall webworm, exacerbating the damage it causes and threatening eleven of the twelve major global ecological projects. With the initiation of the United Nations Decade on Ecosystem Restoration, efforts to restore forests have been intensified worldwide \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. However, many tree species commonly utilized in afforestation, such as \u003cem\u003eAcer\u003c/em\u003e, \u003cem\u003ePrunus\u003c/em\u003e, \u003cem\u003ePopulus\u003c/em\u003e, \u003cem\u003eUlmus\u003c/em\u003e, \u003cem\u003eQuercus\u003c/em\u003e, \u003cem\u003eRobinia\u003c/em\u003e, \u003cem\u003eBeutla\u003c/em\u003e, \u003cem\u003eFraxinus\u003c/em\u003e, are preferred hosts for the fall webworm. The distribution of these species closely aligns with areas heavily impacted by the pest, and their large-scale planting may have further expanded the fall webworm's range. Additionally, the increasing proportion of plantation forests and the decreasing proportion of natural forests are expected to amplify the damage caused by the pest. These findings highlight a significant challenge in the current global restoration paradigm. While afforestation efforts are essential for mitigating climate change and restoring biodiversity\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, the potential damage caused by invasive pests must be carefully considered.\u003c/p\u003e \u003cp\u003eCountries with higher levels of development are more likely to engage in afforestation. Consequently, nations that rank among the top ten in global afforestation efforts, such as China, the United States, Russia, Canada, Sweden, Japan, Finland, and Germany, must remain vigilant against the threat of invasive pests and diseases to newly planted forests. Furthermore, an increasing number of developing countries, particularly those in Africa and South America, have joined afforestation initiatives\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. For these countries, maintaining the outcomes of afforestation presents greater challenges\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This underscores the importance of carefully selecting tree species, prioritizing pest-resistant varieties, and emphasizing the ecological regulation of management practices \u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. For instance, reducing pesticide use while promoting insect and soil microbial diversity could significantly enhance the sustainability and resilience of afforestation projects\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. By analyzing the comprehensive threat indices across different regions, this study provides a global threat distribution map for afforestation projects facing the fall webworm, offering scientific support for relevant departments in developing regional control measures and optimizing planting strategies.\u003c/p\u003e \u003cp\u003eThe fall webworm's adaptation during its invasion process is expected to accelerate its spread from localized hotspots to a global scale. With the increasing hydroclimate volatility (55% higher than the current) caused by global warming, the fall webworm may further invade coastal regions of the Southern Hemisphere and high-latitude areas of the Northern Hemisphere\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Coupled with its adaptation to new hosts, 65.80% of future afforestation areas are likely to be threatened by this species\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In recent years, the \"insect crisis\" has garnered significant attention from scientists, the public, and policymakers, particularly in the context of global climate change\u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. While most insect species face challenges such as declining abundance, habitat loss, and reduced ecosystem services, invasive insect species present a stark contrast\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. These invasive species may continue to expand their habitats in the future, thriving in new regions due to their adaptability to changing climates and the availability of novel ecological niches, thereby posing significant threats to native ecosystems and biodiversity.\u003c/p\u003e "},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eData Source\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eFall webworm data collection\u003c/strong\u003e: To document initial encounters with the fall webworm across invaded regions, an exhaustive investigation was undertaken, encompassing scholarly literature, media reports, species databases, and other relevant resources. This facilitated the chronicling of initial detection dates in different nations and the reconstruction of historical distribution profiles (for detailed data, refer to Zenodo: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.14011611\u003c/span\u003e\u003c/span\u003e). Data on areas impacted by fall webworm defoliation in China from 2000 to 2020, as well as affected areas in seven provinces, fifty cities, and ninety-five counties in 2020, were obtained from the National Forestry and Grassland Administration of China.\u003c/p\u003e\n \u003cp\u003eTo collect information on the fall webworm, we conducted a thorough literature search using the Web of Science and Scopus databases. Additionally, data were gathered from publicly available resources such as GBIF, EPPO, CABI, and the National Forestry and Grassland Administration of China, as well as from news reports and other online platforms (for detailed data, refer to Zenodo: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.14011611\u003c/span\u003e\u003c/span\u003e). After removing duplicate records, we compiled 26,023 data points for each fall webworm occurrence, including information on latitude and longitude, continent, country, province, year of discovery, and data source. We also classified each occurrence as either native or invasive, noting the red/black head type, the number of generations per year, and the source of the data.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHost information acquisition\u003c/strong\u003e: We assembled a comprehensive list of all fall webworm host plants from the literature and then revised the genus and species names for the 606 host plant species to correct outdated nomenclature. This involved removing variants, hybrids, and subspecies, as well as updating naming conventions, resulting in a final list of 606 host species. The list includes species names, taxonomic information, and Forest category classification information (Zenodo: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.14011611\u003c/span\u003e\u003c/span\u003e). Host distribution data was sourced from GBIF (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gbif.org/\u003c/span\u003e\u003c/span\u003e) and includes precise geographical information (i.e., latitude and longitude). Subsequently, due to the lack of distribution data for China in GBIF, we supplemented with several important databases, including: including the 2023 Contributions of Plant Specimen Data in China (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15468/9us6fb\u003c/span\u003e\u003c/span\u003e), plant specimens from the Chengdu Institute of Botany, Chinese Academy of Sciences (CDBI) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15468/pui83d\u003c/span\u003e\u003c/span\u003e), plant specimens from the Wuhan Botanical Garden, Chinese Academy of Sciences (HIB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15468/iuxeth\u003c/span\u003e\u003c/span\u003e), plant specimens from the Guangxi Institute of Botany, Chinese Academy of Sciences (IBK) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15468/dk5gko\u003c/span\u003e\u003c/span\u003e), plant specimens from the Lushan Botanical Garden, Jiangxi, and the Chinese Academy of Sciences (LBG) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15468/kvfygp\u003c/span\u003e\u003c/span\u003e), plant specimens from the Institute of Botany, Jiangsu Province and the Chinese Academy of Sciences (NAS) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15468/r2la8h\u003c/span\u003e\u003c/span\u003e), some plant specimens from KUN, IBSC, and NAS herbaria from 1900 to 1950 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15468/irnwew\u003c/span\u003e\u003c/span\u003e), some plant specimens from PE herbarium from 1900 to 1950 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15468/liiipc\u003c/span\u003e\u003c/span\u003e), and 500,000 plant specimens from the PE herbarium from 1950 to 1999 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15468/44r5e4\u003c/span\u003e\u003c/span\u003e). We then merged all datasets, removing outliers and duplicates to minimize sampling bias. The cleaned dataset includes 16,138,802 geographic information records, stored in Zenodo: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.14011611\u003c/span\u003e\u003c/span\u003e. These data were used to create a global map of host plant distribution using Tableau and ArcGIS software\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal forest data\u003c/strong\u003e: The natural regeneration forest and plantation area from 1990\u0026ndash;2020 was sourced from the Global Forest Resources Assessment (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fra-data.fao.org/assessments/fra/2020/WO/home/overview\u003c/span\u003e\u003c/span\u003e). Data on the area of different forest types and major tree species in various provinces of China were obtained from the 2018 China Forest Resources Report. Our global reforestation opportunity spatial dataset from Griscom et al. (2017) uses modified 1 km resolution maps from the Forest Landscape Restoration Opportunity Atlas, excluding northern ecological zones, grass-dominated ecosystems, and areas with unsuitable tree cover percentages to estimate potential afforestation scope 8. Data on global key ecological engineering projects were obtained from the book World\u0026apos;s Important Ecological Engineerings (ISBN 9787030206770) and the Bonn Challenge (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bonnchallenge.org/\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e40\u003c/sup\u003e. For key projects we retained information on the project name, project duration, project area, project cost, project distribution, and the main tree species involved (Table S7). World forest distribution maps are sourced from High-Resolution Global Maps of 21st-Century Forest Cover Change (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://glad.umd.edu/dataset/global-2010-tree-cover-30-m\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal climate data\u003c/strong\u003e: The climate zone data is sourced from \u0026quot;Present and future K\u0026ouml;ppen-Geiger climate classification maps at 1-km resolution\u0026quot; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/ndownloader/files/12407516\u003c/span\u003e\u003c/span\u003e). Extract the climate zone of each distribution point using spatial interpolation. We obtained bioclimatic variables from WorldClim (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.worldclim.org/\u003c/span\u003e\u003c/span\u003e). The WorldClim dataset provides bioclimatic data (Version 2.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.worldclim.org/\u003c/span\u003e\u003c/span\u003e) for 1970\u0026ndash;2000 and future climate data (i.e., the 2050s: 2041\u0026ndash;2060; 2070s: 2061\u0026ndash;2080) with a 2.5\u0026prime; spatial resolution. To overcome the problem of over-fitting caused by the multicollinearity of 19 climatic factors in the model results, we screened variables using the Pearson\u0026apos;s correlation coefficient. If the value was \u0026gt;\u0026thinsp;.8, it was omitted and deemed to be a highly correlated climate factor (Fig. S5, S6)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Finally, for the climatic differentiation between native and invasive distribution points, we selected nine climate variables for PCA analysis. For the species distribution model, we chose six climate variables to predict its potential range. The importance of environmental factors was assessed using the Jackknife method (Fig. S9).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eData Analysis\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eLogistic regression analysis of the annual total number of countries invaded by fall webworm\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe employed logistic regression to analyze the annual total number of countries invaded by the fall webworm. The dependent variable was the total number of countries invaded each year, while the independent variable was the year of observation. Logistic regression analysis was performed using R software. This approach allowed us to assess how the invasion dynamics changed over time.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eReconstruction of the phylogenetic tree of major host plant families and genera of the fall webworm and enrichment analysis of included species\u003c/strong\u003e: We constructed a phylogenetic tree of the major host plants for the fall webworm using the commontree feature of the NCBI taxonomy database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/Taxonomy/CommonTree/wwwcmt.cgi\u003c/span\u003e\u003c/span\u003e). Subsequently, we conducted a family and genus enrichment analysis by counting the number of species under each family and genus to assess their abundance among the host plants (Table S3,4). To enhance the visual appeal of the phylogenetic tree, we employed evolview3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.evolgenius.info/evolview/\u003c/span\u003e\u003c/span\u003e) to make the generated tree more intuitive and easier to read\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAnalysis of the correlation between fall webworm and host plants\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe used R to spatially grid the species distribution data and calculated the frequency of species occurrence at a 1\u0026deg; \u0026times; 1\u0026deg; grid scale. The distribution of the fall webworm and its potential host plants was converted into a species distribution matrix, with rows representing sample plots, columns representing species, and values indicating species occurrence frequency. For the distribution of host plants, we integrated the data at the family and genus levels, resulting in a distribution matrix for 109 families and a distribution matrix for 190 genera within families exhibiting an Association Coefficient (AC)\u0026thinsp;\u0026gt;\u0026thinsp;0.67, which was subsequently used for correlation analyses.\u003c/p\u003e\n \u003cp\u003eInterspecific correlation was measured using Spearman\u0026apos;s rank correlation and association coefficient (AC) tests. Spearman\u0026apos;s rank correlation assesses the monotonic relationship between species, while the AC test quantifies the degree of association between species.\u003c/p\u003e\n \u003cp\u003eThe Spearman\u0026apos;s correlation coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{S}(i,k)\\)\u003c/span\u003e\u003c/span\u003e is calculated to measure the rank correlation between variable \u003cem\u003ei\u003c/em\u003e and variable \u003cem\u003ek\u003c/em\u003e, providing insight into their monotonic relationship:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$\\:{r}_{S}(i,k)=1-\\frac{6\\sum\\:{d}_{j}^{2}}{{N}^{3}-N}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{S}(i,k)\\)\u003c/span\u003e\u003c/span\u003e is the Spearman\u0026apos;s correlation coefficient between species \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ek\u003c/em\u003e in sample plot \u003cem\u003ej\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:N\\)\u003c/span\u003e\u003c/span\u003e is the total number of sample plots, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{j}\\)\u003c/span\u003e\u003c/span\u003e is the rank difference between species \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ek\u003c/em\u003e in sample plot \u003cem\u003ej\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eThe calculation of the AC follows the method described in references as outlined below \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e\u003cp\u003eAssuming species A and B occur in sample plots, let \u003cem\u003ea\u003c/em\u003e, \u003cem\u003eb\u003c/em\u003e, \u003cem\u003ec\u003c/em\u003e and \u003cem\u003ed\u003c/em\u003e represent the following:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ea:\u0026nbsp;\u003c/strong\u003enumber of sample plots where both species A and B occur\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eb:\u0026nbsp;\u003c/strong\u003enumber of sample plots where only species A occurs\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ec:\u0026nbsp;\u003c/strong\u003enumber of sample plots where only species B occurs\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ed:\u0026nbsp;\u003c/strong\u003enumber of sample plots where neither species A nor B occurs\u003c/p\u003e\u003cp\u003e\u003cstrong\u003en:\u0026nbsp;\u003c/strong\u003etotal number of sample plots, \u003cem\u003en\u0026thinsp;=\u0026thinsp;a\u0026thinsp;+\u0026thinsp;b\u0026thinsp;+\u0026thinsp;c\u0026thinsp;+\u0026thinsp;d\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe correlation between the fall webworm and its hosts is calculated as follows:\u003c/p\u003e\u003cp\u003eIf \u003cem\u003ead\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;\u003cem\u003ebc\u003c/em\u003e: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:AC=\\frac{ad-bc}{\\left(a+b\\right)\\left(b+d\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eIf \u003cem\u003ebc\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003ead\u003c/em\u003e and \u003cem\u003ed\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;\u003cem\u003ea\u003c/em\u003e: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:AC=\\frac{ad-bc}{\\left(a+b\\right)\\left(a+c\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eIf \u003cem\u003ebc\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003ead\u003c/em\u003e and \u003cem\u003ed\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003ea\u003c/em\u003e:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:AC=\\frac{ad-bc}{\\left(b+d\\right)\\left(d+c\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThe AC value ranges from \u0026minus;\u0026thinsp;1 to 1. An AC closer to -1 indicates a strong negative association, while an AC closer to 1 indicates a strong positive association. An AC of 0 means the species are completely independent. Specifically:\u003c/p\u003e\u003cp\u003eAC\u0026thinsp;=\u0026thinsp;0.67 indicates a strong positive association\u003c/p\u003e\u003cp\u003e0.67\u0026thinsp;\u0026gt;\u0026thinsp;AC\u0026thinsp;\u0026gt;\u0026thinsp;0 indicates a weak positive association\u003c/p\u003e\u003cp\u003eAC\u0026thinsp;=\u0026thinsp;0 indicates no association\u003c/p\u003e\u003cp\u003e\u0026minus;0.67\u0026thinsp;\u0026lt;\u0026thinsp;AC\u0026thinsp;\u0026lt;\u0026thinsp;0 indicates a weak negative association\u003c/p\u003e\u003cp\u003eAC\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;0.67 indicates a strong negative association\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCorrelation analysis of afforestation area and fall webworm occurrence\u003c/strong\u003e: Simple linear regression was employed to analyze the correlation between afforestation proportion, natural forest proportion and the occurrence area of the fall webworm from both temporal and spatial perspectives. The R-squared (\u003cem\u003eR\u0026sup2;\u003c/em\u003e) value was used to assess model fit: values of 0.7\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003eR\u0026sup2;\u003c/em\u003e \u0026lt; 0.9 indicate strong explanatory power and a good fit, while \u003cem\u003eR\u0026sup2;\u003c/em\u003e \u0026ge; 0.9 signifies very strong explanatory power and an excellent fit.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePearson correlation analysis of forest types, tree species, and fall webworm occurrence\u003c/strong\u003e: Pearson correlation analysis was performed to evaluate the relationship between the areas of different forest types and the tree species used in afforestation, as well as the occurrence area of the fall webworm. An r value greater than 0 indicates a positive correlation, categorized as follows: 0.7\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1 signifies a strong correlation, 0.5\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.7 indicates a moderate correlation, and 0\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.5 reflects a weak correlation.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEcological niche differentiation of fall webworm during its invasion process\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eIn this study, we extracted relevant climate variable data from the WorldClim database for distribution points of the fall webworm in both its native and invaded regions. Using PCA analysis, we compared the differences in climate niches between the two regions and explored the species\u0026apos; climatic adaptability across different areas.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eThe analysis of latitudinal adaptability differences in the fall webworm\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAn independent sample t test was used for all other assays. GraphPad Prism 5 (GraphPad Software Inc., San Diego, CA, USA) and IBM SPSS 18.0 software (SPSS, Inc., Chicago, IL, USA) were used for statistical analyses. A value of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically different.\u003c/p\u003e\u003c/div\u003e\u003ch3\u003eThe Comprehensive Threat Index of the Fall Webworm to Major Global Afforestation Projects\u003c/h3\u003e\u003cp\u003eTo assess the threat posed by the fall webworm to global afforestation projects, we employed a method that calculates the variation in impact based on host diversity, abundance, and the strength of interaction with the fall webworm.\u003c/p\u003e\u003cp\u003eFor calculating the diversity of host tree species within the project, we used the following formula:\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$\\:HTLP=\\frac{{N}_{host}}{{N}_{total}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere HTLP represents the Host Tree Species Proportion, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{total}\\:\\)\u003c/span\u003e\u003c/span\u003edenotes the total number of tree species commonly used in each ecological project, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{host}\\)\u003c/span\u003e\u003c/span\u003e refers to the number of species within the project that are host tree species of the fall webworm.\u003c/p\u003e\n \u003cp\u003eFor calculating the interaction strength between host plants and the fall webworm, we used the following formula:\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ2\" class=\"mathdisplay\"\u003e$$\\:WSCI=\\sum\\:_{i=1}^{{N}_{host}}\\left(\\frac{{S}_{i}}{{\\sum\\:}_{i=1}^{{N}_{host}}{S}_{i}}\\right)\\times\\:{S}_{i}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWSCI represents the Weighted Spatial Correlation Index, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the spatial correlation index (AC value) of the 𝑖-th host tree species, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{{N}_{i=1}}^{{N}_{host}}{S}_{i}\\)\u003c/span\u003e\u003c/span\u003eis the sum of the spatial correlation indices of all host tree species, used to normalize the spatial correlation of each species. The purpose of the Weighted Spatial Correlation Index is to measure the spatial proximity and intensity of potential threats posed by host tree species within each ecological project, taking into account the contribution of each host tree species to the overall threat.\u003c/p\u003e\n \u003cp\u003eFor the relative abundance of hosts, the following formula is used:\u003c/p\u003e\n \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ3\" class=\"mathdisplay\"\u003e$$\\:AAF=\\frac{{A}_{program}}{{A}_{max}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eAAF represents the Area Adjustment Factor. Here, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{project}\\)\u003c/span\u003e\u003c/span\u003eis the area of each project; larger areas may indicate more host tree species and a wider habitat, potentially leading to greater threats. Area can also influence the spatial distribution density of host tree species, and thus needs to be accounted for in the calculation of the threat index. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{max}\\)\u003c/span\u003e\u003c/span\u003e is the maximum area among all projects, used to standardize the effect of area.\u003c/p\u003e\n \u003cp\u003eThe Comprehensive Threat Index for each major project is calculated using the following formula:\u003c/p\u003e\n \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ4\" class=\"mathdisplay\"\u003e$$\\:CTI=HTLP\\times\\:WSCI\\times\\:AAF$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eBased on this model, different ecological projects can be compared to assess the level of threat posed by the fall webworm to each project.\u003c/p\u003e\n \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eSpecies Distribution Modeling\u003c/h2\u003e\n \u003cp\u003eWe used ensemble methods to predict the potential distribution of the species at a 2.5\u0026apos; \u0026times; 2.5\u0026apos; grid resolution. Specifically, we applied Maximum Entropy (MaxEnt), which is a distinct modeling algorithm. This was accomplished by inputting the occurrence data with climate and human activity factors to generate pseudoabsence records using the \u0026ldquo;gRandom\u0026rdquo; method\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. We then used a 75% random sample of initial data (presence\u0026ndash;absence) as training data and evaluated this against the remaining 25% of the samples. We repeated the split sampling 10 times to account for the uncertainty associated with data partitioning\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. One key metric is the Area Under the Receiver Operating Characteristic Curve (AUC), which measures the rank correlation of predicted values. The AUC of the test data is 0.786 (Fig. S8). The final map of each species\u0026apos; potential suitable habitat under the current climate had a range of values from 0 to 1, with values less than 0.2 representing unsuitable habitats. After using the current climatic data to model the spatial extent of suitable habitat for the fall webworm, modeling projections were performed for future climate scenarios SSP5-8.5-2030s/2050s/2070s to predict the extent of suitable habitat. We assessed the spatial overlap between areas highly suitable for the fall webworm, based on major global ecological projects and the global dataset of reforestation opportunities. Using the approach of Early et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, we converted these areas into a 1 km resolution grid and evaluated the proportion of overlap.\u003c/p\u003e\n \u003c/div\u003e"},{"header":"Declarations","content":" \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCode availability\u003c/h2\u003e \u003cp\u003eData analysis and plotting were processed with the \u0026ldquo;tidyverse\u0026rdquo;, \u0026ldquo;ggtext\u0026rdquo;, \u0026ldquo;camcorder\u0026rdquo;, \u0026ldquo;scales\u0026rdquo;, \u0026ldquo;ggsci\u0026rdquo;, \u0026ldquo;ggdist\u0026rdquo;, \u0026ldquo;gghalves\u0026rdquo;, \u0026ldquo;dplyr\u0026rdquo;, \u0026ldquo;reader\u0026rdquo;, \u0026ldquo;devtools\u0026rdquo;, \u0026ldquo;spaa\u0026rdquo;, \u0026ldquo;jsonlite\u0026rdquo;, \u0026ldquo;dismo\u0026rdquo;, \u0026ldquo;ggplot2\u0026rdquo;, \u0026ldquo;sf\u0026rdquo;, \u0026ldquo;raster\u0026rdquo;, \u0026ldquo;ggcorrplot\u0026rdquo;, \u0026ldquo;CoordinateCleaner\u0026rdquo;, \u0026ldquo;rgbif\u0026rdquo;, \u0026ldquo;maptools\u0026rdquo;, \u0026ldquo;readxl\u0026rdquo;, \u0026ldquo;geodata\u0026rdquo;, \u0026ldquo;ggpubr\u0026rdquo;, \u0026ldquo;maps\u0026rdquo;, \u0026ldquo;patchwork\u0026rdquo;, \u0026ldquo;plotly\u0026rdquo; and \u0026ldquo;reshape2\u0026rdquo; packages in R v.4.1.176.\u003c/p\u003e \u003c/div\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis study was supported by the National Natural Science Foundation of China (3240030502), the Postdoctoral Fellowship Program of CPSF (GZC20230652). the China Scholarship Council Innovative Talent Program (No.2022\u0026ndash;2260).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXu, H., Yue, C., Zhang, Y., Liu, D. \u0026amp; Piao, S. 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Commun 7, 12485 (2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":false,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5939468/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5939468/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobal plantations, crucial for restoring degraded landscapes, face rising invasive pest threats, in recent decades. This study highlights that the invasion and proliferation of the fall webworm have been facilitated by the global afforestation boom in the last 80 years and could pose a threat to all global ecological projects until 2050. Since 1940, this polyphagous pest has spread to 40 countries, following an \u003cem\u003eS-\u003c/em\u003ecurve pattern. The distribution of the fall webworm is positively correlated with the expansion rates of afforestation and host tree plantation areas. China is now the most affected country by the fall webworm, with the highest comprehensive threat index (CTI). The number of host species has risen from 121 in the U.S. to 400 in China, and the host range has expanded from hardwoods to include coniferous trees. Notably, two-thirds of the total 600 host plants are tree species utilized for afforestation purposes. The preferred host species, \u003cem\u003eAcer\u003c/em\u003e, \u003cem\u003eQuercus\u003c/em\u003e, and \u003cem\u003ePopulus\u003c/em\u003e, are predominant in eight major global ecological projects. Additionally, hydroclimate extremes are projected to increase threats to 65.8% of afforestation zones by 2070, highlighting the need for strategic tree species selection to achieve sustainable ecological goals of global ecological projects, and protect against pests.\u003c/p\u003e","manuscriptTitle":"Vulnerability of Global Afforestation Projects to a Polyphagous Invasive Fall Webworm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-13 08:12:55","doi":"10.21203/rs.3.rs-5939468/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"080bd7a3-b8de-469b-bb3e-b81fffd663c9","owner":[],"postedDate":"February 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44181121,"name":"Biological sciences/Ecology/Invasive species"},{"id":44181122,"name":"Earth and environmental sciences/Ecology/Forestry"},{"id":44181123,"name":"Biological sciences/Ecology/Ecosystem services"},{"id":44181124,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts"}],"tags":[],"updatedAt":"2025-07-07T17:25:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-13 08:12:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5939468","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5939468","identity":"rs-5939468","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}