Invasion potential of the recently established woodwasp Sirex obesus (Hymenoptera: Siricidae) across South America pine plantations

Invasion potential of the recently established woodwasp Sirex obesus (Hymenoptera: Siricidae) across South America pine plantations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Invasion potential of the recently established woodwasp Sirex obesus (Hymenoptera: Siricidae) across South America pine plantations Victoria Lantschner, José Villacide This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7775236/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Invasive forest pests are among the greatest threats to global forestry, causing substantial economic losses and disrupting ecosystem dynamics worldwide. The recent detection of the North American woodwasp Sirex obesus (Hymenoptera: Siricidae) in Brazilian pine plantations poses a serious risk to South America’s 4.6 million hectares of commercial pine forests. Here, we present the first comprehensive assessment of its invasion potential across the continent, combining species distribution modeling with a multi-factor invasion risk index. Using occurrence records from the species’ native range, we modeled climatic suitability across South America and addressed invasion risk by integrating bioclimatic suitability, host distribution, proximity to invaded areas, and wood trade volumes with Brazil. Our model predicts suitable climatic conditions in 48% of South American pine plantation areas, particularly in montane and high-altitude regions along the Andean corridor and central-eastern Brazil. The mean temperature of the driest quarter was the most influential predictor of suitability. The invasion risk index identified southern Brazil, northeastern Argentina, Argentine Patagonia, and central Chile as the regions most vulnerable to the establishment, due to the convergence of extensive pine plantations, favorable climate, and either proximity to infested areas or intense trade connections with Brazil. These findings provide a foundation for targeted surveillance and phytosanitary measures aimed at preventing further spread. Early monitoring in high-risk regions, combined with stricter inspections of wood products, will be critical to avoiding widespread establishment and severe economic impacts across South American forestry. Siricidae biological invasions pine plantations risk assessment Figures Figure 1 Figure 2 Figure 3 Introduction The establishment and spread of exotic pests represent one of the most critical challenges to global forestry (Roy et al. 2014 ; Brockerhoff and Liebhold 2017 ). Invasive species can alter forest ecosystems, reduce productivity, and generate significant economic losses (Bradshaw et al. 2016 ). Early detection and accurate prediction of the potential distribution of recently established pests are essential to anticipate their spread and design effective management strategies (Nahrung et al. 2023 ). This is particularly relevant in plantation forestry, where the introduction of a single pest species can compromise large areas of economically valuable timber (Wingfield et al. 2015 ). Woodwasps of the family Siricidae comprise approximately 128 species widely distributed across the forests of the Northern Hemisphere, with no native representatives in South America (Taeger et al. 2010 ; Schiff et al. 2012 ). Several species have been accidentally introduced worldwide through the movement of wood products and packaging materials (Schiff et al. 2012 ). The best-documented case is Sirex noctilio Fabricius, 1793, which has spread across the Southern Hemisphere, becoming one of the most damaging insect pests affecting commercial pine plantations in countries such as South Africa, Australia, New Zealand, and South America (Slippers et al. 2015 ; Corley et al. 2019 ). The spread of S. noctilio exemplifies how the combination of human-mediated transport and Siricid life history traits has facilitated successful invasions of plantation forests across continents. Recently, Sirex obesus Bradley, 1913, a woodwasp native to North America, was detected in pine plantations in southern Brazil (Wilcken et al. 2025 ). Field observations suggest that this species can cause considerable damage to commercial stands, raising concerns about its potential spread to other parts of South America. This continent hosts some of the world’s largest pine plantations, covering approximately 4.6 million hectares. These plantations, established mainly for timber, pulp, and fuel production, and typically developed as intensively managed, even-aged, regularly spaced monocultures (Payn et al. 2015 ). These conditions can facilitate the establishment and spread of invasive pests by providing abundant host resources and reduced natural enemy pressure (Corley et al. 2020 ; Villacide et al. 2023 ). Given the economic importance of pine production in the region, assessing the potential distribution of S. obesus is crucial to guide monitoring and prevention strategies. In this study, we modeled the species’ potential distribution across South American pine plantations and developed a qualitative invasion risk index across the continent based on host abundance, suitable bioclimatic conditions, proximity to the invaded area, and the volume of wood trade between each country and Brazil. By identifying high-risk areas, our study provides a useful foundation for early detection, risk assessment, and the development of regionally tailored management strategies to mitigate the impact of this emerging pest. Methodology Native distribution records We compiled a database of occurrence locations of S. obesus across its native range from published sources (Table S1 ), although its distribution there remains poorly documented. Previous studies suggest that the species likely occurs in Mexico’s Sierra Madre Occidental, a region of high conifer diversity located between known populations in the United States and Mexico (Schiff et al. 2012 ). Thus, to address this knowledge gap, we generated simulated localities in this area (Jiménez-Valverde et al. 2011 ), where eleven points were randomly assigned—matching the number of reported occurrences for the species—within the area connecting populations in both countries. These points were restricted to pine forest regions (Fryer 2018 ), with a minimum distance of 50 km maintained between them (Fig. 1 ; Table S1 ). Potential distribution We modeled the potential distribution of S. obesus in South America using MaxEnt software v.3.4.4 (Phillips et al. 2025 ). For this analysis, we used compiled occurrence data and simulated records from the species’ native range. To reduce spatial bias and improve the uniformity of native occurrence data, we applied a thinning procedure by buffering point locations with a 50 km distance radius and removing adjacent, closely clustered records. We considered climate conditions as potential predictors of the species distribution. We used 19 bioclimatic variables acquired from the WorldClim v2.1 (Fick and Hijmans 2017 ), derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables, obtained from weather stations restricted to records spanning 1970 to 2000 (Fick and Hijmans 2017 ). These include annual values (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest months, and precipitation of the wet and dry quarters) (Booth et al. 2014 ). We used WorldClim data grids of estimates at a resolution of 5 minutes. To avoid collinearity among the variables, a matrix of Pearson’s rank correlation coefficients for all possible pairs of variables was carried out for each occurrence location of the species. Variables that correlated (CC ≥ 0.65) with each other were excluded, leaving only those variables with more biological relevance for the species. The selected variables were maximum temperature of warmest month (bio5), mean temperature of driest quarter (bio9), precipitation of wettest month (bio13), and precipitation seasonality (coefficient of variation, bio15). We defined North and Central America as the training area for the model. To control model complexity and reduce overfitting, we set the regularization multiplier in MaxEnt to 2. We applied the cross-validation method, partitioning the presence data into multiple subsets to iteratively train and evaluate the model and assess its predictive performance. The analysis was run with 10 replicates, and all outputs were averaged to produce a single model for each species. Model accuracy was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), where values range from 0 to 1, with 0.5 indicating no better performance than random expectation and 1 indicating perfect predictive accuracy. The spatial resolution of the output model grid matched that of the input environmental variables (5 arcminutes). To estimate the potential geographical distribution of S. obesus outside of its native range, we designated a threshold probability to define suitable and non-suitable habitat, such that probability values derived from MaxEnt models above that threshold were designated as suitable, while values at or below that threshold were designated as unsuitable. We used “equal training sensitivity and specificity logistic threshold” to convert the continuous probability of occurrence maps from MaxEnt to a binary habitat/ non-habitat map. At the same time, we considered the distribution of suitable hosts in South America to refine predictions of potential establishment. In its native range, S. obesus has been reported on three host species: Pinus ponderosa, P. teocote , and P. leiophylla (Schiff et al. 2012 ). In contrast, invasive populations in Brazil have been recorded on the locally planted species P. taeda, P. caribaea, P. maximinoi, P. tecunumanii , and the hybrids P. caribaea x P. elliottii and P. caribaea x P. tecunumanii (Wilcken et al. 2025 ). Given that this woodwasp has demonstrated the ability to colonize a wide range of hosts within the genus Pinus , including several of the most extensively planted species in South America, we assumed that it could affect any of the species commonly planted in the region: P. taeda, P. elliottii, P. ponderosa, P. contorta , P. caribaea, P. oocarpa, P. patula and P. radiata, P. tecunumanii (Table 1 ). Consequently, we used a map of the distribution of all available pine species in South America -modified from Lantschner et al. ( 2017 )- to constrain the modeled potential distribution of the species. Table 1 Description of the attributes of the different pine-growing regions in South America considered relevant for predicting the qualitative risk of Sirex obesus establishment. We indicate for each region the main planted pine species, and the value and category estimated for each of the four variables used to estimate the invasion risk index: the surface area of pine plantations (in million hectares “M ha”), the percentage of the regions with suitable bioclimatic conditions, the proximity to the region where the pest is established, and the volume of wood products exports from Brazil to each country (in million dollars “M USD”). Finally, we detail the resulting invasion risk index (IRI. Sources: 1 SAGyP ( 2025 ), 2 Sandoval ( 2008 ), 3 da Silva et al. ( 2024 ), 4 CONAF ( 2024 ), 5 MADR ( 2024 ), 6 Ministerio del Ambiente ( 2025 ), 7 MAG ( 2023 ), 8 Schwartz ( 2004 ), 9 MGAyP ( 2024 ), 10 González et al. ( 2004 ). Country (Region) Main planted Pinus species Pine Surface Bioclim suitability Proximity Wood exports IRI M ha Cat % Cat Dist (km) Cat M USD Cat Value Cat Argentina (NE) P. ellioti, P. taeda 0.67 1 high 4.4 low 490 high 523.74 high 10 high Argentina (Patagonia) P. ponderosa, P. contorta 0.11 1 medium 100 high 2387 medium 523.74 high 10 high Argentina (NW) P. ellioti, P. taeda 0.005 1 low 97.3 high 1485 medium 523.74 high 9 medium Argentina (center) P. ellioti, P. taeda 0.018 1 low 100 high 1636 medium 523.74 high 9 medium Bolivia P. radiata, P. patula y P. elliottii 0.011 2 low 33.1 medium 1581 medium 101.59 medium 7 medium Brazil (Minas Gerais) P. taeda, P. elliottii, P caribea, P. oocarpa 0.05 3 medium 86 high 0 high --- high 11 high Brazil (São Paulo) P. taeda, P. elliottii, P. caribaea, P. oocarpa 0.231 3 medium 76.6 high 0 high --- high 11 high Brazil (Paraná) P. ellioti, P. taeda 0.631 3 high 1.1 low 17 high --- high 10 high Brazil (Rio Grande do Sul) P. ellioti, P. taeda 0.271 3 high 2 low 431 high --- high 10 high Brazil (Santa Catarina) P. ellioti, P. taeda 0.617 3 high 0 low 173 high --- high 10 high Brazil (Distrito Federal) P. ellioti, P. taeda 0.0004 3 low 100 high 381 high --- high 10 high Brazil (Mato Grosso do Sul) P. caribaea, P. oocarpa 0.003 3 low 1.3 low 204 high --- high 8 medium Brazil (Goias) P. taeda, P. elliottii, P. caribaea 0.006 3 low 23.7 low 89 high --- high 8 medium Brazil (Espírito Santo) P. ellioti, P. taeda 0.002 3 low 30.7 low 713 high --- high 8 medium Chile (central) P. radiata 1.234 4 high 97.7 high 2213 medium 219.56 high 11 high Chile (South) P. ponderosa, P. radaita 0.03 4 low 58.6 medium 2686 low 219.56 high 6 low Colombia (Coffee Axis & SW) P. patula, P. tecunumanii, P. maximinoi, P. occarpa 0.109 5 medium 3.6 low 3775 low 159.55 medium 6 low Colombia (Orinoquia) P. caribaea, P. patula 0.054 5 medium 2 low 3358 low 159.55 medium 6 low Ecuador P. ellioti, P. taeda 0.03 6 low 43.6 medium 3612 low 70.55 low 5 low Paraguay P. ellioti, P. taeda 0.01 7 low 0 low 531 high 144.95 medium 7 medium Perú P. radiata, P. patula 0.014 8 low 71.6 high 3310 low 186.05 medium 7 medium Uruguay P. ellioti, P. taeda 0.13 9 medium 0 low 966 high 87.37 low 7 medium Venezuela P. caribaea 0.01 10 low 0 low 3347 low 21.5 low 4 low Risk of establishment To qualitatively assess the risk of Sirex obesus invasion, we divided South America’s pine plantation areas into 24 geographic units, delineated according to political boundaries and plantation distribution, and designed to be as homogeneous in size as possible (Table 1 , Fig. 3 ). For each geographical unit, we estimated an invasion risk index by summing the categorical scores of four regionally relevant variables: (1) Host abundance: We compiled information on the area planted with pine in each unit from official inventories and/or other available reports (Table 1 ). We classified pine surface into three categories: high (> 0.5 million ha), medium (0.5 to 0.1 million ha), or low (< 0.1 million ha.) (2) Suitable bioclimatic conditions: Using the previously estimated habitat suitability, we quantified the proportion of each geographic unit classified as suitable and categorized suitability as high (> 66% of the unit area suitable), medium (33 to 66%), or low (< 33%). (3) Proximity to the area where the species is currently established: We estimated the shortest Euclidean distance between the area currently occupied by the species in Brazil and each geographical unit. We categorized proximity as high ( 2500 km). (4) Volume of wood trade: To estimate wood movement as a potential pathway for the spread of S. obesus from the invaded region to other South American countries, we compiled information on Brazil’s wood product exports to each country. Specifically, we obtained data on annual export volumes (in million USD) for the period 2018–2022 (World Bank 2024 ) and calculated an average value for each country. We classified wood exports as high (> USD 200 million), medium (USD 100 to 200 million), or low (< USD 100 million). Finally, we assigned scores to each category of every variable (1 = low, 2 = medium, 3 = high) and calculated the invasion risk index for each geographic unit. Results Suitable bioclimatic conditions Our results indicate that extensive areas of South America provide suitable climatic conditions for the potential establishment of Sirex obesus (Fig. 2 A). The distribution model performed very well with an average AUC of 0.984 (Table S2). In regions with pine plantations (Fig. 2 B), 48% of the regions with pine plantations were classified as suitable for establishment of the species. The region where the species is currently established in Brazil (states of São Paulo and Minas Gerais) was correctly predicted. In addition, a high probability of suitable habitat was identified in central, northwestern, and southern Argentina, in central Chile, and in the Andean regions of northern South America (Bolivia, Peru, Ecuador, and Colombia). Conversely, low habitat suitability was predicted in northeastern Argentina, Uruguay, and southern Brazil. The bioclimatic variable with the highest contribution to the model based on permutation importance was the mean temperature of the driest quarter (86.4%) followed by precipitation seasonality (10.6%). Invasion risk index The regions with the highest host abundance for S. obesus , measured as the area of pine plantations, were observed in central Chile, southern Brazil, and northeastern Argentina. Uruguay, southern Argentina, and Colombia showed intermediate values, while all other regions had low host abundance (Fig. 3 A, Table 1 ). In terms of bioclimatic suitability, the largest proportion of suitable area was identified in central Chile, Patagonia, central and northwestern Argentina, Peru, and the Brazilian regions where the species is currently established (São Paulo and Minas Gerais). Southern Chile, Bolivia, and Ecuador exhibited intermediate suitability, whereas the remaining regions had low suitability (Fig. 3 B, Table 1 ). Proximity to the current invaded area in Brazil was naturally high in the pine-growing regions of Brazil, northeastern Argentina, Paraguay, and Uruguay; intermediate in other parts of Argentina, central Chile, and Bolivia; and low in the remaining regions (Fig. 3 C, Table 1 ). Finally, wood product exports from Brazil were highest to Argentina and Chile. Although national statistics are unavailable, domestic trade within Brazil were assumed to be also high al. Export volumes to Paraguay, Bolivia, Peru, and Colombia ware intermediate, while trade with other countries was low (Fig. 3 D, Table 1 ). Overall, the invasion risk index was highest for southern Brazil, northeastern Argentina, Argentine Patagonia, and central Chile. Meanwhile, the index was intermediate for the remaining regions of Brazil and Argentina, as well as Uruguay, Bolivia, and Peru, and lowest for southern Chile, Ecuador, Colombia, and Venezuela (Fig. 3 E, Table 1 ). Discussion The recent detection of the woodwasp Sirex obesus in South America highlights an urgent need to implement measures aimed at mitigating the risk of its spread and potential impacts on the region’s most productive forestry areas. This study provides the first comprehensive assessment of the species’ invasion potential across the continent, identifying regions of high vulnerability that warrant immediate attention. We identified regions with the highest potential for establishment of S. obesus , based on factors related to environmental invasibility (climate and host availability) and propagule pressure (inferred from timber trade volumes and proximity to already-invaded areas). Our results indicate that this newly established woodwasp species has considerable potential to spread throughout South America’s pine plantation areas. Bioclimatic suitability Our distribution model showed strong predictive performance in identifying suitable habitat for S. obesus across South America. It accurately predicted the species’ current establishment area in the states of São Paulo and Minas Gerais, providing confidence in its ability to identify other potentially suitable regions. The broad climatic suitability predicted across 48% of South American pine plantation areas underscores the significant risk this invasive woodwasp poses to the region's forestry sector. The primary bioclimatic predictor in the model was the mean temperature of the driest quarter. In its native range, the driest quarter coincides with the warmest one, corresponding to the trees’ growth period. This finding suggests that S. obesus establishment may be particularly favored in regions with moderate temperatures during dry seasons, which characterizes much of the suitable habitat identified in our analysis. Interestingly, the potential distribution of S. obesus in South America, as identified in this study, exhibits spatial patterns that contrast with the distribution predicted for its congener, the woodwasp Sirex noctilio (Ireland et al. 2018 ), possibly reflecting differences in their climatic requirements. Sirex noctilio , one of the most important pine pests in the Southern Hemisphere, has caused severe impacts on plantation forestry in South America since its introduction (Corley et al. 2019 ; Lantschner et al. 2019 ; Villacide et al. 2023 ). This species is primarily associated with the temperate and humid regions of southeastern South America, with high suitability predicted in southern Brazil, northeastern Argentina, Uruguay, and the Andean Patagonia of Chile and Argentina (Ireland et al. 2018 ). In contrast, S. obesus shows its greatest climatic suitability along the Andean corridor, particularly in Peru, Bolivia, northern and central Chile, and western Argentina, with additional suitable areas in central-eastern Brazil. While S. noctilio appears to be favored by lowland temperate environments where pine plantations are concentrated, S. obesus is more constrained to montane and high-altitude climates. However, our results should be interpreted with caution. The limited number of S. obesus occurrence records from the native range necessitated the use of simulated localities, which may introduce uncertainty in model predictions. In addition, the species’ native range may be restricted by factors other than climate, including geographic barriers or biotic interactions (natural enemy pressure or interspecific competition) which can significantly influence invasion outcomes (Radomski 2025 ). The reduced natural enemy pressure commonly observed in South American pine plantations (Corley and Villacide 2025 ), compared to native North American forests (where the model was trained), may enhance establishment probability beyond what climate-based models predict. These considerations underscore the need for additional research to more accurately determine the environmental conditions that enable the wasp to establish and persist beyond its native range. Invasion risk index In addition to suitable climatic conditions, our risk assessment highlights the combined influence of three additional drivers of invasion risk—host abundance, proximity to established populations, and high wood trade volumes from Brazil. As a results, the identification of southern Brazil, northeastern Argentina, Argentine Patagonia, and central Chile as the highest-risk regions reflects the convergences of these multiple facilitating drivers. Host abundance is a key determinant of invasive herbivore species distributions, as large and continuous resource availability can sustain higher population densities and facilitate spread (Gougherty et al. 2024 ). In this context, regions with extensive pine plantations—such as central Chile, southern Brazil, and northeastern Argentina (Table 1 ) —represent particularly high-risk areas where suitable climate coincides with extensive areas of susceptible hosts. It should be noted, however, that our assumption that S. obesus can attack all planted pine species in South America, while supported by its observed host flexibility (Wilcken et al. 2025 ), requires empirical validation. Because host suitability can vary significantly among pine species and may influence establishment success and population growth rates (Corley et al. 2019 ), future research should prioritize host-range testing to refine risk predictions. On the other hand, timber importation and proximity to already-invaded areas are key determinants in the spread of invasive forest pests (Skarpaas and Økland 2009 ). These factors facilitate the introduction of new individuals into novel habitats, increasing the likelihood of establishment and population growth. Sirex obesus can disperse naturally by adult flight, but can also be transported unintentionally by human activities, particularly through the movement of infested wood that can transport the immature stages over long distances. Consequently, Brazilian states surrounding the invaded area, together with southern Paraguay, Uruguay, and northeastern Argentina, face a particularly high risk of receiving woodwasp propagules due to their proximity to established populations. In addition, the extensive forest plantation landscapes in this region create largely continuous habitats for many forest pests, facilitating their spread pest (Corley et al. 2020 ; Lantschner et al. 2024 ; Villacide and Fuentealba 2025 ). Countries importing large volumes of wood products from Brazil, such as Argentina and Chile (Table 1 ), as well as Brazilian states with intensive domestic wood trade, are likewise highly vulnerable to accidental pest introduction through transportation pathways. Insights from the invasion history of S. noctilio as a model for S. obesus The invasion history of Sirex noctilio in South America provides valuable lessons for anticipating the spread of S. obesus . This woodwasp was first detected in Uruguay in 1980, followed by northeastern Argentina (1985) and southern Brazil (1988). During the 1990s, it expanded throughout Argentina’s forested regions and reached Chile by 2001 (Corley et al. 2019 ). Reported spread rates varied between regions, ranging from 12 to 82 km per year, and increased with higher mean annual temperatures and greater isothermality (Lantschner et al. 2014 ). In southern Brazil, the rate was 46 km per year, comparable to that observed for S. obesus since its recent establishment, suggesting that, at this rate, the species could potentially spread throughout all major pine plantation areas in Brazil within less than two decades. Currently, S. noctilio has spread across most pine-growing areas of South America south of 21° latitude, largely facilitated by human activities such as the transport of infested wood (Corley et al. 2019 ). Its expansion within these regions is also strongly influenced by natural dispersal, with laboratory studies showing that females can fly an average of 17 km per day, reaching up to 50 km per day (Bruzzone et al. 2009 ). Moreover, the outbreak dynamics of S. noctilio provide useful insights into what might be expected for S. obesus , particularly given the substantial tree mortality already reported in Brazil (Wilcken et al. 2025 ). Sirex noctilio exhibits pulse-like population dynamics, with long periods of low density interrupted by sudden outbreaks (Berryman 1987 ). Outbreaks are often triggered by the availability of stressed trees during extreme climatic events such as droughts (Lantschner et al. 2019 ), and particularly frequent in overstocked, damaged, or unmanaged stands, and in plantations on slopes with drier conditions (Beèche et al. 2012 ; Iede et al. 2012 ; Lantschner and Corley 2015 ). Forest composition also influences outbreak likelihood, with certain pines, such as P. contorta in southern Argentina or P. taeda in Uruguay and Brazil, being more susceptible. This population dynamic is critical for understanding the species’ spread and the damage it causes, as higher abundances also increase the likelihood of accidental transport to new areas. In summary, this invasion history highlights how population dynamics, host condition, and climate interact to amplify spread and impacts, and underscores the importance of determining whether S. obesus exhibits similar traits to refine invasion predictions and inform management strategies in South America. Recommendations for management strategies The potential establishment of S. obesus across South American pine plantations poses substantial economic risks. In Brazil, it is already established, the species has caused tree mortalities of up to 73% in some stands (Wilcken et al. 2025 ). Considering that South American pine plantations cover approximately 4.6 million hectares and represent considerable economic value, even a partial spread of S. obesus could lead to significant economic losses. Based on our risk assessment, a hierarchical management approach should prioritize high-risk regions. Regulatory frameworks should prioritize enhanced phytosanitary inspections of wood products moving between high-risk regions and other South American countries. Given the demonstrated importance of trade pathways, bilateral agreements between Brazil and major trading partners would benefit from establish specific protocols for S. obesus detection and quarantine procedures. Surveillance programs should prioritize southern Brazil, northeastern Argentina, Argentine Patagonia, and central Chile, with monitoring sites strategically placed near major transportation hubs and border crossings. Until species-specific methods are available, effective tools developed for S. noctilio can be employed, including α- and β-pinene–baited traps, trap tree plots (intentionally stressed trees used as attractants), and visual inspections for signs such as crown chlorosis, resin droplets at oviposition sites, and emergence holes (Corley et al. 2019 ). In regions classified as medium risk, surveillance programs are also recommended, although with reduced effort, focusing primarily on major points of entry for imported goods and on plantations located near the Brazilian border. Our study identifies several priority areas for future research. Empirical host-range studies should assess the relative suitability of different pine species planted across South America, leading to more accurate risk predictions. Long-term monitoring of the invasion front in Brazil will provide critical data on dispersal rates and establishment patterns, enabling refinement of predictive models and validation of risk assessments. As knowledge of the species’ biology and management strategies advances, incorporating additional variables into establishment risk models will further improve predictive accuracy. Ultimately, early detection of S. obesus and rapid response, guided by insights from similar pests, represent the most effective management strategies to slow its spread and mitigate impacts on South American forestry. Conclusions Our comprehensive risk assessment provides the first regional-scale evaluation of Sirex obesus invasion potential in South American pine plantations and highlights priority areas for early detection efforts. The convergence of suitable climate conditions, abundant host resources, and active trade pathways results in forest landscapes highly favorable for establishment and spread. Identifying specific high-risk regions provides a practical basis for prioritizing management strategies designed to prevent or mitigate economic and ecological impacts of this emerging pest. Strengthening surveillance and regulatory measures in priority regions represents the most cost-effective approach to limit further spread, while longer-term research will be essential to advance understanding of the species’ ecology in non-native areas. Preventing the expansion of S. obesus populations into new areas will require not only national efforts but also regional cooperation, including coordinated monitoring, joint research initiatives, and harmonized forest health policy frameworks across South American countries. Declarations Ethical Approval This article does not contain any studies with human participants or animals that would require ethical approval. Conflict of Interest The authors declare that they have no conflict of interest. Funding This work was supported by a grant from CONICET (PIP 11220200100764CO) and INTA (PD-I074-2023). Contributions VL and JV conceptualized and designed the study. VL processed and analyzed the data and drafted the initial manuscript. Both authors reviewed and edited the text and approved the final version. Acknowledgements We thank CONICET and INTA for their financial support of this study. References Beèche M, Lanfranco D, Zapata M, Ruiz C (2012) Surveillance and Control of the Sirex Woodwasp: The Chilean Experience. In: Slippers B, de Groot P, Wingfield MJ (eds) The Sirex Woodwasp and its Fungal Symbiont. Springer , Netherlands, pp 229–245 Berryman AA (1987) The theory and classification of outbreaks. In: Barbosa P, Schults J (eds) Inset outbreaks. Academic Press, pp 3–30 Booth TH, Nix HA, Busby JR, Hutchinson MF (2014) BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. 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For Ecol Manage 433:762–770. https://doi.org/https://doi.org/10.1016/j.foreco.2018.11.044 Lantschner MV, Corley JC (2015) Spatial pattern of attacks of the invasive woodwasp Sirex noctilio, at landscape and stand scales. PLoS One 10:. https://doi.org/10.1371/journal.pone.0127099 Lantschner MV, Villacide JM, Garnas JR, et al (2014) Temperature explains variable spread rates of the invasive woodwasp Sirex noctilio in the Southern Hemisphere. Biol Invasions 16:329–339. https://doi.org/10.1007/s10530-013-0521-0 Lantschner V, Gomez DF, Vilardo G, et al (2024) Distribution, Invasion History, and Ecology of Non-native Pine Bark Beetles (Coleoptera: Curculionidae: Scolytinae) in Southern South America. Neotrop Entomol 53:351–363. https://doi.org/10.1007/s13744-023-01125-2 MADR (2024) Boletín estadístico forestal Marzo 2024. Ministerio de Agricultura y Desarrollo Rural. Bogotá, Colombia MAG (2023) VI Censo Agropecuario Nacional 2022. Ministerio de Agricultura y Ganadería, Gobierno Nacional de Paraguay. Asunción, Paraguay. MGAyP (2024) Resultados de la Cartografía Forestal Nacional 2024. Ministerio de Ganadería, Agricultura y Pesca. Montevideo, Uruguay Ministerio del Ambiente (2025) Bosque plantado. In: Ministerio del Ambiente de Ecuador. Available from url: https://ecuadorforestal.org/informacion-s-f-e/bosque-forestal/bosque-plantado/ Nahrung HF, Liebhold AM, Brockerhoff EG, Rassati D (2023) Forest Insect Biosecurity: Processes, Patterns, Predictions, Pitfalls. Annu Rev Entomol 68:211–229. https://doi.org/https://doi.org/10.1146/annurev-ento-120220-010854 Payn T, Carnus J-M, Freer-Smith P, et al (2015) Changes in planted forests and future global implicationsPayn, T., Carnus, J.-M., Freer-Smith, P., Kimberley, M., Kollert, W., Liu, S., Orazio, C., Rodriguez, L., Silva, L.N., Wingfield, M.J., 2015. Changes in planted forests and future global implication. For Ecol Manage 352:57–67. https://doi.org/https://doi.org/10.1016/j.foreco.2015.06.021 Phillips SJ, Dudík M, Schapire RE (2025) Maxent software for modeling species niches and distributions (Version 3.4.4). Available from url: http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed on 2025-8-1 Radomski T (2025) The ecology of geographic range limits. Biol Rev n/a: https://doi.org/https://doi.org/10.1111/brv.70070 Roy BA, Alexander HM, Davidson J, et al (2014) Increasing forest loss worldwide from invasive pests requires new trade regulations. Front Ecol Environ 12:457–465. https://doi.org/https://doi.org/10.1890/130240 SAGyP (2025) Inventario de plantaciones forestales de Argentina. In: Secretaría de Agricultura, Ganadería y Pesca, República Argentina. Access: https://www.magyp.gob.ar/desarrollo-foresto-industrial/cadena-valor.php Sandoval E (2008) Situación de las plantaciones forestales en Bolivia. Universidad Autónoma Gabriel René Moreno. Santa Cruz de la Sierra, Bolivia Schiff NM, Goulet H, Smith DR, et al (2012) Siricidae (Hymenoptera: Symphyta: Siricoidea) of the western hemisphere. Can J Arthropod Identif 21:1–305. https://doi.org/doi:10.3752/cjai.2012.21 Schwartz E (2004) Estudio de tendencias y perspectivas del sector forestal en América Latina documento de trabajo: informe nacional Perú. FAO. Rome, Italy Skarpaas O, Økland B (2009) Timber import and the risk of forest pest introductions. J Appl Ecol 46:55–63. https://doi.org/https://doi.org/10.1111/j.1365-2664.2008.01561.x Slippers B, Hurley BP, Wingfield MJ (2015) Sirex woodwasp: A model for evolving management paradigms of invasive forest pests. Annu Rev Entomol 60:601–619. https://doi.org/doi:10.1146/annurev-ento-010814-021118 Taeger A, Blank SM, Liston AD (2010) World Catalog of Symphyta (Hymenoptera). Zootaxa 2580:1–1064. https://doi.org/10.11646/zootaxa.2580.1.1 Villacide J, Fuentealba A (2025) Perspectives: Pests in plantation forests: Challenging traditional productive paradigms in the Southern Cone of America. For Ecol Manage 597:123127. https://doi.org/https://doi.org/10.1016/j.foreco.2025.123127 Villacide JM, Gomez DF, Perez CA, et al (2023) Forest Health in the Southern Cone of America: State of the Art and Perspectives on Regional Efforts. Forests 14:756. https://doi.org/10.3390/f14040756 Wilcken CF, da Mota TA, de Oliveira CH, et al (2025) Sirex obesus (Hymenoptera: Siricidae) as invasive pest in pine plantations in Brazil. Sci Rep 15:22522. https://doi.org/10.1038/s41598-025-06418-7 Wingfield MJ, Brockerhoff EG, Wingfield BD, Slippers B (2015) Planted forest health: The need for a global strategy. Science (1979) 349:832–836. https://doi.org/DOI:10.1126/science.aac6674 World Bank (2024) World Integrated Trade Solution (WITS) [Database]. Export data of wood products from Brazil to South America, 2018–2022. In: https://wits.worldbank.org Supplementary Files SupplementaryMaterial.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 Oct, 2025 Reviewers invited by journal 08 Oct, 2025 Editor invited by journal 07 Oct, 2025 Editor assigned by journal 06 Oct, 2025 First submitted to journal 06 Oct, 2025 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-7775236","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526641228,"identity":"6157f056-a38e-4513-91c0-8d9891202e1a","order_by":0,"name":"Victoria Lantschner","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDACdgiVAMSMD4AEDx9BLcxAfACihdkApIWNFC1sEiABgloMDjM/e/zhj12eOXvvs8qvOXYybAzMDx/dwKuFzdzgYFtysWXPcbPbstuSgQ5jMzbOwaNFspnBTOJgw4HEDTfS2G5LbmMGauFhk8avhf2bxIE/QC33n7EVS26rJ6yFn5nHTOIAG8gWNjbGj9sOE6WlTOJsW3LihjNpzNKM247zsDET8Asbe/s2iYo/dokbjh9j/PhzW7U9P3vzw8f4tKAAZh4wSaxyEGD8QYrqUTAKRsEoGDEAAPM4REWBIF+DAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-2012-1366","institution":"Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB), INTA Bariloche - CONICET","correspondingAuthor":true,"prefix":"","firstName":"Victoria","middleName":"","lastName":"Lantschner","suffix":""},{"id":526641229,"identity":"93610820-6d40-42e7-9f91-bf3340b36228","order_by":1,"name":"José Villacide","email":"","orcid":"","institution":"Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB), INTA Bariloche - 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10:21:04","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125090,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7775236/v1/cc617dfd34d0faa0e5913fca.html"},{"id":94012155,"identity":"1be0d6a6-3498-437f-96a5-de7d60195db9","added_by":"auto","created_at":"2025-10-21 10:21:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":404779,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution records of \u003cem\u003eSirex obesus\u003c/em\u003eacross its native range. Red dots indicate occurrence records reported in the literature, while black dots represent simulated points generated in this study. Green polygons show the distribution of pine (\u003cem\u003ePinus\u003c/em\u003e) species (Fryer 2018).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7775236/v1/8ec62af6228745b4b0bf5dd8.png"},{"id":94012168,"identity":"2a5e2bc1-9fff-423c-827e-fcd1079227f4","added_by":"auto","created_at":"2025-10-21 10:21:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":416189,"visible":true,"origin":"","legend":"\u003cp\u003eMaps of suitable conditions for the establishment of \u003cem\u003eSirex obesus\u003c/em\u003e in South America. (A) Predicted suitable habitat based on bioclimatic variables, with probability values ranging from 0 (unsuitable) to 1 (highly suitable). (B) Areas suitable for establishment based on the intersection between host availability and suitable bioclimatic conditions. The grid lines indicate the area corresponding to the two Brazilian states where the species has established, São Paulo (Wilcken et al. 2025) and Minas Gerais (Wilcken personal communication, Sep 2025).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7775236/v1/0a6cfddcc3307b36dec9a02c.png"},{"id":94012170,"identity":"62915caa-39c5-4462-a60d-5f5f8584fa69","added_by":"auto","created_at":"2025-10-21 10:21:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":508427,"visible":true,"origin":"","legend":"\u003cp\u003eMaps showing attributes of the pine-growing regions in South America relevant for predicting the qualitative risk of \u003cem\u003eSirex obesus\u003c/em\u003e establishment. (A) Surface area of pine plantations (million hectares), (B) percentage of the regions with suitable bioclimatic conditions; (C) proximity to the region where \u003cem\u003eS. obesus\u003c/em\u003e is established (Km); (D) volume of wood products exports from Brazil to each country (million USD); (E) Invasion risk index, resulting from the sum of the four variables. The grid lines indicate the area corresponding to the two Brazilian states where the species has established, São Paulo (Wilcken et al. 2025) and Minas Gerais (Wilcken personal communication, Sep 2025).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7775236/v1/c368eda1fae36393b79a21db.png"},{"id":94012954,"identity":"d0f2146d-9d1e-4d90-a93a-ba0539f46922","added_by":"auto","created_at":"2025-10-21 10:28:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2025232,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7775236/v1/621a5e20-d795-43aa-8b89-3ba6125d8bcc.pdf"},{"id":94012154,"identity":"b8cac888-5428-489f-8a69-19a5f61cf2ca","added_by":"auto","created_at":"2025-10-21 10:21:02","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":214425,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7775236/v1/280b020adced8d90c6a796bb.pdf"}],"financialInterests":"","formattedTitle":"Invasion potential of the recently established woodwasp Sirex obesus (Hymenoptera: Siricidae) across South America pine plantations","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe establishment and spread of exotic pests represent one of the most critical challenges to global forestry (Roy et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Brockerhoff and Liebhold \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Invasive species can alter forest ecosystems, reduce productivity, and generate significant economic losses (Bradshaw et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Early detection and accurate prediction of the potential distribution of recently established pests are essential to anticipate their spread and design effective management strategies (Nahrung et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This is particularly relevant in plantation forestry, where the introduction of a single pest species can compromise large areas of economically valuable timber (Wingfield et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWoodwasps of the family Siricidae comprise approximately 128 species widely distributed across the forests of the Northern Hemisphere, with no native representatives in South America (Taeger et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Schiff et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Several species have been accidentally introduced worldwide through the movement of wood products and packaging materials (Schiff et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The best-documented case is \u003cem\u003eSirex noctilio\u003c/em\u003e Fabricius, 1793, which has spread across the Southern Hemisphere, becoming one of the most damaging insect pests affecting commercial pine plantations in countries such as South Africa, Australia, New Zealand, and South America (Slippers et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Corley et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The spread of \u003cem\u003eS. noctilio\u003c/em\u003e exemplifies how the combination of human-mediated transport and Siricid life history traits has facilitated successful invasions of plantation forests across continents.\u003c/p\u003e\u003cp\u003eRecently, \u003cem\u003eSirex obesus\u003c/em\u003e Bradley, 1913, a woodwasp native to North America, was detected in pine plantations in southern Brazil (Wilcken et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Field observations suggest that this species can cause considerable damage to commercial stands, raising concerns about its potential spread to other parts of South America. This continent hosts some of the world\u0026rsquo;s largest pine plantations, covering approximately 4.6\u0026nbsp;million hectares. These plantations, established mainly for timber, pulp, and fuel production, and typically developed as intensively managed, even-aged, regularly spaced monocultures (Payn et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These conditions can facilitate the establishment and spread of invasive pests by providing abundant host resources and reduced natural enemy pressure (Corley et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Villacide et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven the economic importance of pine production in the region, assessing the potential distribution of \u003cem\u003eS. obesus\u003c/em\u003e is crucial to guide monitoring and prevention strategies. In this study, we modeled the species\u0026rsquo; potential distribution across South American pine plantations and developed a qualitative invasion risk index across the continent based on host abundance, suitable bioclimatic conditions, proximity to the invaded area, and the volume of wood trade between each country and Brazil. By identifying high-risk areas, our study provides a useful foundation for early detection, risk assessment, and the development of regionally tailored management strategies to mitigate the impact of this emerging pest.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eNative distribution records\u003c/h2\u003e\u003cp\u003eWe compiled a database of occurrence locations of \u003cem\u003eS. obesus\u003c/em\u003e across its native range from published sources (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), although its distribution there remains poorly documented. Previous studies suggest that the species likely occurs in Mexico\u0026rsquo;s Sierra Madre Occidental, a region of high conifer diversity located between known populations in the United States and Mexico (Schiff et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Thus, to address this knowledge gap, we generated simulated localities in this area (Jim\u0026eacute;nez-Valverde et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), where eleven points were randomly assigned\u0026mdash;matching the number of reported occurrences for the species\u0026mdash;within the area connecting populations in both countries. These points were restricted to pine forest regions (Fryer \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), with a minimum distance of 50 km maintained between them (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePotential distribution\u003c/h3\u003e\n\u003cp\u003eWe modeled the potential distribution of \u003cem\u003eS. obesus\u003c/em\u003e in South America using MaxEnt software v.3.4.4 (Phillips et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For this analysis, we used compiled occurrence data and simulated records from the species\u0026rsquo; native range. To reduce spatial bias and improve the uniformity of native occurrence data, we applied a thinning procedure by buffering point locations with a 50 km distance radius and removing adjacent, closely clustered records.\u003c/p\u003e\u003cp\u003eWe considered climate conditions as potential predictors of the species distribution. We used 19 bioclimatic variables acquired from the WorldClim v2.1 (Fick and Hijmans \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables, obtained from weather stations restricted to records spanning 1970 to 2000 (Fick and Hijmans \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These include annual values (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest months, and precipitation of the wet and dry quarters) (Booth et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). We used WorldClim data grids of estimates at a resolution of 5 minutes. To avoid collinearity among the variables, a matrix of Pearson\u0026rsquo;s rank correlation coefficients for all possible pairs of variables was carried out for each occurrence location of the species. Variables that correlated (CC\u0026thinsp;\u0026ge;\u0026thinsp;0.65) with each other were excluded, leaving only those variables with more biological relevance for the species. The selected variables were maximum temperature of warmest month (bio5), mean temperature of driest quarter (bio9), precipitation of wettest month (bio13), and precipitation seasonality (coefficient of variation, bio15).\u003c/p\u003e\u003cp\u003eWe defined North and Central America as the training area for the model. To control model complexity and reduce overfitting, we set the regularization multiplier in MaxEnt to 2. We applied the cross-validation method, partitioning the presence data into multiple subsets to iteratively train and evaluate the model and assess its predictive performance. The analysis was run with 10 replicates, and all outputs were averaged to produce a single model for each species. Model accuracy was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), where values range from 0 to 1, with 0.5 indicating no better performance than random expectation and 1 indicating perfect predictive accuracy. The spatial resolution of the output model grid matched that of the input environmental variables (5 arcminutes).\u003c/p\u003e\u003cp\u003eTo estimate the potential geographical distribution of \u003cem\u003eS. obesus\u003c/em\u003e outside of its native range, we designated a threshold probability to define suitable and non-suitable habitat, such that probability values derived from MaxEnt models above that threshold were designated as suitable, while values at or below that threshold were designated as unsuitable. We used \u0026ldquo;equal training sensitivity and specificity logistic threshold\u0026rdquo; to convert the continuous probability of occurrence maps from MaxEnt to a binary habitat/ non-habitat map.\u003c/p\u003e\u003cp\u003eAt the same time, we considered the distribution of suitable hosts in South America to refine predictions of potential establishment. In its native range, \u003cem\u003eS. obesus\u003c/em\u003e has been reported on three host species: \u003cem\u003ePinus ponderosa, P. teocote\u003c/em\u003e, and \u003cem\u003eP. leiophylla\u003c/em\u003e (Schiff et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In contrast, invasive populations in Brazil have been recorded on the locally planted species \u003cem\u003eP. taeda, P. caribaea, P. maximinoi, P. tecunumanii\u003c/em\u003e, and the hybrids \u003cem\u003eP. caribaea x P. elliottii\u003c/em\u003e and \u003cem\u003eP. caribaea x P. tecunumanii\u003c/em\u003e (Wilcken et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Given that this woodwasp has demonstrated the ability to colonize a wide range of hosts within the genus \u003cem\u003ePinus\u003c/em\u003e, including several of the most extensively planted species in South America, we assumed that it could affect any of the species commonly planted in the region: \u003cem\u003eP. taeda, P. elliottii, P. ponderosa, P. contorta\u003c/em\u003e, \u003cem\u003eP. caribaea, P. oocarpa, P. patula\u003c/em\u003e and \u003cem\u003eP. radiata, P. tecunumanii\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Consequently, we used a map of the distribution of all available pine species in South America -modified from Lantschner et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)- to constrain the modeled potential distribution of the species.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescription of the attributes of the different pine-growing regions in South America considered relevant for predicting the qualitative risk of \u003cem\u003eSirex obesus\u003c/em\u003e establishment. We indicate for each region the main planted pine species, and the value and category estimated for each of the four variables used to estimate the invasion risk index: the surface area of pine plantations (in million hectares \u0026ldquo;M ha\u0026rdquo;), the percentage of the regions with suitable bioclimatic conditions, the proximity to the region where the pest is established, and the volume of wood products exports from Brazil to each country (in million dollars \u0026ldquo;M USD\u0026rdquo;). Finally, we detail the resulting invasion risk index (IRI.\u003c/p\u003e \u003cdiv class=\"Credit\"\u003e\u003cp\u003eSources: \u003csup\u003e1\u003c/sup\u003e SAGyP (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), \u003csup\u003e2\u003c/sup\u003e Sandoval (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), \u003csup\u003e3\u003c/sup\u003e da Silva et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), \u003csup\u003e4\u003c/sup\u003e CONAF (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), \u003csup\u003e5\u003c/sup\u003e MADR (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), \u003csup\u003e6\u003c/sup\u003e Ministerio del Ambiente (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), \u003csup\u003e7\u003c/sup\u003e MAG (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), \u003csup\u003e8\u003c/sup\u003e Schwartz (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), \u003csup\u003e9\u003c/sup\u003e MGAyP (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), \u003csup\u003e10\u003c/sup\u003e Gonz\u0026aacute;lez et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCountry (Region)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMain planted Pinus species\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003ePine Surface\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eBioclim suitability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eProximity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eWood exports\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eIRI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eM ha\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eCat\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eCat\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eDist (km)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eCat\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eM USD\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eCat\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eValue\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eCat\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArgentina (NE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. ellioti, P. taeda\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.67\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e523.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArgentina (Patagonia)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. ponderosa, P. contorta\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e523.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArgentina (NW)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. ellioti, P. taeda\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e523.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArgentina (center)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. ellioti, P. taeda\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e523.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBolivia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. radiata, P. patula y P. elliottii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.011\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e101.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrazil (Minas Gerais)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. taeda, P. elliottii, P caribea, P. oocarpa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrazil (S\u0026atilde;o Paulo)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. taeda, P. elliottii, P. caribaea, P. oocarpa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.231\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrazil (Paran\u0026aacute;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. ellioti, P. taeda\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.631\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrazil (Rio Grande do Sul)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. ellioti, P. taeda\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.271\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrazil (Santa Catarina)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. ellioti, P. taeda\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.617\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrazil (Distrito Federal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. ellioti, P. taeda\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0004\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrazil (Mato Grosso do Sul)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. caribaea, P. oocarpa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrazil (Goias)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. taeda, P. elliottii, P. caribaea\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrazil (Esp\u0026iacute;rito Santo)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. ellioti, P. taeda\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChile (central)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. radiata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.234\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e219.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChile (South)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. ponderosa, P. radaita\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e219.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColombia (Coffee Axis \u0026amp; SW)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. patula, P. tecunumanii, P. maximinoi, P. occarpa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.109\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e159.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColombia (Orinoquia)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. caribaea, P. patula\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.054\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e159.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEcuador\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. ellioti, P. taeda\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e70.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParaguay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. ellioti, P. taeda\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e144.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer\u0026uacute;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. radiata, P. patula\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e186.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUruguay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. ellioti, P. taeda\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.13\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e87.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVenezuela\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP. caribaea\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e21.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eRisk of establishment\u003c/h3\u003e\n\u003cp\u003eTo qualitatively assess the risk of \u003cem\u003eSirex obesus\u003c/em\u003e invasion, we divided South America\u0026rsquo;s pine plantation areas into 24 geographic units, delineated according to political boundaries and plantation distribution, and designed to be as homogeneous in size as possible (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For each geographical unit, we estimated an invasion risk index by summing the categorical scores of four regionally relevant variables:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e(1) Host abundance: We compiled information on the area planted with pine in each unit from official inventories and/or other available reports (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We classified pine surface into three categories: high (\u0026gt;\u0026thinsp;0.5\u0026nbsp;million ha), medium (0.5 to 0.1\u0026nbsp;million ha), or low (\u0026lt;\u0026thinsp;0.1\u0026nbsp;million ha.)\u003c/p\u003e\u003cp\u003e(2) Suitable bioclimatic conditions: Using the previously estimated habitat suitability, we quantified the proportion of each geographic unit classified as suitable and categorized suitability as high (\u0026gt;\u0026thinsp;66% of the unit area suitable), medium (33 to 66%), or low (\u0026lt;\u0026thinsp;33%).\u003c/p\u003e\u003cp\u003e(3) Proximity to the area where the species is currently established: We estimated the shortest Euclidean distance between the area currently occupied by the species in Brazil and each geographical unit. We categorized proximity as high (\u0026lt;\u0026thinsp;1000 km), medium (1000 to 2500 km), or low (\u0026gt;\u0026thinsp;2500 km).\u003c/p\u003e\u003cp\u003e(4) Volume of wood trade: To estimate wood movement as a potential pathway for the spread of \u003cem\u003eS. obesus\u003c/em\u003e from the invaded region to other South American countries, we compiled information on Brazil\u0026rsquo;s wood product exports to each country. Specifically, we obtained data on annual export volumes (in million USD) for the period 2018\u0026ndash;2022 (World Bank \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and calculated an average value for each country. We classified wood exports as high (\u0026gt;\u0026thinsp;USD 200\u0026nbsp;million), medium (USD 100 to 200\u0026nbsp;million), or low (\u0026lt;\u0026thinsp;USD 100\u0026nbsp;million).\u003c/p\u003e\u003cp\u003eFinally, we assigned scores to each category of every variable (1\u0026thinsp;=\u0026thinsp;low, 2\u0026thinsp;=\u0026thinsp;medium, 3\u0026thinsp;=\u0026thinsp;high) and calculated the invasion risk index for each geographic unit.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eSuitable bioclimatic conditions\u003c/h2\u003e\u003cp\u003eOur results indicate that extensive areas of South America provide suitable climatic conditions for the potential establishment of \u003cem\u003eSirex obesus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The distribution model performed very well with an average AUC of 0.984 (Table S2). In regions with pine plantations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), 48% of the regions with pine plantations were classified as suitable for establishment of the species. The region where the species is currently established in Brazil (states of S\u0026atilde;o Paulo and Minas Gerais) was correctly predicted. In addition, a high probability of suitable habitat was identified in central, northwestern, and southern Argentina, in central Chile, and in the Andean regions of northern South America (Bolivia, Peru, Ecuador, and Colombia). Conversely, low habitat suitability was predicted in northeastern Argentina, Uruguay, and southern Brazil. The bioclimatic variable with the highest contribution to the model based on permutation importance was the mean temperature of the driest quarter (86.4%) followed by precipitation seasonality (10.6%).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eInvasion risk index\u003c/h2\u003e\u003cp\u003eThe regions with the highest host abundance for \u003cem\u003eS. obesus\u003c/em\u003e, measured as the area of pine plantations, were observed in central Chile, southern Brazil, and northeastern Argentina. Uruguay, southern Argentina, and Colombia showed intermediate values, while all other regions had low host abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In terms of bioclimatic suitability, the largest proportion of suitable area was identified in central Chile, Patagonia, central and northwestern Argentina, Peru, and the Brazilian regions where the species is currently established (S\u0026atilde;o Paulo and Minas Gerais). Southern Chile, Bolivia, and Ecuador exhibited intermediate suitability, whereas the remaining regions had low suitability (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Proximity to the current invaded area in Brazil was naturally high in the pine-growing regions of Brazil, northeastern Argentina, Paraguay, and Uruguay; intermediate in other parts of Argentina, central Chile, and Bolivia; and low in the remaining regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Finally, wood product exports from Brazil were highest to Argentina and Chile. Although national statistics are unavailable, domestic trade within Brazil were assumed to be also high al. Export volumes to Paraguay, Bolivia, Peru, and Colombia ware intermediate, while trade with other countries was low (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOverall, the invasion risk index was highest for southern Brazil, northeastern Argentina, Argentine Patagonia, and central Chile. Meanwhile, the index was intermediate for the remaining regions of Brazil and Argentina, as well as Uruguay, Bolivia, and Peru, and lowest for southern Chile, Ecuador, Colombia, and Venezuela (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe recent detection of the woodwasp \u003cem\u003eSirex obesus\u003c/em\u003e in South America highlights an urgent need to implement measures aimed at mitigating the risk of its spread and potential impacts on the region\u0026rsquo;s most productive forestry areas. This study provides the first comprehensive assessment of the species\u0026rsquo; invasion potential across the continent, identifying regions of high vulnerability that warrant immediate attention. We identified regions with the highest potential for establishment of \u003cem\u003eS. obesus\u003c/em\u003e, based on factors related to environmental invasibility (climate and host availability) and propagule pressure (inferred from timber trade volumes and proximity to already-invaded areas). Our results indicate that this newly established woodwasp species has considerable potential to spread throughout South America\u0026rsquo;s pine plantation areas.\u003c/p\u003e\n\u003ch3\u003eBioclimatic suitability\u003c/h3\u003e\n\u003cp\u003eOur distribution model showed strong predictive performance in identifying suitable habitat for \u003cem\u003eS. obesus\u003c/em\u003e across South America. It accurately predicted the species\u0026rsquo; current establishment area in the states of S\u0026atilde;o Paulo and Minas Gerais, providing confidence in its ability to identify other potentially suitable regions. The broad climatic suitability predicted across 48% of South American pine plantation areas underscores the significant risk this invasive woodwasp poses to the region's forestry sector. The primary bioclimatic predictor in the model was the mean temperature of the driest quarter. In its native range, the driest quarter coincides with the warmest one, corresponding to the trees\u0026rsquo; growth period. This finding suggests that \u003cem\u003eS. obesus\u003c/em\u003e establishment may be particularly favored in regions with moderate temperatures during dry seasons, which characterizes much of the suitable habitat identified in our analysis.\u003c/p\u003e\u003cp\u003eInterestingly, the potential distribution of \u003cem\u003eS. obesus\u003c/em\u003e in South America, as identified in this study, exhibits spatial patterns that contrast with the distribution predicted for its congener, the woodwasp \u003cem\u003eSirex noctilio\u003c/em\u003e (Ireland et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), possibly reflecting differences in their climatic requirements. \u003cem\u003eSirex noctilio\u003c/em\u003e, one of the most important pine pests in the Southern Hemisphere, has caused severe impacts on plantation forestry in South America since its introduction (Corley et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lantschner et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Villacide et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This species is primarily associated with the temperate and humid regions of southeastern South America, with high suitability predicted in southern Brazil, northeastern Argentina, Uruguay, and the Andean Patagonia of Chile and Argentina (Ireland et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In contrast, \u003cem\u003eS. obesus\u003c/em\u003e shows its greatest climatic suitability along the Andean corridor, particularly in Peru, Bolivia, northern and central Chile, and western Argentina, with additional suitable areas in central-eastern Brazil. While \u003cem\u003eS. noctilio\u003c/em\u003e appears to be favored by lowland temperate environments where pine plantations are concentrated, \u003cem\u003eS. obesus\u003c/em\u003e is more constrained to montane and high-altitude climates.\u003c/p\u003e\u003cp\u003eHowever, our results should be interpreted with caution. The limited number of \u003cem\u003eS. obesus\u003c/em\u003e occurrence records from the native range necessitated the use of simulated localities, which may introduce uncertainty in model predictions. In addition, the species\u0026rsquo; native range may be restricted by factors other than climate, including geographic barriers or biotic interactions (natural enemy pressure or interspecific competition) which can significantly influence invasion outcomes (Radomski \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The reduced natural enemy pressure commonly observed in South American pine plantations (Corley and Villacide \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), compared to native North American forests (where the model was trained), may enhance establishment probability beyond what climate-based models predict. These considerations underscore the need for additional research to more accurately determine the environmental conditions that enable the wasp to establish and persist beyond its native range.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eInvasion risk index\u003c/h2\u003e\u003cp\u003eIn addition to suitable climatic conditions, our risk assessment highlights the combined influence of three additional drivers of invasion risk\u0026mdash;host abundance, proximity to established populations, and high wood trade volumes from Brazil. As a results, the identification of southern Brazil, northeastern Argentina, Argentine Patagonia, and central Chile as the highest-risk regions reflects the convergences of these multiple facilitating drivers.\u003c/p\u003e\u003cp\u003eHost abundance is a key determinant of invasive herbivore species distributions, as large and continuous resource availability can sustain higher population densities and facilitate spread (Gougherty et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this context, regions with extensive pine plantations\u0026mdash;such as central Chile, southern Brazil, and northeastern Argentina (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) \u0026mdash;represent particularly high-risk areas where suitable climate coincides with extensive areas of susceptible hosts. It should be noted, however, that our assumption that \u003cem\u003eS. obesus\u003c/em\u003e can attack all planted pine species in South America, while supported by its observed host flexibility (Wilcken et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), requires empirical validation. Because host suitability can vary significantly among pine species and may influence establishment success and population growth rates (Corley et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), future research should prioritize host-range testing to refine risk predictions.\u003c/p\u003e\u003cp\u003eOn the other hand, timber importation and proximity to already-invaded areas are key determinants in the spread of invasive forest pests (Skarpaas and \u0026Oslash;kland \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These factors facilitate the introduction of new individuals into novel habitats, increasing the likelihood of establishment and population growth. \u003cem\u003eSirex obesus\u003c/em\u003e can disperse naturally by adult flight, but can also be transported unintentionally by human activities, particularly through the movement of infested wood that can transport the immature stages over long distances. Consequently, Brazilian states surrounding the invaded area, together with southern Paraguay, Uruguay, and northeastern Argentina, face a particularly high risk of receiving woodwasp propagules due to their proximity to established populations. In addition, the extensive forest plantation landscapes in this region create largely continuous habitats for many forest pests, facilitating their spread pest (Corley et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lantschner et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Villacide and Fuentealba \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Countries importing large volumes of wood products from Brazil, such as Argentina and Chile (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), as well as Brazilian states with intensive domestic wood trade, are likewise highly vulnerable to accidental pest introduction through transportation pathways.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eInsights from the invasion history of S. noctilio as a model for S. obesus\u003c/h2\u003e\u003cp\u003eThe invasion history of \u003cem\u003eSirex noctilio\u003c/em\u003e in South America provides valuable lessons for anticipating the spread of \u003cem\u003eS. obesus\u003c/em\u003e. This woodwasp was first detected in Uruguay in 1980, followed by northeastern Argentina (1985) and southern Brazil (1988). During the 1990s, it expanded throughout Argentina\u0026rsquo;s forested regions and reached Chile by 2001 (Corley et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Reported spread rates varied between regions, ranging from 12 to 82 km per year, and increased with higher mean annual temperatures and greater isothermality (Lantschner et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In southern Brazil, the rate was 46 km per year, comparable to that observed for \u003cem\u003eS. obesus\u003c/em\u003e since its recent establishment, suggesting that, at this rate, the species could potentially spread throughout all major pine plantation areas in Brazil within less than two decades. Currently, \u003cem\u003eS. noctilio\u003c/em\u003e has spread across most pine-growing areas of South America south of 21\u0026deg; latitude, largely facilitated by human activities such as the transport of infested wood (Corley et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Its expansion within these regions is also strongly influenced by natural dispersal, with laboratory studies showing that females can fly an average of 17 km per day, reaching up to 50 km per day (Bruzzone et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMoreover, the outbreak dynamics of \u003cem\u003eS. noctilio\u003c/em\u003e provide useful insights into what might be expected for \u003cem\u003eS. obesus\u003c/em\u003e, particularly given the substantial tree mortality already reported in Brazil (Wilcken et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). \u003cem\u003eSirex noctilio\u003c/em\u003e exhibits pulse-like population dynamics, with long periods of low density interrupted by sudden outbreaks (Berryman \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Outbreaks are often triggered by the availability of stressed trees during extreme climatic events such as droughts (Lantschner et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and particularly frequent in overstocked, damaged, or unmanaged stands, and in plantations on slopes with drier conditions (Be\u0026egrave;che et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Iede et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lantschner and Corley \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Forest composition also influences outbreak likelihood, with certain pines, such as \u003cem\u003eP. contorta\u003c/em\u003e in southern Argentina or \u003cem\u003eP. taeda\u003c/em\u003e in Uruguay and Brazil, being more susceptible. This population dynamic is critical for understanding the species\u0026rsquo; spread and the damage it causes, as higher abundances also increase the likelihood of accidental transport to new areas. In summary, this invasion history highlights how population dynamics, host condition, and climate interact to amplify spread and impacts, and underscores the importance of determining whether \u003cem\u003eS. obesus\u003c/em\u003e exhibits similar traits to refine invasion predictions and inform management strategies in South America.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eRecommendations for management strategies\u003c/h2\u003e\u003cp\u003eThe potential establishment of \u003cem\u003eS. obesus\u003c/em\u003e across South American pine plantations poses substantial economic risks. In Brazil, it is already established, the species has caused tree mortalities of up to 73% in some stands (Wilcken et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Considering that South American pine plantations cover approximately 4.6\u0026nbsp;million hectares and represent considerable economic value, even a partial spread of \u003cem\u003eS. obesus\u003c/em\u003e could lead to significant economic losses.\u003c/p\u003e\u003cp\u003eBased on our risk assessment, a hierarchical management approach should prioritize high-risk regions. Regulatory frameworks should prioritize enhanced phytosanitary inspections of wood products moving between high-risk regions and other South American countries. Given the demonstrated importance of trade pathways, bilateral agreements between Brazil and major trading partners would benefit from establish specific protocols for \u003cem\u003eS. obesus\u003c/em\u003e detection and quarantine procedures. Surveillance programs should prioritize southern Brazil, northeastern Argentina, Argentine Patagonia, and central Chile, with monitoring sites strategically placed near major transportation hubs and border crossings. Until species-specific methods are available, effective tools developed for \u003cem\u003eS. noctilio\u003c/em\u003e can be employed, including α- and β-pinene\u0026ndash;baited traps, trap tree plots (intentionally stressed trees used as attractants), and visual inspections for signs such as crown chlorosis, resin droplets at oviposition sites, and emergence holes (Corley et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In regions classified as medium risk, surveillance programs are also recommended, although with reduced effort, focusing primarily on major points of entry for imported goods and on plantations located near the Brazilian border.\u003c/p\u003e\u003cp\u003eOur study identifies several priority areas for future research. Empirical host-range studies should assess the relative suitability of different pine species planted across South America, leading to more accurate risk predictions. Long-term monitoring of the invasion front in Brazil will provide critical data on dispersal rates and establishment patterns, enabling refinement of predictive models and validation of risk assessments. As knowledge of the species\u0026rsquo; biology and management strategies advances, incorporating additional variables into establishment risk models will further improve predictive accuracy. Ultimately, early detection of \u003cem\u003eS. obesus\u003c/em\u003e and rapid response, guided by insights from similar pests, represent the most effective management strategies to slow its spread and mitigate impacts on South American forestry.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur comprehensive risk assessment provides the first regional-scale evaluation of \u003cem\u003eSirex obesus\u003c/em\u003e invasion potential in South American pine plantations and highlights priority areas for early detection efforts. The convergence of suitable climate conditions, abundant host resources, and active trade pathways results in forest landscapes highly favorable for establishment and spread. Identifying specific high-risk regions provides a practical basis for prioritizing management strategies designed to prevent or mitigate economic and ecological impacts of this emerging pest. Strengthening surveillance and regulatory measures in priority regions represents the most cost-effective approach to limit further spread, while longer-term research will be essential to advance understanding of the species\u0026rsquo; ecology in non-native areas. Preventing the expansion of \u003cem\u003eS. obesus\u003c/em\u003e populations into new areas will require not only national efforts but also regional cooperation, including coordinated monitoring, joint research initiatives, and harmonized forest health policy frameworks across South American countries.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals that would require ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from CONICET (PIP 11220200100764CO) and INTA (PD-I074-2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVL and JV conceptualized and designed the study. VL processed and analyzed the data and drafted the initial manuscript. Both authors reviewed and edited the text and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank CONICET and INTA for their financial support of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBe\u0026egrave;che M, Lanfranco D, Zapata M, Ruiz C (2012) Surveillance and Control of the Sirex Woodwasp: The Chilean Experience. In: Slippers B, de Groot P, Wingfield MJ (eds) The Sirex Woodwasp and its Fungal Symbiont. Springer , Netherlands, pp 229\u0026ndash;245\u003c/li\u003e\n\u003cli\u003eBerryman AA (1987) The theory and classification of outbreaks. In: Barbosa P, Schults J (eds) Inset outbreaks. 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Forests 14:756. https://doi.org/10.3390/f14040756\u003c/li\u003e\n\u003cli\u003eWilcken CF, da Mota TA, de Oliveira CH, et al (2025) Sirex obesus (Hymenoptera: Siricidae) as invasive pest in pine plantations in Brazil. Sci Rep 15:22522. https://doi.org/10.1038/s41598-025-06418-7\u003c/li\u003e\n\u003cli\u003eWingfield MJ, Brockerhoff EG, Wingfield BD, Slippers B (2015) Planted forest health: The need for a global strategy. Science (1979) 349:832\u0026ndash;836. https://doi.org/DOI:10.1126/science.aac6674\u003c/li\u003e\n\u003cli\u003eWorld Bank (2024) World Integrated Trade Solution (WITS) [Database]. Export data of wood products from Brazil to South America, 2018\u0026ndash;2022. In: https://wits.worldbank.org\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"neotropical-entomology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nent","sideBox":"Learn more about [Neotropical Entomology](https://www.springer.com/journal/13744)","snPcode":"13744","submissionUrl":"https://www.editorialmanager.com/nent/default2.aspx","title":"Neotropical Entomology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Siricidae, biological invasions, pine plantations, risk assessment","lastPublishedDoi":"10.21203/rs.3.rs-7775236/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7775236/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInvasive forest pests are among the greatest threats to global forestry, causing substantial economic losses and disrupting ecosystem dynamics worldwide. The recent detection of the North American woodwasp \u003cem\u003eSirex obesus\u003c/em\u003e (Hymenoptera: Siricidae) in Brazilian pine plantations poses a serious risk to South America\u0026rsquo;s 4.6\u0026nbsp;million hectares of commercial pine forests. Here, we present the first comprehensive assessment of its invasion potential across the continent, combining species distribution modeling with a multi-factor invasion risk index. Using occurrence records from the species\u0026rsquo; native range, we modeled climatic suitability across South America and addressed invasion risk by integrating bioclimatic suitability, host distribution, proximity to invaded areas, and wood trade volumes with Brazil. Our model predicts suitable climatic conditions in 48% of South American pine plantation areas, particularly in montane and high-altitude regions along the Andean corridor and central-eastern Brazil. The mean temperature of the driest quarter was the most influential predictor of suitability. The invasion risk index identified southern Brazil, northeastern Argentina, Argentine Patagonia, and central Chile as the regions most vulnerable to the establishment, due to the convergence of extensive pine plantations, favorable climate, and either proximity to infested areas or intense trade connections with Brazil. These findings provide a foundation for targeted surveillance and phytosanitary measures aimed at preventing further spread. Early monitoring in high-risk regions, combined with stricter inspections of wood products, will be critical to avoiding widespread establishment and severe economic impacts across South American forestry.\u003c/p\u003e","manuscriptTitle":"Invasion potential of the recently established woodwasp Sirex obesus (Hymenoptera: Siricidae) across South America pine plantations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 10:19:27","doi":"10.21203/rs.3.rs-7775236/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-10-08T16:30:46+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-08T16:10:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Neotropical Entomology","date":"2025-10-07T11:37:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-07T01:57:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Neotropical Entomology","date":"2025-10-06T10:20:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"neotropical-entomology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nent","sideBox":"Learn more about [Neotropical Entomology](https://www.springer.com/journal/13744)","snPcode":"13744","submissionUrl":"https://www.editorialmanager.com/nent/default2.aspx","title":"Neotropical Entomology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4d068df2-32fc-4346-bee5-cab001378706","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T15:02:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-21 10:19:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7775236","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7775236","identity":"rs-7775236","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}