Invasive Risk Assessment Research Articles

Future invasion risk assessment of the peacock bass in Neotropical ecoregions: A conceptual and testable model

Protecting freshwater biodiversity from non-native invasive species and their impacts is crucial for minimising both regional and global deterioration of ecosystem services. The yellow peacock bass Cichla ocellaris, a predatory freshwater fish endemic to the Amazon region, has been translocated within several Neotropical ecoregions, exhibiting harmful effects on biodiversity. In order to facilitate the prioritization of management efforts, which are essential for defining conservation actions in extensive and diverse areas, we propose a risk assessment index that integrates multiple ecological and environmental variables within a comprehensive framework, here applied for C. ocellaris. By considering fish species richness, occurrence frequency, and climate suitability, our index provides a more nuanced understanding of invasion risks. Within regions categorized as high and very high risk, where ecological impacts of C. ocellaris have been extensively documented (e.g., the Southern and North-East Brazilian regions, as well as Central American and Caribbean ecoregions), numerous environments are increasingly conducive to the establishment of this invasive species, exacerbated by a surge in dam constructions. Our analysis projects a significant increase in suitable habitats by 5% to 6.5%, with Southern and Northeastern Brazil and Central America identified as critical zones. Thus, our reproducible risk assessment framework, which considers freshwater ecoregions as conservation units, not only directs effective resource allocation for control measures but also enhances our ability to predict and mitigate the ecological impacts of non-native species introductions, providing a valuable tool for both policymakers and conservationists.

Global invasion risk assessment of Lantana camara, a highly invasive weed, under future environmental change

Invasion risk assessments are essential for making informed decisions, allocating resources, and implementing targeted strategies to prevent or minimize the harmful effects of invasive species on native biodiversity, agricultural productivity, and natural ecosystems. In this study, the random forest algorithm was used to assess the spatial invasion risk of Lantana camara, one of the world’s top 100 worst invasive weeds, across all continents under current and future environmental conditions. The current invasion risk was relatively high on four continents (i.e., Africa, Australia, Oceania, and South America) within approximately 35°N and 35°S latitude, estimated to cover at least 68.98 % of the total land surface. Furthermore, projections for future environmental changes suggested a substantial increase in invasion risk across all continents, with the most significant changes (251.52 %) observed in Europe compared with current invasion levels. Additionally, invasion risk was predicted to extend beyond 35°N latitude. Categorizing 200 countries and territories into distinct risk levels, 27 countries had current invasion potential, and introduction and establishment was predicted in 114 countries. Moreover, at least 45 countries, including Canada, India, Italy, and United States, were projected to transition from no or low invasion risk to high invasion risk and 28 countries had a risk increase of over 50 %. Current study provides valuable insights into the global invasion risk posed by L. camara. These results are expected to be of great utility for invasive weed management, facilitating the development of control and sustainable management strategies for this notorious weed at both global and local scales.

Reconstructing the biological invasion of noxious invasive weed Parthenium hysterophorus and invasion risk assessment in China.

Invasive alien plants (IAPs) present a severe threat to native ecosystems and biodiversity. Comprehending the potential distribution patterns of these plant invaders and their responses to climate change is essential. Parthenium hysterophorus, native to the Americas, has become an aggressively invasive species since its introduction to China in the 1930s. This study aims to collect and reconstruct the historical occurrence and invasion of P. hysterophorus. Using the optimal MaxEnt model, the potential geographical distributions of P. hysterophorus were predicted based on screened species occurrences and environmental variables under the current and three future scenarios in the 2030s, 2050s, and 2070s (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5), and the invasion risk of P. hysterophorus in Chinese cities, croplands, forests, and grasslands was assessed. The results show that: (1) The species initially invaded highly suitable areas and further spread to regions with non-analogous climate conditions. (2) Under the current climatic conditions, the overall potential distribution of P. hysterophorus is characterized by more in the southeast and less in the northwest. Climate variables, including mean annual temperature (bio1), precipitation in the wettest month (bio13), isothermality (bio3), and temperature seasonality (bio4), are the primary factors influencing its distribution. (3) The potential distribution of P. hysterophorus will expand further under future climate scenarios, particularly toward higher latitudes. (4) Forests and crop lands are the areas with the most serious potential invasion risk of P. hysterophorus. Therefore, we suggest that the government should strengthen the monitoring and management of P. hysterophorus to prevent its spread and protect agro-ecosystems and human habitats. Depending on the potential risk areas, measures such as quarantine, removal, and publicity should be taken to mitigate the threat of P. hysterophorus invasion and to raise awareness of P. hysterophorus invasion prevention.

Machine learning ensemble model prediction of northward shift in potato cyst nematodes (Globodera rostochiensis and G. pallida) distribution under climate change conditions

Potato cyst nematodes (PCNs) are a significant threat to potato production, having caused substantial damage in many countries. Predicting the future distribution of PCN species is crucial to implementing effective biosecurity strategies, especially given the impact of climate change on pest species invasion and distribution. Machine learning (ML), specifically ensemble models, has emerged as a powerful tool in predicting species distributions due to its ability to learn and make predictions based on complex data sets. Thus, this research utilised advanced machine learning techniques to predict the distribution of PCN species under climate change conditions, providing the initial element for invasion risk assessment. We first used Global Climate Models to generate homogeneous climate predictors to mitigate the variation among predictors. Then, five machine learning models were employed to build two groups of ensembles, single-algorithm ensembles (ESA) and multi-algorithm ensembles (EMA), and compared their performances. In this research, the EMA did not always perform better than the ESA, and the ESA of Artificial Neural Network gave the highest performance while being cost-effective. Prediction results indicated that the distribution range of PCNs would shift northward with a decrease in tropical zones and an increase in northern latitudes. However, the total area of suitable regions will not change significantly, occupying 16–20% of the total land surface (18% under current conditions). This research alerts policymakers and practitioners to the risk of PCNs’ incursion into new regions. Additionally, this ML process offers the capability to track changes in the distribution of various species and provides scientifically grounded evidence for formulating long-term biosecurity plans for their control.

Forecasting potential invaders to prevent future biological invasions worldwide.

The ever-increasing and expanding globalisation of trade and transport underpins the escalating global problem of biological invasions. Developing biosecurity infrastructures is crucial to anticipate and prevent the transport and introduction of invasive alien species. Still, robust and defensible forecasts of potential invaders are rare, especially for species without known invasion history. Here, we aim to support decision-making by developing a quantitative invasion risk assessment tool based on invasion syndromes (i.e., generalising typical attributes of invasive alien species). We implemented a workflow based on 'Multiple Imputation with Chain Equation' to estimate invasion syndromes from imputed datasets of species' life-history and ecological traits and macroecological patterns. Importantly, our models disentangle the factors explaining (i) transport and introduction and (ii) establishment. We showcase our tool by modelling the invasion syndromes of 466 amphibians and reptile species with invasion history. Then, we project these models to amphibians and reptiles worldwide (16,236 species [c.76% global coverage]) to identify species with a risk of being unintentionally transported and introduced, and risk of establishing alien populations. Our invasion syndrome models showed high predictive accuracy with a good balance between specificity and generality. Unintentionally transported and introduced species tend to be common and thrive well in human-disturbed habitats. In contrast, those with established alien populations tend to be large-sized, are habitat generalists, thrive well in human-disturbed habitats, and have large native geographic ranges. We forecast that 160 amphibians and reptiles without known invasion history could be unintentionally transported and introduced in the future. Among them, 57 species have a high risk of establishing alien populations. Our reliable, reproducible, transferable, statistically robust and scientifically defensible quantitative invasion risk assessment tool is a significant new addition to the suite of decision-support tools needed for developing a future-proof preventative biosecurity globally.

Invasion away from roadsides was not driven by adaptation to grassland habitats in Dittrichia graveolens (stinkwort)

Invasive plants along transportation corridors can significantly threaten ecosystems and biodiversity if they spread beyond anthropogenic environments. Rapid evolution may increase the ability of invading plant populations to establish in resident plant communities over time, posing a challenge to invasion risk assessment. We tested for adaptive differentiation in Dittrichia graveolens (stinkwort), an invasive species of ruderal habitat in California that is increasingly spreading away from roadsides into more established vegetation. We collected seeds from eight pairs of vegetated sites and their nearest (presumed progenitor) roadside population. We assessed differentiation between populations in roadside and vegetated habitat for germination behavior and for response to competition in a greenhouse experiment. We also tested for increased performance in vegetated habitat with a grassland field experiment including a neighbor removal treatment. Germination rates were slightly reduced in seeds from vegetated sites, which may indicate lower seed viability. Otherwise, plants did not show consistent differences between the two habitat types. Competition strongly reduced performance of D. graveolens in both the greenhouse and in the field, but plants originating from vegetated sites did not show enhanced competitive ability. Our findings show no evidence of adaptive differentiation between D. graveolens populations from roadside and vegetated habitats to date, suggesting that invasiveness in grasslands has not been enhanced by rapid evolution in the 40 + years since this species was introduced to California. Evolutionary constraints or potentially high levels of gene flow at this small scale may limit adaptation to novel habitats along roadsides.

Predicting species invasiveness with genomic data: Is genomic offset related to establishment probability?

Predicting the risk of establishment and spread of populations outside their native range represents a major challenge in evolutionary biology. Various methods have recently been developed to estimate population (mal)adaptation to a new environment with genomic data via so-called Genomic Offset (GO) statistics. These approaches are particularly promising for studying invasive species but have still rarely been used in this context. Here, we evaluated the relationship between GO and the establishment probability of a population in a new environment using both in silico and empirical data. First, we designed invasion simulations to evaluate the ability to predict establishment probability of two GO computation methods (Geometric GO and Gradient Forest) under several conditions. Additionally, we aimed to evaluate the interpretability of absolute Geometric GO values, which theoretically represent the adaptive genetic distance between populations from distinct environments. Second, utilizing public empirical data from the crop pest species Bactrocera tryoni, a fruit fly native from Northern Australia, we computed GO between "source" populations and a diverse range of locations within invaded areas. This practical application of GO within the context of a biological invasion underscores its potential in providing insights and guiding recommendations for future invasion risk assessment. Overall, our results suggest that GO statistics represent good predictors of the establishment probability and may thus inform invasion risk, although the influence of several factors on prediction performance (e.g., propagule pressure or admixture) will need further investigation.

Predicting global geographical distribution and latitudinal suitability gradient for light brown apple moth

Epiphyas postvittana, commonly known as the light brown apple moth (LBAM), is native to Australia and has a restricted global distribution. Its polyphagous nature and the recent surge in interceptions have emphasized the need for focused risk assessments to guide effective measures to curb the entry of this pest into new countries. This study aimed to perform a detailed global invasion risk assessment using an optimized MaxEnt model that incorporated 19 bioclimatic variables and elevation. The predictive outcomes underscored the significance of key variables, specifically the minimum temperature of the coldest month (bio6), precipitation of the driest month (bio14), and precipitation of the coldest quarter (bio19), in shaping the potential geographical distribution of LBAM. Regions beyond the existing range, including the southeastern United States, southern Brazil, eastern Argentina, Uruguay, southern Chile, and various Western European countries, were identified as susceptible to invasion and establishment by LBAM. An increase in suitability was observed above 45°N and 40°S under future climate scenario. With respect to climate change, LBAM would expand its potential range in Western Europe and the United States, especially under SSP5-8.5, in the 2050s. An upward trend in the latitudinal suitability gradient for LBAM in mid-high latitude areas implies that amid changing climate conditions, LBAM may find favorable habitats in these regions. For countries and regions with invasion risk, it is imperative to implement corresponding inspections and quarantine measures to thwart the introduction of LBAM, particularly in countries with established trade ties with invaded regions.

Assessment of American Bullfrog (Lithobates catesbeianus) spreading in the Republic of Korea using rule learning of elementary cellular automata

The spread of American Bullfrog has a significant impact on the surrounding ecosystem. It is important to study the mechanisms of their spreading so that proper mitigation can be applied when needed. This study analyzes data from national surveys on bullfrog distribution. We divided the data into 25 regional clusters. To assess the spread within each cluster, we constructed temporal sequences of spatial distribution using the agglomerative clustering method. We employed Elementary Cellular Automata (ECA) to identify rules governing the changes in spatial patterns. Each cell in the ECA grid represents either the presence or absence of bullfrogs based on observations. For each cluster, we counted the number of presence location in the sequence to quantify spreading intensity. We used a Convolutional Neural Network (CNN) to learn the ECA rules and predict future spreading intensity by estimating the expected number of presence locations over 400 simulated generations. We incorporated environmental factors by obtaining habitat suitability maps using Maxent. We multiplied spreading intensity by habitat suitability to create an overall assessment of bullfrog invasion risk. We estimated the relative spreading assessment and classified it into four categories: rapidly spreading, slowly spreading, stable populations, and declining populations.

Climate Change Facilitates the Potentially Suitable Habitats of the Invasive Crop Insect Ectomyelois ceratoniae (Zeller)

Invasive alien insects directly or indirectly driven by climate change threaten crop production and increase economic costs worldwide. Ectomyelois ceratoniae (Zeller) is a highly reproductive invasive crop insect that can severely damage fruit commodities and cause significant economic losses globally. Estimating the global potentially suitable habitats (PSH) of E. ceratoniae is an important aspect of its invasive risk assessment and early warning. Here, we constructed an optimized MaxEnt model based on the global distribution records of E. ceratoniae, and nine environmental variables (EVs), to predict its global PSH under current and future climates. Our results showed that the RM value was 2.0 and the mean area under receiver operating characteristic curve (AUC) value was 0.972, indicating the high accuracy of the optimal MaxEnt model. The mean temperature of driest quarter (bio9, 50.2%), mean temperature of wettest quarter (bio8, 16.9%), temperature seasonality (bio4, 9.7%), and precipitation of coldest quarter (bio19, 9.1%) were the significant EVs affecting its distribution patterns. The global PSH of E. ceratoniae are mainly located in western Asia under current climate scenarios (687.57 × 104 km2), which showed an increasing trend under future climate scenarios. The PSH of E. ceratoniae achieved the maximum under the shared socioeconomic pathway (SSP) 1–2.6 in the 2030s and under the SSP2-4.5 in the 2050s. The increased PSH of E. ceratoniae are mainly located in southwestern Asia, northwestern Europe, northwestern South America, northwestern North America, southern Oceania, and northwestern Africa. Our findings suggest that quarantine officials and governmental departments in the above high-risk invasion areas should strengthen monitoring and early warning to control E. ceratoniae; in particular, cultural measures should be taken in areas where its further expansion is expected in the future.

MRI for risk stratification of muscle invasion by upper tract urothelial carcinoma: a feasibility study

BackgroundMagnetic resonance imaging (MRI) is recommended in patients with upper tract urothelial carcinoma (UTUC) only when computed tomography (CT) is contraindicated. However, CT does not allow distinguishing ureter wall layers, making impossible to assess muscle invasion, a factor contributing to differentiate high- from low-risk UTUCs, which require different therapeutic approaches. We investigated the feasibility of MRI assessment of UTUC muscle invasion.MethodsFrom June 2022 to March 2023, we prospectively enrolled patients suspected of UTUC, i.e., with positive urinary tract ultrasound and/or ureteroscopy, or positive urinary cytology and/or hematuria but negative cystoscopy and bladder ultrasound at two Italian centers. They underwent CT followed by MRI (≤ 24 h apart), independently reported by two experienced radiologists, blinded from histopathology results. After imaging confirmation, they all underwent nephroureterectomy and histopathology analysis. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the receiver operating characteristic curve (AUC) were calculated.ResultsThirty-nine lesions were detected in 30 patients on both CT and MRI. Muscle-invasive UTUC prevalence was 81% (21/26) among patients with MRI suspicion and 8% (1/13) among those without MRI suspicion (p < 0.001). Considering the assessment of muscle-layer invasion, the more experienced reader achieved 95% sensitivity (95% confidence interval 82−100), 71% specificity (47−88), 81% PPV (63−93), 92% NPV (70−100), 85% accuracy (67−96), and 0.84 AUC (0.70−0.98). Inter-reader agreement was substantial (κ = 0.73).ConclusionsMRI showed a promising diagnostic performance for the assessment of UTUC risk of muscle invasion.Relevance statementResulting feasible both in technical and clinical terms, MRI could be helpful for upper tract urothelial carcinomas pre-operative risk stratification, to allow a personalized patients’ management. These results play in favor of promoting preoperative MRI for UTUC, as already proven for bladder cancer.Key points• Muscle invasion is a crucial information for tailored treatments of upper tract urothelial carcinomas.• CT does not distinguish ureter wall layers, making muscle invasion risk assessment not feasible.• MRI was shown to reliably diagnose muscle-layer invasion by upper tract urothelial carcinomas (sensitivity 95%, specificity 71%).Graphical

How well do trees fit the city? Lessons from an urban tree survey in Córdoba, Argentina.

Background and aim: Urban tree composition is generally highly diverse and largely non-native in origin. Species selection, however, should consider not only future predicted climate and species' environmental requirements, but also the regional invasion risk assessments and the respiratory health potential of species. Here, we assessed the suitability of the most frequent urban tree species by using available online databases. M&amp;M: Suitability was assessed by analysing tree attributes in relation to their invasive status in the study area, the respiratory health potential of the species given by their known pollination strategy and the bioclimatic match of species with the plantation zone. We grouped species according to their nativeness as non-native, regionally native and local native. After filtering those with undesirable characteristics (i.e., invasive species and/or with anemophilous pollination strategy) we analysed three bioclimatic variables: annual temperature, annual precipitation and precipitation seasonality. Results: Results showed that the composition of Córdoba's street trees is heavily biased towards non-native species, many of which are invasive, have an anemophilous pollination strategy and/or exhibit high bioclimatic mismatch. In addition, the strong bias in the current tree composition towards species from more humid, temperate regions is evident. Conclusions: Our results highlight the importance of revising current policy decisions to adjust urban tree flora in the face of climate change.

Monetary impacts should be considered in biological invasion risk assessments

Abstract Biological invasions pose a significant threat to biodiversity, planetary health and socio‐economies worldwide. Prioritizing invasive species for management is crucial, given the limited economic resources available. While global invasive species risk scoring systems exist, they currently do not incorporate fully quantitative impact metrics. However, the emergence of monetary costs as a standardized measure of invasive species impact across contexts provides an opportunity to compare species in risk assessments. Synthesis and applications: The economic impact of invasive species should now be considered in impact and risk assessments worldwide, given the growing availability and synthesis of cost data. This will allow for more informed decision‐making regarding the allocation of resources for invasive species management.

Climate change impacts the risk of invasion of eucalypt pests in Brazil

Abstract Identifying areas at high risk of invasion is considered an important step for prioritization of phytosanitary measures to prevent biological invasions. Because climate change is likely to affect species' habitat suitability, ideally this should be considered in invasion risk assessments. We evaluated whether climate change affects habitat suitability and invasion risk of eight potentially invasive Eucalyptus pests in Brazil: Eucalyptolyma maideni, Mnesampela privata, Ophelimus maskelli, Orgyia postica, Paropsis atomaria, Paropsisterna beata, Paropsisterna cloelia and Trachymela sloanei. The shifts in the distribution range varied depending on the species, year and climate change scenario. While a decrease in the distribution range was predicted for four species, an increase was predicted for two species. Moreover, a decrease and/or increase in distribution depending on the climate change scenario was predicted for two species. Increases in invasion risk were predicted in major Eucalyptus‐producing areas near international airports and high cargo volume seaports. Our study provides an important contribution to knowledge on the effects of climate change on the distribution of eight potentially invasive Eucalyptus pests and reinforces the need to consider the changes in species' habitat suitability in invasion risk assessments.

Herbaceous ornamental plants with conspicuous aesthetic traits contribute to plant invasion risk in subtropical urban parks

Global ornamental horticulture is a major pathway for plant invasions, while urban parks are key areas for introducing non-native ornamental plants. To react appropriately to the challenges (e.g., biological invasion issues) and opportunities (e.g., urban ecosystem services) of herbaceous ornamentals in urban parks, we conducted a comprehensive invasive risk assessment in 363 urban parks in Chongqing, a subtropical city in China. The results found more than 1/3 of the 119 non-native species recorded in urban parks had a high invasion risk, and more than five species had potential invasion risk in 96.29% of the study area, indicating herbaceous ornamentals in urban parks are potentially a pool of invasive species that deserves attention. Moreover, humans have chosen herbaceous ornamentals with more aesthetic characteristics in urban parks, where exotic species were more prominent than native species in floral traits, such as more conspicuous flowers and longer flowering periods. The findings can inform urban plant management, provide an integrated approach to assessing herbaceous ornamentals' invasion risk, and offer insights into understanding the filtering effects of human aesthetic preferences.

The Global Epidemic of Bactrocera Pests: Mixed-Species Invasions and Risk Assessment.

Throughout the past century, the global spread of Bactrocera pests has continued to pose a significant threat to the commercial fruit and vegetable industry, resulting in substantial costs associated with both control measures and quarantine restrictions. The increasing volume of transcontinental trade has contributed to an escalating rate of Bactrocera pest introductions to new regions. To address the worldwide threat posed by this group of pests, we first provide an overview of Bactrocera. We then describe the global epidemic, including border interceptions, species diagnosis, population genetics, geographical expansion, and invasion tracing of Bactrocera pests. We further consider the literature concerning the invasion co-occurrences, life-history flexibility, risk assessment, bridgehead effects, and ongoing implications of invasion recurrences, as well as a case study of Bactrocera invasions of California. Finally, we call for global collaboration to effectively monitor, prevent, and control the ongoing spread of Bactrocera pests and to share experience and knowledge to combat it.

Ginger, Galangal, and Turmeric Production in Florida

Ginger, galangal, and turmeric (Figure 1) are emerging crops for Florida production. All of these plants are in the Zingiberaceae family and share most aspects of their production. This bulletin describes production in containers or the field under Florida conditions to help guide growers interested in ginger, turmeric, and galangal production or expanding their market. All species have been evaluated by the UF/IFAS Assessment of Non-native Plants in Florida’s Natural Areas (UF/IFAS Assessment) using the Predictive Tool (an invasion risk assessment) and all present a low risk of invasion in Florida (https://assessment.ifas.ufl.edu).

Preoperative assessment of microvascular invasion risk using gadoxetate-enhanced MRI for predicting outcomes after liver transplantation for single hepatocellular carcinoma within the Milan criteria.

To compare therapeutic outcomes after liver transplantation (LT) between hepatocellular carcinomas (HCC) with low and high risk for microvascular invasion (MVI) within the Milan criteria evaluated preoperatively. Eighty patients with a single HCC who underwent LT as the initial therapy between 2008 and 2017 were included from two tertiary referral medical centers in a HBV-predominant population. A preoperative MVI-risk model was used to identify low- and high-risk patients. Recurrence-free survival (RFS) after LT between the two risk groups was compared using Kaplan-Meier curves with the log-rank test. Prognostic factors for RFS were identified using a multivariable Cox hazard regression analysis. Eighty patients were included (mean age, 51.8 years +/- 7.5 [standard deviation], 65 men). Patients were divided into low-risk (n = 64) and high-risk (n = 16) groups for MVI. The RFS rates after LT were significantly lower in the MVI high-risk group compared to the low-risk group at 1 year (75.0% [95% CI: 56.5-99.5%] vs. 96.9% [92.7-100%], p = 0.048), 3 years (62.5% [42.8-91.4%] vs. 95.3% [90.3-100%], p = 0.008), and 5 years (62.5% [42.8-91.4%] vs. and 95.3% [90.3-100%], p = 0.008). In addition, multivariable analysis showed that MVI high risk was the only significant factor for poor RFS (p = 0.016). HCC patients with a high risk of MVI showed significantly lower RFS after LT than those without. This model could aid in selecting optimal candidates in addition to the Milan criteria when considering upfront LT for patients with HCC if alternative treatment options are available. High risk for microvascular invasion (MVI) in hepatocellular carcinoma patients lowered recurrence-free survival after liver transplantation, despite meeting the Milan criteria. Identifying MVI risk could aid candidate selection for upfront liver transplantation, particularly if alternative treatments are available. • A predictive model-derived microvascular invasion (MVI) high- and low-risk groups had a significant difference in the incidence of MVI on pathology. • Recurrence-free survival after liver transplantation (LT) for single hepatocellular carcinoma (HCC) within the Milan criteria was significantly different between the MVI high- and low-risk groups. • The peak incidence of tumor recurrence was 20 months after liver transplantation, probably indicating that HCC with high risk for MVI had a high risk of early (≤ 2 years) tumor recurrence.

Global distribution modelling, assessment of the spatial invasion risk of Asian pied starling (Sturnus contra Linnaeus, 1758) under climate change

Global distribution modelling, assessment of the spatial invasion risk of Asian pied starling (Sturnus contra Linnaeus, 1758) under climate change

Incorporating adaptive genomic variation into predictive models for invasion risk assessment

Global climate change is expected to accelerate biological invasions, necessitating accurate risk forecasting and management strategies. However, current invasion risk assessments often overlook adaptive genomic variation, which plays a significant role in the persistence and expansion of invasive populations. Here we used Molgula manhattensis, a highly invasive ascidian, as a model to assess its invasion risks along Chinese coasts under climate change. Through population genomics analyses, we identified two genetic clusters, the north and south clusters, based on geographic distributions. To predict invasion risks, we employed the gradient forest and species distribution models to calculate genomic offset and species habitat suitability, respectively. These approaches yielded distinct predictions: the gradient forest model suggested a greater genomic offset to future climatic conditions for the north cluster (i.e., lower invasion risks), while the species distribution model indicated higher future habitat suitability for the same cluster (i.e, higher invasion risks). By integrating these models, we found that the south cluster exhibited minor genome-niche disruptions in the future, indicating higher invasion risks. Our study highlights the complementary roles of genomic offset and habitat suitability in assessing invasion risks under climate change. Moreover, incorporating adaptive genomic variation into predictive models can significantly enhance future invasion risk predictions and enable effective management strategies for biological invasions in the future.