Potential Distribution Of Invasive Species Research Articles

Effect of study area extent on the potential distribution of Species: A case study with models for Raoiella indica Hirst (Acari: Tenuipalpidae)

Ecological niche models are used to quantify the relationships between known occurrence records of a given species and environmental variables at these locations. Maxent is among the most widely used algorithms for modeling species distribution and has demonstrated better performance compared to other methods. However, the extent of the study area is a critical issue in the development of presence-only species distribution models because it encompasses the region used to extract the background points employed to characterize the environments accessible to the species. Thus, this study evaluated the effect of the extension of the study area on the species distribution modeling with the Maxent algorithm and occurrence data from the invasive species Raoiella indica Hirst (Acari: Tenuipalpidae). The increase in the study area extent inflated most of the threshold-dependent and -independent metrics used to assess model performance. The selection of the study area also affected the predicted suitable areas for the species (its potential distribution). The analysis shows that models developed with smaller study areas resulted in model overfitting and an increase in false-negative predictions. The extent of the area used during model training has a strong influence on the model outputs, with significant consequences for predicting the potential distribution of invasive species and thus for the areas under risk of invasion.

All that changes is not shift: methodological choices influence niche shift detection in freshwater invasive species

Species distribution models are often used to predict the potential distributions of invasive species outside their native ranges and rely on the assumption of realized niche conservatism. Analyses observed that freshwater invasive species often show high degrees of niche expansion, suggesting limited reliability of species distribution models. However, observed niche shifts can arise because of both actual niche shifts, determined by biological factors, and apparent shifts, due to methodological issues. We compared metrics of niche dynamics calculated using different sets of variables to identify factors that could influence the rate of niche shifts. We collected presence‐only data for 40 freshwater invasive animal species, then measured niche shift dynamics using 14 different combinations of environmental variables. Shifts were assessed measuring niche overlap, expansion and unfilling, and testing for niche conservatism. We then built generalized linear mixed models relating niche shifts to methodological choices and biological features. Our results showed that methodological choices strongly affected all the considered niche dynamics metrics, while the effects of biological features were less prominent. Moreover, different niche dynamic measures sometimes provided contradictory assessments of niche conservatism. Niche analyses are powerful tools to predict areas at risk of invasion, but inappropriate methodological choices can lead to apparent niche shifts, questioning niche model reliability and biological interpretation. The high rate of niche expansion observed in freshwater invasive species highlights the importance of delineating objective criteria to determine the set of variables to be used in niche dynamic assessments.

Current and future potential distribution of the invasive scale Ceroplastes rusci (L., 1758) (Hemiptera: Coccidae) under climate niche.

The fig wax scale, Ceroplastes rusci is an invasive pest that feeds on more than 94 genera from 52 families that is spread across 60 countries, causing negative impacts to agriculture and forestry. Understanding the potential distribution of invasive species under climate change is crucial for the management and monitoring purposes. Thus, we predicted the potential distribution areas of C. rusci using Maximum Entropy (MaxEnt) based on the occurrence data and environmental variables under current and future climatic scenarios. Our results showed that the temperature annual range (Bio 7) and mean temperature of the warmest quarter (Bio 10) attributed to a higher contribution to the current model of the distribution of C. rusci. The potential distribution maps illustrated the main concentrated areas of C. rusci which included South America, Africa, Asia, and Oceania. In addition, potential range expansions or reductions were predicted under different future climate change scenarios, which showed that the total suitable areas of the fig wax scale presented an increasing trend until 2100. Our study provides significant data to understand the potential distribution of C. rusci around the world. It also serves as an early warning for the highly suitable habitat areas that even offers a platform to the currently non-infested regions or countries who are yet to develop monitoring strategies in response to the possible C. rusci outbreak. © 2022 Society of Chemical Industry.

Spread risk assessment of invasive axis deer using bioclimatic niche models

Ecological niche models are useful tools for predicting and managing biological invasions. However, their reliable implementation and derived applications could be biased by niche shifts among native and invaded areas and by non-equilibrium dynamics. We calculated bioclimatic niche overlap between native and invaded ranges, and tuned species distribution models trained on both the native range (native model), and the native and invasive ranges (complete model) for the axis deer (Axis axis) to assess its invasion risk potential worldwide. Our results suggest that the axis deer shifted its realized niche in some invaded areas where most of the climatic niche available was contained within the environmental conditions from the native range. We found signals of non-equilibrium dynamics between native and invaded niches due to several unfilled areas in invaded regions. The native and complete models are similar in their projection for the equatorial region but differ significantly in the tropical regions. The first fails to predict areas where the species was successfully established. This difference reinforces the idea of using all the occurrences for predicting possible invaded areas, and that invasive species can somehow shift their niche. The complete model predicted that the most climatically suitable regions for the species worldwide were in tropical and subtropical climates, particularly the Neotropics and Afrotropics. While the species has been already established in the Neotropics, it hasn’t in the latter. Therefore, our work emphasized the importance of a nuanced understanding of the potential distribution of invasive species to inform more effective management measures.

Testing MaxEnt model performance in a novel geographic region using an intentionally introduced insect

Species distribution models (SDM's), such as the popular MaxEnt software, are frequently used to guide conservation programmes, predict the potential distribution of invasive species, forecast the impacts of climate change and develop applied ecological research (e.g., biological control). Many applications of these models require that SDM's be transferred in either space and/or time. However, few studies to date have tested the transferability, and thus usefulness, of MaxEnt models using fully independent data. Moreover, numerous authors have raised concerns over how model complexity (controlled primarily by feature class and regularisation multiplier settings) may affect MaxEnt model transferability. In this paper, we evaluated the usefulness of MaxEnt models when transferred in space using the native Australian insect Dasineura rubiformis Kolesik (Diptera: Cecidomyiidae) which has been intentionally introduced as a biological control agent of the invasive plant Acacia mearnsii De Wild (Fabaceae) in South Africa. MaxEnt's models were developed using native-range records only (Australia) and projected over South Africa to identify the potential climatic suitability for the insect, using a range of model parameter configurations. Model transferability was assessed using independent post-release data from South Africa. Our results demonstrated that MaxEnt scores were positively correlated with increased probabilities of recording D. rubiformis at field sites in South Africa. However, the accuracy of the MaxEnt models and their climatic suitability projections depended on the parameter configurations (i.e. features classes and regularisation multipliers) used to calibrate the models. Taken together, MaxEnt models can be useful in predicting where a species may establish, even when projected into a novel geographic area, but users need to be aware of how model parameter configurations and model extrapolation can influence model outputs and uncertainty. We demonstrate how statistical validation of the D. rubiformis MaxEnt models can be used to develop an environmental management programme towards improving the biological control of A. mearnsii using D. rubiformis by selecting optimal release sites.

Potential impact of four invasive alien plants on the provision of ecosystem services in Europe under present and future climatic scenarios

Invasive alien species (IAS) are one of the main threats to biodiversity conservation, with significant socio-economic and ecological impacts as they disrupt ecosystem services and compromise human well-being. Global change may exacerbate the impacts of IAS, since rising temperatures and human activities favour their introduction and range expansion. Therefore, anticipating the impacts of biological invasions is crucial to support decision-making for their management. In this work, the potential impacts of four invasive alien plant species: Ailanthus altissima, Baccharis halimifolia, Impatiens glandulifera and Pueraria montana, on the provision of three ecosystem services in Europe were evaluated under current and future climate change scenarios. Using a risk analysis protocol, we determined that the most affected services are food provisioning, soil erosion regulation and the maintenance of biological diversity. To evaluate future impacts, species distribution models were calibrated using bioclimatic, environmental and human impact variables. We found that most of continental Europe is suitable for the establishment of A. altissima, B. halimifolia and I. glandulifera, while the potential distribution of P. montana is more limited. Models anticipate a shift in the distribution range for the species towards the north and east of Europe under future scenarios. Bivariate analysis allowed the identification of trends for future impacts in ecosystem services by simultaneously visualising the potential distribution of invasive species and the provision of ecosystem services. Our models project an increase in critical and high impact areas on the analysed ecosystem services, with Western Europe and the British Isles as the most affected regions. In comparison, lower impacts are projected for the Mediterranean region, likely as a consequence of the northwards expansion of invaders. Measures need to be taken to mitigate the expansion and impact of invasive species as our work shows that it can jeopardise the provision of three key services in Europe.

Supraspecific units in correlative niche modeling improves the prediction of geographic potential of biological invasions.

BackgroundBiological invasions rank among the most significant threats to biodiversity and ecosystems. Correlative ecological niche modeling is among the most frequently used tools with which to estimate potential distributions of invasive species. However, when areas accessible to the species across its native distribution do not represent the full spectrum of environmental conditions that the species can tolerate, correlative studies often underestimate fundamental niches.MethodsHere, we explore the utility of supraspecific modeling units to improve the predictive ability of models focused on biological invasions. Taking into account phylogenetic relationships in correlative ecological niche models, we studied the invasion patterns of three species (Aedes aegypti, Pterois volitans and Oreochromis mossambicus).ResultsUse of supraspecific modeling units improved the predictive ability of correlative niche models in anticipating potential distributions of three invasive species. We demonstrated that integrating data on closely related species allowed a more complete characterization of fundamental niches. This approach could be used to model species with invasive potential but that have not yet invaded new regions.

Oh the places they’ll go: improving species distribution modelling for invasive forest pests in an uncertain world

Species distribution modelling (SDM) is a valuable tool for predicting the potential distribution of invasive species across space and time. Maximum entropy modelling (MaxEnt) is a popular choice for SDM, but questions have been raised about how these models are developed. Without biologically informed baseline assumptions, complex default SDM models could be selected, even though alternative settings may be more appropriate. Here we explored the effects of various SDM design strategies on distribution mapping of four forest invasive species (FIS) in Canada. We found that if we ignored the underlying FIS biology such as use of biologically relevant predictors, appropriate feature selection and inclusion of dispersal and biotic interactions when we developed our SDMs, we obtained complex SDMs (default) that provided an incomplete picture of the potential FIS invasion. We recommend simplifying SDM complexity and including biologically informed assumptions to achieve more accurate dispersal predictions, particularly when projecting FIS spread across time. We strongly encourage SDM users to perform species-specific tuning when modeling FIS distributions with MaxEnt to determine the best SDM design.

Modeling of the potential distribution of Eichhornia crassipes on a global scale: risks and threats to water ecosystems

The water hyacinth (Eichhornia crassipes) is listed among the 100 worst invasive plants and was ranked as the 11th worst invasive species in Europe, being a threat to aquatic biodiversity and water-provision. Predicting species distribution is the first step to understanding niche suitability, forecasting the invasion impact and building resilience against this species. In this study, we used a potential distribution model to assess the global risk of water hyacinth invasion by overlapping maps of highly suitable areas for water hyacinth occurrence and areas of biological importance and water scarcity.  The MaxEnt - Maximum Entropy algorithm was used in the construction of the model and included five global bioclimatic layers and one of urbanized areas. Among the variables used, occurrence is mainly explained by urban areas, highlighting the importance of cities as a source or dispersion mechanism of the water hyacinth. Global biodiversity hotspots are predominantly situated in high suitability regions for the species. Ramsar sites and global protected areas are at a lower risk level compared to hotspots; however, future climate change and urban growth scenarios could put these areas at higher risk for invasion. Threats posed by the water hyacinth are possibly more acute in regions suffering from current or chronic drought. The results suggest that niche models that do not consider anthropic variables may be underestimating potential distribution of invasive species. Furthermore, the ecological plasticity of the water hyacinth and its close association with cities increase the concern about the impact of this species on the environment and on water security. 

Evidence of ecological niche shift in Rhododendron ponticum (L.) in Britain: Hybridization as a possible cause of rapid niche expansion.

Biological invasions threaten global biodiversity and natural resources. Anticipating future invasions is central to strategies for combating the spread of invasive species. Ecological niche models are thus increasingly used to predict potential distribution of invasive species. In this study, we compare ecological niches of Rhododendron ponticum in its native (Iberian Peninsula) and invasive (Britain) ranges. Here, we test the conservation of ecological niche between invasive and native populations of R. ponticum using principal component analysis, niche dynamics analysis, and MaxEnt‐based reciprocal niche modeling. We show that niche overlap between native and invasive populations is very low, leading us to the conclusion that the two niches are not equivalent and are dissimilar. We conclude that R. ponticum occupies novel environmental conditions in Britain. However, the evidence of niche shift presented in this study should be treated with caution because of nonanalogue climatic conditions between native and invasive ranges and a small population size in the native range. We then frame our results in the context of contradicting genetic evidence on possible hybridization of this invasive species in Britain. We argue that the existing contradictory studies on whether hybridization caused niche shift in R. ponticum are not sufficient to prove or disprove this hypothesis. However, we present a series of theoretical arguments which indicate that hybridization is a likely cause of the observed niche expansion of R. ponticum in Britain.

The effect of local species composition on the distribution of an avian invader

Estimating the potential distribution of invasive species has been primarily achieved by employing species distribution models (SDM). Recently introduced joint species distribution models (JSDM) that include species interactions are expected to improve model output. Here we compare the predictive ability of SDM and JSDM by modelling the distribution of one of the most prolific avian invaders in the world, the common myna (Acridotheres tristis), in a recent introduction in Israel. Our results indicate that including information on the local species composition did not improve model accuracy, possibly because of the unique characteristics of this species that include broad environmental tolerance and behavior flexibility. However, the JSDM provided insights into co-occurrence patterns of common mynas and their local heterospecifics, suggesting that at this time point, there is no evidence of species exclusion by common mynas. Our findings suggest that the invasion potential of common mynas depends greatly on urbanization and less so on the local species composition and reflect the major role of anthropogenic impact in increasing the distribution of avian invaders.

InvasiBES: Understanding and managing the impacts of Invasive alien species on Biodiversity and Ecosystem Services

Invasive Alien Species (IAS) are amongst the most significant drivers of species extinction and ecosystem degradation, causing negative impacts on ecosystem services and human well-being. InvasiBES, a project funded by BiodivERsA-Belmont Forum for 2019–2021, will use data and models across scales, habitats and species to understand and anticipate the multi-faceted impacts of IAS and to provide tools for their management. Using Alien Species Narratives as reference, we will design future intervention scenarios focused on prevention, control and eradication of IAS in Europe and the United States, through a participatory process bringing together the expertise of scientists and stakeholders. We will also adapt current impact assessment protocols to assess both the detrimental and beneficial impacts of IAS on biodiversity and ecosystem services. This information will then be combined with maps of the potential distribution of Invasive Species of Interest in Europe under current and future climate-change scenarios. Likewise, we will anticipate areas under risk of invasion by range-shifting plants of concern in the US. Finally, focusing on three local-scale studies that cover a range of habitats (freshwater, terrestrial and marine), invasive species (plants and animals) and ecosystem services (supporting, provisioning, regulating and cultural), we will use empirical field data to quantify the real-world impacts of IAS on biodiversity and ecosystem services and calculate indicators of ecosystem recovery after the invader is removed. Spatial planning tools (InVEST) will be used to evaluate the costs and benefits of species-specific intervention scenarios at the regional scale. Data, models and maps, developed throughout the project, will serve to build scenarios and models of biodiversity and ecosystem services that are relevant to underpin management of IAS at multiple scales.

Combining ecological niche modeling with genetic lineage information to predict potential distribution of Mikania micrantha Kunth in South and Southeast Asia under predicted climate change

Ecological niche models (ENM), an effective tool to predict the potential distribution of invasive species, are often built on assumptions of niche conservatism between native and invasive ranges and genetic uniformity of the invasive population. In this study, we have incorporated genetic information with ENM to generate projected distribution of the invasive species Mikania micrantha for which two genetic clusters were identified in South and Southeast Asia. Climatic niches were compared between native and invasive ranges, as well as between invasive ranges of two lineages by using multivariate and univariate analyses. Ecological niche models were built with MaxEnt, using occurrence data of two lineages separately, together and also using native range data. Predictive abilities of the models were compared and potential distributions of the two lineages were predicted under present and future climate scenarios. The models were projected on the native range to identify climatically suitable areas for each lineage. Significant differences between climatic niches of the two invasive lineages were found and unique climatically suitable areas for each lineage were identified. A large area of South and Southeast Asia was found to be climatically suitable for both lineages. Under climate change scenarios, pole ward range expansion for one lineage and decrease in range size along marginal areas for another lineage were predicted. However, high amount of niche unfilling for both the lineages indicated that if introduced, the lineages can establish in cold and dry areas of the invasive range. The evidence of niche abandonment between native and invasive ranges indicated presence of other lineages in its native range which are not yet introduced in this region. These findings provided baseline data for implementing management strategies at early stage of invasion and quarantine measures to protect this region from future invasions. Climatically suitable areas in the native range were identified for both lineages which can be prioritized for conducting surveys for identification of source populations and biological control agents. Our study highlights the importance of integrating genetic data in future ENM approaches to have finer scale information of species’ distribution, which can be utilized to develop region-specific and climate change-integrated management strategies for invasive species.

Staying ahead of invaders: using species distribution modeling to predict alien species’ potential niche shifts

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 612:127-140 (2019) - DOI: https://doi.org/10.3354/meps12878 Staying ahead of invaders: using species distribution modeling to predict alien species’ potential niche shifts Nicolás Battini1,2,3,*, Nahuel Farías4,5, Clara Belen Giachetti1,2,3, Evangelina Schwindt1,2, Alejandro Bortolus1,6 1Grupo de Ecología en Ambientes Costeros (GEAC), Puerto Madryn 9120, Argentina 2Instituto de Biología de Organismos Marinos (IBIOMAR-CONICET), Puerto Madryn 9120, Argentina 3Facultad de Ciencias Exactas y Naturales (UBA), Buenos Aires 1428, Argentina 4Laboratorio de Invertebrados, FCEyN-UNMDP, Mar del Plata 7602, Argentina 5Instituto de Investigaciones Marinas y Costeras (IIMyC-CONICET), Mar del Plata 7602, Argentina 6Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), CONICET, Puerto Madryn 9120, Argentina *Corresponding author: battini@cenpat-conicet.gob.ar ABSTRACT: Early detection and rapid response are essential to prevent invasive species from thriving in marine environments following their introduction. Species distribution models (SDMs) are widely used to predict the potential distribution of invasive species, providing excellent tools for the design of strategies to prevent or mitigate impacts of non-native species. Niche shifts are among the major drawbacks in the use of SDMs, leading scientists to formulate inaccurate predictions. In this work, we tested the performance of 3 different SDMs (Bioclim, Mahalanobis distance and Maxent) to predict the distribution of a niche-shifting invasive species using native data only. As a model organism, we used the neurotoxic sea-slug Pleurobranchaea maculata, which was recently introduced into the southwestern Atlantic, where it has undergone a niche shift. Our results show that Maxent outperforms the other modeling techniques in predicting the invasive distribution, but that Bioclim provides the most accurate outputs, minimizing over- and underpredictions. Our study strongly suggests that niche decomposition can provide important evidence for the underlying causes of niche shifts, aiding our understanding of why they occur and how they can be addressed by SDMs. This approach will improve the interpretation of SDMs in order to predict the potential spread of invasive species worldwide. KEY WORDS: Pleurobranchaea maculata · Invasive species · Species distribution · SDM · Ecological niche · Niche shift · Neurotoxins Full text in pdf format Supplementary material PreviousNextCite this article as: Battini N, Farías N, Giachetti CB, Schwindt E, Bortolus A (2019) Staying ahead of invaders: using species distribution modeling to predict alien species’ potential niche shifts. Mar Ecol Prog Ser 612:127-140. https://doi.org/10.3354/meps12878 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 612. Online publication date: March 07, 2019 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2019 Inter-Research.

High invasion potential of Hydrilla verticillata in the Americas predicted using ecological niche modeling combined with genetic data.

Ecological niche modeling is an effective tool to characterize the spatial distribution of suitable areas for species, and it is especially useful for predicting the potential distribution of invasive species. The widespread submerged plant Hydrilla verticillata (hydrilla) has an obvious phylogeographical pattern: Four genetic lineages occupy distinct regions in native range, and only one lineage invades the Americas. Here, we aimed to evaluate climatic niche conservatism of hydrilla in North America at the intraspecific level and explore its invasion potential in the Americas by comparing climatic niches in a phylogenetic context. Niche shift was found in the invasion process of hydrilla in North America, which is probably mainly attributed to high levels of somatic mutation. Dramatic changes in range expansion in the Americas were predicted in the situation of all four genetic lineages invading the Americas or future climatic changes, especially in South America; this suggests that there is a high invasion potential of hydrilla in the Americas. Our findings provide useful information for the management of hydrilla in the Americas and give an example of exploring intraspecific climatic niche to better understand species invasion.

A unified framework to model the potential and realized distributions of invasive species within the invaded range

AbstractAimTo propose a species distribution modelling framework and its companion “iSDM” R package for predicting the potential and realized distributions of invasive species within the invaded range.LocationNorthern France.MethodsThe non‐equilibrium distribution of invasive species with the environment within the invaded range affects the environmental representativeness of species presence–absence data collected from the field and introduces uncertainty in observed absences as these may either reflect unsuitable sites or be incidental. To address these issues, we here propose an environmental systematic sampling design to collect presence–absence data from the field and a probability index to sort and subsequently separate environmental absences (EAs: reflecting environmentally unsuitable sites) from dispersal‐limited absences (DLAs: reflecting sites out of dispersal reach). We first conducted a comprehensive test based on a virtual species to evaluate the performance of our framework. Then, we applied it on different life stages of a non‐native tree species (Prunus serotina Ehrh.) invasive in Europe.ResultsRegarding the potential distribution, we found higher model performances for both the virtual species (true skill statistics (TSS) > 0.75) and P. serotina (TSS ≥ 0.68) after carefully selecting absences with a low probability to be DLAs compared with classical models that incorporate both EAs and DLAs (e.g. TSS = 0.11 for P. serotina with 80% of DLAs). On the contrary, both EAs and DLAs as well as dispersal‐related covariates were needed to capture the realized distribution of both the virtual species and P. serotina.Main ConclusionsOur framework helps overcoming the conceptual and methodological limitations of the disequilibrium in species’ distribution models inherent to invasive species and enables managers to robustly estimate both the realized and potential distributions of invasive species. Although more relevant for modelling the distribution of non‐native species, this framework can also be applied to native species.

A multi-scale modelling framework to guide management of plant invasions in a transboundary context

Attention has recently been drawn to the issue of transboundary invasions, where species introduced and naturalized in one country cross international borders and become problematic in neighbouring countries. Robust modelling frameworks, able to identify the environmental drivers of invasion and forecast the current and future potential distribution of invasive species, are needed to study and manage invasions. Limitations due to the lack of species distribution and environmental data, or assumptions of modelling tools, often constrain the reliability of model predictions. We present a multiscale spatial modelling framework for transboundary invasions, incorporating robust modelling frameworks (Multimodel Inference and Ensemble Modelling) to overcome some of the limitations. The framework is illustrated using Hakea sericea Schrad. (Proteaceae), a shrub or small tree native to Australia and invasive in several regions of the world, including the Iberian Peninsula. Two study scales were considered: regional scale (western Iberia, including mainland Portugal and Galicia) and local scale (northwest Portugal). At the regional scale, the relative importance of environmental predictors sets was evaluated and ranked to determine the main general drivers for the species distribution, while the importance of each environmental predictor was assessed at the local scale. The potential distribution of H. sericea was spatially projected for both scale areas. Model projections for western Iberia suggest that a large area is environmentally suitable in both Portugal and Spain. Climate and landscape composition sets were the most important determinants of this regional distribution of the species. Conversely, a geological predictor (schist lithology) was more important in explaining its local-scale distribution. After being introduced to Portugal, H. sericea has become a transboundary invader by expanding in parts of Galicia (Spain). The fact that a larger area is predicted as environmentally suitable in Spain raises concerns regarding its potential continued expansion. This highlights the importance of transboundary cooperation in the early management of invasions. By reliably identifying drivers and providing spatial projections of invasion at multiple scales, this framework provides insights for the study and management of biological invasions, including the assessment of transboundary invasion risk.

Using maximum entropy to predict the potential distribution of an invasive freshwater snail

Summary Ecological niche modelling is a technique used to estimate potential distributions of invasive species based on available occurrence data and associated environmental conditions. Maximum entropy (Maxent) is a powerful method for ecological niche modelling and yet has only rarely been applied to aquatic species. Here we applied Maxent to estimate the potential distribution of the invasive Chinese mystery snail (Cipangopaludina chinesis) in Wisconsin and analysed several methodological issues associated with using Maxent for lentic species. To generate Maxent estimates of the potential distribution of C. chinesis, we used presence records from 292 lakes, five spatially explicit climatic variables, and two lake‐specific environmental data sets (area, conductivity) from 7995 lakes. Our investigations included three aspects that could affect model prediction accuracy and transferability: (i) combining climate and lake variables into a single data set in two different ways, using each lake as a single observation and as a grid of 1 ha cells; (ii) varying the size of the background data set (locations without presences); and (iii) contrasting environmental conditions between locations with and without C. chinesis. The lake‐based model had higher accuracy than the grid‐based model, although both models had accuracy values indicative of good performance. Conductivity and lake area were important predictor variables for both models, but had higher contribution to the lake‐based model accuracy. Decreasing the background sample size minimally affected model accuracy and thus Maxent can be used even when background sampling does not meet the algorithm's default settings. Lastly, lakes that were environmentally dissimilar from lakes with known C. chinesis records were more likely to be predicted unsuitable by both grid‐based and lake‐based models. Overall, the models predicted high potential suitability across Wisconsin lakes for C. chinesis, especially in lakes ≥60 ha. Our study provides evidence that small or environmentally biased presence data sets may underestimate the number of environmentally suitable locations of invasive species.

Predicting the potential distribution of Lantana camara L. under RCP scenarios using ISI-MIP models

Projections of anthropogenically-induced global climate change and its impacts on potential distributions of invasive species are crucial for implementing effective conservation and management strategies. Lantana camara L., a popular ornamental plant native to tropical America, has become naturalized in some 50 countries and is considered one of the world’s worst weeds. To increase our understanding of its potential extent of spread and examine the responses of global geographic distribution, predictive models incorporating global distribution data of L. camara were generated. These models were used to identify areas of environmental suitability and project the effects of future climate change based on an ensemble of the four global climate models (GCMs) within the Inter-Sectoral Impact Model Intercomparis on Project (ISI-MIP). Each model was run under the four emission scenarios (Representative Concentration Pathways, RCPs) using the Maximum entropy (Maxent) approach. Future model predictions through 2050 indicated an overall expansion of L. camara, despite future suitability varying considerably among continents. Under the four RCP scenarios, the range of L. camara expanded further inland in many regions (e.g. Africa, Australia), especially under the RCP85 emission scenario. The global distribution of L. camara, though restricted within geographical regions of similar latitude as at present (35°N ~ 35°S), was projected to expand equator-ward in response to future climate conditions. Considerable discrepancy in predicted environmental suitability for L. camara among GCMs highlights the complexities of the likely effects of climate change on its potential distribution and the need to improve the reliability of predictions in novel climates.

Using district-level occurrences in MaxEnt for predicting the invasion potential of an exotic insect pest in India

Insect pests are a major threat to agricultural biosecurity across the world, causing substantial economic losses. Majority of the species distribution modeling studies use precise coordinates (latitude/longitude) of species occurrences in MaxEnt (or maximum entropy model). However, lack of precise coordinates of insect pest occurrences at national/regional level is a common problem for many countries including India. This is because of the limited resources, lack of nationally coordinated surveys, and growers/farmers’ privacy issues; district-level occurrences are commonly available (e.g., National Agricultural Pest Information System or NAPIS in the United States; http://pest.ceris.purdue.edu/). We demonstrated the use of MaxEnt to generate a preliminary, district-level map of the potential risk of invasion by an exotic cotton mealybug Phenacoccus solenopsis (Tinsley) (Hemiptera: Pseudococcidae) in India. District-level occurrence data were integrated with bioclimatic variables (values averaged within districts) using MaxEnt. The MaxEnt model performed better than random with an average test AUC value of 0.86 (±0.05). Our model predictions matched closely with the documented occurrence of P. solenopsis in all nine cotton growing states, and also predicted suitable habitats in other districts across India. The greatest threat of P. solenopsis infestations were predicted in most districts of Gujarat, Maharashtra, Andhra Pradesh, southwestern Punjab, northwestern Rajasthan, and western Haryana. Precipitation of coldest quarter, temperature annual range, and precipitation seasonality were the strongest predictors associated with P. solenopsis distribution. Precipitation of coldest quarter was negatively correlated with P. solenopsis occurrence. Mapping the potential distribution of invasive species is an iterative process, and our study is the first attempt to model national-level risk assessment of P. solenopsis in India. Our results can be used for selecting monitoring and surveillance sites and designing local, regional and national-level integrated pest management policies for cotton and other cultivated crops in India. The maps of potential pest distributions are urgently needed by agriculture managers and policymakers. Our approach can be used in other countries that lack precise coordinates of insect pest occurrences and generate a preliminary map of potential risk because it may be too late to wait for the precise coordinates of pest occurrences to generate a perfect map.