Comparative analysis of the population structure of Crematogaster subdentata and Lasius neglectus in the primary and secondary ranges (Hymenoptera: Formicidae)

ABSTRACTAimPredicting the future distribution of invasive species is a current challenge for biodiversity assessment. Species distribution models (SDMs) have long been the state‐of‐the‐art to evaluate suitable areas for new invasions, but they may be limited by truncated niches and the uncertainties of species dispersal. Here, we developed a framework based on hierarchical SDMs and dispersal simulations to predict the future distribution and spread of invasive species at the ecoregion level.LocationCantabrian Mixed Forests Ecoregion (SW Europe) with global distribution data.Time Period1950–2063.Major Taxa StudiedVascular plants.MethodsWe used occurrence data from 102 invasive species to fit SDMs with machine‐learning algorithms and to simulate species dispersal. We combined habitat suitability models based on species' global climatic niches together with regional models including local variables (topography, landscape features, human activity, soil properties) in a hierarchical approach. Then, we simulated species dispersal across suitable areas over the next 40 years, considering species dispersal limitations and climate change.ResultsGlobal climatic niches retained a strong contribution in the hierarchical models, followed by local factors such as human population density, sand content and soil pH. In general, the highest suitability was predicted for warm and humid climates close to the coastline and urbanised areas. The inclusion of dispersal abilities identified different trajectories of geographic spread for individual species, predicting regional hotspots of species invasion. The predictions were more dependent on global suitability and species dispersal rather than climatic warming scenarios.Main ConclusionsThis study provides a comprehensive framework for predicting the regional distribution of invasive species. While hierarchical modelling combines non‐truncated global climatic niches with regional drivers of species invasions, the integration of dispersal simulations allows us to anticipate invasibility in new areas. This framework can be useful to assess the current and future distribution of invasive species pools in biogeographical regions.

Dam-induced difference of invasive plant species distribution along the riparian habitats

Invasive species are one of the most serious threats to biodiversity. Up-to-date and accurate information on the distribution of invasive species is an important biosecurity risk analysis tool. Several databases are available to determine the distributions of invasive and native species. However, keeping this information current is a real challenge. Ants are among the most widespread invasive species. Five species of ants are listed in the IUCN list of damaging invasive species, and many other species are also invasive in the Pacific. We sought to determine and update the distribution information for the 18 most problematic invasive ant species in the Pacific to assist Small Island Developing States with risk analysis. We compared the information on six public databases, conducted a literature review, and contacted experts on invasive ants in the Pacific region to resolve conflicting information. While most public records were accurate we found some new records had not yet been incorporated in the public databases, and some information was inaccurate. The maintenance of public databases faces an enormous challenge in balancing completeness (~15 000 ant species in this case) with accuracy (the impossibility of constantly surveying) and utility.

Climate change and invasive species are critical factors affecting native land snail diversity. In South America, the introduced Giant African Snail (Lissachatina fulica) has spread significantly in recent decades into the habitat of the threatened native giant snails of the genus Megalobulimus. We applied species distribution modeling (SDM), using the maximum entropy method (Maxent) and environmental niche analysis, to understand the ecological relationships between these species in a climate change scenario. We compiled a dataset of occurrences of L. fulica and 10 Megalobulimus species in South America and predicted the distribution of the species in current and future scenarios (2040–2060). We found that L. fulica has a broader environmental niche and potential distribution than the South American Megalobulimus species. The distribution of six Megalobulimus species will have their suitable areas decreased, whereas the distribution of the invasive species L. fulica will not change significantly in the near future. A correlation between the spread of L. fulica and the decline of native Megalobulimus species in South America was found due to habitat alteration from climate change, but this relationship does not seem to be related to a robust competitive interaction between the invasive and native species.

Prediction and modeling using integrated datasets and expertise from various disciplines greatly improve the management of invasive species. So far several attempts have been made to predict, handle, and mitigate invasive alien species impacts using specific efforts from various disciplines. Yet, the most persuasive approach is to better control its invasion and subsequent expansion by making use of cross-disciplinary knowledge and principles. However, the information in this regard is limited and experts from several disciplines have sometimes difficulties understanding well each other. In this respect, the focus of this review was to overview challenges and opportunities in integrating bioclimatic, remote sensing variables, and species distribution models (SDM) for predicting invasive species in data-poor regions. Google Scholar search engine was used to collect relevant papers, published between 2005–2020 (15 years), using keywords such as SDM, remote sensing of invasive species, and contribution of remote sensing in SDM, bioclimatic variables, invasive species distribution in data-poor regions, and invasive species distribution in Ethiopia. Information on the sole contribution of remote sensing and bioclimatic datasets for SDM, major challenges, and opportunities for integration of both datasets are systematically collected, analyzed, and discussed in table and figure formats. Several major challenges such as quality of remotely sensed data and its poor interpretation, inappropriate methods, poor selection of variables, and models were identified. Besides, the availability of Earth Observation (EO) data with high spatial and temporal resolution and their capacity to cover large and inaccessible areas at a reasonable cost, as well as progress in remote sensing data integration techniques and analysis are among the opportunities. Also, the impacts of important sensor characteristics such as spatial and temporal resolution are crucial for future research prospects. Similarly important are studies analyzing the impacts of interannual variability of vegetation and land use patterns on invasive SDM. Urgently needed are clearly defined working principles for the selection of variables and the most appropriate SDM.

Characterizing the distribution, mechanism, and behaviour of invasive species is crucial to implementing an effective plan to protect and manage native grassland ecosystems. Hyperspectral remote sensing has been used to map and monitor invasive species at various spatial and temporal scales. However, most studies focus either on invasive tree species mapping or on the landscape level using coarse-spatial resolution imagery. These coarse-resolution images are not fine enough to distinguish individual invasive grasses, especially in a heterogeneous environment where invasive species are small, fragmented, and co-existent with native plants with similar color and texture. To capture the small yet highly dynamic invasive plants at different stages of the growing season and under various topography and hydrological conditions, we use airborne high-resolution narrow-band hyperspectral imagery (HrHSI) to map invasive species in a heterogeneous grassland ecosystem in southern Ontario, Canada. The results show that there is high spectral and textural separability between two invasive species and between invasive and native plants, leading to an overall species classification accuracy of up to 89.6%. The combination of resultant species-level maps and the digital elevation model (DEM) showed that seasonality is the dominant factor that drives the distribution of invasive species at the landscape level, while small-scale topographic variations partially explain local patches of invasive species. This study provides insights into the feasibility of using HrHSI in mapping invasive species in a heterogeneous ecosystem and offers the means to understand the mechanism and behaviour of invasive species for a more effective grassland management strategy.

Habitat distribution models are increasingly used to predict the potential distributions of invasive species and to inform monitoring. However, these models assume that species are in equilibrium with the environment, which is clearly not true for most invasive species. Although this assumption is frequently acknowledged, solutions have not been adequately addressed. There are several potential methods for improving habitat distribution models. Models that require only presence data may be more effective for invasive species, but this assumption has rarely been tested. In addition, combining modeling types to form "ensemble" models may improve the accuracy of predictions. However, even with these improvements, models developed for recently invaded areas are greatly influenced by the current distributions of species and thus reflect near- rather than long-term potential for invasion. Larger scale models from species' native and invaded ranges may better reflect long-term invasion potential, but they lack finer scale resolution. We compared logistic regression (which uses presence/absence data) and two presence-only methods for modeling the potential distributions of three invasive plant species on the Olympic Peninsula in Washington, USA. We then combined the three methods to create ensemble models. We also developed climate envelope models for the same species based on larger scale distributions and combined models from multiple scales to create an index of near- and long-term invasion risk to inform monitoring in Olympic National Park (ONP). Neither presence-only nor ensemble models were more accurate than logistic regression for any of the species. Larger scale models predicted much greater areas at risk of invasion. Our index of near- and long-term invasion risk indicates that < 4% of ONP is at high near-term risk of invasion while 67-99% of the Park is at moderate or high long-term risk of invasion. We demonstrate how modeling results can be used to guide the design of monitoring protocols and monitoring results can in turn be used to refine models. We propose that, by using models from multiple scales to predict invasion risk and by explicitly linking model development to monitoring, it may be possible to overcome some of the limitations of habitat distribution models.

Plant invasion is highly responsive to rising temperature, altered precipitation and various anthropogenic disturbances. Therefore, climate anomalies might provide opportunities to identify the relationship of past climate in deriving the distribution of invasive species and to detect their probable future distribution. In this work, we studied the correlation of climate anomaly i.e. temperature and precipitation with an indicative map of plant invasive species (1° grid) of India. The indicative map was generated through the plant data available from the project ‘Biodiversity Characterization at Landscape Level’. Climate anomaly was calculated and represented by average temperature and precipitation using ‘Climate Research Unit’ data for the period of 1901 to 2000. A comparison of local geographically weighted regression (GWR) model and a global ordinary least square regression (OLS) model was carried out for statistical analysis to depict the correlation at 1° spatial grids. Overall, 20,501 records with a total of 9112 unique plots and 161 unique invasive species were recorded in the database that shows a maximum of 15 invasive species in a 0.04 ha nested quadrat. Cumulative analysis showed a maximum of 53 invasive species at 1° grid. Individually, GWR could reveal a significant correlation with invasive species distribution for temperature anomaly (r2 = 0.73, AIC= 2206) and precipitation anomaly (r2 = 0.74, AIC= 2221), while OLS model did not offer a good correlation (r2 2400) compared to GWR. Combination of temperature and precipitation anomaly (shared model) showed an improved spatial correlation (r2 > 0.75) using GWR. Variation partitioning revealed the dominant influence (> 0.40 of variation) of temperature anomaly over Deccan Peninsula, Himalaya and North East zone. Influence of precipitation anomaly was more prominent over arid and semi-arid zone explaining > 0.35 of variation. Results revealed the strength of GWR to see the interaction of invasive plant species w.r.t. climate anomalies that explain the influence of spatial variation due to heterogeneity at varying neighbour distances. The significant correlation of invasive species with both the anomalies revealed the affinity of invasive species towards warmer, drier and wet places. This gives an indication that the distribution of invasive species could be triggered by climate anomaly. The use of other predictor variables (i.e. edaphic and anthropogenic) could be an inclusive input in a future perspective.

Invasive tree species threaten ecosystems, natural resources, and managed land worldwide. Land cover has been widely used as an environmental variable for predicting global invasive tree species distributions. Recent studies have shown that consensus land cover data can be an effective tool for species distribution modelling. In this paper, consensus land cover data were used as prediction variables to predict the distribution of the 11 most aggressive invasive tree species globally. We found that consensus land cover data could indeed contribute to modelling the distribution of invasive tree species. According to the contribution rate of land cover to the distribution of invasive tree species, we inferred that the cover classes of open water and evergreen broadleaf trees have strong explanatory power regarding the distribution of invasive tree species. Under consensus land cover changes, invasive tree species were mainly distributed near equatorial, tropical, and subtropical areas. In order to limit the damage caused by invasive tree species to global biodiversity, human life, safety, and the economy, strong measures must be implemented to prevent the further expansion of invasive tree species. We suggest the use of consensus land cover data to model global invasive tree species distributions, as this approach has strong potential to enhance the performance of species distribution modelling. Our study provides new insights into the risk assessment and management of invasive tree species globally.

Biological invasions are synonymous with international trade. The direct effects of trade have largely been quantified using relationships between imports and the number of alien species in a region or patterns in the global spread of species linked to shipping and air traffic networks. But trade also has an indirect role on biological invasions by transforming the environments and societies of exporting and importing nations. Here, both the direct and indirect roles of trade on biological invasions, as well as their interaction, are examined for the first time. Future trends in international trade, including e-commerce, new trade routes, and major infrastructure developments, will lead to the pressure on national borders soon outstripping the resources available for intervention. The current legislative and scientific tools targeting biological invasions are insufficient to deal with this growing threat and require a new mindset that focuses on curbing the pandemic risk posed by alien species.

Aim Road networks are common landscape disturbances that can facilitate the spread of invasive plants. This study explored the influence of distance from the road, habitat type and broader environmental factors in shaping the distribution patterns of both invasive and native species in the above‐ground vegetation and soil seed banks. Location Guangxi, China. Methods We collected data on plant species composition from both soil seed banks and above‐ground vegetation at six distances from the road edge: 0 m, 2 m, 4 m, 9 m, 14 m and 24 m in three habitat types, including abandoned land, Eucalyptus plantations and natural secondary forests. We collected data on environmental variables at each sampling location. We examined the compositional similarity of plant communities by non‐metric multidimensional scaling (NMDS) and identified the influence factors by redundancy analysis (RDA). Results Our results indicated that invasive species richness decreased with distance from the road, especially in natural secondary forests. Conversely, native species did not show consistent distribution patterns relative to distance from roads across the various habitats. The composition of invasive plant communities was similar in both soil seed banks and above‐ground vegetation, while only 13.33% of native species identified in the soil were observed in the above‐ground vegetation. Road characteristics, human disturbance and soil properties correlated with the distribution of invasive and native species, with the strength of these correlations varying among habitat types. The richness and density of native plants were associated with the presence of invasive alien plants at various distances from the road across the three types of habitats. Main Conclusions The study highlights that proximity to the road, habitat type and environmental factors are critical in determining the distribution of plant species within nature reserves. Moreover, it underscores the importance of integrating both above‐ground and seed bank perspectives for effective management strategies to control invasive species and promote native plant communities.

Distributions of invasive species are commonly predicted with species distribution models that build upon the statistical relationships between observed species presence data and climate data. We used field observations, climate station data, and Maximum Entropy species distribution models for 13 invasive plant species in the United States, and then compared the models with inputs from a General Circulation Model (hereafter GCM-based models) and a downscaled Regional Climate Model (hereafter, RCM-based models).We also compared species distributions based on either GCM-based or RCM-based models for the present (1990–1999) to the future (2046–2055). RCM-based species distribution models replicated observed distributions remarkably better than GCM-based models for all invasive species under the current climate. This was shown for the presence locations of the species, and by using four common statistical metrics to compare modeled distributions. For two widespread invasive taxa (Bromus tectorum or cheatgrass, and Tamarix spp. or tamarisk), GCM-based models failed miserably to reproduce observed species distributions. In contrast, RCM-based species distribution models closely matched observations. Future species distributions may be significantly affected by using GCM-based inputs. Because invasive plants species often show high resilience and low rates of local extinction, RCM-based species distribution models may perform better than GCM-based species distribution models for planning containment programs for invasive species.

Accurate analysis of present distributions and effective modeling of future distributions of invasive alien species (IAS) are both highly dependent on the availability and accessibility of occurrence data and natural history information about the species. Invasive alien species monitoring and detection networks (such as the Invasive Plant Atlas of New England and the Invasive Plant Atlas of the MidSouth) generate occurrence data at local and regional levels within the United States, which are shared through the US National Institute of Invasive Species Science. The Inter-American Biodiversity Information Network's Invasives Information Network (I3N), facilitates cooperation on sharing invasive species occurrence data throughout the Western Hemisphere. The I3N and other national and regional networks expose their data globally via the Global Invasive Species Information Network (GISIN). International and interdisciplinary cooperation on data sharing strengthens cooperation on strategies and responses to invasions. However, limitations to effective collaboration among invasive species networks leading to successful early detection and rapid response to invasive species include: lack of interoperability; data accessibility; funding; and technical expertise. This paper proposes various solutions to these obstacles at different geographic levels and briefly describes success stories from the invasive species information networks mentioned above. Using biological informatics to facilitate global information sharing is especially critical in invasive species science, as research has shown that one of the best indicators of the invasiveness of a species is whether it has been invasive elsewhere. Data must also be shared across disciplines because natural history information (e.g. diet, predators, habitat requirements, etc.) about a species in its native range is vital for effective prevention, detection, and rapid response to an invasion. Finally, it has been our experience that sharing information, including invasive species dispersal mechanisms and rates, impacts, and prevention and control strategies, enables resource managers and decision-makers to mount a more effective response to biological invasions

Challenges in predicting the future distributions of invasive plant species

SUMMARYAnts are among the worst invasive species, and can have tremendous negative impacts on native biodiversity, agriculture, estates, property and human health. Invasive ants are extremely difficult to control, and thus early detection is essential to prevent ant invasions, in particular through surveillance efforts at ports of entry. This paper assesses the potential distribution of 14 of the worst invasive ant species in France, under current and future climatic conditions. Consensus species distribution models, using five different modelling techniques, three global climate models and two CO2 emission scenarios, indicated that France presented suitable areas for 10/14 species, including five listed on the Invasive Species Specialist Group's selection of the world's 100 worst invasive species. Among these 10 species, eight were predicted to increase their potential range with climate change. Areas with the highest concentration of potential invaders were mainly located along the coastline, especially in the south-west of France, but all departments appeared to be climatically suitable for at least two invasive species. A ranking of climatic suitability per species for 17 major airports and 14 maritime ports indicated that the ports of entry with the highest suitability were located in Biarritz, Toulon and Nice, and the species with the greatest potential distribution in France were Lasius neglectus and Linepithema humile, followed by Solenopsis richteri, Pheidole megacephala and Wasmannia auropunctata.