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

Unveiling the role of climate in spatially synchronized locust outbreak risks

Desert locusts threaten crop production and food security. Spatially synchronized locust outbreaks further exacerbate these crises. Continental-scale understanding of such compound locust risks and underlying climatic drivers is crucial to designing coordinated and predictive control strategies but remains elusive. Here, we develop a data-driven framework to assess the compound risk of locust outbreaks in the Middle East and North Africa (MENA) and elucidate the role of climate in locust dynamics. We find that more than one-fifth of high-risk country pairs faced spatially synchronized locust risks from 1985 to 2020, dominated by concurrent winds or inundations. Individual locusts are more prone to infest arid areas punched by extreme rainfall. The spatial prevalence of locusts is strongly modulated by climate variability such as El Niño-Southern Oscillation. A warming climate will lead to widespread increases in locust outbreaks with emerging hotspots in west central Asia, posing additional challenges to the global coordination of locust control.

Potential sources of time lags in calibrating species distribution models

The Anthropocene is characterized by a rapid pace of environmental change and is causing a multitude of biotic responses, including those that affect the spatial distribution of species. Lagged responses are frequent and species distributions and assemblages are consequently pushed into a disequilibrium state. How the characteristics of environmental change-for example, gradual 'press' disturbances such as rising temperatures due to climate change versus infrequent 'pulse' disturbances such as extreme events-affect the magnitude of responses and the relaxation times of biota has been insufficiently explored. It is also not well understood how widely used approaches to assess or project the responses of species to changing environmental conditions can deal with time lags. It, therefore, remains unclear to what extent time lags in species distributions are accounted for in biodiversity assessments, scenarios and models; this has ramifications for policymaking and conservation science alike. This perspective piece reflects on lagged species responses to environmental change and discusses the potential consequences for species distribution models (SDMs), the tools of choice in biodiversity modelling. We suggest ways to better account for time lags in calibrating these models and to reduce their leverage effects in projections for improved biodiversity science and policy.

Constructing indicator species distribution models to study the potential invasion risk of invasive plants: A case of the invasion of Parthenium hysterophorus in China

Aim

As invasive plants are often in a non-equilibrium expansion state, traditional species distribution models (SDMs) are likely underestimating their suitable habitat. New methods are necessary to identify potential invasion risk areas.

Location

Tropical monsoon rainforest and subtropical evergreen broad-leaved forest regions in China.

Methods

We took Parthenium hysterophorus as a case study to predict its potential invasion risk using climate, terrain, and human activity variables. First, a generalized joint attribute model (GJAM) was constructed using the occurrence of P. hysterophorus and its 27 closely related species in Taiwan, given it is widely distributed in Taiwan. Based on the output correlation values, two positively correlated species (Cardiospermum halicacabum and Portulaca oleracea) and one negatively correlated species (Crassocephalum crepidioides) were selected as indicator species. Second, the distributions of P. hysterophorus and its indicator species in the study area were predicted separately using an ensemble model (EM). Third, when selecting indicator species to construct indicator SDMs, two treatments (indicator species with positive correlation only, or both positive and negative correlation) were considered. The indicator species' EM predictions were overlaid using a weighted average method, and a better indicator SDMs prediction result was selected by comparison. Finally, the EM prediction result of P. hysterophorus was used to optimize the indicator SDMs result by a maximum overlay.

Results

The optimized indicator SDMs prediction showed an expanded range beyond the current geographic range compared to EM and the thresholds for predicting key environmental variables were wider. It also reinforced the human activities' influence on the potential distribution of P. hysterophorus.

Main Conclusions

For invasive plants with expanding ranges, information about indicator species distribution can be borrowed as a barometer for areas not currently invaded. The optimized indicator SDMs allow for more efficient potential invasion risk prediction. On this basis, invasive plants can be prevented earlier.

Expanding the invasion toolbox: including stable isotope analysis in risk assessment

Species introductions are a major concern for ecosystem functioning, socio-economic wealth, and human well-being. Preventing introductions proved to be the most effective management strategy, and various tools such as species distribution models and risk assessment protocols have been developed or applied to this purpose. These approaches use information on a species to predict its potential invasiveness and impact in the case of its introduction into a new area. At the same time, much biodiversity has been lost due to multiple drivers. Ways to determine the potential for successful reintroductions of once native but now extinct species as well as assisted migrations are yet missing. Stable isotope analyses are commonly used to reconstruct a species’ feeding ecology and trophic interactions within communities. Recently, this method has been used to predict potentially arising trophic interactions in the absence of the target species. Here we propose the implementation of stable isotope analysis as an approach for assessment schemes to increase the accuracy in predicting invader impacts as well as the success of reintroductions and assisted migrations. We review and discuss possibilities and limitations of this methods usage, suggesting promising and useful applications for scientists and managers.

Advancing biological invasion hypothesis testing using functional diversity indices

Pioneering investigations on the effects of introduced populations on community structure, ecosystem functioning and services have focused on the effects of invaders on taxonomic diversity. However, taxonomic-based diversity metrics overlook the heterogeneity of species roles within and among communities. As the homogenizing effects of biological invasions on community and ecosystem processes can be subtle, they may require the use of functional diversity indices to be properly evidenced. Starting from the listing of major functional diversity indices, alongside the presentation of their strengths and limitations, we focus on studies pertaining to the effects of invasive species on native communities and recipient ecosystems using functional diversity indices. By doing so, we reveal that functional diversity of the recipient community may strongly vary at the onset of the invasion process, while it stabilizes at intermediate and high levels of invasion. As functional changes occurring during the lag phase of an invasion have been poorly investigated, we show that it is still unknown whether there are consistent changes in functional diversity metrics that could indicate the end of the lag phase. Thus, we recommend providing information on the invasion stage under consideration when computing functional diversity metrics. For the existing literature, it is also surprising that very few studies explored the functional difference between organisms from the recipient communities and invaders of the same trophic levels, or assessed the effects of non-native organism establishment into a non-analogue versus an analogue community. By providing valuable tools for obtaining in-depth diagnostics of community structure and functioning, functional diversity indices can be applied for timely implementation of restoration plans and improved conservation strategies. To conclude, our work provides a first synthetic guide for their use in hypothesis testing in invasion biology.

What Does the Future Hold for Páramo Plants? A Modelling Approach

Understanding the effects of climate change on tropicalpine biota remains a scientific challenge today. The Andean páramo is the largest and most diverse tropicalpine biogeographical region in the world, and also one of the most threatened as it is prone to accelerated environmental changes. My goal was to predict changes in the distribution ranges of the diverse and highly endemic páramo flora on the mid-term (50 years). First, I predicted distribution changes in páramo plant species under novel climates and considering dispersal constraints. Second, I looked for consensus areas of species losses vs. gains in the páramo, expecting to identify a gradient of increasing relative richness with elevation over time. Last, I evaluated the behavior of plant species regarding their climatic refugia since the Last Glacial Maximum (LGM) to establish if they likely remain or transcend them. Based on VegParamo vegetation data and CHELSA bioclimatic information, I performed species distribution models for a 664 species pool, that were then contrasted between the present, future (2070) and past (LGM). About 8.3% of the entire species pool (55 species) were predicted to be extirpated from the páramo by 2070, including 22 species endemics. On average, páramo plants gained 15.52% of additional distribution by 2070 (18.81% for endemics). Models predicted the most area gains for the northern páramos of Colombia and Venezuela, and the highest losses for the eastern Ecuadorian and Peruvian mountains. Moreover, area gains were more pronounced at high elevations, suggesting a future accelerated colonization process toward the northern Andean summits. Finally, only 21.41% of the species’ 2070 distribution coincided with their LGM (19.75% for endemics), and the largest climatic refugia since the LGM were found in southern Ecuador and Peru. This study is pioneer in predicting future distribution shifts for páramo plant species overall and provides solid bases to support climate change research and adaptation strategies in the tropical Andes.

A framework to integrate innovations in invasion science for proactive management

Invasive alien species (IAS) are a rising threat to biodiversity, national security, and regional economies, with impacts in the hundreds of billions of U.S. dollars annually. Proactive or predictive approaches guided by scientific knowledge are essential to keeping pace with growing impacts of invasions under climate change. Although the rapid development of diverse technologies and approaches has produced tools with the potential to greatly accelerate invasion research and management, innovation has far outpaced implementation and coordination. Technological and methodological syntheses are urgently needed to close the growing implementation gap and facilitate interdisciplinary collaboration and synergy among evolving disciplines. A broad review is necessary to demonstrate the utility and relevance of work in diverse fields to generate actionable science for the ongoing invasion crisis. Here, we review such advances in relevant fields including remote sensing, epidemiology, big data analytics, environmental DNA (eDNA) sampling, genomics, and others, and present a generalized framework for distilling existing and emerging data into products for proactive IAS research and management. This integrated workflow provides a pathway for scientists and practitioners in diverse disciplines to contribute to applied invasion biology in a coordinated, synergistic, and scalable manner.

Large-scale forecasting of Heracleum sosnowskyi habitat suitability under the climate change on publicly available data

This research aims to establish the possible habitat suitability of Heracleum sosnowskyi (HS), one of the most aggressive invasive plants, in current and future climate conditions across the territory of the European part of Russia. We utilised a species distribution modelling framework using publicly available data of plant occurrence collected in citizen science projects (CSP). Climatic variables and soil characteristics were considered to follow possible dependencies with environmental factors. We applied Random Forest to classify the study area. We addressed the problem of sampling bias in CSP data by optimising the sampling size and implementing a spatial cross-validation scheme. According to the Random Forest model built on the finally selected data shape, more than half of the studied territory in the current climate corresponds to a suitability prediction score higher than 0.25. The forecast of habitat suitability in future climate was highly similar for all climate models. Almost the whole studied territory showed the possibility for spread with an average suitability score of 0.4. The mean temperature of the wettest quarter and precipitation of wettest month demonstrated the highest influence on the HS distribution. Thus, currently, the whole study area, excluding the north, may be considered as s territory with a high risk of HS spreading, while in the future suitable locations for the HS habitat will include high latitudes. We showed that chosen geodata pre-processing, and cross-validation based on geospatial blocks reduced significantly the sampling bias. Obtained predictions could help to assess the risks accompanying the studied plant invasion capturing the patterns of the spread, and can be used for the conservation actions planning.

Drivers of Solidago species invasion in Central Europe-Case study in the landscape of the Carpathian Mountains and their foreground

AIM

The invasion process is a complex, context-dependent phenomenon; nevertheless, it can be described using the PAB framework. This framework encompasses the joint effect of propagule pressure (P), abiotic characteristics of the environment (A), and biotic characteristics of both the invader and recipient vegetation (B). We analyzed the effectiveness of proxies of PAB factors to explain the spatial pattern of Solidago canadensis and S. gigantea invasion using invasive species distribution models.

LOCATION

Carpathian Mountains and their foreground, Central Europe.

METHODS

The data on species presence or absence were from an atlas of neophyte distribution based on a 2 × 2 km grid, covering approximately 31,200 km2 (7,752 grid cells). Proxies of PAB factors, along with data on historical distribution of invaders, were used as explanatory variables in Boosted Regression Trees models to explain the distribution of invasive Solidago. The areas with potentially lower sampling effort were excluded from analysis based on a target species approach.

RESULTS

Proxies of the PAB factors helped to explain the distribution of both S. canadensis and S. gigantea. Distributions of both species were limited climatically because a mountain climate is not conducive to their growth; however, the S. canadensis distribution pattern was correlated with proxies of human pressure, whereas S. gigantea distribution was connected with environmental characteristics. The varied responses of species with regard to distance from their historical distribution sites indicated differences in their invasion drivers.

MAIN CONCLUSIONS

Proxies of PAB are helpful in the choice of explanatory variables as well as the ecological interpretation of species distribution models. The results underline that human activity can cause variation in the invasion of ecologically similar species.

In situ adaptation and ecological release facilitate the occupied niche expansion of a non-native Madagascan day gecko in Florida

AIM

To investigate whether the frequently advocated climate-matching species distribution modeling approach could predict the well-characterized colonization of Florida by the Madagascar giant day gecko Phelsuma grandis.

LOCATION

Madagascar and Florida, USA.

METHODS

To determine the climatic conditions associated with the native range of P. grandis, we used native-range presence-only records and Bioclim climatic data to build a Maxent species distribution model and projected the climatic thresholds of the native range onto Florida. We then built an analogous model using Florida presence-only data and projected it onto Madagascar. We constructed a third model using native-range presences for both P. grandis and the closely related parapatric species P. kochi.

RESULTS

Despite performing well within the native range, our Madagascar Bioclim model failed to identify suitable climatic habitat currently occupied by P. grandis in Florida. The model constructed using Florida presences also failed to reflect the distribution in Madagascar by overpredicting distribution, especially in western areas occupied by P. kochi. The model built using the combined P. kochi/P. grandis dataset modestly improved the prediction of the range of P. grandis in Florida, thereby implying competitive exclusion of P. grandis by P. kochi from habitat within the former's fundamental niche. These findings thus suggest ecological release of P. grandis in Florida. However, because ecological release cannot fully explain the divergent occupied niches of P. grandis in Madagascar versus Florida, our findings also demonstrate some degree of in situ adaptation in Florida.

MAIN CONCLUSIONS

Our models suggest that the discrepancy between the predicted and observed range of P. grandis in Florida is attributable to either in situ adaptation by P. grandis within Florida, or a combination of such in situ adaptation and competition with P. kochi in Madagascar. Our study demonstrates that climate-matching species distribution models can severely underpredict the establishment risk posed by non-native herpetofauna.

Predicting seasonal movements and distribution of the sperm whale using machine learning algorithms

Implementation of effective conservation planning relies on a robust understanding of the spatiotemporal distribution of the target species. In the marine realm, this is even more challenging for species rarely seen at the sea surface due to their extreme diving behavior like the sperm whales. Our study aims at (a) investigating the seasonal movements, (b) predicting the potential distribution, and (c) assessing the diel vertical behavior of this species in the Mascarene Archipelago in the south-west Indian Ocean. Using 21 satellite tracks of sperm whales and eight environmental predictors, 14 supervised machine learning algorithms were tested and compared to predict the whales' potential distribution during the wet and dry season, separately. Fourteen of the whales remained in close proximity to Mauritius, while a migratory pattern was evidenced with a synchronized departure for eight females that headed towards Rodrigues Island. The best performing algorithm was the random forest, showing a strong affinity of the whales for sea surface height during the wet season and for bottom temperature during the dry season. A more dispersed distribution was predicted during the wet season, whereas a more restricted distribution to Mauritius and Reunion waters was found during the dry season, probably related to the breeding period. A diel pattern was observed in the diving behavior, likely following the vertical migration of squids. The results of our study fill a knowledge gap regarding seasonal movements and habitat affinities of this vulnerable species, for which a regional IUCN assessment is still missing in the Indian Ocean. Our findings also confirm the great potential of machine learning algorithms in conservation planning and provide highly reproductible tools to support dynamic ocean management.

Distribution of alien tetrapods in the Iberian Peninsula

We present a dataset that assembles occurrence records of alien tetrapods (amphibians, reptiles, birds and mammals) in the Iberian Peninsula, a coherent biogeographically unit where introductions of alien species have occurred for millennia. These data have important potential applications for ecological research and management, including the assessment of invasion risks, formulation of preventive and management plans, and research at the biological community level on alien species. This dataset summarizes inventories and data sources on the taxonomy and distribution of alien tetrapods in the Iberia Peninsula, comprising known locations from published literature, expert knowledge and citizen science platforms. An expert-based assessment process allowed the identification of unreliable records (misclassification or natural dispersion from native range), and the classification of species according to their status of reproduction in the wild. Distributional data was harmonized into a common area unit, the 10 × 10 km Universal Transverse Mercator (UTM) system (n = 6,152 cells). The year of observation and/or year of publication were also assigned to the records. In total, we assembled 35,940 unique distribution records (UTM × species × Year) for 253 species (6 amphibians, 16 reptiles, 218 birds and 13 mammals), spanning between 1912 and 2020. The species with highest number of distribution records were the Mediterranean painted frog Discoglossus pictus (n = 59 UTM), the pond slider Trachemys scripta (n = 471), the common waxbill Estrilda astrild (n = 1,275) and the house mouse Mus musculus (n = 4,043), for amphibians, reptiles, birds and mammals, respectively. Most alien species recorded are native to Africa (33%), followed by South America (21%), Asia (19%), North America (12%) and Oceania (10%). Thirty-six species are classified by IUCN as threatened in their native range, namely 2 Critically Endangered (CR), 6 Endangered (EN), 8 Vulnerable (VU), and 20 species Near Threatened (NT). Species maps are provided in DataSet1, as well R code and GIS layers to update them as new records are obtained.

Toward reliable habitat suitability and accessibility models in an era of multiple environmental stressors

Global biodiversity declines, largely driven by climate and land-use changes, urge the development of transparent guidelines for effective conservation strategies. Species distribution modeling (SDM) is a widely used approach for predicting potential shifts in species distributions, which can in turn support ecological conservation where environmental change is expected to impact population and community dynamics. Improvements in SDM accuracy through incorporating intra- and interspecific processes have boosted the SDM field forward, but simultaneously urge harmonizing the vast array of SDM approaches into an overarching, widely adoptable, and scientifically justified SDM framework. In this review, we first discuss how climate warming and land-use change interact to govern population dynamics and species' distributions, depending on species' dispersal and evolutionary abilities. We particularly emphasize that both land-use and climate change can reduce the accessibility to suitable habitat for many species, rendering the ability of species to colonize new habitat and to exchange genetic variation a crucial yet poorly implemented component of SDM. We then unite existing methodological SDM practices that aim to increase model accuracy through accounting for multiple global change stressors, dispersal, or evolution, while shifting our focus to model feasibility. We finally propose a roadmap harmonizing model accuracy and feasibility, applicable to both common and rare species, particularly those with poor dispersal abilities. This roadmap (a) paves the way for an overarching SDM framework allowing comparison and synthesis of different SDM studies and (b) could advance SDM to a level that allows systematic integration of SDM outcomes into effective conservation plans.

Identifying hotspots of invasive alien terrestrial vertebrates in Europe to assist transboundary prevention and control

This study aims to identify environmentally suitable areas for 15 of the most harmful invasive alien terrestrial vertebrates (IATV) in Europe in a transparent and replicable way. We used species distribution models and publicly-available data from GBIF to predict environmental suitability and to identify hotspots of IATV accounting for knowledge gaps in their distributions. To deal with the ecological particularities of invasive species, we followed a hierarchical approach to estimate the global climatic suitability for each species and incorporated this information into refined environmental suitability models within Europe. Combined predictions on environmental suitability identified potential areas of IATV concentrations or hotspots. Uncertainty of predictions identified regions requiring further survey efforts for species detection. Around 14% of Europe comprised potential hotspots of IATV richness, mainly located in northern France, UK, Belgium and the Netherlands. IATV coldspots covered ~ 9% of Europe, including southern Sweden and Finland, and northern Germany. Most of Europe (~ 77% area) comprised uncertain suitability predictions, likely caused by a lack of data. Priorities on prevention and control should focus on potential hotspots where harmful impacts might concentrate. Promoting the collection of presence data within data-deficient areas is encouraged as a core strategy against IATVs.

Do Habitats Show a Different Invasibility Pattern by Alien Plant Species? A Test on a Wetland Protected Area

Biological invasions are deemed to be the second most important global driver of biodiversity loss, right behind habitat destruction and fragmentation. In this study, we aimed at testing if community invasibility, defined as the vulnerability to invasion of a community, could be associated with the characteristics of a given habitat, as described by the composition and structure of its native species. Based on a probabilistic sampling of the alien flora occurring in the temperate wetland Lake Doberdò (Friuli Venezia Giulia region, NE Italy) and using a null-model-based approach, the observed occurrence of Invasive Alien Species (IAS) within sampling units was randomized within habitats. While testing the degree of invasibility for each habitat within the wetland, our null hypothesis postulated that habitats are equally invaded by IAS, as IAS can spread homogeneously in the environment thanks to their plasticity in functional traits that makes them able to cope with different ecological conditions. The obtained results comparing observed IAS frequencies, abundance and richness to those obtained by the null model randomizations show that, for all habitats, invasion was selective. Specifically, a marked preference for habitats with an intermediate disturbance level, a high nutrients level and a medium-high light availability was observed, while an avoidance was detected for habitats characterized by lower levels of nutrients and light availability or extreme conditions caused by prolonged submersion. This method allows us to provide useful information using a simple-to-run simulation for the management of the IAS threat within protected areas. Moreover, the method allows us to infer important ecological characteristics leading to habitat invasion without sampling the environmental characteristic of the habitats, which is an expensive operation in terms of time and money.

A Spatiotemporal Analysis and Dispersal Patterns of the Potato Cyst Nematode Globodera pallida in Idaho

The potato cyst nematode Globodera pallida is a globally regulated potato pest. It was detected for the first time in the United States in the state of Idaho in 2006, and as of February 2019, the infestation is limited to 1,326 hectares. G. pallida is a specialized obligate sedentary endoparasite that can survive in the soil for up to 30 years in the absence of its potato host. In highly infested fields, the nematode can reduce tuber yields up to 80% and is spread mainly through the movement of soil, tubers, or farm equipment. The objectives of this study were to describe the spatiotemporal pattern of G. pallida in infested fields and model its dispersal patterns in southeastern Idaho. We used geostatistical tools and simulation models for precise mapping and to describe the relationships between G. pallida incidence and the spatial configurations. We found that the nematode is spatially clustered and prevalent around edges of fields, and its dispersal pattern followed the direction of cultivation. We found that the absence of potato in an infested field significantly reduced the number of cysts sampled each year subsequent to the initial delimitation sampling in 2007. Phytosanitary measures prohibiting the growth of potato contributed to stopping nematode reproduction, and the use of chemical fumigants and biofumigant cover crops contributed to a significant reduction in egg viability. We observed a process of a nonlinear decline in the prevalence of cysts as the distance separation from the primary infestation focus increased. A power law model was used to fit G. pallida dispersal capabilities. This study contributed to describing G. pallida infestation for Idaho. The goal of this study is to provide information on the spatial pattern and landscape ecology of G. pallida in Idaho for policy makers, industry, and researchers as well as facilitate common understandings on the challenges and opportunities for controlling this pest in Idaho.

Modeling current and potential distributions of mammal species using presence-only data: A case study on British deer

AIM

Decisions on wildlife conservation, management, and epidemiological risk are best based on robust evidence. The continual improvement of species distributions, such that they can be relied upon in decision-making, is important. Here we seek to refine aspects of a generic modelling approach and improve the utility of species distribution maps.

LOCATION

Great Britain (GB).

METHODS

We applied a modeling framework based on hierarchical Bayesian species distribution models exploiting opportunistic occurrence records from citizen science datasets to predict both current and potential distributions for each of the six deer species known to be present in GB. Using the resulting maps, we performed a simple analysis of the overlap between species to illustrate possible contact, which we interpret as the relative risk of potential disease spread given an introduction.

RESULTS

Predicted distribution maps showed good agreement with the broader scale occurrence reported by a recent national deer survey with an average True Skill Statistics and AUC of 0.69 and 0.89, respectively. Aggregation of the maps for all species highlighted regions of central and eastern England as well as parts of Scotland where extensive areas of range overlap could result in interspecific contact with consequences for risk assessments for diseases of deer. However, if populations are allowed to expand to their predicted potential, then areas of overlap, and therefore disease interspecific transmission risk, will become extensive and widespread across all of mainland Britain.

MAIN CONCLUSIONS

The generic modeling approach outlined performed well across all of the deer species tested, offering a robust and reliable tool through which current and potential animal distributions can be estimated and presented. Our application, intended to inform quantitative risk assessments, demonstrates the practical use of such outputs to generate the valuable evidence required to inform policy decisions on issues such as management strategy.

Current and future climatic regions favourable for a globally introduced wild carnivore, the raccoon Procyon lotor

Invasive species are considered as one of the major threats to biodiversity and represent a major challenge in the conservation of natural ecosystems, in preventing damage to agricultural production, and human health risks. Environmental Niche Modelling has emerged as a powerful tool to predict the patterns of range expansion of non-native species and to direct effective strategies for managing biological invasions. The raccoon, Procyon lotor, is a wild mesocarnivore presenting a high adaptability and showing successful introduced populations worldwide. Here, we modelled the current and future climatically favourable areas for the raccoon using two protocols, based on data sets filtrated in geographic and environmental spaces. Projections from these models show extensive current favourable geographical areas covering extensive regions of temperate biomes. Moreover, predictions for 2050 reveals extensive new favourable areas north of the current favourable regions. However, the results of the two modeling approaches differ in the extent of predicted favourable spaces. Protocols using geographically filtered data present more conservative forecasts, while protocol using environmental filtration presents forecasts across greater areas. Given the biological characteristics and the ecological requirements of a generalist carnivore such as the raccoon, the latter forecasts appears more relevant and should be privileged in the development of conservation plans for ecosystems.

The DIARS toolbox: a spatially explicit approach to monitor alien plant invasions through remote sensing

The synergies between remote sensing technologies and ecological research have opened new avenues for the study of alien plant invasions worldwide. Such scientific advances have greatly improved our capacity to issue warnings, develop early-response systems and assess the impacts of alien plant invasions on biodiversity and ecosystem functioning. Hitherto, practical applications of remote sensing approaches to support nature conservation actions are lagging far behind scientific advances. Yet, for some of these technologies, knowledge transfer is difficult due to the complexity of the different data handling procedures and the huge amounts of data it involves per spatial unit.

In this context, the next logical step is to develop clear guidelines for the application of remote sensing data to monitor and assess the impacts of alien plant invasions, that enable scientists, landscape managers and policy makers to fully exploit the tools which are currently available. It is desirable to have such guidelines accompanied by freely available remote sensing data and generated in a free and open source environment that increases the availability and affordability of these new technologies.

Here we present a toolbox that provides an easy-to-use, flexible, transparent and open source set of tools to sample, map, model and assess the impact of alien plant invasions using two high-resolution remote sensing products (hyperspectral and LiDAR images). This online toolbox includes a real case dataset designed to facilitate testing and training in any computer system and processing capacity.

Can species distribution models really predict the expansion of invasive species?

Predictive studies are of paramount importance for biological invasions, one of the biggest threats for biodiversity. To help and better prioritize management strategies, species distribution models (SDMs) are often used to predict the potential invasive range of introduced species. Yet, SDMs have been regularly criticized, due to several strong limitations, such as violating the equilibrium assumption during the invasion process. Unfortunately, validation studies–with independent data–are too scarce to assess the predictive accuracy of SDMs in invasion biology. Yet, biological invasions allow to test SDMs usefulness, by retrospectively assessing whether they would have accurately predicted the latest ranges of invasion. Here, we assess the predictive accuracy of SDMs in predicting the expansion of invasive species. We used temporal occurrence data for the Asian hornet Vespa velutina nigrithorax, a species native to China that is invading Europe with a very fast rate. Specifically, we compared occurrence data from the last stage of invasion (independent validation points) to the climate suitability distribution predicted from models calibrated with data from the early stage of invasion. Despite the invasive species not being at equilibrium yet, the predicted climate suitability of validation points was high. SDMs can thus adequately predict the spread of V. v. nigrithorax, which appears to be—at least partially–climatically driven. In the case of V. v. nigrithorax, SDMs predictive accuracy was slightly but significantly better when models were calibrated with invasive data only, excluding native data. Although more validation studies for other invasion cases are needed to generalize our results, our findings are an important step towards validating the use of SDMs in invasion biology.