Detection Of Invasive Species Research Articles

Community science can inform invasive species management: Melaleuca (Myrtaceae) in South Africa

Abstract Community science initiatives are revolutionising our ability to detect and respond to biological invasions. Non‐native trees and shrubs are among the world's most damaging invasive species and community science data can be used to inform protocols for managing these invasions. This study explores the utility of iNaturalist in informing management practices for the widely cultivated and naturalised genus Melaleuca L. (Myrtaceae; here including the genus Callistemon) in South Africa. We applied data from iNaturalist to assess the distribution and invasion ecology of Melaleuca species in South Africa. Melaleucas, first recorded in South Africa in 1882, have been widely used as garden ornamentals and street trees in the country for over 50 years. Naturalisation of melaleucas in South Africa was first reported in 1998 and the first records of naturalisation/invasiveness for other species are accumulating rapidly. Data on all Melaleuca species in South Africa were downloaded from iNaturalist and analysed using Geographic Information System software. In September 2023, iNaturalist had 3221 records of melaleucas across the country. After checking and filtering, and applying criteria to increase reliability, 2815 records remained, with confirmed identifications of 26 species. These species were recorded in a total of 138 quarter‐degree cells (QDCs) in South Africa (7% of the country); Research Grade (RG, wild‐growing) records occurred in 21 QDCs, records of cultivated plants in 75 QDCs, and 42 QDCs had records of both cultivated and wild‐growing plants. An Invasiveness Index was calculated for each species in the country, provinces, and municipalities, to show which species are already invasive or have substantial invasion debt. Thirty‐two percent of the filtered records were RG (naturalised). The municipality with the highest number of records is the City of Cape Town, with 43% RG records. iNaturalist provided useful information on the occurrence of five Melaleuca species for which no information was available before this study was undertaken. Case studies of invaded habitats highlight that melaleucas have the potential to alter ecosystems incurring substantial control costs. Practical implications: Our study highlights the value of community science data in the detection, monitoring, and management of invasive plant species.

Mosquitoes on a chip-environmental DNA-based detection of invasive mosquito species using high-throughput real-time PCR.

The monitoring of mosquitoes is of great importance due to their vector competence for a variety of pathogens, which have the potential to imperil human and animal health. Until now mosquito occurrence data is mainly obtained with conventional monitoring methods including active and passive approaches, which can be time- and cost-consuming. New monitoring methods based on environmental DNA (eDNA) could serve as a fast and robust complementary detection system for mosquitoes. In this pilot study already existing marker systems targeting the three invasive mosquito species Aedes (Ae.) albopictus, Ae. japonicus and Ae. koreicus were used to detect these species from water samples via microfluidic array technology. We compared the performance of the high-throughput real-time PCR (HT-qPCR) system Biomark HD with real-time PCR (qPCR) and also tested the effect of different filter media (Sterivex® 0.45 µm, Nylon 0.22 µm, PES 1.2 µm) on eDNA detectability. By using a universal qPCR protocol and only 6-FAM-MGB probes we successfully transferred these marker systems on the HT-qPCR platform. All tested marker systems detected the target species at most sites, where their presence was previously confirmed. Filter media properties, the final filtration volume and observed qPCR inhibition did not affect measured Ct values via qPCR or HT-qPCR. The Ct values obtained from HT-qPCR were significantly lower as Ct values measured by qPCR due to the previous preamplification step, still these values were highly correlated. Observed incongruities in eDNA detection probability, as manifested by non-reproducible results and false positive detections, could be the result of methodological aspects, such as sensitivity and specificity issues of the used assays, or ecological factors such as varying eDNA release patterns. In this study, we show the suitability of eDNA-based detection of mosquito species from water samples using a microfluidic HT-qPCR platform. HT-qPCR platforms such as Biomark HD allow for massive upscaling of tested species-specific assays and sampling sites with low time- and cost-effort, thus this methodology could serve as basis for large-scale mosquito monitoring attempts. The main goal in the future is to develop a robust (semi)-quantitative microfluidic-based eDNA mosquito chip targeting all haematophagous culicid species occurring in Western Europe. This chip would enable large-scale eDNA-based screenings to assess mosquito diversity, to monitor species with confirmed or suspected vector competence, to assess the invasion progress of invasive mosquito species and could be used in pathogen surveillance, when disease agents are incorporated.

Integrating UAV imagery and machine learning via Geographic Object Based Image Analysis (GEOBIA) for enhanced monitoring of Yucca gloriosa in Mediterranean coastal dunes

Effective monitoring and early detection of invasive alien plant species (IAPs) are crucial for mitigating their spread and safeguarding native habitats. Unmanned Aerial Vehicles (UAVs) offer a cost-efficient solution, providing high resolution images. In this study, we aimed to develop a semi-automated methodology using a machine learning algorithm, spatial metrics, and clustering techniques on UAV images to monitor, map, and suggest management measures to counteract Yucca gloriosa, an invasive plant colonizing coastal fixed dunes in central Italy.UAV flights were conducted using two drones: one for the visible spectrum and the other for multispectral bands (Blue, Green, Red, Red Edge, and Near Infrared) along with a Digital Surface Model (DSM). Derived vegetation indices were also utilized. For mapping Y. gloriosa distribution, a Geographic Object Based Image Analysis (GEOBIA) approach was applied to the orthophoto segmentation, followed by a Random Forest algorithm in a training phase, considering three variable combinations (DSM + vegetation indices, DSM + spectral bands, DSM + mixed variables). The most accurate Y. gloriosa map was used to suggest management measures combining the spatial pattern of invaded patches (size, height, isolation level, and aggregation degree) and a mixed clustering approach (hierarchical and partitioning).The results highlighted that the most accurate prediction map was based on the DSM + mixed variables dataset, showing the important role of using a combination of spectral bands and vegetation indices. In all three cases, the DSM emerged as the pivotal variable for discriminating Y. gloriosa from the surrounding environment. Additionally, our results demonstrate the advantages of incorporating vegetation indices in discerning the target invasive alien plant (IAP) from the broader environment, particularly considering its distinctive photosynthesis process and biomass production. From a managerial standpoint, our pilot study indicates that the UAV-based mapping methodology represents an optimal balance between field efforts and costs. This approach allows for the precise identification of containment and removal areas of Y. gloriosa, without compromising the accuracy of the method. The generated prediction maps also hold potential significance for the conservation of coastal dune ecosystems, providing a promising tool for the effective management of invasive species and biodiversity conservation by suggesting management measures for Y. gloriosa.

Recommendations for incorporating invasive species into U.S. climate change adaptation planning and policy

AbstractExecutive Order No. 14008 mandated that U.S. federal agencies and departments develop Climate Change Adaptation Plans to enhance the nation's resilience to climate change. Invasive species are documented to reduce the effectiveness of climate adaptation and mitigation actions. In spite of this, only eight of the 26 federal plans directly reference invasive species, and just four meaningfully consider the reciprocal impact of invasive species on climate adaptation efforts. To ensure that climate adaptation planning and processes are effective with associated benefits for biodiversity conservation, a gap analysis of the Climate Change Adaptation Plans was conducted. Five key recommendations were developed that would transform how invasive species are considered within them. Examples of how invasive species have been included in some of the plans are provided, with suggestions for agencies that did not include invasive species on how they might do so, to illustrate that dedicated efforts in prevention, detection, and management of invasive species can safeguard carbon sequestration, protect communities and infrastructure, and minimize economic and biodiversity losses. Although developed based on U.S. federal plans, these recommendations can be used by state and local, as well as international, jurisdictions tasked with implementing climate‐adaptive policies to build resilience across multiple sectors.

Unlocking rivers' hidden diversity and ecological status using DNA metabarcoding in Northwest Spain.

Rivers are crucial ecosystems supporting biodiversity and human well-being, yet they face increasing degradation globally. Traditional river biomonitoring methods based on morphological identification of macroinvertebrates present challenges in terms of taxonomic resolution and scalability. This study explores the application of DNA metabarcoding analysis in both bulk and environmental DNA (eDNA) samples for comprehensive assessment of macrozoobenthic biodiversity, detection of invasive and endangered species, and evaluation of river ecological status in northwestern Spain. DNA metabarcoding of homogenized bulk samples and water eDNA revealed a mean of 100 and 87 macrozoobenthos species per sample respectively. However, the specific composition was significantly different with only 27.3% of the total species being shared. It was not possible to identify all the OTUs to species level; only 17.43% and 49.4% of the OTUs generated could be identified to species level in the bulk and eDNA samples, respectively. Additionally, a total of 11 exotic species (two first records for the Iberian Peninsula and another three first records for Asturias region) and one endangered species were detected by molecular tools. Molecular methods showed significant correlations with morphological identification for EQR values (Ecological Quality Ratio) of IBMWP index, yet differences in inferred river ecological status were noted, with bulk samples tending to indicate higher status. Overall, DNA metabarcoding offers a promising approach for river biomonitoring, providing insights into biodiversity, invasive species, and ecological status within a single analysis. Further optimization and intercalibration are required for its implementation in routine biomonitoring programmes, but its scalability and multi-tasking capabilities position it as a valuable tool for integrated monitoring of river ecosystems.

Research horizons for invasive marine species detection with eDNA/eRNA

The global marine ecosystem is changing rapidly as the result of biogeochemical cycles and ecosystem structure being altered by industrial civilization. Invasive marine species (IMS) are one of the most damaging regional consequences of human activity, and one of the most easily attributable to specific processes. This makes IMS introduction one of most tractable threats for management by appropriate policies. Once established, a different set of policies are required either to restrict IMS spread, or to attempt local eradication. The key ecosystem management tool for IMS damage mitigation is rapid, widely deployable IMS detection. Environmental Nucleic Acids (eNA), combining environmental DNA (eDNA) and environmental RNA (eRNA) analyses, have emerged as valuable tools for sensitive, cost-effective and readily deployable detection of IMS. Methods for IMS detection by eNA are still being developed through a widespread and active research community, so identifying the limitations of current processes will help prioritise eNA-based IMS detection research. We analysed and synthesised the opinions of expert marine ecosystem managers and researchers in Australia and New Zealand about the knowledge gaps and research needs for eNA-based IMS detection. This synthesis was placed in context with current research literature on what eNA technologies are currently providing as an IMS management tool; what problems exist with the current technology; and what could be done to improve this general approach. Our analyses produced a list of priorities that chart a path towards the best possible systems for IMS detection by eNA.

Predictor importance in habitat suitability models for invasive terrestrial plants

AbstractAimDue to the socioeconomic and environmental damages caused by invasive species, predicting the distribution of invasive plants is fundamental for effectively targeting management efforts. A habitat suitability model (HSM) is a powerful tool to predict potential habitat of invasive species to help guide the early detection of invasive plants. Despite numerous studies of the predictors used in HSMs, there is little consensus about the most appropriate predictors to use in creating ecologically realistic predictions from HSMs.LocationThe contiguous United States.MethodsWe explore 220 invasive terrestrial plant species' existing HSMs constructed with consistent modelling algorithms, background generation methods, predictor resolution, and geographic extent, and calculate the relative importance of predictors for each species. We sort predictors into eight groups (topography, temperature, disturbance, atmospheric water, landscape water, substrate, biotic interaction, and radiation) and compare the importance of predictor groups by plant lifeforms and phylogenetic relatedness.ResultsHuman modification and minimum winter temperature were generally the two highest performing individual predictors across the species studied. The highest‐performing predictor groups were disturbance, temperature, and atmospheric water. Across lifeforms, there were minimal differences in the influences of predictor groups, although woody plant models exhibited the largest differences in predictor importance when compared with non‐woody plant models. Additionally, we found no significant relationship between the importance of predictor groups and phylogenetic relatedness.Main ConclusionsThis study has implications for informing predictor selection in invasive plant HSMs, leading to more reliable and accurate models of invasive terrestrial plants. Our results emphasize the need to critically select predictors included in HSMs, with special consideration to temperature and disturbance predictors, to accurately predict habitat of invasive plant for detection and response of invasive plant species. With more accurate predictions, managers will be better prepared to address invasive species and reduce their threats to landscapes.

Evaluation of eDNA qPCR monitoring as an early detection tool for a non-native mysid in Great Lakes Waters

Early detection of aquatic invasive species (AIS) is vital to cost-effective prevention of their spread in the Great Lakes. Unfortunately, AIS surveillance has been generally too slow and geographically limited to support this purpose. Environmental DNA (eDNA) detection using quantitative polymerase chain reaction (qPCR) offers more rapid and affordable detection of likely AIS presence, but it does not directly discern live/dead status. Vital status verification using conventional surveys following positive eDNA qPCR detections could resolve this barrier, but only if the latter are adequately reliable and sensitive. Here we explore the reliability and sensitivity of eDNA qPCR monitoring for the bloody red shrimp (Hemimysis anomala), an AIS established in the southern Great Lakes but not yet widely distributed in Lake Superior, against conventional microscopy-based methods. We conducted this comparison using 1) harbor water from Muskegon Lake, MI where H. anomala is established, and 2) raw ballast water from ships transporting ballast from lower Lake Michigan to western Lake Superior. Our studies showed positive eDNA qPCR detections of H. anomala in all harbor and ballast samples for which conventional detection results were positive, and in some samples for which conventional results were negative. These results suggest that qPCR assays with adequate specificity could be an important tool in support of more effective and affordable early detection of target species in Great Lakes water, especially when combined with confirmatory conventional monitoring.

The third invasive Anoplophora: Citizen science facilitates uncovering massive abundance of non-native A. horsfieldii (Coleoptera: Cerambycidae) in South Korea

The longhorned beetle genus Anoplophora contains highly destructive species in the field of invasive entomology. Recently, the establishment of the non-native member of the genus, Anoplophora horsfieldii (Hope, 1843), has been documented in South Korea. However, the degree of impact of A. horsfieldii in invaded area remains uncertain due to the limited number of individuals surveyed, making it harder to classify them as invasive. This study combines data from both citizen scientists and naturalists to investigate the occurrence status and distribution range of A. horsfieldii. In total, 1,156 individuals were collected, with 1,124 of them collected in 2023 alone. Citizen-contributed data accounted for 52.5%, enabling us to estimate the preliminary occurrence period and population peak. This research identifies A. horsfieldii as the third invasive species within the genus Anoplophora and underscores the importance of citizen science in early detection and ecological surveys of invasive species.

Is it there?—Estimating the invasion of armored sailfin catfish (Pterygoplichthys sp.) in the water bodies of Eastern Ghats, India using the eDNA approach

AbstractEarly detection of invasive species is crucial for effectively controlling the potential damage they can inflict on the ecosystems. In contrast to the many limitations that conventional detection methods like visual surveys and netting hold, environmental DNA assay provides a powerful alternative. This non‐invasive, highly sensitive, and user‐friendly technique offers the advantage of detecting invasive species even in areas where direct observation is challenging, thus addressing the shortcomings of traditional techniques and enhancing overall accuracy in estimating distribution. The spread of invasive Pterygoplichthys sp. has become a cause for concern in biodiversity‐rich countries like India. Despite this, comprehensive studies on the prevalence of this invasive species are limited. The Eastern Ghats of India remain under‐explored with a high potential for supporting diverse lifeforms. Studying the extent of biological invasions in the Eastern Ghats is essential for effective management and to mitigate the ecological and socioeconomic impacts of invasive species. In this study, we have designed and optimized an eDNA‐based quantitative PCR assay to map the presence and spread of invasive Pterygoplichthys sp. in selected freshwater ecosystems of the Eastern Ghats. This assay detected invasive Pterygoplichthys sp. in almost 65% of the total locations sampled. This study can be further extended to larger geographical areas, which can contribute to formulating necessary measures by the authorities to manage invasion and conserve the diversity of the freshwater ecosystem.

New dockside eDNA based protocol to detect the seaweed Asparagopsis armata evaluated by stakeholders

Early detection of invasive species is crucial to deal effectively with biological invasions in ports, which are hotspots of species introductions. In this study, a simplified end-time PCR methodology conducted on eDNA from water samples was developed for rapid detection of the invasive seaweed Asparagopsis armata (four hours from water collection to result visualization). It was tested dockside in four international Spanish ports in presence of stakeholders, whose feedback was obtained to explore the real applicability of this biotechnology. Although biological invasions were not a main concern for them, results indicate a unanimous approval of the methodology by the stakeholders, having detected the presence of A. armata in three of the ports. Stakeholders suggested further developments for easier application of the tool and multiple species detection, to be adopted for the control of invasive species in ports.

Advanced Detection of Invasive Neophytes in Agricultural Landscapes: A Multisensory and Multiscale Remote Sensing Approach

The sustainable provision of ecological products and services, both natural and man-made, faces a substantial threat emanating from invasive plant species (IPS), which inflict considerable economic and ecological harm on a global scale. They are widely recognized as one of the primary drivers of global biodiversity decline and have become the focal point of an increasing number of studies. The integration of remote sensing (RS) and geographic information systems (GIS) plays a pivotal role in their detection and classification across a diverse range of research endeavors, emphasizing the critical significance of accounting for the phenological stages of the targeted species when endeavoring to accurately delineate their distribution and occurrences. This study is centered on this fundamental premise, as it endeavors to amass terrestrial data encompassing the phenological stages and spectral attributes of the specified IPS, with the overarching objective of ascertaining the most opportune time frames for their detection. Moreover, it involves the development and validation of a detection and classification algorithm, harnessing a diverse array of RS datasets, including satellite and unmanned aerial vehicle (UAV) imagery spanning the spectrum from RGB to multispectral and near-infrared (NIR). Taken together, our investigation underscores the advantages of employing an array of RS datasets in conjunction with the phenological stages, offering an economically efficient and adaptable solution for the detection and monitoring of invasive plant species. Such insights hold the potential to inform both present and future policymaking pertaining to the management of invasive species in agricultural and natural ecosystems.

First inventory of Ants (Hymenoptera: Formicidae) with detection of potential invasive species in National Park of Ehotilés islands, Côte d’Ivoire

Estuarine and wetland ecosystems are becoming increasingly altered by the concentration of human population near the coastline. A major threat to biodiversity related to this human footprint is the introduction of invasive alien species, particularly in isolated ecosystems like islands where biological invasion is often harmful. The National Park of Ehotilés Islands is an archipelago of 6 islands and a RAMSAR site subjected to disturbances activities like agriculture, illegal fisheries, and tourism. These factors often act as an accelerator for the introduction of invasive species. However, there is a lack of research conducted on insects, specifically ants on these islands. This study aimed to inventory the present ant fauna and estimate the vulnerability to tramp and potential invasive ant species. Ants were collected using Winkler, pitfall, and funnel traps on five islands. In total, 76 ant species were recorded. These species are distributed into 20 genera and five subfamilies: Dolichoderinae (5 species), Formicinae (11 species), Myrmicinae (49 species), Ponerinae (11 species) and Proceratiinae (1 species). We also detected two tramp and potentially invasive species: the ghost ant Tapinoma melanocephalum and the big-headed ant Pheidole megacephala. Ant communities are dominated by six species, namely Odontomachus troglodytes, Oecophylla longinoda, Nylanderia lepida, Pheidole sp.2, Monomorium invidium, and the invasive ghost ant Tapinoma melanocephalum. This work is the first to inventory ants on the Islands of Ehotilés National Park and may serve as a basis for conservation decisions as it demonstrates that this park is not spared from the introduction of invasive ant species.

Potential invasive species detection of Demospongiae using environmental DNA in Sabang and Lhokseumawe Ports

Sabang and Lhokseumawe Ports are located in the province of Aceh and constitute part of the northern Malacca Strait, sharing direct borders with neighboring countries such as Malaysia, Thailand, and India. Strategic geographical location significantly influences the oceanographic conditions and biodiversity of the area. As marine organisms, sponges play a crucial role in the ecosystem, yet research on their identification in the Sabang and Lhokseumawe regions is limited. The analysis of community structure and identification of species can be conducted using various approaches, including environmental DNA (e-DNA) analysis. This study aimed to assess water quality and identify the presence of potentially invasive Demospongiae using DNA Metabarcoding in Sabang and Lhokseumawe Ports. The measurement results of the water conditions indicate that the waters around Sabang and Lhokseumawe ports generally exhibit values that support sponge life. e-DNA analysis successfully detected the presence of the Demospongiae class, with 260 Operational Taxonomic Units (OTUs) in Sabang waters and 148 OTUs in Lhokseumawe waters. One commonly found genus, Cliona sp., has the potential to become invasive in both locations, posing a risk of bioerosion to corals under specific conditions.

Distribution of the Cannabis sativa L. in the Western Himalayas: A tale of the ecological factors behind its continuous invasiveness

The invasion of species in new regions depends on multiple factors, especially, the prevailing environmental factors. The environmental conditions are essential to understand for planning effective management strategies related to invasive species. Little is known about Cannabis as an established invasive weed. We hypothesized that the successful establishment of this invading species is influenced by the environmental variables; however, some of them have a much stronger influence than the others. Quantitative ecological methods were adopted for sampling the habitats invaded by Cannabis sativa, in a total of 165 quadrats. Soil samples were collected for soil analyses from each of those quadrat. Ecological and statistical approaches including Structure Equation Modeling (SEM) procedures were applied to evaluate the impact of environmental factors, ecological interrelationships, and the resultant invasiveness of the C. sativa. Our findings indicate that elevation, temperature, humidity, anthropogenic pressure, physio-chemical prperties of soil and habitat degradation play significant roles in determining the distribution and abundance of C. sativa. Principal Component Analysis (PCA) of the parameters further clarifies that elevation is the most important driver in explaining the successful establishment of the invader species with a 30.1% variance. Structural equation modeling further confirms the significant role played by elevation, which not only directly affects the abundance of Cannabis but also indirectly influences other variables such as anthropogenic pressure, temperature, and humidity etc. However, the invasion of C. sativa is less affected by soil saturation pH, electrical conductivity, phosphorous, potassium, and CaCO3. Our study provides valuable scientific information that could be used for the early detection of invasive species at the early stage of invasion and in devising policies for their management and control.

Dealing with imperfect data for invasive species detection using multispectral imagery

Detection and monitoring invasive species can provide valuable ecological information to guide management decisions. Multispectral imagery remote sensing may be an ideal tool to address this problem by providing accurate and affordable repeat imagery. However, developing training datasets for remote sensing imagery can be riddled with issues such as matching a training site to a single pixel in target imagery or mixed pixels caused by low invader densities. The multitarget multiple-instance spectral match filter (MTMI-SMF) algorithm accounts for such errors and has low computation costs. Here, we evaluated MTMI-SMF for invasive species detection with National Agriculture Imagery Program (NAIP), RapidEye, and Landsat imagery using Brazilian peppertree (Schinus terebinthifolia) in the Everglades National Park as a case study. MTMI-SMF detected Brazilian peppertree with an area under the curve (AUC) of 0.85 for Landsat, 0.82 for RapidEye, and 0.71 NAIP imagery. Invader classification was developed using a threshold calculated from MTMI-SMF image detection confidence values and compared to a random forest classification. MTMI-SMF and random forest classifications had similar overall accuracies with NAIP imagery performing the lowest (an average of 73.6% and 88.9%, respectively) and Landsat performing the highest (an average of 90.9% and 91.4%, respectively). However, due to the relatively rare nature of Brazilian peppertree across this landscape, the overall classification accuracy was not fully representative of detection capabilities. MTMI-SMF outperformed random forest for user accuracy and had comparable producer accuarcy across all three image sources. Classifications primarily relied on the brighter signature of Brazilian peppertree due to the limited number of spectral bands in the multispectral imagery. As a result, confusion was caused by heterogeneous vegetation communities or shrub habitats, which most likely could be resolved with the increased availability of satellite hyperspectral datasets. Our study indicates that MTMI-SMF, a machine-learning approach that allows flexibility in training data, can detect highly problematic invaders using multispectral imagery. This pipeline could be readily adapted to other invaders and ecosystems, as well as different remote sensing image sources, and has low computational requirements. Our study demonstrates that remote sensing technologies and multiple-instance learning algorithms can provide managers with critical tools for tackling the ever-growing, costly, and challenging problem of tracking invasive species' plant spread.

Deep learning-based image classification of turtles imported into Korea

Although turtles play a key role in maintaining healthy and balanced environments, these are endangered due to global trade to meet the high demand for food, medicine, and pets in Asia. In addition, imported non-native turtles have been controlled as alien invasive species in various countries, including Korea. Therefore, a rapid and accurate classification of imported turtles is needed to conserve and detect those in native ecosystems. In this study, eight Single Shot MultiBox Detector (SSD) models using different backbone networks were used to classify 36 imported turtles in Korea. The images of these species were collected from Google and were identified using morphological features. Then, these were divided into 70% for training, 15% for validation, and 15% for test sets. In addition, data augmentation was applied to the training set to prevent overfitting. Among the eight models, the Resnet18 model showed the highest mean Average Precision (mAP) at 88.1% and the fastest inference time at 0.024 s. The average correct classification rate of 36 turtles in this model was 82.8%. The results of this study could help in management of the turtle trade, specifically in improving detection of alien invasive species in the wild.

Incursion Preparedness, Citizen Science and Early Detection of Invasive Insects: The Case of Aleurocanthus spiniferus (Hemiptera, Aleyrodidae) in France.

We describe the process by which the quarantine whitefly, Aleurocanthus spiniferus (Hemiptera, Aleyrodidae), was detected in France. The initial observation was made by a volunteer who reported a picture of an adult in the Inventaire National du Patrimoine Naturel (INPN Espèces), a citizen science resource developed by l'Office Français de la Biodiversité and the French Muséum National d'Histoire Naturelle. The specimen was suspected to be A. spiniferus from this picture by one of the expert entomologists in charge of the Hemiptera group validation. Once the species was identified, it was mounted on a slide and the information was officially passed on to the ministry in charge of agriculture via a communication channel set up in advance for this type of situation. The ministry then triggered the regulatory actions planned in the event of the suspected detection of quarantine organisms. Sampling was quickly carried out and the specimens collected on this occasion were formally identified as belonging to the species A. spiniferus. This led to the formalization of an outbreak in France. This sequence of decisions took just two months from the first observation to the implementation of a management plan. This case presents how incursion preparedness contributes to a rapid response. Furthermore, this case exemplifies how citizen science can contribute to the early detection of invasive species and highlights the importance of informing both the general public and professionals about major environmental issues.

Research and prospects of environmental DNA (eDNA) for detection of invasive aquatic species in East Asia

Research and prospects of environmental DNA (eDNA) for detection of invasive aquatic species in East Asia

Predicting the spatio-temporal pattern of range expansion under lack of equilibrium with climate

Early detection of invasive species in regions under threat is key for biodiversity conservation. Here we conduct a retrospective study to assess whether correlative Species Distribution Models (SDMs) can predict the spatio-temporal range of expansion in an invasive species, the Asian hornet. Given that modelling invasive species distributions defies one of the main assumptions of SDMs, the equilibrium with climate, we also introduce a quantitative assessment of climatic disequilibrium in the invaded area based on hypervolume comparison between the native and invaded areas (both unoccupied and occupied territories). We evaluate the ability of three different modelling approaches (presence-only, presence-background and presence-absence) calibrated with data until 2013 to predict the species distribution observed the following years (2015, 2017, 2019, and 2021). Our results show that presence-only models based on the BIOCLIM algorithm can effectively predict the spatio-temporal pattern of invasion when predictions are rescaled based on percentiles (i.e. ranked suitability) and the model is calibrated with data from both native and invaded areas. These models predicted higher suitability values for sites that were invaded earlier in time and, importantly, they did not predict low suitability values for sites that were eventually occupied years later. Thus, they can be very useful for decision-makers and managers, as they provide a probabilistic prediction of both (i) the temporal dimension of species range expansion, and (ii) the potential distribution range in the long term. Such information would allow prioritizing monitoring efforts in the short term without losing sight of the potential risks in the long term.