Wild Boar in Flanders, Belgium: (Dis)agreements Between Key Stakeholders on Wild Boar Management Objectives, Actions, and Legal Provisions

Wild boar (Sus scrofa) reappeared in Flanders, Belgium in 2006 after more than half a century of absence. Besides being a native and highly valued game species in Europe, wild boar are also known to be responsible for car collisions, crop damage, disease transmission, and ecological damage at high densities. The management of wild boar therefore seeks to balance these positive and negative impacts. Given the highly fragmented landscape in Flanders and its multifunctional use, coexistence with wild boar is only possible through integrated management involving relevant stakeholder groups. However, to be successful, this requires that the management objectives, the overall wild boar policy of the Flemish authorities, and management actions are supported by the stakeholders. To assess the support for the current management, we conducted a survey among members of the 3 key stakeholder groups: farmers, hunters, and conservationists. Our survey assessed the importance stakeholders attribute to different management objectives, their support for the current legal provisions, and how desirable the different stakeholder groups considered possible management actions. The potential for conflict index was used to analyze the (dis)agreement between and within stakeholder groups. Reducing or preventing crop damage and the risk for car accidents are indicated as being the most important management objectives by all 3 stakeholder groups. Stakeholder groups differ strongly in their support for the current legal provisions. Those stakeholders that have to implement the legal provisions or are mostly affected by these laws are less supportive than others. The desirability of the possible management actions strongly varied according to the different stakeholder groups. Contrary to other studies, the desirability of a possible management action was hardly influenced by the management objective it tried to achieve

Automated recognition of people and identfication of animal species in camera trap images

Camera traps are increasingly being used in wildlife monitoring. The great advantage of camera traps in comparison with other sampling methods is that very accurate data can be collected without the animal being collared or tagged nor the researcher being present. However, such camera trapping frameworks produce high volumes of pictures which often need to be reviewed manually. Convolutional neural networks can be used to automate this labour intensive process. In our work, we use existing manually labelled images from a camera trap study conducted by the Research Institute for Nature and Forest in collaboration with Hasselt University (Belgium) to train a convolutional neural network for identifying animal species. Images were annotated using the camera trap application Agouti (www.agouti.eu). In this way images can be automatically labelled or the network can be incorporated into annotation applications to provide a suggestion to the users and as such speed up the annotation process. In addition to conveying the presence or absence of species, the images may contain other useful information, for example animal attributes and behaviour. Therefore, getting help from wildlife enthusiasts via citizen science may be desirable to review the large amounts of data. However, since cameras are mounted in public nature reserves, there always exists the risk that passers-by have triggered the camera traps. For privacy reasons, images showing people cannot be made public. Removing these images from the dataset can be automated by training the network to recognise people in addition to identifying animals species, before the data can be made available to volunteers

Beyond protocols:improving the reliability of expert-based risk analysis underpinning invasive species policies

Risk assessment tools for listing invasive alien species need to incorporate all available evidence and expertise. Beyond the wealth of protocols developed to date, we argue that the current way of performing risk analysis has several shortcomings. In particular, lack of data on ecological impacts, transparency and repeatability of assessments as well as the incorporation of uncertainty should all be explicitly considered. We recommend improved quality control of risk assessments through formalized peer review with clear feedback between assessors and reviewers. Alternatively, a consensus building process can be applied to better capture opinions of different experts, thereby maximizing the evidential basis. Elaborating on manageability of invasive species is further needed to fully answer all risk analysis requirements. Tackling the issue of invasive species urges better handling of the acquired information on risk and the exploration of improved methods for decision making on biodiversity management. This is crucial for efficient conservation resource allocation and uptake by stakeholders and the public

Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data

Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap-derived Big Data are becoming increasingly solvable with the help of scalable cyber-infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media-based and event-based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in-depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard. Biodiversity data, camera traps, data exchange, data sharing, information standardspublishedVersio

Using structured eradication feasibility assessment to prioritize the management of new and emerging invasive alien species in Europe

Prioritizing the management of invasive alien species (IAS) is of global importance and within Europe integral to the EU IAS regulation. To prioritize management effectively, the risks posed by IAS need to be assessed, but so too does the feasibility of their management. While the risk of IAS to the EU has been assessed, the feasibility of management has not. We assessed the feasibility of eradicating 60 new (not yet established) and 35 emerging (established with limited distribution) species that pose a threat to the EU, as identified by horizon scanning. The assessment was carried out by 34 experts in invasion management from across Europe, applying the Non‐Native Risk Management scheme to defined invasion scenarios and eradication strategies for each species, assessing the feasibility of eradication using seven key risk management criteria. Management priorities were identified by combining scores for risk (derived from horizon scanning) and feasibility of eradication. The results show eradication feasibility score and risk score were not correlated, indicating that risk management criteria evaluate different information than risk assessment. In all, 17 new species were identified as particularly high priorities for eradication should they establish in the future, whereas 14 emerging species were identified as priorities for eradication now. A number of species considered highest priority for eradication were terrestrial vertebrates, a group that has been the focus of a number of eradication attempts in Europe. However, eradication priorities also included a diverse range of other taxa (plants, invertebrates and fish) suggesting there is scope to broaden the taxonomic range of attempted eradication in Europe. We demonstrate that broad scale structured assessments of management feasibility can help prioritize IAS for management. Such frameworks are needed to support evidence‐based decision‐making

Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data

Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap-derived Big Data are becoming increasingly solvable with the help of scalable cyber-infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media-based and event-based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in-depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standar

A horizon scan of emerging technologies for alien species citizen science

Citizen Science (CS) significantly contributes to the study and management of biological invasions. Technological developments such as social media, internet scraping, eDNA, apps, sensors, search engines and predictive analytics can foster projects and increase the reach of CS. The list of tech is long and their potential for alien species CS is unclear. To help research funders and project initiators we performed a horizon scan on the value of emerging technologies for alien species CS. A network of 44 experts from the COST Action Alien-CSI identified and assessed 39 technologies. Assessors rated their usefulness on a 5-point Likert scale with respect to their potential to attract new audiences, improve inclusivity, applicability in daily life, ability to increase engagement, provide feedback, improve data quality and gather new data. The results of the scoring were discussed at a consensus workshop. Consistency among ratings was explored using inter-rater reliability metrics and ordination techniques. Experts were asked to explain and discuss the reasons for inconsistent rating across technologies or criteria, focusing on the evidence used or differences in interpretation of criteria. After this, one new round of individual re-evaluation of technologies followed. The discussions resulted in increased consensus on the potential of the different technologies. Mobile-based data collection platforms and social media (both their use to interact with CS participants as well as their potential for scraping new occurrence records) were the top-ranking technologies for IAS CS, followed by machine learning techniques, the application of AI and collective intelligence. Technologies differed considerably in their potential when different criteria were individually considered. For instance, DNA-based technologies ranked high on acquiring new data, open data technologies scored high on their potential to improve the quality of collected data. Virtual reality and gamification scored high on their potential to increase engagement in IAS CS

A pilot on integrated wildlife monitoring at European scale: environmental detection of selected pathogens in the European Observatory of Wildlife

The European Observatory of Wildlife (EOW) as part of the ENETWILD project aims progressively developing integrated wildlife monitoring (population abundance and pathogens). The present report shows how to link the wildlife population monitoring (by camera trapping) and wildlife disease surveillance at European scale, by using environmental sampling over 15 study areas of the EOW from 10 Countries (4 study areas in 4 countries will be incorporated next). We specifically focused on multi-host pathogens Mycobacterium tuberculosis Complex (bacteria, MTC), and Hepatitis E virus (HEV). The aims of this trial were, first, to evaluate the harmonized implementation of a simple field sampling protocol for detecting zoonotic pathogens in environmental samples (standing water) through a network of wildlife professionals at European level. Secondly, we got insights for future improved strategies of wildlife integrated monitoring through environmental sampling. This trial prioritized the inclusion of a diverse array of study areas and a simple sampling approach rather than complex protocols and illustrated. We evidenced the importance of supporting such a coordinate network of wildlife professionals to progressively improve strategies, protocols, the general design, sampling, target matrix, selected pathogens, preservation and transport of samples, analytical techniques, and sample and data flow. We discuss specific results on pathogens, remarking the detection of the MTC in certain areas.Question number: EFSA-Q-2022-00562.Peer reviewe

Mammal responses to global changes in human activity vary by trophic group and landscape

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.Peer reviewe