Abstract

Social media has become a popular stage for people’s views over climate change. Monitoring how climate change is perceived on social media is relevant for informed decision-making. This work advances the way social media users’ perceptions and reactions towards climate change can be understood over time, by implementing a scalable methodological framework grounded on natural language processing. The framework was tested in over 1771 thousand X/Twitter posts of Spanish, Portuguese, and English discourses from Southwestern Europe. The employed models were successful (i.e., > 84% success rate) in detecting relevant climate change posts. The methodology detected specific climate phenomena in users’ discourse, coinciding with the occurrence of major climatic events in the test area (e.g., wildfires, storms). The classification of sentiments, emotions, and irony was also efficient, with evaluation metrics ranging from 71 to 92%. Most users’ reactions were neutral (> 35%) or negative (> 39%), mostly associated to sentiments of anger and sadness over climate impacts. Almost a quarter of posts showed ironic content, reflecting the common use of irony in social media communication. Our exploratory study holds potential to support climate decisions based on deep learning tools from monitoring people’s perceptions towards climate issues in the online space. 

Abstract: 

Disturbance by fire can significantly influence the spread and establishment of invasive alien plants, such as Acacia dealbata (silver wattle), which pose serious threats to ecosystems. This study assessed the combined effects of fire count density and landscape susceptibility to invasion on the population dynamics of A. dealbata in the Minho region of Portugal using a mechanistic model. The model, developed using STELLA software, comprises four submodels that address: (A) changing trends in land use and land cover (LULC); (B) fire events; (C) changes in the landscape susceptibility to invasion and (D) A. dealbata population dynamics. Higher fire count densities led to higher A. dealbata population growth and regeneration, increasing landscape susceptibility to invasion. Models such as the proposed approach can help identify potential priority areas for management, namely in sites with low initial structural diversity and continued A. dealbata population growth, as demonstrated in our dynamic simulations. The model not only enhances understanding of interactions between fire and plant invasion dynamics but also provides a tool for predicting the dynamics of other invasive plant species to guide proactive and cost-effective management strategies. 

Abstract: 

Hydropower is often promoted as a sustainable solution for meeting rising energy demands while reducing greenhouse gas emissions. However, its impacts on terrestrial wildlife remain poorly understood, even in the case of threatened and declining species such as cliff-nesting raptors. This study assesses the long-term impacts of hydropower development in northeastern Portugal on three cliff-nesting raptors: Golden eagle (Aquila chrysaetos), Egyptian vulture (Neophron percnopterus), and Griffon vulture (Gyps fulvus). Besides the dam impacts, these species were also potentially affected by mitigation measures, including prey management for eagles and the establishment of feeding stations for vultures. Using a Before-After-Control-Impact (BACI)-like approach, we analysed changes in territory occupancy, breeding population size, and breeding productivity before (2010–2014) and after (2015–2022) river damming. The extent of flooding around nests served as a proxy for dam impact severity. The Golden eagle population fluctuated, and breeding productivity declined over time, though without a clear link to flooding. Egyptian vultures showed steeper productivity declines in territories most affected by flooding. Conversely, Griffon vulture occupancy and population size increased after flooding, albeit with a weak tendency towards a smaller population increase where flooding was greater. Our findings highlight species-specific responses to hydropower development. While Griffon vultures likely benefited from supplementary feeding, Egyptian vultures may have been negatively affected by habitat loss and competition with the former species. These contrasting trends highlight the complex ecological consequences of hydropower infrastructure and underscore the need for species-focused mitigation strategies. The study emphasizes the importance of long-term monitoring and adaptive management to balance renewable energy development with the conservation of vulnerable wildlife. 

Abstract: 

Deep learning has advanced the content analysis of digital data, unlocking opportunities for detecting, mapping, and monitoring invasive species. Here, we tested the ability of open source classification and object detection models (i.e., convolutional neural networks: CNNs) to identify and map the invasive plant Cortaderia selloana (pampas grass) in mainland Portugal. CNNs were trained over citizen science images and then applied to social media content (from Flickr, Twitter, Instagram, and Facebook), allowing to classify or detect the species in over 77% of situations. Images where the species was identified were mapped, using their georeferenced coordinates and time stamp, showing previously unreported occurrences of C. selloana, and a tendency for the species expansion from 2019 to 2021. Our study shows great potential from deep learning, citizen science and social media data for the detection, mapping, and monitoring of invasive plants, and, by extension, for supporting follow-up management options. 

Abstract: 

1. We evaluated the potential of data from existing citizen science platforms for early detection of IAS by obtaining 687 first records of species from 30 European countries where there was both an official first record (i.e. published in scientific literature or by a government agency) and a record in a citizen science platform. We tested how the difference between the two (time lag) was related to species traits, popularity in citizen science platforms, public and research attention and regulatory status.

2. We found that for 50% of the time lag records, citizen science platforms reported IAS earlier than or in the same year as the official databases. Although we cannot determine causality (the first official record could have been from a citizen science platform, or contemporaneous with it), this demonstrates that citizen science platforms are effective for IAS early detection.

3. Time lags were largely affected by species traits. Compared with official records, vertebrates were more likely to have earlier records on citizen science platforms, than plants or invertebrates. Greater popularity of the IAS in citizen science platforms and its observation in neighbouring countries resulted in earlier citizen science reporting. In contrast, inclusion in the EU priority list resulted in earlier official recording, reflecting the efficacy of targeted surveillance programmes. However, time lags were not affected by the overall activity of citizen platforms per country.

4. Synthesis and applications. Multi-species citizen science platforms for reporting nature sightings are a valuable source of information on early detection of IAS even though they are not specifically designed for this purpose. We recommend that IAS surveillance programmes should be better connected with citizen science platforms, including greater acknowledgement of the role of citizen scientists and better data flow from smaller citizen science initiatives into global databases, to support efficient early detection.

Abstract: 

Urban biodiversity plays a crucial role in the functioning of urban ecosystems and significantly impacts the well-being and quality of life for city residents. By focusing on the city of Porto as a case study, the influence of local-scale land cover evolution on urban biodiversity is investigated, using species richness of birds, reptiles, and amphibians as indicators, within a multimodel inference framework. The results underscore the importance of past land cover and land cover legacy in shaping urban biodiversity. Birds, reptiles, and amphibians respond positively to older vegetation patches but negatively to long-established urban areas. Birds exhibit adaptability by positively responding to recent vegetation, while amphibians tend to avoid newly vegetated zones. The fragmented distribution of amphibians and reptiles in Porto suggests limited mobility and a potential delayed response to habitat loss and isolation. Reptiles benefit from both wooded and herbaceous habitats, emphasizing the importance of local spatial diversity, while water elements are critical for amphibians, although many aquatic habitats in the urban context may not be suitable. Modeling results guide the identification of priority areas for urban biodiversity conservation in Porto, informing decision-makers, urban planners, and conservationists. This spatially explicit research aids efforts to create more ecologically resilient and biodiverse urban environments. It highlights the role of historical land cover and the unique responses of different vertebrate groups to urbanization, contributing to our understanding of urban biodiversity dynamics and sustainable urban development. 

Abstract: 

Spatial epidemiology tools play a critical role in effectively allocating resources to curb the spread of animal diseases. This study focuses on classical scrapie (CS), an animal prion disease identified in Portugal, which infects small ruminant flocks and has been shown to be experimentally transmissible to wild cervids. Utilising remote sensing technologies and semi-automatic classification models, we aimed to evaluate the risk of interspecies prion transmission from domestic small ruminants to wild cervids (hosts). To achieve this, we gathered data related to hosts and infected small ruminant flocks. Furthermore, we collected and processed freely available, medium-resolution satellite imagery to derive vegetative and biophysical spectral indices capable of representing the primary habitat features. By employing a pixel-based species distribution model, we integrated the compiled geographical distribution data and spectral data with five supervised classification algorithms (random forest, classification tree analysis, artificial neural network, generalised linear model, and generalised additive model). The consensus map allowed accurate predictions of spatialised regions exhibiting spectral characteristics similar to where CS and its hosts were initially identified. By overlapping suitable territories for disease and host occurrence, we created a spatially explicit tool that assesses the risk of prion spill-over from domestic small ruminants to wild cervids. The described methodology is highly replicable and freely accessible, thus emphasising its practical utility. This study underscores the substantial contribution of model-based spatial analysis to disease monitoring and lays the groundwork for defining populations at risk and implementing targeted control and prevention strategies, thus safeguarding both animal and public health. 

Abstract: 

Although invasive alien species have long been recognized as a major threat to nature and people, until now there has been no comprehensive global review of the status, trends, drivers, impacts, management and governance challenges of biological invasions. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Thematic Assessment Report on Invasive Alien Species and Their Control (hereafter ‘IPBES invasive alien species assessment’) drew on more than 13,000 scientific publications and reports in 15 languages as well as Indigenous and local knowledge on all taxa, ecosystems and regions across the globe. Therefore, it provides unequivocal evidence of the major and growing threat of invasive alien species alongside ambitious but realistic approaches to manage biological invasions. The extent of the threat and impacts has been recognized by the 143 member states of IPBES who approved the summary for policymakers of this assessment. Here, the authors of the IPBES assessment outline the main findings of the IPBES invasive alien species assessment and highlight the urgency to act now. 

Abstract: 

E-commerce has become a booming market for wildlife trafficking, as online platforms are increasingly more accessible and easier to navigate by sellers, while still lacking adequate supervision. Artificial intelligence models, and specifically deep learning, have been emerging as promising tools for the automated analysis and monitoring of digital online content pertaining to wildlife trade. Here, we used and fine-tuned freely available artificial intelligence models (i.e., convolutional neural networks) to understand the potential of these models to identify instances of wildlife trade. We specifically focused on pangolin species, which are among the most trafficked mammals globally and receiving increasing trade attention since the COVID-19 pandemic. Our convolutional neural networks were trained using online images (available from iNaturalist, Flickr and Google) displaying both traded and non-traded pangolin settings. The trained models showed great performances, being able to identify over 90 % of potential instances of pangolin trade in the considered imagery dataset. These instances included the showcasing of pangolins in popular marketplaces (e.g., wet markets and cages), and the displaying of commonly traded pangolin parts and derivates (e.g., scales) online. Nevertheless, not all instances of pangolin trade could be identified by our models (e.g., in images with dark colours and shaded areas), leaving space for further research developments. The methodological developments and results from this exploratory study represent an advancement in the monitoring of online wildlife trade. Complementing our approach with other forms of online data, such as text, would be a way forward to deliver more robust monitoring tools for online trafficking. 

Abstract: 

The Kunming-Montreal Global Biodiversity Framework (GBF) calls for a 50% reduction in rates of invasive alien species establishment by 2030. However, estimating changes in rates of introduction and establishment is far from straightforward, particularly on a national scale. Variation in survey effort over time, the absence of data on survey effort, and aspects of the invasion process itself interact in ways that make rate estimates from naive models of invasion trends inaccurate. To support progress toward robust global and national reporting against the GBF invasions target, we illustrate this problem using a combination of simulations, and global and national scale case studies. We provide recommendations and a clear set of steps that are needed for progress. These include routine collection of survey effort data as part of surveillance and monitoring protocols and working closely with researchers to develop meaningful estimates of change in biological invasions. Better awareness of this challenge and investment in developing robust approaches will be required from Parties if progress on Target 6 of the GBF is to be tracked and achieved. 

Abstract: 

Crowdsourced social media data has become popular for assessing cultural ecosystem services (CES). Nevertheless, social media data analyses in the context of CES can be time consuming and costly, particularly when based on the manual classification of images or texts shared by people. The potential of deep learning for automating the analysis of crowdsourced social media content is still being explored in CES research. Here, we use freely available deep learning models, i.e., Convolutional Neural Networks, for automating the classification of natural and human (e.g., species and human structures) elements relevant to CES from Flickr and Wikiloc images. Our approach is developed for Peneda-Gerês (Portugal) and then applied to Sierra Nevada (Spain). For Peneda-Gerês, image classification showed promising results (F1-score ca. 80%), highlighting a preference for aesthetics appreciation by social media users. In Sierra Nevada, even though model performance decreased, it was still satisfactory (F1-score ca. 60%), indicating a predominance of people’s pursuit for cultural heritage and spiritual enrichment. Our study shows great potential from deep learning to assist in the automated classification of human-nature interactions and elements from social media content and, by extension, for supporting researchers and stakeholders to decode CES distributions, benefits, and values. 

Abstract: 

Eucalyptus globulus Labill. stands have been expanding in protected areas (sites) of the Natura 2000 network in the Iberian Peninsula (Iberia). This expansion is mostly human-driven, but there is increasing evidence of plant recruitment and escape from cultivation areas. Therefore, it is important to assess the recruitment and invasion potential of sites and associated habitats and how future climate may change this potential. Here, we use SDMs to project current and future climatic suitability for E. globulus recruitment in Iberia and combine this suitability with local factors to rate the current recruitment potential of eucalypt stands. This potential is then extrapolated to neighbour areas in Natura 2000 sites to assess the invasion potential. The results show a wide recruitment range along coastal regions of western and northern Iberia (83,275 km2) and a northward contraction under climate change, similar to the trend projected for plantation suitability. Recruitment potential of any level was identified in 989 km2, while invasion potential was identified in 878 km2 across 176 Natura 2000 sites. Heathlands and riparian forests were associated with the largest recruitment and invasion potential areas. This study may help in preventing further negative impacts in protected areas and habitats already affected by E. globulus expansion. 

Abstract: 

The Yellow-legged hornet (Vespa velutina nigrithorax), native to regions of Southeast Asia, was accidentally introduced in Europe, South Korea, and Japan, where is has often become invasive. Due to its potential negative impacts at ecologic, economic and social levels, this hornet was included in the “Union list” of the EU legislation for invasive alien species. This means that measures are urgently needed to prevent further introductions, as well as to early-detect and control spread to avoid new populations. In this study we aim to identify the main reported drivers of distribution, ecological preferences, impacts, and methods for preventing introduction, controlling, and managing this invasive species. The supporting information was obtained from a comprehensive literature search. Then, a literature review was performed to classify the records gathered and to extract the relevant information following an adapted Drivers-Pressures-State-Impacts-Responses framework. The achieved results show a growing interest of researchers on V. velutina nigrithorax through time due to its quick spread and impacts on new ecosystems. They also revealed that there is much information on the State of invasions, whereas more knowledge is needed regarding the Drivers of those invasions. Biological traits such as life history traits, morphology, and the sting venom properties are some of the most studied topics regarding V. velutina nigrithorax. In the future, research should focus on the topics that lack information, analyse other Response solutions that meet the intended measures by the EU legislation, and use new methodology to study the impacts caused by this invader under new perspectives.

Abstract: 

Invasive alien species are a major driver of global environmental change. Escalating globalization processes such as international trade and long-distance transport have contributed to an increase in the ecological, economic, and sociocultural impacts of biological invasions. As a result, their management has become an increasingly relevant topic on environmental policy agendas. To better understand the role of policy in invasion science and to identify trends and gaps in policy-oriented research, a systematic literature review was conducted covering 2135 publications. The results highlight that international policy instruments are contributing to an increased interest in pursuing policy-oriented research. Specifically, key historical periods in policy development (e.g., the Convention on Biological Diversity’s COP10 in 2010) coincide with periods of active policy-focused research in invasion science. Research is, however, more applied to local scales (i.e., subnational, and national) and is more focused in places with high research capacity or where severe environmental or economic impacts are well documented. 

Abstract: 

Monitoring the progress parties have made toward meeting global biodiversity targets requires appropriate indicators. The recognition of invasive alien species (IAS) as a biodiversity threat has led to the development of specific targets aiming at reducing their prevalence and impact. However, indicators for adequately monitoring and reporting on the status of biological invasions have been slow to emerge, with those that exist being arguably insufficient. We performed a systematic review of the peer-reviewed literature to assess the adequacy of existing IAS indicators against a range of policy-relevant and scientifically valid properties. We found that very few indicators have most of the desirable properties and that existing indicators are unevenly spread across the components of the Driver-Pressure-State-Response and Theory of Change frameworks. We provide three possible reasons for this: (i) inadequate attention paid to the requirements of an effective IAS indicator, (ii) insufficient data required to populate and inform policy-relevant, scientifically robust indicators, or (iii) deficient investment in the development and maintenance of IAS indicators. This review includes an analysis of where current inadequacies in IAS indicators exist and provides a roadmap for the future development of indicators capable of measuring progress made toward mitigating and halting biological invasions.  

Abstract: 

The spread of invasive alien species promotes ecosystem structure and functioning changes, with detrimental effects on native biodiversity and ecosystem services, raising challenges for local management authorities. Predictions of invasion dynamics derived from modeling tools are often spatially coarse and therefore unsuitable for guiding local management. Accurate information on the occurrence of invasive plants and on the main factors that promote their spread is critical to define successful control strategies. For addressing this challenge, we developed a dual framework combining satellite image classification with predictive ecological modeling. By combining data from georeferenced invaded areas with multispectral imagery with 10-meter resolution from Sentinel-2 satellites, a map of areas invaded by the woody invasive Acacia longifolia in a municipality of northern Portugal was devised. Classifier fusion techniques were implemented through which eight statistical and machine-learning algorithms were ensembled to produce accurate maps of invaded areas. Through a Random Forest (RF) model, these maps were then used to explore the factors driving the landscape-level abundance of A. longifolia. RF models were based on explanatory variables describing hypothesized environmental drivers, including climate, topography/geomorphology, soil properties, fire disturbance, landscape composition, linear structures, and landscape spatial configuration. Satellite-based maps synoptically described the spatial patterns of invaded areas, with classifications attaining high accuracy values (True Skill Statistic, TSS: 0.895, Area Under the Receiver Operating Curve, ROC: 0.988, Kappa: 0.857). The predictive RF models highlighted the primary role of climate, followed by landscape composition and configuration, as the most important drivers explaining the species abundance at the landscape level. Our innovative dual framework—combining image classification and predictive ecological modeling—can guide decision-making processes regarding effective management of invasions by prioritizing the invaded areas and tackling the primary environmental and anthropogenic drivers of the species’ abundance and spread. 

Abstract: 

How institutional stakeholders perceive the supply and demand of ecosystem services (ES) under distinct contexts determines which planning actions are deemed priority or not. Public officers play a crucial role in social-ecological management and decision-making processes, but there is a paucity of research exploring their perceptions on ES supply and demand under a changing climate. We address this gap through an exploratory study that analyses the views of public officers on the potential impacts of climate-change related drivers on multiple ES in a major administrative region from Portugal (EU NUTS 3). We combined qualitative spatial data from participatory maps and semi-quantitative answers from questionnaire-based surveys with 22 officers from public institutions contributing to territorial planning. Contrary to other similar studies, public officers shared a common view on the importance of ES. This view aligns with scientific projections on how a changing climate is expected to influence ES in the region over the next decade. In agreement with other observations in Mediterranean regions, the most perceivably valued ES concerned tangible socio-economic benefits (e.g., periurban agriculture and wine production). Surprisingly, despite the region’s potential for cultural ES, and considering the impacts that climate change may hold on them, recreation and tourism did not seem to be embedded in the officers’ views. We explore the implications of our findings for territorial planning and social-ecological adaptation, considering that the way stakeholders manage the territory in response to climate change depends on the extent to which they are aware and expect to experience climatic consequences in the future. 

Summary: 

This dataset provides georeferenced occurrence records of Cortaderia selloana (pampas grass; hereafter C. selloana) across mainland Portugal, obtained through high-resolution imagery acquired by Uncrewed Aerial Vehicles (UAVs, or drones). produced within the framework of the project LIFE17 NAT/ES/000495 – LIFE STOP CORTADERIA: Urgent measures for controlling the spread of Pampa Grass (Cortaderia selloana) in the Atlantic area. The dataset was generated under the contract “Acquisition of services for the elaboration of the cartography of the priority distribution areas of Cortaderia selloana in the study area and respective distribution analysis, prediction and trends” commissioned by the Municipality of Vila Nova de Gaia (Portugal) to ICETA/CIBIO-InBIO, Universidade do Porto.

Dataset description: 

Each record corresponds to an individual C. selloana plant with a circle roughly encompassing its basal area observed at nadir in aerial images (i.e., orthomosaics), accompanied by its geographic coordinates (x/y of the centroid) and morphometric attributes (e.g., area, perimeter, estimated plant diameter). 

According to UAV-based surveys the presence of C. selloana was found in a wide range of disturbed and semi-natural environments, corresponding to the following general land-use and habitat types:

• Peri-urban industrial or post-industrial areas, including both active and abandoned sites with sparse or degraded vegetation cover;

• (Semi-)abandoned urban lots (“vacant” or “expectant” spaces) characterized by strong anthropogenic disturbance and colonization by ruderal vegetation;

• Road verges and adjacent areas, most frequently along embankments and highway access ramps;

• Other linear features, both artificial (e.g., railways) and natural (e.g., riverbanks and streams);

• Open semi-natural habitats such as shrublands or grasslands with low and discontinuous vegetation cover, often with exposed soil;

• Estuarine margins and sandbanks, notably in systems such as the Lima River and the Ria de Aveiro;

• Abandoned quarries, spoil heaps, and dumping sites showing clear evidence of anthropogenic disturbance;

• Gardens and landscaped areas, both public and private, where the species often occurs as an ornamental planting.

The dataset provides a highly detailed spatial baseline for mapping, monitoring, and modelling the current extent of C. selloana invasion in coastal, peri-urban, industrial (or post-industrial) and urbanized areas of Portugal. It constitutes one of the most comprehensive fine-scale geospatial datasets of this invasive species to date and supports the development of predictive ecological models and management strategies aimed at invasive plant control.

Context: 

The contribution of volunteers in recording invasive alien species (IAS) has been fostered by technological developments such as social media, apps, low-cost sensors, search engines and predictive analytics. These technology developments, an increased attention to citizen science and a cultural change towards collaboration and openness in research within the policy agenda should increase the contribution of volunteer recording. Within the framework of the COST Action CA17122 Increasing Understanding of Alien Species through Citizen Science (Roy et al. 2018) a group of researchers explored the value of emerging technologies for citizen science in the context of alien species, recognizing the contribution of volunteers and reviewing their potential to engage broad audiences, motivate volunteers, improve data collection, increase data quality etc. 

Dataset description: 

This dataset represents the list of technologies (in the broadest sense, including approaches) that were identified collectively by the experts as being relevant technologies in the framework of (alien species) citizen science. The dataset includes the following fields: 

• Name: name of the approach/technology

• Category: broad categorisation of the approach/technology (Hardware and infrastructure, data collection and analysis tools, tools to improve user experience). If some approaches are combinations this is mentioned in description.

• Description: a definition and/or description of the approach/technology

• Reference: a reference on the approach/technology (e.g. paper, online reference), mostly with a doi

• Example: an example of the approach/technology, mostly with reference to an (alien species) citizen science project that applied it

• Notes: any further remarks