Conference Agenda

Invasive alien species (IAS) are an important driver of biodiversity loss worldwide. Their widespread detrimental ecological impacts contribute to important economic impacts, associated with damage and management costs. Despite this, the general public is less aware of biological invasions compared to other drivers of global change. Public perceptions of IAS may be linked to how much they are incorporated into the conservation discourse. Here, we utilized a conservation culturomics approach to analyze human-nature interactions manifested in large digital databases, improving our understanding of IAS salience and highlighting avenues for conservation communication and decision-making regarding IAS. We investigated the relationships between costs incurred by tetrapod IAS (mammals, birds, reptiles, and amphibians) in Europe and their internet salience. We hypothesized that IAS with higher costs would have higher salience, suggesting that conservation communication about IAS costs has been effective in raising popular interest. To test this, we used the InvaCost database to extract IAS management and damage costs, and quantified their salience using Google Health internet search volumes and Wikipedia visitation rates in each of the countries where they occur. This method has much promise in contributing toward our understanding of how conservation communication may influence issue salience and subsequent policies.

Artificial intelligence techniques, and specifically deep learning, have advanced and empowered the content analysis of digital data, opening promising opportunities for detecting, mapping, and monitoring invasive alien species. In this study, we tested the ability of openly available 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, X/Twitter, Instagram, and Facebook), allowing to classify or detect the species in over 77% of situations. Images where the species was correctly identified were mapped, using their georeferenced coordinates and time stamp (whenever available), showing previously unreported occurrences of Cortaderia selloana, and a tendency for the species expansion from 2019 to 2021. This study shows great potential from deep learning models, citizen science and social media data for the early detection, mapping, and monitoring of invasive plants, and, by extension, for supporting follow-up management options.

Invasive alien species negatively impact ecosystems, biodiversity, human societies, and economies. To prevent future invasions, it is crucial to understand both the ecological and the human and social factors determining whether a species is picked up, transported and introduced beyond their native range. However, we often have no or little information on key human and social factors. Here, we present a conceptual framework exploring how alien species introductions are shaped by a combination of ecological, and human and social factors, and highlight the potential of the emerging fields of conservation culturomics and iEcology for disentangling their relative importance. We argue that quantifying and assessing the relative importance of the human and social dimensions of alien species introductions can substantially improve our understanding of the invasion process.

Understanding patterns and drivers of ecological and biological phenomena at different scales, such as biological invasions, increasingly depends on collecting comprehensive data and making the best use of existing data. The proposed talk will address the concept of secondary data, which refers to additional information that is unintentionally captured in species records, especially in multimedia citizen science reports. Secondary data can provide ecologically relevant information that improves our understanding of interactions between native and alien organisms and their impact on biodiversity dynamics. We present the possibilities offered by secondary data, describe their main types and sources and give an overview of selected case studies in invasion biology. Finally, challenges to the wider use of secondary data, including biases, licensing issues, and a lack of awareness of this data source due to a lack of common language, are also discussed, along with possible solutions to overcome these barriers.

Social networks can provide relevant information about the process of biological invasion, as users can share information about the invading species in real time, and societal perceptions of the invading species can provide valuable information about the risks of invasion, such as the likelihood of intentional introduction or potential management support or opposition. The use of historical data in time series analyses could be very helpful in predicting and providing early warning of an impending invasion. Here we use digital data to explore possible relationships between the societal interest that species elicit at different stages of invasion and the outcome of the invasion process. We also explore the identification of specific traits or keywords that characterise different levels of invasion risk and different invasion scenarios, and hypothesise that references to the species vary according to invasion stage. We observed a greater correspondence between the event of first discovery at a site and public interest raised prior to this event, with peaks indicating that awareness increases when the species is in the early stages of its invasion process. This study shows that culturomics data can be used to some extent to predict the risk of a species invading a new habitat.

Biological invasions are considered one of the major threats to biodiversity, having ecological as well as socio-economic effects, frequently with negative impacts. To achieve effective conservation measures, understanding societal interest in invasive alien species is crucial since greater public attention can help mobilise conservation efforts, investments and success. One of the main challenges in monitoring societal interest is developing near-real-time indicators to cover large-scale spatial-temporal dynamics of public attention. The digital revolution has opened up new opportunities to alien species research and management. Here, we focus on the lionfish (Pterois miles) in the Mediterranean Sea and investigate spatial-temporal patterns of public interest in the species along its invasion gradient by using Google search volumes as a proxy for societal attention. Our study revealed that 1) public attention is higher in countries that have already experienced lionfish invasion compared to ones in which the species has yet to arrived; and 2) temporal patterns of societal attention do not seem to be fully related to the year of arrival of lionfish in a given country. While the first results confirm a clear, spatial pattern in public attention, further research is needed to investigate drivers of temporal trends.