Assessing unintended human‐mediated dispersal using visitation networks

Abstract

Human visitors are associated with the unintended dispersal of weeds, seeds and pathogens across ecological communities. With the increasing popularity of nature‐based tourism, access to protected areas has increased, in turn increasing the risks of unintended dispersal of exotic species to these areas. Here, we assess the potential contribution of both international and domestic visitors travelling within New Zealand to the spread of exotic species. To get an overview of the visitors’ travelling patterns across the country, we constructed visitation networks at two spatial scales—a regional scale (which is a coarse scale) and a local territorial scale (which is a finer scale). We then used a Mixed Membership Stochastic Block Model to identify characteristic groups of visitors and places based on the similarities of the visitors’ travelling patterns across the country. Overall, we found that there are 10 characteristic groups of visitors travelling to 12 characteristic groups of places at the regional scale and 6 characteristic groups of visitors travelling to 6 characteristic groups of places at the territorial scale. The resulting characteristic travelling patterns of the visitors across New Zealand further allowed us to estimate the different visitor groups’ likelihood to travel to protected areas. Overall, we found that some visitor groups are much more likely than others to travel to protected areas of high protection status, at both spatial scales. Synthesis and applications. Our results highlight the importance of accounting for human behaviour—that is, understanding how visitors travel to places—when assessing human‐mediated dispersal. More specifically, we illustrate how to assess the relative contribution of a potential vector dispersing exotic species based on their travelling patterns—especially in cases where the target exotic species are not yet identified or when there is limited information regarding the dispersal routes of exotic species and their potential vectors. As a result, our work offers a holistic perspective on human‐mediated dispersal of exotic species. Moreover, it provides a potential baseline against which both field biologists and practitioners can identify areas that would benefit from further investigation to better understand invasion processes in their focal systems.

Biosecurity and post-arrival pathways in New Zealand: relating alien organism detections to tourism indicators

“…For example, some camping grounds and visits to friends and family are not included in the domestic and international guest nights data. Camping trips could make an important difference to the travel statistics, because an unknown proportion will likely involve destinations in more remote or vulnerable areas (see, for example, Runghen at al. 2021).…”

Exploiting node metadata to predict interactions in bipartite networks using graph embedding and neural networks

“…Similarly, even if visitors with consistent characteristics were to come, predicting the dynamics of their travelling patterns can be hard as it is highly dependent of the visitors' behaviour. Accounting for human behaviour has shown to be crucial, especially when predicting their travelling patterns [11,50,51]. For example, seasonal changes, accessibility to places, or even the increasing popularity of particular places owing to social media can influence visitors' behaviour in travelling across a country [50][51][52].…”

“…network properties such as node degree and various centrality measures) are used to predict interactions [6]. Block model-based approaches, such as the probabilistic generative family of Stochastic Block Models (SBMs) (and variants), aggregate nodes into groups based on their similarity of interactions [7][8][9][10][11]. Graph embedding methods on the other hand rely on projecting nodes onto an abstract latent feature space, so that the interaction probabilities depend on these latent features [12][13][14].…”

Flight plan for the future: floatplane pilots and researchers team up to predict invasive species dispersal in Alaska

“…Protecting wilderness ecosystems from biological invasion requires data-driven decisions, often made possible through a participating public (Auffret and Cousins 2013;Bullock et al 2018;Encarnac ¸a ˜o et al 2021;Runghen et al 2021). Yet, too often the global and local processes that drive fast-paced environmental change in Earth's remotest regions remain largely unexplored (Huntington et al 2020).…”

“…These management challenges are exacerbated when the invader is difficult to detect and thus more likely to be established before detection, as is often the case with aquatic invasive species (AIS) (Sytsma and Pennington 2015). Especially in as yet undisturbed wilderness areas, a better understanding of human travel patterns as pathways for invasive species is essential for effective early detection and eradication (McGeoch et al 2016;Runghen et al 2021).…”