No abstract

There is a growing need for collaborative and interdisciplinary research in addressing global ecological challenges, and early career researchers (ECRs) often play a vital role in such ventures. But despite the desire for such approaches, forming new and interdisciplinary collaborations is risky, and disproportionately so for ECRs, whose perspectives on this topic are rarely heard. Here, we present common perceptions among ECRs regarding opportunities for intra‐ and interdisciplinary collaboration, and barriers preventing such collaboration from taking place. We also discuss possible solutions, and the ecological outcomes of fostering more collaboration. The perceptions discussed have been distilled from a two‐day workshop in New Zealand, aiming to investigate the potential for collaboration between 34 ECRs in distinct ecological disciplines across ten research institutes. Commonality in methodology or research aims was vital for potential collaborations to be considered worthwhile, but differences in spatial or temporal scales were a key disconnect that hindered numerous potential crossovers. Individual connectivity and institutional structures were commonly perceived as barriers to acting collaboratively in general. Specifically, barriers included having a small peer network, lack of access to funding, and concerns over the risk/reward ratio of forming new collaborations. Overcoming barriers will require active, practical support from institutions, funding bodies and mentors, and participants commonly called for specific funding support and the creation of ECR‐focused spaces to better foster collaborative behavior. Fostering interdisciplinary ECR collaborations in ecology was perceived to be useful in creating larger and more useful datasets and tools, and more scalable and transferable models and outcomes. Adopting practices that facilitate more ECR‐led interdisciplinary collaboration will help generate a more integrative understanding of ecological systems globally.

transfer learning ancestral character estimation biogeography 1. Despite their importance in many ecological processes, collecting data and information on ecological interactions is an exceedingly challenging task. For this reason, large parts of the world have a data deficit when it comes to species interactions, and how the resulting networks are structured. As data collection alone is unlikely to be sufficient, community ecologists must adopt predictive methods.2. We present a methodological framework that uses graph embedding and transfer learning to assemble a predicted list of trophic interactions of a species pool for which their interactions are unknown. Specifically, we 'learn' the information (latent traits) of species from a known interaction network and infer the latent traits of another species pool for which we have no a priori interaction data based on their phylogenetic relatedness to species from the known network. The latent traits can then be used to predict interactions and construct an interaction network.3. Here we assembled a metaweb for Canadian mammals derived from interactions in the European food web, despite only 4% of common species being shared between the two locations. The results of the predictive model are compared against databases of recorded pairwise interactions, showing that we correctly recover 91% of known interactions.4. The framework itself is robust even when the known network is incomplete or contains spurious interactions making it an ideal candidate as a tool for filling gaps when it comes to species interactions. We provide guidance on how this framework can be adapted by substituting some approaches or predictors in order to make it more generally applicable.

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.

Collecting network interaction data is difficult. Non-exhaustive sampling and complex hidden processes often result in an incomplete data set. Thus, identifying potentially present but unobserved interactions is crucial both in understanding the structure of large scale data, and in predicting how previously unseen elements will interact. Recent studies in network analysis have shown that accounting for metadata (such as node attributes) can improve both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, the dimension of the object we need to learn to predict interactions in a network grows quickly with the number of nodes. Therefore, it becomes computationally and conceptually challenging for large networks. Here, we present a new predictive procedure combining a graph embedding method with machine learning techniques to predict interactions on the base of nodes' metadata. Graph embedding methods project the nodes of a network onto a---low dimensional---latent feature space. The position of the nodes in the latent feature space can then be used to predict interactions between nodes. Learning a mapping of the nodes' metadata to their position in a latent feature space corresponds to a classic---and low dimensional---machine learning problem. In our current study we used the Random Dot Product Graph model to estimate the embedding of an observed network, and we tested different neural networks architectures to predict the position of nodes in the latent feature space. Flexible machine learning techniques to map the nodes onto their latent positions allow to account for multivariate and possibly complex nodes' metadata. To illustrate the utility of the proposed procedure, we apply it to a large dataset of tourist visits to destinations across New Zealand. We found that our procedure accurately predicts interactions for both existing nodes and nodes newly added to the network, while being computationally feasible even for very large networks. Overall, our study highlights that by exploiting the properties of a well understood statistical model for complex networks and combining it with standard machine learning techniques, we can simplify the link prediction problem when incorporating multivariate node metadata. Our procedure can be immediately applied to different types of networks, and to a wide variety of data from different systems. As such, both from a network science and data science perspective, our work offers a flexible and generalisable procedure for link prediction.