Assessing unintended human‐mediated dispersal using visitation networks

Advances in understanding and predicting the spread of invading insect populations

Understanding and predicting the spread of invading insects is a critical challenge in management programs that aim to minimize ecological and economic harm to native ecosystems. Although efforts to quantify spread rates have been well studied over the past several decades, opportunities to improve our ability to estimate rates of spread, and identify the factors, such as habitat suitability and climate, that influence spread, remain. We review emerging sources of data that can be used to delineate distributional boundaries through time and thus serve as a basis for quantifying spread rates. We then address advances in modeling methods that facilitate our understanding of factors that drive invasive insect spread. We conclude by highlighting some remaining challenges in understanding and predicting invasive insect spread, such as the role of climate change and biotic similarity between the native and introduced ranges, particularly as it applies to decision-making in management programs.

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

Networks are increasingly used in various fields to represent systems with the aim of understanding the underlying rules governing observed interactions, and hence predict how the system is likely to behave in the future. Recent developments in network science highlight that accounting for node metadata improves both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, to predict interactions in a network within existing statistical and machine learning frameworks, we need to learn objects that rapidly grow in dimension with the number of nodes. Thus, the task becomes computationally and conceptually challenging for networks. Here, we present a new predictive procedure combining a statistical, low-rank graph embedding method with machine learning techniques which reduces substantially the complexity of the learning task and allows us to efficiently predict interactions from node metadata in bipartite networks. To illustrate its application on real-world data, we apply it to a large dataset of tourist visits across a country. We found that our procedure accurately reconstructs existing interactions and predicts new interactions in the network. Overall, both from a network science and data science perspective, our work offers a flexible and generalizable procedure for link prediction.

Seed Mucilage Promotes Dispersal of Plantago asiatica Seeds by Facilitating Attachment to Shoes

Understanding the mechanisms underlying seed dispersal is a fundamental issue in plant ecology and vegetation management. Several species demonstrate myxospermy, a phenomenon where the seeds form mucilage after absorbing water. Mucilage is thought to act as a glue, enabling seeds to attach to the external surfaces of dispersing agents. However, there have been no quantitative investigations of the efficacy of this function of seed mucilage. We performed a trampling and walking experiment to investigate the seed dispersal of a perennial herb, Asian plantain (Plantago asiatica L.), which forms polysaccharide mucilage upon hydration. Our experiment showed that: (1) after trampling, more seeds of P. asiatica attached to shoes in wet conditions (after rainfall), in which seed mucilage was created, than in dry conditions (no rainfall); and (2) after walking for 1000 m, more seeds remained attached to shoes in wet conditions than in dry conditions. Our results indicate that mucilage promotes the adherence of seeds to the surface of vectors. We therefore provide the first empirical evidence that seed mucilage facilitates epizoochory and human-mediated dispersal.

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

Between-country tourism is established as a facilitator of the spread of invasive alien species; however, little attention has been paid to the question of whether tourism contributes to the arrival and subsequent dispersal of exotic organisms within national borders. To assess the strength of evidence that tourism is a driver for the accidental introducing and dispersal of exotic organisms, we sourced three national databases covering the years 2011 to 2017, namely international and domestic hotel guest nights and national population counts, along with records of exotic organism detections collected by the Ministry for Primary Industries, New Zealand’s government agency that oversees biosecurity. We fitted statistical models to assess the strength of the relationship between monthly exotic organism interception rate, guest nights and population, the latter as a baseline. The analysis showed that levels of incursion detection were significantly related to tourism records reflecting the travel of both international and domestic tourists, even when population was taken into account. There was also a significant positive statistical correlation between the levels of detection of exotic organisms and human population. The core take-home message is that a key indicator of within-country human population movement, namely the number of nights duration spent in specific accommodation, is statistically significantly correlated to the contemporaneous detection of exotic pests. We were unable to distinguish between the effects of international as opposed to domestic tourists. We conclude that this study provides evidence of impact of within-country movement upon the internal spread of exotic species, although important caveats need to be considered.

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

Aircraft can transport aquatic invasive species (AIS) from urban sources to remote waterbodies, yet little is known about this long-distance pathway. In North America and especially Alaska, aircraft with landing gear for water called floatplanes are used for recreation access to remote, often road-less wilderness destinations. Human-mediated dispersal of AIS is particularly concerning for the conservation of pristine wildlands, yet resource managers are often challenged by limited monitoring and response capacity given the vast areas they manage. We collected pathway data through a survey with floatplane pilots and used a Bayesian hierarchical model to inform early detection in a data-limited situation. The study was motivated by Alaska’s first known AIS, Elodea spp. (Elodea) and its floatplane-related dispersal. For 682 identified floatplane destinations, a Bayesian hierarchical model predicts the chance of flights originating from AIS source locations in freshwater and estimates the expected number of flights from these sources. Model predictions show the potential for broad spread across remote regions currently not known to have Elodea and informed monitoring and early detection efforts. Our result underlines the small window of opportunity for Arctic conservation strategies targeting an AIS free Arctic. We recommend management that focuses on long-distance connectivity, keeping urban sources free of AIS. We discuss applicability of the approach for other data-limited situations supporting data-informed AIS management responses.