Early detection of new invasive pest incursions enables faster management responses and more successful outcomes. Formal surveillance programs—such as agency-led pest detection surveys—are thus key components of domestic biosecurity programs for managing invasive species. Independent sources of pest detection, such as members of the public and farm operators, also contribute to early detection efforts, but their roles are less understood. To assess the relative contributions of different detection sources, we compiled a novel dataset comprising reported detections of new plant pests in the US from 2010 through 2018 and analyze when, where, how, and by whom pests were first detected. While accounting for uncertainties arising from data limitations, we find that agency-led activities detected 32% to 56% of new pests, independent sources detected 27% to 60%, and research/extension detected 8% to 17%. We highlight the value of independent sources in detecting high impact pests, diverse pest types, and narrowly distributed pests – with contributions comparable with agency-led surveys. However, in the US, independent sources detect a smaller proportion of new pests than in New Zealand. We suggest opportunities to further leverage independent pest detection sources, including by citizen science, landscaping contractors, and members of the public.
Invasive pests cause significant damages to economic and ecological systems, including to agriculture, biodiversity, and ecosystem service provisioning. Estimates of total annual damage and control costs for invasive species in the United States exceed $160 billion (2019 USD) (Pimentel et al., 2005). With increasing trade, changing climate, expanding source distributions of non-native pests, and increases in difficult-to-manage invasion pathways (such as ecommerce), rates of invasion and costs likely will continue to expand (Essl et al., 2015; Epanchin-Niell et al., 2021).
Efforts to avoid or reduce impacts from invasive pests include offshore and border prevention activities, as well as post-introduction efforts aimed at eradicating or controlling invasions. For invasive species that become established in a new region, detection is critical to initiating eradication and control responses. Earlier detection can lead to better outcomes and lower long-term costs, as smaller invasions generally are easier and less costly to control and adaptation measures can be initiated sooner (Epanchin-Niell et al., 2014; Liebhold et al., 2016; Lodge et al., 2006; Pyšek and Richardson, 2010).
Early detection of pests can arise from multiple sources (Hester and Cacho, 2017). Active surveillance by government agencies for early detection of new pest incursions, which we refer to as agency detections, involves programs at various government levels. Agency detections often include high-risk site surveillance and commodity- and pest-specific surveys (e.g., Acosta et al., 2020; Arndt et al., 2020; USDA, 2019). A second broad source of detections is local extension specialists or researchers, who may encounter pests during their routine activities or more systematic survey efforts (e.g., Hester and Cacho, 2017); we term these research/extension detections. A third broad source consists of members of the public and farm and nursery operators, which we refer to as independent sources. These detections, particularly those by members of the public, are often viewed by agencies as fortuitous, because they contribute to achieving biosecurity objectives but are not the direct outcome of planned investments in surveys (Hester and Cacho, 2017).
Our classification of detection sources is similar to previous pest detection categorizations, which generally are described as spanning from active to passive (e.g., Hester and Cacho, 2017; Pocock et al., 2016; White et al. 2019). However, we employ the term independent instead of passive, in recognition that detections by operators or members of the public may be either passive (i.e., unintentional) or active (e.g., resulting from routine private activities to monitor landscaping or crop health), while nonetheless occurring independently from agency survey efforts.
Independent sources contribute to detecting new incursions and to monitoring spread of established invasions. For example, the Asian longhorned beetle (Anoplophora glabripennis), a major threat to hardwood trees in the US, was first detected and reported in 1996 by a Brooklyn resident who observed damage to street trees (Haack et al., 1997). Subsequent incursions of this species were also first reported by the public, often in private residential gardens (EPPO, 2011; Haack et al., 2010; Straw et al., 2015). Other examples in the US include the Asian shore crab (Hemigrapsus sanguineus) detected by a college student on a field trip and more recently the Asian giant hornet (Vespa mandarinia) discovered by a citizen on their front porch (McDermott 2001, Baker 2020). Independent sources have been prevalent and critical to early invasive species detection and management in other countries as well, see examples from New Zealand and Australia in Bleach (2019) and Hester and Cacho (2017).
The data contributions of the public are an important ongoing area of research across a range of disciplines (Lukyanenko et al. 2020). Specifically, independent pest detection sources have received increasing attention in recent years from agencies and researchers (Bleach, 2018; Hester and Cacho, 2017). Most studies addressing the role of independent detections focus on contributions to monitoring pest spread or control efforts, rather than to early detection of new incursions (e.g., Poland and Rassati, 2019; Cacho et al., 2010; Cacho and Hester, 2011; Keith and Spring, 2013). For example, $1 million invested in public engagement activities as part of a fire ant (Solenopsis invicta) surveillance program in Queensland, Australia, was estimated to have achieved a level of detection that would have required $60 million in active (i.e., agency) surveillance (Cacho et al., 2012; Hester and Cacho, 2017). The role of citizen science in augmenting biological monitoring efforts also has been extensively explored (Blackburn et al. 2020; Conrad and Hilchey, 2011; Crall et al., 2010; Dickinson et al., 2010; Johnson et al., 2014; Larson et al., 2020; McKinley et al., 2017). Empirical studies show that volunteer-based citizen science programs can significantly improve understanding of invasive species distributions (e.g., Crall et al., 2015; Delaney et al., 2008; Gallo and Waitt, 2011; Maistrello et al., 2016; Pocock et al., 2016; Rothenberger et al. 2020; Scyphers et al., 2015). In addition, a recent analysis shows that species characteristics are significant factors in reporting probability, demonstrating that public contributions are not equally distributed across species types (Caley et al. 2020). Many of the lessons drawn from these studies are broadly applicable to detection of invasive species by the public.
Recent research addressing the design and efficacy of surveillance programs for early detection of new pest incursions has largely addressed the questions of how much—and where—survey resources should be deployed to minimize long-term costs and damages (e.g., Epanchin-Niell et al., 2012, 2014, 2017; Hauser and McCarthy, 2009; Holden et al., 2016; Horie et al., 2013; Kaiser and Burnett, 2010; Moore and McCarthy, 2016; Yemshanov et al., 2015). While these studies have focused almost entirely on optimizing targeted agency surveillance (e.g., trapping), sensitivity analyses in Epanchin-Niell et al. (2014) demonstrate that the background rate of invasion detection—in the absence of active surveillance—is an important factor in determining optimal surveillance investment. Specifically, in contexts where pests are unlikely to be detected by other means, the benefits of agency surveillance are greater, all else equal. Therefore, a better understanding of background detection rates can lead to more effective active surveillance program design.
Despite increasing recognition of independent sources for early detection, quantitative understanding of the public’s contribution to detection outcomes, as well as factors affecting detection likelihood, is limited. In New Zealand, Bleach (2018) finds that 63% of investigated detections of new pest incursions over one year were reported by the general public and an additional 10% had been reported by industry. This highlights the important role of independent detections in New Zealand, where residents have a legal mandate to report any pests they detect (Biosecurity Act 1993, Section 44). In Australia, Carnegie and Nahrung (2019) find that 36% of the 34 total forest pest detections over 20 years were by independent sources. The only similar study in the US, Looney et al. (2016), finds that 36% of new pest detections in Washington State over 24 years were by independent sources.
While studies have hypothesized factors likely to contribute to enhanced detection by independent sources, such as detections occurring on private lands or detections of highly conspicuous pests (Cacho et al. 2010; Brown et al. 2017; Hester and Cacho, 2017; Looney et al., 2016; Pocock et al., 2016; Poland and Rassati, 2019), these have been largely untested. Understanding of the types of pests detected by various sources, where detections occur, and how quickly different sources detect pests is an informational gap that hinders effective accounting of independent sources of detection in biosecurity planning.
In this study, we develop and analyze a new dataset to explore detection sources responsible for intercepting and reporting new invasive pests in the United States. We classify detection sources based on the entities that detected and reported each new pest. For each pest detection, we also characterized the setting, geographic location, type of pest, anticipated impact of the pest, and estimated distribution of the pest within the United States when detected. We use these data to evaluate the relative contribution of each source in detecting new pests and to explore factors and circumstances influencing detection frequency across sources. In addition, we consider the potential that new pests could be detected even earlier through close monitoring of citizen science platforms such as iNaturalist. For this we compare the date of detection in our data with the first report date for each pest on iNaturalist to determine if any were reported earlier via that platform.
We provide several contributions relative to the current literature. We present the first national-level analysis of sources of new pest detections in the United States, and compare our findings with those from a national-scale study in New Zealand (Bleach, 2018) and a state-level analysis from Washington in the United States (Looney et al., 2016). We also categorize and evaluate contextual variables about detections that have been suggested as relevant to understanding pest detection activities but have not previously been tested. Specifically, we meet calls for data collection on pest characteristics (Hester and Cacho, 2017; Looney et al., 2016) and detection contexts (Carnegie and Nahrung, 2019) to scrutinize assumptions typically made about the attributes of different sources of detection (Froud et al., 2008). We also outline opportunities to better leverage independent pest detection sources and data documentation and analysis needs to further enhance understanding of pest detection sources.