Effective targeting for early detection of invasive species requires knowledge of an organism’s biology and mechanisms of invasion, as well as the ability to translate that information into an optimized surveillance program [1
]. A popular tool for invasive species managers is the use of species distribution models (SDMs) to define the geographic extent over which an invading species may occur [2
]. Factors that influence realized distributions (e.g., where a species is actually found) may be categorized by the biotic-abiotic-movement (or “BAM”) framework [4
]. In practice, researchers most often define distributions by an organism’s potential niche [5
], estimated by the range of abiotic variability within which it can survive and reproduce. However, potential distributions may be further limited by biotic interactions that arise from other processes, such as competition [6
], predation [8
], and parasitism [9
]. The movement component of the BAM diagram describes habitat accessibility through dispersal, such that areas of the predicted niche are within reach of dispersing organisms [11
]. For invasive species distribution models (iSDMs), dispersal may be assisted by human movement, which can transport organisms across long distances [12
]. The inclusion of dispersal mechanisms or “pathways” within iSDMs may be an effective approach for optimizing early detection programs to areas with high potential for invasion [16
Pathways can be defined as the mechanisms or routes by which species arrive at new regions or ecosystems [19
]. Invasion success is partially defined by the magnitude and spatio-temporal variability of propagule pressure across invasion pathways [20
]. Therefore, development of pathway predictors for iSDMs will be most informative when they are proximate to the mechanism of dispersal [2
] and include information on origin, destination, and rate of movement per time step [22
]. Examples of pathway predictors in iSDMs are sparse and typically involve spatial kernels as dispersal predictors [18
]. Pathways models fitted to large geographic extents are particularly under-represented [28
], and even fewer examples exist of volume-based pathways predictors [30
]. However, there is potential for using such pathway models [31
] to guide early detection and rapid response, or EDRR [32
]. Pathways data in SDMs not only refines potential distribution to more targeted risk areas, but also more explicitly supports the operational use of SDMs for EDRR by targeting the mechanisms for how invasive species enter an ecosystem [22
]. The quantification of major pathways can guide policy programs [33
] to target high risk pathways in outreach campaigns designed to prevent human-assisted spread [34
However, there remain significant challenges [35
] in the development of iSDMs, particularly to target early detection. The process of biological invasion is both spatially and temporally dynamic, which violates assumptions of stationarity in SDMs for invasive species [38
]. The assumption of a species in equilibrium with its environment (i.e., temporal stationarity) results in underprediction of risk area [39
], especially in earlier stages of the invasion process [38
]. Spatial stationarity is often violated, especially over large geographic extents, because abiotic/biotic constraints may vary over space [39
] or the organism exhibits characteristics of stratified dispersal [41
]. Predictive models following suggested guidelines [43
] and iterated over time to test hypothetical processes of invasion [46
] provide a more informative and adaptive framework for managing species distributions than single model development [47
]. Iteratively updating iSDMs improves the detection of new invasion hotspots [48
], expands predicted geographic distributions [50
], and increases the reliability of model predictions [51
], particularly when paired with a targeted survey design [53
]. We apply these principles of handling non-stationarity and iterative modeling to the European gypsy moth (Lymantria dispar dispar
Linnaeus 1758) to increase our understanding of its mechanisms for spread and how to increase effectiveness for early detection within operational contexts.
The European gypsy moth is a forest pest accidentally introduced to Medford, Massachusetts, USA. in 1869 that has steadily spread across the north-eastern United States, establishing as far north as Maine, west toward Wisconsin and Minnesota, and as far south as Virginia. Gypsy moth feed on more than 300 species of trees and shrubs, making it a generalist species [54
]. This destructive forest pest has impacted forests in invaded areas by reducing mast production [55
], quality of timber products [56
], affecting native species [57
], nutrient cycling [59
], and human health [60
], with an economic impact estimated at >$
250 million per year [61
]. Because of these impacts, the gypsy moth is a well-studied species with detailed information on population biology [62
], climate suitability [64
], pathways [67
], detection [72
], and optimal management strategies [74
]. Most of the United States is climatically suitable to gypsy moth [77
]. The combination of broad climatic suitability and extensive host list means that gypsy moth can potentially establish in most of the United States [78
], making early detection of the spread into new areas difficult to target.
However, targeting surveillance to gypsy moth’s pathways for spread may yield efficiency in limited program resources. Gypsy moth naturally disperses by larval ballooning [79
]. While male moths can fly, the females are incapable of flight. Dispersal by larval ballooning generally occurs over a distance of 1–3 km [80
], but may extend over much greater distances depending on topography and wind [81
]. Humans also play a role in transporting the insect [80
], because its sticky egg masses may be deposited on most outdoor objects. Bigsby et al. [67
] developed an anthropogenic model to estimate the contribution of anthropogenic factors on gypsy moth spread for counties with a historical record of infestation. They found that locations with greater proximity to source populations, higher household income, and higher household consumption of firewood were correlated with a higher likelihood of gypsy moth presence. A second study on anthropogenic pathways for gypsy moth [71
] used proxies, such as population density and road accessibility, but it lacked proximate predictors for spread mechanisms. A national model of spread pathways is not likely to be stationary across geographic space, which affects how management actions target different stages of invasion or pathways of spread occurring across the landscape.
Our goal is to develop a species distribution model to support the early detection of European gypsy moth that addresses geographic and temporal variability of spread mechanisms. Our specific objectives are to identify proximate predictors for spread mechanisms, evaluate regional differences in spread, and to demonstrate the utility of iteration. We developed origin-destination data as a predictor for human-assisted spread, developed regionalized models based on geographic changes in predictor strength, and analyzed the value of iterative model development by assessing model performance with following year survey data.
The inclusion of origin-destination pathway predictors in invasive species distribution models brings significant advantages to targeting the early detection of invasive species. Inclusion of pathways were useful in predicting long-range, human-mediated dispersal of mussels [25
] and in intermediate-range, human-mediated dispersal with campground reservations for gypsy moth [69
]. The increasing variable importance of address forwarding, a proximate predictor for long-distance dispersal of gypsy moth egg masses, with a concurrent increase in model performance and precision over time, was similar to the Leung et al.’s [25
] recreational boat movement approximating human-mediated long distance dispersal of mussels. While a change in methodology between 2014 and 2015 accounts for some performance increase, it does not explain the continued increasing trend in subsequent model iterations with stable methodology. The increase in model performance may be due to the accumulation of the address forwarding data as an approximation of propagule pressure, rather than the addition of new detection data. While the risk models were available for the states to target their surveillance, it was optional. There was not an explicit feedback loop between survey design and the risk model, which may have increased the informatic value of new data being collected. Also, given that the gypsy moth program is historically rich in data and collects more than 100,000 new data samples each year, novel detections (new detections in low risk areas) to inform the risk model are rare.
Some research has suggested that predictions of invasion dynamics should be hierarchical, with data gathered from multiple spatial scales [112
]. Specific to invasive species distribution models, however, inclusion of a dispersal kernel, which focuses on short range dispersal, to limit over-prediction of risk has been encouraged [3
]. However, for targeting early detection of invasive species with a human-assisted spread pathway, this suggestion may be too conservative. Dispersal kernels focus detection effort along the spread front, where prevalence is high and fewer samples are required to detect the species. This method fails for early detection in uninfested areas far from the spread front, as illustrated by the gypsy moth 2015 outbreak in the Pacific Northwest. Our regionalized approach addresses the afore-mentioned biological phenomenon of stratified dispersal for gypsy moth. Our approach recognizes the different mechanisms of dispersal by incorporating a dispersal kernel for larval ballooning and other short-range mechanisms, while also addressing human-assisted pathways to explore long-distance dispersal events important for early detection activities overseen by APHIS.
The Slow the Spread program targets a 100-km “transition zone” to suppress gypsy moth spread, which was determined to be the optimal distance for reducing the spread rate to a target rate of 9 km per year [74
]. Our short range model included an additional 100 km beyond the transition zone, and it suggested that both areas had the same spread mechanisms. This result concurs with prior analysis of new colony formation occurring as far as 250 km from the spread front [63
]. While a spread kernel (partially driven by ballooning and wind-driven transport) was the most important predictor, anthropogenic predictors were also important in determining the detection likelihood within that region. The single largest difference between the short and intermediate range regional models was the change in primary predictor from a spread kernel (distance from prior year detections) to the address forwards. This changeover in pathway importance is demonstrated in the prediction surface as the transition of detection likelihood from the spread front to urban area hot spots.
We expected that the long-range model would show an increasing importance of address forwarding as a predictor of gypsy moth detection, as it does in the intermediate model. However, predictors, such as household income, population density, and traffic volume, exhibited consistently higher predictor performance than address forwarding. Areas of previous infestation in the long range model area generally occur in high-density urban areas, where a higher level of household income may be required for the higher cost of living. Urban areas also have smaller census tracts than rural regions (such as those that dominate the intermediate range model area), and gypsy moth detections frequently appear in very close proximity to high move-in areas without actually occurring in one. This phenomenon adds noise to the model and likely explains the poor predictor performance of address forwards in the long range model.
The need for an iterative approach to both sampling and distribution modeling for invasive species has been acknowledged for many years [47
]. Tests of the iterative sampling and modeling framework revealed improvements in secondary models based on new information collected as a result of initial models [53
]. Here, we present an operationalized iterative modeling framework to support adaptive management of invasive species. As with previous work, our example illustrates increased model performance over time with the addition of new information (both detections and predictors with accumulated information such as address forwarding).
Our model serves as a rapid investigative technique to test hypotheses regarding gypsy moth spread pathways and to inform targeted surveillance. The iterative process allowed us to investigate model prediction failures and test improvements for future model iterations. For example, stakeholders and program management have provided feedback that there is too much risk area in Texas, which is supported by the lack of detections in the region. These two lines of evidence indicate the model is overpredicting in this region, leading us to hypothesize that biological limitations, such as supraoptimal temperatures [114
], high winter temperatures precluding a required diapause development stage [115
], or dessication [116
], may be limiting life stage development in this area. We also detected several false negatives in the intermediate range regional model, which may be a result of missing pathway predictors, such as firewood movement [117
] and recreational activity [16
] in non-urban landscapes. The lack of origin-destination data sources to estimate these pathways likely results in underestimation of risk in this region. These examples highlight the importance of incorporating expert knowledge [121
] and proximate predictors [122
] into risk models, ensuring that products are appropriately targeted to the management need.
These operationalized iterative models of an invasive forest pest support management activities at a national scale. Our regionalized approach to model development supports previously identified policy and management objectives to invasive species management at different stages of invasion in different geographic regions [40
]. Efforts to prevent the spread of invasive species by targeting pathways are less costly than even early detection efforts [123
]. Our models can help target public outreach campaigns to prevent human-assisted movement of gypsy moth through our identification of the importance of these pathways. For example, APHIS partnered with the American Moving and Storage Association in the “Remove Before You Move” outreach campaign [124
] to educate the public on how to check their household articles for egg masses before moving [125
]. Our models also support the next step in the invasion process by targeting early detection efforts to areas at high risk for population establishment. We interact with the Slow the Spread program targeted at the spread front, allowing for continuous surveillance effort across management areas. Thus, our framework facilitates efforts across stages of the invasion process and the stages’ associated management options.