Here we compare the environmental niche of a highly polyphagous forest Lepidoptera species, the winter moth (Operophtera brumata), in its native and invaded range. During the last 90 years, this European tree folivore has invaded North America in at least three regions and exhibited eruptive population behavior in both its native and invaded range. Despite its importance as both a forest and agricultural pest, neither the potential extent of this species’ invaded range nor the geographic source of invading populations from its native range are known. Here we fit a climatic niche model, based on the MaxEnt algorithm, to historical records of winter moth occurrence in its native range and compare predictions of suitable distributions to records from the invaded range. We modeled this distribution using three spatial bins to overcome sampling bias for data obtained from public databases and averaged the multi-continental suitable habitat prediction. Results indicate that this species is distributed across a wide range of climates in its native range but occupies a narrower range in its invaded habitat. Furthermore, the lack of a close fit between climatic conditions in parts of its invaded range and its known native range suggests the possibility that this species has adapted to new climatic conditions during the invasion process. These models can be used to predict suitable habitats for winter moth invasions worldwide and to gain insight into possible origins of North American populations.

bioclimatic modelling, biological invasions, climatic envelope, geographical distribution, invasive alien species, range projection, species distribution models

With heightened awareness of the damage caused by biological invasions, biosecurity programs take on increasing importance for preventing new invasions (Hulme 2009). Risk assessment plays a key role in biosecurity programs, providing information on likely invasion success and impacts of species, and is used to prioritize preventative measures (Hayes 2003). A crucial component of the risk assessment process is mapping the potential range of a species should it become established (Venette et al. 2010). Knowledge of the potential geographical distribution of a candidate invasive species can guide implementation of both pre- and post-border biosecurity activities such as surveillance.

Understanding and quantifying the ecological niche of a species in its native range can be used to predict its potential distribution in a novel environment. Ecological niche models use occurrence data and environmental variables to predict habitat suitability (Guisan and Zimmermann 2000). These models typically use available species occurrence records to quantify the association of a species’ distribution with various components of its habitat such as climate. Ecological niche models assume that a species is well adapted to present climatic conditions and this acclimation assumption is carried into future distribution forecasts. For practicality, these models typically ignore the effects of biotic interactions which may limit a species’ potential distribution, and thus are expressed as a realized niche. Application of ecological niche models to predict an invading species future range assumes that successful invasion of novel environments requires similarly matched climates between the native and novel regions (Panetta and Mitchell 1991).

Unfortunately, systematic surveys for most species throughout their ranges are often impractical, though a wealth of distribution information resides in global databases and museum collections worldwide. These datasets, such as the Global Biodiversity Information Facility (GBIF, GBIF.org 2018), assemble occurrence records from many different sources; however, the intensity of sampling behind these records often contains a sampling bias where more records exist in certain areas (such as near research facilities or locations with extensive sampling by hobbyists).

A number of methods can be applied to limit this spatial bias. One method of accounting for sampling bias is to use frequencies of background occurrence records of a conspecific species or an entire genus, often referred to as target group background bias records, as proxies for sampling effort (Yates et al. 2010). Another method involves spatially filtering the occurrence records to remove records at distances greater than the resolution of cells in the environmental layers (Aiello‐Lammens et al. 2015; Boria et al. 2014; Hijmans 2012; Kramer‐Schadt et al. 2013; Pearson et al. 2007; Veloz 2009). Occurrence records can also be thinned based on their environmental/climatic values, discarding repetitive occurrences in similar climatic conditions (Varela et al. 2014), or records can be filtered geographically, splitting occurrence records into geographic bins (Jiménez-Valverde et al. 2011; Radosavljevic and Anderson 2014).

This study focuses the use of ecological niche models for predicting the potential distribution of the winter moth, Operophtera brumata. The native distribution of this species ranges widely through most of Europe (Kozhanchikov 1950) (Fig. 1). Furthermore, larvae of this species are known to have an extremely broad host range, feeding on the foliage of a wide variety of broadleaf trees and occasionally on certain conifer species (Wint 1983). Recurrent outbreaks of this insect occur in several regions of its native range, especially in mountain birch (Betula pubescence) forests in Fennoscandia (Jepsen et al. 2008), but also on other hosts, such as Sitka spruce (Picea sitchensis) planted in the British Isles (Stoakley 1985; Watt and McFarlane 1991) and even on heather (Calluna vulgaris) in Scotland (Kerslake et al. 1996).

Figure 1.

A map showing the distribution of the native range for winter moth (Operophtera brumata), recreated from Kozhanchikov (1950), as well as native winter moth occurrence records used as training records – colored according to three geographic filters (British Isles in red, Fennoscandia in blue and Central/Southern Europe in green) and evaluation records shown in yellow B map of North American records reflecting the invaded range separated into three geographic regions (western Canada in brown, New England in orange and eastern Canada in violet).

Non-native populations of the species exist in portions of North America with alien populations established in Nova Scotia, the Pacific Northwest, and New England (Fig. 1). Winter moth was first accidentally introduced to Nova Scotia in the 1930s (Hawboldt and Cuming 1950), Oregon in the 1950s (Kimberling and Miller 1988), near Vancouver, British Columbia around 1970 (Gillespie et al. 1978), and eastern Massachusetts in the 1990s (Elkinton et al. 2010). It is unknown whether these represent separate introductions from the native range or a single invasion that has spawned several secondary introductions. Following the initial establishment of each of these populations there was some initial range expansion, though in each case, spread may have been limited by climatic conditions, introduction of biological control agents, and/or by hybridization with the native congener, Bruce spanworm, O. bruceata (Elkinton et al. 2010).

The objective of this study was to fit ecological niche models based on winter moth occurrence records from its native range in order to predict the potential invaded range of this species. Furthermore, as there is much uncertainty about where in Europe the non-native populations of winter moth originated from (Andersen et al. 2017; Elkinton et al. 2010; Gwiazdowski et al. 2013), we use geographic filtering to compare the climatic suitability of different potential source populations to each of the invaded North American populations. We then comment on the utility of geographic filtering to reduce sample bias in datasets based on public records.

As expected, each of the three models predicted suitable habitat in portions of Europe from which occurrence data were located (Fig. 2). Averaging predictions across all three models from the native range (Fig. 3A) produces a predicted European range very similar to the range described in Kozhanchikov (1950) (Fig. 1), though the model fails to predict suitable habitat in Ukraine and southeastern Russia. However, the lack of predicted presence in these areas could have arisen simply from inadequate sampling in that region. Winter moth was recently reported from Tunisia (Mannai et al. 2015), and genetic analyses suggest this population is native to the region (Andersen et al. 2019b). Andersen et al. (2019b) also reported winter moth occurrence in southern Spain and Italy. Because dates of these observations fall outside of the range of dates that were used for the climate data (1970–2010), they were not used in our analysis, though their inclusion could further change the predicted distribution of winter moth to include additional Mediterranean locations. The species is also reported as an established non-native species in Iceland (Halldórsson et al. 2019).

The average of predictions from the models based on the three native range regions predicts suitable habitat in northwestern Washington, along the coast of Western Canada and northward along the coast into Alaska (Fig. 3B). Though winter moth has been established in part of this region (i.e. coastal British Columbia) for over 50 years it has not spread into most of the predicted region. It is possible that populations in this region are constrained from expanding into a climatically suitable region by a biotic factor, such as the introduced parasitoid Cyzenis albicans (Elkinton et al. 2015; Roland and Embree 1995), other natural enemies (Broadley et al. 2019) though there are only a few examples where natural enemies are known to constrain the range of an insect species (e.g., Elkinton et al. 2006, Parry 2008). Another possibility is that hybridization with the native congener, Bruce spanworm (Andersen et al. 2019a) alters the fitness of invading populations, thereby constraining their range. Predictions of the model appear to align with invaded portions of Eastern North America more poorly. The model predicts most of the invaded portions of Nova Scotia and New England as low suitability but classifies all of Newfoundland and portions of Labrador, areas that have never been invaded, as either low or medium suitability. However, it is possible that winter moth has never had the opportunity to invade these regions since the Gulf of St. Lawrence separates them from the currently invaded area. As stated earlier, this lack of agreement in portions of the Eastern North America between the model and winter moth occurrence could also be due to hybridization with Bruce spanworm (Havill et al. 2017).

Predictions of suitable habitat based on climatic niche models fit to native occurrence records sometimes do not coincide well with actual invaded regions (Broennimann et al. 2007; Jiménez-Valverde et al. 2011; Ørsted and Ørsted 2019; Roura-Pascual et al. 2006; Steiner et al. 2008), and this may result from factors such as species not being in equilibrium with the local climate, release from competitors and predators, or sampling and dispersal limitations. Fitzpatrick et al. (2007) found similar results of under-predicting the invasive potential of fire ants using native range occurrence records and over-predicting the southern boundary of the native range using invaded range records, which they concluded was due to fire ants establishing in a novel environment similar to their native environment and then expanding into novel climatic environments not available in their native range.

It is not unusual for populations of various species to become locally adapted to their climate and such local adaptation can result in variation in the potential alien range of populations originating from different portions of the native range. In such cases, models built with spatially partitioned occurrence records from the native range may provide some indication of the geographic origins of invaded populations (Steiner et al. 2008). However, without prior knowledge of the extent of local adaptation, such reconstruction of the origins of invading populations may not be possible. In our analysis, models based on records from all three native regions predict suitability in the western Canada invaded range near Vancouver and Victoria (Fig. 2). Analysis of climatic conditions (Fig. 5) indicates considerable overlap in climate between invaded areas of Western Canada and all three native regions. Thus, our analysis does not show any uniquely high similarity of the invaded portion of Western Canada with any single region of Europe. Only two models fit to European regions (Fennoscandia and the Central/Southern Europe) show suitable habitat in Nova Scotia. In the ridgeline plots (Fig. 4) and the PCA plots (Fig. 5) the records from Eastern Canada fall closest to those of Fennoscandia, suggesting this region as a possible origin for the Eastern Canada population. The model fit to the Central/Southern Europe occurrence records is the only model that predicts suitable habitat in New England, specifically near Cape Cod. When plotting climatic conditions using PCA (Fig. 5), the New England records appear to occupy a different niche mainly outside of the distribution of records from all other regions, though perhaps most similar to the Central/Southern Europe native region, supporting the hypothesis of that region being the origin of the invasion. However, we have no evidence of local adaptation to climate in the winter moth’s native range, so such attribution is tentative.

Adaptation to local environments is often observed in species with large geographical ranges (Osborne and Suárez-Seoane 2002; Peterson and Vargas 1993). Differences in predictions from models based on native range occurrence records are apparent in AUC scores (Table 2), the variables of greatest contribution (Table 2) and the averaged prediction (Fig. 3). Probabilities averaged across the three models based on native range regions accurately predicted suitable North American habitat in most of the invaded regions, but predicted particularly high habitat suitability in invaded portions of the south coast of British Columbia (Fig. 3). However, it also predicted large areas of medium to high habitat suitability in uninvaded regions north of both the western and eastern invaded regions. It is suggested that distribution models may be useful in predicting regions where species are likely to invade, but may be flawed in predicting subsequent spread in novel regions (Fitzpatrick et al. 2007; Loo et al. 2007). Given that many of the large areas of predicted suitable habitat are adjacent to currently invaded areas but remain uninvaded, we suspect that there may be unknown biotic or abiotic factors (other than the climatic variables considered here) that limit North American populations into these northern uninvaded areas. However, the identity of such biotic factors remains unknown and we encourage further research into the biotic variables that may be limiting the spread of winter moth in its invaded regions.

We focused our study on a generalist herbivore, the winter moth, to predict areas in North America where this species is likely to invade. We applied MaxEnt, the most widely used species distribution and niche modelling algorithm, to predict the potential range of suitable habitat for winter moth. Preliminary model runs fit to large regions of winter moth occurrence highlighted a high sampling bias in the United Kingdom. We implemented a simple method of applying spatial filters based on geography to reduce sampling bias. Environmental variables were selected based on expectations of climatic factors likely to be important to the biology of this species. We chose to use environmental variables at a coarse grain (25 km) due to the widespread nature of this species and multi-continental areas of interest.

Differentiating the geographic origins for each of the North American winter moth ranges based on predictions from the various native geographic bins is possible, assuming local adaptation to climate in native populations. Based upon climatic similarity, central Europe appears to be the most likely origin of non-native populations in New England. Climatic similarity of the invaded range in Eastern Canada with Fennoscandia and Central Europe suggests those regions as likely origins. However, the Western Canada invaded range appeared equally similar to all native regions and thus there was no evidence regarding possible origins. All of these conclusions regarding origin remain speculative and would require confirmation based on genetic similarities. Combining molecular marker studies in ecological niche modelling approaches can help advance this field (Scoble and Lowe 2010) and improve future predictions of species distribution trajectories. Furthermore, evidence of hybridization with the native Bruce spanworm has been shown in New England, Oregon, British Columbia and Nova Scotia (Andersen et al. 2019a; Elkinton et al. 2010; Havill et al. 2017). If alleles from Bruce spanworm can introgress into winter moth, then this hybridization may increase winter moth’s ability to adapt to a novel environment, leading to a shift in the species’ fundamental niche (Holt and Gaines 1992).

Predicting the potential North American distribution of this invasive species can aid managers in proactively selecting survey locations for this destructive moth. Areas outside the current species’ distribution, which are highly suitable for winter moth, may be prioritized for biosecurity measures to help prevent establishment of this species. However, it remains to be confirmed whether winter moth could establish in the vast regions predicted to be suitable north of currently invaded areas. Given that winter moth is not currently expanding its range into these areas, many of which are adjacent to currently invaded regions, there may be unknown biotic factors that limit the range of this species in ways that are not currently understood.

This research was funded by the USDA Forest Service. LB acknowledges support from University of Tennessee National Institute for Mathematical and Biological Synthesis (NIMBioS) at a tutorial session “Applications of Spatial Data: Ecological Niche Modeling”. AML was supported by grant EVA4.0, No. CZ.02.1.01/0.0/0.0/16_019/0000803 financed by OP RDE. Funding to JSE was from USDA-APHIS grant Nos AP17PPQS&T00C068. AP19PPQFO000C125