The effect of spatial heterogeneity on the establishment and spread

of P. brassicae

MDiG was developed to model invasive species spread in heterogeneous environments (Pitt, 2008) and has been employed in variety of applications (Kriticos et al., 2008; Pittet al., 2009, 2011; Worner et al., In Press). In this study, we use the great white butterfly,

Pieris brassicae, to illustrate some of the capability of MDiG for modelling dynamic range expansion at a regional scale. In this example, we investigate how different representa- tions of spatial heterogeneity in urban landscapes can change the final projected species’ distribution.

P. brassicae is an oligophagous butterfly, native to Europe and Asia that feeds on members of the family Brassicacea, commonly found in home gardens and as crops in agriculture (Phillipset al., 2014). Additionally, New Zealand has a number of threatened native Brassicae species. The species was first detected in Nelson, New Zealand in May 2010, and has since been the focus of intense monitoring and eradication efforts (Phillips

et al., 2014). We investigated the spread dynamic of the species in five administrative districts in the South Island, New Zealand, that were either in contact with or near to the locations invaded by P. brassicae. These districts were Buller, Tasman, Nelson City, Marlborough and Kaikoura (comprising 12,466 sq. ha).

For simplicity, we focus on a dynamic presence/absence model. The initial dispersal site was set in a cell close to Nelson port which is suspected to be the site of P. brassicae

unintentional introduction. The cell resolution was set to 100 m to approximate the median distance of local movement of P. brassicae as reviewed in Feltwell (1982). Estimates of the median distance and average frequency of long-distance dispersal events were obtained

from the dispersal history ofP. brassicaein the United Kingdom for which, well referenced temporal presence data were found in Feltwell (1982), Heathet al.(1984) and the Global Biodiversity Information Facility (GBIF) database. Two survival layers were developed to investigate the effect of urban landscape structure on invasive species spread. The first survival layer (Surv1) included four data sources: climate suitability, degree days, land

cover, and high resolution remotely sensed data (Figure 2.2 - 2.3). The second survival layer (Surv2) included all components used inSurv1, except the high resolution remotely

sensed data (Figure 2.3). High resolution remotely sensed data are needed to distinguish highly suitable home gardens, public parks and untended green spaces from human-made structures such as houses and roads. Underestimation of the complexity of the urban landscape could lead to an over-estimation of the spread ability ofP. brassicae.

Figure 2.2: From Senay (2014), with permission. Suitability maps used to build the survival layer of

P. brassicae: (A) hybrid climate model, (B) land-cover suitability layer (C) accumulated growing degree days layer

Figure 2.3: From Senay (2014), with permission. Zoom on the high resolution SPOT MapR data used

for generating the survival layerSurv1, in which man-made structures can be identified.

Sixteen years of simulations were undertaken representing dispersal from the year 2010 to 2025. The simulation was replicated 1000 times to account for dispersal stochas- ticity. Three thresholds [5%,10%,50%] that corresponded to the number of times a cell was occupied during dispersal for all the replications was used to estimate probability of dispersal into a cell (Pitt et al., 2009). The New Zealand data set of P. brassicae detec- tions and absences, obtained from the Department of Conservation (Phillipset al., 2014), was used to compare the first three years of the dispersal occupancy envelopes of both

Surv1 and Surv2 dispersal model outputs with field data. Three performance measures

- accuracy, sensitivity and specificity - were used to estimate the mean performance of the two dispersal models. Further explanation about the datasets, the parametrization methods and performance measures can be found in Appendix A and Senay (2014).

Both models closely simulated the progression of the Nelson inner city invasion, according to patterns observed from the occurrence data (Figure A.2). For 2011, however, there were more actual occurrences than presence locations predicted by the spread model. This discrepancy could result from high stochasticity in dispersal patterns at early stages of the invasion process (Pitt et al., 2009). Nevertheless, while we assumed that P. brassicae

was first introduced in New Zealand in 2010, it is highly possible that the species had already completed a generation or two before it was detected, which could explain the more dispersed surveillance data when compared to the few presence locations predicted

by the models for the year 2011.

Accounting for the complexity of the urban landscape resulted in a substantial in- crease in the accuracy (68% versus 27%) and specificity (69% versus 25%) of the model predictions, but a lower sensitivity score (48% versus 83%). The high sensitivity obtained from the model using the survival layer Surv2 was due to the high survival value given

to all urban areas. However, unsuitable sites were also incorrectly labelled as suitable, so specificity was low. Higher precision in mapping unsuitable patches among highly suitable urban areas slowed dispersal. When the actual dispersal maps are compared there is an apparent delay in occupation of suitable areas when the first survival layer (Surv1) is used.

By 2020, for example, the model using the survival layer Surv2 predicted high-risk areas

of invasion (> 50%) that reached Renwick and Blenheim, and covered extensive areas beyond the Wairau valley in the Marlborough district; in contrast, the model using the survival layer Surv1 predicted only limited dispersal within the 10−50% envelope that

reached beyond Wairau valley (Figure 2.4). It is also notable, that by the end of 2025, high-risk areas predicted by the model using the survival layer Surv2 covered extensive

areas in the bays, islands and peninsulas of Marlborough Sounds while these areas were still not covered by the high percentage envelope generated by the model using the survival layer Surv1 (Figure 2.4).