Impacts of climate change on high priority fruit fly species in Australia

“unsuitable” conditions) can be subjective. A region classified as unsuitable may not be free of the pest; rather, these areas are considered less likely to support a population compared with regions above the threshold. In reality, the choice of threshold is based upon a comparison of the importance of false positives and false negatives [76]. For invasive species, the latter may be more serious because it can result in an underestimate of the geographic extent of suitable con- ditions, and hence, invasion risk [77]. This, in turn, can lead to poor decision-making and fail- ure to establish appropriate surveillance or containment measures. As such, in this context a precautionary approach to defining a threshold, as undertaken in the present study, is war- ranted. However, since overprediction of suitable habitat can also prove problematic (poten- tially leading to ineffective allocation of monitoring resources), we provide maps of continuous (unthresholded) suitability (S1 File), permitting stakeholders to modify this threshold according to their objectives. Sampling bias is another challenge faced when fitting correlative SDMs, particularly when incorporating data from sources of incidental observations such as museums and natural his- tory collections [78]. As such, it is difficult to determine whether a species is observed in a par- ticular environment because of habitat preferences or because that region has received the largest search effort [75, 78]. For presence-background approaches to habitat modelling, a tar-

get-group background sampling strategy goes some way to handling biased occurrence sam- ples [79]. However, while imposing environmental bias on the background counteracts similar bias in the occurrence sample, this strategy may increase the extent of novel environments to which the model must be extrapolated. While SDMs consider sensitivity to climate change, species responses may also include microevolution [80] or plasticity [81]. As accessibility to genomic data increases, and experi- ments on plasticity are conducted, SDM output can be refined [82]. In addition, as mean con- ditions change, so too will the distribution and magnitude of extremes. Presently, there has been little work undertaken to assess how different fruit fly pest species tolerate extreme weather events such as heatwaves and moisture stress, or their potential for adaptation to cli- mate change. These remain key areas for future research. We also note that our analysis does not take into consideration the potential necessity for horticultural industries to shift geographically to adapt to climate change. Analysing shifts in climatic suitability for horticultural crops is complicated by our capacity to modify the envi- ronment (e.g. through irrigation), and thus was beyond the scope of this study. To conclude, surveillance activities, pre- and post-harvest treatment, and control activities for fruit flies present a substantial cost to Australia’s horticultural industries [2, 4, 14]. Our analysis highlights that the major horticultural production regions are likely to remain suitable

for multiple economically important fruit fly species as climate changes. Furthermore, given that knowledge of species current distributions remains the basis for market access decisions, the potential for range shifts to occur is of critical interest to horticultural industries. Outputs from this study provide guidance to pest managers, such that they can assess pest risks and design appropriate ongoing surveillance strategies. Our results emphasize the importance of vigilance and preparedness across Australia to prevent further range expansion of these 11 spe- cies, and underscore the need for ongoing research and development into monitoring, control, and eradication tools. Supporting information S1 File. (DOCX) Impacts of climate change on high priority fruit fly species in Australia PLOS ONE | https://doi.org/10.1371/journal.pone.0213820 February 13, 2020 15 / 19