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

SDMs are useful for developing a broad understanding of how the distribution of suitable hab- itat may be influenced by climate change. However, the output of SDMs is known to be influ- enced by characteristics of the occurrence sample, including its size [71], sampling bias [72], and spatial autocorrelation [73], as well as the extent of the study area, selection of predictor variables [74], and selection of background points [75]. We addressed these issues by: (1) exploring alternate settings in Maxent to optimise models and reduce overfitting that may gen- erate unreliable estimates [48]; (2) reducing the number of predictor variables by assessing col- linearity; and (3) critically examining response curves. In addition, we acknowledge that the selection of a threshold for converting Maxent’s con- tinuous output into binary data (typically defined as distinguishing between “suitable” and 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 14 / 19

“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

Acknowledgments We gratefully acknowledge our data providers Nick Secomb (Plant Health Operations Biose- curity, PIRSA, South Australia) and Lauren Donaldson (Department of Economic Develop- ment, Jobs, Transport and Resources, Victoria). Special thanks to Phil Taylor, Dan Ryan, and Penny Measham for their feedback and advice. SS was supported by an International Mac- quarie University Research Excellence Scholarship (iMQRES). This research was conducted as part of the SITplus collaborative fruit fly program. Author Contributions Conceptualization: Sabira Sultana, John B. Baumgartner, Linda J. Beaumont. Data curation: Sabira Sultana, John B. Baumgartner, Bernard C. Dominiak, Jane E. Royer, Linda J. Beaumont. Formal analysis: Sabira Sultana, John B. Baumgartner, Linda J. Beaumont. Funding acquisition: Linda J. Beaumont. Methodology: Sabira Sultana, John B. Baumgartner, Bernard C. Dominiak, Jane E. Royer, Linda J. Beaumont. Software: John B. Baumgartner. Supervision: John B. Baumgartner, Linda J. Beaumont. Validation: Bernard C. Dominiak, Jane E. Royer. Visualization: Sabira Sultana, John B. Baumgartner. Writing – original draft: Sabira Sultana. Writing – review & editing: John B. Baumgartner, Bernard C. Dominiak, Jane E. Royer, Linda J. Beaumont. References 1. Plant Health Australia. The Australian Handbook for the Identification of Fruit Flies. Version 3.1.: Plant Health Australia. Canberra, ACT.; 2018. 2. Plant Health Australia. Draft National Fruit Fly Strategy. Plant Health Australia. DEAKIN, ACT, 2600; 2008.

3. Hancock DL, Hamacek EL, Lloyd AC, Elson-Harris MM. The distribution and host plants of fruit flies (Diptera: Tephritidae) in Australia: Department of Primary Industries, Queensland; 2000. 4. Horticultural Policy Council. The impact of fruit flies on Australian horticulture: Report to the Minister for Primary Industries and Energy, Canberra; 1991. 5. Drew RAI. The tropical fruit flies (Diptera: Tephritidae: Dacinae) of the Australasian and Oceanian regions. 1989. Report No.: 0079–8835, 521pp. 6. Smith ESC, Chin D, Allwood AJ, Collins SG. A revised host list of fruit flies (Diptera: Tephritidae) from the Northern Territory of Australia. Queensland Journal of Agricultural and Animal Sciences. 1988; 45 (1):19–28. 7. Royer JE, Hancock DL. New distribution and lure records of Dacinae (Diptera: Tephritidae) from Queensland, Australia, and description of a new species of Dacus Fabricius. Australian Journal of Ento- mology. 2012; 51(4):239–247. 8. Sultana S, Baumgartner JB, Dominiak BC, Royer JE, Beaumont LJ. Potential impacts of climate change on habitat suitability for the Queensland fruit fly. Scientific Reports. 2017; 7(1):13025. https://doi.org/10. 1038/s41598-017-13307-1 PMID: 29026169 9. Dominiak BC, Worsley P. Review of the southern boundary of Jarvis fruit fly ’Bactrocera jarvisi’ (Tyron) (Diptera: Tephritidae: Dacinae) and its likley southern distribution in Australia. General and Applied Entomology: The Journal of the Entomological Society of New South Wales. 2017; 45:1–7. 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 16 / 19

10. De Meyer M, Copeland R, Wharton R, McPheron B. On the geographic origin of the Medfly Ceratitis capitata (Weidemann)(Diptera: Tephritidae). Proceedings of the 6th International Fruit Fly Symposium, Stellenbosch, South Africa. 2002:45–53. 11. White IM, Elson-Harris MM. Fruit flies of economic significance: their identification and bionomics: CAB International; 1992. 12. Permkam S, Hancock D. Australian Ceratitinae (Diptera: Tephritidae). Invertebrate Systematics. 1994; 8(6):1325–1341. 13. Dominiak BC, Mapson R. Revised distribution of Bactrocera tryoni in eastern Australia and effect on possible incursions of Mediterranean fruit fly: development of Australia’s eastern trading block. Journal of Economic Entomology. 2017; 110(6): 2459–2465. https://doi.org/10.1093/jee/tox237 PMID: 29040591 14. Plant Health Australia. Prevent fruit fly. Fruit Fly Research. 2016. 15. Abdalla A, Millist N, Buetre B, Bowen B. Benefit-cost analysis of the national fruit fly strategy action plan. Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra; 2012. 16. PHA. Economic assessment of the implementation of the proposed National Fruit Fly Strategy: Part 1 (Plant Health Australia, August, Canberra., 2009). 17. Australian Pesticides and Veterinary Medicines Authority. Dimethoate Residues and Dietary Risk Assessment Report. August 2011. 18. Clarke AR, Powell KS, Weldon CW, Taylor PW. The ecology of Bactrocera tryoni (Diptera: Tephritidae): what do we know to assist pest management? Annals of Applied Biology. 2011; 158(1):26–54.

19. Australian Pesticides and Veterinary Medicines Authority. Fenthion Residues and Dietary Risk Assess- ment Report. September 2012. 20. Dominiak BC, Ekman JH. The rise and demise of control options for fruit fly in Australia. Crop Protec- tion. 2013; 51:57–67. 21. Jessup A, Dalton S, Slogget R. Determination of host status of table grapes to Queensland fruit fly, ’Bactrocera tryoni’ (Froggatt) (Diptera: Tephritidae), for export to New Zealand. General and Applied Entomology: The Journal of the Entomological Society of New South Wales. 1998; 28:73–75. 22. Stephens AEA, Stringer LD, Suckling DM. Advance, retreat, resettle? Climate change could produce a zero-sum game for invasive species. Austral Entomology. 2016; 55:177–184. 23. Hill MP, Bertelsmeier C, Clusella-Trullas S, Garnas J, Robertson MP, Terblanche JS. Predicted decrease in global climate suitability masks regional complexity of invasive fruit fly species response to climate change. Biological Invasions. 2016; 18(4):1105–1119. 24. Kriticos D. Risks of establishment of fruit flies in New Zealand under climate change. New Zealand For- est Research Institute, Rotorua; 2007. 25. Vera MT, Rodriguez R, Segura DF, Cladera JL, Sutherst RW. Potential geographical distribution of the Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae),m with emphasis on Argentina and Aus- tralia. Environmental entomology. 2002; 31(6):1009–1122. 26. Aguilar G, Blanchon D, Foote H, Pollonais C, Mosee A. Queensland fruit fly invasion of New Zealand:

predicting area suitability under future climate change scenarios. Perspectives in Biosecurity Research Series 2015; 2:1–13. 27. Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distribu- tions. Ecological Modelling. 2006; 190(3):231–259. 28. Kumar S, Neven LG, Yee WL. Evaluating correlative and mechanistic niche models for assessing the risk of pest establishment. Ecosphere. 2014; 5(7):1–23. 29. Kumar S, Neven LG, Yee WL. Assessing the potential for establishment of Western Cherry Fruit Fly using ecological niche modeling. Journal of Economic Entomology. 2014; 107(3):1032–1044. https:// doi.org/10.1603/ec14052 PMID: 25026662 30. Kumar S, Neven LG, Zhu H, Zhang R. Assessing the global risk of establishment of Cydia pomonella (Lepidoptera: Tortricidae) using CLIMEX and MaxEnt niche models. Journal of Economic Entomology. 2015; 108(4):1708–1719. https://doi.org/10.1093/jee/tov166 PMID: 26470312 31. Kumar S, Yee WL, Neven LG. Mapping global potential risk of establishment of Rhagoletis pomonella (Diptera: Tephritidae) using MaxEnt and CLIMEX niche models. Journal of Economic Entomology. 2016; 109(5):2043–2053. https://doi.org/10.1093/jee/tow166 PMID: 27452001 32. Australian horticulture statistics handbook. Horticulture Innovation Australian Limited 2016/17 (www. horticulture.com.au). 33. Dominiak BC, Daniels D. Review of the past and present distribution of Mediterranean fruit fly (Ceratitis capitata Wiedemann) and Queensland fruit fly (Bactrocera tryoni Froggatt) in Australia. Australian Jour-

nal of Entomology. 2012; 51(2):104–115. 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 17 / 19

34. Dominiak BC. Review of grapes Vitis sp. as an occasional host for Queensland fruit fly Bactrocera tryoni (Froggatt)(Diptera: Tephritidae). Crop Protection. 2011; 30(8):958–961. 35. Royer JE, Wright CL, Hancock DL. Bactrocera frauenfeldi (Diptera: Tephritidae), an invasive fruit fly in Australia that may have reached the extent of its spread due to environmental variables. Austral Ento- mology. 2016; 55(1):100–111. 36. May AWS. An investigation of fruit flies (Trypertidae: Diptera) in Queensland 1. Introduction, species, pest status and distribution. Queensland Journal of Agricultural Science. 1963; 20:1–82. 37. Gillespie P. Observations on fruit flies (Diptera: Tephritidae) in New South Wales. Gen Appl Ent Vol. 2003; 32:41–48. 38. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate sur- faces for global land areas. International Journal of Climatology. 2005; 25(15):1965–1978. 39. Bateman MA. The ecology of fruit flies. Annual Review of Entomology. 1972; 17(1):493–518. 40. De Meyer M, Robertson MP, Mansell MW, Ekesi S, Tsuruta K, Mwaiko W, et al. Ecological niche and potential geographic distribution of the invasive fruit fly Bactrocera invadens (Diptera, Tephritidae). Bul- letin of Entomological Research. 2010; 100(01):35–48. 41. Beaumont LJ, Graham E, Duursma DE, Wilson PD, Cabrelli A, Baumgartner JB, et al. Which species distribution models are more (or less) likely to project broad-scale, climate-induced shifts in species ranges? Ecological Modelling. 2016; 342:135–146.

42. CSIRO & BoM. Climate change in Australia information for Australia’s natural resource management regions. Technical Report, CSIRO and Bureau of Meteorology, Australia.; 2015. 43. Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, Van Vuuren DP, et al. The next genera- tion of scenarios for climate change research and assessment. Nature. 2010; 463(7282):747–756. https://doi.org/10.1038/nature08823 PMID: 20148028 44. Peters GP, Andrew RM, Boden T, Canadell JG, Ciais P, Le Que´ re´ C, et al. The challenge to keep global warming below 2 C. Nature Climate Change. 2012; 3(1):1–3. 45. Greenberg J, Mattiuzzi M. gdalUtils: Wrappers for the Geospatial Data Abstraction Library (GDAL) Utili- ties. R Package Version 2.0.1.7. 2015. 46. Team RC. R: A language and Environment for statistical computing. R Foundation for Statistical Com- puting, Vienna, Austria 2014. 47. Elith J, Graham CH, Anderson RP, Dudı´k M, Ferrier S, Guisan A, et al. Novel methods improve predic- tion of species’ distributions from occurrence data. Ecography. 2006; 29:129–151. 48. Merow C, Smith MJ, Silander JA. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography. 2013; 36(10):1058–1069. 49. Elith J, Phillips SJ, Hastie T, Dudı´k M, Chee YE, Yates CJ. A statistical explanation of MaxEnt for ecolo- gists. Diversity and Distributions. 2011; 17(1):43–57. 50. Ihlow F, Dambach J, Engler JO, Flecks M, Hartmann T, Nekum S, et al. On the brink of extinction? How

climate change may affect global chelonian species richness and distribution. Global Change Biology. 2012; 18(5):1520–1530. 51. Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988; 240(4857):1285–1293. https://doi.org/10.1126/science.3287615 PMID: 3287615 52. Wilson KA, Westphal MI, Possingham HP, Elith J. Sensitivity of conservation planning to different approaches to using predicted species distribution data. Biological Conservation. 2005; 122(1):99–112. 53. Elith J, Kearney M, Phillips S. The art of modelling range-shifting species. Methods in Ecology and Evo- lution. 2010; 1(4):330–342. 54. Pebesma E, Roger SB. Classes and methods for spatial data in R. 2005. R News 5 (2):9–13. 55. Hijmans RJ. Raster: Geographic Data Analysis and Modeling. R Package Version 2.4–15. http://CRAN. R-project.org/package=raster 2015. 56. Hijmans RJ, Phillips S, Leathwick J, Elith J. Dismo: Species distribution modeling.R package version 0.8–17. 2013. 57. R Core Team. R: A language and environment for statistical computing: R Foundation for Statistical Computing. Vienna, Austria; 2017. 58. De Meyer M, Robertson M, Peterson A, Mansell M. Ecological niches and potential geographical distri- butions of Mediterranean fruit fly (Ceratitis capitata) and Natal fruit fly (Ceratitis rosa). Journal of Bioge- ography. 2008; 35(2):270–281. 59. Holz G, Grose M, Bennett J, Corney S, White C, Phelan D, et al. Climate Futures for Tasmania: impacts on agriculture technical report. Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, Tasmania, 2010.

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 18 / 19

60. O’Loughlin GT, East RA, Meats A. Survival, development rates and generation times of the Queensland fruit fly, Dacus tryoni, in a marginally favourable climate: experiments in Victoria. Australian Journal of Zoology. 1984; 32(3):353–361. 61. Muthuthantri S, Maelzer D, Zalucki MP, Clarke AR. The seasonal phenology of Bactrocera tryoni (Frog- gatt)(Diptera: Tephritidae) in Queensland. Austral Entomology. 2010; 49(3):221–233. 62. Sutherst RW, Collyer BS, Yonow T. The vulnerability of Australian horticulture to the Queensland fruit fly, Bactrocera (Dacus) tryoni, under climate change. Australian Journal of Agricultural Research. 2000; 51(4):467–480. 63. Szyniszewska AM, Tatem AJ. Global assessment of seasonal potential distribution of Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae). PLoS One. 2014; 9(11):e111582. https://doi.org/10. 1371/journal.pone.0111582 PMID: 25375649 64. Allwood A, Angeles T. Host records of fruit flies (family Tephritidae) in the Northern Territory. Queens- land Journal of Agricultural and Animal Sciences. 1979, 1:105–113. 65. Cameron EC. Fruit Fly Pests of Northwestern Australia [Thesis]: University of Sydney.; 2006. 66. Dominiak BC, Worsley P. Review of cucumber fruit fly, Bactrocera cucumis (French)(Diptera: Tephriti- dae: Dacinae) in Australia: Part 1, host range, surveillance and distribution. Crop Protection. 2018; 106:79–85. 67. Fitt GP. New records of Dacus (Austrodacus) cucumis French from the Northern Territory, Australia (Diptera: Tephritidae). Australian Journal of Entomology. 1980; 19(3):240–240.

68. Gibbs G. The comparative ecology of two closely related, sympatric species of Dacus (Diptera) in Queensland. Australian Journal of Zoology. 1967; 15(6):1123–1139. 69. Wang Y, Yu H, Raphael K, Gilchrist A. Genetic delineation of sibling species of the pest fruit fly Bacto- cera (Diptera: Tephritidae) using microsatellites. Bulletin of Entomological Research. 2003; 93(4):351– 360. https://doi.org/10.1079/ber2003249 PMID: 12908921 70. Drew R, Hooper G, Bateman M. Economic fruit flies of the South Pacific Region. Queensland Depart- ment of Primary Industries.1982. 71. Wisz MS, Hijmans R, Li J, Peterson AT, Graham C, Guisan A, et al. Effects of sample size on the perfor- mance of species distribution models. Diversity and distributions. 2008; 14(5):763–773. 72. Syfert MM, Smith MJ, Coomes DA. The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. PLoS ONE. 2013; 8(2):e55158. https://doi.org/10. 1371/journal.pone.0055158 PMID: 23457462 73. Veloz SD. Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. Journal of Biogeography. 2009; 36(12):2290–2299. 74. Guille´n D, Sa´ nchez R. Expansion of the national fruit fly control programme in Argentina. Area-wide Control of Insect Pests. 2007:653–660. 75. Phillips SJ. Transferability, sample selection bias and background data in presence-only modelling: a response to Peterson et al.(2007). Ecography. 2008; 31(2):272–278.

76. Franklin J. Mapping species distributions: spatial inference and prediction: Cambridge University Press; 2010. 77. Pheloung P, Williams P, Halloy S. A weed risk assessment model for use as a biosecurity tool evaluat- ing plant introductions. Journal of Environmental Management. 1999; 57(4):239–251. 78. Newbold T, Reader T, El-Gabbas A, Berg W, Shohdi WM, Zalat S, et al. Testing the accuracy of species distribution models using species records from a new field survey. Oikos. 2010; 119(8):1326–1334. 79. Elith J. Predicting distributions of invasive species. 2013. arXiv:1312.0851. 80. Salamin N, Wu¨ est RO, Lavergne S, Thuiller W, Pearman PB. Assessing rapid evolution in a changing environment. Trends in Ecology & Evolution. 2010; 25(12):692–698. 81. Charmantier A, McCleery RH, Cole LR, Perrins C, Kruuk LE, Sheldon BC. Adaptive phenotypic plastic- ity in response to climate change in a wild bird population. Science. 2008; 320(5877):800–803. https:// doi.org/10.1126/science.1157174 PMID: 18467590 82. Bush A, Mokany K, Catullo R, Hoffmann A, Kellermann V, Sgrò C, et al. Incorporating evolutionary adaptation in species distribution modelling reduces projected vulnerability to climate change. Ecology Letters. 2016; 19(12):1468–1478. https://doi.org/10.1111/ele.12696 PMID: 27873482 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 19 / 19