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Daily mapping of Australian Plague Locust abundance

  • 1.

    Stige, L. C., Chan, K.-S., Zhang, Z., Frank, D. & Stenseth, N. C. Thousand-year-long Chinese time series reveals climatic forcing of decadal locust dynamics. Proc. Natl. Acad. Sci. 104, 16188–16193 (2007).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 2.

    Walker, F. Catalogue of the Specimens of Dermaptera Saltatoria in Collection of the British Museum. Part III. 485–594 (British Museum (Natural History), 1870).

  • 3.

    Wright, D. E. Analysis of the development of major plagues of the Australian plague locust Chortoicetes terminifera (Walker) using a simulation model. Aust. J. Ecol. 12, 423–437 (1987).

    Article  Google Scholar 

  • 4.

    Deveson, E. D. & Walker, P. W. Not a one-way trip: Historical distribution data for Australian plague locusts support frequent seasonal exchange migrations. J. Orthoptera Res. 14, 91–105 (2005).

    Article  Google Scholar 

  • 5.

    Wang, H. Quantitative assessment of Australian plague locust habitats in the inland of eastern Australia using RS and GIS technologies in Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI vol. 9239 92390D (International Society for Optics and Photonics, 2014).

  • 6.

    Chapuis, M.-P. et al. Challenges to assessing connectivity between massive populations of the Australian plague locust. Proc. R. Soc. B Biol. Sci. 278, 3152–3160 (2011).

    Article  Google Scholar 

  • 7.

    Murray, D. A. H., Clarke, M. B. & Ronning, D. A. Estimating invertebrate pest losses in six major Australian grain crops. Aust. J. Entomol. 52, 227–241 (2013).

    Article  Google Scholar 

  • 8.

    Zhang, L., Lecoq, M., Latchininsky, A. & Hunter, D. Locust and grasshopper management. Annu. Rev. Entomol. 64, 15–34 (2019).

    CAS  PubMed  Article  Google Scholar 

  • 9.

    Adriaansen, C., Woodman, J., Deveson, E. & Drake, V. The Australian Plague Locust: risk and response. Environ. Hazards Risks Disasters Biol https://doi.org/10.1016/B978-0-12-394847-2.00005-X (2016).

    Article  Google Scholar 

  • 10.

    Farrow, R. A. & Longstaff, B. C. Comparison of the annual rates of increase of locusts in relation to the incidence of plagues. Oikos 2, 207–222 (1986).

    Article  Google Scholar 

  • 11.

    Wardhaugh, K. G. The effects of temperature and moisture on the inception of diapause in eggs of the Australian plague locust, Chortoicetes terminifera Walker (Orthoptera: Acrididae). Aust. J. Ecol. 5, 187–191 (1980).

    Article  Google Scholar 

  • 12.

    Wardhaugh, K. G. Diapause strategies in the Australian plague locust (Chortoicetes terminifera Walker). In The evolution of insect life cycles 89–104 (Springer, Berlin, 1986).

    Google Scholar 

  • 13.

    Clark, D. P. Flights after sunset by the Australian plague locust, Chortoicetes terminifera (Walker) and their significance in dispersal and migration. Aust. J. Zool. 19, 159–176 (1971).

    Article  Google Scholar 

  • 14.

    Farrow, R. A. Origin and decline of the 1973 plague locust outbreak in central western New South Wales. Aust. J. Zool. 25, 455–489 (1977).

    Article  Google Scholar 

  • 15.

    Wang, B. et al. Future climate change likely to reduce the Australian plague locust (Chortoicetes terminifera) seasonal outbreaks. Sci. Total Environ. 668, 947–957 (2019).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 16.

    Veran, S. et al. Modeling spatiotemporal dynamics of outbreaking species: influence of environment and migration in a locust. Ecology 96, 737–748 (2015).

    PubMed  Article  Google Scholar 

  • 17.

    Maywald, G., Kriticos, D., Sutherst, R. & Bottomley, W. DYMEX model builder version 3: user’s guide. (2007).

  • 18.

    Meynard, C. N. et al. Climate-driven geographic distribution of the desert locust during recession periods: Subspecies’ niche differentiation and relative risks under scenarios of climate change. Glob. Change Biol. 23, 4739–4749 (2017).

    ADS  Article  Google Scholar 

  • 19.

    Piou, C. et al. Coupling historical prospection data and a remotely-sensed vegetation index for the preventative control of Desert locusts. Basic Appl. Ecol. 14, 593–604 (2013).

    Article  Google Scholar 

  • 20.

    Tratalos, J. A., Cheke, R. A., Healey, R. G. & Stenseth, N. C. Desert locust populations, rainfall and climate change: Insights from phenomenological models using gridded monthly data. Clim. Res. 43, 229–239 (2010).

    Article  Google Scholar 

  • 21.

    Tian, H. et al. Reconstruction of a 1,910-y-long locust series reveals consistent associations with climate fluctuations in China. Proc. Natl. Acad. Sci. 108, 14521–14526 (2011).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 22.

    Ehrlén, J. & Morris, W. F. Predicting changes in the distribution and abundance of species under environmental change. Ecol. Lett. 18, 303–314 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  • 23.

    Croft, S., Chauvenet, A. L. & Smith, G. C. A systematic approach to estimate the distribution and total abundance of British mammals. PLoS ONE 12, e0176339 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 24.

    Woodman, J. D. High-temperature survival is limited by food availability in first-instar locust nymphs. Aust. J. Zool. 58, 323–330 (2011).

    Article  Google Scholar 

  • 25.

    Guisan, A., Edwards, T. C. & Hastie, T. Generalized linear and generalized additive models in studies of species distributions: Setting the scene. Ecol. Model. 157, 89–100 (2002).

    Article  Google Scholar 

  • 26.

    Yee, T. W. & Mitchell, N. D. Generalized additive models in plant ecology. J. Veg. Sci. 2, 587–602 (1991).

    Article  Google Scholar 

  • 27.

    Bučas, M. et al. Empirical modelling of benthic species distribution, abundance, and diversity in the Baltic Sea: Evaluating the scope for predictive mapping using different modelling approaches. ICES J. Mar. Sci. 70, 1233–1243 (2013).

    Article  Google Scholar 

  • 28.

    Heersink, D. K. et al. Statistical modeling of a larval mosquito population distribution and abundance in residential Brisbane. J. Pest Sci. 89, 267–279 (2016).

    Article  Google Scholar 

  • 29.

    Jeffrey, S. J., Carter, J. O., Moodie, K. B. & Beswick, A. R. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Model. Softw. 16, 309–330 (2001).

    Article  Google Scholar 

  • 30.

    Tozer, C. R., Kiem, A. S. & Verdon-Kidd, D. C. On the uncertainties associated with using gridded rainfall data as a proxy for observed. Hydrol. Earth Syst. Sci. 16, 1481–1499 (2012).

    ADS  Article  Google Scholar 

  • 31.

    Gregg, P. Development of the Australian Plague Locust, Chortoicetes terminifera, in relation to weather I. Effects of constant temperature and humidity. Aust. J. Entomol. 22, 247–251 (1983).

    Article  Google Scholar 

  • 32.

    Pruess, K. P. Day-degree methods for pest management. Environ. Entomol. 12, 613–619 (1983).

    Article  Google Scholar 

  • 33.

    McVicar, T. R., Briggs, P. R., King, E. A. & Raupach, M. R. A review of predictive modelling from a natural resource management perspective: the role of remote sensing of the terrestrial environment (CSIRO Land and Water CSIRO Earth Observation Centre, Canberra, 2003).

    Google Scholar 

  • 34.

    Grundy, M. J. et al. Soil and landscape grid of Australia. Soil Res. 53, 835–844 (2015).

    Article  Google Scholar 

  • 35.

    Cressie, N. & Wikle, C. K. Statistics for spatio-temporal data (John Wiley & Sons, New York, 2015).

    Google Scholar 

  • 36.

    James, G., Witten, D., Hastie, T. & Tibshirani, R. An introduction to statistical learning (Springer, Berlin, 2013).

    Google Scholar 

  • 37.

    Nelder, J. A. & Wedderburn, R. W. Generalized linear models. J. R. Stat. Soc. Ser. Gen. 135, 370–384 (1972).

    Article  Google Scholar 

  • 38.

    Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  • 39.

    Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer-Verlag, Berlin, 2002). https://doi.org/10.1007/978-0-387-21706-2.

    Google Scholar 

  • 40.

    Wood, S. N., Goude, Y. & Shaw, S. Generalized additive models for large data sets. J. R. Stat. Soc. Ser. C Appl. Stat. 64, 139–155 (2015).

    MathSciNet  Article  Google Scholar 

  • 41.

    Clark, D. P. The influence of rainfall on the densities of adult Chortoicetes terminifera (Walker) in central western New South Wales, 1965–73. Aust. J. Zool. 22, 365–386 (1974).

    Article  Google Scholar 

  • 42.

    Shelford, V. E. The ecology of North America. Ecol. N. Am. 2, 2 (1963).

    Google Scholar 

  • 43.

    Deveson, E. D. Satellite normalized difference vegetation index data used in managing Australian plague locusts. J. Appl. Remote Sens. 7, 075096 (2013).

    ADS  Article  Google Scholar 

  • 44.

    Kuhnert, P. M. & Lucchesi, L. Vizumap: An R package for visualizing uncertainty in spatial data (Zenodo, Boca Raton, 2018). https://doi.org/10.5281/zenodo.1479951.

    Google Scholar 

  • 45.

    Lucchesi, L. R. & Wikle, C. K. Visualizing uncertainty in areal data with bivariate choropleth maps, map pixelation and glyph rotation. Stat 6, 292–302 (2017).

    MathSciNet  Article  Google Scholar 

  • 46.

    Benfekih, L., Chara, B. & Doumandji-Mitiche, B. Influence of anthropogenic impact on the habitats and swarming risks of Dociostaurus maroccanus and Locusta migratoria (Orthoptera, Acrididae) in the Algerian Sahara and the semi-arid zone. J. Orthoptera Res. 11, 243–250 (2002).

    Article  Google Scholar 

  • 47.

    Štrumbelj, E. & Kononenko, I. Explaining prediction models and individual predictions with feature contributions. Knowl. Inf. Syst. 41, 647–665 (2014).

    Article  Google Scholar 

  • 48.

    Escorihuela, M. J. et al. SMOS based high resolution soil moisture estimates for desert locust preventive management. Remote Sens. Appl. Soc. Environ. 11, 140–150 (2018).

    Google Scholar 

  • 49.

    Myneni, R. B. & Williams, D. L. On the relationship between FAPAR and NDVI. Remote Sens. Environ. 49, 200–211 (1994).

    ADS  Article  Google Scholar 

  • 50.

    Hu, G. et al. Long-term seasonal forecasting of a major migrant insect pest: the brown planthopper in the Lower Yangtze River Valley. J. Pest Sci. 92, 417–428 (2019).

    Article  Google Scholar 


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