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Grasshoppers exhibit asynchrony and spatial non-stationarity in response to the El Niño/Southern and Pacific Decadal Oscillations

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  • Humphreys, John M.
  • Srygley, Robert B.
  • Lawton, Douglas
  • Hudson, Amy R.
  • Branson, David H.

Abstract

Grasshoppers are preeminent herbivores and perhaps the most significant rangeland pests in the United States (US). Despite the important ecosystem functions they provide, grasshopper populations often obtain densities that cause significant economic harm to grazing operations and agricultural production. Although numerous studies conducted at the level of individual field sites have examined potential mechanisms contributing to grasshopper population “boom and bust” cycles, there has yet to be a large, regional scaled analysis that quantified grasshopper variation across the Western US as a whole. While taking steps to account for data collection biases, mediating effects, and variable confounding, we assessed the influence of Pacific Ocean sea surface temperature oscillations on a 40-year record of grasshopper density in the Western US. Central to our analysis was employing spatially varying coefficients to model time and location-specific variation in grasshopper response to climate. Our results quantitatively demonstrated interannual changes in grasshopper density to be indirectly effected by seasonal El Niño/Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) variability and to exhibit spatial asynchrony and non-stationarity such that the relative influence of climate on grasshopper density varied through time and across geographic space. Our model is the first to incorporate climate indices as spatially varying coefficients for assessment of a terrestrial species and represents a critical step towards understanding causal drivers of regional grasshopper density.

Suggested Citation

  • Humphreys, John M. & Srygley, Robert B. & Lawton, Douglas & Hudson, Amy R. & Branson, David H., 2022. "Grasshoppers exhibit asynchrony and spatial non-stationarity in response to the El Niño/Southern and Pacific Decadal Oscillations," Ecological Modelling, Elsevier, vol. 471(C).
  • Handle: RePEc:eee:ecomod:v:471:y:2022:i:c:s0304380022001533
    DOI: 10.1016/j.ecolmodel.2022.110043
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    as
    1. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    2. Cristobal Young, 2019. "The Difference Between Causal Analysis and Predictive Models: Response to “Comment on Young and Holsteen (2017)â€," Sociological Methods & Research, , vol. 48(2), pages 431-447, May.
    3. Alan Gelfand & Alexandra Schmidt & Sudipto Banerjee & C. Sirmans, 2004. "Nonstationary multivariate process modeling through spatially varying coregionalization," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 13(2), pages 263-312, December.
    4. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    5. Brock, Guy & Pihur, Vasyl & Datta, Susmita & Datta, Somnath, 2008. "clValid: An R Package for Cluster Validation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i04).
    6. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
    7. Acharya, Avidit & Blackwell, Matthew & Sen, Maya, 2016. "Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects," American Political Science Review, Cambridge University Press, vol. 110(3), pages 512-529, August.
    8. Martins, Thiago G. & Simpson, Daniel & Lindgren, Finn & Rue, Håvard, 2013. "Bayesian computing with INLA: New features," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 68-83.
    9. Jacob M. Montgomery & Brendan Nyhan & Michelle Torres, 2018. "How Conditioning on Posttreatment Variables Can Ruin Your Experiment and What to Do about It," American Journal of Political Science, John Wiley & Sons, vol. 62(3), pages 760-775, July.
    10. Lindgren, Finn & Rue, Håvard, 2015. "Bayesian Spatial Modelling with R-INLA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i19).
    11. John M. Humphreys & Robert B. Srygley & David H. Branson, 2022. "Geographic Variation in Migratory Grasshopper Recruitment under Projected Climate Change," Geographies, MDPI, vol. 2(1), pages 1-19, January.
    12. Markus Reichstein & Michael Bahn & Philippe Ciais & Dorothea Frank & Miguel D. Mahecha & Sonia I. Seneviratne & Jakob Zscheischler & Christian Beer & Nina Buchmann & David C. Frank & Dario Papale & An, 2013. "Climate extremes and the carbon cycle," Nature, Nature, vol. 500(7462), pages 287-295, August.
    13. Peter J. Diggle & Raquel Menezes & Ting‐li Su, 2010. "Geostatistical inference under preferential sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 191-232, March.
    14. Gabriel A. Vecchi & Andrew T. Wittenberg, 2010. "El Niño and our future climate: where do we stand?," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 1(2), pages 260-270, March.
    15. Dhiman Bhadra & Michael J. Daniels & Sungduk Kim & Malay Ghosh & Bhramar Mukherjee, 2012. "A Bayesian Semiparametric Approach for Incorporating Longitudinal Information on Exposure History for Inference in Case–Control Studies," Biometrics, The International Biometric Society, vol. 68(2), pages 361-370, June.
    16. Hanyu Yang & Runze Li & Robert A. Zucker & Anne Buu, 2016. "Two-stage model for time varying effects of zero-inflated count longitudinal covariates with applications in health behaviour research," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(3), pages 431-444, April.
    17. Pearl Judea, 2010. "An Introduction to Causal Inference," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-62, February.
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