STATISTICS OF EXTREMES: MODELING ECOLOGICAL DISTURBANCES

By R. Katz, G. Brush, Marc B. Parlange. 2005

264 citations recorded by the source corpus.

The potential advantage of extreme value theory in modeling ecological disturbances is the central theme of this paper. The statistics of extremes have played only a very limited role in ecological modeling, despite the disproportionate influence of unusual disturbances on ecosystems. An overview of this theory is provided, with emphasis on recent developments that both make more efficient use of the available data on extremes and enable applications that are more ecologically realistic. Consistent with the emphasis on scale in ecology, scaling properties of extremes are emphasized. It is argued that the existence of distributions whose extreme upper tail is ''heavy'' (i.e., decreases at a relatively slow rate) implies that ecological disturbances are sometimes regarded as more ''surprising'' than they ought to be. The application focuses on modeling disturbances in paleoecology. Two examples are considered: the first, a sediment yield time series for Nicolay Lake in the high Arctic, reflects only the influence of hydrologic disturbances; the second, a sediment rate time series in the Chesapeake Bay, includes both climatic and anthropogenic influences. Strong evidence supports a heavy-tailed distribution for the Nicolay Lake sediment yield, but not necessarily for the Chesapeake Bay sediment rates. For each example, it is demonstrated how the statistics of extremes can readily incorporate information about covariates, such as large-scale atmospheric-oceanic circulation patterns or land use.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/f0dbec615985d4cc6be2b1f55b84dd614fc9d980

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Citations in the corpus

Disturbance and landscape dynamics in a changing world. (2010, background): “Extreme events must be given special consideration because their potential impacts on ecosystems and people are substantial (Katz et al. 2005, Mitchell et al. 2006, Mills 2009).”
Non‐stationary frequency analysis of extreme daily rainfall in Sao Paulo, Brazil (2009, methodology): “…becomes questionable, and often statistical modelling of EV needs to be properly modified to accommodate, for instance, a time-trend component, either linear or non-linear (e.g. Strupczewski et al., 2001a,b; Katz et al., 2002, 2005; Khaliq et al., 2006; El Adlouni et al., 2007; Nogaj et al., 2007).”
Thermal variability alters climatic stress resistance and plastic responses in a globally invasive pest, the Mediterranean fruit fly (Ceratitis capitata) (2010, background): “…(i.e., the magnitude of variability) are clearly important in shaping the strength of selection and thus phenotypes in natural populations (Gaines & Denny, 1993; Gutschick & BassiriRad, 2003; Katz et al., 2005), but are poorly understood for many species, particularly insect pests of agriculture.”
Climate change effects on plant disease: genomes to ecosystems. (2006, unknown): “disturbance agents” (76) in models of vege-”
Ecological Models and Data in R (2008, unknown): “maxima) of samples of other distributions (Katz et al., 2005).”
Physiological Correlates of Geographic Range in Animals (2011, background): “Theoretical models (Katz et al. 2005) as well as empirical data (Easterling et al. 2000) have concluded that climate change also impacts temperature variability.”