Red herrings revisited: spatial autocorrelation and parameter estimation in geographical ecology

By B. Hawkins, J. Diniz-Filho, Luis Mauricio Bini, P. Marco, T. Blackburn. 2007

236 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/c3ee79fe89a517289cb0aa8359a0d0e47e8040d8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. Jens‐Christian Svenning
2. Antoine Guisan
3. Carsten F. Dormann
4. Miguel B. Araújo
5. Wilfried Thuiller
6. Walter Jetz
7. Werner Kissling
8. I. Kühn

In Ecology

1. Jens‐Christian Svenning
2. Carsten F. Dormann
3. Walter Jetz
4. Werner Kissling
5. Miguel B. Araújo
6. Wilfried Thuiller
7. I. Kühn
8. Jorge M. Lobo

In Nature and Landscape Conservation

1. Carsten F. Dormann
2. Jens‐Christian Svenning
3. Wilfried Thuiller
4. Antoine Guisan
5. Miguel B. Araújo
6. Werner Kissling
7. I. Kühn
8. Walter Jetz

Citations in the corpus

• Methods to account for spatial autocorrelation in the analysis of species distributional data : a review (2007, background): “…be very different in real data, where both non-stationarity (Ver Hoef et al. 1993, Brunsdon et al. 1996, Foody 2004, Osborne et al. 2007) and bias in parameter space (e.g. less complete data coverage in warmer regions) can be found (Lennon 2000, but see Hawkins et al. 2007 for an opposite view).”
• Topoclimates, refugia, and biotic responses to climate change (2020, background): “2006), which was not a focus of our modeling, and impacts on parameter estimation and uncertainty are observed under some conditions, depending on scale, but not others (Hawkins et al. 2007; Paciorek 2010).”
• Eight (and a half) deadly sins of spatial analysis (2012, background): “…is that OLS regression coefficients, except with respect to estimating standard errors of coefficients, are not biased by spatial autocorrelation in a statistical sense (Cressie, 1993; Dutilleul, 1993; Fortin & Dale, 2005; Schabenberg & Gotway, 2005; Diniz-Filho et al., 2007; Hawkins et al., 2007).”
• Environment–richness relationships for mammals, birds, reptiles, and amphibians at global and regional scales (2010, background): “…on vertebrate richness shift from water constraints at low latitudes to ambient energy at high latitudes (‘water–energy dynamics hypothesis’, Hawkins et al. 2003a, 2007a), which has previously been documented in a global model of bird richness (Hawkins et al. 2003b) and is indirectly…”
• Global patterns in fruiting seasons (2008, methodology): “For comparison, we also fitted versions of the model specifications without the ‘correction’ for spatial autocorrelation ( Hawkins et al ., 2007 ).”
• Basin-wide variations in foliar properties of Amazonian forest: phylogeny, soils and climate. (2009, background): “existence of autocorrelated residuals does not seriously affect parameter estimates (Hawkins et al., 2007).”
• Predicting the distribution of vulnerable marine ecosystems in the deep sea using presence-background models (2014, background): “A simple solution to this issue is to remove the spatial structures present in the sample data a priori (Hawkins et al., 2007).”
• High tropical net diversification drives the New World latitudinal gradient in palm (Arecaceae) species richness (2008, background): “However, we note that spatial autocorrelation may not bias ordinary least-squares estimation of regression coefficients (Hawkins et al., 2007).”