Red-shifts and red herrings in geographical ecology

By J. Lennon. 2000

541 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/9c21cbc1f4c6e005e7fd127029327efce364553d

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In Ecology

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

Oceanographic influences on seabirds and cetaceans of the eastern tropical Pacific: A review (2006, background): “Species data are often autocorrelated (Lennon, 2000) and habitat variables often exhibit multicollinearity (Neter et al., 1996), further complicating analyses.”
The role of climate in limiting European resident bird populations (2003, unknown): “The effect of spatial autocorrelation on the interpretation of the results is strongest if the spatial structure of the data is a continuous lattice (Lennon, 2000).”
Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France (2006, background): “Spatial autocorrelation can inflate the predictive performance of models (Legendre, 1993; Lennon, 2000; Hampe, 2004; Araújo et al., 2005) when calibration and evaluation plots are too close Table 1 List of available (a) climatic and (b) edaphic variables to model the distribution of Acer campestre i”
Spatial autocorrelation and red herrings in geographical ecology (2003, methodology): “However, Lennon (2000) recently argued that, beyond difficulties in hypothesis testing due to inflated Type I errors, there would also be a systematic bias toward particular kinds of mechanisms associated with variables that have greater spatial autocorrelation.”
Integrating the statistical analysis of spatial data in ecology (2002, unknown): “While this has been known for many years, the importance of this problem is just beginning to be appreciated fully by many ecologists and there is a need to identify appropriate methods for accounting for spatial pattern in the adjustment of statistical tests (Lennon 2000).”
Basin-wide variations in foliar properties of Amazonian forest: phylogeny, soils and climate. (2009, background): “Interpretation of the macroecological patterns through statistical analyses where spatial autocorrelation is present is an issue of active debate (Lennon, 2000; Diniz-Filho et al., 2003), with recent simulations showing that the existence of autocorrelated residuals may not seriously affect paramete”
Incorporating spatial autocorrelation may invert observed patterns (2006, methodology): “Lennon (2000) raised this topic again and pointed out that spatial autocorrelation can alter parameter estimates of linear models by influencing the variance–covariance matrix (Anselin, 1988; Anselin & Bera, 1998).”
Can models of presence‐absence be used to scale abundance? Two case studies considering extremes in life history (2005, methodology): “Given that spatial autocorrelation was likely among sample locations (Lennon 2000) and potentially inflating our Type I error rate (Diniz-Filho et al.”
Identifying appropriate spatial scales of predictors in species distribution models with the random forest algorithm (2013, background): “…residual variance and leading to a bias in the statistical inference if the response and predictor are both spatially autocorrelated (which is often the case in ecological data sets) (Legendre 1993), as the correlation coefficient and AIC can be affected (Lennon 2000; Hoeting et al. 2006).”
Range‐wide patterns of greater sage‐grouse persistence (2008, background): “Patterns of sage-grouse persistence Diversity and Distributions , 14 , 983–994, Journal compilation © 2008 Blackwell Publishing Ltd 987 No claim to original US government works pseudoreplicated data and ensured that inferences about coefficients were correct (Lennon, 2000; Diniz et al ., 2003).”
Residual spatial autocorrelation in macroecological and biogeographical modeling: a review (2019, background): “This simply means that the presence of dependence between pairs of observations across space, where independence between such observations is assumed, can result in falsely rejecting, much more often than expected, the null hypothesis while it is true (Lennon, 2000).”
Beyond description: the active and effective way to infer processes from spatial patterns. (2009, background): “One explanation of this is that endogenous ecological processes operate between neighboring individuals, thus creating autocorrelation (Tilman and Kareiva 1997, Lennon 2000, Stoll and Bergius 2005).”
Accounting for spatial pattern when modeling organism- environment interactions (2002, unknown): “Careless regression (or correlation) of autocorrelated variables across an explicit or implicit spatial surface may, as a consequence, highlight spurious associations (Lennon 2000).”