Incorporating spatial autocorrelation may invert observed patterns

Kühn, Ingolf

doi:10.1111/j.1472-4642.2006.00293.x

Cited by 219 publications

(188 citation statements)

References 19 publications

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“…Black squares represent recorded presences. model distribution trends not related to the spatial structure in the environmental conditions (Borcard et al, 1992;Diniz-Filho et al, 2003;Kühn, 2007), but rather to historical events or to migrations from favourable areas to those less favourable but within reach (Legendre, 1993;Barbosa et al, 2001;Real et al, 2003).…”

Section: Distribution Data and Predictor Variablesmentioning

confidence: 99%

Transferability of environmental favourability models in geographic space: The case of the Iberian desman (Galemys pyrenaicus) in Portugal and Spain

Barbosa

Real

Vargas

2009

Ecological Modelling

100

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a b s t r a c tTransferring distribution models between different geographical areas may be problematic, as the performance of models outside their original scope is hard to predict. A modelling procedure is needed that gets the gist of the environmental descriptors of a distribution area, without either overfitting to the training data or overestimating the species' distribution potential. We tested the transferability power of the favourability function, a generalized linear model, on the distribution of the Iberian desman (Galemys pyrenaicus) in the Iberian territories of Portugal and Spain. We also tested the effects of two of the main potential constraints on model transferability: the analysed ranges of the predictor variables, and the completeness of the species distribution data. We modelled 10 km × 10 km presence/absence data from Portugal and Spain separately, extrapolated each model to the other country, and compared predictions with observations. The Spanish model, despite arguably containing more false absences, showed good predictive ability in Portugal. The Portuguese model, whose predictors ranged between only a subset of the values observed in Spain, overestimated desman distribution when transferred. We discuss possible reasons for this differential model behaviour, and highlight the importance of this kind of models for prediction and conservation applications.

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Section: Distribution Data and Predictor Variablesmentioning

confidence: 99%

Transferability of environmental favourability models in geographic space: The case of the Iberian desman (Galemys pyrenaicus) in Portugal and Spain

Barbosa

Real

Vargas

2009

Ecological Modelling

100

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“…This phenomenon is often observed in species data, and can result from biotic pressures, such as competition or dispersal, as well as from habitat preferences within spatially structured environmental gradients [21]. Recently the statistical effect of spatial autocorrelation in ecological studies has been noted (see [22][23][24]), and methods to incorporate spatial autocorrelation into regression models have become more common [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Exploring Spatial Scale, Autocorrelation and Nonstationarity of Bird Species Richness Patterns

Holloway

Miller

2015

IJGI

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Abstract:In this paper we explore relationships between bird species richness and environmental factors in New York State, focusing particularly on how spatial scale, autocorrelation and nonstationarity affect these relationships. We used spatial statistics, Getis-Ord Gi*(d), to investigate how spatial scale affects the measurement of richness "hot-spots" and "cold-spots" (clusters of high and low species richness, respectively) and geographically weighted regression (GWR) to explore scale dependencies and nonstationarity in the relationships between richness and environmental variables such as climate and plant productivity. Finally, we introduce a geovisualization approach to show how these relationships are affected by spatial scale in order to understand the complex spatial patterns of species richness.

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“…Residual spatial structure is often considered problematic because it can lead to inflated type I error rates (Cliff and Ord 1981;Lichstein et al 2002) or erroneous inference on model parameters (Kühn 2007). However, if the spatial environment is sufficiently represented by explanatory variables and the spatial covariance error structure is modelled appropriately to remove spatial autocorrelation from the residuals, inference on model parameters should be correct (Bannerjee et al 2004;Wagner and Fortin 2005).…”

Section: Introductionmentioning

confidence: 99%

Predicting Microstegium vimineum invasion in natural plant communities of the southern Blue Ridge Mountains, USA

et al. 2012

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Shade-tolerant non-native invasive plant species may make deep incursions into natural plant communities, but detecting such species is challenging because occurrences are often sparse. We developed Bayesian models of the distribution of Microstegium vimineum in natural plant communities of the southern Blue Ridge Mountains, USA to address three objectives: (1) to assess local and landscape factors that influence the probability of presence of M. vimineum; (2) to quantify the spatial covariance error structure in occurrence that was not accounted for by the environmental variables; and (3) to synthesize our results with previous findings to make inference on the spatial attributes of the invasion process. Natural plant communities surrounded by areas with high human activity and low forest cover were at highest risk of M. vimineum invasion. The probability of M. vimineum presence also increased with increasing native species richness and soil pH, and decreasing basal area of ericaceous shrubs. After accounting for environmental covariates, evaluation of the spatial covariance error structure revealed that M. vimineum is invading the landscape by a hierarchical process. Infrequent longdistance dispersal events result in new nascent subpopulations that then spread via intermediate-and short-distance dispersal, resulting in 3-km spatial aggregation pattern of sub-populations. Containment or minimisation of its impact on native plant communities will be contingent on understanding how M. vimineum can be prevented from colonizing new suitable habitats. The hierarchical invasion process proposed here provides a framework to organise and focus research and management efforts.

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Incorporating spatial autocorrelation may invert observed patterns

Cited by 219 publications

References 19 publications

Transferability of environmental favourability models in geographic space: The case of the Iberian desman (Galemys pyrenaicus) in Portugal and Spain

Transferability of environmental favourability models in geographic space: The case of the Iberian desman (Galemys pyrenaicus) in Portugal and Spain

Exploring Spatial Scale, Autocorrelation and Nonstationarity of Bird Species Richness Patterns

Predicting Microstegium vimineum invasion in natural plant communities of the southern Blue Ridge Mountains, USA

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