Analyzing spatial autocorrelation in species distributions using Gaussian and logit models

Carl, Gudrun; Kühn, Ingolf

doi:10.1016/j.ecolmodel.2007.04.024

Cited by 102 publications

(88 citation statements)

References 29 publications

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“…Autocorrelation is a common source of lack of independence encountered in spatial studies in ecology (Carl and Kü hn 2007).The use of multiple logistic models requires the statistical assumption of independence. Spatial autocorrelation may affect error and parameter estimates of the statistical model (Legendre 1993, Lennon 2000, but doesn't necessarily invalidate the statistical model (Boyce et al 2002, Diniz-Filho et al 2003.…”

Section: Predictive Models Of Foraging Distribution Patternsmentioning

confidence: 99%

Foraging behavior of heritage versus recently introduced herbivores on desert landscapes of the American Southwest

et al. 2011

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Abstract. Since the 1800s managed grasslands and shrublands of the arid American Southwest have been grazed predominantly by cattle originally bred for temperate climates in northern Europe. A heritage breed, the criollo cattle, has survived in northern Mexico for more than 400 years under desert-like conditions of low and variable rainfall, hot temperatures in the growing season, and both spatially and temporally scarce levels of primary production. We tested the hypothesis that the heritage breed has a broader spatial foraging distribution under harsh environmental conditions, and that its distribution is driven by environmental variables which differ from those that control the distribution of the introduced European breed. Movements of individual criollo and Angus breed animals were monitored autonomously in the northern Chihuahuan desert of southern New Mexico, USA. Georeferenced foraging locations acquired at 5-minute intervals for each animal were fit to a logistic regression using environmental factors as predictors. In the spring, when forage availability was high and more uniformly distributed across the landscape, animal foraging patterns were similar for both breeds. In the fall when forage availability was low and non-uniformly distributed, the two breeds exhibited very different foraging patterns: heritage animals foraged across a much larger spatial extent whereas their domestic counterparts remained in close proximity to the permanent source of water. These differences in foraging behavior driven by environmental variables have important implications for sustainability of rangelands in spatially and temporally variable environments. Heritage breeds of animals that are generalist foragers during unfavorable conditions can reduce environmental impacts compared to more recently introduced breeds.Key words: Chihuahuan desert; criollo cattle; GPS monitoring; herbivory; landscape foraging patterns; resource selection functions. Ecosphere 2(5):art57.

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Section: Predictive Models Of Foraging Distribution Patternsmentioning

confidence: 99%

Foraging behavior of heritage versus recently introduced herbivores on desert landscapes of the American Southwest

et al. 2011

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“…The correlation within a cluster can be specified by two different structures: (1) quadratic, where the correlation varies with distance class (Carl and Ku¨hn 2007; for example, a block of 2 3 2 sampling stations contains two distance classes, therefore two correlation parameters need to be estimated) and (2) exchangeable. All correlations within a cluster are equal.…”

Section: Methodsmentioning

confidence: 99%

“…Prior to these iterative steps (2 and 3) the data were clustered into smaller blocks of 2 3 2, 3 3 3, or 4 3 4 sampling stations to reduce computation time. Correlations within each cluster were included in the model, while correlations between clusters were assumed to be absent (Carl andKu¨hn 2007, Koper andManseau 2009).…”

Section: Methodsmentioning

confidence: 99%

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The role of environmental variables in structuring landscape‐scale species distributions in seafloor habitats

Kraan

Aarts

Meer

et al. 2010

Ecology

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Abstract. Ongoing statistical sophistication allows a shift from describing species' spatial distributions toward statistically disentangling the possible roles of environmental variables in shaping species distributions. Based on a landscape-scale benthic survey in the Dutch Wadden Sea, we show the merits of spatially explicit generalized estimating equations (GEE). The intertidal macrozoobenthic species, Macoma balthica, Cerastoderma edule, Marenzelleria viridis, Scoloplos armiger, Corophium volutator, and Urothoe poseidonis served as test cases, with median grain-size and inundation time as typical environmental explanatory variables. GEEs outperformed spatially naı¨ve generalized linear models (GLMs), and removed much residual spatial structure, indicating the importance of median grain-size and inundation time in shaping landscape-scale species distributions in the intertidal. GEE regression coefficients were smaller than those attained with GLM, and GEE standard errors were larger. The best fitting GEE for each species was used to predict species' density in relation to median grainsize and inundation time. Although no drastic changes were noted compared to previous work that described habitat suitability for benthic fauna in the Wadden Sea, our predictions provided more detailed and unbiased estimates of the determinants of species-environment relationships. We conclude that spatial GEEs offer the necessary methodological advances to further steps toward linking pattern to process.

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“…With non-normally distributed data, such as binomial or Poisson, we will need a generalization of the methods described above. Generalized Estimating Equations (GEE) are generalizations of Generalized Linear Models (GLM) to include autocorrelation structures (Diggle et al 1995;Yan and Fine 2004;Carl and Kühn 2007). Another option is the use of Generalized linear mixed effect models (Blackburn and Duncan 2001) or the use of and Generalized Least Squares, which are also apt to correct for SAC in analyses with non-normally distributed residuals when a correlation structure is defined.…”

Section: Spatial Non-independence Of Introduction Eventsmentioning

confidence: 99%

The comparative analysis of historical alien introductions

2007

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Analyzing spatial autocorrelation in species distributions using Gaussian and logit models

Cited by 102 publications

References 29 publications

Foraging behavior of heritage versus recently introduced herbivores on desert landscapes of the American Southwest

Foraging behavior of heritage versus recently introduced herbivores on desert landscapes of the American Southwest

The role of environmental variables in structuring landscape‐scale species distributions in seafloor habitats

The comparative analysis of historical alien introductions

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