2016
DOI: 10.1080/11250003.2016.1223186
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Patterns of spatial autocorrelation in the distribution and diversity of carabid beetles and spiders along Alpine glacier forelands

Abstract: Spatial autocorrelation is a common feature of ecological data and can be found in the distribution pattern of many species or in the diversity of many species assemblages. The presence of spatial autocorrelation in species distribution along primary successions on recently deglaciated terrains has been largely overlooked until now, despite its potential consequences for comparisons between glacier forelands. Here, we investigated the occurrence of spatial autocorrelation at different spatial scales in the dis… Show more

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Cited by 11 publications
(11 citation statements)
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“…For all soil variables and plant cumulative ground cover, quantile regression models (Cade & Noon ) were used; thus the median values of each variable were compared amongst landforms. To account for the correlation amongst sampling points within each plot (Gobbi & Brambilla ), a random effect with Laplace distribution was included in each model (Geraci & Bottai ). For the remaining community variables (plant species richness, arthropod species richness and total activity density), generalized linear models with Poisson error were used.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…For all soil variables and plant cumulative ground cover, quantile regression models (Cade & Noon ) were used; thus the median values of each variable were compared amongst landforms. To account for the correlation amongst sampling points within each plot (Gobbi & Brambilla ), a random effect with Laplace distribution was included in each model (Geraci & Bottai ). For the remaining community variables (plant species richness, arthropod species richness and total activity density), generalized linear models with Poisson error were used.…”
Section: Discussionmentioning
confidence: 99%
“…For the remaining community variables (plant species richness, arthropod species richness and total activity density), generalized linear models with Poisson error were used. To account for the correlation amongst sampling points within each plot (Gobbi & Brambilla ), the models were estimated with generalized estimating equation methods (Zeger et al . ; Dormann et al .…”
Section: Discussionmentioning
confidence: 99%
“…We adopted a mixed modelling approach for all analyses by fitting Generalized Linear Mixed Models (GLMMs) using the glmer function of the 'lme4' package in R (Bates, Maechler, Bolker, & Walker, ). Study area was specified as a random effect to account for non‐independence of sampling sites from the same location and study (Dormann et al, ; Gobbi & Brambilla, ). In an initial exploratory analysis, we modelled species richness in relation to elevation using the above model structure and specifying a Poisson distribution.…”
Section: Methodsmentioning
confidence: 99%
“…Given that our data have a clear spatial structure, with three traps within each sampling plot, and that spatial autocorrelation is a key issue for studies investigating invertebrate ecology (and carabids and beetles in particular) along glacier forelands (Gobbi & Brambilla, ), we adopted a modelling technique able to deal with spatially autocorrelated data. We worked with generalised least squares (GLS) models, which can incorporate the spatial structure into the model's error and are one of the best performing methods for similar spatial analyses (Dormann et al ., ; Beale et al ., ).…”
Section: Methodsmentioning
confidence: 99%