Red herrings remain in geographical ecology: a reply to Hawkins et al. (2007)

Beale, Colin M.; Lennon, Jack J.; Elston, David A.; Brewer, Mark J.; Yearsley, Jonathan M.

doi:10.1111/j.2007.0906-7590.05338.x

Cited by 59 publications

(65 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Failure to account for SAC can result in the selection of pre dictors with the greatest level of autocorrelation (Lennon 2000), the selection of broad-scale predictors over finerscale ones (Diniz-Filho et al 2003), and selection of models with too many predictors (Hoeting et al 2006, Latimer et al 2006. Beale et al (2007) showed that precision tends to rapidly de crease when SAC increases when using standard non-spatial models. Dor mann et al (2007), Miller et al (2007), Veloz (2009) andMiller (2012) review SAC in a context of species distribution modelling.…”

Section: Spatial Autocorrelationmentioning

confidence: 99%

Spatial scale and geographic context in benthic habitat mapping: review and future directions

Lecours¹,

Devillers²,

Schneider³

et al. 2015

Mar. Ecol. Prog. Ser.

156

123

View full text Add to dashboard Cite

Understanding the effects of scale is essential to the understanding of natural ecosystems, particularly in marine environments where sampling is more limited and sporadic than in terrestrial environments. Despite its recognized importance, scale is rarely considered in benthic habitat mapping studies. Lack of explicit statement of scale in the literature is an impediment to better characterization of seafloor pattern and process. This review paper highlights the importance of incorporating ecological scaling and geographical theories in benthic habitat mapping. It reviews notions of ecological scale and benthic habitat mapping, in addition to the way spatial scale influences patterns and processes in benthic habitats. We address how scale is represented in geographic data, how it influences their analysis, and consequently how it influences our understanding of seafloor ecosystems. We conclude that quantification of ecological processes at multiple scales using spatial statistics is needed to gain a better characterization of species−habitat relationships. We offer recommendations on more effective practices in benthic habitat mapping, including sampling that covers multiple spatial scales and that includes as many environmental variables as possible, adopting continuum-based habitat characterization approaches, using statistical analyses that consider the spatial nature of data, and explicit statement of the scale at which the research was conducted. We recommend a set of improved standards for defining benthic habitat. With these standards benthic habitats can be defined as 'areas of seabed that are (geo)statistically significantly different from their surroundings in terms of physical, chemical and biological characteristics, when observed at particular spatial and temporal scales'.

show abstract

Section: Spatial Autocorrelationmentioning

confidence: 99%

Spatial scale and geographic context in benthic habitat mapping: review and future directions

Lecours¹,

Devillers²,

Schneider³

et al. 2015

Mar. Ecol. Prog. Ser.

156

123

View full text Add to dashboard Cite

show abstract

“…As in palaeoecology, there is currently a lively debate about the need or otherwise to account for the effects of spatial autocorrelation (e.g. Lennon 2000; Betts et al 2006Betts et al , 2009Beale et al 2007Beale et al , 2010Hawkins et al 2007;Bini et al 2009;Dormann 2009;de Knegt et al 2010) in bioclimate-envelope modelling and other forms of species distribution modelling.…”

Section: Spatial Autocorrelationmentioning

confidence: 99%

Strengths and Weaknesses of Quantitative Climate Reconstructions Based on Late-Quaternary Biological Proxies

Birks

2011

TOECOLJ

343

284

View full text Add to dashboard Cite

Abstract:The importance of reconstructing past environments quantitatively in palaeoecology is reviewed by showing that many ecological questions asked of palaeoecological data commonly involve the reconstructions of past environment. Three basic approaches to reconstructing past climate from palaeoecological data are outlined and discussed in terms of their assumptions, strengths, and weaknesses. These approaches are the indicator-species approach involving bioclimateenvelope modelling; the assemblage approach involving modern analogue techniques and response surfaces; and the multivariate calibration-function approach. Topics common to all approaches are reviewed -presentation and interpretation, evaluation and validation, comparison, and general limitations of climate reconstructions. Challenges and possible future developments are presented and the potential future role of quantitative climate reconstructions in palaeoecology is summarised.

show abstract

“…c Semivariogram of residuals for best autoregressive GLMM as a function of distance (m) differences between non-temporal and temporal models may be because of other factors (e.g., multicollinearity, scale dependence) rather than the presence of autocorrelation in model residuals Hawkins et al 2007). Although much more work is required to find underlying factors causing differences between both model types and thus to solve the controversial puzzle that has recently emerged on this issue Beale et al 2007), what it is clear is that in this study the best temporally structured models were able to characterize satisfactorily the complex system studied and fulfil statistical assumptions.…”

Section: Non-temporal and Temporal Modelsmentioning

confidence: 77%

Analysing spatial and temporal variation in colony size: an approach using autoregressive mixed models and information theory

Olea

2008

Population Ecology

View full text Add to dashboard Cite

The spatial and temporal variation in population sizes of animal colonies are rarely studied simultaneously. I examined factors determining colony size (number of nests) for 23 colonies from the only breeding population of rook Corvus frugilegus in Spain over 7 years. Population sizes within colonies were highly predictable over time, with autocorrelations up to a distance (lag) of 6 years. Autoregressive mixed models were used to explain colony size as a function of environmental factors, while controlling for temporal autocorrelation. These factors included refuse tips, widely used as food resource, and a derived variable that incorporated the two factors most often related to avian colony size (inter-colony competition and foraging habitat around colonies). Autoregressive models provided a better fit to the data than models which did not consider temporal autocorrelation. The information-theoretic (AIC c -based) approach revealed uncertainty in the selection of the best model explaining colony-size, but relatively strong support for certain variables. The highest weights of evidence were for year (x i = 0.90) and the number of competitors per unit of foraging habitat (i.e., derived variable; x i = 0.63), showing that the size of rook colonies in Spain was negatively affected by inter-colony competition relative to the foraging habitat surrounding the colonies. This variable measured within a 6-km radius from the colonies had *30 times higher weight of evidence (more plausible) than the same variable measured within 3 km, indicating that food limitation may occur outside the breeding period. Sizes of colonies tended to decrease when distance between the colony and the nearest refuse tip increased. There was some evidence supporting the idea that the effect of the number of competitors per unit of foraging habitat on colony size varied from year to year, but statistical power was weak. These findings suggest that variation in number of rook nests within colonies reflects spatial and temporal heterogeneity of net food via both inter-colony competition and foraging habitat around the colony.

show abstract

Red herrings remain in geographical ecology: a reply to Hawkins et al. (2007)

Cited by 59 publications

References 8 publications

Spatial scale and geographic context in benthic habitat mapping: review and future directions

Spatial scale and geographic context in benthic habitat mapping: review and future directions

Strengths and Weaknesses of Quantitative Climate Reconstructions Based on Late-Quaternary Biological Proxies

Analysing spatial and temporal variation in colony size: an approach using autoregressive mixed models and information theory

Contact Info

Product

Resources

About