Biological relativity to water–energy dynamics

O’Brien, Eileen M.

doi:10.1111/j.1365-2699.2006.01534.x

Cited by 193 publications

(256 citation statements)

References 40 publications

Supporting

Mentioning

229

Contrasting

Order By: Relevance

“…For example, Hawkins et al (2007) found that the richness gradient of North American and European trees is better explained statistically by rainfall than by annual temperature (r 2 ¼ 0.706 vs. 0.525, respectively), and, unlike the case with temperature, the association of tree richness with rainfall is linear across all climates. Based on an overwhelming amount of evidence for the importance of water to life, any explanation for diversity gradients that depends solely on temperature will probably be incomplete (see also Field et al 2005, O'Brien 2006.…”

Section: Discussionmentioning

confidence: 99%

A Global Evaluation of Metabolic Theory as an Explanation for Terrestrial Species Richness Gradients

Hawkins

Albuquerque

Araújo

et al. 2007

Ecology

154

235

View full text Add to dashboard Cite

Abstract. We compiled 46 broadscale data sets of species richness for a wide range of terrestrial plant, invertebrate, and ectothermic vertebrate groups in all parts of the world to test the ability of metabolic theory to account for observed diversity gradients. The theory makes two related predictions: (1) ln-transformed richness is linearly associated with a linear, inverse transformation of annual temperature, and (2) the slope of the relationship is near À0.65. Of the 46 data sets, 14 had no significant relationship; of the remaining 32, nine were linear, meeting prediction 1. Model I (ordinary least squares, OLS) and model II (reduced major axis, RMA) regressions then tested the linear slopes against prediction 2. In the 23 data sets having nonlinear relationships between richness and temperature, split-line regression divided the data into linear components, and regressions were done on each component to test prediction 2 for subsets of the data. Of the 46 data sets analyzed in their entirety using OLS regression, one was consistent with metabolic theory (meeting both predictions), and one was possibly consistent. Using RMA regression, no data sets were consistent. Of 67 analyses of prediction 2 using OLS regression on all linear data sets and subsets, two were consistent with the prediction, and four were possibly consistent. Using RMA regression, one was consistent (albeit weakly), and four were possibly consistent. We also found that the relationship between richness and temperature is both taxonomically and geographically conditional, and there is no evidence for a universal response of diversity to temperature. Meta-analyses confirmed significant heterogeneity in slopes among data sets, and the combined slopes across studies were significantly lower than the range of slopes predicted by metabolic theory based on both OLS and RMA regressions. We conclude that metabolic theory, as currently formulated, is a poor predictor of observed diversity gradients in most terrestrial systems.

show abstract

Section: Discussionmentioning

confidence: 99%

A Global Evaluation of Metabolic Theory as an Explanation for Terrestrial Species Richness Gradients

Hawkins

Albuquerque

Araújo

et al. 2007

Ecology

154

235

View full text Add to dashboard Cite

show abstract

“…1;Levin 1992;Giladi et al 2011;Arroyo-Rodríguez et al 2017). At the broadest spatial scales and over the longest temporal scales, the proximate drivers such as speciation, extinction, migration, biogeographic history, evolutionary history, and regional species pool, and the ultimate drivers such as climate, energy, water, and topography are more influential (Ricklefs 1987;Hawkins et al 2003;O'Brien 2006;Pärtel et al 2007;Harrison & Cornell 2008;Field et al 2009). At finer spatial scales and over shorter temporal scale, species diversity is directed by proximate drivers such as species' population dynamics, species interactions, and meso-or micro-climate, and…”

Section: Scale Sensitivity Of the Species Diversity Patternsmentioning

confidence: 99%

Scale sensitivity of the relationship between alpha and gamma diversity along an alpine elevation gradient in central Nepal

Bhatta

Grytnes

Vetaas

2018

Journal of Biogeography

View full text Add to dashboard Cite

Aim Components of scale, such as grain, focus and extent, influence the spatial patterns of alpha and gamma diversity and the relationships between them. We explored these scale relations by testing whether the gamma diversity and alpha diversity along an elevation gradient were related independent of scale and whether the elevational patterns of herbaceous and woody species richness were dependent on scale. Location Langtang National Park, Nepal. Methods We estimated alpha diversity (plot richness) for woody and herbaceous plant species along an alpine elevation gradient (3,900–5,000 m a.s.l.) in nested plots of 1 m2, 16 m2 and 100 m2 and gamma diversity (regional richness) from published sources. Generalized linear modelling was used to analyse alpha and gamma diversity and their correspondence at different grain sizes. Results Elevational trends of gamma and alpha diversity were significantly correlated for both woody and herbaceous species at all grain sizes. The concordance increased with increasing grain size and area for gamma diversity estimation, particularly for the monotonously decreasing elevational gamma and alpha diversity patterns of woody species. The hump‐shaped patterns of elevational gamma and alpha diversity for herbaceous species were also significantly correlated, but the concordance between the alpha diversity of herbaceous species and local gamma diversity was stronger. Elevational patterns of alpha diversity were coarsely consistent across grain sizes, although the patterns became more pronounced at larger grain sizes. Main conclusions The correspondence of elevational gamma and alpha diversity was largely scale invariant, implying that elevational and possibly other geographical diversity patterns can reliably be studied at different spatial scales. Nonetheless, the alpha diversity pattern was the least pronounced at fine grain size, particularly for woody life‐forms. This finding suggests that for large‐scale patterns such as elevational gradients at regional or continental scales, coarse grain sizes and large areas for gamma estimation are more appropriate.

show abstract

“…Across all data sets, the r 2 values were substantially lower than the r 2 values from OLS fits (paired t test ¼À5.39; P , 0.001), despite low overall fits of temperature using either method (average r 2 G&A ¼ 0.153; average r 2 OLS ¼ 0.272). Although we currently do not have other environmental predictors for all data sets, previous meta-analyses (Hawkins et al 2003) indicate that r 2 values of other variables (derived from theories related to water-energy balance; e.g., O'Brien [2006]) have much greater statistical explanatory power. Moreover, recent modeling of geographic range overlap explicitly based on MTE generated results with lower explanatory power than those generated using alternative models (Rahbek et al 2007).…”

Section: Hypothesis Testing and Model Selectionmentioning

confidence: 99%