Energy and Large-Scale Patterns of Animal- and Plant-Species Richness

Currie, David J.

doi:10.1086/285144

Cited by 1,525 publications

(1,730 citation statements)

References 38 publications

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“…Frequently, these studies use only simple regression models (Austin, 1999b). More complex statistical models have been used in exploratory analysis of species richness patterns in relation to environment (Currie, 1991;Margules et al, 1987;Pausas, 1994;Austin, et al, 1996). Investigations of global patterns of species richness tend to emphasise indirect gradients such as latitude (Ricklefs and Schluter, 1993;Rosenzweig, 1995).…”

Section: Collective Propertiesmentioning

confidence: 99%

Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

Austin¹

2002

Ecological Modelling

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Neglect of ecological knowledge is a limiting factor in the use of statistical modelling to predict species distribution. Three components are needed for statistical modelling, an ecological model concerning the ecological theory to be used or tested, a data model concerning the collection and measurement of the data, and a statistical model concerning the statistical theory and methods used. This component framework is reviewed with emphasis on ecological theory. The expected shape of a species response curve to an environmental gradient is a central assumption on which agreement has yet to be reached. The nature of the environmental predictors whether indirect variables, e.g. latitude that have no physiological impact on plants, or direct variables, e.g. temperature also influence the type of response expected. Straight-line relationships between organisms and environment are often used uncritically. Many users of canonical correlation analysis use linear (straight-line) functions to relate ordination axes to variables such as slope and aspect though this is not a necessary part of the method. Some statisticians have used straight lines for species/environment relationships without testing, when evaluating new statistical procedures. Assumptions used in one component often conflict with those in another component. Statistical models can be used to explore ecological theory. Skewed species response curves predominate contrary to the symmetric unimodal curves assumed by some statistical methods. Improvements in statistical modelling can be achieved based on ecological concepts. Examples include incorporating interspecific competition from dominant species; more proximal predictors based on water balance models and spatial autocorrelation procedures to accommodate non-equilibrium vegetation. #

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Section: Collective Propertiesmentioning

confidence: 99%

Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

Austin¹

2002

Ecological Modelling

1,473

1,270

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“…At a higher level of complexity, these approaches have also been used to investigate the distribution of plant (Currie and Paquin, 1987;Margules et al, 1987;Pausas, 1994;Heikkinen, 1996;Wohlgemuth, 1998) and animal diversity (Owen, 1989;Currie, 1991;Fraser, 1998).…”

Section: A Framework For Use Of Statistical Models In Ecological Studiesmentioning

confidence: 99%

“…Hence, the predictive capability of such models is frequently low (Roloff and Kernohan, 1999;Pearce and Ferrier, 2000), and most have limited success when applied to other sampling locations (Power, 1993). An exception is the tree species richness model of Currie (1991), developed in North America but which provided acceptable estimates when applied to UK.…”

Section: A Framework For Use Of Statistical Models In Ecological Studiesmentioning

confidence: 99%

Generalized linear and generalized additive models in studies of species distributions: setting the scene

Guisan

Edwards

Hastie

2002

Ecological Modelling

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“…For example, some of the small lakes in China have been separated from larger lakes by anthropogenic reclamation, and hence have relatively more fish species than expected. Previous study in China also showed that lake area did not correlate with fish species richness, but energy available could best explain fish species richness patterns in national scale (Currie, 1991;Zhao et al, 2006).…”

Section: Prediction and Determinants Of Fish Species Richness In Chinmentioning

confidence: 82%

Predicting fish species richness and assemblages with climatic, geographic and morphometric factors: A broad-scale study in Chinese lakes

Guo

Lek

et al. 2015

Limnologica

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a b s t r a c tThe present study was designed to investigate the relative importance of climatic (temperature and precipitation), geographic (altitude) and morphometric (lake area) factors in predicting fish species richness and assemblages in Chinese lakes at a large spatial scale. Two recursive partitioning tree-based approaches: Classification and Regression Trees (CARTs) and Multivariate Regression Trees (MRTs) were employed to generate predictive models respectively. Six fish assemblages were thus defined from the MRT model. The results indicated that lake altitude was the main determinant for predicting fish assemblages in Chinese lakes (30.43%), followed by precipitation of the driest month (10.47%), temperature annual range (3.62%) and annual mean temperature (3.15%). Validated CART model implied that precipitation of driest month, maximum temperature of warmest month and lake area were the main predictors in determining fish species richness patterns. Overall, our results indicated that the altitudinal extent and range of climatic variation was sufficient to overshadow the area effect in predicting fish species richness and assemblages in Chinese lakes. At the macroecological scale, the effect of temperature and precipitation on fish richness and assemblages also suggests future changes in fish diversity as a consequence of climate change.

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Energy and Large-Scale Patterns of Animal- and Plant-Species Richness

Cited by 1,525 publications

References 38 publications

Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

Generalized linear and generalized additive models in studies of species distributions: setting the scene

Predicting fish species richness and assemblages with climatic, geographic and morphometric factors: A broad-scale study in Chinese lakes

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