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Prediction of neotropical tree and liana species richness from soil and climatic data

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TLDR
In this article, the authors present an analysis of local species richness in neotropical forests, based on a number of 0.1 ha samples of woody plants collected by the late Alwyn Gentry.
Abstract
We present an analysis of local species richness in neotropical forests, based on a number of 0.1 ha samples of woody plants collected by the late Alwyn Gentry. For each of 69 forests, soils were analysed and climatic data were collated. Using transformed independent variables and interaction terms, multiple regression equations were developed that explained the greatest possible amount of variation in species richness, and the best equations were selected on the basis of regression diagnostics. The best models are presented for (a) all neotropical forests, (b) forests west of the Andes (transandean) and (c) east of the Andes (cisandean), and for various subsets based on elevation and annual rainfall. For the whole dataset, and for most subsets, annual rainfall and rainfall seasonality were the most important variables for explaining species richness. Soil variables were correlated with precipitation — drier forests have more nutrient-rich soils. After the inclusion of rainfall variables, available soil nutrient concentrations contributed little to explaining or accounting for additional variation in species numbers, indicating that tropical forest species richness is surprisingly independent of soil quality. The results are consistent with the hypothesis that plants in mature tropical forests may obtain nutrients through the process of direct cycling, in which mineral nutrients are extracted from litterfall before they enter the soil. The strong relationship between community species richness and rainfall patterns has implications for biodiversity conservation. Wet forests with an ample year-round moisture supply harbour the greatest number of woody plant species and should be a focus of conservation efforts.

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Journal ArticleDOI

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

TL;DR: This chapter discusses Detecting Influential Observations and Outliers, a method for assessing Collinearity, and its applications in medicine and science.
Journal ArticleDOI

Energy, water, and broad-scale geographic patterns of species richness

TL;DR: In this paper, the authors examine the relationship between climate and biodiversity and conclude that the interaction between water and energy, either directly or indirectly, provides a strong explanation for globally extensive plant and animal diversity gradients, but for animals there also is a latitudinal shift in the relative importance of ambient energy vs. water moving from the poles to the equator.
Journal ArticleDOI

What is the observed relationship between species richness and productivity

TL;DR: The relationship between species richness and productivity has been extensively studied in the literature as discussed by the authors, with a focus on positive, negative, or curvilinear relationships between productivity and species diversity.
Journal ArticleDOI

Scale and species richness: towards a general, hierarchical theory of species diversity

TL;DR: The case is articulated for a top-down approach to theory building, in which scale is addressed explicitly and in which different response variables are clearly distinguished, to articulate the case for a general theory of diversity that must necessarily cover many disparate phenomena, at various scales of analysis.
Journal ArticleDOI

A Mechanistic Explanation for Global Patterns of Liana Abundance and Distribution

TL;DR: Using data from 69 tropical forests worldwide, it is demonstrated that liana abundance is correlated negatively with mean annual precipitation and positively with seasonality, a pattern precisely the opposite of most other plant types.
References
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Book

Applied Regression Analysis and Other Multivariable Methods

TL;DR: In this article, the authors compare two straight line regression models and conclude that the Straight Line Regression Equation does not measure the strength of the Straight-line Relationship, but instead is a measure of the relationship between two straight lines.
Book

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

TL;DR: In this article, the authors present a method for detecting and assessing Collinearity of observations and outliers in the context of extensions to the Wikipedia corpus, based on the concept of Influential Observations.
Book

Resource competition and community structure

David Tilman
TL;DR: This book builds a mechanistic, resource-based explanation of the structure and functioning of ecological communities and explores such problems as the evolution of "super species," the differences between plant and animal community diversity patterns, and the cause of plant succession.
Journal ArticleDOI

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

TL;DR: This chapter discusses Detecting Influential Observations and Outliers, a method for assessing Collinearity, and its applications in medicine and science.
Journal ArticleDOI

Evolution and measurement of species diversity

Robert H. Whittaker
- 01 May 1972 - 
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The strong relationship between community species richness and rainfall patterns has implications for biodiversity conservation.