Authors

D. G. Jenkins

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

Ecosphere

Keywords

allometric scaling; biodiversity and ecosystem functioning; function; macroecology; model selection; productivity; productivity-biomass; relationships; productivity-diversity relationships; species; richness-productivity relationships; structure; standardized major axis; regression; HERBACEOUS PLANT-COMMUNITIES; SPECIES RICHNESS; NONLINEAR-REGRESSION; EUROPEAN GRASSLANDS; CURRENT KNOWLEDGE; POWER-LAWS; ECOSYSTEM; BIODIVERSITY; DIVERSITY; ALLOMETRY; Ecology

Abstract

Productivity-diversity relationships (P/D) are a vital theme in ecology, but productivity is typically not measured directly in that research. Instead, biomass (B) is the most common proxy for productivity, often as a 1: 1 substitute. Unfortunately, this practice may cause error and uncertainty in P/D research, due to the fundamental difference between B and P and variable P/B ratios among and within systems. As a result, P/D research often measures a B/D relationship but interprets it as P/D. Fortunately, plausible, statistically legitimate and predictive P/B relationships can be found with careful analyses based on model selection of alternative allometric scaling equations and tests of model assumptions. Analyses are presented here for P/B relationships of 19 data sets, ranging from plant and animal populations and assemblages to ecosystems and biomes, representing over 2,300 analyzed P/B data. Models included standardized major regression (SMA) and ordinary least squares (OLS) regressions. Simple linear 1: 1 P/B relationships are never supported. Instead, logP-logB transformed data, consistent with allometric scaling approaches, are far more common as the most plausible, statistically legitimate and predictive models. Given these relationships, many P/D studies with only B data may now better estimate P with SMA models, while studies with P and B data in some plots may estimate P in parallel plots with B and D data by using OLS models. Two grassland examples are re-analyzed to evaluate the importance of this approach to P/D research when B was used as a proxy for P; in one case, P had been underestimated by 20%; in the other case, P had been overestimated by 20%. The difference is related to underlying sampling methods and obtained data. The approach presented here may help productivity-diversity research resolve some uncertainty to better understand effects of ecological diversity on biomass production.

Journal Title

Ecosphere

Volume

6

Issue/Number

4

Publication Date

1-1-2015

Document Type

Article

Language

English

First Page

31

WOS Identifier

WOS:000354777300004

ISSN

2150-8925

Download

Share

COinS