Eelgrass, Zostera marina , growth along depth gradients: upper boundaries of the variation as a powerful predictive tool

Eelgrass, Zostera marina , growth along depth gradients: upper boundaries of the variation as a... 1200 measurements of eelgrass (Zostera marina) biomass, shoot density and cover along 19 depth gradients in Øresund, located between Denmark and Sweden, were analysed to characterise growth of eelgrass in relation to depth. The large data set allowed analyses of boundaries of distribution as well as of average trends. Natural variability is large in shallow water where populations are disturbed by wave action and other physical parameters. Models based on average values, therefore, did not adequately describe growth regulation by resources, and only explained a minor part (up to 30%) of the overall variation in data. In contrast, boundary functions, which describe the upper bounds of distributions, focus on the variation produced by the ultimately growth‐regulating resource, and therefore provide models with high predictive power. An exponential model explained up to 90% of the variation in upper bounds of eelgrass shoot density as a function of depth and indicated that shoot density was ultimately regulated by light availability. The boundary functions demonstrated that eelgrass shoot density, biomass and cover followed markedly different patterns as functions of depth and were affected differently by the factors governing their distribution. In addition, boundary functions revealed informative spatial structures in data and illustrated whether a given general trend was caused by changes in maximum values, minimum values or both. For example, upper and lower boundaries of biomass‐shoot density relations changed markedly with depth, demonstrating depth‐related changes in intraspecific succession and competition patterns. Boundary functions are therefore suggested as a promising tool for analysing ultimate regulating factors of distribution and growth of organisms when large data sets are available. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Oikos Wiley

Eelgrass, Zostera marina , growth along depth gradients: upper boundaries of the variation as a powerful predictive tool

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Publisher
Wiley
Copyright
Copyright © 2000 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0030-1299
eISSN
1600-0706
DOI
10.1034/j.1600-0706.2001.910204.x
Publisher site
See Article on Publisher Site

Abstract

1200 measurements of eelgrass (Zostera marina) biomass, shoot density and cover along 19 depth gradients in Øresund, located between Denmark and Sweden, were analysed to characterise growth of eelgrass in relation to depth. The large data set allowed analyses of boundaries of distribution as well as of average trends. Natural variability is large in shallow water where populations are disturbed by wave action and other physical parameters. Models based on average values, therefore, did not adequately describe growth regulation by resources, and only explained a minor part (up to 30%) of the overall variation in data. In contrast, boundary functions, which describe the upper bounds of distributions, focus on the variation produced by the ultimately growth‐regulating resource, and therefore provide models with high predictive power. An exponential model explained up to 90% of the variation in upper bounds of eelgrass shoot density as a function of depth and indicated that shoot density was ultimately regulated by light availability. The boundary functions demonstrated that eelgrass shoot density, biomass and cover followed markedly different patterns as functions of depth and were affected differently by the factors governing their distribution. In addition, boundary functions revealed informative spatial structures in data and illustrated whether a given general trend was caused by changes in maximum values, minimum values or both. For example, upper and lower boundaries of biomass‐shoot density relations changed markedly with depth, demonstrating depth‐related changes in intraspecific succession and competition patterns. Boundary functions are therefore suggested as a promising tool for analysing ultimate regulating factors of distribution and growth of organisms when large data sets are available.

Journal

OikosWiley

Published: Nov 1, 2000

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