4.6 Article

The scaling of biomass variance across trophic levels in stream species communities: a macroecological approach

Journal

HYDROBIOLOGIA
Volume 847, Issue 12, Pages 2705-2723

Publisher

SPRINGER
DOI: 10.1007/s10750-020-04239-5

Keywords

Macroecology; Stream ecosystems; Multi-species communities; Protozoa and meiofauna-dominated systems; Fractal biomass scaling; Biomass invariance; Flow-driven assemblage patterns

Funding

  1. NERC [NER/A/S/2001/00566]
  2. Royal Society
  3. Austrian Science Fund FWF [P15597-B03]

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Much has been published about different aspects of body-size distribution resting on the assumptions of metabolic scaling, although a number of studies in aquatic ecosystems have questioned its generality. This study considers the effects of individual body-mass and biomass variability on scaling properties of multi-species communities (protists, meio- and macroinvertebrates), and their intrinsic variations in assemblage structure. We examine how size traits within communities are distributed on local and regional scales and assess the potential sources of variation affecting whole ecosystems. Our results, built upon seven river catchment communities including 1204 species, revealed micro-meiofauna-dominated biomass distributions driven by stochastic hydrophysical processes that induce a fractal fluctuation scaling, irrespective of trophic levels, shaping local and regional scaling relations. Fractal-scaling differences are largely generated by the frequency of high flow events that influence the biomass assemblage configurations, which are significantly better represented by the Power Fraction model compared to single statistical random models. We conclude that environmental random variability contributes to the decoupling of total biomass and body mass per site from assemblage size, resulting in scale-invariant body-size traits among assemblages and systems. Generally, these findings emphasize that ignoring small-sized species and, thus, the wide range of body sizes makes accurate ecological model predictions, impossible.

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