Towards a compleat theory of ecosystem size spectra

TL;DR

This paper introduces a predator-prey-efficiency theory to explain the regularity of ecosystem size spectra, emphasizing trophic processes and their implications for marine ecosystems and human activities.

Contribution

It presents a comprehensive theory linking trophic dynamics to size spectra, integrating data and models across biological communities and non-living particles.

Findings

Most pelagic ecosystems are controlled by resource and top-down processes.

Size spectra are influenced by trophic processes like resource limitation and regulation.

The theory has implications for fisheries, invasive species, and ecosystem management.

Abstract

The regularity of ecosystem size spectra is one of the most intriguing and relevant phenomena on our planet. Such size spectra generally show a log-linearly downtrending shape, following a power-law distribution. A constant log-linear slope has been reported for many marine pelagic ecosystems, often being approximately b = -1. Conversely, there are variable trophic-level-biomass relationships (trophic pyramids). The contrasting observations of a constant size spectrum and highly variable trophic pyramids may be defined as the constant size spectrum - variable trophic dynamics paradox. Here, a mass-specific predator-prey-efficiency theory of size spectra (PETS) is presented and discussed. A thorough analysis of available data, literature, and models resulted in the conclusion that most pelagic marine ecosystems are controlled by trophic processes such as resource-limit stress (bottom-up control) and top-down regulation, with a key role of the carrying capacity spectrum. This has relevant consequences for the prediction and interpretation of size spectra in the context of fisheries, whaling, and the introduction of exotic predators (e.g., lionfish). The complete size spectrum obtained in situ, including living organisms and non-living particles (e.g., with UVP data) is discussed. This paper is intended as a plea for the integration of modeling approaches, to understand and integrate data and processes across communities, including bacteria, phytoplankton, fish, and mammals, considering the effects of non-organismic particles.