Phytoplankton competition in deep biomass maximum

TL;DR

This study models phytoplankton competition in deep water columns, revealing how resource gradients and environmental factors influence species dominance, with implications for deep ocean ecosystem predictions.

Contribution

It introduces an invasion threshold analysis to link environmental conditions with phytoplankton composition in heterogeneous deep water environments.

Findings

Resource gradients, not local resource levels, determine competition outcomes.

Increased light or bottom nutrients favor light-competitive species.

Enhanced mixing or turbidity favors nutrient-competitive species.

Abstract

Resource competition in heterogeneous environments is still an unresolved problem of theoretical ecology. In this article I analyze competition between two phytoplankton species in a deep water column, where the distributions of main resources (light and a limiting nutrient) have opposing gradients and co-limitation by both resources causes a deep biomass maximum. Assuming that the species have a trade-off in resource requirements and the water column is weakly mixed, I apply the invasion threshold analysis (Ryabov and Blasius 2011) to determine relations between environmental conditions and phytoplankton composition. Although species deplete resources in the interior of the water column, the resource levels at the bottom and surface remain high. As a result, the slope of resources gradients becomes a new crucial factor which, rather than the local resource values, determines the outcome of competition. The value of resource gradients nonlinearly depend on the density of consumers. This leads to complex relationships between environmental parameters and species composition. In particular, it is shown that an increase of both the incident light intensity or bottom nutrient concentrations favors the best light competitors, while an increase of the turbulent mixing or background turbidity favors the best nutrient competitors. These results might be important for prediction of species composition in deep ocean.