Not just who, but how many: the importance of partner abundance in reef coral symbioses.

The performance and function of reef corals depends on the genetic identity of their symbiotic algal partners, with some symbionts providing greater benefits (e.g., photosynthate, thermotolerance) than others. However, these interaction outcomes may also depend on partner abundance, with differences in the total number of symbionts changing the net benefit to the coral host, depending on the particular environmental conditions. We suggest that symbiont abundance is a fundamental aspect of the dynamic interface between reef corals and the abiotic environment that ultimately determines the benefits, costs, and functional responses of these symbioses. This density-dependent framework suggests that corals may regulate the size of their symbiont pool to match microhabitat-specific optima, which may contribute to the high spatiotemporal variability in symbiont abundance observed within and among colonies and reefs. Differences in symbiont standing stock may subsequently explain variation in energetics, growth, reproduction, and stress susceptibility, and may mediate the impacts of environmental change on these outcomes. However, the importance of symbiont abundance has received relatively little recognition, possibly because commonly-used metrics based on surface area (e.g., symbiont cells cm(-2)) may be only weakly linked to biological phenomena and are difficult to compare across studies. We suggest that normalizing symbionts to biological host parameters, such as units of protein or numbers of host cells, will more clearly elucidate the functional role of symbiont abundance in reef coral symbioses. In this article, we generate testable hypotheses regarding the importance of symbiont abundance by first discussing different metrics and their potential links to symbiosis performance and breakdown, and then describing how natural variability and dynamics of symbiont communities may help explain ecological patterns on coral reefs and predict responses to environmental change.