Estimation of fitness from energetics and life‐history data: An example using mussels

Abstract

Changing environments have the potential to alter the fitness of organisms through effects on components of fitness such as energy acquisition, metabolic cost, growth rate, survivorship, and reproductive output. Organisms, on the other hand, can alter aspects of their physiology and life histories through phenotypic plasticity as well as through genetic change in populations (selection). Researchers examining the effects of environmental variables frequently concentrate on individual components of fitness, although methods exist to combine these into a population level estimate of average fitness, as the per capita rate of population growth for a set of identical individuals with a particular set of traits. Recent advances in energetic modeling have provided excellent data on energy intake and costs leading to growth, reproduction, and other life‐history parameters; these in turn have consequences for survivorship at all life‐history stages, and thus for fitness. Components of fitness alone (performance measures) are useful in determining organism response to changing conditions, but are often not good predictors of fitness; they can differ in both form and magnitude, as demonstrated in our model. Here, we combine an energetics model for growth and allocation with a matrix model that calculates population growth rate for a group of individuals with a particular set of traits. We use intertidal mussels as an example, because data exist for some of the important energetic and life‐history parameters, and because there is a hypothesized energetic trade‐off between byssus production (affecting survivorship), and energy used for growth and reproduction. The model shows exactly how strong this trade‐off is in terms of overall fitness, and it illustrates conditions where fitness components are good predictors of actual fitness, and cases where they are not. In addition, the model is used to examine the effects of environmental change on this trade‐off and on both fitness and on individual fitness components.

Only as strong as the weakest link: structural analysis of the combined effects of elevated temperature and pCO2 on mussel attachment

“…In addition to reducing byssal thread structural integrity, elevated temperature decreased thread production. Living at 25°C is energetically costly to M. trossulus (Braby & Somero, 2006); thus, energy reserves may be shifted away from byssus production towards other processes (Carrington et al ., 2015; Sebens et al ., 2018). Previous studies on M. edulis found that thread production increases as temperature ranges from 2°C to 18°C (Moeser & Carrington, 2006; Moeser et al ., 2006; Young, 1985).…”

Detrimental impacts of climate change may be exacerbated by density‐dependent population regulation in blue mussels

“…Because there are no published studies of maximum rates of population growth for blue mussels, we extended our literature search on this topic to include simulation and empirical studies for all marine and freshwater mussel species. Based on our findings from the literature for a diversity of mussel species (Jiao et al., 2008; Jones & Neves, 2011; Katsanevakis, 2009; Madenjian et al., 2010; Sebens et al., 2018), we were able to estimate an informative Gaussian prior for r , distributed as r z ~ Norm(0.21, 0.12 2 ). A similar, exponentially transformed prior was used for λ in the Beverton–Holt model.…”

Resource allocation to a structural biomaterial: Induced production of byssal threads decreases growth of a marine mussel

“…The production of a structural biomaterial, however, requires an investment of energetic resources. The investment might result in energy allocation trade‐offs that shift performance traits on the individual level, and affect population dynamics (Sebens et al., 2018) and spatial distributions of organisms (biomechanical ecotype, Read & Stokes, 2006). For example, the altered growth and development of plants in response to wind or mechanical perturbation (thigmorphogenesis) reduces plant size and fecundity (Chehab et al, 2008; Telewski & Pruyn, 1998).…”

“…Filgueira et al., 2011; Nisbet et al., 2012), but differ in complexity and in their handling of uncertainty (Boersch‐Supan & Johnson, 2019). Energy budget models also provide a flexible framework with which to evaluate trade‐offs with structural materials since structural material production costs correlate with well‐described bioenergetic fluxes (Sanders et al., 2018; Sarà et al., 2013; Sebens et al., 2018), and can have different mass‐specific costs (Brody, 1945; Sanders et al., 2018). SFG models provide a simple conceptual framework where tissue growth is represented as a function of consumption of food minus physiological costs (Bayne et al., 1976; Sebens, 2002; Widdows & Bayne, 1971, Figure 1).…”