A functional trait approach to identifying life history patterns in stochastic environments

Abstract

Temporal variation in demographic processes can greatly impact population dynamics. Perturbations of statistical coefficients that describe demographic rates within matrix models have, for example, revealed that stochastic population growth rates (log(λs)) of fast life histories are more sensitive to temporal autocorrelation of environmental conditions than those of slow life histories. Yet, we know little about the mechanisms that drive such patterns. Here, we used a mechanistic, functional trait approach to examine the functional pathways by which a typical fast life history species, the macrodetrivore Orchestia gammarellus, and a typical slow life history species, the reef manta ray Manta alfredi, differ in their sensitivity to environmental autocorrelation if (a) growth and reproduction are described mechanistically by functional traits that adhere to the principle of energy conservation, and if (b) demographic variation is determined by temporal autocorrelation in food conditions. Opposite to previous findings, we found that O. gammarellus log(λs) was most sensitive to the frequency of good food conditions, likely because reproduction traits, which directly impact population growth, were most influential to log(λs). Manta alfredi log(λs) was instead most sensitive to temporal autocorrelation, likely because growth parameters, which impact population growth indirectly, were most influential to log(λs). This differential sensitivity to functional traits likely also explains why we found that O. gammarellus mean body size decreased (due to increased reproduction) but M. alfredi mean body size increased (due to increased individual growth) as food conditions became more favorable. Increasing demographic stochasticity under constant food conditions decreased O. gammarellus mean body size and increased log(λs) due to increased reproduction, whereas M. alfredi mean body and log(λs) decreased, likely due to decreased individual growth. Our findings signify the importance of integrating functional traits into demographic models as this provides mechanistic understanding of how environmental and demographic stochasticity affects population dynamics in stochastic environments.

Efficient use of harvest data: An integrated population model for exploited animal populations

“…Even in determinate growers such as birds and mammals, evidence is accumulating that body mass is a crucial structuring factor of population dynamics (Gaillard et al 2000 for a review on large herbivores; see Coulson, Tuljapurkar & Childs 2010 for a case study on Soay sheep Ovis aries ), making biologically relevant the use of body mass-structured models in IPM for a large range of exploited populations of vertebrates. More generally, our framework based on a body mass-structured model adds to the spate of studies that have recently flourished in the literature showing that trait-based approaches (such as body mass) and demographic approaches are intertwined (Salguero-Gómez et al 2018; Smallegange & Berg 2019). Another clear advantage of our IPM is the inclusion of the number of dead individuals in the body mass-structured population model, whereas all the IPMs built so far only included the number of individuals alive in a population model (Caswell 2001).…”

Unusually Paced Life History Strategies of Marine Megafauna Drive Atypical Sensitivities to Environmental Variability

“…Using a cross-taxonomical approach, Paniw et al (2018) illustrated that fast life histories were more sensitive to simulated autocorrelation in demographic rates than slow life histories. Smallegange and Berg (2019), on the other hand, found, in a species-specific analysis, that…”

“…Not only environmental autocorrelation determines how demographic rates and population growth vary over time, also good environment frequency (Table 1): the temporal frequency with which favorable environmental conditions occur (Caswell, 2001). For example, Smallegange and Berg (2019) found that the fast life history species O. gammarellus was very sensitive to good environment frequency, in contrast to the slow life history species M. alfredi. With predicted global changes in environmental patterning (García-Carreras and Reuman, 2011) due to, e.g., shifts in environmental autocorrelation or good environment frequency, it is urgent to gain in-depth understanding of the life history processes that result in distinct demographic responses between different life history strategies to such shifts.…”

“…Specifically, E(Y) low is the expected feeding level associated with population decline (λ < 1) and E(Y) high as the expected feeding level associated with population increase (λ > 1). For the rays and sharks, we set E(Y) low = 0.7 and E(Y) high = 1.0 and for the turtles that have a slightly faster life history speed, we set E(Y) low = 0.6 and E(Y) high = 1.0 (Smallegange and Berg, 2019). We set σ(Y) = 0.3.…”

“…In contrast to slow life history populations, fast life history populations are very sensitive to environmental variability, and their population sizes can fluctuate greatly if environmental variability is high (Morris et al, 2008;McDonald et al, 2017). Unraveling how life history strategies and demographic rates are linked can therefore provide in depth understanding of how populations respond to environmental change (Salguero-Gómez et al, 2016;Paniw et al, 2018;Smallegange and Berg, 2019).…”