Trait‐based predictions and responses from laboratory mite populations to harvesting in stochastic environments

Abstract

Predictions on population responses to perturbations are often derived from trait‐based approaches like integral projection models (IPMs), but are rarely tested. IPMs are constructed from functions that describe survival, growth and reproduction in relation to the traits of individuals and their environment. Although these functions comprise biologically non‐informative statistical coefficients within standard IPMs, model parameters of the recently developed dynamic energy budget IPM (DEB‐IPM) are life‐history traits like “length at maturation” and “maximum reproduction rate”. Testing predictions from mechanistic IPMs against empirical observations can therefore provide functional insights into the links between individual life history, the environment and population dynamics.Here, we compared the population dynamics of the bulb mite (Rhizoglyphus robini) predicted by a DEB‐IPM with those observed in an experiment where populations experienced daily food rations that were either positively correlated over time (red noise), negatively (blue noise) or uncorrelated (white noise). We also selectively harvested large adults in half of these populations. The model failed to generate detailed predictions of population structure as juvenile numbers were overestimated; likely because juvenile–adult interference competition was underestimated. The model performed well at the population level as, for both harvested and unharvested populations, simulations matched the observed, long‐term stochastic growth rate λs.We next generalised the model to investigate how stochastic change affects mite λs, which correlated well with the frequency f of experiencing periods of good environment, but, due to the relationship between f and noise colour ρ, did not correlate well with shifts in ρ. The sensitivity of λs to perturbations in life‐history parameters depended on the type of stochastic change, as well as population growth.Our findings show that responses to differential mortality depend on individual life‐history traits, environmental characteristics and population growth. As long‐term climate change causes ever greater environmental fluctuations, trait‐based approaches will be increasingly important in predicting population responses to change. We therefore conclude by illustrating what questions can be examined with mechanistic trait‐based models like the DEB‐IPM, the answers to which will advance our knowledge of the functional links between individual traits, the environment and population dynamics.

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

“…More significantly, O. gammarellus stochastic population growth rate was more sensitive to shifts in good environment frequency than to shifts in temporal autocorrelation of food conditions: the higher the frequency value, the higher was the stochastic population growth rate. Qualitatively equivalent patterns emerged when the same stochastic analysis was applied to another fast life history species, the bulb mite Rhizoglyphus robini (Smallegange & Ens, ).…”

“…Our findings on life history speed sensitivity to shifts in environmental autocorrelation from a mechanistic analysis are opposite to the recent outcome of the phenomenological, cross‐taxonomical analysis by Paniw et al (). Although we only have stochastic functional trait analysis outcomes for three species (this study; Smallegange & Ens, ), we find there is merit in discussing potential reasons for why the two sets of results differ. Such a discussion is also urgent in light of the persistent problems that exist in the construction of standard matrix population models (Kendall et al, ), such that Kendall et al () advocate to be cautious when interpreting results from, for example, cross‐taxonomical analyses that use such models.…”

“…Unraveling the drivers of population growth in variable environments is critical for answering eco‐evolutionary questions (Ruokolainen, Linden, Kaitala, & Fowler, ; Smallegange, Deere, & Coulson, ; Tuljapurkar, ), developing conservation management strategies (Clark, ; Metcalf & Koons, ), predicting the time course of epidemics (Chaves, Scott, Morrison, & Takada, ; Keeling & Gilligan, ) and in influencing the yield of harvested populations (Cameron et al, ; Higgins, Hastings, Sarvela, & Botsford, ; Smallegange & Ens, ). Both demographic processes and temporal variation in environmental conditions are important drivers of population growth (Higgins et al, ; Lande, Engen, & Sæther, ; Turchin & Taylor, ).…”

“…Yet, we are only beginning to unravel whether links between life history characteristics and population responses to environmental fluctuations identified for uncorrelated environments (Dalgleish et al, ; Morris et al, ) hold in biologically more realistic, autocorrelated environments. For example, in mite microcosm experiments, life stage responses to environmental perturbations (harvesting) differ between autocorrelated and uncorrelated environments (Cameron et al, ; Smallegange & Ens, ). Theoretical studies have shown that a change in the serial correlation of demographic rates may increase or decrease population growth rate depending on the structure of the life history (Tuljapurkar, Gaillard, & Coulson, ).…”

Delivering the promises of trait‐based approaches to the needs of demographic approaches, and vice versa

“…The authors find significant interactions between early life cycle stages, increases in sea surface temperature and multiple functional traits, as well as cross‐seasonal carry‐over effects on population growth rate. Smallegange and Ens () investigate the predictive performance of a mechanistic, trait‐based demographic model. They construct an integral projection model (IPM) for which the vital rates of survival, growth and reproduction are informed by a dynamic energy budget (DEB; Kooijman & Troost, ; van der Meer, ).…”