The role of harvesting in age-structured populations: Disentangling dynamic and age truncation effects

Wikström, Anders; Ripa, Jörgen; Jonzén, Niclas

doi:10.1016/j.tpb.2011.12.008

Cited by 20 publications

(23 citation statements)

References 29 publications

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“…A recent theoretical study flagged the need for experimental studies that would help determine under what types of environmental variation harvesting compounds or ameliorates population variability in structured populations living in dynamic environments (Wikstrom et al. ).…”

Section: Introductionmentioning

confidence: 99%

Harvested populations are more variable only in more variable environments

Cameron

O’Sullivan

Reynolds

et al. 2016

Ecology and Evolution

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The interaction between environmental variation and population dynamics is of major importance, particularly for managed and economically important species, and especially given contemporary changes in climate variability. Recent analyses of exploited animal populations contested whether exploitation or environmental variation has the greatest influence on the stability of population dynamics, with consequences for variation in yield and extinction risk. Theoretical studies however have shown that harvesting can increase or decrease population variability depending on environmental variation, and requested controlled empirical studies to test predictions. Here, we use an invertebrate model species in experimental microcosms to explore the interaction between selective harvesting and environmental variation in food availability in affecting the variability of stage‐structured animal populations over 20 generations. In a constant food environment, harvesting adults had negligible impact on population variability or population size, but in the variable food environments, harvesting adults increased population variability and reduced its size. The impact of harvesting on population variability differed between proportional and threshold harvesting, between randomly and periodically varying environments, and at different points of the time series. Our study suggests that predicting the responses to selective harvesting is sensitive to the demographic structures and processes that emerge in environments with different patterns of environmental variation.

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Section: Introductionmentioning

confidence: 99%

Harvested populations are more variable only in more variable environments

Cameron

O’Sullivan

Reynolds

et al. 2016

Ecology and Evolution

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“…Models without age-structure show an increased variability in population abundance as a consequence of exploitation [14]. In a structured model, fishing can either increase or decrease the stock size variability [15]. We find that the CV of population size has been reduced in all investigated HCRs, and especially with N -targeted BR.…”

Section: Discussionmentioning

confidence: 75%

“…The induced periodic population variability in predator-prey interactions is well known in ecology [49], but the result of the dynamic interaction between the fish stock and the harvest control is much less studied [15]. It has been hypothesized that populations truncated by fishing have a shorter inherent periodicity, and track environmental fluctuations more closely.…”

Section: Discussionmentioning

confidence: 99%

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A Concept of Bayesian Regulation in Fisheries Management

et al. 2014

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Stochastic variability of biological processes and uncertainty of stock properties compel fisheries managers to look for tools to improve control over the stock. Inspired by animals exploiting hidden prey, we have taken a biomimetic approach combining catch and effort in a concept of Bayesian regulation (BR). The BR provides a real-time Bayesian stock estimate, and can operate without separate stock assessment. We compared the performance of BR with catch-only regulation (CR), alternatively operating with N-target (the stock size giving maximum sustainable yield, MSY) and F-target (the fishing mortality giving MSY) on a stock model of Baltic Sea herring. N-targeted BR gave 3% higher yields than F-targeted BR and CR, and 7% higher yields than N-targeted CR. The BRs reduced coefficient of variance (CV) in fishing mortality compared to CR by 99.6% (from 25.2 to 0.1) when operated with F-target, and by about 80% (from 158.4 to 68.4/70.1 depending on how the prior is set) in stock size when operated with N-target. Even though F-targeted fishery reduced CV in pre-harvest stock size by 19–22%, it increased the dominant period length of population fluctuations from 20 to 60–80 years. In contrast, N-targeted BR made the periodic variation more similar to white noise. We discuss the conditions when BRs can be suitable tools to achieve sustainable yields while minimizing undesirable fluctuations in stock size or fishing effort.

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“…The type of compensating responses of the different life stages to selective harvesting, in turn, depended on noise colour. Indeed, Wikström, Ripa, and Jonzén () predicted from their age‐structured model with stochastic recruitment that proportional adult harvesting leads to stabilising effects only in a small percentage of cases. These potentially complex responses to environmental stochasticity highlight the need to unravel the underlying mechanisms of how environmental variables translate into population‐dynamical processes via individual life‐history traits and competition, not just at the level of species (e.g.…”

Section: Discussionmentioning

confidence: 99%

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

Smallegange

Ens

2018

Journal of Animal Ecology

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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.

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The role of harvesting in age-structured populations: Disentangling dynamic and age truncation effects

Cited by 20 publications

References 29 publications

Harvested populations are more variable only in more variable environments

Harvested populations are more variable only in more variable environments

A Concept of Bayesian Regulation in Fisheries Management

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

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