On signals of phase transitions in salmon population dynamics

Krkošek, Martin; Drake, John M.

doi:10.1098/rspb.2013.3221

Cited by 32 publications

(27 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, we found strong fluctuations in nonlinearity estimates, especially in the exploitation scenario (electronic supplementary material, figure S2f). Changes in variance and autocorrelation can also be unreliable in the presence of short time series [51], high levels of stochasticity [57], fast changing stress drivers [54,58] or due to portfolio effects [56] and life-history strategies [59]. Further research is needed to find if similar constraints hold for the elevated nonlinearity indicator we propose here.…”

Section: Discussionmentioning

confidence: 99%

“…Our findings suggest that an ecosystem manager could estimate both CSD and EDM metrics in order to rank populations according to their stability [55]. For instance, Krkosek & Drake [56] looked at patterns of CV and AR1 for Pacific salmon populations and found that they were higher for pink salmon stocks that had a population growth rate close to zero. In this work, the authors assumed that salmon populations would suffer a transcritical bifurcation due to growth rates approaching zero.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress

Dakos

Glaser

Hsieh

et al. 2017

J. R. Soc. Interface.

View full text Add to dashboard Cite

Populations occasionally experience abrupt changes, such as local extinctions, strong declines in abundance or transitions from stable dynamics to strongly irregular fluctuations. Although most of these changes have important ecological and at times economic implications, they remain notoriously difficult to detect in advance. Here, we study changes in the stability of populations under stress across a variety of transitions. Using a Ricker-type model, we simulate shifts from stable point equilibrium dynamics to cyclic and irregular boom-bust oscillations as well as abrupt shifts between alternative attractors. Our aim is to infer the loss of population stability before such shifts based on changes in nonlinearity of population dynamics. We measure nonlinearity by comparing forecast performance between linear and nonlinear models fitted on reconstructed attractors directly from observed time series. We compare nonlinearity to other suggested leading indicators of instability (variance and autocorrelation). We find that nonlinearity and variance increase in a similar way prior to the shifts. By contrast, autocorrelation is strongly affected by oscillations. Finally, we test these theoretical patterns in datasets of fisheries populations. Our results suggest that elevated nonlinearity could be used as an additional indicator to infer changes in the dynamics of populations under stress.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress

Dakos

Glaser

Hsieh

et al. 2017

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…The speed at which a population approaches a critical transition is influenced by the rate of change of the system from a steady state. In our case, this is largely governed by the parameter c. As this approach speed could alter the prevalence of early warning signals (Brock and Carpenter 2010;Krkošek and Drake 2014), we simulated 10 time series where the value of c increased linearly (from 1) at different rates (0.025, 0.03, 0.035, . .…”

Section: Detectability Of Early Warning Signals 51mentioning

confidence: 99%

“…These generic leading indicators arise from mathematical properties of stochastic dynamical systems as they approach a tipping point and appear to be present in vastly different systems, including stock markets, global climatic change, and populations (for a review, see Scheffer et al 2009). The development and testing of these statistical early warning signals suggests that a predictive framework for population risk might be developed that requires only a relatively limited amount of demographic data (Hefley et al 2013;Krkošek and Drake 2014). From a conservation perspective, such a framework could provide an important tool for prioritizing conservation efforts and funding to target those species approaching a critical transition.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Factors Influencing the Detectability of Early Warning Signals of Population Collapse

Clements

Drake

Griffiths

et al. 2015

The American Naturalist

Self Cite

View full text Add to dashboard Cite

The recent description of potentially generic early warning signals is a promising development that may help conservationists to anticipate a population's collapse prior to its occurrence. So far, the majority of such warning signals documented have been in highly controlled laboratory systems or theoretical models. Data from wild populations, however, are typically restricted both temporally and spatially due to limited monitoring resources and intrinsic ecological heterogeneity -limitations that may affect the detectability of generic early warning signals, as they add additional stochasticity to population abundance estimates. Consequently, spatial and temporal subsampling may serve either to muffle or magnify early warning signals. Using a combination of theoretical models and analysis of experimental data, we evaluate the extent to which statistical warning signs are robust to data corruption. Online enhancements: appendixes, zip file.abstract: The recent description of potentially generic early warning signals is a promising development that may help conservationists to anticipate a population's collapse prior to its occurrence. So far, the majority of such warning signals documented have been in highly controlled laboratory systems or in theoretical models. Data from wild populations, however, are typically restricted both temporally and spatially due to limited monitoring resources and intrinsic ecological heterogeneity-limitations that may affect the detectability of generic early warning signals, as they add additional stochasticity to population abundance estimates. Consequently, spatial and temporal subsampling may serve to either muffle or magnify early warning signals. Using a combination of theoretical models and analysis of experimental data, we evaluate the extent to which statistical warning signs are robust to data corruption.

show abstract

Early warning signals detect critical impacts of experimental warming

Jarvis

McCann

Tunney

et al. 2016

Ecology and Evolution

View full text Add to dashboard Cite

Earth's surface temperatures are projected to increase by ~1–4°C over the next century, threatening the future of global biodiversity and ecosystem stability. While this has fueled major progress in the field of physiological trait responses to warming, it is currently unclear whether routine population monitoring data can be used to predict temperature‐induced population collapse. Here, we integrate trait performance theory with that of critical tipping points to test whether early warning signals can be reliably used to anticipate thermally induced extinction events. We find that a model parameterized by experimental growth rates exhibits critical slowing down in the vicinity of an experimentally tested critical threshold, suggesting that dynamical early warning signals may be useful in detecting the potentially precipitous onset of population collapse due to global climate change.

show abstract

On signals of phase transitions in salmon population dynamics

Cited by 32 publications

References 43 publications

Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress

Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress

Factors Influencing the Detectability of Early Warning Signals of Population Collapse

Early warning signals detect critical impacts of experimental warming

Contact Info

Product

Resources

About