Poor environmental tracking can make extinction risk insensitive to the colour of environmental noise

Pol, Martijn van de; Vindenes, Yngvild; Sæther, Bernt Erik; Engen, Steinar; Ens, Bruno J.; Oosterbeek, Kees; Tinbergen, Joost M.

doi:10.1098/rspb.2011.0487

Cited by 29 publications

(27 citation statements)

References 56 publications

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“…However, it is not unreasonable to think that one or more of our proposed environmental filters are operating in most animal populations. As a result, an imperfect translation of covariate autocorrelation into PEA may be quite common and recent empirical results are beginning to support this viewpoint (Knape and de Valpine, 2010; Garcia-Carreras and Reuman, 2011; van de Pol et al, 2011; Engen et al, 2013). …”

Section: Discussionmentioning

confidence: 99%

“…However, recent work has found that the presence of autocorrelation in a temperature dependent, stage-structured model of Eurasian oystercatchers ( Haematopus ostralegus ) only weakly affected population extinction risk primarily due to nonlinear interactions between demographic rates and environmental covariates that led to poor environmental tracking (van de Pol et al, 2011) and estimates in four other age-structured populations were found to be quite small (Engen et al, 2013). These results provide an empirical example that coupling real life-history complexity to nonlinear environmental responses leads to reductions in the influence of autocorrelated environments.…”

Section: Discussionmentioning

confidence: 99%

“…The presence of demographic stochasticity leads to the addition of white noise to the total population variance, potentially reducing the overall importance of PEA at low abundances (van de Pol et al, 2011). Although demographic stochasticity is at work to some degree in all populations, our analysis is primarily focused on long-term studies of stable populations that are likely to have relatively low amounts of demographic stochasticity.…”

Section: Introductionmentioning

confidence: 99%

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An updated perspective on the role of environmental autocorrelation in animal populations

et al. 2015

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Ecological theory predicts that the presence of temporal autocorrelation in environments can considerably affect population extinction risk. However, empirical estimates of autocorrelation values in animal populations have not decoupled intrinsic growth and density feedback processes from environmental autocorrelation. In this study we first discuss how the autocorrelation present in environmental covariates can be reduced through nonlinear interactions or by interactions with multiple limiting resources. We then estimated the degree of environmental autocorrelation present in the Global Population Dynamics Database using a robust, model-based approach. Our empirical results indicate that time series of animal populations are affected by low levels of environmental autocorrelation, a result consistent with predictions from our theoretical models. Claims supporting the importance of autocorrelated environments have been largely based on indirect empirical measures and theoretical models seldom anchored in realistic assumptions. It is likely that a more nuanced understanding of the effects of autocorrelated environments is necessary to reconcile our conclusions with previous theory. We anticipate that our findings and other recent results will lead to improvements in understanding how to incorporate fluctuating environments into population risk assessments.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An updated perspective on the role of environmental autocorrelation in animal populations

et al. 2015

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“…The autocorrelation of environmental variables is also changing due to climate change [64]. It would be possible, using a stage-structured model, to compare the relative effects of changes in mean, variance, and autocorrelation of the environment on population dynamics (as done for a single oystercatcher population in [65]). Finally, the sensitivities of are linear approximations of the functions that relate to and , and therefore assume small changes in the environment.…”

Section: Discussionmentioning

confidence: 99%

Are Changes in the Mean or Variability of Climate Signals More Important for Long-Term Stochastic Growth Rate?

García‐Carreras

Reuman

2013

PLoS ONE

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Population dynamics are affected by changes in both the mean and standard deviation of climate, e.g., changes in average temperature are likely to affect populations, but so are changes in the strength of year-to-year temperature variability. The impacts of increases in average temperature are extensively researched, while the impacts of changes in climate variability are less studied. Is the greater attention given to changes in mean environment justified? To help answer this question we developed a simple population model, explicitly linked to an environmental process. We used the model to compare the sensitivities of a population's long-term stochastic growth rate, a measure of fitness, to changes in the mean and standard deviation of the environment. Results are interpreted in light of a comparative analysis of the relative magnitudes of change in means and standard deviations of biologically relevant climate variables in the United States. Results show that changes in the variability of the environment can be more important for many populations. Changes in mean conditions are likely to have a greater impact than changes in variability on populations far from their ideal environment, for example, populations near species range boundaries and potentially of conservation concern. Populations near range centres and close to their ideal environment are more likely to be affected by changes in variability. Among pest and insect disease vectors, as well as species of commercial value, populations likely to be of greatest economic and public health significance are those near species range centers, living in a near-ideal environment for the species. Observed changes in the variability of climate variables may benefit these populations.

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“…[26]). We, therefore, have a rather poor understanding of how a failure to simultaneously consider microhabitat heterogeneity, behavioural thermoregulation [27], physiological polymorphism [7], and environmental predictability [28] affects forecasts of the ecological impacts of climate change [29].…”

Section: Introductionmentioning

confidence: 99%

Untangling the roles of microclimate, behaviour and physiological polymorphism in governing vulnerability of intertidal snails to heat stress

et al. 2017

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Biogeographic distributions are driven by cumulative effects of smaller scale processes. Thus, vulnerability of animals to thermal stress is the result of physiological sensitivities to body temperature (T b ), microclimatic conditions, and behavioural thermoregulation. To understand interactions among these variables, we analysed the thermal tolerances of three species of intertidal snails from different latitudes along the Chinese coast, and estimated potential T b in different microhabitats at each site. We then empirically determined the temperatures at which heart rate decreased sharply with rising temperature (Arrhenius breakpoint temperature, ABT) and at which it fell to zero (flat line temperature, FLT) to calculate thermal safety margins (TSM). Regular exceedance of FLT in sun-exposed microhabitats, a lethal effect, was predicted for only one mid-latitude site. However, ABTs of some individuals were exceeded at sun-exposed microhabitats in most sites, suggesting physiological impairment for snails with poor behavioural thermoregulation and revealing inter-individual variations (physiological polymorphism) of thermal limits. An autocorrelation analysis of T b showed that predictability of extreme temperatures was lowest at the hottest sites, indicating that the effectiveness of behavioural thermoregulation is potentially lowest at these sites. These results illustrate the critical roles of mechanistic studies at small spatial scales when predicting effects of climate change.

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Poor environmental tracking can make extinction risk insensitive to the colour of environmental noise

Cited by 29 publications

References 56 publications

An updated perspective on the role of environmental autocorrelation in animal populations

An updated perspective on the role of environmental autocorrelation in animal populations

Are Changes in the Mean or Variability of Climate Signals More Important for Long-Term Stochastic Growth Rate?

Untangling the roles of microclimate, behaviour and physiological polymorphism in governing vulnerability of intertidal snails to heat stress

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