Circumventing structural uncertainty: A Bayesian perspective on nonlinear forecasting for ecology

Munch, Stephan B.; Poynor, Valerie; Arriaza, Juan Lopez

doi:10.1016/j.ecocom.2016.08.006

Cited by 39 publications

(52 citation statements)

References 43 publications

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“…) and are constantly improved to deal with issues such as observation error and nonstationarity of ecological systems (Munch et al. ). The variant of these methods we use in this manuscript is based on the simplex projection and S‐map method (Sugihara ) through the rEDM package ( available online )…”

Section: Methodsmentioning

confidence: 99%

The intrinsic predictability of ecological time series and its potential to guide forecasting

Pennekamp

Iles

Garland

et al. 2019

Ecological Monographs

103

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Successfully predicting the future states of systems that are complex, stochastic, and potentially chaotic is a major challenge. Model forecasting error (FE) is the usual measure of success; however model predictions provide no insights into the potential for improvement. In short, the realized predictability of a specific model is uninformative about whether the system is inherently predictable or whether the chosen model is a poor match for the system and our observations thereof. Ideally, model proficiency would be judged with respect to the systems’ intrinsic predictability, the highest achievable predictability given the degree to which system dynamics are the result of deterministic vs. stochastic processes. Intrinsic predictability may be quantified with permutation entropy (PE), a model‐free, information‐theoretic measure of the complexity of a time series. By means of simulations, we show that a correlation exists between estimated PE and FE and show how stochasticity, process error, and chaotic dynamics affect the relationship. This relationship is verified for a data set of 461 empirical ecological time series. We show how deviations from the expected PE–FE relationship are related to covariates of data quality and the nonlinearity of ecological dynamics. These results demonstrate a theoretically grounded basis for a model‐free evaluation of a system's intrinsic predictability. Identifying the gap between the intrinsic and realized predictability of time series will enable researchers to understand whether forecasting proficiency is limited by the quality and quantity of their data or the ability of the chosen forecasting model to explain the data. Intrinsic predictability also provides a model‐free baseline of forecasting proficiency against which modeling efforts can be evaluated.

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Section: Methodsmentioning

confidence: 99%

The intrinsic predictability of ecological time series and its potential to guide forecasting

Pennekamp

Iles

Garland

et al. 2019

Ecological Monographs

103

View full text Add to dashboard Cite

show abstract

“…Bakker, Schouten, Giles, Takens, & Van Den Bleek, ). Here, we used Gaussian process regression (Munch et al., ; Rasmussen & Williams, ) to estimate the map from the past to the future and quantify the intrinsic component of recruitment dynamics (defined below). The GP approach extends the EDM toolkit by allowing automatic lag selection, incorporating information from multiple sources using hierarchical modelling and allowing for nonstationary dynamics (Munch et al., ).…”

Section: Methodsmentioning

confidence: 99%

“…Here, we used Gaussian process regression (Munch et al., ; Rasmussen & Williams, ) to estimate the map from the past to the future and quantify the intrinsic component of recruitment dynamics (defined below). The GP approach extends the EDM toolkit by allowing automatic lag selection, incorporating information from multiple sources using hierarchical modelling and allowing for nonstationary dynamics (Munch et al., ). Gaussian process regression has been used in population modelling to estimate the form of density dependence (Munch, Kottas, & Mangel, ), test for the presence of Allee effects (Sugeno & Munch, ,b) and to assess model misspecification (Thorson, Ono, & Munch, ).…”

Section: Methodsmentioning

confidence: 99%

“…Thus, rather than conducting an exhaustive search, we used automatic relevance determination (Neal, ) to select lags and identify a parsimonious model. In particular, we used a penalty function to shrink ϕ i towards zero, which automatically selects relevant lags (Munch et al., ). Further details on GP implementation are provided in the Supporting Information (Appendix S2).…”

Section: Methodsmentioning

confidence: 99%

“…From this larger data set, we reconsider the question of whether biomass and recruits are causally coupled using convergent cross‐mapping (Sugihara et al., ). Next, using Gaussian process–EDM (Munch, Poynor, & Arriaza, ), we explicitly quantify what fraction of the observed variation in recruitment can be explained by intrinsic dynamics. We then benchmark our predictions against several commonly used models.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear dynamics and noise in fisheries recruitment: A global meta‐analysis

2018

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The relative importance of environmental and intrinsic controls on recruitment in fishes has been studied for over a century. Despite this, we are not much closer to predicting recruitment. Rather, recent analyses suggest that recruitment is virtually independent of stock size and, instead, seems to occur in distinct environmental regimes. This issue of whether or not recruitment and subsequent production are coupled to stock size is highly relevant to management. Here, we apply empirical dynamical modelling (EDM) to a global database of 185 fish populations to address the questions of whether or not variation in recruitment is (a) predictable and (b) coupled to stock size. We find that a substantial fraction of recruitment variation is predictable using only the observed history of fluctuations (~40% on average). In addition, although recruitment is often coupled to stock size (107 of 185 stocks), stock size alone explains very little of the variation in recruitment; In ~90% of the stocks analysed, EDM forecasts have substantially lower prediction error than models based solely on stock size. We find that predictability varies across taxa and improves with the number of generations that have been sampled. In the light of these results, we suggest that EDM will be of greatest use in managing relatively short‐lived species.

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Circumventing structural uncertainty: A Bayesian perspective on nonlinear forecasting for ecology

Cited by 39 publications

References 43 publications

The intrinsic predictability of ecological time series and its potential to guide forecasting

The intrinsic predictability of ecological time series and its potential to guide forecasting

Nonlinear dynamics and noise in fisheries recruitment: A global meta‐analysis

Prediction-based approach for quantifying phenological mismatch across landscapes under climate change

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