Quantitative argument for long-term ecological monitoring

Girón‐Nava, Alfredo; James, Chase C.; Johnson, Andrew F.; Dannecker, David; Kolody, Bethany; Lee, Adrienne; Nagarkar, Maitreyi; Pao, Gerald M.; Ye, Hao; Johns, David G.; Sugihara, George

doi:10.3354/meps12149

Cited by 59 publications

(54 citation statements)

References 31 publications

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“…Of course, when additional driving variables are known they can be readily incorporated into this framework (Deyle et al., ; Dixon et al., ; Ye et al., ). As prediction skill increases with the number of generations sampled, we expect these methods to be of greatest use for relatively short‐lived fishes (see also Giron‐Nava et al., ).…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

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

2018

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“…We acknowledge these as fairly stringent requirements for ecological time series. Growth rate, r Time series measured at the appropriate time scales over long periods of time are rare, despite the knowledge that they are among the most effective approaches at resolving long-standing questions regarding environmental drivers (Lindenmayer et al 2012, Giron-Nava et al 2017, Hughes et al 2017). This problem is beginning to be resolved with automated measurements of system states, such as chlorophyll a concentrations in aquatic systems (Blauw et al 2018, Thomas et al 2018, assessment of community dynamics in microbiology (Trosvik et al 2008, Faust et al 2015, Martin-Platero et al 2018, and phenological (Pau et al 2011) and flux measurements (Dietze 2017).…”

Section: Reliable Assessment Of Intrinsic Predictabilitymentioning

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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“…Permutation entropy requires time series data of suitable length and sampling frequency to infer the correct permutation order and time delay (Riedl et al 2013). Long ecological time series measured at the appropriate time scales are rare, despite the knowledge that they are among the most effective approaches at resolving long-standing questions regarding environmental drivers (Lindenmeyer et al 2012, Giron-Nava et al 2017, Hughes et al 2017). This problem is beginning to be resolved with automated measurements of system states, such as chlorophyll-a concentrations in aquatic systems (Thomas et al 2018, Blauw et al 2018, assessment of community dynamics in microbiology (Martin-Platero et al 2018, Faust et al 2015, Trosvik et al 2008), and phenological (Pau et al 2011) and flux measurements (Dietze 2017).…”

Section: Discussionmentioning

confidence: 99%

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

Pennekamp

Iles

Garland

et al. 2018

Preprint

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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 v. 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 dataset 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. GlossaryActive information: The amount of information that is available to forecasting models (redundant information minus lost information; Fig. 1). Forecasting error (FE):A measure of the discrepancy between a model's forecasts and the observed dynamics of a system. Common measures of forecast error are root mean squared error and mean absolute error. Entropy: Measures the average amount of information in the outcome of a stochastic process. Information: Any entity that provides answers and resolves uncertainty about a process. When information is calculated using logarithms to the base two (i.e. information in bits), it is the minimum number of yes/no questions required, on average, to determine the identity of the symbol (Jost 2006). The information in an observation consists of information inherited from the past (redundant information), and of new information. Intrinsic predictability: the maximum achievable predictability of a system (Beckage et al. 2011). Lost information:The part of the redundant information lost due to measurement or sampling error, or transformations of the data (Fig. 1). New information, Shannon entropy rate: The Shannon entropy rate quantifies the average amount of information per observation in a t...

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Quantitative argument for long-term ecological monitoring

Cited by 59 publications

References 31 publications

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

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

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

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