A semiparametric Bayesian approach to estimating maximum reproductive rates at low population sizes

Sugeno, Masatoshi; Munch, Stephan B.

doi:10.1890/12-0453.1

Cited by 9 publications

(9 citation statements)

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“…For example, meta-analysis has previously been applied to estimating the presence or absence of depensation in stock-recruit functions (Liermann and Hilborn 1997) and the ratio between management targets and life history parameters (Zhou et al 2012). Such metaanalysis may be more robust if conclusions are less influenced by whatever parametric form is assumed a priori, i.e., using a Gaussian process (Sugeno and Munch 2013).…”

Section: Discussionmentioning

confidence: 99%

“…One possible prior for z that is flexible and computationally feasible is a Gaussian process (which we hereafter refer to as the ''GP'' approach; Rasmussen and Williams 2006). GPs have been used widely in spatial ecology under the guise of Kriging (Banerjee et al 2003) and have since been applied to modeling density dependence (Munch et al 2005), detecting Allee effects (Sugeno and Munch 2013), and determining seasonal variation in growth potential (Sigourney et al 2012). An obvious alternative would be to model z using a basis expansion (spline, Fourier, and so forth), as is commonly done in generalized additive models (Wood and Augustin 2002).…”

Section: Population Growth Modelsmentioning

confidence: 99%

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A Bayesian approach to identifying and compensating for model misspecification in population models

Thorson

Ono

Munch

2014

Ecology

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State-space estimation methods are increasingly used in ecology to estimate productivity and abundance of natural populations while accounting for variability in both population dynamics and measurement processes. However, functional forms for population dynamics and density dependence often will not match the true biological process, and this may degrade the performance of state-space methods. We therefore developed a Bayesian semiparametric state-space model, which uses a Gaussian process (GP) to approximate the population growth function. This offers two benefits for population modeling. First, it allows data to update a specified "prior" on the population growth function, while reverting to this prior when data are uninformative. Second, it allows variability in population dynamics to be decomposed into random errors around the population growth function ("process error") and errors due to the mismatch between the specified prior and estimated growth function ("model error"). We used simulation modeling to illustrate the utility of GP methods in state-space population dynamics models. Results confirmed that the GP model performs similarly to a conventional state-space model when either (1) the prior matches the true process or (2) data are relatively uninformative. However, GP methods improve estimates of the population growth function when the function is misspecified. Results also demonstrated that the estimated magnitude of "model error" can be used to distinguish cases of model misspecification. We conclude with a discussion of the prospects for GP methods in other state-space models, including age and length-structured, meta-analytic, and individual-movement models.

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Section: Discussionmentioning

confidence: 99%

Section: Population Growth Modelsmentioning

confidence: 99%

A Bayesian approach to identifying and compensating for model misspecification in population models

Thorson

Ono

Munch

2014

Ecology

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“…The latter includes both non‐parametric components and a parametric effect of temperature. I therefore follow previous studies in calling it a “semiparametric” forecast model (Shelton, Thorson, Ward, & Feist, ; Sugeno & Munch, ; Thorson, Ono, & Munch, ). I fit this model using package VAST (https://github.com/James-Thorson/VAST) in the R statistical environment (R Core Team ).…”

Section: Methodsmentioning

confidence: 96%

“…The latter includes both non-parametric components and a parametric effect of temperature. I therefore follow previous studies in calling it a "semiparametric" forecast model (Shelton, Thorson, Ward, & Feist, 2014;Sugeno & Munch, 2013;Thorson, Ono, & Munch, 2014 identical sampling design across years, future research could apply the same method to data sets that have different sampling intensity or design among years (e.g., Thorson, Ianelli, et al, 2016).…”

Section: Vector-autoregressive Spatio-temporal (Vast) Forecastsmentioning

confidence: 96%

Forecast skill for predicting distribution shifts: A retrospective experiment for marine fishes in the Eastern Bering Sea

Thorson

2018

Fish and Fisheries

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Forecasting distribution shifts under novel environmental conditions is a major task for ecologists and conservationists. Researchers forecast distribution shifts using several tools including: predicting from an empirical relationship between a summary of distribution (population centroid) and annual time series (“annual regression,” AR); or fitting a habitat‐envelope model to historical distribution and forecasting given predictions of future environmental conditions (“habitat envelope,” HE). However, surprisingly little research has estimated forecast skill by fitting to historical data, forecasting distribution shifts and comparing forecasts with subsequent observations of distribution shifts. I demonstrate the important role of retrospective skill testing by forecasting poleward movement over 1‐, 2‐ or 3‐year periods for 20 fish and crab species in the Eastern Bering Sea and comparing forecasts with observed shifts. I specifically introduce an alternative vector‐autoregressive spatio‐temporal (VAST) forecasting model, which can include species temperature responses, and compare skill for AR, HE and VAST forecasts. Results show that the HE forecast has 30%–43% greater variance than predicting that future distribution is identical to the estimated distribution in the final year (a “persistence” forecast). Meanwhile, the AR explains 2%–6% and VAST explains 8%–25% of variance in poleward movement, and both have better performance than a persistence forecast. HE and AR both generate forecast intervals that are too narrow, while VAST models with or without temperature have appropriate width for forecast intervals. Retrospective skill testing for more regions and taxa should be used as a test bed to guide future improvements in methods for forecasting distribution shifts.

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“…The use of non-parametric and semiparametric growth models is becoming increasingly common in ecology and fisheries (Munch et al 2005;Sugeno and Munch 2012). The use of non-parametric and semiparametric growth models is becoming increasingly common in ecology and fisheries (Munch et al 2005;Sugeno and Munch 2012).…”

Section: Gompertzmentioning

confidence: 99%

Multimodel approaches in shark and ray growth studies: strengths, weaknesses and the future

et al. 2016

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Multimodel frameworks are common in contemporary elasmobranch growth literature. These techniques offer a proposed improvement over individual growth functions by incorporating additional candidate models with alternative characteristics. Sigmoid functions (e.g. Gompertz and logistic) are a popular alternative to the commonly used von Bertalanffy growth function (VBGF) as they are hypothesized to better suit certain taxa based on body shape (such as batoids) or reproductive mode (such as egg‐layers). However, this hypothesis has never been tested. This study examined 74 elasmobranch multimodel growth studies by comparing the growth curves of their respective candidate models. Hypotheses regarding model performances were rejected as the VBGF was equally likely to fit best for all taxa and reproductive modes. Subsequently, no individual model was suited to be used a priori. Differences between candidate model fits were greatest at age zero with Gompertz and logistic functions providing estimates that were 15% and 23% larger on average than the VBGF, respectively. However, length‐at‐age estimates of the different models became negligible at older ages. Differences between candidate models were mostly small (≤5%), and the multimodel framework only marginally affected length‐at‐age estimates. However, there were cases where some candidate models provided inappropriate fits that contrasted considerably to the best fitting model. In some of these instances, a single‐model framework could have yielded biologically unrealistic growth estimates. Therefore, no study could pre‐empt whether or not it required a multimodel framework. A framework was subsequently recommended to maximize the accuracy of model fits for elasmobranch length‐at‐age estimates using multimodel approaches.

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A semiparametric Bayesian approach to estimating maximum reproductive rates at low population sizes

Cited by 9 publications

References 50 publications

A Bayesian approach to identifying and compensating for model misspecification in population models

A Bayesian approach to identifying and compensating for model misspecification in population models

Forecast skill for predicting distribution shifts: A retrospective experiment for marine fishes in the Eastern Bering Sea

Multimodel approaches in shark and ray growth studies: strengths, weaknesses and the future

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