Review of State-Space Models for Fisheries Science

Aeberhard, William H.; Flemming, Joanna Mills; Nielsen, Anders

doi:10.1146/annurev-statistics-031017-100427

Cited by 71 publications

(58 citation statements)

References 65 publications

(80 reference statements)

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“…This was mainly due to the difference in recruitment assumption, which matters when forecasting fish stocks. Estimating process errors in fisheries models should result in more robust parameters and derived outputs (Aeberhard et al, ) even if process errors are misspecified or their variances underestimated (EM1, EM1b) because this more closely reflects the emergent complexity of the biological processes in fisheries ecosystems.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to multispecies models, there is an incentive to develop state‐space stock assessment models that account for uncertainty in sampling that generates observations (observations errors) and also in unobserved biological processes responsible for stochastic changes in the population over time (process errors). These state‐space models treat the process errors as random effects which are integrated out to estimate fixed effects parameters from the marginal likelihood of the observations (Aeberhard, Mills Flemming, & Nielsen, ). By estimating both types of errors, these state‐space models become a more realistic illustration of the uncertainty that exists in our understanding of the fisheries systems.…”

Section: Introductionmentioning

confidence: 99%

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Performance of a state‐space multispecies model: What are the consequences of ignoring predation and process errors in stock assessments?

Trijoulet

Fay

Miller

2019

Journal of Applied Ecology

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Having a realistic representation of ecosystems in fisheries models is important in the context of ecosystem‐based fisheries management (EBFM). While different modelling approaches support EBFM, accounting for trophic interactions and uncertainty in stock dynamics is important for management advice. Multispecies models exist, but are rarely used for assessments. Most stock assessments are single species models and predation is subsumed into natural mortality, which is often an assumed known value. The use of state‐space assessment models, which account for stochasticity in unobserved processes (process errors), is increasing. However, many stocks are managed assuming deterministic processes. Little is known of how ignoring predation and process errors in stock assessment can impact the perception of the stocks and therefore fisheries management. We developed an age‐structured multispecies operating model that simulated data with errors in observations, recruitment and fish abundance. Four estimation models (EMs) that differed according to whether or not they accounted for predation or process errors were fitted to the simulated data. Relative differences between true and predicted outputs were estimated as a measure of bias. Equilibrium unfished biomass was estimated for each model as a proxy reference point. Ignoring predation had the largest impact on stock perception and resulted in large bias in parameters, derived outputs and absolute or relative reference points. Estimating unobserved processes was not sufficient in limiting the bias when natural mortality was misspecified. Ignoring process errors had limited bias but the bias increased when no contrasts existed in fishing mortality over time. Looking solely at likelihood values to choose among models is misleading and predictive ability could be used to prevent selecting models that overfit the data. Synthesis and applications. Ignoring trophic interactions that occur in marine ecosystems induces bias in stock assessment outputs and results in low model predictive ability with subsequently biased reference points. While it may be difficult to estimate natural mortality when no data exist to inform it, stock managers should remember that, if predation is large, assuming a constant mortality over time and/or age could have large consequences on stock perception and reference point estimates and affect resulting management advice.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance of a state‐space multispecies model: What are the consequences of ignoring predation and process errors in stock assessments?

Trijoulet

Fay

Miller

2019

Journal of Applied Ecology

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“…By explicitly modeling both process and measurement error, we were able to monitor canopy dynamics, despite temporal variation in Landsat data. While state-space models have been applied to analyze noisy time series for a range of fields, from fisheries science (Aeberhard et al 2018) to epidemiology (Cauchemez and Ferguson 2008), this modeling approach is not widely used for Earth observation applications. Instead, remote sensing has focused more on minimizing measurement error than on modeling ecological process.…”

Section: Discussionmentioning

confidence: 99%

Monitoring tropical forest succession at landscape scales despite uncertainty in Landsat time series

Caughlin

Barber

Asner

et al. 2020

Ecological Applications

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Forecasting rates of forest succession at landscape scales will aid global efforts to restore tree cover to millions of hectares of degraded land. While optical satellite remote sensing can detect regional land cover change, quantifying forest structural change is challenging. We developed a state-space modeling framework that applies Landsat satellite data to estimate variability in rates of natural regeneration between sites in a tropical landscape. Our models work by disentangling measurement error in Landsat-derived spectral reflectance from process error related to successional variability. We applied our modeling framework to rank rates of forest succession between 10 naturally regenerating sites in Southwestern Panama from about 2001 to 2015 and tested how different models for measurement error impacted forecast accuracy, ecological inference, and rankings of successional rates between sites. We achieved the greatest increase in forecasting accuracy by adding intra-annual phenological variation to a model based on Landsatderived normalized difference vegetation index (NDVI). The best-performing model accounted for inter-and intra-annual noise in spectral reflectance and translated NDVI to canopy height via Landsat-lidar fusion. Modeling forest succession as a function of canopy height rather than NDVI also resulted in more realistic estimates of forest state during early succession, including greater confidence in rank order of successional rates between sites. These results establish the viability of state-space models to quantify ecological dynamics from time series of space-borne imagery. State-space models also provide a statistical approach well-suited to fusing high-resolution data, such as airborne lidar, with lower-resolution data that provides better temporal and spatial coverage, such as the Landsat satellite record. Monitoring forest succession using satellite imagery could play a key role in achieving global restoration targets, including identifying sites that will regain tree cover with minimal intervention.

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“…We tested the hypothesis of metapopulation structure within the EEC and estimated the level of mixing between sub-populations. For that purpose, we developped a capture-recapture model (Royle et al 2013;McCrea & Morgan 2014) built in a state-space model framework (recently reviewed in Aeberhard et al 2018 in the context of fisheries) to estimate adult and subadult movement probabilities. Finally, based on our observations, we consider the implications for management of the common sole stock in the EEC (and more widely) of acknowledging and incorporating population substructure in management and assessment.…”

Section: Introductionmentioning

confidence: 99%

State-space modeling of multidecadal mark–recapture data reveals low adult dispersal in a nursery-dependent fish metapopulation

Lecomte

Pape

Baillif

et al. 2020

Can. J. Fish. Aquat. Sci.

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Quantifying connectivity within fish metapopulations is an important component in understanding population dynamics and providing an evidence base for assessment and management. We investigate metapopulation connectivity of the common sole (Solea solea) in the Eastern English Channel (EEC). The EEC common sole stock is currently assessed as a single and spatially homogeneous population, but connectivity induced through adult movements within this stock and with nearby stocks remains unknown. To fill this knowledge gap, we developed a state-space mark–recovery model, designed to estimate adult connectivity using mark–recapture data from multiple release experiments from 1970 to 2018 across the EEC and adjacent management areas. The model estimates seasonal fish movements between five predefined areas, Western English Channel, Eastern English Channel (split into three discrete sub-areas), and North Sea. Over 32 000 fish were tagged, 4036 of which were recovered via fisheries. Our results suggest minimal large-scale adult movements between these areas; movements among spatial units within the EEC were very low with even lower levels of immigration from areas adjoining the EEC. Our results support the hypothesis of segregated populations within the EEC. The importance of accommodating population substructure in the fisheries management is considered.

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Review of State-Space Models for Fisheries Science

Cited by 71 publications

References 65 publications

Performance of a state‐space multispecies model: What are the consequences of ignoring predation and process errors in stock assessments?

Performance of a state‐space multispecies model: What are the consequences of ignoring predation and process errors in stock assessments?

Monitoring tropical forest succession at landscape scales despite uncertainty in Landsat time series

State-space modeling of multidecadal mark–recapture data reveals low adult dispersal in a nursery-dependent fish metapopulation

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