Spatial stock assessment methods: A viewpoint on current issues and assumptions

Punt, André E.

doi:10.1016/j.fishres.2019.01.014

Cited by 74 publications

(74 citation statements)

References 70 publications

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“…Similarly, implementation of reserves or protected areas relies on being able to accurately monitor spatiotemporal dynamics of animals and harvesters (White, 2015). Thus, spatially stratified IPMs are warranted to help develop sustainable and well-informed spatial conservation strategies (Berger et al, 2017;Chandler & Clark, 2014;Punt, 2019aPunt, ,2019b. Furthermore, increased collection and utilization of tagging data, including conventional, electronic and natural tags, can help differentiate whether observed population fluctuations are due to changes in availability or abundance (Saunders et al, 2019), especially as fish distributions begin F I G U R E 6 Time series of bias (relative percent difference) in population-specific and system-wide spawning biomass and recruitment for the no movement (NO_T) and the 2YR+2AG (i.e., the min-max solution as determined by the lowest maximum MARE across movement OMs; Table 2) IPMs for the climate-induced movement (CLM) OM.…”

Section: Discussionmentioning

confidence: 99%

“…Open dialogue among scientists will help ensure that tagging experimental designs address estimation of any potential tagging "nuisance" parameters, even though these issues are often thoroughly addressed in most tagging programs (Brenden et al, 2010;Goethel et al, 2015b;Vincent et al, 2020). In many instances, though, spatially stratified IPMs can still be implemented without incorporating tagging data (Punt, 2019a(Punt, ,2019b. For instance, the NO_TAG IPM demonstrated adequate performance for many of the movement scenarios (except the climate-induced movement OM), corroborating the conclusions of Hulson et al, (2011), Hulson et al, (2013), McGilliard et al (2015, Goethel et al (2019) and Punt (2019a) that spatial IPMs are often feasible without incorporating tagging data.…”

Section: Discussionmentioning

confidence: 99%

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Where do you think you’re going? Accounting for ontogenetic and climate‐induced movement in spatially stratified integrated population assessment models

et al. 2020

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Understanding spatial population structure and biocomplexity is critical for determining a species' resilience to environmental and anthropogenic perturbations. However, integrated population models (IPMs) used to develop management advice for harvested populations have been slow to incorporate spatial dynamics. Therefore, limited research has been devoted to understanding the reliability of movement parameter estimation in spatial population models, especially for spatially dynamic marine fish populations. We implemented a spatial simulation-estimation framework that emulated a generic marine fish metapopulation to explore the impact of ontogenetic movement and climate-induced distributional shifts between two populations. The robustness of spatially stratified IPMs was explored across a range of movement parametrizations, including ignoring connectivity or estimating movement with various levels of complexity. Ignoring connectivity was detrimental to accurate estimation of population-specific biomass, while implementing spatial IPMs with intermediate levels of complexity (e.g. estimating movement in two-year and two-age blocks) performed best when no a priori information about underlying movement was available. One-way distributional shifts mimicking climate-induced poleward migrations presented the greatest estimation difficulties, but the incorporation of auxiliary information on connectivity (e.g. tag-recapture data) reduced bias. The continued development of spatially stratified modelling approaches should allow harvested resources to be better utilized without increased risk. Additionally, expanded collection and incorporation of unique spatially explicit data will enhance the robustness of IPMs in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Where do you think you’re going? Accounting for ontogenetic and climate‐induced movement in spatially stratified integrated population assessment models

et al. 2020

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“…Sensitivity analysis is performed using fuzzy C-means clustering to the models. Punt [23] analysis outlines some of the key decisions that need to be made when conducting a spatial stock assessment. Reasons for including special structure in stock assessments explain how to select the stocks fleets and areas included in the stock assessment.…”

Section: Introductionmentioning

confidence: 99%

Economic fish production inventory model for perishable fish items with the detoriation rate and the added value under pentagonal fuzzy number

Kuppulakshmi

Sugapriya

Nagarajan

2020

Complex Intell. Syst.

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In marine fishing, a considerable planning is required for developing socio-economic value of fishermen. This research explores the discussion in optimal fish manufacturing quantity for perishable fish items in the vessel during yachting. The rate of deterioration is treated as a Pentagonal Fuzzy Number (PFN) to obtain the optimal total cost. The convexity of the model is proved by satisfying the constraint equation in a fuzzy environment. An efficient procedure is applied to find the annual fish production quantity and the production in a single period to avoid the faulty measurement in the demand for fish items and the supply to the retailers. In addition, a few sensitivity analyses are carried out for the repair cost and the added value cost to indicate the existence of the total cost in the least possible range. Some managerial discrimination is also included.

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“…However, this is rarely the case. Therefore, spatially aggregated population models are likely to yield biased estimates of population quantities (Conroy, Cohen, James, Matsinos, & Maurer, ; Goethel, Legault, & Cadrin, ; Guan et al, ; Punt, ; Sampson & Scott, ; Turner et al, ), depending on the extent to which the underlying spatial structures of the population and fisheries are mis‐specified (Punt et al, ). Spatially explicit models have been increasingly developed to represent population spatial structure since the 1990s (Fournier, Hampton, & Sibert, ; Fu & Fanning, ; Goethel, Legault, & Cadrin, ; Goethel et al, ; Hulson, Miller, Ianelli, & Quinn, ; Quinn, Deriso, & Neal, ; Vincent, Brenden, & Bence, ).…”

Section: Introductionmentioning

confidence: 99%

“…Methods have been developed to include spatial structure in assessments, either implicitly or explicitly (Punt, ). Models that do and do not explicitly model spatial heterogeneity are hereinafter referred to as spatially explicit and spatially aggregated models, respectively.…”

Section: Introductionmentioning

confidence: 99%

A novel spatiotemporal stock assessment framework to better address fine‐scale species distributions: Development and simulation testing

et al. 2019

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Characterizing population distribution and abundance over space and time is central to population ecology and conservation of natural populations. However, species distribution models and population dynamic models have rarely been integrated into a single modelling framework. Consequently, fine‐scale spatial heterogeneity is often ignored in resource assessments. We develop and test a novel spatiotemporal assessment framework to better address fine‐scale spatial heterogeneities based on theories of fish population dynamic and spatiotemporal statistics. The spatiotemporal model links species distribution and population dynamic models within a single statistical framework that is flexible enough to permit inference for each state variable through space and time. We illustrate the model with a simulation–estimation experiment tailored to two exploited marine species: snow crab (Chionoecetes opilio, Oregoniidae) in the Eastern Bering Sea and northern shrimp (Pandalus borealis, Pandalidae) in the Gulf of Maine. These two species have different types of life history. We compare the spatiotemporal model with a spatially aggregated model and systematically evaluate the spatiotemporal model based on simulation experiments. We show that the spatiotemporal model can recover spatial patterns in population and exploitation pressure as well as provide unbiased estimates of spatially aggregated population quantities. The spatiotemporal model also implicitly accounts for individual movement rates and can outperform spatially aggregated models by accounting for time‐and‐size varying selectivity caused by spatial heterogeneity. We conclude that spatiotemporal modelling framework is a feasible and promising approach to address the spatial structure of natural resource populations, which is a major challenge in understanding population dynamics and conducting resource assessments and management.

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Spatial stock assessment methods: A viewpoint on current issues and assumptions

Cited by 74 publications

References 70 publications

Where do you think you’re going? Accounting for ontogenetic and climate‐induced movement in spatially stratified integrated population assessment models

Where do you think you’re going? Accounting for ontogenetic and climate‐induced movement in spatially stratified integrated population assessment models

Economic fish production inventory model for perishable fish items with the detoriation rate and the added value under pentagonal fuzzy number

A novel spatiotemporal stock assessment framework to better address fine‐scale species distributions: Development and simulation testing

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