A framework to incorporate environmental effects into stock assessments informed by fishery-independent surveys: a case study with American lobster (<i>Homarus americanus</i>)

Hodgdon, Cameron T.; Tanaka, Kisei R.; Runnebaum, Jocelyn; Cao, Jie; Chen, Yong

doi:10.1139/cjfas-2020-0076

Cited by 10 publications

(6 citation statements)

References 40 publications

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“…Under this method, it is assumed that the base case results are the most "correct" compared to the other scenarios. Given that American lobster is one of the most heavily studied and surveyed fisheries on the planet (Chen et al, 2006b;ASMFC, 2015;Hodgdon et al, 2020), confidence in this assumption was relatively high. A spatially average difference metric was calculated as follows…”

Section: Bioclimate Model Comparative Diagnosticsmentioning

confidence: 99%

Consequences of model assumptions when projecting habitat suitability: a caution of forecasting under uncertainties

Hodgdon

Mazur

Friedland

et al. 2021

ICES Journal of Marine Science

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Climate change is continuing to influence spatial shifts of many marine species by causing changes to their respective habitats. Habitat suitability as a function of changing environmental parameters is a common method of mapping these changes in habitat over time. The types of models used for this process (e.g. bioclimate models) can be used for projecting habitat if appropriate forecasted environmental data are used. However, the input data for this process must be carefully selected as less reliable results can incite mis-management. Thus, a knowledge of the organism and its environment must be known a priori. This paper demonstrates that these assumptions about a species’ life history and the environment are critical when applying certain types of bioclimate models that utilize habitat suitability indices. Inappropriate assumptions can lead to model results that are not representative of environmental and biological realities. Using American lobster (Homarus americanus) of the Gulf of Maine as a case study, it is shown that the choice of extrapolation data, spatial scale, environmental parameters, and appropriate subsetting of the population based on life history are all key factors in determining appropriate biological realism necessary for robust bioclimate model results.

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Section: Bioclimate Model Comparative Diagnosticsmentioning

confidence: 99%

Consequences of model assumptions when projecting habitat suitability: a caution of forecasting under uncertainties

Hodgdon

Mazur

Friedland

et al. 2021

ICES Journal of Marine Science

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“…Previous comparisons of VAST versus design‐based indices have also found that the latter exaggerated temporal variability (Cao et al, 2017). However, Hodgdon et al, (2020) found that model‐based abundance indices were not intrinsically better than design‐based indices and should be tested for each species individually, as has been done in this study.…”

Section: Discussionmentioning

confidence: 76%

“…Recent development of a vector autoregressive spatiotemporal (VAST) model (Thorson & Barnett, 2017) provides a tool to estimate biomass in defined spatial areas and could be useful in quota allocation (Thorson, 2019). In the Northwest Atlantic Ocean, VAST has been used to standardise indices for the northern shrimp ( Pandalus borealis Krøyer) assessment (Cao et al, 2017); to combine data from multiple fishery‐independent surveys to predict density estimates for cusk ( Brosme brosme (Ascanius)) for use in habitat suitability indices (Runnebaum et al, 2018), as well as to predict hotspots of cusk bycatch in the American lobster ( Homarus americanus Milne‐Edwards) fishery (Runnebaum et al, 2020); to test the incorporation of environmental covariates in a length‐structured assessment of the American lobster (Hodgdon et al, 2020); and to examine distribution shifts for cod (Guan et al, 2017) and summer flounder ( Paralichthys dentatus (L.)) in the region (Perretti & Thorson, 2019).…”

Section: Introductionmentioning

confidence: 99%

Quota allocation for stocks that span multiple management zones: analysis with a vector autoregressive spatiotemporal model

Adams

Brooks

Legault

et al. 2021

Fisheries Management Eco

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Allocating quotas among stakeholders requires an agreed‐upon formula. If the stock unit spans multiple management jurisdictions, the formula may require updated biomass estimates of the stock's spatial distribution with respect to those jurisdictions. Data for calculating stock biomass often come from fishery‐independent surveys. While stratified random sampling is a common design, strata boundaries may not always align with state or national jurisdictions, requiring post hoc stratification and imputation to calculate area‐specific biomass. The vector autoregressive spatiotemporal (VAST) model was explored as a tool for calculating fish biomass within subareas of a defined stock unit for three different stocks jointly managed by the United States and Canada on Georges Bank in the Northwest Atlantic Ocean. VAST estimated proportions of stock biomass in each nation's waters were compared with an existing allocation algorithm that utilises a loess smooth through the average design‐based swept area biomass from three fishery‐independent surveys. The ability of VAST to impute biomass when no tows occur in a subarea of a survey stratum was demonstrated, as well as the relatively smoother biomass trend compared with design‐based estimates, which may be desirable if the intent is to avoid large inter‐annual swings in allocated quota.

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“…More information about the Maine-New Hampshire Inshore Bottom Trawl survey procedures, protocols, or specifics can be found in Sherman et al (2005). This survey has been found to yield informative data for studying lobster distributions and habitats in the GOM (Tanaka and Chen, 2016;Tanaka et al, 2019;Hodgdon et al, 2020).…”

Section: Study Area and Data Sourcesmentioning

confidence: 99%

Examining Scale Dependent Environmental Effects on American Lobster (Homarus americanus) Spatial Distribution in a Changing Gulf of Maine

Behan

Chen

2021

Front. Mar. Sci.

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The Gulf of Maine (GOM) is a highly complex environment and previous studies have suggested the need to account for spatial nonstationarity in species distribution models (SDMs) for the American lobster (Homarus americanus). To explore impacts of spatial nonstationarity on species distribution, we compared models with the following three assumptions : (1) large-scale and stationary relationships between species distributions and environmental variables; (2) meso-scale models where estimated relationships differ between eastern and western GOM, and (3) finer-scale models where estimated relationships vary across eastern, central, and western regions of the GOM. The spatial scales used in these models were largely determined by the GOM coastal currents. Lobster data were sourced from the Maine-New Hampshire Inshore Bottom Trawl Survey from years 2000–2019. We considered spatial and environmental variables including latitude and longitude, bottom temperature, bottom salinity, distance from shore, and sediment grain size in the study. We forecasted distributions for the period 2028–2055 using each of these models under the Representative Concentration Pathway (RCP) 8.5 “business as usual” climate warming scenario. We found that the model with the third assumption (i.e., finest scale) performed best. This suggests that accounting for spatial nonstationarity in the GOM leads to improved distribution estimates. Large-scale models revealed a tendency to estimate global relationships that better represented a specific location within the study area, rather than estimating relationships appropriate across all spatial areas. Forecasted distributions revealed that the largest scale models tended to comparatively overestimate most season × sex × size group lobster abundances in western GOM, underestimate in the western portion of central GOM, and overestimate in the eastern portion of central GOM, with slightly less consistent and patchy trends amongst groups in eastern GOM. The differences between model estimates were greatest between the largest and finest scale models, suggesting that fine-scale models may be useful for capturing effects of unique dependencies that may operate at localized scales. We demonstrate how estimates of season-, sex-, and size- specific American lobster spatial distribution would vary based on the spatial scale assumption of nonstationarity in the GOM. This information may help develop appropriate local adaptation measures in a region that is susceptible to climate change.

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A framework to incorporate environmental effects into stock assessments informed by fishery-independent surveys: a case study with American lobster (Homarus americanus)

Cited by 10 publications

References 40 publications

Consequences of model assumptions when projecting habitat suitability: a caution of forecasting under uncertainties

Consequences of model assumptions when projecting habitat suitability: a caution of forecasting under uncertainties

Quota allocation for stocks that span multiple management zones: analysis with a vector autoregressive spatiotemporal model

Examining Scale Dependent Environmental Effects on American Lobster (Homarus americanus) Spatial Distribution in a Changing Gulf of Maine

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