Impacts of fisheries-dependent spatial sampling patterns on catch-per-unit-effort standardization: A simulation study and fishery application
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Help
Clear All

Query Builder

Query box

Help
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

Impacts of fisheries-dependent spatial sampling patterns on catch-per-unit-effort standardization: A simulation study and fishery application

Filetype[PDF-13.12 MB]


Select the Download button to view the document
This document is over 5mb in size and cannot be previewed

Details:

  • Journal Title:
    Fisheries Research
  • Description:
    Abundance indices derived from fisheries-dependent data (catch-per-unit-effort or CPUE) are known to have potential for bias, in part because of the usual non-random nature of fisheries spatial distributions. However, given the cost and lack of availability of fisheries-independent surveys, fisheries-dependent CPUE remains a common and informative input to fisheries stock assessments. Recent research efforts have focused on the development of spatiotemporal delta-generalized linear mixed models (GLMMs) which simultaneously standardize the CPUE and predict abundance in unfished areas when estimating the abundance index. These models can include local seasonal environmental covariates (e.g. sea surface temperature) and a spatially varying response to regional annual indices (e.g. the El Niño Southern Oscillation) to interpolate into unfished areas. Spatiotemporal delta-GLMMs have been demonstrated in simulation studies to perform better than conventional, non-spatial delta-generalized linear models (GLMs). However, spatiotemporal delta-GLMMs have rarely been evaluated in situations where fisheries spatial sampling patterns change over time (e.g. fisheries expansion or spatial closures). This study develops a simulation framework to evaluate 1) how the nature of fisheries-dependent spatial sampling patterns may bias estimated abundance indices, 2) how shifts in spatial sampling over time impact our ability to estimate temporal changes in catchability, and 3) how including seasonal environmental covariates and/or regional annual indices in spatiotemporal delta-GLMMs can improve the estimation of abundance indices given shifts in spatial sampling. Spatiotemporal delta-GLMMs are then applied to a case study example where the spatial sampling pattern changed dramatically over time (contraction of the Japanese pole-and-line fishery for skipjack tuna Katsuwonus pelamis in the western and central Pacific Ocean). Results from simulations indicate that spatial sampling in proportion to the underlying biomass can produce similar abundance indices to those produced under random sampling. Though estimated abundance indices were not perfect, spatiotemporal GLMMs were generally able to disentangle shifts in spatial sampling from temporal changes in catchability when shifts in spatial sampling were not too extreme. Lastly, the inclusion of seasonal environmental covariates and/or regional oceanographic indices in spatiotemporal GLMMs did not improve abundance index estimation and in some cases resulted in degraded model performance.
  • Source:
    Fisheries Research 246 (2022) 106169
  • Document Type:
  • Rights Information:
    Accepted Manuscript
  • Compliance:
    Submitted
  • Main Document Checksum:
  • File Type:

Supporting Files

  • No Additional Files

More +

You May Also Like

Checkout today's featured content at repository.library.noaa.gov

Version 3.26