Version 1
: Received: 12 February 2020 / Approved: 14 February 2020 / Online: 14 February 2020 (02:15:33 CET)
Version 2
: Received: 7 January 2021 / Approved: 8 January 2021 / Online: 8 January 2021 (13:24:03 CET)
Version 3
: Received: 17 June 2021 / Approved: 23 June 2021 / Online: 23 June 2021 (11:50:11 CEST)
How to cite:
Tobias, V. Simulated Fishing to Untangle Catchability and Availability in Fish Abundance Monitoring. Preprints2020, 2020020177. https://doi.org/10.20944/preprints202002.0177.v2
Tobias, V. Simulated Fishing to Untangle Catchability and Availability in Fish Abundance Monitoring. Preprints 2020, 2020020177. https://doi.org/10.20944/preprints202002.0177.v2
Tobias, V. Simulated Fishing to Untangle Catchability and Availability in Fish Abundance Monitoring. Preprints2020, 2020020177. https://doi.org/10.20944/preprints202002.0177.v2
APA Style
Tobias, V. (2021). Simulated Fishing to Untangle Catchability and Availability in Fish Abundance Monitoring. Preprints. https://doi.org/10.20944/preprints202002.0177.v2
Chicago/Turabian Style
Tobias, V. 2021 "Simulated Fishing to Untangle Catchability and Availability in Fish Abundance Monitoring" Preprints. https://doi.org/10.20944/preprints202002.0177.v2
Abstract
In fisheries monitoring, catch is assumed to be a product of fishing intensity, catchability, and availability, where availability is defined as the number or biomass of fish present and catchability refers to the relationship between catch rate and the true population. Ecological monitoring programs use catch per unit of effort (CPUE) to standardize catch and monitor changes in fish populations; however, CPUE is proportional to the portion of the population that is vulnerable to the type of gear that is used in sampling, which is not necessarily the entire population. Programs often deal with this problem by assuming that catchability is constant, but if catchability is not constant, it is not possible to separate the effects of catchability and population size using monitoring data alone. This study uses individual-based simulation to separate the effects of changing environmental conditions on catchability and availability in environmental monitoring data. The simulation combines a module for sampling conditions with a module for individual fish behavior to estimate the proportion of available fish that would escape from the sample. The method is applied to the case study of the well-monitored fish species Delta Smelt (Hypomesus transpacificus) in the San Francisco Estuary, where it has been hypothesized that changing water clarity may affect catchability for long-term monitoring studies. Results of this study indicate that given constraints on Delta Smelt swimming ability, it is unlikely that the apparent declines in Delta Smelt abundance are due to an effect of changing water clarity on catchability.
bias; simulation; long-term monitoring; Delta Smelt; San Francisco Estuary
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
8 January 2021
Commenter:
Vanessa Tobias
Commenter's Conflict of Interests:
Author
Comment:
This version adds the vertical dimension to the simulated gear escape behavior and updates the results accordingly. It also provides a link to the code respository containing the full code to implement the simulation described in the text.
Commenter: Vanessa Tobias
Commenter's Conflict of Interests: Author