rorcs: Refined ORCS approach

In cfree14/datalimited2: More Stock Assessment Methods for Data-limited Fisheries

Refined ORCS approach

Description

Estimates stock status (i.e., under, fully, or overexploited) from 12 stock- and fishery-related predictors using the refined ORCS approach from Free et al. 2017. Stock status categories are defined as follows: (1) B/BMSY > 1.5 = underexploited; (2) 0.5 < B/BMSY < 1.5 = fully exploited; and (3) B/BMSY < 0.5 = overexploited.

Usage

rorcs(scores)

Arguments

scores

A numeric vector of length twelve containing scores for the following "Table of Attributes" questions (see Free et al. 2017 for more details): TOA 1 - Status of assessed stocks in fishery TOA 3 - Behavior affecting capture (2 or 3 only) TOA 5 - Discard rate TOA 6 - Targeting intensity TOA 7 - M compared to dominant species TOA 8 - Occurence in catch TOA 9 - Value (US$/lb) - continuous value TOA 10 - Recent trends in catch TOA 11 - Habitat loss TOA 12 - Recent trend in effort TOA 13 - Recent trend in abundance index TOA 14 - Proportion of population protected

Details

The refined ORCS approach (rORCS) uses a boosted classification tree model trained on the RAM Legacy Database to estimate stock status (i.e., under, fully, or overexploited) from twelve stock- and fishery-related predictors, the most important of which are the value of the taxa, status of the assessed stocks in the fishery, targeting intensity, discard rate, and occurrence in the catch (Free et al. 2017). The approach also includes a step for estimating the overfishing limit (OFL) as the product of a historical catch statistic and scalar based on stock status and risk policy.

Value

A data frame containing the probability that a stock is under, fully, or overexploited with stock status identified by the most probable category.

References

Free CM, Jensen OP, Wiedenmann J, Deroba JJ (2017) The refined ORCS approach: a catch-based method for estimating stock status and catch limits for data-poor fish stocks. Fisheries Research 193: 60-70. https://doi.org/10.1016/j.fishres.2017.03.017

Examples

# Create vector of TOA scores and estimate status
scores <- c(1, 2, NA, 2, 2, 3, 1.93, 2, 1, 2, 1, 3)
rorcs(scores)

cfree14/datalimited2 documentation built on Aug. 21, 2023, 2:26 p.m.