Background
salmonMSE is a quantitative and stochastic decision-support tool for Pacific salmon focusing on strategic trade-offs among harvest, hatchery and habitat management levers. salmonMSE can be used for risk-based analyses to evaluate the performance of and prioritize management actions and identify trade-offs towards achieving biological and harvest objectives
Initial development of salmonMSE is based on the All-H Analyzer (AHA) spreadsheet which models salmon dynamics of individual stocks by life stages over successive generations to obtain the long-term equilibrium properties of the state dynamics.
Currently, salmonMSE is intended to be used similarly, but expands upon the population dynamics modeling in several ways. To accommodate more complex life histories, salmonMSE is age-structured which is useful for species that have multiple cohorts overlapping in the return. Multi-stock models can be developed to evaluate outcomes at a stock management unit. Finally, stochasticity can be incorporated in the model to incorporate uncertainty in our understanding of the stock productivity.
openMSE is the population dynamics model underlying salmonMSE. While openMSE is intended for the evaluation of management strategies in marine fisheries, the functionality of the software can be re-purposed to model salmon life stages. Most state variables are therefore directly modeled within openMSE, while additional state variables that are needed for salmon life history are calculated in salmonMSE. For more information, see the article on the population dynamics model.
Use of openMSE allows for rigorous review of the source code by a wider user base. However, typical users of salmonMSE will not need to see the internal conversion between salmonMSE and openMSE operating models. Inputs and outputs of salmonMSE use salmon-specific terminology.
Getting started
A salmon operating model contains the parameters for the population
dynamics and the management levers to be implemented. In salmonMSE, an
object of class SOM can be created from constituent objects
of class Bio, Habitat, Hatchery,
and Harvest as follows:
library(salmonMSE)
Bio <- new("Bio", ...)
Habitat <- new("Habitat", ...)
Hatchery <- new("Hatchery", ...)
Harvest <- new("Harvest", ...)
SOM <- new("SOM", Bio, Hatchery, Habitat, Harvest)- The
Bioclass specify the natural production, for example, maturity, fecundity, stock-recruit relationship, and marine survival. - The
Habitatclass specify the change in the stock-recruit relationship (density-dependent smolt production), for example, from improvements to stream habitat. - The
Hatcheryclass specify the parameters surrounding hatchery production, such as the number of target releases, removal of hatchery spawners to maintain high proportions of natural spawners, and the population fitness parameters arising from interbreeding of hatchery and natural spawners. - The
Harvestclass specify the exploitation rate and harvest control rules for the fishery.
Additional slots in the SOM class control the
projections, for example, the number of years and simulation
replicates.
Details on the slots of the various S4 classes can be obtained by
typing class?SOM in the R console.
The simulation can then run with the salmonMSE()
function:
MSE <- salmonMSE(SOM)The output is a class SMSE object containing the state
variables and some performance metrics pertaining to hatchery dynamics
(fitness, PNI, etc.) as arrays typically indexed by simulation, stock,
age, and year. For example, SMSE@NOS reports the natural
origin spawners.
For convenience and comparison purposes, salmonMSE distributes an implementation of AHA in R as well:
SAHA <- AHA(SOM)The resulting output is a named list following the format of the
SMSE object, but indexed by generation instead of year.
More details are also provided in the example article.