Overview

The purpose of atlantisom is to use existing Atlantis ecosystem model output to generate input datasets for a variety of simpler population models, so that the performance of these models can be evaluated against known (simulated) ecosystem dynamics. Atlantis models simulate a wide range of physical and ecological processes, include a full food web, and can be run using different climate forcing, fishing, and other scenarios.

Users of atlantisom specify fishery independent and fishery dependent sampling in space and time, as well as species-specific catchability, selectivty, and other observation processes to simulate survey and fishery “data” from stored Atlantis scenario output. atlantisom outputs internally consistent multispecies and ecosystem datasets with known observation error characteristics for use in individual model performance testing, comparing performance of alternative models, and performance testing of model ensembles against “true” Atlantis outputs.

Development of atlantisom began at the 2015 Atlantis Summit in Honolulu, Hawaii, USA. Substantial progress since then was made possible by a NOAA NMFS International Fellowship and the Institute of Marine Research in Bergen, Norway.

Credits

Sarah Gaichas, Christine Stawitz, Kelli Johnson, Alexander Keth, Allan Hicks, Sean Lucey, Emma Hodgson, and Gavin Fay

Get atlantisom https://github.com/r4atlantis/atlantisom

# install.packages("devtools")
devtools::install_github("r4atlantis/atlantisom")

Detailed data flow

Have these Atlantis input files in a directory:

At present,atlantisom uses the following Atlantis output files, where […] is a model-specific prefix. They should be in the same directory as the input files above:

Get the truth:

Generate the data:

Background

Assessing the ability of models to predict key processes is essential if the models are to be used in decision-making dependent on those processes. Skill assessment compares model predictions with observations; good agreement between predictions and observations indicates high skill (see, e.g. Stow et al 2009). However, in many natural resrouce management contexts, the observations themselves are highly uncertain, so assessing model skill can be difficult. In these cases, creating an observational dataset with a simulation model can provide alternative information for model skill assessment.

End-to-end ecosystem operating models as dataset generators

Atlantis is a spatially resolved mechanistic end-to-end ecosystem modeling framework: Fulton et al. 2011, Fulton and Smith 2004. Atlantis models have been implemented for regional ecosystems around the world, including:

Norwegian-Barents Sea

Hansen et al. 2016, 2018

NoBa model areas

California Current

Marshall et al. 2017, Kaplan et al. 2017

CCA model areas

Atlantis models can incoroporate physical drivers from global change projections and simulate complex biological responses throughout the ecosystem: Hodgson et al. 2018, Olsen et al. 2018

Why use Atlantis?

Why generate datasets instead of simulating within Atlantis?

How does atlantisom make “data” for stock assessments?

The atlantisom user must specify uncertainty in assessment “data”:

  1. Survey specification:

    1. timing and spatial coverage?

    2. which species are captured?

    3. species-specific survey efficiency (“q”)?

    4. selectivity at age for each species?

  2. Survey uncertainty:

    1. additional observation error (survey cv for index)?

    2. effective sample size for biological samples?

  3. Fishery uncertainty:

    1. additional observation error (catch cv for total)?

    2. catch sampled for length/age in all areas?

    3. effective sample size for biological samples?

Using these specifications, atlantisom generates survey data from the true biomass outputs of the Atlantis model, and generates fishery data from the true fishery outputs of the Atlantis model. The simulated data can then be used as inputs to a variety of single species or multispecies assessment models.

Make Atlantis output into assessment model input

Example atlantisom workflows:

  1. Get true biomass, abundance, age composition, length composition, weight at age, fishery catch, fishery catch at age, fishery length composition, and fishery weight age age for a “sardine-like species”: https://sgaichas.github.io/poseidon-dev/FullSardineTruthEx.html

  2. Format these outputs and get other life history parameters for input into a stock assessment model (Stock Synthesis, using r4ss): https://sgaichas.github.io/poseidon-dev/CreateStockSynthesis.html

  3. Get true and observed input data, format inputs, and run the assessment model: https://sgaichas.github.io/poseidon-dev/SardinesHakeatlantisom2SStest.html

  4. In progress: compare assessment results with truth: https://sgaichas.github.io/poseidon-dev/SkillAssessInit.html

  5. Simplified dataset extraction with wrapper functions: https://sgaichas.github.io/poseidon-dev/NOBAcod.html

Visualizing atlantisom outputs

Survey census test NOBA

True length composition NOBA

Standard survey test CCA

Survey length composition CCA

Example: a “sardine” assessment

Need: assessment model data inputs and life history parameters

(model based on actual Sardine assessment in Stock Synthesis 3)

Data:

Parameters:

A “sardine” assessment: setup

A “sardine” assessment: fits to data

survey index fit

length fit

catch at age fit 1 catch at age fit 2

A “sardine” assessment: skill? (proof of concept)

Biomass

Fishing mortality

Recruitment

Key: True SS3 estimate

More information