Climate Test
Rating the Climate Readiness of Fishery Management Options
Tom Carruthers tom@bluematterscience.com
08 December, 2025
1 Overview
Forecasts of climate impacts on marine populations have often struggled to pass peer-review. Typically, those forecasts are also highly uncertain. These forecasting challenges have proven an impediment to the adoption of fishery management practices that are considerate of climate change.
To alleviate the need for a defensible forecast, the Climate Test rephrases the problem, focusing on the relative performance of management options given increasingly extreme climate scenarios. This is analogous to testing pilots in a simulator. It is possible to simulation test and then rate pilots according to their ability to fly under increasingly challenging weather conditions. This pilot rating process is unrelated to a particular weather forecast.
The aim of Climate Test is to provide strategic information to fishery managers about the relative climate robustness of various candidate management options. For a set of candidate management procedures that are otherwise comparable, managers have the option of adopting a procedure that has been shown to withstand more extreme climate tests.
2 Project Details
Title: ‘Creating and demonstrating a practical, science-based framework that enables fisheries managers to evaluate and adopt climate-resilient management procedures’
| Term | Sept 2025 - Nov 2027 |
| Funded by | Paul M. Angell Family Foundation, Oceankind, The Pew Charitable Trusts |
| Coordinated by | The Ocean Foundation (TOF) |
| Initiative | www.HarvestStrategies.org |
| Project Partners | Blue Matter Science Ltd., University of Cape Town (UCT) |
| TOF Project Leader | Rebecca Scott |
| Blue Matter Team | Tom Carruthers, Adrian Hordyk, Quang Huynh |
| UCT Collaborators | Kelly Ortega-Cisneros, Carryn de Moor |
3 Introduction
3.1 Hypothesized Climate Impacts on Exploited Marine Populations
Changing climatic conditions have the potential to substantially alter the abundance, productivity and species composition of pelagic marine ecosystems (Beaugrand and Kirby 2023, Bell et al. 2011). Due to increasing anthropogenic greenhouse gas emissions, climate processes affecting marine ecosystems are forecasted to intensify (Hoehy-Guldberg et al. 2018; Kwiatkowski et al., 2020).
Climate processes (e.g., thermal regime, concentration of greenhouse gasses) are linked to oceanographic conditions (e.g., ocean temperature, ocean mixing, stratification, pH & salinity)(Figure 1, Table 1). A large number of mechanisms have been proposed for how individual fish stocks may be impacted by those changing oceanographic conditions (Table 2). The mechanistic linkages are numerous and can relate to changing ecological processes (e.g., primary productivity, community dynamics) and the location and amount of suitable habitat. The large number of mechanisms ultimately impact a relatively concise list of individual stock dynamics relating to species distribution, survival of various life stages, condition factor, fecundity and somatic growth (Walther et al., 2002; Doney et al., 2012; Free et al., 2019).
Figure 1. A simplified diagram of linkages among climate processes, oceanographic conditions and individual species dynamics. The size of each box is intended to represent the relative number of processes / mechanisms that have been proposed in the literature, highlighting the relatively small number of hypothesized impacts on individual species.
In their review of impacts on large pelagic fish in the Northwest Atlantic, Dell’Apa et al. (2023) provide a useful example of changes in ocean conditions that may impact stock dynamics:
“… the Northwest Atlantic Ocean has been characterized by several climate-driven changes in regional environmental conditions over recent decades, including warmer sea surface temperature (SST) (Karnauskas et al., 2013; Loder and Wang, 2015) and bottom water temperature (Brickman et al., 2018), increased summertime stratification of shelf waters (Li et al., 2015), changes in dissolved oxygen concentration (DO) levels (Stendardo and Gruber, 2012) and acidification (Cai et al., 2011), and altered oceanographic processes (Karnauskas et al., 2015)”.
Establishing climate-ready management is a key priority for many regional fishery management organizations in light of persistent declines in various species (Pacoureau et al. 2021) and concern that fishing may increase the sensitivity of marine populations to climate change (Hsieh et al. 2008).
Evaluating the climate-readiness of current and alternative management options requires models that can predict fishery and population dynamics. Unfortunately, forecasting the impacts of climate changes on managed pelagic species is highly uncertain. In theory, it is possible for example, to combine models of emissions (e.g., Algieri et al. 2023, Wang et al. 2017), earth systems (Kawamiya et al. 2020), ecosystems (e.g., Beaugrand and Kirby 2018, Lehodey et al. 2010; 2011), behaviour (e.g., Bushnell and Brill 1991, Cayré and Marsac 1993) and physiology (e.g., Gooding et al. 1981, Graham et al. 1989, Essington 2003, Checkley et al. 2009). In doing so, forecasting combines a complex series of linked projections that include greenhouse gas emissions (least uncertain), response of climate processes (uncertain), linkages with oceanographic conditions (more uncertain) and the expected impact of those on pelagic communities and individual species (most uncertain). It follows that any climate change scenario for establishing climate-ready management advice for a given fish stock is firmly hypothetical, and the relative credibility of scenarios should be considered highly uncertain.
Table 1. Proposed linkages between climatological processes and oceanographic properties impacting fish habitat.
This large uncertainty over climate impact scenarios poses a problem for the provision of ‘climate ready’ fishery management advice using the contemporary stock assessment and management strategy evaluation (MSE) frameworks. That is because those frameworks rely on the specification of models that represent climate impacts and the assigned credibility of those models could strongly affect the advice provided. For example, there may be small or large future changes in natural survival. Advice arising from scenarios with low survival changes would likely lead to the provision of strongly differing advice from scenarios with high natural survival, yet the relative credibility of those scenarios is not easily evaluated.
Although there are very few quantitative forecasts of climate impacts on fisheries, qualitatively the way in which climate can impact individual populations has been hypothesized in numerous papers (Table 2). The most frequently hypothesized population changes relate to recruitment (carrying capacity, spawning habitat, larval survival), adult survival (natural mortality rate), somatic growth, spatial distribution (range contraction, catchability) age-at-maturity and condition factor (weight-at-length, fecundity). Additionally, the problematic direction of change in those variables is established: lower recruitment strength, decreased survival, lower somatic growth rate, reduced spatial distribution, older age-at-maturity and poorer condition factor. The incredibly large body of theoretical work is distilled into a very small set of single stock climate impacts.
Table 2. Mechanistic linkages between oceanographic properties impacting fish habitat and the dynamics of individual stocks (or operating models).
3.2 Climate Test: Metrics of Climate Robustness
Rather than leaving the investigation of climate resilience stalled in the (perhaps indefinite) wait for scientifically defensible forecasts of climate impacts, the Climate Test presents an alternative approach. The solution proposed here is to shift the focus from forecast-based tests of climate robustness in favor of performance metrics of climate robustness. This is analogous to testing pilots in a simulator under an increasingly difficult set of weather conditions. We do not need a weather forecast to know those pilots that are better at flying in more extreme weather conditions. The Climate Test increases the degree of climate impact to identify those management options that most robust.
3.3 Management Strategy Evaluation
The Climate Test is proposed as an extension to management strategy evaluation (MSE), a process that has two essential components for climate test already specified: (1) models of system dynamics (operating models) and (2) candidate management options (management procedures, harvest strategies). The intention of Climate Test is to be a straightforward addition to any MSE process established in the openMSE framework (an extension R package) allowing for climate-considerate management advice in more than 20 current MSEs.
4 Methods
4.1 Robustness Threshold
The climate test compares when management options ‘break’ due to climate impacts and hence requires a definition of that break point, a ‘Robustness threshold’. The Robustness threshold has three parts: (1) an unacceptable level of impact, (2) a quantity that is impacted and (3) a time horizon.
In Figure 2 a climate test is presented for two management procedures for Atlantic Blue Shark (Carruthers 2024a;b). In this case the robustness threshold is a 30% decline in spawning stock biomass (SSB) over a 30 year time horizon. The two management procedures were climate tested for declines in somatic growth (K). The top row shows how future spawning biomass is impacted by increasingly large impacts on somatic growth (phrased as annual percentage declines in K).
The first management procedure (top row) crosses the robustness threshold after 30 years given a decline in growth of 8%. The second management procedure (bottom row) is much more robust and it takes an 18% decline in somatic growth before projected biomass crosses the performance threshold.
Figure 2. An exploratory climate test of decreasing somatic growth for two management procedures applied to Atlantic blueshark operating model (Carruthers 2024b)
4.2 Exploratory Tests
For any case study it is possible to create a set of marginal (one parameter at a time) climate tests to rate the climate robustness of candidate management procedures. Despite a large number of proposed mechanisms, there are only six frequently cited impacts on individual stocks and the problematic direction for management is known (Table 3)
Table 3. Six exploratory climate tests identified in Carruthers 2024a
| Parameter | Code | Description |
|---|---|---|
| Somatic Growth | K | Declines in somatic growth may lead to fewer mature fecund individuals in the population, a higher fraction of fish caught per catch weight, and a lower rate of sustainable harvest |
| Recruitment Strength | R | Declines in recruitment strength require fewer catches to maintain stock sizes |
| Natural Mortality | M | Declining natural survival (increasing M) reduces the number of cohorts in the population, and reduces the likelihood of reaching maturity |
| Condition Factor | C | Declining condition factor reduces spawning biomass, increases the number of fish caught per catch weight and reduces the rate of sustainable harvest |
| Spatial distribution | S | Spatial range contration towards fishing can increase catchability (fishing efficiency) leading to higher catch per effort and hyperstability in relative abundance indices |
These marginal climate tests can be used to rate MPs according to the level of climate impact before the robustness threshold is crossed. Table 4 shows four marginal tests for six example management procedures for blue shark (Carruthers 2024b).
Table 4. An example of exploratory climate tests of decreasing: natural survival (M), mean recruitment strength (R), condition factor (C) and somatic growth (K) for six generic management procedures applied to a Atlantic blueshark operating model (Carruthers 2024b). Numbers represent the percentage change in each exploratory test before the robustness threshold was crossed. Higher numbers indicate higher impacts and hence a more climate robust management procedure.
4.3 Conceptual Test
Conceptual tests differ from exploratory tests in that they use a theoretical model to predict the combined impacts of climate at the same time. For example, rather than separately rating MP robustness given increasing the natural mortality rate (M) and reductions in somatic growth (K), the conceptual test makes a paired projection of these parameters, linking them by a theoretical ecosystem model.
Figure 3. An example of three individual future projections of natural mortality rate (M, increasing) and somatic growth rate (K, decreasing). In this example, made-up for didactic purposes, the two parameters are negatively correlated, and constrained theoretically by the equations of the ecosystem model. The three lines represent three individual projections at low, medium and high climate impacts (green, orange, red). Changes are phrased as a mulitple of current levels (e.g. they start at 1 in 2024).
Figure 4. As Figure 3 but including the projected values of 10 simulations combined.
In combined tests, all parameters change with increasing climate impact so results are easily presented graphically along a continuum of climate impact (Figure 5).
Figure 5. The Combined Climate test. The robustness of MPs can be rated over a finer resolution of climate impacts than presented in Figure 4 (which only had three). In this case MPs are rated according to whether they could remain above 80% of current spawning stock biomass (dashed horizontal line, with increasing ocean warming scenario). When they cross this robustness threshold they are scored according to the degrees of warming when this occurred. In this case MP1 is the most robust crossing the robustness threshold after a warming of 0.9 degrees.
4.4 Tailored Test
Tailored tests are identical to conceptual tests but they are based on ecosystem models specified and calibrated for the particular case study stock. For example, the conceptual test might apply model-based projections for a lower trophic level pelagic fish stock. The tailored test could be based on, for example, impacts on natural mortality, growth and recruitment for Sarding in an Atlantis ecosystem model for the Benguela Ecosystem.
5 Using Climate Test
5.1 Overview
There are four distinct steps to conducting a Climate Test for any given case study:
- Prepare operating models to have no climate change impacts
- Tune candidate management procedures so they have identical outcomes given no climate impacts
- Test the MPs over increasingly stringent climate impacts
- Summarize results
These steps are connected:
Step 1. Since the Climate Test manipulates future productivity and catchability dynamics to represent possible climate impacts, any existing changes must first be removed. There are for example, cases where operating models in a reference grid include systematic changes in recruitment. It is possible that these are more extreme than the various climate impacts simulated in the climate tests which would lead to outcomes that are more optimistic in the tests and hence invalidate the underlying concept of stress testing MPs until they cross a robustness threshold. In this step the historical fishery and stock is also reconstructed including the calculation of various refernece points. This step only has to be conducted once saving computation due to historical reconstruction in all future climate test calculations such as MP tuning.
Step 2. Once operating models have had climate impacts removed the various candidate MPs must be tuned to obtain identical future outcomes. The default is to tune them such that they have the same spawning stock biomass (SSB) in projection year 30. They must be tuned in this way to ensure that reductions in SSB due to climate tests can be meaningfully compared among MPs. The tuning process uses the MSEtool function tune_MP(). This is a newton search for a tuning parameter that minimizes the difference between current (last historical year, e.g., 2019) SSB and SSB in projection year 30 (e.g., 2059). In this way all tuned MPs will provide a stable SSB outcome without climate impacts.
Step 3. In the third step various escalating climate impacts are imposed on the operating models. The tuned MPs are projected in these simulated conditions generating predicted yield and SSB outcomes (amongst many other outputs).
Step 4. The fourth step analyses these projected data to evaluate the climate robustness of the tuned MPs. For example, by calculating the level of climate impact before an MP crosses a specified Robustness Threshold. This could be 80% of current SSB in projection year 30. For example, if MPs 1 and 2 cross the robustness threshold given 4% and 8% increases in natural mortality rate over 30 years we might say MP 2 is twice as climate robust as MP 1. We could derive a language based on this robustness such as ‘M4’ and ‘M8’ robust.
The rest of this section goes into detail on how to carry out the Climate Test in code, for a real application.
5.2 Installation
5.2.1 R and RStudio
Climate Test uses the R statistical environment and the openMSE libraries to manipulate operating models, management procedures and conduct closed-loop simulations. To start it is recommended that you install the latest versions of R and RStudio.
R can be downloaded from the R Project for Statistical Computing webpage.
Currently the ClimateTest package is compatible with R version R version 4.5.1 (2025-06-13 ucrt).
RStudio can be downloaded from the Posit webpage.
Currently the ClimateTest package is working in RStudio version 2025.05.1 Build 513.
The ClimateTest package has only been tested on a Windows x64 system.
5.2.2 Installing R Packages
Open RStudio and run the following lines of code at the R command prompt:
install.packages("remotes")
remotes::install_github('blue-matter/ClimateTest')To test that installation worked correctly run the following line of code to produce a multi-panel summary of an example Exploratory Climate Test:
library(ClimateTest)
CT_4_summary(CT_data, tests = "M", RT = 0.6, horizon = 30)
5.3 Demonstration Using OM annd MPs of the ClimateTest Package
5.3.1 Prerequisites
To start using ClimateTest functions you need to load up the library and initialize a cluster on your computer for parallel processing. Parallel processing is important because ClimateTest is very computationally demanding, involving numerous simulated projections of stock dynamics in the tuning of management procedures and the testing of climate robustness.
library('ClimateTest') # ClimateTest functions and demo objects
packageVersion('ClimateTest') # Check ClimateTest version
setup() # Set up parallel processing5.3.2 Examine Demonstration Operating Models and Management Procedures
The ClimateTest R package comes with small (few simulations) operating models and fast (empirical) management procedures that are ideal for demonstrating the various steps of the Climate Test approach.
You can list these objects using the objs() function:
objs('MP') # Generic management procedures## Searching for objects of class MP in package: ClimateTest## [1] "CE_b" "CE_c" "CE_d" "CE_e" "Ir" "It" "MCC11_b"
## [8] "MCC11_c" "MCC11_d" "MCC11_e" "MCC9_b" "MCC9_c" "MCC9_d" "MCC9_e"objs('OM') # Demo operating models## Searching for objects of class OM in package: ClimateTest## [1] "BET_1" "BET_2" "BSH_1" "BSH_2"Here we can see two example operating models based on recent stock assessments of Bigeye Tuna (BET) and Blue Shark (BSH) in the North Atlantic Ocean.
There are two operating models for each species, the first is the base model derived directly from the recent Stock Synthesis 3 assessment for those stocks. The second (e.g. BET_2, BSH_2) is identical but has a lower level of resilience (Beverton Holt stock-recruitment steepness value is lower).
The example management procedures are empirical (i.e. do not include models that estimate stock and fishery dynamics) and aim for a constant harvest rate (Index-ratio, ‘Ir’, a constant ratio of TAC to relative abundance index level) or a target index level (Index-target, ‘It’, a specified level of a relative abundance index).
To learn more about the management procedures and operating models you can call up the in-line R help:
?Ir # Help documentatation for the Index-ratio empirical MP included in the ClimateTest package
?BET_1 # Help documentatation for the N. Atl. Bigeye operating model included in the ClimateTest package5.3.3 Step 1, Prepare Operating Models
A user defined list of operating models (openMSE class ‘OM’ or ‘MOM’) are standardized to have no climate impacts, returning the historical simluations spooled-up for projections.
OM_list = list(BET_1, BET_2) # A list of operating models
Hist_list = CT_1_prep(OM_list) # Same OMs but without climate impacts and included historical reconstruction5.3.4 Step 2, Tune MPs
The user identifies (by name) management procedures (openMSE class ‘MP’) that are to be tuned to have the same projected outcome:
Ir1 = Ir2 = ClimateTest::Ir # Generic Index ratio MP from Climate Test package
formals(Ir2)$maxchng = 0.15 # TAC updates for Ir2 can now vary by 15% (default is 10%)
It = ClimateTest::It # Generic Index target MP from Climate Test package
# Tuning options
horizon = 30 # Tuned to be same as current in 30 projected years
# Tuning: defaults to MP parameter 'tune' over range 1/3x to 3x the default MP argument value
MPs_tuned = CT_2_tune(Hist_list,
MPs = c("Ir1","Ir2","It"), # Names of MPs to be tuned and tested
type = "SSB", # Spawning stock biomass tuning
horizon) # SSB is same as current in 30 yearsThe R console should look something like this:
As the optimizer runs, it reports both the value of the tuning parameter (in this case ‘tune’) and the ratio of SSB in horizon years: SSB current (the ‘Target ratio’). When this ratio is close to 1 and the tuning parameter is changing less than the specified tolerance (user defined parameter ‘near_enough’ - the default is 1E-4), the optimizer stops and moves to the next MP.
If all MPs are tuned such that they are ‘near_enough’ to the target ratio of 1 (SSB_horizon ~= SSB_current) the tuning will end with output confirming that all MPs were tuned successfully:
5.3.5 Step 3, Conduct Climate Tests
The tuned MPs are now projected for incrementally increasing climate tests.
tests = c(S = 200, # Up to 200% increase in catchability over the specified horizon
M = 25, # Up to 25% increases in natural mortality over the specified horizon
R = 50, # Up to 50% decreases in recruitment strength over the specified horizon
K = 75, # Up to 75% decreases in somatic growth over the specified horizon
C = 75) # Up to 75% decrease in condition factor (W / L^b) over the specified horizon
nlev = 8 # Number of levels for each test (to interpolate over)
CT_data = CT_3_test(Hist_list, # From Step 1
MPs_tuned, # From Step 2
nlev, horizon, tests) # Over 8 increments for 30 projected yearsRunning the climate test is effectively running a very large number of projections. Lets say you have 5 tests and 8 levels of each test for 2 operating models, 3 MPs over 100 simulations and 35 projection years. That is 5 x 8 x 2 x 3 x 100 x 35 = 840,000 simulation-years of calculations. Some model-based MPs take time to run for each of these simulation-year combinations. You can first run a small (low simulation number, single OM) climate test to get an idea of the running time.
The test is run in parallel across the levels of each marginal test (8 levels by default) so providing you have enough threads that may not impact running time substantially.
Once the climate test has run you should see something like this output:
5.3.6 Step 4, Summarize Results
The results data arising from the test can be plotted and presented in various ways:
# Raw results
results = CT_metrics(CT_data, horizon) # Calculate SSB and yield loss metrics
results$SSB_relative # SSB loss metrics across tests and MPs## $C
## 0 10.714 21.429 32.143 42.857 53.571 64.286 75
## Ir1_CT 1.00004 0.91549 0.81518 0.71008 0.61055 0.51931 0.45807 0.39847
## Ir2_CT 1.00041 0.94129 0.88453 0.87068 0.85126 0.82961 0.80831 0.79482
## It_CT 0.99930 0.89949 0.78687 0.69627 0.60343 0.55414 0.54809 0.54463
##
## $S
## 0 28.571 57.143 85.714 114.286 142.857 171.429 200
## Ir1_CT 1.00004 0.88435 0.80317 0.75763 0.72541 0.71178 0.70195 0.69220
## Ir2_CT 1.00041 0.82705 0.70300 0.60213 0.51097 0.42732 0.35750 0.29018
## It_CT 0.99930 0.85444 0.82748 0.80152 0.76913 0.73528 0.71918 0.71882
##
## $M
## 0 3.571 7.143 10.714 14.286 17.857 21.429 25
## Ir1_CT 1.00004 0.85923 0.72753 0.60313 0.49397 0.39326 0.30361 0.23668
## Ir2_CT 1.00041 0.87942 0.76290 0.66259 0.59118 0.52443 0.46238 0.40812
## It_CT 0.99930 0.84765 0.70444 0.57352 0.46821 0.37163 0.29788 0.24372
##
## $R
## 0 7.143 14.286 21.429 28.571 35.714 42.857 50
## Ir1_CT 1.00004 0.88866 0.79205 0.69487 0.61318 0.53911 0.46659 0.40214
## Ir2_CT 1.00041 0.90536 0.82138 0.74371 0.68687 0.64259 0.60249 0.56399
## It_CT 0.99930 0.88018 0.77301 0.67167 0.58096 0.50899 0.44262 0.38031
##
## $K
## 0 10.714 21.429 32.143 42.857 53.571 64.286 75
## Ir1_CT 1.00004 0.89357 0.76386 0.62411 0.49437 0.39095 0.32687 0.26271
## Ir2_CT 1.00041 0.92470 0.86214 0.83470 0.79973 0.76065 0.72512 0.68823
## It_CT 0.99930 0.87717 0.73308 0.60862 0.49706 0.46252 0.46416 0.46040# Robustness metrics
RT = 0.85 # Robustness threshold, when SSB drops below this fraction of starting SSB
tab = CT_tabulate(results$SSB_relative, RT) # Calculate the test level where RT is crossed
dt = makeCTtab(tab) # HTML table
dtCT_proj(CT_data, horizon, "M", RT = RT) # Explanatory figure
CT_4_summary(CT_data, RT = RT, horizon = horizon) # Summary figure
5.4 Exploratory Test For N. Atl. Swordfish
In this example we use a single reference set operating model from the North Atlantic Swordfish MSE (MOM_005), strip it to a small number of simulations for demonstration purposes, and then Climate Test the adopted MP (‘mostly constant catch’, MCC11_b, Duprey 2024) and a competing candidate MP assuming approximately constant exploitation rate (CE_b, Hordyk 2024)
5.4.1 Prerequisites
For this real-world application you will need to install the SWOMSE R package from github, and then load it and the ClimateTest package:
install.packages('pak') # Install package management
pak::pkg_install('https://github.com/ICCAT/nswo-mse') # Install SWOMSE R package from ICCAT repo
library(SWOMSE) # Load N. Atl. swordfish MSE library
library('ClimateTest') # ClimateTest functions and demo objects5.4.2 Step 1, Prepare Operating Models
Here we take one of the swordfish multi-operating model objects (MOM_005) and reduce it to just the first 8 simulations so that the code runs fast enough for demonstration purposes. After which, all climate impacts are removed and the historical simulation is spooled-up using CT_1_prep.
small_OM = SubCpars(MOM_005,1:8)
OM_list = list(small_OM)
Hist_list = CT_1_prep(OM_list) # Spool up with no climate effects5.4.3 Step 2, Tune MPs
In step 2 we tune two empirical MPs, CE_b and MCC11_b, the latter being the MP adopted for use in N. Atl. Swordfish management. The projection runs for something like 32 years so we are using a 25 year horizon.
horizon = 25
CE_b = ClimateTest::CE_b
MCC11_b = ClimateTest::MCC11_b
MPs_tuned = CT_2_tune(Hist_list,
MPs = c("CE_b","MCC11_b"), # Names of MPs to be tuned and tested
MP_par_nams = rep("tunepar",2), # tuning parameter names for each MP
type = "SSB", # Spawning stock biomass tuning,
horizon, # Returns a list of tuned MPs
parallel=F) # No need to run in parallel (only 1 OM)5.4.4 Step 3, Conduct Climate Tests
The spooled-up operating models (Hist_list) and the tuned MPs (MPs_tuned) are then used in the Climate Test. This is run in parallel across the various levels of each type of test (e.g., 8 levels of natural mortality rate increase (M) from 0 to 25% over 25 years). To make sure everthing needed is available to the cluster for parallel computation, we use the sfLibrary() function to export all SWOMSE functions and variables to the cluster:
setup() # Initialize cluster for parallel computation
sfLibrary(SWOMSE) # Send SWOMSE objects and functions to the cluster
tests = c(C = 75, # Up to 75% decrease in condition factor (W / L^b)
S = 200, # Up to 200% increase in catchability
M = 25, # Up to 25% increases in natural mortality
R = 50, # Up to 50% decreases in recruitment strength
K = 75) # Up to 75% decreases in somatic growth
nlev = 8 # Number of increments for each test (to interpolate over)
CT_data = CT_3_test(Hist_list, MPs_tuned, nlev, horizon, tests)5.4.5 Step 4, Summarize Results
As before, the climate test outputs can be presented as tables or figures:
RT = 0.8
results = CT_metrics(CT_data, horizon=horizon) # Calculate SSB and yield loss metrics
results$SSB_relative # SSB loss metrics
tab = CT_tabulate(results$SSB_relative, RT=RT) # Calculate the test level where RT is crossed
makeCTtab(tab) # HTML table
CT_proj(CT_data, horizon, "M", RT=RT) # Plot the projected SSB outcomes and test level
CT_4_summary(CT_data, RT = RT, horizon = horizon) # Summary plot of all tests6 Recent Presentations
7 Climate Test Papers
8 References
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9 Acknowledgements
ClimateTest is funded by:
The Paul M. Angell Family Foundation Oceankind The Pew Charitable Trusts
and
The Ocean Foundation in partnership with www.HarvestStrategies.org