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Setup Case Study Area and
Environmental Data
Identify Fishing Positions
Compute Fishing Effort
Identify Homogeneous
Regions
Load Vessel Characteristics
Estimate LPUE
Compute Total Production
Slice the biological resources into Age
Cohorts
Simulate Management
Scenarios
Evaluate the biological status of
the stocks
Input8. Assess
Interactions
Population
Parameters
MALK
R₀
The starting input for the Assess Module has
been prepared in all the previous steps. The user
can choose to perform either a Single Species or
a Multi-Species stock assessment.
For the single species, the user can inspect the
estimated starting parameters and eventually
modify as preferred. For the multi-species
assessment, in addition to the starting parameter
tweaking, the user must supply the interaction
network between the studied species as prey/
predator interaction, included cannibalism.
The Assess Module performs an optimization
of the starting parameters to estimate the
critical descriptors of the studied species. The
optimized parameters include point estimates
and variability of the number of recruits, the
stock biomass, and fishing mortality.
The assessment follows the framework of a
cohort model with Statistical Catch At Age
implementation. Specifically, the method is
referred to as a model of intermediate
complexity or MICE.
Operations
Input7. SimulationThe Simulation Module performs a stochastic optimization of the individual Fishing Pattern of the studied vessel, seeking the maximization of the fisher profit (revenues minus costs). Other than the explcit input to be provided by the user (species size/price at the market, activity costs, and management strategy), the simulator employs all the intermediate output from the previous steps (observed Fishing Pattern, Fishing Grounds, LPUE matrix, Age/Length Key).
Strategy
Costs
The size/price dataset is a collection of price of species at the market. Format: CSV file, with minimum and maximum prices by species and harbor.
The Costs dataset is built from a sample of vessel with individual based measures of costs. Format: CSV file, with vessel IDs, fixed costs, and variable costs.
The Management Strategy is made by the different scenario foreseeable by the user. Format: the builtin function allows users to select areas subject to fishery restrictions.
Price
.csv
.csv
Operations
OptimizedEffort
Input6. Mixture
L∞K
t₀Age/Length
KeyGrowth
Parameters
The Mixture Module performs a mixture decomposition to identify the age cohorts from the Length Frequency Distribution of the provided species. The Fishery and the Survey dataset are elaborated separately to estimate the growth parameters. The spatial distribution of the species can be merged or it is possible to chose one of the two.
The Fishery dataset has the same format of the Survey data but it is built from samples provided by the fishers.Format: CSV file, with haul position, timestamp, species, weight and length.
The Survey dataset is built from samples collected during a scientific survey. Each specimen in the sample is classified, weighted and measured. Format: CSV file, with haul position, timestamp, species, weight and length.
Fishery
Survey
.csv
.csv
Operations
InputThe Production Module loads the raw landings data and connects, for the available vessels, the Effort Pattern to the landed species and quantities. The Logit sub-model discriminates between targeting and by-catch activity. The LANDER model estimates landings rates (LPUE - Landings Per Unit of Effort) for each Fishing Ground.
The Landings dataset is made of records of the landed quantity by species of a single trip of a sample of vessels. Format: CSV file, with vessel IDs, timestamp, species and the landed quantity
The other required input is the observed Pattern of Effort (Fishing Hours aggregated by Fishing Ground) as the unit of effort and the landing records (with vessel ID, Timestamp, Species, and Quant i ty information) both at the individual level.
Landings
LPUEmatrix
LogitEffort
Pattern
5. Production Operations
InputThe Fleet Register stores the vessel specific information as Length Over All (LOA), engine power, and the port of registration. Format: CSV file, with vessel IDs, LOA, Power and Port of Registration.
The set of ports names is geocoded to obtain the coordinates of each harbour. The other input is the Fishing Ground configuration from the previous module. The GUI allows users to graphically explore the summary characteristics of the fleet.
.csv
The Register Module connects the individual characteristics of each vessel to the performed fishing activity. The collected information is employed twice.
F i r s t , t h e p o r t o f r e g i s t r a t i o n i s georeferenced and the average distance between each fishing ground and harbour is computed.
Successively, the LOA and power of the vessel are used to calibrate the individual fishing power in the Production Module.
FishingGrounds
FleetRegister
4. Register Operations
InputThe input for the Fishing Ground
Module is the grid topology, a vector of
depth values, the presence/absence
matrix of the seabed habitats, and the
cell-aggregated Effort Pattern.
It is possible to supply other custom input.
Directly if the provided data conforms to
the format, otherwise it is required to
adapt the procedure.Effort
Pattern
Fishing GroundConfiguration
Environment
Data
3. Fishing Grounds‘Regionalization is a classification procedure applied to spatial objects with an areal representation, which groups them into homogeneous contiguous regions’The grid topology is then aggregated into g r o u p o f a d j a c e n t c e l l s w i t h homogenoues conditions. The output of the routine is the regionalised fishing ground configuration.
Operations
InputThe Effort Module is designed to download already processed data stored in the standard database format of the vmsbase package. The data is made by the positions of individual vessels recorded by the VMS or AIS system.
Format: List of dataframes, one for each year, with vessel IDs, coordinates, timestamp, speed and heading.
vmsbaseDB
or custom
Operations2. EffortThe GUI extracts the effort data from one or more vmsbase SQLite databases.
It identifies the fishing position, based on the gear characteristics, and it computes the individual Effort Pattern aggregated to the grid cells (as individual vessel measures of daily fishing hours by cell).
Effort Pattern
InputThe Environment Module loads the Grid to define the case study’ extent and the minimal spatial unit of the fishery.
With the marmap package, the bathymetry data is automatically downloaded and stored as a continuous variable (vector) measured at the grid centers.
The user provided presence/absence matrix for the type of seabed is then employed, along with the other variables, to define the Fishing Grounds.Seabed
Bathymetry
Grid
The Grid defines the physical boundaries of the case study. The cell size determines the smallest geographical unit. Format: Regular square grid as a shape-file.
Bathymetric information of the area of interest as numerical matrix with the seafloor depth at the center of each cell.
Binary data of the bedfloor characteristics as a Presence/Absence matrix of the predominant substrate type in each cell.
1. Environment Operations
.csv
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11 12 13 14 15
Longitude
Lat
itu
de
Case Study Cells
Grid
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11 12 13 14 15
Longitude
Latitude
SeabedDC
DL
HP
SFBC
VB−PSF
VB−VC
VB−VSG
VTC
Seabed
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37
38
11 12 13 14 15
Longitude
Latitude
Depth
35
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37
38
11 12 13 14 15
Longitude
Lat
itu
de
Status At sea In harbour
Sample raw points − 2012
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11 12 13 14 15
Longitude
Lat
itu
de
Status Not fishing Fishing
Sample fishing points − 2012
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11 12 13 14 15
Longitude
Lat
itu
de
100 100.5 101 101.5
Count
Fishing Effort − 2012
vesselv timet
FTvtg1 ..FTvtgn
landedQtyvt
g v − eth
t − eth
Targeting : Tvts =
1 = target, landedQtyvt ≥ thresholds
0 = notarget,
v t
s
LPUEs s t
g LPUEs
W = αLβ
α β
CFT vg
FTvtg1 ..FTvtgn v − eth
wg
spatialIndexv =
∑G
g=1wgCFT v
g∑G
g=1wg
daysAtSeav =D∑
d=1
Outvd
At Sea Status : Outvd =
1 = at sea
0 = in harbour
at sea
v − eth d − eth
productionIndexv =T∑
t=1
S∑
s=1
Lvst
spatialIndexv LOAv v − eth
βsc1
βsc2
spatialIndex LOA
spatialCostv = βsc1spatialIndexv + βsc
2LOAv
daysAtSeav LOAv Kwv v − eth
daysAtSea Index
βec1
βec2
βec3
daysAtSea LOA Kw
effortCostv = βec1daysAtSeav + βec
2LOAv + βec
3Kwv
productionIndexv v − eth
βpc1
productionCostv = βpc1productionIndexv
Z a y
Zya = Ma + SaFy
Ma a Sa
a Fy y
Nya =
R0eεy , a = 0
Ny−1a−1e−Zy−1a−1 , 1 ≤ a ≤ x
Ny−1x−1e−Zy−1x−1 +Ny−1xe
−Zy−1x , a = x
Nya a y R0
eεy x
Cya
Cya =SaFy
Zya
Nya(1− e−Zya)
SSBy =x∑
a=0
wamaNyae−0.5Zya
SSB
SSB
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