The FLR project Objectives To develop a platform for quantitative - - PowerPoint PPT Presentation

Fisheries modelling in R: the FLR (Fisheries Library in R) project

• P. Grosjean, R. Hillary, E. Jardim, L. T. Kell, I. Mosqueira,
• J. J. Poos, R. Scott

UseR2006 – p. 1

The FLR project

• Objectives
• To develop a platform for quantitative work in fisheries

biology, assessment and management based on R.

• To encourage open and transparent collaboration in

fisheries research.

• To introduce new tools and procedures already in use

in other fields.

• To improve upon the quality of the scientific work

carried out for fisheries management.

• Research and management applications
• The project

UseR2006 – p. 2

The FLR project

• Objectives
• Research and management applications
• Support for data collection and analysis of sampling

design issues

• Exploratory data analysis, data aggregation and error

checking

• Stock assessment and estimation of stock status

indicators

• Simulation testing of management scenarios
• The project

UseR2006 – p. 3

The FLR project

• Objectives
• Research and management applications
• The project
• http://flr-project.org
• A small team in charge of FLCore, general design and

package release

• EU-funded research projects

UseR2006 – p. 4

The FLQuant class

• Basic “building block” of FLCore, holds most fisheries data

(biological, technological, economic)

• A five dimensional array (soon to be 6D)
• Dimensions: quant, year, unit, season, area, (iter)
• Attribute: units

UseR2006 – p. 5

The FLQuant class

North Sea Plaice, area 4

catch−at−age

0 2 4 6 8 1960 1980 2000

1

020 60 100 1960 1980 2000

2

50 150 250 1960 1980 2000

3

50 150 1960 1980 2000

4

50 100150 1960 1980 2000

5

20 60 100 1960 1980 2000

6

0 20 40 60 80 1960 1980 2000

7

0 510 20 30 1960 1980 2000

8

5 10 15 1960 1980 2000

9

5 10 1960 1980 2000

10

0 2 4 6 8 10 1960 1980 2000

11

2 4 6 8 1960 1980 2000

12

1 2 3 4 1960 1980 2000

13

0.5 1.5 1960 1980 2000

14

1 2 3 4 5 1960 1980 2000

15 UseR2006 – p. 6

FLCore: classes

• Fully designed around S4 classes
• Inheritance provides good extensibility (FLAssess)
• Method overloading reduces command set for

interactive use and simplifies modular development (assess)

• C++ classes
• FLCore classes have been replicated in C++ to use

with R headers

• To help integrating legacy code and speed up slow

calculations

• Accesor and replacement functions automatically

generated at package compile time

UseR2006 – p. 7

FLCore: classes

• Example: FLStock

UseR2006 – p. 8

FLCore: methods

• Extensive use of lattice to deal with plots of

multi-dimensional data

• Minimum set of methods required for new classes
• show, summary, plot
• window
• A number of new generic methods covering common
• perations
• Overloading of many S3 methods in R base

UseR2006 – p. 9

Other packages

• FLEDA: exploratory data analysis, lattice plots,
• FLBayes: Bayesian fisheries models, McMC S4 class
• FLAssess + FLXSA: Stock assessment using VPA methods
• FLOE: Observation error
• FLOM: Fisheries Operating Model conditioned on

age-structured assessment results

• FLEcon

UseR2006 – p. 10

Stock assessment with FLAssess

• Stock assessment is a fundamental task in fisheries

science.

• Separate implementations of sometimes similar methods

require and return input and output files in different formats

• Data available as FLR objects can be input to a range of

assessment methods

• Output diagnostics and standard plots are available with

the same syntax for different assessment models

• ICES advice system requires yearly evaluation of stock

status and trends

• Exploration of data and results is limited by time constraints

and the difficulty of moving data between incompatible software

UseR2006 – p. 11

Management Strategy Evaluation

• Computer simulation of stock, fishery, advice and

management systems

• Exploration of uncertainties and their impact on

management

• Comparison of complex models and simpler management

rules under varipous scenarios

• Objective is the design of management procedures robust

to present and future uncertainties

• Pioneered by the development of the Revised

Management Procedure of the IWC

• Operational Model of the fishery system (stock & fleet)
• Data collection and stock assessment
• Harvest Control Rule for management decision making
• Interaction through Bayesian Belief Networks

UseR2006 – p. 12

Management Strategy Evaluation

• Fisheries operating models: Northern Hake (Garcia, D.,

Mosqueira, I.)

• Variability in SSB, F and TAC due to uncertainty in

recruitment and indices of abundance

UseR2006 – p. 13

Future developments

• Increase its adoption on various fora (ICES, Tuna

Commissions)

• Packages in development
• Cluster and grid computation
• Storage of FLR objects in SQL databases
• File format based on XML and StatDataML
• Implementation of unit testing

UseR2006 – p. 14