product user manual – tuna population models outputs ... · the oceanic fisheries are dominated...

INDESO

Product User Manual – tuna population mod-els outputs (yellowfin, shipjack, bigeye)

Reference: IN-WP6.2-PUM-296

Nomenclature: -

Issue: 1. 0

Date: Sep. 4, 15

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PRODUCT USER MANUAL – TUNA POPULATION MODEL

IN-WP6.2-PUM-296 - V 1.0

Proprietary information: no part of this document may be reproduced, divulged or used in any form without prior permission from CLS.

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Chronology Issues:

Issue: Date: Reason for change: Author:

0.1 22/04/15 Preliminary version V. Rosmorduc

0.2 31/08/15 expert revision I. Senina

1.0 04/09/15 Initial version V. Rosmorduc

People involved in this issue:

Written by (*):

V. Rosmorduc Date + Signature:( visa ou réf)

Checked by (*):

B. Pirrotta Date + Signature:( visa ou réf)

Approved by (*): Date + Signature:( visa ou réf)

Application author-ized by (*):

R. De Dianous Date + Signature:( visa ou réf)

*In the opposite box: Last and First name of the person + company if different from CLS

Index Sheet:

Context:

Keywords: [Mots clés ]

Hyperlink:

Distribution:

Company Means of distribution Names

CLS Notification CLS Management review team

CLS Soft copy CLS INDESO team

Balitbang KP Soft copy Balitbang KP INDESO team

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List of tables and figures

List of tables:

Table 1: Tuna model output datasets ........................................................................... 7

List of figures:

Figure 3.3. Mean primary production predicted from the PISCES biogeochemical model and from two empirical models based on satellite ocean colour data. ................................. 3

Figure 3.4. Comparison between biomass distribution of epipelagic and bathypelagic micronekton predicted with SEAPODYM global model using primary production estimates from VGPM and EPPLEY-VGPM (week of 14 Dec 2014). ............................................... 4

Figure 2.1. SEAPODYM: an integrated system for operational modeling of tuna stock .............. 6

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List of Contents

1. Purpose ...................................................................................... 1

2. Processing ................................................................................... 1

2.1. Introduction .......................................................................... 1

2.2. Input data ............................................................................. 2

2.2.1. Global SEAPODYM forcing .......................................................................... 2

2.2.1.1. Physical forcing (temperature and currents), ............................................. 2

2.2.1.2. biogeochemical forcing ........................................................................ 2

2.2.1.3. Fishery forcing (catch by species and size): Pacific Tuna Fishing data ............... 5

2.3. Method: SEAPODYM model ......................................................... 5

3. Description of the product specification ............................................. 7

3.1. Product general content and specifications .................................... 7

3.2. Nomenclature of files ............................................................... 7

3.3. Acknowledgments ................................................................... 9

4. Data format ................................................................................. 9

4.1. NetCdf ................................................................................ 10

4.2. Structure and semantic of NetCDF files ........................................ 10

4.3. Structure and semantic of ASCII files ........................................... 11

4.4. Structure ofPDF files ............................................................... 12

5. How to download a product ............................................................ 12

5.1. Registration .......................................................................... 12

5.2. Access Services ..................................................................... 13

6. References ................................................................................. 14

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1. PURPOSE

This document is prepared for the users of INDESO project datasets in order to provide necessary information to understand the tuna population model outputs (yellowfin, skipjack and bigeye)..

This document is organized as follows:

- Chapter 2: input data and method.

- Chapter 3: the product description, files provided, the nomenclature

- Chapter 4: the data formats

- Chapter 5: how to download products.

- Chapter 6: bibliographical references

2. PROCESSING

2.1. INTRODUCTION

The tuna dynamic model used for the INDESO project is a regionalized version of the SEAPODYM model (Bertignac et al., 1998; Lehodey et al., 2003; Lehodey et al., 2008; Senina et al., 2008). The SEAPODYM model simulates the spatial and temporal dynamics of age-structured pelagic fish popu-lations under the combined pressure of fisheries and oceanic variability. SEAPODYM predicts the catch using reported fishing effort and the characteristics (catchability and selectivity) of the fish-ing gear. The SEAPODYM (Spatial Ecosystem And Populations Dynamics Model) adapted to the Indo-nesian archipelago includes a representation of several functional groups of intermediate trophic levels (mesozooplankton and micronekton) and three tuna species: skipjack (katsuwonus pelamis), yellowfin (Thunnus albacores) and bigeye (Thunnus obesus).

The regional SEAPODYM model is resolved at a horizontal resolution of 1/12° and 3 vertical layers. The horizontal grid is Arakawa A-type with grid center being used to evaluate model variables. The vertical pelagic layers are defined by the euphotic depth. The model is forced by physical and bio-logical variables provided by the INDESO project (see Physical model and Micronekton model out-

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puts) and by the climatological fishing effort data. At the lateral open boundaries, the regional SEAPODYM model uses analysis and forecast fields from a global version of the SEAPODYM model implemented in CLS.

Tuna biomass distributions are provided by life stage, i.e. larvae, juvenile, young (immature) and adult. The number of recruits and total population biomass are also available. Predicted catch are saved in ASCII format files containing the summary of fish population biomass and fisheries statistics (effort, catch, number of spatial observations and CPUE) for Indonesian Fisheries Management Are-as. Validation metrics are provided as pdf files.

2.2. INPUT DATA

Given the high-migratory nature of these species the regional INDESO model needs to be con-strained at its open boundaries by the conditions defined with a global model. The quality of the regional model outputs are strongly linked to the outputs of the global model.

2.2.1. GLOBAL SEAPODYM FORCING

The quality of SEAPODYM model optimization and simulation outputs is linked to the accuracy of its forcing variables. These include the physical forcing (3D temperature and currents), the biogeo-chemical (primary production and euphotic depth; dissolved oxygen concentration) and the tuna biomass removal due to fishing.

2.2.1.1. PHYSICAL FORCING (TEMPERATURE AND CURRENTS),

The SEAPODYM model requires temperature and horizontal currents produced by global ocean circu-lation models. The first phase, i.e., the optimization, requires long hindcasts or reanalyses together with historical fishing data to achieve the best possible model parameterization.

2.2.1.2. BIOGEOCHEMICAL FORCING

Primary production (PP) and associated euphotic depth can be estimated either from the sum of small and large phytoplankton production simulated with the PISCES biogeochemical model, or with satellite ocean colour data.

The NCEP-ORCA2 hindcast simulation was coupled to the biogeochemical model PISCES (Pelagic Interaction Scheme for Carbon and Ecosystem Studies; Aumont and Bopp, 2006). PISCES incorpo-rates both multi-nutrient limitation (NO3, NH4, PO4, SiO3 and Fe) and a description of the plankton community structure with four plankton functional groups (Diatoms, Nano-phytoplankton, Micro-zooplankton and Meso-zooplankton). This model provided biogeochemical variables needed to run SEAPODYM

For the ocean reanalyses the primary production and euphotic depth were derived from satellite data. Because ocean reanalyses assimilate satellites (SST and altimetry) and in situ data, their pre-dicted fields of temperature and currents are globally coherent with those of primary production derived from ocean color data (Behrenfeld and Falkowski, 1997). However, there are many satellite "chlorophyll-based" models with empirically determined functions that can predict quite different estimates of primary production (Figure 2.1).

The first series of optimization experiments were conducted with VGPM primary production. How-ever, given the uncertainty on this key input variable and the contrasted distributions predicted at basin scale, a second series of optimization is currently conducted with the Eppley-VGPM model estimates, being different from VGPM model (Behrenfeld and Falkowski, 1997) by the relashionship of the phytoplanktonic growth and the temperature (Eppley, 1972). Based on the difference ob-

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tained between predicted biomass distributions of micronekton groups (Figure 2.2), it can be ex-pected substantial differences also in the optimal parameterization achieved with these two forc-ings.

PIS

CES

VG

PM

Epple

y V

GPM

Figure 2.1. Mean primary production predicted from the PISCES biogeochemical model and from two empirical models based on satellite ocean colour data.

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Epip

ela

gic

(V

GPM

)

Epip

ela

gic

(Epple

y-V

GPM

)

Bath

ypela

gic

(V

GPM

)

Bath

ypela

gic

(Epple

y-V

GPM

)

Figure 2.2. Comparison between biomass distribution of epipelagic and bathypelagic micronek-ton predicted with SEAPODYM global model using primary production estimates

from VGPM and EPPLEY-VGPM (week of 14 Dec 2014).

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2.2.1.3. FISHERY FORCING (CATCH BY SPECIES AND SIZE): PACIFIC TUNA FISHING DATA

In addition to be an essential variable to account for the fishing mortality in the population dynam-ics simulations, the fishing data are used for the model optimization. This optimization is conducted at the scale of the Pacific Ocean using data provided through collaboration with the Oceanic Fisher-ies Programme of the Secretariat of the Pacific Community and the Western Central Pacific Fisher-ies Commission (project WCPFC 62).

Since tuna are highly migratory species, they are managed by International Commissions requiring from their Country Members a detailed collect of fishing information. These Commissions maintain databases archiving the historical tuna catch, fishing effort and size frequencies of catch for the fisheries in their convention area, ie Indian Ocean for IOTC, Western Central Pacific Ocean (WCPO) for the WCPFC and Eastern Pacific Ocean for the IATTC. These ocean basin-scale datasets are used both to run optimization experiments at Pacific basin-scale and to take into account the fishing mortality due to all fisheries.

The oceanic fisheries are dominated by skipjack, yellowfin and bigeye tuna and albacore, which together represent > 90% of the total catch taken by industrial fleets. The industrial tuna fisheries are based on the use of large vessels owned by major fishing companies, with much of the catch marketed by multinational fish trading corporations. The largest of the two main fisheries is com-monly referred to as the surface fishery, where purse-seine and pole-and-line vessels target schools of skipjack tuna, and the smaller size classes (< 80 cm) of yellowfin tuna, in the equatorial regions. The catch from the surface fishery is used for canning. Although juvenile bigeye tuna are not the target of the surface fishery, the use of floating fish aggregating devices (FADs) now aids the cap-ture of this species. The surface fishery also includes small-scale artisanal fisheries using various fishing gear such as handlines and ringnets. With the development of the purse seine fishery since the 1980s, tuna catch in the Pacific Ocean has continuously increased despite year to year variabil-ity. The provisional total WCPO tuna catch for 2011 was estimated at 2,244,776 mt (Williams and Terawasi 2012), 300,000 mt below the 2009 record. The longline fishery account for around 10–13% of the total WCPO catch with several thousand boats of different size categories.

Pacific tuna fisheries were stratified with the best available fishing data sets that have been pro-vided by the WCPFC (through SPC), and the IATTC at a resolution of 1° x month or 5°x month. The resulting fisheries definition by species is:

- Fourteen skipjack fisheries, mainly purse seine or pole and line fleets. - Twenty three fisheries for bigeye including 10 longline fisheries and 6 purse seine fisheries

in the WCPO and 5 longline and 1 purse seine fishery in Indian ocean area . - Nineteen fisheries for yellowfin including twelve longline fisheries and six purse seine fish-

eries and one pole-and-line fishery operating in the WCPO region.

Due to too low quality of fishing data for the domestic Philippines-Indonesia fishery, they are not used for optimization. However they were used to compute the overall fishing mortality.

To each fishery is associated a dataset of size frequencies of catch. These data are included in the Maximum Likelihood estimation approach and provide critical information on the population struc-ture and the fishing gear selectivity by size. The spatial resolution for these data varies between 5 and 20 degree square.

2.3. METHOD: SEAPODYM MODEL

SEAPODYM is a model developed for investigating spatial tuna population dynamics, under the influ-ence of both fishing and environmental effects. The model (Figure 2.3) simulates micronektonic groups (prey species) and their predators (tunas). The main features of the model are:

Integration of the effects of environmental variables, such as temperature, currents, primary production and dissolved oxygen concentration, on tuna populations;

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Prediction of the temporal and spatial distributions of functional groups of micronektonic prey representing the mid-trophic level (MTL) of the ecosystem (Lehodey et al. 2010), and age-structured predator (tuna) populations (Lehodey et al. 2008);

Prediction of the total catch and the size-frequency of catch by fleet;

Parameter optimization based on fishing data assimilation techniques (Senina et al., 2008).

Figure 2.3. SEAPODYM: an integrated system for operational modeling of tuna stock

(physics to fish)

The mid-trophic level model describes vertical and horizontal dynamics of prey groups. Dynamics of tuna populations are estimated using habitat indices, movements, growth and mortality. The feed-ing habitat is based on the accessibility of tuna to the groups of prey. The spawning habitat com-bines temperature preference and coincidence of spawning with presence or absence of predators and food for larvae. Successful larval recruitment is linked to spawning stock biomass and mortality during the drift with currents. Older tuna can swim in addition to being advected by currents. A food requirement index is computed to adjust locally the natural mortality of cohorts, based on food demand and accessibility to available forage components. The model includes a representation of fisheries and predicts total catch and size frequency of catch by fleet when fishing data (catch and effort) are available. A Maximum Likelihood Estimation approach is used to optimize the model parameters.

Development and results from this modeling approach are regularly presented to the Western Cen-tral Pacific Fisheries Commission (Lehodey, 2004a, b, 2005a, b, 2008; Lehodey et al. 2008, 2009,

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2010, 2011) and published in peer-reviewed scientific journals (Bertignac et al 1998; Lehodey 2001; Lehodey et al. 1998, 2003, 2008, 2010a; 2010b; 2012; Senina et al 2008; Sibert et al 2012; Bell et al 2013).

3. DESCRIPTION OF THE PRODUCT SPECIFICATION

3.1. PRODUCT GENERAL CONTENT AND SPECIFICATIONS

Each Indeso product includes a series of related datasets. Those datasets are delivered with differ-ent names (see nomenclature), contents (see NetCDF, Ascii and PDF contents) and format (below).

Note that the datasets available for a given user depend on the user profile.

Dataset Name Dataset time cove-

rage

Production frequency

Geographical coverage

Spatial Re-solution

File for-mat

Bigeye daily historical & real-time biomass distribution

from start to (T0+10 days)

weekly 20S-25N/90E-144E

1/12° regu-lar grid

Netcdf

Bigeye daily historical biomass distribution from start to (T0-30 days)



Netcdf

Bigeye daily historical & real-time predicted catch - zone xx



NA Ascii

Bigeye daily historical predicted catch - zone xx

from start to (T0-30 days)


NA Ascii

Skipjack daily historical & real-time biomass distribution




Netcdf

Skipjack daily historical biomass distribution from start to (T0-30 days)



Netcdf

Skipjack daily historical & real-time predicted catch - zone xx



NA Ascii

Skipjack daily historical predicted catch - zone xx



NA Ascii

Yellowfin daily historical & real-time biomass distribution




Netcdf

Yellowfin daily historical biomass distribution from start to (T0-30 days)



Netcdf

Yellowfin daily historical & real-time predict-ed catch - zone xx



NA Ascii

Yellowfin daily historical predicted catch - zone xx



NA Ascii

Critical tuna model historical & real-time metrics


weekly NA NA Pdf

Critical tuna model historical metrics from start to (T0-30 days)

weekly NA NA Pdf

Long term tuna model metrics On demand On demand NA NA pdf

Table 1: list of tuna model output datasets

3.2. NOMENCLATURE OF FILES

Files downloaded using Indeso downloading services are named using a unique identifier (13 digits, corresponding to the current time (downloading time) in milliseconds since January 1, 1970 mid-night UTC.) at the end of the file name. The metrics pdf and catch ascii files are compressed within a zip file (nomenclature of both the zip file and the pdf/ascii within are listed here).

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Bigeye daily historical&real-time biomass distribution INDESO_BIGEYE-RT_%nnnnnnnnnnnnn.nc

Bigeye daily historical biomass distribution INDESO_BIGEYE_%nnnnnnnnnnnnn.nc

Bigeye daily historical&real-time predicted catch - zone xx INDESO_BIGEYE_Summary_%Zone-RT_%nnnnnnnnnnnnn.zip INDESO_BIGEYE_Summary_%Zone_%Y%m%d(prod).asc

Bigeye daily historical predicted catch - zone xx INDESO_BIGEYE_Summary_%Zone_%nnnnnnnnnnnnn.zip INDESO_BIGEYE_Summary_%Zone_%Y%m%d(prod).asc

Yellowfin daily historical&real-time biomass distribution INDESO_YELLOWFIN-RT_%nnnnnnnnnnnnn.nc

Yellowfin daily historical biomass distribution INDESO_YELLOWFIN_%nnnnnnnnnnnnn.nc

Yellowfin daily historical&real-time predicted catch - zone xx INDESO_YELLOWFIN_Summary_%Zone-RT_%nnnnnnnnnnnnn.zip INDESO_YELLOWFIN_Summary_%Zone_%Y%m%d(prod).asc

Yellowfin daily historical predicted catch - zone xx INDESO_YELLOWFIN_Summary_%Zone_%nnnnnnnnnnnnn.zip INDESO_YELLOWFIN_Summary_%Zone_%Y%m%d(prod).asc

Skipjack daily historical&real-time biomass distribution INDESO_SKIPJACK-RT_%nnnnnnnnnnnnn.nc

Skipjack daily historical biomass distribution INDESO_SKIPJACK_%nnnnnnnnnnnnn.nc

Skipjack daily historical&real-time predicted catch - zone xx INDESO_SKIPJACK_Summary_%Zone-RT_%nnnnnnnnnnnnn.zip INDESO_SKIPJACK_Summary_%Zone_%Y%m%d(prod).asc

Skipjack daily historical predicted catch - zone xx INDESO_SKIPJACK_Summary_%Zone_%nnnnnnnnnnnnn.zip INDESO_SKIPJACK_Summary_%Zone_%Y%m%d(prod).asc

Critical tuna model historical&real-time metrics INDESO_TUNA_CMetrics-RT_%nnnnnnnnnnnnn.zip INDESO_TUNA_CMetrics_%Y%m%d(prod).pdf

Critical tuna model historical metrics INDESO_TUNA_CMetrics_%nnnnnnnnnnnnn.zip INDESO_TUNA_CMetrics_%Y%m%d(prod).pdf

Long term tuna model metrics INDESO_TUNA_LTMetrics_%nnnnnnnnnnnnn.zip INDESO_TUNA_LTMetrics_%Y%m%d(Min)_%Y%m%d(Max)_%Y%m%d(prod).pdf

Where

%nnnnnnnnnnnnn is the identifier inserted by the downloading service

%Zone is one of the FMA (Fisheries Management Area) zones defined within Indonesian EEZ below:

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Figure XX. Indonesian Fisheries Managament Areas

and

Date Macro used

# digits Ex: Date

2001/03/20 9H15M20S

Year %Y 4 2001

Year %y 2 01

Month %m 2 03

Day in month %d 2 20

Day of the year %j 3 079

Hour %H 2 09

Minute %M 2 15

Second %S 2 20

3.3. ACKNOWLEDGMENTS

Original INDESO Products - or Value Added Products or Derivative Works developed from INDESO Products including pictures - shall include the following credit conspicuously displayed and written in full:

"© INDESO, 2013, a system implemented by CLS for Balitbang KP, all rights reserved".

(b) In case of any publication, the Licensees will ensure credit INDESO in the following manner:

"© INDESO, 2013, a system implemented by CLS for Balitbang KP, all rights reserved".

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4. DATA FORMAT

4.1. NETCDF

The products are stored using the NetCDF CF format. NetCDF (network Common Data Form) is an interface for array-oriented data access and a library that provides an implementation of the inter-face. The netCDF library also defines a machine-independent format for representing scientific data. Together, the interface, library, and format support the creation, access, and sharing of sci-entific data. The netCDF software was developed at the Unidata Program Center in Boulder, Colo-rado. The netCDF libraries define a machine-independent format for representing scientific data. Please see Unidata NetCDF pages for more information, and to retreive NetCDF software package on: http://www.unidata.ucar.edu/packages/netcdf/

NetCDF data is:

Self-Describing. A netCDF file includes information about the data it contains.

Architecture-independent. A netCDF file is represented in a form that can be accessed by computers with different ways of storing integers, characters, and floating-point numbers.

Direct-access. A small subset of a large dataset may be accessed efficiently, without first reading through all

the preceding data.

Appendable. Data can be appended to a netCDF dataset along one dimension without copy-ing the dataset or redefining its structure. The structure of a netCDF dataset can be changed, though this sometimes causes the dataset to be copied.

Sharable. One writer and multiple readers may simultaneously access the same netCDF file.

4.2. STRUCTURE AND SEMANTIC OF NETCDF FILES

Variable name Standard_name Dimensions Units

Daily BIGEYE biomass distribution

INDESO_BIGEYE-RT_%nnnnnnnnnnnn.nc or INDESO_BIGEYE_%nnnnnnnnnnnn.nc

Netcdf-CF Grid

Dimensions: lon, lat, time=1

lon longitude (lon) degrees_east

lat latitude (lat) degrees_north

time time (time) seconds since 2007-01-03 12:00:00

bet_juv bigeye_potential_juvenile_density (time,lat,lon) nb/km2

bet_you bigeye_potential_young_biomass (time,lat,lon) mt/km2

bet_tot bigeye_potential_total_biomass (time,lat,lon) mt/km2

bet_lar bigeye_potential_larvae_density (time,lat,lon) nb/km2

bet_rec bigeye_potential_recruits_density (time,lat,lon) nb/km2

bet_adu bigeye_potential_adult_biomass (time,lat,lon) mt/km2

http://www.unidata.ucar.edu/packages/netcdf/

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Daily YELLOWFIN biomass distribution

INDESO_YELLOWFIN-RT_%nnnnnnnnnnnn.nc or INDESO_YELLOWFIN_%nnnnnnnnnnnn.nc

Netcdf-CF Grid





yft_juv yellowfin_potential_juvenile_density (time,lat,lon) nb/km2

yft_you yellowfin_potential_young_biomass (time,lat,lon) mt/km2

yft_tot yellowfin_potential_total_biomass (time,lat,lon) mt/km2

yft_lar yellowfin_potential_larvae_density (time,lat,lon) nb/km2

yft_rec yellowfin_potential_recruits_density (time,lat,lon) nb/km2

yft_adu yellowfin_potential_adult_biomass (time,lat,lon) mt/km2


Daily SKIPJACK biomass distribution

INDESO_SKIPJACK-RT_%nnnnnnnnnnnn.nc or INDESO_SKIPJACK_%nnnnnnnnnnnn.nc

Netcdf-CF Grid





skj_juv skipjack_potential_juvenile_density (time,lat,lon) nb/km2

skj_you skipjack_potential_young_biomass (time,lat,lon) mt/km2

skj_tot skipjack_potential_total_biomass (time,lat,lon) mt/km2

skj_lar skipjack_potential_larvae_density (time,lat,lon) nb/km2

skj_rec skipjack_potential_recruits_density (time,lat,lon) nb/km2

skj_adu skipjack_potential_adult_biomass (time,lat,lon) mt/km2

4.3. STRUCTURE AND SEMANTIC OF ASCII FILES

Ascii file

Daily BIGEYE predicted catch

INDESO_BIGEYE_Summary_%Zone_%Y%m%d(field).asc

%Zone= FMAXXX (XXX=571, 572, 573, 711, 712, 713, 714, 715, 716, 717, 718)

Year; month; day; species_N_Larvae; species_N_Juv.; species_N_Rec.; species_B_Young; species_B_Adult; spe-cies_B_tot.; Nb.obs_fishery_code; effort_fishery_code; obs_C_species_fishery_code; pred_C_species_fishery_code; obs_CPUE_species_fishery_code; obs.err_CPUE_species_fishery_code; pred_CPUE_species_fishery_code

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Where fishery_code is a number encoding the fishery (from 1 to 34). Not all species are predicted for all fisheries.

Ascii file

Daily YELLOWFIN predicted catch

INDESO_YELLOWFIN_Summary_%Zone_%Y%m%d(field).asc

%Zone= FMAXXX (XXX=571, 572, 573, 711, 712, 713, 714, 715, 716, 717, 718)



Ascii file

Daily SKIPJACK predicted catch

INDESO_SKIPJACK_Summary_%Zone_%Y%m%d(field).asc

%Zone= FMAXXX (XXX=571, 572, 573, 711, 712, 713, 714, 715, 716, 717, 718)



4.4. STRUCTURE OFPDF FILES

The metrics files are automated pdf reports, computed for all three species of tuna.

Typical contents of those reports is:

1. Compliance table for the number of valid points in the grid 2. Compliance table for the values of the tuna model Fields

In both these tables the short names used are the variables names as defined in the NetCDF files (see above).

5. HOW TO DOWNLOAD A PRODUCT

5.1. REGISTRATION

To access data, registration is required. During registration process, the user shall accept using licenses for the use of INDESO products and services.

License shall include:

Data use conditions,

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Legal and contractual clauses

5.2. ACCESS SERVICES

Different services enable registered users to access the data. Depending on the dataset, not all of them are relevant.

Dataset Name File format Discover View Get

Fish population dynamics model daily historical & real-time predicted catch (Bigeye, Yellowfin, Skip-jack)

ascii Yes No Yes

Fish population dynamics model daily historical pre-dicted catch (Bigeye, Yellowfin, Skipjack)

ascii Yes No Yes

Critical fish population dy-namics model historical & real-time metrics

(Bigeye, Yellowfin, Skip-jack, Micronekton)

pdf Yes No Yes

Critical fish population dy-namics model historical metrics

(Bigeye, Yellowfin, Skip-jack, Micronekton)

pdf Yes No Yes

Fish population dynamics model daily historical & real-time biomass distribu-tion (Bigeye, Yellowfin, Skipjack, Micronekton)

netcdf CF Yes Yes Yes

Fish population dynamics model daily historical bio-mass distribution (Bigeye, Yellowfin, Skipjack, Micronekton)

netcdf CF Yes Yes Yes

Long term fish population dynamics model metrics (Bigeye, Yellowfin, Skip-jack, Micronekton)

pdf Yes No Yes

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6. REFERENCES

Aumont, O. and L. Bopp, 2006 : Globalizing results from ocean in situ iron fertilization studies, Global Biogeochem. Cycles, 20, GB2017, doi:10.1029/2005GB002591

Behrenfeld M.J., Falkowski P.G., (1997). A consumer's guide to phytoplankton primary productivity models. Limnol. Oceanogr. 42(7): 1479-1491.

Bell JD, Ganachaud A., Gehrke PC, Griffiths SP, Hobday AJ, Hoegh-Guldberg O, Johnson, JE Le Bor-gne R, Lehodey P, Lough JM, Matear RJ, Pickering TD, Pratchett MS, Sen Gupta A, Senina I and Waycott M., (2013) Tropical Pacific fisheries and aquaculture will respond differently to climate change. Nature Climate Change, 3: 591-599

Bertignac, M., Lehodey, P., Hampton, J., 1998. A spatial population dynamics simulation model of tropical tunas using a habitat index based on environmental parameters. Fisheries Oceanography 7, 326–334.

Eppley, RW (1972). Temperature and phytoplankton growth in the sea. Fishery Bulletin, 70: 1063-1085.

Ivanov Y. A., Lebedev K. V., Sarkisyan A. S . (1997). Generalized Hydrodynamic Adjustment Method (GHDAM). Izvestiya, Atmospheric and Oceanic Physics, 33(6): 752–757.

Lehodey P., Murtugudde R., Senina I. (2010a). Bridging the gap from ocean models to population dynamics of large marine predators: a model of mid-trophic functional groups. Progress in Oceanog-raphy, 84: 69–84

Lehodey, P., Chai, F., Hampton, J., 2003. Modeling climate-related variability of tuna populations from a coupled ocean-biogeochemical-populations dynamics model. Fisheries Oceanography 12 (4), 483–494.

Lehodey P., Senina I., & Murtugudde R. (2008). A Spatial Ecosystem And Populations Dynamics Model (SEAPODYM) - Modelling of tuna and tuna-like populations. Progress in Oceanography, 78: 304-318.

Lehodey P., Senina I., Calmettes B, Hampton J, Nicol S. (2013). Modelling the impact of climate change on Pacific skipjack tuna population and fisheries. Climatic Change, 119: 95–109.

Lehodey P., Senina I., Calmettes B., Hampton J., Nicol S., Williams P., Jurado Molina J., Ogura M., Kiyofuji H., Okamoto S. (2011). SEAPODYM working progress and applications to Pacific skipjack tuna population and fisheries. 7th regular session of the Scientific Steering Committee, 8-17 August 2011, Pohnpei, Federate States of Micronesia. WCPFC-SC7-2011/EB- WP 06.

Lehodey P., Senina I., Sibert J., Bopp L, Calmettes B., Hampton J., Murtugudde R. (2010b). Pre-liminary forecasts of population trends for Pacific bigeye tuna under the A2 IPCC scenario. Progress in Oceanography. 86: 302–315

Lehodey, P., Hampton, J., Brill, R.W., Nicol, S., Senina, I., Calmettes, B., Pörtner, H.O., Bopp, L., Ilyina,T., Johann D. Bell, and J. Sibert (2011). Vulnerability of oceanic fisheries in the tropical Pa-cific to climate change. In Bell J., Johnson JE, Hobday AJ (Ed.), Vulnerability of Tropical Pacific Fisheries and Aquaculture to Climate Change. Secretariat of the Pacific Community. Noumea New Caledonia.

Lehodey, P., Senina, I., Hampton, J, Nicol, S, Williams, P., Jurado Molina J., Abecassis, M., Polovina J. ( ). Project 62: SEAPODYM working progress and applications to Pacific tuna and billfish populations and fisheries. 8th regular session of the Scientific Steering Committee, 7-15 August 2012, Busan, Korea. WCPFC-SC8-2012/EB-IP-06.

Senina I, Royer F, Lehodey P, Hampton J, Nicol S, Ogura M, Kiyofuji H, Sibert J (2012). Integrating conventional and electronic tagging data into SEAPODYM. Pelagic Fisheries Research Program, Newsletter 16(1): 9-14;

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Senina I., Sibert J., & Lehodey P. (2008). Parameter estimation for basin-scale ecosystem-linked population models of large pelagic predators: application to skipjack tuna. Progress in Oceanogra-phy, 78: 319-335.

Sibert J, Senina I, Lehodey P, Hampton J. (2012). Shifting from marine reserves to maritime zoning for conservation of Pacific bigeye tuna (Thunnus obesus). Proceedings of the National Academy of Sciences 109(44): 18221-18225.

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