Alternative hypotheses of two mixing stocks of South African sardine: Some projections assuming no future catch

Similar documents
Additional perspectives for the Stock Assessment Review Panel on penguin population modelling for decision making

The 3 rd Annual Review Workshop. FIP for the Orkney creel fishery. 20 Jun 2016

Using follow-up data to adjust for selective non-participation in cross-sectional setting

Joint U.S.-Canada Scientific Review Group Report Watertown Hotel Seattle Washington USA February 18-21, 2014

3 The backwards approach effectively projects population trajectories

The Classification Accuracy of Measurement Decision Theory. Lawrence Rudner University of Maryland

Rob Dorrington, Debbie Bradshaw and Debbie Budlender

Systematic review with multiple treatment comparison metaanalysis. on interventions for hepatic encephalopathy

Reinforcement Learning : Theory and Practice - Programming Assignment 1

Multilevel IRT for group-level diagnosis. Chanho Park Daniel M. Bolt. University of Wisconsin-Madison

RESPONSE SURFACE MODELING AND OPTIMIZATION TO ELUCIDATE THE DIFFERENTIAL EFFECTS OF DEMOGRAPHIC CHARACTERISTICS ON HIV PREVALENCE IN SOUTH AFRICA

1 Institute of Social and Preventive Medicine, University of Bern, Finkenhubelweg 11, 3012 Bern, Switzerland

Study Design Considerations for Aerial Surveys in the Spring and Fall to Estimate the Abundance of Pacific Walrus

Sequence balance minimisation: minimising with unequal treatment allocations

DRAFT REPORT. 1. Introductions The meeting was convened at 9:00 am. Ryan Okano served as the Chair.

Combining Risks from Several Tumors Using Markov Chain Monte Carlo

OBSERVATION OF TOOTH SCARS ON THE HEAD OF MALE SPERM WHALE, AS AN INDICATION OF INTRA-SEXUAL FIGHTINGS

Bayesian Confidence Intervals for Means and Variances of Lognormal and Bivariate Lognormal Distributions

A COMPARISON OF IMPUTATION METHODS FOR MISSING DATA IN A MULTI-CENTER RANDOMIZED CLINICAL TRIAL: THE IMPACT STUDY

THE INDIRECT EFFECT IN MULTIPLE MEDIATORS MODEL BY STRUCTURAL EQUATION MODELING ABSTRACT

Sustainable marine ingredients and their role in fish nutrition, health and welfare

AP Statistics TOPIC A - Unit 2 MULTIPLE CHOICE

Marianne Robert, Abdelmalek Faraj, Murdoc Mcallister, Etienne Rivot

GLOSSARY OF GENERAL TERMS

Appendix 1. Sensitivity analysis for ACQ: missing value analysis by multiple imputation

Quantitative Literacy: Thinking Between the Lines

Suggested Answers Part 1 The research study mentioned asked other family members, particularly the mothers if they were available.

Homicides and suicides in Cape Town,

Mathematical Structure & Dynamics of Aggregate System Dynamics Infectious Disease Models 2. Nathaniel Osgood CMPT 394 February 5, 2013

Fundamental Clinical Trial Design

Focus Points 4/5/2017. Estimating 1 2 and p 1 p 2. Section 7.4. Independent Samples and Dependent Samples

ISC- GRADE XI HUMANITIES ( ) PSYCHOLOGY. Chapter 2- Methods of Psychology

APPENDIX 9 NOTIFIABLE AVIAN INFLUENZA (NAI) SURVEILLANCE

E 490 FE Exam Prep. Engineering Probability and Statistics

Statistical Techniques. Masoud Mansoury and Anas Abulfaraj

(BANTU AND NILOTIC) MEN

Module 5. The Epidemiological Basis of Randomised Controlled Trials. Landon Myer School of Public Health & Family Medicine, University of Cape Town

Running head: How large denominators are leading to large errors 1

Exploring the Influence of Particle Filter Parameters on Order Effects in Causal Learning

MOROCCAN SARDINE FIP LEADING AMBIENT FOOD SPECIALIST PREPARED FOR SEAFISH COMMON LANGUAGE GROUP BY CAITLIN SCHINDLER, YOUSSEF JARIDI, JO GASCOIGNE

Lecture 1 An introduction to statistics in Ichthyology and Fisheries Science

Variation in Measurement Error in Asymmetry Studies: A New Model, Simulations and Application

Health effects of consuming 2 portions per week of Scottish farmed salmon raised on different feeding regimes. Baukje de Roos

STAT 113: PAIRED SAMPLES (MEAN OF DIFFERENCES)

1. Evaluate the methodological quality of a study with the COSMIN checklist

Types of Studies. There are three main types of studies. These are ways to collect data.

1.2.7 (a) This situation was an experiment. The researchers applied the treatment.

A Genetic Analysis of Taste Deficiency in the American Negro

Introduction to Bayesian Analysis 1

(ii) The effective population size may be lower than expected due to variability between individuals in infectiousness.

Erica J. Yoon Introduction

Lesson 8 Descriptive Statistics: Measures of Central Tendency and Dispersion

Shrimp adjust their sex ratio to fluctuating age distributions

STATISTICS & PROBABILITY

The National Longitudinal Study of Gambling Behaviour (NLSGB): Preliminary Results. Don Ross Andre Hofmeyr University of Cape Town

Introduction to Statistical Data Analysis I

Essentials of Aggregate System Dynamics Infectious Disease Models. Nathaniel Osgood CMPT 858 FEBRUARY 3, 2011

Mediation Analysis With Principal Stratification

A Proposal for the Validation of Control Banding Using Bayesian Decision Analysis Techniques

Bayesian Mediation Analysis

Bayesian and Frequentist Approaches

Hypothesis Testing. Richard S. Balkin, Ph.D., LPC-S, NCC

Chapter 11. Experimental Design: One-Way Independent Samples Design

EC352 Econometric Methods: Week 07

A Case Study: Two-sample categorical data

AN EPIC COMPUTATIONAL MODEL OF VERBAL WORKING MEMORY D. E. Kieras, D. E. Meyer, S. T. Mueller, T. L. Seymour University of Michigan Sponsored by the

Risk Aversion in Games of Chance

Stunting and growth development for adolescents perinatally infected by HIV: males and females evolve differently.

STA Module 9 Confidence Intervals for One Population Mean

Increasing Motor Learning During Hand Rehabilitation Exercises Through the Use of Adaptive Games: A Pilot Study

Gamal M. Abd El-Nasser Central Lab for Aquaculture Research Abbassa, Sharkia, Egypt. Key words: Clarias gariepinus, larvae, feeding, primary nursing.

Research Methods in Human Computer Interaction by J. Lazar, J.H. Feng and H. Hochheiser (2010)

Advanced/Advanced Subsidiary. You must have: Mathematical Formulae and Statistical Tables (Blue)

A STUDY OF THE PLASMA SODIUM AND POTASSIUM LEVELS IN NORMAL MERINO SHEEP

A novel approach to estimation of the time to biomarker threshold: Applications to HIV

Bayesian Statistics Estimation of a Single Mean and Variance MCMC Diagnostics and Missing Data

Boston Biometrics International Validation Study

Research Methods in Forest Sciences: Learning Diary. Yoko Lu December Research process

Cognitive styles sex the brain, compete neurally, and quantify deficits in autism

Bayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm

Probability Models for Sampling

Bayesian Bi-Cluster Change-Point Model for Exploring Functional Brain Dynamics

1. The figure below shows the lengths in centimetres of fish found in the net of a small trawler.

Welcome. Recreational Enhancements on the Lehigh River Public Information Workshop 18 February 2010

ANNEX A5 CHANGES IN THE ADMINISTRATION AND SCALING OF PISA 2015 AND IMPLICATIONS FOR TRENDS ANALYSES

Design of Experiments & Introduction to Research

STRATEGIC COST ADVICE AND THE EFFECT OF THE AVAILABILITY HEURISTIC C J FORTUNE

A Comparison of Robust and Nonparametric Estimators Under the Simple Linear Regression Model

Cownose ray population changes: Do the reports match the biology?

Comparison of the Approaches Taken by EFSA and JECFA to Establish a HBGV for Cadmium 1

Slide 1 - Introduction to Statistics Tutorial: An Overview Slide notes

! Which nut has the most. stored energy? By: Andrea Rendon. Chemistry. April, 18th, 2014

Managing Precocious Maturation in Chinook Salmon Captive Broodstock

1 Introduction. st0020. The Stata Journal (2002) 2, Number 3, pp

Study Endpoint Considerations: Final PRO Guidance and Beyond

GMAC. Scaling Item Difficulty Estimates from Nonequivalent Groups

Understanding Statistics for Research Staff!

Digital Disease Detection: MERS, Ebola, and Measles. Maimuna S. Majumder

on the Lehigh River Public Information Workshop

Transcription:

Alternative hypotheses of two mixing stocks of outh African sardine: ome projections assuming no future catch C.L. de Moor Correspondence email: carryn.demoor@uct.ac.za Introduction This document shows projections of the sardine 1+ biomasses under a no catch scenario for a number of the alternative hypotheses considered by de Moor et al. (214). The alternatives are summarised again below for ease of reference, together with details of any changes in the simulation testing framework of de Moor and Butterworth (213). Projections are shown from the results at the joint posterior mode (even though some of these have not converged, see de Moor et al. (214)), which thus only incorporate simulated future random error, and from the posterior distributions of a limited number of hypotheses which did converge at the joint posterior mode. Given the limited time available to run MCMC, full convergence diagnostics were not checked for any results presented here. amples were randomly drawn with resampling from a segment of the chain 123. For simplicity, the average sardine catch weight at age is kept constant at the baseline values for all hypotheses. Alternative Two tock Hypotheses Alternative A (Effective pawning Biomasses) This alternative hypothesis assumes that a portion of the west/south spawning stock biomass (B) forms part of the effective spawning biomass of the other stock. The hypotheses considered are as follows: : % of south B contributes to effective west B % of west B contributes to effective south B Alt A-1: 1% of south B contributes to effective west B % of west B contributes to effective south B Alt A-2: 18% of south B and 1% of west B contributes to effective west B % of west B and 82% of south B contributes to effective south B Alt A-3: 18% of south B and 98% of west B contributes to effective west B 2% of west B and 82% of south B contributes to effective south B MARAM (Marine Resource Assessment and Management Group), Department of Mathematics and Applied Mathematics, University of Cape Town, Rondebosch, 771, outh Africa. 1 The chain for A-1 was 25 9 long. amples were drawn after a burn-in of 4 was discarded and the remaining chain thinned at intervals of. 2 The chain for B-1, B-2 and B-3 were 29 5, 26 9 and 27 1 long, respectively. amples were drawn after a burn-in of 5 was discarded and the remaining chain thinned at intervals of. 3 The chain for D-1 was 27 5 long. amples were drawn after a burn-in of 5 was discarded and the remaining chain thinned at intervals of. 1

Alt A-4: Alt A-5: 1% of south B and 29% of west B contributes to effective west B.5% of west B and 46% of south B contributes to effective south B 13% of south B and 29% of west B contributes to effective west B.5% of west B and 59% of south B contributes to effective south B The changes to the operating model (de Moor and Butterworth 213) are as follows: Given the spawning stock biomass: 5 = + j, y a= 2 j, y, a j, y, a B N w y = y1, K, y (1) n the effective spawning stock biomass is calculated as: eff B 1, y = Bw _ w, y + Bs _ w, y eff 1, y = Bw _ s, y + Bs _ s B B, 2 y and the recruitment is related to the effective rather than actual spawning stock biomass as follows: N eff ( B ) ε f e j, y j, y, = j, y y y1, K, yn Here: N j y, a w j y, a = (2), is the model predicted number (in billions) of sardine of age a at the beginning of November in year y of stock j ;, is the mean mass (in grams) of sardine of age a of stock j sampled during the November survey w w of year y; B _ is the proportion of the west B contributing to the effective west B; B _ is the proportion of the west B contributing to the effective south B; w s s B _ is the proportion of the south B contributing to the effective west B; s w B _ is the proportion of the south B contributing to the effective south B; s f ( ) denotes the assumed stock-recruitment relationship; and j, y ε is the annual lognormal deviation of sardine recruitment. Alternative B (Varied Recruit Distributions) In this hypothesis, the recruits surveyed west of Cape Infanta thus consist of west stock recruits and a portion of south stock recruits. For simplicity as an initial test, it is assumed that these south stock recruits surveyed west of Cape Infanta are not caught west of Cape Agulhas. The alternatives tested are: : % of south stock recruits distributed west of Cape Infanta at the time of the survey Alt B-1: 1% of south stock recruits distributed west of Cape Infanta at the time of the survey Alt B-2: 2% of south stock recruits distributed west of Cape Infanta at the time of the survey Alt B-3: 5% of south stock recruits distributed west of Cape Infanta at the time of the survey 2

The changes to the model (de Moor and Butterworth 213) are as follows: N N, obs 1, y, r = k j, r, pred, pred ( N + obs N ) 1, y, r, pred ( obs) N ) 2, y, r e ε 1, y, rec, obs ε 2, y, rec 2, y, r = k j, r 1 2, y, r e (3) Here: pred N, j y, r j r, is the model predicted number (in billions) of juvenile sardine of stock j at the time of the recruit survey in year y; and k, is the constant of proportionality (multiplicative bias) between survey estimated and model predicted 1+ biomass, drawn from posterior distributions estimated by the operating models; and obs is the time-invariant proportion of south stock recruits surveyed west of Cape Infanta. The errors, ε j. y. rec, are calculated in the same manner as before (de Moor and Butterworth 213). de Moor and Butterworth (213) took account of both the estimated stock-recruitment relationship, with autocorrelated error and the survey estimate of recruitment west of Cape Infanta 212 when calculating the November 211 model predicted west stock recruitment. As there was no survey estimate of recruitment east of Cape Infanta in 212, only the stock-recruitment relationship and autocorrelated error was used to calculate south stock recruitment in November 211. Due to the mix in the recruitment assumed surveyed west of Cape Infanta under these hypotheses, it was decided to simply calculate the November 211 recruitment for both the west and south stocks from the stock recruitment relationships. For comparative purposes, therefore, the baseline results shown with Alternative B has been re-run without the information of recruitment west of Cape Agulhas in May 212. Alternative D (Varied Adult Movement) This hypothesis assumes some adult west stock sardine migrate to the south stock in addition to the recruits which are modelled to migrate in the November in which they become 1 year olds. The alternatives tested are: : Only west stock recruits migrate to the south stock as they turn 1 year old. The proportion migrating is estimated annually from 1994. D-1: West stock recruits and 1 year olds migrate to the south stock as they turn 1 and 2 years old, respectively. The age-independent proportion migrating is estimated annually from 1994. D-2: West stock sardine of all ages migrate to the south stock. The age-independent proportion migrating is estimated annually from 1994. D-3: West stock recruits and 1 year olds migrate to the south stock as they turn 1 and 2 years old, respectively. The proportion migrating is estimated annually from 1994, and the proportion of 1 year olds is assumed to be half that of the recruits. D-4: West stock sardine of all ages migrate to the south stock. The proportion migrating is estimated annually from 1994, and the proportion of adults migrating is assumed to be half that of the recruits. 3

Results and Discussion Alternative A (Effective pawning Biomasses) Figure 1 shows the different model predicted 1+ biomasses for the west and south stocks from results at the joint posterior mode, under the movement hypothesis MoveAutoC, while Figure 2 compares the medians of the future projections between the alternative A hypotheses. These indicate a faster growth of the 1+ biomass in the short term under A-2 to A-5. The 1+ biomass after 2 years is projected to be 1-2% higher under A-2 to A-5 compared to the baseline and 25% higher under A-1. Figures 3 and 4 are a repeat of Figures 1 and 2, but for the movement hypothesis NoMove, showing little difference between the alternative A hypotheses in the south stock projections, but a lower plateau in the increase of west stock 1+ biomass under A-2 to A-5 compared to the and A-1. The trajectories based on the posterior distributions show a similar pattern, although the differences between the and A-1 are smaller for MoveAutoC and larger for NoMove. Unfortunately, since A-2 to A-5 did not converge at the joint posterior mode, trajectories from posterior distributions of these hypotheses cannot be shown. Thus, although de Moor et al. (214) suggested A-3 to A-5 would be preferred a priori to A-1 and A-2, we cannot currently conclude whether the differences between the and A-2 to A-5 would be of large consequence. Alternative B (Varied Recruit Distributions) There is little difference between the model predicted future 1+ biomass of the baseline and Alternative B hypotheses from projections from the joint posterior mode (Figures 9, 1, 13, 14). The difference is larger for projections are based on results from the posterior distributions (Figures 11, 12, 15, 16). The recovery of the sardine is slower under B-1 to B-3 compared to the baseline for MoveAutoC. The median west stock 1+ biomass at the end of the projection years is 76% (54%) of the for B-2 (B-3), while the median south stock 1+ biomass at the end of the projection years is 91% (82%) of the for B-2 (B-3). Recall, however, that de Moor et al. (214) considered B-3 to be an extreme. Alternative D (Varied Adult Movement) The projected 1+ biomasses are worse under the alternative D hypotheses than the (Figures 17, 18, 21, 22) for MoveAutoC. This may be because even though the proportion moving may be lower than the, the number of fish moving is higher. Under the NoMove hypothesis, the west stock 1+ biomass for D-1 plateaus at a higher level (Figures 19, 2, 23, 24) given the higher west stock carrying capacity for D-1 compared to the and D-2 to D-4. References de Moor, C.L. and Butterworth, D.. 213. The simulation testing framework used during the development of OMP-13. DAFF Branch Fisheries document FIHERIE/213/OCT/WG-PEL/26. 27pp. 4

de Moor, C.L., Butterworth, D.., van der Lingen C.D., and Coetzee J.C. 214. Alternative hypotheses of two mixing stocks of outh African sardine: Initial testing. MARAM International tock Assessment Workshop Report No MARAM/IW/DEC14/ardine/P2. 2pp. 5

a) ('t) 6 5 4 3 2 b) ('t) 6 5 4 3 2 c) ('t) 6 5 4 3 2 A2 21 22 23 21 22 23 21 22 23 d) ('t) 6 5 4 3 2 A3 e) ('t) 6 5 4 3 2 A4 f) ('t) 6 5 4 3 2 A5 21 22 23 21 22 23 21 22 23 Figure 1a. The median and 9% probability interval of future projected sardine 1+ biomass for the west stock under a no catch scenario and alternative A hypotheses, with the movement hypothesis MoveAutoC, and projections from the joint posterior mode. 6

a) ('t) d) ('t) 2 175 15 125 75 5 25 2 175 15 125 21 22 23 75 5 25 21 22 23 ('t) 15 125 21 22 23 MARAM/IW/DEC14/ardine/P4 Figure 1b. The median and 9% probability interval of future projected sardine 1+ biomass for the south stock under a no catch scenario and alternative A hypotheses, with the movement hypothesis MoveAutoC, and projections from the joint posterior mode. A3 b) e) ('t) 2 175 75 5 25 2 175 15 125 75 5 25 21 22 23 A4 c) ('t) f) ('t) 2 175 15 125 75 5 25 2 175 15 125 75 5 25 21 22 23 21 22 23 A2 A5 7

('t) 2 18 16 14 12 8 6 4 2 A2 A3 A4 A5 MoveAutoC 21 215 22 225 23 235 ('t) 2 18 16 14 12 8 6 4 2 A2 A3 A4 A5 MoveAutoC 21 215 22 225 23 235 Figure 2. The median probability intervals from Figure 1. 8

a) ('t) d) ('t) 6 5 4 3 2 6 5 4 3 2 21 22 23 21 22 23 ('t) 6 5 4 3 2 21 22 23 MARAM/IW/DEC14/ardine/P4 Figure 3a. The median and 9% probability interval of future projected sardine 1+ biomass for the west stock under a no catch scenario and alternative A hypotheses, with the movement hypothesis NoMove, and projections from the joint posterior mode. A3 b) e) ('t) 6 5 4 3 2 21 22 23 A4 c) ('t) f) ('t) 6 5 4 3 2 6 5 4 3 2 21 22 23 21 22 23 A2 A5 9

a) ('t) d) ('t) 2 175 15 125 75 5 25 2 175 15 125 75 5 25 21 22 23 21 22 23 ('t) 2 175 15 125 21 22 23 MARAM/IW/DEC14/ardine/P4 Figure 3b. The median and 9% probability interval of future projected sardine 1+ biomass for the south stock under a no catch scenario and alternative A hypotheses, with the movement hypothesis NoMove, and projections from the joint posterior mode. A3 b) e) ('t) 75 5 25 2 175 15 125 75 5 25 21 22 23 A4 c) ('t) f) ('t) 2 175 15 125 75 5 25 2 175 15 125 21 22 23 75 5 25 21 22 23 A2 A5 1

('t) 35 3 25 2 15 5 A2 A3 A4 A5 NoMove 21 215 22 225 23 235 Figure 4. The median probability intervals from Figure 3, and projections from the joint posterior mode. ('t) 2 18 16 14 12 8 6 4 2 A2 A3 A4 A5 NoMove 21 215 22 225 23 235 a) 6 5 4 3 2 21 215 22 225 23 235 ('t) b) 6 5 4 3 2 21 215 22 225 23 235 Figure 5a. The median and 9% probability interval of future projected sardine 1+ biomass for the west stock under a no catch scenario and alternative A hypotheses, with the movement hypothesis MoveAutoC, and projections from the posterior distribution. ('t) a) 2 175 15 125 75 5 25 21 215 22 225 23 235 ('t) b) 2 175 15 125 75 5 25 21 215 22 225 23 235 Figure 5b. The median and 9% probability interval of future projected sardine 1+ biomass for the south stock under a no catch scenario and alternative A hypotheses, with the movement hypothesis MoveAutoC, and projections from the posterior distribution. ('t) 11

('t) 35 3 25 2 15 5 NoMove 21 215 22 225 23 235 Figure 6. The median probability intervals from Figure 5. ('t) 2 18 16 14 12 8 6 4 2 NoMove 21 215 22 225 23 235 a) 6 5 4 3 2 21 215 22 225 23 235 ('t) b) 6 5 4 3 2 21 215 22 225 23 235 Figure 7a. The median and 9% probability interval of future projected sardine 1+ biomass for the west stock under a no catch scenario and alternative A hypotheses, with the movement hypothesis NoMove, and projections from the posterior distribution. ('t) a) 2 175 15 125 75 5 25 21 215 22 225 23 235 ('t) b) 2 175 15 125 75 5 25 21 215 22 225 23 235 Figure 7b. The median and 9% probability interval of future projected sardine 1+ biomass for the south stock under a no catch scenario and alternative A hypotheses, with the movement hypothesis NoMove, and projections from the posterior distribution. ('t) 12

('t) 4 35 3 25 2 15 5 NoMove 21 215 22 225 23 235 Figure 8. The median probability intervals from Figure 7. ('t) 2 18 16 14 12 8 6 4 2 NoMove 21 215 22 225 23 235 a) ('t) c) ('t) 6 5 4 3 2 6 5 4 3 2 21 22 23 Figure 9a. The median and 9% probability interval of future projected sardine 1+ biomass for the west stock under a no catch scenario and alternative B hypotheses, with the movement hypothesis MoveAutoC, and projections from the joint posterior mode. 21 22 23 b) ('t) d) ('t) 6 5 4 3 2 6 5 4 3 2 21 22 23 21 22 23 13

a) ('t) c) ('t) 2 175 15 125 75 5 25 2 175 15 125 21 22 23 75 5 25 MARAM/IW/DEC14/ardine/P4 Figure 9b. The median and 9% probability interval of future projected sardine 1+ biomass for the south stock under a no catch scenario and alternative B hypotheses, with the movement hypothesis MoveAutoC, and projections from the joint posterior mode. 21 22 23 b) ('t) 2 175 15 125 75 5 25 d) 2 175 ('t) 15 125 75 5 25 21 22 23 21 22 23 14

('t) 35 3 25 2 15 5 MoveAutoC 21 215 22 225 23 235 Figure 1. The median probability intervals from Figure 9. ('t) 2 18 16 14 12 8 6 4 2 MoveAutoC 21 215 22 225 23 235 a) ('t) c) ('t) 6 5 4 3 2 6 5 4 3 2 Figure 11a. The median and 9% probability interval of future projected sardine 1+ biomass for the west stock under a no catch scenario and alternative B hypotheses, with the movement hypothesis NoMove, and projections from the joint posterior mode. 21 22 23 21 22 23 b) ('t) d) ('t) 6 5 4 3 2 6 5 4 3 2 21 22 23 21 22 23 15

a) ('t) c) ('t) 2 175 15 125 75 5 25 2 175 15 125 21 22 23 75 5 25 MARAM/IW/DEC14/ardine/P4 Figure 11b. The median and 9% probability interval of future projected sardine 1+ biomass for the south stock under a no catch scenario and alternative B hypotheses, with the movement hypothesis NoMove, and projections from the joint posterior mode. 21 22 23 b) ('t) d) ('t) 2 175 15 125 75 5 25 2 175 15 125 75 5 25 21 22 23 21 22 23 ('t) 35 3 25 2 15 5 NoMove 21 215 22 225 23 235 Figure 12. The median probability intervals from Figure 11. ('t) 2 18 16 14 12 8 6 4 2 NoMove 21 215 22 225 23 235 16

a) 6 5 4 3 2 21 215 22 225 23 235 ('t) c) 6 5 4 3 2 21 215 22 225 23 235 ('t) MARAM/IW/DEC14/ardine/P4 b) 6 5 4 3 2 21 215 22 225 23 235 Figure 13a. The median and 9% probability interval of future projected sardine 1+ biomass for the west stock under a no catch scenario and alternative B hypotheses, with the movement hypothesis MoveAutoC, and projections from the posterior distribution. ('t) d) 6 5 4 3 2 21 215 22 225 23 235 ('t) 17

a) 2 175 15 125 75 5 25 21 215 22 225 23 235 ('t) c) 2 175 15 125 75 5 25 21 215 22 225 23 235 ('t) MARAM/IW/DEC14/ardine/P4 b) 2 175 15 125 75 5 25 21 215 22 225 23 235 Figure 13b. The median and 9% probability interval of future projected sardine 1+ biomass for the south stock under a no catch scenario and alternative B hypotheses, with the movement hypothesis MoveAutoC, and projections from the posterior distribution. ('t) d) 2 175 15 125 75 5 25 21 215 22 225 23 235 ('t) ('t) 35 3 25 2 15 5 MoveAutoC 21 215 22 225 23 235 Figure 14. The median probability intervals from Figure 13. ('t) 2 18 16 14 12 8 6 4 2 MoveAutoC 21 215 22 225 23 235 18

a) 6 5 4 3 2 21 215 22 225 23 235 ('t) c) 6 5 4 3 2 21 215 22 225 23 235 ('t) MARAM/IW/DEC14/ardine/P4 b) 6 5 4 3 2 21 215 22 225 23 235 Figure 15a. The median and 9% probability interval of future projected sardine 1+ biomass for the west stock under a no catch scenario and alternative B hypotheses, with the movement hypothesis NoMove, and projections from the posterior distribution. ('t) d) 6 5 4 3 2 21 215 22 225 23 235 ('t) 19

a) 2 175 15 125 75 5 25 21 215 22 225 23 235 ('t) c) 2 175 15 125 75 5 25 21 215 22 225 23 235 ('t) MARAM/IW/DEC14/ardine/P4 b) 2 175 15 125 75 5 25 21 215 22 225 23 235 Figure 15b. The median and 9% probability interval of future projected sardine 1+ biomass for the south stock under a no catch scenario and alternative B hypotheses, with the movement hypothesis NoMove, and projections from the posterior distribution. ('t) d) 2 175 15 125 75 5 25 21 215 22 225 23 235 ('t) ('t) 4 35 3 25 2 15 5 NoMove 21 215 22 225 23 235 Figure 16. The median probability intervals from Figure 15. ('t) 2 18 16 14 12 8 6 4 2 NoMove 21 215 22 225 23 235 2

a) ('t) 6 5 4 3 2 21 215 22 225 23 235 b) 6 5 4 3 2 21 215 22 225 23 235 ('t) d) 6 D3 5 4 3 2 21 215 22 225 23 235 ('t) c) 6 D2 5 4 3 2 21 215 22 225 23 235 Figure 17a. The median and 9% probability interval of future projected sardine 1+ biomass for the west stock under a no catch scenario and alternative D hypotheses, with the movement hypothesis MoveAutoC, and projections from the joint posterior mode. ('t) e) 6 D4 5 4 3 2 21 215 22 225 23 235 ('t) 21

a) ('t) 2 175 15 125 75 5 25 21 215 22 225 23 235 b) 2 175 15 125 75 5 25 21 215 22 225 23 235 ('t) c) ('t) 2 175 D2 15 125 75 5 25 21 215 22 225 23 235 d) ('t) 2 175 D3 15 125 75 5 25 21 215 22 225 23 235 e) 2 175 D4 15 125 75 5 25 21 215 22 225 23 235 ('t) Figure 17b. The median and 9% probability interval of future projected sardine 1+ biomass for the south stock under a no catch scenario and alternative D hypotheses, with the movement hypothesis MoveAutoC, and projections from the joint posterior mode. 22

('t) 2 18 16 14 12 8 6 4 2 D2 D3 D4 MoveAutoC 21 215 22 225 23 235 Figure 18. The median probability intervals from Figure 17. ('t) 2 18 16 14 12 8 6 4 2 D2 D3 D4 MoveAutoC 21 215 22 225 23 235 23

a) ('t) 6 5 4 3 2 21 215 22 225 23 235 b) 6 5 4 3 2 21 215 22 225 23 235 ('t) d) 6 D3 5 4 3 2 21 215 22 225 23 235 ('t) c) 6 D2 5 4 3 2 21 215 22 225 23 235 Figure 19a. The median and 9% probability interval of future projected sardine 1+ biomass for the west stock under a no catch scenario and alternative D hypotheses, with the movement hypothesis NoMove, and projections from the joint posterior mode. ('t) e) 6 D4 5 4 3 2 21 215 22 225 23 235 ('t) 24

a) ('t) 2 175 15 125 75 5 25 21 215 22 225 23 235 b) 2 175 15 125 75 5 25 21 215 22 225 23 235 ('t) d) 2 175 D3 15 125 75 5 25 21 215 22 225 23 235 ('t) c) 2 175 D2 15 125 75 5 25 21 215 22 225 23 235 Figure 19b. The median and 9% probability interval of future projected sardine 1+ biomass for the south stock under a no catch scenario and alternative D hypotheses, with the movement hypothesis NoMove, and projections from the joint posterior mode. ('t) e) 2 175 D4 15 125 75 5 25 21 215 22 225 23 235 ('t) 25

('t) 5 45 4 35 3 25 2 15 5 D2 D3 D4 NoMove 21 215 22 225 23 235 Figure 2. The median probability intervals from Figure 19. ('t) 2 18 16 14 12 8 6 4 2 D2 D3 D4 NoMove 21 215 22 225 23 235 a) 6 5 4 3 2 21 215 22 225 23 235 ('t) b) 6 5 4 3 2 21 215 22 225 23 235 Figure 21a. The median and 9% probability interval of future projected sardine 1+ biomass for the west stock under a no catch scenario and alternative D hypotheses, with the movement hypothesis MoveAutoC, and projections from the posterior distribution. ('t) a) 2 175 15 125 75 5 25 21 215 22 225 23 235 ('t) b) 2 175 15 125 75 5 25 21 215 22 225 23 235 Figure 21b. The median and 9% probability interval of future projected sardine 1+ biomass for the south stock under a no catch scenario and alternative D hypotheses, with the movement hypothesis MoveAutoC, and projections from the posterior distribution. ('t) 26

('t) 2 175 15 125 75 5 25 MoveAutoC 21 215 22 225 23 235 Figure 22. The median probability intervals from Figure 21. ('t) 2 175 15 125 75 5 25 MoveAutoC 21 215 22 225 23 235 a) 6 5 4 3 2 21 215 22 225 23 235 ('t) b) 6 5 4 3 2 21 215 22 225 23 235 Figure 23a. The median and 9% probability interval of future projected sardine 1+ biomass for the west stock under a no catch scenario and alternative D hypotheses, with the movement hypothesis NoMove, and projections from the posterior distribution. ('t) a) 2 175 15 125 75 5 25 21 215 22 225 23 235 ('t) b) 2 175 15 125 75 5 25 21 215 22 225 23 235 Figure 23b. The median and 9% probability interval of future projected sardine 1+ biomass for the south stock under a no catch scenario and alternative D hypotheses, with the movement hypothesis NoMove, and projections from the posterior distribution. ('t) 27

('t) 5 45 4 35 3 25 2 15 5 NoMove 21 215 22 225 23 235 Figure 24. The median probability intervals from Figure 23. ('t) 2 18 16 14 12 8 6 4 2 NoMove 21 215 22 225 23 235 28