Modeling Selectivity in Stock Synthesis Modeling population processes 2009 IATTC workshop.

Slides:

Advertisements

Similar presentations

Introduction to Stock Synthesis

Advertisements

Modeling Recruitment in Stock Synthesis

Sardine Two-Stock Hypothesis: Results at the Posterior Mode SPSWG Meeting 28 th August 2013 Carryn de Moor Doug Butterworth Marine Resource Assessment.

Randomized Complete Block and Repeated Measures (Each Subject Receives Each Treatment) Designs KNNL – Chapters 21,

Research Methods 2 Study Block 4 Workshop Statistical Analysis of Quantitative Data.

An exploration of alternative methods to deal with time-varying selectivity in the stock assessment of YFT in the eastern Pacific Ocean CAPAM – Selectivity.

2.1 Describing Graphs of Functions. If we examine a typical graph the function y = f(x), we can observe that for an interval throughout which the function.

I can order decimal numbers up to three decimal places (4a)

Sheng-Ping Wang 1,2, Mark Maunder 2, and Alexandre Aires-Da-Silva 2 1.National Taiwan Ocean University 2.Inter-American Tropical Tuna Commission.

458 Delay-difference models Fish 458, Lecture 14.

Gene Shaving – Applying PCA Identify groups of genes a set of genes using PCA which serve as the informative genes to classify samples. The “gene shaving”

HST Rough Toolpaths. Cut Parameters Note: The cut parameters will change based on the toolpath type. When possible the same settings will be brought into.

Black Sea Bass – Northern Stock Coastal-Pelagic/ASMFC Working Group Review June 15, 2010.

The current status of fisheries stock assessment Mark Maunder Inter-American Tropical Tuna Commission (IATTC) Center for the Advancement of Population.

Growth in Length and Weight Rainer Froese SS 2008.

Lec 24, Ch.16, pp : Superelevation runoff (objectives)

Output Transistors Current gain / input impedance is a vital parameter of a power amplifier. In the class A analysis, the load impedance is scaled by a.

Descriptive statistics (Part I)

Case Study - Dover Sole Range from Baja California to the Bering Sea. On mud or muddy-sand, at 35 to 1400 m depths. Feed on polychaete worms, shrimp, brittle.

2.3. Measures of Dispersion (Variation):

Introduction to Regression with Measurement Error STA431: Spring 2013.

Generalized Linear Models

Status of Exploited Marine Fishes and Invertebrates in German Marine Waters Rainer Froese, GEOMAR Cluster Meeting ökosystemgerechte Fischerei Bundesamt.

The (potential) value and use of empirical estimates of selectivity in integrated assessments John Walter, Brian Linton, Will Patterson and Clay Porch.

Revisiting Stock-Recruitment Relationships Rainer Froese Mini-workshop on Fisheries: Ecology, Economics and Policy CAU, Kiel, Germany.

Modeling Menstrual Cycle Length in Pre- and Peri-Menopausal Women Michael Elliott Xiaobi Huang Sioban Harlow University of Michigan School of Public Health.

Modeling Parameters in Stock Synthesis Modeling population processes 2009 IATTC workshop.

Modelling non-independent random effects in multilevel models William Browne Harvey Goldstein University of Bristol.

Maximum likelihood estimates of North Pacific albacore tuna ( Thunnus alalunga ) von Bertalanffy growth parameters using conditional-age-at-length data.

A statistical method for testing whether two or more dependent variable means are equal (i.e., the probability that any differences in means across several.

Future needs for Stock Synthesis. Last years requests See website ( index.php?page=Report+from+the+SS2+Tutorial+2007)

ASSESSMENT OF BIGEYE TUNA (THUNNUS OBESUS) IN THE EASTERN PACIFIC OCEAN January 1975 – December 2006.

Biodiversity of Fishes Summary

Evaluation of a practical method to estimate the variance parameter of random effects for time varying selectivity Hui-Hua Lee, Mark Maunder, Alexandre.

Highway accident severities and the mixed logit model: An exploratory analysis John Milton, Venky Shankar, Fred Mannering.

Modeling Natural Mortality in Stock Synthesis Modeling population processes 2009 IATTC workshop.

Biodiversity of Fishes Growth Rainer Froese

FTP Yield per recruit models. 2 Objectives Since maximizing effort does not maximize catch, the question is if there is an optimum fishing rate that would.

Lecture 3 Linear random intercept models. Example: Weight of Guinea Pigs Body weights of 48 pigs in 9 successive weeks of follow-up (Table 3.1 DLZ) The.

The Stock Synthesis Approach Based on many of the ideas proposed in Fournier and Archibald (1982), Methot developed a stock assessment approach and computer.

Workshop on Stock Assessment Methods 7-11 November IATTC, La Jolla, CA, USA.

28. Multiple regression The Practice of Statistics in the Life Sciences Second Edition.

Modeling Growth in Stock Synthesis Modeling population processes 2009 IATTC workshop.

Describing Data Descriptive Statistics: Central Tendency and Variation.

Evaluation of harvest control rules (HCRs): simple vs. complex strategies Dorothy Housholder Harvest Control Rules Workshop Bergen, Norway September 14,

SEDAR 42: US Gulf of Mexico Red grouper assessment Review Workshop Introduction SEFSC July , 2015.

Biodiversity of Fishes Stock-Recruitment Relationships

CHAPTER 2: Basic Summary Statistics

Yellowfin Tuna Major Changes Catch, effort, and length-frequency data for the surface fisheries have been updated to include new data for 2005.

Empirical comparison of historical data and age- structured assessment models for Prince William Sound and Sitka Sound Pacific herring Peter-John F. Hulson,

UALG Statistical catch at age models Einar Hjörleifsson.

Influence of selectivity and size composition misfit on the scaling of population estimates and possible solutions: an example with north Pacific albacore.

Analysis of Variance and Design of Experiments (Math 446/546) January 28, 2013.

NWFSC A short course on data weighting and process error in Stock Synthesis Allan Hicks CAPAM workshop October 19, 2015.

MONTE CARLO SIMULATION MODEL. Monte carlo simulation model Computer based technique length frequency samples are used for the model. In addition randomly.

Data requirement of stock assessment. Data used in stock assessments can be classified as fishery-dependent data or fishery-independent data. Fishery-dependent.

Population Dynamics and Stock Assessment of Red King Crab in Bristol Bay, Alaska Jie Zheng Alaska Department of Fish and Game Juneau, Alaska, USA.

9.4: Models for Population Growth. Logistic Model.

Pacific-Wide Assessment of Bigeye Tuna

Sardine Two-Stock Hypothesis: Results at the Posterior Mode

Generalized Linear Models

ASAP Review and Discussion

Biodiversity of Fishes Summary

Mathematical Foundations of BME Reza Shadmehr

SAFS Quantitative Seminar

Effects of Catch-at-Age Sample Size on Gulf of Mexico Gray Triggerfish Spawning Stock Biomass Estimates Jeff Isely SEFSC Miami.

JABBA-Select: Simulation-Testing Henning Winker

2.3. Measures of Dispersion (Variation):

Bristol Bay Red King Crab Assessment in Spring 2019

Presentation transcript:

Modeling Selectivity in Stock Synthesis Modeling population processes 2009 IATTC workshop

Outline General notes Functional forms Details on double normal Retention and discard mortality Male offset

Notes on selectivity in SS Wide variety of options Selectivity can be a function of either age or length or a combination of both Parameters of the selectivity curves have all the functionality as other parameters: –time blocks, random variation, covariates, priors, etc.

Functional forms Full selectivity for all ages/lengths –typically used for age with length is also used and vice versa Logistic –commonly used for asymptotic selectivity Double logistic Double normal –most commonly used selectivity, allows a declining right limb Exponential-logistic Piecewise linear (in log space) function of length One value per age Random walk across ages Selex = spawning biomass, recruitment, or rec dev Mirror selectivity of another fleet/survey

Double normal Comprised of the outer sides of two adjacent normal curves with separate variance parameters and peaks joined by a horizontal line. The parameters include –the selectivity at the smallest and largest ages/sizes, –the age/size where the selectivity first reaches full selectivity, –the length of the plateau, and –two parameters controlling the slope of the ascending and descending limbs. Can be made asymptotic by fixing some parameters Can also be configured to ignore the parameter representing the selectivity at the oldest age

Double normal Can be similar to double logistic Simpler, better behaved function

Retention and discard mortality Retention is a logistic function of size with parameter for asymptotic retention rate Offset from inflection parameter for males Discard mortality also logistic with asymptote and male offset dead fish = sel∙(ret + (1-ret)*disc)

Retention and discard mortality

Male offset Male (or female) selectivity is modeled two ways 1. Offset from female selectivity using a broken stick with parameters: –age/size at the break point –log( male / female selectivity ) at min, max, and break point 2. A function of parameters which are computed as offsets from parameters for other gender (only available for logistic and double normal)

Male offset