scholarly journals Mixture of time‐dependent growth models with an application to blue swimmer crab length‐frequency data

Biometrics ◽  
2016 ◽  
Vol 72 (4) ◽  
pp. 1255-1265 ◽  
Author(s):  
Luke R. Lloyd‐Jones ◽  
Hien D. Nguyen ◽  
Geoffrey J. McLachlan ◽  
Wayne Sumpton ◽  
You‐Gan Wang
Keyword(s):  
Growth Models ◽  
Time Dependent ◽  
Frequency Data ◽  
Length Frequency ◽  
Dependent Growth ◽  
Length Frequency Data

scholarly journals Fitting growth models to length frequency data

2004 ◽  
Vol 61 (2) ◽  
pp. 218-230 ◽  
Author(s):  
Geoff M. Laslett ◽  
J. Paige Eveson ◽  
Tom Polacheck
Keyword(s):  
Growth Models ◽  
Time Interval ◽  
Age Group ◽  
Frequency Data ◽  
Sampling Errors ◽  
Length Frequency ◽  
Error Structure ◽  
Second Stage ◽  
Time Period ◽  
Length Frequency Data

Abstract A novel two-stage procedure for fitting growth curves to length frequency data collected from commercial fisheries is described. The method is suitable for species in which cohorts are spawned over a limited time period, and samples of length frequency data are collected regularly (e.g. in weekly, fortnightly, or monthly time intervals) over an extended time period. In the first stage of analysis, Gaussian mixtures are fitted separately to the data for each time interval, and summary statistics (component means and standard errors) are extracted. In the second stage, parametric growth models, such as the von Bertalanffy seasonal growth curve, are fitted to the summary data. The error structure in this second stage of analysis incorporates random between-year effects, random within-year age-group effects, random within-year time-interval effects, random within-year age-group and time-interval interactions, and sampling errors. This complex error structure incorporating unbalanced crossed and nested random effects acknowledges that commercial fishing is not an exercise in random sampling, and allows for the inevitable additional sources of random variation in such an enterprise. The method is applied to South Australian southern bluefin tuna length frequency data collected from 1964 to 1989, and leads to the conclusion that juvenile tuna grew faster in the 1980s than in the 1960s, with the 1970s being a decade of highly variable growth.

scholarly journals POPULATION DYNAMICS OF MALAYAN LEAF FISH (Pristolepisgrooti Blkr.) IN RANAU LAKE, SOUTH SUMATRA

2018 ◽  
Vol 24 (2) ◽  
pp. 125
Author(s):  
Sevi Sawetri ◽  
Subagdja Subagdja ◽  
Dina Muthmainnah
Keyword(s):  
Mortality Rate ◽  
Growth Parameters ◽  
Total Mortality ◽  
Natural Mortality ◽  
Fishing Mortality ◽  
Frequency Data ◽  
Length Frequency ◽  
Lake Ecosystems ◽  
Exploitation Rate ◽  
Length Frequency Data

The Malayan leaf fish or locally named as kepor (Pristolepis grooti) is one of important biotic components in Ranau Lake ecosystems. This study aimed to estimate population dynamic and exploitation rate of kepor in Ranau Lake, South Sumatera. The population parameters are estimated based on length frequency data which were collected in March to October 2013. Growth parameters and fishing mortality rates were calculated using FiSAT software package. The results showed that kepor’s growth was negative allometric, which tended to gain length faster than weight. Kepor population was dominated (42%) by individual length of 10.0 to 11.0 cm. Predicted length infinity (L) was 17.28 cm with high value of growth rates (K) of 1.4 year-1. The natural mortality rate (M) is 2.57 year-1, the fishing mortality rate (F) is 5.36 year-1 and total mortality rate (Z) is 7.93 year-1. The exploitation rate of Malayan leaf fish in Ranau Lake (E = 0.68 year-1) has passed the optimum score.  

scholarly journals Investigation and Development of Post-Season Modelling of Arrow Squid in the Snares and Auckland Islands

2021 ◽  
Author(s):  
◽  
Vidette Louise McGregor
Keyword(s):  
Automatic Differentiation ◽  
Integrated Model ◽  
Current Data ◽  
Management Approach ◽  
Frequency Data ◽  
Catch Per Unit Effort ◽  
Length Frequency ◽  
Model Builder ◽  
Set Up ◽  
Length Frequency Data

<p>Squid fisheries require a different management approach to most fish species which are much longer living. Most squid live for around one year, spawn and then die. The result of this is an entirely new stock each year with little or no relationship of stock sizes between the years. Hence, it is difficult to set appropriate catch limits prior to the season. Currently, there is nothing set up for modelling the New Zealand squid fishery in-season or post-season. In-season management would allow for adjustments of catch limits during a season. Post-season management would provide information on how much the stock was exploited during a season (described as the escapement). I have produced an integrated model using ADMB (Automatic Differentiation Model Builder) (Fournier et al., 2011) which models length frequency data, CPUE (Catch Per Unit Effort) indices and catch weights from a season. It calculates escapement which indicates how much the fishery is currently being exploited. In running the model against data from four area and year combinations, I found the escapement calculation to be stable. The results suggest this modelling approach could be used with the current data collected for post-season modelling of the fishery. I am less confident about in-season modelling with the current data collected. The integrated model fits quite poorly to the CPUE data, suggesting some discrepancy either between the data or the assumptions made of them. Sampling from a greater number of tows is recommended to improve the length frequency data and this may also improve the ability of the model to fit both to these and the CPUE.</p>

scholarly journals Investigation and Development of Post-Season Modelling of Arrow Squid in the Snares and Auckland Islands

2021 ◽  
Author(s):  
◽  
Vidette Louise McGregor
Keyword(s):  
Automatic Differentiation ◽  
Integrated Model ◽  
Current Data ◽  
Management Approach ◽  
Frequency Data ◽  
Catch Per Unit Effort ◽  
Length Frequency ◽  
Model Builder ◽  
Set Up ◽  
Length Frequency Data

<p>Squid fisheries require a different management approach to most fish species which are much longer living. Most squid live for around one year, spawn and then die. The result of this is an entirely new stock each year with little or no relationship of stock sizes between the years. Hence, it is difficult to set appropriate catch limits prior to the season. Currently, there is nothing set up for modelling the New Zealand squid fishery in-season or post-season. In-season management would allow for adjustments of catch limits during a season. Post-season management would provide information on how much the stock was exploited during a season (described as the escapement). I have produced an integrated model using ADMB (Automatic Differentiation Model Builder) (Fournier et al., 2011) which models length frequency data, CPUE (Catch Per Unit Effort) indices and catch weights from a season. It calculates escapement which indicates how much the fishery is currently being exploited. In running the model against data from four area and year combinations, I found the escapement calculation to be stable. The results suggest this modelling approach could be used with the current data collected for post-season modelling of the fishery. I am less confident about in-season modelling with the current data collected. The integrated model fits quite poorly to the CPUE data, suggesting some discrepancy either between the data or the assumptions made of them. Sampling from a greater number of tows is recommended to improve the length frequency data and this may also improve the ability of the model to fit both to these and the CPUE.</p>

scholarly journals KORELASI PARAMETER MORFOMETRIK, NISBAH KELAMIN DAN KOMPOSISI UKURAN IKAN PEDANG (Xiphias gladius L.) DI SAMUDERA HINDIA

2015 ◽  
Vol 6 (3) ◽  
pp. 155
Author(s):  
Bram Setyadji ◽  
Budi Nugraha
Keyword(s):  
Body Size ◽  
Standard Form ◽  
Fork Length ◽  
Secondary Data ◽  
Primary Data ◽  
Frequency Data ◽  
Length Frequency ◽  
Lower Jaw ◽  
Length Measurements ◽  
Length Frequency Data

Model pengkajian stok melalui data frekuensi panjang lebih banyak digunakan karena data tersebut paling banyak tersedia dan mudah didapatkan dibandingkan data pengukuran jaringan keras (sisik, otolith, sirip dan tulang belakang) dan tagging. Khusus untuk ikan pedang, data panjang yang tersedia sebagian besar tidak standar dikarenakan ikan pedang yang tertangkap langsung diproses di laut yang mana bagian kepala, sirip, isi perut dibuang. Oleh karena itu dibutuhkan persamaan empiris untuk konversi dari ukuran non-standar ke standar sehingga bisa digunakan sebagai basis data pengkajian stok yang berbasis data tersebut. Data primer merupakan hasil observasi laut selama kurun waktuMaret 2011 sampai dengan Desember 2013, sedangkan data sekunder merupakan data observasi ilmiah Loka Penelitian Perikanan Tuna periode 2005-2013. Hasil penelitian menunjukkan terdapat korelasi yang signifikan antara beberapa parametermorfometrik ikan pedang yang diukur yakni panjang dari pangkal sirip dada ke ujung lekukan tengah sirip ekor (LJFL), panjang dari mata ke ujung lekukan tengah sirip ekor (EFL) dan panjang dari ujung rahang bawah ke ujung lekukan tengah sirip ekor (PFL) (R2 > 0,97; P < 0,01), akan tetapi tidak ada perbedaan yang nyata antara morfometri ikan pedang dan jenis kelamin (EFL-LJFL, P > 0,05 dan PFL-LJFL, P > 0,05). Hubungan antara nisbah kelamin dengan panjang ikan signifikan (Nisbah Kelamin = 0,0175 LJFL – 3,1001; n = 6, selang kelas 5 cm; P < 0,01) yang mana ikan pedang dengan ukuran lebih dari 260 cmadalah betina.Stock assessment models using length frequency data are more frequently used by Indonesian scientist due to its availability and easily obtained rather than skeletal parts or tagging data. As for swordfish most of the data vailable are not in standard form because most of swordfish landed are usually dressed at sea with various ways, so the length measurement are possible done afterward. There fore conversion among different length measurements is a necessity for assessment and management purposes. Primary data was collected from scientific observer program conducted between March 2011 and December 2013, while secondary data was obtained from 2005-2013. The results showed that the models are fit quite well for Lower Jaw Fork Length (LJFL), Eye Orbit Fork Length (EOFL) and Pectoral Fork Length (PFL) (R2> 0.97; P < 0.01) and there was no significant relationship between morphometric and sex (EFL-LJFL, P > 0.05 and PFL-LJFL, P > 0.05). Correlation between sex ratio and body size proved to be significant with nearly all of the swordfish >260 cm was female.

Using length-frequency data to elucidate the population dynamics of Argulus foliaceus (Crustacea: Branchiura)

Parasitology ◽  
2009 ◽  
Vol 136 (9) ◽  
pp. 1023-1032 ◽  
Author(s):  
N. G. H. TAYLOR ◽  
R. WOOTTEN ◽  
C. SOMMERVILLE
Keyword(s):  
Population Dynamics ◽  
Growth Rates ◽  
Egg Laying ◽  
Frequency Data ◽  
Length Frequency ◽  
Reliable Prediction ◽  
Egg Incubation ◽  
Novel Method ◽  
Argulus Foliaceus ◽  
Length Frequency Data

SUMMARYThis study uses a novel method for discriminating cohorts and investigating the population dynamics of the parasitic crustacean, Argulus foliaceus. Analysis of parasite length-frequency data was carried out in order to elucidate the timings and drivers behind the parasite's life cycle. Up to 6 cohorts of the parasite emerge through the course of 1 year in still-water trout fisheries in England. Recruitment ceases over the winter months; however, 3 cohorts of the parasite over-winter, 2 as eggs and 1 as a hatched stage. The technique, when used in conjunction with temperature data, also allowed for the reliable prediction of growth rates and provided estimates of egg incubation times and the length of hatching periods. These data showed that growth rates increased exponentially between the observed temperatures of 4 to 22°C. The method allowed for the time taken from hatching to egg laying under field conditions to be predicted and produced estimates that were validated against independent laboratory studies on the growth of the parasite.

scholarly journals On the pile-up effect and priors for Linf and M/K: response to a comment by Hordyk et al. on “A new approach for estimating stock status from length frequency data”

2019 ◽  
Vol 76 (2) ◽  
pp. 461-465 ◽  
Author(s):  
Rainer Froese ◽  
Henning Winker ◽  
Gianpaolo Coro ◽  
Nazli Demirel ◽  
Athanassios C Tsikliras ◽  
...  
Keyword(s):  
Frequency Data ◽  
Length Frequency ◽  
New Approach ◽  
Stock Status ◽  
Pile Up ◽  
K Response ◽  
Length Frequency Data

Evaluation of management procedures for a length-frequency data-limited fishery

2019 ◽  
Vol 26 (16) ◽  
pp. 15894-15904
Author(s):  
Richard Kindong ◽  
Jiangfeng Zhu ◽  
Feng Wu ◽  
Libing Dai ◽  
Xiaojie Dai ◽  
...  
Keyword(s):  
Frequency Data ◽  
Length Frequency ◽  
Management Procedures ◽  
Length Frequency Data
Sign in / Sign up

Export Citation Format

Share Document