scholarly journals A Standardized Abundance Index from Fishery Independent Data: A Case Study of Swordfish (Xiphias Gladius) from Indonesian Tuna Longline Fishery

2020 ◽  
Vol 147 ◽  
pp. 02016
Author(s):  
Bram Setyadji ◽  
Zulkarnaen Fahmi
Keyword(s):  
Relative Abundance ◽  
Fishery Management ◽  
Negative Binomial ◽  
Stock Assessment ◽  
Information Criterion ◽  
Catch Per Unit Effort ◽  
Hurdle Models ◽  
Abundance Indices ◽  
Longline Fisheries ◽  
Tuna Longline

Most varieties of the billfish caught in the Indian Ocean are either swordfish or Indo-Pacific sailfish. Swordfish is mostly considered as by-catch from tuna longline fisheries, except for South African, Spanish and Portuguese fleets. Despite of its importance, little are known about their abundance. Relative abundance indices are the input data for stock assessment analyses that provide useful information for decision making and fishery management. In this paper, a Generalized Linear Model (GLM) was utilized to systematize the catch-per-unit-effort (CPUE) and to estimate relative abundance indices based on the Indonesian longline dataset. The data was collected by scientific observers from August 2005 to December 2016. Conventional models for counting data were used, but zero-inflated and hurdle models also considered, due to the high number of zero-catchper-set. Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were applied to select the best models among all those evaluated. Both AIC and BIC suggested that the simple negative binomial (NB) model is the best option. The trends were relatively similar to the nominal series, but with smoother peaks. In general, there was a tendency of positive trends in the last decade, with the series varying throughout the period.

Simulating spatial dynamics to evaluate methods of deriving abundance indices for tropical tunas

2010 ◽  
Vol 67 (9) ◽  
pp. 1409-1427 ◽  
Author(s):  
Thomas R. Carruthers ◽  
Murdoch K. McAllister ◽  
Robert N. M. Ahrens
Keyword(s):  
Population Dynamics ◽  
Relative Abundance ◽  
Spatial Dynamics ◽  
Selection Criterion ◽  
Information Criterion ◽  
Fishing Effort ◽  
Catch Per Unit Effort ◽  
Applied Model ◽  
Thunnus Obesus ◽  
Abundance Indices

Relative abundance indices derived from nominal catch-per-unit-effort (CPUE) data are a principle source of information for the majority of stock assessments. A particular problem with formulating such abundance indices for pelagic species such as tuna is the interpretation of CPUE data from fleets that have changed distribution over time. In this research, spatial population dynamics are simulated to test the historical pattern of fishing effort as a basis for making inferences about relative abundance. A number of age-structured, spatially disaggregated population dynamics models are described for both Atlantic yellowfin tuna ( Thunnus albacares ) and bigeye tuna ( Thunnus obesus ) to account for uncertainty in spatial distribution and movement. These models are used to evaluate the reliability of standardization methods and a commonly applied model selection criterion, Akaike’s information criterion (AIC). The simulations demonstrate the pitfalls of aggregating CPUE data over spatial areas and highlight the need for data imputation. Simulations support simpler models than those selected using AIC for extracting reliable indices of relative abundance.

scholarly journals THE INFLUENCE OF THE COASTAL CURRENT ON THE ESTIMATION OF RELATIVE ABUNDANCE INDICES IN SMALL-SCALE SHRIMP FISHERY

2021 ◽  
Vol 47 ◽  
pp. e665
Author(s):  
Paulo Victor do Nascimento ARAÚJO ◽  
Alex Barbosa de MORAES ◽  
Flávia LUCENA FRÉDOU ◽  
Fúlvio Aurélio de Morais FREIRE
Keyword(s):  
Relative Abundance ◽  
Stock Assessment ◽  
Northeastern Brazil ◽  
Small Scale ◽  
Coastal Current ◽  
Sampling Effort ◽  
Catch Per Unit Effort ◽  
Abundance Index ◽  
Shrimp Fishery ◽  
Abundance Indices

The aim of this scientific note was to evaluate the influence of the coastal current on the estimation of relative abundance indices for small-scale marine shrimp trawling to indicate the best relative abundance index to be used for stock assessment and conservation. Georeferenced experimental trawls were carried out with standardized equipment and capture time on the coast of Rio Grande do Norte, northeastern Brazil. Drags followed convergent and divergent orientations in relation to the flow of the local coastal current. The results showed that the direction of the coastal current flow directly influences the distances and drag shifts, generating variations in the sampling effort and, consequently, bias when using Catch per Unit Effort (CPUE) as a relative abundance index. Conversely, the adoption of Catch per Unit of Swept Area (CPUA) as an index of relative abundance for shrimp trawling becomes more suitable since the variations in the distances of trawl shifts are perceptible through this index.

scholarly journals DELTA MODEL APPROACH FOR CPUE STANDARDIZATION OF SWORDFISH (Xiphias gladius Linnaeus, 1978) CAUGHT BY INDONESIAN LONGLINE FLEET IN THE EASTERN INDIAN OCEAN

2017 ◽  
Vol 23 (1) ◽  
pp. 7
Author(s):  
Irwan Jatmiko ◽  
Humber Andrade ◽  
Budi Nugraha
Keyword(s):  
Relative Abundance ◽  
Fixed Effects ◽  
Fishery Management ◽  
Stock Assessment ◽  
Short Branch ◽  
Abundance Indices ◽  
Configuration Data ◽  
Catch Rates ◽  
Proportional Decrease ◽  
Model Approach

Relative abundance indices as calculated based on commercial catches are the input data to run stock assessment models to gather useful information for decision making in fishery management. A Generalized Linear Model (GLM) was used to calculate relative abundance indices and effect of longline fishing gear configuration. Data were collected by a scientific observer program from August 2005 to November 2013. Most of the boats monitored were based in the Benoa Port, Bali. Catches are often equal to zero because swordfish is a bycatch for Indonesian longline fleets. Therefore, a hurdle model and a binomial distribution was used to model the proportion of positive catch rates, while a gamma distribution were used to model the positive longline sets. Correlations between the proportion of positive sets and year () and quarter () were weak. However, linear correlation between the proportion of positive sets and the length of branch lines () and number of hooks between floats () were negative and significant. The probability of success is higher for surface longline with small number of hooks and short branch lines. Models with year in interactions as random effects did not converge. Models with year in interactions as fixed effects did converge, but the estimation of standard errors of year coefficients were high. Meaningful estimations were obtained only when using the simplest model, in which year is not in interactions. The low proportional decrease of deviance indicates that most of the variability of catch rates of swordfish caught by Indonesian longline boats are not related to year, quarter, number of hooks between floats and the length of branch lines. Other variables and information, like the daytime while the longlines deployed in the water (day or night), type of bait, size and type of hooks, and if the fishermen use light-sticks to attract the fish, are necessary to better understand the catch rate, and improve the estimations of the relative abundance indices.

scholarly journals Integrating spatial synchrony/asynchrony of population distribution into stock assessment models: a spatial hierarchical Bayesian statistical catch-at-age approach

2016 ◽  
Vol 73 (7) ◽  
pp. 1725-1738 ◽  
Author(s):  
Yan Jiao ◽  
Rob O'Reilly ◽  
Eric Smith ◽  
Don Orth ◽  
Keyword(s):  
Relative Abundance ◽  
Bayesian Model ◽  
Population Distribution ◽  
Stock Assessment ◽  
Spatial Models ◽  
Reference Points ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Spatial Synchrony ◽  
Abundance Indices

Abstract In many marine fisheries assessments, population abundance indices from surveys collected by different states and agencies do not always agree with each other. This phenomenon is often due to the spatial synchrony/asynchrony. Those indices that are asynchronous may result in discrepancies in the assessment of temporal trends. In addition, commonly employed stock assessment models, such as the statistical catch-at-age (SCA) models, do not account for spatial synchrony/asynchrony associated with spatial autocorrelation, dispersal, and environmental noise. This limits the value of statistical inference on key parameters associated with population dynamics and management reference points. To address this problem, a set of geospatial analyses of relative abundance indices is proposed to model the indices from different surveys using spatial hierarchical Bayesian models. This approach allows better integration of different surveys with spatial synchrony and asynchrony. We used Atlantic weakfish (Cynoscion regalis) as an example for which there are state-wide surveys and expansive coastal surveys. We further compared the performance of the proposed spatially structured hierarchical Bayesian SCA models with a commonly used Bayesian SCA model that assumes relative abundance indices are spatially independent. Three spatial models developed to mimic different potential spatial patterns were compared. The random effect spatially structured hierarchical Bayesian model was found to be better than the commonly used SCA model and the other two spatial models. A simulation study was conducted to evaluate the uncertainty resulting from model selection and the robustness of the recommended model. The spatially structured hierarchical Bayesian model was shown to be able to integrate different survey indices with/without spatial synchrony. It is suggested as a useful tool when there are surveys with different spatial characteristics that need to be combined in a fisheries stock assessment.

scholarly journals BEBERAPA PARAMETER POPULASI IKAN PEDANG (Xiphias gladius) DI SAMUDERA HINDIA BAGIAN TIMUR

2017 ◽  
Vol 8 (2) ◽  
pp. 117
Author(s):  
Bram Setyadji ◽  
I Wayan Arthana ◽  
I Wayan Kasa
Keyword(s):  
Indian Ocean ◽  
Stock Assessment ◽  
Total Mortality ◽  
Growth Parameter ◽  
Assessment Model ◽  
Eastern Indian Ocean ◽  
Optimum Level ◽  
Xiphias Gladius ◽  
Longline Fisheries ◽  
Tuna Longline

Komposisi hasil tangkapan ikan berparuh (Istiophoridae dan Xiphiidae) menduduki peringkat kedua terbesar setelah tuna (Thunnus sp.) pada perikanan rawai tuna. Sekitar 90% jenis ikan berparuh yang di daratkan didominasi oleh ikan pedang (Xiphias gladius), yang mana merupakan hasil tangkap sampingan dari perikanan rawai tuna, terutama di Samudera Hindia bagian timur. Meskipun dikategorikan sebagai ikan dengan nilai ekonomis tinggi, akan tetapi studi mengenai parameter populasi untuk spesies ini masih terbatas, terutama di Indonesia. Penelitian ini bertujuan untuk menduga parameter pertumbuhan, laju mortalitas dan laju ekploitasi ikan pedang berdasarkan data ukuran panjang. Model pengkajian stok menggunakan data frekuensi panjang dipilih karena ketersediaan dan kemudahan pengambilan data tersebut dibandingkan dengan metode lainnya. Penelitian ini menggunakan data pemantau ilmiah tahun 2005 sampai dengan 2014 dan data pengamatan harian pendaratan tuna dan sejenisnya tahun 2002 sampai dengan 2014 di Samudera Hindia. Hasil penelitian menunjukkan pertumbuhan ikan pedang relatif cepat, terutama pada awal masa pertumbuhan dengan nilai K = 0,12/tahun, t0 = -0,76025 tahun dan L = 302,4 cmFL. Nilai F (0,28/tahun) sedikit lebih besar daripada nilai M (0,24/tahun), yang berarti kematian ikan pedang lebih banyak disebabkan oleh penangkapan. Nilai E sebesar 0,55 mengindikasikan bahwa ikan pedang yang tertangkap oleh armada rawai tuna di Samudera Hindia berada pada kondisi optimum. Billfishes (Istiophoridae and Xiphiidae) are the second largest catch in tuna longline fisheries. About 90% of billfishes landed dominated by swordfish (Xiphias gladius) which was a by-catch from tuna longline fisheries, especially in eastern Indian Ocean. Despite of its high economic value, study on stock assessment for this species is limited, especially in Indonesia. The catch-at-size based stock assessment model was applied, to its availability and ease on collecting the data. The Objectives of this study are to estimate growth parameter, mortality rate and exploitation rate based on catch-at-size data. The primary data was obtained from scientific observer program from 2005 to 2014 and port sampling data from 2002 to 2014. The result showed that swordfish were relatively fast growth, especially on their early age (K = 0.12/year) with t0 estimated around -0.76 year and Linf about 302.4 cmLJFL. The estimated of total mortality (Z), natural mortality (M) and fishing mortality (F) from the model were 0.52/year, 0.24/year and 0.28/year respectively. The explitation rate of swordfish in the eastern Indian Ocean is on optimum level (E=0.55).

scholarly journals Stock depletion of yellowfin tuna Thunnus albacares (Bonnaterre 1788) in the Northeastern Indian Ocean

2021 ◽  
Vol 322 ◽  
pp. 05004
Author(s):  
Bram Setyadji ◽  
Hety Hartaty ◽  
Arief Wujdi ◽  
Ririk K. Sulistyaningsih
Keyword(s):  
Indian Ocean ◽  
Data Collection ◽  
Relative Abundance ◽  
Yellowfin Tuna ◽  
Coastal Communities ◽  
Thunnus Albacares ◽  
The Indian Ocean ◽  
Stock Depletion ◽  
Longline Fisheries ◽  
Tuna Longline

The stock of yellowfin tuna (Thunnus albacares) has been in a declining trend in the last five years. Although the noticeable decline mainly occurred in the western part of the Indian Ocean, uncertainty lingers on how this phenomenon will affect the opposite leg. The study aimed to investigate the dynamics of stock through monitoring several indicators by utilizing logbooks, scientific port sampling, and observer data available. The result showed that both relative abundance and estimated catch trend are declining in recent years, a sign that the negative global inclination also influences Indonesian tuna longline fisheries. Further studies are needed to understand whether this phenomenon also impacts other gears. Hence, mitigation on conserving the resource by reducing the catch and strengthening the data collection should be the priority to maintain the livelihood and welfare of many coastal communities.

scholarly journals Interpreting catch per unit effort data to assess the status of individual stocks and communities

2006 ◽  
Vol 63 (8) ◽  
pp. 1373-1385 ◽  
Author(s):  
Mark N. Maunder ◽  
John R. Sibert ◽  
Alain Fonteneau ◽  
John Hampton ◽  
Pierre Kleiber ◽  
...  
Keyword(s):  
Relative Abundance ◽  
Stock Assessment ◽  
Limited Information ◽  
Catch Per Unit Effort ◽  
Fish Stocks ◽  
Unit Effort ◽  
Strategy Evaluation ◽  
The Status ◽  
Catch Rates ◽  
Management Advice

AbstractDespite being one of the most common pieces of information used in assessing the status of fish stocks, relative abundance indices based on catch per unit effort (cpue) data are notoriously problematic. Raw cpue is seldom proportional to abundance over a whole exploitation history and an entire geographic range, because numerous factors affect catch rates. One of the most commonly applied fisheries analyses is standardization of cpue data to remove the effect of factors that bias cpue as an index of abundance. Even if cpue is standardized appropriately, the resulting index of relative abundance, in isolation, provides limited information for management advice or about the effect of fishing. In addition, cpue data generally cannot provide information needed to assess and manage communities or ecosystems. We discuss some of the problems associated with the use of cpue data and some methods to assess and provide management advice about fish populations that can help overcome these problems, including integrated stock assessment models, management strategy evaluation, and adaptive management. We also discuss the inappropriateness of using cpue data to evaluate the status of communities. We use tuna stocks in the Pacific Ocean as examples.

Hierarchical analysis of multiple noisy abundance indices

2010 ◽  
Vol 67 (1) ◽  
pp. 108-120 ◽  
Author(s):  
Paul B. Conn
Keyword(s):  
Relative Abundance ◽  
Sampling Error ◽  
Atlantic Coast ◽  
Implicit Assumption ◽  
Hierarchical Analysis ◽  
Catch Per Unit Effort ◽  
Spanish Mackerel ◽  
Process Error ◽  
The Us ◽  
Abundance Indices

Fisheries analysts often rely on relative abundance indices for assessing stock status. However, trends in abundance can be difficult to discern when there are multiple indices or when correlation among indices is weak or negative. In this paper, I present a hierarchical framework for analyzing multiple, noisy indices with the goal of estimating a single time series of relative abundance. An implicit assumption is that each index is measuring the same quantity (relative abundance) but that each is subject to process error (attributable to variation in catchability, spatial distribution, etc.) in addition to an estimable level of within-survey variance (i.e., sampling error). I use simulation to explore estimator performance under a number of scenarios, including several that violate underlying assumptions. In general, the hierarchical approach produced estimators with reasonable properties. I illustrate the method with an analysis of seven fishery-dependent catch-per-unit-effort indices of Spanish mackerel ( Scomberomorus maculatus ) off the US Atlantic coast and provide several suggestions for how this approach can be used in practice.

scholarly journals STANDARDIZING CPUE OF ALBACORE TUNA (Thunnus alalunga Bonnaterre, 1788) ON TUNA LONGLINE FISHERY IN EASTERN INDIAN OCEAN

2017 ◽  
Vol 23 (1) ◽  
pp. 29
Author(s):  
Fathur Rochman ◽  
Bram Setyadji ◽  
Arief Wujdi
Keyword(s):  
Indian Ocean ◽  
Relative Abundance ◽  
Eastern Indian Ocean ◽  
Series Data ◽  
Longline Fishery ◽  
Albacore Tuna ◽  
Fishing Vessels ◽  
Abundance Indices ◽  
Thunnus Alalunga ◽  
Tuna Longline

Albacore (Thunnus alalunga) is the third dominant catch of Indonesian tuna longline fishery operating in the eastern Indian Ocean. The percentage production of albacore catch was reaching up 6% of the total catch of tuna groups in Indonesia. Thi study aims to examine a relative abundance indices using standardized catch per unit of effort (CPUE) of longliner based on albacore tuna. This information will give a valuable input and information to support stock assessment particularly in the regional basis. In this study, we use Generalized Linear Model (GLM) with Tweedie distribution to standardize the CPUE and to estimate relative abundance indices based on the Indonesian longline dataset time series. Data were collected from January 2006 to October 2015 (106 trip observer and 8.989 fishing days) by conducting direct onboard observation on tuna longline vessels operating in the Indian Ocean. The result show that year, area,hooks between floats, year*season, year*area and year* hooks between floats significantly influenced the nominal CPUE of albacore. The highest value of Standardized CPUE appeared in 2014 and probably related to the large number of foreign fishing vessels with a high capacity (over 60 GT) targeting frozen tuna including albacore. In 2015, standardized CPUE value was sharply decreased due to the ban of foreign vessels in Indonesia. 

scholarly journals Comparing Zero-inflated Poisson, Zero-inflated Negative Binomial and Zero-inflated Geometric in Count Data with Excess Zero

2019 ◽  
pp. 1-10 ◽  
Author(s):  
M. I. Adarabioyo ◽  
R. A. Ipinyomi
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Likelihood Estimation ◽  
Information Criterion ◽  
Skewed Distribution ◽  
Hurdle Models ◽  
Mean And Variance ◽  
Over Dispersion ◽  
Mean Vector ◽  
Inflation Parameter

Count data often violate the assumptions of a normal distribution due to the fact that they are bounded by their lowest value which is zero. The Poison distribution is sometimes suggested but when the assumption of equal mean and variance is violated due to over-dispersion and presence of zeros we tend to look in the direction of other models. Zero-inflated data falls in this category. The zero-inflated and hurdle models have been found to fit this scenario. The proportions of zero in the data often affect the choice of the models. Our study used the Monte Carlo design to sample 1000 cases from positively skewed distribution with 1.25 as mean vector and 0.10 as zero-inflation parameter. The data was analysed using the method of the maximum likelihood estimation. The Zero-Inflated Poisson, Zero-Inflated Negative Binomial and Zero-Inflated Geometric were fitted; the standard error and Akaike Information Criterion were obtained as measures of model validation with ZIP outperformed ZINB and ZIG.

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