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 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.

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.

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.

A GEOSTATISTICAL APPROACH TO THE ASSESSMENT OF THE SPATIAL DISTRIBUTION OF PARAPENAEUS LONGIROSTRIS (LUCAS, 1846) IN THE CENTRAL-SOUTHERN TYRRHENIAN SEA

Crustaceana ◽  
1999 ◽  
Vol 72 (9) ◽  
pp. 1093-1108 ◽  
Author(s):  
Pierluigi Carbonara ◽  
Teresa Silecchia ◽  
Maria Spedicato ◽  
Alessandra Acrivulis ◽  
Giuseppe Lembo
Keyword(s):  
Spatial Distribution ◽  
Small Scale ◽  
Tyrrhenian Sea ◽  
Temporal Dimension ◽  
Abundance Index ◽  
Spatial Continuity ◽  
Geostatistical Approach ◽  
Abundance Indices ◽  
Parapenaeus Longirostris ◽  
Southern Tyrrhenian Sea

AbstractThe spatial distribution of the abundance indices of the deep-water rose shrimp Parapenaeus longirostris was investigated applying geostatistical techniques on data collected in the central southern Tyrrhenian Sea from bottom trawl surveys carried out in the autumn since 1994. Experimental variograms (auto and cross) were constructed on the variable "abundance index", expressed in kg/km2, and those variogram models best describing the spatial continuity were detected and validated by the jackknife technique. The spatial structure of the "abundance index", exhibiting a similar pattern throughout the surveys, was described by a spherical model and characterized by a spatial continuity at a small scale level in the whole area. The linear geostatistical approach was applied by different kriging techniques and the estimates extended to the spatio-temporal dimension, in this case adopting the co-regionalized models and applying the cokriging technique. This method applied to the spatial dimension (abundance index and depth). Also, linking the spatial and temporal dimension of the abundance indices, measured in two different years, contributed to represent a more accurate picture of the abundance distribution, and allowed the detection of a temporal persistence of the localization of areas with higher abundance, reducing the standard deviation of the estimation error. This information, if coupled with an analysis of the geographical allocation of the fishing effort, could be of importance in stock assessment, allowing some variant application of the composite surplus production models. La distribution spatiale des indices d'abondance de la crevette rose d'eau profonde Parapenaeus longirostris a ete etudiee en appliquant les techniques de la geostatistique aux donnees collectees dans le centre-sud de la mer Tyrrhenienne au cours des campagnes de chalutage demersal realisees pendant l'automne, depuis 1994. Les variogrammes experimentaux (auto et cross) ont ete construits sur la variable "indice d'abondance", exprimee en kg/km2, et les modeles de variogramme decrivants le mieux la continuite spatiale ont ete determines et valides par la technique du "jackknife". La structure spatiale de l'indice d'abondance a presente le meme aspect pour tous les echantillonages; elle a ete decrite au moyen d'un modele spherique et caracterisee par une continuite spatiale a petite echelle dans toute la zone. La geostatistique lineaire a ete appliquee en utilisant differentes techniques du krigeage, et les estimations ont ete etendues a la dimension spatio-temporelle en appliquant les modeles coregionalises et la technique du cokrigeage. Cette methode, appliquee soit dans la dimension spatiale (indice d'abondance et profondeur), soit dans la dimension spatio-temporelle en considerant l'indice d'abondance echantillonne en deux annees differentes, a contribue a representer une image plus precise de la distribution de l'abondance, et a permis de detecter une persistance temporelle de la localisation des aires a plus grande abondance, en reduisant l'ecart type de l'erreur d'estimation. Cette information, avec l'analyse de l'allocation geografique de l'effort de peche, pourrait etre importante dans l'evaluation des stocks, en permettant l'application, avec quelques variantes, des modeles composites de production.

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 The use of acoustic accelerometer tags to determine seasonal changes in activity and catchability of a recreationally caught marine teleost

2015 ◽  
Vol 72 (8) ◽  
pp. 2512-2520 ◽  
Author(s):  
Kilian M. Stehfest ◽  
Jeremy M. Lyle ◽  
Jayson M. Semmens
Keyword(s):  
Seasonal Changes ◽  
Acoustic Telemetry ◽  
Stock Assessment ◽  
Lower Probability ◽  
Small Scale ◽  
Accurate Information ◽  
Activity Levels ◽  
Catch Per Unit Effort ◽  
Basic Study ◽  
Activity Data

Abstract Understanding the way catchability of exploited fish species varies due to changes in individual fish behaviour is a seldom addressed but important requisite for extracting accurate information on fish abundance from catch per unit effort (cpue) data, particularly from baited gears. In this study, cpue data analysis was combined with analysis of movement and activity data from acoustic telemetry tags to determine the nature of seasonal changes in cpue of the recreationally caught sand flathead (Platycephalus bassensis) in a coastal bay and estuary in southeast Tasmania, Australia. Water temperature had a significant influence on cpue of sand flathead with lower catches at lower temperatures. Yet, even at the relatively small scale of this study (10 s of km), the mechanisms in which temperature affects cpue were highly area specific. In the shallow, estuarine part of the study area, changes in cpue were driven by changes in availability, due to seasonal movements of fish in and out of the area. In the deeper bay at the mouth of the estuary, on the other hand, changes in cpue with temperature were most likely driven by temperature-related changes in activity. At lower temperatures, fish were less active, indicating that fish will have a lower probability of encountering bait as well as lower feeding motivation due to lower metabolic debt. This shows the importance of the inclusion of an environmentally influenced catchability parameter in stock assessment models that utilize cpue data from baited gear types, which is often only done implicitly on a coarse temporal scale by accounting for changes in cpue with season. Our study furthermore highlights the usefulness of acoustic telemetry in a fisheries context beyond the basic study of fish movement, allowing the monitoring of activity levels of exploited fish in relation to environmental parameters in the field.

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