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.

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 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 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.

Ecological and environmental factors affecting the foraging activity of the White-bellied Heron Ardea insignis (Hume, 1878) in Bhutan

2021 ◽  
pp. 1-18
Author(s):  
PEMA KHANDU ◽  
GEORGE A. GALE ◽  
SARA BUMRUNGSRI
Keyword(s):  
Relative Abundance ◽  
Habitat Restoration ◽  
Prey Species ◽  
Catch Per Unit Effort ◽  
Factors Affecting ◽  
Prey Biomass ◽  
Gravel Mining ◽  
Unit Effort ◽  
Prey Preferences ◽  
The Government

Summary White-bellied Heron Ardea insignis (WBH) is critically endangered, but we lack data on many aspects of its basic ecology and threats to the species are not clearly understood. The goal of this study was to analyse WBH foraging microhabitat selection, foraging behaviour, and prey preferences in two river basins (Punatsangchhu and Mangdechhu) in Bhutan which are likely home to one of the largest remaining populations of WBH. We also explored the relationship between the relative abundance of the WBH and prey biomass catch per unit effort within four foraging river microhabitats (pool, pond, riffle and run). Prey species were sampled in 13 different 100-m thalweg lengths of the rivers using cast nets and electrofishing gear. Riffles and pools were the most commonly used microhabitats; relative abundance was the highest in riffles. The relative abundance of WBH and prey biomass catch per unit effort (CPUE) also showed a weak but significant positive correlation (rs = 0.22). The highest biomass CPUE was observed in riffles while the lowest was found in the ponds. From the 97 prey items caught by the WBH, 95% of the prey were fish. The WBH mainly exploited three genera of fish (Garra, Salmo, and Schizothorax) of which Schizothorax (64%) was the most frequently consumed. This study provides evidence in support of further protection of critical riverine habitat and fish resources for this heron. Regular monitoring of sand and gravel mining, curbing illegal fishing, habitat restoration/mitigation, and developing sustainable alternatives for local people should be urgently implemented by the government and other relevant agencies. Further study is also required for understanding the seasonal variation and abundance of its prey species in their prime habitats along the Punatsangchhu and Mangdechhu basins.

scholarly journals Standardizing catch rates: is logbook information by itself enough?

2008 ◽  
Vol 65 (2) ◽  
pp. 255-266 ◽  
Author(s):  
J. Bishop ◽  
W. N. Venables ◽  
C. M. Dichmont ◽  
D. J. Sterling
Keyword(s):  
Relative Abundance ◽  
Prediction Models ◽  
Expert Knowledge ◽  
External Information ◽  
Linear Regression Models ◽  
Catch Per Unit Effort ◽  
Estimation Model ◽  
Catch Rates ◽  
Logbook Data ◽  
Estimation Models

Abstract Bishop, J., Venables, W. N., Dichmont, C. M., and Sterling, D. J. 2008. Standardizing catch rates: is logbook information by itself enough? – ICES Journal of Marine Science, 65: 255–266. The goal of the work was to maximize the accuracy of standardized catch per unit effort as an index of relative abundance. Linear regression models were fitted to daily logbook data from a multispecies penaeid trawl fishery in which within-vessel changes in efficiency are common. Two model-fitting strategies were compared. The predictive strategy focused on maximizing the explained variance, and the estimation strategy on finding realistic coefficients for important components of changing catchability. Realistic values could not always be obtained, because the regression factors were not orthogonal, and data on the presence of technology were sometimes unreliable or systematically incomplete. It was not possible to separate fishing power from abundance by analysing logbook data alone; it was necessary to incorporate external information within the standardization model. Therefore, the resultant estimation models incorporated external information and expert knowledge by offsets. There was no single best estimation model. Instead, a series of models provided an envelope of possible changes in relative fishing power and prawn abundance since 1970. Compared with the prediction models, the estimation models revealed different trends in relative fishing power and relative abundance.

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.

Comparison of odontocete populations of the Marquesas and Society Islands (French Polynesia)

2008 ◽  
Vol 89 (5) ◽  
pp. 931-941 ◽  
Author(s):  
Alexandre Gannier
Keyword(s):  
Relative Abundance ◽  
Bottlenose Dolphin ◽  
Bottlenose Dolphins ◽  
Sperm Whale ◽  
French Polynesia ◽  
Society Islands ◽  
Spotted Dolphin ◽  
Abundance Indices ◽  
Beaked Whales ◽  
Using Data

Small boat surveys were organized to study cetaceans of the Marquesas (9°S and 140°W) and the Society Islands (17°S and 150°W) in French Polynesia. Prospecting took place from 12–15 m sailboats, between 1996 and 2001 with systematic visual searching. Boats moved according to sea conditions, at a mean speed of 10 km/h. Effective effort of 4856 km in the Marquesas and 10,127 km in the Societies were logged. Relative abundance indices were processed for odontocetes using data obtained with Beaufort 4 or less. In the Marquesas, 153 on-effort sightings were obtained on 10 delphinids species including the spotted dolphin, spinner dolphin, bottlenose dolphin, melon-headed whale and rough-toothed dolphin. In the Societies, 153 sightings of 12 odontocetes included delphinids (spinner, rough-toothed and bottlenose dolphins, short-finned pilot and melon-headed whales, Fraser's dolphin, Risso's dolphin and pygmy killer whale) and two species of beaked whales, the sperm whale and dwarf sperm whale. Relative abundance indices were higher in the Marquesas than in the Societies both inshore (0.93 ind/km2 against 0.36 ind/km2) and offshore (0.28 ind/km2 against 0.14 ind/km2). Differences in remote-sensed primary production were equally important, the Marquesas waters featuring an annual average of 409 mgC.m−2 · day−1 and the Societies of only 171 mgC · m−2 · day−1. The presence of a narrow shelf around the Marquesas also accounted for differences in odontocete populations, in particular the delphinids.

scholarly journals Factors affecting relative abundance of low-mobility fishing resources: spiny lobster in the Galapagos Marine Reserve

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7278 ◽  
Author(s):  
Juan Carlos Murillo-Posada ◽  
Silvia Salas ◽  
Iván Velázquez-Abunader
Keyword(s):  
Monitoring System ◽  
Relative Abundance ◽  
Spatial Scales ◽  
Marine Reserve ◽  
Spiny Lobster ◽  
Fishing Effort ◽  
Additive Models ◽  
Catch Per Unit Effort ◽  
Monthly Basis ◽  
Galapagos Marine Reserve

Management of low-mobility or benthic fisheries is a difficult task because variation in the spatial distribution and population dynamics of the resources make the monitoring and assessment of these fisheries challenging. We assumed that environmental, spatial, and temporal factors can contribute to the variability of the relative abundance of such species; we used Generalized Additive Models for Location Scale and Shape (GAMLSS) to test this hypothesis using as a case study the lobster fishery (targeting two species) in the Galapagos Marine Reserve, Ecuador. We gathered data on each of the two species of lobster on a monthly basis over seven years, including: (a) onboard observers’ records of catch data, fishing effort, and ground location by trip, and (b) data from interviews undertaken with fishers at their arrival to port, recording the same type of information as obtained from onboard observers. We use this information to analyze the effect of the measured variables and to standardize the Catch per Unit Effort (CPUE) in each case, using the GAMLSS. For both species, the temperature, region, fishing schedule, month, distance, and the monitoring system were significant variables of the selected models associated with the variability of the catch rate. ForPanulirus penicillatus, CPUE was higher at night than during the day, and forPanulirus gracilisit was higher during the day. Increased temperature resulted in a decrease of CPUE values. It was evident that temporal, spatial scales and monitoring system can influence the variability of this indicator. We contend that the identification of drivers of change of relative abundance in low-mobility species can help to support the development of monitoring and assessment programs for this type of fisheries.

scholarly journals Preliminary prediction of the potential distribution and consequences of Haemaphysalis longicornis using a simple rule-based climate envelope model

2018 ◽  
Author(s):  
Krisztian Magori
Keyword(s):  
Atlantic Coast ◽  
The United States ◽  
Simple Rule ◽  
Potential Range ◽  
Haemaphysalis Longicornis ◽  
Rule Based ◽  
The Us ◽  
Climate Envelope ◽  
Envelope Model ◽  
The Many

AbstractHaemaphysalis longicornis, the Asian longhorned (or bush) tick has been detected on a sheep in August 2017 in Hunterdon County, New Jersey. By October 26, 2018, this tick has been detected in 44 counties in 9 states along the Atlantic coast of the United States, with the first detection backdated to 2010. Here, I use a simple rule-based climate envelope model, based on a prior analysis in New Zealand, to provide a preliminary analysis of the potential range of this introduced tick species in North America. After validating this model against the counties where the tick has been already detected, I highlight the counties where this tick might cause considerable economic harm. I discuss the many limitations of this simple approach, and potential remedies for these limitations, and more sophisticated approaches. Finally, I conclude that substantial areas of the US, especially along the Gulf and Atlantic coast, are suitable for the establishment of this tick, putting millions of heads of livestock potentially at risk.

Does habitat or depth influence catch rates of pelagic species?

2007 ◽  
Vol 64 (11) ◽  
pp. 1581-1594 ◽  
Author(s):  
Keith A Bigelow ◽  
Mark N Maunder
Keyword(s):  
Relative Abundance ◽  
Environmental Conditions ◽  
Environmental Data ◽  
Catch Per Unit Effort ◽  
Blue Shark ◽  
Species Vulnerability ◽  
Thunnus Obesus ◽  
Catch Rates ◽  
The Relationship ◽  
The Vertical Distribution

The efficiency of a pelagic longline fishing operation and the species composition of the resulting catch is influenced primarily by the relationship between the distribution of hooks and species vulnerability, with vulnerability described by either depth or some suite of environmental variables. We therefore fitted longline catch rate models to determine whether catch is estimated better by vertically distributing a species by depth or by environmental conditions (e.g., temperature, thermocline gradient, and oxygen concentration). Catch rates were estimated by two methods: (i) monitoring longlines where the vertical distribution of hooks and catch in relation to depth and environmental conditions is known, and (ii) applying a statistical habitat-based standardization (statHBS) model to fishery and environmental data to develop relative abundance indices for bigeye tuna (Thunnus obesus) and blue shark (Prionace glauca). Results indicated that an understanding of gear dynamics and environmental influences are important for analyzing catch-per-unit-effort (CPUE) data correctly. Analyses based on depth-specific catch rates can lead to serious misinterpretation of abundance trends, despite the use of sophisticated statistical techniques (e.g., generalized linear mixed models). This illustrates that inappropriate inclusion or exclusion of important covariates can bias estimates of relative abundance, which may be a common occurrence in CPUE analysis.

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