scholarly journals Spatiotemporal analysis of compositional data: increased precision and improved workflow using model-based inputs to stock assessment

2019 ◽  
Vol 76 (3) ◽  
pp. 401-414 ◽  
Author(s):  
James T. Thorson ◽  
Melissa A. Haltuch
Keyword(s):  
Sample Size ◽  
Compositional Data ◽  
Stock Assessment ◽  
Future Research ◽  
Sex Composition ◽  
Abundance Index ◽  
Composition Data ◽  
Model Based ◽  
Stock Assessments ◽  
The Impact

Stock assessment models are fitted to abundance-index, fishery catch, and age–length–sex composition data that are estimated from survey and fishery records. Research has developed spatiotemporal methods to estimate abundance indices, but there is little research regarding model-based methods to generate age–length–sex composition data. We demonstrate a spatiotemporal approach to generate composition data and a multinomial sample size that approximates the estimated imprecision. A simulation experiment comparing spatiotemporal and design-based methods demonstrates a 32% increase in input sample size for the spatiotemporal estimator. A Stock Synthesis assessment used to manage lingcod (Ophiodon elongatus) in the California Current also shows a 17% increase in sample size and better model fit using the spatiotemporal estimator, resulting in smaller standard errors when estimating spawning biomass. We conclude that spatiotemporal approaches are feasible for estimating both abundance-index and compositional data, thereby providing a unified approach for generating inputs for stock assessments. We hypothesize that spatiotemporal methods will improve statistical efficiency for composition data in many stock assessments and recommend that future research explore the impact of including additional habitat or sampling covariates.

Simulation testing a new multi-stage process to measure the effect of increased sampling effort on effective sample size for age and length data

2020 ◽  
Vol 77 (5) ◽  
pp. 1728-1737 ◽  
Author(s):  
James T Thorson ◽  
Meaghan D Bryan ◽  
Peter-John F Hulson ◽  
Haikun Xu ◽  
André E Punt
Keyword(s):  
Sample Size ◽  
Stock Assessment ◽  
Model Misspecification ◽  
Size Analysis ◽  
Effective Sample Size ◽  
Sampling Effort ◽  
Age Composition ◽  
Composition Data ◽  
Multi Stage ◽  
The Impact

Abstract Ocean management involves monitoring data that are used in biological models, where estimates inform policy choices. However, few science organizations publish results from a recurring, quantitative process to optimize effort spent measuring fish age. We propose that science organizations could predict the likely consequences of changing age-reading effort using four independent and species-specific analyses. Specifically we predict the impact of changing age collections on the variance of expanded age-composition data (“input sample size”, Analysis 1), likely changes in the variance of residuals relative to stock-assessment age-composition estimates (“effective sample size”, Analysis 2), subsequent changes in the variance of stock status estimates (Analysis 3), and likely impacts on management performance (Analysis 4). We propose a bootstrap estimator to conduct Analysis 1 and derive a novel analytic estimator for Analysis 2 when age-composition data are weighted using a Dirichlet-multinomial likelihood. We then provide two simulation studies to evaluate these proposed estimators and show that the bootstrap estimator for Analysis 1 underestimates the likely benefit of increased age reads while the analytic estimator for Analysis 2 is unbiased given a plausible mechanism for model misspecification. We conclude by proposing a formal process to evaluate changes in survey efforts for stock assessment.

scholarly journals Standardizing compositional data for stock assessment

2014 ◽  
Vol 71 (5) ◽  
pp. 1117-1128 ◽  
Author(s):  
James T. Thorson
Keyword(s):  
Sample Size ◽  
Approximation Method ◽  
Compositional Data ◽  
Normal Approximation ◽  
Stock Assessment ◽  
Simulated Data ◽  
Future Research ◽  
Effective Sample Size ◽  
Sampling Intensity ◽  
Index Standardization

Abstract Stock assessment models frequently integrate abundance index and compositional (e.g. age, length, sex) data. Abundance indices are generally estimated using index standardization models, which provide estimates of index standard errors while accounting for: (i) differences in sampling intensity spatially or over time; (ii) non-independence of available data; and (iii) the effect of covariates. However, compositional data are not generally processed using a standardization model, so effective sample size is not routinely estimated and these three issues are unresolved. I therefore propose a computationally simple “normal approximation” method for standardizing compositional data and compare this with design-based and Dirichlet-multinomial (D-M) methods for analysing compositional data. Using simulated data from a population with multiple spatial strata, heterogeneity within strata, differences in sampling intensity, and additional overdispersion, I show that the normal-approximation method provided unbiased estimates of abundance-at-age and estimates of effective sample size that are consistent with the imprecision of these estimates. A conventional design-based method also produced unbiased age compositions estimates but no estimate of effective sample size. The D-M failed to account for known differences in sampling intensity (the proportion of catch for each fishing trip that is sampled for age) and hence provides biased estimates when sampling intensity is correlated with variation in abundance-at-age data. I end by discussing uses for “composition-standardization models” and propose that future research develop methods to impute compositional data in strata with missing data.

Performance of a fisheries catch-at-age model (Stock Synthesis) in data-limited situations

2011 ◽  
Vol 62 (8) ◽  
pp. 927 ◽  
Author(s):  
Chantell R. Wetzel ◽  
André E. Punt
Keyword(s):  
Stock Assessment ◽  
Estimation Method ◽  
Assessment Method ◽  
Assessment Model ◽  
Limited Data ◽  
Composition Data ◽  
Spawning Stock ◽  
Age Model ◽  
Age Structured ◽  
The Impact

Limited data are a common challenge posed to fisheries stock assessment. A simulation framework was applied to examine the impact of limited data and data type on the performance of a widely used catch-at-age stock-assessment method (Stock Synthesis). The estimation method provided negatively biased estimates of current spawning-stock biomass (SSB) relative to the unfished level (final depletion) when only recent survey indices were available. Estimation of quantities of management interest (unfished SSB, virgin recruitment, target fishing mortality and final depletion) improved substantially even when only minimal-length-composition data from the survey were available. However, the estimates of some quantities (final depletion and unfished SSB) remained biased (either positively or negatively) even in the scenarios with the most data (length compositions, age compositions and survey indices). The probability of overestimating yield at the target SSB relative to the true such yield was ~50%, a risk-neutral result, for all the scenarios that included length-composition data. Our results highlight the importance of length-composition data for the performance of an age-structured assessment model, and are encouraging for the assessment of data-limited stocks.

scholarly journals “The Elephant in the Room”: Exploring Natural Mortality Uncertainty in Statistical Catch at Age Models

2020 ◽  
Vol 7 ◽  
Author(s):  
Alessandro Mannini ◽  
Cecilia Pinto ◽  
Christoph Konrad ◽  
Paraskevas Vasilakopoulos ◽  
Henning Winker
Keyword(s):  
Stock Assessment ◽  
Assessment Process ◽  
Sensitivity Analyses ◽  
Fish Stock ◽  
Natural Mortality ◽  
Fishing Mortality ◽  
Food And Agriculture ◽  
Stock Assessments ◽  
The Mediterranean ◽  
The Impact

The natural mortality rate (M) of a fish stock is typically highly influential on the outcome of age-structured stock assessment models, but at the same time extremely difficult to estimate. In data-limited stock assessments, M usually relies on a range of empirically or theoretically derived M estimates, which can vary vastly. This article aims at evaluating the impact of this variability in M using seven Mediterranean stocks as case studies of statistical catch-at-age assessments for information-limited fisheries. The two main bodies carrying out stock assessments in the Mediterranean and Black Seas are European Union’s Scientific Technical Economic Committee for Fisheries (STECF) and Food and Agriculture Organization’s General Fisheries Commission for the Mediterranean (GFCM). Current advice in terms of fishing mortality levels is based on a single “best” M assumption which is agreed by stock assessment expert working groups, but uncertainty about M is not taken into consideration. Our results demonstrate that not accounting for the uncertainty surrounding M during the assessment process can lead to strong underestimation or overestimation of fishing mortality, potentially biasing the management process. We recommend carrying out relevant sensitivity analyses to improve stock assessment and fisheries management in data-limited areas such as the Mediterranean basin.

scholarly journals Impact of humorous advertising on purchase decision: In context of Pakistan’s telecom industry

2020 ◽  
Vol 10 (2) ◽  
pp. 144-150
Author(s):  
Abbiha Waqar
Keyword(s):  
Sample Size ◽  
Secondary Data ◽  
Mobile Network ◽  
Google Scholar ◽  
Mobile Users ◽  
Future Research ◽  
Purchase Decision ◽  
Telecom Industry ◽  
Humorous Appeal ◽  
The Impact

The purpose was to study the impact of humorous advertisement on purchase decision, and in order to reach this objective, Uf one ads were analysed and compared to other mobile network ads which are being aired, especially in Pakistan’s telecom industry. Mobile users of Pakistan filled the questionnaires which were administered via distributing hard copies and online through Google Forms, from January 2017 to January 2018. Secondary data were collected using different research journals, which included JSTOR, Science Direct and Google Scholar. The planned sample size was 127 respondents. The results showed that humorous advertisement is one of the appeals which breaks the clutter.90% of the respondents said that humorous advertisements greatly affect the purchase decision. Hence, Ufone’s ads are effective. Recommendation for future research would be to study humour in detail, that is, by dividing the humorous appeal in categories like dark humour, slice of life humour and studying their respective impact on customer’s purchase decision.   Keywords: Humorous advertisement, advertisement effectiveness, purchase decision, telecom companies.

A framework to incorporate environmental effects into stock assessments informed by fishery-independent surveys: a case study with American lobster (Homarus americanus)

2020 ◽  
Vol 77 (10) ◽  
pp. 1700-1710
Author(s):  
Cameron T. Hodgdon ◽  
Kisei R. Tanaka ◽  
Jocelyn Runnebaum ◽  
Jie Cao ◽  
Yong Chen
Keyword(s):  
Environmental Variability ◽  
Stock Assessment ◽  
Homarus Americanus ◽  
Assessment Model ◽  
American Lobster ◽  
Multiple Model ◽  
Environmental Covariates ◽  
Model Based ◽  
Stock Assessments ◽  
Abundance Indices

Stock assessments for a majority of the world’s fisheries often do not explicitly consider the effects of environmental conditions on target species, which can raise model uncertainty and potentially reduce forecasting quality. Model-based abundance indices were developed using a delta generalized linear mixed model that incorporates environmental variability for use in stock assessment to understand how the incorporation of environmental variability impacts our understanding of population dynamics. For this study, multiple model-based abundance indices were developed to test the incorporation of environmental covariates in a length-structured assessment of the American lobster (Homarus americanus) stock in the Gulf of Maine – Georges Bank on the possible improvement of stock assessment quality. Comparisons reveal that modelled indices with environmental covariates appear to be more precise than traditional indices, but model performance metrics and hindcasted fishery statuses revealed that these improvements to indices may not necessarily mean an improved assessment. Model-based abundance indices are not intrinsically better than design-based indices and should be tested for each species individually.

Using model-based inference to evaluate global fisheries status from landings, location, and life history data

2012 ◽  
Vol 69 (4) ◽  
pp. 645-655 ◽  
Author(s):  
James T. Thorson ◽  
Trevor A. Branch ◽  
Olaf P. Jensen
Keyword(s):  
Logistic Regression ◽  
Life History ◽  
Research Agenda ◽  
Cross Validation ◽  
Stock Assessment ◽  
Location Data ◽  
Life History Data ◽  
Model Based ◽  
Stock Assessments ◽  
Fishery Status

Assessing fishery collapses worldwide is hindered by the lack of biomass data for most stocks, leading to the use of landings-based proxies or the assumption that existing stock assessments are globally representative. We argue that the use of sparse assessments to evaluate fishery status requires model-based inference because assessment availability varies spatially and temporally, and we derive a model that extrapolates from assessment results to available landings, life history, and location data. This model uses logistic regression to classify stocks into different prediction bins and estimates the probability of collapse in each using cross-validation. Results show that landings, life history, and location are informative to discriminate among different probabilities of collapse. We find little evidence that regions with fewer assessments have a greater proportion of collapsed stocks, while acknowledging weak inferential support regarding regions with one or fewer assessments. Our extrapolation suggests that 4.5%–6.5% of stocks defined by landings data are collapsed, but that this proportion is increasing. Finally, we propose a research agenda that combines stock assessment and landings databases while overcoming limitations in each.

scholarly journals Improving stock assessments through data prioritization

2016 ◽  
Vol 73 (12) ◽  
pp. 1703-1711 ◽  
Author(s):  
Kate I. Siegfried ◽  
Erik H. Williams ◽  
Kyle W. Shertzer ◽  
Lewis G. Coggins
Keyword(s):  
Data Collection ◽  
Stock Assessment ◽  
Integrated Analysis ◽  
Data Types ◽  
Age Composition ◽  
Composition Data ◽  
Relative Paucity ◽  
Southeastern Us ◽  
Multiple Data ◽  
Stock Assessments

The need for “better data” is a common response of stakeholders and managers when confronted with the uncertainty of advice resulting from quantitative stock assessments. Most contemporary stock assessments are based on an integrated analysis of multiple data types, each with their associated cost to collect. Data collection resources are inevitably limited; therefore, it is important to quantify the relative value of increased sampling for alternative data types in terms of improving stock assessments. We approached this universal problem using a simulation study of a hypothetical, amalgam species developed from eight separate stock assessments conducted for species found in southeastern US Atlantic waters. We simulated a population and a stock assessment from the amalgam species and then individually improved alternative data types (indices, age compositions, landings, and discards) by increasing either precision or sample size. We also simulated the effects of increased sampling for alternative groupings of data that might be collected in concert (e.g., commercial, recreational, or survey). Our results show that for the snapper–grouper complex we modeled, age composition data have the largest effect on the accuracy of assessments, with commercial age compositions being the most influential. This is due in part to the relative paucity of age composition data for many southeast US marine stocks, so that modest increases in collection efforts have relatively high benefits for age-based assessment models currently in use for the region. Though this study used data from a particular region of the US, our investigative framework is broadly applicable for quantitatively evaluating the benefits of improved data collection in terms of the precision of stock assessments in any region.

Entrepreneurial characteristics amongst university students: insights for understanding entrepreneurial intentions amongst youths in a developing economy

2020 ◽  
Vol 63 (1) ◽  
pp. 71-84
Author(s):  
Ayodotun Stephen Ibidunni ◽  
Dumebi Mozie ◽  
Adebanji Wlliam A.A. Ayeni
Keyword(s):  
University Students ◽  
Sample Size ◽  
Developing Economies ◽  
Risk Tolerance ◽  
Entrepreneurial Intentions ◽  
Future Research ◽  
Content Type ◽  
Entrepreneurial Characteristics ◽  
Business Opportunities ◽  
The Impact

PurposeThis study focussed on investigating the impact of entrepreneurial characteristics on the entrepreneurial intention of university students in Nigeria.Design/methodology/approachThis research adopted a survey research design via a well-constructed questionnaire. The study's sample size consisted of 354 aspiring student entrepreneurs.FindingsThe result from the statistical analysis revealed that the entrepreneurial characteristics, especially risk tolerance, the need for achievement and the locus of control (LoC) significantly influence students' entrepreneurial intentions.Research limitations/implicationsOne implication of this study is that risk tolerance has a positive influence on the ability to identify business opportunities. Thus, when persons pay adequate attention to tolerating risks, they have more chances of identifying business opportunities. Despite the valuable contribution made by this research, an important area of future research is to carry out investigations that use a more robust sample size and a multivariate analysis to identify the impact of entrepreneurial competencies on entrepreneurial intentions of university youths from a cross-country perspective amongst developing economies.Originality/valueThere are very little understanding and empirical evidence about how the entrepreneurial characteristics of the youths, especially those in the formal university system of developing countries like Nigeria, can determine and direct their intentions to venture into entrepreneurship endeavours. This study, therefore, undertakes an interventionist role to investigate the relationship between entrepreneurial characteristics and entrepreneurial intentions of university students in Nigeria.

Sign in / Sign up

Export Citation Format

Share Document