Semidiscrete biomass dynamic modeling: an improved approach for assessing fish stock responses to pulsed harvest events

2012 ◽  
Vol 69 (10) ◽  
pp. 1710-1721 ◽  
Author(s):  
Michael E. Colvin ◽  
Clay L. Pierce ◽  
Timothy W. Stewart
Keyword(s):  
Time Series Data ◽  
Reference Points ◽  
Series Data ◽  
Fish Stock ◽  
Parameter Estimates ◽  
Maximum Sustained Yield ◽  
Biomass Dynamic ◽  
Biomass Dynamics ◽  
Discrete Manner ◽  
Dynamics Models

Continuous harvest over an annual period is a common assumption of continuous biomass dynamics models (CBDMs); however, fish are frequently harvested in a discrete manner. We developed semidiscrete biomass dynamics models (SDBDMs) that allow discrete harvest events and evaluated differences between CBDMs and SDBDMs using an equilibrium yield analysis with varying levels of fishing mortality (F). Equilibrium fishery yields for CBDMs and SDBDMS were similar at low fishing mortalities and diverged as F approached and exceeded maximum sustained yield (FMSY). Discrete harvest resulted in lower equilibrium yields at high levels of F relative to continuous harvest. The effect of applying harvest continuously when it was in fact discrete was evaluated by fitting CBDMs and SDBDMs to time series data generated from a hypothetical fish stock undergoing discrete harvest and evaluating parameter estimates bias. Violating the assumption of continuous harvest resulted in biased parameter estimates for CBDM while SDBDM parameter estimates were unbiased. Biased parameter estimates resulted in biased biological reference points derived from CBDMs. Semidiscrete BDMs outperformed continuous BDMs and should be used when harvest is discrete, when the time and magnitude of harvest are known, and when F is greater than FMSY.

A Bayesian life-cycle model to estimate escapement at maximum sustained yield in salmon based on limited information

2019 ◽  
Vol 76 (2) ◽  
pp. 299-307
Author(s):  
Jan Ohlberger ◽  
Samuel J. Brenkman ◽  
Patrick Crain ◽  
George R. Pess ◽  
Jeffrey J. Duda ◽  
...  
Keyword(s):  
Life Cycle ◽  
Time Series Data ◽  
Coho Salmon ◽  
Reference Points ◽  
Series Data ◽  
Limited Information ◽  
Maximum Sustained Yield ◽  
Sustained Yield ◽  
Life Cycle Models ◽  
Marine Survival

Life-cycle models combine several strengths for estimating population parameters and biological reference points of harvested species and are particularly useful for those exhibiting distinct habitat shifts and experiencing contrasting environments. Unfortunately, time series data are often limited to counts of adult abundance and harvest. By incorporating data from other populations and by dynamically linking the life-history stages, Bayesian life-cycle models can be used to estimate stage-specific productivities and capacities as well as abundance of breeders that produce maximum sustained yield (MSY). Using coho salmon (Oncorhynchus kisutch) as our case study, we show that incorporating information on marine survival variability from nearby populations can improve model estimates and affect management parameters such as escapement at MSY. We further show that the expected long-term average yield of a fishery managed for a spawner escapement target that produces MSY strongly depends on the average marine survival. Our results illustrate the usefulness of incorporating information from other sources and highlight the importance of accounting for variation in marine survival when making inferences about the management of Pacific salmon.

scholarly journals Finding the signal in the noise: objective data-selection criteria improve the assessment of western Baltic spring-spawning herring

2009 ◽  
Vol 66 (8) ◽  
pp. 1673-1680 ◽  
Author(s):  
Mark R. Payne ◽  
Lotte Worsøe Clausen ◽  
Henrik Mosegaard
Keyword(s):  
Selection Criteria ◽  
Time Series Data ◽  
Stock Assessment ◽  
Clupea Harengus ◽  
Data Selection ◽  
Series Data ◽  
Distribution Area ◽  
Fish Stock ◽  
Objective Data ◽  
Spatial Coverage

Abstract Payne, M. R., Clausen, L. W., and Mosegaard, H. 2009. Finding the signal in the noise: objective data-selection criteria improve the assessment of western Baltic spring-spawning herring. – ICES Journal of Marine Science, 66: 1673–1680. In the art of fish-stock assessment, it is common practice to include all available data without properly testing their validity in terms of their signal-to-noise ratio. The western Baltic spring-spawning herring (Clupea harengus) stock has been historically difficult to assess in a reliable manner. The population is spread between the Skagerrak, Kattegat, the Danish islands, and the western Baltic, but the distribution depends on age and season. Although the distribution area is covered by five separate surveys, none covers the entire stock. Using all time-series data may cause high noise levels and could lead to a poor-quality assessment. We examine the temporal and spatial coverage of each survey in terms of current biological understanding of stock distribution and, employing the observed internal consistency between age classes within cohorts as additional criteria, select the most appropriate data subsets. Assessments based on the revised dataset show greatly improved quality in terms of both accuracy and precision. The results highlight the often-ignored principle that a judicious choice of input data, based on rational and justifiable selection criteria, can enhance the ultimate quality of a stock assessment.

Bias in the Estimation of Functional Relationships from Time Series Data

1985 ◽  
Vol 42 (1) ◽  
pp. 147-149 ◽  
Author(s):  
Carl J. Walters
Keyword(s):  
Time Series ◽  
Measurement Errors ◽  
Time Series Data ◽  
Series Data ◽  
Parameter Estimates ◽  
Natural Experiments ◽  
Independent Variables ◽  
Functional Relationships ◽  
Stock Recruitment ◽  
The Relationship

Functional relationships, such as stock–recruitment curves, are generally estimated from time series data where natural "random" factors have generated both deviations from the relationship and also informative variation in the independent variables. Even in the absence of measurement errors, such natural experiments can lead to severely biased parameter estimates. For stock–recruitment models, the bias is misleading for management: the stock will appear too productive when it is low, and too unproductive when it is large. The likely magnitude of such biases can and should be determined for any particular case by Monte Carlo simulations.

scholarly journals Time Stand Still: Effects of Temporal Window Selection on Eye Tracking Analysis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Jonathan E. Peelle ◽  
Kristin J. Van Engen
Keyword(s):  
Eye Tracking ◽  
Degrees Of Freedom ◽  
Time Series Data ◽  
Time Window ◽  
Time Windows ◽  
Basis Set ◽  
Series Data ◽  
Parameter Estimates ◽  
Start Time ◽  
Window Selection

The number of possible approaches to conducting and analyzing a research study—often referred to as researcher degrees of freedom—has been increasingly under scrutiny as a challenge to the reproducibility of experimental results. Here we focus on the specific instance of time window selection for time series data. As an example, we use data from a visual world eye tracking paradigm in which participants heard a word and were instructed to click on one of four pictures corresponding to the target (e.g., “Click on the hat”). We examined statistical models for a range of start times following the beginning of the carrier phrase, and for each start time a range of window lengths, resulting in 8281 unique time windows. For each time window we ran the same logistic linear mixed effects model, including effects of time, age, noise, and word frequency on an orthogonalized polynomial basis set. Comparing results across these time ranges shows substantial changes in both parameter estimates and p values, even within intuitively “reasonable” boundaries. In some cases varying the window selection in the range of 100–200 ms caused parameter estimates to change from positive to negative. Rather than rush to provide specific recommendations for time window selection (which differs across studies), we advocate for transparency regarding time window selection and awareness of the effects this choice may have on results. Preregistration and multiverse model exploration are two complementary strategies to help mitigate bias introduced by any particular time window choice.

scholarly journals Does Financial Liberalization Lead to Poverty Alleviation? New Evidence from Nigeria

2020 ◽  
Vol 8 (3) ◽  
pp. p22
Author(s):  
Onwuka Ifeanyi Onuka ◽  
Nwadiubu Anthony Odinakachukwu
Keyword(s):  
Private Sector ◽  
Poverty Alleviation ◽  
Agricultural Sector ◽  
Financial Liberalization ◽  
Time Series Data ◽  
Manufacturing Sector ◽  
Financial System ◽  
Series Data ◽  
Parameter Estimates ◽  
Bounds Testing

The study examined anew the empirical question of whether financial liberalization induces poverty alleviation. There is a theoretical expectation that liberalizing the financial market will lead to greater savings mobilization, greater access to credit facilities and poverty alleviation. Using a time-series data spanning 38 years (1980-2018), the study analyzed the effect of financial liberalization on credit availability to the private sector, the manufacturing sector especially the small & medium enterprises and the agricultural sector in Nigeria. The Bounds testing approach to co-integration employed within the framework of Autoregressive Distributed Lag model (ARDL) was used to generate the coefficients. The coefficient of financial liberalization-though positive in all the parameter estimates, it is not significant. This lead us to the conclusion that despite the advantages of financial liberalization, its benefits is yet to bring about significant positive increases or changes in the volume of credit to the private sector and in poverty alleviation. Inferring upon this, we deduced that the continued liberalization of the financial system though indicating a positive long run impact on financial widening (or financial deepening as the case may be), its manifestation on quantum of credit to the private sector and on poverty alleviation is yet to be realized in Nigeria. The study recommended, amongst others, that government should re-think and re-tool the process in ways that will generate stability in the financial system and unleash the potentials of the process to generate greater savings and ultimately greater investment in the real sectors of the economy.

scholarly journals Inferring Ecosystem Networks as Information Flows

2021 ◽  
Author(s):  
Jie Li ◽  
Matteo Convertino
Keyword(s):  
Species Interactions ◽  
Network Inference ◽  
Time Series Data ◽  
Ecosystem Model ◽  
Series Data ◽  
Fish Stock ◽  
Community Patterns ◽  
Predator Prey Model ◽  
Biodiversity Changes ◽  
Structural Networks

AbstractThe detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed Optimal Information Flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and α-diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective α-diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities.

scholarly journals A jump distance based parameter inference scheme for particulate trajectories in biological settings

2017 ◽  
Author(s):  
Rebecca Menssen ◽  
Madhav Mani
Keyword(s):  
Time Series Data ◽  
Temporal Dynamics ◽  
Mean Square Displacement ◽  
Parameter Extraction ◽  
Biological Data ◽  
Series Data ◽  
Parameter Estimates ◽  
Selection Scheme ◽  
Jump Distance ◽  
Competing Models

ABSTRACTOne type of biological data that needs more quantitative analytical tools is particulate trajectories. This type of data appears in many different contexts and across scales in biology: from the trajectory of bacteria performing chemotaxis to the mobility of ms2 spots within nuclei. Presently, most analyses performed on data of this nature has been limited to mean square displacement (MSD) analyses. While simple, MSD analysis has several pitfalls, including difficulty in selecting between competing models, handling systems with multiple distinct sub-populations, and parameter extraction from limited time-series data. Here, we provide an alternative to MSD analysis using the jump distance distribution (JDD). The JDD resolves several issues: one can select between competing models of motion, have composite models that allow for multiple populations, and have improved error bounds on parameter estimates when data is limited. A major consequence is that you can perform analyses using a fraction of the data required to get similar results using MSD analyses, thereby giving access to a larger range of temporal dynamics when the underlying stochastic process is not stationary. In this paper, we construct and validate a derivation of the JDD for different transport models, explore the dependence on dimensionality of the process, and implement a parameter estimation and model selection scheme. We demonstrate the power of this scheme through an analysis of bacterial chemotaxis data, highlighting the interpretation of results and improvements upon MSD analysis. We expect that our proposed scheme provides quantitative insights into a broad spectrum of biological phenomena requiring analysis of particulate trajectories.

A hierarchical approach to determining reference points for Pandalid shrimp

2004 ◽  
Vol 61 (8) ◽  
pp. 1373-1391 ◽  
Author(s):  
Steven X Cadrin ◽  
James A Boutillier ◽  
Josef S Idoine
Keyword(s):  
Life Histories ◽  
Reference Points ◽  
Hierarchical Approach ◽  
Precautionary Approach ◽  
Research Programs ◽  
Production Models ◽  
Biomass Dynamics ◽  
Using Data ◽  
Dynamics Models ◽  
Data Requirements

Reference points for harvesting Pandalid shrimp are categorized into five general approaches: historical proxies, biomass dynamics models, dynamic pool models, stock–recruit models, and demographic production models. Each of these approaches has different data requirements and underlying assumptions. Estimation of biological reference points from these methods can be viewed as a hierarchy, using data-poor proxies in the lowest tier to applying more informative demographic production models in the highest. Based on a review of Pandalid life histories, precautionary-approach reference points, and methodologies for estimating reference points and their applications to Pandalid shrimp stocks, we advocate a progression from proxies to more informative models and the requisite advancement of research programs to develop reliable reference points for Pandalid shrimp stocks.

Exploring Territorial Differences in the Relationship between Marketing Variables

1977 ◽  
Vol 14 (2) ◽  
pp. 145-155 ◽  
Author(s):  
Dick R. Wittink
Keyword(s):  
Time Series ◽  
Market Share ◽  
Response Function ◽  
Time Series Data ◽  
Series Data ◽  
Price Sensitivity ◽  
Parameter Estimates ◽  
Second Stage ◽  
Marketing Variables ◽  
The Relationship

The parameters of a market share response function are estimated by using time series data separately for several sales territories. In a second stage an attempt is made to explain territorial differences in parameter estimates. It appears that for the brand studied advertising may serve to increase the price sensitivity.

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