Frequency Domain Filtering of Age-Structured Population Data

1987 ◽  
Vol 44 (3) ◽  
pp. 605-618 ◽  
Author(s):  
David W. Welch
Keyword(s):  
Time Series ◽  
Frequency Domain ◽  
Population Data ◽  
Complete Removal ◽  
Parameter Estimates ◽  
New Approach ◽  
Structured Population ◽  
Stock Recruitment ◽  
Age Structured ◽  
Frequency Domain Filtering

A new approach is described for removing part of the density-independent noise present in stock–recruitment (SR) data. The method is based on filtering the time series of recruitment data in the frequency domain, an approach that allows complete removal of the identifiable environmental noise without reducing the length of the time series. Results from Monte Carlo simulations demonstrate that recruitment filtering allows more reliable estimation of SR parameters than is possible without filtering and that negligible bias is introduced into the parameter estimates obtained for commonly used SR models.

Time series bias in the estimation of density-dependent mortality in stock-recruitment models

1995 ◽  
Vol 52 (1) ◽  
pp. 223-232 ◽  
Author(s):  
Ransom A. Myers ◽  
N. J. Barrowman
Keyword(s):  
Time Series ◽  
Gadus Morhua ◽  
Large Time ◽  
Atlantic Cod ◽  
Model Misspecification ◽  
Parameter Estimates ◽  
Density Dependent Mortality ◽  
Stock Recruitment ◽  
Iteroparous Species ◽  
The Relationship

Large biases can occur in parameter estimates for stock–recruitment models because the stock sizes are not chosen independently, being correlated with variability in recruitment. We examine the importance of this "time series bias" by a comprehensive analysis of available stock–recruitment data and the use of simulations. For semelparous species, i.e., species that reproduce only once, time series bias is important for all populations for which we had data. For iteroparous species, i.e., species that reproduce more than once, large biases occur if the populations are exploited at close to the maximum that is biologically possible. Notably, when there is autocorrelation in natural mortality, for univoltine species, the direction of bias is reversed due to model misspecification. Given moderate sample sizes and moderate levels of exploitation, time series bias is small for species such as Atlantic cod (Gadus morhua), for which α, the slope of the relationship between recruitment and number of spawners as the number of spawners goes to zero, is large. Time series bias will usually be important in species such as hakes (Merluccius) for which α appears to be relatively small.

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.

Identifying the Stock–Recruitment Relationship for Age-Structured Populations Using Time-Invariant Matched Linear Filters

1986 ◽  
Vol 43 (1) ◽  
pp. 108-123 ◽  
Author(s):  
David W. Welch
Keyword(s):  
Substantial Improvement ◽  
Structured Populations ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Linear Filters ◽  
Statistical Precision ◽  
Stock Recruitment ◽  
Age Structured ◽  
Definition Of ◽  
Time Invariant

Theory is introduced which permits the identification and removal of part of the environmental (density-independent) noise influencing the recruitment to age-structured populations. The biological assumptions under which this noise may be safely removed from the recruitment data without distorting the analysis of the density-dependent dynamics are outlined, and the permissible level of filtering is shown to depend on the age structure of the population in question. As examples of the method, filters were designed and applied to stock–recruitment data available for 16 commercially exploited marine fish populations. A substantial improvement is demonstrated in the statistical precision with which the stock–recruitment relationship can be defined, with standard errors on parameter estimates frequently decreasing by a factor of 2 or 3 after filtering. In general, use of filtering theory should allow a more precise definition of the nature of the stock–recruitment relationship in essentially all age-structured populations.

A Partial Bias Correction Factor for Stock–Recruitment Parameter Estimation in the Presence of Autocorrelated Environmental Effects

1990 ◽  
Vol 47 (3) ◽  
pp. 516-519 ◽  
Author(s):  
Carl J. Walters
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Parameter Estimation ◽  
Environmental Effects ◽  
Bias Correction ◽  
Parameter Estimates ◽  
Common Procedure ◽  
Stock Recruitment ◽  
The Common ◽  
Recruitment Time

Stock–recruitment time series often give a distorted picture of average recruitment rates, with high productivities per spawner being overrepresented at low stock sizes. This distortion is exaggerated by autocorrelation among years in environmental effects on productivity. The common procedure of fitting a stock–recruit curve and then analysing residuals from the curve will result in a substantial underestimate of the autocorrelation among environmental effects. Previous studies have recommended using Monte Carlo simulations to estimate the bias in stock–recruit model parameter estimates. These simulations can generally be avoided by using a simple correction equation. However, deviations from the corrected stock–recruit curve will not give better estimates of autocorrelation patterns in environmental effects, and hence will not help to provide better forecasts and stronger tests for factors that may be causing the effects.

On the use of time series representations of population models

1986 ◽  
Vol 23 (A) ◽  
pp. 345-353 ◽  
Author(s):  
C. C. Heyde
Keyword(s):  
Time Series ◽  
Population Models ◽  
Stationary Time Series ◽  
Time Series Models ◽  
Vital Rates ◽  
Use Of Time ◽  
Structured Population ◽  
Series Representations ◽  
The Matrix ◽  
Age Structured

Many population models which are far from stationarity can nevertheless be written in autoregressive format, perhaps with random coefficient. It is the thesis of this paper that procedures developed for stationary time series models are a useful guide to inferential results for population processes and may indeed be directly applicable. The illustrations concentrate on estimation of the matrix of mean vital rates in an age-structured population.

Nonlinear Feedback Dynamics in Fisheries: Analysis of the Deriso–Schnute Model

1994 ◽  
Vol 51 (7) ◽  
pp. 1462-1473 ◽  
Author(s):  
Shripad Tuljapurkar ◽  
Carl Boe ◽  
Kenneth W. Wachter
Keyword(s):  
Density Dependence ◽  
Nonlinear Feedback ◽  
Large Magnitude ◽  
Structured Population ◽  
Sustained Oscillations ◽  
Global View ◽  
Local View ◽  
Schnute Model ◽  
Stock Recruitment ◽  
Age Structured

Fishery models of the Deriso–Schnute form are based on the dynamics of an age-structured population, together with a nonlinear stock–recruitment relationship. Cyclical dynamics are commonly observed in fisheries and have been attributed to feedback between stock and recruitment. In this paper, we do four things. First, we present analytical results on sustained oscillations driven by nonlinear recruitment. These results show explicitly how density dependence near equilibrium determines the character of sustained population oscillations. Second, we briefly characterize the dynamics of the Deriso–Schnute model when the density-dependent response becomes very strong. We find that the Deriso–Schnute model displays sustained, complex (probably chaotic) variability of large magnitude, but only typically when reproduction is concentrated at very few ages. Third, we dissect the nature of density dependence in recruitment, contrasting a "local" view that uses information about response to small variations in stock with a "global" view that uses a function such as Schnute's over the entire range of stock levels. Finally, we argue that the global approach leads to practical and theoretical difficulties and that a local view may be more biologically realistic.

On the use of time series representations of population models

1986 ◽  
Vol 23 (A) ◽  
pp. 345-353 ◽  
Author(s):  
C. C. Heyde
Keyword(s):  
Time Series ◽  
Population Models ◽  
Stationary Time Series ◽  
Time Series Models ◽  
Vital Rates ◽  
Use Of Time ◽  
Structured Population ◽  
Series Representations ◽  
The Matrix ◽  
Age Structured

Many population models which are far from stationarity can nevertheless be written in autoregressive format, perhaps with random coefficient. It is the thesis of this paper that procedures developed for stationary time series models are a useful guide to inferential results for population processes and may indeed be directly applicable. The illustrations concentrate on estimation of the matrix of mean vital rates in an age-structured population.

scholarly journals The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters

2018 ◽  
Vol 7 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Adekunlé Akim Salami ◽  
Ayité Sénah Akoda Ajavon ◽  
Mawugno Koffi Kodjo ◽  
Seydou Ouedraogo ◽  
Koffi-Sa Bédja
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Series Data ◽  
Empirical Method ◽  
Likelihood Method ◽  
Series Data ◽  
New Approach ◽  
Weibull Parameters ◽  
Distribution Histogram ◽  
Mean Wind Speed

In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.https://doi.org/10.14710/ijred.7.2.139-150

scholarly journals Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Dalton J. Hance ◽  
Katie M. Moriarty ◽  
Bruce A. Hollen ◽  
Russell W. Perry
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Gaussian State ◽  
Parameter Estimates ◽  
Accelerometer Data ◽  
Two Stage ◽  
Location Data ◽  
Gps Measurement ◽  
Activity Measurements ◽  
Behavioral States

Abstract Background Studies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (Pekania pennanti) based on GPS and accelerometer data. Methods We developed a two-stage modelling approach to estimate when and where GPS-collared fishers were resting for 21 separate collar deployments on 9 individuals in southern Oregon. For each deployment, we first fit independent hidden Markov models (HMMs) to the time series of accelerometer-derived activity measurements and apparent step lengths to identify periods of movement and resting. Treating the state assignments as given, we next fit a set of linear Gaussian state space models (SSMs) to estimate the location of each resting event. Results Parameter estimates were similar across collar deployments. The HMMs successfully identified periods of resting and movement with posterior state assignment probabilities greater than 0.95 for 97% of all observations. On average, fishers were in the resting state 63% of the time. Rest events averaged 5 h (4.3 SD) and occurred most often at night. The SSMs allowed us to estimate the 95% credible ellipses with a median area of 0.12 ha for 3772 unique rest events. We identified 1176 geographically distinct rest locations; 13% of locations were used on > 1 occasion and 5% were used by > 1 fisher. Females and males traveled an average of 6.7 (3.5 SD) and 7.7 (6.8 SD) km/day, respectively. Conclusions We demonstrated that if auxiliary data are available (e.g., accelerometer data), a two-stage approach can successfully resolve both problems of latent behavioral states and GPS measurement error. Our relatively simple two-stage method is repeatable, computationally efficient, and yields directly interpretable estimates of resting site locations that can be used to guide conservation decisions.

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