Evidence for skipped spawning in a potamodromous cyprinid, humpback chub (Gila cypha), with implications for demographic parameter estimates

Abstract Determining the population status of endangered Humpback Chub Gila cypha is a major component of the adaptive management program designed to inform operation of Glen Canyon Dam upstream from Grand Canyon, Arizona. In recent decades, resource managers have identified a portfolio of management actions (with intermittent implementation) to promote population recovery of Humpback Chub, including nonnative fish removal, changes in water release volumes and discharge ramping schedules, and reductions in hydropower peaking operations. The Humpback Chub population in Grand Canyon has increased over this same period, causal factors for which are unclear. We took advantage of unusual hydrology in the Colorado River basin in 2011 to assess trends in juvenile Humpback Chub length–weight relationships and condition in the Colorado River below Glen Canyon Dam as well as in the unregulated Little Colorado River. Within each river, we observed higher length–weight b-parameter estimates (exponent of the standard power equation) at higher water temperatures. We also found higher slope estimates for the length–weight relationship at higher temperatures in the Little Colorado River. Slope estimates were more variable in the Colorado River, where mean water temperatures were more uniform. The next step is to examine whether Humpback Chub length–weight relationships influence population metrics such as abundance or survival. If these relationships exist, then monitoring condition in juvenile Humpback Chub would provide a quick and low-cost technique for assessing population response to planned management experiments or changing environmental conditions.

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When Celibacy Matters: Incorporating Non-Breeders Improves Demographic Parameter Estimates

PLoS ONE ◽

10.1371/journal.pone.0060389 ◽

2013 ◽

Vol 8 (3) ◽

pp. e60389 ◽

Cited By ~ 10

Author(s):

Deborah Pardo ◽

Henri Weimerskirch ◽

Christophe Barbraud

Keyword(s):

Parameter Estimates ◽

Demographic Parameter

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Characterizing fish populations: effects of sample size and population structure on the precision of demographic parameter estimates

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/f01-098 ◽

2001 ◽

Vol 58 (8) ◽

pp. 1557-1568 ◽

Cited By ~ 47

Author(s):

J P Kritzer ◽

C R Davies ◽

B D Mapstone

Keyword(s):

Sample Size ◽

Growth Parameters ◽

General Rule ◽

Reef Fishes ◽

Sensitivity Analyses ◽

Parameter Estimates ◽

Sample Sizes ◽

Demographic Parameter ◽

Order Of Magnitude ◽

Optimum Sample

We examined precision of size, age, growth, and mortality parameters for four reef fishes at sample sizes ranging from 25 to 1000 using bootstrapped population samples. The results are illustrative rather than prescriptive in that we do not determine "optimum" sample sizes, but rather describe improvements in precision with increasing sample size. Furthermore, we do not address the related issue of accuracy. In general, a sample size needed to be tripled to halve precision at that sample size. Mean lengths and ages were most precise, reaching 10% by a sample size of 75 for all species. von Bertalanffy growth parameters were up to an order of magnitude more precise when constraints were placed upon the fitting process. Asymptotic lengths, L[Formula: see text], were up to eight times as precise as Brody growth coefficients, K. Catch curves were generally less precise than two other mortality estimators, but we cannot advocate any estimator until accuracy is addressed. We propose a general rule of collecting an average of 710 fish per age-class to estimate a variety of parameters. However, we more strongly suggest applying similar analyses for focal species and, where possible, with consideration of the application of parameters (e.g., sensitivity analyses).

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Approximating Variance of Demographic Parameters Using the Delta Method: A Reference for Avian Biologists

The Condor ◽

10.1093/condor/109.4.949 ◽

2007 ◽

Vol 109 (4) ◽

pp. 949-954 ◽

Cited By ~ 51

Author(s):

Larkin A. Powell

Keyword(s):

Growth Rate ◽

Population Growth Rate ◽

Survival Rates ◽

Delta Method ◽

Parameter Estimates ◽

Demographic Parameters ◽

Sampling Variance ◽

Demographic Parameter ◽

Daily Nest Survival ◽

Combine Parameter

Abstract Avian biologists routinely estimate sampling variance for parameter estimates such as daily nest survival, fecundity, annual survival, and density. However, many biologists are not certain of methods to derive sampling variance for parameters when survival rates change temporal scales. Similar methods are needed to obtain sampling variance when biologists combine parameter estimates to calculate an indirect demographic parameter, such as population growth rate. The delta method is a useful technique for approximating sampling variance when the desired demographic parameter is a function of at least one other demographic parameter. However, the delta method is rarely taught in most graduate-level biology or ecology courses, and application of this method may be discouraged by seemingly daunting formulas in reference books. Here, I provide five examples of sampling variance approximations for common situations encountered by avian ecologists, with step-by-step explanations of the equations involved.

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To catch or to sight? A comparison of demographic parameter estimates obtained from mark-recapture and mark-resight models

Biodiversity and Conservation ◽

10.1007/s10531-014-0748-9 ◽

2014 ◽

Vol 23 (11) ◽

pp. 2781-2800 ◽

Cited By ~ 12

Author(s):

K. A. Lee ◽

C. Huveneers ◽

O. Gimenez ◽

V. Peddemors ◽

R. G. Harcourt

Keyword(s):

Parameter Estimates ◽

Demographic Parameter ◽

Mark Recapture

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Benefits from Computerized Adaptive Testing as Seen in Simulation Studies

European Journal of Psychological Assessment ◽

10.1027//1015-5759.15.2.91 ◽

1999 ◽

Vol 15 (2) ◽

pp. 91-98 ◽

Cited By ~ 10

Author(s):

Lutz F. Hornke

Keyword(s):

Measurement Error ◽

Computerized Adaptive Testing ◽

Test Procedure ◽

Adaptive Testing ◽

Parameter Estimates ◽

Simulation Studies ◽

Computerized Adaptive Test ◽

Item Banks ◽

Item Parameters ◽

General Reliability

Summary: Item parameters for several hundreds of items were estimated based on empirical data from several thousands of subjects. The logistic one-parameter (1PL) and two-parameter (2PL) model estimates were evaluated. However, model fit showed that only a subset of items complied sufficiently, so that the remaining ones were assembled in well-fitting item banks. In several simulation studies 5000 simulated responses were generated in accordance with a computerized adaptive test procedure along with person parameters. A general reliability of .80 or a standard error of measurement of .44 was used as a stopping rule to end CAT testing. We also recorded how often each item was used by all simulees. Person-parameter estimates based on CAT correlated higher than .90 with true values simulated. For all 1PL fitting item banks most simulees used more than 20 items but less than 30 items to reach the pre-set level of measurement error. However, testing based on item banks that complied to the 2PL revealed that, on average, only 10 items were sufficient to end testing at the same measurement error level. Both clearly demonstrate the precision and economy of computerized adaptive testing. Empirical evaluations from everyday uses will show whether these trends will hold up in practice. If so, CAT will become possible and reasonable with some 150 well-calibrated 2PL items.

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A Cautionary Note on Incremental Fit Indices Reported by LISREL

Methodology ◽

10.1027/1614-1881.1.2.81 ◽

2005 ◽

Vol 1 (2) ◽

pp. 81-85 ◽

Cited By ~ 5

Author(s):

Stefan C. Schmukle ◽

Jochen Hardt

Keyword(s):

Factor Analysis ◽

Maximum Likelihood ◽

Structural Equation ◽

Structural Equation Models ◽

Null Model ◽

Likelihood Estimation ◽

Parameter Estimates ◽

Fit Indices ◽

Cautionary Note ◽

Confirmatory Factor

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

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The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽

10.1027/1614-2241/a000095 ◽

2015 ◽

Vol 11 (3) ◽

pp. 89-99 ◽

Cited By ~ 1

Author(s):

Leslie Rutkowski ◽

Yan Zhou

Keyword(s):

Sparse Matrix ◽

Small Body ◽

Auxiliary Information ◽

Poor Quality ◽

Quality Data ◽

Estimation Methods ◽

Parameter Estimates ◽

Population Parameter ◽

Conditioning Model ◽

The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

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Effect of the Number of (Multiple) Imputations on Parameter Estimates and P-Values

PsycEXTRA Dataset ◽

10.1037/e634232010-001 ◽

2010 ◽

Author(s):

Kevin A. Kupzyk

Keyword(s):

Parameter Estimates ◽

P Values ◽

Multiple Imputations

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Missing Data - Better "Not to Have Them", but What If You Do? (Part 1)

Marketing ZFP ◽

10.15358/0344-1369-2019-4-21 ◽

2019 ◽

Vol 41 (4) ◽

pp. 21-32

Author(s):

Dirk Temme ◽

Sarah Jensen

Keyword(s):

Missing Data ◽

Statistical Power ◽

Missing Values ◽

Graphical Representation ◽

Marketing Research ◽

Likelihood Estimation ◽

Parameter Estimates ◽

Full Information Maximum Likelihood ◽

Definition Of ◽

Traditional Approaches

Missing values are ubiquitous in empirical marketing research. If missing data are not dealt with properly, this can lead to a loss of statistical power and distorted parameter estimates. While traditional approaches for handling missing data (e.g., listwise deletion) are still widely used, researchers can nowadays choose among various advanced techniques such as multiple imputation analysis or full-information maximum likelihood estimation. Due to the available software, using these modern missing data methods does not pose a major obstacle. Still, their application requires a sound understanding of the prerequisites and limitations of these methods as well as a deeper understanding of the processes that have led to missing values in an empirical study. This article is Part 1 and first introduces Rubin’s classical definition of missing data mechanisms and an alternative, variable-based taxonomy, which provides a graphical representation. Secondly, a selection of visualization tools available in different R packages for the description and exploration of missing data structures is presented.

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