A Model for Predicting Age-Specific Body Weights of Nutria Without Age Determination

Willner, Gale R.; Dixon, Kenneth R.; Chapman, Joseph A.; Stauffer, Jay R.

doi:10.2307/2402330

Cited by 7 publications

(10 citation statements)

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“…An alternative and simpler explanation for model discrepancy is that the Dixon et al (1979) models were not accurate because of a lack of empirical age data for the animals used to derive them. The validation of the Dixon et al (1979) models presented by Willner et al (1980) had little potential to illustrate model inaccuracy because it was based on tooth wear (Aliev 1965), an aging technique that is subjective, insensitive to changes in age ,1 year, and potentially influenced by food quality (Kinler et al 1987). In contrast, our models are based on a technique that has been shown to accurately predict the age of nutria to within 1 month .…”

Section: Evaluation Of Existing Modelsmentioning

confidence: 99%

“…Other parameters, such as seasonal survival rates, are also known to vary between male and female nutria Micol 1990, Gosling andBaker 1991), suggesting that population control efforts should account for physiological differences between the sexes to the extent possible. Willner et al (1980) concluded there was a decline in body weight for older (.2 yr) females, but this conclusion was based on 13 3-year-old and 2 4-year-old nutria. With a greater number of animals in age classes .24 months, our data suggested that mean body mass did not vary across age classes after asymptotes were reached (Fig.…”

Section: Growth Modelsmentioning

confidence: 99%

“…The models subsequently were used as predictors of nutria age from body weight (Willner et al 1980:345) after being evaluated with data from nutria aged by tooth development and wear (Aliev 1965). To predict ages, Willner et al (1980) rearranged the equations developed by Dixon et al (1979), retaining the original parameter estimates. Predicting age in this manner, however, results in biased estimates of the former independent variable (i.e., inverse prediction of age; Seber 1977).…”

mentioning

confidence: 99%

“…These time periods should be positively correlated with, but not necessarily a direct measure of, age. In validating the models, Willner et al (1980) assigned nutria (n ¼ 133 F, 233 M) to 1 of 5 discrete age classes (0.5, 1, 2, 3, or 4 yr) based on tooth wear and compared mean predicted ages with mean observed ages. This approach is limited by age-related change in nutria body weight, which reaches an asymptote at about 2 years (Willner et al 1980).…”

mentioning

confidence: 99%

“…In validating the models, Willner et al (1980) assigned nutria (n ¼ 133 F, 233 M) to 1 of 5 discrete age classes (0.5, 1, 2, 3, or 4 yr) based on tooth wear and compared mean predicted ages with mean observed ages. This approach is limited by age-related change in nutria body weight, which reaches an asymptote at about 2 years (Willner et al 1980). Therefore, this classification essentially consisted of 3 groups (0.5, 1, or 2þ yr), providing a relatively insensitive test of b as a predictor of growth rate.…”

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confidence: 99%

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A Reexamination of Age-Related Variation in Body Weight and Morphometry of Maryland Nutria

Sherfy

Mollett

McGOWAN

et al. 2006

Journal of Wildlife Management

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Age‐related variation in morphometry has been documented for many species. Knowledge of growth patterns can be useful for modeling energetics, detecting physiological influences on populations, and predicting age. These benefits have shown value in understanding population dynamics of invasive species, particularly in developing efficient control and eradication programs. However, development and evaluation of descriptive and predictive models is a critical initial step in this process. Accordingly, we used data from necropsies of 1,544 nutria (Myocastor coypus) collected in Maryland, USA, to evaluate the accuracy of previously published models for prediction of nutria age from body weight. Published models underestimated body weights of our animals, especially for ages <3. We used cross‐validation procedures to develop and evaluate models for describing nutria growth patterns and for predicting nutria age. We derived models from a randomly selected model‐building data set (n = 192–193 M, 217–222 F) and evaluated them with the remaining animals (n = 487–488 M, 642–647 F). We used nonlinear regression to develop Gompertz growth‐curve models relating morphometric variables to age. Predicted values of morphometric variables fell within the 95% confidence limits of their true values for most age classes. We also developed predictive models for estimating nutria age from morphometry, using linear regression of log‐transformed age on morphometric variables. The evaluation data set corresponded with 95% prediction intervals from the new models. Predictive models for body weight and length provided greater accuracy and less bias than models for foot length and axillary girth. Our growth models accurately described age‐related variation in nutria morphometry, and our predictive models provided accurate estimates of ages from morphometry that will be useful for live‐captured individuals. Our models offer better accuracy and precision than previously published models, providing a capacity for modeling energetics and growth patterns of Maryland nutria as well as an empirical basis for determining population age structure from live‐captured animals.

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Section: Evaluation Of Existing Modelsmentioning

confidence: 99%

Section: Growth Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Reexamination of Age-Related Variation in Body Weight and Morphometry of Maryland Nutria

Sherfy

Mollett

McGOWAN

et al. 2006

Journal of Wildlife Management

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The Chinese water deer, Hydropotes inermis—A fast-growing and productive ruminant

et al. 2011

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Paternity share predicts sons’ fetal testosterone

Fishman,

Koren,

Ben-Shlomo

et al. 2023

Sci Rep

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Multiple paternity is common in many species. While its benefits for males are obvious, for females they are less clear. Female indirect benefits may include acquiring ‘good genes’ for offspring or increasing litter genetic diversity. The nutria (Myocastor coypus) is a successful invasive species. In its native habitat, it is polygynous, with larger and more aggressive males monopolizing paternity. Here, using culled nutria we genetically examined multiple paternity in-utero and found a high incidence of multiple paternity and maintenance of the number of fathers throughout gestation. Moreover, male fetuses sired by the prominent male have higher testosterone levels. Despite being retained, male fetuses of ‘rare’ fathers, siring commonly only one of the fetuses in the litter, have lower testosterone levels. Considering the reproductive skew of nutria males, if females are selected for sons with higher future reproductive success, low testosterone male fetuses are expected to be selected against. A possible ultimate explanation for maintaining multiple paternity could be that nutria females select for litter genetic diversity e.g., a bet-hedging strategy, even at the possible cost of reducing the reproductive success of some of their sons. Reproductive strategies that maintain genetic diversity may be especially beneficial for invasive species, as they often invade through a genetic bottleneck.

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A Model for Predicting Age-Specific Body Weights of Nutria Without Age Determination

Cited by 7 publications

References 2 publications

A Reexamination of Age-Related Variation in Body Weight and Morphometry of Maryland Nutria

A Reexamination of Age-Related Variation in Body Weight and Morphometry of Maryland Nutria

The Chinese water deer, Hydropotes inermis—A fast-growing and productive ruminant

Paternity share predicts sons’ fetal testosterone

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