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Bryan F.J. Manly

Publications and source records attributed to Bryan F.J. Manly.

7 recordsLinked to original sources
USGS2005

Introduction to the handbook

In September of 1802, Pierre Simon Laplace (1749–1827) used a capture– recapture type of approach to estimate the size of the human population of France (Cochran 1978; Stigler 1986). At that time, live births were recorded for all of France on an annual basis. In the year prior to September 1802, Laplace estimated the number of such births to be approximately X = 1,000,000. These newly born individuals constituted a marked population. Laplace then obtained census and live birth data from several communities “with zealous and intelligent mayors” across all of France. Recognizing some variation in annual birth rates, Laplace summed the number of births reported in these sample communities for the three years leading up to the time of his estimate, and divided by three to determine that there were x = 71,866 births per year (marked individuals) in those communities. The ratio of these marked individuals to the total number of individuals in the sampled communities, y = 2,037,615, was then the estimate p = 71,866/2,037,615 = 0.0353 of the proportion of the total population in France that was newly born. On this basis, the one million marked individuals in the whole of France is related to the total population N as N p ≈ 1,000,000 so that N ≈ 1,000,000/0.0353 =28,328,612 This estimation procedure is equivalent to the Lincoln-Peterson capture-recapture estimator described in chapter 2.

Book chapter↗
USGS2003

Improving size estimates of open animal populations by incorporating information on age

Around the world, a great deal of effort is expended each year to estimate the sizes of wild animal populations. Unfortunately, population size has proven to be one of the most intractable parameters to estimate. The capture-recapture estimation models most commonly used (of the Jolly-Seber type) are complicated and require numerous, sometimes questionable, assumptions. The derived estimates usually have large variances and lack consistency over time. In capture–recapture studies of long-lived animals, the ages of captured animals can often be determined with great accuracy and relative ease. We show how to incorporate age information into size estimates for open populations, where the size changes through births, deaths, immigration, and emigration. The proposed method allows more precise estimates of population size than the usual models, and it can provide these estimates from two sample occasions rather than the three usually required. Moreover, this method does not require specialized programs for capture-recapture data; researchers can derive their estimates using the logistic regression module in any standard statistical package.

BioScience↗
USGS2001

Effects of gull predation and weather on survival of emperor goose goslings

Numbers of emperor geese ( Chen canagica ) have remained depressed since the mid-1980s. Despite increases in glaucous gulls ( Larus hyperboreus ), a primary predator of goslings, little information existed to assess whether recent patterns of gosling survival have been a major factor affecting population dynamics. We used observations of known families of emperor geese to estimate rates of gosling survival during 1993-96 on the Yukon-Kuskokwim Delta, Alaska. Survival of goslings to 30 days of age varied among years from 0.332 during 1994 to 0.708 during 1995. Survival was lowest during 1993-94, which corresponded with the years of highest frequency of disturbance of goose broods by glaucous gulls. Rainfall during early brood rearing was much higher in 1994 than other years, and this corresponded to low survival among goslings ≤5 days of age. Numbers of juveniles in families during fall staging were negatively related to rainfall during early brood rearing (n = 23 yr). Although there are no data to assess whether gosling survival in emperor geese has declined from some previous level, current survival rates of emperor goose goslings are as high as or higher than those observed in other goose species that are rapidly increasing. A proposed reduction of glaucous gull numbers by managers may not be the most effective means for increasing population growth in emperor geese.

Alaska↗
USGS2001

Estimation of brood and nest survival: Comparative methods in the presence of heterogeneity

The Mayfield method has been widely used for estimating survival of nests and young animals, especially when data are collected at irregular observation intervals. However, this method assumes survival is constant throughout the study period, which often ignores biologically relevant variation and may lead to biased survival estimates. We examined the bias and accuracy of 1 modification to the Mayfield method that allows for temporal variation in survival, and we developed and similarly tested 2 additional methods. One of these 2 new methods is simply an iterative extension of Klett and Johnson's method, which we refer to as the Iterative Mayfield method and bears similarity to Kaplan-Meier methods. The other method uses maximum likelihood techniques for estimation and is best applied to survival of animals in groups or families, rather than as independent individuals. We also examined how robust these estimators are to heterogeneity in the data, which can arise from such sources as dependent survival probabilities among siblings, inherent differences among families, and adoption. Testing of estimator performance with respect to bias, accuracy, and heterogeneity was done using simulations that mimicked a study of survival of emperor goose ( Chen canagica ) goslings. Assuming constant survival for inappropriately long periods of time or use of Klett and Johnson's methods resulted in large bias or poor accuracy (often >5% bias or root mean square error) compared to our Iterative Mayfield or maximum likelihood methods. Overall, estimator performance was slightly better with our Iterative Mayfield than our maximum likelihood method, but the maximum likelihood method provides a more rigorous framework for testing covariates and explicity models a heterogeneity factor. We demonstrated use of all estimators with data from emperor goose goslings. We advocate that future studies use the new methods outlined here rather than the traditional Mayfield method or its previous modifications.

Journal of Wildlife Management↗
USGS2001

Negative binomial models for abundance estimation of multiple closed populations

Counts of uniquely identified individuals in a population offer opportunities to estimate abundance. However, for various reasons such counts may be burdened by heterogeneity in the probability of being detected. Theoretical arguments and empirical evidence demonstrate that the negative binomial distribution (NBD) is a useful characterization for counts from biological populations with heterogeneity. We propose a method that focuses on estimating multiple populations by simultaneously using a suite of models derived from the NBD. We used this approach to estimate the number of female grizzly bears (Ursus arctos) with cubs-of-the-year in the Yellowstone ecosystem, for each year, 1986-1998. Akaike's Information Criteria (AIC) indicated that a negative binomial model with a constant level of heterogeneity across all years was best for characterizing the sighting frequencies of female grizzly bears. A lack-of-fit test indicated the model adequately described the collected data. Bootstrap techniques were used to estimate standard errors and 95% confidence intervals. We provide a Monte Carlo technique, which confirms that the Yellowstone ecosystem grizzly bear population increased during the period 1986-1998.

Idaho, Montana, Wyoming↗