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Mark S. Udevitz

Publications and source records attributed to Mark S. Udevitz.

44 records · Page 3Linked to original sources
USGS1998

Estimating survival rates with age-structure data

We developed a general statistical model that provides a comprehensive framework for inference about survival rates based on standing age-structure and ages-at-death data. Previously available estimators are maximum likelihood under the general model, but they use only 1 type of data and require the assumption of a stable age structure and a known population growth rate. We used the general model to derive new survival rate estimators that use both types of data and require only the assumption of a stable age structure or a known population growth rate. Our likelihood-based approach allows use of standard model-selection procedures to test hypotheses about age-structure stability, population growth rates, and age-related patterns in survival. We used this approach to estimate survival rates for female sea otters ( Enhydra lutris ) in Prince William Sound, Alaska.

Journal of Wildlife Management↗
USGS1997

Adaptation of a stage-projection model for species with multiple year reproductive cycles

We apply stage projection matrices to multiple age and reproductive categories, deriving the stable age - reproductive category distribution for populations with multi-year reproductive cycles and identifying conditions for the existence of a dominant latent root. Given the stable distribution of age and reproductive category and the number of female young for each reproductive category, the age specific fecundity rates can be calculated. For species with multi-year reproductive cycles, the fecundity rate associated with the stable age distribution is often not a smooth function of age, but fluctuates as a larger or smaller fraction of females becomes available to breed. We suggest that fecundity rates for species with multi-year reproductive cycles be defined in terms of litter size and probabilities of conception and successful gestation rather than average fecundity.

Ecological Modelling↗
USGS1995

Using effort information with change-in-ratio data for population estimation

Most change-in-ratio (CIR) methods for estimating fish and wildlife population sizes have been based only on assumptions about how encounter probabilities vary among population subclasses. When information on sampling effort is available, it is also possible to derive CIR estimators based on assumptions about how encounter probabilities vary over time. This paper presents a generalization of previous CIR models that allows explicit consideration of a range of assumptions about the variation of encounter probabilities among subclasses and over time. Explicit estimators are derived under this model for specific sets of assumptions about the encounter probabilities. Numerical methods are presented for obtaining estimators under the full range of possible assumptions. Likelihood ratio tests for these assumptions are described. Emphasis is on obtaining estimators based on assumptions about variation of encounter probabilities over time.

Biometrics↗
USGS1995

Kodiak brown bears

Brown bears ( Ursus arctos middendorffi ) on the Kodiak Archipelago are famous for their large size and seasonal concentrations at salmon streams. Sport hunting of Kodiak bears has been popular since World War II. Their value as captivating subjects to observe or photograph is a more recent development that is increasing rapidly; visitors from around the world come to experience brown bears on Kodiak, adding substantially to Alaska's economy. An equally important contribution of brown bears is their value as an indicator of ecosystem vitality. Despite high population numbers, Kodiak bears are vulnerable to the environmental effects that have seriously depleted brown bear populations in Europe and parts of North America (Cowan 1972; Servheen 1990). They are long-lived mammals that require large expanses of land to meet biological needs, and their low reproductive rate limits population recovery. Energy development, depletion of salmon resources, and recreational growth are factors that can adversely affect bears and, in doing so, signal a loss of environmental quality affecting many species. Management of Kodiak brown bears is directed at maintaining current density, distribution, and habitat-use patterns. This goal is challenged by growing levels of commercial and private use throughout the region. An immediate concern is cabin and lodge development on 121,500 ha (300,000 acres), formerly part of the Kodiak National Wildlife Refuge, that were deeded to Alaska Natives via the Alaska Native Claims Settlement Act. Much of that Native-conveyed land is coastal or riparian habitat especially important to brown bears during summer and fall. Concurrently, recreational use of the Kodiak refuge is increasing about 10% annually (USFWS 1987). Sport fishing, bear photography, and deer and elk hunting often put bears and humans in direct conflict (Smith et al. 1989). Timber harvest on Afognak Island, uncertain trends of salmon populations due to natural or human-caused events (e.g., Exxon Valdez oil spill), and hydroelectric development (Smith and Van Daele 1990) could impose additional long-term effects on localized bear populations.

Alaska↗
USGS1994

An intersection model for estimating sea otter mortality along the Kenai Peninsula

We developed an intersection model to integrate parameters estimated from three distinct data sets that resulted from the Exxon Valdez oil spill: (1) the distribution, amount, and movements of spilled oil; (2) the distribution and abundance of sea otters along the Kenai Peninsula; and (3) the estimates of site-specific sea otter mortality relative to oil exposure from otters captured for rehabilitation and from collected carcasses. In this chapter, we describe the data sets and provide examples of how they can be used in the model to generate acute loss estimates. We also examine the assumptions required for the model and provide suggestions for improving and applying the model.

Alaska↗
USGS1992

Change-in-ratio methods for estimating population size

Change-in-ratio (CIR) methods can provide an effective, low cost approach for estimating the size of wildlife populations. They rely on being able to observe changes in proportions of population subclasses that result from the removal of a known number of individuals from the population. These methods were first introduced in the 1940’s to estimate the size of populations with 2 subclasses under the assumption of equal subclass encounter probabilities. Over the next 40 years, closed population CIR models were developed to consider additional subclasses and use additional sampling periods. Models with assumptions about how encounter probabilities vary over time, rather than between subclasses, also received some attention. Recently, all of these CIR models have been shown to be special cases of a more general model. Under the general model, information from additional samples can be used to test assumptions about the encounter probabilities and to provide estimates of subclass sizes under relaxations of these assumptions. These developments have greatly extended the applicability of the methods. CIR methods are attractive because they do not require the marking of individuals, and subclass proportions often can be estimated with relatively simple sampling procedures. However, CIR methods require a carefully monitored removal of individuals from the population, and the estimates will be of poor quality unless the removals induce substantial changes in subclass proportions. In this paper, we review the state of the art for closed population estimation with CIR methods. Our emphasis is on the assumptions of CIR methods and on identifying situations where these methods are likely to be effective. We also identify some important areas for future CIR research.

Book chapter↗
USGS1991

Change-in-ratio estimators for populations with more than two subclasses

Change-in-ratio methods have been developed to estimate the size of populations with two or three population subclasses. Most of these methods require the often unreasonable assumption of equal sampling probabilities for individuals in all subclasses. This paper presents new models based on the weaker assumption that ratios of sampling probabilities are constant over time for populations with three or more subclasses. Estimation under these models requires that a value be assumed for one of these ratios when there are two samples. Explicit expressions are given for the maximum likelihood estimators under models for two samples with three or more subclasses and for three samples with two subclasses. A numerical method using readily available statistical software is described for obtaining the estimators and their standard errors under all of the models. Likelihood ratio tests that can be used in model selection are discussed. Emphasis is on the two-sample, three-subclass models for which Monte-Carlo simulation results and an illustrative example are presented.

Biometrics↗