Len H. Carpenter

Adaptive Impact Management: An Integrative Approach to Wildlife Management

Wildlife professionals need better ways to integrate ecological and human dimensions of wildlife management. A focus on impacts, guided by a structured decision process, will orient wildlife management toward rigorous, integrative decision making. Impacts are important socially defined effects of events and interactions related to wildlife that merit management. To manage impacts we propose adaptive impact management (AIM). This approach has seven primary components: situational analysis, objective setting, development of system model(s), identification and selection of management alternatives, actual management interventions, monitoring, and refinement of models and eventually interventions. Adaptive impact management builds upon strengths of systems thinking and conventional adaptive management, yet differs in that fundamental objectives of management are impacts on society, rather than conditions of a wildlife population or habitat. Emphasis is placed on stakeholder involvement in management and shared learning among scientists, managers, and stakeholders. We describe and assess adaptive impact management with respect to black bear management in New York.

Survival of mule deer in Northwest Colorado

Survival of mule deer (Odocoileus hemionus) in Piceance Basin, Colorado, was measured with radio telemetry for 3 years on 1 study area and 4 years on another. Survival rates for deer >6 months old were estimated for yearly intervals beginning 1 December. Male and female fawns had similar (P = 0.254) survival, as did adult and yearling females (P = 0.567). There were significant differences (P 6 months old, yearling female, and adult female mule deer; (2) identify the timing and causes of mortality for ach age class; and (3) use a Cox model analysis to test for relationships between fawn size and overwinter survival. Financial support was provided by the U.S. Dep. Energy Contract W-7405-ENG-36 to Los Alamos Natl. Lab. and DE-FG02-85ER60297 to Colorado State Univ. Supplemental funding was provided by Colo. Fed. Aid Wildl. Restor. Proj. FW 26P and Cathedral Bluffs Shale Oil Co. We thank D. A. Garrott, M. A. Sovada, and D. L. Weybright for field assistance and the Bur. Land Manage. and local ranchers for their coop ration. B. L. Dupire, J. E. Morris and many other Colo. Div. Wildl. personnel aided in innumerable ways. Special thanks to T. E. Hakonson during the formulation of this study and K. V. Bostick for administration and logistic expertise. We appreciate the cooperation of T. H. Pysto of Cathedral Bluffs Shale Oil Co. in facilitating access to the shale oil lease tract and in helping trap deer. Numerous individuals participated in the annual trapping sessions. D. J. Freddy, T. M. Pojar, and W. D. Snyder reviewed the manuscript.

Stakeholder Acceptance Capacity in Wildlife management

Abstract Capacity understanding has been a central driving force in both the biological and human dimensions of wildlife management. The concept of biological carrying capacity has been recognized for a long time. For many years, reference has been made to another form of carrying capacity, that of society to tolerate or accept the impacts of wildlife in particular situations. Attempts to articulate this concept have taken several forms, but all generally recognize the economic or attitudinal limits of society to “carry”; wildlife (e.g., risk perception and acceptance). We compare and contrast elements of the biological and human dimensions concepts of carrying capacity. We then describe the concept “stakeholder acceptance capacity”; in wildlife management and a theoretical model for weighting stakes, including algorithms to illustrate conceptually how stakes can be weighted in management decision making.

Aerial mark-recapture estimates of confined mule deer in pinyon-juniper woodland

Counts from a helicopter of known numbers of marked (radio-collared) and reasonably well known numbers of unmarked mule deer (Odocoileus hemionus hemionus) in 4 58-70-ha pastures were used to calculate 114 Lincoln-Petersen estimates of population size. Three approaches to combine Lincoln-Petersen estimates were explored: simple arithmetic mean, median, and joint hypergeometric maximum likelihood. Compared to individual estimates, all 3 produced narrower confidence intervals and increased the percentage of confidence intervals that covered true population values. The median was least sensitive to outliers, but the joint hypergeometric maximum likelihood provided -40% smaller confidence intervals. About the same percentage of confidence intervals for all 3 estimators (64-73%) overlapped at least part of the ranges of true population size. For all 3 estimators, a large proportion (>45%) of a small population should be marked to obtain more reliable estimates and greatest confidence interval coverage. However, there is still high probability that mean population estimates will be low. J. WILDL. MANAGE. 51(1):41-46 Testing Petersens mark-recapture method (Petersen 1896, Lincoln 1930) using aerial sampling has received scant attention with regard to big game population estimates. Woolf (1973) used the technique to estimate size of a confined white-tailed deer (0. virginianus) population in Pennsylvania, and Rice and Harder (1977) initially evaluated the technique with known numbers of confined white-tailed deer in Ohio. To our knowledge, research to assess accuracy of the technique to estimate size of mule deer populations is lacking. During tests of aerial counting accuracy with mule deer in fenced enclosures (Bartmann et al. 1986), considerable numbers of unmarked deer were present along with radio-collared deer that were stocked, thus presenting a unique opportunity to test aerial mark-recapture estimation procedures. In the experimental protocol used in this study, animals are marked with radio collars so that the number of marked animals in an area can be determined with biotelemetry prior to an aerial survey. Since unmarked animals sighted during an aerial survey are not marked (radio-collared) for subsequent surveys, the Schnabel-Darroch multiple recapture protocol (Model M, of Otis et al. 1978) is not applicable for this situation. Rather, multiple applications of the Lincoln-Petersen estimator are computed where the initial marking (radio collaring) of animals constitutes the 1st sample, and the numbers of marked and unmarked animals counted during the aerial survey provide the 2nd sample. Each aerial survey then produces a new Lincoln-Petersen estimate. Our objective in this paper is to evaluate performance of 3 procedures (simple arithmetic mean, median, and joint hypergeometric maximum likelihood estimate) to combine repeated Lincoln-Petersen estimates using aerial markrecapture data where total numbers of deer were reasonably well known. This research was funded by Colo. Fed. Aid Wildl. Restor. Proj. FW 26P, and by the U.S. Dep. Energy, Contract W-4305-36 to Los Alamos Natl. Lab. and DE-FG02-85ER60297 to Colorado State Univ. We thank D. R. Anderson for critically reviewing the manuscript. STUDY AREA AND METHODS Four contiguous 58-70-ha pastures on pinyon pine (Pinus edulis)-Utah juniper (Juniperus osteosperma) winter range in Piceance Basin, northwestern Colorado, were stocked with a total of 48 mule deer in November and December 1983. Each deer was fitted with a radio transmitter mounted on a white collar 5.1 cm wide. Deer were not marked for individual identification during aerial surveys, as this was 1 Present address: Department of Fisheries and Wildlife, University of Minnesota, St. Paul, MN 55108.

A conservation institution for the 21st century: implications for state wildlife agencies.

Abstract The wildlife conservation institution (Institution) needs to reform to maintain legitimacy and relevancy in the 21st century. Institutional reform is inherently slow. Limitations resulting from historical and resource dependencies between state wildlife agencies and hunters have left the Institution poorly positioned to meet changing ecological and social complexities. In this paper, we suggest that an ideal Institution would have the following 4 components: broad-based funding, trustee-based governance, multidisciplinary science as the basis of recommendations from professional staff, and involvement of diverse stakeholders and partners. Our suggestions reflect the fundamental tenets of the Public Trust Doctrine, which we believe is the foundation of the Institution. In bringing forth these ideas, we hope to encourage discussion about how the Institution should reform to meet the changing needs of society.

Integrating Ecological and Human Dimensions in Adaptive Management of Wildlife-Related Impacts

Abstract Adaptive wildlife management seeks to improve the integration of science and management by focusing decision-making on hypothesis-testing and structuring management actions as field experiments. Since the early 1990s, adaptive resource management (ARM) has advocated enhancing scientific rigor in evaluating management actions chosen to achieve “enabling objectives” typically directed at wildlife habitat or population characteristics. More recently, the concept of adaptive impact management (AIM) has emphasized a need to articulate “fundamental objectives” in terms of wildlife-related impacts to be managed. Adaptive impact management seeks to clarify why management is undertaken in a particular situation. Understanding the “why” question is viewed in AIM as a prerequisite for establishing enabling objectives, whether related to changes in wildlife habitats and populations or to human beliefs and behaviors. This article describes practical aspects of AIM by exploring relationships between AIM and ARM within a comprehensive model of decision-making for wildlife management. Adaptive impact management clarifies and differentiates fundamental objectives (i.e., wildlife-related impacts to be modified) and enabling objectives (i.e., conditions that affect levels of impacts), whereas ARM reduces uncertainty about how to achieve enabling objectives and seeks an optimal management alternative through hypothesis-testing. The 2 concepts make different contributions to development of management hypotheses about alternative actions and policies and should be nested for optimal application to comprehensive wildlife management. Considered in the context of the entire management process, AIM and ARM are complementary ideas contributing to adaptive wildlife management.

Temporal, Spatial, and Age-Specific Changes in Genotypic Composition of Mule Deer

Spatial, temporal, and age-specific genetic variation is described for a mule deer ( Odocoileus hemionus ) herd from northwestern Colorado during 1983—1986. Little genetic differentiation was observed between major migratory herd subdivisions, although spatial structuring and inbreeding within subdivisions is indicated during some years. Significant differences in common allele frequencies at six of 11 variable loci and changes in apportionment of genetic variance among individuals within and among locations occurred during 4 years of study. Common allele frequencies increased with year of study. Significant differences in allele frequencies also were observed between fawns and adult females. Temporal and age-specific genetic differences probably were influenced most by changes in breeding structure.

Developing the wildlife stakeholder acceptance capacity concept: Research needed

Abstract The concept of wildlife stakeholder acceptance capacity (WSAC) has developed from different perspectives. Depending on the perspective taken by the scholars addressing the concept, it may be seen as primarily cultural, social, or economic in nature. Despite the “fuzziness,”; the development of these various perspectives on the concept has enhanced its emerging place as a fundamental element of modern wildlife management. However, our own experiences and observations of attempts to articulate and apply the concept indicate a need to refine it. The primary needs as we see them include: (1) develop a robust theoretical framework and standard terminology to articulate WSAC, (2) develop standard measures, (3) identify and understand relationships of principal factors that affect acceptance capacity of various stakeholder groups, (4) determine how to aggregate multiple stakeholder acceptance capacities in a particular situation, (5) experimentation in manipulating variables influencing WSAC for different stakeholder groups, and (6) understand stakeholder acceptance of techniques that managers employ to influence wildlife populations or to influence WSAC.

A Case for Standardized Ungulate Surveys and Data Management in the Western United States

Abstract