Jody W. Enck

Deer populations up, hunter populations down: implications of interdependence of deer and hunter population dynamics on management.

Abstract White-tailed deer (Odocoileus virginianus) are managed to yield diverse impacts, including effects to ecosystems. Many conventional hunting systems manage deer abundance through rules that strive to produce recreation opportunities and an equitable distribution of antlered bucks among hunters. To protect against excessive harvests, antlerless deer harvests often are regulated through quotas. This approach is effective when deer productivity does not outstrip capacity of the hunter population to harvest required numbers of antlerless deer. In many areas of North America, abundance of white-tailed deer has increased dramatically in the past two decades, which has caused many wildlife managers to ask whether deer populations can be controlled with conventional harvest strategies. We used population reconstruction modeling to simulate deer populations from mixed hardwood forests in southern New York, determined antlerless deer harvests needed to stabilize or reduce populations, and evaluated whether current hunting systems can effectively achieve potential ecosystem objectives. Current hunter willingness to seek or use antlerless deer permits likely is inadequate to stabilize or reduce deer densities. This situation may be exacerbated in the future with occurrence of diseases in deer or other factors that diminish hunter participation. We discuss implications for effectiveness of ecosystem management.

Can citizen science enhance public understanding of science

Over the past 20 years, thousands of citizen science projects engaging millions of participants in collecting and/or processing data have sprung up around the world. Here we review documented outcomes from four categories of citizen science projects which are defined by the nature of the activities in which their participants engage – Data Collection, Data Processing, Curriculum-based, and Community Science. We find strong evidence that scientific outcomes of citizen science are well documented, particularly for Data Collection and Data Processing projects. We find limited but growing evidence that citizen science projects achieve participant gains in knowledge about science knowledge and process, increase public awareness of the diversity of scientific research, and provide deeper meaning to participants’ hobbies. We also find some evidence that citizen science can contribute positively to social well-being by influencing the questions that are being addressed and by giving people a voice in local environmental decision making. While not all citizen science projects are intended to achieve a greater degree of public understanding of science, social change, or improved science -society relationships, those projects that do require effort and resources in four main categories: (1) project design, (2) outcomes measurement, (3) engagement of new audiences, and (4) new directions for research.

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.

Human dimensions of wildlife management: Knowledge for agency survival in the 21st century

The need to understand the human dimensions of fish and wildlife management has increased in recent years as societal values have changed pertaining to wildlife and its use. For management agencies...

Examining assumptions in wildlife management: A contribution of human dimensions inquiry

Abstract Making good decisions in wildlife management is difficult given the needs and desires of competing stakeholder groups. Good decisions require good information, but in the absence of such information managers must operate on assumptions. Managers’ ability to make the most appropriate decisions can be enhanced if assumptions about peoples beliefs, attitudes, preferences, and behaviors are scrutinized through human dimensions inquiry to assess their reliability. To demonstrate this value of human dimensions inquiry, we consider a set of assumptions that have formed the bases for some wildlife management decisions, drawing primarily on our experience in New York. For each assumption, we review examples of human dimensions findings that have helped wildlife managers verify or revise a particular application of the assumption. In the examples, examination of assumptions has helped wildlife managers provide increased benefits for wildlife stakeholders.