Duration: 10/01/2002 to 09/30/2008

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

Statement of Issues and Justification

As a result of increases in the distribution and abundance of white-tailed deer (Odocoileus virginianus), stakeholders throughout the Midwest and Northeast Regions of the United States incur numerous positive (e.g., recreational viewing, hunting, and associated economic benefits) and negative effects (e.g., crop and forest depredation, vehicle collisions, and disease). Increased knowledge about white-tailed deer ecology and stakeholder values about deer is needed to match the level of acceptable impacts with capabilities of deer management. Specific needs include determining: (1) the landscape scale factors affecting the distribution and abundance of white-tailed deer throughout different types of landscapes; (2) stakeholder defined impacts desired from deer, and factors affecting the willingness of stakeholders to accept impacts from deer; and (3) changes necessary in land management, education, communication, and hunting regulations to enhance the effectiveness of wildlife management.

ISSUES AND JUSTIFICATION

Once rare in many regions of the United States (US), white-tailed deer (Odocoileus virginianus) now represent one of the greatest challenges facing communities and natural resource managers in the 21^st century (Warren 1997). What was once a wildlife resource of interest only to hunters and state management agencies, deer now are of widespread interest to the general public.

As a result of events such as herbivory on commercial crops and ornamental vegetation, deer-vehicle collisions, and outbreaks of bovine tuberculosis and Lyme disease, stakeholders throughout the Midwest and Northeast Regions of the US are incurring an array of economic, aesthetic, psychological, and health-related impacts. Concurrently, societal values regarding wildlife have changed over the past few decades and managing wildlife for human benefit (e.g., hunting and viewing) has become increasingly controversial (Muth and Jamison 2000). White-tailed deer are considered a keystone herbivore in agricultural and forested ecosystems by some biologists and ecologists (e.g., Ostfield et al. 1996, Waller and Alverson 1997). White-tailed deer have been implicated in restricting forest regeneration (Tilghman 1989, Campa et al. 1993, Healy 1997), decreasing songbird abundance (DeCalesta 1994), and diminishing the aesthetic attributes of landscapes (Underwood and Porter 1991). Concurrently, deer provide positive economic, recreational, and aesthetic attributes such as recreation from hunting, viewing of wildlife, as well as a significant role in ecosystems functions (Conover 1997). Balancing the positive and negative impacts of deer on the environmental and social landscapes requires increased knowledge about stakeholders and the ecological relationships of deer and their environment.

A great deal has been learned about white-tailed deer in the last few decades (e.g., Halls 1984, Warren 1997). However, the ability to control deer numbers has not kept pace with the growth and expansion of deer populations in most areas (Warren 1997). Very little empirical data exist on deer population distributions and dynamics at large scales, nor have researchers adequately attempted to investigate the ways habitat and population management can affect deer populations at these scales across the Midwest and Northeast Regions of the US. For example, do deer in landscapes dominated by agricultural crops use woodlots as refugia during difficult winter periods (i.e., prolonged deep snow or ice) or as metapopulation activity centers and as a result influence crop production and forest characteristics? In more southern states, because of mild fall and winter conditions, are deer more widely distributed among many different vegetation types, such as residual cropland that provide food and thus have relatively less impact on the regeneration of natural vegetation types? How can deer populations be regulated at the landscape scale under changing human demographics (Brown et al. 2000)? And lastly, can deer population and habitat management programs be designed to affect the distribution and abundance of deer to meet management objectives?

Just as there are questions about how landscapes throughout the Midwest and Northeastern Regions influence deer distributions there are also many questions regarding how landscape characteristics influence deer population structure and demographic processes. For example, how does the distribution of vegetation types throughout the Midwest influence the relative abundance of antlerless deer (vs. antlered) in specific landscapes? What are the cause-specific mortality patterns between sexes and productivity patterns among age classes in various landscapes? These are examples of critical questions that wildlife managers need to address to meet ecological and social management objectives regarding deer impacts.

Wildlife managers are recognizing that management of white-tailed deer is also an extremely controversial societal issue (Swihart and DeNicola 1997). For example, some wildlife management is refocusing its purpose to one of managing impacts of wildlife on people (Decker et al. in review). Impacts are the beneficial and detrimental effects, defined and weighted by human values, of events or interactions involving: (a) individual organisms, populations, and communities, (b) wildlife management interventions, and (c) stakeholder-to-stakeholder interactions vis-`-vis wildlife. Impacts, therefore, are the subset of effects of wildlife-related interactions and events that are determined to be sufficiently important to warrant management attention; they can be considered the priority effects from a management perspective. Events or interactions can be of several types: inter- and intraspecific among wildlife, between wildlife and their habitat, between wildlife and people, between people and wildlife habitat, and among people where wildlife is the reason for the interaction.

The aggregated acceptance of stakeholders or their desire for relief from impacts related to interactions with deer can be manifested in the concept of wildlife stakeholder acceptance capacity (WSAC) (Attachment A, Figure 1). WSAC represents the wildlife population level (or effects from the perceived population) acceptable to stakeholders; it is a function of the perceived benefits and costs associated with a wildlife population (Carpenter et al. 2000). WSAC can provide guidance for wildlife managers who are attempting to balance benefits and concerns of wildlife perceived by stakeholders. However, a serious shortcoming of WSAC considerations in deer management is a lack of detailed knowledge about factors influencing WSAC for different stakeholder groups. By examining several factors that influence WSAC and are influenced by WSAC, this study also will attempt to determine the complex causal relationships contributing to WSAC and acceptability of deer management actions.

White-tailed deer ecology, and the values associated with deer, have been investigated historically within individual states. However, the Midwest and Northeastern environmental and socioeconomic landscapes, and white-tailed deer abundance have changed dramatically in the past several decades. Given these changes, natural resources managers are now challenged with managing an abundance of deer in different areas than they once occurred for many, oftentimes, conflicting human values. For example, conventional methods to manage deer populations, such as hunting, have become infeasible in some areas due to changing human values and land use patterns (Muth and Jamison 2000, Chase et al. in review), and the extent of these areas where conventional methods are not acceptable is expected to increase in the future (Brown et al. 2000, Muth and Jamison 2000).

Research is needed that can contribute to a knowledge base that informs wildlife managers how to better match the level of impacts acceptable to stakeholders with current and future capabilities of deer management. Specific research needs include: (1) a better understanding of landscape scale factors affecting the distribution and abundance of white-tailed deer; (2) a better definition and understanding of stakeholders= willingness to accept impacts from deer; and (3) development of processes to affect necessary changes in land management, hunting regulations, education, and communication to enhance the effectiveness of deer management.

To properly address the research objectives, a multi-state multi-region project is required. A basic premise of this project is that deer population density, demographic vital rates, management, and stakeholder acceptance capacity will vary across a continuum of landscapes. In particular, we expect these ecological and management characteristics to vary with the relative proportion of potential deer habitat, particularly the ratio and types of forests and cropland. Many of these ecological and management characteristics will reflect the state-specific historic pressures to which human and deer populations have been exposed (e.g., deer hunting regulations). Therefore, many of the factors to be examined do not vary enough within one state or are too confounded within the state to allow for valid state-by-state comparisons. To achieve the necessary level of variability and replication of characteristics, large landscapes (i.e., from the Northeastern region of the United States through the Midwest region) are required. Additionally, given the size of the required study sites and costs associated with collecting data on these sites, no single state can afford the required replication (even if it existed).

The goal of this project is to improve the capabilities of state wildlife management agencies, local governments, and other stakeholders to make decisions about the management of white-tailed deer throughout their northern range in the United States. To achieve this goal, improved understanding is needed about the effects of landscape characteristics on the biological, environmental, and human dimensions of white-tailed deer management, and the components needed for effective outreach to stakeholders. Initiating a multidisciplinary collaboration among researchers that addresses these specific needs is essential to comprehensively assess and manage the white-tailed deer in diverse regions throughout the United States. The committee members in the proposed study are leaders in their respective fields of wildlife biology and human dimensions of wildlife management. Adaptive impact management (AIM), developed by several of the members, can serve as a framework for guiding the collection of relevant data and providing meaningful assistance to natural resource managers (Riley et al. in review). One aspect of this multidisciplinary, multi-state project is to modify research designs of many similar but geographically separated studies to develop new and comparable data sets. An opportunity to gain fresh, innovative insights, and to affect large-scale change in wildlife management will be created if this project is implemented.

Related, Current and Previous Work

A search of the Multistate Regional Project CRIS database identified only 1 project pertaining to white-tailed deerCNCT-185, the project from which this proposal was developed. A full text search of the CRIS database yielded 114 projects that pertained to white-tailed deer to some extent. None of the identified projects, however, involved quantifying variations in deer landscape use patterns, demographic characteristics, and social components of deer management across a gradient of landscape types (i.e., agriculture dominated to various agro-forest dominated landscapes; we define agro-forest landscapes as those composed of a mix of agriculture crop types and forest vegetation types). The projects that were described for individual states are addressing some of the similar habitat, population, or human dimension components as we propose to investigate (e.g., deer population demographics), however, none of the projects listed have addressed or integrated the results from the three components and none of the studies are linking results and analyses among regions in the US. All of the investigators that will be involved with this project (Attachment B) have an extensive amount of research experience with the ecological or social dimensions of deer management within their respective institutions.

Objectives

1. Assess dynamic interactions among physical landscape characteristics and white-tailed deer demographics.
   
2. Assess dynamic interactions among human dimensions characteristics of the landscape, wildlife stakeholder acceptance capacity (WSAC, Attachment A, Figure 1), and white-tailed deer demographics and management.
   
3. Develop communication and outreach strategies from the research findings to assist in white-tailed deer management.
   
4. 
   

Methods

The overall design attempts to determine whether deer ecology and human attitudes about management vary in a predictable way from areas in the eastern portion of the region, where forest vegetation types dominate the agro-forest landscape, to areas in the western region of the Midwest, where row cropland dominate the landscape. Ideally, all 3 objectives will be achieved in all states, depending upon financial resources, with key investigators being responsible for the analysis of different data set for all states (i.e., landscape use-Iowa, Missouri and Michigan; deer demographics-Iowa and Michigan; human dimensions-New York and Michigan). Project leaders from Michigan State University (see Attachment B) will coordinate the compilation and analyses of the regional data.

At scales as large as landscapes and states an experimental manipulation of the system is not feasible. Our study is observational (Eberhardt and Thomas 1991) and we will focus on estimation and testing of parameters, model revision and variable selection, and goodness of fit of models. As such, our inferential statements apply to characteristics of the models and the implications for the real world (Hilborn and Mangel 1997).

At the simplest level we envision building models of the form logit (P) = f (landscape composition and configuration variables), where P represents a demographic parameter such as deer density or survival. Models can be constructed using standard statistical approaches for regression, logistic regression, or categorical modeling. Modeling and inference will be based on information criteria (Akaike's information criteria, Burnham and Anderson 1998), which provide a framework for model selection and weighting and determination of variable importance. An advantage of this approach is that it can be applied to human attitudes and deer ecology data. Model inferences derived in each state can then be combined using concepts of meta-analysis (Burnham et al. 1996). In effect the approach is a way to answer questions like: Is the qualitative list of variables important to deer demography or human attitudes the same across the Midwest and Northeast Regions of the United States? Is the quantitative influence of these variables on deer demography or human attitudes the same across the region?

Objective 1: Assess dynamic interactions among physical landscape characteristics and white-tailed deer demographics

The first task for Objectives 1 and 2 will be to select specific research landscapes across the geographical range. Study landscapes will represent a gradient of agricultural, forested, and suburban environments to facilitate comparisons across the dominant landscapes occupied by white-tailed deer.

Determine interactions among recruitment, mortality, movement patterns and landscape characteristics.

Linking demographic vital rates and deer movement patterns and distributions to landscape characteristics throughout the study sites ultimately requires a common land use/cover classification scheme and a means to quantify landscape composition and configuration. Many of the participating states have completed or are about to complete a Gap Analysis (Scott and Jennings 1997). Gap Analysis is a geographic information system (GIS) approach that could be used to assess biological resources and their distributions throughout our study landscapes.

The classification of vegetation will follow the standards of the National Vegetation Classification (NVC) (Anderson et al. 1998) which has been adopted as the standard by the Federal Geographic Data Committee (FGDC 1997). Drake and Faber-Langendoen (1997) refined the approach into "An alliance level classification of the Midwestern United States." It is likely that vegetation classified at the alliance level will be too detailed for landscape analyses but when aggregation of alliances is necessary, we will follow a prescribed classification hierarchy.

Landsat Thematic Mapper (TM) will be the primary source of data for constructing base maps of vegetation distribution. Use of TM data and various GIS software (i.e., ArcInfo, ArcView, Imagine, Grid) will be used to facilitate mapping, particular estimation of quantities that characterize the forest vegetation type/cropland continuum.

Characteristics of landscape configuration, such as size and shape of forest types, and fragmentation measures such as habitat interspersion or juxtaposition, will be quantified using ArcView and software specifically developed to quantify landscape metrics such as FRAGSTATS (McGarigal and Marks 1995) or Patch Analyst (i.e., an extension of ArcView). We will link landscape variables to demographic parameters at multiple scales of selection based on the biology of white-tailed deer and the ecology of the habitat types deer use within the participating states.

White-tailed deer will be captured during winter months within each state. Trapping sites within states will represent the mix of dominant agricultural crops and forest vegetation types in each state-with some states having relatively more agricultural land while other states have less. Within each state's landscape, a minimum of 3 trapping sites will be used to radio-collar deer in order to describe deer movement patterns throughout the landscape.

Deer will be manually restrained in traps, given color-coded eartags and sexed. Ages will be classified as fawns (<7 months), yearlings, or adults based on tooth wear and replacement (Severinghaus 1949). A subsample of trapped deer (minimum of 35 females, 10 males) will be fitted with radio-collars equipped with a 6-hour, time-delayed mortality sensor. All capturing and handling procedures for white-tailed deer will be reviewed by the animal care and use committee from each respective state.

Radio-collared deer will be located a minimum of 3 times a week from the time they are captured until they die, are censored (Pollock et al. 1989), or the study ends. Locations will be obtained using hand-held receivers to triangulate from known map positions or by researchers systematically moving around a minimum of 3 sides of the animal until the occupied vegetation type can be identified (Beyer 1987). Fixed (95%) kernel home range estimates (Worton 1987, 1989) of deer will be computed within the Animal Movement ArcView extension (Hooge and Eichenlaub 1997). Based on the recommendations of Seaman et al. (1999), home ranges will only be estimated for radio-collared deer with $ 30 locations.

To determine if landscape variables are correlated with home range sizes, we will use deer home range sizes as the response variable in a general linear regression model (McCullagh and Nelder 1989, Neter et al. 1990) of the form

Deer home range size = f(landscape composition variables).

Across the multi-state landscape, we will evaluate several covariates within each deer home range, such as the edge density, patch density, mean patch size, path richness density, contagion index, interspersion/juxtaposition index, the composition of vegetation types, and the mean patch size of each vegetation type to determine if landscape features are correlated with deer home range sizes. Landscape metrics will be calculated in FRAGSTATS (McGarigal and Marks 1995) and ArcView.

Total deer observed/km driven (see below), sex and age ratios, and survival probabilities of age groups will be determined for each year, within each state. These annual within state values will be the inputs for the multi-state comparisons. Deer population characteristics will be compared among states with respect to year and landscape composition and patterns using standard nonparametric techniques (Siegel 1956). Because of the expected low sample size (< 25 animals per group), survival rates of the 3 groups of deer (i.e., fawns, yearlings, adults) will be determined using the Mayfield survival estimator (Mayfield 1975, Winterstein et al. 2001) with a staggered entry approach as discussed by Pollock et al. (1989). In addition, cause-specific mortality factors will be summarized for all deer groups each year. Quantifying survival rates, cause-specific mortality factors, and the timing of deer death within different landscapes and making comparisons in these parameters among states are essential for understanding the ecology of white-tailed deer in the U.S. since wide variations in the composition of landscapes are likely to influence populations characteristics.

Assess factors affecting resource selection by white-tailed deer in a gradient of agro-forest landscapes.

To assess deer resource selection, we will use discrete choice analysis (Cooper and Millspaugh 1999, 2001) or logistic regression (Manly et al. 1993). The data necessary to estimate the multinomial logit form of the discrete choice model are similar to those required for standard logistic regression. Data consist of either continuous or categorical resource attributes used and those available, but not used at that time. In this study, resource use and availability will be determined using our standardized GIS. To determine resource use, seasonal coordinates for all radio-collared deer locations will be imported into the GIS. At each location point, the values of each of the habitat-related parameters will be recorded. The patch, as opposed to the specific location within a patch, is what constitutes a "choice." For this study, a vector of all habitat-related parameters defines each choice. Seasonal home range boundaries for each radio-collared deer or a finer-scale boundary (Cooper and Millspaugh 1999, 2001) will be used to estimate resource availability.

In discrete choice analysis, typical "avoidance" or "preference" is replaced by a framework in which animals receive greater or lesser "utility" from a specific resource (Cooper and Millspaugh 1999, 2001). The effect of each habitat-related parameter on the utility of selecting a patch by deer will be evaluated as in any regression model (McCullagh and Nelder 1989, Neter et al. 1990).

Develop regional methodology for the assessment of deer density.

Accurate and repeatable measures of deer populations are necessary to link demographic processes to deer density and landscape features. Aerial helicopter surveys have the greatest potential for accurate counts but they can be very costly. Helicopter surveys can be very accurate and precise because they can be flown thoroughly thus making it easier to detect deer (Kufeld et al. 1980, Ludwig 1981). Detectability has been as great as nearly 100% (119 out of 120) in intensively farmed landscapes in Ohio (Stoll et al. 1991) to an average of 78.5% for 10 repeated trials in Missouri where the landscapes consisted of extensive oak-hickory forest (Berringer et al. 1998). A major drawback to aerial surveys in general is the potentially short, unpredictable window when adequate snow cover exists for the surveys and if a study landscape is composed of a large proportion of conifer vegetation types. In some of the proposed study landscapes adequate conditions may never exist.

Variation in detectability and survey conditions make it imperative that an efficient survey design be used when selecting the amount and types of habitat alliances to be flown. Standard statistical stratification procedures based on habitat have been used to reduce the overall sample variance in aerial surveys of wildlife (Gasaway et al. 1986, Noyes et al. 2000). For example, standard stratification would divide a study landscape into strata based on the amount, configuration, and type of forest cover mapped in the GIS (Roseberry and Woolf 1998) and the expectation of deer density (low, moderate and high potential density). Transects would be 200 m wide, arranged along the cardinal directions of the compass to insure consistent flight, and cover an entire study landscape. Each transect will be divided into distinct segments using existing roads or other easily distinguished features. Samples can be drawn with known probability depending on habitat, time to sample all transects, and the number of days with adequate snow cover. The habitat within each segment will be used to post-stratify segments in relation to landscape elements so that continuous transects can be flown.

A priori stratification has advantages over random sampling but it is likely that an adaptive sampling design (Thompson and Seber 1996) would further improve sampling efficiency. Adaptive sampling uses observed clusters in the deer population relative to landscape strata to increase sampling intensity during surveys (Smith et al. 1995, Christman 1997). Specific stopping rules are used to determine when to resume original flight paths.

Because snow cover, dominance of coniferous vegetation types, or financial constraints may make it impossible to conduct aerial surveys in some states, results of deer capture efforts and roadside observation surveys will also be used to quantify deer population characteristics (e.g., sex and age ratios) associated with the agro-forest landscapes in each state. Monthly roadside observational surveys will be conducted within each study landscape to quantify sex and age ratios. These surveys will be along 150 km standardized routes that encompass the diversity of vegetation types (e.g., agricultural and nonagricultural lands) within each study landscape. Alternating 75 km sections of each route will be driven 2 hours before dusk on each of 4 consecutive nights monthly from February to December and approximately 1 hour before dusk on at least 4 consecutive nights monthly from January to October (Sitar 1996). Route direction will be alternated on a consecutive nights and routes should not be driven during heavy rain, snow, or winds since they are likely to impact deer behavior (Gladfelter 1980). Total number of deer observed and location of observations along the survey route will be recorded as well as the number of deer in each sex and age category (i.e., adult, subadults) when possible.

Objective 2: Assess dynamic interactions among human dimensions characteristics of the landscape, wildlife stakeholder acceptance capacity (WSAC, Attachment A, Figure 1), and white-tailed deer demographics and management.

This objective will be achieved by procedures that sequentially assess and measure human dimensions characteristics, determine stakeholder-defined desirable impacts from deer, refine and apply methods to implement the concept of WSAC, and integrate the biological and human components to help wildlife management agencies achieve socially acceptable objectives and management actions. Qualitative, quantitative, and integrative methods will be used to achieve this objective (Vaske et al. 2001, Riley et al. in review).

From the outset, this project seeks to determine the potentially relevant impacts of deer, as defined by stakeholders, and qualitatively and quantitatively describe the management environment in which pertinent impacts occur. This step will draw upon the existing base of information to create a background of what is known about the relevant impacts and to develop a first generation model of the management system (Starfield 1990, Riley et al. in review). Knowledge gained in this initial step will also provide direction for tailoring context-specific inquiry of stakeholders, outreach, and management strategies.

Assess and measure human dimensions characteristics including socioeconomic variables, desired impacts from deer, attitudes toward deer, experience with damage and other factors that may influence WSAC and be influenced by WSAC.

Data collection methods for Objective 2 will be tested and refined in pilot sites in a subset of states (e.g., New York, Massachusetts, and Michigan) before being widely implemented. Initial data collection will rely on qualitative methods to construct a framework that describes relationships between human dimensions characteristics and WSAC. Qualitative methods include informal, unstructured interviews of stakeholders and wildlife managers, documents reviews, and observations of relevant events (Patton 1990). These techniques will be used to elicit the component factors of WSAC (e.g., Appendix A, Figure 1) and to generate hypotheses regarding their relationships.

Based on qualitative findings, quantitative methods such as structured survey instruments will be developed and applied to several cases in each participating state. For each research site, similar methods will be used for selecting samples and implementing survey instruments. To allow collaborators to address unique considerations in different contexts, data collection processes may not be identical in every site. Data collection, however, will follow appropriate protocol to ensure that data sets have ample consistency to facilitate comparisons among states.

The range and importance of positive and negative impacts that result from human-deer interactions will be identified through quantitative survey instruments. Examples are positive aesthetic impacts that result from the enjoyment of watching and photographing deer and deer hunting. Negative economic or psychological impacts may result from real or perceived risks associated with deer-vehicle accidents; damage to crops, forest, or gardens; Lyme disease; or bovine tuberculosis.

A process for weighting the importance of various impacts will be developed because, as a practical matter, management programs can address only a limited number of impacts. The weighting across impacts will serve to determine the importance of the components of WSAC and how they may be used to help set priorities for deer management objectives and actions. The applications and subsequent evaluations will examine the validity, consistency, and transferability of the methods, the WSAC concept, and components of WSAC.

We hypothesize that several human dimensions characteristics are expected to influence WSAC or be influenced by WSAC. Socioeconomic variables that may be relevant include gender, age, income, level of education, employment, marital status, parental status, current place of residence (e.g., farm, rural village, city, suburb), and place of residence during childhood (Riley 1998). Personal experiences with wildlife in general as well as recent experiences with the reference species, white-tailed deer, are expected to influence WSAC (Craven et al. 1992, Riley and Decker 2000).

Develop, apply, and evaluate methods for measuring WSAC and its component factors, including value-based impacts.

Recent research has developed quantitative models to estimate WSAC for specific species in specific locations (Riley and Decker 2000). However, accepted standardized methods for measurement of WSAC are lacking, and routine applications of WSAC to contemporary wildlife management issues have not been adopted by most wildlife management agencies. Whereas specific contextual concerns are important to the measurement of WSAC, a multi-state collaborative approach will allow development of methods that can serve as general guidelines for measuring WSAC in most situations relevant to the management of white-tailed deer.

Societal values regarding wildlife will be evaluated with a focus on factors contributing to changes in the sociocultural landscape as possible determinants of impact definition and WSAC (Muth 1991). We also will measure wildlife value orientations including philosophies regarding the use of wildlife for human benefits, the importance of wildlife to recreational pursuits, the importance of knowing that wildlife exist in the state and ensuring the continued existence of wildlife for future generations of humans, philosophies about wildlife rights, and the importance of wildlife in the neighborhood and around the home (Fulton et al. 1996, Purdy and Decker 1989). Attitudes toward deer will be measured using a standard attitudinal scale (Chase et al. 1999, Loker et al. 1999).

A comparison between perceptions of deer population trends and the acceptability of wildlife management actions will be conducted. Acceptability of management actions has been shown to vary across stakeholder groups with different demographic characteristics, experiences with wildlife, and perceptions of population trends (Loker et al. 1999, Siemer et al. in press, Chase et al. in review). We hypothesize that acceptability of wildlife management actions is correlated with WSAC; however, previous research has not identified causal relationships. Acceptability of potential management actions will be measured with quantitative survey instruments.

Determine the relationships between human dimensions characteristics, physical features, WSAC, and socially acceptable management objectives and actions.

Stakeholder-defined impacts and factors affecting WSAC will be compared with the physical and social landscape characteristics determined through Objectives 1 and 2 to: (1) describe the interactions between these large-scale variables and WSAC, and (2) inform development of regional and local AIM for white-tailed deer. Comparisons will be facilitated and displayed with GIS technologies. Decision-support systems models (Forrester 1994, Riley et al. in review) will be developed that integrate the biological, environmental, and human dimensions insights about deer ecology and management. These models will simulate the relationships between WSAC and its component factors with variables that are influenced by WSAC. The models will: (1) facilitate communication and analysis among states participating in the research; (2) allow for initial testing of proposed management interventions on impacts; and, (3) function as policy screens to define an acceptable set of management options to be carried forward through the policy process. Participation of wildlife managers and stakeholders, through methods such as focus groups and surveys, will contribute to the development and implementation of the AIM models.

Objective 3: Develop communication and outreach strategies from the research findings to assist in white-tailed deer management.

Determine key target audiences for extension strategies.

Key stakeholders will be identified throughout our initial study period and detailed information about their needs will be achieved through Objective 2. Collaboration with state wildlife management agencies throughout the study will help insure that current key deer management audiences are identified.

Develop the framework for a interactive web-based models to facilitate understanding and integration of knowledge about landscape characteristics, deer demographics, and WSAC.

A secure web site will be develop to exchange deer research and management information, experiences, and data among committee members. The goal of developing and using this web site is to enhance the exchange of information that typically only occurs at annual coordination meetings. Research techniques, progress reports, proposed conference presentation abstracts, and pictures of field research activities will be exchanged and updated through this central web site. The editorial policy of this site will be established during the first year's coordination meeting, and updated thereafter. A second phase will include development of a public web site for disseminating project progress, accomplishments, and publications.

Collaborate with university extension systems, state wildlife management agencies, and communities to develop appropriate approaches that links the findings of research to management influencing WSAC and deer populations and the landscapes they live in.

Extension materials from other deer research projects across the country will be compiled and compared with products from this project for opportunities to make improvements. The web sites created described above will be shared nationally through the extension system. Committee members will coordinate and present research findings at state, regional, and national symposia on deer ecology and management.

Measurement of Progress and Results

Outputs

• In addition to the analyses of the state and regional ecological and sociological data, project outputs will be:
• a description of landscape features associated with perceived over- and under-abundant deer populations,
• an understanding of landscape effects on deer populations at large, meaningful spatial scales
• a description of the impacts that society desires from white-tailed deer,
• models that quantify the variation in wildlife stakeholder acceptance capacity across the gradient of landscapes, and
• web-based extension products that will be targeted to stakeholders based on the predicted distributions of landscape elements, deer densities, and stakeholder attitudes.

Outcomes or Projected Impacts

• The project outputs will provide natural resources managers (e.g., wildlife extension specialists, state wildlife biologists) with an understanding of how landscape composition and patterns throughout the Midwest and Northeast regions of the US affect deer distributions, population dynamics, and stakeholder wildlife acceptance capacity. This will allow for more effective decisions about balancing the positive and negative impacts of white-tailed deer throughout the Midwest and Northeastern regions of the US. White-tailed deer management is presently done on a state-by-state basis. The project outputs will provide a framework for multi-state, multi-jurisdictional deer management programs. Additionally, the data on deer population dynamics will be used to develop and refine population models for setting harvest quotas and other management decisions.

Milestones

(0):the modeling and web-based extension outputs (2005 <FONT FACE="WP TypographicSymbols">B</FONT> 2006) described above are dependent upon collection and synthesis of the raw data (2001 <FONT FACE="WP TypographicSymbols">B</FONT> 2004). The application and precision of certain techniques (e.g., measurement of WSAC, development of regional methodology to assess deer density) need to be examined concurrent with the development of the models and web-based outputs.

(0):0

Projected Participation

View Appendix E: Participation

Outreach Plan

The audience for the project outputs includes university-based researchers, natural resources managers with federal, state or local jurisdiction, and the general public. Appropriate project outputs will be made available to university-based researchers primarily through publication in refereed publications and presentations at professional meetings. Natural resources managers will have access to outputs through the periodic reports required by granting agencies, non-refereed publications, and workshops. The general public has access to the professional literature, but will undoubtedly receive desired information through the web-based products, public forums and workshops, articles in specialty magazines (e.g., out-doors or deer hunting magazines) and extension pamphlets. In particular, web-based products, magazine articles and pamphlets can be targeted to meet the needs of specific stakeholders.

Organization/Governance

The NC Committee will be organized according to the guidelines provided in the USDA Multi-state Research Manual. Members on the Committee will include the Administrative Advisor, a yet to be assigned CSREES representative, and a minimum of 13 investigators from 10 states (Attachment B). One or two committee members will take the lead in serving as the repository of data/information for each of the four primary parts of the project: landscape composition and structure impacts on deer distribution, landscape impacts on deer population demographics, stakeholder defined impacts, and changes needed in communication and education. Additional states have expressed interest in the project, however, may join in subsequent years if the project is initiated.

Annual meetings (i.e., 1.5-2 days) will be held in December in association with the Midwest Fish and Wildlife Conference since the majority of the investigators attend this meeting regularly and would, therefore, minimize additional travel. Each year a new secretary will be elected by the NC Committee and the previous secretary will serve as the vice chair and the previous vice chair will serve as the new chair. The NC Executive Committee will consist of the current officers, the past chair, and the administrative advisor.

The responsibilities of the chair will be to develop the agenda for the upcoming meeting in consultation with the Executive Committee. During the annual meetings, investigators (i.e., faculty representatives and graduate students) from each state will be responsible for providing an oral presentation and a written technical report of their results to date, a summary of future research activities and collaborations, and summaries of meetings they have had with collaborating conservation groups and/or state agency partners who are providing matching financial resources. During each annual meeting the secretary will take notes. Following the meeting, the secretary will send copies of the minutes to all the investigators and the administrative advisor in a timely manner. Each year the chair will also be responsible for preparing an annual report. A draft of this report will be circulated among all primary participating investigators for comment prior to submitting it to the administrative advisor and posting it on the web.

The success of the NC committee will be aided by the strong collaborations investigators already have with one another and the need to enhance these collaborations to address multidisciplinary research questions that could not be easily addressed by one individual (i.e., comparisons across the Midwest and Northeastern Regions). In addition, many investigators have already contacted partners from state management agencies and/or conservation organizations that are supportive of the project objectives and have expressed interest in providing matching financial resources. These external collaborations will be essential for planning and conducting the research as well as applying research findings to address deer management questions across the Midwest and Northeast Regions of the US.

POTENTIAL FUNDING SOURCES

Successful completion of the regional project requires that participants obtaining funding from a variety of sources. In addition to AES funds, potential funding sources include state and federal wildlife management agencies and private organizations. As an example, project leaders from Michigan State University have received funding commitments from the MAES, Michigan Department of Natural Resources, and Pierce Cedar Creek Institute. They have a proposal for additional funding pending with the Michigan Department of Military and Veterans Affairs.

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