Geology topics

Bryan L. Swift

Publications and source records attributed to Bryan L. Swift.

3 recordsLinked to original sources

USGS2017

Addressing wild turkey population declines using structured decision making

We present a case study from New York, USA, of the use of structured decision making (SDM) to identify fall turkey harvest regulations that best meet stakeholder objectives, in light of recent apparent declines in abundance of wild turkeys in the northeastern United States. We used the SDM framework to incorporate the multiple objectives associated with turkey hunting, stakeholder desires, and region-specific ecological and environmental factors that could influence fall harvest. We identified a set of 4 fall harvest regulations, composed of different season lengths and bag limits, and evaluated their relative achievement of the objectives. We used a stochastic turkey population model, statistical modeling, and expert elicitation to evaluate the consequences of each harvest regulation on each of the objectives. We conducted a statewide mail survey of fall turkey hunters in New York to gather the necessary information to evaluate tradeoffs among multiple objectives associated with hunter satisfaction. The optimal fall harvest regulation was a 2-week season and allowed for the harvest of 1 bird/hunter. This regulation was the most conservative of those evaluated, reflecting the concerns about recent declines in turkey abundance among agency wildlife biologists and the hunting public. Depending on the region of the state, the 2-week, 1-bird regulation was predicted to result in 7–32% more turkeys on the landscape after 5 years. The SDM process provided a transparent framework for setting fall turkey harvest regulations and reduced potential stakeholder conflict by explicitly taking the multiple objectives of different stakeholder groups into account.

New York↗

USGS2016

Structured decision making as a framework for large-scale wildlife harvest management decisions

Fish and wildlife harvest management at large spatial scales often involves making complex decisions with multiple objectives and difficult tradeoffs, population demographics that vary spatially, competing stakeholder values, and uncertainties that might affect management decisions. Structured decision making (SDM) provides a formal decision analytic framework for evaluating difficult decisions by breaking decisions into component parts and separating the values of stakeholders from the scientific evaluation of management actions and uncertainty. The result is a rigorous, transparent, and values-driven process. This decision-aiding process provides the decision maker with a more complete understanding of the problem and the effects of potential management actions on stakeholder values, as well as how key uncertainties can affect the decision. We use a case study to illustrate how SDM can be used as a decision-aiding tool for management decision making at large scales. We evaluated alternative white-tailed deer ( Odocoileus virginianus ) buck-harvest regulations in New York designed to reduce harvest of yearling bucks, taking into consideration the values of the state wildlife agency responsible for managing deer, as well as deer hunters. We incorporated tradeoffs about social, ecological, and economic management concerns throughout the state. Based on the outcomes of predictive models, expert elicitation, and hunter surveys, the SDM process identified management alternatives that optimized competing objectives. The SDM process provided biologists and managers insight about aspects of the buck-harvest decision that helped them adopt a management strategy most compatible with diverse hunter values and management concerns.

Ecosphere↗

USGS2011

Variation in spring harvest rates of male wild turkeys in New York, Ohio, and Pennsylvania

Spring harvest rates of male wild turkeys ( Meleagris gallapavo ) influence the number and proportion of adult males in the population and turkey population models have treated harvest as additive to other sources of mortality. Therefore, hunting regulations and their effect on spring harvest rates have direct implications for hunter satisfaction. We used tag recovery models to estimate survival rates, investigate spatial, temporal, and demographic variability in harvest rates, and assess how harvest rates may be related to management strategies and landscape characteristics. We banded 3,266 male wild turkeys throughout New York, Ohio, and Pennsylvania during 2006–2009. We found little evidence that harvest rates varied by year or management zone. The proportion of the landscape that was forested within 6.5 km of the capture location was negatively related to harvest rates; however, even though the proportion forested ranged from 0.008 to 0.96 across our study area, this corresponded to differences in harvest rates of only 2–5%. Annual survival was approximately twice as high for juveniles as adults . In turn, spring harvest rates for adult turkeys were greater for adults than juveniles . We estimated the population of male turkeys in New York and Pennsylvania ranged from 104,000 to 132,000 in all years and ranged from 63,000 to 75,000 in Ohio. Because of greater harvest rates for adult males, the proportion of adult males in the population was less than in the harvest and ranged from 0.40 to 0.81 among all states and years. The high harvest rates observed for adults may be offset by greater recruitment of juveniles into the adult age class the following year such that these states can sustain high harvest rates yet still maintain a relative high proportion of adult males in the harvest and population.

New York, Ohio, Pennsylvania↗