Identifying target reference points for harvesting assessment‐limited wildlife populations: a case study

Abstract

Identifying appropriate strategies for sustainable harvest is a challenge for many terrestrial vertebrate species because of uncertain system dynamics, limited data to inform population models, and potentially conflicting objectives that seek to harvest and maintain populations at desirable levels. The absence of monitoring and assessment infrastructure needed to regularly estimate abundance accentuates this challenge for many species, and limits application of rigorous state-dependent frameworks for decision making that are commonly advocated in natural resource management. Reference points, which define management targets or triggers for changing management, are often used to guide decision-making, but suffer from ambiguity when developed without explicit consideration of uncertainty or trade-offs among competing objectives. We describe an approach for developing unambiguous target reference points for assessment-limited species using structured decision making, and demonstrate the approach to develop target harvest rates for management of fall Wild Turkey (Meleagris gallopavo) harvests in the face of uncertain population and harvest dynamics. We use simulation and decision analyses to identify harvest rates that are optimal for accomplishing explicit management objectives in the face of uncertainty, and harvest rates with robust performance over broad regions of the demographic and harvest model parameter space. We demonstrate that population and harvest parameters commonly uncertain to wildlife managers interact to determine appropriate target harvest rates for Wild Turkeys, and that formally acknowledging a range of plausible values for structurally uncertain parameters results in more conservative target reference points than suggested by previously published studies. The structured decision making framework described here provides a natural conceptual and quantitative framework for extending our approach to develop unambiguous harvest targets for other assessment-limited wildlife populations while formally acknowledging structural uncertainty in system dynamics.

Harvest Regulations and Implementation Uncertainty in Small Game Harvest Management

“…Similar concerns were raised by Stevens et al (2017) in a recent study using structured decision making models to establish wild turkey (Meleagris gallopavo) harvest reference points. Such uncertainty makes it hard to design harvest management plans based on first principles (e.g., proportional harvest strategies), because even if the managers change TAC in response to fluctuations in population density, a proportionate change in realized harvest rates do not necessarily follow.…”

“…Despite the lack of terrestrial studies focusing on implementation of harvest regulations, we know from the fishery literature that such uncertainty might be even more influential than the other types (Deroba and Bence, 2008), it seems to a large extent to be glossed over in the wildlife harvest management (Bischof et al, 2012). In small game management implementation uncertainty may severely limit our ability to predict the outcome and sustainability of different harvest regulations (Andersen, 2015;Stevens et al, 2017).…”

Comparison of Merriam's Turkey Harvest Strategies and Survival in Northern Arizona

“…The 0.039 fall harvest rate of female turkeys in this study was less than the 0.10 maximum harvest rate recommended for eastern turkeys based on modeling by Vangilder and Kurzejeski (1995), Healy and Powell (1999), Alpizar‐Jara et al (2001), and McGhee et al (2008), or the 0.20 maximum harvest rate recommended by Weaver and Mosby (1979). Recently, Stevens et al (2017 a ) recommended that fall harvest rates for eastern turkeys ( M. g. silvestris ) should be lower than those currently recommended (5–10%; Healy and Powell 1999) without detailed information on productivity, vulnerability, and spring harvest rates. Many turkey population models were developed during a period of rapid population increase that was common during the restoration phase of management (Vangilder and Kurzejeski 1995).…”

Simulated performance of multi‐year harvest regulation cycles for wild turkeys

“…Lastly, we modeled partial control of spring male harvest consistent with previous work, where realized spring harvest rates varied annually from a lognormal distribution: , , 0.175, and = 0.15 or 0.30 (McGhee et al , Stevens et al ). These median spring harvest values were intended to approximate light (0.15) to moderate (0.3) harvest rates for males during spring (Stevens et al , b).…”

“…In turkey management, such models were parameterized using data from field studies or literature review (Vangilder and Kurzejeski , Rolley et al , McGhee et al , Stevens et al ). Early studies sought to identify harvest levels that would enable continued population growth while turkeys were being actively restored (Vangilder and Kurzejeski , Rolley et al , Alpizar‐Jara et al ); however, more recent efforts have focused on harvest maximization (McGhee et al ) or balancing harvest and population objectives in the post‐restoration era of management (Stevens et al , b). Moreover, these models incorporated multiple types of uncertainty to reflect the stochastic nature of population and harvest processes (Nichols et al , Williams ), including environmental uncertainty (Lobdell et al , Vangilder and Kurzejeski , Alpizar‐Jara et al ), partial controllability (McGhee et al , McGhee and Berkson ), structural uncertainty (Rolley et al , Stevens et al ), or some combination of these (McGhee et al ; Robinson et al ; Stevens et al , b).…”