An Adaptive Decision Framework for the Conservation of a Threatened Plant

Abstract

Mead's milkweed Asclepias meadii, a long-lived perennial herb of tallgrass prairie and glade communities of the central United States, is a species designated as threatened under the U.S. Endangered Species Act. Challenges to its successful management include the facts that much about its life history is unknown, its age at reproductive maturity is very advanced, certain life stages are practically unobservable, its productivity is responsive to unpredictable environmental events, and most of the known populations occur on private lands unprotected by any legal conservation instrument. One critical source of biological uncertainty is the degree to which fire promotes growth and reproductive response in the plant. To aid in its management, we developed a prototype population-level state-dependent decision-making framework that explicitly accounts for this uncertainty and for uncertainties related to stochastic environmental effects and vital rates. To parameterize the decision model, we used estimates found in the literature, and we analyzed data from a long-term monitoring program where fates of individual plants were observed through time. We demonstrate that different optimal courses of action are followed according to how one believes that fire influences reproductive response, and we show that the action taken for certain population states is informative for resolving uncertainty about competing beliefs regarding the effect of fire. We advocate the use of a model-predictive approach for the management of rare populations, particularly when management uncertainty is profound. Over time, an adaptive management approach should reduce uncertainty and improve management performance as predictions of management outcome generated under competing models are continually informed and updated by monitoring data.

Adaptive management: where are we now?

“…Apart Not given from providing clear direction, this promotes a common understanding of intentions, actions and both anticipated and actual outcomes, which fosters shared learning and promotes progress (Runge 2011). Additionally this would aid more efficient policy formulation and the provision of incentives to overcome barriers to the implementation of AM, including technical challenges (see Moore et al 2011) Many who use the term AM rarely define it clearly or specify just how to apply it. Too often it seems to be used as a buzzword: for example, we identified one paper where AM featured in the title but nowhere else in the paper.…”

Detection and Plant Monitoring Programs: Lessons from an Intensive Survey of Asclepias meadii with Five Observers

“…Gray)), a rare prairie plant. In past studies, we found detection probabilities considerably less than one, and utilized this information to improve estimates of population size, survival rates, and population growth rates [10], [13], [14], [23]. As we expanded our work to new sites we faced the classic challenge: how does one accurately determine the numbers of plants in a defined area within a reasonable amount of time?…”

From planning to implementation: explaining connections between adaptive management and population models

“…In species conservation problems, objectives are usually related to population viability (Converse et al, 2013). This objective may be explicit (either maximizing viability or minimizing probability of extinction; Bakker and Doak, 2009) or defined indirectly through maximizing population abundance (Bearlin et al, 2002;Runge, 2013;Smith et al, 2013), number of populations (Moore et al, 2011) or population growth rate (McDonald-Madden et al, 2010), and may include thresholds (e.g., productivity above some level, Martin et al, 2010) or multiple competing objectives (e.g., harvest and conservation, Varley and Boyce, 2006;Smith et al, 2013). All these metrics require population models for their calculation.…”

“…All these metrics require population models for their calculation. Again, the uncertainty that complicates decisionmaking may be related to demographic parameters (e.g., survival, Rout et al, 2009;Runge, 2013;or productivity, Smith et al, 2013) or model structure (Martin et al, 2010;Moore et al, 2011). The issue of population control is in a way inverse to that of conservation: managers attempt to keep the population of the problem species under some level, or even aim at eradicating it in the case of invasive species.…”

“…The theory of AM was developed several decades ago in fisheries science (Walters andHilborn, 1976, 1978;Smith and Walters, 1981) and then spread to other areas of NRM (Parma et al, 1998;Williams et al, 2007), particularly waterfowl hunting management (Nichols et al, 1995;Johnson et al, 1997). The last decade has seen a renewed interest in its application to biodiversity conservation, for both plant (Moore et al, 2011) and animal species (Rout et al, 2009;McDonald-Madden et al, 2010;Runge, 2013), as well as habitat-based approaches (Gerber et al, 2005;Moore and Conroy, 2006;McCarthy and Possingham, 2007). Although often dealing with a single species, multi-species problems have also been addressed using AM, including predator-prey systems with linked dynamics (Varley and Boyce, 2006;Martin et al, 2010) and sympatric species (Sainsbury, 1991;Smith et al, 2013).…”