A Markov Decision Process for Managing Habitat for Florida Scrub-Jays

Abstract

Florida scrub-jays Aphelocoma coerulescens are listed as threatened under the Endangered Species Act due to loss and degradation of scrub habitat. This study concerned the development of an optimal strategy for the restoration and management of scrub habitat at Merritt Island National Wildlife Refuge, which contains one of the few remaining large populations of scrub-jays in Florida. There are documented differences in the reproductive and survival rates of scrub-jays among discrete classes of scrub height (<120 cm or “short”; 120–170 cm or “optimal”; >170 cm or “tall”; and a combination of tall and optimal or “mixed”), and our objective was to calculate a state-dependent management strategy that would maximize the long-term growth rate of the resident scrub-jay population. We used aerial imagery with multistate Markov models to estimate annual transition probabilities among the four scrub-height classes under three possible management actions: scrub restoration (mechanical cutting followed by burning), a prescribed burn, or no intervention. A strategy prescribing the optimal management action for management units exhibiting different proportions of scrub-height classes was derived using dynamic programming. Scrub restoration was the optimal management action only in units dominated by mixed and tall scrub, and burning tended to be the optimal action for intermediate levels of short scrub. The optimal action was to do nothing when the amount of short scrub was greater than 30%, because short scrub mostly transitions to optimal height scrub (i.e., that state with the highest demographic success of scrub-jays) in the absence of intervention. Monte Carlo simulation of the optimal policy suggested that some form of management would be required every year. We note, however, that estimates of scrub-height transition probabilities were subject to several sources of uncertainty, and so we explored the management implications of alternative sets of transition probabilities. Generally, our analysis demonstrated the difficulty of managing for a species that requires midsuccessional habitat, and suggests that innovative management tools may be needed to help ensure the persistence of scrub-jays at Merritt Island National Wildlife Refuge. The development of a tailored monitoring program as a component of adaptive management could help reduce uncertainty about controlled and uncontrolled variation in transition probabilities of scrub-height and thus lead to improved decision making.

Integrating Land Cover Modeling and Adaptive Management to Conserve Endangered Species and Reduce Catastrophic Fire Risk

“…Because scrub regrowth is often a function of underground biomass, many ecologists have observed that tall scrub recovers much faster than scrub that has been subjected to frequent fires [9]. Fire managers usually rely on a return interval to determine when a managed area needs to burn, which differs from the typical ARM approach, which recommends using current values of time-specific state variables (e.g., habitat states from Table 1) to determine the optimal management action [18].…”

“…Funding for conducting prescribed fires is often specified for fuel management and not necessarily for species habitat management; administrators from agencies often measure the performance of prescribed fire programs using the acreage burned each year, which provides little information on scrub habitat quality trajectories, because fire intensity and outcomes are heterogeneous [10]. Our studies have shown that an emphasis on fuel management will not achieve species conservation goals on many conservation lands [9,18]. Wildland fire managers and ecologists agree that prescribed fires can be beneficial, and we believe that differences in management approaches are reconcilable, but require stakeholders to acknowledge multiple values and agree: (1) on the legitimacy of objectives arising from these values; (2) on how to measure whether management is attaining those objectives; and (3) on how decisions should be made.…”

“…We describe two competing models, where the first model predicts that the two types of prescribed fires can be implemented successfully to produce different consequences (probabilistically), while the second model predicts that the consequences of these two types of prescribed fires cannot be differentiated (i.e., that it is not really possible to implement habitat fires; thus both kinds of fires produce the same system responses). In our example, we rely on estimates already made for fuel reduction fires [9,18] and estimates solicited from managers and biologists that can be updated by monitoring.…”

“…Johnson et al [18] describe an adaptive management program in which the FSJ population growth rate is modeled as a function of scrub-height classes ( Table 1). The management objective is to maximize population growth rates by implementing habitat treatments at the management unit level, and the utility function increases linearly with the growth rate.…”

“…When land cover states can be classified in terms that represent progress towards or away from something we hope to achieve, estimates of land cover transition probabilities under differing management regimes can be used to guide optimal decision-making for realizing positive impacts on resources, processes and ecosystem services [18]. Most habitats are subject to natural succession dynamics, and the application of a management action is intended, by definition, to transition land cover states in a desired direction, although often with considerable uncertainty.…”