A niche for null models in adaptive resource management.

As global systems rapidly change, our collective ability to predict future ecological dynamics will become increasingly important for successful natural resource management. By merging stakeholder objectives with system uncertainty, and by adapting actions to changing systems and knowledge, adaptive resource management (ARM) provides a rigorous platform for making sound decisions in a changing world. Critically, however, applications of ARM could be improved by employing benchmarks (i.e., points of reference) for determining when learning is occurring through the cycle of monitoring, modeling, and decision-making steps in ARM. Many applications of ARM use multiple model-based hypotheses to identify and reduce systematic uncertainty over time, but generally lack benchmarks for gauging discovery of scientific evidence and learning. This creates the danger of thinking that directional changes in model weights or rankings are indicative of evidence for hypotheses, when possibly all competing models are inadequate. There is thus a somewhat obvious, but yet to be filled niche for including benchmarks for learning in ARM. We contend that carefully designed "ecological null models," which are structured to produce an expected ecological pattern in the absence of a hypothesized mechanism, can serve as suitable benchmarks. Using a classic case study of mallard harvest management that is often used to demonstrate the successes of ARM for learning about ecological mechanisms, we show that simple ecological null models, such as population persistence (Nt +1 = Nt ), provide more robust near-term forecasts of population abundance than the currently used mechanistic models. More broadly, ecological null models can be used as benchmarks for learning in ARM that trigger the need for discarding model parameterizations and developing new ones when prevailing models underperform the ecological null model. Identifying mechanistic models that surpass these benchmarks will improve learning through ARM and help decision-makers keep pace with a rapidly changing world.

Evaluation of a two-season banding program to estimate and model migratory bird survival.

The management of North American waterfowl is predicated on long-term, continental-scale banding implemented prior to the hunting season (i.e., July-September) and subsequent reporting of bands recovered by hunters. However, single-season banding and encounter operations have a number of characteristics that limit their application to estimating demographic rates and evaluating hypothesized limiting factors throughout the annual cycle. We designed and implemented a two-season banding program for American Black Ducks (Anas rubripes), Mallards (A. platyrhynchos), and hybrids in eastern North America to evaluate potential application to annual life cycle conservation and sport harvest management. We assessed model fit and compared estimates of annual survival among data types (i.e., pre-hunting season only [July-September], post-hunting season only [January-March], and two-season [pre- and post-hunting season]) to evaluate model assumptions and potential application to population modeling and management. There was generally high agreement between estimates of annual survival derived using two-season and pre-season only data for all age and sex cohorts. Estimates of annual survival derived from post-season banding data only were consistently higher for adult females and juveniles of both sexes. We found patterns of seasonal survival varied by species, age, and to a lesser extent, sex. Hunter recovered birds exhibited similar spatial distributions regardless of banding season suggesting banded samples were from the same population. In contrast, goodness-of-fit tests suggest this assumption was statistically violated in some regions and years. We conclude that estimates of seasonal and annual survival for Black Ducks and Mallards based on the two-season banding program are valid and accurate based on model fit statistics, similarity in survival estimates across data and models, and similarities in the distribution of recoveries. The two-season program provides greater precision and insight into the survival process and will improve the ability of researchers and managers to test competing hypotheses regarding population regulation resulting in more effective management.

State-Dependent Resource Harvesting with Lagged Information about System States.

Markov decision processes (MDPs), which involve a temporal sequence of actions conditioned on the state of the managed system, are increasingly being applied in natural resource management. This study focuses on the modification of a traditional MDP to account for those cases in which an action must be chosen after a significant time lag in observing system state, but just prior to a new observation. In order to calculate an optimal decision policy under these conditions, possible actions must be conditioned on the previous observed system state and action taken. We show how to solve these problems when the state transition structure is known and when it is uncertain. Our focus is on the latter case, and we show how actions must be conditioned not only on the previous system state and action, but on the probabilities associated with alternative models of system dynamics. To demonstrate this framework, we calculated and simulated optimal, adaptive policies for MDPs with lagged states for the problem of deciding annual harvest regulations for mallards (Anas platyrhynchos) in the United States. In this particular example, changes in harvest policy induced by the use of lagged information about system state were sufficient to maintain expected management performance (e.g. population size, harvest) even in the face of an uncertain system state at the time of a decision.

Allowable levels of take for the trade in Nearctic songbirds.

The take of Nearctic songbirds for the caged-bird trade is an important cultural and economic activity in Mexico, but its sustainability has been questioned. We relied on the theta-logistic population model to explore options for setting allowable levels of take for 11 species of passerines that were subject to legal take in Mexico in 2010. Because estimates of population size necessary for making-periodic adjustments to levels of take are not routinely available, we examined the conditions under which a constant level of take might contribute to population depletion (i.e., a population below its level of maximum net productivity). The chance of depleting a population is highest when levels of take are based on population sizes that happen to be much lower or higher than the level of maximum net productivity, when environmental variation is relatively high and serially correlated, and when the interval between estimation of population size is relatively long (> or = 5 years). To estimate demographic rates of songbirds involved in the Mexican trade we relied on published information and allometric relationships to develop probability distributions for key rates, and then sampled from those distributions to characterize the uncertainty in potential levels of take. Estimates of the intrinsic rate of growth (r) were highly variable, but median estimates were consistent with those expected for relatively short-lived, highly fecund species. Allowing for the possibility of nonlinear density dependence generally resulted in allowable levels of take that were lower than would have been the case under an assumption of linearity. Levels of take authorized by the Mexican government in 2010 for the 11 species we examined were small in comparison to relatively conservative allowable levels of take (i.e., those intended to achieve 50% of maximum sustainable yield). However, the actual levels of take in Mexico are unknown and almost certainly exceed the authorized take. Also, the take of Nearctic songbirds in other Latin American and Caribbean countries ultimately must be considered in assessing population-level impacts.