Structured decision making for management of warm-water habitat for manatees. Final report to U.S. Fish and Wildlife Service
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Report↗
Geology topics
Fred A. Johnson
Publications and source records attributed to Fred A. Johnson.
Adaptive harvest management for the Svalbard population of pink-footed geese: 2015 progress summary
This document describes progress to date on the development of an adaptive harvest management strategy for maintaining the Svalbard population of pink‐footed geese (Anser brachyrhynchus) near their agreed target level (60,000) by providing for sustainable harvests in Norway and Denmark. This report provides an assessment of the most recent monitoring information (1991-2014) and its implications for the harvest management strategy, and it is an update of an initial assessment for 2013-2015 (see http://pinkfootedgoose.aewa.info/). By combining varying hypotheses about survival and reproduction, a suite of nine models have been developed that represent a wide range of possibilities concerning the extent to which demographic rates are density dependent or independent. Current updated model weights suggest little evidence for density-dependent survival and reproduction, suggesting that the population may have recently experienced a release from density-dependent mechanisms, corresponding to the period of most rapid growth in population size. The optimal harvest strategy for the 2013–2015 hunting seasons prescribed a harvest quota of 15,000 per year. The harvest in the 2014 hunting season was 14,991, compared to 11,081 in 2013, mostly due to an increase in harvest in Denmark during January 2015. The percentage of young in the fall of 2014 was 10.3%, which is lower than average. The observed population size of 59,000 in May 2015 was much lower than expected. For the 2015 hunting season, observed population size and temperature days suggest that an emergency closure should be considered. In the event a harvest of 15,000 is maintained, predicted population size in May 2016 is 51,700 (95% CL: 41,600-64,300), based on observed TempDays = 9 in May 2015 and the most recent model weights. On the other hand, if the season were closed this year, we would expect a population size of 66,700 (95% CL: 53,600-82,900) in May 2016. A total harvest of 6,700 would be expected to result in a 2016 population size at goal (i.e., 60,000).
Technical Report from DCE - Danish Centre for Envi↗
Dynamic reserve design in the face of climate change and urbanization
Reserve design is a process that must address many ecological, social, and political factors to successfully identify parcels of land in need of protection to sustain wildlife populations and other natural resources. Making land acquisition choices for a large, terrestrial protected area is difficult because it occurs over a long timeframe and may involve consideration future conditions such as climate and urbanization changes. Decision makers need to consider factors including: order of parcel purchasing given budget constraints, future uncertainty, potential future landscape‐scale changes from urbanization and climate. In central Florida, two new refuges and the expansion of a third refuge are in various stages of USFWS planning. The Everglades Headwaters National Wildlife Refuge (EHNWR) has recently been established, is at the top of the Presidential Administration’s priority conservation areas, and is cited by the Secretary of DOI routinely in the context of conservation. The new refuges were strategically located for both for species adaptation from climate change impacts as well as currently being host to a number of important threatened and endangered species and habitats. We plan to combine a structured decision making framework, optimal solution theory, and output from ecological and sociological models (these modeling efforts were previously funded by DOI partners) that incorporate climate change to provide guidance for EHNWR reserve design. Utilizing a SDM approach and optimal solution theory, decision support tools will be developed that will incorporate stakeholder and agency objectives into targeting conservation lands both through fee simple purchase and other incentives such as easements based on ecological and socioeconomic modeling outputs driven by climate change.
Report↗
Uncertainty, robustness, and the value of information in managing a population of northern bobwhites
The abundance of northern bobwhites (Colinus virginianus) has decreased throughout their range. Managers often respond by considering improvements in harvest and habitat management practices, but this can be challenging if substantial uncertainty exists concerning the cause(s) of the decline. We were interested in how application of decision science could be used to help managers on a large, public management area in southwestern Florida where the bobwhite is a featured species and where abundance has severely declined. We conducted a workshop with managers and scientists to elicit management objectives, alternative hypotheses concerning population limitation in bobwhites, potential management actions, and predicted management outcomes. Using standard and robust approaches to decision making, we determined that improved water management and perhaps some changes in hunting practices would be expected to produce the best management outcomes in the face of uncertainty about what is limiting bobwhite abundance. We used a criterion called the expected value of perfect information to determine that a robust management strategy may perform nearly as well as an optimal management strategy (i.e., a strategy that is expected to perform best, given the relative importance of different management objectives) with all uncertainty resolved. We used the expected value of partial information to determine that management performance could be increased most by eliminating uncertainty over excessive-harvest and human-disturbance hypotheses. Beyond learning about the factors limiting bobwhites, adoption of a dynamic management strategy, which recognizes temporal changes in resource and environmental conditions, might produce the greatest management benefit. Our research demonstrates that robust approaches to decision making, combined with estimates of the value of information, can offer considerable insight into preferred management approaches when great uncertainty exists about system dynamics and the effects of management.
Florida↗
Uncertainty, robustness, and the value of information in managing an expanding Arctic goose population
We explored the application of dynamic-optimization methods to the problem of pink-footed goose (Anser brachyrhynchus) management in western Europe. We were especially concerned with the extent to which uncertainty in population dynamics influenced an optimal management strategy, the gain in management performance that could be expected if uncertainty could be eliminated or reduced, and whether an adaptive or robust management strategy might be most appropriate in the face of uncertainty. We combined three alternative survival models with three alternative reproductive models to form a set of nine annual-cycle models for pink-footed geese. These models represent a wide range of possibilities concerning the extent to which demographic rates are density dependent or independent, and the extent to which they are influenced by spring temperatures. We calculated state-dependent harvest strategies for these models using stochastic dynamic programming and an objective function that maximized sustainable harvest, subject to a constraint on desired population size. As expected, attaining the largest mean objective value (i.e., the relative measure of management performance) depended on the ability to match a model-dependent optimal strategy with its generating model of population dynamics. The nine models suggested widely varying objective values regardless of the harvest strategy, with the density-independent models generally producing higher objective values than models with density-dependent survival. In the face of uncertainty as to which of the nine models is most appropriate, the optimal strategy assuming that both survival and reproduction were a function of goose abundance and spring temperatures maximized the expected minimum objective value (i.e., maxi–min). In contrast, the optimal strategy assuming equal model weights minimized the expected maximum loss in objective value. The expected value of eliminating model uncertainty was an increase in objective value of only 3.0%. This value represents the difference between the best that could be expected if the most appropriate model were known and the best that could be expected in the face of model uncertainty. The value of eliminating uncertainty about the survival process was substantially higher than that associated with the reproductive process, which is consistent with evidence that variation in survival is more important than variation in reproduction in relatively long-lived avian species. Comparing the expected objective value if the most appropriate model were known with that of the maxi–min robust strategy, we found the value of eliminating uncertainty to be an expected increase of 6.2% in objective value. This result underscores the conservatism of the maxi–min rule and suggests that risk-neutral managers would prefer the optimal strategy that maximizes expected value, which is also the strategy that is expected to minimize the maximum loss (i.e., a strategy based on equal model weights). The low value of information calculated for pink-footed geese suggests that a robust strategy (i.e., one in which no learning is anticipated) could be as nearly effective as an adaptive one (i.e., a strategy in which the relative credibility of models is assessed through time). Of course, an alternative explanation for the low value of information is that the set of population models we considered was too narrow to represent key uncertainties in population dynamics. Yet we know that questions about the presence of density dependence must be central to the development of a sustainable harvest strategy. And while there are potentially many environmental covariates that could help explain variation in survival or reproduction, our admission of models in which vital rates are drawn randomly from reasonable distributions represents a worst-case scenario for management. We suspect that much of the value of the various harvest strategies we calculated is derived from the fact that they are state dependent, such that appropriate harvest rates depend on population abundance and weather conditions, as well as our focus on an infinite time horizon for sustainability.
Ecological Modelling↗
Tailoring point counts for inference about avian density: dealing with nondetection and availability
Point counts are commonly used for bird surveys, but interpretation is ambiguous unless there is an accounting for the imperfect detection of individuals. We show how repeated point counts, supplemented by observation distances, can account for two aspects of the counting process: (1) detection of birds conditional on being available for observation and (2) the availability of birds for detection given presence. We propose a hierarchical model that permits the radius in which birds are available for detection to vary with forest stand age (or other relevant habitat features), so that the number of birds available at each location is described by a Poisson-gamma mixture. Conditional on availability, the number of birds detected at each location is modeled by a beta-binomial distribution. We fit this model to repeated point count data of Florida scrub-jays and found evidence that the area in which birds were available for detection decreased with increasing stand age. Estimated density was 0.083 (95%CI: 0.060–0.113) scrub-jays/ha. Point counts of birds have a number of appealing features. Based on our findings, however, an accounting for both components of the counting process may be necessary to ensure that abundance estimates are comparable across time and space. Our approach could easily be adapted to other species and habitats.
Natural Resource Modeling↗
Maximizing the social and ecological value of Cape Romain National Wildlife Refuge, South Carolina as the effects of global change processes increase.
Coastal ecosystems in the eastern U.S. have been severely altered by processes associated with human development, including drainage of coastal wetlands, changes in hydrology that alter sediment and freshwater delivery to the coast, land clearing, agricultural and forestry activity, and the construction of seawalls and other structures that “harden” the coast. Sea-level rise and the changing frequency of extreme events associated with climate change are now further degrading the capacity of those ecological and social systems to remain resilient in the face of disturbance, largely through the degradation and loss of land and habitat. The goal was to develop a problem framing that would broadly reflect the type, scale and scope of SLR-adaptation decisions faced by refuges in general, and that could be used by individual refuges to help understand how their specific problems fit into a larger context of SLR planning and implementation. We worked with refuge managers in developing a common frame of reference for some shared SLR-related management problems, while also facilitating communication about these problems with other refuges and with surrounding jurisdictions.
South Carolina↗
Snow conditions as an estimator of the breeding output in high-Arctic pink-footed geese Anser brachyrhynchus
The Svalbard-breeding population of pink-footed geese Anser brachyrhynchus has increased during the last decades and is giving rise to agricultural conflicts along their migration route, as well as causing grazing impacts on tundra vegetation. An adaptive flyway management plan has been implemented, which will be based on predictive population models including environmental variables expected to affect goose population development, such as weather conditions on the breeding grounds. A local study in Svalbard showed that snow cover prior to egg laying is a crucial factor for the reproductive output of pink-footed geese, and MODIS satellite images provided a useful estimator of snow cover. In this study, we up-scaled the analysis to the population level by examining various measures of snow conditions and compared them with the overall breeding success of the population as indexed by the proportion of juveniles in the autumn population. As explanatory variables, we explored MODIS images, satellite-based radar measures of onset of snow melt, winter NAO index, and the May temperature sum and May thaw days. To test for the presence of density dependence, we included the number of adults in the population. For 2000–2011, MODIS-derived snow cover (available since 2000) was the strongest indicator of breeding conditions. For 1981–2011, winter NAO and May thaw days had equal weight. Interestingly, there appears to have been a phase shift from density-dependent to density-independent reproduction, which is consistent with a hypothesis of released breeding potential due to the recent advancement of spring in Svalbard.
Svalbard↗
Confronting dynamics and uncertainty in optimal decision making for conservation
The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a critically endangered population through captive breeding, control of invasive species, construction of biodiversity reserves, design of landscapes to increase habitat connectivity, and resource exploitation. Although these decision making problems and their solutions present significant challenges, we suggest that a systematic and effective approach to dynamic decision making in conservation need not be an onerous undertaking. The requirements are shared with any systematic approach to decision making--a careful consideration of values, actions, and outcomes.
Environmental Research Letters↗
Resilience thinking and a decision-analytic approach to conservation: strange bedfellows or essential partners?
There has been some tendency to view decision science and resilience theory as opposing approaches, or at least as contending perspectives, for natural resource management. Resilience proponents have been especially critical of optimization in decision science, at least for those cases where it is focused on the aggressive pursuit of efficiency. In general, optimization of resource systems is held to reduce spatial, temporal, or organizational heterogeneity that would otherwise limit efficiency, leading to homogenization of a system and making it less able to cope with unexpected changes or disturbances. For their part, decision analysts have been critical of resilience proponents for not providing much practical advice to decision makers. We believe a key source of tension between resilience thinking and application of decision science is the pursuit of efficiency in the latter (i.e., choosing the “best” management action or strategy option to maximize productivity of one or few resource components), vs. a desire in the former to keep options open (i.e., maintaining and enhancing diversity). It seems obvious, however, that with managed natural systems, there must be a principle by which to guide decision making, which at a minimumallows for a comparison of projected outcomes associated with decision alternatives. This is true even if the primary concern of decision making is the preservation of system resilience. We describe how a careful framing of conservation problems, especially in terms of management objectives and predictive models, can help reduce the purported tension between resiliencethinking and decision analysis. In particular, objective setting in conservation problems needs to be more attuned to the dynamics of ecological systems and to the possibility of deep uncertainties that underlie the risk of unintended, if not irreversible, outcomes. Resilience thinking also leads to the suggestion that model development should focus more on process rather than pattern, on multiple scales of influence, and on phenomena that can create alternative stability regimes. Although we acknowledge the inherent difficulties in modeling ecological processes, we stress that formulation of useful models need not depend on a thorough mechanistic understanding or precise parameterization, assuming that uncertainty is acknowledged and treated in a systematic manner.
Ecology and Society↗
Adaptive harvest management for the Svalbard population of pink-footed geese: assessment for the 2013-2015 hunting seasons
This report describes progress on the development of an adaptive harvestmanagement strategy for maintaining the Svalbard population of pink-footed geese near their agreed target level (60,000) by providing for sustainable harvests in Norway and Denmark. Specifically, this report provides an optimal harvest quota for the 2013-2015 hunting seasons and describes a process for evaluating whether emergency hunting closures would be needed during that period. By combining varying hypotheses about survival and reproduction, a suite of nine models have been developed that represent a wide range of possibilities concerning the extent to which demographic rates are density dependent or independent, and the extent to which spring temperatures are important. The most current set of monitoring information was used to update model weights for the 1991 – 2012 period. Current model weights suggest no evidence for density-dependent survival. These results suggest that the pink-footed goose population may have recently experienced a release from density-dependent mechanisms, corresponding to the period of most rapid growth in population size. There was equivocal evidence for the effect of May temperature days (number of days with temperatures above freezing) on survival and on reproduction. The optimal harvest strategy suggests that the appropriate annual harvest quota for the 20132015 period is 15,000; hence there is no need to take emergency measures to close the upcoming hunting season. For comparison, the estimated harvest in 2012 was 11,000. If the harvest quota of 15,000 were met, the autumn 2013 population count is expected to be 76,000. If only the most recent 3-year mean harvest were realized (11,500), an autumn population size of 80,000 thousand is expected. Thus, it may be that harvest is approaching the magnitude needed to stabilize the population.
Svalbard↗
Adaptive harvest management for the Svalbard population of pink-footed geese: cooperator report
This document describes progress to date on the development of a harvest‐management strategy for maintaining pink‐footed goose abundance near their target level by providing for sustainable harvests in Norway and Denmark. Many goose populations in western Europe have increased dramatically in recent decades. The Svalbard population of pink‐footed geese (Anser brachyrhynchus) is a good example, increasing from about 10 thousand individuals in the early 1960’s to roughly 80 thousand today. Although these geese are a highly valued resource, the growing numbers of geese are causing agricultural conflicts in wintering and staging areas. The African‐Eurasian Waterbird Agreement (AEWA; http://www.unep‐aewa.org/) calls for means to manage populations which cause conflicts with certain human economic activities. We compiled relevant demographic and weather data and specified an annual‐cycle model for pink-footed geese that reconciles the different dates of monitoring activities and the timing of harvest-management decisions. We then developed dynamic models for survival and reproductive processes and parameterized them using available data. By combining varying hypotheses about survival and reproduction, we developed a suite of nine models that represent a wide range of possibilities concerning the extent to which demographic rates are density dependent or independent, and the extent to which spring temperatures are important. These nine models varied significantly in their predictions of the harvest required to stabilize current population size, ranging from a low of about 500 to a high of about 17 thousand. For comparison, the harvest in Norway and Denmark was about 11 thousand in 2011 and the population increased from 70 to 80 thousand. We relied on the passive form of adaptive management in formulating a harvest strategy. In passive adaptive management, alternative population models and their associated weights of evidence are explicitly considered in the development of an optimal harvest strategy. Unlike active adaptive management, however, there is no explicit consideration of how harvest management actions could reduce uncertainty as to the most appropriate model of population dynamics. In optimizing a harvest strategy, we assumed equal probabilities for all nine models and assumed relatively course control over harvest. We used a management objective that seeks to maximize sustainable harvest, but avoids harvest decisions that are expected to result in a subsequent population size different than the population goal of 60 thousand. Optimal harvest strategies were calculated using stochastic dynamic programming, and Monte Carlo simulations were used to investigate expected strategy performance. The optimal passive adaptive‐management strategy is expected to maintain mean population size near 60 thousand, regardless of the most appropriate model. However, mean harvest rates and harvests varied substantially depending on the most appropriate model of population dynamics. With an average number of days above freezing in May in Svalbard, optimal harvest rates (i.e., the proportion of the population to be harvested in autumn) increase rapidly once there are more than about 50 thousand birds in the population. Generally, optimal harvests were on the order of 10 – 20 thousand for population sizes > 60 thousand, and 0 – 5 thousand for population sizes < 60 thousand. For the observations of young of 15.4 thousand and adults of 54.6 thousand in autumn 2010, and 10 days above freezing in May 2011 (a relatively warm spring compared to the average of about 7), the optimal harvest rate in autumn of 2011 would have been 0.16, or a harvest of about 14 thousand. Based on the optimal strategy, hunting‐season closures would be required as the number of adults in the autumn population falls below about 52 thousand, regardless of the number of young in the population. As the number of adults and young decrease, the number of warm days in May required to keep the hunting season open increases. We also investigated the ability of the optimal strategy to stabilize the population at around 60 thousand birds, assuming varying values of the maximum harvest rate that could be implemented. Harvest strategies that contained a maximum harvest rate of 0.16 (equivalent to a harvest of about 17 thousand) were effective at stabilizing the population at 60 thousand within 4‐5 years, regardless of climate scenario. Harvest strategies with a maximum harvest rate of 0.12 (harvest ≈ 13 thousand) were also able to stabilize the population near 60 thousand, although it took more time. Harvest strategies with a maximum harvest rate of 0.08 (harvest ≈ 8 thousand) were unsuccessful at stabilizing the population at 60 thousand. Continued monitoring of the pink‐footed goose population on an annual basis is critical to an informed harvest management strategy. At a minimum, the ground census in November should be continued to determine population size and proportion of young. Continued estimates of harvest from Norway and Denmark are also necessary to help judge the credibility of the alternative population models. However, an adaptive management process that relies on periodic updating of model weights will depend on acquiring either estimates of the realized harvest rate of adults or the age composition of the harvest. We also recommend that a census conducted during spring migration be operationalized, and that estimates of survival based on mark‐recapture data be updated. Finally, the International Working Group has expressed a desire to adopt a three‐year cycle of decision making related to the regulation of pink‐footed goose harvests. The idea is that once a target harvest level is adopted, it would remain in place for three years, after which time population status would be assessed and a potentially new management action chosen. We have developed a preliminary framework to implement a three‐year cycle using stochastic dynamic programming, and we hope to have it fully operational later this year . We note, however, that application of this 3‐year framework will still require annual resource monitoring and assessments to facilitate learning, and to allow managers the opportunity to respond to any unforeseen change in resource conditions.
Report↗
Adaptive harvest management for the Svalbard population of pink-footed geese: briefing summary
The African-Eurasian Waterbird Agreement (AEWA; http://www.unep-aewa.org/) calls for means to manage populations which cause conflicts with certain human economic activities. The Svalbard population of the pink-footed goose has been selected as the first test case for such an international species management plan to be developed. This document describes progress to date on the development of an adaptive harvest management (AHM) strategy for maintaining pink-footed goose abundance near their target level by providing for sustainable harvasts in Norway and Denmark. This briefing supplements material provided in the Progress Summary distributed to the International Working Group on February 1, 2013. We emphasize that peer review is an essential aspect of the process of developing and implementing an AHM program for pink-footed geese, and we will continue to solicit reviews by the International Working Group and their staff, as well as scientists not engaged in this effort. We wish to make the Working Group aware the the following two manuscripts have been submitted recently to refereed journals and are available upon request from the senior authors: Jensen, G.H., J. Madsen, F.A. Johnson, and M. Tamstorf. Snow conditions as an estimator of the breeding output in high-Arctic pink-footed geese Anser brachyrhynchus. Polar Biology: In review. Johnson, F.A., G.H. Jensen, J. Madsen, and B.K. Williams. Uncertainity, robustness, and the value of information in managing an expanding Arctic goose population. Ecological Modeling: In review. In addition to these manuscripts, the Progress Summary (February 1, 2013), and this Briefing Summary (April 23, 2013) an annual report will be produced in August 2013 and every summer thereafter. Additional manuscripts for journal publication are also anticipated.
Report↗
Adaptive harvest management for the Svalbard population of pink-footed geese: a response to a review of the progress summary
Herein we provide excerpts of the reviewers' comments and our response. We did not duplicate the portion of the reviewers' comments in which they summarized our findings.
Report↗
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 (≥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.
Ecological Applications↗
Learning and adaptation in the management of waterfowl harvests
A formal framework for the adaptive management of waterfowl harvests was adopted by the U.S. Fish and Wildlife Service in 1995. The process admits competing models of waterfowl population dynamics and harvest impacts, and relies on model averaging to compute optimal strategies for regulating harvest. Model weights, reflecting the relative ability of the alternative models to predict changes in population size, are used in the model averaging and are updated each year based on a comparison of model predictions and observations of population size. Since its inception the adaptive harvest program has focused principally on mallards ( Anas platyrhynchos ), which constitute a large portion of the U.S. waterfowl harvest. Four competing models, derived from a combination of two survival and two reproductive hypotheses, were originally assigned equal weights. In the last year of available information (2007), model weights favored the weakly density-dependent reproductive hypothesis over the strongly density-dependent one, and the additive mortality hypothesis over the compensatory one. The change in model weights led to a more conservative harvesting policy than what was in effect in the early years of the program. Adaptive harvest management has been successful in many ways, but nonetheless has exposed the difficulties in defining management objectives, in predicting and regulating harvests, and in coping with the tradeoffs inherent in managing multiple waterfowl stocks exposed to a common harvest. The key challenge now facing managers is whether adaptive harvest management as an institution can be sufficiently adaptive, and whether the knowledge and experience gained from the process can be reflected in higher-level policy decisions.
Journal of Environmental Management↗
A Markov decision process for managing habitat for Florida scrub-jays
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 scrubjays 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.
Florida↗
Adaptive harvest management of North American waterfowl populations: a brief history and future prospects
Since 1995, the US Fish and Wildlife Service has used an adaptive approach to the management of sport harvest of mid-continent Mallard ducks (Anas platyrhynchos) in North America. This approach differs from many current approaches to conservation and management in requiring close collaboration between managers and scientists. Key elements of this process are objectives, alternative management actions, models permitting prediction of system responses, and a monitoring program. The iterative process produces optimal management decisions and leads to reduction in uncertainty about response of populations to management. This general approach to management has a number of desirable features and is recommended for use in many other programs of management and conservation.
Journal of Ornithology↗