G. Scott Boomer

Climate change, uncertainty, and natural resource management†

ABSTRACT Climate change and its associated uncertainties are of concern to natural resource managers. Although aspects of climate change may be novel (e.g., system change and nonstationarity), natural resource managers have long dealt with uncertainties and have developed corresponding approaches to decision-making. Adaptive resource management is an application of structured decision-making for recurrent decision problems with uncertainty, focusing on management objectives, and the reduction of uncertainty over time. We identified 4 types of uncertainty that characterize problems in natural resource management. We examined ways in which climate change is expected to exacerbate these uncertainties, as well as potential approaches to dealing with them. As a case study, we examined North American waterfowl harvest management and considered problems anticipated to result from climate change and potential solutions. Despite challenges expected to accompany the use of adaptive resource management to address problems associated with climate change, we conclude that adaptive resource management approaches will be the methods of choice for managers trying to deal with the uncertainties of climate change.

On formally integrating science and policy: walking the walk

The contribution of science to the development and implementation of policy is typically neither direct nor transparent. In 1995, the U.S. Fish and Wildlife Service (FWS) made a decision that was unprecedented in natural resource management, turning to an unused and unproven decision process to carry out trust responsibilities mandated by an international treaty. The decision process was adopted for the establishment of annual sport hunting regulations for the most economically important duck population in North America, the 6 to 11 million mallards Anas platyrhynchos breeding in the mid-continent region of north-central United States and central Canada. The key idea underlying the adopted decision process was to formally embed within it a scientific process designed to reduce uncertainty (learn) and thus make better decisions in the future. The scientific process entails use of models to develop predictions of competing hypotheses about system response to the selected action at each decision point. These predictions not only are used to select the optimal management action, but also are compared with the subsequent estimates of system state variables, providing evidence for modifying degrees of confidence in, and hence relative influence of, these models at the next decision point. Science and learning in one step are formally and directly incorporated into the next decision, contrasting with the usual ad hoc and indirect use of scientific results in policy development and decision-making. Application of this approach over the last 20 years has led to a substantial reduction in uncertainty, as well as to an increase in transparency and defensibility of annual decisions and a decrease in the contentiousness of the decision process. As resource managers are faced with increased uncertainty associated with various components of global change, this approach provides a roadmap for the future scientific management of natural resources. Science and policy

Estimating migratory game-bird productivity by integrating age ratio and banding data

Context Reproduction is a critical component of fitness, and understanding factors that influence temporal and spatial dynamics in reproductive output is important for effective management and conservation. Although several indices of reproductive output for wide-ranging species, such as migratory birds, exist, there has been no theoretical justification for their estimators or associated measures of variance. Aims The aims of our research were to develop statistical justification for an estimator of reproduction and associated variances on the basis of an existing national wing-collection survey and banding data, and to demonstrate the applicability of this estimator to a migratory game bird. Methods We used a Bayesian hierarchical modelling approach to integrate wing-collection data, which provides information on population age ratios, and band-recovery data, which provides information on recovery probabilities of various age classes, for American woodcock (Scolopax minor) to estimate productivity and associated measures of variance. We present two models of relative vulnerability between age classes: one model assumed that adult recovery probabilities were higher, but that annual fluctuations were synchronous between the two age classes (i.e. an additive effect of age and year). The second model assumed that adults, on average, had higher recovery probabilities than did juveniles and that annual fluctuations were asynchronous through time (i.e. an interaction between age and year). Key results Fitting our models within a hierarchical Bayesian framework efficiently incorporates the two data types into a single estimator and derives appropriate variances for the productivity estimator. Further, use of Bayesian methods enabled us to derive credible intervals that avoid the reliance on asymptotic assumptions. When applied to American woodcock data, the additive model resulted in biologically realistic and more precise age-ratio estimates each year and is adequate when the relative vulnerability to sampling only slightly varies or does not vary among components of a population (e.g. age, sex class) among years. Therefore, we recommend using woodcock indices from our analysis based on this model. Conclusions We provide a flexible modelling framework for estimating productivity and associated variances that can incorporate ecological covariates to explore various factors that could drive annual dynamics in productivity. Applying our model to the American woodcock data indicated that assumptions about the variability in relative recovery probabilities could greatly influence the precision of our productivity estimator. Therefore, researchers should carefully consider the assumption of temporally variable relative recovery probabilities (i.e. ratio of juvenile to adults’ recovery probability) for different age classes when applying this estimator. Implications Several national and international management strategies for migratory game birds in North America rely on measures of productivity from harvest survey parts collections, without a justification of the estimator or providing estimates of precision. We derive an estimator of productivity with realistic measures of uncertainty that can be directly incorporated into management plans or ecological studies across large spatial scales.

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.

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.

Integrating Breeding Bird Survey and demographic data to estimate Wood Duck population size in the Atlantic Flyway

ABSTRACT The U.S. Fish and Wildlife Service (USFWS) uses data from the North American Breeding Bird Survey (BBS) to assist in monitoring and management of some migratory birds. However, BBS analyses provide indices of population change rather than estimates of population size, precluding their use in developing abundance-based objectives and limiting applicability to harvest management. Wood Ducks (Aix sponsa) are important harvested birds in the Atlantic Flyway (AF) that are difficult to detect during aerial surveys because they prefer forested habitat. We integrated Wood Duck count data from a ground-plot survey in the northeastern U.S. with AF-wide BBS, banding, parts collection, and harvest data to derive estimates of population size for the AF. Overlapping results between the smaller-scale intensive ground-plot survey and the BBS in the northeastern U.S. provided a means for scaling BBS indices to the breeding population size estimates. We applied these scaling factors to BBS results for portions of the AF lacking intensive surveys. Banding data provided estimates of annual survival and harvest rates; the latter, when combined with parts-collection data, provided estimates of recruitment. We used the harvest data to estimate fall population size. Our estimates of breeding population size and variability from the integrated population model (N̄ = 0.99 million, SD = 0.04) were similar to estimates of breeding population size based solely on data from the AF ground-plot surveys and the BBS (N̄ = 1.01 million, SD = 0.04) from 1998 to 2015. Integrating BBS data with other data provided reliable population size estimates for Wood Ducks at a scale useful for harvest and habitat management in the AF, and allowed us to derive estimates of important demographic parameters (e.g., seasonal survival rates, sex ratio) that were not directly informed by data.

Spatially explicit dynamic N-mixture models

Knowledge of demographic parameters such as survival, reproduction, emigration, and immigration is essential to understand metapopulation dynamics. Traditionally the estimation of these demographic parameters requires intensive data from marked animals. The development of dynamic N-mixture models makes it possible to estimate demographic parameters from count data of unmarked animals, but the original dynamic N-mixture model does not distinguish emigration and immigration from survival and reproduction, limiting its ability to explain important metapopulation processes such as movement among local populations. In this study we developed a spatially explicit dynamic N-mixture model that estimates survival, reproduction, emigration, local population size, and detection probability from count data under the assumption that movement only occurs among adjacent habitat patches. Simulation studies showed that the inference of our model depends on detection probability, local population size, and the implementation of robust sampling design. Our model provides reliable estimates of survival, reproduction, and emigration when detection probability is high, regardless of local population size or the type of sampling design. When detection probability is low, however, our model only provides reliable estimates of survival, reproduction, and emigration when local population size is moderate to high and robust sampling design is used. A sensitivity analysis showed that our model is robust against the violation of the assumption that movement only occurs among adjacent habitat patches, suggesting wide applications of this model. Our model can be used to improve our understanding of metapopulation dynamics based on count data that are relatively easy to collect in many systems.

Population monitoring and modelling of yellow-shouldered parrot on Bonaire, Caribbean Netherlands

Abundance estimates based on adequate survey design and count methodology are needed for population monitoring and modelling, and for assessing the results of conservation actions taken to boost or maintain population size at desired target levels. We monitored Bonaires population of yellow-shouldered parrot Amazona barbadensis rothschildi using systematic distance sampling surveys in 2009–2017, and developed a Bayesian state-space logistic model to predict changes in abundance resulting from increased human-induced mortality in 2018–2066. Survey-based abundance estimates (mean ± bootstrapped SE) were 0.172 ± 0.020 parrots ha-1 and 2924 ± 340 parrots at a survey region covering 17 000 ha. Model-based posterior distribution estimates (mean ± MCMC SD) of maximum population growth rate, maximum sustainable mortality rate, maximum sustainable mortality, population carrying capacity and equilibrium population size were 0.179 ± 0.129, 0.090 ± 0.064, 219 ± 135, 5623 ± 2043 and 2811 ± 1022 parrots. With low to moderate mortality rates (0.001– 0.100, 0.101–0.250), predicted population sizes (mean ± MCMC SD) were 2963 ± 668 and 2703 ± 1660 parrots in 2018, and 2754 ± 690 and 2297 ± 1301 parrots in 2066. With high mortality rates (0.251–0.500), predicted population sizes were 1780 ± 1160 parrots in 2018 and 26 ± 139 parrots in 2066. Because the relative importance and magnitude of human–parrot conflicts are unknown but may be unsustainable, we consider the parrot population vulnerable to the risk of extinction during the modelled time horizon. Therefore, we recommend long-term monitoring and modelling for assessing changes in abundance and the results of conservation actions taken to keep the population above 2800 parrots in the survey region (i.e. population size N > 2.5% percentile of the posterior distribution of population carrying capacity K).

Sustainability assessment of Plain Pigeons and White-crowned Pigeons illegally hunted in Puerto Rico

ABSTRACT The Puerto Rican Plain Pigeon (Patagioenas inornata wetmorei) and the White-crowned Pigeon (P. leucocephala) are hunted illegally in Puerto Rico, despite being protected. Data are lacking to estimate how many are hunted illegally each year. For this reason, we used abundance estimates derived from distance sampling surveys conducted in 1986–2014 to (1) fit a Bayesian state-space model, (2) estimate posterior distributions for population and harvest management parameters (e.g., growth rate, carrying capacity, and maximum sustainable harvest rate), and (3) predict abundance in 2025 as a function of potential illegal hunting in 2015–2024. For the Plain Pigeon and White-crowned Pigeon, respectively, the intrinsic rate of population growth was 0.351 (95% credible interval = 0.086–0.737) and 0.352 (0.094–0.699), population carrying capacity was 55,840 (29,649–96,505) and 73,692 (47,225–98,434) individuals, maximum sustainable harvest rate was 0.176 (0.043–0.369 and 0.047–0.349), and predicted abundance was 20,536 (8,167–89,040) and 29,361 (1,779–100,937) individuals in 2025. Both pigeon populations increased from low numbers in the 1980–1990s, recovered quickly after hurricanes in 1989 and 1998, surpassed carrying capacity in 1995–2008, and decreased sharply at the same time that legal hunting of columbids increased in 2008–2014. Our monitoring and modeling results suggest that an increase in illegal hunting may be responsible for some of the abundance decline in 2008–2014, and that population sustainability may be affected by illegal hunting in 2015–2025. Therefore, data collection and the control of illegal hunting should be considered management priorities. Because we are updating model-based abundance predictions annually with monitoring data, we can inform management decisions, evaluate the results of conservation actions taken to maintain the pigeon populations fluctuating around carrying-capacity levels, and learn from the comparison of estimated and predicted abundances. Our monitoring and modeling scheme is applicable to other resident and migratory bird populations in the Caribbean.