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Gitte H. Jensen

Publications and source records attributed to Gitte H. Jensen.

5 recordsLinked to original sources
USGS2020

Using integrated population models for insights into monitoring programs: An application using pink-footed geese

Development of integrated population models (IPMs) assume the absence of systematic bias in monitoring programs, yet many potential sources of systematic bias in monitoring data exist (e.g., under-counts of abundance). By integrating multiple sources of data, we can assess whether various sources of monitoring data provide consistent inferences about changes in population size and, thus, whether monitoring programs appear unbiased. For the purposes of understanding how IPMs could provide insights for monitoring programs, we used the Svalbard breeding population of pink-footed goose ( Anser brachyrhynchus ) as a case study. The Svalbard pink-footed goose is a well-studied species, the focus of the first adaptive-harvest-management program in Europe, and the subject of a variety of long-term monitoring programs. We examined two formulations of an IPM, but ultimately relied on the one that provided a satisfactory fit to all the available data as based on Chi-squared goodness of fit tests. Our analyses suggest a negative bias in November counts (-20 %), a negative bias in capture-mark-recapture estimates of survival (-3 %), and a negative bias in indices of productivity (-23 %). We offer possible explanations for these biases, whether the degree of bias seems reasonable considering those explanations, and how bias might be investigated directly and ultimately avoided or corrected. Finally, we discuss implications of our work for developing IPMs and associated monitoring programs for managing pink-footed geese and other waterbird species.

Ecological Modelling↗
USGS2015

Training conservation practitioners to be better decision makers

Traditional conservation curricula and training typically emphasizes only one part of systematic decision making ( i.e. , the science), at the expense of preparing conservation practitioners with critical skills in values-setting, working with decision makers and stakeholders, and effective problem framing. In this article we describe how the application of decision science is relevant to conservation problems and suggest how current and future conservation practitioners can be trained to be better decision makers. Though decision-analytic approaches vary considerably, they all involve: (1) properly formulating the decision problem; (2) specifying feasible alternative actions; and (3) selecting criteria for evaluating potential outcomes. Two approaches are available for providing training in decision science, with each serving different needs. Formal education is useful for providing simple, well-defined problems that allow demonstrations of the structure, axioms and general characteristics of a decision-analytic approach. In contrast, practical training can offer complex, realistic decision problems requiring more careful structuring and analysis than those used for formal training purposes. Ultimately, the kinds and degree of training necessary depend on the role conservation practitioners play in a decision-making process. Those attempting to facilitate decision-making processes will need advanced training in both technical aspects of decision science and in facilitation techniques, as well as opportunities to apprentice under decision analysts/consultants. Our primary goal should be an attempt to ingrain a discipline for applying clarity of thought to all decisions.

Sustainability↗
USGS2014

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↗
USGS2013

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↗