Geology topics

Jesper Madsen

Publications and source records attributed to Jesper Madsen.

At least 19 recordsLinked to original sources

USGS2026

Waterfowl move less in heterogeneous and human-populated landscapes, with implications for spread of avian influenza viruses

Animal movements contribute to the spread of infectious diseases and are driven in part by environmental conditions. We investigated the links among the environment, animal movement, and infectious disease dynamics in waterfowl, which are among the primary wildlife hosts of avian influenza viruses. By combining telemetry data on 4606 individuals from 26 waterfowl species with data on land cover, weather, and vegetation, we found that waterfowl moved less in areas of higher land cover heterogeneity and higher human population density. Moreover, predicted waterfowl movement distances were weakly but positively correlated with distances between detections of H5N1 highly pathogenic avian influenza in wild waterfowl, suggesting that environmental conditions might contribute to the spread of this disease via their effects on bird movements. By considering wildlife movements alongside other drivers of infectious disease dynamics, such as livestock production and human mobility, we move closer to predicting outbreaks and informing interventions.

Ecology Letters↗

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↗

USGS2020

Ecological insights from three decades of animal movement tracking across a changing Arctic

The Arctic is entering a new ecological state, with alarming consequences for humanity. Animal-borne sensors offer a window into these changes. Although substantial animal tracking data from the Arctic and subarctic exist, most are difficult to discover and access. Here, we present the new Arctic Animal Movement Archive (AAMA), a growing collection of more than 200 standardized terrestrial and marine animal tracking studies from 1991 to the present. The AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. With AAMA-based case studies, we document climatic influences on the migration phenology of eagles, geographic differences in the adaptive response of caribou reproductive phenology to climate change, and species-specific changes in terrestrial mammal movement rates in response to increasing temperature.

Science↗

USGS2019

Harvest assessment for Taiga bean geese in the Central Management Unit: 2019

In 2016 the European Goose Management International Working Group (EGM IWG) began development of an Adaptive Harvest Management (AHM) program for Taiga Bean Geese. In 2017, the IWG adopted an Interim Harvest Strategy consisting of a constant harvest rate (on adults) of 3% for the Central Management Unit (MU) of Taiga Bean Geese. The interim strategy is intended to provide limited hunting opportunity while rebuilding the population. Based on a January count of 41,927, the harvest quota for the 2019 hunting season is 1,740 Taiga Bean Geese (compared to 2,335 and 1,610 for the 2017 and 2018 seasons, respectively). We emphasize that these quotas include both, harvest during the regular season and derogation shooting. Going forward, we describe how an Integrated Population Model (IPM) will use counts at multiple times during the year, along with other demographic information, to estimate population size (and its precision). The IPM can be used to develop an adaptive harvest strategy if unambiguous management objectives can be agreed upon. We provide some initial guidance for formulating those objectives.

Conference Paper↗

USGS2019

Adaptive harvest management for the Svalbard population of pink‐footed geese: 2019 progress summary

This report describes an Adaptive Harvest Management (AHM) program designed to maintain the Svalbard population of Pink-footed Geese (Anser brachyrhynchus) near their target level (60,000) by providing sustainable harvests in Norway and Denmark. Specifically, this report provides recent monitoring and assessment results and their implications for the 2019 hunting season. In this report we provide results for both the set of nine discrete population models used since 2013 and for a recently developed Integrated Population Model (IPM). Updating model weights (set of nine models) and model parameters (IPM) use the most recent monitoring information available (i.e. through spring 2019). We also compare optimal harvest strategies based on the two modeling approaches. Of growing concern, however, has been the observation that the predictive ability of the original population models has declined over time. Going forward, we suggest that use of the IPM is a superior modeling approach for setting hunting seasons for Pink-footed Geese. Population sizes in May as estimated by the IPM are in general agreement with counts and capture-markrecapture estimates but are more precise. Estimates of survival from natural causes were relatively constant over time with a mean of 0.93 and estimates of adult kill rate ranged from 0.04 to 0.13. Estimates of countryspecific kill rates suggest that most of the overall increase in kill rate in recent years is attributed to increasing harvest pressure in Denmark. With respect to productivity, the IPM produced estimates of preseason age ratio that were variable over time, but on average suggested that young constituted about 18% of the population just prior to the hunting season. Estimates of the preseason age ratio correlated well with the number of days above freezing in May in Svalbard. Our results suggest that population size has stabilized because of declining survival rates that have accompanied an increase in kill rates. Adoption of the IPM this year would result in a harvest quota for the 2019 hunting season of 22,000, based on a May population estimate of 76,500 (95% CI: 66,800 – 86,600) and 8 days above freezing in Svalbard. Using the agreed upon allocation of the total allowable harvest, the quotas are 6,600 for Norway and 15,400 for Denmark. Assuming the total quota is met, the IPM predicts a population size in May 2020 of 64,900 (95% CI: 50,300 – 83,700). We note that the updated set of original models used since 2013 suggests an allowable harvest of 40,000 in 2019 based on a November 2018 count of 91,900 geese (comprised of 12,900 young and 79,000 adults). We are skeptical of this large quota, however. Using all available demographic data suggests that the November 2018 population size was lower than that indicated by the count and that the winter population experienced more natural mortality than expected. These insights demonstrate the inherent risk of basing a harvest quota on a single population count, irrespective of other demographic data.

Report↗

USGS2018

Making do with less: Must sparse data preclude informed harvest strategies for European waterbirds?

The demography of many European waterbirds is not well understood because most countries have conducted little monitoring and assessment, and coordination among countries on waterbird management has little precedent. Yet intergovernmental treaties now mandate the use of sustainable, adaptive harvest strategies, whose development is challenged by a paucity of demographic information. In this study, we explore how a combination of allometric relationships, fragmentary monitoring and research information, and expert judgment can be used to estimate the parameters of a theta-logistic population model, which in turn can be used in a Markov decision process to derive optimal harvesting strategies. We show how to account for considerable parametric uncertainty, as well as for different management objectives. We illustrate our methodology with a poorly understood population of taiga bean geese ( Anser fabalis fabalis ), which is a popular game bird in Fennoscandia. Our results for taiga bean geese suggest that they may have demographic rates similar to other, well-studied species of geese, and our model-based predictions of population size are consistent with the limited monitoring information available. Importantly, we found that by using a Markov decision process, a simple scalar population model may be sufficient to guide harvest management of this species, even if its demography is age-structured. Finally, we demonstrated how two different management objectives can lead to very different optimal harvesting strategies, and how conflicting objectives may be traded off with each other. This approach will have broad application for European waterbirds by providing preliminary estimates of key demographic parameters, by providing insights into the monitoring and research activities needed to corroborate those estimates, and by producing harvest management strategies that are optimal with respect to the managers’ objectives, options, and available demographic information.

Ecological Applications↗

USGS2018

An interim harvest strategy for Taiga Bean geese

In 2016 the AEWA European Goose Management International Working Group (EGM IWG) adopted document AEWA/EGM IWG 1.8 (Johnson et al. 2016), which contained initial elements of an Adaptive Harvest Management programme for Taiga Bean Geese. This report addresses a number of limitations with the population model presented in that document, and provides up-to-date population projections for the Central Management Unit under a range of constant harvest rates. Based on simulations for the 2017-2025 timeframe, median population size was near the median goal of 70,000 in 2019, 2020, and 2021 for harvest rates of birds aged one year or more of 0.00, 0.02, and 0.04, respectively. Simulated population sizes generally increased over the timeframe, albeit with a lot of variation and with the degree of uncertainty increasing over time. With a harvest rate of 0.02, harvests averaged 1,848 (95% CI: 1,403 – 2,492) over the timeframe; a harvest rate of 0.04 produced an average harvest of 3,484 (95% CI: 2,617 – 4,884). Future work for the Central Management Unit will involve development of a dynamic harvest strategy by employing a Markov decision process, in which multiple, possibly competing, management objectives can be addressed.

Conference Paper↗

USGS2018

Adaptive harvest management for the Svalbard population of pink-footed geese: 2018 progress summary

This report describes progress on development and implementation of an adaptive harvest management program for maintaining the Svalbard population of Pink-footed Geese (Anser brachyrhynchus) near their target level (60,000) by providing sustainable harvests in Norway and Denmark. Specifically, this report provides an assessment of recent monitoring information and its implications for hunting seasons in 2018. An Adaptive Harvest Management (AHM) program requires specification of four elements: (a) A set of alternative population models, which bound the uncertainty about population dynamics; (b) A set of weights describing the relative credibility of the alternative models, which are updated each year based on a comparison of model predictions and monitoring information; (c) A set of alternative harvest quotas from which to choose; and (d) An objective function, by which alternative harvest strategies are evaluated and an optimal strategy chosen. The most current set of monitoring information was used to update model weights for the 1991-2017 period. Current model weights suggest little evidence for density-dependent survival and reproduction. These results suggest that the Pink-footed Goose population may have experienced a release from density-dependent mechanisms, corresponding to the period of rapid growth in population size. There is equivocal evidence for the effect of the number of days above freezing in May in Svalbard on survival, but the evidence for an effect on reproduction has been increasing in recent years. Since the 2016 hunting season, harvest quotas are prescribed on an annual basis rather than every three years because of the potential to better meet management objectives. Based on updated model probabilities, the November 2017 population size (72,000), the proportion of the population comprised of one-year-old birds (0.076), and days above freezing in Svalbard in May 2018 (27), the optimal harvest quota for the 2018 hunting season is approximately 27,000. With the agreed upon harvest allocation of 30% Norway and 70% Denmark, the national quotas are 8,100 and 18,900, respectively, which are higher than the harvests realized in previous years. In 2017 the quota for the two countries combined was 36,000, but only a harvest of about 11,400 was realized. The decrease in harvest quota for 2018 is largely attributable to the apparent decline in population size. We also describe the ongoing development of an Integrated Population Model (IPM), which uses all available demographic data for Pink-footed Geese in a single, unified analysis. IPM estimates of harvest rates of adult geese were variable and relatively low prior to the implementation of AHM (2013), and have been relatively high since. The increase in harvest rates has been accompanied by a decline in annual survival. The ratio of young-of-the-year to older birds just prior to the hunting season has been variable over time, and since about 2005 has been highly correlated with the number of days above freezing in May in Svalbard. IPM estimates of population size suggest that abundance of Pink-footed Geese has been relatively stable, or declining slightly, in recent years. Based on the IPM estimate of population size in November 2017 of 68,800 (95% credible interval: 58,200 – 79,400), the optimal harvest quota for the 2018 hunting season is 15,000. This is lower than that derived from the set of nine discrete models because the IPM estimate of November population size is lower than the November count, and because the IPM model does not consider May temperatures in Svalbard, but rather assumes reproductive success varies randomly about the mean.

Report↗

USGS2018

Taiga bean goose: Harvest assessment for the Central Management Unit: 2018

In 2016 the European Goose Management International Working Group (EGM IWG) began development of an adaptive harvest management program for Taiga Bean Geese (TBG). In 2017, the EGM IWG adopted an interim harvest strategy consisting of a constant harvest rate (on adults) of 3% for the Central Management of Taiga Bean Geese. The interim strategy is intended to provide limited hunting opportunity while rebuilding the population. Recent efforts have involved development of a dynamic strategy in which the harvest rate can vary each year with changes in population size, and in which multiple, possibly competing, management objectives can be addressed. This report provides examples of dynamic harvest strategies and compares them with the interim, constant harvest-rate strategy. Until such time that a dynamic strategy is adopted by the EGM IWG, the annual harvest quota and its allocation among Range States is predicated on the interim strategy. Based on a January count of 38,717, the harvest quota for the 2018 hunting season is 1,610 Taiga Bean Geese (compared to 2,335 for the 2017 season). We emphasize that these quotas include both harvest during the regular season and derogation shooting. We acknowledge that the January 2018 count of Taiga Bean Geese in the Central Management Unit was likely biased low, as counts in the autumn and spring in Sweden were higher. Additionally, the size of the harvest during the fall and winter of 2017-18 is unknown, due to an inability to differentiate taiga and Tundra Bean Geese in the harvest, compilation of data too late to be used in this report, and a lack of reporting. Because of problems with both the population and harvest monitoring programs it is difficult to estimate a harvest quota for 2018 with any degree of confidence.

Report↗

USGS2017

Implementation of the first adaptive management plan for a European migratory waterbird population: The case of the Svalbard pink-footed goose Anser brachyrhynchus

An International Species Management Plan for the Svalbard population of the pink-footed goose was adopted under the Agreement on the Conservation of African-Eurasian Migratory Waterbirds in 2012, the first case of adaptive management of a migratory waterbird population in Europe. An international working group (including statutory agencies, NGO representatives and experts) agreed on objectives and actions to maintain the population in favourable conservation status, while accounting for biodiversity, economic and recreational interests. Agreements include setting a population target to reduce agricultural conflicts and avoid tundra degradation, and using hunting in some range states to maintain stable population size. As part of the adaptive management procedures, adjustment to harvest is made annually subject to population status. This has required streamlining of monitoring and assessment activities. Three years after implementation, indicators suggest the attainment of management results. Dialogue, consensus-building and engagement among stakeholders represent the major process achievements.

Ambio↗

USGS2016

Regulation of the hunting season as a tool for adaptive harvest management — First results for pink-footed geese Anser brachyrhynchus

Adjustment of hunting season length is often used to regulate harvest of waterbirds but the effects are disputed. We describe the first results of season length extension on the harvest of the pink-footed goose, which has been selected as the first test case of adaptive harvest management of waterbirds in Europe. In Denmark, the season (previously 1 September to 31 December) was extended to include January in 2014-15 with the aim to increase the harvest and, in the longer term, reduce the population size. The total harvest in Denmark increased by 52% compared to previous years, and almost 50% of the Danish harvest was taken in the January extension. In the course of the hunting season, the proportion of adults in the bag increased. In this case, the outcomes from the first extension of season suggest that season length adjustment can be an effective tool to regulate harvest, though dependent on winter weather conditions and hunters’ motivation for shooting geese.

Wildlife Biology↗

USGS2016

Adaptive harvest management for the Svalbard population of pink‐footed geese: 2016 progress summary: Technical Report from DCE – Danish Centre for Environment and Energy, No. 86

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 (1991-2015) and its implications for the harvest management strategy. 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. 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. Beginning with the 2016 hunting season, harvest quotas will be prescribed on an annual basis rather than every three years because of the potential to better meet population management objectives. Based on updated model weights, the recent observations of population size (74,800), the proportion of the population comprised of one-year-old birds (0.138), and temperature days in Svalbard (20), the optimal harvest quota for the 2016 hunting season is 25,000. The large increase in quota compared to that during first three years of AHM reflects stakeholders’ desire to reduce population size to the goal of 60,000, recognizing that population size remains relatively high and above-average production is expected in 2016 due to a warm spring.

Report↗

USGS2016

Development of an adaptive harvest management program for Taiga bean geese

This report describes recent progress in specifying the elements of an adaptive harvest program for taiga bean goose. It describes harvest levels appropriate for first rebuilding the population of the Central Management Unit and then maintaining it near the goal specified in the AEWA International Single Species Action Plan (ISSAP). This report also provides estimates of the length of time it would take under ideal conditions (no density dependence and no harvest) to rebuild depleted populations in the Western and Eastern Management Units. We emphasize that our estimates are a first approximation because detailed demographic information is lacking for taiga bean geese. Using allometric relationships, we estimated parameters of a thetalogistic matrix population model. The mean intrinsic rate of growth was estimated as r = 0.150 (90% credible interval: 0.120 – 0.182). We estimated the mean form of density dependence as   2.361 (90% credible interval: 0.473 – 11.778), suggesting the strongest density dependence occurs when the population is near its carrying capacity. Based on expert opinion, carrying capacity (i.e., population size expected in the absence of hunting) for the Central Management Unit was estimated as K  87,900 (90% credible interval: 82,000 – 94,100). The ISSAP specifies a population goal for the Central Management Unit of 60,000 – 80,000 individuals in winter; thus, we specified a preliminary objective function as one which would minimize the difference between this goal and population size. Using the concept of stochastic dominance to explicitly account for uncertainty in demography, we determined that optimal harvest rates for 5, 10, 15, and 20-year time horizons were h = 0.00, 0.02, 0.05, and 0.06, respectively. These optima represent a tradeoff between the harvest rate and the time required to achieve and maintain a population size within desired bounds. We recognize, however, that regulation of absolute harvest rather than harvest rate is more practical, but our matrix model does not permit one to calculate an exact harvest associated with a specific harvest rate. Approximate harvests for current population size in the Central Management Unit are 0, 1,200, 2,300, and 3,500 for the 5, 10, 15, and 20-year time horizons, respectively. Populations of taiga bean geese in the Western and Eastern Units would require at least 10 and 13 years, respectively, to reach their minimum goals under the most optimistic of scenarios. The presence of harvest, density dependence, or environmental variation could extend these time frames considerably. Finally, we stress that development and implementation of internationally coordinated monitoring programs will be essential to further development and implementation of an adaptive harvest management program.

Conference Paper↗

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↗

USGS2015

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↗

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

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↗

USGS2013

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↗