Geology topics

Mikko Alhainen

Publications and source records attributed to Mikko Alhainen.

5 recordsLinked to original sources

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↗

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

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↗

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↗