| Literature DB >> 28215011 |
Jesper Madsen1, James Henty Williams2, Fred A Johnson3, Ingunn M Tombre4, Sergey Dereliev5, Eckhart Kuijken6.
Abstract
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.Entities:
Keywords: Adaptive harvest management; Human–wildlife conflict; Population target; Stakeholder involvement; Structured decision-making; Tundra degradation
Mesh:
Year: 2017 PMID: 28215011 PMCID: PMC5316328 DOI: 10.1007/s13280-016-0888-0
Source DB: PubMed Journal: Ambio ISSN: 0044-7447 Impact factor: 5.129
Fig. 1Changes in annual population size and harvest of the Svalbard pink-footed goose, 1989/1990 to 2015/2016 (years on x-axis represent autumn). Annual harvest is based on reported bag records in the two countries (starting in Norway in 1992; after Madsen et al. 2016b)
Fig. 2Hierarchy of objectives for the International Species Management Plan for the pink-footed goose. Top level goal (or strategic objective); second level fundamental objectives (which are supposed to be SMART, i.e. Specific, Measurable, Achievable, Results-oriented and Time-fixed); lower levels means objectives (or alternative key actions) to reach the fundamental objectives. Red arrows show positive feedbacks between objectives
Procedures of annual adaptive harvest management of the pink-footed goose population in a nutshell
| The development of an adaptive harvest management (AHM) strategy requires the specification of four elements: (a) a set of alternative population models, which bound the uncertainty about effects of harvest and other environmental factors, (b) a set of probabilities describing the relative credibility of the alternative models, (c) a set of alternative harvest quotas from which to choose and (d) an objective function, by which alternative harvest strategies can be evaluated. An optimal management strategy prescribes a harvest quota for each and every possible set of model probabilities, and for population abundance and environmental conditions that may be observed at the time a decision is made |
| Nine models of pink-footed goose dynamics describe competing hypotheses about how reproductive and survival rates might vary over time. The models focus on whether spring temperature and density dependence influence survival and/or reproduction. Bayesian probabilities are used to express the relative ability of each model to accurately predict the changes in population size that actually occur, and they are updated each year using monitoring information. In the figure below are the time sequences of the aggregate probabilities on models that incorporate (A) density-dependent survival, (B) density-dependent reproduction and (C) days above freezing in May in Svalbard in the reproductive and survival processes |
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| The four elements of AHM (models, model probabilities, alternative quotas and objective function) are used each year to calculate an optimal harvest strategy designed to maintain the population near the goal of 60 000. The optimal harvest strategy is a large lookup table that is difficult to display graphically. Below is a simplified representation of the strategy for model probabilities in 2016, in which a series of yes–no questions are asked (yes is the left branch; no is the right branch) about the abundance of adults and young (A and Y in thousands, respectively) and the number of days above freezing in May in Svalbard (DAYS). The approximate harvest quota (in thousands, to the nearest 2.5) is given at the ends of the branches |
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Fig. 3Annual cycle in monitoring, assessment and decision-making in the adaptive harvest management of the pink-footed goose
Fig. 4Projection of pink-footed goose population size (in thousands) based on current model weights and assuming a harvest of 11 300 (average of 2011–2013) (a) and 15 000 (b). Vertical lines represent 95% confidence limits, boxes are the interquartile ranges, horizontal lines are medians, and the open circle characters represent the means. Projections of population size were based on observed, post-harvest population size in 2013, random variation in positive temperature days in Svalbard in May (as a proxy of advancement of spring) and model process error. Each time series was simulated five thousand times (after Johnson et al. 2014b)