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Data from: Quantifying risk of overharvest when implementation is uncertain

Cite this dataset

Eriksen, Lasse F.; Moa, Pål F.; Nilsen, Erlend B. (2018). Data from: Quantifying risk of overharvest when implementation is uncertain [Dataset]. Dryad.


1. Sustainable harvest management implies an ability to control harvest rates. This is challenging in systems that have limited control of resources and resource users, which is often the case in small game harvest management. The difference between management strategies and actual harvest bag size (i.e. implementation uncertainty) may be substantial, but few studies have explored this. 2. We investigated how different management strategies and ecosystem variables affected realised harvest of willow ptarmigan (Lagopus lagopus L.) among nine independently-managed, state-owned hunting areas in Central and South Norway during 2008-2015. First, we focused our empirical analysis around three response variables of interest: hunting bag (scaled by area), hunting effort (number of hunting days scaled by area) and hunter efficiency (shot birds per hunting day). Akaike’s Information Criteria (AIC) guided model selection among candidate GLMMs. Then, we used model-averaged parameter estimating from the statistical models in numerical simulations to explore risk of overharvest due to implementation uncertainty. 3. The most parsimonious model explaining hunting bag included total allowable catch (TAC) and willow ptarmigan density. Hunting effort was explained by number of permits sold and type of quota (daily vs. weekly quota). The most parsimonious model describing hunter efficiency only included the effect of willow ptarmigan density. 4. Our results show that managers have only partial control over harvest rates in this system, and that hunters were relatively more efficient and harvest rates higher at low densities. This effect was present for all management strategy scenarios, including when managers adjusted TAC according to population estimates from monitoring programs. 5. Synthesis and applications. Quantifying risk of unsustainable harvest rates under different scenarios enables managers to make informed decisions, when dealing with competing objectives of harvest opportunities and sustainability. The substantial risk of high harvest rates at low densities reported here should encourage frequent use of threshold strategies. This study is one of the first approaches for quantifying implementation uncertainty in small game harvest, and shows how estimates from empirical analyses could be used to quantify risk of overharvest.

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