More is more? Forest management allocation at different spatial scales to mitigate conflicts between ecosystem services

Abstract

Context. Multi-objective management can mitigate conflicts among land-use objectives. However, the effectiveness of a multi-objective solution depends on the spatial scale at which land-use is optimized. This is because the ecological variation within the planning region influences the potential for site-specific prioritization according to the different objectives. Objectives. We optimized the allocation of forest management strategies to maximize the joint production of two conflicting objectives, timber production and carbon storage, at increasing spatial scales. We examined the impacts of the extent of the planning region on the severity of the conflict, the potential for its mitigation, and the strategies that were identified as optimal. Methods. Using forecasted data from a forest simulator, we constructed Pareto frontiers optimizing the joint provision of the objectives in production forests in Finland. Optimization was conducted within increasing hierarchical spatial scales and outcomes were compared in terms of the severity of the conflict and the solution to mitigate it. Results. The trade-offs between timber production and carbon storage appeared less severe and could be mitigated more effectively the larger the planning regions were, but the improvements became minor beyond the scale of 'large forest holding'. The results thus indicate that this scale, approximately 100 stands or 200 ha, is large enough to effectively mitigate the conflict between timber production and carbon storage. Conclusions. Management planning over relatively small forest areas (200 ha) can mitigate ecosystem service trade-offs effectively. Thus the effective use of multi-objective optimization tools may be feasible even in small-scale forestry.

Forest management optimization across spatial scales to reconcile economic and conservation objectives

“…Overall, our findings are similar to those of our earlier study [27], where we examined the effects of planning region size on the trade-off between timber harvesting and carbon storage and on the optimal forest management to reconcile them, and found the effects to be small beyond the ‘large holding’ scale. Clearly, there is variation among stands that makes it valuable to optimize management allocation across multiple stands, be it to reconcile timber harvesting and deadwood availability or timber harvesting and carbon storage, but the amount of this variation is not significantly increased beyond scales of approximately 100 stands or 200 ha (corresponding to the ‘large holding’ scale).…”

“…In an earlier study [27] we showed that the effectiveness of forest management optimization to promote the co-production of carbon storage and income from timber harvests in Finland is positively affected by the size of the planning region, but only marginally beyond relatively small areas (approximately 200 ha). In this article, we tackle the question of how the spatial scale of management optimization affects the capability to mitigate the conflict between timber harvesting and deadwood availability.…”

Enhancing Resilience of Boreal Forests Through Management Under Global Change: a Review

“…The desired variation in forest structure across the landscape can be obtained by using variable cutting patterns which can be performed with existing sophisticated machinery [100••]. Secondly, the implementation of management plans critically needs to shift from stand-or small-scale to landscapelevel management planning to better address conflicting objectives [135]. Lastly, we need to improve the framework for an efficient implementation of advanced computational methods such as multi-objective optimization to evaluate complex outcomes of different management scenarios [136].…”

Interactions between ecosystem services and land use in France: A spatial statistical analysis

“…Specifically, in the case of large-scale (single country to European scale) ES modeling, parameter estimations are performed using maps of drivers and ESs (Cord et al, 2017;Teillard et al, 2017;Accatino et al, 2019;Shi et al, 2021). With relationships (mechanistic or statistical) linking drivers to ESs, it is possible to run scenarios to explore what consequences changes in drivers have for ES provision (Seppelt et al, 2013;Kindu et al, 2018) or optimization scenarios to investigate the combinations of drivers that lead to maximization of one or more ESs (Pohjanmies et al, 2017;Accatino et al, 2019;Shi et al, 2021).…”