Abstract
Landscapes are often patchworks of private properties, where composition and configuration patterns result from cumulative effects of the actions of multiple landowners. Securing the delivery of services in such multi-ownership landscapes is challenging, because it is difficult to assure tight compliance to spatially explicit management rules at the level of individual properties, which may hinder the conservation of critical landscape features. To deal with these constraints, a multi-objective simulation-optimization procedure was developed to select non-spatial management regimes that best meet landscape-level objectives, while accounting for uncoordinated and uncertain response of individual landowners to management rules. Optimization approximates the non-dominated Pareto frontier, combining a multi-objective genetic algorithm and a simulator that forecasts trends in landscape pattern as a function of management rules implemented annually by individual landowners. The procedure was demonstrated with a case study for the optimum scheduling of fuel treatments in cork oak forest landscapes, involving six objectives related to reducing management costs (1), reducing fire risk (3), and protecting biodiversity associated with mid- and late-successional understories (2). There was a trade-off between cost, fire risk and biodiversity objectives, that could be minimized by selecting management regimes involving ca. 60% of landowners clearing the understory at short intervals (around 5 years), and the remaining managing at long intervals (ca. 75 years) or not managing. The optimal management regimes produces a mosaic landscape dominated by stands with herbaceous and low shrub understories, but also with a satisfactory representation of old understories, that was favorable in terms of both fire risk and biodiversity. The simulation-optimization procedure presented can be extended to incorporate a wide range of landscape dynamic processes, management rules and quantifiable objectives. It may thus be adapted to other socio-ecological systems, particularly where specific patterns of landscape heterogeneity are to be maintained despite imperfect management by multiple landowners.
Highlights
In his seminal paper, Hardin [1] hypothesized that a system based on a common resource that is not managed will inevitably tend to exhaustion as a consequence of every user maximizing its own benefit
Most non-dominated solutions involved three groups of landowners with different management regimes, suggesting that this strategy provided the largest potential and flexibility in fully exploring the feasible objective space. Those that best minimized the distance to the ideal solution, assuming equal-weighing of cost, fire risk and biodiversity objectives, were characterized by a group with about 40% of landowners clearing the understory at short intervals, another group with about 30% clearing with long intervals (75–80 years) and the remaining not managing (Figure 2A)
Designing management strategies to secure the services provided by landscapes made up of patchworks of private properties is challenging, due to the inherent stochasticity in landowners’ responses to management rules, which in turn result in temporal and spatial variations in landscape composition and configuration that are hard to predict
Summary
Hardin [1] hypothesized that a system based on a common resource that is not managed will inevitably tend to exhaustion as a consequence of every user maximizing its own benefit. A landscape used by multiple landowners can be viewed as such a common resource [2], where the resource units may be equated to the products and services the landscape provides [3,4] Many of these products and services, including those of direct interest to the landowners such as fire risk regulation [5,6,7] and water quality regulation [8], are dependent on whole-landscape structure [4,9]. A key challenge is to optimize rules that once implemented by landowners will contribute to successfully achieving landscape scale objectives that are relevant at the individual and societal levels [19,26]
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