The Use of Stochastic Dynamic Programming in Optimal Landscape Reconstruction for Metapopulations

Abstract

Abstract. A decision theory framework can be a powerful technique to derive optimal management decisions for endangered species. We built a spatially realistic stochastic metapopulation model for the Mount Lofty Ranges Southern Emu-wren (Stipiturus malachurus intermedius), a critically endangered Australian bird. Using discrete-time Markov chains to describe the dynamics of a metapopulation and stochastic dynamic programming (SDP) to find optimal solutions, we evaluated the following different management decisions: enlarging existing patches, linking patches via corridors, and creating a new patch. This is the first application of SDP to optimal landscape reconstruction and one of the few times that landscape reconstruction dynamics have been integrated with population dynamics. SDP is a powerful tool that has advantages over standard Monte Carlo simulation methods because it can give the exact optimal strategy for every landscape configuration (combination of patch areas and presence of corridors) and pattern of metapopulation occupancy, as well as a trajectory of strategies. It is useful when a sequence of management actions can be performed over a given time horizon, as is the case for many endangered species recovery programs, where only fixed amounts of resources are available in each time step. However, it is generally limited by computational constraints to rather small networks of patches. The model shows that optimal metapopulation management decisions depend greatly on the current state of the metapopulation, and there is no strategy that is universally the best. The extinction probability over 30 yr for the optimal state-dependent management actions is 50-80% better than no management, whereas the best fixed state-independent sets of strategies are only 30% better than no management. This highlights the advantages of using a decision theory tool to investigate conservation strategies for metapopulations. It is clear from these results that the sequence of management actions is critical, and this can only be effectively derived from stochastic dynamic programming. The model illustrates the underlying difficulty in determining simple rules of thumb for the sequence of management actions for a metapopulation. This use of a decision theory framework extends the capacity of population viability analysis (PVA) to manage threatened species.

When do conservation planning methods deliver? Quantifying the consequences of uncertainty

“…Some reserve selection methods do attempt to directly include persistence in the problem statement and objective function to attempt to optimize it directly (Haight et al, 2002;Moilanen and Cabeza, 2002;Westphal et al, 2003;Drechsler and Burgman, 2004;Nicholson et al, 2006). We believe that this is a step in the right direction; however, for computational reasons, these approaches necessarily make use of fast methods for approximating persistence and usually ignore complexities like barriers to movement such as roads and ocean currents.…”

Metapopulation modelling and area-wide pest management strategies evaluation. An application to the Pine processionary moth

“…In this respect, metapopulation models have unexplored potentiality (Hunter, 2002;Grimm et al, 2003;Gilioli et al, 2008;van Nouhuys, 2009). However, metapopulation models should be embedded in a decision-making framework to give managers the capability of ranking alternative decisions (Westphal et al, 2003). This means that the objectives of the management should be explicitly and clearly stated in terms of metapopulation model variables (Possingham et al, 2001).…”

Developing a Cell-Based Spatial Optimization Model for Land-Use Patterns Planning

“…The determination of specific habitat type ratios and their spatial configuration is required in many conservation strategies for the reconstruction of landscapes [5,22]. In many cases, the configuration of habitat structure should be considered at a finer scale, particularly in terms of both composition and functional connectivity for ecological integrity [23].…”