Search

As declines in biodiversity accelerate, there is an urgent imperative to ensure that every dollar spent on conservation counts toward species protection. Systematic conservation planning is a widely used approach to achieve this, but there is growing concern that it must better integrate the human social dimensions of conservation to be effective. Yet, fundamental insights about when social data are most critical to inform conservation planning decisions are lacking. To address this problem, we derived novel principles to guide strategic investment in social network information for systematic conservation planning. We considered the common conservation problem of identifying which social actors, in a social network, to engage with to incentivize conservation behavior that maximizes the number of species protected. We used simulations of social networks and species distributed across network nodes to identify the optimal state‐dependent strategies and the value of social network information. We did this for a range of motif network structures and species distributions and applied the approach to a small‐scale fishery in Kenya. The value of social network information depended strongly on both the distribution of species and social network structure. When species distributions were highly nested (i.e., when species‐poor sites are subsets of species‐rich sites), the value of social network information was almost always low. This suggests that information on how species are distributed across a network is critical for determining whether to invest in collecting social network data. In contrast, the value of social network information was greatest when social networks were highly centralized. Results for the small‐scale fishery were consistent with the simulations. Our results suggest that strategic collection of social network data should be prioritized when species distributions are un‐nested and when social networks are likely to be centralized. Article impact statement: The value of collecting social network information for conservation planning depends on both species distributions and social network structure. [ABSTRACT FROM AUTHOR]

Land-acquisition strategies employed by conservation organizations vary in their flexibility. Conservation-planning theory largely fails to reflect this by presenting models that are either extremely inflexible-parcel acquisitions are irreversible and budgets are fixed-or extremely flexible-previously acquired parcels can readily be sold. This latter approach, the selling of protected areas, is infeasible or problematic in many situations. We considered the value to conservation organizations of increasing the flexibility of their land-acquisition strategies through their approach to financing deals. Specifically, we modeled 2 acquisition-financing methods commonly used by conservation organizations: borrowing and budget carry-over. Using simulated data, we compared results from these models with those from an inflexible fixed-budget model and an extremely flexible selling model in which previous acquisitions could be sold to fund new acquisitions. We then examined 3 case studies of how conservation organizations use borrowing and budget carry-over in practice. Model comparisons showed that borrowing and budget carry-over always returned considerably higher rewards than the fixed-budget model. How they performed relative to the selling model depended on the relative conservation value of past acquisitions. Both the models and case studies showed that incorporating flexibility through borrowing or budget carry-over gives conservation organizations the ability to purchase parcels of higher conservation value than when budgets are fixed without the problems associated with the selling of protected areas. [ABSTRACT FROM AUTHOR]

Threatened species often exist in a small number of isolated subpopulations. Given limitations on conservation spending, managers must choose from strategies that range from managing just one subpopulation and risking all other subpopulations to managing all subpopulations equally and poorly, thereby risking the loss of all subpopulations. We took an economic approach to this problem in an effort to discover a simple rule of thumb for optimally allocating conservation effort among subpopulations. This rule was derived by maximizing the expected number of extant subpopulations remaining given n subpopulations are actually managed. We also derived a spatiotemporally optimized strategy through stochastic dynamic programming. The rule of thumb suggested that more subpopulations should be managed if the budget increases or if the cost of reducing local extinction probabilities decreases. The rule performed well against the exact optimal strategy that was the result of the stochastic dynamic program and much better than other simple strategies (e.g., always manage one extant subpopulation or half of the remaining subpopulation). We applied our approach to the allocation of funds in 2 contrasting case studies: reduction of poaching of Sumatran tigers (Panthera tigris sumatrae ) and habitat acquisition for San Joaquin kit foxes (Vulpes macrotis mutica ). For our estimated annual budget for Sumatran tiger management, the mean time to extinction was about 32 years. For our estimated annual management budget for kit foxes in the San Joaquin Valley, the mean time to extinction was approximately 24 years. Our framework allows managers to deal with the important question of how to allocate scarce conservation resources among subpopulations of any threatened species. [ABSTRACT FROM AUTHOR]