ConsNet is a comprehensive software package for the design of conservation area networks (CANs). The software selects areas to be potentially placed under conservation management for the representation of biodiversity surrogates. Additionally, ConsNet optimizes spatial criteria including compactness, connectivity, replication, and alignment, as well as socio‐economic criteria as specified by users. ConsNet uses an advanced tabu search engine to identify efficient alternatives quickly, offering capabilities beyond existing planning software. The ability to perform ongoing interactive analysis with multi‐criteria objectives makes ConsNet an ideal decision support tool for large scale planning exercises.

This paper presents a new approach to the solution of the well-studied conservation area network design problem (CANP), which is closely related to the classical set cover problem (SCP). The goal is to find the smallest amount of land that (when placed under conservation) will contain and protect a specified representation level of biodiversity resources. A new tabu search methodology is applied to an extension of the "basic" CANP which explicitly considers additional spatial requirements for improved conservation planning. The underlying search engine, modular adaptive self-learning tabu search (MASTS), incorporates state-of-the-art techniques including adaptive tabu search, dynamic neighborhood selection, and rule-based objectives. The ability to utilize intransitive orderings within a rule-based objective gives the search flexibility, improving solution quality while saving computation. This paper demonstrates how rule-based objectives can be used to design near optimal conservation area networks in which the individual conservation areas are well connected. The results represent a considerable improvement over classical techniques that do not consider spatial features. This paper provides an initial description of ConsNet, a comprehensive software package for systematic conservation planning.

A framework was developed for the construction of an objectives hierarchy for multicriteria decisions in land use planning. The process began through identification of fundamental objectives; these were iteratively decomposed into a hierarchy of subobjectives until a level was reached at which subobjectives had measurable attributes. Values were derived for attributes through a variety of methods and weights assigned to objectives through preference elicitation.The framework assumed that the objectives could be incorporated into a linear value function; this required attributes to satisfy preference and difference independence conditions. Strategies were developed to address typical features that distinguish land use decisions from many other multicriteria decisions. The methodology was illustrated with a case study of land use planning in a forestry concession in the Merauke region of Papua Province, Indonesia. The problem involved severe hard constraints; the analysis showed how these can be accommodated within the framework. Results integrated interests and preferences of a diverse set of stakeholders (resident peoples, developers, and conservation professionals) and were intended for implementation. This methodology is extendible to other land use problems.

BackgroundMéxico is one of the world's centers of species diversity (richness) for Opuntia cacti. Yet, in spite of their economic and ecological importance, Opuntia species remain poorly studied and protected in México. Many of the species are sparsely but widely distributed across the landscape and are subject to a variety of human uses, so devising implementable conservation plans for them presents formidable difficulties. Multi–criteria analysis can be used to design a spatially coherent conservation area network while permitting sustainable human usage.Methods and FindingsSpecies distribution models were created for 60 Opuntia species using MaxEnt. Targets of representation within conservation area networks were assigned at 100% for the geographically rarest species and 10% for the most common ones. Three different conservation plans were developed to represent the species within these networks using total area, shape, and connectivity as relevant criteria. Multi–criteria analysis and a metaheuristic adaptive tabu search algorithm were used to search for optimal solutions. The plans were built on the existing protected areas of México and prioritized additional areas for management for the persistence of Opuntia species. All plans required around one–third of México's total area to be prioritized for attention for Opuntia conservation, underscoring the implausibility of Opuntia conservation through traditional land reservation. Tabu search turned out to be both computationally tractable and easily implementable for search problems of this kind.Conclusions Opuntia conservation in México require the management of large areas of land for multiple uses. The multi-criteria analyses identified priority areas and organized them in large contiguous blocks that can be effectively managed. A high level of connectivity was established among the prioritized areas resulting in the enhancement of possible modes of plant dispersal as well as only a small number of blocks that would be recommended for conservation management.