ConsNet: new software for the selection of conservation area networks with spatial and multi‐criteria analyses

Abstract

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.

Complementarity and the selection of nature reserves: algorithms and the origins of conservation planning, 1980–1995

“…21 Even though the term "metaheuristic" was introduced as early as 1986 in the context of tabu search (Glover 1986), and simulated annealing is a metaheuristic algorithm, it was not used in this context until 2006 . 22 For recent extensions, see Ciarleglio (2008); Ciarleglio et al (2009). 23 For a historically sophisticated scientific review of this problem, which is only slightly more optimistic about exact algorithms, see Williams et al (2005).…”

“…The metaheuristic algorithms of the 1990s used shape as a criterion (Sect. 6); more recent such algorithms have introduced connectivity (and a variety of other spatial criteria) (Ciarleglio et al 2009). Was this a return to the geometric rules produced by Diamond and others in the 1970s (Sect.…”

Area prioritization and performance evaluation of the conservation area network for the Moroccan herpetofauna: a preliminary assessment

“…As the current objective is to determine the smallest set of cells such that each species meets its representation target, ConsNet tries to minimize the area of selected land that is sufficient to contain and protect a specified representation level of biodiversity resources whilst simultaneously optimizing a variety of costs and spatial criteria such as size, compactness, replication, connectivity and alignment (for a more detailed explanation of ConsNet see Ciarleglio et al 2008Ciarleglio et al , 2009Ciarleglio et al , 2010. For performance evaluation of the existing CAN, ConsNet runs were initialized with the RF4 adjacency algorithm (Ciarleglio et al 2008(Ciarleglio et al , 2009(Ciarleglio et al , 2010, meaning that those cells that contain the rarest species which have not met the representation target are chosen first, and in the event of a tie, cells are chosen based on complementarity. Rarity-based initialization was chosen because it is known to result in more effective area selection (Pawar et al, 2007) than species richness-based initialization .…”

Planejamento sistemático das unidades de conservação no Estado do Tocantins

“…It supports objectives based on rules and a dynamic neighbourhood selection that controls possible movements during the search for solutions, and intelligently arranges the structure of the spatial problems (Ciarleglio et al, 2009). Using the surrogates probability distribution for each cell in a geographic grid, ConsNet makes a binary decision (to select or not a cell to be put under a conservation plan) and orders each cell hierarchically on the basis of its biodiversity value.…”

“…ConsNet software (Ciarleglio et al, 2008(Ciarleglio et al, , 2009) was used to design the CAN scenarios. The probability of occurrence of each species in each cell above previously defined thresholds was obtained from niche model outputs in MAXENT, and total representation for each species was the sum of all the probabilities across the planning region.…”