Revisiting the minimum set cover, the maximal coverage problems and a maximum benefit area selection problem to make climate‐change‐concerned conservation plans effective

Abstract

1. Informed decisions for the selection of protected areas (PAs) are grounded in two general problems in Operations Research: the minimum set covering problem (minCost), where a set of ecological constraints are established as conservation targets and the minimum cost PAs are found, and the maximal coverage problem (maxCoverage) where the constraint is uniquely economic (i.e. a fixed budget) and the goal is to maximize the number of species having conservation targets adequately covered. 2. We adjust minCost and maxCoverage to accommodate the dynamic effects of climate change on species' ranges. The selection of sites is replaced by the selection of time-ordered sequences of sites (climate change corridors), and an estimate of the persistence of each species in corridors is calculated according to the expected suitability of each site in the respective time period and the capacity of species to disperse between consecutive sites along corridors. In these problems, conservation targets are expressed as desired (and attainable) species persistence levels. We also introduce a novel problem (minShortfall) that combines minCost and maxCoverage. Unlike these two problems, minShortfall allows persistence targets to be missed and minimizes the sum of those gaps (i.e. target shortfalls), subject to a limited budget. 3. We illustrate the three problems with a case study using climatic suitability estimates for 10 mammal species in the Iberian Peninsula under a climate change scenario until 2080. We compare solutions of the three problems with respect to species persistence and PA costs, under distinct settings of persistence targets, number of target-fulfilled species and budgets. The solutions from different problems differed with regard to the areas to prioritize, their timings and the species whose persistence targets were fulfilled. This analysis also allowed identifying groups of species sharing corridors in optimal solutions, thus allowing important financial savings in site protection. 4. We suggest that enhancing species persistence is an adequate approach to cope with habitat shifts due to climate change. We trust the three problems discussed can provide complementary and valuable support for planners to anticipate decisions in order that the negative effects of climate change on species' persistence are minimized.

Protected areas in Central Mexico - are they fit in promoting species persistence under climate and land use changes?

“…At this level, more specific analyses on habitat availability and socio-environmental factors are required, such that the areas that offer the best adaptive potential for species to be fixed, or move along, are protected and/or restored (Shoo et al, 2013). For example, Alagador et al (2014) and Alagador and Cerdeira (2020) have introduced a set of models to obtain the trajectories that species are more likely to follow, given the directionality of drivers of change at multiple scales. These trajectories define full conservation units where species are able to persist through either local adaptation or adaptive movements along suitable areas, and therefore may give rise to core zones of PAs or transitional areas to protect in a less formal and permanent manner.…”

Spatially explicit models for decision‐making in animal conservation and restoration

“…A range of optimisation software is available (Ball et al 2009, Moilanen et al 2011) that has traditionally been applied to static biodiversity information, such as optimising the representation or number of species or sites. Alagador and Cerdeira (2020) showed how existing prioritisation software such as ‘Marxan' (Ball et al 2009) and ‘Zonation' (Moilanen et al 2011) could be reformulated to optimise persistence goals under transient climate change dynamics, yet at the expense of high computational load. New prioritisation methods are currently being developed to make use of machine learning and artificial intelligence (Chadès et al 2017).…”

Coding for Life: Designing a Platform for Projecting and Protecting Global Biodiversity

“…socioeconomic benefits (Alagador and Cerdeira 2020). For instance, models might use real estate values to guide reserve design during climate change, thus minimizing both financial and biodiversity losses.…”