Germplasm collections play a significant role among strategies for conservation of diversity. It is common to select a core collection to represent the genetic diversity of a germplasm collection, in order to minimize the cost of conservation, while ensuring the maximization of genetic variation. We aimed to solve two main problems: (1) to select a set of individuals, from an in situ data set, that is genetically complementary to an existing germplasm collection, and (2) to define a core collection for a germplasm collection. We proposed a new multi-objective optimization (MOO) approach based on principles of systematic conservation planning (SCP) incorporating heterozygosity information; therefore, optimization takes genotypic diversity and variability patterns into account as well. As a case study, we used Dipteryx alata microsatellite loci information from two sources, an ex situ germplasm collection located at the Agronomy School of the Federal University of Goiás (UFG-AS), and an in situ data set composed of 642 sampled individual trees. We were able to identify within a population of several individuals, the exact accessions/samples that should be chosen in order to preserve the species diversity. We found that material from nine in situ individual trees are enough to complement the UFG-AS germplasm collection as it is, and that it is possible to define a core collection of 20 individual trees representing all studied genetic diversity. Moreover, we defined a method (a protocol) to deal with large amounts of accessions in the context of MOO. The proposed approach can be used to help constructing collections with maximal allelic richness and can also be extended to the in situ conservation. As far as we know, this is the first time that principles of SCP and the MOO approach are applied to the problem of complementing a germplasm collection and of finding a core collection for a germplasm collection.
ABSTRACT. Biodiversity crises have led scientists to develop strategies for achieving conservation goals. The underlying principle of these strategies lies in systematic conservation planning (SCP), in which there are at least 2 conflicting objectives, making it a good candidate for multi-objective optimization. Although SCP is typically applied at the species level (or hierarchically higher), it can be used at lower hierarchical levels, such as using alleles as basic units for analysis, for conservation genetics. Here, we propose a method of SCP using a multiobjective approach. We used non-dominated sorting genetic algorithm II in order to identify the smallest set of local populations of Dipteryx alata (baru) (a Brazilian Cerrado species) for conservation, representing the known genetic diversity and using allele frequency information associated with heterozygosity and Hardy-Weinberg equilibrium. We worked in 3 variations for the problem. First, we reproduced a previous experiment, but using a multi-objective approach. We found that the smallest set of populations needed to represent all alleles under study was 7, corroborating the results of the previous study, but with more distinct solutions. In the 2nd and 3rd variations, we performed simultaneous optimization of 4 and 5 objectives, respectively. We found similar but refined results for 7 populations, and a larger portfolio considering intraspecific diversity and persistence with populations ranging from 8-22. This is the first study to apply multi-objective algorithms to an SCP problem using alleles at the population level as basic units for analysis.
Abstract. Biodiversity conservation has been since long an academic community concern, leading scientists to propose strategies to effectively meet conservation goals. In particular, Systematic Conservation Planning (SCP) aims to determine the most cost effective way of investing in conservation actions. SCP can be formalized by the Set-Covering Problem, which is NP-hard. SCP is inherently multi-objective, although it has been usually treated with a monobjective and static approach. Here, we propose a multi-objective solution for SCP, increasing its flexibility and complexity, and, at the same time, augmenting the quality of provided information, which reinforces decision-making. We used ensemble forecasting, considering future climate simulations to estimate species occurrence projected to 2080. Our method identifies sites: 1) of high priority for conservation; 2) with significant risk of investment; and, 3) that may become attractive in the future. To the best of our knowledge, this application to a real-world problem in ecology is the first attempt to apply multi-objective optimization to SCP associated to climate forecasting, in a dynamic spatial prioritization analysis for biodiversity conservation.
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