Opuntia in México: Identifying Priority Areas for Conserving Biodiversity in a Multi-Use Landscape

BACKGROUND: México 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 FINDINGS: Species 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

Otimização multiobjetivo aplicada ao planejamento sistemático de conservação para espécies de plantas do cerrado brasileiro

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2015.Nesta tese, propôs-se a aplicação de conceitos de Otimização Multiobjetivo (MOO) e de Computação Bioinspirada a problemas de Planejamento Sistemático de Conservação (SCP). Foram estudados três problemas específicos. No primeiro, buscou-se o menor conjunto de populações locais a serem conservadas para representar a diversidade genética de uma espécie vegetal do Cerrado. O método proposto foi capaz de identificar uma maior diversidade de soluções com a quantidade mínima de populações ao mesmo tempo em que refinou os resultados, indicando as combinações com maior diversidade intraespecífica e maior possibilidade de persistência ao longo do tempo. No segundo problema, buscou-se: (i) selecionar um conjunto de amostras geneticamente complementares a uma coleção de germoplasma de plantas já existente; (ii) definir uma core collection para uma coleção de germoplasma. Com a utilização de MOO foi possível identificar os indivíduos exatos que deveriam ser selecionados para complementar o germoplasma. Ademais, definiu-se um protocolo para tratar um grande volume de amostras a fim de estabelecer uma core collection. A abordagem proposta pode ser usada para construir core collections com máxima riqueza alélica, bem como ser estendido a casos de conservação in situ. Por fim, no terceiro problema, SCP foi associado à estimativa da ocorrência de espécies projetada para o futuro com base em simulações climáticas objetivando definir prioridades de conservação. O método proposto identificou locais com: (i) alta prioridade para conservação; (ii) risco significativo de investimento; e, (iii) que poderiam tornar-se atrativos no futuro. Foi proposto, também, um algoritmo multiobjetivo baseado em Sistemas Imunológicos Artificiais, o Multi-Objective Artificial Immune System (MAIS). MOO permitiu trabalhar com instâncias de problemas com mais de duas dimensões, possibilitando maior confiabilidade na indicação do portfolio de soluções, aumentando, assim, o poder de decisão do método computacional e a qualidade da informação fornecida aos tomadores de decisão. O presente trabalho é pioneiro no país ao resolver problemas de SCP usando técnicas avançadas de otimização, colaborando para a implantação da área de Ecoinformática no Brasil.This thesis proposes a more sophisticated, yet general, solution to the systematic conservation planning problem (SCP) based on multi-objective optimization (MOO) and bio-inspired computing. We worked with three problems using data from plants of the Brazilian Cerrado biome. In the first problem, we looked for the smallest set of local populations of a plant species aiming its conservation. The method was able to find a larger portfolio of solutions and to refine the results as well, indicating solutions with more intra-specific diversity and higher probability of persistence throughout time. In the second problem, we aimed: (i) to select a set of individuals genetically complementary to an existing plant germplasm collection; and, (ii) to define a core collection for a germplasm collection. 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. 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. Finally, in the third problem, we applied MOO to SCP associated to climate forecasting, in a dynamic spatial prioritization analysis for biodiversity conservation. Our method was able to identify sites: (i) of high priority for conservation; (ii) with significant risk of investment; and, (iii) that may become attractive in the future. We also proposed a constrained multi-objective artificial immune system algorithm (MAIS). The MOO approach to SCP increases reliability by including additional objectives, which while increasing the complexity, significantly augments the amount and quality of information used to provide users with an improved decision support system. This thesis is pioneer in solving the SCP problem using advanced optimization techniques contributing to the insertion and consolidation of the new area of ecoinformatics in Brazil