In biology, coevolution occurs when two or more species reciprocally affect each other's evolution through the process of natural selection. The term sometimes is used for two traits in the same species affecting each other's evolution, as well as gene-culture coevolution.
Charles Darwin mentioned evolutionary interactions between flowering plants and insects in On the Origin of Species (1859). Although he did not use the word coevolution, he suggested how plants and insects could evolve through reciprocal evolutionary changes. Naturalists in the late 1800s studied other examples of how interactions among species could result in reciprocal evolutionary change. Beginning in the 1940s, plant pathologists developed breeding programs that were examples of human-induced coevolution. Development of new crop plant varieties that were resistant to some diseases favored rapid evolution in pathogen populations to overcome those plant defenses. That, in turn, required the development of yet new resistant crop plant varieties, producing an ongoing cycle of reciprocal evolution in crop plants and diseases that continues to this day.
Coevolution as a major topic for study in nature expanded rapidly from the 1960s, when Daniel H. Janzen showed coevolution between acacias and ants (see below) and Paul R. Ehrlich and Peter H. Raven suggested how coevolution between plants and butterflies may have contributed to the diversification of species in both groups. The theoretical underpinnings of coevolution are now well-developed (e.g., the geographic mosaic theory of coevolution), and demonstrate that coevolution can play an important role in driving major evolutionary transitions such as the evolution of sexual reproduction or shifts in ploidy. More recently, it has also been demonstrated that coevolution can influence the structure and function of ecological communities, the evolution of groups of mutualists such as plants and their pollinators, and the dynamics of infectious disease.
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Explores predicting protein structure from sequence data and inferring interaction partners through Direct Coupling Analysis and the Iterative Pairing Algorithm.
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