Journal articles on the topic 'Coevolution'

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1

Hibbitt, Cate. "Coevolution." American Biology Teacher 78, no. 8 (October 1, 2016): 689. http://dx.doi.org/10.1525/abt.2016.78.8.689.

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2

Yasukawa, Ken. "Coevolution." Ethology and Sociobiology 6, no. 4 (January 1985): 265–66. http://dx.doi.org/10.1016/0162-3095(85)90020-2.

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3

Lewis, R. E. "Coevolution." Bulletin of the Entomological Society of America 33, no. 3 (September 1, 1987): 195. http://dx.doi.org/10.1093/besa/33.3.195.

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4

FUTUYMA, D. J., and J. KIM. "Phylogeny and Coevolution: Coevolution and Systematics." Science 237, no. 4813 (July 24, 1987): 441–42. http://dx.doi.org/10.1126/science.237.4813.441.

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5

FUTUYMA, D. J. "Coevolution: Cautious Views: Chemical Mediation of Coevolution." Science 245, no. 4921 (September 1, 1989): 991–92. http://dx.doi.org/10.1126/science.245.4921.991-a.

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6

Lamm, Ehud, and Ohad Kammar. "Inferring Coevolution." Philosophy of Science 81, no. 4 (October 2014): 592–611. http://dx.doi.org/10.1086/678045.

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7

Ash, Caroline. "Fermenting coevolution." Science 369, no. 6505 (August 13, 2020): 784.1–785. http://dx.doi.org/10.1126/science.369.6505.784-a.

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8

Berenbaum, M. "Coevolution Reconsidered." Science 267, no. 5199 (February 10, 1995): 910–11. http://dx.doi.org/10.1126/science.267.5199.910.

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9

Stower, Hannah. "Coevolution revealed." Nature Reviews Genetics 13, no. 11 (October 16, 2012): 758. http://dx.doi.org/10.1038/nrg3365.

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10

Stoy, Kayla S., Joselyne Chavez, Valeria De Las Casas, Venkat Talla, Aileen Berasategui, Levi T. Morran, and Nicole M. Gerardo. "Evaluating coevolution in a horizontally transmitted mutualism." Evolution 77, no. 1 (December 8, 2022): 166–85. http://dx.doi.org/10.1093/evolut/qpac009.

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Abstract Many interspecific interactions are shaped by coevolution. Transmission mode is thought to influence opportunities for coevolution within symbiotic interactions. Vertical transmission maintains partner fidelity, increasing opportunities for coevolution, but horizontal transmission may disrupt partner fidelity, potentially reducing opportunities for coevolution. Despite these predictions, the role of coevolution in the maintenance of horizontally transmitted symbioses is unclear. Leveraging a tractable insect–bacteria symbiosis, we tested for signatures of pairwise coevolution by assessing patterns of host–symbiont specialization. If pairwise coevolution defines the interaction, we expected to observe evidence of reciprocal specialization between hosts and their local symbionts. We found no evidence for local adaptation between sympatric lineages of Anasa tristis squash bugs and Caballeronia spp. symbionts across their native geographic range. We also found no evidence for specialization between three co-localized Anasa host species and their native Caballeronia symbionts. Our results demonstrate generalist dynamics underlie the interaction between Anasa insect hosts and their Caballeronia symbionts. We predict that selection from multiple host species may favor generalist symbiont traits through diffuse coevolution. Alternatively, selection for generalist traits may be a consequence of selection by hosts for fixed cooperative symbiont traits without coevolution.
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11

Yamamichi, Masato, and Stephen P. Ellner. "Antagonistic coevolution between quantitative and Mendelian traits." Proceedings of the Royal Society B: Biological Sciences 283, no. 1827 (March 30, 2016): 20152926. http://dx.doi.org/10.1098/rspb.2015.2926.

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Coevolution is relentlessly creating and maintaining biodiversity and therefore has been a central topic in evolutionary biology. Previous theoretical studies have mostly considered coevolution between genetically symmetric traits (i.e. coevolution between two continuous quantitative traits or two discrete Mendelian traits). However, recent empirical evidence indicates that coevolution can occur between genetically asymmetric traits (e.g. between quantitative and Mendelian traits). We examine consequences of antagonistic coevolution mediated by a quantitative predator trait and a Mendelian prey trait, such that predation is more intense with decreased phenotypic distance between their traits (phenotype matching). This antagonistic coevolution produces a complex pattern of bifurcations with bistability (initial state dependence) in a two-dimensional model for trait coevolution. Furthermore, with eco-evolutionary dynamics (so that the trait evolution affects predator–prey population dynamics), we find that coevolution can cause rich dynamics including anti-phase cycles, in-phase cycles, chaotic dynamics and deterministic predator extinction. Predator extinction is more likely to occur when the prey trait exhibits complete dominance rather than semidominance and when the predator trait evolves very rapidly. Our study illustrates how recognizing the genetic architectures of interacting ecological traits can be essential for understanding the population and evolutionary dynamics of coevolving species.
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12

Desjardins, Eric. "On the Meaning of “Coevolution” in Social-Ecological Studies." Philosophical Topics 47, no. 1 (2019): 45–64. http://dx.doi.org/10.5840/philtopics20194713.

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Researchers studying linked Social-Ecological Systems (SESs) often use the notion of coevolution in describing the relation between humans and the rest of nature. However, most descriptions of the concept of socio-ecological coevolution remain elusive and poorly articulated. The objective of the following paper is to further specify and enrich the meaning of “coevolution” in social-ecological studies. After a critical analysis of two accounts of coevolution in ecological economics, the paper uses the frameworks of Niche Construction Theory and the Geographic Mosaic Theory to define social-ecological coevolution as the reciprocal adaptation of human-social and ecological ensembles through human and ecological niche construction activities. In sum, this conceptual analysis suggests that an ecologization of Darwinian coevolution can bring clarity to profound functional integration that takes place between humans and ecological systems, and at the same time opens fruitful avenues for social-ecological research.
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13

Vignieri, Sacha. "Coevolution in India." Science 371, no. 6527 (January 21, 2021): 359.5–360. http://dx.doi.org/10.1126/science.371.6527.359-e.

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14

Rausher, Mark D. "Is Coevolution Dead?" Ecology 69, no. 4 (August 1988): 898–901. http://dx.doi.org/10.2307/1941240.

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15

Tosta, Carlos Eduardo. "Infectrons and coevolution." Revista da Sociedade Brasileira de Medicina Tropical 34, no. 1 (February 2001): 1–3. http://dx.doi.org/10.1590/s0037-86822001000100001.

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16

Brooks, Daniel R., and Susan M. Bandoni. "Coevolution and Relicts." Systematic Zoology 37, no. 1 (March 1988): 19. http://dx.doi.org/10.2307/2413186.

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17

Jeffrey, C., A. R. Stone, and D. L. Hawksworth. "Coevolution and Systematics." Kew Bulletin 43, no. 2 (1988): 366. http://dx.doi.org/10.2307/4113753.

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18

Hassall, M., and J. N. Thompson. "Interaction and Coevolution." Journal of Ecology 73, no. 3 (November 1985): 1083. http://dx.doi.org/10.2307/2260187.

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19

Apaloo, Joseph. "Ecological Species Coevolution." Journal of Biological Systems 05, no. 01 (March 1997): 17–34. http://dx.doi.org/10.1142/s0218339097000035.

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The concepts of evolutionary stable strategies (ESS) and neighborhood invader strategies (NIS) are used to examine the dynamics of coevolving species. When an ESS as well as its near neighbors are ecologically stable and phenotypic space is unconstrained, the strong NIS concept shows that each member of the ESS coalition can always be repelled by some coalition strategies that are sufficiently close to the ESS. Thus an ESS for coevolving species may not be attained by selection unless phenotypic constraints are imposed.
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20

AHMADJIAN, VERNON. "Coevolution in Lichens." Annals of the New York Academy of Sciences 503, no. 1 Endocytobiolo (July 1987): 307–15. http://dx.doi.org/10.1111/j.1749-6632.1987.tb40617.x.

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21

Ash, C. "Human-microbiota coevolution." Science 353, no. 6297 (July 21, 2016): 359–61. http://dx.doi.org/10.1126/science.353.6297.359-n.

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22

Thompson, J. N. "Coevolution and Maladaptation." Integrative and Comparative Biology 42, no. 2 (April 1, 2002): 381–87. http://dx.doi.org/10.1093/icb/42.2.381.

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23

Boucot, Arthur J. "Coevolution and Mutualism." Paleontological Society Special Publications 8 (1996): 42. http://dx.doi.org/10.1017/s2475262200000447.

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24

May, R. M., and R. M. Anderson. "Parasite—host coevolution." Parasitology 100, S1 (June 1990): S89—S101. http://dx.doi.org/10.1017/s0031182000073042.

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In this paper we wish to develop three themes, each having to do with evolutionary aspects of associations between hosts and parasites (with parasite defined broadly, to include viruses, bacteria and protozoans, along with the more conventionally defined helminth and arthropod parasites). The three themes are: the evolution of virulence; the population dynamics and population genetics of host–parasite associations; and invasions by, or ‘emergence’ of, new parasites.
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25

VRIJENHOEK, ROBERT C. "Host-Parasite Coevolution." Science 232, no. 4746 (April 4, 1986): 112.1–112. http://dx.doi.org/10.1126/science.232.4746.112.

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26

Hill, Geoffrey E. "Mitonuclear Compensatory Coevolution." Trends in Genetics 36, no. 6 (June 2020): 403–14. http://dx.doi.org/10.1016/j.tig.2020.03.002.

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27

Thompson, John N. "Concepts of coevolution." Trends in Ecology & Evolution 4, no. 6 (June 1989): 179–83. http://dx.doi.org/10.1016/0169-5347(89)90125-0.

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28

Toft, Catherine A., and Andrew J. Karter. "Parasite-host coevolution." Trends in Ecology & Evolution 5, no. 10 (October 1990): 326–29. http://dx.doi.org/10.1016/0169-5347(90)90179-h.

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29

O'Connor, Martin. "Complexity and coevolution." Futures 26, no. 6 (July 1994): 610–15. http://dx.doi.org/10.1016/0016-3287(94)90032-9.

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30

Golden, Michael, Benjamin Murrell, Darren Martin, Oliver G. Pybus, and Jotun Hein. "Evolutionary Analyses of Base-Pairing Interactions in DNA and RNA Secondary Structures." Molecular Biology and Evolution 37, no. 2 (October 30, 2019): 576–92. http://dx.doi.org/10.1093/molbev/msz243.

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Abstract Pairs of nucleotides within functional nucleic acid secondary structures often display evidence of coevolution that is consistent with the maintenance of base-pairing. Here, we introduce a sequence evolution model, MESSI (Modeling the Evolution of Secondary Structure Interactions), that infers coevolution associated with base-paired sites in DNA or RNA sequence alignments. MESSI can estimate coevolution while accounting for an unknown secondary structure. MESSI can also use graphics processing unit parallelism to increase computational speed. We used MESSI to infer coevolution associated with GC, AU (AT in DNA), GU (GT in DNA) pairs in noncoding RNA alignments, and in single-stranded RNA and DNA virus alignments. Estimates of GU pair coevolution were found to be higher at base-paired sites in single-stranded RNA viruses and noncoding RNAs than estimates of GT pair coevolution in single-stranded DNA viruses. A potential biophysical explanation is that GT pairs do not stabilize DNA secondary structures to the same extent that GU pairs do in RNA. Additionally, MESSI estimates the degrees of coevolution at individual base-paired sites in an alignment. These estimates were computed for a SHAPE-MaP-determined HIV-1 NL4-3 RNA secondary structure. We found that estimates of coevolution were more strongly correlated with experimentally determined SHAPE-MaP pairing scores than three nonevolutionary measures of base-pairing covariation. To assist researchers in prioritizing substructures with potential functionality, MESSI automatically ranks substructures by degrees of coevolution at base-paired sites within them. Such a ranking was created for an HIV-1 subtype B alignment, revealing an excess of top-ranking substructures that have been previously identified as having structure-related functional importance, among several uncharacterized top-ranking substructures.
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31

Steglich, Christian, Tom A. B. Snijders, and Patrick West. "Applying SIENA." Methodology 2, no. 1 (January 2006): 48–56. http://dx.doi.org/10.1027/1614-2241.2.1.48.

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We give a nontechnical introduction into recently developed methods for analyzing the coevolution of social networks and behavior(s) of the network actors. This coevolution is crucial for a variety of research topics that currently receive a lot of attention, such as the role of peer groups in adolescent development. A family of dynamic actor-driven models for the coevolution process is sketched, and it is shown how to use the SIENA software for estimating these models. We illustrate the method by analyzing the coevolution of friendship networks, taste in music, and alcohol consumption of teenagers.
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32

Pedraza, Fernando, and Jordi Bascompte. "The joint role of coevolutionary selection and network structure in shaping trait matching in mutualisms." Proceedings of the Royal Society B: Biological Sciences 288, no. 1957 (August 18, 2021): 20211291. http://dx.doi.org/10.1098/rspb.2021.1291.

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Coevolution can sculpt remarkable trait similarity between mutualistic partners. Yet, it remains unclear which network topologies and selection regimes enhance trait matching. To address this, we simulate coevolution in topologically distinct networks under a gradient of mutualistic selection strength. We describe three main insights. First, trait matching is jointly influenced by the strength of mutualistic selection and the structural properties of the network where coevolution is unfolding. Second, the strength of mutualistic selection determines the network descriptors better correlated with higher trait matching. While network modularity enhances trait matching when coevolution is weak, network connectance does so when coevolution is strong. Third, the structural properties of networks outrank those of modules or species in determining the degree of trait matching. Our findings suggest networks can both enhance or constrain trait matching, depending on the strength of mutualistic selection.
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33

Gintis, Herbert. "Gene–culture coevolution and the nature of human sociality." Philosophical Transactions of the Royal Society B: Biological Sciences 366, no. 1566 (March 27, 2011): 878–88. http://dx.doi.org/10.1098/rstb.2010.0310.

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Human characteristics are the product of gene–culture coevolution, which is an evolutionary dynamic involving the interaction of genes and culture over long time periods. Gene–culture coevolution is a special case of niche construction. Gene–culture coevolution is responsible for human other-regarding preferences, a taste for fairness, the capacity to empathize and salience of morality and character virtues.
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34

Sisterson, Mark S., and Anne L. Averill. "Coevolution across landscapes: a spatially explicit model of parasitoid-host coevolution." Evolutionary Ecology 18, no. 1 (January 2004): 29–49. http://dx.doi.org/10.1023/b:evec.0000017692.23250.d1.

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35

Li, Guangdi, Kristof Theys, Jens Verheyen, Andrea-Clemencia Pineda-Peña, Ricardo Khouri, Supinya Piampongsant, Mónica Eusébio, Jan Ramon, and Anne-Mieke Vandamme. "A new ensemble coevolution system for detecting HIV-1 protein coevolution." Biology Direct 10, no. 1 (2015): 1. http://dx.doi.org/10.1186/s13062-014-0031-8.

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36

Dennehy, John J. "What Can Phages Tell Us about Host-Pathogen Coevolution?" International Journal of Evolutionary Biology 2012 (November 18, 2012): 1–12. http://dx.doi.org/10.1155/2012/396165.

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The outcomes of host-parasite interactions depend on the coevolutionary forces acting upon them, but because every host-parasite relation is enmeshed in a web of biotic and abiotic interactions across a heterogeneous landscape, host-parasite coevolution has proven difficult to study. Simple laboratory phage-bacteria microcosms can ameliorate this difficulty by allowing controlled, well-replicated experiments with a limited number of interactors. Genetic, population, and life history data obtained from these studies permit a closer examination of the fundamental correlates of host-parasite coevolution. In this paper, I describe the results of phage-bacteria coevolutionary studies and their implications for the study of host-parasite coevolution. Recent experimental studies have confirmed phage-host coevolutionary dynamics in the laboratory and have shown that coevolution can increase parasite virulence, specialization, adaptation, and diversity. Genetically, coevolution frequently proceeds in a manner best described by the Gene for Gene model, typified by arms race dynamics, but certain contexts can result in Red Queen dynamics according to the Matching Alleles model. Although some features appear to apply only to phage-bacteria systems, other results are broadly generalizable and apply to all instances of antagonistic coevolution. With laboratory host-parasite coevolutionary studies, we can better understand the perplexing array of interactions that characterize organismal diversity in the wild.
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37

Schulte, Rebecca D., Carsten Makus, Barbara Hasert, Nico K. Michiels, and Hinrich Schulenburg. "Host–parasite local adaptation after experimental coevolution of Caenorhabditis elegans and its microparasite Bacillus thuringiensis." Proceedings of the Royal Society B: Biological Sciences 278, no. 1719 (February 9, 2011): 2832–39. http://dx.doi.org/10.1098/rspb.2011.0019.

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Coevolving hosts and parasites can adapt to their local antagonist. In studies on natural populations, the observation of local adaptation patterns is thus often taken as indirect evidence for coevolution. Based on this approach, coevolution was previously inferred from an overall pattern of either parasite or host local adaptation. Many studies, however, failed to detect such a pattern. One explanation is that the studied system was not subject to coevolution. Alternatively, coevolution occurred, but remained undetected because it took different routes in different populations. In some populations, it is the host that is locally adapted, whereas in others it is the parasite, leading to the absence of an overall local adaptation pattern. Here, we test for overall as well as population-specific patterns of local adaptation using experimentally coevolved populations of the nematode Caenorhabditis elegans and its bacterial microparasite Bacillus thuringiensis . Furthermore, we assessed the importance of random interaction effects using control populations that evolved in the absence of the respective antagonist. Our results demonstrate that experimental coevolution produces distinct local adaptation patterns in different replicate populations, including host, parasite or absence of local adaptation. Our study thus provides experimental evidence of the predictions of the geographical mosaic theory of coevolution, i.e. that the interaction between parasite and host varies across populations.
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38

Holding, Matthew L., James E. Biardi, and H. Lisle Gibbs. "Coevolution of venom function and venom resistance in a rattlesnake predator and its squirrel prey." Proceedings of the Royal Society B: Biological Sciences 283, no. 1829 (April 27, 2016): 20152841. http://dx.doi.org/10.1098/rspb.2015.2841.

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Measuring local adaptation can provide insights into how coevolution occurs between predators and prey. Specifically, theory predicts that local adaptation in functionally matched traits of predators and prey will not be detected when coevolution is governed by escalating arms races, whereas it will be present when coevolution occurs through an alternate mechanism of phenotype matching. Here, we analyse local adaptation in venom activity and prey resistance across 12 populations of Northern Pacific rattlesnakes and California ground squirrels, an interaction that has often been described as an arms race. Assays of venom function and squirrel resistance show substantial geographical variation (influenced by site elevation) in both venom metalloproteinase activity and resistance factor effectiveness. We demonstrate local adaptation in the effectiveness of rattlesnake venom to overcoming present squirrel resistance, suggesting that phenotype matching plays a role in the coevolution of these molecular traits. Further, the predator was the locally adapted antagonist in this interaction, arguing that rattlesnakes are evolutionarily ahead of their squirrel prey. Phenotype matching needs to be considered as an important mechanism influencing coevolution between venomous animals and resistant prey.
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39

Roughgarden, Jonathan. "Community Coevolution: A Comment." Evolution 41, no. 5 (September 1987): 1130. http://dx.doi.org/10.2307/2409199.

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40

Bell, A. E., and K. C. Spencer. "Chemical Mediation of Coevolution." Journal of Applied Ecology 27, no. 2 (August 1990): 769. http://dx.doi.org/10.2307/2404333.

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41

Agrawal, M., A. S. Raghubanshi, J. S. Singh, and B. K. Roy. "Coevolution and species interactions." Journal of Palaeosciences 41 (December 31, 1992): 132–43. http://dx.doi.org/10.54991/jop.1992.1114.

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Ecosystems are characterized by a network of interrelationships among species and species group which leads to community homeostasis. Occasional mutations or recombination in one component of the coevolving species pair may lead to a new set of defense characteristics making it possible for that component to enter a new adaptation zone from which evolutionary radiation might follow. The other component of the pair through genetic feedback might evolve to develop morphological, behavioural or biochemical features to overcome the new characteristics of the other component. Selection would carry this population to new adaptive zone allowing it to diversify further. Coevolution thus is a manifestation of selective evolutionary interactions between species or species group with a close ecological relationship. Coevolutionary instances are illustrated in plant-herbivore, host-parasite, plant-pollinator and several similar relationships. In coevolution, the biotic environment plays an active role within a relatively passive physical environment. however, change in the physical environment might favour or disfavor one component of the coevolved pair more than the other component. This differential response might lead to disruption of the relationship and may even result in loss of species.
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42

Holland, J. Nathaniel. "Evolving Theory of Coevolution." Ecology 86, no. 12 (December 2005): 3425–27. http://dx.doi.org/10.1890/0012-9658(2005)86[3425:etoc]2.0.co;2.

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43

Mo, Pak-Hung. "Institutions' Complementarity and Coevolution." Malaysian Journal of Economic Studies 55, no. 1 (May 14, 2018): 133–50. http://dx.doi.org/10.22452/mjes.vol55no1.8.

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44

YOKOYAMA, JUN. "Molecular Phylogeny and Coevolution." Plant Species Biology 9, no. 3 (December 1994): 163–67. http://dx.doi.org/10.1111/j.1442-1984.1994.tb00097.x.

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45

Schmidt, M. D., and H. Lipson. "Coevolution of Fitness Predictors." IEEE Transactions on Evolutionary Computation 12, no. 6 (December 2008): 736–49. http://dx.doi.org/10.1109/tevc.2008.919006.

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46

Gomes, Jorge, Pedro Mariano, and Anders Lyhne Christensen. "Novelty-Driven Cooperative Coevolution." Evolutionary Computation 25, no. 2 (June 2017): 275–307. http://dx.doi.org/10.1162/evco_a_00173.

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Cooperative coevolutionary algorithms (CCEAs) rely on multiple coevolving populations for the evolution of solutions composed of coadapted components. CCEAs enable, for instance, the evolution of cooperative multiagent systems composed of heterogeneous agents, where each agent is modelled as a component of the solution. Previous works have, however, shown that CCEAs are biased toward stability: the evolutionary process tends to converge prematurely to stable states instead of (near-)optimal solutions. In this study, we show how novelty search can be used to avoid the counterproductive attraction to stable states in coevolution. Novelty search is an evolutionary technique that drives evolution toward behavioural novelty and diversity rather than exclusively pursuing a static objective. We evaluate three novelty-based approaches that rely on, respectively (1) the novelty of the team as a whole, (2) the novelty of the agents’ individual behaviour, and (3) the combination of the two. We compare the proposed approaches with traditional fitness-driven cooperative coevolution in three simulated multirobot tasks. Our results show that team-level novelty scoring is the most effective approach, significantly outperforming fitness-driven coevolution at multiple levels. Novelty-driven cooperative coevolution can substantially increase the potential of CCEAs while maintaining a computational complexity that scales well with the number of populations.
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47

Nachenberg, Carey. "Computer virus-antivirus coevolution." Communications of the ACM 40, no. 1 (January 1997): 46–51. http://dx.doi.org/10.1145/242857.242869.

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48

Thompson, J. N. "The Role of Coevolution." Science 335, no. 6067 (January 26, 2012): 410–11. http://dx.doi.org/10.1126/science.1217807.

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49

Mitscher, Lester A. "Coevolution: Mankind and Microbes⊥." Journal of Natural Products 71, no. 3 (March 2008): 497–509. http://dx.doi.org/10.1021/np078017j.

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50

Nijholt, Jurriaan J., and Jos Benders. "Coevolution in Management Fashions." Group & Organization Management 32, no. 6 (December 2007): 628–52. http://dx.doi.org/10.1177/1059601106293781.

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