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Updated: Feb 26, 2026

Manipulation of Gene Function in Mexican Cavefish
Published on: April 22, 2019
Investigating gene flow between the blind cavefish Garra barreimiae and its conspecific surface populations
Sandra Kirchner1,2, Helmut Sattmann3, Elisabeth Haring4,5
1University of Vienna, Department of Integrative Zoology, Althanstraße 14, 1090, Vienna, Austria. sandra.kirchner@nhm-wien.ac.at.
Abstract:
Cave-dwelling taxa often share the same phenotypic modifications like absence of eyes and pigmentation. These "troglomorphic characters" are expressed in the populations of Garra barreimiae from the Al Hoota Cave and nearby Hoti Pit in Northern Oman. Surface morphotypes of this cyprinid species are common throughout the distribution area. Very rarely individuals with intermediate phenotypes can be found. In the present study, potential gene flow between cave and surface populations was tested and population structure within five sampling sites was assessed. Overall, 213 individuals were genotyped at 18 microsatellite loci. We found that the cave populations have lower genetic diversity and are clearly isolated from the surface populations, which seem to be sporadically in contact with each other. The results indicate a recent genetic bottleneck in the cave populations. Thus, it can be assumed that during climatic changes the connection between cave and surface water bodies was disjoined, leaving a subpopulation trapped inside. Nevertheless, occasional gene flow between the morphotypes is detectable, but hybridisation seems only possible in cave habitat with permanent connection to surface water. Individuals from surface sites bearing intermediate phenotypes but cave genotypes imply that phenotypic plasticity might play a role in the development of the phenotype.
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Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

