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Genetic distinction between contiguous urban and rural multimammate mice in Tanzania despite gene flow

S. Gryseels, J. Goüy de Bellocq, R. Makundi, K. Vanmechelen, J. Broeckhove, V. Mazoch, R. Šumbera, J. Zima, H. Leirs, SJ. Baird,

. 2016 ; 29 (10) : 1952-1967. [pub] 20160709

Language English Country Switzerland

Document type Journal Article

Persistent link   https://www.medvik.cz/link/bmc18017204

Special conditions are required for genetic differentiation to arise at a local geographical scale in the face of gene flow. The Natal multimammate mouse, Mastomys natalensis, is the most widely distributed and abundant rodent in sub-Saharan Africa. A notorious agricultural pest and a natural host for many zoonotic diseases, it can live in close proximity to humans and appears to compete with other rodents for the synanthropic niche. We surveyed its population genetic structure across a 180-km transect in central Tanzania along which the landscape varied between agricultural land in a rural setting and natural woody vegetation, rivers, roads and a city (Morogoro). We sampled M. natalensis across 10 localities and genotyped 15 microsatellite loci from 515 individuals. Hierarchical STRUCTURE analyses show a K-invariant pattern distinguishing Morogoro suburbs (located in the centre of the transect) from nine surrounding rural localities. Landscape connectivity analyses in Circuitscape and comparison of rainfall patterns suggest that neither geographical isolation nor natural breeding asynchrony could explain the genetic differentiation of the urban population. Using the isolation-with-migration model implemented in IMa2, we inferred that a split between suburban and rural populations would have occurred recently (<150 years ago) with higher urban effective population density consistent with an urban source to rural sink of effective migration. The observed genetic differentiation of urban multimammate mice is striking given the uninterrupted distribution of the animal throughout the landscape and the high estimates of effective migration (2Ne M = 3.0 and 29.7), suggesting a strong selection gradient across the urban boundary.

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$a Special conditions are required for genetic differentiation to arise at a local geographical scale in the face of gene flow. The Natal multimammate mouse, Mastomys natalensis, is the most widely distributed and abundant rodent in sub-Saharan Africa. A notorious agricultural pest and a natural host for many zoonotic diseases, it can live in close proximity to humans and appears to compete with other rodents for the synanthropic niche. We surveyed its population genetic structure across a 180-km transect in central Tanzania along which the landscape varied between agricultural land in a rural setting and natural woody vegetation, rivers, roads and a city (Morogoro). We sampled M. natalensis across 10 localities and genotyped 15 microsatellite loci from 515 individuals. Hierarchical STRUCTURE analyses show a K-invariant pattern distinguishing Morogoro suburbs (located in the centre of the transect) from nine surrounding rural localities. Landscape connectivity analyses in Circuitscape and comparison of rainfall patterns suggest that neither geographical isolation nor natural breeding asynchrony could explain the genetic differentiation of the urban population. Using the isolation-with-migration model implemented in IMa2, we inferred that a split between suburban and rural populations would have occurred recently (<150 years ago) with higher urban effective population density consistent with an urban source to rural sink of effective migration. The observed genetic differentiation of urban multimammate mice is striking given the uninterrupted distribution of the animal throughout the landscape and the high estimates of effective migration (2Ne M = 3.0 and 29.7), suggesting a strong selection gradient across the urban boundary.
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$a Makundi, R $u Pest Management Centre, Sokoine University of Agriculture, Morogoro, Tanzania.
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$a Vanmechelen, K $u Computational Modelling and Programming, Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium.
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$a Broeckhove, J $u Computational Modelling and Programming, Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium.
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$a Mazoch, V $u Department of Zoology, University of South Bohemia, České Budějovice, Czech Republic.
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$a Zima, J $u Institute of Vertebrate Biology, Research Facility Studenec, Academy of Sciences of the Czech Republic, Brno, Czech Republic. Department of Zoology, University of South Bohemia, České Budějovice, Czech Republic.
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$a Leirs, H $u Evolutionary Ecology Group, Department of Biology, University of Antwerp, Antwerp, Belgium.
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