Plant species identification using fecal DNAs from red-eared slider and Reeves’ pond turtle in agricultural canals for rural ecosystem conservation View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-10

AUTHORS

Noriyuki Koizumi, Atsushi Mori, Takuya Mineta, Eiji Sawada, Keiji Watabe, Takeshi Takemura

ABSTRACT

Fecal DNA samples from the red-eared slider and Reeves’ pond turtle, suspected pests of lotus root paddies, were used to identify the plant species eaten by these turtles in order to develop a strategy for rural ecosystem conservation. The fecal samples were obtained from young and adult individuals (mostly female) of both species living in agricultural canals surrounding lotus root paddies in Tokushima Prefecture, Japan. The samples were screened for the presence or absence of DNA from nine plant species using PCR and plant species-specific primers for the rbcL gene of chloroplast DNA. In the red-eared slider, our analysis identified seven plant species in the fecal DNA samples of adults and three plant species in those of young individuals. In Reeves’ pond turtle, our analysis identified two plant species from adult fecal samples and one species from those of young individuals. Thus, adult red-eared sliders consume a greater range of plants than young red-eared sliders or Reeves’ pond turtles. Both turtle species, independently of age, consumed lotus plants and were likely to cause feeding damage to lotus roots. Considering the plant species detected in adult red-eared sliders and these plant habitats, we suggest that this adult turtle is likely to travel between the agricultural canals and the lotus root paddies. These findings will help the development of strategies for preventing damage to lotus roots by these turtles; furthermore, they indicate that fecal DNA analysis will be applicable to investigation of the feeding habits of other animal species. More... »

PAGES

723-730

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10333-016-0576-5

DOI

http://dx.doi.org/10.1007/s10333-016-0576-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085459023


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