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Identifying conservation priority ponds of an endangered minnow, Pseudorasbora pumila, in the area invaded by Pseudorasbora parva

Abstract

We investigated the occurrence pattern of the pond-living endangered cyprinid, Pseudorasbora pumila, and also compared its habitat characteristics with those of the congeneric invasive species, Pseudorasbora parva, in the contact zone. Comparison of 16 environmental variables among the P. pumila habitats, P. parva habitats, and unoccupied ponds revealed that conductivity was a common limitation factor of distribution of both species. We found that emergent vegetation occupancy along the pond bank was the most important factor determining P. pumila occurrence and that ponds with steep banks may have a low probability of containing P. parva. We constructed a logistic regression model to predict the establishment risk of P. parva in ponds occupied by P. pumila. The model demonstrated that more than half of the ponds exhibited a high establishment risk of P. parva. Principal component analysis using six parameters selected from stepwise logistic regression analysis revealed that seven unoccupied ponds had the potential to sustain P. pumila, suggesting that our study site is capable of supporting more P. pumila populations and expanding the current range.

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Acknowledgments

We admire the efforts of the late Y. Shimizu for protection of native wildlife. We are grateful to K. Takata and T. Natsumeda for their invaluable comments on an early draft manuscript and to M. Taguchi for assistance in the field survey. We thank the Nagano City landowners for permission to survey their ponds. This research was partly funded by the Fisheries Agency of Japan.

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Correspondence to Mayu Konishi.

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Konishi, M., Sakano, H. & Iguchi, K. Identifying conservation priority ponds of an endangered minnow, Pseudorasbora pumila, in the area invaded by Pseudorasbora parva . Ichthyol Res 56, 346 (2009). https://doi.org/10.1007/s10228-009-0106-1

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Keywords

  • Priority habitat
  • Irrigation pond
  • Prediction
  • Logistic regression analysis
  • Invasive species