Diatoms from the genus Achnanthidium in flowing waters of the Appalachian Mountains (North America): Ecology, distribution and taxonomic notes |
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Authors: | Karin C. Ponader Marina G. Potapova |
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Affiliation: | aHarvard University Herbaria, 22 Divinity Avenue, Cambridge, MA 02138, USA;bPatrick Center for Environmental Research, The Academy of Natural Sciences, Philadelphia, PA 19103, USA |
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Abstract: | Diatoms from the genus Achnanthidium are abundant in rivers, streams, and springs of the Appalachian Mountains. They inhabit clean and polluted waters, including those affected by acid mine drainage. The identification of Achnanthidium taxa is difficult due to their small cell size and insufficient information in the diatom floras. We studied the taxonomy and ecology of Achnanthidium in Appalachian rivers by analyzing a data set of benthic diatom samples and corresponding water chemistry data collected during several water-quality surveys from 181 sampling sites. Ten species were identified using scanning electron and light microscopy: A. alpestre (Lowe & Kociolek) Lowe & Kociolek, A. atomus (Hustedt) Monnier, Lange-Bertalot, & Ector, A. deflexum (Reimer) Kingston, A. duthii (Sreenivasa) Edlund, A. eutrophilum (Lange-Bertalot) Lange-Bertalot, A. cf. gracillimum (Meister) Lange-Bertalot, A. cf. latecephalum Kobayasi, A. minutissimum (Kützing) Czarnecki (sensu lato), A. reimeri (Camburn) comb. nov., and A. rivulare Potapova & Ponader. The distribution of common taxa in relation to water chemistry was studied by fitting non-parametric regression models (generalized additive models, GAM, and non-parametric multiplicative regression models, NPMR) to species relative abundances. Studied Achnanthidium species differed considerably in their responses to water chemistry. These results suggest that species-level identifications will lead to more accurate bioassessments. |
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Keywords: | Diatoms Achnanthidium Appalachian Mountains Rivers Taxonomy Ecology Distribution Generalized additive models Non-parametric multiplicative regression models Water-quality assessment |
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