2021 Fisheries Index of Biotic Integrity (IBI) survey data from Great Smoky Mountains National Park

Description

These data represent an assessment of Ichthyofaunal community structure in selected streams located in Great Smoky Mountains National Park (GRSM). Sites are sampled on a rotational basis occurring once every two years. Currently (2021), there are ten watersheds being sampled using IBI protocols. In 2021, five sites were surveyed: Cataloochee Creek, Middle Prong Little Pigeon River, Deep Creek (two sites), and Big Creek, all within GRSM. Fish community assemblage assessments are made after collecting specimens using backpack electrofishing and seining techniques. An IBI is used to determine how many species of fish are represented in a stream segment by surveying the various types of habitat: riffles, runs, pools, and shoreline. Techniques for IBI sampling also use seining in conjunction with electrofishing for fish capture. The point of IBI sampling isn’t to remove all the fish present, but rather to determine the community assemblage, i.e., species diversity within the stream segment. The GRSM Fishery Division works in conjunction with aquatics biologists at the Tennessee Valley Authority (TVA), who then provide “scores” regarding the species’ representation or robustness given the site criteria. IBI scores and metrics are determined using the TVA IBI Stream Survey Recording System v2.02. Once data have been entered by GRSM staff in the TVA Stream Survey database, results are sent to TVA biologists, who then provide GRSM with the scores associated with the species observed. Scores for 2021 survey data were provided to GRSM by TVA in December, 2021.

Resources

Name Format Description Link
GSMNP_2021_IBI_UPDATED.xlsx XLS Open resource
20210713_DeepCreek_3_RMI_4.3.xlsx XLS Open resource
GSMNP_2021_IBI.xlsx XLS Open resource
20210715_IBI_BIG_1.pdf PDF Open resource
20210712_IBI_CAT_1.pdf PDF Open resource
20210713_DPC_1.pdf PDF Open resource
20210713_IBI_DPC_2.pdf PDF Open resource
20210714_IBI_MPLP_1.pdf PDF Open resource

Tags

Topics

Categories