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Classifying rarity and abundance at a regional scale: Implementation within a new ecoinformatics tool
Citation:
Saarinen, E. V., H. LEE, II, D. REUSSER, AND M. R. FRAZIER. Classifying rarity and abundance at a regional scale: Implementation within a new ecoinformatics tool. Presented at Ecological Society of America, Austin, TX, August 07 - 12, 2011.
Impact/Purpose:
One factor that determines a species vulnerability to extinction is its rarity in the environment and a goal of many species analyses is to evaluate geographic patterns of abundance.
Description:
One factor that determines a species vulnerability to extinction is its rarity in the environment and a goal of many species analyses is to evaluate geographic patterns of abundance. In an attempt to assess vulnerability to climate change, we evaluated relative species abundance across several invertebrate taxonomic groups along the Pacific Coast of the U.S. by synthesizing multiple data sources. We capture both qualitative and quantitative data from field surveys (e.g. EMAP), natural history texts, and global databases that provide distributional data (e.g. GBIF, OBIS). We registered and stored abundance information in the Pacific Coast Ecosystem Information System (PCEIS), a joint EPA-USGS marine ecosystem resource. PCEIS is an MS Office Access database, though an effort is underway to migrate the system to the internet. Currently, PCEIS stores geographical, biological, and population-level data for over 18,000 species from the marine/estuarine near-shore communities. In order to classify abundance as captured from disparate data sources, we used a hierarchical, multi-tier system. Species were first diagnosed as being present or absent in a region, then classified using an assessment of their abundance (abundant, moderate, or rare), and finally classified using a tertiary level that further qualifies abundance and rarity. We classified species’ abundances in Marine Ecological Regions of the World (MEOW) ecoregions although our framework can be applied to other scales. To synthesize information from the disparate data sources, we generated a list of keywords and other criteria to use when classifying species’ abundances at the ecoregion level. We also provide a method for assessing uncertainty and its application to the analysis. Our results produced lists of rare and abundant invertebrate species (per ecoregion) as well as statistics regarding rarity in taxonomic groups of near-shore communities. In this endeavor we give a numerical representation to species that are at risk due to their rarity in the environment.