Why combine databases?

1. Abstract #OS42C-140 Background -Ten species of sea anemonee host anemonefish (Family Pomacentridae) : Cryptodendrum adaesivum, Entacmaea quadricolor, Macrodactyla doreensis, Heteractis magnifica, H. crispa, H. aurora, H. malu, Stichodactyla haddoni, S. gigantea, and S. mertensii. -The host anemones possess endosymbiotic zooxanthellae (dinoflagellates), so they must live in shallow, clear water -Anemones are brightly colored and their symbiosis with anemonefish makes them distinctive -Their habitat preferences and ecology are well known, but their distribution patterns have not been well studied -All 27 species of anemonefish are obligate symbionts of sea anemones during their entire lives except for a brief planktonic larval stage -Therefore, the geographic range of these fish is constrained by that of their host anemones -Unlike the fish, the anemones can exist without a fish symbiont in nature (with the exception of one species of anemone in one part of its range) -The anemone-anemonefish symbiosis is confined to the Indian and Western Pacific Oceans The Anemone-Anemonefish example:Anemonefish never occur in the absence of a host anemone, so can be used to extend analysis of anemone occurrence data – an example of Hexacorallia – Fishbase interaction (with environment!) Objectives and Methods -Determine which environmental variables characterize the habitat of the host sea anemones -Localities from which host sea anemones are known were gathered ultimately from the literature (assembled in Biogeoinformatics of Hexacorallia) -The environmental attributes associated with those localities were determined -Predict, based on the environmental attributes, where else the anemones might occur -Places characterized by the set of environmental attributes, but where there were no host anemone records, were identified -Assess the accuracy of the prediction by overlaying on the predicted distribution of anemonefishes -Localities from which anemonefish are known were taken from FishBase -Demonstrate a working ability to exchange data and information between two independent databases Why combine databases? -Relate organism distributions to habitat and environmental characteristics -Compare and combine distributions of taxonomically or functionally similar organisms -Create a product worth more than the sum of its parts Interoperability permits rapid, seamless integration of data and product preparation Synonymy -Until recently, the nomenclature of the host anemones was not standard. Using names in the published records results in more taxa, each with a smaller range -Syngraph finds the accepted name for an individual from any of the names in past literature, standardizing the names of host anemones and other species Funding NSF OCE 00-03970 (NOPP) to Daphne G. Fautin and Robert W. Buddemeier NSF DBI 0097223 (REU) to Helen M. Alexander NSF DEB 9978106 (PEET) to Daphne G. Fautin

2. Environmental data associated with geo-referenced localities can be used for statistical habitat analysis and to predict occurrences -LOICZView geospatial clustering provides similarity analysis of environmental variables -This analysis used seven variables: Minimum Sea Surface Temperature, Minimum Salinity, Average Depth, Wave Height, Tidal Range, Ocean Color, and Reef Occurrences -All ten species of anemones were included in the overlay function -The areas included in the study are all shallow basins and coastal areas in the Indo-Pacific Basin. Resolution is the half degree cell. -These areas were subdivided into 16 clusters (Map A.), which were analyzed for anemone/anemonefish occurrence -The anemone overlay reveals that 77.7% of known host anemones occur in 8 of 16 clusters (Map B.) -The anemonefish overlay reveals that 83.7% of known anemonefish occur in 6 of 16 clusters (Map C.) A. B. C. Single Species Example -Macrodactyla doreensis -100% of geo-referenced points occur in these four clusters -Severe over prediction – most likely due to a small number of geo-referenced points -Eight records probably not enough to predict a distribution Tools: -LOICZView – K-means clustering tool for geo-spatial data http://www.palantir.swarthmore.edu/loicz/ -FishBase – Provided the anemonefish data http://www.fishbase.org/search.cfm -Syngraph – Used to link synonymous names of host anemones http://www.nhm.ku.edu/inverts/syngraph/ -Arcview GIS http://www.esri.com Conclusions -Two independent databases can share and exchange information and data to produce conclusions important to biodiversity and biogeography -Host sea anemones occur in a specific type of environment: they prefer high salinity, warm temperatures, clear water, and areas that reefs occur -The occurrence of soft-substrate anemones and reef-substrate anemones correlates with reef occurrence Acknowledgements Dr. Robert W. Buddemeier, Jeremy Bartley, Girmay Misgna, Keith Hunsinger