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The shortest distance between two points isn't always a great circle: getting around landmasses in the calibration of marine geodisparity

Published online by Cambridge University Press:  08 April 2016

Shuang-Ye Wu  and
Arnold I. Miller
Shuang-Ye Wu*
Affiliation:
Department of Geology, University of Dayton, 300 College Park Avenue, Dayton, Ohio 45469, U.S.A. E-mail: [email protected].
Arnold I. Miller
Affiliation:
Department of Geology, University of Cincinnati, Cincinnati, Ohio 45221-0013, U.S.A. E-mail: [email protected]
*
Corresponding author
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Abstract

In the assessment of Phanerozoic marine global biodiversity, there has been longstanding interest in quantifying compositional similarities among sampling points as a function of their distances from one another (geodisparity). Previous research has demonstrated that faunal similarity between any two locations tends to decrease significantly as the great circle distance (GCD) between the locations increases, but the rate of decrease begins to stabilize at transoceanic distances. The accuracy of these assessments, and comparisons among different temporal intervals, may suffer, however, because of intervening landmasses that are not accounted for when distance is calibrated simply as GCD. Here, we present a new method for determining the shortest overwater distance (WD) between two marine locations, and we use the method to recalibrate for several Phanerozoic intervals previous measures of global geodisparity in the taxonomic compositions of marine biotas. WD was determined by using a cost-distance approach in ArcGIS, modified to work on a spherical, as opposed to a planar, surface. Results demonstrate two notable effects of using WD. First, mean compositional similarity between locations tends to decrease more continuously as a function of distance with WD than with GCD. Second, pairs of locations with WDs that are at least 50% greater than their GCDs tend to have lower compositional similarity to one another than those with more closely matching WDs and GCDs. These differences are expected as WD better represents the “true” distance between locations; they diminish at GCDs of 5000 km or more when clear, transoceanic paths between locations become more common. Despite these effects, using WD does not alter fundamental temporal trends in global geodisparity through the Phanerozoic observed in previous research, but it is likely to have more significant ramifications for more confined paleobiogeographic investigations.

Type
Articles
Information
Paleobiology , Volume 40 , Issue 3 , Summer 2014 , pp. 428 - 439
Copyright
Copyright © The Paleontological Society 

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References

Literature Cited

Bambach, R. K. 1977. Species richness in marine benthic habitats through the Phanerozoic. Paleobiology 3:152–167.Google Scholar
Bush, A. M., and Bambach, R. K. 2004. Did alpha diversity increase during the Phanerozoic? Lifting the veils of taphonomic, latitudinal, and environmental biases. Journal of Geology 112:625–642.CrossRefGoogle Scholar
Clapham, M. E., Shen, S. Z., and Bottjer, D. J. 2009. The double mass extinction revisited: reassessing the severity, selectivity, and causes of the end-Guadalupian biotic crisis (Late Permian). Paleobiology 35:32–50.Google Scholar
Dijkstra, E. W. 1959. A note on two problems in connexion with graphs. Numerische Mathematik 1:269–271.Google Scholar
ESRI. 2011. ArcGIS Desktop Help 10.1: how cost distance tools work. http://resources.arcgis.com/en/help/main/10.1/index.html#//009z00000025000000, accessed on 11 September 2013.Google Scholar
Furrer, R., Nychka, D., and Sain, S. 2011. fields: tools for spatial data. R package, Version 6.6.2. http://CRAN.R-project.org/package=fields.Google Scholar
Holland, S. M. 2010. Additive diversity partitioning in palaeobiology: revisiting Sepkoski's question. Palaeontology 53:1237–1254.Google Scholar
Lagomarcino, A. J., and Miller, A. I. 2012. The relationship between genus richness and geographic area in Late Cretaceous marine biotas: epicontinental sea versus open-ocean-facing settings. PLoS ONE 7:e40472.CrossRefGoogle ScholarPubMed
Miller, A. I., and Foote, M. 2009. Epicontinental seas versus open-ocean settings: the kinetics of mass extinction and origination. Science 326:1106–1109.Google Scholar
Miller, A. I., Aberhan, M., Buick, D. P., Bulinski, K. V., Ferguson, C. A., Hendy, A. J. W., and Kiessling, W. 2009. Phanerozoic trends in the global geographic disparity of marine biotas. Paleobiology 35:612–630.Google Scholar
Qian, H., Badgley, C., and Fox, D. L. 2009. The latitudinal gradient in beta diversity in relation to climate and topography for mammals in North America. Global Ecology and Biogeography 18:111–122.Google Scholar
R Development Core Team. 2011. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN 3-900051-07-0. http://www.R-project.org/.Google Scholar
Sepkoski, J. J. Jr. 1988. Alpha, beta, or gamma: where does all the diversity go? Paleobiology 14:221–234.Google Scholar
Sepkoski, J. J. Jr., Bambach, R. K., Raup, D. M., and Valentine, J. W. 1981. Phanerozoic marine diversity and the fossil record. Nature 293:435–437.Google Scholar
Valentine, J. W. 1969. Patterns of taxonomic and ecological structure of the shelf benthos during Phanerozoic time. Palaeontology 12:684–709.Google Scholar
Valentine, J. W. 1970. How many marine invertebrate fossil species? A new approximation. Journal of Paleontology 44:410–415.Google Scholar
Valentine, J. W., Foin, T. C., and Peart, D. 1978. A provincial model of Phanerozoic marine diversity. Paleobiology 4:55–66.Google Scholar
van Etten, J. 2011. gdistance: distances and routes on geographical grids. R package, Version 1.1–2. http://CRAN.R-project.org/package=gdistanceGoogle Scholar