Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-27T11:28:06.885Z Has data issue: false hasContentIssue false
  • English
  • Français

An abundance- and morphology-based similarity index

Published online by Cambridge University Press:  29 October 2021

Daniel G. Dick Open the ORCID record for Daniel G. Dick [Opens in a new window]  and
Marc Laflamme
Daniel G. Dick*
Affiliation:
Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario, L5L 1C6, Canada. E-mail: [email protected], [email protected]
Marc Laflamme
Affiliation:
Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario, L5L 1C6, Canada. E-mail: [email protected], [email protected]
*
*Corresponding author.
Get access Rights & Permissions [Opens in a new window]

Abstract

Classic similarity indices measure community resemblance in terms of incidence (the number of shared species) and abundance (the extent to which the shared species are an equivalently large component of the ecosystem). Here we describe a general method for increasing the amount of information contained in the output of these indices and describe a new “soft” ecological similarity measure (here called “soft Chao-Jaccard similarity”). The new measure quantifies community resemblance in terms of shared species, while accounting for intraspecific variation in abundance and morphology between samples. We demonstrate how our proposed measure can reconstruct short ecological gradients using random samples of taxa, recognizing patterns that are completely missed by classic measures of similarity. To demonstrate the utility of our new index, we reconstruct a morphological gradient driven by river flow velocity using random samples drawn from simulated and real-world data. Results suggest that the new index can be used to recognize complex short ecological gradients in settings where only information about specimens is available. We include open-source R code for calculating the proposed index.

Type
Articles
Information
Paleobiology , Volume 48 , Issue 2 , May 2022 , pp. 284 - 301
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Paleontological Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Booth, R. 2001. Ecology of testate amoebae (Protozoa) in two Lake Superior coastal wetlands: implications for paleoecological environmental monitoring. Wetlands 21:564–576.CrossRefGoogle Scholar
Bradfield, G., and Kenkel, N.. 1987. Nonlinear ordination using flexible shortest path adjustment of ecological distances. Ecology 68:750–753.CrossRefGoogle Scholar
Bray, J. R., and Curtis, J. T.. 1957. An ordination of the upland forest communities in southern Wisconsin. Ecological Monographs 27:325–349.CrossRefGoogle Scholar
Brett, C., Bartholomew, A., and Baird, G.. 2007. Biofacies recurrence in the Middle Devonian of New York State: an example with implications for evolutionary paleoecology. Palaios 22:306–324.CrossRefGoogle Scholar
Camiz, S. 2005. The Guttman effect: its interpretation and a new redressing method. Data Analysis Bulletin 5:7–34.Google Scholar
Chao, A., Chazdon, R. L., Colwell, R. K., and Shen, T-J.. 2005. A new statistical approach for assessing similarity of species composition with incidence and abundance data. Ecology Letters 8:148–159.CrossRefGoogle Scholar
Chao, A., Chazdon, R. L., Colwell, R. K., and Shen, T-J.. 2006. Abundance-based similarity indices and their estimation when there are unseen species in samples. Biometrics 62:361–371.CrossRefGoogle ScholarPubMed
Chauvin, L., Kumar, K., Wachinger, C., Vangel, M., de Guise, J., Desrosiers, C., Wells, W., Toews, M., and Alzheimer's Disease Neuroimaging Initiative. 2020. Neuroimage signature from salient keypoints is highly specific to individuals and shared by close relatives. NeuroImage 204:116208.CrossRefGoogle ScholarPubMed
Chum, O., Philbin, J., and Zisserman, A.. 2008. Near duplicate image detection: min-Hash and tf-idf weighting. Pp. 50.1–50.10 in M. Everingham and C. Needham, eds. Proceedings of the British Machine Conference. BMVA Press. doi: 10.5244/C.22.50.CrossRefGoogle Scholar
Clapham, M., Narbonne, G., and Gehling, J.. 2003. Paleoecology of the oldest known animal communities: Ediacaran assemblages at Mistaken Point, Newfoundland. Paleobiology 29:527–544.2.0.CO;2>CrossRefGoogle Scholar
Dale, M. 1994. Straightening the horseshoe: a Riemannian resolution. Coenoses 9:43–53.Google Scholar
Darroch, S., Laflamme, M., and Clapham, M.. 2013. Population structure of the oldest known macroscopic communities from Mistaken Point, Newfoundland. Paleobiology 39:591–608.CrossRefGoogle Scholar
Dekking, F., Kraaikamp, C., Lopuhaä, H., and Meester, L.. 2005. A modern introduction to probability and statistics: understanding why and how. Springer-Verlag, London.CrossRefGoogle Scholar
Dunn, J. 1973. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics 3(3):32–57.CrossRefGoogle Scholar
Einbinder, S., Mass, T., Brokovich, E., Dubinsky, Z., Erez, J., and Tchernov, D.. 2009. Changes in morphology and diet of the coral Stylophora pistillata along a depth gradient. Marine Ecology Progress Series 381:167–174.CrossRefGoogle Scholar
Fulton, E. A., Smith, A. D. M., and Punt, A. E.. 2005. Which ecological indicators can robustly detect effects of fishing? ICES Journal of Marine Science. 62:540–551.CrossRefGoogle Scholar
Gower, J. C. 1971. A general coefficient of similarity and some of its properties. Biometrics 27:857–871.CrossRefGoogle Scholar
Hill, M., and Gauch, H.. 1980. Detrended correspondence analysis: an improved ordination technique. Pp. 47–58 in Classification and ordination. Springer, Nijmegen, Netherlands.CrossRefGoogle Scholar
Holland, S., and Patzkowsky, M.. 2007. Gradient ecology of a biotic invasion: biofacies of the type Cincinnatian series (Upper Ordovician), Cincinnati, Ohio region, USA. Palaios 22:392–407.CrossRefGoogle Scholar
Holland, S., Miller, A., Meyer, D., and Dattilo, B.. 2001. The detection and importance of subtle biofaces within a single lithofacies: the Upper Ordovician Kope Formation of the Cincinnati, Ohio region. Palaios 16:205–217.2.0.CO;2>CrossRefGoogle Scholar
Jaccard, P. 1901. Étude comparative de la distribution florale dans une portion des Alpes et du Jura. Bulletin de la Société Vaudoise des Sciences Naturelles 37:547–579.Google Scholar
Jimenez, S., Gonzalez, F., and Gelbukh, A.. 2010. Text comparison using soft cardinality. Pp. 297–302 in International symposium on string processing and information retrieval. Springer, Berlin.CrossRefGoogle Scholar
Jimenez, S., Gonzalez, F. A., and Gelbukh, A.. 2016. Mathematical properties of soft cardinality: enhancing Jaccard, Dice and cosine similarity measures with element-wise distance. Information Sciences 367–368:373–389.CrossRefGoogle Scholar
Kowalewski, M., Flessa, K., and Aggen, J.. 1994. Taphofacies analysis of recent shelly cheniers (beach ridges), northeastern Baja California, Mexico. Facies 31:209–242.CrossRefGoogle Scholar
Kramer, N., Tamir, R., Eyal, G., and Loya, Y.. 2020. Coral morphology portrays the spatial distribution and population size-structure along a 5–100m depth gradient. Frontiers in Marine Science 7:1–13.CrossRefGoogle Scholar
Legendre, P., and Gallagher, E.. 2001. Ecologically meaningful transformations for ordination of species data. Oecologia 129:271–280.CrossRefGoogle ScholarPubMed
Legendre, P., and Legendre, L.. 2012. Numerical ecology. 3rd ed. Elsevier, Oxford, UK.Google Scholar
Leinster, T., and Cobbold, C.. 2012. Measuring diversity: the importance of species similarity. Ecology 93:477–489.CrossRefGoogle ScholarPubMed
Li, C., Zhang, X., Liu, X., Luukkanen, O., and Berninger, F.. 2006. Leaf morphological and physiological responses of Quercus aquifolioides along an altitudinal gradient. Silva Fennica 40:5.CrossRefGoogle Scholar
MacQueen, J. 1967. Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1(14):281–297.Google Scholar
Magurran, A. 2004. Measuring biological diversity. Blackwell Science, Oxford.Google Scholar
Miles, D., and Ricklefs, R.. 1984. The correlation between ecology and morphology in deciduous forest passerine birds. Ecology 65:1629–1640.CrossRefGoogle Scholar
Molodtsov, D. 1999. Soft set theory—first results. Computers and Mathematics with Applications 47(4–5):19–31.CrossRefGoogle Scholar
Nanglu, K., Caron, J-B., and Gaines, R.. 2020. The Burgess Shale paleocommunity with new insights from Marble Canyon, British Columbia. Paleobiology 46:58–81.CrossRefGoogle Scholar
Novack-Gottshall, P. 2007. Using a theoretical ecospace to quantify the ecological diversity of Paleozoic and modern marine biotas. Paleobiology 33:273–294.CrossRefGoogle Scholar
Økland, R. 1999. On the variation explained by ordination and constrained ordination axes. Journal of Vegetation Science 10:131–136.CrossRefGoogle Scholar
Patzkowsky, M. 1995. Gradient analysis of Middle Ordovician brachiopod biofacies: biostratigraphic, biogeographic, and macroevolutionary implications. Palaios 10:154–179.CrossRefGoogle Scholar
Podani, J. 1999. Extending Gower's general coefficient of similarity to ordinal characters. Taxon 48:331–340.CrossRefGoogle Scholar
Podani, J., and Miklós, I.. 2002. Resemblance coefficients and the horseshoe effect in principal coordinates analysis. Ecology 83:3331–3343.CrossRefGoogle Scholar
Price, T. 1991. Morphology and ecology of breeding warblers along an altitudinal gradient in Kashmir, India. Journal of Animal Ecology 60:643–664.CrossRefGoogle Scholar
Puijalon, S., and Bornette, G.. 2004. Morphological variation of two taxonomically distant plant species along a natural flow velocity gradient. New Phytologist 163:651–660.CrossRefGoogle ScholarPubMed
Scarponi, D., and Kowalewski, M.. 2004. Stratigraphic paleoecology: bathymetric signatures and sequence overprint of mollusk associations from upper Quaternary sequences of the Po Plain, Italy. Geology 32:989–992.CrossRefGoogle Scholar
Schubert, E., and Rousseeuw, P.. 2019. Faster k-medoids clustering: improving the PAM, CLARA, and CLARANS algorithms. Pp. 171–187 in Amato, G., Gennaro, C., Oria, V., and Radovanović, M., eds. Similarity search and applications. Springer, Berlin.CrossRefGoogle Scholar
Sidorov, G., Gelbukh, A., Gómez-Adorno, H., and Pinto, D.. 2014. Soft similarity and soft cosine measure: similarity of features in vector space model. Computación y Sistemas 18:491–504.CrossRefGoogle Scholar
Soto, D., De Palmas, S., Ho, M., Denis, V., and Chen, C.. 2018. Spatial variation in the morphological traits of Pocillopora verrucose along a depth gradient in Taiwan. PLoS ONE 13(8):e0202586.CrossRefGoogle Scholar
Swan, J. 1970. An examination of some ordination problems by use of simulated vegetational data. Ecology 51:89–102.CrossRefGoogle Scholar
Ter Braak, C. 1986. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67:1167–1179.CrossRefGoogle Scholar
Walker, S. 2015. Indirect gradient analysis by Markov-chain Monte Carlo. Plant Ecology 216:697–708.CrossRefGoogle Scholar
Whittaker, R. 1967. Gradient analysis of vegetation. Biological Reviews 42:207–264.CrossRefGoogle ScholarPubMed
Williamson, M. 1978. The ordination of incidence data. Journal of Ecology 66:911–920.CrossRefGoogle Scholar
Wolda, H. 1981. Similarity indices, sample size and diversity. Oecologia 50:296–302.CrossRefGoogle ScholarPubMed