Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-02T20:46:55.317Z Has data issue: false hasContentIssue false
  • English
  • Français

Automated leaf physiognomic character identification from digital images

Published online by Cambridge University Press:  20 October 2015

Norman MacLeod  and
David Steart
Norman MacLeod
Affiliation:
The Natural History Museum, Cromwell Road, London SW7 5BD, UK Department of Earth Sciences, University College, London, Gower Street, London WC1E 6BT, UK Nanjing Institute of Geology and Palaeontology, 39 Beijing Donglu, Nanjing, China. E-mail: [email protected]
David Steart
Affiliation:
The Natural History Museum, Cromwell Road, London SW7 5BD, UK and LaTrobe University, Melbourne Victoria 3086, Australia. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Research into the relationship between leaf form and climate over the last century has revealed that, in many species, the sizes and shapes of leaf characters exhibit highly structured and predictable patterns of variation in response to the local climate. Several procedures have been developed that quantify covariation between the relative abundance of plant character states and the states of climate variables as a means of estimating paleoclimate parameters. One of the most widely used of these is the Climate Leaf Analysis Multivariate Program (CLAMP). The consistency, accuracy and reliability with which leaf characters can be identified and assigned to CLAMP character-state categories is critical to the accuracy of all CLAMP analyses. Here we report results of a series of performance tests for an image-based, fully automated at the point of use, leaf character scoring system that can be used to generate CLAMP leaf character state data for: leaf bases (acute, cordate and round), leaf apices (acute, attenuate), leaf shapes (ovate, elliptical and obovate), leaf lobing (unlobed, lobed), and leaf aspect ratios (length/width). This image-based system returned jackknifed identification accuracy ratios of between 87% and 100%. These results demonstrate that automated image-based identification systems have the potential to improve paleoenvironmental inferences via the provision of accurate, consistent and rapid CLAMP leaf-character identifications. More generally, our results provide strong support for the feasibility of using fully automated, image-based morphometric procedures to address the general problem of morphological character-state identification.

Type
Articles
Information
Paleobiology , Volume 41 , Issue 4 , September 2015 , pp. 528 - 553
Copyright
Copyright © 2015 The Paleontological Society. All rights reserved. 

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

Bailey, I. W., and Sinnott, E. W.. 1915. A botanical index of Cretaceous and Tertiary climates. Science 41:831–834.CrossRefGoogle ScholarPubMed
Bailey, I. W., and Sinnott, E. W.. 1916. The climatic distribution of certain types of angiosperm leaves. American Journal of Botany 3:24–39.CrossRefGoogle Scholar
Belman, R. 1961. Adaptive control processes: a guided tour. Princeton University Press, Princeton.CrossRefGoogle Scholar
Clark, J. Y., Corney, D. P. A., and Tang, H. L.. 2012. Automated plant identification using artificial neural networks. Pp. 342–348. 2012 IEEE Symposium on computational intelligence in bioinformatics and computational biology (CIBCB). IEEE, San Diego, California.CrossRefGoogle Scholar
Colquhoun, W. P. 1959. The effect of a short rest pause on inspection efficiency. Ergonomics 2:367–372.CrossRefGoogle Scholar
Colquhoun, W. P 1982. Biological rhythms and performance. Pp. 59–86In W. B. Webb, ed. Biological rhythms, sleep, and performance. Wiley, Cichester.Google Scholar
Cope, J. S., Corney, D., Clark, J. Y., Remagnino, P., and Wilkin, P.. 2012. Plant species identification using digital morphometrics: a review. Expert Systems with Applications 39:7562–7573.CrossRefGoogle Scholar
Corney, D. P. A., Tang, H. L., Clark, J. Y., Hu, Y., and Jin, J.. 2012a. Automating digital leaf measurement: The tooth, the whole tooth, and nothing but the tooth. PLoS One. Public Library of Science.CrossRefGoogle Scholar
Corney, D. P. A., Clark, J. Y., Tang, H. L., and Wilkin, P.. 2012b. Automatic extraction of leaf characters from herbarium specimens. Taxon 61:231–244.CrossRefGoogle Scholar
Culverhouse, P. F., MacLeod, N., Williams, R., Benfield, M. C., Lopes, R. M., and Picheral, M.. 2013. An empirical assessment of the consistency of taxonomic identifications. Marine Biology Research 10(1), 73–84.CrossRefGoogle Scholar
Culverhouse, P. F., Williams, R., Reguera, B., Herry, V., and González-Gil, S.. 2003. Do experts make mistakes? A comparison of human and machine identification of dinoflagellates. Marine Ecology Progress Series 247:17–25.CrossRefGoogle Scholar
Culverhouse, P. F. R., MacLeod, N., Williams, R., Benfield, M. C., Lopes, R. M., and Picheral, M.. 2013. An empirical assessment of the consistency of taxonomic identifications. Marine Biology Research 10:73–84.CrossRefGoogle Scholar
Evans, J. S. B. T. 1987. Bias in human reasoning: causes and consequences. Laurence Erlbuam Associates, Hove.Google Scholar
Fox, J. G. 1971. Background music and industrial efficiency — a review. Applied Ergonomics June, 70–73.CrossRefGoogle ScholarPubMed
Ginsburg, R. N. 1997a. An attempt to resolve the controversy over the end-Cretaceous extinction of planktic foraminifera at El Kef, Tunisia using a blind test. Introduction: background and procedures. Marine Micropaleontology 29:67–68.CrossRefGoogle Scholar
Ginsburg, R. N 1997b. Perspectives on the blind test. Marine Micropaleontology 29:101–103.CrossRefGoogle Scholar
Godfrey-Smith, P. 2003. Theory and reality: an introduction to the philosophy of science. University of Chicago Press, Chicago, Ill., London.CrossRefGoogle Scholar
Gregory-Wodzicki, K. M. 2000. Relationships between leaf morphology and climate, Bolivia: implications for estimating paleoclimate from fossil floras. Paleobiology 26:668–688.2.0.CO;2>CrossRefGoogle Scholar
Huff, P. M., Wilf, P., and Azumah, E. J.. 2003. Digital Future for paleoclimate estimation from fossil leaves? Preliminary results. Palaios 18(3), 266–274.2.0.CO;2>CrossRefGoogle Scholar
Jacques, F. M. B., Sub, T., Spicer, R. A., Xing, Y., Huang, Y., Wanga, W., and Zhoub, Z.. 2011. Leaf physiognomy and climate: Are monsoon systems different? Global and Planetary Change 76:56–62.CrossRefGoogle Scholar
Kaesler, R. L. 1993. A window of opportunity: peering into a new century of paleontology. Journal of Paleontology 67:329–333.CrossRefGoogle Scholar
Kovach, W. L., and Spicer, R. A.. 1996. Canonical correspondence analysis of leaf physiognomy:a contribution to the development of a new palaeoclimatological tool. Palaeoclimates 2:125–138.Google Scholar
MacLeod, N. 2002a. Geometric morphometrics and geological form-classification systems. Earth-Science Reviews 59:27–47.CrossRefGoogle Scholar
MacLeod, N 2002b. Phylogenetic signals in morphometric data. Pp. 100–138. In N. MacLeod, and P. L. Forey, eds. Morphology, shape and phylogeny. Taylor & Francis, London.CrossRefGoogle Scholar
MacLeod, N 2005. Shape models as a basis for morphological analysis in paleobiological systematics: dicotyledonous leaf physiography. Bulletins of American Paleontology 369:219–238.Google Scholar
MacLeod, N 2007. Automated taxon identification in systematics: theory, approaches, and applications. CRC Press, Taylor & Francis Group, London.CrossRefGoogle Scholar
MacLeod, N——. 2015. The direct analysis of digital images (eigenimage) with a comment on the use of discriminant analysis in morphometrics. Pp. 156–182. In P. E. Lestrel, ed. Proceedings of the Third International Symposium on Biological Shape Analysis. World Scientific, Singapore.Google Scholar
MacLeod, N., Benfield, M., and Culverhouse, P. F.. 2010. Time to automate identification. Nature 467:154–155.CrossRefGoogle ScholarPubMed
MacLeod, N., Krieger, J., and Jones, K. E.. 2013. Geometric morphometric approaches to acoustic signal analysis in mammalian biology. Hystrix 24:116–125.Google Scholar
Manly, B. F. J. 1994. Multivariate statistical methods: a primer. Chapman & Hall, Bury, St. Edmonds, Suffolk.Google Scholar
Manly, B. F. J 1997. Randomization, bootstrap and Monte Carlo methods in biology. Chapman Hall, London.Google Scholar
Mitteröcker, P., and Bookstein, F. L.. 2011. Linear discrimination, ordination, and the visualization of selection gradients in modern morphometrics. Evolutionary Biology 38:100–114.CrossRefGoogle Scholar
Royer, D. L., Wilf, P., Janesko, D. A., Kowalski, E. A., and Dilcher, D. L.. 2005. Correlations of climate and plant ecology to leaf size and shape: potential proxies for the fossil record. American Journal of Botany 92:1141–1151.CrossRefGoogle ScholarPubMed
Royer, D. L., Peppe, D. J., Wheeler, E. A., and Niinemets, U.. 2012. Roles of climate and functional traits in controlling toothed vs. untoothed leaf margins. American Journal of Botany 99(5), 915–922.CrossRefGoogle ScholarPubMed
Sokal, R. R. 1974. Classification: purposes, principles, progress, prospects. Science 185:1115–1123.CrossRefGoogle ScholarPubMed
Spicer, R. A. 2000. Leaf physiognomy and climate change. Pp. 244–264. In C. S., and R. P., eds. Biotic Response to Global change: the Last 145 Million Years. Cambridge University Press, Cambridge, U.K.CrossRefGoogle Scholar
Spicer, R. A 2008. CLAMP. Pp. 156–158. In V. Gornitz, ed. Encyclopedia of paleoclimatology and ancient environments. Dordrecht, Germany.Google Scholar
Spicer, R. A., Bera, S., De Bera, S., Spicer, T. E. V., Srivastava, G., Mehrotra, R., Mehrotra, N., and Yang, J.. 2011. Why do foliar physiognomic climate estimates sometimes differ from those observed? Insights from taphonomic information loss and a CLAMP case study from the Ganges Delta. Palaeogeography, Palaeoclimatology, Palaeoecology 302(3–4):381–395.CrossRefGoogle Scholar
Spicer, R. A., Valdes, P. J., Spicer, T. E. V., Craggs, H. J., Srivastava, G., Mehrotra, R. C., and Yang, J.. 2009. New developments in CLAMP: calibration using global gridded meteorological data. Palaeogeography, Palaeoclimatology, Palaeoecology 283:91–98.CrossRefGoogle Scholar
Spicer, R. A., Valdes, P. J., Spicer, T. E. V., Craggs, H. J., Srivastava, G., Mehrotra, R. C., and Yang, J.. 2010. Quantification of uncertainties in fossil leaf paleoaltimetry: Does leaf size matter? Tectonics 29(6), TC6001.CrossRefGoogle Scholar
Spicer, R. A., and Yang, J.. 2010. Quantification of uncertainties in fossil leaf paleoaltimetry: Does leaf size matter? Tectonics 29(6):TC6001.CrossRefGoogle Scholar
Steart, D. C., Spicer, R. A., and Bamford, M. K.. 2010. Is southern Africa different? An investigation of the relationship between leaf physiognomy and climate in southern African mesic vegetation. Review of Palaeobotany and Palynology 162:607–620.CrossRefGoogle Scholar
Su, T., Xing, Y.-W., Liu, Y.-S., Jacques, F. M. B., Chen, W.-Y., Huang, Y.-J., and Zhou, Z.-K.. 2010. Leaf margin analysis: a new equation from humid to mesic forests in China. Palaios 25:234–238.CrossRefGoogle Scholar
Sykes, B., Mullis, R. J., Hagenmuller, C., Melton, T., and Sartori, M.. 2014. Genetic analysis of hair samples attributed to yeti, bigfoot and other anomalous primates. Proceedings of the Royal Society B: Biological Sciences 281(1789):10.1098/rspb.2014.0161.Google ScholarPubMed
Weimann, M. C., Manchester, S. R., Dilcher, D. L., Hinojosa, L. F., and Wheeler, E. A.. 1998. Estimation of temperature and precipitation from morphological characters of dicotyledonous leaves. American Journal of Botany 85:1796–1802. Biological Sciences.CrossRefGoogle Scholar
Wilf, P. 1997. When are leaves good thermometers? A new case for leaf margin analysis. Paleobiology 23:373–390.CrossRefGoogle Scholar
Wilf, P., Wing, S. L., Greenwood, D. R., and Greenwood, C. L.. 1998. Using fossil leaves as paleoprecipitation indicators: an Eocene example. Geology 26:203–206.2.3.CO;2>CrossRefGoogle Scholar
Wolfe, J. A. 1995. Paleoclimatic estimates from Tertiary leaf assemblages. Annual Review of Earth and Planetary Science 23:119–142.CrossRefGoogle Scholar
Wolfe, J. A., and Upchurch, G. R.. 1987. North American nonmarine climates and vegetation during the Late Cretaceous. Palaeogeography, Palaeoclimatology, Palaeoecology 61:33–77.CrossRefGoogle Scholar
Zachariasse, W. J., Riedel, W. R., Sanfilippo, A., Schmidt, R. R., Brolsma, M. J., Schrader, H. J., Gersonde, R., Drooger, M. M., and Brokeman, J. A.. 1978. Micropaleontological counting methods and techniques — an exercise on an eight meters section of the Lower Pliocene of Capo Rossello, Sicily. Utrecht Micropaleontological Bulletins 17:1–265.Google Scholar
Zimek, A., Schubert, E., and Kriegel, H.-P.. 2012. A survey on unsupervised outlier detection in high-dimensional numerical data. Statistical Analysis and Data Mining 5(5), 363–387.CrossRefGoogle Scholar