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A comparative analysis of butterfly richness detection capacity of Pollard transects and general microhabitat surveys

Published online by Cambridge University Press:  02 July 2012

Abstract

Assessing biodiversity is essential in conservation biology but the resources needed are often limited. Citizen science, by which volunteers gather data at low cost, represents a potential solution for the lack of resources if it produces usable data for scientific means. Scientific inventories for butterflies are often performed with a Pollard transect, a standardised surveying technique that generates high-quality data. General microhabitat surveys (GMSs) are potentially more appealing to amateurs participating in citizen science projects because they are less constrained. We compare estimates of butterfly species richness acquired by Pollard transects to those obtained by GMSs. We demonstrate that GMSs allow surveyors to detect more butterfly species and a more complete portrait of local butterfly assemblages for the same number of individuals captured.

Résumé

La quantification de la biodiversité est indispensable en biologie de la conservation mais les ressources nécessaires sont souvent limitées. La participation citoyenne à la science, par laquelle des bénévoles récoltent à peu de frais des données, représente une solution potentielle au manque de ressources si les techniques d'inventaires utilisées par les amateurs peuvent produire des données utilisables à des fins scientifiques. Les inventaires scientifiques de papillons sont généralement effectués avec des transects Pollard, un type d'inventaire standardisé générant des données d'excellente qualité. L'inventaire général des microhabitats n'est pas aussi contraignant et est potentiellement plus approprié pour les amateurs désirant s'impliquer dans des projets de participation citoyenne à la science. Nous comparons les estimés obtenus par des transects Pollard à ceux obtenus par des inventaires généraux des microhabitats afin d’évaluer leur capacité respective à mesurer la richesse spécifique en papillons. Nous démontrons que les inventaires généraux des microhabitats détectent plus d'espèces de papillons et produisent un portrait plus complet de la richesse locale des assemblages de papillons que le transect Pollard pour le même nombre de captures.

Type
Original Article
Copyright
Copyright © Entomological Society of Canada 2012

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References

Bates, D., Maechler, M., Bolker, B. 2011. lme4: linear mixed-effects models using S4 classes, R package version 0.999375-41 [online]. Available from http://CRAN.R-project.org/package=lme4 [accessed 26 September 2011].Google Scholar
Bonney, R., Cooper, C.B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K.V., et al. 2009. Citizen science: a developing tool for expanding science knowledge and scientific literacy. BioScience, 59: 977984.CrossRefGoogle Scholar
Buddle, C.M., Beguin, J., Bolduc, E., Mercado, A., Sackett, T.E., Selby, R.D., et al. 2005. The importance and use of taxon sampling curves for comparative biodiversity research with forest arthropod assemblages. The Canadian Entomologist, 137: 120127.CrossRefGoogle Scholar
Burke, R.J., Fitzsimmons, J.M., Kerr, J.T. 2011. A mobility index for Canadian butterfly species based on naturalists’ knowledge. Biodiversity and Conservation, 20: 22732295.CrossRefGoogle Scholar
Chao, A., Chazdon, R.L., Colwell, R.K., Shen, T.-J. 2005. A new statistical approach for assessing similarity of species composition with incidence and abundance data. Ecology Letters, 8(2), 148159.CrossRefGoogle Scholar
Chazdon, R.L., Colwell, R.K., Denslow, J.S., Guariguata, M.R. 1998. Statistical methods for estimating species richness of woody regeneration in primary and secondary rain forests of northeastern Costa Rica. In Forest biodiversity research, monitoring and modeling: conceptual background and Old World case studies. Edited by F. Dallmeier and J.A. Comiskey. Parthenon Publishing Group, Carnforth, Lancashire. pp. 285309.Google Scholar
Colwell, R.K. 2009. EstimateS: Statistical estimation of species richness and shared species from samples. Version 8.2. User's guide and application [online]. Available from http://viceroy.eeb.uconn.edu/estimates [accessed 30 April 2012].Google Scholar
Colwell, R.K., Mao, C.X., Chang, J. 2004. Interpolating, extrapolating, and comparing incidence-based species accumulation curves. Ecology, 85: 27172727.CrossRefGoogle Scholar
Crall, A.W., Newman, G.J., Stohlgren, T.J., Holfelder, K.A., Graham, J., Waller, D.M. 2011. Assessing citizen science data quality: an invasive species case study. Conservation Letters, 4: 433442; doi: 10.1111/j.1755-263X.2011.00196.x.CrossRefGoogle Scholar
Davros, N.M., Debinski, D.M., Reeder, K.F., Hohman, W.L. 2006. Butterflies and the continuous conservation reserve program filter strips: landscape considerations. Wildlife Society Bulletin, 34: 936943.CrossRefGoogle Scholar
Fox, J. Weisberg, S. 2010. An {R} companion to applied regression, second edition [online]. Sage, Thousand Oaks, California. Available from http://socserv.socsci.mcmaster.ca/jfox/Books/Companion [accessed 26 September 2011].Google Scholar
Grill, A. Cleary, D.F.R. 2003. Diversity patterns in butterfly communities of the Greek nature reserve Dadia. Biological Conservation, 114: 427436.CrossRefGoogle Scholar
Kerr, J.T., Sugar, A., Packer, L. 2000. Indicator taxa, rapid biodiversity assessment, and nestedness in an endangered ecosystem. Conservation Biology, 14: 17261734.CrossRefGoogle Scholar
Kuefler, D., Haddad, N.M., Hall, S., Hudgens, B., Bartel, B., Hoffman, E. 2008. Distribution, population structure, and habitat use of the endangered St. Francis’ satyr butterfly, Neonympha mitchellii francisci . American Midland Naturalist, 159: 298320.CrossRefGoogle Scholar
Kühn, E., Feldmann, R., Harpke, A., Hirneisen, N., Musche, M., Leopold, P., et al. 2008. Getting the public involved in butterfly conservation: lessons learned from a new monitoring scheme in Germany. Israel Journal of Ecology and Evolution, 54: 89103.CrossRefGoogle Scholar
Layberry, R.A., Hall, P.W., Lafontaine, J.D. 1998. The butterflies of Canada. University of Toronto Press, Toronto.CrossRefGoogle Scholar
May, R.M. 1988. How many species are there on earth? Science, 241(4872), 14411449.CrossRefGoogle ScholarPubMed
Natuhara, Y., Imai, C., Takahashi, M. 1999. Pattern of land mosaics affecting butterfly assemblages at Mt. Ikoma, Osaka, Japan. Ecological Research, 14: 105118.CrossRefGoogle Scholar
Opler, P.A., Lotts, K., Naberhaus, T. 2011. Butterflies and moths of North America [online]. Big Sky Institute, Bozeman, Montana. Available from http://www.butterfliesandmoths.org [accessed 25 June 2011].Google Scholar
Pollard, E. Yates, T.J. 1993. Monitoring butterflies for ecology and conservation. Chapman & Hall, London.Google Scholar
R Development Core Team. 2010. R: a language and environment for statistical computing [online]. R Foundation for Statistical Computing, Vienna. Available from http://www.R-project.org [accessed 27 December 2010].Google Scholar
Sutcliffe, O.L., Thomas, C.D., Moss, D. 1996. Spatial synchrony and asynchrony in butterfly population dynamics. Journal of Animal Ecology, 65: 8595.CrossRefGoogle Scholar
Warren, M.S., Hill, J.K., Thomas, J.A., Asher, J., Fox, R., Huntley, B., et al. 2001. Rapid responses of British butterflies to opposing forces of climate and habitat change. Nature, 414: 6569.CrossRefGoogle ScholarPubMed