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Predicting the distribution of the air pollution sensitive lichen species Usnea hirta

Published online by Cambridge University Press:  08 June 2012

Gajendra SHRESTHA ,
Steven L. PETERSEN  and
Larry L. ST. CLAIR
Gajendra SHRESTHA
Affiliation:
Biology Department and M. L. Bean Life Science Museum, Brigham Young University, Provo, Utah, USA. Email: gssm_us@yahoo.com
Steven L. PETERSEN
Affiliation:
Department of Plant and Wildlife Sciences, Brigham Young University, Provo, Utah, USA
Larry L. ST. CLAIR
Affiliation:
Biology Department and M. L. Bean Life Science Museum, Brigham Young University, Provo, Utah, USA. Email: gssm_us@yahoo.com
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Abstract

Usnea hirta, an important member of the lichen family Parmeliaceae, has long been used as a bio-monitor of air pollution, particularly of sulphur dioxide in North America. Although U. hirta has a wide geographical distribution, it is important to be able to identify accurately the optimal habitat conditions for air pollution-sensitive species, thus making it possible to more effectively and efficiently establish air quality bio-monitoring stations. We modelled the distribution of U. hirta as a function of nine variables, five macroclimatic variables: average monthly precipitation, average monthly minimum temperature, average monthly maximum temperature, solar radiation, and integrated moisture index, and four topographic variables: elevation, slope, aspect, and land forms and uses for the White River National Forest, Colorado. The response variable was developed based on the presence or absence of U. hirta at each of 72 bio-monitoring baseline sites established in selected portions of four intermountain area states. Our model was developed using Non-Parametric Multiplicative Regression (NPMR) analysis, a modelling approach that analyzes environmental gradients, or predictor variables, against known locations for individuals of the model species. Finally, we evaluated our model on the basis of log β values and overall improvement over a naïve model and the Monte Carlo Permutation Test with 1000 randomized runs. The best model for U. hirta included four variables – solar radiation, average monthly precipitation, and average monthly minimum and maximum temperatures (log β=3·68). Among these four variables, average monthly maximum temperature was the most influential predictor (sensitivity=0·71) for the distribution of U. hirta. The occurrence rate for U. hirta, based on field validation, was 45·5%, 65·4%, and 70·4% for low, medium, and high probability areas, respectively. This study showed that our model was successful in predicting the distribution of U. hirta in the White River National Forest. Based on these results, the north-eastern and western portions of the forest appear to offer the most favourable conditions for the installation of future air quality bio-monitoring baseline sites.

Keywords

habitat niche modelNPMRWhite River National ForestGIS
Type
Research Article
Information
The Lichenologist , Volume 44 , Issue 4 , July 2012 , pp. 511 - 521
Creative Commons
This is the work of the US Government and is not subject to copyright protection in the US
Copyright
Copyright © British Lichen Society2012. This is the work of the US Government and is not subject to copyright protection in the US

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References

Austin, M. P. (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modeling. Ecological Modelling 157: 101–118.CrossRefGoogle Scholar
Bergamini, A., Stofer, S., Bolliger, J. & Scheidegger, C. (2007) Evaluating macrolichens and environmental variables as predictors of the diversity of epiphytic microlichens. Lichenologist 39: 475–489.CrossRefGoogle Scholar
Berryman, S. & McCune, B. (2006) Estimating epiphytic macrolichen biomass from topography, stand structure and lichen community data. Journal of Vegetation Science 17: 157–170.CrossRefGoogle Scholar
Binder, M. D. & Ellis, C. J. (2008) Conservation of the rare British lichen Vulpicida pinastri: changing climate, habitat loss and strategies for mitigation. Lichenologist 40: 63–79.CrossRefGoogle Scholar
Bolliger, J., Bergamini, A., Stofer, S., Kienast, F. & Scheidegger, C. (2007) Predicting the potential spatial distributions of epiphytic lichen species at the landscape scale. Lichenologist 39: 279–291.CrossRefGoogle Scholar
Brodo, I. M. (1961) Transplant experiments with corticolous lichens using a new technique. Ecology 42: 838–841.CrossRefGoogle Scholar
Brodo, I. M., Sharnoff, S. D. & Sharnoff, S. (2001) Lichens of North America. New Haven and London: Yale University Press.Google Scholar
Casazza, G., Zappa, E., Mariotti, M. G., Médail, F. & Minuto, L. (2008) Ecological and historical factors affecting distribution pattern and richness of endemic plant species: the case of the Maritime and Ligurian Alps hotspot. Diversity and Distributions 14: 47–58.CrossRefGoogle Scholar
Clerc, P. (1997) Notes on the genus Usnea Dill. ex Adan-son. Lichenologist 29: 209–215.CrossRefGoogle Scholar
Conti, M. E. & Cecchetti, G. (2001) Biological monitoring: lichens as bioindicators of air pollution assessment – a review. Environmental Pollution 114: 471–492.CrossRefGoogle ScholarPubMed
Coxson, D. S. & Stevenson, S. K. (2007) Growth rate responses of Lobaria pulmonaria to canopy structure in even-aged and old-growth cedar-hemlock forests of central-interior British Columbia, Canada. Forest Ecology and Management 242: 5–16.CrossRefGoogle Scholar
Cristofolini, F., Giordani, P., Gottardini, E. & Modenesi, P. (2008) The response of epiphytic lichens to air pollution and subsets of ecological predictors: a case study from the Italian Prealps. Environmental Pollution 151: 308–317.CrossRefGoogle ScholarPubMed
Dietrich, M. & Scheidegger, C. (1997) Frequency, diversity and ecological strategies of epiphytic lichens in the Swiss Central Pleateau and the Pre-Alps. Lichenologist 29: 237–258.CrossRefGoogle Scholar
Edwards, J. T. C., Cutler, D. R., Zimmermann, N. E., Geiser, L. & Moisen, G. G. (2006) Effects of sample survey design on the accuracy of classification tree models in species distribution models. Ecological Modelling 199: 132–141.CrossRefGoogle Scholar
Ellis, C. J. & Coppins, B. J. (2006) Contrasting functional traits maintain lichen epiphyte diversity in response to climate and autogenic succession. Journal of Biogeography 33: 1643–1656.CrossRefGoogle Scholar
Ellis, C. J., Coppins, B. J. & Dawson, T. P. (2007) Predicted response of the lichen epiphyte Lecanora populicola to climate change scenarios in a clean-air region of Northern Britain. Biological Conservation 135: 396–404.CrossRefGoogle Scholar
Fenton, N. J. & Bergeron, Y. (2008) Does time or habitat make old-growth forests species rich? Bryophyte richness in boreal Picea mariana forests. Biological Conservation 141: 1389–1399.CrossRefGoogle Scholar
Garty, J., Kauppi, M. & Kauppi, A. (1997) The influence of air pollution on the concentration of airborne elements and on the production of stress-ethylene in the lichen Usnea hirta (L.) Weber em. Mot. transplanted in urban sites in Oulu, N. Finland. Archives of Environmental Contamination and Toxicology 32: 993–1009.CrossRefGoogle Scholar
Giordani, P. (2007). Is the diversity of epiphytic lichens a reliable indicator of air pollution? A case study from Italy. Environmental Pollution 146: 317–323.CrossRefGoogle ScholarPubMed
Giordani, P. & Will-Wolf, S. (2006). Variables influencing the distribution of epiphytic lichens in heterogeneous areas: a case study for Liguria, NW Italy. Journal of Vegetation Science 17: 195–206.CrossRefGoogle Scholar
Glavich, D. A., Geiser, L. H. & Mikulin, A. G. (2005) Rare epiphytic coastal lichen habitats, modeling, and management in the Pacific Northwest. Bryologist 108: 377–390.CrossRefGoogle Scholar
Grundel, R. & Pavlovic, N. E. (2007) Response of bird species densities to habitat structure and fire history along a midwestern open-forest gradient. The Condor 109: 734–749.CrossRefGoogle Scholar
Gustafsson, L., Appelgren, L., Jonsson, F., Nordin, U., Persson, A. A. & Weslien, J. O. (2004) High occurrence of red-listed bryophytes and lichens in mature managed forests in boreal Sweden. Basic and Applied Ecology 5: 123–129.CrossRefGoogle Scholar
Hyvärinen, M., Halonen, P. & Kauppi, M. (1992) Influence of stand age and structure on the epiphytic lichen vegetation in the middle-boreal forests of Finland. Lichenologist 24: 165–180.CrossRefGoogle Scholar
Iverson, L. R., Dale, M. E., Scott, C. T. & Prasad, A. (1997) A GIS-derived intergrated moisture index to predict forest composition and productivity of Ohio forests (USA). Landscape Ecology 12: 331–348.CrossRefGoogle Scholar
Jeffreys, H. (1961) Some tests of significance, treated by the theory of probability. Proceedings of the Cambridge Philosophical Society 31: 203–222.CrossRefGoogle Scholar
Jozwiak, M. (2009) Influence of cement industry on accumulation of heavy metals in bioindicators. Ecological Chemistry and Engineering 16: 323–334.Google Scholar
Kohler, G. R. (2007) Predators associated with hemlock woolly adelgid (Hemiptera: Adelgidae) infested western hemlock in the Pacific Northwest. Ph. D. thesis, Oregon State University.CrossRefGoogle Scholar
Leavitt, S. D., Johnson, L. A., Goward, T. & St. Clair, L. L. (2011) Species delimitation in taxonomically difficult lichen-forming fungi: an example from morphologically and chemically diverse Xanthoparmelia (Parmeliaceae) in North America. Molecular Phylogenetics and Evolution 60: 317–32.CrossRefGoogle ScholarPubMed
Loppi, S. (1996) Lichens as bioindicators of geothermal air pollution in central Italy. Bryologist 99: 41–48.CrossRefGoogle Scholar
McCune, B. (2006) Non-parametric habitat models with automatic interactions. Journal of Vegetation Science 17: 819–830.Google Scholar
McCune, B. & Mefford, M. J. (2004) HyperNiche. Nonparametric Multiplicative Habitat Modeling (Version 1). Gleneden Beach, Oregon, USA: MjM Software.Google Scholar
Radies, D., Coxson, D., Johnson, C. & Konwicki, K. (2009) Predicting canopy macrolichen diversity and abundance within old-growth inland temperate rainforests. Forest Ecology and Management 259: 86–97.CrossRefGoogle Scholar
St. Clair, S. B., St. Clair, L. L., Mangelson, N. F. & Weber, D. J. (2002 a) Influence of growth form on the accumulation of airborne copper by lichens. Atmospheric Environment 36: 5637–5644.CrossRefGoogle Scholar
St. Clair, S. B., St. Clair, L. L., Weber, D. J., Mangelson, N. F. & Eggett, D. L. (2002 b) Element accumulation patterns in foliose and fruticose lichens from rock and bark substrates in Arizona. Bryologist 105: 415–421.CrossRefGoogle Scholar
Thomson, J. W. (1984) American Arctic Lichens 1. The Macrolichens. New York: Columbia University Press.Google Scholar
Thuiller, W. (2004) Patterns and uncertainties of species' range shifts under climate change. Global Change Biology 10: 2020–2027.CrossRefGoogle Scholar
Thuiller, W., Araújo, M. B. & Lavorel, S. (2004) Do we need land-cover data to model species distributions in Europe? Journal of Biogeography 31: 353–361.CrossRefGoogle Scholar
van Herk, C. M., Mathijissen-Spiekman, E. A. & de Zwart, D. (2003) Long distance nitrogen air pollution effects on lichens in Europe. Lichenologist 35: 347–359.CrossRefGoogle Scholar
Werth, S., Tommervik, H. & Elvebakk, A. (2005) Epiphytic macrolichen communities along regional gradients in northern Norway. Journal of Vegetation Science 16: 199–208.CrossRefGoogle Scholar
Yost, A. C. (2006) Probabilistic modeling of understory vegetation species in a northeastern Oregon industrial forest. Ph.D. dissertation, Oregon State University.Google Scholar
Yost, A. C. (2008) Probabilistic modeling and mapping of plant indicator species in a Northeast Oregon industrial forest, USA. Ecological Indicators 8: 46–56.CrossRefGoogle Scholar