Title: Occurrence and dominance of six Pacific Northwest conifer species
Abstract: Journal of Vegetation ScienceVolume 21, Issue 3 p. 586-596 Occurrence and dominance of six Pacific Northwest conifer species Todd A. Schroeder, Todd A. Schroeder Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC, Canada.Search for more papers by this authorAndreas Hamann, Andreas Hamann Department of Renewable Resources, University of Alberta, Edmonton, 739 General Services Building, Edmonton, AB, Canada.Search for more papers by this authorTongli Wang, Tongli Wang Department of Forest Sciences, Centre for Forest Conservation Genetics, University of British Columbia, 2424 Main Mall, Vancouver, BC Canada.Search for more papers by this authorNicholas C. Coops, Nicholas C. Coops Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC, Canada.Search for more papers by this author Todd A. Schroeder, Todd A. Schroeder Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC, Canada.Search for more papers by this authorAndreas Hamann, Andreas Hamann Department of Renewable Resources, University of Alberta, Edmonton, 739 General Services Building, Edmonton, AB, Canada.Search for more papers by this authorTongli Wang, Tongli Wang Department of Forest Sciences, Centre for Forest Conservation Genetics, University of British Columbia, 2424 Main Mall, Vancouver, BC Canada.Search for more papers by this authorNicholas C. Coops, Nicholas C. Coops Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC, Canada.Search for more papers by this author First published: 13 April 2010 https://doi.org/10.1111/j.1654-1103.2009.01163.xCitations: 18 Schroeder, T. A. (corresponding author, [email protected]) & Coops, N. C. ([email protected]), Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC, Canada.Hamann, A. ([email protected]), Department of Renewable Resources, University of Alberta, Edmonton, 739 General Services Building, Edmonton, AB, Canada.Wang, T. ([email protected]), Department of Forest Sciences, Centre for Forest Conservation Genetics, University of British Columbia, 2424 Main Mall, Vancouver, BC Canada. Co-ordinating Editor: Ingolf Kühn. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Abstract Questions: Can probability of occurrence and dominance be accurately estimated for six important conifer species with varying range sizes? Does range size impact the accuracy of species probability of occurrence models? Is species predicted probability of occurrence significantly related to observed dominance? Location: Pacific Northwest region, North America (60°–40°N, 140°–110°W). Methods: This study develops near range-wide predictive distribution maps for six important conifer species (Pseudotsuga menziesii, Tsuga heterophylla, Pinus contorta, Thuja plicata, Larix occidentalis, and Picea glauca) using forest inventory data collected across the United States and Canada. Species model accuracies are compared with range size using a rank scoring system. A suite of climate and topographic predictor variables are used to investigate environmental constraints that limit species range and quantify relationships between species predicted probability of occurrence and dominance at both plot and landscape scales. Results: Evaluation statistics revealed significant and accurate probability of occurrence models were developed for all six species. Based on ranked evaluation statistics, Tsuga heterophylla had highest overall model accuracy (statistic rank score=5) and Pinus contorta the lowest (statistic rank score=17). Across species, ranked evaluation statistics also revealed a pattern of decreasing model accuracy with increasing range size. At plot level, correlations between dominance and probability of occurrence were weakly positive for all species with only half of the species having statistically significant correlations. Pseudotsuga menziesii had the highest correlation (r=0.36, P<0.001) and Thuja plicata lowest (r=0.038, P=0.799). At the 50-km scale, correlations between dominance and probability of occurrence improved for all species except Pinus contorta. Pseudotsuga menziesii displayed the highest correlation (r=0.68, P<0.001) and Thuja plicata the lowest (r=0.07, P>0.709). Conclusions: Species probability of occurrence model accuracy decreased with increasing range size. The strength and significance of correlations between probability of occurrence and dominance varied considerably by species and across spatial scales. Apart from Pseudotsuga menziesii and L. occidentalis, the results suggest that probability of occurrence is not a consistently reliable surrogate for species dominance in Pacific Northwest forests. We demonstrate how the degree of correlation between species occurrence and dominance can be used as an indicator of how well predictions of occurrence characterize the optimal niche of a species. Supporting Information Appendix S1. Species probability of occurrence maps for a.) Pseudotsuga menziesii, b.) Tsuga heterophylla, c.) Pinus contorta, d.) Thuja plicata, e.) Larix occidentalis, and f.) Picea glauca. Appendix S2. Predictor variables and evaluation statistics for the top three NPMR models per species. Appendix S3. Dominance maps for a.) Pseudotsuga menziesii, b.) T.suga heterophylla, c.) Pinus contorta, d.) Thuja plicata, e.) Larix occidentalis, and f.) Picea glauca. Bold lines indicate Little Jr. (1971) range boundary. Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article. Filename Description JVS_1163_sm_001.ppt1.2 MB Supporting info item Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. References Anon. (Alberta Sustainable Resource Development) 2005. Permanent sample plot (PSP) field procedures manual. Public Lands and Forests Division, Forest Management Branch, Edmonton, AB, CA. Anon. (B.C. Ministry of Forests) 2001. Mensuration data from the provincial ecology program. B.C. Ministry of Forests Workshop paper 62. Araújo, M.B. & New, M. 2006. Ensemble forecasting of species distributions. Trends in Ecology and Evolution 22: 42–47. Austin, M.P., Nicholls, A.O. & Margules, C.R. 1990. Measurement of the realized qualitative niche: environmental niches of five Eucalyptus species. Ecological Monographs 60: 161–177. Bechtold, W.A. & Patterson, P.L. 2005. The enhanced forest inventory and analysis program – national sampling design and estimation procedures. General Technical Report SRS-80, U.S. Department of Agriculture, Forest Service, Southern Research Station, Ashville, NC, US Clifford, P., Richardson, S. & Hemon, D. 1989. Assessing the significance of the correlation between 2 spatial processes. Biometrics 45: 123–134. Daly, C., Taylor, G.H., Gibson, W.P., Parzybok, T.W., Johnson, G.L. & Pasteris, P.A. 2000. High-quality spatial climate data sets for the United States and beyond. Transactions of the ASAE 43: 1957–1962. Daly, C., Gibson, W.P., Taylor, G.H., Johnson, G.L. & Pasteris, P. 2002. A knowledge-based approach to the statistical mapping of climate. Climate Research 22: 99–113. Dormann, C.F., Purschke, O., Garćia Márquez, J.R., Lautenbach, S. & Schröder, B. 2008. Components of uncertainty in species distributions analysis: a case study of the great grey shrike. Ecology 89: 3371–3386. Dutilleul, P. 1993. Modifying the t-test for assessing the correlation between 2 spatial processes. Biometrics 49: 305–314. Flora of North America Editorial Comittee (eds). 1993. Flora of North America North of Mexico. 15 vols., New York and Oxford. Fortin, M-J. & Payette, S. 2002. How to test the significance of the relation between spatially autocorrelated data at the landscape scale: a case study using fire and forest maps. Ecoscience 9: 213–218. Franklin, J. & Dyrness, C. 1988. Natural Vegetation of Oregon and Washington. Oregon State University Press, Corvallis, OR, US. Gaston, K.J. 1996. The multiple forms of the interspecific abundance–distribution relationship. Oikos 76: 211–220. Hamann, A. & Wang, T.L. 2005. Models of climatic normals for genecology and climate change studies in British Columbia. Agricultural and Forest Meteorology 128: 211–221. Hamann, A. & Wang, T. 2006. Potential effects of climate change on ecosystem and tree species distribution in British Columbia. Ecology 87: 2773–2786. Hanley, J.A. & McNeil, B.J. 1982. The meaning and use of the area under a ROC curve. Radiology 143: 29–36. Hutchinson, G.E. 1957. Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology 22: 415–427. Hutchinson, G.E. 1965. The niche: an abstractly inhabited hypervolume. In: The Ecological Theatre and the Evolutionary Play. pp. 26–78. Yale University Press, New Haven, CT, US. Iverson, L.R. & Prasad, A.M. 1998. Predicting abundance of 80 tree species following climate change in the eastern United States. Ecological Monographs 68: 465–485. Iverson, L.R., Prasad, A. & Schwartz, M.W. 1999. Modeling potential future individual tree-species distributions in the eastern United States under a climate change scenario: a case study with Pinus virginiana. Ecological Modeling 115: 77–93. Kühn, I. 2007. Incorporating spatial autocorrelation may invert observed patterns. Diversity and Distributions 13: 66–69. Landsberg, J.J. & Waring, R.H. 1997. A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance, and partitioning. Forest Ecology and Management 95: 209–228. Little, E.L. Jr. 1971. Atlas of United States trees. Conifers and Important Hardwoods. Vol. 1, USDA Miscellaneous Publication 1146, Washington, DC, US. McCune, B. & Mefford, M.J. 2004. HyperNiche. Non-parametric multiplicative habitat modeling. Version 1.38. MjM Software, Gleneden Beach, OR, US. McCune, B. 2006. Non-parametric habitat models with automatic interactions. Journal of Vegetation Science 17: 819–830. Moisen, G.G., Freeman, E.A., Blackard, J.A. Frescino, T.S., Zimmerman, N.E. & Edwards, T.C. 2006. Predicting tree species presence and basal area in Utah: a comparison of stochastic gradient boosting, generalized additive models, and tree-based methods. Ecological Modelling 199: 176–187. Nielson, S.E., Johnson, C.J., Heard, D.C. & Boyce, M.S. 2005. Can models of presence–absence be used to scale abundance? Two case studies considering extremes in life history. Ecography 28: 197–208. Pearson, R.G. & Dawson, T.P. 2003. Predicting the impacts of climate change on the distribution of species: are bioclimatic envelope models useful? Global Ecology and Biogeography 12: 361–371. Pierce, K.B., Lookingbill, T. & Urban, D. 2005. A simple method for estimating potential relative radiation (PRR) for landscape-scale vegetation analysis. Landscape Ecology 20: 137–147. Rehfeldt, G.E., Crookson, N.L., Warwell, M.V. & Evans, J.S. 2006. Empirical analysis of plant–climate relationships for the western United States. International Journal of Plant Science 167: 1123–1150. Shafer, S.L., Bartlein, P.J. & Thompson, R.S. 2001. Potential changes in the distributions of western North America tree and shrub taxa under future climate scenarios. Ecosystems 4: 200–215. Sibson, R. 1981. A brief description of natural neighbour interpolation. In: V. Barnett (ed.) Interpreting multivariate data. pp. 21–36. John Wiley & Sons, New York, NY, US. Wang, T., Hamann, A., Spittlehouse, D.L. & Aitken, S.N. 2006. Development of scale-free climate data for western Canada for use in resource management. International Journal of Climatology 26: 383–397. Wright, D.H. 1991. Correlations between incidence and abundance are expected by chance. Journal of Biogeography 18: 463–466. Yost, A.C. 2008. Probabilistic modeling and mapping of plant indicator species in a Northeast Oregon industrial forest, USA. Ecological Indicators 8: 46–56. Citing Literature Volume21, Issue3June 2010Pages 586-596 ReferencesRelatedInformation