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2 - Mortality: the essence of a healthy forest

Published online by Cambridge University Press:  05 June 2012

By
L. Zhang ,
B.D. Rubin  and
P.D. Manion
Edited by
John D. Castello  and
Stephen A. Teale
L. Zhang
Affiliation:
State University of New York
B.D. Rubin
Affiliation:
Western Ontario University
P.D. Manion
Affiliation:
Cazenovia, NY
John D. Castello
Affiliation:
State University of New York College of Environmental Science and Forestry
Stephen A. Teale
Affiliation:
State University of New York College of Environmental Science and Forestry
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Summary

Introduction

Chapter 1 explained the importance of diameter distributions to assess sustainability as a component of forest health. In this chapter, we further develop the method and its application, and we explore complimentary statistical tools that have been developed to analyze other aspects of forest condition. The main objective of this chapter is to present the methodology used to calculate baseline mortality, the role of diameter distributions and model fitting in this process, and to review additional conceptual methods to assess aspects of forest health. We make no effort to distinguish among different mortality-inducing agents. We first will look at diameter distributions and how to determine baseline mortality, and then examine other techniques that consider management objectives and spatial scales.

Recently, the importance of determining the causes and consequences of tree mortality at a large spatial scale has been realized (Hansen and Goheen 2000; Holdenrieder et al., 2004). Determining the normal or baseline amount of disease or mortality is an essential part of this effort. If disease or other disturbance is considered an impediment to the optimal use of forest resources, then the ideal disease level is zero. However, if disturbances, pests and diseases are viewed in their full ecological context, then some abundance greater than zero must be considered a “healthy amount of disease” (Manion 2003). We use the term “a healthy amount of disease” to refer to the level of mortality-inducing agents that will induce the needed mortality, regardless of the cause of the mortality.

Type
Chapter
Information
Forest Health
An Integrated Perspective
 , pp. 17 - 49
Publisher: Cambridge University Press
Print publication year: 2011

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References

Abrams, M.D. 1992. Fire and development of oak forests. BioScience 42: 346–353.CrossRefGoogle Scholar
Abrams, M.D. 1998. The red maple paradox. BioScience 48: 355–364.CrossRefGoogle Scholar
Alerich, C.L., Klevgard, L., Liff, C., and Miles, P.D. 2004. The Forest Inventory and Analysis Database: Database Description and User's Guide Version 1.7. 193.US Forest Service Forest Inventory and Analysis Program, Arlington, VA.Google Scholar
Avery, T. E. and Burkhart, H. E. 2002. Forest Measurements. 5th edition. McGraw-Hill, NY.Google Scholar
Bailey, R. L. and Dell, T. R. 1973. Quantifying diameter distributions with the Weibull function. Forest Science 19: 97–104.Google Scholar
Boscoe, F. P., McLaughlin, C., Schymura, M. J., and Kielb, C. L. 2003. Visualization of the spatial scan statistic using nested circles. Health & Place 9: 273–277.CrossRefGoogle ScholarPubMed
Castello, J. D., Leopold, D. J., and Smallidge, P. J. 1995. Pathogens, patterns and processes in forest ecosystems. BioScience 45: 16–24.CrossRefGoogle Scholar
Clutter, J. L., Fortson, J. C., Pienaar, L. V., et al. 1992. Timber Management: a Quantitative Approach. Krieger Publishing Company. Malabar, FL.Google Scholar
Condit, R., Sukumar, R., Hubbell, S. P., and Foster, R. B. 1998. Predicting population trends from size distributions: a direct test in a tropical tree community. American Naturalist 152: 495–509.CrossRefGoogle Scholar
Coomes, D. A., Duncan, R. P., Allen, R. B., and Truscott, J. 2003. Disturbances prevent stem size-density distributions in natural forests from following scaling relationships. Ecology Letters 6: 980–989.CrossRefGoogle Scholar
Coulston, J. W. and Riitters, K. H. 2003. Geographic analysis of forest health indicators using spatial scan statistics. Environmental Management 31: 764–773.CrossRefGoogle ScholarPubMed
Liocourt, F. 1898. De l'amenagement des sapinieres. Bulletin Triemestriel Societe Forestriere de Franche Comte et Belfort, Besancon: 396–409.Google Scholar
Dobbertin, M. 2005. Tree growth as an indicator of tree vitality and of tree reaction to environmental stress: a review. European Journal of Forest Research 124: 319–333.CrossRefGoogle Scholar
Duchesne, L., Ouimet, R., Moore, J. -D., and Paquin, R. 2005. Changes in structure and composition of maple-beech stands following sugar maple decline in Québec, Canada. Forest Ecology & Management 208: 223–236.CrossRefGoogle Scholar
Enquist, B. J. and Niklas, K. J. 2001. Invariant scaling relationships across tree-dominated communities. Nature 410: 655–660.CrossRefGoogle ScholarPubMed
Franklin, J. F., Shugart, H. H., and Harmon, M. E. 1987. Tree death as an ecological process: The causes, consequences, and variability of tree mortality. BioScience 37: 550–556.CrossRefGoogle Scholar
Gillis, M. D., Omule, A. Y., and Brierley, T. 2005. Monitoring Canada's forests: the National Forest Inventory. Forestry Chronicle 81: 214–221.CrossRefGoogle Scholar
Goff, F. G. and West, D. 1975. Canopy-understory interaction effects on forest -population structure. Forest Science 21: 98–108.Google Scholar
Goodburn, J. M. and Lorimer, C. G. 1999. Population structure in old-growth and managed northern hardwoods: an examination of the balanced diameter distribution concept. Forest Ecology & Management 118: 11–29.CrossRefGoogle Scholar
Grenier, Y., Blais, L., and Lavoie, É. 1991. Aire minimum d'échantillonnage ou nombre se points de prisme nécessaires pour établir la structure d'un peuplement inéquienne. Canadian Journal of Forest Research 21: 1632–1638.CrossRefGoogle Scholar
Hafley, W. L. and Schreuder, H. T. 1977. Statistical distributions for fitting diameter and height data in even-aged stands. Canadian Journal of Forest Research 7: 481–487.CrossRefGoogle Scholar
Hansen, E. M. and Goheen, E. M. 2000. Phellinus weirii and other native root pathogens as determinants of forest structure and process in western North America. Annual Review Phytopathology 38: 315–339.CrossRefGoogle ScholarPubMed
Hartmann, H., Beaudet, M., and Messier, C. 2008. Using longitudinal survival probabilities to test field vigor estimates in sugar maple (Acer saccharum Marsh.). Forest Ecology & Management 256: 1771–1779.CrossRefGoogle Scholar
Hawksworth, F. G. 1977. The 6-class dwarf mistletoe rating system. Gen. Tech. Rep. RM-48. USDA Forest Service, Rocky Mountain Forest and Range Experiment Station., Ft. Collins, CO.Google Scholar
Hyink, D. M. and Moser, J r., J. W. 1983. A generalized framework for projecting forest yield and stand structure using diameter distributions. Forest Science 29: 85–95.Google Scholar
Hett, J. M. 1971. A dynamic analysis of age in sugar maple seedlings. Ecology 52: 1071–1074.CrossRefGoogle Scholar
Hett, J. M. and Loucks, O. L. 1968. Application of life-table analyses to tree seedlings in Quetico Park, Ontario. Forestry Chronicle 44: 29–32.CrossRefGoogle Scholar
Hett, J. M. and Loucks, O. L. 1971. Sugar maple (Acer saccharum Marsh.) seedling mortality. Journal Ecology 59: 507–520.CrossRefGoogle Scholar
Hett, J .M. and Loucks, O. L. 1976. Age structure models of balsam fir and eastern hemlock. Journal Ecology 64: 1029–1044.CrossRefGoogle Scholar
Holdenrieder, O., Pautasso, M., Weisberg, P. J., and Lonsdale, D. 2004. Tree diseases and landscape processes: the challenge of landscape pathology. Trends in Ecology & Evolution 19: 446–452.CrossRefGoogle ScholarPubMed
Huang, L., Tiwari, R., Zuo, J., Kulldorff, M., and Feuer, E. J. 2009. Weighted normal spatial scan statistic for heterogeneous population data. Journal American Statistical Association 104: 886–898.CrossRefGoogle Scholar
Kamei, M. and Nakagoshi, N. 2006. Geographic assessment of present protected areas in Japan for representativeness of forest communities. Biodiversity Conservation 15: 4583–4600.CrossRefGoogle Scholar
Kohira, M. and Ninomiya, I. 2003. Detecting tree populations at risk for forest conservation management: using single-year vs. long-term inventory data. Forest Ecology & Management 174: 423–435.CrossRefGoogle Scholar
Kulldorff, M. 1997. A spatial scan statistic. Communication in Statistics – Theory & Methods 26: 1481–1496.CrossRefGoogle Scholar
Kulldorff, M. 2009. SaTScan User's Guide. Version 8.0. 65.Online at: www.satscan.org [Accessed November 2010].
Kulldorff, M., Heffernan, R., Hartman, J., et al. 2005. A space-time permutation scan statistic for disease outbreak detection. PLoS Medicine 2: e59.CrossRefGoogle ScholarPubMed
Kulldorff, M., Huang, L., Pickle, L., and Duczmal, L. 2006. An elliptical spatial scan statistic. Statistics in Medicine 25: 3929–3943.CrossRefGoogle Scholar
Kulldorff, M., Tango, T., and Park, P. J. 2003. Power comparisons for disease clustering tests. Computational Statistics and Data Analysis 42: 665–684.CrossRefGoogle Scholar
Kulldorff, M., Zhang, Z., Hartman, J., et al. 2004. Benchmark data and power calculations for evaluating disease outbreak detection systems. Morbidity & Mortality Weekly Report 53: 144–151.Google Scholar
Leak, W. B. 1964. An expression of diameter distribution for unbalanced, uneven-aged stands and forests. Forest Science 10: 39–50.Google Scholar
Leak, W. B. 1965. The J-shaped probability distribution. Forest Science 11: 405–409.Google Scholar
Leak, W. B. 1996. Long-term structural change in uneven-aged northern hardwoods. Forest Science 42: 160–165.Google Scholar
Liu, C., Zhang, L., Davis, C. J., et al. 2002. A finite mixture model for characterizing the diameter distribution of mixed-species forest stands. Forest Science 48: 653–661.Google Scholar
Lorimer, C. G. and Frelich, L. E. 1984. A simulation of equilibrium diameter distribution of sugar maple (Acer saccharum). Bulletin Torrey Botanical Club 111: 193–199.CrossRefGoogle Scholar
Lorimer, C. G. and Frelich, L. E. 1998. A structural alternative to chronosequence analysis for uneven-aged northern hardwood stands. Journal Sustainable Forestry 6: 347–366.CrossRefGoogle Scholar
Lundquist, J. E. and Beatty, J. S. 1999. A conceptual model for defining and assessing condition of forest stands. Environmental Management 23: 519–525.CrossRefGoogle ScholarPubMed
Lundquist, J. E., Goheen, E. M., and Goheen, D. J. 2002. Measuring positive, negative, and null impacts of forest disturbances: a case study using dwarf mistletoe on douglas fir. Environmental Management 30: 793–800.CrossRefGoogle ScholarPubMed
Manion, P. D. 2003. Evolution of concepts in forest pathology. Phytopathology 93: 1052–1055.CrossRefGoogle ScholarPubMed
Manion, P. D. and Griffin, D. H. 2001. Large landscape scale analysis of tree death in the Adirondack Park, New York. Forest Science 47: 542–549.Google Scholar
Manion, P. D., Griffin, D. H., and Rubin, B. D. 2001. Ice damage impacts on the health of the northern New York State forest. Forestry Chronicle 77: 619–625.CrossRefGoogle Scholar
Manion, P. D. and Lachance, D. 1992. Forest Decline Concepts. American Phytopathological Society, St. Paul, MN.Google Scholar
Marquis, D. A., Eckert, P. L., and Roach, B. A. 1976. Acorn weevils, rodents, and deer all contribute to oak-regeneration difficulties in Pennsylvania. Res. Pap. NE-356, 5.USDA Forest Service, Upper Derby, PA.Google Scholar
Mathiasen, R. L., Nickrent, D. L., Shaw, D. C., and Watson, D. M. 2008. Mistletoes: pathology, systematics, ecology and management. Plant Disease 92: 998–1006.CrossRefGoogle Scholar
Meyer, H. A. 1952. Structure, growth, and drain in balanced uneven-aged forests. Journal Forestry 50: 85–92.Google Scholar
Meyer, H. A. and Stevenson, D. D. 1943. The structure and growth of virgin beech-birch-maple-hemlock forests in northern Pennsylvania. Journal Agricultural Research 67: 465–484.Google Scholar
Midgley, J. J. 2001. Do mixed-species mixed-size indigenous forests also follow the self thinning line?Trends in Ecology & Evolution 16: 661–662.CrossRefGoogle Scholar
Morin, R. S., Liebhold, A. M., and Gottschalk, K. W. 2004. Area-wide analysis of hardwood defoliator effects on tree conditions in the Allegheny Plateau. Northern Journal Applied Forestry 21: 31–39.Google Scholar
Muller-Landau, H. C., Condit, R. S., et al. 2006. Comparing tropical forest tree size distributions with the predictions of metabolic ecology and equilibrium models. Ecology Letters 9: 589–602.CrossRefGoogle ScholarPubMed
Munck, I. A. and Manion, P. D. 2006. Landscape-scale impact of beech bark disease in response to slope and aspect in New York State. Forest Science 52: 503–510.Google Scholar
,OMNR. 2004. Ontario Tree Marking Guide. version 1.1. Ontario Ministry of Natural Resources, Toronto, ON.Google Scholar
Pulido, F. J., Díaz, M., and Hidalgo de Trucios, S. J. 2001. Size structure and regeneration of Spanish holm oak Quercus ilex forests and dehesas: effects of agroforestry use on their long-term sustainability. Forest Ecology & Management 146: 1–13.CrossRefGoogle Scholar
Rawlings, J. O., Pantula, S. G., and Dickey, D. A. 1998. Applied Regression Analysis. A Research Tool. 2nd edition. Springer-Verlag, New York.CrossRefGoogle Scholar
Riitters, K. H. and Coulston, J. W. 2005. Hot spots of perforated forest in the eastern United States. Environmental Management 35: 483–492.CrossRefGoogle ScholarPubMed
Rouvinen, S. and Kuuluvainen, T. 2005. Tree diameter distributions in natural and managed old Pinus sylvestris-dominated forests. Forest Ecology & Management 208: 45–61.CrossRefGoogle Scholar
Rubin, B. D. and MacFarlane, D. W. 2008. Using the space-time permutation scan statistic to map anomalous diameter distributions drawn from landscape-scale forest inventories. Forest Science 54: 523–533.Google Scholar
Rubin, B. D. and Manion, P. D. 2001. Landscape-scale forest structure in northern New York and potential successional impacts of the 1998 ice storm. Forestry Chronicle 77: 613–618.CrossRefGoogle Scholar
Rubin, B. D. and Manion, P. D. 2005. Characterizing regional forest health and sustainability from diameter distributions, baseline mortality, and cumulative liabilities. In: Lundquist, J. E. and Hamlin, R. C. (eds). Forest Pathology: from Genes to Landscapes. American Phyopathological Society Press, St. Paul, MN.Google Scholar
Rubin, B. D., Manion, P. D., and Faber-Langendoen, D. 2006. Diameter distributions and structural sustainability in forests. Forest Ecology & Management 222: 427–438.CrossRefGoogle Scholar
Schwartz, J. W., Nagel, L. M., and Webster, C. R. 2005. Effects of uneven-aged management on diameter distribution and species composition of northern hardwoods in Upper Michigan. Forest Ecology & Management 211: 356–370.CrossRefGoogle Scholar
Solberg, S. and Strand, L. 1999. Crown density assessments, control surveys and reproducibility. Environmental Monitoring & Assessment 56: 75–86.CrossRefGoogle Scholar
Song, C. and Kulldorff, M. 2003. Power evaluation of disease clustering tests. Journal of Health Geographics 2: 1–8.Google ScholarPubMed
Steinman, J. 2000. Tracking the health of trees over time on forest health monitoring plots. In: Proceedings of the IUFRO Conference Integrated Tools for Natural Resources: Inventories for the 21st Century, USDA Forest Service Gen. Tech. Rep. NC-GTR-212. Hansen, M. and Burk, T. (eds).
Tuia, D., Ratle, F., Lasaponara, R., et al. 2008. Scan statistics analysis of forest fire clusters. Communications in Nonlinear Science and Numerical Simulation 13: 1689–1694.CrossRefGoogle Scholar
Vadrevu, K. P. 2008. Analysis of fire events and controlling factors in Eastern India using spatial scan and multivariate statistics. Geografiska Annaler: Series A, Physical Geography 90: 315–328.CrossRefGoogle Scholar
Vanclay, J. K. 1994. Modeling Forest Growth and Yield: Application to Mixed Tropical Forests. CAB International, Oxford, UK.Google Scholar
Weiss, N. A. 2008. Introductory Statistics. 8th edition. Pearson, San Fransisco.Google Scholar
West, D. C., Shugart, H. H., and Ranney, R. W. 1981. Population structure of forests over a large area. Forest Science 27: 701–710.Google Scholar
Westphal, C., Tremer, N., Oheimb, G., et al. 2006. Is the reverse J-shaped diameter distribution universally applicable in European virgin beech forests?Forest Ecology & Management 223: 75–83.CrossRefGoogle Scholar
Zhang, L., Packard, K. C., and Liu, C. 2003. A comparison of estimation methods for fitting Weibull and Johnson's SB distributions to mixed spruce-fir stands in northeastern North America. Canadian Journal of Forest Research 33: 1340–1347.CrossRefGoogle Scholar
Zhang, L., Gove, J. H., Liu, C., and Leak, W. B. 2001. A finite mixture of two Weibull distributions for modeling the diameter distributions of rotated-sigmoid, uneven-aged stands. Canadian Journal Forest Research 31: 1654–1659.CrossRefGoogle Scholar

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