Modelling probability of snow and wind damage in Scots pine stands using tree characteristics |
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| Institution: | 1. Swedish University of Agricultural Sciences, Faculty of Forestry, Department of Silviculture, S-901 83 Umeå, Sweden;2. Swedish University of Agricultural Sciences, Faculty of Forestry, Department of Forest Resource Management and Geomatics, S-901 83 Umeå, Sweden;1. Otto Von Guericke Universität Magdeburg, Germany;2. Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany;1. Swedish Biodiversity Centre, Swedish University of Agricultural Sciences, Box 7016, SE-750 07 Uppsala, Sweden;2. Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden;3. Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, SE-901 83 Umeå, Sweden;4. Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Box 7050, SE-750 07 Uppsala, Sweden;5. Department of Ecology, Swedish University of Agricultural Sciences, Box 7044, SE-750 07 Uppsala, Sweden;1. Environmental Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany;2. Institute of Environmental Medicine, Helmholtz Center Munich – German Research Center for Environmental Health, Augsburg, Germany;3. Albrecht Daniel Thaer Institute of Agricultural and Horticultural Sciences, Department of Phytomedicine, Humboldt University of Berlin, Berlin, Germany;4. Terrestrial Ecology and Climate Change, Department of Ecology, School of Biology, Faculty of Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece;5. Physical Geography/Landscape Ecology and Sustainable Ecosystem Development, Catholic University of Eichstätt-Ingolstadt, Eichstätt, Germany;6. Christine-Kühne Center for Allergy Research and Education (CK-Care), Davos, Switzerland;1. Swedish University of Agricultural Sciences, Faculty of Forest Sciences, School for Forest Management, SE-739 21 Skinnskatteberg, Sweden;2. Inland Norway University of Applied Sciences, Faculty of Applied Ecology, Agricultural Sciences and Biotechnology, Department of Forestry and Wildlife Management, Campus Evenstad, N-2480 Koppang, Norway;3. University of Groningen, Faculty of Spatial Sciences, Theme Sustainable Society, Postbus 800, 9700 AV Groningen, the Netherlands;4. Gävleborg County Administration, SE-801 70 Gävle, Sweden;5. OT Boxdorf, Waldteichstr. 47, D-01468 Moritzburg, Landkreis Meißen, Sachsen, Germany;6. Engelmark Ecology, Högtorp 1, SE-642 63 Mellösa, Sweden;7. Chernivtsi National University, Department of Ecology and Biomonitoring, 2 Kotsyubynskyi Street, Chernivtsi 58012, Ukraine;8. Leibniz Institute of Ecological Urban and Regional Development, Weberplatz 1, 01217 Dresden, Germany;9. Eberswalde University for Sustainable Development, Centre for Econics and Ecosystem Management, Alfred-Möller-Str.1, 16225 Eberswalde, Germany;10. Realtid Media AB, Birger Jarlsgatan 9, c/o Camp Jarl, SE-111 45 Stockholm, Sweden;11. Vytautas Magnus University, Faculty of Forest Science and Ecology, Akademija, Kaunas District LT-53361, Lithuania;12. Magnus Nilsson Produktion, c/o Context, Kungsgatan 84, SE-112 27 Stockholm, Sweden;13. Forest Sector Insights AB, TT Banan 12, SE-77 693 Hedemora, Sweden;14. WWF, Ulriksdals Slott, SE-170 81 Solna, Sweden;15. Estonian University of Life Sciences, Environmental Protection and Landscape Management, Kreutzwaldi 1, 51006 Tartu, Estonia;p. Grangärdebygdens Intresseförening, Stakhedsvägen 11, SE-770 14 Nyhammar, Sweden;q. National Forestry University of Ukraine, Institute of Ecological Economics and Management, Lviv, Ukraine;r. Czech University of Life Sciences, Faculty of Forestry and Wood Sciences, Department Forest Ecology, Prague, Czech Republic;s. Institute of System Forest Research, Bauman Moscow State Technical University, 1st Institutskaya Street, 1, 141005 Mytischi, Moscow Region, Russia;t. Perstorp, SE-695 97 Tived, Sweden;u. Department of Urban and Rural Development, Swedish University of Agricultural Sciences, SE-750 07 Uppsala, Sweden;v. Dalagård i Västanberg, Stensänget 1, SE-774 97 Fors, Sweden |
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| Abstract: | Predictions of damage risk from snow and wind at sites using tree characteristics of Scots pine (Pinus sylvestris L.), were made using a subset of data from permanent sample plots within the Swedish National Forest Inventory (NFI). The plots were sampled twice at five-year intervals between 1983 and 1992. A logistic risk assessment model was developed using data originating from 286 plots, dominated by Scots pine (> 65% of basal area), within one county situated in the boreal zone in northern Sweden (Västerbotten). The model was evaluated with NFI-data from two other counties, one adjacent to Västerbotten (Västermorrland, 99 plots), which is also in the boreal zone, and one (Kalmar, 138 plots) in the hemi-boreal zone in southern Sweden. In each plot, measurements at first inventory of tree characteristics for the largest undamaged sample tree, and measurements at second inventory of damage from snow and wind on all sample trees were used to develop a logistic model that predicts the damage probability for each site. The best predictors were upper diameter (ud, diameter at 3 or 5 m) and the ratio of height/diameter at breast height (rhd). According to the model calculations, the overall damage probability never exceeded 0.26 for any of the sample plots used for model development. At a given ud the probability of damage is higher for a site with trees of low rhd. The fit of the model was better for the adjacent Västernorrland county than for the southern county, Kalmar. This inferior predictability was explained by differences in tree characteristics between Kalmar and the other counties. The results show that it is possible to predict damage from snow and wind at a site by using only single tree characteristics. |
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