Refining the Forest Vegetation Simulator for projecting the effects of spruce budworm defoliation in the Acadian Region of North America
The Forest Vegetation Simulator (FVS) is an individual-tree growth model widely used in the US and parts of Canada, which has been developed to predict stand dynamics in response to various disturbance-causing agents. In this study, we developed annualized modifiers for the Acadian variant of FVS (F...
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Canadian Institute of Forestry
2018-10-01
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| Series: | The Forestry Chronicle |
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| Online Access: | https://pubs.cif-ifc.org/doi/10.5558/tfc2018-037 |
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| author | Cen Chen Aaron Weiskittel Mohammad Bataineh David A. MacLean |
| author_facet | Cen Chen Aaron Weiskittel Mohammad Bataineh David A. MacLean |
| author_sort | Cen Chen |
| collection | DOAJ |
| description | The Forest Vegetation Simulator (FVS) is an individual-tree growth model widely used in the US and parts of Canada, which has been developed to predict stand dynamics in response to various disturbance-causing agents. In this study, we developed annualized modifiers for the Acadian variant of FVS (FVS-ACD) to account for effects of spruce budworm (SBW), Choristoneura fumiferana (Clem.), defoliation on forest development. These modifiers were developed using individual tree data collected annually from 375 permanent sample plots that covered > 40 000 km2 of varying ranges of defoliation and forest conditions during the last SBW outbreak in the 1970s-1980s in Maine. FVS-ACD refined by our modifiers consistently had smaller mean biases (predicted – observed; 64% and 79% lower in Maine and New Brunswick, respectively) and root mean squared errors (RMSE; 26% lower in both Maine and New Brunswick) of predicted stand volumes than FVS-ACD refined by STAMAN (a Canadian growth model) SBW modifiers, which was superior to FVSACD alone. Our refined FVS-ACD can be used to predict stand dynamics and support management as well as protection activities against SBW defoliation in the Acadian Region to reflect the defoliation impact relationships measured in Maine during the last SBW outbreak. |
| format | Article |
| id | doaj-art-7ce7b83b71814fa593aeb56933ea8a03 |
| institution | Kabale University |
| issn | 0015-7546 1499-9315 |
| language | English |
| publishDate | 2018-10-01 |
| publisher | Canadian Institute of Forestry |
| record_format | Article |
| series | The Forestry Chronicle |
| spelling | doaj-art-7ce7b83b71814fa593aeb56933ea8a032025-08-20T03:39:00ZengCanadian Institute of ForestryThe Forestry Chronicle0015-75461499-93152018-10-01940324025310.5558/tfc2018-037Refining the Forest Vegetation Simulator for projecting the effects of spruce budworm defoliation in the Acadian Region of North AmericaCen Chen0Aaron Weiskittel1Mohammad Bataineh2David A. MacLean3University of Maine, School of Forest Resources, Orono, ME 04469, United StatesUniversity of Maine, School of Forest Resources, Orono, ME 04469, United StatesUniversity of Arkansas System Division of Agriculture, Agricultural Experiment Station – University of Arkansas at Monticello, School of Forestry & Natural Resources, Monticello, AR 71655, United StatesUniversity of New Brunswick, Faculty of Forestry and Environmental Management, P.O. Box 4400, Fredericton, NB, E3B 5A3 CanadaThe Forest Vegetation Simulator (FVS) is an individual-tree growth model widely used in the US and parts of Canada, which has been developed to predict stand dynamics in response to various disturbance-causing agents. In this study, we developed annualized modifiers for the Acadian variant of FVS (FVS-ACD) to account for effects of spruce budworm (SBW), Choristoneura fumiferana (Clem.), defoliation on forest development. These modifiers were developed using individual tree data collected annually from 375 permanent sample plots that covered > 40 000 km2 of varying ranges of defoliation and forest conditions during the last SBW outbreak in the 1970s-1980s in Maine. FVS-ACD refined by our modifiers consistently had smaller mean biases (predicted – observed; 64% and 79% lower in Maine and New Brunswick, respectively) and root mean squared errors (RMSE; 26% lower in both Maine and New Brunswick) of predicted stand volumes than FVS-ACD refined by STAMAN (a Canadian growth model) SBW modifiers, which was superior to FVSACD alone. Our refined FVS-ACD can be used to predict stand dynamics and support management as well as protection activities against SBW defoliation in the Acadian Region to reflect the defoliation impact relationships measured in Maine during the last SBW outbreak.https://pubs.cif-ifc.org/doi/10.5558/tfc2018-037individual-tree growth modelred spruceblack sprucewhite sprucebalsam firmodèle de croissance arbre par arbre |
| spellingShingle | Cen Chen Aaron Weiskittel Mohammad Bataineh David A. MacLean Refining the Forest Vegetation Simulator for projecting the effects of spruce budworm defoliation in the Acadian Region of North America The Forestry Chronicle individual-tree growth model red spruce black spruce white spruce balsam fir modèle de croissance arbre par arbre |
| title | Refining the Forest Vegetation Simulator for projecting the effects of spruce budworm defoliation in the Acadian Region of North America |
| title_full | Refining the Forest Vegetation Simulator for projecting the effects of spruce budworm defoliation in the Acadian Region of North America |
| title_fullStr | Refining the Forest Vegetation Simulator for projecting the effects of spruce budworm defoliation in the Acadian Region of North America |
| title_full_unstemmed | Refining the Forest Vegetation Simulator for projecting the effects of spruce budworm defoliation in the Acadian Region of North America |
| title_short | Refining the Forest Vegetation Simulator for projecting the effects of spruce budworm defoliation in the Acadian Region of North America |
| title_sort | refining the forest vegetation simulator for projecting the effects of spruce budworm defoliation in the acadian region of north america |
| topic | individual-tree growth model red spruce black spruce white spruce balsam fir modèle de croissance arbre par arbre |
| url | https://pubs.cif-ifc.org/doi/10.5558/tfc2018-037 |
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