Managed forests are an important contributor to both economic and non-timber values, but the ecological contribution of managed, including planted, forests to biodiversity objectives at the landscape scale needs to be better understood.

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This landscape-level project will:

1. Use LiDAR point cloud statistics combined with other forest and site data to identify habitat for priority wildlife species across forest landscapes;

2. Project forest and wildlife habitat under planned management and natural disturbance scenarios to determine changes in composition and structure over time;

3. Compare current and projected Black Brook District (intensively managed) with areas with less intensive management and protected areas;

4. Assess effects of management intensity on water quality and aquatic habitat;

5. Monitor selected taxa in various habitat types in both Black Brook and less intensively managed (Crown) forest and reserves; and

6. Conduct spatial analyses of effects of current and planned management scenarios on amount and structure of habitat.

 

Taxa to be emphasized in the studies include specific bird species of concern, bryophytes and beetle species associated with old forest conditions, habitats identified with listed species of concern, and deer populations that support recreational hunting opportunities. Metrics of forest composition and structure (e.g., species composition, density, age-class distribution, downed and standing deadwood, large trees) and of water quality, aquatic habitat, and fish community will be evaluated at present and projected for future conditions, and compared between intensively managed and less intensively and unmanaged or reserve landscapes. Research results will be applicable in future to many areas in Canada.

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Co-applicants:  Joseph Bennett (Carleton U), Nicole Fenton (UQAT), Graham Forbes (UNB), Michelle Gray (UNB), Karen Kidd (McMaster), David Kreutzweiser (CFS), Gaétan Moreau (U Moncton), Lisa Venier (CFS)

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Gray Lab Students:   Carson White MScF candidate, Zach Bourque MEM candidate, Xu Gong MF student

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