A harvest failure approach to assess the threat from an invasive species

We present the idea of using potential infringements on annual allowable harvest targets as an approach to estimate threats from invasive species to the forest products sector. The approach uses present-day harvest levels as a reference level to estimate when and where the impact of a nonnative forest pest could become economically damaging. We use a generic model that simulates spread and damage by nonnative invasive species, basic harvest and forest growth through time. The concept is illustrated with a case study of a new nonnative invasive pest, Sirex noctilio Fabricius on pine resources in eastern Canada. Impacts of invasion on wood supply, in particular, the point at which present-day harvest levels are not attainable, were identified for 77 non-overlapping geographical regions that delimit the primary wood supply areas around large mills and wood processing facilities in eastern Canada. The results identify the minimum area of a pest outbreak that could trigger harvest shortages (approximately 12.5–14 M ha of pine forests in Ontario and Quebec). Beyond this level, the amount of host resource available for harvesting in any given year declines rapidly. The failure to sustain broad-scale harvest targets may be an attractive and intuitive indicator for policy makers and regulators interested in developing control and “slow-the-spread” programs for non-native forest pests.     

Mapping forest composition from the Canadian National Forest Inventory and land cover classification maps

Canada's National Forest Inventory (CanFI) provides coarse-grained, aggregated information on a large number of forest attributes. Though reasonably well suited for summary reporting on national forest resources, the coarse spatial nature of this data limits its usefulness in modeling applications that require information on forest composition at finer spatial resolutions. An alternative source of information is the land cover classification produced by the Canadian Forest Service as part of its Earth Observation for Sustainable Development of Forests (EOSD) initiative. This product, which is derived from Landsat satellite imagery, provides relatively high resolution coverage, but only very general information on forest composition (such as conifer, mixedwood, and deciduous). Here we link the CanFI and EOSD products using a spatial randomization technique to distribute the forest composition information in CanFI to the forest cover classes in EOSD. The resultant geospatial coverages provide randomized predictions of forest composition, which incorporate the fine-scale spatial detail of the EOSD product and agree in general terms with the species composition summaries from the original CanFI estimates. We describe the approach and provide illustrative results for selected major commercial tree species in Canada.