/VIIIC #" 7785'-51 '=-::: 1: ....-- ---- . - w: .:w Forestry • Foresterie ___...... Northern Ontario ---._,., ....__r-- Development Agreement NODA Note No. 22 QUANTIFYING SPECIES DISTRIBUTIONS FOR BIODIVERSITY ASSESSMENTS: SOME EXAMPLES APPLIED TO TREES, HERPETOFAUNA, AND BIRDS IN ONTARIO D.W. McKenney,' B.G. Mackey/ R.A. Sims,' Y. Wang,' K.L. Campbell,' D. Welsh.' and M. Oldham" /""'" INTRODUCTION Biodiversity conservation is a major operational challenge for resource managers and a major policy issue internation- ally(McKenney et al. 1994). One type of data required for quantitative analyses is accurate, scientificallybased descrip- tions of species' potential and actual distributions. This is not a trivial exercise, as species are affected by numerous processes, includingdisturbancehistories, climate,and nutrient regimes. Given the need for sound spatial descriptions, a major goal of the Bio-environmental Indices Project (BIP) has been the development of landscape-level descriptions of biodiversity (Mackey and McKenney 1994). This informa- tion is important for ecological restoration activities and reserve selection; it also creates a context for the contro- versiesthat exist over trade-offs between biodiversity conser- vation and wood production. Because the outputs are spatial data, results can be easily transferred and used by planners in geographic information systems (GIS). The approach described here makes use of biological site data that are typically collected by field ecologists. Through various empirical methods, such data can be "spatially extended" across landscapes if spatial data on the drivers of ecological response are available (Nix 1986; Mackey 1993, 1994). This note describes a process that uses field observa- tions to develop quantitative data on the distributions of species. Three examples are provided, using taxa that are of interest from a biodiversity conservation or wood pro- duction perspective in Ontario: jack pine (Pinus banksiana Lamb.), the five-lined skink (Eumecesfasciatus), Ontario's only lizard, and the American black duck (Anas rubripes), an important waterfowl species. The results are from various works in progress, and future papers by the authors will provide more detailed results and ecological interpretations of these analyses. The methods are being applied to a wide variety of Ontario's wildlife species and many commercial and noncommercial plant species. It is worth noting a fundamental distinction between this landscape-level modeling and what often occurs in forest planning. Forest planning models (e.g., Ontario's new Strategic Forest Management Model; Davis 1995) typically aggregate Forest Resource Inventory (FRJ) data that describe the major commercial tree species present and likely wood volumes in stands. These data are generated by mapping land units based on interpretation of aerial photographs, sometimes underpinned by field survey. Al- though a useful method of resource inventory, it provides no information about the causal processes-information that is essential for resource management. Also, links to other aspects ofbiodiversity are often made through expert opinions rather than through application of quantitative modeling and analysis. Examples include habitat suitability indices that relate forest type and stand age to a species' predicted preferred habitat. Habitat suitability indices are rarely spatially based and are projected based on assumed associations. The methods used here attempt to address more rigorously spatial variations that occur in forests and provide a more empirical basis to habitat suitability indices (e.g., Norton et al. 1992). 1 Canadian Forest Service-Sault Ste. Marie, Sault Ste. Marie, Ontario. 2 Department of Geography, The Australian National University, Canberra, Australia. 3 Canadian Wildlife Service, Ottawa, Ontario. 4 Ontario Natural Heritage Information Centre, Peterborough, Ontario. .+. Natural Resources Canada Canadian Forest Service Ressources naturelles Canada Service canadien des fOrt3ts ® Ontario Ministry of Ministere des Natural Richesses Resources naturelles