Pierre River Mine Project

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TERRESTRIAL AENV SIRS 44 – 78 References Section 13.1 when appropriate data are available. It is also important that models are conceptually validated to ensure that the theories and assumptions each model is based on are correct (Rykiel 1996). Model structure and output for the black bear, beaver and yellow rail HSI models correspond with well understood habitat associations and ecological relationships for those species. Black bears in central Alberta will opportunistically eat meat, but rely on berries in summer and early fall (Ruff 1978; Young and Ruff 1982) while using dense shrub cover and mature forest for security cover (Zapisocki et al. 1998). Black bears often avoid human disturbances (Aune 1994). When black bears are habituated and do not avoid disturbance, mortality risk increases (Manville 1983). The black bear HSI model incorporates expert ecological knowledge from the region to combine these well-established habitat associations and habitat risks into a mathematical equation. Beavers feed primarily on deciduous shrubs and trees within 100 m of water (Jenkins 1980, Novak 1999), and the beaver HSI expresses these known relationships. Yellow rail breeding habitat generally consists of fresh or brackish shallow wet meadows and sedge marshes with little to no woody vegetation (Goldade 2002; Prescott et al. 2002). In the Oil Sands Region, this equates to graminoid fen (FONG), shrubby fen (FONS) or graminoid marsh (MONG) wetlands types (Halsey et al. 2003), which are identified as habitat by the yellow rail HSI model. Model validation is important for evaluating model reliability (Conroy et al. 1995). However, the presence and abundance of wildlife species is affected by many factors, and can become uncoupled from habitat quality (Van Horne 1983). Therefore, validation results based on wildlife distribution data can be deceptive. For example, a species may inadvertently select habitats that contain elevated mortality risk, in which case a successfully validated model will predict that hazardous habitats are in fact high quality (Falcucci et al. 2009). Ideally, validation data would represent the survival rate, birth rate, and carrying capacity per habitat type, which is needed to truly assess habitat quality (Van Horne 1983). Unfortunately, data on demographic parameters are very difficult to collect, and are rarely available. This is not to suggest that habitat suitability models should not be validated with presence or abundance data, as this is commonly all that are available. Rather, it is important to note that an impact assessment is limited by the availability of data and assessment tools. The intent of an EIA is to use the best available data to assess the effects of the project and to outline uncertainty around the effects assessment. In the absence of available validation data or validated models, the alternative to using expert-based, but unvalidated, HSI models would be to use no quantitative tools for assessing the effects of the project on black bear, beaver and yellow rail habitat. To remove the output of these useful models from the assessment would be a step backwards in the quality and thoroughness of the assessment for wildlife key indicator resources (KIRs). Aune, K.E. 1994. Comparative ecology of black and grizzly bears on the Rocky Mountain Front, Montana. International Conference on Bear Research and Management 9: 365-74. April 2010 Shell Canada Limited 13-75 CR029
TERRESTRIAL AENV SIRS 44 – 78 Section 13.1 Conroy, M.J., Y. Cohen, F.C. James, Y. G. Matsinos, B.A. Maurer. 1995. Parameter estimation, reliability, and model improvement for spatially explicit models of animal populations. Ecological Applications 5(1):17- 19. Falcucci, A., P. Ciucci, L. Maiorano, L. Gentile and L. Boitani. Assessing habitat quality for conservation using an integrated occurrence-mortality model. Journal of Applied Ecology 46: 600-609. Goldade, C.M., J.A. Dechant, D.H. Johnson, A.L. Zimmerman, B.E. Jamison, J.O. Church and B.R.Euliss. 2002. Effects of Management Practices on Wetland Birds: Yellow Rail. North Prairie Research Center, Jamestown. Halsey, L.A., D.H. Vitt, D. Beilman, S. Crow, S. Mehelcic and R. Wells. 2003. Alberta Wetlands Inventory Standards, Version 2.0. Alberta Sustainable Resource Development, Resource Data Branch, Edmonton, AB. 54 pp. Jenkins, S.H. 1980. A Size-Distance Relation in Food Selection by Beavers. Ecology. 61: 740-746. Manville, A.M. 1983. Human impact on the black bear in Michigan's lower peninsula. International Conference on Bear Research and Management 5: 20-33. Novak, M. 1999. Beaver. Wild Furbearer Management and Conservation in North America, Section IV: Species Biology, Management and Conservation. 25: 282-312. Prescott, D.R.C., M.R. Norton and I.M.G. Michaud. 2002. Night surveys of yellow rails, Corturnicops noveboracensis, and Virginia rails, Rallus limicola, in Alberta using call playbacks. The Canadian Field Naturalist. 116(3): 408-415. Ruff, R.L. 1978. A Study of the Natural Regulatory Mechanisms Acting on an Unhunted Population of Black Bears near Cold Lake, AB. Proj. Rep. Dept Wildl. Ecol., Univ. Wisc., Madison. 107 pp. Rykiel Jr., E.J. 1996. Testing ecological models: the meaning of validation. Ecological Modelling 90: 229-244. Young, B.F. and R.L. Ruff. 1982. Population Dynamics and Movements of Black Bears in East Central Alberta. Journal of Wildlife Management. 46: 845- 860. Van Horne, B. 1983. Density as a Misleading Indicator of Habitat Quality. Journal of Wildlife Management 47(4):893-901. Zapisocki, R., M. Todd, R. Bonar, J. Beck, B. Beck and R. Quinlan. 1998. Black Bear Summer/Fall Habitat: Habitat Suitability Index Model Version 5. Foothills Model Forest. Hinton, AB. 13-76 Shell Canada Limited April 2010 CR029
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Application for Approval of the Pie
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PIERRE RIVER MINE SUPPLEMENTAL INFO
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CONTENTS LIST OF ILLUSTRATIONS Tabl
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INTRODUCTION OVERVIEW Table 1-1: Gu
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INTRODUCTION NAVIGABLE WATERS Secti
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INTRODUCTION NAVIGABLE WATERS Secti
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Section 2.2 ERRATA EIA, EIA UPDATE
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Section 2.2 ERRATA EIA, EIA UPDATE
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Section 2.3 ERRATA SUPPLEMENTAL INF
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Section 2.3 ERRATA SUPPLEMENTAL INF
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MINING AND PROCESSING ERCB SIRS 3 -
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AIR ERCB SIRS 40 - 45 Request 41b B
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AIR ERCB SIRS 40 - 45 Table ERCB 41
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WATER ERCB SIRS 46 - 79 Section 7.1
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WATER ERCB SIRS 46 - 79 References
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WATER ERCB SIRS 46 - 79 References
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GENERAL AENV SIRS 1 - 5 Figure AENV
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AIR AENV SIRS 6 - 14 Question No. 7
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WATER AENV SIRS 15 - 43 Section 12.
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WATER AENV SIRS 15 - 43 Decay Rates
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TERRESTRIAL AENV SIRS 44 - 78 Refer
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GLOSSARY CEAA The abbreviation for
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GLOSSARY ESR The abbreviation for e
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GLOSSARY landform The configuration
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GLOSSARY OSDG The abbreviation for
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GLOSSARY SEWG The abbreviation for
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