USING NATURAL VARIABILITY PATTERN STRATEGIES TO UNDERSTAND ANTHROPOGENIC DISTURBANCES IN ALBERTA Paul D. Pickell 1 , Nicholas C. Coops 1 and David W. Andison 2 1 Integrated Remote Sensing Studio, University of British Columbia 2 Bandaloop Landscape-Ecosystem Services, Belcarra, British Columbia Contact: ppickell@interchange.ubc.ca, Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia 2231-2424 Main Mall Vancouver, BC V6T 1Z4 Why study anthropogenic disturbances? Objectives Disturbances maintain landscape heterogeneity, biodiversity and ecosystem resilience [1,2,3] Design of forest harvests which emulate patterns of natural disturbances may enhance best practices [4,5] The extraction of timber, bitumen and natural gas results in unmitigated cumulative impacts in Alberta [6,7,8] How do we study anthropogenic disturbances? i. Discriminate disturbance patterns of two industrial stakeholders in Alberta: forestry and energy ii. Compare spatial patterns of industrial stakeholders to NRV and report on preliminary analyses iii. Develop analyses for study of two additional forest management areas in the foothills of Alberta 1. Select spatial pattern metrics which quantify biologically-relevant information [10,11, 12] References [1] Turner, M. G. (2010). Ecology, 91(10), 2833-2849. [2] Work, T. T., et al. (2003). The Forestry Chronicle, 79(5), 906-916. [3] Drever, C. R., et al. (2006). Canadian Journal of Forest Research, 36(9), 2285-2299. [4] Bergeron, Y., et al. (2007). The Forestry Chronicle, 83(3), 326-337. [5] Hunter, M. L. J. (1993). Biological Conservation, 65, 115-120. [6] Schneider, R., et al. (2003). Conservation Ecology, 7(1), 8. [7] Timoney, K., & Lee, P. (2001). Journal of Environmental Management, 63(4), 387-405. [8] Paine, R. T., et al. (1998). Ecosystems, 1(6), 535-545. [9] Boreal Forest Photo Project. (2007). http://www.flickr.com/photos/44269235@N06/4066852693/ [10] Gillanders, S. N., et al. (2008). Progress in Physical Geography, 32(5), 503-528. [11] Buddle, C. M., et al. (2006). Biological Conservation, 128(3), 346-357. [12] Nappi, A., et al. (2005). The Auk, 120(2), 505-511. [13] McGarigal, K., et al. (2002). www.umass.edu/landeco/research/fragstats/fragstats.html [14] Andison, D. W. (2012). Personal communication. 2. Stratify frequency, intensity and distribution of anthropogenic disturbances across sub-areas A (high impact) to D (low impact). 3. Parse polygons of airborne Alberta Vegetation Inventory (AVI) data to 30m cells and use FRAGSTATS [13] to quantify spatial patterns What differences did we find? Photographer Dan Riedlhuber, Pilot Phil Wilmer, © Dan Riedlhuber/Boreal Forest Photo Project [9] http://www.flickr.com/photos/44269235@N06/4066852693/ 1. Edge density of forestry and energy features appears to be directly related and are generally outside the NRV for the industrialised sub-areas A, B and C (see figure below). Spatial Pattern Metric Description Edge density (ED) A measure of shape edge per unit area (m · ha -1 ) Edge density-to-area disturbed ratio A measure of edge density per unit area disturbed (km 2 ) Mean patch size (MPS) The mean areal size of a class of patches (ha) Fractal dimension (FD) A measure of shape complexity (D) such that patch area (A) is related to patch perimeter (P) where P ≈ √AD 2. Energy features tend to have much higher edge- to-disturbed area ratios in all sub-areas (see figure top). 3. Energy and forestry disturbances tend to create smaller patches than NRV (see figure right). 4. Energy features tend to have much lower mean fractal dimension than forestry and NRV features (see figure right). Future Work Historical aerial photography provides a dataset to model NRV across all of Alberta The decision support tool NEPTUNE [14] provides a spatial language for quantifying spatial patterns of disturbance events Analyse two other forest management areas in Alberta using NEPTUNE and NRV Expectation that best forestry practices may be enhanced by emulating NRV spatial patterns Event size, shape Patch size, shape Undisturbed residuals size, % of event