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