Reference: Weisberg, P. J., Ko, D., Py, C., & Bauer, J. M. (2008). Modeling fire and landform influences on the distribution of old-growth pinyon-juniper woodland. Landscape Ecology, 23(8), 931-943.
I chose to review this presentation by Bowen Lan and Leian Cindy Chen because I am interested to learn more about the relationship between fire and woodland, particularly in the Western US where we are seeing a rising frequency of forest fires, and how this trend is influences the distribution of species across the landscape.
Old-growth Pinyon and Utah juniper forests are commonly distributed throughout the Western US. A key knowledge gap is the reason for the lack of diversity in the area – is this driven by fire history? The authors created models of old growth distribution, taking into account topography and fuel in order to address the following research objectives:
- Determine which fire risk component is most strongly associated with old growth distribution.
- Assess the strength of the association between fire and spatial distribution in pinyon-juniper woodland.
The study site was a watershed area in Nevada. The authors mapped the current distribution of old-growth woodland and made models for predicted old growth distribution using cellular automated simulation to predict old growth distribution by adding fire and topographic variables to their models.
Model 1:
In ArcGIS, the authors modelled both the spatial variability in fuel loading in addition to the topographic convergence and solar radiation indices. These indices were z-standardisted into the three categories: low, moderate, and high. Patterns were then identified, including the observation that upper slope sites have a low probability of fire development due to the low fuel availability and greater exposure.
Model 2:
The authors also modelled the topographic barriers to the spread of fire, using GIS to identify and mark the locations of ridge lines, proximity to rock outcrops, and rating on the wind exposure index (which combines meteorological and topographic data).
Model 3:
The final model was a combination of fuel loading factors and topographic barriers (thus, a mix of biotic and abiotic variables).
Ultimately, it was determined that the spatial distribution of fuel material influenced the agre structure of the landscape to a greater extent than topographic barriers.
Bowen and Cindy gave the study a 7/10 because the models were novel and robust and could be applied to future spatial distribution studies, however landscape dynamics are complex so they believe the authors could have incorporated more independent variables into their models.