I spent the majority of this class in preliminary stages of my research.  While I was able to go through nearest neighbor analysis and hot spot analysis following the “Spatial Pattern Analysis of Dengue Fever”, which was helpful, I did not analyze my own data.  Nonetheless, I do intend to use spatial statistical tools to analyze the data that I do collect for my summer research, described below.

This summer, I’ll be working with the BLM to assess stream temperature in South Sister Creek.  In 2009, the BLM placed several stream enhancement structures (~90 according to some reports) in South Sister, a 3- 4 order tributary to the Smith River in the Coast Range of Oregon nested in the Umpqua Basin. The Creek serves as spawning ground for Oregon Coastal Coho salmon (ESA listed), steelhead trout, and pacific  lamprey.  Over time, the creek has been degraded by human use.    Recent work verified that the Creek had been used as a log drive to transport logs during the 19^th and 20^th centuries which may have contributed to the simplification of the stream (Miller, 2010).    Stream cleaning, a restoration practice that removed log jams from the stream, has also reportedly occurred along the stream in the 1980’s (Bureau of Land Management, 2009).

In 2006, 2011, and 2012 the BLM collected data from 9 temperature gages along the stream and its tributaries during the summer months (from mid or late June through September).  This summer, those same sites will be monitored (see Figure 1).    The BLM is interested in whether or not their efforts made a detectable difference on stream temperature.  However, the data collection record is not long enough to detect a change from a temporal perspective.

Nonetheless, there are interesting questions to be asked regarding the relative influence of the enhancement structures, riparian and topographic shade, and substrate on stream temperature at fine spatial scales.   Previous research has indicated the importance of several variables on stream temperature, with surface and groundwater inputs, riparian shade/solar inputs, discharge, and hyporheic exchange exerting moderate to high influence (Poole & Berman 2003).  Large wood in streams have been linked to increased channel and stream-bed complexity, which may be indicative of hyporheic exchange (Arrigoni et al 2008).  

Specifically, the following questions are being asked:

1. What is the spatial and temporal variability of temperature at a small scale?
2. How well are the current stationary data loggers (i.e.  hobo tidbits) representing the ambient fine-scale patterns of stream temperature?
3. Which factors dominate or explain the most spatial variation in stream temperature at a fine scale?
   1. Log jam density
   2. Presence or absence of alluvial substrate (a proxy for hyporheic exchange)
   3. Topographic shading
   4. Riparian shading

Over the past few weeks I’ve been working on a research design to help answer these questions.    To map the heterogeneity and relative influence of log jam density, shade, and substrate on the spatial distribution of stream temperature, the study is employing a stratified sampling approach designed to capture as many combinations as possible of the variables outlined in Figure 2 at varying elevations of the stream.     LiDAR data and a shade modeler program will be used to identify areas that are most likely to have 10-2 shade (both topographic and vegetation).  An analysis of log jam density (data from previously gathered GPS points taken at the right bank of each in-stream log jam) will be undertaken in ArcMap to divide the study area into reaches that are densely, moderately, and sparsely populated by jams.  Data regarding the final stratification layer, streambed substrate, will be gathered during the site layout and field mapping phase of the protocol due to the lack of a priori, spatially explicit knowledge of stream-bed condition (alluvial or bedrock).   The field map will be made during the first survey and be used as a reference for locating temperature sampling sites and processing data.

Once my data is collected, I would like to explore the use of Geographically Weighted Regression – however much of my data will be categorical, which may mean GWR will have to be avoided.  I welcome any comments related to what types of analysis I should consider doing that might also inform my study design.