GEOG 566

         Advanced spatial statistics and GIScience

June 11, 2018

Considering Beaver Dam Occurrence based on Stream Habitat and Landscape Characteristics

Filed under: Final Project @ 1:18 pm
  1. Research Question

Q1: How does the Suzuki and McComb Habitat Suitability Index (HSI) relate to observed beaver dams and the West Fork Cow Creek drainage?

 Q2: What other factors explain the selection of suitable habitat?

 At the beginning of this effort, the question I was most interested in was “How well does the Habitat Suitability Index (HSI) developed by Suzuki and McComb (1998) beaver dams and the West Fork Cow Creek drainage located in the South Umpqua River Basin in Southern Oregon.  The HSI suggests that beaver dams are mostly likely to occur where stream gradients are less than 3%, active channel widths are three to six meters, and valley bottom widths are at least 25 meters.

The second question I had was what other variables might explain the selection of some suitable stream reaches for dam building, while others were seemingly ignored.  Other models such as the Beaver Restoration Assessment Tool, developed by McFarland et al (2017) out of Utah weight a number of other variables including flow permanence and vegetation as possible limitations on dam building that I wanted to consider.  However, because of dataset availability I decided, instead to follow another line of inquiry from landscape ecology that considers the influence of habitat size and connectivity.

  1. Datasets:

To answer this question I considered two primary datasets. The first dataset is, Netmap, a proprietary stream network layer generated through a combination of digital elevation models (DEM) and data that was collected through state and federal agency stream surveys. This included estimates of stream gradient, active channel width, and valley bottom width.  The second data set was a collection of survey locations randomly selected from the stream network layer, stratified by locations considered to be suitable and unsuitable for beaver damming based on the HSI criteria .  Given the rarity, however, beaver dam occurrences in a watershed, the sample was weighted toward sites considered suitable for beaver damming. This dataset also included observations from surveys that took place in the fall months of 2017.  In total, 48 beaver dams were observed from the survey locations, principally on two streams in the drainage.


Map 1: West Fork Cow Creek








  1. Hypotheses:

H1: All observed dams will occur in stream reaches classified as suitable by the Suzuki and McComb HSI. 

 H2: Suitable stream reaches that are longer and located in closer proximity to other suitable reaches will are more likely to be occupied than those that are smaller and more isolated.

Figure from Dunning et al. 1992 showing how habitats A and B, while both too small to support a population, may be occupied (A) if in close proximity to other habitats









  1. Approaches:

The most challenging but most informative approaches I used all related to geoprocessing in ArcMap, which I was largely unfamiliar with as a software package prior this class.

Using SQL to identify suitable damming habitat:

The first effort included using SQL to identify reaches in the stream network data that fit the criteria.  The language was a little clunky at first but after a little time was simple enough to select the appropriate thresholds for each of the habitat variables from the HSI .

Converting suitable reaches into habitat patches:
The second approach required that I convert contiguous reaches of suitable habitat into habitat ‘patches’ so that I could eventually calculate the length of each patch and the distance of that patch to its nearest neighboring patch.  While seemingly simple, there was not one tool in Arc to accomplish this task.  All told I used a combination of functions in Arc including Buffer, Dissolve, Join and Spatial Join to accomplish this.

Calculating distance between patches:

To accomplish the last approach, I burrowed a tool designed for traffic engineers that can calculate the distance from one point to another via a specified network.  In most cases the network is a series of roads but for this problem, I used the stream network polylines, since there is literature to support that beavers will have high fidelity to water as they move through the landscape.


  1. Results:

Exercise 1 produced two metrics that I mapped.  The first was the patch length, and the second was distance to the nearest neighboring patch.

Map 2: Suitable dam habitat and survey locations


In Exercise 2, I demonstrated that the habitat criteria from the HSI correctly identified all areas where dams were observed, but that not all reaches meeting the HSI criteria had dams sites.  In short, based on these data, the HSI criteria seemed to be necessary but not ultimately sufficient for damming.




In Exercise 3, I then looked at the relationship between patch length, distance to nearest neighbor, and a final metric that consider the size of the nearest neighbor weighed by the distance to that neighbor. As a result of aggregating continuous stream reaches into single features, the number of patches with observed dams decreases to 4 and was problematic for a logistic regression analysis because the small sample size can lead to Type 1 errors.


Map 3. Habitat Patch Length based on HSI criteria

Map 4. Distance to Nearest Neighboring Patch









  1. Significance

I found two important take-home messages from these efforts. The first is that the HSI developed by Suzuki and McComb (1998) seems to work relatively well for identifying suitable dam habitat in the West Fork Cow Creek in sites, in that observed dams all fell within the criteria thresholds.  However, there is still quite of bit of variation in the suitable habitat locations where beaver dams were observed that cannot be explained by these data.  The second take-home message is that the selection within habitats appears to coincide with the landscape hypothesis that habitats are more likely to be selected based on their size and connectivity.  Due to sample size limitations, however, the strength of evidence for this is not conclusive.

These results are important to managers because there is a growing emphasis on the use of beavers to improve stream characteristics thought to be conducive to salmonids, particularly, Threatened Oregon Coast Coho Salmon (Oncorhynchus kisutch).  To date most management efforts in this regard have focused on relocating beavers from locations where dam building is problematic (e.g. road culverts) to areas where dams will not be a nuisance and accessible to anadromous fish.  Yet, our anecdotal understanding from conversations with managers in the Umpqua Basin is that these efforts have not been guided by an evaluation of suitable habitats for this area.  In other words, this work suggests that, at a minimum, relocation efforts should focus on streams that meet the HSI criteria.

  1. Geo-processing learning

These efforts were helpful to me in two ways.  First, it was useful to consider the HSI criteria relative to the observed dam sites that we found and improves my confidence that these may be necessary criteria for dam building, at least in similar drainages.  Secondly, I had relatively little experience with ArcMap, and what I did was from nearly a decade ago.  These exercises greatly improved my familiarity and confidence using this software.  This is particularly important as we scale this research up to the larger Umpqua Basin where the number of stream reaches increase by a factor 10.  I’ve even begun automating the geo-processesing steps for the patching and OD Cost Matrix in model builder which surpasses any expectations I had in the beginning of the quarter.

  1. Statistical learning

From statistical standpoint I am disappointed that I was not able to consider more sophisticated analyses.  In particular I had hope to apply logistic regression and apply a predictive map to inform my survey selection for the rest of the Umpqua basin but that was not feasible because of my small sample sizes.  As it was, I was limited to very basic analyses such as the permutation test given the small and lopsided data.  Yet, as I turn to defining my protocol for surveys in the rest of the Umpqua I have a much better appreciation for the relative rarity of dam site occurrences and that I will need to generate my sample accordingly so that I can develop a larger dataset to allow more robust statistical procedures.  Despite this, it was helpful to see how other students approached their problems and consider what of those tools I may apply to future datasets.


Dunning, J. B., Danielson, B. J., & Pulliam, H. R. (1992). Ecological Processes That Affect Populations in Complex Landscapes. Oikos, 65(1), 169–175.

Suzuki, N., & McComb, W. C. (1998). Habitat classification models for beaver (Castor canadensis) in the streams of the central Oregon Coast Range. Retrieved from





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  1.   leatherl — June 15, 2018 @ 2:53 pm    

    John, such a cool project! For H1, I’m curious why you used SQL to select habitats that fit the criteria? I like SQL as a code-based data managment tool, but as a self-proclaimed R evangelist, I’m sure there’s a way to have a more replicable R script that would do the same thing– one of the key advantages being that you could more flexibly adjust the criteria to assess which of them might be most influential! Also, based on your maps, it looks like the observed dams are only in two of the stream reaches, total? You note that the small sample size is a limitation– however, I’m curious if you analyzed the reaches separately, you would get the same results? On the other side, do you have any ability or ideas to obtain more data on observed beaver dams?

  2.   jonesju — June 15, 2018 @ 7:08 am    

    Well done. Please clarify criteria for grouping reaches into patches. Could you use all the reaches in a logistic regression? Consider what other variables you might include, such as vegetation. What are the effects of the random sample on this overall analysis? Is it subject to circularity? How to avoid this?

  3.   swanssam — June 15, 2018 @ 6:13 am    

    Nice work, John. I’m sorry I missed your presentation on Monday. I’m curious what Type 1 errors in a logistic regression are. Those must be results of small sample sizes. Hopefully if you’re able to compute those three attributes (patch length, distance to nearest neighbor, and size of the nearest neighbor weighed by the distance to that neighbor) automatically over a study area, repeating this analysis across your entire basin will return a large enough sample size that Type 1 errors aren’t an issue. Either way, good luck with your future endeavors!

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