GEOG 566






         Advanced spatial statistics and GIScience

June 7, 2018

Aggregating Suitable Dam Habitat to Consider the Role of Habitat Size and Connectivity

Filed under: 2018,Exercise/Tutorial 3 2018 @ 11:47 am

Question:

How does the size and connectivity of beaver dam habitat relate to observed dam sites in West Fork Cow Creek?

Approach:

In Exercise 2, I considered if stream gradient, active channel width, and valley bottom width, described by Suzuki & McComb (1998) as the most important predictors of beaver damming, corresponded to the observed dam sites collected in our field surveys last fall. And while I found that the their thresholds defining suitable damming habitat captured all observed dam sites, it was also clear that there were was also a significant amount of suitable habitat where no observed dams occurred.  In other words, there was still quite a bit of ‘available’ habitat.

To understand why some of these habitats were used and others were not I turned to work from landscape ecology, that posits the size and connectivity of habitat is an important consideration in resource selection of animals (Dunning et al. 1992).  To consider if this may help explain why dams were observed in some suitable habitats and not others, I conducted the ‘patching’ process and OD Cost Matrix to estimate the size and connectivity of suitable damming habitats using the Suzuki & McComb (1998) criteria and which are considered in this final exercise.

Steps used to complete the analysis:

Data preparation: Despite having already generated the size and connectivity measures for patches of damming habitat, there was still some geoprocessing required.  In particular, I need to identify what patches of habitat had actually been surveyed during our fall 2018 field work as well as identify what patches were observed to have beaver dams somewhere in them.  To do this, I used the Spatial Join Tool, choosing the ‘INTERSECT’ method of identifying overlapping patches and survey sites. I repeated this processes to identify patches with observed dams sites.  Of the 49 habitat patches generated in Exercise 1, 29 of those coincided with our survey points.  Of those, 48 dams sites were observed on only 4 of the 29 patches. All patches that were not surveyed were removed from the dataset and fields for a dummy variable along with dam counts were added for observed dams.

Variable generation: Using the data from the OD Cost Matrix analysis in Exercise 1 I had two variables of interest: 1) length of contiguous habitat reaches, that I refer to as patch ‘Length’, and another which is the distance of a patch to it’s nearest neighboring patch if traveling through the stream network; referred to hear as ‘Nearest Neighbor (NN) Distance.  For this exercise I was curious, however, if the size of the NN was important, surmising that a patch would be more likely to be occupied if it was located 100 meters away from a large patch (e.g. 900m) as opposed to a small patch (e.g. 100m).  As a result I calculated a NN Length, weighted by the distance to the NN patch (NN Length/NN Distance); referred to as ‘NNLwDist’.

Analysis: Similar to Exercise 2 I used the easyGgplot2 package to generate overlapping histograms of habitat patches with and without observed dam sites for these three variables. I also used this same package to generate density plots to show the relative distribution of patches with and without observed dams.  I then computed the means, standard deviation, and range for each variable with and without observed dams.

Lastly, I compared the difference in means using a permutation test because a standard Welches t-test was inappropriate due to the small and unequal sample sizes in each group.  I also applied a Wilcoxon rank sum test but this proved problematic due to a high number of ties in these data.

Results:

Distributions and Summary Statistics:

Table 1

 

 

 

 

 

Patch length: The histogram and density plot in figures 1 and 2 show a clear delineation in the patch length between those with and without observed dams.  On average, patches where dams were observed were more than 900 meters longer (z=3.6, p<0.0001) than those without (table 1).

 

Figure 1

Figure 2

 

 

 

 

 

 

 

 

 

Distance to Nearest Neighbor Patch: On average, patches with dams were nearly 350 meters closer to their nearest neighboring patch and overall are highly skewed to the shortest distances (95m to 100m) and clearly visible in the density plot (figure 4).  Patches without dams had a much wider distribution of distances (96m to 1591m). However, the permutation test did not find the difference in distances to be significant (z= -1.5, p=0.14).

Figure 3

Figure 4

 

 

 

 

 

 

 

 

 

Nearest Neighbor weighed by Distance: The mean difference between patches with and without observed dams was 6.4m with the permutation test suggesting this was significant (z=3.001, p=0.003).  Patches with observed dams were more distributed than in the other metrics (2m to 15m) which is particularly apparent in the density plots.

Figure 5

Figure 6

 

 

 

 

 

 

 

 

 

Lastly, figures 7, 8, and 9 show plots of Patch Length by NN Distance and NNLwDist, as well as NN Distance by NNLwDist, respectively.  The clustering of patch with observed dams by patch length is consistent with the above discussion, however, patches with observed dams also seem bounded or highly clustered by only small distances to the NN.  Given a larger sample size, I suspect, that differences in patches with and without dams would be more significant.  Conversely, patches with observed dams are more distributed by NNLwDist and is curious why those are significant.

 

Figure 7

Figure 8

Figure 9

 

 

 

 

 

 

 

 

 

Critique of methods:

Given the simplicity of the analysis in this exercise, I don’t have a lot of critiques to offer. The fundamental challenge with analyzing these data were that they are ‘0’ inflated, meaning that of the 29 patches generated, only 4 of them had observed dam sites.  I had initially applied a logistic regression model to consider the probability of dams as a function of patch length, distance to nearest neighbor and the weighted neighbor length, but refrained from providing the results and opting instead to offer the simpler analysis above given the small and lopsided samples in this analysis. That said, the overall effort confirms that patch geometry seems to have a role in the occurrence of beaver dams.  In particular, there is strong evidence of a difference in the size of patches where dams were observed compared to those where they were not and is consistent with theory from landscape ecology that suggests animals will preferentially select larger habitat over smaller habitats all else being equal.  Curiously, these data do not show compelling evidence of a difference in the NN Distance between patches with and without observed dams sites, but do for the NN Length weighted by the distance to that patch.

 

 

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