This week we presented our landscape ecology article reviews to the class. The article I reviewed is called “a modelling approach to infer the effects of wind farms on landscape connectivity for bats”. (Article Link Here) The authors wanted to better understand the impact of wind farms on bat commuting routes. The method used for this research is summarized in the diagram shown below, which is divided into three main sections:

1) Creating a Species Distribution Map (SDM)
– Creating variables from existing environmental data (land cover, digital terrain model, and hydrographic maps)
– Input variables into model along with 70% of bat presence data
– Test model accuracy with remaining 30% of bat presence data
– Repeat procedure 50 times to create binary SDM

2) Determining connectivity
– Use 3 proxies of linear variables from previous research with SDM to produce resistance surface
– Select 50 random points in and connect them together by calculating least cost pathways
– Repeat procedure 10 times and summarize results in Potential Commuting Corridor map (PCC)

3) Wind farm interference
– Create map of all existing and future wind turbine locations and add 150m buffer to each
– Overlay wind turbine map with PCC
– If a PCC crosses a buffer, that turbine will be labeled as “affected”

In the end the researchers found areas with the most windfarm impact therefore they suggest those turbines be curtailed during bat migration seasons as well as putting limitations on future turbine construction.

Overall I would rate this article a 6  out of 10. Despite having successfully completing the goals they set out with and having good replicability in their results, 2 potential limitations made me rate the article lower:

1) The researchers chose this specific bat species because of its vulnerabilities to wind farms and migratory nature. They also noted how lots of turbine deaths were bats on their migration. But then the researchers decided to assume all the PCCs they produced were for commuting (daily) rather than migrating (annual) routes because of the lack of information of migration. Why ignore migration completely after putting so much emphasis on it?

2) I felt that the binary SDM was too simple for representing a complex factor like suitability. In nature there obviously isn’t a clear boundary where suddenly it becomes unsuitable for bats. Having a gradient or several classes would have been a better way of representing suitability.

I also found it interesting that several other studies presented used the same methodology and spatial analysis program (MaxEnt), and that this research was just following some of the “standard” processes set by previous research.

Bat Connectivity Map

We had a review of many statistical ideas to help with lab 3. The major topic was on regression since it can help model patterns. Ordinary Least Squares (OLS) is simple and one of the most commonly used regression models that can show overall patterns. However OLS has a “global” nature, which means it assumes everything is the same over space. This is a limitation when working with spatial data with heterogeneity. So Geographically Weighed Regression (GWR) has become one of the more popular methods recently due to its ability to create a “local” model that varies over space.

The lab for this week was also focused on GWR and children’s language skills in relation to several variables. Firstly we used the Explanatory Regression Analysis tool to determine which set of variables provides the best relations. We then used OLS tool to produce a general global result, and compared that to results produced using the GWR tool. Finally we produced grouping clusters using the Grouping Analysis tool to see if we can relate the results from OLS and GWR to other variables used to create the groups. I found that only two of the three variables are statistically significant from OLS, and the map of OLS residuals showed no significant spatial patterns. As for the GWR results, the surface map of social score showed good correlation with GWR R² values, so it would seem that social scores and language scores have the strongest relation.

GWR R2 results with Social Score surface.

Spatial patterns vary greatly in space and it is difficult to work with only one scale because it won’t capture everything we want. Spatial Autocorrelation is a common measurement method used to analyse patterns, and it is the distribution of objects or events on the landscape, and how clustered or dispersed they are. However there is likely more than one pattern that exists when we work with different scales. As a result the patterns are classified as either first-order or second-order. First-order processes show patterns determined by environment, for example rabbits clustering around a food source. Second-order processes show patterns caused by the objects or events themselves, for example rabbits clustering around each other for protection. Stationarity is another process that was discussed. It is a measure of how well patterns stay the same over space. Changes in space measured are relative locations as opposed to absolute locations because this allows us to see the patterns among the points. First-order stationary is when the patterns remain the same everywhere in space. Second-order stationary is when the process is random but will depend on time or space. Direction plays an important part here, since processes that favour certain directions (anisotropic pattern) can create distinct patterns such as having a slope or mountain ridge. On the other hand patterns can also favour any direction equally (isotropic).

We also discussed the different processes that can affect the landscape. Abiotic processes are physical factors like climate, topography, and soil composition. Biotic processes are caused by living organisms and their interactions, such as the power of keystone species in controlling other species and the environment. Two other effects that are becoming more important are anthropogenic processes and natural disturbances like earthquakes and fires. These processes can contribute to local uniqueness, phase difference, and also dispersal, which all cause spatial patterns. There are also many metrics that can help measure different patterns, such as diversity, connectivity, and dominance. However it is important to know what processes are at work, and also knowing a good scale for the processes.