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.