Almost all ecological processes in nature occur in space and time (Fortin and Dale, 2011) and non-randomness seems to be the dominant spatial structure (Perry et al., 2002). Starting from the 1990s, ecologists increasingly considered space and it was even described as “the final frontier for ecological theory” (Kareiva, 1994). Even though connected to several challenges, such as scaling issues (Levin, 1992; Wiens, 1989), different processes leading to the same spatial pattern (Wiegand et al., 2003) or interactions of several processes (Dovčiak et al., 2001; Wiegand et al., 2009), it is generally agreed upon that space should be explicitly considered in ecological studies.

In ecology, commonly three different types of spatial data are used, namely (I) discrete raster data (e.g. land use classes), (II) continuous raster data (e.g. soil characteristics as pH-values) and lastly (III) point pattern data (e.g. tree locations and size of trees) (Wiegand and Moloney, 2014). Addtionally, in the last years also vector data has become more prominent. While for each kind of data different analysis techniques exist, this course deals with point pattern analysis.

A point pattern is a map of the locations of all individuals within a defined observation window or study plot (Velázquez et al., 2016). Based on the assumption that the pattern contains information about the underlying processes (Law et al., 2009), ecologist try to infer these processes by analysing the spatial structure of the pattern and comparing it to null model data based on ecological theory (Wiegand and Moloney, 2014, 2004).

In the following videos, international experts will present you some exemplary studies that used spatial point pattern analysis to infer underlying processes from the patterns.