Spatial point pattern analysis of aerial survey data to assess clustering in wildlife distributions

Abstract

Assessing clustering in wildlife populations is crucial for understanding their dynamics. This assessment is made difficult for data obtained through aerial surveys because the shape and size of sampling units (strip transects) result in poor data supports, which generally hampers spatial analysis of these data. The problem may be solved by having more detailed data where exact locations of observed animal groups are recorded. These data, obtainable through GPS technology, are amenable to spatial analysis, thereby allowing spatial point pattern analysis to be used to assess observed spatial patterns relative to environmental factors like vegetation. Distance measures like the G-statistic and K-function classify such patterns into clustered, regular or completely random patterns, while independence between species is assessed through a multivariate extension of the K-function. Quantification of clustering is carried out using spatial regression. The techniques are illustrated with field data on three ungulates observed in an ecosystem in Kenya. Results indicate a relation between species spatial distribution and their dietary requirements, thereby concluding the usefulness of spatial point pattern analysis in investigating species spatial distribution. It also provides a technique for explaining and differentiating the distribution of wildlife species.