When trying to understand how wildlife, for example a bird species, may react to climate change scientists generally study how species numbers vary in relation to climatic or weather variables (e.g. Renwick et al. 2012, Johnston et al. 2013). The way this tends to be done is by gathering information (data!) about bird numbers as well as the weather variables (for example temperature) in several locations (i.e. in space) and fitting a regression model to these data to detect and illustrate how bird numbers go up or down with temperature.
Data on bird numbers and temperatures in several locations lets researchers see the relationship between the two.
This relationship is then used to forecast how bird numbers may change along with potential temperature changes in the future (i.e. in time), for example due to climate change.
Relationships between bird numbers and temperature in a given location are often used to forecast changes in bird numbers with expected changes in temperatures over time.
Ordination and clustering methods are widely applied to ecological data that are non-negative (like species abundances or biomasses). These methods rely on a measure of multivariate proximity that quantifies differences between the sampling units (e.g. individuals, stations, time points), leading to results such as:
Ordinations of the units, where interpoint distances optimally display the measured differences
Clustering the units into homogeneous clusters
Assessing differences between pre-specified groups of units (e.g. regions, periods, treatment–control groups)
In this video, Michael Greenacre introduces his new article, ‘‘Size’ and ‘Shape’ in the Measurement of Multivariate Proximity’, published in Methods in Ecology and Evolution, May 2017. In the context of species abundances, for example, he explains how much a chosen proximity measure captures the difference in “size” between two samples, i.e. difference in overall abundances, and differences in “shape”, i.e. differences in compositions or relative abundances. He shows that the popular Bray-Curtis dissimilarity inevitably includes a part of the “size” difference in its measurement of multivariate proximity.