This poster was presented for the International Biometric Conference, held in Florence from 6 to 11 July 2014.
- Ecological inference and management decisions often depend on data from many species.
- A proper and useful statistical analysis quantifies the important patterns of variation, whilst reducing the complexity in multi-species data.
- Currently, analysis is frequently done by: 1) performing species-by-species analyses (e.g. univariate regression and extensions) and then
- combining results, or 2) by combining data (clustering) and then performing a group-by-group analysis.
- Neither of the standard approaches are entirely satisfactory as important aspects of the variance in the data can be lost when moving from step
- to step. Also, the propagation of uncertainty is difficult and is subsequently (often) ignored.
- We introduce two models, based on mixture models, that address these issues. One model type, species archetype models (SAMs) exploits
- similarities in individual species’ responses to the environment. The second type, regions of common profile (RCP) models, exploits similarities in
the assemblage patterns at each site.
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