The systematics, zoogeography and evolution of Dolly Varden and bull trout in British Columbia
Creators: name: Haas, Gordon Robert
Publisher: name: University of British Columbia
Year: 2010

description: An analysis of the systematics, zoogeography and evolution of the Dolly Varden char species complex in British Columbia is presented. These features of this species complex and the morphometric statistical procedures used in these analyses have both long been the subjects of strong debate and also have recently seen much renewed interest and work. This thesis assesses both these areas and is divided into those two parts. The first section deals with these three biological topics, and the second section contains a synthesis and exploratory data assessment of the commonly used morphometric techniques and provides some new methodology for understanding their requirements and interpreting their results. PART I 1. The systematics of the Dolly Varden char species complex is examined by using principal component analysis (PCA) to designate typological species groupings and then employing linear discriminant function analysis on a reduced set of significant characters to classify the remaining specimens. This typological distinction is verified with distributional information that reveals no interbreeding of the species in areas of parapatry and sympatry, and with preliminary information regarding intra- and inter- specific crosses, spawning colouration, skull osteology, cytology and embryology. This data is also suggestive of competitive exclusion and character displacement. All these results indicate that the Dolly Varden char species complex in B.C. is composed of two species, Dolly Varden (Salvelinus malma) and bull trout (Salvelinus confluentus). 2. The zoogeography of these two species is analyzed using canonical trend surface analysis (CTS). CTS can potentially separate confounding non-geographic morphometric information from the data and thus could allow historical zoogeograpbic patterns to be inferred from that data which corresponds to geography. Such a reconstruction reveals the possible glacial refuge origins and post-glacial recolonization patterns of these two species for each of the major river drainages in B.C.. 3. The evolution of these two species is assessed through the implementation of PCA to fit the cross-sectional morphometric data to an ontogenetic model. The resultant PCA size and shape vectors effectively portray allometric trends which indicate that Dolly Varden could have evolved from bull trout through neotenic paedomorphosis. This result is supported with data on growth rates and developmental homeostasis. PART II 4. A synthesis of the available but widely scattered and disparate information on the data and statistical requirements for morphometric statistics reveals the analytical problems that can result from not approximating underlying test assumptions. These assumptions are important, but are not appreciated or often assessed. Simple recommendations and rarely used tests for dealing with these requirements are provided. 5. The effectiveness and compatability of four bivariate morphometric techniques (ratios, log₁₀ ratios, allometric regression, regression residuals) are assessed. All methods provide similar but ineffective individual ordination and group separation. Their effects on characters differ greatly and are often unrealistic. None of these methods effectively removes all the confounding allometric size information, but allometric regression will usually be the best bivariate procedure. 6. A similar assessment of four multivariate morphometric procedures (covariance matrix PCA, correlation matrix PCA, shear matrix PCA, size-constrained matrix PCA) is undertaken. Size-constrained PCA results in non-orthogonal vectors that also do not represent the traditional multivariate morphometric size and shape vectors. As well, the character and individual information it provides is unrealistic. The other three techniques result in similar and effective individual ordination, group separation and removal of confounding allometric size information. PCA on a covariance matrix is likely the best multivariate method since it provides the most realistic size adjustment and character information. 7. PCA is often carried out on data which has been previously adjusted through bivariate procedures. An examination of this method demonstrates that it results in no benefits since the multivariate morphometric size and shape vectors are lost, and the data variation is no longer synthesized into only two or three resultant significant vectors. 8. PCA is also performed on mixed character data sets (continuous and discontinuous data). An assessment of this procedure shows that it provides improved group separation, but the representation of characters, individuals and multivariate morphometric size and shape relationships is confounded and unrealistic. There also is a slight reduction in data synthesis. 9. A methodology for back-transforming PCA output into the original and more intuitively comprehensible data scale, format and dimensions is given. This back-transformation also verifies the traditional belief that the first resultant PCA morphometric vector is size and that the second is shape. Separate unconfounded matrices for size and shape information in which only the significant data variation is accounted for can thus be independently back transformed.