Chapter 1 - Núria BONADA

Local scale: Distribution patterns in Trichoptera
Both matrixes, caddis-max and caddis-mod, were compared using a Mantel test (Mantel,
1967) with the PCORD program (McCune & Mefford, 1999). This statistic method test
differences between two similarity or distance matrices with the same objects (samples) to
determine if distances among objects in one matrix (e.g., caddis-max) are or are not linearly
correlated with the ones in the second matrix (e.g., caddis-mod). This test is equivalent to a
non-parametric and multivariate test useful when biological data with many zeros is used. The
result is a Mantel’s standardized correlation coefficient (rM) tested by random permutations
(999 runs).
Spatial changes in caddisfly assemblages
Two ordination techniques of multivariate data were applied to analyze distribution patterns of
caddisflies. Firstly, an indirect analysis of Correspondence Analysis (CA) using biological data
was performed. This ordination technique allows us to relate objects (samples) and descriptors
(taxa) in a low-dimensional space. The measure used is the χ 2, appropriated for
semiquantitative data. It has been considered to produce better results than Principal
Coordinate Analysis (PCA) with biological data, because matrices usually have numerous null
values and χ 2 distance exclude double-zeros (Legendre & Legendre, 1998). Eigenvalues results
(an indication of the percentage of variability explained by each canonical axis) were kept and
compared with the ones obtained using a partial Canonical Correspondence Analysis (pCCA)
to understand the proportion of caddisfly distribution explained by measured environmental
variables. Partial CCA analysis is a direct ordination method similar to partial Redundancy
Analysis (pRDA) but using χ 2 rather than Euclidean distances. This method obtains samples
ordination according to the environmental constrains provided by an environmental variables
matrix, and extracting the influence of some covariates on the biological data. A pCCA analysis
was performed in front of a simple CCA to extract the influence of seasonality in sampling
samples, because it presented a significant effect after a MRPP test (Multi-response
Permutation Procedures) comparing four sampled seasons (A=0.003, p-value=0.022).
Seasonality was included as four dummy covariables (spring, summer, autumn and winter).
Rare species were down weighted to avoid bias in the final results in CA and pCCA analysis.
Environmental data matrix was built up using the variables measured in GUADALMED Project
(Table 1). Physical and chemical parameters included are those measured in the field or
obtained in the lab. Oxygen was removed from the analysis because the incomplete data set.
Biological indicators of the composition and diversity of the macroinvertebrate community
were also used, as IBMWP (Alba-Tercedor y Sánchez-Ortega, 1988; Alba-Tercedor, 1996; Alba-
Tercedor & Pujante, 2000), and the IASPT (the ratio between IBMWP and number of taxa).
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