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BarEcoRe data analysis workshop day 3: status

BarEcoRe data analysis workshop day 3: status. Group 2. Functional disversity calculation: General strategy. traits. species. dendrogram. TR. Dist. species. species. species. 1. P/A. FD. GRAPHS & DIAGNOSTICS. env. stations. stations. ENV. stations. Done.

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BarEcoRe data analysis workshop day 3: status

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  1. BarEcoRe data analysis workshop day 3: status Group 2

  2. Functional disversity calculation: General strategy traits species dendrogram TR Dist species species species 1 P/A FD GRAPHS & DIAGNOSTICS env stations stations ENV stations

  3. Done Data available: • Ecosystem survey (2009) • Fish functionaltraits • Species namesmatchedbetweenthesetwo! R scripting of FD • Import of data • Data transformed to presence/absence • Functionaltraitsdendrogramcomputed

  4. Methodological issues • Choice of traits • Habitat (demersal, pelagic, bathy-demersal), Max Length, Trophic level (3,4,5), Migration (yes, no, little) • List of species (61) • Distance metric • Gower • Clustering method • Single, complete, average, McQuitty, median, centroid • Functional diversity metrics • Petchey Gaston

  5. Dendrograms

  6. Relative FD vs. sp richness

  7. Threshold in FD for high sp. Richness? FD Richness/FD Sp. richness Sp. richness

  8. Richness/FD vs. Richness

  9. Is the observed FD vs. Richness particular? • Compare with other systems • Compare with ‘null model’ • Random species association

  10. Species incidence Number of species Number of stations

  11. Species richness Number of species

  12. Functional diversity (size) Community – Bray Curtis cluster (colors)

  13. Community – Bray Curtis cluster (colors)

  14. Ratio=FD/S Should rather be the deviation from the 0-model

  15. Conclusions • High FD implies high adaptability • NE Barents Sea has relatively low FD • Environmental drivers of FD: Low temperature low FD Low salinity low FD Shallow stations <150m low FD • Higher FD than expected in communities assembled randomly

  16. What’snext? • Improvement of the trait matrix Functional traits (e.g. shape analysis) • Evaluation of the effects of traits & species selection on FD estimation • Quantitative measurement of differences between clustering methods • Environmental drivers of FD • Compare FD patterns to null models

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