Firstwrkshnotes » History » Version 4
Corinna Gries, 03/10/2014 01:50 PM
1 | 1 | Corinna Gries | h1. Workshop Notes |
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3 | 3 | Corinna Gries | are on etherpad: https://epad.nceas.ucsb.edu/p/commdyn-20140105 |
4 | 4 | Corinna Gries | |
5 | Metrics brainstorming: |
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6 | what are you currently using |
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7 | what would you like to use |
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8 | how widely is it used |
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9 | can it be applied to different biological community datasets (sampling approach) |
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10 | is it already coded {in R} |
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12 | # Diversity (all of these are generally in R, mostly in vegan) |
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13 | > # Jaccard index |
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14 | > # Simpson's diversity |
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15 | > # Shannons index |
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16 | > # Turnover - different ways to calculate |
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17 | > # Dominance |
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18 | > # Evenness |
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19 | > # Richness |
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20 | > # Rank abundance shift |
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21 | > # Beta diversity |
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22 | # Community metrics/ordination |
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23 | # NMDS (vegan) |
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24 | # PCA (vegan) |
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25 | # Bray curtis (vegan) |
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26 | # Variance tracking, quantify variability change |
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27 | # And spatial... |
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28 | # patch scale |
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29 | # spatial autoregression |
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30 | # meta community statistics |
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31 | # Mechanistic models |
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32 | # MAR, needs driver matrix, problem auto-corelation, mostly fresh water or marine (Eli Holmes has state-space MAR in R implemented, not sure if it's on CRAN) http://cran.r-project.org/web/packages/MARSS/index.html |
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33 | # MANOVA (vegan? Also, permanova is in vegan) |
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34 | # interaction population models - inter specific competition (Ben Bolker's book and corresponding package) |
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35 | # Food webs |
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36 | # connectance |
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37 | # network analysis |
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38 | # Traits/phylogentic |
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39 | # species aggregation (functional groups, trophic levels |
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40 | # phylogenetic dispersion (ape etc. -- this stuff is all in R) |
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41 | # Native/exotic |
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42 | # Temporal indices |
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43 | # Variance ratio |
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44 | # Mean-variance scaling |
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45 | # Spectral analysis |
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46 | # Regresssion windows (strucchange) |
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47 | # null models |
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49 | issues identified by four breakout groups: |
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50 | 1. length of time series relative to lifespan of organisms |
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51 | WMI toolbox |
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53 | 2. high frequency data needed |
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54 | sample too frequently then don't see signal, sample to far about miss all dynamics |
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56 | 3. type of variable being measured |
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57 | abundance, biomass, production |
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60 | Rare species as background noise |
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61 | rank abundance curves back again |
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62 | Comparative analysis of small noise vs large noise systems. What drives differences? |