Firstwrkshnotes » History » Version 11
Corinna Gries, 03/10/2014 03:04 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 | 1 | Corinna Gries | |
6 | 10 | Corinna Gries | |
7 | h1. Breakout session 1: Metrics brainstorming |
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8 | |||
9 | 7 | Corinna Gries | * what are you currently using |
10 | * what would you like to use |
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11 | * how widely is it used |
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12 | * can it be applied to different biological community datasets (sampling approach) |
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13 | * is it already coded {in R} |
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14 | 1 | Corinna Gries | |
15 | 8 | Corinna Gries | |
16 | 5 | Corinna Gries | h2. Metrics |
17 | 6 | Corinna Gries | |
18 | 5 | Corinna Gries | # *Diversity* (all of these are generally in R, mostly in vegan) |
19 | ## Jaccard index |
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20 | ## Simpson's diversity |
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21 | ## Shannons index |
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22 | ## Turnover - different ways to calculate |
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23 | ## Dominance |
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24 | ## Evenness |
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25 | ## Richness |
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26 | ## Rank abundance shift |
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27 | 9 | Corinna Gries | ## Proportion of overall diversity |
28 | 5 | Corinna Gries | ## Beta diversity |
29 | # *Community metrics/ordination* |
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30 | ## NMDS (vegan) |
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31 | ## PCA (vegan) |
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32 | ## Bray curtis (vegan) |
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33 | ## Variance tracking, quantify variability change |
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34 | 9 | Corinna Gries | ## Position in ordination-space |
35 | 5 | Corinna Gries | # *Spatial* |
36 | ## patch scale |
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37 | ## spatial autoregression |
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38 | 9 | Corinna Gries | ## Endemism |
39 | ## Summary of species' positions within their ranges |
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40 | 5 | Corinna Gries | ## meta community statistics |
41 | # *Mechanistic models* |
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42 | ## 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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43 | ## MANOVA (vegan? Also, permanova is in vegan) |
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44 | 9 | Corinna Gries | ## Ecosystem function (e.g. N deposition) |
45 | 5 | Corinna Gries | ## interaction population models - inter specific competition (Ben Bolker's book and corresponding package) |
46 | 9 | Corinna Gries | ## Economically/legally relevant metrics (e.g. Maximum sustainable yield) |
47 | 5 | Corinna Gries | # *Food webs* |
48 | ## connectance |
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49 | ## network analysis |
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50 | # *Traits/phylogentic* |
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51 | 9 | Corinna Gries | ## functional/phylogenetic diversity |
52 | 1 | Corinna Gries | ## species aggregation (functional groups, trophic levels |
53 | 9 | Corinna Gries | ## phylogenetic dispersion |
54 | 1 | Corinna Gries | ## Native/exotic |
55 | 9 | Corinna Gries | ## Phylogeographic history |
56 | 1 | Corinna Gries | # *Temporal indices* |
57 | 9 | Corinna Gries | ## species turnover |
58 | ## rate of return |
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59 | 4 | Corinna Gries | ## Variance ratio |
60 | ## Mean-variance scaling |
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61 | 5 | Corinna Gries | ## Spectral analysis |
62 | 1 | Corinna Gries | ## Regresssion windows (strucchange) |
63 | 9 | Corinna Gries | ## time series models of abundance -- metric would be parameters of model |
64 | 1 | Corinna Gries | # *null models* |
65 | 9 | Corinna Gries | # *Comparative analysis of small noise vs large noise systems. What drives differences?* |
66 | 5 | Corinna Gries | |
67 | 1 | Corinna Gries | h2. Issues: |
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69 | 11 | Corinna Gries | # length of time series relative to lifespan of organisms |
70 | 9 | Corinna Gries | > WMI toolbox |
71 | 11 | Corinna Gries | # high frequency data needed |
72 | 9 | Corinna Gries | > sample too frequently then don't see signal, sample to far about miss all dynamics |
73 | 11 | Corinna Gries | # type of variable being measured |
74 | 9 | Corinna Gries | > abundance, biomass, production |
75 | # Rare species as background noise |
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76 | 8 | Corinna Gries | |
77 | |||
78 | h2. Coded in R |
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79 | |||
80 | * Richness/diversity metrics: http://cran.r-project.org/web/packages/vegan/index.html |
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81 | * Diversity metrics (alpha, beta, gamma): http://cran.r-project.org/web/packages/vegetarian/index.html |
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82 | * Hubble metrics: http://cran.r-project.org/web/packages/untb/index.html |
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83 | * Leading indicators, variance, autocorrelation, skew, heteroscedasticity: http://cran.at.r-project.org/web/packages/earlywarnings/index.html |
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84 | |||
85 | not yet coded: |
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86 | 1 | Corinna Gries | * state-space models and community level resilience |
87 | 10 | Corinna Gries | |
88 | h1. Breakout Session 2: Identify research questions: |
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89 | |||
90 | # Data set transformation to allow compute of many metrics |
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91 | # Time series analysis of community level metrics (consider higher freq data too)(earlywarnings R package) |
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92 | # New R code for capturing climate variance at seasonal and interannual scales and residuals |
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93 | # Review of non-stationarity |
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94 | 11 | Corinna Gries | ## How do you figure out if 3 or more spp are changing together? |
95 | ## Developing/adopting/adapting new techniques |
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96 | 10 | Corinna Gries | |
97 | h2. Report back from working groups |
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98 | |||
99 | h3. Group 1 and 2 |
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100 | |||
101 | * Downloaded datasets, created common format |
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102 | * Eric made script to calculate metrics over time |
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103 | * lots of time to clean up and reformat the data |
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104 | * Dataformat most useful: long format, some standardization is useful: NAN, date format, separate file with site conditions, spelling of words, outliers, how to deal with large missing data chunks, functional groups for species |
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105 | * How to deal with data where individuals have not been keyed to species, but higher and varying taxonomic ranks |
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106 | * more exploration is necessary |
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107 | * specific site conditions were partly unknown (e.g. CAP landuse) |
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108 | * Continue work on development of workflows for data cleanup |
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109 | * Ecological analysis of temporal change data sets |
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110 | |||
111 | h3. Group 3 |
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112 | |||
113 | * Eric's LTER project to develop the R model for analyzing more spatial variability, could collaborate with long term data |
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114 | |||
115 | h3. Group 4 |
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116 | |||
117 | *Temporal and spatial variance* |
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118 | * ecosystems strongly influenced by temporal and spatial variation bi-variate plot |
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119 | * limitations: other data, e.g. biochemical conditions |
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120 | * time turnover |
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121 | * spatial heterogeneity |
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122 | * cross site LTER datasets to see if there is some traction to the idea |
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123 | * datasets in long form because they need to be manipulated a little for each new exploratory step |
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124 | * Work on common data format for various communities |
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125 | * Work on script to calculate variance in space and time metrics for each of those communities |
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126 | * One limitation was that many available data don't lend themselves to both the spatial and temporal analyses |
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127 | |||
128 | *Variance partitioning* |
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129 | * climate, intra annual seasonal, inter annual |
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130 | * new metrics: variance components analysis |
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131 | * explore different temporal scale |
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132 | * which part of the annual 'weather' is important |