Community Dynamics Toolbox

h1. Workshop Notes

are on etherpad: https://epad.nceas.ucsb.edu/p/commdyn-20140105

h1. Breakout session 1: Metrics brainstorming

* what are you currently using

* what would you like to use

* how widely is it used

* can it be applied to different biological community datasets (sampling approach)

* is it already coded {in R}

h2. Metrics

# *Diversity* (all of these are generally in R, mostly in vegan)

## Jaccard index

## Simpson's diversity 

## Shannons index

## Turnover - different ways to calculate

## Dominance 

## Evenness

## Richness

## Rank abundance shift

## Proportion of overall diversity

## Beta diversity

# *Community metrics/ordination*

## NMDS (vegan)

## PCA (vegan)

## Bray curtis (vegan)

## Variance tracking, quantify variability change

## Position in ordination-space

# *Spatial*

## patch scale 

## spatial autoregression

## Endemism

## Summary of species' positions within their ranges

## meta community statistics

# *Mechanistic models*

## 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

## MANOVA (vegan? Also, permanova is in vegan)

## Ecosystem function (e.g. N deposition)

## interaction population models - inter specific competition (Ben Bolker's book and corresponding package)

## Economically/legally relevant metrics (e.g. Maximum sustainable yield)

# *Food webs*

## connectance

## network analysis

# *Traits/phylogentic*

## functional/phylogenetic diversity

## species aggregation (functional groups, trophic levels

## phylogenetic dispersion

## Native/exotic

## Phylogeographic history

# *Temporal indices*

## species turnover

## rate of return

## Variance ratio

## Mean-variance scaling

## Spectral analysis

## Regresssion windows (strucchange)

## time series models of abundance -- metric would be parameters of model

# *null models*

# *Comparative analysis of small noise vs large noise systems. What drives differences?*

h2. Coded in R

* Richness/diversity metrics: http://cran.r-project.org/web/packages/vegan/index.html

* Diversity metrics (alpha, beta, gamma): http://cran.r-project.org/web/packages/vegetarian/index.html

* Hubble metrics: http://cran.r-project.org/web/packages/untb/index.html

* Leading indicators, variance, autocorrelation, skew, heteroscedasticity: http://cran.at.r-project.org/web/packages/earlywarnings/index.html

not yet coded:

* state-space models and community level resilience

* variance components analysis

h1. Breakout Session 2: Identify research questions

# Data set transformation to allow compute of many metrics

# Time series analysis of community level metrics (consider higher freq data too)(earlywarnings R package)

# New R code for capturing climate variance at seasonal and interannual scales and residuals

# R model for analyzing more spatial variability (Eric's LTER project)

# Review of non-stationarity

## Variance partitioning 

## Temporal and spatial variance

h1. Discussion and Feedback: Collaboration Approaches

*Most Important Limitations*

* Data

** Time necessary to clean data

** Quality control data and deal with problems

** Data sharing permission issues 

* Collaboration

** Scattered resources: data and code in different locations, hard to move back and forth, hard to work on the code together, hard to know who's working on which parts of the code

** Workspace integration and accessibility

** Project management/tool integration

** Time investment in learning different tools

*Recommendations*

* Data

** Dataset format: long format with columns for species and count/biomass, plus columns for site (plot, subplot, etc.) and date. Separate table with species name to be able to add functional groups, taxonomic rank, etc.. Separate table for site descriptions (manipulations, land use, etc.)

** Gather additional data on biogeochemistry, climate etc.

** Develop standard methods for dealing with outliers, large gaps, species names and spellings

* Collaboration Tool

** git repository, has been used successfully in this workshop when some people were familiar with it a could bootstrap the use for other people quickly

** Way to replicate or interface with services like {Google open refine, db constraints, taxize, TNRS)

*Datasets*

* small mammal (VCR, SEV)

* arthropod data (CAP, KNZ, FCE)

* datasets on kelp published in ESA journal

* Cedar Creek : 

** species compostion data Accessible at: http://doi.org/10.6073/pasta/50db8bde41c9ea8b32dfbdde8bb0fad2

** climate data accessible at http://doi.org/10.6073/pasta/24eb99ad3102cdcb2f8d02de93dd551e

* PISCO intertidal biodiversity surveys

** Methods: http://cbsurveys.ucsc.edu/sampling/images/dataprotocols.pdf

** Point contact data (percent cover, good for sessile/common spp): https://knb.ecoinformatics.org/m/#view/doi:10.6085/AA/pisco_intertidal.50.6

** Quadrat data (percent cover, good for mobile spp): https://knb.ecoinformatics.org/m/#view/doi:10.6085/AA/pisco_intertidal.52.7

** Swath data (extensive, only select rare species like seastars): https://knb.ecoinformatics.org/m/#view/doi:10.6085/AA/pisco_intertidal.51.6

* Konza

** climate data (KNZ headquarters): doi:10.6073/pasta/ac19b27f2c28a63890d59ece32f5116b

** Konza species composition (belowground experiment for N addition contrasts): doi:10.6073/pasta/b6653594d336bddf9d5f7f72c7d9200c Konza only collects cover for N addition treatments every 5 years, so we will abandon for now