Elita Baldridge's dissertation repository
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sad-data
violins
.gitignore
Fig1.pdf
Fig1.png
RAD-comparison-graphs.py
README.md
Table1.docx
chapter1.docx
chapter1.md
chapter1.pdf
chapter1_diff.pdf
chapter2.md
chapter2.pdf
chapter3.md
chapter3.pdf
check-likelihood-consistency.R
check-outputs.R
check.py
connolly_data_extraction.py
data-cleaning.py
defense_pres.pdf
defense_pres.tex
defense_transcript.md
defense_transcript.pdf
ecology.csl
example-plot.py
format.sty
intro.md
intro.pdf
intro_refs.bib
makefile
misc-data-query.py
miscDB-graphs.py
miscDB-ref-formatting.py
miscDB_paper_refs.bib
miscDB_refs.bib
neutral_comparison_refs.bib
outline.md
peerj_response_letter.md
sad-comparison-graphs.py
sad-comparisons.py
sad-ll-pln.R
sad-postprocess.R
sad-process-db.py
sad_comparison_functions.py
sad_comparison_refs.bib
sad_neutral_analysis.py
summary.md
summary.pdf
summary_refs.bib
total_wins.png
wins-by-dataset.png

README.md

sad-comparison

DOI

####Repository for comparisons among species abundance distribution (SAD) models.

#####Data:
Community data the same as used in White EP, Thibault KM, Xiao X. 2012. Characterizing species abundance distributions across taxa and ecosystems using a simple maximum entropy model. Ecology. 93(8):1772-1778. Further details and scripts for data extraction and processing can be found in the GitHub repository for that paper at https://github.com/weecology/white-etal-2012-ecology Additional community data for Actinopterygii, Reptilia, Coleoptera, Arachnida, and Amphibia were mined from the literature and are publicly available for import through the EcoData Retriever (https://github.com/weecology/retriever) or on figshare (Baldridge, Elita (2013): Community abundance data. figshare. http://dx.doi.org/10.6084/m9.figshare.769251)

.

#####Python dependencies:
METE: https://github.com/weecology/METE.git
macroecotools: https://github.com/weecology/macroecotools.git
matplotlib
basemap for matplotlib: http://matplotlib.org/basemap/users/installing.html#installation numpy
scipy
pandas
seaborn

The METE module and the macroecotools module can be installed from the command line (with appropriate permissions)
git clone https://github.com/weecology/METE.git
cd METE
python setup.py install (sudo python setup.py install on Linux)
cd ..
git clone https://github.com/weecology/macroecotools.git
cd macroecotools
python setup.py install (with sudo for Linux)

#####SAD models tested and packages used:
Maximum Entropy Theory of Ecology (METE) (METE)
Logseries (macroecotools/macroeco_distributions)
Poisson lognormal (macroecotools/macroeco_distributions)
Negative binomial (macroecotools/macroeco_distributions)
Geometric series (macroecotools/macroeco_distributions)

Neutral theory: Because neutral theory predicts the negative binomial distribution at the local scale (Connolly et al. 2014. Commonness and rarity in the marine biosphere. PNAS 111: 8524-8529. http://www.pnas.org/content/111/23/8524.abstract), we used the prediction for the negative binomial distribution (macroecotools/macroecodistributions) instead of fitting the neutral theory model directly. The AICc for neutral theory was calculated with the appropriate number of parameters for neutral theory.

To reproduce the workflow and analyses in this repository, run:

misc-data-query.py to extract the Baldridge 2013 data used in addition to the White 2012 data (Data were first imported into an sqlite database with the EcoData Retriever).

AND

sad-comparisons.py to perform the analyses

sad-process-db.py to create a database from the analysis results from sad-comparisons.py

sad-comparison-graphs.py to generate the figures

OR

sad_neutral_analysis.py to perform the analysis and generate the figures.