Informatics & code

Code and public tools associated with past, current, and ongoing lab projects

See also GitHub – Enquist Lab, Oxford Ecosystems Lab, Enquist Lab Git/Computational Tutorials



We have several that are under development. What are they? and why important? Well, in short, they are the future of open science and will be the savior of our data and science. Don’t take our word – just ask Ethan White.

Taxonomic Name Resolution –  The Taxonomic Name Resolution Service (TNRS) API is a tool for the computer-assisted standardization of plant scientific names. The TNRS corrects spelling errors and alternative spellings to a standard list of names, and converts out of date names (synonyms) to the current accepted name. see

Native Species Resolver – The Native Species Resolver (NSR) API uses a database of regional taxonomic checklists to determine if observations of a species within political divisions (country, state/province, county/parish, etc.) are native or introduced.see

Global Plant Traits – Access global plant trait data – coming soon in collaboration with rOpenSci 


  • The Taxonomic Name Resolution Service – Standardize taxonomy and integrate biological names open source general solutions for correcting and standardizing biological names. Code available via Github here. The TNRS was even featured in Nature which you can read here.
  • Plant -O- Matic open software for integrating biodiversity layers with phone apps and the use of APIs to bring biodiversity data to your phone. See our 2016 publication in Methods in Ecology and Evolution. Watch for new upcoming updates too! Code available via Github here.
  • BIEN – BETA R package for accessing global plant trait, habit, geographic range maps, observation records, and plot data. ~ query and download 85Million botanical observations and ~100,000 species range maps.  Global coverage but most intensive coverage for North and South America.  We are finalizing beta version for submission to CRAM but email us for access.  More information? see or

Statistics/New Methods

  • Hypervolumes  R package to measure a niche size, niche overlap and climate hyper volumes via methodology outlined in Blonder et al. (2014) Global Ecology and Biogeography.  These functions estimates the shape and volume of high-dimensional objects and performs set operations: intersection / overlap, union, unique components, inclusion test, and non-convex feature detection. Can measure the n-dimensional ecological hypervolume and perform species distribution modeling. We created a new methodological approach to the study and quantification of hyper volumes. You can download the R package by B. Blonder here.
  • Community Climate Statistics – Community climate statistics – From Blonder et al. (2015) Ecology R package by B. Blonder . “Computes community climate statistics for volume and mismatch using species’ climate niches either unscaled or scaled relative to a regional species pool. These statistics can be used to describe biogeographic patterns and infer community assembly processes. Includes a vignette outlining usage.” From the paper Blonder, B., Nogués-Bravo, D., Borregaard, M.K., Donoghue II, J.C., Jørgensen, P.M., Kraft, N.J.B., Lessard, J-P., Morueta-Holme, N., Sandel, B., Svenning, J.C., Violle, C., Rahbek, C. and B.J. Enquist (2015) Linking environmental filtering and disequilibrium to biogeography with a community climate framework. Ecology 96:972–985.
  • Inference of Species Association Networks from Co-Occurrence Data R package ‘netassoc’ by B. Blonder and N. Morueta-Holme. “Infers species associations from community matrices. Uses local and (optional) regional- scale co-occurrence data by comparing observed partial correlation coefficients between species to those estimated from regional species distributions. Extends Gaussian graphical models to a null modeling framework. Provides interface to a variety of inverse covariance matrix estimation methods.” From the paper Morueta-Holme, N., Blonder, B., et al. (In Press) A network approach for inferring species associations from co-occurrence data. Ecography.
  • Alternative regression and allometric methods – OLS, RMA, and OLS Bisector methods – R code for fitting a series of differing regression models including OLS Bisector from Isobe et al. 1990. Using the slopes.s routine, which was originally written in S+ by Andrew P. Allen, borrowing heavily from a Fortran program written by Isobe et al. this code computes estimates and standard deviations for the slopes and intercepts of six different regression models. Details and methodological considerations are available in Isobe et al. 1990.

Plant Functional Ecology, Evolution and Macroecology

  • Leaf venation metrics and traits –  MATLAB code to automatically calculate venation traits. From images of leaf venation, these programs calculate the ‘venation’ vein density, vein distance, and vein loopiness, all in SI units (m^-1, m, m^-2). The function also returns the density, distance, loopiness, and a skeletonized representation of the image.
  • Leaf Venation and the Leaf Economic Spectrum Inclusion of vein traits improves the correlation between observed and predicted values of leaf economics spectrum traits. The code analysed our previously published data (or any other Leaf Economics Spectrum Trait data and venation data) to assess if the fitted r2 values for a regression of predicted against observed values (not constrained to the origin) are significantly higher than for the same regressions when vein density was sampled uniformly randomly from its global range. Input data include Am, mass-normalized photosynthetic rate; LL, leaf lifespan; LMA, leaf mass per area; Nm, nitrogen content. You can download the supplemental file with the associated R Code here ( Blonder_etal_R_code_2014).


  • BIEN – BETA R package for accessing global plant trait, habit, geographic range maps, observation records, and plot data. ~ query and download 85Million botanical observations and ~100,000 species range maps.  Global coverage but most intensive coverage for North and South America.  We are finalizing beta version for submission to CRAM but email us for access.  More information? see or
  • Simulation model for Eco-Evolutionary Community Diversity Dynamics:  R code from Stegen et al. 2011.  This simulation model builds upon earlier work (Stegen et al. 2009) and presents a unified approach to the study of how the speed of evolution, the influence of species richness on diversification, and niche-based coexistence. The model simulates community invasibility and species richness across a broad thermal gradient. In the model, the evolution of body size influences the ecological structure and dynamics of a trophic network, and organismal metabolism ties temperature to eco-evolutionary processes. The framework distinguishes ecological invasibility (governed by ecological interactions) from evolutionary invasibility (governed by local ecology and constraints imposed by small phenotypic effects of mutation).

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