Below are a number of links that provide code and scripts for ecological data analysis from our own projects.

Trait data integration

• We have developed an open-source workflow for harmonizing heterogeneous trait measurements of plants (e.g. lengths, widths, counts and angles of stems, leaves, fruits and inflorescence parts) as well as additional information related to taxonomy, measurement or fact and occurrence. The details are published in Lenters et al. (2021). The code and scripts are available as:

  • R code of the workflow (GitHub)

  • Bookdown tutorial

Species richness modelling

• R scripts for regression models analysing species richness of vertebrates (birds, mammals, amphibians) in relation to climatic and geological predictor variables across the worlds mountains, as analysed in Antonelli et al. (2018)

LiDAR

• Laserchicken software for handling massive amounts of LiDAR point clouds created by airborne laser scanning (ALS)

  • Laserchicken on GitHub

  • Laserchicken documentation

• Laserfarm pipeline providing a free and open source software wrapper to Laserchicken, supporting data preparation to scheduling and execution of distributed processing across a cluster of compute nodes.

  • Laserfarm on GitHub

  • Laserfarm documentation

  • Laserfarm on Zenodo

• See our GitHub repository for the eEcoLiDAR project with scripts and information about processing LiDAR point cloud data

• Code for analysing LiDAR point cloud data to identify and map linear vegetation elements (hedges, tree lines) in agricutlural landscapes, as analysed in Lucas et al. (2019)

• Code and workflow (R scripts) for classifying wetland‐related land cover types and habitats using fine‐scale lidar metrics derived from country‐wide airborne laser scanning data, as analysed in Koma et al. (2021)

   

Macroevolutionary analyses

• R scripts to perform the Binary State Speciation and Extinction (BiSSE) and Multiple State Speciation and Extinction (MuSSE) analyses as done in Onstein et al. (2017).

• R scripts to simulate a trait-dependent diversification process with a shift in rates at a given point in time as done in Onstein et a. (2018)

Spatial autocorrelation and regression modelling

• See Appendix S1 and S3 of Kissling & Carl (2008) for R code and data to construct simultaneous autoregressive models in R

• See the Appendix of Dormann et al. (2007) for R code and data to test different methods to account for spatial autocorrelation in the analysis of species distributional data