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

Ecological applications of LiDAR remote sensing

• Laserfarm pipeline providing a free and open source workflow for scheduling and execution of distributed LiDAR vegetation metric calculations across a cluster of compute nodes.

  • Laserfarm on GitHub

  • Laserfarm documentation

  • Laserfarm on Zenodo

• Examples of Laserfarm Jupyter Notebooks for MAMBO demonstration sites

  • All Laserfarm Jupyter Notebooks for MAMBO demonstration sites

  • Laserfarm Jupyter Notebook Salisbury Plain

  • Laserfarm Jupyter Notebook Oostvaardersplassen

  • Laserfarm Jupyter Notebook Monks Wood

  • Laserfarm Jupyter Notebook Mols Bjerge National Park

  • Laserfarm Jupyter Notebook Knepp Estate

  • Laserfarm Jupyter Notebook Comino

  • Laserfarm Jupyter Notebook Reserve Naturelle Nationale du Bagnas

  • Zenodo repository with all Laserfarm Jupyter Notebooks, derived data products (GeoTIFF files), metric visualization (maps in PDF format), and study site boundaries (shapefiles), together with a detailed description of the methodology and a README file

•  Laserfarm Jupyter Notebooks for processing country-wide ALS point clouds of the Netherlands (AHN1–AHN4)

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

  • Laserchicken on GitHub

  • Laserchicken documentation

• 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)

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  Code for retiling large LAZ files to smaller tiles and clipping 3D LiDAR point clouds of LAS/LAZ format with polygons of ESRI shapefiles.

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)

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