Species turnover does not rescue biodiversity in fragmented landscapes

Zenodo repository

Table of Contents

1. Introduction
2. Directory Structure
3. Requirements
4. Usage
5. Scripts Description
6. Figures and Results
7. License

Introduction

This is the repository with data and scripts to replicate all analyses run in the paper "Species turnover does not rescue biodiversity in fragmented landscapes".

While habitat fragmentation generally reduces biodiversity at the patch-scale (𝛂 diversity)1, there is ongoing controversy regarding whether such negative effects can be alleviated at the landscape-scale (𝛾 diversity) if among-patch diversity (𝛃 diversity) increases as a result of fragmentation2–6. However, this controversial view has not been rigorously tested. We use a dataset of 4,006 taxa across 37 studies from six continents to test the effects of fragmentation on biodiversity across scales by explicitly comparing continuous and fragmented landscapes. We find that fragmented landscapes consistently had both lower 𝛂- and lower 𝛾 diversity. While fragmented landscapes did tend to have higher 𝛃 diversity, this did not translate into higher 𝛾 diversity. Our findings refute claims that habitat fragmentation can increase biodiversity at landscape-scales and emphasise the need to restore habitat and increasing connectivity, in order to minimise biodiversity loss at ever increasing scales.

Directory Structure

The project is organized as follows:

- figures/ # Contains the outputs of Figures 2 and 3 - processed_data/ # Folder for processed datasets after running analyses such as rarefaction - renv/ # Directory for R environment configurations - results/ # Summary and relevant results files - 01_study_design.R # Script for estimating alpha diversity based on study design - 02_pairwise_div_all.R # Script for estimating pairwise diversity (psd) using all plot pairs - 03_pairwise_div_near.R # Script for estimating pairwise diversity (psd) using the nearest plot pairs - 04_pairwise_div_raref.R # Script for estimating pairwise diversity (psd) using q = 0 and 2 (all pairs and nearest pairs) - 05_pairwise_div_raref_samp_cov.R # Further rarefaction analysis to estimate pairwise diversity with a coverage based approach - 06_figures.R # Script for generating figures 2 and 3 - 07_glmm.R # Script for GLMMs to test the effects of landscape type on species diversity - 08_meta_analysis.R # Script for performing meta-analysis to test the effects of landscape type on species diversity - Engel_novel_code.R # Relevant functional to calculate beta diversity (coverage based) from Engel et al. 2021, Ecosphere, https://doi.org/10.1002/ecs2.3745 - biod_loss_frag.Rproj # R project file - renv.lock # Lock file for R environment - utility_functions.R # Utility functions used across scripts, specified as needed in scripts 01 to 08

Requirements

To run these scripts, you'll need:

• R https://cran.r-project.org/ and RStudio https://posit.co/download/rstudio-desktop/ installed on your computer
• Packages defined in the renv.lock file
• Any additional software or dependencies required should be installed
• You must run the scripts from 01 to 08 to make sure all required files are created.

Usage

You may want to download this repository (and associated R Project) to fully reproduce the scripts. All required packages and explanations for the usage can be run on each scripts, number from 01 (01studydesign.R) to 08 (08metaanalysis.R). All required data is available in the folders data and processed_data.

1. Clone the repository:

bash git clone https://github.com/yourusername/yourproject.git cd yourproject

1. Open biod_loss_frag.Rproj in RStudio to activate the R project environment.

2. Ensure all dependencies are installed, typically handled via the renv package:

R renv::restore()

1. Run the analysis scripts in order as follows:

• source("01_study_design.R")
• source("02_pairwise_div_all.R")
• (Repeat for other scripts...)

Scripts Description

• 01studydesign.R: Estimates alpha diversity based on study design
• 02pairwisediv_all.R: Estimates pairwise diversity (psd) using all plot pairs
• 03pairwisediv_near.R: Estimates pairwise diversity (psd) using the nearest plot pairs
• 04pairwisediv_raref.R: Estimates pairwise diversity (psd) using q = 0 and 2 (all pairs and nearest pairs)
• 05pairwisedivrarefsamp_cov.R: Further rarefaction analysis to estimate pairwise diversity with a coverage based approach
• 06_figures.R: Generates figures 2 and 3
• 07_glmm.R: Runs GLMMs to test the effects of landscape type on species diversity
• 08metaanalysis.R: Runs meta-analysis to test the effects of landscape type on species diversity

Figures and Results

Images and results generated from the 06_figures.R and other relevant scripts can be found in the figures/ and results/ directories.

License

CC-BY-4.0 License