DOI: 10.5061/dryad.dr7sqvb5m
title: Regional Biomes outperform broader spatial units in capturing biodiversity responses to land-use change

description: Biogeographic context, such as biome type, has a critical influence on
 ecological resilience, as climatic and environmental conditions impact how
 communities respond to anthropogenic threats. For example, land-use change
 causes a greater loss of biodiversity in tropical biomes compared to
 temperate biomes. Furthermore, the nature of threats impacting ecosystems
 varies geographically. Therefore, monitoring the state of biodiversity at
 a high spatial resolution is crucial to capture variation in
 threat-responses caused by biogeographical context. However such
 fine-scale ecological data collection could be prohibitively resource
 intensive. In this study, we aim to find the spatial scale that could best
 capture variation in community-level threat responses whilst keeping data
 collection requirements feasible. Using a database of biodiversity records
 with extensive global coverage, we modelled species richness and total
 abundance (the responses) across land-use types (reflecting threats),
 considering three different spatial scales: biomes, biogeographical
 realms, and regional biomes (the interaction between realm and biome). We
 then modelled data from three highly sampled biomes to ask how responses
 to threat differ between regional biomes and taxonomic group. We found
 strong support for regional biomes in explaining variation in species
 richness and total abundance compared to biomes or realms alone. Our biome
 case studies demonstrate that there is variation in magnitude and
 direction of threat responses across both regional biomes and taxonomic
 group, although the interpretation is limited by sampling bias in the
 literature. All groups in tropical forest showed a consistently negative
 response, whilst many taxon-regional biome groups showed no clear response
 to threat in temperate forest and tropical grassland. Our results provide
 the first empirical evidence that the taxon-regional biome unit has
 potential as a reasonable spatial unit for monitoring how ecological
 communities respond to threats and designing effective conservation
 interventions to bend the curve on biodiversity loss.
description: # Regional Biomes outperform broader spatial units in capturing
 biodiversity responses to land-use change
 [https://doi.org/10.5061/dryad.dr7sqvb5m](https://doi.org/10.5061/dryad.dr7sqvb5m) ## Description of the data and file structure Analysing biodiversity responses to land use change across regional biomes ### Files and variables Data must be downloaded from original sources and placed in this folder. ### PREDICTS data This project uses data from the 2016 release of the PREDICTS database (Hudson et al., 2016). The dataset can be found at [https://doi.org/10.5519/0066354](https://doi.org/10.5519/0066354) This analysis starts with the RDS format of the data, called 'database.rds'. Place this in a folder called 'PredictsData' #### **Terrestrial Ecoregions of the world Map** Ecoregion data was originally downloaded from the The Nature Conservancy's (TNC) conservation atlas ([http://maps.tnc.org/gis_data.html](http://maps.tnc.org/gis_data.html)). At the time of publishing, the link to this dataset is broken , but the same dataset can be downloaded from ResourceWatch [here](https://resourcewatch.org/data/explore/bio021a-Terrestrial-Ecoregions?section=Discover&selectedCollection=&zoom=3&lat=0&lng=0&pitch=0&bearing=0&basemap=dark&labels=light&layers=%255B%257B%2522dataset%2522%253A%2522d0968f74-f5c1-40a1-b2b5-5bac5de5cb15%2522%252C%2522opacity%2522%253A1%252C%2522layer%2522%253A%252201152647-80b6-41fb-9ebc-48a5f2411327%2522%257D%255D&aoi=&page=1&sort=most-viewed&sortDirection=-1). Note, the labelling of ecoregions in this dataset follows the same format as the PREDICTS database. Other ecoregions maps are available, for example from [WWF](https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world), but will require some editing to fit with the version of the PREDICTS database used here. All ecoregion maps are based on Olson et al., 2001.  #### References: Lawrence Hudson; Tim Newbold; Sara Contu; Samantha L L Hill et al. (2016). *The 2016 release of the PREDICTS database [SUPERSEDED]* [Data set]. Natural History Museum. [https://doi.org/10.5519/0066354](https://doi.org/10.5519/0066354) Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. ## Code/software **Package requirements:** *The versions listed here are the versions used at publication, but future versions may be usable.* * R Version - 4.0.4 * dplyr Version 1.1.3 * sf Version 1.0-16 * devtools Version 2.4.0 * predictsFunctions Version 1.0 * knitr Version 1.31 * tidyr Version 1.3.0 * reshape2 Version 1.4.4 * flextable Version 0.6.4 * officer Version 0.3.17 * lme4 Version 1.1-28 * sjPlot Version 2.8.7 * data.table Version 1.14.2 * ggplot2 Version 3.4.1 * performance Version 0.7.0 * StatisticalModels Version 0.1 * cowplot Version 1.1.1 * patchwork Version 1.1.1 #### **Description of R Scripts** The analysis uses a series of R scripts. The entire analysis can be run by calling 'RunAllScriptsHere.R'. This should take approximately 2.5 hours depending on compute power. 01_CreateSiteMetrics.R - uses database.rds from Data/PredictsData folder - this script uses the mergesites function to group observations from a study to one row to give species richness and total abundance - here we also choose which columns to retain. Output: 02_PREDICTSDivMetrics.csv 02_PreProcessingModelData.R - remove 'urban' and 'Cannot decide' land use types - edit land use & use intensity factor names & add regional biome names, create landuse:use intensity factor for all 5 land use types - create taxa variable Output: 03_PREDICTSModelData.csv ; 03_PREDICTSModelData.rds 03_ExploreModelData.R - use this script to create summary tables of data - number of observations in each biome and regional biome - number of observations in each land use type by biome and regional biome - number of observations in each taxon group by biome and regional biome - it would be useful to save these tables as CSVs that can be brought back in later to subset 04_GlobalModels&Figs.R - run global model of species richness/abundance with land use change - create figures for: biodiversity change with land use - biodiversity change with land use and regional biome - biodiversity change with land use and regional biome and taxa 05_BiomeCaseStudies.R - Subset predicts database to 3 biomes, run regional biome model on species richness and abundance and plot predictions in change in metric over land-use change. Explore the impact of evening out sample sizes. Explore the impact of adding taxon to the species richness model. PredictGLMERfunction.R - a copy of the PredictGLMER function from the StatisticalModels Package, called in case there are issues loading this package.