DOI: 10.5061/dryad.34tmpg4zf
title: Shifting baselines increase the risk of misinterpreting biodiversity trends

description: Ecological studies quantifying the impact of land-use change on
 biodiversity may be sensitive to the choice of reference points – or
 baselines – particularly when sampling across human land-use
 gradients and other space-for-time comparisons. Much depends on whether
 the chosen baseline has already undergone shifts in species composition
 because of hunting, habitat loss, and degradation. However, few studies
 have assessed the influence of shifting baselines on estimates of
 anthropogenic impacts. Using new survey data from five West African
 land-use gradients, we examine how habitat patch size and structure
 influence the estimated impact of land-use change on bird species richness
 and functional diversity. We show that smaller forests have already lost
 many forest-dependent birds, particularly those with large body size or
 specialised ecological niches, leading to reduced estimates of
 biodiversity loss after deforestation. The steepest biodiversity loss was
 found in mid-sized forests, whereas relatively shallow declines were
 estimated for the most extensive forests, despite their richer taxonomic
 and functional diversity. In these larger forest blocks, accurate
 estimates of biodiversity loss may require longer transects extending
 beyond the biodiversity ‘shadow’ caused by the more extensive spillover of
 forest species into the surrounding landscape, potentially linked to
 source-sink dynamics. These findings suggest that biodiversity assessments
 are highly sensitive to baseline selection and transect design,
 highlighting the risk of underestimating land-use impacts unless shifting
 baselines are carefully considered.
description: # Shifting baselines increase the risk of misinterpreting biodiversity
 trends This DRYAD repository contains the code and data associated with
 the manuscript: Shifting baselines increase the risk of misinterpreting
 biodiversity trends published in the journal Ecography. **Before you
 start** To reconstruct our analyses, all our scripts are run through R and
 can be executed on a standard PC. We have depended on the following R
 packages to produce analyses and figures: glmmTMB DHARMa multcomp psych
 emmeans scales sf geosphere lwgeom rgeos sp dplyr ggplot2 ggeffects
 tidyverse patchwork ggpubr These should be installed before rerunning code
 (using "install.packages(*name_of_packages*)" in R). After
 installation, the required packages to run each script will be loaded
 automatically when the script is run. #### **Data** The Excel file `Data
 S1.xlsx` contains the raw survey data used in our analyses. The Excel file
 is organised into the following sheets: ``` Data_S1.xlsx ├── Metadata ├──
 Survey metadata ├── Survey results ├── Traits ├── Total diversity ├──
 Literature ├── Sources ``` The sheets `Metadata`, `Literature` and
 `Sources` provide contextual information but do not contain data necessary
 to rerun analyses. The sheets `Survey metadata`, `Survey results`,
 `Traits` and `Total diversity` have to be converted to .csv files and
 stored in a folder called `data` before rerunning scripts. Descriptions of
 all variables can be found in `Metadata`. #### Data processing
 **Description of the data and file structure** The folder structure of
 \_Shifted_baselines_-*Data_processing*.zip is as follows: ``` Shifted
 baselines - Data processing.zip ├── [Shifted baselines - Data processing]
 Calculating SR, RA and FDis.R ├── [Shifted baselines - Data processing]
 Calculating distance to forest edge.R ├── GEE canopy cover.txt ├── GEE
 canopy height.txt ├── Forest shapefiles ├──ahokwa.cpg ├──ahokwa.dbf
 ├──ahokwa.prj ├──ahokwa.qmd ├──ahokwa.shp ├──ahokwa.shx ├──ankasa.cpg
 ├──ankasa.dbf ├──ankasa.prj ├──ankasa.qmd ├──ankasa.shp ├──ankasa.shx
 ├──dompim.cpg ├──dompim.dbf ├──dompim.prj ├──dompim.qmd ├──dompim.shp
 ├──dompim.shx ├──gola.cpg ├──gola.dbf ├──gola.prj ├──gola.qmd ├──gola.shp
 ├──gola.shx ├──kakum.cpg ├──kakum.dbf ├──kakum.prj ├──kakum.qmd
 ├──kakum.shp └──kakum.shx ``` #### Calculating diversity metrics The
 script `[Shifted baselines - Data processing] Calculating SR, RA and
 FDis.R` calculates the diversity metrics used in all downstream analyses
 from the raw bird point-count data in `Survey_results`. It formats the
 data so that each row has the landscape variables (taken from
 'Survey_metadata') and the diversity metrics associated to one
 assemblage survey. The script outputs a formatted .csv called `data/Output
 script 1.csv` which is used for analyses. The script has to be run before
 scripts in `Shifted baselines - Analyses.zip` can be run. **NOTE: the only
 script in** **`Shifted baselines - Data processing.zip`** **that has to be
 run ahead of rerunning analyses and reproducing figures is** **`[Shifted
 baselines - Data processing] Calculating SR, RA and FDis.R`. Other scripts
 in this folder are provided for reference only, to be able to reproduce
 methods used to calculate landscape variables (i.e. canopy cover etc.)
 in** **`Survey_metadata.csv`.** #### Calculating landscape variables Using
 the shapefiles provided in the folder `Forest shapefiles` and the script
 `Shifted baselines - Data processing] Calculating distance to forest
 edge.R` the variable `Distance.to.Forest.km` provided in
 `Survey_metadata.csv` can be calculated. The text files `GEE canopy
 cover.txt` and `GEE canopy height.txt` are java code used to calculate the
 `Forest.Cover.200m` and `Canopy.Height.200m.mean` variables provided in
 `Survey_metadata.csv`. This code was run in Google Earth Engine using the
 Hansen et al. 2013 and Potapov et al. 2022 canopy cover and height layers.
 The code is provided as an example and does not need to be run to
 reproduce analyses. #### Analyses **Description of the data and file
 structure** The folder structure of \_Shifted_baselines_-*Analyses*.zip is
 as follows: ``` Shifted baselines - Analyses.zip ├── [Shifted baselines -
 Analysis] FDis ~ Canopy cover.R ├── [Shifted baselines - Analysis] FDis ~
 Distance to edge.R ├── [Shifted baselines - Analysis] FDis ~ Forest size.R
 ├── [Shifted baselines - Analysis] RA ~ Forest size.R ├── [Shifted
 baselines - Analysis] Sensitivity analysis slopes.R ├── [Shifted baselines
 - Analysis] Sensitivity analysis points.R ├── [Shifted baselines -
 Analysis] SR ~ Canopy cover.R ├── [Shifted baselines - Analysis] SR ~
 Distance to forest.R ├── [Shifted baselines - Analysis] SR ~ Forest size.R
 ``` #### Modelling diversity in reponse to forest size The script
 `[Shifted baselines - Analysis] SR ~ Forest size.R` uses generalised
 linear mixed models to model bird assemblage species richness across
 forest size, as presented in Fig. 3. The script uses `Output script 1.csv`
 (generated using the script `[Shifted baselines - Data processing]
 Calculating SR, RA and FDis.R`) as input and generates Fig. 3 as output.
 Similarly, the scripts `[Shifted baselines - Analysis] RA ~ Forest size.R`
 and `[Shifted baselines - Analysis] FDis ~ Forest size.R` model bird
 assemblage relative abundance and functional dispersion across forest
 size, as presented in Fig. S6 and Fig. S7. The script uses `Output script
 1.csv` (generated using the script `[Shifted baselines - Data processing]
 Calculating SR, RA and FDis.R`) as input. #### Modelling diversity slopes
 in reponse to land-use change The scripts `[Shifted baselines - Analysis]
 SR ~ Canopy cover.R` and `[Shifted baselines - Analysis] SR ~ Distance to
 forest.R` use generalised linear mixed models to test whether there are
 differences in species richness slopes across land-use change gradients
 between study areas. Land-use change is measured as canopy cover
 percentage and distance to forest edge respectively. The scripts use
 `Output script 1.csv` (generated using the script `[Shifted baselines -
 Data processing] Calculating SR, RA and FDis.R`) as input. Results are
 presented in Fig. 4 and Fig. 5. The scripts `[Shifted baselines -
 Analysis] FDis ~ Canopy cover.R` and `[Shifted baselines - Analysis] FDis
 ~ Distance to forest.R` repeat the same analysis but for bird assemblage
 functional dispersion. The scripts use `Output script 1.csv` (generated
 using the script `[Shifted baselines - Data processing] Calculating SR, RA
 and FDis.R`) as input. Results are presented in Fig. S9 and Fig. S10. ####
 Sensitivity analyses The script `[Shifted baselines - Analysis]
 Sensitivity analysis points` uses t-tests to determine whether there are
 significant difference between the bird assemblage species richness
 detected in 3-point versus 4-point assemblages. Results are reported in
 Fig. S4. The script `[Shifted baselines - Analysis] Sensitivity analysis
 slopes` repeats the analyses presented in `[Shifted baselines - Analysis]
 SR ~ Canopy cover.R` and `[Shifted baselines - Analysis] SR ~ Distance to
 forest.R` but excluding 4-point assemblages to test whether unevenness in
 assemblage sampling may have biased results. Outputs are reported in Table
 S10. --- #### Figures **Description of the data and file structure** The
 folder structure of \_Shifted_baselines_-*Figures*.zip is as follows: ```
 Shifted baselines - Figures.zip ├── [Shifted baselines - Figures] Plotting
 Fig. 4.R ├── [Shifted baselines - Figures] Plotting Fig. 5.R ├── [Shifted
 baselines - Figures] Plotting Fig. S1.R ├── [Shifted baselines - Figures]
 Plotting Fig. S2 and Fig. S3.R ├── [Shifted baselines - Figures] Plotting
 Fig. S8.R ├── [Shifted baselines - Figures] Plotting Fig. S9.R ├──
 [Shifted baselines - Figures] Plotting Fig. S10.R ├── [Shifted baselines -
 Figures] Plotting phylogeny Fig. 2.R ├── Tree output.nex ``` Phylogenetic
 trees used to visualise the phylogenetic diversity of surveyed species
 were downloaded from [https://birdtree.org/](https://birdtree.org/). For
 reproducibility, copies of the exact trees used are provided in `Shifted
 baselines - Figures.zip` as `Tree output.nex`.