DOI: 10.5061/dryad.n5tb2rc0d
title: The undetectability of global biodiversity trends using local species richness

description: Although species are being lost at alarming rates, previous research has
 provided conflicting results on the extent and even direction of global
 biodiversity change at the local scale. Here, we assessed the ability to
 detect global biodiversity trends using local species richness and how it
 is affected by the number of monitoring sites, sampling interval (i.e.,
 time between original survey and re-survey of the site), measurement error
 (error of the measurement of the local species richness), spatial grain of
 monitoring (a proxy for the taxa mobility), and spatial sampling biases
 (i.e., site-selection biases). We use PREDICTS model-based estimates as a
 proxy for the real-world distribution of biodiversity and randomly
 selected monitoring sites to calculate local species richness trends. We
 found that while a monitoring network with hundreds of sites could detect
 global change in species richness within a 30-year period, the number of
 sites for detecting trends doubled for a decade, increased 10-fold within
 three years, and yearly trends were undetectable. Measurement errors had a
 non-linear effect on statistical power, with a 1% error reducing
 statistical power by a slight margin and a 5% error drastically reducing
 the power to reliably detect any trend. The ability to detect global
 change in local species richness was also related to spatial grain, making
 it harder to detect trends for sites sampled at smaller plot sizes.
 Spatial sampling biases not only reduced the ability to detect negative
 global biodiversity trends but sometimes yielded positive trends. We
 conclude that detecting accurate global biodiversity trends using local
 richness may simply be unfeasible with current approaches. We suggest that
 monitoring a representative network of sites implemented at the national
 level, combined with models accounting for errors and biases, can help
 improve our understanding of global biodiversity change.
description: We used modeled estimates of local within-sample historical
 species richness across the terrestrial world surface from Hill, et al.
 (2018, https://doi.org/10.1101/311787).
 Estimates were derived from the PREDICTS (Projecting Responses of
 Ecological Diversity In Changing Terrestrial Systems) database, a
 spatially heterogeneous and globally comprehensive collation of site-level
 data from over 32,000 sites and over 51,000 species, covering a wide range
 of taxonomic groups across 767 studies (Hudson, et al. 2017,https://doi.org/10.1002/ece3.2579,
 Purvis, et al. 2018, https://doi.org/10.1016/bs.aecr.2017.12.003).
 A linear mixed-effects model was used to model site-level species richness
 using the site-level data extracted from PREDICTS (Hudson, et al. 2017),
 with historical land use and related pressures (land-use intensity, and
 human population density) as explanatory variables (Hurtt, et al. 2020,
 https://doi.org/10.5194/gmd-13-5425-2020). The spatial pattern of the expected site-level species richness was then projected by combining the coefficients of this model with global raster data of these pressures for each focal year (Hill, et al. 2018).
description: <strong>RStudio</strong>
 <em>R
 packages:</em> library(rgdal)  #
 package to work with
 shapefiles<br>library(tidyverse)<br>library(raster) # package
 to work with rasters<br>library(boot) # package to
 bootstrap<br>library(ggplot2)<br>library(reshape)<br>library(dplyr)<br>library(rms) #ordinary least square models<br>library(wesanderson) #color palette<br>library(pwr) #power analyses<br>library(ebvcube)#work with netcdf files<br>library(rhdf5)#for the netCDF