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.

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).

RStudio

R packages:

library(rgdal)  # package to work with shapefiles
library(tidyverse)
library(raster) # package to work with rasters
library(boot) # package to bootstrap
library(ggplot2)
library(reshape)
library(dplyr)
library(rms) #ordinary least square models
library(wesanderson) #color palette
library(pwr) #power analyses
library(ebvcube)#work with netcdf files
library(rhdf5)#for the netCDF