The undetectability of global biodiversity trends using local species richness

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, incre..., 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 spe..., RStudio
R packages:
library(rgdal)  # package to work with shapefileslibrary(tidyverse)library(raster) # package to work with rasterslibrary(boot) # package to bootstraplibrary(ggplot2)library(reshape)library(dplyr)library(rms) #ordinary least square modelslibrary(wesanderson) #color palettelibrary(pwr) #power analyseslibrary(ebvcube)#work with netcdf fileslibrary(rhdf5)#for the netCDF