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, 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.