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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.

Uncertainty in projections of global change impacts on biodiversity over the 21st century is high. Improved predictive accuracy is needed, highlighting the importance of using different types of models when predicting species range shifts. However, this is still rarely done. Our approach integrates the outputs of a spatially-explicit physiologically inspired model of extinction and correlative species distribution models to assess climate-change induced range shifts of three European reptile species (Lacerta lepida, Iberolacerta monticola, and Hemidactylus turcicus) in the coming decades. We integrated the two types of models by mapping and quantifying agreement and disagreement between their projections. We analyzed the relationships between climate change and projected range shifts. Agreement between model projections varied greatly between species and depended on whether or not they consider dispersal ability. Under our approach, the reliability of predictions is greatest where the predictions of these different types of models converge, and in this way uncertainty is reduced; sites where this convergence occurs are characterized by both current high temperatures and significant future temperature increase, suggesting they may become hotspots of local extinctions. Moreover, this approach can be readily implemented with other types of models.

It has been hypothesized that total speciation rate may depend on species diversity (Seposky 1978), population sizes (Hubbell 2001), and island area (Losos and Schluter 2000). In a recent paper, Emerson and Kolm (2005) tested the hypothesis that species diversity might promote speciation, by comparing the proportion of endemics in a taxon with the number of species in that taxon, in the Canary Islands and the Hawaiian Islands. They show that the number of species of the taxon is a good predictor of the island endemics in that taxon, and suggest that this result demonstrates the role of species diversity in speciation. We propose an alternative explanation: that the same abiotic variables that control species diversity also influence speciation, and that this phenomenon may render the analysis of Emerson and Kolm circular. Emerson and Kolm performed a stepwise regression of the speciation rate of a taxon, measured as the proportion of endemics of that taxon, against a set of independent variables comprising abiotic factors and the numberofspeciesofthattaxon.However,asweshallsee, if the number of species itself is dependent on the same set of abiotic variables as the speciation rate, then the stepwise regression will select the number of species as the explanatory variable for the speciation rate and lead to the wrong conclusion that the abiotic variables are less important. This statistical artefact happens because the number of species accounts for the compound effect of the abiotic variables. Cadena et al. (2005) point out a particular case of this artefact which happens when both species richness and endemism depend on attributes of islands that influence the average age of resident populations and reduce extinction rates, such as area. Here we show that the statistical artefact can occur more generally for any type of abiotic variable that influences both species richness and speciation or extinction rates.