Probabilistic models of species discovery and biodiversity comparisons.

Inferring large-scale processes that drive biodiversity hinges on understanding the phylogenetic and spatial pattern of species richness. However, clades and geographic regions are accumulating newly described species at an uneven rate, potentially affecting the stability of currently observed diversity patterns. Here, we present a probabilistic model of species discovery to assess the uncertainty in diversity levels among clades and regions. We use a Bayesian time series regression to estimate the long-term trend in the rate of species description for marine bivalves and find a distinct spatial bias in the accumulation of new species. Despite these biases, probabilistic estimates of future species richness show considerable stability in the currently observed rank order of regional diversity. However, absolute differences in richness are still likely to change, potentially modifying the correlation between species numbers and geographic, environmental, and biological factors thought to promote biodiversity. Applied to scallops and related clades, we find that accumulating knowledge of deep-sea species will likely shift the relative richness of these three families, emphasizing the need to consider the incomplete nature of bivalve taxonomy in quantitative studies of its diversity. Along with estimating expected changes to observed patterns of diversity, the model described in this paper pinpoints geographic areas and clades most urgently requiring additional systematic study-an important practice for building more complete and accurate models of biodiversity dynamics that can inform ecological and evolutionary theory and improve conservation practice.