Evidence of cryptic and pseudocryptic speciation in the Paracalanus parvus species complex (Crustacea, Copepoda, Calanoida), supplementary material

Introduction Many marine planktonic crustaceans such as copepods have been considered as widespread organisms. However, the growing evidence for cryptic and pseudo-cryptic speciation has emphasized the need of re-evaluating the status of copepod species complexes in molecular and morphological studies to get a clearer picture about pelagic marine species as evolutionary units and their distributions. This study analyses the molecular diversity of the ecologically important Paracalanus parvus species complex. Its seven currently recognized species are abundant and also often dominant in marine coastal regions worldwide from temperate to tropical oceans. Results COI and Cytochrome b sequences of 160 specimens of the Paracalanus parvus complex from all oceans were obtained. Furthermore, 42 COI sequences from GenBank were added for the genetic analyses. Thirteen distinct molecular operational taxonomic units (MOTU) and two single sequences were revealed with cladistic analyses (Maximum Likelihood, Bayesian Inference), of which seven were identical with results from species delimitation methods (barcode gaps, ABDG, GMYC, Rosenberg's P(AB)). In total, 10 to 12 putative species were detected and could be placed in three categories: (1) temperate geographically isolated, (2) warm-temperate to tropical wider spread and (3) circumglobal warm-water species. Conclusions The present study provides evidence of cryptic or pseudocryptic speciation in the Paracalanus parvus complex. One major insight is that the species Paracalanus parvus s.s. is not panmictic, but may be restricted in its distribution to the northeastern Atlantic.

Detection histories for eight species of Amazona parrots in Venezuela during the NeoMaps bird surveys in 2010

Documenting changes in distribution is necessary for understanding species' response to environmental changes, but data on species distributions are heterogeneous in accuracy and resolution. Combining different data sources and methodological approaches can fill gaps in knowledge about the dynamic processes driving changes in species-rich, but data-poor regions. We combined recent bird survey data from the Neotropical Biodiversity Mapping Initiative (NeoMaps) with historical distribution records to estimate potential changes in the distribution of eight species of Amazon parrots in Venezuela. Using environmental covariates and presence-only data from museum collections and the literature, we first used maximum likelihood to fit a species distribution model (SDM) estimating a historical maximum probability of occurrence for each species. We then used recent, NeoMaps survey data to build single-season occupancy models (OM) with the same environmental covariates, as well as with time- and effort-dependent detectability, resulting in estimates of the current probability of occurrence. We finally calculated the disagreement between predictions as a matrix of probability of change in the state of occurrence. Our results suggested negative changes for the only restricted, threatened species, Amazona barbadensis, which has been independently confirmed with field studies. Two of the three remaining widespread species that were detected, Amazona amazonica, Amazona ochrocephala, also had a high probability of negative changes in northern Venezuela, but results were not conclusive for Amazona farinosa. The four remaining species were undetected in recent field surveys; three of these were most probably absent from the survey locations (Amazona autumnalis, Amazona mercenaria and Amazona festiva), while a fourth (Amazona dufresniana) requires more intensive targeted sampling to estimate its current status. Our approach is unique in taking full advantage of available, but limited data, and in detecting a high probability of change even for rare and patchily-distributed species. However, it is presently limited to species meeting the strong assumptions required for maximum-likelihood estimation with presence-only data, including very high detectability and representative sampling of its historical distribution.