Rediscovery of Nebela ansata (Amoebozoa: Arcellinida) in eastern North America: biogeographical implications

Aim The question whether free‐living protists are generally cosmopolitan is currently a matter of debate. In this study we investigate the geographical distribution of a distinctive testate amoeba species, Nebela ansata, and use our data to assess the potential for highly restricted distribution patterns in some protist species. Location Global. Methods We analysed (1) 3400 testate amoeba publications from North America and other continents, (2) unpublished slides of the Penard Collection of the Natural History Museum, London, UK, and (3) 104 Sphagnum samples from eastern North America. Non‐metric multidimensional scaling (NMDS) was used to visualize the similarities in testate amoeba community composition among 1012 North American samples, including two communities that contained N. ansata. Results We rediscovered N. ansata at a site in New Jersey located close to its type locality, and in Nova Scotia. We also report the existence of an apparently unpublished museum specimen originally collected from New Jersey. Our extensive literature survey confirmed the presence of this species only in the temperate part of eastern North America. The NMDS revealed that communities with N. ansata were less similar to each other than to communities from other parts of North America, suggesting that favourable habitats for N. ansata occur in other Sphagnum‐dominated peatlands, a habitat type that has been extensively sampled in North America and elsewhere. Main conclusions These data provide an unusually convincing case of a free‐living microorganism with a very limited distribution range in the temperate part of eastern North America. The remarkably restricted distribution of N. ansata highlights the extent of our ignorance about the natural history of free‐living microorganisms, and raises questions about the lack of attention to microbial diversity in conservation biology.     

A study on the minimum number of loci required for genetic evaluation using a finite locus model

For a finite locus model, Markov chain Monte Carlo (MCMC) methods can be used to estimate the conditional mean of genotypic values given phenotypes, which is also known as the best predictor (BP). When computationally feasible, this type of genetic prediction provides an elegant solution to the problem of genetic evaluation under non-additive inheritance, especially for crossbred data. Successful application of MCMC methods for genetic evaluation using finite locus models depends, among other factors, on the number of loci assumed in the model. The effect of the assumed number of loci on evaluations obtained by BP was investigated using data simulated with about 100 loci. For several small pedigrees, genetic evaluations obtained by best linear prediction (BLP) were compared to genetic evaluations obtained by BP. For BLP evaluation, used here as the standard of comparison, only the first and second moments of the joint distribution of the genotypic and phenotypic values must be known. These moments were calculated from the gene frequencies and genotypic effects used in the simulation model. BP evaluation requires the complete distribution to be known. For each model used for BP evaluation, the gene frequencies and genotypic effects, which completely specify the required distribution, were derived such that the genotypic mean, the additive variance, and the dominance variance were the same as in the simulation model. For lowly heritable traits, evaluations obtained by BP under models with up to three loci closely matched the evaluations obtained by BLP for both purebred and crossbred data. For highly heritable traits, models with up to six loci were needed to match the evaluations obtained by BLP.     

The Physical Mechanism for Retinal Discrete Dark Noise: Thermal Activation or Cellular Ultraweak Photon Emission?

For several decades the physical mechanism underlying discrete dark noise of photoreceptors in the eye has remained highly controversial and poorly understood. It is known that the Arrhenius equation, which is based on the Boltzmann distribution for thermal activation, can model only a part (e.g. half of the activation energy) of the retinal dark noise experimentally observed for vertebrate rod and cone pigments. Using the Hinshelwood distribution instead of the Boltzmann distribution in the Arrhenius equation has been proposed as a solution to the problem. Here, we show that the using the Hinshelwood distribution does not solve the problem completely. As the discrete components of noise are indistinguishable in shape and duration from those produced by real photon induced photo-isomerization, the retinal discrete dark noise is most likely due to ‘internal photons’ inside cells and not due to thermal activation of visual pigments. Indeed, all living cells exhibit spontaneous ultraweak photon emission (UPE), mainly in the optical wavelength range, i.e., 350–700 nm. We show here that the retinal discrete dark noise has a similar rate as UPE and therefore dark noise is most likely due to spontaneous cellular UPE and not due to thermal activation.