Where is positional uncertainty a problem for species distribution modelling?

Species data held in museum and herbaria, survey data and opportunistically observed data are a substantial information resource. A key challenge in using these data is the uncertainty about where an observation is located. This is important when the data are used for species distribution modelling (SDM), because the coordinates are used to extract the environmental variables and thus, positional error may lead to inaccurate estimation of the species–environment relationship. The magnitude of this effect is related to the level of spatial autocorrelation in the environmental variables. Using local spatial association can be relevant because it can lead to the identification of the specific occurrence records that cause the largest drop in SDM accuracy. Therefore, in this study, we tested whether the SDM predictions are more affected by positional uncertainty originating from locations that have lower local spatial association in their predictors. We performed this experiment for Spain and the Netherlands, using simulated datasets derived from well known species distribution models (SDMs). We used the K statistic to quantify the local spatial association in the predictors at each species occurrence location. A probabilistic approach using Monte Carlo simulations was employed to introduce the error in the species locations. The results revealed that positional uncertainty in species occurrence data at locations with low local spatial association in predictors reduced the prediction accuracy of the SDMs. We propose that local spatial association is a way to identify the species occurrence records that require treatment for positional uncertainty. We also developed and present a tool in the R environment to target observations that are likely to create error in the output from SDMs as a result of positional uncertainty.     

A novel genetic locus linked to pro-inflammatory cytokines after virulent H5N1 virus infection in mice

Background

Genetic variation in the human population is a key determinant of influenza disease severity. A single nucleotide polymorphism in the antiviral gene IFITM3 was linked to outcomes during the 2009 H1N1 pandemic. To identify variant host genes associated with increased virus replication and severe disease, we performed a quantitative trait locus analysis on pro-inflammatory cytokine production 48 hours after intranasal infection with highly pathogenic H5N1 influenza virus.

Results

Pro-inflammatory cytokines CCL2, TNFα and IFN-α, were measured by ELISA in lung homogenates of DBA/2J (D2), C57BL/6J (B6) and 44 different BXD recombinant inbred mouse strains. Virus titer was also assessed in a subset of these animals. CCL2 (8-fold), TNFα (24-fold) and IFN-α (8-fold) concentrations varied significantly among the different BXD RI strains. Importantly, cytokine concentration correlated very well (r =0.86-0.96, P <0.0001) with virus titer suggesting that early cytokine production is due to increased virus infection and replication. Linkage analysis of cytokine concentration revealed a significant locus on chromosome 6 associated with differences in TNFα, IFN-α and CCL2 cytokine concentration (LRS =26). This locus accounted for nearly 20% of the observed phenotypic variation in the BXD population studied. Sequence and RNA expression analysis identified several candidate host genes containing missense mutations or deletions; Samd9l, Ica1, and Slc25a13. To study the role of Slc25a13, we obtained Slc25a13 knockout line, but upon challenge with H5N1 influenza virus observed no effect on CCL2 production, or morbidity and mortality.

Conclusion

A novel genetic locus on chromosome 6 modulates early pro-inflammatory cytokine production and virus replication after highly pathogenic influenza virus infection. Candidate genes, Samd9l and Ica1, may be important for the control of influenza virus infection and pathogenesis.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1017) contains supplementary material, which is available to authorized users.     

A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses

Background

To obtain predictions that are not biased by selection, the conditional mean of the breeding values must be computed given the data that were used for selection. When single nucleotide polymorphism (SNP) effects have a normal distribution, it can be argued that single-step best linear unbiased prediction (SS-BLUP) yields a conditional mean of the breeding values. Obtaining SS-BLUP, however, requires computing the inverse of the dense matrix G of genomic relationships, which will become infeasible as the number of genotyped animals increases. Also, computing G requires the frequencies of SNP alleles in the founders, which are not available in most situations. Furthermore, SS-BLUP is expected to perform poorly relative to variable selection models such as BayesB and BayesC as marker densities increase.

Methods

A strategy is presented for Bayesian regression models (SSBR) that combines all available data from genotyped and non-genotyped animals, as in SS-BLUP, but accommodates a wider class of models. Our strategy uses imputed marker covariates for animals that are not genotyped, together with an appropriate residual genetic effect to accommodate deviations between true and imputed genotypes. Under normality, one formulation of SSBR yields results identical to SS-BLUP, but does not require computing G or its inverse and provides richer inferences. At present, Bayesian regression analyses are used with a few thousand genotyped individuals. However, when SSBR is applied to all animals in a breeding program, there will be a 100 to 200-fold increase in the number of animals and an associated 100 to 200-fold increase in computing time. Parallel computing strategies can be used to reduce computing time. In one such strategy, a 58-fold speedup was achieved using 120 cores.

Discussion

In SSBR and SS-BLUP, phenotype, genotype and pedigree information are combined in a single-step. Unlike SS-BLUP, SSBR is not limited to normally distributed marker effects; it can be used when marker effects have a t distribution, as in BayesA, or mixture distributions, as in BayesB or BayesC π. Furthermore, it has the advantage that matrix inversion is not required. We have investigated parallel computing to speedup SSBR analyses so they can be used for routine applications.

Electronic supplementary material

The online version of this article (doi:10.1186/1297-9686-46-50) contains supplementary material, which is available to authorized users.     

Group 1 LEA proteins contribute to the desiccation and freeze tolerance of Artemia franciscana embryos during diapause

Water loss either by desiccation or freezing causes multiple forms of cellular damage. The encysted embryos (cysts) of the crustacean Artemia franciscana have several molecular mechanisms to enable anhydrobiosis—life without water—during diapause. To better understand how cysts survive reduced hydration, group 1 late embryogenesis abundant (LEA) proteins, hydrophilic unstructured proteins that accumulate in the stress-tolerant cysts of A. franciscana, were knocked down using RNA interference (RNAi). Embryos lacking group 1 LEA proteins showed significantly lower survival than control embryos after desiccation and freezing, or freezing alone, demonstrating a role for group 1 LEA proteins in A. franciscana tolerance of low water conditions. In contrast, regardless of group 1 LEA protein presence, cysts responded similarly to hydrogen peroxide (H₂O₂) exposure, indicating little to no function for these proteins in diapause termination. This is the first in vivo study of group 1 LEA proteins in an animal and it contributes to the fundamental understanding of these proteins. Knowing how LEA proteins protect A. franciscana cysts from desiccation and freezing may have applied significance in aquaculture, where Artemia is an important feed source, and in the cryopreservation of cells for therapeutic applications.     

Groundwater input affecting plant distribution by controlling ammonium and iron availability

Question: How does groundwater input affect plant distribution in Alnus glutinosa (black alder) carrs? Location: Alder carrs along the river Meuse, SE Netherlands. Methods: Three types of site, characterized by groundwater flow, were sampled in 17 A. glutinosa carrs. Vegetation and abiotic data (soil and water chemistry) were collected and analysed using a Canonical Correspondence Analysis. Based on the results, a laboratory experiment tested the effect of groundwater input (Ca²⁺) on pore water chemistry (NH₄⁺ availability). Results: Environmental factors indicating groundwater input (Ca²⁺ and Fe²⁺), correlating with the NH⁺₄ concentration in the pore water, best explained the variation in plant distribution. NH₄⁺ availability was determined by Ca²⁺ input via the groundwater and subsequent competition for exchange sites in the sediment. As a result, nutrient‐poor seepage locations fully fed by groundwater were dominated by small iron resistant plants such as Caltha palustris and Equisetum fluviatile. More nutrient‐rich locations, fed by a combination of groundwater and surface water, allowed the growth of taller iron resistant plant species such as Carex paniculata. Nutrient‐rich locations with stagnating surface water were hardly fed by groundwater, allowing the occurrence of fast growing and less iron tolerant wetland grasses such as Glyceria fluitans and G. maxima. Conclusion: Groundwater input affects plant composition in A. glutinosa carrs along the river Meuse by determining nutrient availability (ammonium) and concentrations of toxic iron.     

Ultrastructural localization of amiloride-sensitive sodium channels and Na+,K(+)-ATPase in the rat's olfactory epithelial surface

Several studies have indicated that olfactory responses are impeded by amiloride. Therefore, it was of interest to see whether, and if so which, olfactory epithelial cellular compartments have amiloride- sensitive structures. Using ultrastructural methods that involved rapid freezing, freeze-substitution and low temperature embedding of olfactory epithelia, this study shows that, in the rat, this tissue is immunoreactive to antibodies against amiloride sensitive Na(+)- channels. However, microvilli of olfactory supporting cells, as opposed to receptor cilia, contained most of the immunoreactive sites. Apices from which the microvilli sprout and receptor cell dendritic knobs had much less if any of the amiloride-antibody binding sites. Using a direct ligand-binding cytochemical method, this study also confirms earlier ones that showed that olfactory receptor cell cilia have Na+, K(+)-ATPase. It is proposed that supporting cell microvilli and the receptor cilia themselves have mechanisms, different but likely complementary, that participate in regulating the salt concentration around the receptor cell cilia. In this way, both structures help to provide the ambient mucous environment for receptor cells to function properly. This regulation of the salt concentration of an ambient fluid environment is a function that the olfactory epithelium shares with cells of transporting epithelia, such as those of kidney.      

Cupin: A candidate molecular structure for the Nep1-like protein family

Background  

NEP1-like proteins (NLPs) are a novel family of microbial elicitors of plant necrosis. Some NLPs induce a hypersensitive-like response in dicot plants though the basis for this response remains unclear. In addition, the spatial structure and the role of these highly conserved proteins are not known.