What controls variation in carbon use efficiency among Amazonian tropical forests?

Why do some forests produce biomass more efficiently than others? Variations in Carbon Use Efficiency (CUE: total Net Primary Production (NPP)/ Gross Primary Production (GPP)) may be due to changes in wood residence time (Biomass/NPP_wood), temperature, or soil nutrient status. We tested these hypotheses in 14, one ha plots across Amazonian and Andean forests where we measured most key components of net primary production (NPP: wood, fine roots, and leaves) and autotrophic respiration (R_a; wood, rhizosphere, and leaf respiration). We found that lower fertility sites were less efficient at producing biomass and had higher rhizosphere respiration, indicating increased carbon allocation to belowground components. We then compared wood respiration to wood growth and rhizosphere respiration to fine root growth and found that forests with residence times <40 yrs had significantly lower maintenance respiration for both wood and fine roots than forests with residence times >40 yrs. A comparison of rhizosphere respiration to fine root growth showed that rhizosphere growth respiration was significantly greater at low fertility sites. Overall, we found that Amazonian forests produce biomass less efficiently in stands with residence times >40 yrs and in stands with lower fertility, but changes to long‐term mean annual temperatures do not impact CUE.     

Diversification of porcine MHC class II genes: evidence for selective advantage

The major histocompatibility complex (MHC) is an immunological gene-dense region of high diversity in mammalian species. Sus scrofa was domesticated by at least six independent events over Eurasia during the Holocene period. It has been hypothesized that the level and distribution of MHC variation in pig populations reflect genetic selection and environmental influences. In an effort to define the complexity of MHC polymorphisms and the role of selection in the generation of class II gene diversity (DQB, DRB1, and pseudogene ΨDRB3), DNA from globally distributed unrelated domestic pigs of European and Asian origins and a Suidae out-group was analyzed. The number of pseudogene alleles identified (ΨDRB3 33) was greater than those found in the expressed genes (DQB 20 and DRB1 23) but the level of observed heterozygosity (ΨDRB3 0.452, DQB 0.732, and DRB1 0.767) and sequence diversity (ΨDRB3 0.029, DQB 0.062, and DRB1 0.074) were significantly lower in the pseudogene, respectively. The substitution ratios reflected an excess of d _N (DQB 1.476, DRB1 1.724, and ΨDRB3 0.508) and the persistence of expressed gene alleles suggesting the influence of balancing selection, while the pseudogene was undergoing purifying selection. The lack of a clear MHC phylogeographic tree, coupled with close genetic distances observed between the European and Asian populations (DQB 0.047 and DRB1 0.063) suggested that unlike observations using mtDNA, the MHC diversity lacks phylogeographic structure and appears to be globally uniform. Taken together, these results suggest that, despite regional differences in selective breeding and environments, no skewing of MHC diversity has occurred. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Solar radiation and functional traits explain the decline of forest primary productivity along a tropical elevation gradient

One of the major challenges in ecology is to understand how ecosystems respond to changes in environmental conditions, and how taxonomic and functional diversity mediate these changes. In this study, we use a trait‐spectra and individual‐based model, to analyse variation in forest primary productivity along a 3.3 km elevation gradient in the Amazon‐Andes. The model accurately predicted the magnitude and trends in forest productivity with elevation, with solar radiation and plant functional traits (leaf dry mass per area, leaf nitrogen and phosphorus concentration, and wood density) collectively accounting for productivity variation. Remarkably, explicit representation of temperature variation with elevation was not required to achieve accurate predictions of forest productivity, as trait variation driven by species turnover appears to capture the effect of temperature. Our semi‐mechanistic model suggests that spatial variation in traits can potentially be used to estimate spatial variation in productivity at the landscape scale.     

High-throughput single-nucleotide polymorphism discovery and the search for candidate genes for long-term SIVmac nonprogression in Chinese rhesus macaques (Macaca mulatta)

Background Genetic differences between Indian and Chinese rhesus macaques contribute to the phenotypic variance of clinical trials, including infection with SIVmac. The completion of the rhesus genome has facilitated the discovery of several thousand markers. Methods We developed a genome‐wide SNP map for rhesus macaques containing 3869 validated markers with an average distance of 0.88 Mb and used the program VarLD to identify genomic areas with significant differences in linkage disequilibrium (LD) between Indian‐derived and Chinese rhesus macaques. Results Forty‐one statistically significant differences in LD between Chinese and Indian‐origin rhesus were detected on chromosomes 1, 4, 5 and 11. The region of greatest LD difference was located on the proximal end of chromosome one, which also contained the genes ELAVL4, MAST2 and HIVEP3. Conclusion These genomic areas provide entry to more detailed studies of gene function. This method is also applicable to the study of differences in biomarkers between regional populations of other species.     

Gross Primary Productivity of a High Elevation Tropical Montane Cloud Forest

For decades, the productivity of tropical montane cloud forests (TMCF) has been assumed to be lower than in tropical lowland forests due to nutrient limitation, lower temperatures, and frequent cloud immersion, although actual estimates of gross primary productivity (GPP) are very scarce. Here, we present the results of a process-based modeling estimate of GPP, using a soil–plant–atmosphere model, of a high elevation Peruvian TMCF. The model was parameterized with field-measured physiological and structural vegetation variables, and driven with meteorological data from the site. Modeled transpiration corroborated well with measured sap flow, and simulated GPP added up to 16.2 ± SE 1.6 Mg C ha^?1 y^?1. Dry season GPP was significantly lower than wet season GPP, although this difference was 17% and not caused by drought stress. The strongest environmental controls on simulated GPP were variation of photosynthetic active radiation and air temperature (T _air). Their relative importance likely varies with elevation and the local prevalence of cloud cover. Photosynthetic parameters (V _cmax and J _max) and leaf area index were the most important non-environmental controls on GPP. We additionally compared the modeled results with a recent estimate of GPP of the same Peruvian TMCF derived by the summing of ecosystem respiration and net productivity terms, which added up to 26 Mg C ha^?1 y^?1. Despite the uncertainties in modeling GPP we conclude that at this altitude GPP is, conservatively estimated, 30–40% lower than in lowland rainforest and this difference is driven mostly by cooler temperatures than changes in other parameters.     

Grass allometry and estimation of above‐ground biomass in tropical alpine tussock grasslands

The puna/páramo grasslands span across the highest altitudes of the tropical Andes, and their ecosystem dynamics are still poorly understood. In this study we examined the above‐ground biomass and developed species specific and multispecies power‐law allometric equations for four tussock grass species in Peruvian high altitude grasslands, considering maximum height (h_max), elliptical crown area and elliptical basal area. Although these predictors are commonly used among allometric literature, they have not previously been used for estimating puna grassland biomass. Total above‐ground biomass was estimated to be of 6.7 ± 0.2 Mg ha^?1 (3.35 ± 0.1 Mg C ha^?1). All allometric relationships fitted to similar power‐law models, with basal area and crown area as the most influential predictors, although the fit improved when tussock maximum height was included in the model. Multispecies allometries gave better fits than the other species‐specific equations, but the best equation should be used depending on the species composition of the target grassland. These allometric equations provide an useful approach for measuring above‐ground biomass and productivity in high‐altitude Andean grasslands, where destructive sampling can be challenging and difficult because of the remoteness of the area. These equations can be also applicable for establishing above‐ground reference levels before the adoption of carbon compensation mechanisms or grassland management policies, as well as for measuring the impact of land use changes in Andean ecosystems.