PEATmoss (Physcomitrella Expression Atlas Tool): a unified gene expression atlas for the model plant Physcomitrella patens

Physcomitrella patens is a bryophyte model plant that is often used to study plant evolution and development. Its resources are of great importance for comparative genomics and evo‐devo approaches. However, expression data from Physcomitrella patens were so far generated using different gene annotation versions and three different platforms: CombiMatrix and NimbleGen expression microarrays and RNA sequencing. The currently available P. patens expression data are distributed across three tools with different visualization methods to access the data. Here, we introduce an interactive expression atlas, Physcomitrella Expression Atlas Tool (PEATmoss), that unifies publicly available expression data for P. patens and provides multiple visualization methods to query the data in a single web‐based tool. Moreover, PEATmoss includes 35 expression experiments not previously available in any other expression atlas. To facilitate gene expression queries across different gene annotation versions, and to access P. patens annotations and related resources, a lookup database and web tool linked to PEATmoss was implemented. PEATmoss can be accessed at https://peatmoss.online.uni-marburg.de     

Global mapping of infectious disease

The primary aim of this review was to evaluate the state of knowledge of the geographical distribution of all infectious diseases of clinical significance to humans. A systematic review was conducted to enumerate cartographic progress, with respect to the data available for mapping and the methods currently applied. The results helped define the minimum information requirements for mapping infectious disease occurrence, and a quantitative framework for assessing the mapping opportunities for all infectious diseases. This revealed that of 355 infectious diseases identified, 174 (49%) have a strong rationale for mapping and of these only 7 (4%) had been comprehensively mapped. A variety of ambitions, such as the quantification of the global burden of infectious disease, international biosurveillance, assessing the likelihood of infectious disease outbreaks and exploring the propensity for infectious disease evolution and emergence, are limited by these omissions. An overview of the factors hindering progress in disease cartography is provided. It is argued that rapid improvement in the landscape of infectious diseases mapping can be made by embracing non-conventional data sources, automation of geo-positioning and mapping procedures enabled by machine learning and information technology, respectively, in addition to harnessing labour of the volunteer ‘cognitive surplus’ through crowdsourcing.     

Using independent nest survey data to validate changes in reporting rates of Martial Eagles between the Southern African Bird Atlas Project 1 and 2

Repeat monitoring is vital to measure biodiversity change. However, monitoring protocols may change, as survey techniques improve or different questions are asked. Such modifications may cause difficulties when examining changes in wildlife populations. The Southern African Bird Atlas Project (SABAP) 1 and 2 are repeat national bird surveys undertaken 20 years apart. These surveys therefore offer unrivalled potential to examine bird population changes in an African context. However, changes in protocols, both spatially and temporally, between the two surveys have raised concerns over using these data to infer population changes. In this study we use independ- ent nest survey data to test whether changes in reporting rates of Martial Eagles in the Kalahari Gemsbok National Park between the two SABAP surveys were reflected in real change in numbers of nesting pairs. From 11 quarter degree squares (QDS), covering c. 8 000 km2, both SABAP and nest surveys suggested a near identical 44% decline. Levels of agreement were weaker at the individual QDS scale, although in 67% of cases the direction of change was the same using both surveys. These results suggest that comparisons in the reporting rates between SABAP 1 and SABAP 2 accurately reflect changes in the breeding population size of this species.     

Fine scale waterbody data improve prediction of waterbird occurrence despite coarse species data

While modelling habitat suitability and species distribution, ecologists must deal with issues related to the spatial resolution of species occurrence and environmental data. Indeed, given that the spatial resolution of species and environmental datasets range from centimeters to hundreds of kilometers, it underlines the importance of choosing the optimal combination of resolutions to achieve the highest possible modelling prediction accuracy. We evaluated how the spatial resolution of land cover/waterbody datasets (meters to 1 km) affect waterbird habitat suitability models based on atlas data (grid cell of 12 × 11 km). We hypothesized that the area, perimeter and number of waterbodies computed from high resolution datasets would explain distributions of waterbirds better because coarse resolution datasets omit small waterbodies affecting species occurrence. Specifically, we investigated which spatial resolution of waterbodies better explain the distribution of seven waterbirds nesting on ponds/lakes with areas ranging from 0.1 ha to hundreds of hectares. Our results show that the area and perimeter of waterbodies derived from high resolution datasets (raster data with 30 m resolution, vector data corresponding with map scale 1:10 000) explain the distribution of the waterbirds better than those calculated using less accurate datasets despite the coarse grain of the species data. Taking into account the spatial extent (global vs regional) of the datasets, we found the Global Inland Waterbody Dataset to be the most suitable for modelling distribution of waterbirds. In general, we recommend using land cover data of a resolution sufficient to capture the smallest patches of the habitat suitable for a given species’ presence for both fine and coarse grain habitat suitability and distribution modelling.     

DNA指纹图谱技术在土壤微生物多样性研究中的应用

土壤中的微生物多样性是十分丰富的,传统培养方法对土壤微生物多样性的研究有很大局限性。近年来,各种基于16S rDNA基因的指纹图谱分析技术取得了长足的进步,并广泛应用于土壤微生物多样性的研究。这些技术主要有变性梯度凝胶电泳(DGGE)/温度梯度凝胶电泳(TGGE)、单链构象多态性(SSCP)、随机引物扩增多态性DNA(RAPD)、限制性片段长度多态性(RFLP)和扩增核糖体DNA限制性分析(ARDRA)等。对这些技术近年来在土壤微生物多样性研究领域的应用予以简短综述,并初步探讨未来几年土壤微生物分子生态学发展的方向。     

The role of land cover in bioclimatic models depends on spatial resolution

Aim We explored the importance of climate and land cover in bird species distribution models on multiple spatial scales. In particular, we tested whether the integration of land cover data improves the performance of pure bioclimatic models. Location Finland, northern Europe. Methods The data of the bird atlas survey carried out in 1986–89 using a 10 × 10 km uniform grid system in Finland were employed in the analyses. Land cover and climatic variables were compiled using the same grid system. The dependent and explanatory variables were resampled to 20‐km, 40‐km and 80‐km resolutions. Generalized additive models (GAM) were constructed for each of the 88 land bird species studied in order to estimate the probability of occurrence as a function of (1) climate and (2) climate and land cover variables. Model accuracy was measured by a cross‐validation approach using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Results In general, the accuracies of the 88 bird–climate models were good at all studied resolutions. However, the inclusion of land cover increased the performance of 79 and 78 of the 88 bioclimatic models at 10‐km and 20‐km resolutions, respectively. There was no significant improvement at the 40‐km resolution. In contrast to the finer resolutions, the inclusion of land cover variables decreased the modelling accuracy at 80km resolution. Main conclusions Our results suggest that the determinants of bird species distributions are hierarchically structured: climatic variables are large‐scale determinants, followed by land cover at finer resolutions. The majority of the land bird species in Finland are rather clearly correlated with climate, and bioclimate envelope models can provide useful tools for identifying the relationships between these species and the environment at resolutions ranging from 10 km to 80 km. However, the notable contribution of land cover to the accuracy of bioclimatic models at 10–20‐km resolutions indicates that the integration of climate and land cover information can improve our understanding and model predictions of biogeographical patterns under global change.     

Towards a human proteome atlas: high-throughput generation of mono-specific antibodies for tissue profiling

A great need exists for the systematic generation of specific antibodies to explore the human proteome. Here, we show that antibodies specific to human proteins can be generated in a high-throughput manner involving stringent affinity purification using recombinant protein epitope signature tags (PrESTs) as immunogens and affinity-ligands. The specificity of the generated affinity reagents, here called mono-specific antibodies (msAb), were validated with a novel protein microarray assay. The success rate for 464 antibodies generated towards human proteins was more than 90% as judged by the protein array assay. The antibodies were used for parallel profiling of patient biopsies using tissue microarrays generated from 48 human tissues. Comparative analysis with well-characterized monoclonal antibodies showed identical or similar specificity and expression patterns. The results suggest that a comprehensive atlas containing extensive protein expression and subcellular localization data of the human proteome can be generated in an efficient manner with mono-specific antibodies.     

Determinants of the biogeographical distribution of butterflies in boreal regions

Aim  We explored the relative contributions of climatic and land-cover factors in explaining the distribution patterns of butterflies in a boreal region.
Location  Finland, northern Europe.
Methods  Data from a national butterfly atlas survey carried out during 1991–2003, with a 10-km grain grid system, were used in these analyses. We used generalized additive models (GAM) and hierarchical partitioning (HP) to explore the main environmental correlates (climate and land-cover) of the realized niches of 98 butterfly species. The accuracy of the distribution models (GAMs) was validated by resubstitution and cross-validation approaches, using the area under the curve (AUC) derived from the receiver operating characteristic (ROC) plots.
Results  Predictive accuracies of the 98 individual environment–butterfly models varied from low to very high (cross-validated AUC values 0.48–0.99), with a mean of 0.79. The results of both the GAM and HP analyses were broadly concordant. Most of the variation in butterfly distributions is associated with growing degree-days, mean temperature of the coldest month and cover of built-up area in all six phylogenetic groups (butterfly families). There were no statistically significant differences in predictive accuracy among the different butterfly families.
Main conclusions  About three-quarters of the distributions of butterfly species in Finland appear to be governed principally by climatic, predominantly temperature-related, factors. This indicates that many butterfly species may respond rapidly to the projected climate change in boreal regions. By determining the ecological niches of multiple species, we can project their range shifts in response to changes in climate and land-cover, and identify species that are particularly sensitive to forecasted global changes.