Nationally Representative Plot Network Reveals Contrasting Drivers of Net Biomass Change in Secondary and Old-Growth Forests

Uncertainty about the mechanisms driving biomass change at broad spatial scales limits our ability to predict the response of forest biomass storage to global change. Here we use a spatially representative network of 874 forest plots in New Zealand to examine whether commonly hypothesised drivers of forest biomass and biomass change (diversity, disturbance, nutrients and climate) differ between old-growth and secondary forests at a national scale. We calculate biomass stocks and net biomass change for live above-ground biomass, below-ground biomass, deadwood and litter pools. We combine these data with plot-level information on forest type, tree diversity, plant functional traits, climate and disturbance history, and use structural equation models to identify the major drivers of biomass change. Over the period 2002–2014, secondary forest biomass increased by 2.78 (1.68–3.89) Mg ha^?1 y^?1, whereas no significant change was detected in old-growth forests (+0.28; ?0.72 to 1.29 Mg ha^?1 y^?1). The drivers of biomass and biomass change differed between secondary and old-growth forests. Plot-level biomass change of old-growth forest was driven by recent disturbance (large tree mortality within the last decade), whereas biomass change of secondary forest was determined by current biomass and past anthropogenic disturbance. Climate indirectly affected biomass change through its relationship with past anthropogenic disturbance. Our results highlight the importance of disturbance and disturbance history in determining broad-scale patterns of forest biomass change and suggest that explicitly modelling processes driving biomass change within secondary and old-growth forests is essential for predicting future changes in global forest biomass.     

Primates Decline Rapidly in Unprotected Forests: Evidence from a Monitoring Program with Data Constraints

Growing threats to primates in tropical forests make robust and long-term population abundance assessments increasingly important for conservation. Concomitantly, monitoring becomes particularly relevant in countries with primate habitat. Yet monitoring schemes in these countries often suffer from logistic constraints and/or poor rigor in data collection, and a lack of consideration of sources of bias in analysis. To address the need for feasible monitoring schemes and flexible analytical tools for robust trend estimates, we analyzed data collected by local technicians on abundance of three species of arboreal monkey in the Udzungwa Mountains of Tanzania (two Colobus species and one Cercopithecus), an area of international importance for primate endemism and conservation. We counted primate social groups along eight line transects in two forest blocks in the area, one protected and one unprotected, over a span of 11 years. We applied a recently proposed open metapopulation model to estimate abundance trends while controlling for confounding effects of observer, site, and season. Primate populations were stable in the protected forest, while the colobines, including the endemic Udzungwa red colobus, declined severely in the unprotected forest. Targeted hunting pressure at this second site is the most plausible explanation for the trend observed. The unexplained variability in detection probability among transects was greater than the variability due to observers, indicating consistency in data collection among observers. There were no significant differences in both primate abundance and detectability between wet and dry seasons, supporting the choice of sampling during the dry season only based on minimizing practical constraints. Results show that simple monitoring routines implemented by trained local technicians can effectively detect changes in primate populations in tropical countries. The hierarchical Bayesian model formulation adopted provides a flexible tool to determine temporal trends with full account for any imbalance in the data set and for imperfect detection.     

Climate-tree-growth relationships of European beech (Fagus sylvatica L.) in the French Permanent Plot Network (RENECOFOR)

The influence of climate on the radial growth of Fagus sylvatica was investigated using 15 chronologies developed from mature stands of the French Permanent Plot Network (RENECOFOR) growing under different climatic and soil conditions. The relationships between climate and ring widths were analyzed using extreme growth years, simple correlations and response functions analysis. Monthly climatic regressors were derived by a physiological water balance model that used daily climatic data and stand parameters to estimate soil water deficits. The three most frequent negative pointer years (1959, 1989, 1976) result from a particularly intense and durable drought, whereas positive years (1977, 1958) coincide with wet conditions. The total ring chronology variance attributable to climate averages 34.1% (15.8% –57%). Current early-summer soil water deficit enters in 10 models and the deficit in June explains alone a large part of the radial growth variability (mean value: 26.6%). Temperature or soil water deficit for the other months and weather conditions during the previous season were of little consistency across stands. The response pattern of earlywood is very similar and the percentage of variance explained is higher (16.2% –57.8%). Latewood widths present a different response pattern. High minimum temperature in August and/or September often favour wide latewood widths and monthly water deficits play a secondary role. The percentage of variance explained ranges from 8.8% to 67.4%. Soil water capacity strongly modulates ring characteristics and climate-growth relationships. Mean sensitivity, expressed population signal, signal-to-noise ratio and the strength of growth-climate correlations increase with decreasing soil water capacity.     

The Eucalypt Lignotuber: a Position-dependent Organ

Observational and experimental evidence is presented to showthat the capacity to form a lignotuber is not restricted tothe cotyledonary, and a few succeeding, nodes on the primaryseedling stems of certain eucalypts, but is also a propertyof shoots arising from their accessory buds. Erect secondaryshoots from the lignotuber, may bear lignotubers at a few nodes,rhizostolons at many nodes along their length, and, like stolons,rhizomes may give rise to leafy shoots bearing basal lignotubers.These facts describe a so-called position effect for the productionof lignotubers, in the sense that the term has been used forother position-dependent phenomena in plants. Seedlings of lignotuberousspecies remain competent to form lignotubers over a long periodof adversity during which lignotubers are not formed. Shootsof non-lignotuberous species do not inhibit lignotuber developmentwhen grafted to lignotuberous species. eucalypts, lignotubers, position effect, vegetative propagation, tissue culture, rhizostolons, rhizomes     

Macroecology meets invasion ecology: performance of Australian acacias and eucalypts around the world revealed by features of their native ranges

Native geographical range extent has frequently emerged as a correlate of invasiveness, especially for plant species. We tested whether dimensions of the native range (measured by the area-of-occupancy and its scaling patterns) of 720 Australian eucalypts (genera Angophora, Eucalyptus and Corymbia) could explain introduction and invasion success. We also compared our results with a previous study on 979 Australian acacias (previously grouped in Acacia subgenus Phyllodineae) to investigate whether features of their native ranges explained the much higher invasion success in this group compared to eucalypts. From nine databases we found records that 373 eucalypts have been introduced to areas outside their native ranges; 82 of these have become naturalised, and eight are invasive. A similar proportion of Australian acacias have been introduced, but almost three times as many are invasive (384 species introduced, 71 of which are naturalized and 23 invasive). Eucalypts with large native ranges are more likely to have been introduced and subsequently naturalise, as is the case with acacias. Unlike acacias, however, the native range size of invasive eucalypts was not significantly greater than naturalised (but not invasive) species. Intriguingly, the human preference for introducing species with larger ranges was much greater for acacias than for eucalypts as the geometric mean range sizes of introduced, naturalised and invasive acacias are 2.04, 1.88 and 3.59 times those of eucalypts at the same stage. Moreover, the percolation exponent (i.e. the slope of occupancy scaling) becomes more extreme towards the end of the introduction–naturalization–invasion continuum, decreasing for acacias but increasing for eucalypts, with acacias, except for non-introduced species, having a lower exponent than eucalypts. The selection preference of acacias during introduction is thus for species that can rapidly expand their range; in contrast, slow-spreading eucalypts have been selected for dissemination. In other words, humans appear to have selected for highly invasive acacias but against introducing highly invasive eucalypts.     

Volunteers in bird conservation: Insights from the Australian Threatened Bird Network

Summary   Volunteers play an important role in recovery efforts on threatened Australian birds. We surveyed the demography, motivation and preferences of people registered with the Threatened Bird Network, a network dedicated to placing volunteers in recovery efforts. Most were employed or studying and were members of conservation or natural history groups. The main reason for volunteering was an interest in conservation, and most volunteers considered habitat conservation of primary importance. They preferred outdoor projects on highly threatened species and where organizers set clear goals, provided feedback and supervised in a friendly and helpful manner. We also surveyed the recovery projects to determine the extent of volunteer involvement required, and the expectations recovery teams had of volunteers. Volunteers made a major contribution to recovery efforts, with $3.4 million being directed to 32 recovery efforts in the period 1996−2000 − a contribution that has been increasing in recent years. Most activities involved surveying and searching and most activities were available in spring, which coincides with the breeding season of the taxa involved. Volunteers travelled considerable distances, yet only a few projects provided financial support.     

Identification of a Metabolic Reaction Network from Time-Series Data of Metabolite Concentrations

Recent development of high-throughput analytical techniques has made it possible to qualitatively identify a number of metabolites simultaneously. Correlation and multivariate analyses such as principal component analysis have been widely used to analyse those data and evaluate correlations among the metabolic profiles. However, these analyses cannot simultaneously carry out identification of metabolic reaction networks and prediction of dynamic behaviour of metabolites in the networks. The present study, therefore, proposes a new approach consisting of a combination of statistical technique and mathematical modelling approach to identify and predict a probable metabolic reaction network from time-series data of metabolite concentrations and simultaneously construct its mathematical model. Firstly, regression functions are fitted to experimental data by the locally estimated scatter plot smoothing method. Secondly, the fitted result is analysed by the bivariate Granger causality test to determine which metabolites cause the change in other metabolite concentrations and remove less related metabolites. Thirdly, S-system equations are formed by using the remaining metabolites within the framework of biochemical systems theory. Finally, parameters including rate constants and kinetic orders are estimated by the Levenberg–Marquardt algorithm. The estimation is iterated by setting insignificant kinetic orders at zero, i.e., removing insignificant metabolites. Consequently, a reaction network structure is identified and its mathematical model is obtained. Our approach is validated using a generic inhibition and activation model and its practical application is tested using a simplified model of the glycolysis of Lactococcus lactis MG1363, for which actual time-series data of metabolite concentrations are available. The results indicate the usefulness of our approach and suggest a probable pathway for the production of lactate and acetate. The results also indicate that the approach pinpoints a probable strong inhibition of lactate on the glycolysis pathway.     

Baseline Data from a Belgium-Wide Survey of Campylobacter Species Contamination in Chicken Meat Preparations and Considerations for a Reliable Monitoring Program

From February to November 2007, chicken meat preparations (n = 656) were sampled at 11 processing companies across Belgium. All samples were tested for Campylobacter by enrichment culture and by direct plating according to standard methods. Almost half (48.02%) of the samples were positive for Campylobacter spp. The mean Campylobacter count was 1.68 log₁₀ CFU/g with a standard deviation of ± 0.64 log₁₀ CFU/g. The study revealed a statistically significant variation in Campylobacter contamination levels between companies; processors with a wider frequency distribution range of Campylobacter counts provided chicken meat preparations with higher Campylobacter incidences and concentrations. There was no significant difference between the counts of Campylobacter spp. in various preparation types. However, the Campylobacter counts and incidences in chicken wings were the highest and portioned-form products (legs, wings, and breasts) showed a higher probability of being Campylobacter positive compared to minced-form products (sausages, burgers, and minced meat). The proportion of Campylobacter-positive samples was significantly higher in July than in other months. Recovery of Campylobacter spp. recovery by direct plating was higher (41.0%) compared to detection after enrichment (24.2%). Statistical modeling of the survey data showed that the likelihood of obtaining a positive result by enrichment culture increases with an increase in the Campylobacter concentration in the sample. In the present study, we provide the first enumeration data on Campylobacter contamination in Belgian chicken meat preparations and address proposals for improving Campylobacter monitoring programs.     

Estimation of Global Network Statistics from Incomplete Data

Complex networks underlie an enormous variety of social, biological, physical, and virtual systems. A profound complication for the science of complex networks is that in most cases, observing all nodes and all network interactions is impossible. Previous work addressing the impacts of partial network data is surprisingly limited, focuses primarily on missing nodes, and suggests that network statistics derived from subsampled data are not suitable estimators for the same network statistics describing the overall network topology. We generate scaling methods to predict true network statistics, including the degree distribution, from only partial knowledge of nodes, links, or weights. Our methods are transparent and do not assume a known generating process for the network, thus enabling prediction of network statistics for a wide variety of applications. We validate analytical results on four simulated network classes and empirical data sets of various sizes. We perform subsampling experiments by varying proportions of sampled data and demonstrate that our scaling methods can provide very good estimates of true network statistics while acknowledging limits. Lastly, we apply our techniques to a set of rich and evolving large-scale social networks, Twitter reply networks. Based on 100 million tweets, we use our scaling techniques to propose a statistical characterization of the Twitter Interactome from September 2008 to November 2008. Our treatment allows us to find support for Dunbar''s hypothesis in detecting an upper threshold for the number of active social contacts that individuals maintain over the course of one week.     

The Textile Plot: A New Linkage Disequilibrium Display of Multiple-Single Nucleotide Polymorphism Genotype Data

Linkage disequilibrium (LD) is a major concern in many genetic studies because of the markedly increased density of SNP (Single Nucleotide Polymorphism) genotype markers. This dramatic increase in the number of SNPs may cause problems in statistical analyses, such as by introducing multiple comparisons in hypothesis testing and colinearity in logistic regression models, because of the presence of complex LD structures. Inferences must be made about the underlying genetic variation through the LD structure before applying statistical models to the data. Therefore, we introduced the textile plot to provide a visualization of LD to improve the analysis of the genetic variation present in multiple-SNP genotype data. The plot can accentuate LD by displaying specific geometrical shapes, and allowing for the underlying haplotype structure to be inferred without any haplotype-phasing algorithms. Application of this technique to simulated and real data sets illustrated the potential usefulness of the textile plot as an aid to the interpretation of LD in multiple-SNP genotype data. The initial results of LD mapping and haplotype analyses of disease genes are encouraging, indicating that the textile plot may be useful in disease association studies.     

Epidemiologically Optimal Static Networks from Temporal Network Data

One of network epidemiology''s central assumptions is that the contact structure over which infectious diseases propagate can be represented as a static network. However, contacts are highly dynamic, changing at many time scales. In this paper, we investigate conceptually simple methods to construct static graphs for network epidemiology from temporal contact data. We evaluate these methods on empirical and synthetic model data. For almost all our cases, the network representation that captures most relevant information is a so-called exponential-threshold network. In these, each contact contributes with a weight decreasing exponentially with time, and there is an edge between a pair of vertices if the weight between them exceeds a threshold. Networks of aggregated contacts over an optimally chosen time window perform almost as good as the exponential-threshold networks. On the other hand, networks of accumulated contacts over the entire sampling time, and networks of concurrent partnerships, perform worse. We discuss these observations in the context of the temporal and topological structure of the data sets.     

Glycine mosaic virus: a comovirus from Australian native Glycine species

Two strains of a virus designated Glycine mosaic virus (GMV) were found in Glycine clandestina and G. tabacina, legumes indigenous to Australia and the western Pacific region. When transmitted by sap inoculation, GMV infected mostly leguminous species, and caused mosaic and mottling symptoms. The virus was not found naturally in soybean G. max, but it infected all of the 21 cultivars tested. GMV has isometric particles of c. 28 nm diameter, and produces three components with sedimentation coefficients of 60 S (top), 103 S (middle), and 130 S (bottom). Both middle and bottom components are required for infectivity. The virions contain two major proteins with molecular weights of c. 21 500 and 42 000. GMV produces large aggregates of particles in the cytoplasm of the mesophyll cells of pea Pisum sativum, and also induces amorphous membrane-bound bodies and cytoplasmic vesicles. The type strain (from New South Wales) reacts with antisera to Echtes Ackerbohnenmosaik, broad bean stain, and a Californian isolate of squash mosaic virus. The GW strain (from Queensland) reacts with all of the latter antisera, as well as with antisera to cowpea mosaic virus (Sb and Ark strains), bean pod mottle, and red clover mottle viruses, and is serologically related to, but not identical with, the type strain. These properties clearly establish GMV as a new member of the comovirus group.     

VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data

A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs. Current approaches are primarily based on mutation frequencies of single-genes, which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. These interactors are selected and optimized using the Random Walk with Restart algorithm in a protein-protein interaction network. We applied the method in >300 tumor genomes in two large-scale NGS benchmark datasets: 183 lung adenocarcinoma samples and 121 melanoma samples. In each cancer, we derived a consensus mutation subnetwork containing significantly enriched consensus cancer genes and cancer-related functional pathways. These cancer-specific mutation networks were then validated using independent datasets for each cancer. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data.     

Ecological Monitoring and Health Research in Luambe National Park,Zambia: Generation of Baseline Data Layers

Classifying, describing and understanding the natural environment is an important element of studies of human, animal and ecosystem health, and baseline ecological data are commonly lacking in remote environments of the world. Human African trypanosomiasis is an important constraint on human well-being in sub-Saharan Africa, and spillover transmission occurs from the reservoir community of wild mammals. Here we use robust and repeatable methodology to generate baseline datasets on vegetation and mammal density to investigate the ecology of warthogs (Phacochoerus africanus) in the remote Luambe National Park in Zambia, in order to further our understanding of their interactions with tsetse (Glossina spp.) vectors of trypanosomiasis. Fuzzy set theory is used to produce an accurate landcover classification, and distance sampling techniques are applied to obtain species and habitat level density estimates for the most abundant wild mammals. The density of warthog burrows is also estimated and their spatial distribution mapped. The datasets generated provide an accurate baseline to further ecological and epidemiological understanding of disease systems such as trypanosomiasis. This study provides a reliable framework for ecological monitoring of wild mammal densities and vegetation composition in remote, relatively inaccessible environments.     

Tall tales from sly dwarves: novel functions of gibberellins in plant development

Gibberellins (GAs) are endogenous hormones controlling numerous aspects of plant growth and development. Our present understanding of GA physiology is based largely on genetic analysis in model plants such as Arabidopsis. In spite of the success of this approach, the discovery of additional physiological roles for GAs in seed development, pollen tube growth and meristem development indicates that the existing collection of GA-related mutants (identified partially or entirely on the basis of vegetative phenotypes) has failed to uncover all aspects of plant development that are controlled by GAs. The continued use of ever improving forward and reverse genetic techniques is expected to lead to the discovery of further novel roles for GAs in plant development.     

Association of Blood Lead Levels with the Risk of Depressive Symptoms in the Elderly Chinese Population: Baseline Data of a Cohort Study

Biological Trace Element Research - Previous studies have reported an association between blood lead (BPb) levels and depressive symptoms in adults. However, the relationship between BPb levels and...