Honeybees Use Celestial and/or Terrestrial Compass Cues for Inter‐Patch Navigation

Foraging honeybees (Apis mellifera) are well known to fly straight from the hive, their primary hub, to distal goals as well as between familiar feeding sites. More recently, it was shown that a distal feeding site may be used as a secondary hub. If not fully satiated, the foraging bee may decide to depart the first feeding site in a new compass direction straight to one of many other feeding sites (inter‐patch foraging). Using a recently developed recording method, we discovered that the chosen departure direction at a secondary hub can be guided exclusively by either celestial or terrestrial compass cues. Given our data, we draw two theoretical inferences. First, the bees must be capable of learning and remembering multiple, spatially distinct, navigation vectors between the hive and among multiple feeding sites. Second, this documented and useful representation of multiple navigation vectors between multiple, identified target locations logically implies composite place‐vector mapping, stored in long‐term memory.     

State-vector models of the cell cycle 1. Parametrization and fits to labeled mitoses data

In this paper we study the Hahn model of the cell cycle from the point of view that a cell population's age distribution is more relevant to labeled mitoses data than is the distribution of its transit times.Closed-form relationships are derived between the transition probabilities of the Hahn model and the transit time of the mean of a cohort of labeled cells (with the variance of their transit time through mitosis). Constraints result which define the acceptable values for the number of ages in the state vector and the length of the time step (rarely does the dimension of the state vector equal the number of time steps in the generation time).A generalization to distinct probabilities for G₁, S and G₂M is presented, and the automatic fitting of fraction-labeled mitoses (FLM) data is described. The doubling time of the population is used to define the daughter factor, via the largest eigenvalue of the state transition matrix. The performance of the generalized Hahn model is compared to that of other commonly used fitting methods using two sets of FLM data from the literature. The synthesis of continuous labeling curves is discussed as an independent check of the parametrization. Based on the stable age distribution resulting from fits to experimental FLM data, it is shown that a nonlinear relationship exists between biological age and time.     

Mosquito vector‐associated microbiota: Metabarcoding bacteria and eukaryotic symbionts across habitat types in Thailand endemic for dengue and other arthropod‐borne diseases

Vector‐borne diseases are a major health burden, yet factors affecting their spread are only partially understood. For example, microbial symbionts can impact mosquito reproduction, survival, and vectorial capacity, and hence affect disease transmission. Nonetheless, current knowledge of mosquito‐associated microbial communities is limited. To characterize the bacterial and eukaryotic microbial communities of multiple vector species collected from different habitat types in disease endemic areas, we employed next‐generation 454 pyrosequencing of 16S and 18S rRNA amplicon libraries, also known as metabarcoding. We investigated pooled whole adult mosquitoes of three medically important vectors, Aedes aegypti, Ae. albopictus, and Culex quinquefasciatus, collected from different habitats across central Thailand where we previously characterized mosquito diversity. Our results indicate that diversity within the mosquito microbiota is low, with the majority of microbes assigned to one or a few taxa. Two of the most common eukaryotic and bacterial genera recovered (Ascogregarina and Wolbachia, respectively) are known mosquito endosymbionts with potentially parasitic and long evolutionary relationships with their hosts. Patterns of microbial composition and diversity appeared to differ by both vector species and habitat for a given species, although high variability between samples suggests a strong stochastic element to microbiota assembly. In general, our findings suggest that multiple factors, such as habitat condition and mosquito species identity, may influence overall microbial community composition, and thus provide a basis for further investigations into the interactions between vectors, their microbial communities, and human‐impacted landscapes that may ultimately affect vector‐borne disease risk.     

A note on the low-dimensional display of multivariate data using neural networks

A novel neural network technique has been proposed (Livingstone et al. J. Mol. Graphics 1991, 9, 115-118) which is useful for a low-dimensional display of multivariate data sets. The method makes use of the activity values of the hidden neurons in a trained three-layer feed-forward network to produce the low-dimensional display. It was claimed that in contrast to conventional techniques, such as principal components analysis or nonlinear mapping, this technique could be used also to reconstruct, from a given point in the low-dimensional display, the corresponding multivariate input vector via the completely known weight matrices of a suitably trained network. We show here that this claim is unjustified in this general form. When previously unknown, grossly different input vectors are presented to the trained network, they can occupy, for example, exactly the same point in the low-dimensional display which is occupied also by a given training vector, if certain linear relationships between the vector components are fulfilled. Thus, an infinite set of different linearly dependent input vectors is projected onto one single point in the low-dimensional display. Reconstruction of a multivariate vector, starting from this point in the low-dimensional display, is able to lead back to only one multivariate vector (in the example given, to the original training vector).     

Fine‐scale distribution modeling of avian malaria vectors in north‐central Kansas

Infectious diseases increasingly play a role in the decline of wildlife populations. Vector‐borne diseases, in particular, have been implicated in mass mortality events and localized population declines are threatening some species with extinction. Transmission patterns for vector‐borne diseases are influenced by the spatial distribution of vectors and are therefore not uniform across the landscape. Avian malaria is a globally distributed vector‐borne disease that has been shown to affect endemic bird populations of North America. We evaluated shared habitat use between avian malaria vectors, mosquitoes in the genus Culex and a native grassland bird, the Greater Prairie‐Chicken (Tympanuchus cupido), by (1) modeling the distribution of Culex spp. occurrence across the Smoky Hills of north‐central Kansas using detection data and habitat variables, (2) assessing the occurrence of these vectors at nests of female Greater Prairie‐Chickens, and (3) evaluating if shared habitat use between vectors and hosts is correlated with malarial infection status of the Greater Prairie‐Chicken. Our results indicate that Culex occurrence increased at nest locations compared to other available but unoccupied grassland habitats; however the shared habitat use between vectors and hosts did not result in an increased prevalence of malarial parasites in Greater Prairie‐Chickens that occupied habitats with high vector occurrence. We developed a predictive map to illustrate the associations between Culex occurrence and infection status with malarial parasites in an obligate grassland bird that may be used to guide management decisions to limit the spread of vector‐borne diseases.     

Conceptualizing ecosystem tipping points within a physiological framework

Connecting the nonlinear and often counterintuitive physiological effects of multiple environmental drivers to the emergent impacts on ecosystems is a fundamental challenge. Unfortunately, the disconnect between the way “stressors” (e.g., warming) is considered in organismal (physiological) and ecological (community) contexts continues to hamper progress. Environmental drivers typically elicit biphasic physiological responses, where performance declines at levels above and below some optimum. It is also well understood that species exhibit highly variable response surfaces to these changes so that the optimum level of any environmental driver can vary among interacting species. Thus, species interactions are unlikely to go unaltered under environmental change. However, while these nonlinear, species‐specific physiological relationships between environment and performance appear to be general, rarely are they incorporated into predictions of ecological tipping points. Instead, most ecosystem‐level studies focus on varying levels of “stress” and frequently assume that any deviation from “normal” environmental conditions has similar effects, albeit with different magnitudes, on all of the species within a community. We consider a framework that realigns the positive and negative physiological effects of changes in climatic and nonclimatic drivers with indirect ecological responses. Using a series of simple models based on direct physiological responses to temperature and ocean pCO₂, we explore how variation in environment‐performance relationships among primary producers and consumers translates into community‐level effects via trophic interactions. These models show that even in the absence of direct mortality, mismatched responses resulting from often subtle changes in the physical environment can lead to substantial ecosystem‐level change.     

A Rapid Method for Characterization of Protein Relatedness Using Feature Vectors

We propose a feature vector approach to characterize the variation in large data sets of biological sequences. Each candidate sequence produces a single feature vector constructed with the number and location of amino acids or nucleic acids in the sequence. The feature vector characterizes the distance between the actual sequence and a model of a theoretical sequence based on the binomial and uniform distributions. This method is distinctive in that it does not rely on sequence alignment for determining protein relatedness, allowing the user to visualize the relationships within a set of proteins without making a priori assumptions about those proteins. We apply our method to two large families of proteins: protein kinase C, and globins, including hemoglobins and myoglobins. We interpret the high-dimensional feature vectors using principal components analysis and agglomerative hierarchical clustering. We find that the feature vector retains much of the information about the original sequence. By using principal component analysis to extract information from collections of feature vectors, we are able to quickly identify the nature of variation in a collection of proteins. Where collections are phylogenetically or functionally related, this is easily detected. Hierarchical agglomerative clustering provides a means of constructing cladograms from the feature vector output.     

Vector preference and host defense against infection interact to determine disease dynamics

Vector preference based on host infection status has long been recognized for its importance in disease dynamics. Prior theoretical work has assumed that all hosts are uniformly susceptible to the pathogen. Here we investigated disease dynamics when this assumption is relaxed using a series of vector–host epidemiological compartment models with variable levels of host resistance or tolerance to infection – collectively termed defense. In our models, vectors cannot acquire the infection from resistant hosts but can acquire from tolerant hosts. Specifically, we investigated the interacting effects of vector preference and host defense in a series of single‐ and two‐patch models. Results indicate that resistant host types generally reduce disease prevalence and pathogen spillover, independent of vector preference. The epidemiological consequences of host tolerance, however, depended on vector preference. When vectors preferred diseased hosts, tolerance reduced incidence compared to susceptible hosts; when vectors avoided diseased hosts, tolerance enhanced disease prevalence. Finally, a variation of the model that included preference‐based vector patch leaving rates suggests that both resistance and tolerance can promote pathogen spillover if vectors prefer diseased hosts, because of increased vector dispersal into susceptible patches. Collectively, we found complex, context‐dependent effects of vector preference and host defense on disease dynamics. In the context of management programs for vector‐borne diseases, managers should consider both the precise form of host defense present in a population, breed, or cultivar, as well as vector feeding behavior.     

Is breathing in infants chaotic? Dimension estimates for respiratory patterns during quiet sleep

Wedescribe an analysis of dynamic behavior apparent in times-seriesrecordings of infant breathing during sleep. Three principal techniqueswere used: estimation of correlation dimension, surrogate dataanalysis, and reduced linear (autoregressive) modeling (RARM). Correlation dimension can be used to quantify the complexity of timeseries and has been applied to a variety of physiological andbiological measurements. However, the methods most commonly used toestimate correlation dimension suffer from some technical problems thatcan produce misleading results if not correctly applied. We used a newtechnique of estimating correlation dimension that has fewer problems.We tested the significance of dimension estimates by comparingestimates with artificial data sets (surrogate data). On the basis ofthe analysis, we conclude that the dynamics of infant breathing duringquiet sleep can best be described as a nonlinear dynamic system withlarge-scale, low-dimensional and small-scale, high-dimensionalbehavior; more specifically, a noise-driven nonlinear system with atwo-dimensional periodic orbit. Using our RARM technique, we identifiedthe second period as cyclic amplitude modulation of the same period asperiodic breathing. We conclude that our data are consistent withrespiration being chaotic.

     

Picky hitch‐hikers: vector choice leads to directed dispersal and fat‐tailed kernels in a passively dispersing mite

Dispersal is a central life‐history trait for most animals and plants: it allows to colonize new habitats, escape from competition or avoid inbreeding. Yet, not all species are mobile enough to perform sufficient dispersal. Such passive dispersers may use more mobile animals as dispersal vectors. If multiple potential vectors are available, an active choice can allow to optimize the dispersal process and to determine the distribution of dispersal distances, i.e. an optimal dispersal kernel. We explore dispersal and vector choice in the neotropical flower mite Spadiseius calyptrogynae using a dual approach which combines experiments with an individual‐based simulation model. Spadiseius calyptrogynae is found in lowland rainforests in Costa Rica. It inhabits inflorescences of the understorey palm Calyptrogyne ghiesbreghtiana and is phoretic on a number of flower visitors including bats, beetles and stingless bees. We hypothesised that the mites should optimise their dispersal kernel by actively choosing a specific mix of potential phoretic vectors. In a simple olfactometer setup we showed that the flower mites do indeed discriminate between potential vectors. Subsequently we used an individual‐based model to analyse the evolutionary forces responsible for the observed patterns of vector choice. The mites combine vectors exhibiting long‐distance dispersal with those allowing for more localized dispersal. This results in a fat‐tailed dispersal kernel that guarantees the occasional colonization of new host plant patches (long distance) while optimizing the exploitation of clumped resources (local dispersal). Additionally, kin competition results in a preference for small vectors that transport only few individuals at a time. At the same time, these vectors lead to directed dispersal towards suitable habitat, which increases the stability of this very specialized interaction. Our findings can be applied to other phoretic systems but also to vector‐based seed dispersal, for example.     

A trait‐based trade‐off between growth and mortality: evidence from 15 tropical tree species using size‐specific relative growth rates

A life‐history trade‐off between low mortality in the dark and rapid growth in the light is one of the most widely accepted mechanisms underlying plant ecological strategies in tropical forests. Differences in plant functional traits are thought to underlie these distinct ecological strategies; however, very few studies have shown relationships between functional traits and demographic rates within a functional group. We present 8 years of growth and mortality data from saplings of 15 species of Dipterocarpaceae planted into logged‐over forest in Malaysian Borneo, and the relationships between these demographic rates and four key functional traits: wood density, specific leaf area (SLA), seed mass, and leaf C:N ratio. Species‐specific differences in growth rates were separated from seedling size effects by fitting nonlinear mixed‐effects models, to repeated measurements taken on individuals at multiple time points. Mortality data were analyzed using binary logistic regressions in a mixed‐effects models framework. Growth increased and mortality decreased with increasing light availability. Species differed in both their growth and mortality rates, yet there was little evidence for a statistical interaction between species and light for either response. There was a positive relationship between growth rate and the predicted probability of mortality regardless of light environment, suggesting that this relationship may be driven by a general trade‐off between traits that maximize growth and traits that minimize mortality, rather than through differential species responses to light. Our results indicate that wood density is an important trait that indicates both the ability of species to grow and resistance to mortality, but no other trait was correlated with either growth or mortality. Therefore, the growth mortality trade‐off among species of dipterocarp appears to be general in being independent of species crossovers in performance in different light environments.     

High‐resolution DNA melt‐curve analysis for cost‐effective mass screening of pairwise species interactions

Ecological studies of pairwise interactions are constrained by the methods available for rapid species identification of the interacting organisms. The resolution of data required to characterize species interaction networks at multiple spatio‐temporal scales can be intensive, and therefore laborious and costly to collect. We explore the utility of high‐resolution DNA melt‐curve analysis (HRM) as a rapid species identification method. An approach was developed to identify organisms at the pairwise interaction level, with particular application to cryptic species interactions that are traditionally difficult to study. Here, we selected a challenging application; to identify the presence/absence of pathogenic fungi (Sporothrix inflata, Ophiostoma nigrocarpum and Ophiostoma galeiforme) transported by bark beetle vectors (Hylastes ater and Hylurgus ligniperda). The technique was able to distinguish between different species of DNA within a single, pooled sample. In test applications, HRM was effective in the mass screening and identification of pathogenic fungal species carried by many individual bark beetle vectors (n = 455 beetles screened) across large geographic scales. For two of the fungal species, there was no difference in the frequency of association with either of their vectors, but for the third fungal species there was a shift in vector–pathogen associations across locations. This technique allows rapid, mass screening and characterization of species interactions at a fraction of the time and cost of traditional methods. It is anticipated that this method can be readily applied to explore other cryptic species interactions, or other studies requiring rapid generation of large data sets and/or high‐throughput efficiency.     

Bayesian inference on age-specific survival for censored and truncated data

1. Traditional estimation of age-specific survival and mortality rates in vertebrates is limited to individuals with known age. Although this subject has been studied extensively using effective capture-recapture and capture-recovery models, inference remains challenging because of large numbers of incomplete records (i.e. unknown age of many individuals) and because of the inadequate duration of the studies. 2. Here, we present a hierarchical model for capture-recapture/recovery (CRR) data sets with large proportions of unknown times of birth and death. The model uses a Bayesian framework to draw inference on population-level age-specific demographic rates using parametric survival functions and applies this information to reconstruct times of birth and death for individuals with unknown age. 3. We simulated a set of CRR data sets with varying study span and proportions of individuals with known age, and varying recapture and recovery probabilities. We used these data sets to compare our method to a traditional CRR model, which requires knowledge of individual ages. Subsequently, we applied our method to a subset of a long-term CRR data set on Soay sheep. 4. Our results show that this method performs better than the common CRR model when sample sizes are low. Still, our model is sensitive to the choice of priors with low recapture probability and short studies. In such cases, priors that overestimate survival perform better than those that underestimate it. Also, the model was able to estimate accurately ages at death for Soay sheep, with an average error of 0.94 years and to identify differences in mortality rate between sexes. 5. Although many of the problems in the estimation of age-specific survival can be reduced through more efficient sampling schemes, most ecological data sets are still sparse and with a large proportion of missing records. Thus, improved sampling needs still to be combined with statistical models capable of overcoming the unavoidable limitations of any fieldwork. We show that our approach provides reliable estimates of parameters and unknown times of birth and death even with the most incomplete data sets while being flexible enough to accommodate multiple recapture probabilities and covariates.     

Genetic sex assignment in wild populations using genotyping‐by‐sequencing data: A statistical threshold approach

Establishing the sex of individuals in wild systems can be challenging and often requires genetic testing. Genotyping‐by‐sequencing (GBS) and other reduced‐representation DNA sequencing (RRS) protocols (e.g., RADseq, ddRAD) have enabled the analysis of genetic data on an unprecedented scale. Here, we present a novel approach for the discovery and statistical validation of sex‐specific loci in GBS data sets. We used GBS to genotype 166 New Zealand fur seals (NZFS, Arctocephalus forsteri) of known sex. We retained monomorphic loci as potential sex‐specific markers in the locus discovery phase. We then used (i) a sex‐specific locus threshold (SSLT) to identify significantly male‐specific loci within our data set; and (ii) a significant sex‐assignment threshold (SSAT) to confidently assign sex in silico the presence or absence of significantly male‐specific loci to individuals in our data set treated as unknowns (98.9% accuracy for females; 95.8% for males, estimated via cross‐validation). Furthermore, we assigned sex to 86 individuals of true unknown sex using our SSAT and assessed the effect of SSLT adjustments on these assignments. From 90 verified sex‐specific loci, we developed a panel of three sex‐specific PCR primers that we used to ascertain sex independently of our GBS data, which we show amplify reliably in at least two other pinniped species. Using monomorphic loci normally discarded from large SNP data sets is an effective way to identify robust sex‐linked markers for nonmodel species. Our novel pipeline can be used to identify and statistically validate monomorphic and polymorphic sex‐specific markers across a range of species and RRS data sets.     

Multiple shRNA expressions in a single plasmid vector improve RNAi against the XPA gene

To improve the efficiency of stable knockdown with short hairpin RNA (shRNA), we inserted multiple shRNA expression sequences into a single plasmid vector. In this study, the DNA repair factor XPA was selected as a target gene since it is not essential for cell viability and it is easy to check the functional knockdown of this gene. The efficiency of knockdown was compared among single and triple expression vectors. The single shRNA-expressing vector caused limited knockdown of the target protein in stable transfectants, however, the multiple expression vectors apparently increased the frequency of knockdown transfectants. There were correlations between the knockdown level and marker expression in multiple-expressing transfectants, whereas poorer correlations were observed in single vector transfectants. Multiple-transfectants exhibited reduced efficiency of repair of UV-induced DNA damage and an increased sensitivity to ultraviolet light-irradiation. We propose that multiple shRNA expression vectors might be a useful strategy for establishing knockdown cells.     

Pathogen effects on vegetative and floral odours mediate vector attraction and host exposure in a complex pathosystem

Pathogens can alter host phenotypes in ways that influence interactions between hosts and other organisms, including insect disease vectors. Such effects have implications for pathogen transmission, as well as host exposure to secondary pathogens, but are not well studied in natural systems, particularly for plant pathogens. Here, we report that the beetle‐transmitted bacterial pathogen Erwinia tracheiphila – which causes a fatal wilt disease – alters the foliar and floral volatile emissions of its host (wild gourd, Cucurbita pepo ssp. texana) in ways that enhance both vector recruitment to infected plants and subsequent dispersal to healthy plants. Moreover, infection by Zucchini yellow mosaic virus (ZYMV), which also occurs at our study sites, reduces floral volatile emissions in a manner that discourages beetle recruitment and therefore likely reduces the exposure of virus‐infected plants to the lethal bacterial pathogen – a finding consistent with our previous observation of dramatically reduced wilt disease incidence in ZYMV‐infected plants.     

Nomograms Aid Interpretation of Complex Regression Models

ABSTRACT Ecologists often develop complex regression models that include multiple categorical and continuous variables, interactions among predictors, and nonlinear relationships between the response and predictor variables. Nomograms, which are graphical devices for presenting mathematical functions and calculating output values, can aid biologists in interpreting and presenting these complex models. To illustrate benefits of nomograms, we developed a logistic regression model of elk (Cervus elaphus) resource selection. With this model, we demonstrated how a nomogram helps scientists and managers interpret interactions among variables, compare the relative biological importance of variables, and examine predicted shapes of relationships (e.g., linear vs. nonlinear) between response and predictor variables. Although our example focused on logistic regression, nomograms are equally useful for other linear and nonlinear models. Regardless of the approach used for model development, nomograms and other graphical summaries can help scientists and managers develop, interpret, and apply statistical models.     

Single nucleotide polymorphisms and haplotypes in Native American populations

Autosomal DNA polymorphisms can provide new information and understanding of both the origins of and relationships among modern Native American populations. At the same time that autosomal markers can be highly informative, they are also susceptible to ascertainment biases in the selection of the markers to use. Identifying markers that can be used for ancestry inference among Native American populations can be considered separate from identifying markers to further the quest for history. In the current study, we are using data on nine Native American populations to compare the results based on a large haplotype‐based dataset with relatively small independent sets of single nucleotide polymorphisms. We are interested in what types of limited datasets an individual laboratory might be able to collect are best for addressing two different questions of interest. First, how well can we differentiate the Native American populations and/or infer ancestry by assigning an individual to her population(s) of origin? Second, how well can we infer the historical/evolutionary relationships among Native American populations and their Eurasian origins? We conclude that only a large comprehensive dataset involving multiple autosomal markers on multiple populations will be able to answer both questions; different small sets of markers are able to answer only one or the other of these questions. Using our largest dataset, we see a general increasing distance from Old World populations from North to South in the New World except for an unexplained close relationship between our Maya and Quechua samples. Am J Phys Anthropol, 2011. © 2011 Wiley Periodicals, Inc.     

Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples

Identifying causal relations from time series is the first step to understanding the behavior of complex systems. Although many methods have been proposed, few papers have applied multiple methods together to detect causal relations based on time series generated from coupled nonlinear systems with some unobserved parts. Here we propose the combined use of three methods and a majority vote to infer causality under such circumstances. Two of these methods are proposed here for the first time, and all of the three methods can be applied even if the underlying dynamics is nonlinear and there are hidden common causes. We test our methods with coupled logistic maps, coupled Rössler models, and coupled Lorenz models. In addition, we show from ice core data how the causal relations among the temperature, the CH₄ level, and the CO₂ level in the atmosphere changed in the last 800,000 years, a conclusion also supported by irregularly sampled data analysis. Moreover, these methods show how three regions of the brain interact with each other during the visually cued, two-choice arm reaching task. Especially, we demonstrate that this is due to bottom up influences at the beginning of the task, while there exist mutual influences between the posterior medial prefrontal cortex and the presupplementary motor area. Based on our results, we conclude that identifying causality with an appropriate ensemble of multiple methods ensures the validity of the obtained results more firmly.     

A comprehensive whole genome bacterial phylogeny using correlated peptide motifs defined in a high dimensional vector space

As whole genome sequences continue to expand in number and complexity, effective methods for comparing and categorizing both genes and species represented within extremely large datasets are required. Methods introduced to date have generally utilized incomplete and likely insufficient subsets of the available data. We have developed an accurate and efficient method for producing robust gene and species phylogenies using very large whole genome protein datasets. This method relies on multidimensional protein vector definitions supplied by the singular value decomposition (SVD) of a large sparse data matrix in which each protein is uniquely represented as a vector of overlapping tetrapeptide frequencies. Quantitative pairwise estimates of species similarity were obtained by summing the protein vectors to form species vectors, then determining the cosines of the angles between species vectors. Evolutionary trees produced using this method confirmed many accepted prokaryotic relationships. However, several unconventional relationships were also noted. In addition, we demonstrate that many of the SVD-derived right basis vectors represent particular conserved protein families, while many of the corresponding left basis vectors describe conserved motifs within these families as sets of correlated peptides (copeps). This analysis represents the most detailed simultaneous comparison of prokaryotic genes and species available to date.