Aggregations from using inadvertent social information: a form of ideal habitat selection

Social information in breeding site selection has received extensive study; however, few attempts have been made to link this process to pre‐existing models. We examine the importance of social information to three pertinent models of habitat selection that describe breeding aggregations and spatial patterns: 1) the ideal despotic distribution (IDD) which considers conspecific competition and habitat availability, 2) the perceptual constraints model which accounts for patch selection when animals experience a threshold of undetectable difference in quality, and 3) the “neighbourhood model” which predicts concordance between resources and settlers can be disrupted by conspecific attraction when resources are patchy. These models all predict initial settlers will select a high quality patch first. However, their predictions of subsequent settlement behaviour in remaining patches differ: the IDD predicts subsequent settlers will be distributed regularly, the perceptual constraints model predicts a random distribution, and the neighbourhood model predicts clustering from conspecific attraction. We examined which model best described settlement patterns of bobolink Dolichonyx oryzivorus and savannah sparrow Passerculus sandwichensis, in the context of social information. We observed settlement timing, quantified available resources, and determined where they occurred in the highest (local population “core”) and lowest densities (local population “periphery”). We then assessed whether individuals in the periphery settled in greater concordance with resources or conspecific presence. Core territories were clustered strongly on relevant resources, and these territory holders were older than in the periphery. Peripheral territories were likewise clustered but did not always co‐occur with the best available resources, matching the neighbourhood model prediction that social information may not always direct them to the best sites available. This suggests older individuals used their own experience to locate ideal habitat, whereas younger individuals attempted to aggregate on seemingly ideal habitat by using conspecific location; such information asymmetry due to age can be viewed as an “ideal aggregative distribution”.     

基于标记-重捕模型开展野生动物红外相机种群监测的方法及案例

红外相机技术的广泛应用推动了动物种群生态学研究方法的发展和革新, 特别是基于标记-重捕模型框架通过非损伤取样方式对物种数量和密度等种群参数的可靠估计, 为保护濒危物种和评估保护成效提供了有力的科学依据。对于身体上具有独特天然标记的动物(如多数猫科动物), 可依据红外相机拍摄身体上的独特斑点或条纹鉴别个体, 再运用标记-重捕模型, 估计动物种群数量、密度等参数。本文概述了标记-重捕模型的基本原理、特点以及国内外的应用, 特别是近年来发展出的空间标记-重捕模型。总结了从相机布设到数据分析的具体流程、操作原则, 并以青城山家猫为实例, 展示了应用红外相机数据通过空间标记-重捕模型估计种群密度和数量的基本步骤。最后展望了该模型在种群动态、景观廊道设计、资源选择等方面的应用和发展趋势。     

The Environmental Polymorphisms Registry: a DNA resource to study genetic susceptibility loci

The National Institute of Environmental Health Sciences is establishing a DNA repository named the Environmental Polymorphisms Registry (EPR). The goal is to recruit 20,000 subjects from the greater Research Triangle Park region of North Carolina and collect a sample of each subject’s DNA for genetic study. Personal information is obtained from each EPR subject and linked to their sample in coded form. Once individuals with the genotypes of interest are identified, their samples are decoded, and their names and contact information are given to scientists for follow-up studies in which genotype is important. “Recruit-by-genotype” resources such as the EPR require a transparent consent process and rigorous human subjects protection measures. Unlike the EPR, most US DNA resources are anonymous. Once scientists identify potentially significant genetic variants, they must screen new populations to find individuals with the variants of interest to study. The EPR eliminates this time consuming and expensive step. In designing the EPR, consideration was given to achieving high response rates, minimizing attrition and maximizing usefulness for future research studies. Subjects are recruited from outpatient clinics in area medical centers as well as from the general population to ascertain individuals in diverse states of health. Data are collected on race, ethnicity, gender and age, and are monitored for demographic diversity. As of November 2007, 7,788 individuals have been recruited into the EPR and their DNA samples have been used in numerous genetic studies. EPR subjects have also been solicited for several follow-up studies with high response rates (>90%). The success of the EPR based on the number of subjects recruited and genetic studies underway, suggests that it will be a model for future DNA resources.     

Les sépultures italiennes du Paléolithique supérieur. Reconstitutions du régime alimentaire

The identification of dietary habits is increasingly seen as a fundamental aspect for studying the ancient human populations. Accordingly, several projects aiming to identify Paleolithic individuals’ dietary patterns were developed to analyze the organic component of bone tissue and identify isotopic markers to reconstruct the food typology. Bone fragments from six individuals were selected for carbon and nitrogen stable isotopes analysis. The interpretation of human isotopic data was framed through a dataset of twenty-one Italian Paleolithic individuals. The isotopic data generated for the Paleolithic individuals agree with the information already provided by the archaeological record concerning the Italian hunter and gatherer communities. Their subsistence economy was essentially grounded upon the exploitation of high protein foods, either from terrestrial fauna resources or inland lacustrine or riverine species.     

Understanding the coevolutionary dynamics of mutualism with population genomics

Decades of research on the evolution of mutualism has generated a wealth of possible ways whereby mutually beneficial interactions between species persist in spite of the apparent advantages to individuals that accept the benefits of mutualism without reciprocating — but identifying how any particular empirical system is stabilized against cheating remains challenging. Different hypothesized models of mutualism stability predict different forms of coevolutionary selection, and emerging high‐throughput sequencing methods allow examination of the selective histories of mutualism genes and, thereby, the form of selection acting on those genes. Here, I review the evolutionary theory of mutualism stability and identify how differing models make contrasting predictions for the population genomic diversity and geographic differentiation of mutualism‐related genes. As an example of the possibilities offered by genomic data, I analyze genes with roles in the symbiosis of Medicago truncatula and nitrogen‐fixing rhizobial bacteria, the first classic mutualism in which extensive genomic resources have been developed for both partners. Medicago truncatula symbiosis genes, as a group, differ from the rest of the genome, but they vary in the form of selection indicated by their diversity and differentiation — some show signs of selection expected from roles in sanctioning noncooperative symbionts, while others show evidence of balancing selection expected from coevolution with symbiont signaling factors. I then assess the current state of development for similar resources in other mutualistic interactions and look ahead to identify ways in which modern sequencing technology can best inform our understanding of mutualists and mutualism.     

Family-based association tests for genomewide association scans

With millions of single-nucleotide polymorphisms (SNPs) identified and characterized, genomewide association studies have begun to identify susceptibility genes for complex traits and diseases. These studies involve the characterization and analysis of very-high-resolution SNP genotype data for hundreds or thousands of individuals. We describe a computationally efficient approach to testing association between SNPs and quantitative phenotypes, which can be applied to whole-genome association scans. In addition to observed genotypes, our approach allows estimation of missing genotypes, resulting in substantial increases in power when genotyping resources are limited. We estimate missing genotypes probabilistically using the Lander-Green or Elston-Stewart algorithms and combine high-resolution SNP genotypes for a subset of individuals in each pedigree with sparser marker data for the remaining individuals. We show that power is increased whenever phenotype information for ungenotyped individuals is included in analyses and that high-density genotyping of just three carefully selected individuals in a nuclear family can recover >90% of the information available if every individual were genotyped, for a fraction of the cost and experimental effort. To aid in study design, we evaluate the power of strategies that genotype different subsets of individuals in each pedigree and make recommendations about which individuals should be genotyped at a high density. To illustrate our method, we performed genomewide association analysis for 27 gene-expression phenotypes in 3-generation families (Centre d'Etude du Polymorphisme Humain pedigrees), in which genotypes for ~860,000 SNPs in 90 grandparents and parents are complemented by genotypes for ~6,700 SNPs in a total of 168 individuals. In addition to increasing the evidence of association at 15 previously identified cis-acting associated alleles, our genotype-inference algorithm allowed us to identify associated alleles at 4 cis-acting loci that were missed when analysis was restricted to individuals with the high-density SNP data. Our genotype-inference algorithm and the proposed association tests are implemented in software that is available for free.     

Functional data analysis with covariate-dependent mean and covariance structures

Functional data analysis has emerged as a powerful tool in response to the ever-increasing resources and efforts devoted to collecting information about response curves or anything that varies over a continuum. However, limited progress has been made with regard to linking the covariance structures of response curves to external covariates, as most functional models assume a common covariance structure. We propose a new functional regression model with covariate-dependent mean and covariance structures. Particularly, by allowing variances of random scores to be covariate-dependent, we identify eigenfunctions for each individual from the set of eigenfunctions that govern the variation patterns across all individuals, resulting in high interpretability and prediction power. We further propose a new penalized quasi-likelihood procedure that combines regularization and B-spline smoothing for model selection and estimation and establish the convergence rate and asymptotic normality of the proposed estimators. The utility of the developed method is demonstrated via simulations, as well as an analysis of the Avon Longitudinal Study of Parents and Children concerning parental effects on the growth curves of their offspring, which yields biologically interesting results.     

Invertebrate resources on the internet

The use of invertebrates in research laboratories, classroom teaching, and public displays has greatly increased over the past 20 years, accompanied by a corresponding increase in the amount of online information and literature about invertebrates. This brief overview of Internet resources is intended to aid both novice and experienced individuals in the search for such information.     

Applying evolutionary models to the laboratory study of social learning

Cultural evolution is driven, in part, by the strategies that individuals employ to acquire behavior from others. These strategies themselves are partly products of natural selection, making the study of social learning an inherently Darwinian project. Formal models of the evolution of social learning suggest that reliance on social learning should increase with task difficulty and decrease with the probability of environmental change. These models also make predictions about how individuals integrate information from multiple peers. We present the results of microsociety experiments designed to evaluate these predictions. The first experiment measures baseline individual learning strategy in a two-armed bandit environment with variation in task difficulty and temporal fluctuation in the payoffs of the options. Our second experiment addresses how people in the same environment use minimal social information from a single peer. Our third experiment expands on the second by allowing access to the behavior of several other individuals, permitting frequency-dependent strategies like conformity. In each of these experiments, we vary task difficulty and environmental fluctuation. We present several candidate strategies and compute the expected payoffs to each in our experimental environment. We then fit to the data the different models of the use of social information and identify the best-fitting model via model comparison techniques. We find substantial evidence of both conformist and nonconformist social learning and compare our results to theoretical expectations.     

MASCP Gator: an aggregation portal for the visualization of Arabidopsis proteomics data

Proteomics has become a critical tool in the functional understanding of plant processes at the molecular level. Proteomics-based studies have also contributed to the ever-expanding array of data in modern biology, with many generating Web portals and online resources that contain incrementally expanding and updated information. Many of these resources reflect specialist research areas with significant and novel information that is not currently captured by centralized repositories. The Arabidopsis (Arabidopsis thaliana) community is well served by a number of online proteomics resources that hold an abundance of functional information. These sites can be difficult to locate among a multitude of online resources. Furthermore, they can be difficult to navigate in order to identify specific features of interest without significant technical knowledge. Recently, members of the Arabidopsis proteomics community involved in developing many of these resources decided to develop a summary aggregation portal that is capable of retrieving proteomics data from a series of online resources on the fly. The Web portal is known as the MASCP Gator and can be accessed at the following address: http://gator.masc-proteomics.org/. Significantly, proteomics data displayed at this site retrieve information from the data repositories upon each request. This means that information is always up to date and displays the latest data sets. The site also provides hyperlinks back to the source information hosted at each of the curated databases to facilitate more in-depth analysis of the primary data.     

The limits to cost-free signalling of need between relatives

Theoretical models have demonstrated the possibility of stable cost-free signalling of need between relatives. The stability of these cost-free equilibria depends on the indirect fitness cost of cheating and deceiving a donor into giving away resources. We show that this stability is highly sensitive to the distribution of need among signallers and receivers. In particular, cost-free signalling is likely to prove stable only if there is very large variation in need (such that the least-needy individuals stand to gain much less than the most-needy individuals from additional resources). We discuss whether these conditions are likely to be found in altricial avian breeding systems--the most intensively studied instance of signalling of need between relatives. We suggest that cost-free signalling is more likely to prove stable and will provide parents with more information during the earlier phases of chick growth, when parents can more easily meet the demands of a brood (and chicks are more likely to reach satiation). Later, informative yet cost-free signalling is unlikely to persist.     

A research agenda for malaria eradication: diagnoses and diagnostics

Many of malaria's signs and symptoms are indistinguishable from those of other febrile diseases. Detection of the presence of Plasmodium parasites is essential, therefore, to guide case management. Improved diagnostic tools are required to enable targeted treatment of infected individuals. In addition, field-ready diagnostic tools for mass screening and surveillance that can detect asymptomatic infections of very low parasite densities are needed to monitor transmission reduction and ensure elimination. Antibody-based tests for infection and novel methods based on biomarkers need further development and validation, as do methods for the detection and treatment of Plasmodium vivax. Current rapid diagnostic tests targeting P. vivax are generally less effective than those targeting Plasmodium falciparum. Moreover, because current drugs for radical cure may cause serious side effects in patients with glucose-6-phosphate dehydrogenase (G6PD) deficiency, more information is needed on the distribution of G6PD-deficiency variants as well as tests to identify at-risk individuals. Finally, in an environment of very low or absent malaria transmission, sustaining interest in elimination and maintaining resources will become increasingly important. Thus, research is required into the context in which malaria diagnostic tests are used, into diagnostics for other febrile diseases, and into the integration of these tests into health systems.     

From Experiment to Theory: What Can We Learn from Growth Curves?

Finding an appropriate functional form to describe population growth based on key properties of a described system allows making justified predictions about future population development. This information can be of vital importance in all areas of research, ranging from cell growth to global demography. Here, we use this connection between theory and observation to pose the following question: what can we infer about intrinsic properties of a population (i.e., degree of heterogeneity, or dependence on external resources) based on which growth function best fits its growth dynamics? We investigate several nonstandard classes of multi-phase growth curves that capture different stages of population growth; these models include hyperbolic–exponential, exponential–linear, exponential–linear–saturation growth patterns. The constructed models account explicitly for the process of natural selection within inhomogeneous populations. Based on the underlying hypotheses for each of the models, we identify whether the population that it best fits by a particular curve is more likely to be homogeneous or heterogeneous, grow in a density-dependent or frequency-dependent manner, and whether it depends on external resources during any or all stages of its development. We apply these predictions to cancer cell growth and demographic data obtained from the literature. Our theory, if confirmed, can provide an additional biomarker and a predictive tool to complement experimental research.     

Collection of antimicrobial peptides database and its derivatives: Applications and beyond

Collection of antimicrobial peptides (CAMP), CAMPSign, and ClassAMP are open‐access resources that have been developed to enhance research on antimicrobial peptides (AMPs). Comprehensive information on AMPs and machine learning‐based predictive models are made available for users through these resources. As of date, CAMP_R3 has 10,247 sequences, 757 structures, and 114 family‐specific signatures of AMPs along with associated tools for AMP sequence and structure analysis. CAMPSign uses family‐specific sequence conservation, in the form of patterns and hidden Markov models for identification of AMPs. ClassAMP can be used to classify AMPs as antibacterial, antifungal, or antiviral based on sequence information. Here we describe CAMP and its derivatives and illustrate, with a few examples, the contribution of these online resources to the advancement of our current understanding of AMPs.     

Application of random effects to the study of resource selection by animals

1. Resource selection estimated by logistic regression is used increasingly in studies to identify critical resources for animal populations and to predict species occurrence. 2. Most frequently, individual animals are monitored and pooled to estimate population-level effects without regard to group or individual-level variation. Pooling assumes that both observations and their errors are independent, and resource selection is constant given individual variation in resource availability. 3. Although researchers have identified ways to minimize autocorrelation, variation between individuals caused by differences in selection or available resources, including functional responses in resource selection, have not been well addressed. 4. Here we review random-effects models and their application to resource selection modelling to overcome these common limitations. We present a simple case study of an analysis of resource selection by grizzly bears in the foothills of the Canadian Rocky Mountains with and without random effects. 5. Both categorical and continuous variables in the grizzly bear model differed in interpretation, both in statistical significance and coefficient sign, depending on how a random effect was included. We used a simulation approach to clarify the application of random effects under three common situations for telemetry studies: (a) discrepancies in sample sizes among individuals; (b) differences among individuals in selection where availability is constant; and (c) differences in availability with and without a functional response in resource selection. 6. We found that random intercepts accounted for unbalanced sample designs, and models with random intercepts and coefficients improved model fit given the variation in selection among individuals and functional responses in selection. Our empirical example and simulations demonstrate how including random effects in resource selection models can aid interpretation and address difficult assumptions limiting their generality. This approach will allow researchers to appropriately estimate marginal (population) and conditional (individual) responses, and account for complex grouping, unbalanced sample designs and autocorrelation.     

Genomic imprinting, sibling solidairity and the logic of collective action

Genomic imprinting has been proposed to evolve when a gene's expression has fitness consequences for individuals with different coefficients of matrilineal and patrilineal relatedness, especially in the context of competition between offspring for maternal resources. Previous models have focused on pre-emptive hierarchies, where conflict arises with respect to resource allocation between present and future offspring. Here we present a model in which imprinting arises from scramble competition within litters. The model predicts paternal-specific expression of a gene that increases an offspring's fractional share of resources but reduces the size of the resource pool, and maternal-specific expression of a gene with opposite effects. These predictions parallel the observation in economic models that individuals tend to underprovide public goods, and that the magnitude of this shortfall increases with the number of individuals in the group. Maternally derived alleles are more willing than their paternally derived counterparts to contribute to public goods because they have a smaller effective group size.     

Advancing science through mining libraries, ontologies, and communities

Life scientists today cannot hope to read everything relevant to their research. Emerging text-mining tools can help by identifying topics and distilling statements from books and articles with increased accuracy. Researchers often organize these statements into ontologies, consistent systems of reality claims. Like scientific thinking and interchange, however, text-mined information (even when accurately captured) is complex, redundant, sometimes incoherent, and often contradictory: it is rooted in a mixture of only partially consistent ontologies. We review work that models scientific reason and suggest how computational reasoning across ontologies and the broader distribution of textual statements can assess the certainty of statements and the process by which statements become certain. With the emergence of digitized data regarding networks of scientific authorship, institutions, and resources, we explore the possibility of accounting for social dependences and cultural biases in reasoning models. Computational reasoning is starting to fill out ontologies and flag internal inconsistencies in several areas of bioscience. In the not too distant future, scientists may be able to use statements and rich models of the processes that produced them to identify underexplored areas, resurrect forgotten findings and ideas, deconvolute the spaghetti of underlying ontologies, and synthesize novel knowledge and hypotheses.     

Intra-cultural Differences in the Importance of Plant Resources and Their Impact on Management Intensification in the Tehuacán Valley,Mexico

Management of plant resources is a complex process that involves the interaction of numerous cultural, environmental, economic, and ecological factors Therefore, understanding factors influencing management decisions requires multidisciplinary approaches, including both quantitative and qualitative perspectives. This study explores quantitative approaches as methodological tools with which to identify patterns underlying more intensive management of plant resources. The research was conducted in the village of Santa María Tecomavaca, Oaxaca, in south-central México, within the semiarid Tehuacán-Cuicatlán Biosphere Reserve. Three models explaining the management intensity tendencies were identified, consistent with the pattern of multiple variables influencing plant management.     

Improved surveillance for early detection of a potential invasive species: the alien Rose-ringed parakeet <Emphasis Type="Italic">Psittacula krameri</Emphasis> in Australia

The Rose-ringed parakeet Psittacula krameri is the most widely introduced parrot in the world, and is an important agricultural pest and competitor with native wildlife. In Australia, it is classified as an ‘extreme threat’, yet captive individuals frequently escape into the wild. The distribution and frequency of incursions are currently unknown, as are the potential impacts of the species in Australia. This lack of critical ecological information greatly limits effective biosecurity surveillance and decision-making efforts. We compiled a unique dataset, which combined passive surveillance sources from government and online resources, for all available information on parakeet detections at-large in Australia. We investigated whether geographic variables successfully predicted parakeet incursions, and used species distribution models to assess the potential distribution and economic impacts on agricultural assets. We recorded 864 incursions for the period 1999–2013; mostly escaped birds reported to missing animal websites. Escapes were reported most frequently within, or around, large cities. Incursions were best predicted by factors related to human presence and activity, such as global human footprint and intensive land uses. We recommend surveillance of high (predicted) establishment areas adjacent to cities where a feral parakeet population could most affect horticultural production. Novel passive surveillance datasets combined with species distribution models can be used to identify the regions where potential invasive species are most likely to establish. Subsequently, active surveillance can be targeted to the areas of highest predicted potential risk. We recommend an integrated approach that includes outreach programs involving local communities, as well as traditional biosecurity surveillance, for detecting new incursions.     

An integrative approach to understanding host–parasitoid population dynamics in real landscapes

Many of our advances regarding the spatial ecology of predators and prey have been attributed to research with insect parasitoids and their hosts. Host–parasitoid systems are ideal for spatial-ecological studies because of the small size of the organisms, the often discrete distribution of their resources, and the relative ease with which host mortality from parasitoids can be determined. We outline an integrated approach to studying host–parasitoid interactions in heterogeneous natural landscapes. This approach involves conducting experiments to obtain critically important information on dispersal and boundary behavior of the host and parasitoid, large-scale manipulations of landscape structure to reveal the impacts of landscape change on host–parasitoid interactions and temporal population dynamics, and the development of spatially realistic, behavior-based landscape models. The dividends from such an integrative approach are far reaching, as is illustrated in our research on the prairie planthopper Prokelisia crocea and its egg parasitoid Anagrus columbi that occurs in the tall-grass prairies of North America. Here, we describe the population structure of this system which is based on a long-term survey of planthoppers and parasitoids among host–plant patches. We also outline novel approaches to experimentally quantify and model the movement and boundary behavior of animals in general. The value of this information is revealed in a landscape-level field experiment that was designed to test predictions about how landscape change affects the spatial and temporal population dynamics of the host and parasitoid. Finally, with these empirical data as the foundation, we describe novel simulation models that are spatially realistic and behavior based. Drawing from this integrated approach and case study, we identify key research questions for the future.