Measuring ecological niche overlap from occurrence and spatial environmental data

Aim Concerns over how global change will influence species distributions, in conjunction with increased emphasis on understanding niche dynamics in evolutionary and community contexts, highlight the growing need for robust methods to quantify niche differences between or within taxa. We propose a statistical framework to describe and compare environmental niches from occurrence and spatial environmental data. Location Europe, North America and South America. Methods The framework applies kernel smoothers to densities of species occurrence in gridded environmental space to calculate metrics of niche overlap and test hypotheses regarding niche conservatism. We use this framework and simulated species with pre‐defined distributions and amounts of niche overlap to evaluate several ordination and species distribution modelling techniques for quantifying niche overlap. We illustrate the approach with data on two well‐studied invasive species. Results We show that niche overlap can be accurately detected with the framework when variables driving the distributions are known. The method is robust to known and previously undocumented biases related to the dependence of species occurrences on the frequency of environmental conditions that occur across geographical space. The use of a kernel smoother makes the process of moving from geographical space to multivariate environmental space independent of both sampling effort and arbitrary choice of resolution in environmental space. However, the use of ordination and species distribution model techniques for selecting, combining and weighting variables on which niche overlap is calculated provide contrasting results. Main conclusions The framework meets the increasing need for robust methods to quantify niche differences. It is appropriate for studying niche differences between species, subspecies or intra‐specific lineages that differ in their geographical distributions. Alternatively, it can be used to measure the degree to which the environmental niche of a species or intra‐specific lineage has changed over time.     

Does the anthropometric model influence whole-body center of mass calculations in gait?

Examining whole-body center of mass (COM) motion is one of method being used to quantify dynamic balance and energy during gait. One common method for estimating the COM position is to apply an anthropometric model to a marker set and calculate the weighted sum from known segmental COM positions. Several anthropometric models are available to perform such a calculation. However, to date there has been no study of how the anthropometric model affects whole-body COM calculations during gait. This information is pertinent to researchers because the choice of anthropometric model may influence gait research findings and currently the trend is to consistently use a single model. In this study we analyzed a single stride of gait data from 103 young adult participants. We compared the whole-body COM motion calculated from 4 different anthropometric models (Plagenhoef et al., 1983; Winter, 1990; de Leva, 1996; Pavol et al., 2002). We found that anterior-posterior motion calculations are relatively unaffected by the anthropometric model. However, medial-lateral and vertical motions are significantly affected by the use of different anthropometric models. Our findings suggest that the researcher carefully choose an anthropometric model to fit their study populations when interested in medial-lateral or vertical motions of the COM. Our data can provide researchers a priori information on the model determination depending on the particular variable and how conservative they may want to be with COM comparisons between groups.     

The Value of Opinion in Science and the Forest Service Research Organization

ABSTRACT There is confusion about conflicts of interest between sources of funding and the extent to which Forest Service researchers are free to publish their findings. Forest Service Research is an independent entity with no administrative accountability to policy makers up to the office of the Chief of the Forest Service. Congressional mandate ensures that research will be free from the influence of politics that land management necessarily entails. Because politics involves opinions, it is important to note that opinions per se are not scientific and must be appropriately compared with empirical data before they can be considered so. It is the quantitative test of an opinion that renders it scientific, not the opinion itself.     

Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension

The present article describes how to use eye tracking methodologies to study the cognitive processes involved in text comprehension. Measuring eye movements during reading is one of the most precise methods for measuring moment-by-moment (online) processing demands during text comprehension. Cognitive processing demands are reflected by several aspects of eye movement behavior, such as fixation duration, number of fixations, and number of regressions (returning to prior parts of a text). Important properties of eye tracking equipment that researchers need to consider are described, including how frequently the eye position is measured (sampling rate), accuracy of determining eye position, how much head movement is allowed, and ease of use. Also described are properties of stimuli that influence eye movements that need to be controlled in studies of text comprehension, such as the position, frequency, and length of target words. Procedural recommendations related to preparing the participant, setting up and calibrating the equipment, and running a study are given. Representative results are presented to illustrate how data can be evaluated. Although the methodology is described in terms of reading comprehension, much of the information presented can be applied to any study in which participants read verbal stimuli.     

Futility Determination as a Process: Problems with Medical Sovereignty, Legal Issues and the Strengths and Weakness of the Procedural Approach

Futility is not a purely medical concept. Its subjective nature requires a balanced procedural approach where competing views can be aired and in which disputes can be resolved with procedural fairness. Law should play an important role in this process. Pure medical models of futility are based on a false claim of medical sovereignty. Procedural approaches avoid the problems of such claims. This paper examines the arguments for and against the adoption of a procedural approach to futility determination.     

Simulated Predation Risk Influences Female Choice in Túngara Frogs,Physalaemus pustulosus

While the presence of predators can influence female mate choice, few studies have investigated how females respond to quantitative variation in predation risk. In addition, we know little of how females respond to multiple, independent cues of risk. In this study, we investigated the effects of simulated predation risk on mate choice in túngara frogs, Physalaemus pustulosus, using the advertisement calls of predatory frogs, variation in ambient light, and simulated distance. Females showed aversion to conspecific calls associated with the calls of predators, and females were significantly less likely to travel perceived longer distances while the calls of predatory frogs were broadcast. In both the laboratory and field, females chose among potential mates significantly faster under higher light levels. Female responses to acoustic cues of predation risk were significantly influenced by light level, but decisions about travel distances were not. These results demonstrate that female choice is strongly influenced by perceived predation risk and that females can simultaneously evaluate quantitative variation in different cues of predation risk. The changes in search behavior and mate evaluation we demonstrate indicate that predation plays a strong role in limiting signal evolution and possibly reproductive isolation.     

Comparative bioacoustics: a roadmap for quantifying and comparing animal sounds across diverse taxa

Animals produce a wide array of sounds with highly variable acoustic structures. It is possible to understand the causes and consequences of this variation across taxa with phylogenetic comparative analyses. Acoustic and evolutionary analyses are rapidly increasing in sophistication such that choosing appropriate acoustic and evolutionary approaches is increasingly difficult. However, the correct choice of analysis can have profound effects on output and evolutionary inferences. Here, we identify and address some of the challenges for this growing field by providing a roadmap for quantifying and comparing sound in a phylogenetic context for researchers with a broad range of scientific backgrounds. Sound, as a continuous, multidimensional trait can be particularly challenging to measure because it can be hard to identify variables that can be compared across taxa and it is also no small feat to process and analyse the resulting high-dimensional acoustic data using approaches that are appropriate for subsequent evolutionary analysis. Additionally, terminological inconsistencies and the role of learning in the development of acoustic traits need to be considered. Phylogenetic comparative analyses also have their own sets of caveats to consider. We provide a set of recommendations for delimiting acoustic signals into discrete, comparable acoustic units. We also present a three-stage workflow for extracting relevant acoustic data, including options for multivariate analyses and dimensionality reduction that is compatible with phylogenetic comparative analysis. We then summarize available phylogenetic comparative approaches and how they have been used in comparative bioacoustics, and address the limitations of comparative analyses with behavioural data. Lastly, we recommend how to apply these methods to acoustic data across a range of study systems. In this way, we provide an integrated framework to aid in quantitative analysis of cross-taxa variation in animal sounds for comparative phylogenetic analysis. In addition, we advocate the standardization of acoustic terminology across disciplines and taxa, adoption of automated methods for acoustic feature extraction, and establishment of strong data archival practices for acoustic recordings and data analyses. Combining such practices with our proposed workflow will greatly advance the reproducibility, biological interpretation, and longevity of comparative bioacoustic studies.     

Gene Ontology annotation quality analysis in model eukaryotes

Functional analysis using the Gene Ontology (GO) is crucial for array analysis, but it is often difficult for researchers to assess the amount and quality of GO annotations associated with different sets of gene products. In many cases the source of the GO annotations and the date the GO annotations were last updated is not apparent, further complicating a researchers’ ability to assess the quality of the GO data provided. Moreover, GO biocurators need to ensure that the GO quality is maintained and optimal for the functional processes that are most relevant for their research community. We report the GO Annotation Quality (GAQ) score, a quantitative measure of GO quality that includes breadth of GO annotation, the level of detail of annotation and the type of evidence used to make the annotation. As a case study, we apply the GAQ scoring method to a set of diverse eukaryotes and demonstrate how the GAQ score can be used to track changes in GO annotations over time and to assess the quality of GO annotations available for specific biological processes. The GAQ score also allows researchers to quantitatively assess the functional data available for their experimental systems (arrays or databases).     

Using interpolation to estimate system uncertainty in gene expression experiments

The widespread use of high-throughput experimental assays designed to measure the entire complement of a cell's genes or gene products has led to vast stores of data that are extremely plentiful in terms of the number of items they can measure in a single sample, yet often sparse in the number of samples per experiment due to their high cost. This often leads to datasets where the number of treatment levels or time points sampled is limited, or where there are very small numbers of technical and/or biological replicates. Here we introduce a novel algorithm to quantify the uncertainty in the unmeasured intervals between biological measurements taken across a set of quantitative treatments. The algorithm provides a probabilistic distribution of possible gene expression values within unmeasured intervals, based on a plausible biological constraint. We show how quantification of this uncertainty can be used to guide researchers in further data collection by identifying which samples would likely add the most information to the system under study. Although the context for developing the algorithm was gene expression measurements taken over a time series, the approach can be readily applied to any set of quantitative systems biology measurements taken following quantitative (i.e. non-categorical) treatments. In principle, the method could also be applied to combinations of treatments, in which case it could greatly simplify the task of exploring the large combinatorial space of future possible measurements.     

Emotion, decision making, and the amygdala

Emotion plays a critical role in many contemporary accounts of decision making, but exactly what underlies its influence and how this is mediated in the brain remain far from clear. Here, we review behavioral studies that suggest that Pavlovian processes can exert an important influence over choice and may account for many effects that have traditionally been attributed to emotion. We illustrate how recent experiments cast light on the underlying structure of Pavlovian control and argue that generally this influence makes good computational sense. Corresponding neuroscientific data from both animals and humans implicate a central role for the amygdala through interactions with other brain areas. This yields a neurobiological account of emotion in which it may operate, often covertly, to optimize rather than corrupt economic choice.     

How the Statistics of Sequential Presentation Influence the Learning of Structure

Recent work has shown that humans can learn or detect complex dependencies among variables. Even learning a simple dependency involves the identification of an underlying model and the learning of its parameters. This process represents learning a structured problem. We are interested in an empirical assessment of some of the factors that enable humans to learn such a dependency over time. More specifically, we look at how the statistics of the presentation of samples from a given structure influence learning. Participants engage in an experimental task where they are required to predict the timing of a target. At the outset, they are oblivious to the existence of a relationship between the position of a stimulus and the required temporal response to intercept it. Different groups of participants are either presented with a Random Walk where consecutive stimuli were correlated or with stimuli that were uncorrelated over time. We find that the structural relationship implicit in the task is only learned in the conditions where the stimuli are independently drawn. This leads us to believe that humans require rich and independent sampling to learn hidden structures among variables.     

Estimating correlation coefficient between two variables with repeated observations using mixed effects model

We estimate the correlation coefficient between two variables with repeated observations on each variable, using linear mixed effects (LME) model. The solution to this problem has been studied by many authors. Bland and Altman (1995) considered the problem in many ad hoc methods. Lam, Webb and O'Donnell (1999) solved the problem by considering different correlation structures on the repeated measures. They assumed that the repeated measures are linked over time but their method needs specialized software. However, they never addressed the question of how to choose the correlation structure on the repeated measures for a particular data set. Hamlett et al. (2003) generalized this model and used Proc Mixed of SAS to solve the problem. Unfortunately, their method also cannot implement the correlation structure on the repeated measures that is present in the data. We also assume that the repeated measures are linked over time and generalize all the previous models, and can account for the correlation structure on the repeated measures that is present in the data. We study how the correlation coefficient between the variables gets affected by incorrect assumption of the correlation structure on the repeated measures itself by using Proc Mixed of SAS, and describe how to select the correlation structure on the repeated measures. We also extend the model by including random intercept and random slope over time for each subject. Our model will also be useful when some of the repeated measures are missing at random.     

Modeling physical growth using mixed effects models

This article demonstrates the use of mixed effects models for characterizing individual and sample average growth curves based on serial anthropometric data. These models are advancement over conventional general linear regression because they effectively handle the hierarchical nature of serial growth data. Using body weight data on 70 infants in the Born in Bradford study, we demonstrate how a mixed effects model provides a better fit than a conventional regression model. Further, we demonstrate how mixed effects models can be used to explore the influence of environmental factors on the sample average growth curve. Analyzing data from 183 infant boys (aged 3–15 months) from rural South India, we show how maternal education shapes infant growth patterns as early as within the first 6 months of life. The presented analyses highlight the utility of mixed effects models for analyzing serial growth data because they allow researchers to simultaneously predict individual curves, estimate sample average curves, and investigate the effects of environmental exposure variables. Am J Phys Anthropol, 2013. © 2012 Wiley Periodicals, Inc.     

Annotator: postprocessing software for generating function-based signatures from quantitative mass spectrometry

Mass spectrometry is used to investigate global changes in protein abundance in cell lysates. Increasingly powerful methods of data collection have emerged over the past decade, but this has left researchers with the task of sifting through mountains of data for biologically significant results. Often, the end result is a list of proteins with no obvious quantitative relationships to define the larger context of changes in cell behavior. Researchers are often forced to perform a manual analysis from this list or to fall back on a range of disparate tools, which can hinder the communication of results and their reproducibility. To address these methodological problems, we developed Annotator, an application that filters validated mass spectrometry data and applies a battery of standardized heuristic and statistical tests to determine significance. To address systems-level interpretations, we incorporated UniProt and Gene Ontology keywords as statistical units of analysis, yielding quantitative information about changes in abundance for an entire functional category. This provides a consistent and quantitative method for formulating conclusions about cellular behavior, independent of network models or standard enrichment analyses. Annotator allows for "bottom-up" annotations that are based on experimental data and not inferred by comparison to external or hypothetical models. Annotator was developed as an independent postprocessing platform that runs on all common operating systems, thereby providing a useful tool for establishing the inherently dynamic nature of functional annotations, which depend on results from ongoing proteomic experiments. Annotator is available for download at http://people.cs.uchicago.edu/～tyler/annotator/annotator_desktop_0.1.tar.gz .     

A Bayesian method for analysing spotted microarray data

In the decade since their invention, spotted microarrays have been undergoing technical advances that have increased the utility, scope and precision of their ability to measure gene expression. At the same time, more researchers are taking advantage of the fundamentally quantitative nature of these tools with refined experimental designs and sophisticated statistical analyses. These new approaches utilise the power of microarrays to estimate differences in gene expression levels, rather than just categorising genes as up- or down-regulated, and allow the comparison of expression data across multiple samples. In this review, some of the technical aspects of spotted microarrays that can affect statistical inference are highlighted, and a discussion is provided of how several methods for estimating gene expression level across multiple samples deal with these challenges. The focus is on a Bayesian analysis method, BAGEL, which is easy to implement and produces easily interpreted results.     

A conceptual model for the identification, organization, and measure of influence of fertility policies and programs.

The fertility program of any government is the procedural action intended to effect the government fertility policy. There may also be unintended results of any fertility program. A conceptual model was designed to measure the effect of governmental action on fertility and to identify sociocultural factors that have the greatest influence on fertility. The model uses sociocultural variables as intervening variables between fertility programs and changes in actual fertility. The structural variables considered by the model are characteristics of the pattern of social order; the cultural variables consist of shared values and norms. Fertility trend analysis is effected by polynomial regression formulae. Puerto Rican data from 1946 to 1970 were used to test the proposed model. Analysis of the data indicated that emigration between 1940-1962 had little effect on national fertility. Governmental family planning programs, beginning in 1946, contributed to a sharp decline in fertility. It is recommended that the model be used with data from other countries to assess the effects of their family planning programs.     

Time matching between grooming partners: Do methodological distinctions between short versus long‐term reciprocation matter?

Primatologists have long focused on grooming exchanges to examine aspects of social relationships, co‐operation, and social cognition. One particular interest is the extent to which reciprocating grooming partners time match, and the time frame over which they do so. Conclusions about time matching vary across species. Generally, researchers focus on the duration of pauses between grooming episodes that involve a switch in partner roles and choose a cut‐off point to distinguish short from longer‐term reciprocation. Problematically, researchers have made inconsistent choices about cut‐offs. Such methodological variations are potentially concerning, as it is unclear whether inconsistent conclusions about short‐term time matching are attributable to species/ecological differences, or are due in part to methodological inconsistency. We ask whether various criteria for separating short versus long‐term reciprocation influence conclusions about short‐term time matching using data from free‐ranging rhesus ( Macaca mulatta) and captive‐crested macaques ( Macaca nigra). We compare several commonly used cut‐offs to ones generated by the currently preferred approach—survival analysis. Crested macaques displayed a mild degree of time matching regardless of the cutoff used. For rhesus macaques, whereas most cut‐offs yielded similar degrees of time matching as the one derived from survival analysis, very short ones significantly underestimated both the degree of time matching and the influence of rank distance on time matching. Although researchers may have some flexibility in their choice of cut‐offs, we suggest that they employ caution by using survival analysis when possible, and when not possible, by avoiding very short time windows.     

Genomics of natural history collections for understanding evolution in the wild

A long‐standing question in biology is how organisms change through time and space in response to their environment. This knowledge is of particular relevance to predicting how organisms might respond to future environmental changes caused by human‐induced global change. Usually researchers make inferences about past events based on an understanding of current static genetic patterns, but these are limited in their capacity to inform on underlying past processes. Natural history collections (NHCs) represent a unique and critical source of information to provide temporally deep and spatially broad time‐series of samples. By using NHC samples, researchers can directly observe genetic changes over time and space and link those changes with specific ecological/evolutionary events. Until recently, such genetic studies were hindered by the intrinsic challenges of NHC samples (i.e. low yield of highly fragmented DNA). However, recent methodological and technological developments have revolutionized the possibilities in the novel field of NHC genomics. In this Special Feature, we compile a range of studies spanning from methodological aspects to particular case studies which demonstrate the enormous potential of NHC samples for accessing large genomic data sets from the past to advance our knowledge on how populations and species respond to global change at multiple spatial–temporal scales. We also highlight possible limitations, recommendations and a few opportunities for future researchers aiming to study NHC genomics.