Emigration effects on estimates of age‐ and sex‐specific survival of two sciurids

Age‐ and sex‐specific survival estimates are crucial to understanding important life history characteristics, and variation in these estimates can be a key driver of population dynamics. When estimating survival using Cormack–Jolly–Seber (CJS) models, emigration is typically unknown but confounded with apparent survival. Consequently, especially for populations or age classes with high dispersal rates, apparent survival estimates are often biased low and temporal patterns in survival might be masked when site fidelity varies temporally. We used 9 years of annual mark–recapture data to estimate age‐, sex‐, and time‐specific apparent survival of Humboldt''s flying squirrels (Glaucomys oregonensis) and Townsend''s chipmunks (Neotamias townsendii). For Humboldt''s flying squirrels, these estimates support a small body of research investigating potential variation in survival among age and sex classes, but age‐ and sex‐specific survival has not been evaluated for Townsend''s chipmunks. We also quantified the effects of age‐ and sex‐specific emigration on confounded estimates of apparent survival. Our estimates of juvenile flying squirrel survival were high relative to other small mammal species and estimates for both species were variable among years. We found survival differed moderately among age and sex classes for Humboldt''s flying squirrels, but little among age and sex classes for Townsend''s chipmunks, and that the degree to which emigration confounded apparent survival estimates varied substantially among years. Our results demonstrate that emigration can influence commonly used estimates of apparent survival. Unadjusted estimates confounded the interpretation of differences in survival between age and sex classes and masked potential temporal patterns in survival because the magnitude of adjustment varied among years. We conclude that apparent survival estimators are robust during some time periods; however, when emigration rates vary in time, the effects of emigration should be carefully considered and accounted for.     

Stepping inside the niche: microclimate data are critical for accurate assessment of species' vulnerability to climate change

To assess a species'' vulnerability to climate change, we commonly use mapped environmental data that are coarsely resolved in time and space. Coarsely resolved temperature data are typically inaccurate at predicting temperatures in microhabitats used by an organism and may also exhibit spatial bias in topographically complex areas. One consequence of these inaccuracies is that coarsely resolved layers may predict thermal regimes at a site that exceed species'' known thermal limits. In this study, we use statistical downscaling to account for environmental factors and develop high-resolution estimates of daily maximum temperatures for a 36 000 km² study area over a 38-year period. We then demonstrate that this statistical downscaling provides temperature estimates that consistently place focal species within their fundamental thermal niche, whereas coarsely resolved layers do not. Our results highlight the need for incorporation of fine-scale weather data into species'' vulnerability analyses and demonstrate that a statistical downscaling approach can yield biologically relevant estimates of thermal regimes.     

Implications of Catlin's American Indian population estimates for revision of Mooney's estimate

Catlin's population estimates of 37 American Indian tribes for the period 1832 to 1839 are compared with Mooney's estimates for the same tribes that range from the year 1600 to the year 1780. The remarkably close correspondence between the two totals despite the difference in the time periods indicates considerable inaccuracy on the part of either Mooney or Catlin. If Catlin is given credibility, then the necessity of current upward revisions of Mooney's population estimate is supported.     

Linking macrotrends and microrates: Re‐evaluating microevolutionary support for Cope's rule

Cope's rule, wherein a lineage increases in body size through time, was originally motivated by macroevolutionary patterns observed in the fossil record. More recently, some authors have argued that evidence exists for generally positive selection on individual body size in contemporary populations, providing a microevolutionary mechanism for Cope's rule. If larger body size confers individual fitness advantages as the selection estimates suggest, thereby explaining Cope's rule, then body size should increase over microevolutionary time scales. We test this corollary by assembling a large database of studies reporting changes in phenotypic body size through time in contemporary populations, as well as studies reporting average breeding values for body size through time. Trends in body size were quite variable with an absence of any general trend, and many populations trended toward smaller body sizes. Although selection estimates can be interpreted to support Cope's rule, our results suggest that actual rates of phenotypic change for body size cannot. We discuss potential reasons for this discrepancy and its implications for the understanding of Cope's rule.     

Inferring on the Intentions of Others by Hierarchical Bayesian Learning

Inferring on others'' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to “player” or “adviser” roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser''s incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players'' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers'' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser''s intentions were presently stable. Finally, our model of the players'' inference predicted the players'' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers'' helpfulness and the advisers'' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition.     

The hockey-stick method to estimate evening dim light melatonin onset (DLMO) in humans

The onset of melatonin secretion in the evening is the most reliable and most widely used index of circadian timing in humans. Saliva (or plasma) is usually sampled every 0.5–1 hours under dim-light conditions in the evening 5–6 hours before usual bedtime to assess the dim-light melatonin onset (DLMO). For many years, attempts have been made to find a reliable objective determination of melatonin onset time either by fixed or dynamic threshold approaches. The here-developed hockey-stick algorithm, used as an interactive computer-based approach, fits the evening melatonin profile by a piecewise linear-parabolic function represented as a straight line switching to the branch of a parabola. The switch point is considered to reliably estimate melatonin rise time. We applied the hockey-stick method to 109 half-hourly melatonin profiles to assess the DLMOs and compared these estimates to visual ratings from three experts in the field. The DLMOs of 103 profiles were considered to be clearly quantifiable. The hockey-stick DLMO estimates were on average 4 minutes earlier than the experts' estimates, with a range of ?27 to +13 minutes; in 47% of the cases the difference fell within ±5 minutes, in 98% within ?20 to +13 minutes. The raters' and hockey-stick estimates showed poor accordance with DLMOs defined by threshold methods. Thus, the hockey-stick algorithm is a reliable objective method to estimate melatonin rise time, which does not depend on a threshold value and is free from errors arising from differences in subjective circadian phase estimates. The method is available as a computerized program that can be easily used in research settings and clinical practice either for salivary or plasma melatonin values.     

Approaches to Refining Estimates of Global Burden and Economics of Dengue

Dengue presents a formidable and growing global economic and disease burden, with around half the world''s population estimated to be at risk of infection. There is wide variation and substantial uncertainty in current estimates of dengue disease burden and, consequently, on economic burden estimates. Dengue disease varies across time, geography and persons affected. Variations in the transmission of four different viruses and interactions among vector density and host''s immune status, age, pre-existing medical conditions, all contribute to the disease''s complexity. This systematic review aims to identify and examine estimates of dengue disease burden and costs, discuss major sources of uncertainty, and suggest next steps to improve estimates. Economic analysis of dengue is mainly concerned with costs of illness, particularly in estimating total episodes of symptomatic dengue. However, national dengue disease reporting systems show a great diversity in design and implementation, hindering accurate global estimates of dengue episodes and country comparisons. A combination of immediate, short-, and long-term strategies could substantially improve estimates of disease and, consequently, of economic burden of dengue. Suggestions for immediate implementation include refining analysis of currently available data to adjust reported episodes and expanding data collection in empirical studies, such as documenting the number of ambulatory visits before and after hospitalization and including breakdowns by age. Short-term recommendations include merging multiple data sources, such as cohort and surveillance data to evaluate the accuracy of reporting rates (by health sector, treatment, severity, etc.), and using covariates to extrapolate dengue incidence to locations with no or limited reporting. Long-term efforts aim at strengthening capacity to document dengue transmission using serological methods to systematically analyze and relate to epidemiologic data. As promising tools for diagnosis, vaccination, vector control, and treatment are being developed, these recommended steps should improve objective, systematic measures of dengue burden to strengthen health policy decisions.     

Short Lag Times for Invasive Tropical Plants: Evidence from Experimental Plantings in Hawai'i

Background

The lag time of an invasion is the delay between arrival of an introduced species and its successful spread in a new area. To date, most estimates of lag times for plants have been indirect or anecdotal, and these estimates suggest that plant invasions are often characterized by lag times of 50 years or more. No general estimates are available of lag times for tropical plant invasions. Historical plantings and documentation were used to directly estimate lag times for tropical plant invasions in Hawai''i.

Methodology/Principal Findings

Historical planting records for the Lyon Arboretum dating back to 1920 were examined to identify plants that have since become invasive pests in the Hawaiian Islands. Annual reports describing escape from plantings were then used to determine the lag times between initial plantings and earliest recorded spread of the successful invaders. Among 23 species that eventually became invasive pests, the average lag time between introduction and first evidence of spread was 14 years for woody plants and 5 years for herbaceous plants.

Conclusions/Significance

These direct estimates of lag times are as much as an order of magnitude shorter than previous, indirect estimates, which were mainly based on temperate plants. Tropical invaders may have much shorter lag times than temperate species. A lack of direct and deliberate observations may have also inflated many previous lag time estimates. Although there have been documented cases of long lag times due to delayed arrival of a mutualist or environmental changes over time, this study suggests that most successful invasions are likely to begin shortly after arrival of the plant in a suitable habitat, at least in tropical environments. Short lag times suggest that controlled field trials may be a practical element of risk assessment for plant introductions.     

Determining Consistency of Spatial Dispersion of Nematodes in Small Plots

Nematode population densities in field plots were estimated by collecting samples consisting of 12 soil cores. Plots encompassed a variety of plant hosts and sampling dates, and provided data on the population densities of seven species of plant-parasitic nematodes. Three separate samples were collected per plot on each sampling date to obtain estimates of the mean and variance of numbers for each species. For each nematode species, these estimates were used to derive the Taylor''s Power Law regression over plots having identical hosts and sampling dates. For some nematode species, comparisons of regression equations among different sampling dates on the same host revealed similarities in values of a and b from Taylor''s Power Law. Parameters of Taylor''s Power Law relationships were used to develop sampling plans and to obtain estimates of sample precision. Precision estimates from specific and general sampling plans are illustrated for Belonolaimus longicaudatus.     

Human Migration Patterns in Yemen and Implications for Reconstructing Prehistoric Population Movements

Population migration has played an important role in human evolutionary history and in the patterning of human genetic variation. A deeper and empirically-based understanding of human migration dynamics is needed in order to interpret genetic and archaeological evidence and to accurately reconstruct the prehistoric processes that comprise human evolutionary history. Current empirical estimates of migration include either short time frames (i.e. within one generation) or partial knowledge about migration, such as proportion of migrants or distance of migration. An analysis of migration that includes both proportion of migrants and distance, and direction over multiple generations would better inform prehistoric reconstructions. To evaluate human migration, we use GPS coordinates from the place of residence of the Yemeni individuals sampled in our study, their birthplaces and their parents'' and grandparents'' birthplaces to calculate the proportion of migrants, as well as the distance and direction of migration events between each generation. We test for differences in these values between the generations and identify factors that influence the probability of migration. Our results show that the proportion and distance of migration between females and males is similar within generations. In contrast, the proportion and distance of migration is significantly lower in the grandparents'' generation, most likely reflecting the decreasing effect of technology. Based on our results, we calculate the proportion of migration events (0.102) and mean and median distances of migration (96 km and 26 km) for the grandparent''s generation to represent early times in human evolution. These estimates can serve to set parameter values of demographic models in model-based methods of prehistoric reconstruction, such as approximate Bayesian computation. Our study provides the first empirically-based estimates of human migration over multiple generations in a developing country and these estimates are intended to enable more precise reconstruction of the demographic processes that characterized human evolution.     

A Method for the Automated,Reliable Retrieval of Publication-Citation Records

Background

Publication records and citation indices often are used to evaluate academic performance. For this reason, obtaining or computing them accurately is important. This can be difficult, largely due to a lack of complete knowledge of an individual''s publication list and/or lack of time available to manually obtain or construct the publication-citation record. While online publication search engines have somewhat addressed these problems, using raw search results can yield inaccurate estimates of publication-citation records and citation indices.

Methodology

In this paper, we present a new, automated method that produces estimates of an individual''s publication-citation record from an individual''s name and a set of domain-specific vocabulary that may occur in the individual''s publication titles. Because this vocabulary can be harvested directly from a research web page or online (partial) publication list, our method delivers an easy way to obtain estimates of a publication-citation record and the relevant citation indices. Our method works by applying a series of stringent name and content filters to the raw publication search results returned by an online publication search engine. In this paper, our method is run using Google Scholar, but the underlying filters can be easily applied to any existing publication search engine. When compared against a manually constructed data set of individuals and their publication-citation records, our method provides significant improvements over raw search results. The estimated publication-citation records returned by our method have an average sensitivity of and specificity of (in contrast to raw search result specificity of less than 10%). When citation indices are computed using these records, the estimated indices are within of the true value, compared to raw search results which have overestimates of, on average, .

Conclusions

These results confirm that our method provides significantly improved estimates over raw search results, and these can either be used directly for large-scale (departmental or university) analysis or further refined manually to quickly give accurate publication-citation records.     

Reliability of distance estimation by doctors and patients: cross sectional study.

OBJECTIVE: To assess the reliability and accuracy of distance estimated by doctors and patients. DESIGN: Comparison between estimated and measured distances of six familiar sites around Guy''s Hospital, London. SUBJECTS: 100 hospital consultants and 100 patients. MAIN OUTCOME MEASURES: Median (range) of estimated distances, and mean (SD) of the difference between estimated and measured distances. RESULTS: Both doctors and patients gave a wide range of estimates of distance. The estimates differed by up to 14.6-fold from the measured distances, and the difference between minimum and maximum estimates was up to 62.5-fold. CONCLUSION: Doctors and patients were inaccurate at estimating distances, which implies that estimates of distances walked are not reliable indicators of a person''s health.     

FLCRM: Functional linear cox regression model

Summary

We consider a functional linear Cox regression model for characterizing the association between time‐to‐event data and a set of functional and scalar predictors. The functional linear Cox regression model incorporates a functional principal component analysis for modeling the functional predictors and a high‐dimensional Cox regression model to characterize the joint effects of both functional and scalar predictors on the time‐to‐event data. We develop an algorithm to calculate the maximum approximate partial likelihood estimates of unknown finite and infinite dimensional parameters. We also systematically investigate the rate of convergence of the maximum approximate partial likelihood estimates and a score test statistic for testing the nullity of the slope function associated with the functional predictors. We demonstrate our estimation and testing procedures by using simulations and the analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Our real data analyses show that high‐dimensional hippocampus surface data may be an important marker for predicting time to conversion to Alzheimer's disease. Data used in the preparation of this article were obtained from the ADNI database ( adni.loni.usc.edu ).     

Birth wantedness reports: A look forward and a look back

Abstract

The majority of research indicates that estimates of heritability are not generalizable beyond the specific sample on which they have been calculated. This research has been limited, however, in its scope. The present set of investigations center upon an examination of previously reported heritability estimates for the scales of the Minnesota Multiphasic Personality Inventory (MMPI). The first investigation correlates the estimates for eight samples derived from a variety of published studies. The second investigation correlates the DZ twin pair and MZ twin pair intraclass correlations from four of the first investigation's samples. The third investigation correlates the estimates from one study in which the same sample was tested twice over a period of less than five years. It is concluded that no evidence is available for the generalizability of these estimates of MMPI scale heritability either across samples or even across time with the same sample.     

Trading Speed and Accuracy by Coding Time: A Coupled-circuit Cortical Model

Our actions take place in space and time, but despite the role of time in decision theory and the growing acknowledgement that the encoding of time is crucial to behaviour, few studies have considered the interactions between neural codes for objects in space and for elapsed time during perceptual decisions. The speed-accuracy trade-off (SAT) provides a window into spatiotemporal interactions. Our hypothesis is that temporal coding determines the rate at which spatial evidence is integrated, controlling the SAT by gain modulation. Here, we propose that local cortical circuits are inherently suited to the relevant spatial and temporal coding. In simulations of an interval estimation task, we use a generic local-circuit model to encode time by ‘climbing’ activity, seen in cortex during tasks with a timing requirement. The model is a network of simulated pyramidal cells and inhibitory interneurons, connected by conductance synapses. A simple learning rule enables the network to quickly produce new interval estimates, which show signature characteristics of estimates by experimental subjects. Analysis of network dynamics formally characterizes this generic, local-circuit timing mechanism. In simulations of a perceptual decision task, we couple two such networks. Network function is determined only by spatial selectivity and NMDA receptor conductance strength; all other parameters are identical. To trade speed and accuracy, the timing network simply learns longer or shorter intervals, driving the rate of downstream decision processing by spatially non-selective input, an established form of gain modulation. Like the timing network''s interval estimates, decision times show signature characteristics of those by experimental subjects. Overall, we propose, demonstrate and analyse a generic mechanism for timing, a generic mechanism for modulation of decision processing by temporal codes, and we make predictions for experimental verification.     

Measuring Under-Five Mortality: Validation of New Low-Cost Methods

Background

There has been increasing interest in measuring under-five mortality as a health indicator and as a critical measure of human development. In countries with complete vital registration systems that capture all births and deaths, under-five mortality can be directly calculated. In the absence of a complete vital registration system, however, child mortality must be estimated using surveys that ask women to report the births and deaths of their children. Two survey methods exist for capturing this information: summary birth histories and complete birth histories. A summary birth history requires a minimum of only two questions: how many live births has each mother had and how many of them have survived. Indirect methods are then applied using the information from these two questions and the age of the mother to estimate under-five mortality going back in time prior to the survey. Estimates generated from complete birth histories are viewed as the most accurate when surveys are required to estimate under-five mortality, especially for the most recent time periods. However, it is much more costly and labor intensive to collect these detailed data, especially for the purpose of generating small area estimates. As a result, there is a demand for improvement of the methods employing summary birth history data to produce more accurate as well as subnational estimates of child mortality.

Methods and Findings

We used data from 166 Demographic and Health Surveys (DHS) to develop new empirically based methods of estimating under-five mortality using children ever born and children dead data. We then validated them using both in- and out-of-sample analyses. We developed a range of methods on the basis of three dimensions of the problem: (1) approximating the average length of exposure to mortality from a mother''s set of children using either maternal age or time since first birth; (2) using cohort and period measures of the fraction of children ever born that are dead; and (3) capturing country and regional variation in the age pattern of fertility and mortality. We focused on improving estimates in the most recent time periods prior to a survey where the traditional indirect methods fail. In addition, all of our methods incorporated uncertainty. Validated against under-five estimates generated from complete birth histories, our methods outperformed the standard indirect method by an average of 43.7% (95% confidence interval [CI] 41.2–45.2). In the 5 y prior to the survey, the new methods resulted in a 53.3% (95% CI 51.3–55.2) improvement. To illustrate the value of this method for local area estimation, we applied our new methods to an analysis of summary birth histories in the 1990, 2000, and 2005 Mexican censuses, generating subnational estimates of under-five mortality for each of 233 jurisdictions.

Conclusions

The new methods significantly improve the estimation of under-five mortality using summary birth history data. In areas without vital registration data, summary birth histories can provide accurate estimates of child mortality. Because only two questions are required of a female respondent to generate these data, they can easily be included in existing survey programs as well as routine censuses of the population. With the wider application of these methods to census data, countries now have the means to generate estimates for subnational areas and population subgroups, important for measuring and addressing health inequalities and developing local policy to improve child survival. Please see later in the article for the Editors'' Summary     

Response to ‘The European nitrogen cycle: response to Schulze et al,Global Change Biology (2010) 16, pp. 1451–1469’

Winniwarter and colleagues present alternative estimates for several of the nitrogen (N) fluxes provided by Schulze and colleagues. They reason that numeric discrepancies between largely dependent estimates and lack of detail in Schulze's estimates urges caution in interpreting these numbers. In this reply we provide methodological details enhancing the transparency of Schulze's estimates and argue that convergence between land‐ and atmosphere‐based estimates should be reached before individual estimates can be rejected. Only for the nitrous oxide and NO_x fluxes a balance between atmosphere and land‐based estimates has been reached. Convergence between independent estimates has not been reached yet for NO‐, NH₃‐ and N‐deposition estimates. As stated by Schulze and colleagues these N‐fluxes remain potentially biased and therefore come with a large uncertainty, irrespective of the reported precision.     

Estimating age of immobilized elephants from teeth impressions using dental silicon

High precision condensation dental silicon, Zetalabor^TM, was used to create moulds of the lower jaw molars from 22 immobilized African elephants (Loxodonta africana Blumenback) during radio collaring operations. These moulds were used to determine the elephant's age using Laws and Jachmann's molar aging criteria. The technique proved easy and fast and produced useful imprints in 90% of the cases. We found our age estimates, based on physical appearance, made prior to immobilizations were relatively accurate, with 75% within ±3 years and 95% within ±5 years from the age indicated from molar evaluation. When re‐collaring the same individuals in 2–3 years, new moulds will be made to compare a known time period with the degree of tooth wear. This will provide verification of Laws age estimates from free‐ranging elephants.     

EM-REML estimation of covariance parameters in Gaussian mixed models for longitudinal data analysis

This paper presents procedures for implementing the EM algorithm to compute REML estimates of variance covariance components in Gaussian mixed models for longitudinal data analysis. The class of models considered includes random coefficient factors, stationary time processes and measurement errors. The EM algorithm allows separation of the computations pertaining to parameters involved in the random coefficient factors from those pertaining to the time processes and errors. The procedures are illustrated with Pothoff and Roy''s data example on growth measurements taken on 11 girls and 16 boys at four ages. Several variants and extensions are discussed.     

Capture method affects survival estimates and subsequent interpretation of ecological covariates for a long‐lived cervid

Understanding what variables affect ungulate neonate survival is imperative to successful conservation and management of the species. Predation is commonly cited as a cause‐specific source of mortality, and ecological covariates often influence neonate survival. However, variation in survival estimates related to capture methodology has been documented with opportunistically captured neonates generally displaying greater survival than those captured via aid of vaginal implant transmitters (VITs), likely because of increased left truncation observed in the opportunistically captured datasets. Our goal was to assess whether 3‐ and 6‐month survival estimates varied by capture method while simultaneously assessing whether capture method affected model selection and interpretation of ecological covariates for white‐tailed deer neonates captured from three study sites from 2014 to 2015 in North Dakota and South Dakota, USA. We found survival varied by capture method for 3‐month neonate survival with opportunistically captured neonates displaying up to 26% greater survival than their counterparts captured via VITs; however, this relationship was not present for 6‐month survival. We also found model selection and subsequent interpretation of ecological covariates varied when analyzing datasets comprised of neonates captured via VITs, neonates captured opportunistically, and all neonates combined regardless of capture method. When interpreting results from our VIT‐only analysis for 3‐month survival, we found survival varied by three time intervals and was lowest in the first two weeks of life. Capture method did not affect 6‐month survival, which was most influenced by total precipitation occurring during 3 – 8 weeks of a neonate''s life and percent canopy cover found at a neonate''s capture site. Our results support previous research that capture method must be accounted for when deriving survival estimates for ungulate neonates as it can impact derived estimates and subsequent interpretation of results.