Can population growth rates vary with the spatial scale at which they are measured?

1.  The ratio of successive population censuses is often assumed to reflect population growth rates. We identify three simple potential sources of bias in the estimation of population growth rates that relate to either the total number of censused individuals or the spatial areas over which censuses are conducted.
2.  The commonly used method of adding a constant to time series data to avoid problems caused by division by zero can lead to underestimation of growth rates at low densities in increasing populations.
3.  Variances associated with density estimates can lead to positive bias in estimation of growth rates when populations are distributed in ephemeral patches. The spatial variance and spatio-temporal covariance in bank vole census data suggest that this bias could be severe when small trapping grids are used. Use of logged estimators of growth rate avoids this problem.
4.  Using census data from non-randomly placed trapping grids that are smaller than twice the maximum range of natal dispersal to estimate population growth rates can lead to negatively biased estimates, particularly at low population densities.
5.  These three sources of bias are evaluated as explanations for scale-dependent changes in the estimates of growth rates identified in populations of snowshoe hare ( Lepus americanus ), bank voles ( Clethrionomys glareolus ) and lemmings ( Lemmus lemmus ).     

Explaining Spatial Variation in the Recording Effort of Citizen Science Data across Multiple Taxa

The collation of citizen science data in open-access biodiversity databases makes temporally and spatially extensive species’ observation data available to a wide range of users. Such data are an invaluable resource but contain inherent limitations, such as sampling bias in favour of recorder distribution, lack of survey effort assessment, and lack of coverage of the distribution of all organisms. Any technical assessment, monitoring program or scientific research applying citizen science data should therefore include an evaluation of the uncertainty of its results. We use ‘ignorance’ scores, i.e. spatially explicit indices of sampling bias across a study region, to further understand spatial patterns of observation behaviour for 13 reference taxonomic groups. The data is based on voluntary observations made in Sweden between 2000 and 2014. We compared the effect of six geographical variables (elevation, steepness, population density, log population density, road density and footpath density) on the ignorance scores of each group. We found substantial variation among taxonomic groups in the relative importance of different geographic variables for explaining ignorance scores. In general, road access and logged population density were consistently important variables explaining bias in sampling effort, indicating that access at a landscape-scale facilitates voluntary reporting by citizen scientists. Also, small increases in population density can produce a substantial reduction in ignorance score. However the between-taxa variation in the importance of geographic variables for explaining ignorance scores demonstrated that different taxa suffer from different spatial biases. We suggest that conservationists and researchers should use ignorance scores to acknowledge uncertainty in their analyses and conclusions, because they may simultaneously include many correlated variables that are difficult to disentangle.     

Role of geographic information systems in birth defects surveillance and research

BACKGROUND: With the significant advancement of geographic information systems (GIS), mapping and evaluating the spatial distribution of health events has become easier. We examine the role of GIS in birth defects surveillance and research. METHODS: We briefly describe the geocoding process and potential problems in accuracy of the obtained geocodes, and some of the capabilities and limitations of GIS. We illustrate how GIS has been applied using the Metropolitan Atlanta Congenital Defects Program geocoded dataset. We provide some comments on potential data quality and confidentiality issues with birth defects in relation to GIS. RESULTS: It is desirable to geocode addresses using a multistrategy approach to achieve a high-quality and accurate GIS dataset. Beyond the basic but important function of mapping, sophisticated statistical approaches and software are available to analyze the spatial or spatial-temporal occurrence of birth defects, alone or in association with environmental hazards, and to present this information without compromising the confidentiality of the subjects. CONCLUSIONS: We recommend a broad and systematic use of GIS in birth defects spatial surveillance and research.     

A Maximum-Likelihood Method to Correct for Allelic Dropout in Microsatellite Data with No Replicate Genotypes

Allelic dropout is a commonly observed source of missing data in microsatellite genotypes, in which one or both allelic copies at a locus fail to be amplified by the polymerase chain reaction. Especially for samples with poor DNA quality, this problem causes a downward bias in estimates of observed heterozygosity and an upward bias in estimates of inbreeding, owing to mistaken classifications of heterozygotes as homozygotes when one of the two copies drops out. One general approach for avoiding allelic dropout involves repeated genotyping of homozygous loci to minimize the effects of experimental error. Existing computational alternatives often require replicate genotyping as well. These approaches, however, are costly and are suitable only when enough DNA is available for repeated genotyping. In this study, we propose a maximum-likelihood approach together with an expectation-maximization algorithm to jointly estimate allelic dropout rates and allele frequencies when only one set of nonreplicated genotypes is available. Our method considers estimates of allelic dropout caused by both sample-specific factors and locus-specific factors, and it allows for deviation from Hardy–Weinberg equilibrium owing to inbreeding. Using the estimated parameters, we correct the bias in the estimation of observed heterozygosity through the use of multiple imputations of alleles in cases where dropout might have occurred. With simulated data, we show that our method can (1) effectively reproduce patterns of missing data and heterozygosity observed in real data; (2) correctly estimate model parameters, including sample-specific dropout rates, locus-specific dropout rates, and the inbreeding coefficient; and (3) successfully correct the downward bias in estimating the observed heterozygosity. We find that our method is fairly robust to violations of model assumptions caused by population structure and by genotyping errors from sources other than allelic dropout. Because the data sets imputed under our model can be investigated in additional subsequent analyses, our method will be useful for preparing data for applications in diverse contexts in population genetics and molecular ecology.     

景观生态学中空间数据的模拟和显示方法概述

为了准确描述连续数据在景观中变化的规律,介绍了常用的7种生态学空间数据模拟方法和4种空间数据显示方法的基本原理和方法,以及不同的空间数据取样方法的特点和适用范围,并初步探讨了影响生态学空间数据表达方法的主要因素。     

mvmapper: Interactive spatial mapping of genetic structures

Characterizing genetic structure across geographic space is a fundamental challenge in population genetics. Multivariate statistical analyses are powerful tools for summarizing genetic variability, but geographic information and accompanying metadata are not always easily integrated into these methods in a user‐friendly fashion. Here, we present a deployable Python‐based web‐tool, mvmapper , for visualizing and exploring results of multivariate analyses in geographic space. This tool can be used to map results of virtually any multivariate analysis of georeferenced data, and routines for exporting results from a number of standard methods have been integrated in the R package adegenet , including principal components analysis (PCA), spatial PCA, discriminant analysis of principal components, principal coordinates analysis, nonmetric dimensional scaling and correspondence analysis. mvmapper 's greatest strength is facilitating dynamic and interactive exploration of the statistical and geographic frameworks side by side, a task that is difficult and time‐consuming with currently available tools. Source code and deployment instructions, as well as a link to a hosted instance of mvmapper , can be found at https://popphylotools.github.io/mvMapper/ .     

Density estimation in live-trapping studies

Unbiased estimation of population density is a major and unsolved problem in animal trapping studies. This paper describes a new and general method for estimating density from closed-population capture–recapture data. Many estimators exist for the size (N) and mean capture probability ( p ) of a closed population. These statistics suffer from an unknown bias due to edge effect that varies with trap layout and home range size. The mean distance between successive captures of an individual (     ) provides information on the scale of individual movements, but is itself a function of trap spacing and grid size. Our aim is to define and estimate parameters that do not depend on the trap layout. In the new method, simulation and inverse prediction are used to estimate jointly the population density (D) and two parameters of individual capture probability, magnitude (g₀) and spatial scale (σ), from the information in     , p and     . The method uses any configuration of traps (e.g. grid, web or line) and any choice of closed-population estimator. It is assumed that home ranges have a stationary distribution in two dimensions, and that capture events may be simulated as the outcome of competing Poisson processes in time. The method is applied to simulated and field data. The estimator appears unusually robust and free from bias.     

Decomposed pairwise regression analysis of genetic and geographic distances reveals a metapopulation structure of stream-dwelling Dolly Varden charr

Isolation by distance is usually tested by the correlation of genetic and geographic distances separating all pairwise populations' combinations. However, this method can be significantly biased by only a few highly diverged populations and lose the information of individual population. To detect outlier populations and investigate the relative strengths of gene flow and genetic drift for each population, we propose a decomposed pairwise regression analysis. This analysis was applied to the well-described one-dimensional stepping-stone system of stream-dwelling Dolly Varden charr ( Salvelinus malma ). When genetic and geographic distances were plotted for all pairs of 17 tributary populations, the correlation was significant but weak ( r ² = 0.184). Seven outlier populations were determined based on the systematic bias of the regression residuals, followed by Akaike's information criteria. The best model, 10 populations included, showed a strong pattern of isolation by distance ( r ² = 0.758), suggesting equilibrium between gene flow and genetic drift in these populations. Each outlier population was also analysed by plotting pairwise genetic and geographic distances against the 10 nonoutlier populations, and categorized into one of the three patterns: strong genetic drift, genetic drift with a limited gene flow and a high level of gene flow. These classifications were generally consistent with a priori predictions for each population (physical barrier, population size, anthropogenic impacts). Combined the genetic analysis with field observations, Dolly Varden in this river appeared to form a mainland-island or source-sink metapopulation structure. The generality of the method will merit many types of spatial genetic analyses.     

Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity

Leveraging information in aggregate data from external sources to improve estimation efficiency and prediction accuracy with smaller scale studies has drawn a great deal of attention in recent years. Yet, conventional methods often either ignore uncertainty in the external information or fail to account for the heterogeneity between internal and external studies. This article proposes an empirical likelihood-based framework to improve the estimation of the semiparametric transformation models by incorporating information about the t-year subgroup survival probability from external sources. The proposed estimation procedure incorporates an additional likelihood component to account for uncertainty in the external information and employs a density ratio model to characterize population heterogeneity. We establish the consistency and asymptotic normality of the proposed estimator and show that it is more efficient than the conventional pseudopartial likelihood estimator without combining information. Simulation studies show that the proposed estimator yields little bias and outperforms the conventional approach even in the presence of information uncertainty and heterogeneity. The proposed methodologies are illustrated with an analysis of a pancreatic cancer study.     

Effect of different sampling strategies for a single geographic region in Yemen on standard genetic analyses of mitochondrial DNA sequence data

Collection of biological samples is the foundation of genetic studies ranging from estimation of genetic diversity to reconstruction of population history. Sample collections are intended to accurately represent the genetic, biological, ecological, cultural, geographic, and/or linguistic diversity of a particular region or population by providing a small, but representative, set of samples. In this study, we analyze human mitochondrial DNA variation in samples collected using four different sampling strategies to represent the same geographic region. Specifically, samples were collected from a village, a rural area, a regional clinic, and a national university in the governorate of Dhamar in Yemen. All samples were assayed for mitochondrial hypervariable region I DNA sequence variation and data were subjected to standard molecular genetic analyses. Our results suggest that analyses in which individual DNA sequences are explicitly compared or evaluated, e.g. phylogenetic and network analyses, may be more sensitive to sample collection design than analyses in which data are averaged across individuals or are analyzed more indirectly, e.g. summary statistics.     

The potential of a population genomics approach to analyse geographic mosaics of plant--insect coevolution

A central issue in the evolutionary ecology of species interactions is coevolution, which involves the reciprocal selection between individuals of interacting species. Understanding the importance of coevolution in shaping species interactions requires the consideration of spatial variation in their strength. This is exactly what the, recently developed, geographic mosaic theory of coevolution addresses. Another major development in the study of population ecology is the introduction of the population genomics approach in this field of research. This approach addresses spatial processes through molecular methods. It is of particular interest that population genomics is especially applicable to natural populations of non-model species. We describe how population genomics can be used in the context of the geographic mosaic of coevolution, specifically to identify coevolutionary hot-spots, and to attribute genetic variation found at specific loci to processes of selection versus trait remixing. The proposed integration of the population genomics approach with the conceptual framework of the geographic mosaic of coevolution is illustrated with a few selected, particularly demonstrative, examples from the realm of insect--plant interactions.     

Is more data always better? A simulation study of benefits and limitations of integrated distribution models

Species distribution models are popular and widely applied ecological tools. Recent increases in data availability have led to opportunities and challenges for species distribution modelling. Each data source has different qualities, determined by how it was collected. As several data sources can inform on a single species, ecologists have often analysed just one of the data sources, but this loses information, as some data sources are discarded. Integrated distribution models (IDMs) were developed to enable inclusion of multiple datasets in a single model, whilst accounting for different data collection protocols. This is advantageous because it allows efficient use of all data available, can improve estimation and account for biases in data collection. What is not yet known is when integrating different data sources does not bring advantages. Here, for the first time, we explore the potential limits of IDMs using a simulation study integrating a spatially biased, opportunistic, presence-only dataset with a structured, presence–absence dataset. We explore four scenarios based on real ecological problems; small sample sizes, low levels of detection probability, correlations between covariates and a lack of knowledge of the drivers of bias in data collection. For each scenario we ask; do we see improvements in parameter estimation or the accuracy of spatial pattern prediction in the IDM versus modelling either data source alone? We found integration alone was unable to correct for spatial bias in presence-only data. Including a covariate to explain bias or adding a flexible spatial term improved IDM performance beyond single dataset models, with the models including a flexible spatial term producing the most accurate and robust estimates. Increasing the sample size of presence–absence data and having no correlated covariates also improved estimation. These results demonstrate under which conditions integrated models provide benefits over modelling single data sources.     

TESS3: fast inference of spatial population structure and genome scans for selection

Geography and landscape are important determinants of genetic variation in natural populations, and several ancestry estimation methods have been proposed to investigate population structure using genetic and geographic data simultaneously. Those approaches are often based on computer‐intensive stochastic simulations and do not scale with the dimensions of the data sets generated by high‐throughput sequencing technologies. There is a growing demand for faster algorithms able to analyse genomewide patterns of population genetic variation in their geographic context. In this study, we present TESS3 , a major update of the spatial ancestry estimation program TESS . By combining matrix factorization and spatial statistical methods, TESS3 provides estimates of ancestry coefficients with accuracy comparable to TESS and with run‐times much faster than the Bayesian version. In addition, the TESS3 program can be used to perform genome scans for selection, and separate adaptive from nonadaptive genetic variation using ancestral allele frequency differentiation tests. The main features of TESS3 are illustrated using simulated data and analysing genomic data from European lines of the plant species Arabidopsis thaliana.     

Bias in estimating association parameters for longitudinal binary responses with drop-outs

This paper considers the impact of bias in the estimation of the association parameters for longitudinal binary responses when there are drop-outs. A number of different estimating equation approaches are considered for the case where drop-out cannot be assumed to be a completely random process. In particular, standard generalized estimating equations (GEE), GEE based on conditional residuals, GEE based on multivariate normal estimating equations for the covariance matrix, and second-order estimating equations (GEE2) are examined. These different GEE estimators are compared in terms of finite sample and asymptotic bias under a variety of drop-out processes. Finally, the relationship between bias in the estimation of the association parameters and bias in the estimation of the mean parameters is explored.     

The ‘rotiferologist’ effect and other global correlates of species richness in monogonont rotifers

Global biodiversity patterns are often driven by different environmental variables at different scales. However, it is still controversial whether there are general trends, whether similar processes are responsible for similar patterns, and/or whether confounding effects such as sampling bias can produce misleading results. Our aim is twofold: 1) assessing the global correlates of diversity in a group of microscopic animals little analysed so far, and 2) inferring the influence of sampling intensity on biodiversity analyses. As a case study, we choose rotifers, because of their high potential for dispersal across the globe. We assembled and analysed a new worldwide dataset of records of monogonont rotifers, a group of microscopic aquatic animals, from 1960 to 1992. Using spatially explicit models, we assessed whether the diversity patterns conformed to those commonly obtained for larger organisms, and whether they still held true after controlling for sampling intensity, variations in area, and spatial structure in the data. Our results are in part analogous to those commonly obtained for macroorganisms (habitat heterogeneity and precipitation emerge as the main global correlates), but show some divergence (potential absence of a latitudinal gradient and of a large‐scale correlation with human population). Moreover, the effect of sampling effort is remarkable, accounting for >50% of the variability; this strong effect may mask other patterns such as latitudinal gradients. Our study points out that sampling bias should be carefully considered when drawing conclusions from large‐scale analyses, and calls for further faunistic work on microorganisms in all regions of the world to better understand the generality of the processes driving global patterns in biodiversity.     

Geographic Assessment of Present Protected Areas in Japan for Representativeness of Forest Communities

Most assessments of present protected areas have focused on which features are or are not represented and to what extent, but they have not considered the environmental gradients and the geographic context within each biodiversity feature under a conservation network. We examined how protected areas are distributed with respect to the distribution of six forest community types. Three analyses were applied to the dataset in the Red Data Book of Plant Communities in Japan and the related survey: (1) recursive partitioning was used to contrast environmental factors of conserved communities with nonconserved communities; (2) point pattern analysis, based on Ripley's K function, was used to describe the spatial pattern of conserved communities; and (3) the spatial scan statistic was used to detect spatial representation gaps. Overall, environmental bias was greatest in relation to soil and topography. However, the results of point pattern analysis showed that the spatial pattern of conserved communities did not depend entirely on the distribution of environmental factors. Four types of gaps in spatial representation were detected by the spatial scan statistic, irrespective of environmental bias. These results showed that although a community type might be well protected in total or along the environmental gradients, conserved communities might not capture the full range of geographic context. To ensure appropriate representation or protection, it is important that conservation planning for protected areas take into account both the environmental gradients and the geographic context within each biodiversity feature.     

New methods and technologies for regional-scale abundance estimation of land-breeding marine animals: application to Adélie penguin populations in East Antarctica

Land-breeding marine animals such as penguins, flying seabirds and pinnipeds are important components of marine ecosystems, and their abundance has been used extensively as an indication of ecosystem status and change. Until recently, many efforts to measure and monitor abundance of these species’ groups have focussed on smaller populations and spatial scales, and efforts to account for perception bias and availability bias have been variable and often ad hoc. We describe a suite of new methods, technologies and estimation procedures for cost-effective, large-scale abundance estimation within a general estimation framework and illustrate their application on large Adélie penguin populations in two regions of East Antarctica. The methods include photographic sample counts, automated cameras for collecting availability data, and bootstrap estimation to adjust counts for the sampling fraction, perception bias, and availability bias, and are applicable for a range of land-breeding marine species. The methods will improve our ability to obtain population data over large spatial and population scales within tight logistic, environmental and time constraints. This first application of the methods has given new insights into the biases and uncertainties in abundance estimation for penguins and other land-breeding marine species. We provide guidelines for applying the methods in future surveys.     

Nonparametric correction for covariate measurement error in a stratified Cox model

Stratified Cox regression models with large number of strata and small stratum size are useful in many settings, including matched case-control family studies. In the presence of measurement error in covariates and a large number of strata, we show that extensions of existing methods fail either to reduce the bias or to correct the bias under nonsymmetric distributions of the true covariate or the error term. We propose a nonparametric correction method for the estimation of regression coefficients, and show that the estimators are asymptotically consistent for the true parameters. Small sample properties are evaluated in a simulation study. The method is illustrated with an analysis of Framingham data.