wisepair: a computer program for individual matching in genetic tracking studies

Individual‐based data sets tracking organisms over space and time are fundamental to answering broad questions in ecology and evolution. A ‘permanent’ genetic tag circumvents a need to invasively mark or tag animals, especially if there are little phenotypic differences among individuals. However, genetic tracking of individuals does not come without its limits; correctly matching genotypes and error rates associated with laboratory work can make it difficult to parse out matched individuals. In addition, defining a sampling design that effectively matches individuals in the wild can be a challenge for researchers. Here, we combine the two objectives of defining sampling design and reducing genotyping error through an efficient Python‐based computer‐modelling program, wisepair . We describe the methods used to develop the computer program and assess its effectiveness through three empirical data sets, with and without reference genotypes. Our results show that wisepair outperformed similar genotype matching programs using previously published from reference genotype data of diurnal poison frogs (Allobates femoralis) and without‐reference (faecal) genotype sample data sets of harbour seals (Phoca vitulina) and Eurasian otters (Lutra lutra). In addition, due to limited sampling effort in the harbour seal data, we present optimal sampling designs for future projects. wisepair allows for minimal sacrifice in the available methods as it incorporates sample rerun error data, allelic pairwise comparisons and probabilistic simulations to determine matching thresholds. Our program is the lone tool available to researchers to define parameters a priori for genetic tracking studies.     

A 34K SNP genotyping array for Populus trichocarpa: Design,application to the study of natural populations and transferability to other Populus species

Genetic mapping of quantitative traits requires genotypic data for large numbers of markers in many individuals. For such studies, the use of large single nucleotide polymorphism (SNP) genotyping arrays still offers the most cost‐effective solution. Herein we report on the design and performance of a SNP genotyping array for Populus trichocarpa (black cottonwood). This genotyping array was designed with SNPs pre‐ascertained in 34 wild accessions covering most of the species latitudinal range. We adopted a candidate gene approach to the array design that resulted in the selection of 34 131 SNPs, the majority of which are located in, or within 2 kb of, 3543 candidate genes. A subset of the SNPs on the array (539) was selected based on patterns of variation among the SNP discovery accessions. We show that more than 95% of the loci produce high quality genotypes and that the genotyping error rate for these is likely below 2%. We demonstrate that even among small numbers of samples (n = 10) from local populations over 84% of loci are polymorphic. We also tested the applicability of the array to other species in the genus and found that the number of polymorphic loci decreases rapidly with genetic distance, with the largest numbers detected in other species in section Tacamahaca. Finally, we provide evidence for the utility of the array to address evolutionary questions such as intraspecific studies of genetic differentiation, species assignment and the detection of natural hybrids.     

When can noninvasive samples provide sufficient information in conservation genetics studies?

Noninvasive sampling, of faeces and hair for example, has enabled many genetic studies of wildlife populations. However, two prevailing problems common to these studies are small sample sizes and high genotyping errors. The first problem stems from the difficulty in collecting noninvasive samples, particularly from populations of rare or elusive species, and the second is caused by the low quantity and quality of DNA extracted from a noninvasive sample. A common question is therefore whether noninvasive sampling provides sufficient information for the analyses commonly conducted in conservation genetics studies. Here, we conducted a simulation study to investigate the effect of small sample sizes and genotyping errors on the precision and accuracy of the most commonly estimated genetic parameters. Our results indicate that small sample sizes cause little bias in measures of expected heterozygosity, pairwise F_ST and population structure, but a large downward bias in estimates of allelic diversity. Allelic dropouts and false alleles had a much smaller effect than missing data, which effectively reduces sample size further. Overall, reasonable estimates of genetic variation and population subdivision are obtainable from noninvasive samples as long as error rates are kept below a frequency of 0.2. Similarly, unbiased estimates of population clustering can be made with genotyping error rates below 0.5 when the populations are highly differentiated. These results provide a useful guide for researchers faced with studying the conservation genetics of small, endangered populations from noninvasive samples.     

The effects of seaweed harvesting on fishes: a critique

Synopsis Black & Miller (1991) concluded that there was no large impact of an experimental harvest of rockweed,Ascophyllum nodosum, on fishes. A critique of their study demonstrates that this conclusion cannot be substantiated by their data because of sampling biases, errors in experimental design and low statistical power. Further, evidence is presented which supports the hypothesis that rockweed may provide an important nursery habitat for juvenile fishes.     

Genotyping strategy matters when analyzing hypervariable major histocompatibility complex‐Experience from a passerine bird

Genotyping of classical major histocompatibility complex (MHC) genes is challenging when they are hypervariable and occur in multiple copies. In this study, we used several different approaches to genotype the moderately variable MHC class I exon 3 (MHCIe3) and the highly polymorphic MHC class II exon 2 (MHCIIβe2) in the bluethroat (Luscinia svecica). Two family groups (eight individuals) were sequenced in replicates at both markers using Ion Torrent technology with both a single‐ and a dual‐indexed primer structure. Additionally, MHCIIβe2 was sequenced on Illumina MiSeq. Allele calling was conducted by modifications of the pipeline developed by Sommer et al. (BMC Genomics, 14, 2013, 542) and the software AmpliSAS. While the different genotyping strategies gave largely consistent results for MHCIe3, with a maximum of eight alleles per individual, MHCIIβe2 was remarkably complex with a maximum of 56 MHCIIβe2 alleles called for one individual. Each genotyping strategy detected on average 50%–82% of all MHCIIβe2 alleles per individual, but dropouts were largely allele‐specific and consistent within families for each strategy. The discrepancies among approaches indicate PCR biases caused by the platform‐specific primer tails. Further, AmpliSAS called fewer alleles than the modified Sommer pipeline. Our results demonstrate that allelic dropout is a significant problem when genotyping the hypervariable MHCIIβe2. As these genotyping errors are largely nonrandom and method‐specific, we caution against comparing genotypes across different genotyping strategies. Nevertheless, we conclude that high‐throughput approaches provide a major advance in the challenging task of genotyping hypervariable MHC loci, even though they may not reveal the complete allelic repertoire.     

Conditional Markov Chain Design for Accrual Clinical Trials

Randomization in a comparative experiment has, as one aim, the control of bias in the initial selection of experimental units. When the experiment is a clinical trial employing the accrual of patients, two additional aims are the control of admission bias and control of chronologic bias. This can be accomplished by using a method of randomization, such as the “biased coin design” of Efron, which sequentially forces balance. As an extension of Efron's design, this paper develops a class of conditional Markov chain designs. The detailed randomization employed utilizes the sequential imbalances in the treatment allocation as states in a Markov process. Through the use of appropriate transition probabilities, a range of possible designs can be attained. An additional objective of physical randomization is to provide a model for data analysis. Such a randomization theoretic analysis is presented for the current designs. In addition, Monte Carlo sampling results are given to support the proposed normal theory approximation to the exact randomization distribution.     

Unraveling hierarchical genetic structure in a marine metapopulation: A comparison of three high‐throughput genotyping approaches

Marine metapopulations often exhibit subtle population structure that can be difficult to detect. Given recent advances in high‐throughput sequencing, an emerging question is whether various genotyping approaches, in concert with improved sampling designs, will substantially improve our understanding of genetic structure in the sea. To address this question, we explored hierarchical patterns of structure in the coral reef fish Elacatinus lori using a high‐resolution approach with respect to both genetic and geographic sampling. Previously, we identified three putative E. lori populations within Belize using traditional genetic markers and sparse geographic sampling: barrier reef and Turneffe Atoll; Glover's Atoll; and Lighthouse Atoll. Here, we systematically sampled individuals at ~10 km intervals throughout these reefs (1,129 individuals from 35 sites) and sequenced all individuals at three sets of markers: 2,418 SNPs; 89 microsatellites; and 57 nonrepetitive nuclear loci. At broad spatial scales, the markers were consistent with each other and with previous findings. At finer spatial scales, there was new evidence of genetic substructure, but our three marker sets differed slightly in their ability to detect these patterns. Specifically, we found subtle structure between the barrier reef and Turneffe Atoll, with SNPs resolving this pattern most effectively. We also documented isolation by distance within the barrier reef. Sensitivity analyses revealed that the number of loci (and alleles) had a strong effect on the detection of structure for all three marker sets, particularly at small spatial scales. Taken together, these results illustrate empirically that high‐throughput genotyping data can elucidate subtle genetic structure at previously‐undetected scales in a dispersive marine fish.     

Random sampling does not exclude spatial dependence: The importance of neutral models for ecological hypothesis testing

Lájer (2007) notes that, to investigate phytosociological and ecological relationships, many authors apply traditional inferential tests to sets of relevés obtained by non-random methods. Unfortunately, this procedure does not provide reliable support for hypothesis testing because non-random sampling violates the assumptions of independence required by many parametric inferential tests. Instead, a random sampling scheme is recommended. Nonetheless, random sampling will not eliminate spatial autocorrelation. For instance, a classical law of geography holds that everything in a piece of (biotic) space is interrelated, but near objects are more related than distant ones. Because most ecological processes that shape community structure and species coexistence are spatially explicit, spatial autocorrelation is a vital part of almost all ecological data. This means that, independently from the underlying sampling design, ecological data are generally spatially autocorrelated, violating the assumption of independence that is generally required by traditional inferential tests. To overcome this drawback, randomization tests may be used. Such tests evaluate statistical significance based on empirical distributions generated from the sample and do not necessarily require data independence. However, as concerns hypothesis testing, randomization tests are not the universal remedy for ecologists, because the choice of inadequate null models can have significant effects on the ecological hypotheses tested. In this paper, I emphasize the need of developing null models for which the statistical assumptions match the underlying biological mechanisms.     

Development of SNP‐genotyping arrays in two shellfish species

Use of SNPs has been favoured due to their abundance in plant and animal genomes, accompanied by the falling cost and rising throughput capacity for detection and genotyping. Here, we present in vitro (obtained from targeted sequencing) and in silico discovery of SNPs, and the design of medium‐throughput genotyping arrays for two oyster species, the Pacific oyster, Crassostrea gigas, and European flat oyster, Ostrea edulis. Two sets of 384 SNP markers were designed for two Illumina GoldenGate arrays and genotyped on more than 1000 samples for each species. In each case, oyster samples were obtained from wild and selected populations and from three‐generation families segregating for traits of interest in aquaculture. The rate of successfully genotyped polymorphic SNPs was about 60% for each species. Effects of SNP origin and quality on genotyping success (Illumina functionality Score) were analysed and compared with other model and nonmodel species. Furthermore, a simulation was made based on a subset of the C. gigas SNP array with a minor allele frequency of 0.3 and typical crosses used in shellfish hatcheries. This simulation indicated that at least 150 markers were needed to perform an accurate parental assignment. Such panels might provide valuable tools to improve our understanding of the connectivity between wild (and selected) populations and could contribute to future selective breeding programmes.     

Propensity Score Matching in Randomized Clinical Trials

Summary Cluster randomization trials with relatively few clusters have been widely used in recent years for evaluation of health‐care strategies. On average, randomized treatment assignment achieves balance in both known and unknown confounding factors between treatment groups, however, in practice investigators can only introduce a small amount of stratification and cannot balance on all the important variables simultaneously. The limitation arises especially when there are many confounding variables in small studies. Such is the case in the INSTINCT trial designed to investigate the effectiveness of an education program in enhancing the tPA use in stroke patients. In this article, we introduce a new randomization design, the balance match weighted (BMW) design, which applies the optimal matching with constraints technique to a prospective randomized design and aims to minimize the mean squared error (MSE) of the treatment effect estimator. A simulation study shows that, under various confounding scenarios, the BMW design can yield substantial reductions in the MSE for the treatment effect estimator compared to a completely randomized or matched‐pair design. The BMW design is also compared with a model‐based approach adjusting for the estimated propensity score and Robins‐Mark‐Newey E‐estimation procedure in terms of efficiency and robustness of the treatment effect estimator. These investigations suggest that the BMW design is more robust and usually, although not always, more efficient than either of the approaches. The design is also seen to be robust against heterogeneous error. We illustrate these methods in proposing a design for the INSTINCT trial.     

Randomization Analysis of Incomplete Data in Some Basic Designs

The first two randomization moments of W = Tt.S.S./(Tt.S.S. + Error S.S.) are derived in case of (a) a completely randomized design with one observation missing using (I) Yates method of fitting constants and (II) the available observations only and in case of (b) a randomized block design in which one treatment is tested twice by mistake in a block and another treatment is not tested at all in that block using (I) all the available observations and (II) the data after deleting the observation due to the treatment tested by mistake in place of another treatment in a block. It is concluded that in each of the two cases for a completely randomized design, the F-test is unbiased whereas in each of the two cases for a randomized block design the F-test is in general not unbiased. However, if one treatment is tested twice by mistake in randomly chosen plots of some block, the F-test is unbiased in both cases for a randomized block design. For a completely randomized design the F-test is found to be a good approximation to the corresponding randomized test if N ≧ 80 in case (I) whereas in case (II) this approximation is of the same accuracy as that of case (I) if N ≧ 90. For a randomized block design it is seen that in case (I), the F-test provides a good approximation to the corresponding randomization test if v≧r≧7 and in case (II) this approximation is of the same accuracy if r≧6 and r(v-1)≧45. The analysis of several uniformity trial data shows that for values of “N” in the neighbourhood of 50 the agreement of the first two moments of “W” under the randomization theory and normal theory are reasonably close in all the four cases considered.     

Anonymous fecal sampling and NIRS studies of diet quality: Problem or opportunity?

Investigating the drivers of diet quality is a key issue in wildlife ecology and conservation. Fecal near infrared reflectance spectroscopy (f‐NIRS) is widely used to assess dietary quality since it allows for noninvasive, rapid, and low‐cost analysis of nutrients. Samples for f‐NIRS can be collected and analyzed with or without knowledge of animal identities. While anonymous sampling allows to reduce the costs of individual identification, as it neither requires physical captures nor DNA genotyping, it neglects the potential effects of individual variation. As a consequence, regression models fitted to investigate the drivers of dietary quality may suffer severe issues of pseudoreplication. I investigated the relationship between crude protein and ecological predictors at different time periods to assess the level of individual heterogeneity in diet quality of 22 marked chamois Rupicapra rupicapra monitored over 2 years. Models with and without individual grouping effect were fitted to simulate identifiable and anonymous fecal sampling, and model estimates were compared to evaluate the consequences of anonymizing data collection and analysis. The variance explained by the individual random effect and the value of diet repeatability varied with seasons and peaked in winter. Despite the occurrence of individual variation in dietary quality, ecological parameter estimates under identifiable or anonymous sampling were consistently similar. This study suggests that anonymous fecal sampling may provide robust estimates of the relationship between dietary quality and ecological correlates. However, since the level of individual heterogeneity in dietary quality may vary with species‐ or study‐specific features, inconsequential pseudoreplication should not be assumed in other taxa. When individual differences are known to be inconsequential, anonymous sampling allows to optimize the trade‐off between sampling intensity and representativeness. When pseudoreplication is consequential, however, no conclusive remedy exists to effectively resolve nonindependence.     

Distance models as a tool for modelling detection probability and density of native bumblebees

Effective monitoring of native bee populations requires accurate estimates of population size and relative abundance among habitats. Current bee survey methods, such as netting or pan trapping, may be adequate for a variety of study objectives but are limited by a failure to account for imperfect detection. Biases due to imperfect detection could result in inaccurate abundance estimates or erroneous insights about the response of bees to different environments. To gauge the potential biases of currently employed survey methods, we compared abundance estimates of bumblebees (Bombus spp.) derived from hierarchical distance sampling models (HDS) to bumblebee counts collected from fixed‐area net surveys (“net counts”) and fixed‐width transect counts (“transect counts”) at 47 early‐successional forest patches in Pennsylvania. Our HDS models indicated that detection probabilities of Bombus spp. were imperfect and varied with survey‐ and site‐covariates. Despite being conspicuous, Bombus spp. were not reliably detected beyond 5 m. Habitat associations of Bombus spp. density were similar across methods, but the strength of association with shrub cover differed between HDS and net counts. Additionally, net counts suggested sites with more grass hosted higher Bombus spp. densities whereas HDS suggested that grass cover was associated with higher detection probability but not Bombus spp. density. Density estimates generated from net counts and transect counts were 80%–89% lower than estimates generated from distance sampling. Our findings suggest that distance modelling provides a reliable method to assess Bombus spp. density and habitat associations, while accounting for imperfect detection caused by distance from observer, vegetation structure, and survey covariates. However, detection/non‐detection data collected via point‐counts, line‐transects and distance sampling for Bombus spp. are unlikely to yield species‐specific density estimates unless individuals can be identified by sight, without capture. Our results will be useful for informing the design of monitoring programs for Bombus spp. and other pollinators.     

Estimating occupancy dynamics for large‐scale monitoring networks: amphibian breeding occupancy across protected areas in the northeast United States

Regional monitoring strategies frequently employ a nested sampling design where a finite set of study areas from throughout a region are selected and intensive sampling occurs within a subset of sites within the individual study areas. This sampling protocol naturally lends itself to a hierarchical analysis to account for dependence among subsamples. Implementing such an analysis using a classic likelihood framework is computationally challenging when accounting for detection errors in species occurrence models. Bayesian methods offer an alternative approach for fitting models that readily allows for spatial structure to be incorporated. We demonstrate a general approach for estimating occupancy when data come from a nested sampling design. We analyzed data from a regional monitoring program of wood frogs (Lithobates sylvaticus) and spotted salamanders (Ambystoma maculatum) in vernal pools using static and dynamic occupancy models. We analyzed observations from 2004 to 2013 that were collected within 14 protected areas located throughout the northeast United States. We use the data set to estimate trends in occupancy at both the regional and individual protected area levels. We show that occupancy at the regional level was relatively stable for both species. However, substantial variation occurred among study areas, with some populations declining and some increasing for both species. In addition, When the hierarchical study design is not accounted for, one would conclude stronger support for latitudinal gradient in trends than when using our approach that accounts for the nested design. In contrast to the model that does not account for nesting, the nested model did not include an effect of latitude in the 95% credible interval. These results shed light on the range‐level population status of these pond‐breeding amphibians, and our approach provides a framework that can be used to examine drivers of local and regional occurrence dynamics.     

Batch effects in a multiyear sequencing study: False biological trends due to changes in read lengths

High‐throughput sequencing is a powerful tool, but suffers biases and errors that must be accounted for to prevent false biological conclusions. Such errors include batch effects; technical errors only present in subsets of data due to procedural changes within a study. If overlooked and multiple batches of data are combined, spurious biological signals can arise, particularly if batches of data are correlated with biological variables. Batch effects can be minimized through randomization of sample groups across batches. However, in long‐term or multiyear studies where data are added incrementally, full randomization is impossible, and batch effects may be a common feature. Here, we present a case study where false signals of selection were detected due to a batch effect in a multiyear study of Alpine ibex (Capra ibex). The batch effect arose because sequencing read length changed over the course of the project and populations were added incrementally to the study, resulting in nonrandom distributions of populations across read lengths. The differences in read length caused small misalignments in a subset of the data, leading to false variant alleles and thus false SNPs. Pronounced allele frequency differences between populations arose at these SNPs because of the correlation between read length and population. This created highly statistically significant, but biologically spurious, signals of selection and false associations between allele frequencies and the environment. We highlight the risk of batch effects and discuss strategies to reduce the impacts of batch effects in multiyear high‐throughput sequencing studies.     

State‐dependent cooperation in burying beetles: parents adjust their contribution towards care based on both their own and their partner's size

Handicapping experiments on species with biparental care show that a focal parent increases its contribution when its partner is handicapped. Such results are interpreted as evidence for negotiation, whereby each parent adjusts its amount of care to that of its partner. However, it is currently unclear whether the focal parent responds to a change in its handicapped partner's behaviour or state. To address this gap, we conducted an experiment on the burying beetle Nicrophorus vespilloides where we first generated different‐sized males and females by varying the duration of larval development. We then used a 2 × 2 factorial design in which a small or large male was paired with a small or large female. Small females provided less direct care (food provisioning and interactions with larvae) than large females, and both males and females provided less direct care when paired with a small partner. Thus, the focal parent adjusted its contribution towards care based on both its own state and that of its partner. There was also evidence for negotiation between the two parents as the focal parent adjusted its contribution based on the amount of care by its partner. However, there was no evidence that negotiation accounted for how the focal parent responded to its partner's size. Our results have important implications for our understanding of biparental cooperation as they show that each parent adjusts its contribution not only based on the amount of care provided by its partner but also based on its own state and its partner's state.     

Blood parasites shape extreme major histocompatibility complex diversity in a migratory passerine

Pathogens are one of the main forces driving the evolution and maintenance of the highly polymorphic genes of the vertebrate major histocompatibility complex (MHC). Although MHC proteins are crucial in pathogen recognition, it is still poorly understood how pathogen‐mediated selection promotes and maintains MHC diversity, and especially so in host species with highly duplicated MHC genes. Sedge warblers (Acrocephalus schoenobaenus) have highly duplicated MHC genes, and using data from high‐throughput MHC genotyping, we were able to investigate to what extent avian malaria parasites explain temporal MHC class I supertype fluctuations in a long‐term study population. We investigated infection status and infection intensities of two different strains of Haemoproteus, that is avian malaria parasites that are known to have significant fitness consequences in sedge warblers. We found that prevalence of avian malaria in carriers of specific MHC class I supertypes was a significant predictor of their frequency changes between years. This finding suggests that avian malaria infections partly drive the temporal fluctuations of the MHC class I supertypes. Furthermore, we found that individuals with a large number of different supertypes had higher resistance to avian malaria, but there was no evidence for an optimal MHC class I diversity. Thus, the two studied malaria parasite strains appear to select for a high MHC class I supertype diversity. Such selection may explain the maintenance of the extremely high number of MHC class I gene copies in sedge warblers and possibly also in other passerines where avian malaria is a common disease.     

Effect of environmental factors on the abundance variations of two native rodents in agricultural systems of Buenos Aires,Argentina

Our aim was to assess the effect of environmental factors on short temporal abundance variations of the two most abundant native rodents of agricultural agroecosystems, Akodon azarae and Calomys laucha. We conducted a 3‐year longitudinal sampling of rodents, and recorded meteorological data such as temperature and precipitation, predation rate by Leopardus geoffroyi, Tyto furcata and Athene cunicularia, vegetation cover and height, characteristics of cropfields and their borders. The effect of these factors on rodent abundance was evaluated through generalized linear mixed models. Abundance variations of both rodent species were explained by characteristics of both cropfields and their borders. At the studied temporal scale, meteorological variables did not have a direct effect on abundance variations, but probably influenced through vegetation characteristics and were expressed in seasonal variations. For A. azarae there was also an effect of predation by L. geoffroyi (positive) and T. furcata (negative), while predation by A. cunicularia did not contribute to explain abundance variations of any species.     

Evaluation of genotyping concordance for commercial bovine SNP arrays using quality‐assurance samples

SNP arrays are widely used in genetic research and agricultural genomics applications, and the quality of SNP genotyping data is of paramount importance. In the present study, SNP genotyping concordance and discordance were evaluated for commercial bovine SNP arrays based on two types of quality assurance (QA) samples provided by Neogen GeneSeek. The genotyping discordance rates (GDRs) between chips were on average between 0.06% and 0.37% based on the QA type I data and between 0.05% and 0.15% based on the QA type II data. The average genotyping error rate (GER) pertaining to single SNP chips, based on the QA type II data, varied between 0.02% and 0.08% per SNP and between 0.01% and 0.06% per sample. These results indicate that genotyping concordance rate was high (i.e. from 99.63% to 99.99%). Nevertheless, mitochondrial and Y chromosome SNPs had considerably elevated GDRs and GERs compared to the SNPs on the 29 autosomes and X chromosome. The majority of genotyping errors resulted from single allotyping errors, which also included the opposite instances for allele ‘dropout’ (i.e. from AB to AA or BB). Simultaneous allotyping errors on both alleles (e.g. mistaking AA for BB or vice versa) were relatively rare. Finally, a list of SNPs with a GER greater than 1% is provided. Interpretation of association effects of these SNPs, for example in genome‐wide association studies, needs to be taken with caution. The genotyping concordance information needs to be considered in the optimal design of future bovine SNP arrays.