Relative accuracy of three common methods of parentage analysis in natural populations

Parentage studies and family reconstructions have become increasingly popular for investigating a range of evolutionary, ecological and behavioural processes in natural populations. However, a number of different assignment methods have emerged in common use and the accuracy of each may differ in relation to the number of loci examined, allelic diversity, incomplete sampling of all candidate parents and the presence of genotyping errors. Here, we examine how these factors affect the accuracy of three popular parentage inference methods (colony , famoz and an exclusion‐Bayes’ theorem approach by Christie (Molecular Ecology Resources, 2010a, 10, 115) to resolve true parent–offspring pairs using simulated data. Our findings demonstrate that accuracy increases with the number and diversity of loci. These were clearly the most important factors in obtaining accurate assignments explaining 75–90% of variance in overall accuracy across 60 simulated scenarios. Furthermore, the proportion of candidate parents sampled had a small but significant impact on the susceptibility of each method to either false‐positive or false‐negative assignments. Within the range of values simulated, colony outperformed FaMoz, which outperformed the exclusion‐Bayes’ theorem method. However, with 20 or more highly polymorphic loci, all methods could be applied with confidence. Our results show that for parentage inference in natural populations, careful consideration of the number and quality of markers will increase the accuracy of assignments and mitigate the effects of incomplete sampling of parental populations.     

APIS: An auto‐adaptive parentage inference software that tolerates missing parents

In the context of parentage assignment using genomic markers, key issues are genotyping errors and an absence of parent genotypes because of sampling, traceability or genotyping problems. Most likelihood‐based parentage assignment software programs require a priori estimates of genotyping errors and the proportion of missing parents to set up meaningful assignment decision rules. We present here the R package APIS, which can assign offspring to their parents without any prior information other than the offspring and parental genotypes, and a user‐defined, acceptable error rate among assigned offspring. Assignment decision rules use the distributions of average Mendelian transmission probabilities, which enable estimates of the proportion of offspring with missing parental genotypes. APIS has been compared to other software (CERVUS, VITASSIGN), on a real European seabass (Dicentrarchus labrax) single nucleotide polymorphism data set. The type I error rate (false positives) was lower with APIS than with other software, especially when parental genotypes were missing, but the true positive rate was also lower, except when the theoretical exclusion power reached 0.99999. In general, APIS provided assignments that satisfied the user‐set acceptable error rate of 1% or 5%, even when tested on simulated data with high genotyping error rates (1% or 3%) and up to 50% missing sires. Because it uses the observed distribution of Mendelian transmission probabilities, APIS is best suited to assigning parentage when numerous offspring (>200) are genotyped. We have demonstrated that APIS is an easy‐to‐use and reliable software for parentage assignment, even when up to 50% of sires are missing.     

Reliability assessment of null allele detection: inconsistencies between and within different methods

Microsatellite loci are widely used in population genetic studies, but the presence of null alleles may lead to biased results. Here, we assessed five methods that indirectly detect null alleles and found large inconsistencies among them. Our analysis was based on 20 microsatellite loci genotyped in a natural population of Microtus oeconomus sampled during 8 years, together with 1200 simulated populations without null alleles, but experiencing bottlenecks of varying duration and intensity, and 120 simulated populations with known null alleles. In the natural population, 29% of positive results were consistent between the methods in pairwise comparisons, and in the simulated data set, this proportion was 14%. The positive results were also inconsistent between different years in the natural population. In the null‐allele‐free simulated data set, the number of false positives increased with increased bottleneck intensity and duration. We also found a low concordance in null allele detection between the original simulated populations and their 20% random subsets. In the populations simulated to include null alleles, between 22% and 42% of true null alleles remained undetected, which highlighted that detection errors are not restricted to false positives. None of the evaluated methods clearly outperformed the others when both false‐positive and false‐negative rates were considered. Accepting only the positive results consistent between at least two methods should considerably reduce the false‐positive rate, but this approach may increase the false‐negative rate. Our study demonstrates the need for novel null allele detection methods that could be reliably applied to natural populations.     

Performance of different SNP panels for parentage testing in two East Asian cattle breeds

The International Society for Animal Genetics (ISAG) proposed a panel of single nucleotide polymorphisms (SNPs) for parentage testing in cattle (a core panel of 100 SNPs and an additional list of 100 SNPs). However, markers specific to East Asian taurine cattle breeds were not included, and no information is available as to whether the ISAG panel performs adequately for these breeds. We tested ISAG's core (100 SNP) and full (200 SNP) panels on two East Asian taurine breeds: the Korean Hanwoo and the Japanese Wagyu, the latter from the Australian herd. Even though the power of exclusion was high at 0.99 for both ISAG panels, the core panel performed poorly with 3.01% false‐positive assignments in the Hanwoo population and 3.57% in the Wagyu. The full ISAG panel identified all sire–offspring relations correctly in both populations with 0.02% of relations wrongly excluded in the Hanwoo population. Based on these results, we created and tested two population‐specific marker panels: one for the Wagyu population, which showed no false‐positive assignments with either 100 or 200 SNPs, and a second panel for the Hanwoo, which still had some false‐positive assignments with 100 SNPs but no false positives using 200 SNPs. In conclusion, for parentage assignment in East Asian cattle breeds, only the full ISAG panel is adequate for parentage testing. If fewer markers should be used, it is advisable to use population‐specific markers rather than the ISAG panel.     

Statistical approaches to account for false‐positive errors in environmental DNA samples

Environmental DNA (eDNA) sampling is prone to both false‐positive and false‐negative errors. We review statistical methods to account for such errors in the analysis of eDNA data and use simulations to compare the performance of different modelling approaches. Our simulations illustrate that even low false‐positive rates can produce biased estimates of occupancy and detectability. We further show that removing or classifying single PCR detections in an ad hoc manner under the suspicion that such records represent false positives, as sometimes advocated in the eDNA literature, also results in biased estimation of occupancy, detectability and false‐positive rates. We advocate alternative approaches to account for false‐positive errors that rely on prior information, or the collection of ancillary detection data at a subset of sites using a sampling method that is not prone to false‐positive errors. We illustrate the advantages of these approaches over ad hoc classifications of detections and provide practical advice and code for fitting these models in maximum likelihood and Bayesian frameworks. Given the severe bias induced by false‐negative and false‐positive errors, the methods presented here should be more routinely adopted in eDNA studies.     

Bayesian parentage analysis reliably controls the number of false assignments in natural populations

Parentage analysis in natural populations is a powerful tool for addressing a wide range of ecological and evolutionary questions. However, identifying parent–offspring pairs in samples collected from natural populations is often more challenging than simply resolving the Mendelian pattern of shared alleles. For example, large numbers of pairwise comparisons and limited numbers of genetic markers can contribute to incorrect assignments, whereby unrelated individuals are falsely identified as parent–offspring pairs. Determining which parentage methods are the least susceptible to making false assignments is an important challenge facing molecular ecologists. In a recent paper, Harrison et al. (2013a) address this challenge by comparing three commonly used parentage methods, including a Bayesian approach, in order to explore the effects of varied proportions of sampled parents on the accuracy of parentage assignments. Unfortunately, Harrison et al. made a simple error in using the Bayesian approach, which led them to incorrectly conclude that this method could not control the rate of false assignment. Here, I briefly outline the basic principles behind the Bayesian approach, identify the error made by Harrison et al., and provide detailed guidelines as to how the method should be correctly applied. Furthermore, using the exact data from Harrison et al., I show that the Bayesian approach actually provides greater control over the number of false assignments than either of the other tested methods. Lastly, I conclude with a brief introduction to solomon , a recently updated version of the Bayesian approach that can account for genotyping error, missing data and false matching.     

A model‐based solution for observational errors in laboratory studies

Molecular techniques for detecting microorganisms, macroorganisms and infectious agents are susceptible to false‐negative and false‐positive errors. If left unaddressed, these observational errors may yield misleading inference concerning occurrence, prevalence, sensitivity, specificity and covariate relationships. Occupancy models are widely used to account for false‐negative errors and more recently have even been used to address false‐positive errors, too. Current modelling options assume false‐positive errors only occur in truly negative samples, an assumption that yields biased inference concerning detection because a positive sample could be classified as such not because the target agent was successfully detected, but rather due to a false‐positive test result. We present an extension to the occupancy modelling framework that allows false‐positive errors in both negative and positive samples, thereby providing unbiased inference concerning occurrence and detection, as well as reliable conclusions about the efficacy of sampling designs, handling protocols and diagnostic tests. We apply the model to simulated data, showing that it recovers known parameters and outperforms other approaches that are commonly used when confronted with observation errors. We then apply the model to an experimental data set on Batrachochytrium dendrobatidis, a pathogenic fungus that is implicated in the global decline or extinction of hundreds of amphibian species. The model‐based approach we present is not only useful for obtaining reliable inference when data are contaminated with observational errors, but also eliminates the need for establishing arbitrary thresholds or decision rules that have hidden and unintended consequences.     

Do marker‐based paternity assignments favour heterozygous and unrelated males?

Genetic marker‐based parentage analyses are widely applied to studies of natural populations in the fields of evolutionary biology, conservation biology and ecology. When the same markers used in a parentage analysis are used together with the inferred parentage in a downstream analysis, such as the analysis of mate choice in terms of heterozygosity or relatedness, a bias may be incurred because a subset of the genotypes are favoured in parentage assignments or non‐exclusions. A previous simulation study shows that exclusion‐based paternity analyses are biased in favour of heterozygous males, and males less related to the mothers than expected under random mating. In this study, I investigated the biases of genetic paternity analyses achieved by both exclusion‐ and likelihood‐based methods, using both analytical and simulation approaches. It is concluded that while both exclusion‐ and likelihood‐based methods can lead to biased paternity assignments or non‐exclusions in favour of a subset of genotypes, the bias is not consistently towards heterozygous males or males apparently less related to mothers. Both the direction and extent of the bias depend heavily on the allele frequency distribution and the number of markers, the methods used for paternity assignments, and the estimators of relatedness. There exist important differences in the patterns of the biases between exclusion‐ and likelihood‐based paternity analysis methods. It is concluded that the markers, except when they are highly informative to yield accurate paternity assignments or exclusions, should be split into two subsets which are used separately in the paternity and downstream analyses.     

The consequences of using indirect signs that decay to determine species' occupancy

Statistical models of species' distributions rely on data on species' occupancy, or use, of sites across space and/or time. For rare or cryptic species, indirect signs, such as dung, may be the only realistic means of determining their occupancy status across broad spatial extents. However, the consequences of sign decay for errors in estimates of occupancy have not previously been considered. If signs decay very rapidly, then false‐negative errors may occur because signs at an occupied site have decayed by the time it is surveyed. On the other hand, if signs decay very slowly, false‐positive errors may occur because signs remain present at sites that are no longer occupied. We addressed this issue by quantifying, as functions of sign decay and accumulation rates: 1) the false‐negative error rate due to sign decay and, 2) the expected time interval prior to a survey within which signs indicate the species was present; as this time interval increases, false‐positives become more likely. We then applied this to the specific example of koala Phascolarctos cinereus occupancy derived from faecal pellet surveys using data on faecal pellet decay rates. We show that there is a clear trade‐off between false‐negative error rates and the potential for false‐positive errors. For the koala case study, false‐negative errors were low on average and the expected time interval prior to surveys that detected pellets indicate the species was present within less than 2–3 yr. However, these quantities showed quite substantial spatial variation that could lead to biased parameter estimates for distribution models based on faecal pellet surveys. This highlights the importance of observation errors arising from sign decay and we suggest some modifications to existing methods to deal with this issue.     

Estimation of genotyping error rate from repeat genotyping,unintentional recaptures and known parent–offspring comparisons in 16 microsatellite loci for brown rockfish (Sebastes auriculatus)

Genotyping errors are present in almost all genetic data and can affect biological conclusions of a study, particularly for studies based on individual identification and parentage. Many statistical approaches can incorporate genotyping errors, but usually need accurate estimates of error rates. Here, we used a new microsatellite data set developed for brown rockfish (Sebastes auriculatus) to estimate genotyping error using three approaches: (i) repeat genotyping 5% of samples, (ii) comparing unintentionally recaptured individuals and (iii) Mendelian inheritance error checking for known parent–offspring pairs. In each data set, we quantified genotyping error rate per allele due to allele drop‐out and false alleles. Genotyping error rate per locus revealed an average overall genotyping error rate by direct count of 0.3%, 1.5% and 1.7% (0.002, 0.007 and 0.008 per allele error rate) from replicate genotypes, known parent–offspring pairs and unintentionally recaptured individuals, respectively. By direct‐count error estimates, the recapture and known parent–offspring data sets revealed an error rate four times greater than estimated using repeat genotypes. There was no evidence of correlation between error rates and locus variability for all three data sets, and errors appeared to occur randomly over loci in the repeat genotypes, but not in recaptures and parent–offspring comparisons. Furthermore, there was no correlation in locus‐specific error rates between any two of the three data sets. Our data suggest that repeat genotyping may underestimate true error rates and may not estimate locus‐specific error rates accurately. We therefore suggest using methods for error estimation that correspond to the overall aim of the study (e.g. known parent–offspring comparisons in parentage studies).     

Effects of genotyping errors on parentage exclusion analysis

Genetic markers are widely used to determine the parentage of individuals in studies of mating systems, reproductive success, dispersals, quantitative genetic parameters and in the management of conservation populations. These markers are, however, imperfect for parentage analyses because of the presence of genotyping errors and undetectable alleles, which may cause incompatible genotypes (mismatches) between parents and offspring and thus result in false exclusions of true parentage. Highly polymorphic markers widely used in parentage analyses, such as microsatellites, are especially prone to genotyping errors. In this investigation, I derived the probabilities of excluding a random (related) individual from parentage and the probabilities of Mendelian-inconsistent errors (mismatches) and Mendelian-consistent errors (which do not cause mismatches) in parent-offspring dyads, when a marker having null alleles, allelic dropouts and false alleles is used in a parentage analysis. These probabilities are useful in evaluating the impact of various types of genotyping errors on the information content of a set of markers in and thus the power of a parentage analysis, in determining the threshold number of genetic mismatches that is appropriate for a parentage exclusion analysis and in estimating the rates of genotyping errors and frequencies of null alleles from observed mismatches between known parent-offspring dyads. These applications are demonstrated by numerical examples using both hypothetical and empirical data sets and discussed in the context of practical parentage exclusion analyses.     

Adaptive Sex‐Specific Cognitive Bias in Predation Behaviours of Japanese Pygmy Squid

Errors in decision‐making in animals can be partially explained by adaptive evolution, and error management theory explains that cognitive biases result from the asymmetric costs of false‐positive and false‐negative errors. Error rates that result from the cognitive bias may differ between sexes. In addition, females are expected to have higher feeding rates than males because of the high energy requirements of gamete production. Thus, females may suffer relatively larger costs from false‐negative errors (i.e. non‐feeding) than males, and female decisions would be biased to reduce these costs if the costs of false‐positive errors are not as high. Females would consequently overestimate their capacity in relation to the probability of predation success. We tested this hypothesis using the Japanese pygmy squid Idiosepius paradoxus. Our results show that size differences between the squid and prey shrimp affected predatory attacks, and that predatory attacks succeeded more often when the predator was relatively larger than the prey. Nevertheless, compared to male predatory attacks, female squid frequently attacked even if their size was relatively small compared to the prey, suggesting that the females overestimated their probability of success. However, if the females failed in the first attack, they subsequently adjusted their attack threshold: squid did not attack again if the prey size was relatively larger. These results suggest a sex‐specific cognitive bias, that is females skewed judgment in decision‐making for the first predation attack, but they also show that squid can modify their threshold to determine whether they should attack in subsequent encounters.     

Combining noninvasive genetics and a new mammalian sex‐linked marker provides new tools to investigate population size,structure and individual behaviour: An application to bats

Monitoring wild populations is crucial for their effective management. Noninvasive genetic methods provide robust data from individual free‐ranging animals, which can be used in capture–mark–recapture (CMR) models to estimate demographic parameters without capturing or disturbing them. However, sex‐ and status‐specific behaviour, which may lead to differences in detection probabilities, is rarely considered in monitoring. Here, we investigated population size, sex ratio, sex‐ and status‐related behaviour in 19 Rhinolophus hipposideros maternity colonies (Northern France) with a noninvasive genetic CMR approach (using faeces) combined with parentage assignments. The use of the DDX3X/Y‐Mam sexual marker designed in this study, which shows inter‐ and intrachromosomal length polymorphism across placental mammals, together with eight polymorphic microsatellite markers, produced high‐quality genetic data with limited genotyping errors and allowed us to reliably distinguish different categories of individuals (males, reproductive and nonreproductive females) and to estimate population sizes. We showed that visual counts represent well‐adult female numbers and that population composition in maternity colonies changes dynamically during the summer. Before parturition, colonies mainly harbour pregnant and nonpregnant females with a few visiting males, whereas after parturition, colonies are mainly composed of mothers and their offspring with a few visiting nonmothers and males. Our approach gives deeper insight into sex‐ and status‐specific behaviour, a prerequisite for understanding population dynamics and developing effective monitoring and management strategies. Provided sufficient samples can be obtained, this approach can be readily applied to a wide range of species.     

Quality and quantity of genetic relatedness data affect the analysis of social structure

Kinship plays a fundamental role in the evolution of social systems and is considered a key driver of group living. To understand the role of kinship in the formation and maintenance of social bonds, accurate measures of genetic relatedness are critical. Genotype‐by‐sequencing technologies are rapidly advancing the accuracy and precision of genetic relatedness estimates for wild populations. The ability to assign kinship from genetic data varies depending on a species’ or population's mating system and pattern of dispersal, and empirical data from longitudinal studies are crucial to validate these methods. We use data from a long‐term behavioural study of a polygynandrous, bisexually philopatric marine mammal to measure accuracy and precision of parentage and genetic relatedness estimation against a known partial pedigree. We show that with moderate but obtainable sample sizes of approximately 4,235 SNPs and 272 individuals, highly accurate parentage assignments and genetic relatedness coefficients can be obtained. Additionally, we subsample our data to quantify how data availability affects relatedness estimation and kinship assignment. Lastly, we conduct a social network analysis to investigate the extent to which accuracy and precision of relatedness estimation improve statistical power to detect an effect of relatedness on social structure. Our results provide practical guidance for minimum sample sizes and sequencing depth for future studies, as well as thresholds for post hoc interpretation of previous analyses.     

Reliable eDNA detection and quantification of the European weather loach (Misgurnus fossilis)

The European weather loach (Misgurnus fossilis) is a cryptic and poorly known fish species of high conservation concern. The species is experiencing dramatic population collapses across its native range to the point of regional extinction. Although environmental DNA (eDNA)-based approaches offer clear advantages over conventional field methods for monitoring rare and endangered species, accurate detection and quantification remain difficult and quality assessment is often poorly incorporated. In this study, we developed and validated a novel digital droplet PCR (ddPCR) eDNA-based method for reliable detection and quantification, which allows accurate monitoring of M. fossilis across a number of habitat types. A dilution experiment under laboratory conditions allowed the definition of the limit of detection (LOD) and the limit of quantification (LOQ), which were set at concentrations of 0.07 and 0.14 copies μl^–1, respectively. A series of aquarium experiments revealed a significant and positive relationship between the number of individuals and the eDNA concentration measured. During a 3 year survey (2017–2019), we assessed 96 locations for the presence of M. fossilis in Flanders (Belgium). eDNA analyses on these samples highlighted 45% positive detections of the species. On the basis of the eDNA concentration per litre of water, only 12 sites appeared to harbour relatively dense populations. The other 31 sites gave a relatively weak positive signal that was typically situated below the LOQ. Combining sample-specific estimates of effective DNA quantity (Q_e) and conventional field sampling, we concluded that each of these weak positive sites still likely harboured the species and therefore they do not represent false positives. Further, only seven of the classified negative samples warrant additional sampling as our analyses identified a substantial risk of false-negative detections (i.e., type II errors) at these locations. Finally, we illustrated that ddPCR outcompetes conventional qPCR analyses, especially when target DNA concentrations are critically low, which could be attributed to a reduced sensitivity of ddPCR to inhibition effects, higher sample concentrations being accommodated and higher sensitivity obtained.     

Comparison of F(ST) outlier tests for SNP loci under selection

Genome scans with many genetic markers provide the opportunity to investigate local adaptation in natural populations and identify candidate genes under selection. In particular, SNPs are dense throughout the genome of most organisms and are commonly observed in functional genes making them ideal markers to study adaptive molecular variation. This approach has become commonly employed in ecological and population genetics studies to detect outlier loci that are putatively under selection. However, there are several challenges to address with outlier approaches including genotyping errors, underlying population structure and false positives, variation in mutation rate and limited sensitivity (false negatives). In this study, we evaluated multiple outlier tests and their type I (false positive) and type II (false negative) error rates in a series of simulated data sets. Comparisons included simulation procedures (FDIST2, ARLEQUIN v.3.5 and BAYESCAN) as well as more conventional tools such as global F(ST) histograms. Of the three simulation methods, FDIST2 and BAYESCAN typically had the lowest type II error, BAYESCAN had the least type I error and Arlequin had highest type I and II error. High error rates in Arlequin with a hierarchical approach were partially because of confounding scenarios where patterns of adaptive variation were contrary to neutral structure; however, Arlequin consistently had highest type I and type II error in all four simulation scenarios tested in this study. Given the results provided here, it is important that outlier loci are interpreted cautiously and error rates of various methods are taken into consideration in studies of adaptive molecular variation, especially when hierarchical structure is included.     

Microsatellite null alleles in parentage analysis

Highly polymorphic microsatellite markers are widely employed in population genetic analyses (eg, of biological parentage and mating systems), but one potential drawback is the presence of null alleles that fail to amplify to detected levels in the PCR assays. Here we examine 233 published articles in which authors reported the suspected presence of one or more microsatellite null alleles, and we review how these purported nulls were detected and handled in the data analyses. We also employ computer simulations and analytical treatments to determine how microsatellite null alleles might impact molecular parentage analyses. The results indicate that whereas null alleles in frequencies typically reported in the literature introduce rather inconsequential biases on average exclusion probabilities, they can introduce substantial errors into empirical assessments of specific mating events by leading to high frequencies of false parentage exclusions.     

Finding the right coverage: the impact of coverage and sequence quality on single nucleotide polymorphism genotyping error rates

Restriction‐enzyme‐based sequencing methods enable the genotyping of thousands of single nucleotide polymorphism (SNP) loci in nonmodel organisms. However, in contrast to traditional genetic markers, genotyping error rates in SNPs derived from restriction‐enzyme‐based methods remain largely unknown. Here, we estimated genotyping error rates in SNPs genotyped with double digest RAD sequencing from Mendelian incompatibilities in known mother–offspring dyads of Hoffman's two‐toed sloth (Choloepus hoffmanni) across a range of coverage and sequence quality criteria, for both reference‐aligned and de novo‐assembled data sets. Genotyping error rates were more sensitive to coverage than sequence quality and low coverage yielded high error rates, particularly in de novo‐assembled data sets. For example, coverage ≥5 yielded median genotyping error rates of ≥0.03 and ≥0.11 in reference‐aligned and de novo‐assembled data sets, respectively. Genotyping error rates declined to ≤0.01 in reference‐aligned data sets with a coverage ≥30, but remained ≥0.04 in the de novo‐assembled data sets. We observed approximately 10‐ and 13‐fold declines in the number of loci sampled in the reference‐aligned and de novo‐assembled data sets when coverage was increased from ≥5 to ≥30 at quality score ≥30, respectively. Finally, we assessed the effects of genotyping coverage on a common population genetic application, parentage assignments, and showed that the proportion of incorrectly assigned maternities was relatively high at low coverage. Overall, our results suggest that the trade‐off between sample size and genotyping error rates be considered prior to building sequencing libraries, reporting genotyping error rates become standard practice, and that effects of genotyping errors on inference be evaluated in restriction‐enzyme‐based SNP studies.     

Handling long targets and errors in sequencing by hybridization.

Sequencing by hybridization (SBH) is a DNA sequencing technique, in which the sequence is reconstructed using its k-mer content. This content, which is called the spectrum of the sequence, is obtained by hybridization to a universal DNA array. Standard universal arrays contain all k-mers for some fixed k, typically 8 to 10. Currently, in spite of its promise and elegance, SBH is not competitive with standard gel-based sequencing methods. This is due to two main reasons: lack of tools to handle realistic levels of hybridization errors and an inherent limitation on the length of uniquely reconstructible sequence by standard universal arrays. In this paper, we deal with both problems. We introduce a simple polynomial reconstruction algorithm which can be applied to spectra from standard arrays and has provable performance in the presence of both false negative and false positive errors. We also propose a novel design of chips containing universal bases that differs from the one proposed by Preparata et al. (1999). We give a simple algorithm that uses spectra from such chips to reconstruct with high probability random sequences of length lower only by a squared log factor compared to the information theoretic bound. Our algorithm is very robust to errors and has a provable performance even if there are both false negative and false positive errors. Simulations indicate that its sensitivity to errors is also very small in practice.     

Conditionally autoregressive models improve occupancy analyses of autocorrelated data: An example with environmental DNA

Site occupancy‐detection models (SODMs) are statistical models widely used for biodiversity surveys where imperfect detection of species occurs. For instance, SODMs are increasingly used to analyse environmental DNA (eDNA) data, taking into account the occurrence of both false‐positive and false‐negative errors. However, species occurrence data are often characterized by spatial and temporal autocorrelation, which might challenge the use of standard SODMs. Here we reviewed the literature of eDNA biodiversity surveys and found that most of studies do not take into account spatial or temporal autocorrelation. We then demonstrated how the analysis of data with spatial or temporal autocorrelation can be improved by using a conditionally autoregressive SODM, and show its application to environmental DNA data. We tested the autoregressive model on both simulated and real data sets, including chronosequences with different degrees of autocorrelation, and a spatial data set on a virtual landscape. Analyses of simulated data showed that autoregressive SODMs perform better than traditional SODMs in the estimation of key parameters such as true‐/false‐positive rates and show a better discrimination capacity (e.g., higher true skill statistics). The usefulness of autoregressive SODMs was particularly high in data sets with strong autocorrelation. When applied to real eDNA data sets (eDNA from lake sediment cores and freshwater), autoregressive SODM provided more precise estimation of true‐/false‐positive rates, resulting in more reasonable inference of occupancy states. Our results suggest that analyses of occurrence data, such as many applications of eDNA, can be largely improved by applying conditionally autoregressive specifications to SODMs.