Effect of sampling strategy on estimation of fine-scale spatial genetic structure in Androsace tapete (Primulaceae), an alpine plant endemic to Qinghai-Tibetan Plateau

The fine-scale spatial genetic structure (SGS) of alpine plants is receiving increasing attention, from which seed and pollen dispersal can be inferred. However, estimation of SGS may depend strongly on the sampling strategy,including the sample size and spatial sampling scheme. Here, we examined the effects of sample size and three spatial schemes, simple-random, line-transect, and random-cluster sampling, on the estimation of SGS in Androsace tapete, an alpine cushion plant endemic to Qinghai-Tibetan Plateau. Using both real data and simulated data of dominant molecular markers, we show that: (i) SGS is highly sensitive to sample strategy especially when the sample size is small (e.g., below 100); (ii) the commonly used SGS parameter (the intercept of the autocorrelogram) is more susceptible to sample error than a newly developed Sp statistic; and (iii) the random-cluster scheme is susceptible to obvious bias in parameter estimation even when the sample size is relatively large (e.g., above 200). Overall,the line-transect scheme is recommendable, in that it performs slightly better than the simple-random scheme in parameter estimation and is more efficient to encompass broad spatial scales. The consistency between simulated data and real data implies that these findings might hold true in other alpine plants and more species should be examined in future work.     

Performance of sampling strategies in the presence of known spatial patterns

In crop protection and ecology accurate and precise estimates of insect populations are required for many purposes. The spatial pattern of the organism sampled, in relation to the sampling scheme adopted, affects the difference between the actual and estimated population density, the bias, and the variability of that estimate, the precision. Field monitoring schemes usually adopt time‐efficient sampling regimes involving contiguous units rather than the most efficient for estimation, the completely random sample. This paper uses spatially‐explicit ecological field data on aphids and beetles to compare common sampling regimes. The random sample was the most accurate method and often the most precise; of the contiguous schemes the line transect was superior to more compact arrangements such as a square block. Bias depended on the relationship between the size and shape of the group of units comprising the sample and the dominant cluster size underlying the spatial pattern. Existing knowledge of spatial pattern to inform the choice of sampling scheme may provide considerable improvements in accuracy. It is recommended to use line transects longer than the grain of the spatial pattern, where grain is defined as the average dimension of clusters over both patches and gaps, and with length at least twice the dominant cluster size.     

Bayesian Estimation of Hunting Success Rate and Harvest for Spatially Correlated Post‐stratified Data

We consider the estimation of success rate and harvest under post survey stratification at the sub‐domain (county) level. Often in this situation, the population size for the sub‐domain is unknown and the random mechanism that dictates the sample size for sub‐domains is ignored. Finding good estimators of success rate and harvest is very important for wildlife abundance. A Bayesian hierarchical model is developed to estimate both success rate and harvest simultaneously. The model includes a random sub‐domain sample size correlated with the number of successes in the sub‐domain, fixed week effects, random geographic effects, and spatial correlations between neighboring sub‐domains. The computation is done by Gibbs sampling and adaptive rejection sampling techniques. The method developed is illustrated using data from the Missouri Turkey Hunting Survey. The estimation of success rate is improved by treating the the sub‐domain sample size as a random variable instead of a fixed constant. The Bayesian model yields a reasonable harvest estimation. The spatial pattern of the estimated harvest matches the pattern of the check station data.     

Spatially autocorrelated sampling falsely inflates measures of accuracy for presence‐only niche models

Aim Environmental niche models that utilize presence‐only data have been increasingly employed to model species distributions and test ecological and evolutionary predictions. The ideal method for evaluating the accuracy of a niche model is to train a model with one dataset and then test model predictions against an independent dataset. However, a truly independent dataset is often not available, and instead random subsets of the total data are used for ‘training’ and ‘testing’ purposes. The goal of this study was to determine how spatially autocorrelated sampling affects measures of niche model accuracy when using subsets of a larger dataset for accuracy evaluation. Location The distribution of Centaurea maculosa (spotted knapweed; Asteraceae) was modelled in six states in the western United States: California, Oregon, Washington, Idaho, Wyoming and Montana. Methods Two types of niche modelling algorithms – the genetic algorithm for rule‐set prediction (GARP) and maximum entropy modelling (as implemented with Maxent) – were used to model the potential distribution of C. maculosa across the region. The effect of spatially autocorrelated sampling was examined by applying a spatial filter to the presence‐only data (to reduce autocorrelation) and then comparing predictions made using the spatial filter with those using a random subset of the data, equal in sample size to the filtered data. Results The accuracy of predictions from both algorithms was sensitive to the spatial autocorrelation of sampling effort in the occurrence data. Spatial filtering led to lower values of the area under the receiver operating characteristic curve plot but higher similarity statistic (I) values when compared with predictions from models built with random subsets of the total data, meaning that spatial autocorrelation of sampling effort between training and test data led to inflated measures of accuracy. Main conclusions The findings indicate that care should be taken when interpreting the results from presence‐only niche models when training and test data have been randomly partitioned but occurrence data were non‐randomly sampled (in a spatially autocorrelated manner). The higher accuracies obtained without the spatial filter are a result of spatial autocorrelation of sampling effort between training and test data inflating measures of prediction accuracy. If independently surveyed data for testing predictions are unavailable, then it may be necessary to explicitly account for the spatial autocorrelation of sampling effort between randomly partitioned training and test subsets when evaluating niche model predictions.     

Predicting species distributions based on incomplete survey data: the trade‐off between precision and scale

Systematic species surveys over large areas are mostly not affordable, constraining conservation planners to make best use of incomplete data. Spatially explicit species distribution models (SDM) may be useful to detect and compensate for incomplete information. SDMs can either be based on standardized, systematic sampling in a restricted subarea, or – as a cost‐effective alternative – on data haphazardly collated by “volunteer‐based monitoring schemes” (VMS), area‐wide but inherently biased and of heterogeneous spatial precision. Using data on capercaillie Tetrao urogallus, we evaluated the capacity of SDMs generated from incomplete survey data to localise unknown areas inhabited by the species and to predict relative local observation density. Addressing the trade‐off between data precision, sample size and spatial extent of the sampling area, we compared three different sampling strategies: VMS‐data collected throughout the whole study area (7000 km²) using either 1) exact locations or 2) locations aggregated to grid cells of the size of an average individual home range, and 3) systematic transect counts conducted within a small subarea (23.8 km²). For each strategy, we compared two sample sizes and two modelling methods (ENFA and Maxent), which were evaluated using cross‐validation and independent data. Models based on VMS‐data (strategies 1 and 2) performed equally well in predicting relative observation density and in localizing “unknown” occurrences. They always outperformed strategy 3‐models, irrespective of sample size and modelling method, partly because the VMS‐data provided the more comprehensive clues for setting the discrimination‐threshold for predicting presence or absence. Accounting for potential errors due to extrapolation (e.g. projections outside the environmental domain or potentially biasing variables) reduced, but did not fully compensate for the observed discrepancies. As they cover a broader range of species‐habitat relations, the area‐wide data achieved a better model quality with less a‐priori knowledge. Furthermore, in a highly mobile species like capercaillie a sampling resolution corresponding to an individuals' home range can lead to equally good predictions as the use of exact locations. Consequently, when a trade‐off between the sampling effort and the spatial extent of the sampling area is necessary, less precise data unsystematically collected over a large representative region are preferable to systematically sampled data from a restricted region.     

Statistical Inference for a Two‐Stage Outcome‐Dependent Sampling Design with a Continuous Outcome

Summary The two‐stage case–control design has been widely used in epidemiology studies for its cost‐effectiveness and improvement of the study efficiency ( White, 1982 , American Journal of Epidemiology 115, 119–128; Breslow and Cain, 1988 , Biometrika 75, 11–20). The evolution of modern biomedical studies has called for cost‐effective designs with a continuous outcome and exposure variables. In this article, we propose a new two‐stage outcome‐dependent sampling (ODS) scheme with a continuous outcome variable, where both the first‐stage data and the second‐stage data are from ODS schemes. We develop a semiparametric empirical likelihood estimation for inference about the regression parameters in the proposed design. Simulation studies were conducted to investigate the small‐sample behavior of the proposed estimator. We demonstrate that, for a given statistical power, the proposed design will require a substantially smaller sample size than the alternative designs. The proposed method is illustrated with an environmental health study conducted at National Institutes of Health.     

Extensive spatial genetic structure revealed by AFLP but not SSR molecular markers in the wind-pollinated tree, Fagus sylvatica

Studies of fine-scale spatial genetic structure (SGS) in wind-pollinated trees have shown that SGS is generally weak and extends over relatively short distances (less than 30-40 m) from individual trees. However, recent simulations have shown that detection of SGS is heavily dependent on both the choice of molecular markers and the strategy used to sample the studied population. Published studies may not always have used sufficient markers and/or individuals for the accurate estimation of SGS. To assess the extent of SGS within a population of the wind-pollinated tree Fagus sylvatica, we genotyped 200 trees at six microsatellite or simple sequence repeat (SSR) loci and 250 amplified fragment length polymorphisms (AFLP) and conducted spatial analyses of pairwise kinship coefficients. We re-sampled our data set over individuals and over loci to determine the effect of reducing the sample size and number of loci used for SGS estimation. We found that SGS estimated from AFLP markers extended nearly four times further than has been estimated before using other molecular markers in this species, indicating a persistent effect of restricted gene flow at small spatial scales. However, our SSR-based estimate was in agreement with other published studies. Spatial genetic structure in F. sylvatica and similar wind-pollinated trees may therefore be substantially larger than has been estimated previously. Although 100-150 AFLP loci and 150-200 individuals appear sufficient for adequately estimating SGS in our analysis, 150-200 individuals and six SSR loci may still be too few to provide a good estimation of SGS in this species.     

Optimal sampling strategy for estimation of spatial genetic structure in tree populations

Fine-scale spatial genetic structure (SGS) in natural tree populations is largely a result of restricted pollen and seed dispersal. Understanding the link between limitations to dispersal in gene vectors and SGS is of key interest to biologists and the availability of highly variable molecular markers has facilitated fine-scale analysis of populations. However, estimation of SGS may depend strongly on the type of genetic marker and sampling strategy (of both loci and individuals). To explore sampling limits, we created a model population with simulated distributions of dominant and codominant alleles, resulting from natural regeneration with restricted gene flow. SGS estimates from subsamples (simulating collection and analysis with amplified fragment length polymorphism (AFLP) and microsatellite markers) were correlated with the 'real' estimate (from the full model population). For both marker types, sampling ranges were evident, with lower limits below which estimation was poorly correlated and upper limits above which sampling became inefficient. Lower limits (correlation of 0.9) were 100 individuals, 10 loci for microsatellites and 150 individuals, 100 loci for AFLPs. Upper limits were 200 individuals, five loci for microsatellites and 200 individuals, 100 loci for AFLPs. The limits indicated by simulation were compared with data sets from real species. Instances where sampling effort had been either insufficient or inefficient were identified. The model results should form practical boundaries for studies aiming to detect SGS. However, greater sample sizes will be required in cases where SGS is weaker than for our simulated population, for example, in species with effective pollen/seed dispersal mechanisms.     

A field test of the distance sampling method using Golden‐cheeked Warblers

ABSTRACT Criteria for delisting Golden‐cheeked Warblers (Dendroica chrysoparia) include protection of sufficient breeding habitat to ensure the continued existence of 1000 to 3000 singing males in each of eight recovery regions for ≥10 consecutive years. Hence, accurate abundance estimation is an integral component in the recovery of this species. I conducted a field test to determine if the distance sampling method provided unbiased abundance estimates for Golden‐cheeked Warblers and develop recommendations to improve the accuracy of estimates by minimizing the effects of violating this method's assumptions. To determine if observers could satisfy the assumptions that birds are detected at the point with certainty and at their initial locations, I compared point‐transect sampling estimates from 2‐, 3‐, 4‐, and 5‐min time intervals to actual abundance determined by intensive territory monitoring. Point‐transect abundance estimates were 15%, 29%, 43%, and 59% greater than actual abundance (N= 156) for the 2‐, 3‐, 4‐, and 5‐min intervals, respectively. Point‐transect sampling produced unbiased estimates of Golden‐cheeked Warbler abundance when counts were limited to 2 min (N= 154–207). Abundance estimates derived from point‐transect sampling were likely greater than actual abundance because observers did not satisfy the assumption that birds were detected at their initial locations due to the frequent movements and large territory sizes of male Golden‐cheeked Warblers. To minimize the effect of movement on abundance estimates, I recommend limiting counts of singing males to 2‐min per point. Counts for other species in similar habitats with similar behavior and movement patterns also should be limited to 2 min when unbiased estimates are important and conducting field tests of the point‐transect distance sampling method is not possible.     

Distinguishing colonisation modes from spatial structures in populations of the cushion plant Azorella madreporica in the high‐Andes of central Chile

Abstract We developed multiple a priori hypotheses to link the observed spatial patterns with colonisation processes in the high alpine cushion plant, Azorella madreporica. We conducted this study in the Molina River basin (33°20′ S, 70°16′ W, 3600 m a.s.l.), in the Andes of central Chile, approximately 50 km east of Santiago. We mapped and measured size (as a surrogate for age) of individual cushions in two populations and used a standard spatial analytical tool (semivariograms) to test our alternative a priori hypotheses related to colonisation mode of the cushion species. In both populations, the size distribution of A. madreporica reflected a negative exponential or inverse‐J pattern, typical of uneven‐aged populations, where most of the cushions belonged to relatively smaller size classes, in effect, a regular success in the establishment of seedlings, where all size classes of cushions were represented in the population. The results were site‐specific, where best‐fit semivariograms for spatial cushion's size distribution suggested a gradual colonisation in one population and an episodic colonisation in the other population. Microsite distribution proved to be homogeneous at both sites. Thus, the study of the spatial explicit size‐age population distribution of an alpine species provides valuable information about the frequency, magnitude and site variation of the reproductive pulses in these harsh environments.     

Looking into the black box: simulating the role of self‐fertilization and mortality in the genetic structure of Macrocystis pyrifera

Patterns of spatial genetic structure (SGS), typically estimated by genotyping adults, integrate migration over multiple generations and measure the effective gene flow of populations. SGS results can be compared with direct ecological studies of dispersal or mating system to gain additional insights. When mismatches occur, simulations can be used to illuminate the causes of these mismatches. Here, we report a SGS and simulation‐based study of self‐fertilization in Macrocystis pyrifera, the giant kelp. We found that SGS is weaker than expected in M. pyrifera and used computer simulations to identify selfing and early mortality rates for which the individual heterozygosity distribution fits that of the observed data. Only one (of three) population showed both elevated kinship in the smallest distance class and a significant negative slope between kinship and geographical distance. All simulations had poor fit to the observed data unless mortality due to inbreeding depression was imposed. This mortality could only be imposed for selfing, as these were the only simulations to show an excess of homozygous individuals relative to the observed data. Thus, the expected data consistently achieved nonsignificant differences from the observed data only under models of selfing with mortality, with best fits between 32% and 42% selfing. Inbreeding depression ranged from 0.70 to 0.73. The results suggest that density‐dependent mortality of early life stages is a significant force in structuring Macrocystis populations, with few highly homozygous individuals surviving. The success of these results should help to validate simulation approaches even in data‐poor systems, as a means to estimate otherwise difficult‐to‐measure life cycle parameters.     

Sampling strategies for the assessment of tree species diversity

Abstract. This paper aims at proposing efficient vegetation sampling strategies. It describes how the estimation of species richness and diversity of moist evergreen forest is affected by (1) sampling design (simple random sampling, random cluster sampling, systematic cluster sampling, stratified cluster sampling); (2) choice of species richness estimators (number of observed species vs. non-parametric estimators) and (3) choice of diversity index (Simpson vs. Shannon). Two sites are studied: a 28-ha area situated in the Western Ghats of India and a 25-ha area located at Pasoh in Peninsular Malaysia. The results show that: (1) whatever the sampling strategy, estimates of species richness depend on sample size in these very diverse forest ecosystems which contain many rare species; (2) Simpson's diversity index reaches a stable value at low sample sizes while Shannon's index is affected more by the addition of rare species with increasing sample size; (3) cluster sampling strategies provide a good compromise between cost and statistical efficiency; (4) 300 - 400 sample trees grouped in small clusters (10–50 individuals) are enough to obtain unbiased and precise estimates of Simpson's index; (5) the local topography of the Western Ghats has a major influence on forest composition, the steep slopes being richer and more diverse than the ridges and gentle slopes; (6) stratified cluster sampling is thus an interesting alternative to systematic cluster sampling.     

Associated LAH Numbers,Factorial Series Distributions and Unbiased Estimation of a Size Parameter

A finite population consists of kN individuals of N different categories with k individuals each. It is required to estimate the unknown parameter N, the number of different classes in the population. A sequential sampling scheme is considered in which individuals are sampled until a preassigned number of repetitions of already observed categories occur in the sample. Corresponding fixed sample size schemes were considered by Charalambides (1981). The sequential sampling scheme has the advantage of always allowing unbiased estimation of the size parameter N. It is shown that relative to Charalambides' fixed sample size scheme only minor adjustments are required to account for the sequential scheme. In particular, MVU estimators of parametric functions are expressible in terms of the C-numbers introduced by Charalambides.     

Fine‐scale genetic structure and inferences on population biology in the threatened Mediterranean red coral,Corallium rubrum

Identifying microevolutionary processes acting in populations of marine species with larval dispersal is a challenging but crucial task because of its conservation implications. In this context, recent improvements in the study of spatial genetic structure (SGS) are particularly promising because they allow accurate insights into the demographic and evolutionary processes at stake. Using an exhaustive sampling and a combination of image processing and population genetics, we highlighted significant SGS between colonies of Corallium rubrum over an area of half a square metre, which sheds light on a number of aspects of its population biology. Based on this SGS, we found the mean dispersal range within sites to be between 22.6 and 32.1 cm, suggesting that the surveyed area approximately corresponded to a breeding unit. We then conducted a kinship analysis, which revealed a complex half‐sib family structure and allowed us to quantify the level of self‐recruitment and to characterize aspects of the mating system of this species. Furthermore, significant temporal variations in allele frequencies were observed, suggesting low genetic drift. These results have important conservation implications for the red coral and further our understanding of the microevolutionary processes acting within populations of sessile marine species with a larval phase.     

Quantifying soil organic carbon in complex landscapes: an example of grassland undergoing encroachment of woody plants

The invasion of woody plants into grass‐dominated ecosystems has occurred worldwide during the past century with potentially significant impacts on soil organic carbon (SOC) storage, ecosystem carbon sequestration and global climate warming. To date, most studies of tree and shrub encroachment impacts on SOC have been conducted at small scales and results are equivocal. To quantify the effects of woody plant proliferation on SOC at broad spatial scales and to potentially resolve inconsistencies reported from studies conducted at fine spatial scales, information regarding spatial variability and uncertainty of SOC is essential. We used sequential indicator simulation (SIS) to quantify spatial uncertainty of SOC in a grassland undergoing shrub encroachment in the Southern Great Plains, USA. Results showed that both SOC pool size and its spatial uncertainty increased with the development of woody communities in grasslands. Higher uncertainty of SOC in new shrub‐dominated communities may be the result of their relatively recent development, their more complex above‐ and belowground architecture, stronger within‐community gradients, and a greater degree of faunal disturbance. Simulations of alternative sampling designs demonstrated the effects of spatial uncertainty on the accuracy of SOC estimates and enabled us to evaluate the efficiency of sampling strategies aimed at quantifying landscape‐scale SOC pools. An approach combining stratified random sampling with unequal point densities and transect sampling of landscape elements exhibiting strong internal gradients yielded the best estimates. Complete random sampling was less effective and required much higher sampling densities. Results provide novel insights into spatial uncertainty of SOC and its effects on estimates of carbon sequestration in terrestrial ecosystem and suggest effective protocol for the estimating of soil attributes in landscapes with complex vegetation patterns.     

Geostatistical approaches and optimal additional sampling schemes for spatial patterns and future sampling of bird diversity

Aim To evaluate geostatistical approaches, namely kriging, co‐kriging and geostatistical simulation, and to develop an optimal sampling design for mapping the spatial patterns of bird diversity, estimating their spatial autocorrelations and selecting additional samples of bird diversity in a 2450 km² basin. Location Taiwan. Methods Kriging, co‐kriging and simulated annealing are applied to estimate and simulate the spatial patterns of bird diversity. In addition, kriging and co‐kriging with a genetic algorithm are used to optimally select further samples to improve the kriging and co‐kriging estimations. The association between bird diversity and elevation, and bird diversity and land cover, is analysed with estimated and simulated maps. Results The Simpson index correlates spatially with the normalized difference vegetation index (NDVI) within the micro‐scale and the macro‐scale in the study basin, but the Shannon diversity index only correlates spatially with NDVI within the micro‐scale. Co‐kriging and simulated annealing simulation accurately simulate the statistical and spatial patterns of bird diversity. The mean estimated diversity and the simulated diversity increase with elevation and decrease with increasing urbanization. The proposed optimal sampling approach selects 43 additional sampling sites with a high spatial estimation variance in bird diversity. Main conclusions Small‐scale variations dominate the total spatial variation of the observed diversity due to a lack of spatial information and insufficient sampling. However, simulations of bird diversity consistently capture the sampling statistics and spatial patterns of the observed bird diversity. The data thus accumulated can be used to understand the spatial patterns of bird diversity associated with different types of land cover and elevation, and to optimize sample selection. Co‐kriging combined with a genetic algorithm yields additional optimal sampling sites, which can be used to augment existing sampling points in future studies of bird diversity.     

Robustness of close‐kin mark–recapture estimators to dispersal limitation and spatially varying sampling probabilities

1. Close‐kin mark–recapture (CKMR) is a method for estimating abundance and vital rates from kinship relationships observed in genetic samples. CKMR inference only requires animals to be sampled once (e.g., lethally), potentially widening the scope of population‐level inference relative to traditional monitoring programs.
2. One assumption of CKMR is that, conditional on individual covariates like age, all animals have an equal probability of being sampled. However, if genetic data are collected opportunistically (e.g., via hunters or fishers), there is potential for spatial variation in sampling probability that can bias CKMR estimators, particularly when genetically related individuals stay in close proximity.
3. We used individual‐based simulation to investigate consequences of dispersal limitation and spatially biased sampling on performance of naive (nonspatial) CKMR estimators of abundance, fecundity, and adult survival. Population dynamics approximated that of a long‐lived mammal species subject to lethal sampling.
4. Naive CKMR abundance estimators were relatively unbiased when dispersal was unconstrained (i.e., complete mixing) or when sampling was random or subject to moderate levels of spatial variation. When dispersal was limited, extreme variation in spatial sampling probabilities negatively biased abundance estimates. Reproductive schedules and survival were well estimated, except for survival when adults could emigrate out of the sampled area. Incomplete mixing was readily detected using Kolmogorov–Smirnov tests.
5. Although CKMR appears promising for estimating abundance and vital rates with opportunistically collected genetic data, care is needed when dispersal limitation is coupled with spatially biased sampling. Fortunately, incomplete mixing is easily detected with adequate sample sizes. In principle, it is possible to devise and fit spatially explicit CKMR models to avoid bias under dispersal limitation, but development of such models necessitates additional complexity (and possibly additional data). We suggest using simulation studies to examine potential bias and precision of proposed modeling approaches prior to implementing a CKMR program.

     

Site‐to‐site variation in the demography of a fire‐affected perennial. Acacia suaveolens,at Ku‐ring‐gai Chase National Park,New South Wales,Australia

Abstract Acacia suaveolens (Sm.) Willd is a perennial shrub that forms even‐aged stands, recruited from a soil seed‐bank following fire. It has previously been subject to demographic studies, which used a space‐for‐time substitution to investigate temporal patterns following fire. In the present study the potential for spatial variation across sites was investigated by sampling at several similarly aged populations in Ku‐ring‐gai Chase National Park, northern Sydney, Australia. Significant variation in mean size and fecundity of A. suaveolens individuals was observed among sites, over a 2‐4.6‐fold range in plant size, and a sevenfold range in mean fecundity. The observed variation at 3 years after fire encapsulated most of the variation previously observed among sites 0‐17 years since fire, emphasizing the importance of spatial variation in this species. For each site a two‐stage (seed, plant) matrix model was constructed, and projected from 3 to 25 years following fire. Population growth was measured as number of seeds per 3‐year‐old plant, and found to vary 1.4‐fold across models for different sites. This site‐to‐site variation, as well as that in size, fecundity and survival, was statistically significant. Variation in projected seeds per plant could mostly be attributed to differences in fecundity rather than plant survival. Sensitivity analyses emphasized the biological significance of the variation in fecundity. Whereas previous studies have focused on temporal variation, this work demonstrates the importance of extending our understanding of a species to include the spatial component of population dynamics.     

Sampling related individuals within ponds biases estimates of population structure in a pond‐breeding amphibian

Effective conservation and management of pond‐breeding amphibians depends on the accurate estimation of population structure, demographic parameters, and the influence of landscape features on breeding‐site connectivity. Population‐level studies of pond‐breeding amphibians typically sample larval life stages because they are easily captured and can be sampled nondestructively. These studies often identify high levels of relatedness between individuals from the same pond, which can be exacerbated by sampling the larval stage. Yet, the effect of these related individuals on population genetic studies using genomic data is not yet fully understood. Here, we assess the effect of within‐pond relatedness on population and landscape genetic analyses by focusing on the barred tiger salamanders (Ambystoma mavortium) from the Nebraska Sandhills. Utilizing genome‐wide SNPs generated using a double‐digest RADseq approach, we conducted standard population and landscape genetic analyses using datasets with and without siblings. We found that reduced sample sizes influenced parameter estimates more than the inclusion of siblings, but that within‐pond relatedness led to the inference of spurious population structure when analyses depended on allele frequencies. Our landscape genetic analyses also supported different models across datasets depending on the spatial resolution analyzed. We recommend that future studies not only test for relatedness among larval samples but also remove siblings before conducting population or landscape genetic analyses. We also recommend alternative sampling strategies to reduce sampling siblings before sequencing takes place. Biases introduced by unknowingly including siblings can have significant implications for population and landscape genetic analyses, and in turn, for species conservation strategies and outcomes.