Estimating the encounter rate variance in distance sampling

Summary .  The dominant source of variance in line transect sampling is usually the encounter rate variance. Systematic survey designs are often used to reduce the true variability among different realizations of the design, but estimating the variance is difficult and estimators typically approximate the variance by treating the design as a simple random sample of lines. We explore the properties of different encounter rate variance estimators under random and systematic designs. We show that a design-based variance estimator improves upon the model-based estimator of Buckland et al. (2001, Introduction to Distance Sampling. Oxford: Oxford University Press, p. 79) when transects are positioned at random. However, if populations exhibit strong spatial trends, both estimators can have substantial positive bias under systematic designs. We show that poststratification is effective in reducing this bias.     

A practical sampling design for acoustic surveys of bats

Acoustic surveys are widely used for describing bat occurrence and activity patterns and are increasingly important for addressing concerns for habitat management, wind energy, and disease on bat populations. Designing these surveys presents unique challenges, particularly when a probabilistic sample is required for drawing inference to unsampled areas. Sampling frame errors and other logistical constraints often require survey sites to be dropped from the sample and new sites added. Maintaining spatial balance and representativeness of the sample when these changes are made can be problematic. Spatially balanced sampling designs recently developed to support aquatic surveys along rivers provide solutions to a number of practical challenges faced by bat researchers and allow for sample site additions and deletions, support unequal-probability selection of sites, and provide an approximately unbiased local neighborhood-weighted variance estimator that is efficient for spatially structured populations such as is typical for bats. We implemented a spatially balanced design to survey canyon bat (Parastrellus hesperus) activity along a stream network. The spatially balanced design accommodated typical logistical challenges and yielded a 25% smaller estimated standard error for the mean activity level than the usual simple random sampling estimator. Spatially balanced designs have broad application to bat research and monitoring programs and will improve studies relying on model-based inference (e.g., occupancy models) by providing flexibility and protection against violations of the independence assumption, even if design-based estimators are not used. Our approach is scalable and can be used for pre- and post-construction surveys along wind turbine arrays and for regional monitoring programs. © 2011 The Wildlife Society.     

Standard errors for attributable risk for simple and complex sample designs

Adjusted attributable risk (AR) is the proportion of diseased individuals in a population that is due to an exposure. We consider estimates of adjusted AR based on odds ratios from logistic regression to adjust for confounding. Influence function methods used in survey sampling are applied to obtain simple and easily programmable expressions for estimating the variance of AR. These variance estimators can be applied to data from case-control, cross-sectional, and cohort studies with or without frequency or individual matching and for sample designs with subject samples that range from simple random samples to (sample) weighted multistage stratified cluster samples like those used in national household surveys. The variance estimation of AR is illustrated with: (i) a weighted stratified multistage clustered cross-sectional study of childhood asthma from the Third National Health and Examination Survey (NHANES III), and (ii) a frequency-matched case-control study of melanoma skin cancer.     

Empirically simulated study to compare and validate sampling methods used in aerial surveys of wildlife populations

This paper compares the distribution, sampling and estimation of abundance for two animal species in an African ecosystem by means of an intensive simulation of the sampling process under a geographical information system (GIS) environment. It focuses on systematic and random sampling designs, commonly used in wildlife surveys, comparing their performance to an adaptive design at three increasing sampling intensities, using the root mean square errors (RMSE). It further assesses the impact of sampling designs and intensities on estimates of population parameters. The simulation is based on data collected during a prior survey, in which geographical locations of all observed animals were recorded. This provides more detailed data than that usually available from transect surveys. The results show precision of estimates to increase with increasing sampling intensity, while no significant differences are observed between estimates obtained under random and systematic designs. An increase in precision is observed for the adaptive design, thereby validating the use of this design for sampling clustered populations. The study illustrates the benefits of combining statistical methods with GIS techniques to increase insight into wildlife population dynamics.     

不同采样设计评估鱼类群落效果比较

鱼类群落生态学研究结果的准确性很大程度上依赖于采样设计的合理性和准确性,正确的采样调查设计不仅可以降低调查成本,其结果也对渔业资源的评估或者管理起到相当重要的作用.本文利用计算机模拟定点采样、简单随机采样和分层采样,比较了3种采样设计的采样效果、相对误差及相对偏差.结果表明： 定点采样设计的采样效果 (采样效果平均值为3.37)要弱于简单随机采样和分层随机采样 (采样效果平均值为0.961).3种采样设计中,分层采样设计在鱼类群落丰富度评估时表现最好,其采样效果、相对误差和相对偏差表现最佳.随着采样数的增加,分层采样设计的采样效果有所下降,但其采样精度提高.     

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.     

Monitoring Svalbard rock ptarmigan: Distance sampling and occupancy modeling

The only resident terrestrial herbivorous bird species in high-Arctic Svalbard, Norway is the endemic Svalbard rock ptarmigan (Lagopus muta hyperborea) of which little is known of its population dynamics. We assessed temporal and spatial variability of the pre-breeding population of Svalbard rock ptarmigan males using: 1) distance sampling to estimate density (2000–2009) and 2) occupancy modeling to determine the proportion of survey points being occupied in relation to a habitat index for ptarmigan habitat suitability (2005–2009). Data were collected using a point-transect sampling design. We split the analysis according to type of survey point (non-random, random, and survey points combined). Our estimated spring densities were low (1.3–3.1 territorial male/km², non-random survey points, 2000–2009) with limited annual variability. The best models describing occupancy rates of territorial males at 2 different spatial scales (ptarmigan males observed ≤250 m and ≤450 m from the sampling point) were independent of spatial scales and the type of survey points. Occupancy dynamics were related to the habitat index whereas detection probability was year dependent. Extinction probability was negatively related to habitat quality (good habitats had lower extinction probability). We could not estimate the habitat effect on colonization precisely because initial occupancy rates were high at both spatial scales (estimated average initial occupancy at scale ≤250 m = 0.96; scale ≤450 m = 0.97). Colonization appeared to be positively related to the habitat index for the random survey points (including mainly marginal habitats), but the small sample size led to large uncertainty in the parameter estimate. Detection probabilities varied greatly between study years, thus demonstrating the importance of estimating detection probability annually. We recommend that future surveys are stratified with respect to habitat quality and to integrate the 2 methodologies in population monitoring of Svalbard rock ptarmigan. © 2011 The Wildlife Society.     

Variance of Stratified Survey Estimators With Probability of Detection Adjustments

Abstract: Estimates of wildlife population sizes are frequently constructed by combining counts of observed animals from a stratified survey of aerial sampling units with an estimated probability of detecting animals. Unlike traditional stratified survey designs, stratum-specific estimates of population size will be correlated if a common detection model is used to adjust counts for undetected animals in all strata. We illustrate this concept in the context of aerial surveys, considering 2 cases: 1) a single-detection parameter is estimated under the assumption of constant detection probabilities, and 2) a logistic-regression model is used to estimate heterogeneous detection probabilities. Naïve estimates of variance formed by summing stratum-specific estimates of variance may result in significant bias, particularly if there are a large number of strata, if detection probabilities are small, or if estimates of detection probabilities are imprecise. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):837–844; 2008)     

Atlantic bluefin tuna in the Gulf of Maine,II: precision of sampling designs in estimating seasonal abundance accounting for tuna behaviour

A primary challenge of animal surveys is to understand how to reliably sample populations exhibiting strong spatial heterogeneity. Building upon recent findings from survey, tracking and tagging data, we investigate spatial sampling of a seasonally resident population of Atlantic bluefin tuna in the Gulf of Maine, Northwestern Atlantic Ocean. We incorporate empirical estimates to parameterize a stochastic population model and simulate measurement designs to examine survey efficiency and precision under variation in tuna behaviour. We compare results for random, systematic, stratified, adaptive and spotter-search survey designs, with spotter-search comprising irregular transects that target surfacing schools and known aggregation locations (i.e., areas of expected high population density) based on a priori knowledge. Results obtained show how survey precision is expected to vary on average with sampling effort, in agreement with general sampling theory and provide uncertainty ranges based on simulated variance in tuna behaviour. Simulation results indicate that spotter-search provides the highest level of precision, however, measurable bias in observer-school encounter rate contributes substantial uncertainty. Considering survey bias, precision, efficiency and anticipated operational costs, we propose that an adaptive-stratified sampling alone or a combination of adaptive-stratification and spotter-search (a mixed-layer design whereby a priori information on the location and size of school aggregations is provided by sequential spotter-search sampling) may provide the best approach for reducing uncertainty in seasonal abundance estimates.

Multiple sensitive estimation and optimal sample size allocation in the item sum technique

For surveys of sensitive issues in life sciences, statistical procedures can be used to reduce nonresponse and social desirability response bias. Both of these phenomena provoke nonsampling errors that are difficult to deal with and can seriously flaw the validity of the analyses. The item sum technique (IST) is a very recent indirect questioning method derived from the item count technique that seeks to procure more reliable responses on quantitative items than direct questioning while preserving respondents' anonymity. This article addresses two important questions concerning the IST: (i) its implementation when two or more sensitive variables are investigated and efficient estimates of their unknown population means are required; (ii) the determination of the optimal sample size to achieve minimum variance estimates. These aspects are of great relevance for survey practitioners engaged in sensitive research and, to the best of our knowledge, were not studied so far. In this article, theoretical results for multiple estimation and optimal allocation are obtained under a generic sampling design and then particularized to simple random sampling and stratified sampling designs. Theoretical considerations are integrated with a number of simulation studies based on data from two real surveys and conducted to ascertain the efficiency gain derived from optimal allocation in different situations. One of the surveys concerns cannabis consumption among university students. Our findings highlight some methodological advances that can be obtained in life sciences IST surveys when optimal allocation is achieved.     

Catch estimation in the presence of declining catch rate due to gear saturation

One strategy for estimating total catch is to employ two separate surveys that independently estimate total fishing effort and catch rate with the estimator for total catch formed by their product. Survey designs for estimating catch rate often involve interviewing the fishermen during their fishing episodes. Such roving designs result in incomplete episode data and characteristically have employed a model in which the catch rate is assumed to be constant over time. This article extends the problem to that of estimating total catch in the presence of a declining catch rate due, e.g., to gear saturation. Using a gill net fishery as an example, a mean-of-ratios type of estimator for the catch rate together with its variance estimator are developed. Their performance is examined using simulations, with special attention given to effects of restrictions on the roving survey window. Finally, data from a Fraser River gill net fishery are used to illustrate the use of the proposed estimator and to compare results with those from an estimator based on a constant catch rate.     

Estimating abundance and spatial distribution patterns of the bubble crab Dotilla fenestrata (Crustacea: Brachyura)

Abstract The bubble crab Dotilla fenestrata forms very dense populations on the sand flats of the eastern coast of Inhaca Island, Mozambique, making it an interesting biological model to examine spatial distribution patterns and test the relative efficiency of common sampling methods. Due to its apparent ecological importance within the sandy intertidal community, understanding the factors ruling the dynamics of Dotilla populations is also a key issue. In this study, different techniques of estimating crab density are described, and the trends of spatial distribution of the different population categories are shown. The studied populations are arranged in discrete patches located at the well‐drained crests of nearly parallel mega sand ripples. For a given sample size, there was an obvious gain in precision by using a stratified random sampling technique, considering discrete patches as strata, compared to the simple random design. Density average and variance differed considerably among patches since juveniles and ovigerous females were found clumped, with higher densities at the lower and upper shore levels, respectively. Burrow counting was found to be an adequate method for large‐scale sampling, although consistently underestimating actual crab density by nearly half. Regression analyses suggested that crabs smaller than 2.9 mm carapace width tend to be undetected in visual burrow counts. A visual survey of sampling plots over several patches of a large Dotilla population showed that crab density varied in an interesting oscillating pattern, apparently following the topography of the sand flat. Patches extending to the lower shore contained higher densities than those mostly covering the higher shore. Within‐patch density variability also pointed to the same trend, but the density increment towards the lowest shore level varied greatly among the patches compared.     

Improving inference for aerial surveys of bears: The importance of assumptions and the cost of unnecessary complexity

Obtaining useful estimates of wildlife abundance or density requires thoughtful attention to potential sources of bias and precision, and it is widely understood that addressing incomplete detection is critical to appropriate inference. When the underlying assumptions of sampling approaches are violated, both increased bias and reduced precision of the population estimator may result. Bear (Ursus spp.) populations can be difficult to sample and are often monitored using mark‐recapture distance sampling (MRDS) methods, although obtaining adequate sample sizes can be cost prohibitive. With the goal of improving inference, we examined the underlying methodological assumptions and estimator efficiency of three datasets collected under an MRDS protocol designed specifically for bears. We analyzed these data using MRDS, conventional distance sampling (CDS), and open‐distance sampling approaches to evaluate the apparent bias‐precision tradeoff relative to the assumptions inherent under each approach. We also evaluated the incorporation of informative priors on detection parameters within a Bayesian context. We found that the CDS estimator had low apparent bias and was more efficient than the more complex MRDS estimator. When combined with informative priors on the detection process, precision was increased by >50% compared to the MRDS approach with little apparent bias. In addition, open‐distance sampling models revealed a serious violation of the assumption that all bears were available to be sampled. Inference is directly related to the underlying assumptions of the survey design and the analytical tools employed. We show that for aerial surveys of bears, avoidance of unnecessary model complexity, use of prior information, and the application of open population models can be used to greatly improve estimator performance and simplify field protocols. Although we focused on distance sampling‐based aerial surveys for bears, the general concepts we addressed apply to a variety of wildlife survey contexts.     

Proportionate spatial sampling and equal‐time sampling of mobile animals: A dilemma for inferring areal dependence

Abstract Patch or island area is one of the most frequently used variables for inference in conservation biology and biogeography, and is often used in ecological applications. Given that all of these disciplines deal with large spatial scales, exhaustive censusing is not often possible, especially when there are large numbers of patches (e.g. for replication and control purposes). Therefore, data for patches or islands are usually collected by sampling. We argue that if area is to be used as an inferential factor, then the objects under study (i.e. the patches) must be characterized on an areal basis. This necessarily means that fixed‐area sampling is inadequate (e.g. a single standard quadrat or transect set within patches irrespective of the patch area) and that some form of area‐proportionate sampling is needed (e.g. a fixed areal proportion of each patch is surveyed by random allocation of standard quadrats across each patch). However, use of area‐proportionate sampling is not usually dissociated from the increased temporal intensity of sampling that arises from using this approach. The dilemma we see is deciding how much of the area‐specificity of variables such as species richness, rare‐species indices or probabilities of occurrence of individual species is related to the area‐proportionate survey protocol and how much is due to the temporal intensity of surveys. We undertook a study in which we balanced temporal and spatial effects by increasing the time spent surveying smaller patches of vegetation to account for the area‐ratio difference. The estimated species richness of birds of the box–ironbark system of central Victoria, Australia, was found to depend strongly upon area when area‐proportionate sampling alone was performed. When time‐balancing was imposed upon area‐proportionate sampling, the differences between smaller (10‐ha) and larger (40‐ha) areas were much reduced or effectively disappeared. We show that species found in the additional surveys used to conduct the time‐balancing were significantly less abundant than species recorded in area‐proportionate sampling. This effect is probably most severe for mobile animals, but may emerge in other forms of sampling.     

The consequences of spatial structure for the design and analysis of ecological field surveys

In ecological field surveys, observations are gathered at different spatial locations. The purpose may be to relate biological response variables (e.g., species abundances) to explanatory environmental variables (e.g., soil characteristics). In the absence of prior knowledge, ecologists have been taught to rely on systematic or random sampling designs. If there is prior knowledge about the spatial patterning of the explanatory variables, obtained from either previous surveys or a pilot study, can we use this information to optimize the sampling design in order to maximize our ability to detect the relationships between the response and explanatory variables?
The specific questions addressed in this paper are: a) What is the effect (type I error) of spatial autocorrelation on the statistical tests commonly used by ecologists to analyse field survey data? b) Can we eliminate, or at least minimize, the effect of spatial autocorrelation by the design of the survey? Are there designs that provide greater power for surveys, at least under certain circumstances? c) Can we eliminate or control for the effect of spatial autocorrelation during the analysis? To answer the last question, we compared regular regression analysis to a modified t‐test developed by Dutilleul for correlation coefficients in the presence of spatial autocorrelation.
Replicated surfaces (typically, 1000 of them) were simulated using different spatial parameters, and these surfaces were subjected to different sampling designs and methods of statistical analysis. The simulated surfaces may represent, for example, vegetation response to underlying environmental variation. This allowed us 1) to measure the frequency of type I error (the failure to reject the null hypothesis when in fact there is no effect of the environment on the response variable) and 2) to estimate the power of the different combinations of sampling designs and methods of statistical analysis (power is measured by the rate of rejection of the null hypothesis when an effect of the environment on the response variable has been created).
Our results indicate that: 1) Spatial autocorrelation in both the response and environmental variables affects the classical tests of significance of correlation or regression coefficients. Spatial autocorrelation in only one of the two variables does not affect the test of significance. 2) A broad‐scale spatial structure present in data has the same effect on the tests as spatial autocorrelation. When such a structure is present in one of the variables and autocorrelation is found in the other, or in both, the tests of significance have inflated rates of type I error. 3) Dutilleul's modified t‐test for the correlation coefficient, corrected for spatial autocorrelation, effectively corrects for spatial autocorrelation in the data. It also effectively corrects for the presence of deterministic structures, with or without spatial autocorrelation.
The presence of a broad‐scale deterministic structure may, in some cases, reduce the power of the modified t‐test.     

Simulations inform design of regional occupancy‐based monitoring for a sparsely distributed,territorial species

Sparsely distributed species attract conservation concern, but insufficient information on population trends challenges conservation and funding prioritization. Occupancy‐based monitoring is attractive for these species, but appropriate sampling design and inference depend on particulars of the study system. We employed spatially explicit simulations to identify minimum levels of sampling effort for a regional occupancy monitoring study design, using white‐headed woodpeckers (Picoides albolvartus), a sparsely distributed, territorial species threatened by habitat decline and degradation, as a case study. We compared the original design with commonly proposed alternatives with varying targets of inference (i.e., species range, space use, or abundance) and spatial extent of sampling. Sampling effort needed to achieve adequate power to observe a long‐term population trend (≥80% chance to observe a 2% yearly decline over 20 years) with the previously used study design consisted of annually monitoring ≥120 transects using a single‐survey approach or ≥90 transects surveyed twice per year using a repeat‐survey approach. Designs that shifted inference toward finer‐resolution trends in abundance and extended the spatial extent of sampling by shortening transects, employing a single‐survey approach to monitoring, and incorporating a panel design (33% of units surveyed per year) improved power and reduced error in estimating abundance trends. In contrast, efforts to monitor coarse‐scale trends in species range or space use with repeat surveys provided extremely limited statistical power. Synthesis and applications. Sampling resolutions that approximate home range size, spatially extensive sampling, and designs that target inference of abundance trends rather than range dynamics are probably best suited and most feasible for broad‐scale occupancy‐based monitoring of sparsely distributed territorial animal species.     

Geochemical surveys in the United States in relation to health.

Geochemical surveys in relation to health may be classified as having one, two or three dimensions. One-dimensional surveys examine relations between concentrations of elements such as Pb in soils and other media and burdens of the same elements in humans, at a given time. The spatial distributions of element concentrations are not investigated. The primary objective of two-dimensional surveys is to map the distributions of element concentrations, commonly according to stratified random sampling designs based on either conceptual landscape units or artificial sampling strata, but systematic sampling intervals have also been used. Political units have defined sample areas that coincide with the units used to accumulate epidemiological data. Element concentrations affected by point sources have also been mapped. Background values, location of natural or technological anomalies and the geographic scale of variation for several elements often are determined. Three-dimensional surveys result when two-dimensional surveys are repeated to detect environmental changes.     

Evaluating sampling designs by computer simulation: a case study with the Missouri bladderpod

To effectively manage rare populations, accurate monitoring data are critical. Yet many monitoring programs are initiated without careful consideration of whether chosen sampling designs will provide accurate estimates of population parameters. Obtaining accurate estimates is especially difficult when natural variability is high, or limited budgets determine that only a small fraction of the population can be sampled. The Missouri bladderpod, Lesquerella filiformis Rollins, is a federally threatened winter annual that has an aggregated distribution pattern and exhibits dramatic interannual population fluctuations. Using the simulation program SAMPLE, we evaluated five candidate sampling designs appropriate for rare populations, based on 4 years of field data: (1) simple random sampling, (2) adaptive simple random sampling, (3) grid-based systematic sampling, (4) adaptive grid-based systematic sampling, and (5) GIS-based adaptive sampling. We compared the designs based on the precision of density estimates for fixed sample size, cost, and distance traveled. Sampling fraction and cost were the most important factors determining precision of density estimates, and relative design performance changed across the range of sampling fractions. Adaptive designs did not provide uniformly more precise estimates than conventional designs, in part because the spatial distribution of L. filiformis was relatively widespread within the study site. Adaptive designs tended to perform better as sampling fraction increased and when sampling costs, particularly distance traveled, were taken into account. The rate that units occupied by L. filiformis were encountered was higher for adaptive than for conventional designs. Overall, grid-based systematic designs were more efficient and practically implemented than the others. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Ranked set sampling for replicated sampling designs

In practical ecological sampling studies, a certain design (such as plot sampling or line-intercept sampling) is usually replicated more than once. For each replication, the Horvitz-Thompson estimation of the objective parameter is considered. Finally, an overall estimator is achieved by averaging the single Horvitz-Thompson estimators. Because the design replications are drawn independently and under the same conditions, the overall estimator is simply the sample mean of the Horvitz-Thompson estimators under simple random sampling. This procedure may be wisely improved by using ranked set sampling. Hence, we propose the replicated protocol under ranked set sampling, which gives rise to a more accurate estimation than the replicated protocol under simple random sampling.     

Optimizing noninvasive sampling of a zoonotic bat virus

Outbreaks of infectious viruses resulting from spillover events from bats have brought much attention to bat‐borne zoonoses, which has motivated increased ecological and epidemiological studies on bat populations. Field sampling methods often collect pooled samples of bat excreta from plastic sheets placed under‐roosts. However, positive bias is introduced because multiple individuals may contribute to pooled samples, making studies of viral dynamics difficult. Here, we explore the general issue of bias in spatial sample pooling using Hendra virus in Australian bats as a case study. We assessed the accuracy of different under‐roost sampling designs using generalized additive models and field data from individually captured bats and pooled urine samples. We then used theoretical simulation models of bat density and under‐roost sampling to understand the mechanistic drivers of bias. The most commonly used sampling design estimated viral prevalence 3.2 times higher than individual‐level data, with positive bias 5–7 times higher than other designs due to spatial autocorrelation among sampling sheets and clustering of bats in roosts. Simulation results indicate using a stratified random design to collect 30–40 pooled urine samples from 80 to 100 sheets, each with an area of 0.75–1 m², and would allow estimation of true prevalence with minimum sampling bias and false negatives. These results show that widely used under‐roost sampling techniques are highly sensitive to viral presence, but lack specificity, providing limited information regarding viral dynamics. Improved estimation of true prevalence can be attained with minor changes to existing designs such as reducing sheet size, increasing sheet number, and spreading sheets out within the roost area. Our findings provide insight into how spatial sample pooling is vulnerable to bias for a wide range of systems in disease ecology, where optimal sampling design is influenced by pathogen prevalence, host population density, and patterns of aggregation.