A Field Evaluation of Distance Measurement Error in Auditory Avian Point Count Surveys

ABSTRACT Detection distance is an important and common auxiliary variable measured during avian point count surveys. Distance data are used to determine the area sampled and to model the detection process using distance sampling theory. In densely forested habitats, visual detections of birds are rare, and most estimates of detection distance are based on auditory cues. Distance sampling theory assumes detection distances are measured accurately, but empirical validation of this assumption for auditory detections is lacking. We used a song playback system to simulate avian point counts with known distances in a forested habitat to determine the error structure of distance estimates based on auditory detections. We conducted field evaluations with 6 experienced observers both before and after distance estimation training. We conducted additional studies to determine the effect of height and speaker orientation (toward or away from observers) on distance estimation error. Distance estimation errors for all evaluations were substantial, although training reduced errors and bias in distance estimates by approximately 15%. Measurement errors showed a nonlinear relationship to distance. Our results suggest observers were not able to differentiate distances beyond 65 m. The height from which we played songs had no effect on distance estimation errors in this habitat. The orientation of the song source did have a large effect on distance estimation errors; observers generally doubled their distance estimates for songs played away from them compared with distance estimates for songs played directly toward them. These findings, which we based on realistic field conditions, suggest measures of uncertainty in distance estimates to auditory detections are substantially higher than assumed by most researchers. This means aural point count estimates of avian abundance based on distance methods deserve careful scrutiny because they are likely biased.     

Identifying error and accurately interpreting environmental DNA metabarcoding results: A case study to detect vertebrates at arid zone waterholes

Environmental DNA (eDNA) metabarcoding surveys enable rapid, noninvasive identification of taxa from trace samples with wide‐ranging applications from characterizing local biodiversity to identifying food‐web interactions. However, the technique is prone to error from two major sources: (a) contamination through foreign DNA entering the workflow, and (b) misidentification of DNA within the workflow. Both types of error have the potential to obscure true taxon presence or to increase taxonomic richness by incorrectly identifying taxa as present at sample sites, but multiple error sources can remain unaccounted for in metabarcoding studies. Here, we use data from an eDNA metabarcoding study designed to detect vertebrate species at waterholes in Australia's arid zone to illustrate where and how in the workflow errors can arise, and how to mitigate those errors. We detected the DNA of 36 taxa spanning 34 families, 19 orders and five vertebrate classes in water samples from waterholes, demonstrating the potential for eDNA metabarcoding surveys to provide rapid, noninvasive detection in remote locations, and to widely sample taxonomic diversity from aquatic through to terrestrial taxa. However, we initially identified 152 taxa in the samples, meaning there were many false positive detections. We identified the sources of these errors, allowing us to design a stepwise process to detect and remove error, and provide a template to minimize similar errors that are likely to arise in other metabarcoding studies. Our findings suggest eDNA metabarcoding surveys need to be carefully conducted and screened for errors to ensure their accuracy.     

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.     

The false classification of extinction risk in noisy environments

Abundance trends are the basis for many classifications of threat and recovery status, but they can be a challenge to interpret because of observation error, stochastic variation in abundance (process noise) and temporal autocorrelation in that process noise. To measure the frequency of incorrectly detecting a decline (false-positive or false alarm) and failing to detect a true decline (false-negative), we simulated stable and declining abundance time series across several magnitudes of observation error and autocorrelated process noise. We then empirically estimated the magnitude of observation error and autocorrelated process noise across a broad range of taxa and mapped these estimates onto the simulated parameter space. Based on the taxa we examined, at low classification thresholds (30% decline in abundance) and short observation windows (10 years), false alarms would be expected to occur, on average, about 40% of the time assuming density-independent dynamics, whereas false-negatives would be expected to occur about 60% of the time. However, false alarms and failures to detect true declines were reduced at higher classification thresholds (50% or 80% declines), longer observation windows (20, 40, 60 years), and assuming density-dependent dynamics. The lowest false-positive and false-negative rates are likely to occur for large-bodied, long-lived animal species.     

Observer error in vegetation surveys: a review

Aims Vegetation sampling employing observers is prone to both inter-observer and intra-observer error. Three types of errors are common: (i) overlooking error (i.e. not observing species actually present), (ii) misidentification error (i.e. not correctly identifying species) and (iii) estimation error (i.e. not accurately estimating abundance). I conducted a literature review of 59 articles that provided quantitative estimates or statistical inferences regarding observer error in vegetation studies.Important findings Almost all studies (92%) that tested for a statistically significant effect of observer error found at least one significant comparison. In surveys of species composition, mean pseudoturnover (the percentage of species overlooked by one observer but not another) was 10–30%. Species misidentification rates were on the order of 5–10%. The mean coefficient of variation (CV) among observers in surveys of vegetation cover was often several hundred % for species with low cover, although CVs of 25–50% were more representative of species with mean covers of>50%. A variety of metrics and indices (including commonly used diversity indices) and multivariate data analysis techniques (including ordinations and classifications) were found to be sensitive to observer error. Sources of error commonly include both characteristics of the vegetation (e.g. small size of populations, rarity, morphology, phenology) and attributes of the observers (e.g. mental fatigue, personal biases, differences in experience, physical stress). The use of multiple observers, additional training including active feedback approaches, and continual evaluation and calibration among observers are recommended as strategies to reduce observer error in vegetation surveys.     

Effects of ambient noise on detectability and localization of avian songs and tones by observers in grasslands

Probability of detection and accuracy of distance estimates in aural avian surveys may be affected by the presence of anthropogenic noise, and this may lead to inaccurate evaluations of the effects of noisy infrastructure on wildlife. We used arrays of speakers broadcasting recordings of grassland bird songs and pure tones to assess the probability of detection, and localization accuracy, by observers at sites with and without noisy oil and gas infrastructure in south‐central Alberta from 2012 to 2014. Probability of detection varied with species and with speaker distance from transect line, but there were few effects of noisy infrastructure. Accuracy of distance estimates for songs and tones decreased as distance to observer increased, and distance estimation error was higher for tones at sites with infrastructure noise. Our results suggest that quiet to moderately loud anthropogenic noise may not mask detection of bird songs; however, errors in distance estimates during aural surveys may lead to inaccurate estimates of avian densities calculated using distance sampling. We recommend caution when applying distance sampling if most birds are unseen, and where ambient noise varies among treatments.     

A multispecies dependent double‐observer model: A new method for estimating multispecies abundance

Conservation of biological communities requires accurate estimates of abundance for multiple species. Recent advances in estimating abundance of multiple species, such as Bayesian multispecies N‐mixture models, account for multiple sources of variation, including detection error. However, false‐positive errors (misidentification or double counts), which are prevalent in multispecies data sets, remain largely unaddressed. The dependent‐double observer (DDO) method is an emerging method that both accounts for detection error and is suggested to reduce the occurrence of false positives because it relies on two observers working collaboratively to identify individuals. To date, the DDO method has not been combined with advantages of multispecies N‐mixture models. Here, we derive an extension of a multispecies N‐mixture model using the DDO survey method to create a multispecies dependent double‐observer abundance model (MDAM). The MDAM uses a hierarchical framework to account for biological and observational processes in a statistically consistent framework while using the accurate observation data from the DDO survey method. We demonstrate that the MDAM accurately estimates abundance of multiple species with simulated and real multispecies data sets. Simulations showed that the model provides both precise and accurate abundance estimates, with average credible interval coverage across 100 repeated simulations of 94.5% for abundance estimates and 92.5% for detection estimates. In addition, 92.2% of abundance estimates had a mean absolute percent error between 0% and 20%, with a mean of 7.7%. We present the MDAM as an important step forward in expanding the applicability of the DDO method to a multispecies setting. Previous implementation of the DDO method suggests the MDAM can be applied to a broad array of biological communities. We suggest that researchers interested in assessing biological communities consider the MDAM as a tool for deriving accurate, multispecies abundance estimates.     

Ambient noise increases missed detections in nestling birds

Ambient noise can mask acoustic cues, making their detection and discrimination difficult for receivers. This can result in two types of error: missed detections, when receivers fail to respond to the appropriate cues, and false alarms, when they respond to inappropriate cues. Nestling birds are error-prone, sometimes failing to beg when parents arrive with food (committing missed detections) or begging in response to stimuli other than a parent's arrival (committing false alarms). Here, we ask whether the frequency of these errors by nestling tree swallows (Tachycineta bicolor) increases in the presence of noise. We found that nestlings exposed to noise had more missed detections than their unexposed counterparts. We also found that false alarms remained low overall and did not differ significantly between noise and quiet treatments. Our results suggest that nestlings living in noisy environments may be less responsive to their parents than nestlings in quieter environments.     

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.     

Abundance estimates to inform butterfly management: double-observer versus distance sampling

Abundance estimates are used to establish baselines, set recovery targets, and assess management actions, all of which are essential aspects of evidence-based natural resource management. For many rare butterflies, these estimates do not exist, and conservation decisions rely instead on expert opinion. Using Bartram’s scrub-hairstreak (Strymon acis bartrami, US Endangered) as a case study, we present a novel comparison of two methods that permit the incorporation of detection probabilities into abundance estimates, distance sampling and double-observer surveys. Additionally we provide a framework for establishing a systematic sampling scheme for monitoring very rare butterflies. We surveyed butterflies monthly in 2013, increasing intensity to weekly when butterflies were detected. We conducted 19 complete, island-wide surveys on Big Pine Key in the Florida Keys, detecting a total of 59 Bartram’s scrub-hairstreaks across all surveys. Peak daily abundances were similar as estimated with distance sampling, 156 butterflies (95 % CI 65–247), and double-observer, 169 butterflies (95 % CI 65–269). Selecting a method for estimating abundance of rare species involves evaluating trade-offs between methods. Distance sampling requires at least 40 detections, but only one observer, while double-observer requires only 10 detections, but two observers. Double-observer abundance estimates agreed with distance sampling estimates, which suggests that double-observer is a reasonable alternative method to use for estimating detection probability and abundance for rare species that cannot be surveyed with other, more commonly used methods.     

Replication levels,false presences and the estimation of the presence/absence from eDNA metabarcoding data

Environmental DNA (eDNA) metabarcoding is increasingly used to study the present and past biodiversity. eDNA analyses often rely on amplification of very small quantities or degraded DNA. To avoid missing detection of taxa that are actually present (false negatives), multiple extractions and amplifications of the same samples are often performed. However, the level of replication needed for reliable estimates of the presence/absence patterns remains an unaddressed topic. Furthermore, degraded DNA and PCR/sequencing errors might produce false positives. We used simulations and empirical data to evaluate the level of replication required for accurate detection of targeted taxa in different contexts and to assess the performance of methods used to reduce the risk of false detections. Furthermore, we evaluated whether statistical approaches developed to estimate occupancy in the presence of observational errors can successfully estimate true prevalence, detection probability and false‐positive rates. Replications reduced the rate of false negatives; the optimal level of replication was strongly dependent on the detection probability of taxa. Occupancy models successfully estimated true prevalence, detection probability and false‐positive rates, but their performance increased with the number of replicates. At least eight PCR replicates should be performed if detection probability is not high, such as in ancient DNA studies. Multiple DNA extractions from the same sample yielded consistent results; in some cases, collecting multiple samples from the same locality allowed detecting more species. The optimal level of replication for accurate species detection strongly varies among studies and could be explicitly estimated to improve the reliability of results.     

Improving geographically extensive acoustic survey designs for modeling species occurrence with imperfect detection and misidentification

Acoustic recording units (ARUs) enable geographically extensive surveys of sensitive and elusive species. However, a hidden cost of using ARU data for modeling species occupancy is that prohibitive amounts of human verification may be required to correct species identifications made from automated software. Bat acoustic studies exemplify this challenge because large volumes of echolocation calls could be recorded and automatically classified to species. The standard occupancy model requires aggregating verified recordings to construct confirmed detection/non‐detection datasets. The multistep data processing workflow is not necessarily transparent nor consistent among studies. We share a workflow diagramming strategy that could provide coherency among practitioners. A false‐positive occupancy model is explored that accounts for misclassification errors and enables potential reduction in the number of confirmed detections. Simulations informed by real data were used to evaluate how much confirmation effort could be reduced without sacrificing site occupancy and detection error estimator bias and precision. We found even under a 50% reduction in total confirmation effort, estimator properties were reasonable for our assumed survey design, species‐specific parameter values, and desired precision. For transferability, a fully documented r package, OCacoustic, for implementing a false‐positive occupancy model is provided. Practitioners can apply OCacoustic to optimize their own study design (required sample sizes, number of visits, and confirmation scenarios) for properly implementing a false‐positive occupancy model with bat or other wildlife acoustic data. Additionally, our work highlights the importance of clearly defining research objectives and data processing strategies at the outset to align the study design with desired statistical inferences.     

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.     

Potentiality and limitations of N-mixture and Royle-Nichols models to estimate animal abundance based on noninstantaneous point surveys

Reliable and accurate information on animal abundance is fundamental for the conservation and management of wildlife. Recently, a number of innovative devices (such as camera traps) have been widely used in field surveys and have largely improved survey efficiency. However, these devices often constitute noninstantaneous point surveys, resulting in the multiple counts of the same animal individuals within a single sampling occasion (i.e., false-positive errors). Many commonly-used statistical models do not explicitly account for the false-positive error, with its effects on estimates being poorly understood. Here, I tested the performance of the commonly-used Poisson-binomial N-mixture and the Royle-Nichols model in the presence of both false-positive and negative errors (i.e., individuals in a population might not be detected). I also implemented the Poisson-Poisson mixture model in the Bayesian framework to evaluate its reliability. The results of the simulation using random walks based on Ornstein-Uhlenbeck processes showed that the Poisson-binomial model was not robust to false-positive errors. In comparison, the Royle-Nichols and Poisson-Poisson models provided reasonable estimates of the number of animals whose home range included the survey point. However, the number of animals whose home range included the survey point is inherently influenced by the size of animal home ranges, and thus cannot be used as a surrogate of animal density. Although the N-mixture and Royle-Nichols models are widely used, their utility might be restricted by this limitation. In conclusion, studies should clearly define the objective of surveys and carefully consider whether the models used are valid.     

Handling dolphin detections from C-PODs,with the development of acoustic parameters for verification and the exploration of species identification possibilities

C-PODs are static passive acoustic monitoring devices used to detect odontocete vocalizations in the range of 20–160 kHz. However, falsely classified detections may be an issue, particularly with broadband species (i.e. many dolphin species) due to anthropogenic and other noise occurring at the same frequency. While porpoise detections are verified using species-specific acoustic parameters, the equivalent does not currently exist for verifying dolphin detections. Development of such parameters would increase the accuracy of dolphin detections and eliminate the need for additional monitoring techniques or devices, reducing the cost of monitoring programmes. Herein, we present parameters based on acoustic characteristics of bottlenose (n = 29), common (n = 19) and Risso’s (n = 99) dolphin click trains, sighted within 1 km of C-PODs during land-based surveys, for in-software verification. Overlap of click train parameters among dolphin species prevented robust species identification; therefore, parameters were devised for these dolphin species collectively using frequency, inter-click interval and click train duration. A data set of 4898 Detection Positive Hours was visually verified using these parameters. The temporal and spatial patterns in the visually verified data were similar to land-based observations, suggesting the parameters operate at an acceptable accuracy. However, 68% of high-, moderate- and low-quality KERNO detections were false-positive. Our results suggest that the accuracy of classifiers and quality class weightings are site-specific, and we highlight the importance of data exploration to make the most appropriate software choices based on the aims of a study.     

Geographical patterns in prediction errors of species distribution models

Aim To describe and explain geographical patterns of false absence and false presence prediction errors that occur when describing current plant species ranges with species distribution models. Location Europe. Methods We calibrated species distribution models (generalized linear models) using a set of climatic variables and gridded distribution data for 1065 vascular plant species from the Atlas Florae Europaeae. We used randomly selected subsets for each species with a constant prevalence of 0.5, modelled the distribution 1000 times, calculated weighted averages of the model parameters and used these to predict the current distribution in Europe. Using a threshold of 0.5, we derived presence/absence maps. Comparing observed and modelled species distribution, we calculated the false absence rates, i.e. species wrongly modelled as absent, and the false presence rates, i.e. species wrongly modelled as present, on a 50 × 50 km grid. Subsequently, we related both error rates to species range properties, land use and topographic variability within grid cells by means of simultaneous autoregressive models to correct for spatial autocorrelation. Results Grid‐cell‐specific error rates were not evenly distributed across Europe. The mean false absence rate was 0.16 ± 0.12 (standard deviation) and the mean false presence rate was 0.22 ± 0.13. False absence rates were highest in central Spain, the Alps and parts of south‐eastern Europe, while false presence rates were highest in northern Spain, France, Italy and south‐eastern Europe. False absence rates were high when range edges of species accumulated within a grid cell and when the intensity of human land use was high. False presence rates were positively associated with relative occurrence area and accumulation of range edges. Main conclusions Predictions for various species are not only accompanied by species‐specific but also by grid‐cell‐specific errors. The latter are associated with characteristics of the grid cells but also with range characteristics of occurring species. Uncertainties of predictive species distribution models are not equally distributed in space, and we would recommend accompanying maps of predicted distributions with a graphical representation of predictive performance.     

Testing the importance of auditory detections in avian point counts

ABSTRACT.   Recent advances in the methods used to estimate detection probability during point counts suggest that the detection process is shaped by the types of cues available to observers. For example, models of the detection process based on distance-sampling or time-of-detection methods may yield different results for auditory versus visual cues because of differences in the factors that affect the transmission of these cues from a bird to an observer or differences in an observer's ability to localize cues. Previous studies suggest that auditory detections predominate in forested habitats, but it is not clear how often observers hear birds prior to detecting them visually. We hypothesized that auditory cues might be even more important than previously reported, so we conducted an experiment in a forested habitat in North Carolina that allowed us to better separate auditory and visual detections. Three teams of three observers each performed simultaneous 3-min unlimited-radius point counts at 30 points in a mixed-hardwood forest. One team member could see, but not hear birds, one could hear, but not see, and the third was nonhandicapped. Of the total number of birds detected, 2.9% were detected by deafened observers, 75.1% by blinded observers, and 78.2% by nonhandicapped observers. Detections by blinded and nonhandicapped observers were the same only 54% of the time. Our results suggest that the detection of birds in forest habitats is almost entirely by auditory cues. Because many factors affect the probability that observers will detect auditory cues, the accuracy and precision of avian point count estimates are likely lower than assumed by most field ornithologists.     

Errors in aerial survey count data: Identifying pitfalls and solutions

Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. However, these surveys can yield unreliable data if not carefully executed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. Through an extensive review of the aerial survey literature over the last 50 years, we evaluated how common problems encountered in the data (including nondetection, counting error, and species misidentification) can manifest, the potential difficulties conferred, and the history of how these challenges have been addressed. Additionally, we used a double‐observer case study focused on waterbird data collected via aerial surveys and an online group (flock) counting quiz to explore the potential extent of each challenge and possible resolutions. We found that nearly three quarters of the aerial survey methodology literature focused on accounting for nondetection errors, while issues of counting error and misidentification were less commonly addressed. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. Our results illustrate how each issue can act to bias inferences, highlighting the importance of considering individual methods for mitigating potential problems separately during survey design and analysis. We synthesized the information gained from our analyses to evaluate strategies for overcoming the challenges of using aerial survey data to estimate wildlife abundance, such as digital data collection methods, pooling species records by family, and ordinal modeling using binned data. Recognizing conditions that can lead to data collection errors and having reasonable solutions for addressing errors can allow researchers to allocate resources effectively to mitigate the most significant challenges for obtaining reliable aerial survey data.     

Field assessment of C‐POD performance in detecting echolocation click trains of bottlenose dolphins (Tursiops truncatus)

We evaluated the performance of dolphin echolocation detectors (C‐PODs) in the New River, North Carolina, by ground‐truthing echolocation detections with digital acoustic recordings. We deployed C‐PODs at three sites for a total of 204 monitoring hours. We also performed detection range trials at two sites where water depths ranged from 1.0 to 4.5 m. We used Detection Positive Minutes (DPMs), minutes of C‐POD recordings that contained at least one echolocation click train, to indicate the presence of at least one dolphin. The C‐PODs performed well in detecting dolphin click trains, although all units performed conservatively by failing to detect some echolocation events and therefore underestimated the true occurrence of dolphins. C‐PODs reported only a small number of false detections, as indicated by low false positive rates ranging between 1% and 4% for individual units. Overall, C‐PODs performed with a high accuracy (72%–91%) and detected echolocation at a distance of at least 933 m. We conclude that C‐PODs hold considerable promise in future monitoring studies of this species, but recommend a careful study design especially in complex, coastal environments.     

Sampling Plant Diversity and Rarity at Landscape Scales: Importance of Sampling Time in Species Detectability

Documenting and estimating species richness at regional or landscape scales has been a major emphasis for conservation efforts, as well as for the development and testing of evolutionary and ecological theory. Rarely, however, are sampling efforts assessed on how they affect detection and estimates of species richness and rarity. In this study, vascular plant richness was sampled in 356 quarter hectare time-unlimited survey plots in the boreal region of northeast Alberta. These surveys consisted of 15,856 observations of 499 vascular plant species (97 considered to be regionally rare) collected by 12 observers over a 2 year period. Average survey time for each quarter-hectare plot was 82 minutes, ranging from 20 to 194 minutes, with a positive relationship between total survey time and total plant richness. When survey time was limited to a 20-minute search, as in other Alberta biodiversity methods, 61 species were missed. Extending the survey time to 60 minutes, reduced the number of missed species to 20, while a 90-minute cut-off time resulted in the loss of 8 species. When surveys were separated by habitat type, 60 minutes of search effort sampled nearly 90% of total observed richness for all habitats. Relative to rare species, time-unlimited surveys had ∼65% higher rare plant detections post-20 minutes than during the first 20 minutes of the survey. Although exhaustive sampling was attempted, observer bias was noted among observers when a subsample of plots was re-surveyed by different observers. Our findings suggest that sampling time, combined with sample size and observer effects, should be considered in landscape-scale plant biodiversity surveys.