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.     

An Evaluation of Survey Methods for Estimating Northern Bobwhite Abundance in Southern Texas

Abstract: Distance sampling has been identified as a reliable and well-suited method for estimating northern bobwhite (Colinus virginianus) density. However, distance sampling using walked transects requires intense sampling to obtain precise estimates, thus making the technique impractical for large acreages. Researchers have addressed this limitation by either resorting to the use of indices (e.g., morning covey-call surveys) or incorporating the use of aerial surveys with distance sampling. Both approaches remain relatively untested. Our objectives were to 1) compare density estimates among morning covey-call surveys, helicopter transects, and walked transects; 2) test a critical assumption of distance sampling pertinent to helicopter surveys (i.e., all objects on line are detected); and 3) evaluate the underlying premise of morning covey-call surveys (i.e., that the no. of calling coveys correlates with bobwhite density). Our study was conducted on 3 study sites in Brooks County, Texas, USA, during October to December, 2001 to 2005. Comparisons between walked transects and morning covey-call surveys involved the entire 5-year data set, whereas helicopter transects involved only the latter 2 years. Density estimates obtained from helicopter transects were similar to walked transect estimates for both years. We documented a detection probability on the helicopter transect line of 70 ± 10.2% (% ± SE; n = 20 coveys). Morning covey-call surveys yielded similar density estimates to walked transect estimates during only 2 of 5 years, when walked transect estimates were the least accurate and precise. We detected a positive relationship (R² = 0.51; 95% CI for slope: 29.5–53.1; n = 63 observations) between covey density and number of coveys heard calling. We conclude that helicopter transects appear to be a viable alternative to walked transects for estimating density of bobwhites. Morning covey-call surveys appear to be a poor method to estimate absolute abundance and to depict general population trajectories.     

Detection Probabilities for Ground-Based Breeding Waterfowl Surveys

ABSTRACT Current methods for conducting ground-based surveys of breeding waterfowl pairs make the unlikely assumption that detection probabilities are constant and approach 100%. To test this assumption, we conducted independent double-observer pair surveys in North Dakota, USA, to evaluate sources of variation in detection probabilities for 8 common species of prairie-nesting ducks. An experienced observer had 0.911 detection probability averaged over all 8 species (range = 0.866-0.944) versus 0.790 (range = 0.537-0.890) for a novice observer. Detection probabilities also varied substantially among species, but patterns were not consistent between observers. Detection probabilities declined as number of ducks per wetland increased, presumably due to difficulty in identifying large numbers of flushing ducks. Other covariates affecting detection probabilities included size of social groups, precipitation, survey methodology (roadside vs. walk-up), cloud cover, time of day, and amount of wetland vegetation, but these covariates only affected detection probabilities by 2–5%. Our results demonstrated that the assumption of 100% detection probabilities for ground-based waterfowl counts was clearly false and surveys based on this erroneous assumption underestimated population size by 10–29%. We recommend that future investigators measure detection probabilities explicitly by using double-observer methodologies.     

Raising the bar for the next generation of biological atlases: using existing data to inform the design and implementation of atlas monitoring

Selecting a sampling design to monitor multiple species across a broad geographical region can be a daunting task and often involves tradeoffs between limited resources and the accurate estimation of population abundance and occurrence. Since the 1950s, biological atlases have been implemented in various regions to document the occurrence of plant and animal species. As next‐generation atlases repeat original surveys, investigators often seek to raise the rigour of atlases by incorporating species abundances. We present a repeatable framework that incorporates existing monitoring data, hierarchical modelling and sampling simulations to augment existing atlas occurrence and breeding status maps with a secondary sampling of species abundances. Using existing information on three bird species with varying abundance and detectability, we evaluated several sampling scenarios for the 2nd Wisconsin Breeding Bird Atlas. In general, we found that most sampling schemes produced accurate mean statewide abundance estimates for species with medium to high abundance and detection probability, but estimates varied significantly for species with low abundance and low detection probability. Our approach provided a statewide point‐count sampling design that: provided precise and unbiased abundance estimates for species of varied prevalence and detectability; ensured suitable spatial coverage across the state and its habitats; and reduced spending on total survey costs. Our framework could benefit investigators conducting atlases and other broad‐scale avian surveys that seek to add systematic, multi‐species sampling for estimating density and abundance across broad geographical regions.     

Using the double-observer method to estimate detection probability of two cavity-nesting seabirds in Antarctica: the snow petrel (Pagodroma nivea) and the Wilson��s storm petrel (Oceanites oceanicus)

When estimating the size of seabird populations, count data may be biased due to various factors such as detection probability. Failing to account for detection probability in surveys may lead to an underestimate of population size and may compromise the ability to monitor trends if detection probability varies among surveys. Here, we use the double-observer method to estimate detection probability of cavity-nesting snow petrels (Pagodroma nivea) and Wilson’s storm petrels (Oceanites oceanicus) in East Antarctica. Estimates of single-visit detection probability of nesting/roosting adult snow petrels during the incubation stage of the breeding cycle ranged from 0.86 (SE = 0.04) to 0.87 (SE = 0.04) depending upon observers. Both observers found snow petrel chicks were easier to detect than adults, with estimated detection probability for chicks ranging from 0.92 (SE = 0.03) to 1.00 (SE = 0.34 × 10⁻⁵). Detection probability of adult and chick snow petrels increased as cavity volume increased. Compared to snow petrels, estimated detection probability was considerably lower for nesting/roosting Wilson’s storm petrels, ranging from 0.27 (SE = 0.09) to 0.50 (SE = 0.13) for each observer. These estimates of detection probability apply only to those individuals in the population that were potentially viewable or audible. Nevertheless, our results indicate that double-observer counts for ground surveys of cavity-nesting seabirds should improve estimates of population abundance in comparison with single-visit counts. Accounting for observer effects, habitat characteristics and stage of the breeding season on detection probability should also improve estimation of population trends.     

Estimation of Detection Probability in Manatee Aerial Surveys at a Winter Aggregation Site

Abstract: Estimating components of detection probability is crucial to improving the design of aerial surveys for wildlife populations, and this is especially true for species of marine mammals that are threatened or endangered. To evaluate the probability that Florida manatees (Trichechus manatus latirostris) will be detected by observers during aerial surveys, we conducted 6 series of survey flights, during mornings and afternoons on 14-16 consecutive days over the Tampa Electric Company's (TECO) Big Bend power plant discharge canal in Tampa Bay, Florida, USA (winter 2000 through 2003). Our objective was to understand how our ability to detect manatees at a winter aggregation site affects aerial survey counts, so that we may improve techniques for estimating manatee population size. We estimated the probability that manatees would be present at the warm-water discharge of the plant during winter cold fronts and estimated the overall detection probability of manatees present at the plant and the 2 components that make up the probability of detection (the probability of being available and the probability of being detected given they are available). We used telemetry tags and marker flags (n = 15) to facilitate capture-recapture analyses. The probability that marked manatees would be at the plant varied from 48% to 68% across flight series and was inversely related to the ambient water temperature. Based on sightings of marked animals, estimates of the overall probability of detecting a manatee ranged from 45% to 69% across flight series (x̄ = 58%, n = 6). The probability that a manatee would be available to an observer ranged from 73% to 94% across flight series (x̄ = 83%) but was constant among years (83%, 81%, and 78%; x̄ = 81%). The probability that an available manatee would be detected by an aerial observer was variable across flight series (55-95%) and years (73%, 86%, and 66%, x̄ = 73%). Independent estimates of the probability that a manatee would be available to the observer on one pass were obtained from time-depth data loggers and ranged from 5% to 33% (x̄ = 19%, SE = 3.7%), and the probability that a manatee would be available during ≥1 of 10 passes ranged from 41% to 98% (x̄ = 88%, 95% confidence bounds 0.71-0.95). We adjusted survey counts using measures of detectability. Although corrected counts presented here are site-specific, adjusting counts based on detection probability will greatly improve reliability of population estimates from all aerial surveys. Special sampling to estimate components of detection probability should be built into all aerial surveys to ensure that reliable and unbiased information on species abundance is used to evaluate wildlife populations.     

Separating Components of the Detection Process With Combined Methods: An Example With Northern Bobwhite

Abstract: There are various methods of estimating detection probabilities for avian point counts. Distance and multiple-observer methods require the sometimes unlikely assumption that all birds in the population are available (i.e., sing or are visible) during a count, but the time-of-detection method allows for the possibility that some birds are unavailable during the count. We combined the dependent double-observer method with the time-of-detection method and obtained field-based estimates of the components of detection probability for northern bobwhite (Colinus virginianus). Our approach was a special case of Pollock's robust capture-recapture design where the probability that a bird does not sing is analogous to the probability that an animal is a temporary emigrant. Top models indicated that observers' detection probabilities were similar (0.78–0.84) if bobwhite were available, but bobwhite only had an approximately 0.61 probability of being available during a 2.5-minute sampling interval. Additionally, observers' detection probabilities increased substantially after the initial encounter with an individual bobwhite (analogous to a trap-happy response on the part of the observer). A simulated data set revealed that the combined method was precise when availability and detection given availability were substantially lower. Combined methods approaches can provide critical information for researchers and land managers to make decisions regarding survey length and personnel requirements for point-count-based 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.     

Road-Based Surveys for Estimating Wild Turkey Density in the Texas Rolling Plains

Abstract: Line-transect-based distance sampling has been used to estimate density of several wild bird species including wild turkeys (Meleagris gallopavo). We used inflatable turkey decoys during autumn (Aug-Nov) and winter (Dec-Mar) 2003-2005 at study sites in the Texas Rolling Plains, USA, to simulate Rio Grande wild turkey (M. g. intermedia) flocks. We evaluated detectability of flocks using logistic regression models. Our modeling effort suggested that distance to a flock and flock size played important roles in flock detectability. We also conducted surveys from roads for wild turkeys during November 2004-January 2006. The detection probability of decoy flocks was similar to wild turkey flocks during winter (decoy flock, 69.3 ± 6.2% [x̄ ± 95% CI]; wild turkey flock, 62.2 ± 18.3%) and autumn (decoy flock, 44.1 ± 5.1%; wild turkey flock, 44.7 ± 25.6%), which suggested that using decoys was appropriate for evaluating detectability of wild turkey flocks from roads. We conducted computer simulations to evaluate the performance of line-transect-based distance sampling and examined the power to detect trends in population change. Simulations suggested that population density may be underestimated by 12% during inter and 29% during autumn. Such bias occurred because of incomplete detectability of flocks near roads. Winter surveys tended to have less bias, lower relative variability, and greater power than did autumn surveys. During winter surveys, power was sufficient (≥0.80) to detect a 10-25% change in population density in 8-12 years using ≥100 16-km transects or ≥80 32-km transects. We concluded line-transect-based distance sampling from roads is an efficient, effective, and inexpensive technique for monitoring Rio Grande wild turkey populations across large scales.     

A Unimodal Model for Double Observer Distance Sampling Surveys

Distance sampling is a widely used method to estimate animal population size. Most distance sampling models utilize a monotonically decreasing detection function such as a half-normal. Recent advances in distance sampling modeling allow for the incorporation of covariates into the distance model, and the elimination of the assumption of perfect detection at some fixed distance (usually the transect line) with the use of double-observer models. The assumption of full observer independence in the double-observer model is problematic, but can be addressed by using the point independence assumption which assumes there is one distance, the apex of the detection function, where the 2 observers are assumed independent. Aerially collected distance sampling data can have a unimodal shape and have been successfully modeled with a gamma detection function. Covariates in gamma detection models cause the apex of detection to shift depending upon covariate levels, making this model incompatible with the point independence assumption when using double-observer data. This paper reports a unimodal detection model based on a two-piece normal distribution that allows covariates, has only one apex, and is consistent with the point independence assumption when double-observer data are utilized. An aerial line-transect survey of black bears in Alaska illustrate how this method can be applied.     

Estimating Detection Probabilities of Waterfowl Broods From Ground-Based Surveys

ABSTRACT Brood:pair ratios could provide an economical method for assessing spatial or temporal variation in waterfowl productivity, but such estimators are severely biased by incomplete detection of broods. We conducted 3 sequential counts of 1,357 waterfowl broods in northeastern North Dakota, USA, and used closed-population mark-recapture models to estimate total brood abundance while controlling for variation in detection probabilities (p). Blue-winged teal (Anas discors) broods had the lowest average detection probability (p = 0.305), whereas diving-duck broods had the highest average detectability (p = 0.571). Detection was generally highest in morning or evening, but temporal patterns varied among species and there was no survey window that maximized detection probabilities for all species. Detection probabilities averaged 0.108 (SD = 0.056) higher for an experienced observer versus an inexperienced observer. Detection probabilities were 0.044 higher for roadside versus walk-up surveys and increased with increasing brood size, total brood abundance, survey date, wind speed, temperature, cloud cover, and amount of time spent surveying each wetland. Detection probabilities declined with increasing wetland size and amount of tall peripheral vegetation. Our mark-recapture results indicated that a traditional unreplicated brood survey would have missed 67.5% of estimated broods, summed over all species. Use of closed-population mark-recapture techniques provided an effective method for reducing this bias and identifying and quantifying factors that reduce detection probabilities of waterfowl broods. We recommend that future brood surveys incorporate 2 or 3 temporally segregated replicate counts to allow for formal estimation of detection probabilities.     

A sampling design and model for estimating abundance of Nile crocodiles while accounting for heterogeneity of detectability of multiple observers

As part of the development of a management program for Nile crocodiles in Lake Nasser, Egypt, we used a dependent double-observer sampling protocol with multiple observers to compute estimates of population size. To analyze the data, we developed a hierarchical model that allowed us to assess variation in detection probabilities among observers and survey dates, as well as account for variation in crocodile abundance among sites and habitats. We conducted surveys from July 2008–June 2009 in 15 areas of Lake Nasser that were representative of 3 main habitat categories. During these surveys, we sampled 1,086 km of lake shore wherein we detected 386 crocodiles. Analysis of the data revealed significant variability in both inter- and intra-observer detection probabilities. Our raw encounter rate was 0.355 crocodiles/km. When we accounted for observer effects and habitat, we estimated a surface population abundance of 2,581 (2,239–2,987, 95% credible intervals) crocodiles in Lake Nasser. Our results underscore the importance of well-trained, experienced monitoring personnel in order to decrease heterogeneity in intra-observer detection probability and to better detect changes in the population based on survey indices. This study will assist the Egyptian government establish a monitoring program as an integral part of future crocodile harvest activities in Lake Nasser. © 2012 The Wildlife Society.     

Addressing Temporal Variability in Bird Calling with Design and Estimation: A Northern Bobwhite Example

Imprecise or biased density estimates can lead to inadequate conservation action, overexploitation of game species, or lost recreational opportunities. Common approaches to estimating density of avian populations often either ignore the probability that an individual is present within the sampling area but is not available to be sampled (e.g., not vocalizing), or do not consider covariates that could influence availability. Additionally, management decisions made at the management unit scale are often informed by inadequate monitoring practices, such as limited sampling intensity. In such cases, management agencies calculate density by applying correction factors (e.g., detection probabilities estimated using empirical data from a different study system) to count data, rather than estimating a detection function directly using statistical models. We conducted a simulation study using northern bobwhite (Colinus virginianus; bobwhite) as a model species to quantify the consequences of mis-specifying avian point count models on bias and precision of density estimates. We compared bias and precision of estimates from a fully specified distance-sampling model that estimates availability and detection to 4 different mis-specified approaches, including 2 approaches to calculating density using correction factors. Using correction factors to calculate density produced estimates with low bias but relatively lower precision compared to the fully specified model (CV of density estimates at 35 sites over 5 years: fully specified = 10%, correction factors = 25% and 30%). Although the mean precision and bias of the fully specified model improved with more data (70 sites over 5 years, CV = 9%; 35 sites over 10 years, CV = 9%), precision of correction factors did not (70 sites over 5 years, CV = 22% and 27%; 35 sites over 10 years, CV = 24% and 29%). The fully specified model captured the underlying temporal variation in detection and availability. Increasing sampling duration from 5 to 10 years improved modeled estimates of growth rate, even for mis-specified models, but not derived growth rates using pre-determined detection functions. We demonstrated that conducting point counts 3 times/year at a feasible number of sites can produce relatively unbiased estimates of bobwhite density. Pre-determined detection functions can be fortuitously unbiased for certain years, but they are not a reliable method for determining density or identifying trends in density over time. © 2020 The Wildlife Society.     

A Double-Observer Method to Estimate Detection Rate During Aerial Waterfowl Surveys

Abstract We evaluated double-observer methods for aerial surveys as a means to adjust counts of waterfowl for incomplete detection. We conducted our study in eastern Canada and the northeast United States utilizing 3 aerial-survey crews flying 3 different types of fixed-wing aircraft. We reconciled counts of front- and rear-seat observers immediately following an observation by the rear-seat observer (i.e., on-the-fly reconciliation). We evaluated 6 a priori models containing a combination of several factors thought to influence detection probability including observer, seat position, aircraft type, and group size. We analyzed data for American black ducks (Anas rubripes) and mallards (A. platyrhynchos), which are among the most abundant duck species in this region. The best-supported model for both black ducks and mallards included observer effects. Sample sizes of black ducks were sufficient to estimate observer-specific detection rates for each crew. Estimated detection rates for black ducks were 0.62 (SE = 0.10), 0.63 (SE = 0.06), and 0.74 (SE = 0.07) for pilot-observers, 0.61 (SE = 0.08), 0.62 (SE = 0.06), and 0.81 (SE = 0.07) for other front-seat observers, and 0.43 (SE = 0.05), 0.58 (SE = 0.06), and 0.73 (SE = 0.04) for rear-seat observers. For mallards, sample sizes were adequate to generate stable maximum-likelihood estimates of observer-specific detection rates for only one aerial crew. Estimated observer-specific detection rates for that crew were 0.84 (SE = 0.04) for the pilot-observer, 0.74 (SE = 0.05) for the other front-seat observer, and 0.47 (SE = 0.03) for the rear-seat observer. Estimated observer detection rates were confounded by the position of the seat occupied by an observer, because observers did not switch seats, and by land-cover because vegetation and landform varied among crew areas. Double-observer methods with on-the-fly reconciliation, although not without challenges, offer one viable option to account for detection bias in aerial waterfowl surveys where birds are distributed at low density in remote areas that are inaccessible by ground crews. Double-observer methods, however, estimate only detection rate of animals that are potentially observable given the survey method applied. Auxiliary data and methods must be considered to estimate overall detection rate.     

Detection probability in aerial surveys of feral horses

Observation bias pervades data collected during aerial surveys of large animals, and although some sources can be mitigated with informed planning, others must be addressed using valid sampling techniques that carefully model detection probability. Nonetheless, aerial surveys are frequently employed to count large mammals without applying such methods to account for heterogeneity in visibility of animal groups on the landscape. This often leaves managers and interest groups at odds over decisions that are not adequately informed. I analyzed detection of feral horse (Equus caballus) groups by dual independent observers from 24 fixed-wing and 16 helicopter flights using mixed-effect logistic regression models to investigate potential sources of observation bias. I accounted for observer skill, population location, and aircraft type in the model structure and analyzed the effects of group size, sun effect (position related to observer), vegetation type, topography, cloud cover, percent snow cover, and observer fatigue on detection of horse groups. The most important model-averaged effects for both fixed-wing and helicopter surveys included group size (fixed-wing: odds ratio = 0.891, 95% CI = 0.850–0.935; helicopter: odds ratio = 0.640, 95% CI = 0.587–0.698) and sun effect (fixed-wing: odds ratio = 0.632, 95% CI = 0.350–1.141; helicopter: odds ratio = 0.194, 95% CI = 0.080–0.470). Observer fatigue was also an important effect in the best model for helicopter surveys, with detection probability declining after 3 hr of survey time (odds ratio = 0.278, 95% CI = 0.144–0.537). Biases arising from sun effect and observer fatigue can be mitigated by pre-flight survey design. Other sources of bias, such as those arising from group size, topography, and vegetation can only be addressed by employing valid sampling techniques such as double sampling, mark–resight (batch-marked animals), mark–recapture (uniquely marked and identifiable animals), sightability bias correction models, and line transect distance sampling; however, some of these techniques may still only partially correct for negative observation biases. © 2011 The Wildlife Society.     

Pooling robustness in distance sampling: Avoiding bias when there is unmodelled heterogeneity

The pooling robustness property of distance sampling results in unbiased abundance estimation even when sources of variation in detection probability are not modeled. However, this property cannot be relied upon to produce unbiased subpopulation abundance estimates when using a single pooled detection function that ignores subpopulations. We investigate by simulation the effect of differences in subpopulation detectability upon bias in subpopulation abundance estimates. We contrast subpopulation abundance estimates using a pooled detection function with estimates derived using a detection function model employing a subpopulation covariate. Using point transect survey data from a multispecies songbird study, species-specific abundance estimates are compared using pooled detection functions with and without a small number of adjustment terms, and a detection function with species as a covariate. With simulation, we demonstrate the bias of subpopulation abundance estimates when a pooled detection function is employed. The magnitude of the bias is positively related to the magnitude of disparity between the subpopulation detection functions. However, the abundance estimate for the entire population remains unbiased except when there is extreme heterogeneity in detection functions. Inclusion of a detection function model with a subpopulation covariate essentially removes the bias of the subpopulation abundance estimates. The analysis of the songbird point count surveys shows some bias in species-specific abundance estimates when a pooled detection function is used. Pooling robustness is a unique property of distance sampling, producing unbiased abundance estimates at the level of the study area even in the presence of large differences in detectability between subpopulations. In situations where subpopulation abundance estimates are required for data-poor subpopulations and where the subpopulations can be identified, we recommend the use of subpopulation as a covariate to reduce bias induced in subpopulation abundance estimates.     

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

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

On the importance of estimating detection probabilities from at‐sea surveys of flying seabirds

The primary and accepted method used to estimate seabird densities at sea from ships is the strip transect method, designed to correct for the effect of random directional bird movement relative to that of the ship. However, this method relies on the critical assumption that all of the birds within the survey strip are detected. We used the distance sampling method from line‐transects to estimate detection probability of a number of species of flying seabirds, and to test whether distance from the ship and bird body size affected detectability. Detection probability decreased from 0.987 (SE=0.029) to 0.269 (SE=0.035) with increasing strip half‐width from 100 to 1400 m. Detection probability also varied between size‐groups of species with strip half‐width. For all size‐groups, this probability was close to 1 for strip half‐width of 100 m, but was 0.869 (SE=0.115), 0.725 (SE=0.096) and 0.693 (SE=0.091) for strip half‐width of 300 m, a typical strip width used in seabird surveys, for respectively large, medium and small size flying seabirds. For larger strip half‐width, detection probability was higher for large sized species, intermediate for medium sized species and lower for smaller sized species. For strip half‐width larger than 100 m we suggest that more attention should be paid to testing the assumption of perfect detectability, because abundance estimates may be underestimated when this assumption is violated. Finally, the effect of the speed of travel of flying seabird on the detection probability was estimated in a simulation study, which suggests that detection probability was underestimated with increasing flying speed.     

Standardizing the double-observer survey method for estimating mountain ungulate prey of the endangered snow leopard

Mountain ungulates around the world have been threatened by illegal hunting, habitat modification, increased livestock grazing, disease and development. Mountain ungulates play an important functional role in grasslands as primary consumers and as prey for wild carnivores, and monitoring of their populations is important for conservation purposes. However, most of the several currently available methods of estimating wild ungulate abundance are either difficult to implement or too expensive for mountainous terrain. A rigorous method of sampling ungulate abundance in mountainous areas that can allow for some measure of sampling error is therefore much needed. To this end, we used a combination of field data and computer simulations to test the critical assumptions associated with double-observer technique based on capture-recapture theory. The technique was modified and adapted to estimate the populations of bharal (Pseudois nayaur) and ibex (Capra sibirica) at five different sites. Conducting the two double-observer surveys simultaneously led to underestimation of the population by 15%. We therefore recommend separating the surveys in space or time. The overall detection probability for the two observers was 0.74 and 0.79. Our surveys estimated mountain ungulate populations (± 95% confidence interval) of 735 (± 44), 580 (± 46), 509 (± 53), 184 (± 40) and 30 (± 14) individuals at the five sites, respectively. A detection probability of 0.75 was found to be sufficient to detect a change of 20% in populations of >420 individuals. Based on these results, we believe that this method is sufficiently precise for scientific and conservation purposes and therefore recommend the use of the double-observer approach (with the two surveys separated in time or space) for the estimation and monitoring of mountain ungulate populations.     

Accommodating unmodeled heterogeneity in double-observer distance sampling surveys

Mark-recapture models applied to double-observer distance sampling data neglect the information on relative detectability of objects contained in the distribution of observed distances. A difference between the observed distribution and that predicted by the mark-recapture model is symptomatic of a failure of the assumption of zero correlation between detection probabilities implicit in the mark-recapture model. We develop a mark-recapture-based model that uses the observed distribution to relax this assumption to zero correlation at only one distance. We demonstrate its usefulness in coping with unmodeled heterogeneity using data from an aerial survey of crabeater seals in the Antarctic.