Evaluation of three methods to estimate density and detectability from roadside point counts

Roadside point counts are often used to estimate trends of bird populations. The use of aural counts of birds without adjustment for detection probability, however, can lead to incorrect population trend estimates. We compared precision of estimates of density and detectability of whistling northern bobwhites (Colinus virginianus) using distance sampling, independent double-observer, and removal methods from roadside surveys. Two observers independently recorded each whistling bird heard, distance from the observer, and time of first detection at 362 call-count stops in Ohio. We examined models that included covariates for year and observer effects for each method and distance from observer effects for the double-observer and removal methods using Akaike's Information Criterion (AIC). The best model of detectability from distance sampling included observer and year effects. The best models from the removal and double-observer techniques included observer and distance effects. All 3 methods provided precise estimates of detection probability (CV = 2.4–4.4%) with a range of detectability of 0.44–0.95 for a 6-min survey. Density estimates from double-observer surveys had the lowest coefficient of variation (2005 = 3.2%, 2006 = 1.7%), but the removal method also provided precise estimates of density (2005 CV = 3.4%, 2006 CV = 4.8%), and density estimates from distance sampling were less precise (2005 CV = 9.6%, 2006 CV = 7.9%). Assumptions of distance sampling were violated in our study because probability of detecting bobwhites near the observer was <1 or the roadside survey points were not randomly distributed with respect to the birds. Distances also were not consistently recorded by individual members of observer pairs. Although double-observer surveys provided more precise estimates, we recommend using the removal method to estimate detectability and abundance of bobwhites. The removal method provided precise estimates of density and detection probability and requires half the personnel time as double-observer surveys. Furthermore, the likelihood of meeting model assumptions is higher for the removal survey than with independent double-observers. © 2011 The Wildlife Society.     

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.     

Using distance sampling and hierarchical models to improve estimates of Dall's sheep abundance

Management of large mammal populations has often been based on aerial minimum count surveys that are uncorrected for incomplete detection and lack estimates of precision. These limitations can be particularly problematic for Dall's sheep (Ovis dalli dalli) due to the high cost of surveys and variation in detection probability across time and space. The limitations of these methods have been recognized for some time, but previously proposed alternatives for sheep surveys proved to be too costly and logistically unfeasible in most circumstances (Udevitz et al. 2006). We assessed the potential for a combination of distance sampling surveys and a hierarchical modeling approach to provide a more efficient means for estimating Dall's sheep abundance by conducting aerial contour transect surveys over all sheep habitat in Gates of the Arctic National Park and Preserve (GAAR), Alaska in 2009 and 2010. We estimated the population of Dall's sheep was 8,412 (95% CI: 6,517–11,090) and 10,072 (95% CI 8,081–12,520) in 2009 and 2010, respectively. Abundance within the Itkillik Preserve area within GAAR was 1,898 (95% CI: 1,421–2,578) and 1,854 (95% CI: 1,342–2,488) in 2009 and 2010, respectively. Estimates of lamb abundance in 2010 were more than double those of 2009 after correcting for detection bias related to group size, suggesting that the apparent estimate of lambs in the population may be biased in some years depending on the degree of aggregation. Overall, the contour transect surveys were feasible logistically, cost 70–80% less than minimum count surveys, and produced precise estimates of abundance, indicating that the application of these methods could be used effectively to increase the statistical rigor and spatial extent of Dall's sheep abundance surveys throughout Alaska. These methods could be used to improve the assessment of long-term trends in populations and productivity and provide valuable information for harvest management at both local and landscape scales at reduced costs in comparison to traditional minimum count surveys. © 2011 The Wildlife Society.     

Estimating abundance from detection–nondetection data for randomly distributed or aggregated elusive populations

Estimating abundance is important in many ecological studies in order to understand the spatial and temporal dynamics of a population, which can assist in management and conservation. However, direct estimates of abundance can be difficult and expensive to obtain, particularly for wide-ranging, rare or elusive species. An alternative – estimating from detection-nondetection data – is a challenging but alluring concept to ecologists since the cost and effort of a study can be greatly reduced. This paper describes a method for estimating the abundance of randomly distributed or aggregated populations by using binary data where the probability of detection is less than one. The performances of the models were evaluated by computer simulations comprising 1620 cases. The results show that the accuracy of the abundance estimates increases as the sampling rate, efficiency of survey method, and the number of repeated surveys increase, whereas the accuracy declines as individuals become more aggregated. For a randomly distributed population, using a sampling rate of 0.05 in a survey method with a detection probability of 0.5, and repeating surveys three times provides sufficient accuracy of abundance. For an aggregated population, to achieve reasonably accurate abundance estimates the sampling rate should be doubled and each cell should be repeatedly surveyed on 4 to 6 occasions.     

Heard but not seen: an acoustic survey of the African forest elephant population at Kakum Conservation Area, Ghana

This study, designed to survey forest elephants ( Loxodonta africana cyclotis ) at Kakum Conservation Area, Ghana, is the first to apply acoustic methods to elephant abundance estimation and to compare results with independent survey estimates. Nine acoustic sensors gathered sound continuously for 38 days. Low-frequency calling rates have been established as useful elephant abundance indices at a Namibian watering hole and a central African forest clearing. In this study, we estimated elephant population size by applying an abundance index model and detection function developed in central Africa to data from simultaneous sampling periods on Kakum sensors. The sensor array recorded an average of 1.81 calls per 20-min sampling period from an effective detection area averaging 10.27 km². The resulting estimate of 294 elephants (95% CI: 259–329) falls within confidence bounds of recent dung-based surveys. An extended acoustic model, estimating the frequency with which elephants are silent when present, yields an estimate of 350 elephants (95% CI: 315–384). Acoustic survey confidence intervals are at least half as wide as those from dung-based surveys. This study demonstrates that acoustic surveying is a valuable tool for estimating elephant abundance, as well as for detecting other vocal species and anthropogenic noises that may be associated with poaching.     

Monitoring of the Flat-Tailed Horned Lizard With Methods Incorporating Detection Probability

Abstract: Difficulty in monitoring the flat-tailed horned lizard (Phrynosoma mcallii) has led to controversy over its conservation status. The difficulty in detecting this species has discouraged large-scale estimates of abundance and led to uncertainty over whether the species exists in population sizes of sufficient size for long-term persistence. We incorporated detection probability into monitoring of this species using closed mark—recapture and distance-sampling methods. Density estimation from mark—recapture abundance estimates was improved using an estimate of the proportion of time lizards were on the plot. We estimated the probability of detection on the line for distance sampling and adjusted density estimates accordingly. We estimated the populations of the Yuha Basin Management Area in 2002 and the East Mesa Management Area, Imperial County, California, USA, in 2003 to be 25,514 (95% CI 14,444-38,970) and 42,619 (95% CI 23,161-67,639), respectively. Two estimates of detection probability on the line in distance sampling by different methods were 0.45 and 0.65. Density estimates derived from distance analyses for 3 East Mesa Management Area plots and the Yuha Basin Management Area were 1.55 per ha (95% CI 0.64-3.76) and 0.41 per ha (95% CI 0.22-0.7), respectively. These are the first large-scale estimates of abundance and density for P. mcallii.     

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.     

Double-Observer Line Transect Methods: Levels of Independence

Summary .  Double-observer line transect methods are becoming increasingly widespread, especially for the estimation of marine mammal abundance from aerial and shipboard surveys when detection of animals on the line is uncertain. The resulting data supplement conventional distance sampling data with two-sample mark–recapture data. Like conventional mark–recapture data, these have inherent problems for estimating abundance in the presence of heterogeneity. Unlike conventional mark–recapture methods, line transect methods use knowledge of the distribution of a covariate, which affects detection probability (namely, distance from the transect line) in inference. This knowledge can be used to diagnose unmodeled heterogeneity in the mark–recapture component of the data. By modeling the covariance in detection probabilities with distance, we show how the estimation problem can be formulated in terms of different levels of independence. At one extreme, full independence is assumed, as in the Petersen estimator (which does not use distance data); at the other extreme, independence only occurs in the limit as detection probability tends to one. Between the two extremes, there is a range of models, including those currently in common use, which have intermediate levels of independence. We show how this framework can be used to provide more reliable analysis of double-observer line transect data. We test the methods by simulation, and by analysis of a dataset for which true abundance is known. We illustrate the approach through analysis of minke whale sightings data from the North Sea and adjacent waters.     

New technologies in the mix: Assessing N‐mixture models for abundance estimation using automated detection data from drone surveys

1. Reliable estimates of abundance are critical in effectively managing threatened species, but the feasibility of integrating data from wildlife surveys completed using advanced technologies such as remotely piloted aircraft systems (RPAS) and machine learning into abundance estimation methods such as N‐mixture modeling is largely unknown due to the unique sources of detection errors associated with these technologies.
2. We evaluated two modeling approaches for estimating the abundance of koalas detected automatically in RPAS imagery: (a) a generalized N‐mixture model and (b) a modified Horvitz–Thompson (H‐T) estimator method combining generalized linear models and generalized additive models for overall probability of detection, false detection, and duplicate detection. The final estimates from each model were compared to the true number of koalas present as determined by telemetry‐assisted ground surveys.
3. The modified H‐T estimator approach performed best, with the true count of koalas captured within the 95% confidence intervals around the abundance estimates in all 4 surveys in the testing dataset (n = 138 detected objects), a particularly strong result given the difficulty in attaining accuracy found with previous methods.
4. The results suggested that N‐mixture models in their current form may not be the most appropriate approach to estimating the abundance of wildlife detected in RPAS surveys with automated detection, and accurate estimates could be made with approaches that account for spurious detections.

     

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.     

Accounting for imperfect detection of groups and individuals when estimating abundance

If animals are independently detected during surveys, many methods exist for estimating animal abundance despite detection probabilities <1. Common estimators include double‐observer models, distance sampling models and combined double‐observer and distance sampling models (known as mark‐recapture‐distance‐sampling models; MRDS). When animals reside in groups, however, the assumption of independent detection is violated. In this case, the standard approach is to account for imperfect detection of groups, while assuming that individuals within groups are detected perfectly. However, this assumption is often unsupported. We introduce an abundance estimator for grouped animals when detection of groups is imperfect and group size may be under‐counted, but not over‐counted. The estimator combines an MRDS model with an N‐mixture model to account for imperfect detection of individuals. The new MRDS‐Nmix model requires the same data as an MRDS model (independent detection histories, an estimate of distance to transect, and an estimate of group size), plus a second estimate of group size provided by the second observer. We extend the model to situations in which detection of individuals within groups declines with distance. We simulated 12 data sets and used Bayesian methods to compare the performance of the new MRDS‐Nmix model to an MRDS model. Abundance estimates generated by the MRDS‐Nmix model exhibited minimal bias and nominal coverage levels. In contrast, MRDS abundance estimates were biased low and exhibited poor coverage. Many species of conservation interest reside in groups and could benefit from an estimator that better accounts for imperfect detection. Furthermore, the ability to relax the assumption of perfect detection of individuals within detected groups may allow surveyors to re‐allocate resources toward detection of new groups instead of extensive surveys of known groups. We believe the proposed estimator is feasible because the only additional field data required are a second estimate of group size.     

Evaluating Survey Methods for Monitoring a Rare Vertebrate,the Sonoran Desert Tortoise

Abstract: Effective conservation requires strategies to monitor populations efficiently, which can be especially difficult for rare or elusive species where field surveys require high effort and considerable cost. Populations of many reptiles, including Sonoran desert tortoises (Gopherus agassizii), are challenging to monitor effectively because they are cryptic, they occur at low densities, and their activity is limited both seasonally and daily. We compared efficiency and statistical power of 2 survey methods appropriate for tortoises and other rare vertebrates, line-transect distance sampling and site occupancy. In 2005 and 2006 combined, we surveyed 120 1-km transects to estimate density and 40 3-ha plots 5 times each to estimate occupancy of Sonoran desert tortoises in 2 mountain ranges in southern Arizona, USA. For both mountain ranges combined, we estimated density to be 0.30 adult tortoises/ha (95% CI = 0.17–0.43) and occupancy to be 0.72 (95% CI = 0.56–0.89). For the sampling designs we evaluated, monitoring efforts based on occupancy were 8–36% more efficient than those based on density, when contrasting only survey effort, and 17–30% more efficient when contrasting total effort (surveying, hiking to and from survey locations, and radiotracking). Occupancy had greater statistical power to detect annual declines in the proportion of area occupied than did distance sampling to detect annual declines in density. For example, we estimated that power to detect a 5% annual decline with 10 years of annual sampling was 0.92 (95% CI = 0.75–0.98) for occupancy and 0.43 (95% CI = 0.35–0.52) for distance sampling. Although all sampling methods have limitations, occupancy estimation offers a promising alternative for monitoring populations of rare vertebrates, including tortoises in the Sonoran Desert.     

Challenges and opportunities for estimating abundance of a low-density moose population

Monitoring large herbivores across their core range has been readily accomplished using aerial surveys and traditional distance sampling. But for peripheral populations, where individuals may occur in patchy, low-density populations, precise estimation of population size and trend remains logistically and statistically challenging. For moose (Alces alces) along their southern range margin in northern New York, USA, we sought robust estimates of moose distribution, abundance, and population trend (2016–2019) using a combination of aerial surveys (line transect distance-sampling), repeated surveys in areas where moose were known to occur to boost the number of detections, and density surface modeling (DSM) with spatial covariates. We achieved a precise estimate of density (95% CI = 0.00–0.29 moose/km²) for this small population (656 moose, 95% CI = 501–859), which was patchily distributed across a large and heavily forested region (the 24,280-km² Adirondack Park). Local moose abundance was positively related to active timber management, elevation, and snow cover, and negatively related to large bodies of water. As expected, moose abundance in this peripheral population was low relative to its core range in other northern forest states. Yet, in areas where abundance was greatest, moose densities in New York approached those where epizootics of winter tick (Dermacentor albipictus) have been reported, underscoring the need for effective and efficient monitoring. By incorporating autocorrelation in observations and landscape covariates, DSM provided spatially explicit estimates of moose density with greater precision and no additional field effort over traditional distance sampling. Combined with repeated surveys of areas with known moose occurrence to achieve viable sample sizes, DSM is a useful tool for effectively monitoring low density and patchy populations.     

Evaluating abundance estimates and evidence of breeding for Bobolinks from transect and point-count surveys

Estimating the abundance and breeding success of territorial songbirds is challenging. Various types of surveys and analyses are available, but all receive some criticism in the literature, and most methods are rarely compared with results obtained using intensive monitoring efforts. We assessed the efficacy of transect and point-count surveys to estimate the abundance of male Bobolinks (Dolichonyx oryzivorus) and detect evidence of nesting and fledging by comparing the results of those surveys to results from more intensive monitoring (i.e., spot mapping and nest monitoring). We monitored 36 fields (254 ha) of late-harvest hay, restored grassland, and fallow fields in the Luther Marsh Wildlife Management Area and on four farms in southern Ontario, Canada, in 2018. Compared to the number of territories identified based on spot mapping (197), distance sampling analysis of transect survey data provided a more accurate estimate of the abundance of male Bobolinks (230, 95% CI: 187, 282) than N-mixture models of transect (668, 95% CI: 332, 1342) and point-count (337, 95% CI: 203, 559) data. Three visits to survey transects and five to point counts did not effectively detect evidence of Bobolink breeding (i.e., nesting or fledging) in fields compared to spot mapping and nest monitoring. Distance sampling analysis of transect data appears promising for estimating the number of Bobolink territories in an area, e.g., those impacted by conservation programs. If estimates of the number of nesting Bobolinks and frequency of fledging are of interest, spot mapping and nest monitoring could be implemented at a subset of sampled fields. Our results suggest that additional studies to evaluate model-based estimates of abundance with the best available information (e.g., from spot mapping of marked or unmarked populations and nest monitoring) would be useful to ensure that robust estimates are provided to support population estimates and conservation actions.     

Estimating grizzly and black bear population abundance and trend in Banff National Park using noninvasive genetic sampling

We evaluated the potential of two noninvasive genetic sampling methods, hair traps and bear rub surveys, to estimate population abundance and trend of grizzly (Ursus arctos) and black bear (U. americanus) populations in Banff National Park, Alberta, Canada. Using Huggins closed population mark-recapture models, we obtained the first precise abundance estimates for grizzly bears (N=?73.5, 95% CI?=?64-94 in 2006; N=?50.4, 95% CI?=?49-59 in 2008) and black bears (N=?62.6, 95% CI?=?51-89 in 2006; N=?81.8, 95% CI?=?72-102 in 2008) in the Bow Valley. Hair traps had high detection rates for female grizzlies, and male and female black bears, but extremely low detection rates for male grizzlies. Conversely, bear rubs had high detection rates for male and female grizzlies, but low rates for black bears. We estimated realized population growth rates, lambda, for grizzly bear males (λ=?0.93, 95% CI?=?0.74-1.17) and females (λ=?0.90, 95% CI?=?0.67-1.20) using Pradel open population models with three years of bear rub data. Lambda estimates are supported by abundance estimates from combined hair trap/bear rub closed population models and are consistent with a system that is likely driven by high levels of human-caused mortality. Our results suggest that bear rub surveys would provide an efficient and powerful means to inventory and monitor grizzly bear populations in the Central Canadian Rocky Mountains.     

Estimating abundance of mountain lions from unstructured spatial sampling

Mountain lions (Puma concolor) are often difficult to monitor because of their low capture probabilities, extensive movements, and large territories. Methods for estimating the abundance of this species are needed to assess population status, determine harvest levels, evaluate the impacts of management actions on populations, and derive conservation and management strategies. Traditional mark–recapture methods do not explicitly account for differences in individual capture probabilities due to the spatial distribution of individuals in relation to survey effort (or trap locations). However, recent advances in the analysis of capture–recapture data have produced methods estimating abundance and density of animals from spatially explicit capture–recapture data that account for heterogeneity in capture probabilities due to the spatial organization of individuals and traps. We adapt recently developed spatial capture–recapture models to estimate density and abundance of mountain lions in western Montana. Volunteers and state agency personnel collected mountain lion DNA samples in portions of the Blackfoot drainage (7,908 km²) in west-central Montana using 2 methods: snow back-tracking mountain lion tracks to collect hair samples and biopsy darting treed mountain lions to obtain tissue samples. Overall, we recorded 72 individual capture events, including captures both with and without tissue sample collection and hair samples resulting in the identification of 50 individual mountain lions (30 females, 19 males, and 1 unknown sex individual). We estimated lion densities from 8 models containing effects of distance, sex, and survey effort on detection probability. Our population density estimates ranged from a minimum of 3.7 mountain lions/100 km² (95% CI 2.3–5.7) under the distance only model (including only an effect of distance on detection probability) to 6.7 (95% CI 3.1–11.0) under the full model (including effects of distance, sex, survey effort, and distance × sex on detection probability). These numbers translate to a total estimate of 293 mountain lions (95% CI 182–451) to 529 (95% CI 245–870) within the Blackfoot drainage. Results from the distance model are similar to previous estimates of 3.6 mountain lions/100 km² for the study area; however, results from all other models indicated greater numbers of mountain lions. Our results indicate that unstructured spatial sampling combined with spatial capture–recapture analysis can be an effective method for estimating large carnivore densities. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.     

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.     

Estimating Abundance From One-Dimensional Passive Acoustic Surveys

ABSTRACT Conventional distance sampling, the most-used method of estimating animal density and abundance, requires ranges to detected individuals, which are not easily measured for vocalizations. However, in some circumstances the sequential pattern of detection of vocalizations along a transect line gives information about the range of detection. Thus, from a one-dimensional acoustic point-transect survey (i.e., records of vocalizations detected or not detected at regularly spaced listening stations) it is possible to obtain a useful estimate of density or abundance. I developed equations for estimation of density for one-dimensional surveys. Using simulations I found that for the method to have little bias when both range of detection and rate of vocalization need to be estimated, stations needed to be spaced at 30–80% of the range of detection and the rate of vocalization should be >0.7. If either the range of detection or rate of vocalization is known, conditions are relaxed, and when both parameters are known the method works well almost universally. In favorable conditions for one-dimensional methods, estimated abundances have overall errors not much larger than those from conventional line-transect distance sampling. The methods appeared useful when applied to real acoustic data from whale surveys. The techniques may also be useful in surveys with nonacoustic detection of animals.     

Large numbers of marine mammals winter in the North Water polynya

The importance of the North Water polynya in Smith Sound as an overwintering area for marine mammals has been questioned. One way to address the issue is to assess the abundance of selected marine mammals that are present during winter in the North Water. Visual aerial surveys involving double observer platforms were conducted over the eastern part of the North Water polynya in April 2014. Four species of marine mammals were included in strip-census estimation of abundance. Perception bias was addressed using a double-platform survey protocol, a Chapman mark–recapture estimator for whales, seals and walruses (Odobenus rosmarus) on ice and a mark–recapture distance sampling estimation technique for walruses in water. Availability bias was addressed by correcting abundance estimates by the percentage of time animals detected in water that were available for detection at the surface. The resulting estimates suggested that 2544 walruses (95 % CI 1513–4279), 6005 bearded seals (Erignathus barbatus, 95 % CI 4070–8858), 2324 belugas (Delphinapterus leucas, 95 % CI 968–5575) and 3059 narwhals (Monodon monoceros, 95 % CI 1760–5316) wintered in the eastern part of the North Water polynya in April 2014. The walrus estimate is larger than previous summer estimates, and it emphasizes the importance of the habitat along the Greenland coast as a walrus wintering ground. The estimate of belugas is likely negatively biased due to the partial coverage of the potential habitat. The estimate of narwhals is large compared to the few previous observations of narwhals in winter in the North Water, and it demonstrates that large numbers of narwhals winter there. The overall conclusion is that the North Water is indeed an important wintering area for at least walruses, belugas, narwhals and bearded seals.     

Encounter Rates From Road-Based Surveys of Rio Grande Wild Turkeys in Texas

ABSTRACT Traditional index-based techniques have indicated declines in Rio Grande wild turkey (Meleagris gallopavo intermedia; hereafter, wild turkey) populations across much of Texas, USA. However, population indices can be unreliable. Research has indicated that road-based surveys may be an efficacious technique for monitoring wild turkey populations on an ecoregion level. Therefore, our goal was to evaluate applicability of road-based distance sampling in the Cross Timbers, Edwards Plateau, Rolling Plains, and South Texas ecoregions of Texas. We conducted road-based surveys in each ecoregion during December 2007—March 2008 to estimate wild turkey flock encounter rates and to determine survey effort (i.e., km of roads) required to obtain adequate sample sizes for distance sampling in each ecoregion. With simulations using inflatable turkey decoys, we also evaluated effects of distance to a flock, flock size, and vegetative cover on turkey flock detectability. Encounter rates of wild turkey flocks from road-based surveys varied from 0.1 (95% CI = 0.0–0.6) to 2.2 (95% CI = 0.8–6.0) flocks/100 km surveyed. Encounter rates from surveys restricted to riparian communities (i.e., areas ≤1 km from a river or stream) varied from 0.2 (95% CI = 0.1–0.6) to 2.9 (95% CI = 1.5–6.7) flocks/100 km surveyed. Flock detection probabilities from field simulations ranged from 22.5% (95% CI = 16.3–29.8%) to 25.0% (95% CI = 13.6–39.6%). Flock detection probabilities were lower than expected in all 4 ecoregions, which resulted in low encounter rates. Estimated survey effort required to obtain adequate sample sizes for distance sampling ranged from 2,765 km (95% CI = 2,597–2,956 km) in the Edwards Plateau to 37,153 km (95% CI = 12,861–107,329 km) in South Texas. When we restricted road-based surveys to riparian communities, estimated survey effort ranged from 2,222 km (95% CI = 2,092–2,370 km) in the Edwards Plateau to 22,222 km (95% CI = 19,782–25,349 km) in South Texas.