Interobserver error in grassland vegetation surveys: sources and implications

人类观测误差是植被测量中不可避免的一个问题。我们量化了与高草草原植被长期监测相关的观测者间误差的四个组成部分：忽略误 差、误识别误差、谨慎误差和估计误差。由于观察者会产生误差，我们还评估了地块大小与伪周转率的关系，以及对比了物种组成和丰度的伪变化与四年间植被变化之间的关系。这项研究是在美国堪萨斯州的高草草原国家保护区进行的。监测点包括10个地块，每个地块由一系列的四个嵌套框架(0.01, 0.1, 1和10 m²)组成。在每个嵌套框架中记录了所有的草本物种，并且在10 m²的空间尺度下，视觉估计了7个覆盖类别内的叶面覆盖。总共调查了300个地块(30个地点)，并随机选择28个地块重新进行测量以评估观测者的误差。所有的调查由四名观测者分两组完成。研究结果表明，在10 m²空间尺度上，由忽略误差引起的伪周转率平均为18.6%，而由误识别误差和谨慎误差引起的伪周转率平均值分别为1.4%和0.6%。尽管由重新定位引起的误差可能也起一定的作用，由忽略误差导致的伪周转率随样地面积的减小而增 加。物种组成在四年期间的变化(排除潜在的误识别误差和谨慎误差)为30.7%，其中包括由忽略误差和实际变化引起的伪周转率。18.6%的忽略误差表明四年期间的实际变化只有12.1%。对于估计误差，26.2%会记录为不同的覆盖等级。在四年的时间内，46.9%的记录显示了不同的覆盖等级，这表明两个时间段间覆盖率变化的56%是由于观测者误差造成的。     

Observer variation in field assessments of vegetation condition: Implications for biodiversity conservation

Summary   Uncertainty in assessments of vegetation condition that are used to inform land management and planning decisions for biodiversity conservation in Australia may lead to unexpected outcomes, including loss of biodiversity. This study investigates observer error in field estimates of vegetation attributes, one component of uncertainty in assessments of vegetation condition. Ten observers conducted vegetation condition assessments using two assessment protocols (BioMetric and Habitat Hectares) on 20 sites in a grassy woodland community. Observers' estimates varied substantially across multiple scoring categories for all vegetation attributes on almost all sites. Across all sites, the average coefficient of variation in total vegetation condition scores was 15–18% for both protocols, with a maximum of 60%. The primary cause of variation in total vegetation condition scores was random error in raw estimates of vegetation attributes, although sensitivity of some highly weighted attributes to error exacerbated variation in some cases. Observers generally agreed on the total scores and ranks of highly degraded (pasture) sites, but were less consistent on other sites. Rank correlations between pairs of observers were stronger for Habitat Hectares, suggesting BioMetric may be slightly more sensitive to observer error. It is recommended that: (i) research is undertaken into methods for reducing observer error; (ii) review is made of the sensitivity of index scoring structures to observer error; (iii) field observers estimate uncertainty around point estimates of vegetation condition; and, (iv) decision-makers explicitly incorporate uncertainty into the decision-making processes and aim for outcomes that are robust to this uncertainty.     

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.     

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.     

Scale‐dependent biases in species counts in a grassland

Abstract. Numbers of plant species were recorded in species‐rich meadows in the Bílé Karpaty Mts., SE Czech Republic, with the aim to evaluate the sampling error made by well‐trained observers. Five observers recorded vascular plants in seven plots ranging from 9.8 cm² to 4 m² independently and were not time‐limited. In larger plots a discrepancy of 10–20% was found between individual estimates, in smaller plots discrepancy increased to 33%, on average. The gain in observed species richness by combining records of individual observers (in comparison with the mean numbers estimated by single observers) decreased from the smallest plot (27–82% for two to five observers) to the largest one (13–25%). However, after misidentified and suspicious records were eliminated, the gain was much lower and became scale‐independent; two observers added 12% species, on average, and the increase by combining species lists made by three or more observers was negligible (3% more on average). It is concluded that most discrepancies between individual observers were caused by misidentification of rare seedlings and young plants. We suggest that in species‐rich meadows plants should be recorded by at least three observers together and that they should consult all problematic plant specimens together in the field, to minimize errors.     

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.     

Computer‐aided calibration for visual estimation of vegetation cover

Question: What precision and accuracy of visual cover estimations can be achieved after repeated calibration with images of vegetation in which the true cover is known, and what factors influence the results? Methods: Digital images were created, in which the true cover of vegetation was digitally calculated. Fifteen observers made repeated estimates with immediate feedback on the true cover. The effects on precision and accuracy through time were evaluated with repeated proficiency tests. In a field trial, cover estimates, before and after calibration, were compared with point frequency data. Results: Even a short time of calibration greatly improves precision and accuracy of the estimates, and can also reduce the influence of different backgrounds, aggregation patterns and experience. Experienced observers had a stronger tendency to underestimate the cover of narrow‐leaved grasses before calibration. The field trial showed positive effects of computer‐based calibration on precision, in that it led to considerably less between‐observer variation for one of the two species groups. Conclusions: Computer‐aided calibration of vegetation cover estimation is simple, self‐explanatory and time‐efficient, and might possibly reduce biases and drifts in estimate levels over time. Such calibration can also reduce between‐observer variation in field estimates, at least for some species. However, the effects of calibration on estimations in the field must be further evaluated, especially for multilayered vegetation.     

Monitoring vegetation change caused by trampling: a study in the Cairngorms,Scotland

Summary

A study was made in the Cairngorms, Scotland to make recommendations for a monitoring scheme capable of detecting changes in the vegetation caused by recreational pressure following the development of a funicular railway. Four methods were used in field trials to assess percentage cover of plant species and gravel, rock and bare ground, where appropriate, in two vegetation types (open and closed). The methods used were visual estimates in 50 × 40 cm quadrats (Q), the mean of visual estimates in twenty 10 × 10 cm sub-quadrats of the 50 × 40 cm quadrats (Q₂₀), a modified point intercept method (RL) and photography. Variances between observers and between-quadrats were estimated for the different methods. The sampling design for detecting change was based on a model of variance, constructed from field trial data.

Between-observer and between-quadrat variances were related to mean percentage cover and approximated to a binomial distribution. The between-quadrat variance was larger than observer variance. The Q₂₀ method achieved appreciably better precision than the other methods. Analysis of half of the 10 × 10 cmsub-quadrats (1/2Q₂₀) selected in a checker board design achieved a relative efficiency of 78% compared with the Q₂₀. This result suggests that comparable precision to the Q₂₀ method could be achieved by choosing about 14 sub-quadrats in a larger quadrat, thus saving some time. Variation between quadrats also suggested that the Q₂₀ method was the one of choice for maximising precision. The precision of the photographic method was based on fewer data points, so is less accurate than other estimates.

Minimum sample sizes were estimated for detecting a 10% relative change of a species in open vegetation with 30% cover (i.e. a change from 30% to <27 or to >33% cover). With a 10 % Type II error rate and 5 % Type I error rate the minimum sample sizes were 47 quadrats for Q, 18 for Q ₂₀, 43 for RL, and 23 for the means of ten 10 × 10 cm sub-quadrats in open vegetation.

The most time-efficient field recording appeared to be the use of Q despite the required sample size being 2.6 times higher than that of Q₂₀. The far lower time requirement per quadrat, however, compensated for the higher numbers. The number of quadrats would depend on the specified change in percentage cover and on the statistical significance level used. For example, to detect a 10% absolute change in cover (i.e. from 30% to either <20 % or >40 % cover) at 95 % probability the net effective recording time is estimated at 5 h per vegetation type while to detect a 5 % change at 99 % probability would require c. 25 h. Larger samples may be required for other species or for species with a low initial cover.     

Are Plant Censuses Carried Out on Small Quadrats More Reliable than on Larger Ones?

Plant censuses are known to be significantly affected by observers’ biases. In this study, we checked whether the magnitude of observer effects (defined as the % of total variance) varied with quadrat size: we expected the census repeatability (% of the total variance that is not due to measurement errors) to be higher for small quadrats than for larger ones. Variations according to quadrat size of the repeatability of species richness, Simpson equitability and reciprocal diversity indices, Ellenberg indicator values, plant cover and plant frequency were assessed using 359 censuses of vascular plants. These were carried out independently by four professional botanists during spring 2002 on the same 18 forest plots, each comprising one 400-m² quadrat, four 4-m² and four 2-m² quadrats. Time expenditure was controlled for. General Linear Models using random effects only were applied to the ecological indices to estimate variance components and magnitude of the following effects (if possible): plot, quadrat, observer, plant species and two-way interactions. High repeatability was obtained for species richness and Ellenberg indicator values. Species richness and Ellenberg indicator values were generally more accurate but also more biased in large quadrats. Simpson reciprocal diversity and equitability indices were poorly repeatable (especially equitability) probably because plant cover estimates varied widely among observers, irrespective of quadrat size. Grouping small quadrats usually increased the repeatability of the variable considered (e.g. species richness, Simpson diversity, plant cover) but the number of plant species found on those pooled 16 m² was much lower than if large plots were sampled. We therefore recommend to use large, single quadrats for forest vegetation monitoring.     

Validity and reproducibility of benthic cover estimates made during broadscale surveys of coral reefs by manta tow

 There is limited information on the validity and reproducibility of estimates of benthic cover from manta tow surveys. To address this, benthic cover estimates from the same reef area were compared (i) among observers and (ii) with an independent assessment using under-water video. Benthic cover was classified into 11 categories. There was generally unbiased agreement within one cover category, both among observers (89%) and for comparisons between manta tow and video (86%). While estimates of dead coral cover were reproducible, they were not valid because the concordance between observer estimates and video estimates was not greater than would be expected by chance. Manta tow estimates of the cover of sand and rubble were biased in that they consistently overestimated sand and rubble cover in comparison with estimates from video. The results indicate that manta towing is generally effective for the broadscale estimation of live coral cover, providing observers receive adequate training. Accepted: 25 June 1999     

Scale‐dependent variation in visual estimates of grassland plant cover

Abstract. Plant cover was visually estimated by five observers, independent of each other, in a species‐rich grassland in the Bílé Karpaty Mts., southeastern Czech Republic, in seven plots ranging from 0.001 to 4 m². Variation of total plant cover among the observers was high at small scales: 0.001–0.016 m²; coefficient of variation, CV = 35 to 45%, but much lower at larger scales: 0.06–4 m²; CV = 7 to 15%. Differences between visual estimates of plant cover of individual species made by different observers were affected by plot size, total cover and morphology of particular plants. CV of the cover of individual species ranged from 0 to 225% and decreased with increasing plot size. For abundant plants the CV attained ca. 50%, independent of plot size. In spite of a very high number of sterile plants with similar leaf morphology and colour, the observed variation in cover estimates in the studied grassland was comparable with results reported from other vegetation types. Differences between estimates by individual observers were often larger than usual year to year changes in undisturbed grasslands. Therefore, I suggest that to avoid difficulties in the interpretation of results based on plant cover data obtained from visual estimates, several observers should always work together, adjusting their extreme estimates.     

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.     

Spatial relationship between climatologies and changes in global vegetation activity

Vegetation forms a main component of the terrestrial biosphere and plays a crucial role in land‐cover and climate‐related studies. Activity of vegetation systems is commonly quantified using remotely sensed vegetation indices (VI). Extensive reports on temporal trends over the past decades in time series of such indices can be found in literature. However, little remains known about the processes underlying these changes at large spatial scales. In this study, we aimed at quantifying the spatial relationship between changes in potential climatic growth constraints (i.e. temperature, precipitation and incident solar radiation) and changes in vegetation activity (1982–2008). We demonstrate an additive spatial model with 0.5° resolution, consisting of a regression component representing climate‐associated effects and a spatially correlated field representing the combined influence of other factors, including land‐use change. Little over 50% of the spatial variance could be attributed to changes in climatologies; conspicuously, many greening trends and browning hotspots in Argentina and Australia. The nonassociated model component may contain large‐scale human interventions, feedback mechanisms or natural effects, which were not captured by the climatologies. Browning hotspots in this component were especially found in subequatorial Africa. On the scale of land‐cover types, strongest relationships between climatologies and vegetation activity were found in forests, including indications for browning under warming conditions (analogous to the divergence issue discussed in dendroclimatology).     

Assessing species misidentification rates through quality assurance of vegetation monitoring

Most studies of observer discrepancies in vegetation recording have beenlimited in the extent to which they can separate different sources of error. Itis straightforward to quantify the degree of disparity between two specieslistsbut not clear how to allocate a particular discrepancy to a specific cause.Misidentification is especially difficult to detect, and is rarely discussed inthe literature. The vegetation monitoring protocol devised by the UnitedKingdomEnvironmental Change Network (ECN) splits each plot to be recorded into cells,within each of which a species list is compiled. This provides an objectivemeasure of the frequency of occurrence of individual species, in place of themore subjective estimation of cover, and allows within-plot variation to bequantified. An added advantage of the ECN methodology is that botanicalexpertise and the use of cells can be combined in quality assurance (QA)studiesto detect instances of consistent misidentification of species, therebyincreasing the repeatability of vegetation recording and enhancing thepossibility of detecting change.This paper reports an analysis of the data obtained from a 1996 ECN QA exerciseand describes the methods used to pinpoint the most likely sources ofdiscrepancies between the original site surveys and the QA survey. Overall itisestimated that 5.9% of specimens were misidentified at species level and 1.9%atgenus level, though it is detectable that sites employing consultant surveyorsachieved slightly better results. Misidentification rates are particularly highfor the lower plants and for woodland plots. The number of unmatched records(the pseudoturnover rate) is high, 24%, but comparable to other studies. Thisdoes not seem to be the result, to any great extent, of seasonal changes oridentification problems, but appears to be largely due to overlooking andpartlya result of relocation problems. The overall percentage agreement betweensurveyors was 57%, also comparable with other studies.     

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.     

Experimental Investigation of Observation Error in Anuran Call Surveys

ABSTRACT Occupancy models that account for imperfect detection are often used to monitor anuran and songbird species occurrence. However, presence—absence data arising from auditory detections may be more prone to observation error (e.g., false-positive detections) than are sampling approaches utilizing physical captures or sightings of individuals. We conducted realistic, replicated field experiments using a remote broadcasting system to simulate simple anuran call surveys and to investigate potential factors affecting observation error in these studies. Distance, time, ambient noise, and observer abilities were the most important factors explaining false-negative detections. Distance and observer ability were the best overall predictors of false-positive errors, but ambient noise and competing species also affected error rates for some species. False-positive errors made up 5% of all positive detections, with individual observers exhibiting false-positive rates between 0.5% and 14%. Previous research suggests false-positive errors of these magnitudes would induce substantial positive biases in standard estimators of species occurrence, and we recommend practices to mitigate for false positives when developing occupancy monitoring protocols that rely on auditory detections. These recommendations include additional observer training, limiting the number of target species, and establishing distance and ambient noise thresholds during surveys.     

Empirically testing the effectiveness of thermal imaging as a tool for identification of large mammals in the African bushveldt

Monitoring animal populations often relies on direct visual observations. This is problematic at night when spotlighting can cause misidentification and inaccurate counting. Using infrared thermography (IRT) could potentially solve these difficulties, but reliability is uncertain. Here, we test the accuracy of 24 observers, differing in experience and skill levels, in identifying antelope species from IRT photographs taken in the African bush. Overall, 38% of identifications were correct to species level, and 50% were correct to genus/subfamily level. Identification accuracy depended on the confidence and skill of the observer (positive relationship), the number of animals present (positive relationship), and the distance at which it was taken (negative relationship). Species with characteristic features, horn morphology, or posture were identified with ~80% accuracy (e.g. wildebeest, kudu and impala) while others were considerably lower (e.g. blesbok and waterbuck). Experience significantly improved identification accuracy but the effect was not consistent between species and even experienced observers struggled to identify red hartebeest, reedbuck and eland. Counting inaccuracies were commonplace, particularly when group size was large. We conclude that thermal characteristics of species and experience of observers can pose challenges for African field ecologists, but IRT can be used to identify and count some species accurately, especially <100 m.     

Dealing with detection error in site occupancy surveys: what can we do with a single survey?

Aim Site occupancy probabilities of target species are commonly used in various ecological studies, e.g. to monitor current status and trends in biodiversity. Detection error introduces bias in the estimators of site occupancy. Existing methods for estimating occupancy probability in the presence of detection error use replicate surveys. These methods assume population closure, i.e. the site occupancy status remains constant across surveys, and independence between surveys. We present an approach for estimating site occupancy probability in the presence of detection error that requires only a single survey and does not require assumption of population closure or independence. In place of the closure assumption, this method requires covariates that affect detection and occupancy.Methods Penalized maximum-likelihood method was used to estimate the parameters. Estimability of the parameters was checked using data cloning. Parametric boostrapping method was used for computing confidence intervals.Important findings The single-survey approach facilitates analysis of historical datasets where replicate surveys are unavailable, situations where replicate surveys are expensive to conduct and when the assumptions of closure or independence are not met. This method saves significant amounts of time, energy and money in ecological surveys without sacrificing statistical validity. Further, we show that occupancy and habitat suitability are not synonymous and suggest a method to estimate habitat suitability using single-survey data.     

Estimation of Species Identification Error: Implications for Raptor Migration Counts and Trend Estimation

Abstract: One of the primary assumptions associated with many wildlife and population trend studies is that target species are correctly identified. This assumption may not always be valid, particularly for species similar in appearance to co-occurring species. We examined size overlap and identification error rates among Cooper's (Accipiter cooperii) and sharp-shinned (A. striatus) hawks specific to a raptor migration count station along the Pacific Coast of North America. Illustrating the difficulty of distinguishing between these 2 species, we found overlap in 7 metrics among species-sex groups and in 2 metrics between species, and a principal components analysis revealed a continuum of discrete clusters for each species-sex combination in morphospace. Among juvenile hawks (n = 940), we found the greatest misidentification rate for male Cooper's hawks (23% of the 156 males were identified as sharp-shinned), lesser error rates for female Cooper's (8%, n = 339) and female sharp-shinned (6%, n = 246), and the lowest misidentification rate for male sharp-shinned hawks (0%, n = 199). We observed a similar pattern of misidentification among adult hawks (n = 48). We attempted to use conditional probabilities (identification rates) from calibration data to calculate the true number of adult and juvenile Cooper's hawks and sharp-shinned hawks. Discrepancies between total number of observed accipiters and estimated number using calibration data suggest that daily observer misclassification rates are higher than misclassification rates estimated from calibration data and prevent correction of the raw data. Our results illustrate the importance of testing for and quantifying observer error in species identification in wildlife census and population trend studies particularly when target species may be easily confused with other nontarget species.     

Observer aging and long‐term avian survey data quality

Long‐term wildlife monitoring involves collecting time series data, often using the same observers over multiple years. Aging‐related changes to these observers may be an important, under‐recognized source of error that can bias management decisions. In this study, we used data from two large, independent bird surveys, the Atlas of the Breeding Birds of Ontario (“OBBA”) and the North American Breeding Bird Survey (“BBS”), to test for age‐related observer effects in long‐term time series of avian presence and abundance. We then considered the effect of such aging phenomena on current population trend estimates. We found significantly fewer detections among older versus younger observers for 13 of 43 OBBA species, and declines in detection as an observer ages for 4 of 6 vocalization groups comprising 59 of 64 BBS species. Consistent with hearing loss influencing this pattern, we also found evidence for increasingly severe detection declines with increasing call frequency among nine high‐pitched bird species (OBBA); however, there were also detection declines at other frequencies, suggesting important additional effects of aging, independent of hearing loss. We lastly found subtle, significant relationships between some species' published population trend estimates and (1) their corresponding vocalization frequency (n ≥ 22 species) and (2) their estimated declines in detectability among older observers (n = 9 high‐frequency, monotone species), suggesting that observer aging can negatively bias long‐term monitoring data for some species in part through hearing loss effects. We recommend that survey designers and modelers account for observer age where possible.