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

2007 ◽  
Vol 71 (8) ◽  
pp. 2759-2766 ◽  
Author(s):  
MATHEW W. ALLDREDGE ◽  
THEODORE R. SIMONS ◽  
KENNETH H. POLLOCK
Keyword(s):  
Measurement Error ◽  
Field Evaluation ◽  
Distance Measurement ◽  
Point Count ◽  
Count Surveys

Field evaluation of distance-estimation error during wetland-dependent bird surveys

2012 ◽  
Vol 39 (4) ◽  
pp. 311 ◽  
Author(s):  
Christopher P. Nadeau ◽  
Courtney J. Conway
Keyword(s):  
Detection Probability ◽  
Estimation Error ◽  
Distance Estimation ◽  
Field Evaluation ◽  
Field Trials ◽  
Survey Period ◽  
Estimation Errors ◽  
Point Count ◽  
Survey Point ◽  
Count Surveys

Context The most common methods to estimate detection probability during avian point-count surveys involve recording a distance between the survey point and individual birds detected during the survey period. Accurately measuring or estimating distance is an important assumption of these methods; however, this assumption is rarely tested in the context of aural avian point-count surveys. Aims We expand on recent bird-simulation studies to document the error associated with estimating distance to calling birds in a wetland ecosystem. Methods We used two approaches to estimate the error associated with five surveyor’s distance estimates between the survey point and calling birds, and to determine the factors that affect a surveyor’s ability to estimate distance. Key results We observed biased and imprecise distance estimates when estimating distance to simulated birds in a point-count scenario (error = –9 m, s.d.error = 47 m) and when estimating distances to real birds during field trials (error = 39 m, s.d.error = 79 m). The amount of bias and precision in distance estimates differed among surveyors; surveyors with more training and experience were less biased and more precise when estimating distance to both real and simulated birds. Three environmental factors were important in explaining the error associated with distance estimates, including the measured distance from the bird to the surveyor, the volume of the call and the species of bird. Surveyors tended to make large overestimations to birds close to the survey point, which is an especially serious error in distance sampling. Conclusions Our results suggest that distance-estimation error is prevalent, but surveyor training may be the easiest way to reduce distance-estimation error. Implications The present study has demonstrated how relatively simple field trials can be used to estimate the error associated with distance estimates used to estimate detection probability during avian point-count surveys. Evaluating distance-estimation errors will allow investigators to better evaluate the accuracy of avian density and trend estimates. Moreover, investigators who evaluate distance-estimation errors could employ recently developed models to incorporate distance-estimation error into analyses. We encourage further development of such models, including the inclusion of such models into distance-analysis software.

Analysis of “Distance-Loss” Model Influence Factors in Vehicular Ad Hoc Networks

2012 ◽  
Vol 490-495 ◽  
pp. 1481-1485
Author(s):  
Jiang Feng Wang ◽  
Xue Dong Yan ◽  
Shuo Nie
Keyword(s):  
Measurement Error ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Influence Factors ◽  
Distance Measurement ◽  
Experimental Sample ◽  
Position Information ◽  
Loss Model ◽  
Accurate Position ◽  
Hoc Networks

In vehicular ad hoc network(VANETs), it is crucial for obtaining more accurate position information which variation rules between “distance-loss” model influence factors and distance measurement error is analyzed. Based on qualitative analysis of “distance-loss” model, field experimental scene and scheme are proposed. Using experimental sample data, the relation of environmental factor and shelter factor and distance measurement error is studied quantitatively. The experimental results show that distance measurement performance of optimized “distance-loss” model is significantly improved, and 6.7% is the largest reduction proportion of distance measurement error.

scholarly journals Singing in the suburbs: point count surveys efficiently reveal habitat associations for nocturnal Orthoptera across an urban-to-rural gradient

2020 ◽  
Vol 24 (6) ◽  
pp. 1031-1043
Author(s):  
Darin J. McNeil ◽  
Christina M. Grozinger
Keyword(s):  
Site Occupancy ◽  
Habitat Associations ◽  
Survey Method ◽  
Population Declines ◽  
Detection Rates ◽  
Point Count ◽  
Point Counts ◽  
Focal Species ◽  
Habitat Gradient ◽  
Count Surveys

Abstract As evidence for global insect population declines continues to amass, several studies have indicated that Orthoptera (grasshoppers, crickets, and katydids) are among the most threatened insect groups. Understanding Orthoptera populations across large spatial extents requires efficient survey protocols, however, many previously established methods are expensive and/or labor-intensive. One survey method widely employed in wildlife biology, the aural point count, may work well for crickets and katydids (suborder: Ensifera) because males produce conspicuous, species-specific mating calls. We conducted repeated point count surveys across an urban-to-rural gradient in central Pennsylvania. Occupancy analyses of ten focal species indicated that, although detection probability rates varied by species from 0.43 to 0.98, detection rates compounded over five visits such that all focal species achieved cumulative > 0.90. Factors associated with site occupancy varied among species with some positively associated with urbanization (e.g., Greater Anglewing, Microcentrum rhombifolium), some negatively associated with urbanization (e.g., Sword-bearing Conehead, Neoconocephalus ensiger), and others exhibiting constant occupancy across a habitat gradient (e.g., Common True Katydid, Pterophylla camellifolia). Our community-level analysis revealed that different species’ habitat associations interacted such that intermediate levels of urbanization (i.e., suburbs) hosted the highest number of species. Implications for insect conservation Ultimately, our analyses clearly support the concept that aural point counts paired with static occupancy modeling can serve as an important tool for monitoring night-singing Orthoptera populations. Applications of point count surveys by both researchers and citizen scientists may improve our understanding Ensifera populations and help in the global conservation of these threatened insects.

scholarly journals Estimating Detection Probability and Density from Point-Count Surveys: A Combination of Distance and Double-Observer Sampling

The Auk ◽  
2006 ◽  
Vol 123 (3) ◽  
pp. 735-752 ◽  
Author(s):  
Michelle L. Kissling ◽  
Edward O. Garton
Keyword(s):  
Detection Probability ◽  
Survey Methods ◽  
Effective Area ◽  
Survey Method ◽  
Density Estimates ◽  
Point Count ◽  
Detection Probabilities ◽  
Fixed Radius ◽  
Count Surveys ◽  
Double Observer

Abstract Point counts are the method most commonly used to estimate abundance of birds, but they often fail to account properly for incomplete and variable detection probabilities. We developed a technique that combines distance and double-observer sampling to estimate detection probabilities and effective area surveyed. We applied this paired-observer, variable circular-plot (POVCP) technique to point-count surveys (n = 753) conducted in closed-canopy forests of southeast Alaska. Distance data were analyzed for each species to model a detection probability for each observer and calculate an estimate of density. We then multiplied each observer's density estimates by a correction factor to adjust for detection probabilities <1 at plot center. We compared analytical results from four survey methods: single-observer fixed-radius (50-m) plot; single-observer, variable circular-plot (SOVCP); double-observer fixed-radius (50-m) plot; and POVCP. We examined differences in detection probabilities at plot center, effective area surveyed, and densities for five bird species: Pacific-slope Flycatcher (Empidonax difficilis), Winter Wren (Troglodytes troglodytes), Golden-crowned Kinglet (Regulus satrapa), Hermit Thrush (Catharus guttatus), and Townsend's Warbler (Dendroica townsendi). Average detection probabilities for paired observers increased ≈8% (SE = 2.9) for all species once estimates were corrected for birds missed at plot center. Density estimators of fixed-radius survey methods were likely negatively biased, because the key assumption of perfect detection was not met. Density estimates generated using SOVCP and POVCP were similar, but standard errors were much lower for the POVCP survey method. We recommend using POVCP when study objectives require precise estimates of density. Failure to account for differences in detection probabilities and effective area surveyed results in biased population estimators and, therefore, faulty inferences about the population in question. Estimaciones de la Densidad y de las Probabilidades de Detección a Partir de Muestreos Utilizando Conteos en Puntos: Una Combinación de Muestreos de Distancia y de Doble Observador

scholarly journals Characterizing urban butterfly populations: the case for purposive point-count surveys

2019 ◽  
Vol 22 (6) ◽  
pp. 1083-1096
Author(s):  
Bret J. Lang ◽  
Philip M. Dixon ◽  
Robert W. Klaver ◽  
Jan R. Thompson ◽  
Mark P. Widrlechner
Keyword(s):  
Point Count ◽  
Count Surveys

scholarly journals EFFECTS OF VEGETATION AND BACKGROUND NOISE ON THE DETECTION PROCESS IN AUDITORY AVIAN POINT-COUNT SURVEYS

The Auk ◽  
2008 ◽  
Vol 125 (4) ◽  
pp. 998-998 ◽  
Author(s):  
KRISHNA PACIFICI ◽  
THEODORE R. SIMONS ◽  
KENNETH H. POLLOCK
Keyword(s):  
Background Noise ◽  
Point Count ◽  
Detection Process ◽  
Count Surveys

Improvement of distance measurement error by attenuation of ultrasonic transmitter and receiver signal for the ultrasonic positioning system

2018 ◽  
Vol 2018 (0) ◽  
pp. OS4-7
Author(s):  
Shunsuke Matsuoka ◽  
Hiromichi Ito ◽  
Shingo Takahashi ◽  
Takuma Moritani ◽  
Hideki Kawaguchi ◽  
...  
Keyword(s):  
Measurement Error ◽  
Distance Measurement ◽  
Positioning System ◽  
Ultrasonic Transmitter
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