Double-observer distance sampling improves the accuracy of density estimates for a threatened arboreal mammal

2021 ◽  
Author(s):  
Jemma K. Cripps ◽  
Jenny L. Nelson ◽  
Michael P. Scroggie ◽  
Louise K. Durkin ◽  
David S. L. Ramsey ◽  
...  
Keyword(s):  
Distance Sampling ◽  
Density Estimates ◽  
Arboreal Mammal ◽  
Double Observer

Double-observer evaluation of pronghorn aerial line-transect surveys

2016 ◽  
Vol 43 (6) ◽  
pp. 474 ◽  
Author(s):  
Timothy J. Smyser ◽  
Richard J. Guenzel ◽  
Christopher N. Jacques ◽  
Edward O. Garton
Keyword(s):  
Cluster Size ◽  
Distance Sampling ◽  
Landscape Composition ◽  
Probability Of Detection ◽  
Line Transect ◽  
Front Seat ◽  
Density Estimates ◽  
Seat Position ◽  
Analysis Techniques ◽  
Double Observer

Context Distance sampling is used to estimate abundance for several taxa, including pronghorn (Antilocapra americana). Comparisons between population estimates derived from quadrat sampling and distance sampling suggest that distance sampling underestimates pronghorn density, likely owing to violations of the critical assumption of distance sampling that all pronghorn within the innermost distance band (A band; nearest to the aircraft) are detected. Aims We sought to rigorously test the assumption that all pronghorn clusters are detected within the innermost distance band by applying a double-observer approach to an established pronghorn aerial-survey protocol. Additionally, we evaluated potential effects of cluster size, landscape composition and seat position (front seat versus rear) on the probability of detection. Methods We conducted aerial line-transect distance-sampling surveys using independent, paired observers and modelled the probability of detection with mark–recapture distance-sampling (MRDS) analysis techniques that explicitly estimate the probability of detection for pronghorn clusters in the innermost distance band. We compared density estimates produced by the MRDS analysis with those produced by multiple-covariate distance sampling (MCDS), a method that assumes complete detection for clusters on the transect line. Key results We identified violations of the assumption that all clusters within the innermost distance band were detected, which would contribute to proportional biases in density estimates for analysis techniques that assume complete detection. The frequency of missed clusters was modest from the front-seat position, with 45 of the 47 (96%) clusters in the A band detected. In contrast, the frequency of missed clusters was more substantial for the rear position, from which 37 of 47 (79%) clusters in the A band were detected. Further, our analysis showed that cluster size and landscape composition were important factors for pronghorn sightability. Conclusions When implementing standard survey methodologies, pronghorn aerial-line transect surveys underestimated population densities. A double-observer survey configuration allowed us to quantify and correct for the bias caused by the failure of observers to detect all pronghorn clusters within the innermost distance band. Implications Population monitoring programs should incorporate double-observer validation trials to quantify the extent of bias owing to undetected clusters within the innermost distance band realised under typical survey conditions. Wildlife managers can improve the precision of pronghorn aerial line-transect surveys by incorporating cluster size and measures of landscape composition and complexity into detection models without incurring additional survey costs.

scholarly journals Evaluating Alternative Flight Plans in Thermal Drone Wildlife Surveys—Simulation Study

2021 ◽  
Vol 13 (6) ◽  
pp. 1102
Author(s):  
Julia Witczuk ◽  
Stanisław Pagacz
Keyword(s):  
Computer Simulations ◽  
Survey Design ◽  
Distance Sampling ◽  
Line Of Sight ◽  
Density Estimates ◽  
Accuracy And Precision ◽  
Survey Costs ◽  
Random Survey ◽  
Density Population

The rapidly developing technology of unmanned aerial vehicles (drones) extends to the availability of aerial surveys for wildlife research and management. However, regulations limiting drone operations to visual line of sight (VLOS) seriously affect the design of surveys, as flight paths must be concentrated within small sampling blocks. Such a design is inferior to spatially unrestricted randomized designs available if operations beyond visual line of sight (BVLOS) are allowed. We used computer simulations to assess whether the VLOS rule affects the accuracy and precision of wildlife density estimates derived from drone collected data. We tested two alternative flight plans (VLOS vs. BVLOS) in simulated surveys of low-, medium- and high-density populations of a hypothetical ungulate species with three levels of effort (one to three repetitions). The population density was estimated using the ratio estimate and distance sampling method. The observed differences in the accuracy and precision of estimates from the VLOS and BVLOS surveys were relatively small and negligible. Only in the case of the low-density population (2 ind./100 ha) surveyed once was the VLOS design inferior to BVLOS, delivering biased and less precise estimates. These results show that while the VLOS regulations complicate survey logistics and interfere with random survey design, the quality of derived estimates does not have to be compromised. We advise testing alternative survey variants with the aid of computer simulations to achieve reliable estimates while minimizing survey costs.

A Comparison of White-Winged Dove Zenaida asiatica Densities Estimated During Morning and Evening Surveys

2012 ◽  
Vol 3 (1) ◽  
pp. 158-163 ◽  
Author(s):  
Michael F. Small ◽  
Joseph A. Veech ◽  
John T. Baccus
Keyword(s):  
Distance Sampling ◽  
Visual Observation ◽  
Probability Of Detection ◽  
Reasonable Assumption ◽  
Zenaida Asiatica ◽  
Density Estimates ◽  
Bird Populations ◽  
Game Birds ◽  
Population Sizes ◽  
Cost Efficient

Abstract Surveying bird populations through visual observation is generally limited to morning. The focus on morning surveys is based on the reasonable assumption that detection is more likely when birds are most active. However, population surveys could become more time- and cost-efficient if both morning and evening sampling were equally effective, particularly for game birds, such as white-winged dove Zenaida asiatica. Texas Parks and Wildlife Department has recently implemented distance sampling to estimate population sizes and monitor an ongoing range expansion of this species. We compared morning vs. evening density estimates for white-winged doves sampled in Mason, Texas, on six separate occasions during summer 2006. Program DISTANCE (version 5.0) calculated similar detection probabilities and density estimates between paired morning and evening sampling periods. Probability of detection ranged from 0.27 to 0.46 for both morning and evening samples. Densities, in individuals/ha, ranged from 2.54 to 4.02 for morning sampling and 2.48 to 4.31 for evening sampling. In addition, variables (number of observations, cluster size, distance to cluster) used by DISTANCE did not vary substantially between morning and evening surveys. Our results suggest evening surveys are as effective as the conventional protocol of surveying white-winged doves only in the morning. Additional studies, using Program DISTANCE, should be conducted to similarly evaluate other species.

scholarly journals Landbird Counting Techniques: Current Practices and an Alternative

The Auk ◽  
2002 ◽  
Vol 119 (1) ◽  
pp. 46-53 ◽  
Author(s):  
Steven S. Rosenstock ◽  
David R. Anderson ◽  
Kenneth M. Giesen ◽  
Tony Leukering ◽  
Michael F. Carter
Keyword(s):  
Distance Sampling ◽  
Field Studies ◽  
Empirical Models ◽  
Viable Alternative ◽  
Density Estimates ◽  
Abundance Index ◽  
Point Counts ◽  
Density Index ◽  
Conceptual Overview ◽  
Current Practices

AbstractCounting techniques are widely used to study and monitor terrestrial birds. To assess current applications of counting techniques, we reviewed landbird studies published 1989–1998 in nine major journals and one symposium. Commonly used techniques fell into two groups: procedures that used counts of bird detections as an index to abundance (index counts), and procedures that used empirical models of detectability to estimate density. Index counts rely upon assumptions concerning detectability that are difficult or impossible to meet in most field studies, but nonetheless remain the technique of choice among ornithologists; 95% of studies we reviewed relied upon point counts, strip transects, or other index procedures. Detectability-based density estimates were rarely used and deserve wider application in landbird studies. Distance sampling is a comprehensive extension of earlier detectability-based procedures (variable-width transects, variable circular plots) and a viable alternative to index counts. We provide a conceptual overview of distance sampling, specific recommendations for applying this technique to studies of landbirds, and an introduction to analysis of distance sampling data using the program DISTANCE.

scholarly journals Improving Estimates of Bird Density Using Multiple- Covariate Distance Sampling

The Auk ◽  
2007 ◽  
Vol 124 (4) ◽  
pp. 1229-1243 ◽  
Author(s):  
Tiago A. Marques ◽  
Len Thomas ◽  
Steven G. Fancy ◽  
Stephen T. Buckland
Keyword(s):  
Distance Sampling ◽  
Probability Of Detection ◽  
Density Estimates ◽  
Bird Density ◽  
Multiple Covariates ◽  
Model Probability ◽  
Point Transect ◽  
Space Distance ◽  
True Population

Abstract Inferences based on counts adjusted for detectability represent a marked improvement over unadjusted counts, which provide no information about true population density and rely on untestable and unrealistic assumptions about constant detectability for inferring differences in density over time or space. Distance sampling is a widely used method to estimate detectability and therefore density. In the standard method, we model the probability of detecting a bird as a function of distance alone. Here, we describe methods that allow us to model probability of detection as a function of additional covariates—an approach available in DISTANCE, version 5.0 (Thomas et al. 2005) but still not widely applied. The main use of these methods is to increase the reliability of density estimates made on subsets of the whole data (e.g., estimates for different habitats, treatments, periods, or species), to increase precision of density estimates or to allow inferences about the covariates themselves. We present a case study of the use of multiple covariates in an analysis of a point-transect survey of Hawaii Amakihi (Hemignathus virens). Amélioration des estimations de densité d’oiseaux par l’utilisation de l’échantillonnage par la distance avec covariables multiples

Estimating western ringtail possum (Pseudocheirus occidentalis) density using distance sampling

2010 ◽  
Vol 32 (2) ◽  
pp. 197 ◽  
Author(s):  
G. R. Finlayson ◽  
A. N. Diment ◽  
P. Mitrovski ◽  
G. G. Thompson ◽  
S. A. Thompson
Keyword(s):  
Urban Development ◽  
Population Size ◽  
Western Australia ◽  
Distance Sampling ◽  
Reliable Estimate ◽  
Population Estimates ◽  
Density Estimates ◽  
The Subject ◽  
Ringtail Possum

A reliable estimate of population size is of paramount importance for making management decisions on species of conservation significance that may be impacted during development. The western ringtail possum (Pseudocheirus occidentalis) is regularly encountered during urban development and is the subject of numerous surveys to estimate its abundance. A variety of techniques have been used for this species with mixed results. This paper reports on a case study using distance sampling to estimate density of P. occidentalis in a small habitat remnant near Busselton, Western Australia. Density estimates obtained were within the range of previous studies of this species and we suggest that this technique should be employed in future surveys to improve the accuracy of population estimates for this species before development.

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 Correction: A Unimodal Model for Double Observer Distance Sampling Surveys

PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0140913
Author(s):  
Earl F. Becker ◽  
Aaron M. Christ
Keyword(s):  
Distance Sampling ◽  
Double Observer

scholarly journals Extending density surface models to include multiple and double-observer survey data

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12113
Author(s):  
David L. Miller ◽  
David Fifield ◽  
Ewan Wakefield ◽  
Douglas B. Sigourney
Keyword(s):  
Spatial Model ◽  
Distance Sampling ◽  
Additive Model ◽  
Data Sources ◽  
Multiple Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Surface Models ◽  
Density Surface ◽  
Double Observer

Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains.

scholarly journals Field Comparison of Removal and Modified Double-Observer Modeling for Estimating Detectability and Abundance of Birds

The Auk ◽  
2004 ◽  
Vol 121 (3) ◽  
pp. 865-876
Author(s):  
Jeffrey E. Moore ◽  
Daniel M. Scheiman ◽  
Robert K. Swihart
Keyword(s):  
Count Data ◽  
Bird Abundance ◽  
Density Estimates ◽  
North Central ◽  
Point Count ◽  
Detection Probabilities ◽  
True Detection ◽  
Sources Of Variation ◽  
True Population ◽  
Double Observer

Abstract For point-count data to reliably index bird abundance or density, estimates must be corrected for variation in detection probabilities across species, observers, and environmental conditions. Removal and double-observer modeling are two recently developed statistical techniques for estimating detection probabilities and bird abundance. We collected point-count data in north-central Indiana and used a Huggins closed-capture model in MARK to directly compare those two methods. We found that when detection probabilities were relatively high for individual observers, the two methods yielded similar estimates of density for nearly all 17 species modeled. However, when true detection probabilities for observers were relatively low, removal estimates of detectability and density were biased high and low, respectively, perhaps because of the effect of low-detection probability on the removal estimator or smaller sample sizes associated with less-skilled observers. In general, we consider removal modeling a more desirable approach than double-observer modeling because it requires half as many observers, allows more sources of variation in detectability to be modeled, and estimates abundance or density of the true population of birds. By contrast, double-observer modeling estimates only the abundance of the “apparent” population (i.e. those birds that are visually or audibly conspicuous). For species that vocalize infrequently or are otherwise elusive, the apparent population may be significantly smaller than the true population. However, double-observer modeling is more robust to violations of the assumption of population closure and may outperform removal methods when data are collected by less-experienced observers.

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