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.

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 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

Conventional distance sampling versus strip transects and abundance indices for estimating abundance of greater gliders (Petauroides volans) and eastern ringtail possums (Pseudocheirus peregrinus)

2019 ◽  
Vol 46 (6) ◽  
pp. 518
Author(s):  
Luke D. Emerson ◽  
Guy-Anthony Ballard ◽  
Karl Vernes
Keyword(s):  
Distance Sampling ◽  
Probability Of Detection ◽  
Density Estimate ◽  
Imperfect Detection ◽  
Density Estimates ◽  
Transect Line ◽  
Eucalypt Forest ◽  
Arboreal Marsupials ◽  
Abundance Indices ◽  
Strip Transect

Abstract ContextAccurate estimates of abundance are extremely useful for wildlife management and conservation. Estimates generated from distance sampling are typically considered superior to strip transects and abundance indices, as the latter do not account for probability of detection, thereby risking significant error. AimTo compare density estimates generated from conventional distance sampling (CDS) of arboreal marsupials with strip transect density estimates and abundance indices. MethodsOff-track CDS and strip transects were used to estimate densities of P. volans and P. peregrinus across ~2.6km2 of remnant eucalypt forest at Mt Duval in north-eastern New South Wales. Key resultsCDS density estimates for P. volans (1.36ha−1, 95% confidence interval (CI) of 1.07–1.72ha−1) and P. peregrinus (0.28ha−1, 95% CI 0.22–0.35ha−1) were consistent with densities reported in other studies conducted in open eucalypt forests. A strip transect width of 40m for P. volans resulted in a collective set of values for density (1.35ha−1), error (s.e.±0.14), precision (cv 0.10) and 95% CI (1.07–1.62ha−1) closest to those associated with the CDS-generated density estimate (1.36ha−1, s.e.±0.15, cv 0.10, 95% CI 1.07–1.72ha−1). Strip widths of 10 to 40m resulted in density estimates for P. peregrinus closest to those generated through CDS, but much less precise. ConclusionsAlthough a 40-m wide strip transect provided a robust density estimate for P. volans at Mt Duval, this is unlikely to be consistent across different study areas. Strip transects provided less precise density estimates, or underestimated P. peregrinus density at Mt Duval, when compared with CDS density estimates. CDS should be favoured over strip transects or abundance indices for estimating P. volans and P. peregrinus abundance, because it is capable of providing more meaningful and robust abundance estimates by accounting for the probability of detection from the transect line across different habitats. ImplicationsResearchers, conservation managers and decision makers should be aware that common methods for assessing arboreal marsupial abundance have serious potential weaknesses. Thus, it would be prudent to invest in studies that address imperfect detection to improve the quality of monitoring data.

scholarly journals Seabirds At-Sea Surveys: The Line-Transect Method Outperforms the Point-Transect Alternative

2017 ◽  
Vol 10 (1) ◽  
pp. 42-52 ◽  
Author(s):  
François Bolduc ◽  
David A. Fifield
Keyword(s):  
Detection Probability ◽  
Goodness Of Fit ◽  
Probability Of Detection ◽  
Line Transect ◽  
Density Estimates ◽  
Coefficients Of Variation ◽  
Line Transects ◽  
Point Transect ◽  
Line Transect Method ◽  
Seabirds At Sea

Introduction:Knowledge of seasonal distribution and abundance of species is paramount in identifying key areas. Field data collection and analysis must provide best information concerning seabirds at-sea to optimize conservation efforts.Methods:We tested whether modeling of detection probabilities, and density estimates with their coefficients of variation obtained from the point-transect method provided more robust and precise results than the more commonly used line-transect method. We subdivided our data by species groups (alcids, and aerialist species), and into two behavior categories (flyingvs.swimming). We also computed density estimates from the strip-transect and point count methods, to relate differences between transect methods to their counterparts that do not consider a decreasing probability of detection with distance from the observer. We used data collected in the Gulf of St. Lawrence between 2009 and 2010 when observers simultaneously conducted line- and point-transect sampling.Results:Models of detection probability using the line-transect method had a good fit to the observed data, whereas detection probability histograms of point-transect analyses suggested substantial evasive movements within the 0-50 m interval. This resulted in point-transect detection probability models displaying poor goodness of fit. Line transects yielded density estimates 1.2-2.6 times higher than those obtained using the point-transect method. Differences in percent coefficients of variation between line-transect and point-transect density estimates ranged between 0.2 and 5.9.Conclusion:Using 300 m wide line-transects provided the best results, while other methods could lead to biased conclusions regarding species density in the local landscape and the relative composition of seabird communities among species and behavior groups.

scholarly journals Developing and assessing a density surface model in a Bayesian hierarchical framework with a focus on uncertainty: insights from simulations and an application to fin whales (Balaenoptera physalus)

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8226
Author(s):  
Douglas B. Sigourney ◽  
Samuel Chavez-Rosales ◽  
Paul B. Conn ◽  
Lance Garrison ◽  
Elizabeth Josephson ◽  
...  
Keyword(s):  
Distance Sampling ◽  
Habitat Model ◽  
Line Transect ◽  
Surface Model ◽  
Two Stage ◽  
Balaenoptera Physalus ◽  
Density Estimates ◽  
Bayesian Hierarchical ◽  
Density Surface ◽  
Fin Whales

Density surface models (DSMs) are an important tool in the conservation and management of cetaceans. Most previous applications of DSMs have adopted a two-step approach to model fitting (hereafter referred to as the Two-Stage Method), whereby detection probabilities are first estimated using distance sampling detection functions and subsequently used as an offset when fitting a density-habitat model. Although variance propagation techniques have recently become available for the Two-Stage Method, most previous applications have not propagated detection probability uncertainty into final density estimates. In this paper, we describe an alternative approach for fitting DSMs based on Bayesian hierarchical inference (hereafter referred to as the Bayesian Method), which is a natural framework for simultaneously propagating multiple sources of uncertainty into final estimates. Our framework includes (1) a mark-recapture distance sampling observation model that can accommodate two team line transect data, (2) an informed prior for the probability a group of animals is at the surface and available for detection (i.e. surface availability) (3) a density-habitat model incorporating spatial smoothers and (4) a flexible compound Poisson-gamma model for count data that incorporates overdispersion and zero-inflation. We evaluate our method and compare its performance to the Two-Stage Method with simulations and an application to line transect data of fin whales (Balaenoptera physalus) off the east coast of the USA. Simulations showed that both methods had low bias (<1.5%) and confidence interval coverage close to the nominal 95% rate when variance was propagated from the first step. Results from the fin whale analysis showed that density estimates and predicted distribution patterns were largely similar among methods; however, the coefficient of variation of the final abundance estimate more than doubled (0.14 vs 0.31) when detection variance was correctly propagated into final estimates. An analysis of the variance components demonstrated that overall detectability as well as surface availability contributed substantial amounts of variance in the final abundance estimates whereas uncertainty in mean group size contributed a negligible amount. Our method provides a Bayesian alternative to DSMs that incorporates much of the flexibility available in the Two-Stage Method. In addition, these results demonstrate the degree to which uncertainty can be underestimated if certain components of a DSM are assumed fixed.

scholarly journals Using encounter rates as surrogates for density estimates makes monitoring of heavily-traded grey parrots achievable across Africa

Oryx ◽  
2015 ◽  
Vol 50 (4) ◽  
pp. 617-625 ◽  
Author(s):  
Stuart J. Marsden ◽  
Emmanuel Loqueh ◽  
Jean Michel Takuo ◽  
John A. Hart ◽  
Robert Abani ◽  
...  
Keyword(s):  
Local Density ◽  
Conservation Status ◽  
Distance Sampling ◽  
Central Africa ◽  
High Mobility ◽  
Encounter Rate ◽  
Line Transect ◽  
African Countries ◽  
Density Estimates ◽  
Encounter Rates

AbstractEstimating population sizes in the heavily traded grey parrots of West and Central Africa would provide insights into conservation status and sustainability of harvests. Ideally, density estimates would be derived from a standardized method such as distance sampling, but survey efforts are hampered by the extensive ranges, patchy distribution, variable abundance, cryptic habits and high mobility of the parrots as well as by logistical difficulties and limited resources. We carried out line transect distance sampling alongside a simpler encounter rate method at 10 sites across five West and Central African countries. Density estimates were variable across sites, from 0–0.5 individuals km−2 in Côte d'Ivoire and central Democratic Republic of the Congo to c. 30 km−2 in Cameroon and > 70 km−2 on the island of Príncipe. Most significantly, we identified the relationship between densities estimated from distance sampling and simple encounter rates, which has important applications in monitoring grey parrots: (1) to convert records of parrot groups encountered in a day's activities by anti-poaching patrols within protected areas into indicative density estimates, (2) to confirm low density in areas where parrots are so rare that distance sampling is not feasible, and (3) to provide a link between anecdotal records and local density estimates. Encounter rates of less than one parrot group per day of walking are a reality in most forests within the species’ ranges. Densities in these areas are expected to be one individual km−2 or lower, and local harvest should be disallowed on this basis.

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

scholarly journals Techniques for Estimating the Size of Low-Density Gopher Tortoise Populations

2017 ◽  
Vol 8 (2) ◽  
pp. 377-386 ◽  
Author(s):  
Jonathan M. Stober ◽  
Rocio Prieto-Gonzalez ◽  
Lora L. Smith ◽  
Tiago A. Marques ◽  
Len Thomas
Keyword(s):  
Habitat Management ◽  
Distance Sampling ◽  
Probability Of Detection ◽  
Line Transect ◽  
Efficient Design ◽  
Gopher Tortoise ◽  
Gopherus Polyphemus ◽  
Line Transect Survey ◽  
Estimate Population Size ◽  
Survey Designs

Abstract Gopher tortoises (Gopherus polyphemus) are candidates for range-wide listing as threatened under the U.S. Endangered Species Act. Reliable population estimates are important to inform policy and management for recovery of the species. Line transect distance sampling has been adopted as the preferred method to estimate population size. However, when tortoise density is low, it can be challenging to obtain enough tortoise observations to reliably estimate the probability of detection, a vital component of the method. We suggest a modification to the method based on counting usable tortoise burrows (more abundant than tortoises) and separately accounting for the proportion of burrows occupied by tortoises. The increased sample size of burrows can outweigh the additional uncertainty induced by the need to account for the proportion of burrows occupied. We demonstrate the method using surveys conducted within a 13,118-ha portion of the Gopher Tortoise Habitat Management Unit at Fort Gordon Army Installation, Georgia. We used a systematic random design to obtain more precise estimates, using a newly developed systematic variance estimator. Individual transects had a spatially efficient design (pseudocircuits), which greatly improved sampling efficiency on this large site. Estimated burrow density was 0.091 ± 0.011 burrows/ha (CV = 12.6%, 95% CI = 0.071–0.116), with 25% of burrows occupied by a tortoise (CV = 14.4%), yielding a tortoise density of 0.023 ± 0.004 tortoise/ha (CV = 19.0%, 95% CI = 0.016–0.033) and a population estimate of 297 tortoises (95% CI = 210–433). These techniques are applicable to other studies and species. Surveying burrows or nests, rather than animals, can produce more reliable estimates when it leads to a significantly larger sample of detections and when the occupancy status can reliably be ascertained. Systematic line transect survey designs give better precision and are practical to implement and analyze.

scholarly journals Getting the Most From Surveys: How Method Selection and Method Modification Impact Butterfly Survey Data

2021 ◽  
Author(s):  
Katherine C Kral-O’Brien ◽  
Adrienne K Antonsen ◽  
Torre J Hovick ◽  
Ryan F Limb ◽  
Jason P Harmon
Keyword(s):  
Species Richness ◽  
New Species ◽  
Count Data ◽  
Distance Sampling ◽  
Line Transect ◽  
Similar Species ◽  
Method Selection ◽  
M 10 ◽  
Sampling Procedures

Abstract Many methods are used to survey butterfly populations, with line transect and area surveys being prominent. Observers are typically limited to search within 5 or 10 m from the line, while observers are unrestricted in larger specified search regions in area surveys. Although methods differ slightly, the selection is often based on producing defendable data for conservation, maximizing data quality, and minimizing effort. To guide method selection, we compared butterfly surveys using 1) line versus area methods and 2) varying width transects (5 m, 10 m, or unrestricted) using count data from surveys in North Dakota from 2015 to 2018. Between line and area surveys, we detected more individuals with area surveys, even when accounting for effort. However, both methods accumulated new species at similar rates. When comparing transect methodology, we detected nearly 60% more individuals and nine more species when transect width increased from 5 m to unrestricted, despite similar effort across methodology. Overall, we found line surveys slightly less efficient at detecting individuals, but they collected similar species richness to area surveys when accounting for effort. Additionally, line surveys allow the use of unrestricted-width transects with distance sampling procedures, which were more effective at detecting species and individuals while providing a means to correct count data over the same transect length. Methods that reduce effort and accurately depict communities are especially important for conservation when long-term datasets are unavailable.

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.

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