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.

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.

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.

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.

An estimate of population sizes of burrowing seabirds at the Diego Ramirez archipelago, Chile, using distance sampling and burrow-scoping

Polar Biology ◽  
2005 ◽  
Vol 29 (3) ◽  
pp. 229-238 ◽  
Author(s):  
Kieran Lawton ◽  
Graham Robertson ◽  
Roger Kirkwood ◽  
José Valencia ◽  
Roberto Schlatter ◽  
...  
Keyword(s):  
Distance Sampling ◽  
Population Sizes ◽  
Burrowing Seabirds

scholarly journals Tendencias poblacionales recientes de la avutarda hubara en las Islas Canarias: análisis metodológico y estado de conservación

2012 ◽  
Vol 35 (1) ◽  
pp. 125-139
Author(s):  
C. Schuster ◽  
◽  
J. J. Iglesias-Lebrija ◽  
L. M. Carrascal ◽  
◽  
...  
Keyword(s):  
Canary Islands ◽  
Conservation Status ◽  
Distance Sampling ◽  
Small Variation ◽  
Population Trends ◽  
Line Transect ◽  
Sampling Area ◽  
Average Value ◽  
Houbara Bustard ◽  
Population Sizes

Recent population trends of the houbara bustard in the Canary Islands. Methods and conservation status Determining conservation status requires rigorous and reliable data about population sizes and trends, especially if they have to be applied to islands where the species have small populations. The Canary bustard houbara (Chlamydotis undulata fuertaventurae) is catalogued as ‘in danger’ by the Red Book of the Birds of Spain. This work analyzes the value of previously published information on the species using the method of adjacent linear transects separated by 200 m, as a baseline for establishing robust population trends in 30 important areas (ranging from 1.3 to 12.8 km2) for the houbara in the islands of Lanzarote and Fuerteventura (Canary Islands). Censuses were repeated on the same dates (from November to December) and localities as those carried out in 1994, 2004 and 2006. The detection probability of the houbara was estimated by means of distance sampling, being 0.42 up to 250 m from the observer, and 0.82 in the main census belt of 100 m on either side of the line transect. The method of adjacent linear transects —counting the maximum number of hubaras detected— provides accurate figures of population densities (detection of 95.2% of the birds). The previous estimations of houbara densities can therefore be considered highly trustworthy, with a probable average underestimation of only 5 %. Nevertheless, the confidence intervals of density estimations using only one census were very large. Therefore, with only one census per sampling area and year it is not possible to obtain precise estimates of houbara densities with small variation around the average value. This raises concern when trying to obtain solid evidence about the increases–decreases of houbara populations comparing different dates or study areas. The density of the Canary Island hubara bustard decreased significantly from 2004/2006 to 2011 in eight areas of Fuerteventura (Vega Vieja, Los Alares–Pocetas, Matas Blancas, Lorenzo– Diviso, Corralejo, Lajares and Fimapaire) and in two areas of Lanzarote (Argana and La Santa). Conversely, the density of the houbara significantly increased in three areas of Lanzarote (Zonzamas, Guatiza and Llano de las Maretas). As a whole, average population density did not differ significantly between 2004/2006 and 2011 in Fuerteventura (slight decrease of 29% in 2011), but there was a significant increase in Lanzarote (increase of 60% from 2004/2006 to 2011). The change in density from 2004/2006 to 2011 tended to be an increase in areas further from paved roads, and a decrease in locations predominantly covered by loose sandy soils.

scholarly journals Development of a species distribution model for fin whales (Balaenoptera physalus) within a Bayesian hierarchical framework: Implications for uncertainty

2018 ◽  
Author(s):  
Douglas B Sigourney ◽  
Samuel Chavez-Rosales ◽  
Paul Conn ◽  
Lance Garrison ◽  
Elizabeth Josephson ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Distance Sampling ◽  
Distribution Model ◽  
Balaenoptera Physalus ◽  
Density Estimates ◽  
Bayesian Hierarchical ◽  
Hierarchical Framework ◽  
Final Density ◽  
Fin Whales

Species distribution models (SDMs) have proven to be an integral tool in the conservation and management of cetaceans. Many applications have adopted a two-step approach where a detection function is estimated using conventional distance sampling in the first step and subsequently used as an offset to a density-habitat model in the second step. A drawback to this approach, hereafter referred to as the conventional species distribution model (CSDM), is the difficulty in propagating the uncertainty from the first step to the final density estimates. We describe a Bayesian hierarchical species distribution model (BHSDM) which has the advantage of simultaneously propagating multiple sources of uncertainty. Our framework includes 1) a mark-recapture distance sampling observation model that can accommodate two team line transect data, 2) an informed prior for surface availability 3) spatial smoothers using spline-like bases and 4) a compound Poisson-gamma likelihood which is a special case of the Tweedie distribution. We compare our approach to the CSDM method using a simulation study and a case study of fin whales (Balaenoptera physalus) off the East Coast of the USA. Simulations showed that the BHSDM method produced estimates with lower precision but with confidence interval coverage closer to the nominal 95% rate (94% for the BSHDM vs 85% for the CSDM). Results from the fin whale analysis showed that density estimates and predicted distribution patterns were largely similar among methods. Abundance estimates were also similar though modestly higher for the CSDM (4700, CV=0.13) than the BHSDM (4526, CV=0.26). Estimated sampling error differed substantially among the two methods where the average CV for density estimates from BHSDM method was approximately 3.5 times greater than estimates from the CSDM method. Successful wildlife management hinges on the ability to properly quantify uncertainty. Underestimates of uncertainty can result in ill-informed management decisions. Our results highlight the additional sampling uncertainty that is propagated in a hierarchical framework. Future applications of SDMs should consider techniques that allow all sources of error to be fully represented in final density predictions.

Sign in / Sign up

Export Citation Format

Share Document