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Incorporating covariates into standard line transect analyses
 Biometrics
, 2003
"... Summary. An implicit assumption of standard line transect methodology is that detection probabilities depend solely on the perpendicular distance of detected objects to the transect line. Heterogeneity in detection probabilities is commonly minimized using stratification,but this may be precluded by ..."
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Cited by 38 (7 self)
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Summary. An implicit assumption of standard line transect methodology is that detection probabilities depend solely on the perpendicular distance of detected objects to the transect line. Heterogeneity in detection probabilities is commonly minimized using stratification,but this may be precluded by small sample sizes. We develop a general methodology which allows the effects of multiple covariates to be directly incorporated into the estimation procedure using a conditional likelihood approach. Small sample size properties of estimators are examined via simulations. As an example the method is applied to eastern tropical Pacific dolphin sightings data.
Estimating animal abundance: review III
 Statistical Science
, 1999
"... The literature describing methods for estimating animal abundance and related parameters continues to grow. This paper reviews recent developments in the subject over the past seven years and updates two previous reviews. ..."
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Cited by 23 (0 self)
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The literature describing methods for estimating animal abundance and related parameters continues to grow. This paper reviews recent developments in the subject over the past seven years and updates two previous reviews.
INCORPORATING MEASUREMENT ERROR AND DENSITY GRADIENTS IN DISTANCE SAMPLING SURVEYS
, 2007
"... Distance sampling is one of the most commonly used methods for estimating density and abundance. Conventional methods are based on the distances of detected animals from the center of point transects or the center line of line transects. These distances are used to model a detection function: the pr ..."
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Distance sampling is one of the most commonly used methods for estimating density and abundance. Conventional methods are based on the distances of detected animals from the center of point transects or the center line of line transects. These distances are used to model a detection function: the probability of detecting an animal, given its distance from the line or point. The probability of detecting an animal in the covered area is given by the mean value of the detection function with respect to the available distances to be detected. Given this probability, a HorvitzThompsonlike estimator of abundance for the covered area follows, hence using a modelbased framework. Inferences for the wider survey region are justified using the survey design. Conventional distance sampling methods are based on a set of assumptions. In this thesis I present results that extend distance sampling on two fronts. Firstly, estimators are derived for situations in which there is measurement error in the distances. These estimators use information about the measurement error in two ways: (1) a biased estimator based on the contaminated distances is multiplied by an appropriate correction factor, which is a function of the errors (P DF approach), and
A NOTE ON TESTING THE SHOULDER CONDITION IN LINE TRANSECT SAMPLING
"... We propose a new method to examine the shoulder condition of the detection function in line transect sampling. This is an improvement on the method suggested in Mack (1998). We show that our method has a lower rejection rate (Type I error) when the data are in fact from a population satisfying the s ..."
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We propose a new method to examine the shoulder condition of the detection function in line transect sampling. This is an improvement on the method suggested in Mack (1998). We show that our method has a lower rejection rate (Type I error) when the data are in fact from a population satisfying the shoulder condition. It is also shown that our method is much more sensitive than Mack’s method to the departures from the shoulder condition, therefore has a higher power. KEY WORDS: Line transect sampling, kernel density estimation; end point kernel, optimal bandwidth. 1.
Communications in Statistics  Theory and Methods 1999 28(10)
"... this paper can also be employed for the case with sizebias. Results similar to Theorems 26 in Mack and Quang, op. cit., can be established as in the Theorem above ..."
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this paper can also be employed for the case with sizebias. Results similar to Theorems 26 in Mack and Quang, op. cit., can be established as in the Theorem above
Combining Population Density Estimates in Line Transect Sampling Using the Kernel Method
"... Line transect methods that are “pooling robust ” (Buckland et al, 1993;Burnham et al, 1980) allow data from different transects or locations to be pooled for estimation of population density. This is particularly important in situations where data from individual transects are sparse and pooling is ..."
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Line transect methods that are “pooling robust ” (Buckland et al, 1993;Burnham et al, 1980) allow data from different transects or locations to be pooled for estimation of population density. This is particularly important in situations where data from individual transects are sparse and pooling is done out of necessity. In this study we investigate a method for combining estimates from individual transects when each transect has sufficient data to support estimation with the kernel method. It is based on a minimizer of the asymptotic mean squared error of a linear combination of the individual population density estimators. The asymptotic mean squared error of the simple pooled estimator is always at least as large as the optimally combined estimator. We apply this combination to two estimates from data on a real population of mussels. Using a variety of simulations, we demonstrate the better finite sample efficiency for combining unbalanced cases. In practice, if the detection functions were identical, it can be better to pool; but the gains are modest. On the other hand, when the detection functions are different, it can be substantially better to combine. This recommends the new linear combination. Key words: ecology, environmental survey, nonparametric, pooling 1.
Parametric Detection Function for Kernel Estimator Using Line Transect Data
"... Among different candidate parametric detection functions, it is suggested to use Akaike Information Criterion (AIC) to select the most appropriate one of them to fit line transect data. Four different detection functions are considered in this paper. Two of them are taken to satisfy the shoulder con ..."
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Among different candidate parametric detection functions, it is suggested to use Akaike Information Criterion (AIC) to select the most appropriate one of them to fit line transect data. Four different detection functions are considered in this paper. Two of them are taken to satisfy the shoulder condition assumption and the other two estimators do not satisfy this condition. Once the appropriate detection function is determined, it also can be used to select the smoothing parameter of the nonparametric kernel estimator. For a wide range of target densities, a simulation results show the reasonable and good performances of the resulting estimators comparing with some existing estimator, particularly the usual kernel estimator when the half normal model is use as a reference to
ESTIMATING MOOSE ABUNDANCE IN LINEAR SUBARCTIC HABITATS IN LOW SNOW CONDITIONS WITH DISTANCE SAMPLING AND A KERNEL ESTIMATOR
"... ABSTRACT: Moose (Alces alces) are colonizing previously unoccupied habitat along the tributaries of the lower Kuskokwim River within the Yukon Delta National Wildlife Refuge (YDNWR) of western Alaska. We delineated a new survey area to encompass these narrow (0.7–4.3 km) riparian corridors that ar ..."
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ABSTRACT: Moose (Alces alces) are colonizing previously unoccupied habitat along the tributaries of the lower Kuskokwim River within the Yukon Delta National Wildlife Refuge (YDNWR) of western Alaska. We delineated a new survey area to encompass these narrow (0.7–4.3 km) riparian corridors that are bounded by open tundra and routinely experience winter conditions that limit snow cover and depth necessary for traditional moose surveys. We tested a linetransect distance sampling approach as an alternative to estimate moose abundance in this region. Additionally, we compared standard semiparametric detection functions available in the program Distance to a nonparametric kernelbased estimator not previously used for moose distance data. A doubleobserver technique was used to verify that the probability of detection at the minimum sighting distance was 1.0 (standard assumption). Average moose group size was 2.03 and not correlated with distance from the transect line. The top semiparametric model in the program Distance was a hazardrate key function with no expansion terms. This model estimated average probability of detection as 0.70 with an estimated abundance of 352 moose (95 % CI = 237–540). The CV for the semiparametric model was 20 % and had an estimated bias of 1.4%. The nonparametric kernelbased model had an average probability of detection of 0.73 and an estimated abundance of 340 (95 % CI = 238–472) moose. The CV for