Javascript must be enabled to continue!
Aerial surveys of multiple species: critical assumptions and sources of bias in distance and mark–recapture estimators
View through CrossRef
Recent developments in the application of line-transect models to aerial surveys have used double-observer sampling to account for undercounting on the transect line, a crucial step in obtaining correct population estimates. This method is commonly called the mark–recapture line-transect sampling method and estimates the detection probability at zero distance to correct line-transect estimates of abundance. An alternative approach, which uses the same methodology during data collection, is to use a range of covariates, including distance from the transect, in a mark–recapture model. This approach overcomes the implicit assumption of uniform distribution of distances in line-transect estimators. In this paper, we use three alternative approaches (a multiple-covariates distance method, a distance method incorporating adjustment for incomplete detection on the transect line using mark–recapture sampling, and a mark–recapture method with distance as a covariate) to estimate the abundance of several medium-sized mammals in semiarid ecosystems. Densities determined with the three estimators varied considerably within species and sites. In some cases distance estimates were larger than mark–recapture estimates and vice versa. Despite large numbers of observations, distance uniformity was not observed for any species at any site, nor for any species where sites were combined. Possible reasons, which include sampling variability, movement in response to the aircraft and failure of the mark–recapture independence assumption, are discussed in detail.
CSIRO Publishing
Title: Aerial surveys of multiple species: critical assumptions and sources of bias in distance and mark–recapture estimators
Description:
Recent developments in the application of line-transect models to aerial surveys have used double-observer sampling to account for undercounting on the transect line, a crucial step in obtaining correct population estimates.
This method is commonly called the mark–recapture line-transect sampling method and estimates the detection probability at zero distance to correct line-transect estimates of abundance.
An alternative approach, which uses the same methodology during data collection, is to use a range of covariates, including distance from the transect, in a mark–recapture model.
This approach overcomes the implicit assumption of uniform distribution of distances in line-transect estimators.
In this paper, we use three alternative approaches (a multiple-covariates distance method, a distance method incorporating adjustment for incomplete detection on the transect line using mark–recapture sampling, and a mark–recapture method with distance as a covariate) to estimate the abundance of several medium-sized mammals in semiarid ecosystems.
Densities determined with the three estimators varied considerably within species and sites.
In some cases distance estimates were larger than mark–recapture estimates and vice versa.
Despite large numbers of observations, distance uniformity was not observed for any species at any site, nor for any species where sites were combined.
Possible reasons, which include sampling variability, movement in response to the aircraft and failure of the mark–recapture independence assumption, are discussed in detail.
Related Results
Comparing fecal DNA capture‐recapture to mark‐resight for estimating abundance of mule deer on winter ranges
Comparing fecal DNA capture‐recapture to mark‐resight for estimating abundance of mule deer on winter ranges
AbstractMonitoring big game populations is necessary for making well‐informed management decisions. In the eastern Sierra Nevada in California, USA, mule deer (Odocoileus hemionus)...
Mark‐Recapture Distance Sampling for Aerial Surveys of Ungulates on Rangelands
Mark‐Recapture Distance Sampling for Aerial Surveys of Ungulates on Rangelands
ABSTRACT
Aerial surveys are an efficient technique for counting animals over large geographic areas such as rangelands. In southwestern range...
Costs and Precision of Fecal DNA Mark–Recapture versus Traditional Mark–Resight
Costs and Precision of Fecal DNA Mark–Recapture versus Traditional Mark–Resight
ABSTRACT
Wildlife managers often need to estimate population abundance to make well‐informed decisions. However, obtaining such estimates can...
Generalized Estimator of Population Variance utilizing Auxiliary Information in Simple Random Sampling Scheme
Generalized Estimator of Population Variance utilizing Auxiliary Information in Simple Random Sampling Scheme
In this study, using the Simple Random Sampling without Replacement (SRSWOR) method, we propose a generalized estimator of population variance of the primary variable. Up to the fi...
Efficient Class of Variance Estimators for Population using Supplementary Information in Stratified Random Sampling
Efficient Class of Variance Estimators for Population using Supplementary Information in Stratified Random Sampling
This paper addresses an efficient class of variance estimators for population using stratified random sampling. The suggested class of estimators using supplementary information ha...
Improved Mean Estimators for Population utilizing Dual Supplementary Characteristics under Simple Random Sampling
Improved Mean Estimators for Population utilizing Dual Supplementary Characteristics under Simple Random Sampling
This paper makes another addition to the existing literature of population mean estimation. An improved family of mean estimators for the population is suggested using simple rando...
An Assessment of the Geographic Closure Assumption in Mark–Recapture Abundance Estimates of Anadromous Steelhead Populations
An Assessment of the Geographic Closure Assumption in Mark–Recapture Abundance Estimates of Anadromous Steelhead Populations
Abstract
Closed population models are commonly used to estimate stream salmonid abundances using mark–recapture information collected during electrofishing surveys. ...
Machine Learning for Causal Inference: On the Use of Cross-fit Estimators
Machine Learning for Causal Inference: On the Use of Cross-fit Estimators
Background:
Modern causal inference methods allow machine learning to be used to weaken parametric modeling assumptions. However, the use of machine learning may result...

