Return to search

Assessing estimators of feral goat (Capra hircus) abundance

(1) Reliable measures of population abundance are essential for managing wildlife
effectively. Aerial surveys provide a rapid and efficient means of surveying large mammals
and many techniques have been developed to adjust for the inability to count all animals
within transects. The probability of detection varies according to a range of factors which
are important to consider when estimating density. Standardised survey methods developed
in flat country are not readily transferable to steep terrain due to safety, access and
difficulties delineating transect widths. Other methods have logistic constraints and must
adhere to various other assumptions.
(2) Density estimators are seldom examined using actual population size, hence their
ability to correct for true bias is unknown. Studies that compare techniques are difficult to
interpret because of the uncertainty of adherence to their respective assumptions. Factors
influencing detection probability, estimators that correct for bias, the validity of their
assumptions and how these relate to true density are important considerations for selecting
suitable methods. The aim of this study was to obtain accurate and reliable methods for
estimating the density of feral goats by improving predictions of detection probability,
investigating the assumptions of aerial surveys, and examining the accuracy of 15 density
estimators by comparing with total counts of feral goats.
(3) Group size, vegetation and observer were the most important factors influencing the
probability of observing a group of goats during aerial surveys. However, different
approaches to analysing these data influenced the significance of variables and the
predicted probabilities. Goat colour, type of helicopter, site and rear observer experience in
hours were also found to be significant (P<0.05) when using likelihood equations based on
all animals in the population rather than only those in the sample. The slope of the terrain
was also shown to significantly (P=0.014) affect the probability of detection.
(4) Indices are commonly used in wildlife management for their simplicity and
practicality, but their validity has been questioned because of variable probability of
detection. Results of this study suggest aerial survey indices are useful in monitoring a

range of medium-sized mammal species across space and time if differences in detection
probability between species, group size, vegetation and observer are considered and their
effects are standardised.
(5) An assumption of most sampling regimes that is fundamental but rarely examined is
that animals are not counted more than once. In this study the behavioural responses of
feral goats to helicopters were investigated as a basis for estimating the probability that
goats were recounted. No long-term consequences were evident in feral goat behaviour of
responses to helicopters. However, helicopter surveys were found to alter the structure of
42% of groups observed, with 28% of groups merging with others and 14% splitting into
separate groups. Therefore, group size estimated from the air should not be considered as
biologically important, and when estimating density, researchers should also avoid using
group sizes determined from independent ground observations to correct group sizes
determined from aerial surveys. Goats were also more likely to flush further when
helicopters were within 150 m, which is close to or within standard helicopter strip widths.
Substantial movement occurred between transects and 21% of goats were estimated to be
available for recounting in adjacent transects.
(6) Different detection probabilities between groups of goats may be particularly
relevant when using double-counting, where multiple observers are �capturing� and
�recapturing� animals in the same instant. Many analyses test and adjust for this �unequal
catchability� assumption in different ways, with the approaches of Huggins and Alho
allowing prediction of unique probability values for a range of co-variates. The approach of
Chao attempts to correct for skewed distributions in small samples. The Horvitz-Thompson
approach provides a useful basis for estimating abundance (or density) when detection
probability can be estimated and is known to vary between observations according to a
range of independent variables, and also avoids errors associated with averaging group size.
(7) After correcting for recounting, the Alho estimator applied to helicopter surveys
was the most accurate (Bias = 0.02) and reliable of all techniques, which suggests that
estimates were improved by taking into account unconditional detection probability and
correcting individual observations according to their characteristics. The positive bias

evident in the Chao (Bias = 0.28) and Petersen (Bias = 0.15) aerial survey estimators may
have been a result of averaging detection probability across all observations. The
inconsistency and inaccuracy of the ground-based area-count technique emphasises the
importance of other assumptions in density estimation, such as representative sampling and
availability bias. The accuracy of index-manipulation-index techniques was dependent on
the indices used. Capture-recapture estimates using mustering showed slight negative bias
(Bias = -0.08), which was likely a result of increased probability of re-capture (i.e. trap
happy). Ground-based capture-resight estimates were labour intensive and positively biased
(Bias = 0.13), likely due to underestimating the area sampled, or overestimating the number
of unmarked individuals with each sample.
(8) Helicopter survey using double-counting is recommended for estimating the density
of feral goats in steep terrain. However, consideration of recounting under intensive
sampling regimes and adjustments for the factors that influence unconditional detection
probability is required.

Identiferoai:union.ndltd.org:ADTP/218640
Date January 2004
CreatorsTracey, John Paul, n/a
PublisherUniversity of Canberra. Resource, Environmental & Heritage Management
Source SetsAustraliasian Digital Theses Program
LanguageEnglish
Detected LanguageEnglish
Rights), Copyright John Paul Tracey

Page generated in 0.0029 seconds