- Top of page
- Materials and methods
- Conflict of interest
- Supporting Information
As expected, kiwi call counts conducted by observers in the field were more sensitive than those from autonomous recorders. Misclassification and false-negative rates for the field counts were low, confirming that surveys using trained volunteers, many with no previous experience of bird counts, can yield accurate results. While autonomous recorders were less sensitive, their areal coverage was comparable with field surveys and adequate for many applications. For example, assuming an LSK territory size of 3 ha for a dense population (Colbourne & Robertson 1997; Holzapfel et al. 2008), spectrogram inspection was able to monitor approximately 16 territories, compared with 19 for the field count. The fully automatic survey sampled about half the number of territories, as a result of its inability to detect the fainter, lower quality calls. Assuming a uniform density of calling kiwi, the number of calls will increase with the square of distance, but the amplitude of those calls will decrease by the same ratio. A sensitivity-limited sample will therefore contain a high proportion of lower quality calls, and the area sampled will decrease strongly with sensitivity. This skew to lower quality calls is evident in Fig. 2, which also shows that the automatic method had a constantly high recall (>80%) up to a quality of seven, but after that recall declined sharply. This is because these lower quality calls would mostly be beyond the 300 m reliable detection limit of the automatic method. A larger area could therefore be surveyed simply using more recorders, spaced according to this distance limit. Although the automatic detection recall was relatively low, the very few false positives suggest that it was unaffected by noise conditions, which often cause problems for automatic signal detection and classification (Bardeli et al. 2010).
It should be noted that the performance of the field survey is likely overestimated in this study, since distant detections could not be confirmed by the recorders. Conversely, the recall from spectrogram inspection was a lower limit, since very faint calls that were missed during spectrogram scanning were seen upon re-inspection and would likely have been detected with more careful examination. However, these caveats would make at most only minor adjustments to the performance of each method and do not alter the overall conclusions.
Automatic detection software is ideally suited to species such as LSK that have low call variability. For animals with a wider repertoire, such as song birds, or for detection of multiple species, a larger training set is necessary and detector performance is likely to be reduced (Bardeli et al. 2010). A further restriction is that the detection software in this study was often unable to resolve calls that overlapped with other sources, either conspecifics or other species. This would limit its usefulness in measuring abundance in areas of high population density or where there are high numbers of acoustically competing species. However, field counts are also prone to such bias (Hutto & Stutzman 2009), which is caused by either the insufficiency of human auditory ability in temporally or spectrally resolving simultaneous calls or the requirement that observers record call details as they occur. The latter also affects the quality of observations made, since call parameters were occasionally omitted or inaccurate during periods of high calling activity. It is significant that spectrogram inspection tended to perform better than the field observers during high call rates.
The principal advantage of using autonomous recorders is the gain in efficiency, with the spectrogram inspection around 30 times more efficient than traditional counts. These substantial gains in productivity offered by recorders could be utilised by sampling over longer periods each night and at other times of year to reduce temporal biases. The only significant time input required for the fully automated method is in resupplying recorders and data management, and a nightly single point survey could be achieved with a time expenditure of only about an hour per month. For remote areas or in determining species presence or absence, this method is extremely attractive.
The automatic recorder surveys also offer a major benefit in reduction in temporal and observer bias that can affect field surveys. The automatic detection method is fully repeatable as it requires no subjective input at all and so is well suited for assessing variation in counts between sites or time periods. Minimal disturbance of the target species and a permanent data record are further benefits of the autonomous methods.
A disadvantage of autonomous recorders is that each is restricted to a single fixed location. In contrast, human observers can move around the survey area to make further observations if necessary. Field-based observers can also discern between signals from different directions to improve abundance estimates or determine territories, which is not possible with a single-channel recorder. The simple use of autonomous recorders in this experiment is therefore limited compared with field counts when estimating numbers of individuals or territories. However, stereo recorders can yield directional information from the difference in signal arrival time at each microphone (Benesty et al. 2008). With spatially separated microphones, species densities can be estimated from relative signal intensities (Dawson & Efford 2009), and synchronised microphone arrays allow accurate determination of caller position (Collier et al. 2010; Mennill et al. 2012). Acoustic recorders therefore offer the potential to provide population and behavioural information beyond that available to human listeners (Fitzsimmons et al. 2008; Mennill & Vehrencamp 2008; Kirschel et al. 2011)
The generalised linear models suggested that wind noise affected the autonomous detections more than the field survey. This may have been due to the observers being slightly above the canopy and so surrounded by fewer scattering surfaces and sources of noise in windy conditions than the microphones, which were below canopy level. Also, unlike the static microphones, the observers were able to move to minimise the effect of wind noise. This reflects an inherent restriction of acoustic recorders, which are adversely affected by wind noise on microphones and should be placed in a sheltered position.
The significant impact of ground conditions on the field counts, but not on the autonomous counts, is a more serious concern. Pierce & Westbrooke (2003) reported an increase in call rate with increasing ground moisture index for brown kiwi (Apteryx mantelli), suggesting that this is a real effect. The inability of the autonomous methods to detect a significant influence may have been a result of their reduced sensitivity. This led to fewer counts and wider effect confidence intervals and also reduced areal sampling that may have affected the ground moisture dependency if that effect was nonuniform throughout the study site. Additionally, higher call rates under damp or wet ground conditions may have reduced the recall of the fully automated method. However, the ground moisture coefficient estimates for the autonomous methods are similar to the field counts, albeit nonsignificant. A longer sampling period is necessary to sufficiently reduce confidence intervals for the autonomous methods. This would be easily performed given their efficiency, and it should be noted that the short recording period of 1 h per night is artificially low, used only to provide direct comparison with the field survey.
The year effect would of main interest in most conservation applications and shows no difference between the three methods. It is slightly surprising that no call increase is apparent between years, given that the LSK population in Zealandia is growing at c. 10% per annum (H. Robertson, pers. comm.). However, counts taken over many years would be required to measure such a trend, and call rates do not always follow population changes (Robertson et al. 2003).
This study has evaluated the use of autonomous recorders for point call counts, applied to a typical, volunteer-run monitoring programme, rather than a one-off research experiment. These results demonstrate that while trained lay observers provide accurate surveys, autonomous recorders offer a viable alternative. Spectrogram inspection can yield comparable coverage with field observers for considerably less effort, and fully automated methods offer robust alternatives that are well suited to longer-term projects and those in remote or unmonitored areas. Our modelling results show that autonomous methods can provide similar ecological inferences to field counts, but that care must be taken to ensure sufficient temporal sampling to adjust for the different biases these are subject to.
The detection ability of our autonomous recording methods depended upon signal strength, with recall dropping substantially for the faintest calls. We demonstrated this using a subjective measure of call spectrogram quality, but wider adoption of autonomous techniques would be aided by measuring performance against a quantitative signal-to-noise measure. A plot of recall against signal-to-noise, or ideally the distance to which a species can be reliably detected, would provide a useful definition of the detection limits of a survey method that would guide the spacing of recorders.
Field call counts can offer significant benefits for conservation advocacy and community engagement. We recommend that autonomous recorders do not replace these, but are utilised to increase the spatial and temporal coverage of existing call count regimes. Acoustic recorders should also be used during call counts to verify the accuracy of observations (Hutto & Stutzman 2009), particularly during periods of high call rates. Autonomous recorders would also be suitable for areas where existing surveys are not established and are particularly appropriate for determining presence or absence of a species in remote areas where field call counts are costly and inefficient.