Primera etapa (1991 – 1995)

The increase in human activities worldwide has resulted in a growing need for monitoring programs to measure their effects on protected species and their environments. In addition to having the power to detect effects such as changes in abundance and distribution, monitoring programs also need to be cost-effective because of economic constraints that governments, industries or environmental departments face.

PAM can offer cost benefits needed in monitoring programs of marine species including baleen whales. However, for this technique to be most effective in monitoring changes in population parameters, some knowledge gaps need to be filled. The following are some of the current key knowledge gaps:

 Acoustic repertoire knowledge: Baleen whales usually vocalise at particular frequencies and produce sounds specific to their species and often populations. This attribute allows species to be identified by the sounds they produce. To attribute sounds to a baleen whale species, the acoustical repertoire of each needs to be known. Thus, verification of sounds produced by the species is required. A number of methods are available to achieve this. Tags that directly attach to the body of a whale and record sound, such as ‘D-tags’, are a reliable way to confidently attribute sounds to the whale producing them. However, these methods are expensive, result in low sample sizes, and affect the overall cost of PAM. Visual observations of individual whales can be combined with acoustic recordings more remotely. This approach requires some knowledge of the vocal behaviour, movement patterns, and distribution of whales to be able to match vocalisations recorded or heard with individuals observed. For some species that have well-studied repertoires, additional new sounds recorded by remote underwater recorders can be attributed to the species when the new sounds are produced with known sounds. However, it is important to note that the acoustic repertoire of most baleen whale species, with some exceptions (i.e. humpback

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whales), remains unknown. As a consequence the sources of many sounds remain unidentified. Further studies that describe the acoustic repertoire of baleen whales using such techniques would improve our knowledge base for using PAM to monitor whales into the future.

 Using vocalisations as indices of relative abundance: Most long-term acoustic monitoring studies use single underwater recorders deployed within a target study site. These studies are cost-effective, but do not allow localising individuals in space. This limitation is partially mitigated by using knowledge of their stereotypical songs and comparing signal-to-noise ratios to distinguish individuals from each other and count them. However, when a large number of whales are vocalising simultaneously, this approach is not possible. Multiple recorders (three or more) or the use of directional sonobuoys can provide acoustic tracking capabilities to identify the direction and/or location of individuals vocalising. In these cases, individuals can be distinguished and counted. However, the use of multiple recorders and sonobuoys increases the cost of a program, often beyond what is affordable. Consequently, studies using a single recorder have either used counts of vocalising whales nearby (within a designated high signal-to-noise ratio limit) or have used acoustic energy as an index of relative abundance. However, the accuracy of using sound energy metrics as proxies for relative abundance has not been adequately explored. For instance, accuracy may be affected by changes in relative distance of vocalising animals from the recorder, with closer animals increasing energy levels more than animals far away. Thus, it is important to understand how energy level metrics correlate with actual numbers of vocalising whales and conditions that may affect this relationship.

 Biological, environmental and anthropogenic conditions influencing the

variability in vocalisation rates: Vocalisation rates can be influenced by

different environmental, biological, ecological and anthropogenic conditions and combinations of these. While some of the conditions that influence variability in vocalisation rates have been investigated on a species-by-species basis, few studies have explored the influence of combinations of these on multiple species at a site. A broader understanding of the conditions influencing baleen whale detectability and vocal behaviour is important for identifying potential sources of bias. This knowledge can then be used to adjust detection probability estimates to improve the accuracy of abundance estimates.

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 Acoustic detection probability: Finally, abundance estimates (and other population parameters) using PAM that include an estimation of detection probability have a higher accuracy than those that use raw counts. Estimation of the availability component of the overall detection probability is one of the main challenges associated with PAM, because it depends upon knowledge of the vocalisation rates and behaviour of whales at the target study site during the period monitored. For most baleen whale species, information on vocal behaviour and vocalisation rates is scarce or non-existent (Oleson, Calambokidis, Burgess et al. 2007; Marques et al. 2013). Some studies have measured individual vocalisation rates using D-tags, and this information has been used in density estimation by dividing the number of individuals visually observed by cue rates. However, behavioural studies have suggested differences in vocal rates among individuals and cohorts of a population that vary in different environmental conditions. Thus, by estimating detection probabilities using parameters known to influence the variability in vocalisation rates as covariates (observer bias, environmental conditions, distance, technical specifications), and making adjustments based on the probability that the animal is available for detection (an animal is vocalising), both availability and detectability can be further adjusted for and incorporated into abundance estimates or population parameters.

Availability bias can be approximated using two different survey platforms that count different cues for detection, such as visual and acoustic cues. This approach has been used in a few studies aimed at adjusting for imperfect detection of dolphins (e.g. Akamatsu et al. 2008; Ichikawa et al. 2009; Akamatsu et al. 2014; Richman et al. 2014) and gray and minke whales (Rankin et al. 2007; Van Parijs et al. 2009; Ponce et al 2012). It is important to note that double observations from visual and acoustic platforms approximate estimates of detection probability bias, since animals that are not detected by either platform are not counted. Studies that consider a large number of sources of detectability bias are still under development.

 Optimal monitoring protocols: The use of PAM has increased over recent decades because of the development of acoustic sensors that are affordable, non-invasive and can be used for long periods of time. Therefore, PAM has become an important tool used in cost-effective monitoring of species on land and in aquatic environments, and for monitoring anthropogenic impacts on their populations. However, the use of this tool for long-term monitoring of focal

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species requires knowledge of their biology, behaviour and habitats to optimise the selection of acoustic sensors, survey design and data analysis. Ultimately, the estimation of population parameters of target species can be improved through the development of guidelines for developing optimal monitoring protocols.