Distance sampling is widely used to estimate animal population densities by accounting for imperfect detection of individuals with increasing distance from an observer. Distance sampling assumes that distances are measured without error; however, it is often applied to human estimated distances, which are known to be inconsistent, inaccurate, and biased. We present an objective technique for estimating distance to vocalizing individuals that relies on the relative sound level (RSL) of the vocalization extracted from autonomous recording unit (ARU) recordings and show the error is less than human estimated error extracted from a literature case study. RSL predicted distances can be obtained by manual measurement in sound viewing software, or automatically with automated signal recognition software. We built calibration datasets of Ovenbirds (Seiurus aurocapilla) and Common Nighthawks (Chordeiles minor) recorded at known distances and used regression of RSL from those recordings to predict distance. There was no error bias of RSL predicted distances when compared to known distances for Common Nighthawk, minimal error bias for Ovenbird, and error from all RSL predicted distances was less than human estimated error extracted from the literature. We then simulated ARU point count surveys with a known density and estimated that density with distance sampling to test whether RSL distance prediction does not violate the assumption that distances are measured without error. There was no difference in density estimates from known distance and density estimates obtained from RSL predicted distance, while density estimates contaminated with human estimated error were significantly lower than density estimates from known distance. We found that a calibration dataset of approximately 300 vocalizations was suitable to minimize error for both species, and so conclude that RSL distance prediction is an accessible method of improving distance estimates relative to human estimation. We provide general recommendations on how to collect calibration recordings for the application of RSL distance prediction to other species and areas.
Within populations, individuals often show repeatable variation in behaviour, called ‘animal personality’. In the last few decades, numerous empirical studies have attempted to elucidate the mechanisms maintaining this variation, such as life‐history trade‐offs. Theory predicts that among‐individual variation in behavioural traits could be maintained if traits that are positively associated with reproduction are simultaneously associated with decreased survival, such that different levels of behavioural expression lead to the same net fitness outcome. However, variation in resource acquisition may also be important in mediating the relationship between individual behaviour and fitness components (survival and reproduction). For example, if certain phenotypes (e.g. dominance or aggressiveness) are associated with higher resource acquisition, those individuals may have both higher reproduction and higher survival, relative to others in the population. When individuals differ in their ability to acquire resources, trade‐offs are only expected to be observed at the within‐individual level (i.e. for a given amount of resource, if an individual increases its allocation to reproduction, it comes at the cost of allocation to survival, and vice versa), while among individuals traits that are associated with increased survival may also be associated with increased reproduction. We performed a systematic review and meta‐analysis, asking: (i) do among‐individual differences in behaviour reflect among‐individual differences in resource acquisition and/or allocation, and (ii) is the relationship between behaviour and fitness affected by the type of behaviour and the testing environment? Our meta‐analysis consisted of 759 estimates from 193 studies. Our meta‐analysis revealed a positive correlation between pairs of estimates using both survival and reproduction as fitness proxies. That is, for a given study, behaviours that were associated with increased reproduction were also associated with increased survival, suggesting that variation in behaviour at the among‐individual level largely reflects differences among individuals in resource acquisition. Furthermore, we found the same positive correlation between pairs of estimates using both survival and reproduction as fitness proxies at the phenotypic level. This is significant because we also demonstrated that these phenotypic correlations primarily reflect within‐individual correlations. Thus, even when accounting for among‐individual differences in resource acquisition, we did not find evidence of trade‐offs at the within‐individual level. Overall, the relationship between behaviour and fitness proxies was not statistically different from zero at the among‐individual, phenotypic, and within‐individual levels; this relationship was not affected by behavioural category nor by the testing condition. Our meta‐analysis highlights that variation in resource acquisition may be more important in driving the relationship between behaviour and fitness than previously thought, ...
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In the last several years, there has been a surge in the number of studies addressing the causes and consequences of among‐individual variation in cognitive ability and behavioural plasticity. Here, we use a recent publication by Herczeg et al. (2019: 32(3), 218–226) to highlight three shortcomings common to this newly emerging field. In their study, Herczeg et al. attempted to link variation in cognitive ability and behavioural plasticity by testing whether selection lines of guppies (Poecilia reticulata) that differ in relative brain size also differ in behavioural plasticity, as might be expected if the costs to plasticity are predominantly derived from the cost of developing large brains. First, residual brain size may not be a suitable proxy for ‘cognitive ability’. Recent work has shown that intraspecific variation in cognitive ability can be better understood by considering variation in the specific brain areas responsible for the relevant behaviours as opposed to whole‐brain mass. Second, the measure of behavioural plasticity, habituation, is unlikely to fulfil the assumptions that plasticity is both adaptive and costly. Finally, we point out several misconceptions regarding animal personality that continue to contribute to the choice of traits that are not well aligned with study objectives. Elucidating the mechanisms underlying among‐individual variation in cognition and behavioural plasticity within populations requires integration between behavioural ecology and comparative cognition, and the study system developed by Herczeg et al. has the potential to provide important mechanistic insights. We hope that by articulating and critically appraising the underlying assumptions that are common in these traditionally separate disciplines, a strong foundation can emerge to allow for more fruitful integration of these fields.
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