Nest attendance of lactating red squirrels (<i>Tamiasciurus hudsonicus</i>): influences of biological and environmental correlates

Studd, Emily K.; Boutin, Stan; McAdam, Andrew G.; Humphries, Murray M.

doi:10.1093/jmammal/gyw010

Cited by 17 publications

(29 citation statements)

References 50 publications

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“…For each day of recordings (12 p.m.-12 p.m.), we used k-means clustering constrained to two clusters to determine a daily threshold temperature, above which a squirrel was considered to be in the nest (Studd et al, 2016). For each day of recordings (12 p.m.-12 p.m.), we used k-means clustering constrained to two clusters to determine a daily threshold temperature, above which a squirrel was considered to be in the nest (Studd et al, 2016).…”

Section: In Nest Versus Out Of Nestmentioning

confidence: 99%

“…The accelerometer units recorded temperature in addition to acceleration ( Figure 1). Following a similar method used by Studd et al (2016), we inferred nest-use based on the concept that the ambient temperature of the local environment (i.e., surrounding the squirrel) is warm and stable when in the nest, and cold and variable when out of the nest. Before analysis, all temperature data were smoothed to filter out erroneous recordings (see Supporting Information Appendix S1: Section 1.4).…”

Section: In Nest Versus Out Of Nestmentioning

confidence: 99%

“…Before analysis, all temperature data were smoothed to filter out erroneous recordings (see Supporting Information Appendix S1: Section 1.4). For each day of recordings (12 p.m.-12 p.m.), we used k-means clustering constrained to two clusters to determine a daily threshold temperature, above which a squirrel was considered to be in the nest (Studd et al, 2016). This threshold is unique to each squirrel and day to account for changes in nest insulation (Guillemette et al, 2009), orientation of collar, and daily ambient temperature.…”

Section: In Nest Versus Out Of Nestmentioning

confidence: 99%

“…This includes in (black bars) and out (gray bars) of the nest in the temperature data during both winter and summer, and running, feeding, not moving, and foraging signatures in acceleration data temperature can provide important supplementary information about an individual and its thermal micro-environment. When attached externally to an animal, the temperature recorded is often intermediate between body temperature and the ambient temperature of the environment immediately surrounding the individual (Osgodd & Weigl, 1972;Studd, Boutin, McAdam, & Humphries, 2016;Tremblay, Cherel, Oremus, Tveraa, & Chastel, 2003). This temperature intermediacy likely accounts for their rarity of use; collar temperature is not a reliable measure of body temperature or air temperature (Audet & Thomas, 1996;van Beest, Moorter, & Milner, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal

Studd

Landry‐Cuerrier

Menzies

et al. 2018

Ecology and Evolution

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The miniaturization and affordability of new technology is driving a biologging revolution in wildlife ecology with use of animal‐borne data logging devices. Among many new biologging technologies, accelerometers are emerging as key tools for continuously recording animal behavior. Yet a critical, but under‐acknowledged consideration in biologging is the trade‐off between sampling rate and sampling duration, created by battery‐ (or memory‐) related sampling constraints. This is especially acute among small animals, causing most researchers to sample at high rates for very limited durations. Here, we show that high accuracy in behavioral classification is achievable when pairing low‐frequency acceleration recordings with temperature. We conducted 84 hr of direct behavioral observations on 67 free‐ranging red squirrels (200–300 g) that were fitted with accelerometers (2 g) recording tri‐axial acceleration and temperature at 1 Hz. We then used a random forest algorithm and a manually created decision tree, with variable sampling window lengths, to associate observed behavior with logger recorded acceleration and temperature. Finally, we assessed the accuracy of these different classifications using an additional 60 hr of behavioral observations, not used in the initial classification. The accuracy of the manually created decision tree classification using observational data varied from 70.6% to 91.6% depending on the complexity of the tree, with increasing accuracy as complexity decreased. Short duration behavior like running had lower accuracy than long‐duration behavior like feeding. The random forest algorithm offered similarly high overall accuracy, but the manual decision tree afforded the flexibility to create a hierarchical tree, and to adjust sampling window length for behavioral states with varying durations. Low frequency biologging of acceleration and temperature allows accurate behavioral classification of small animals over multi‐month sampling durations. Nevertheless, low sampling rates impose several important limitations, especially related to assessing the classification accuracy of short duration behavior.

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Section: In Nest Versus Out Of Nestmentioning

confidence: 99%

Section: In Nest Versus Out Of Nestmentioning

confidence: 99%

Section: In Nest Versus Out Of Nestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal

Studd

Landry‐Cuerrier

Menzies

et al. 2018

Ecology and Evolution

Self Cite

View full text Add to dashboard Cite

show abstract

“…We set audio recorders to record in 44.1kHz/16bit WAVE format, and recorded 245 in 2-channel surround mode with the stereo width set to +6 dB and the mic gain set to 10. We 246 allowed audio recorders to run for a full 24 hours, but in this study we only use data collected 247 between 0700-1300 h, which is the period during which squirrels are typically most active 248 between early summer and early autumn (Studd, Boutin, McAdam, & Humphries, 2016;249 Williams et al, 2014). We deployed audio recorders for 137 squirrels (N = 109 males and N = 28 250 females) and recorded each squirrel for 5 consecutive days on average (range: 1-13 days; N = 251 714 days or 4284 hours over 137 individuals).…”

Section: Measuring Familiarity 162mentioning

confidence: 99%

Red squirrels mitigate costs of territory defence through social plasticity

Siracusa

Wilson

Studd

et al. 2018

Preprint

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19For territorial species, the ability to be behaviourally plastic in response to changes in their social 20 environment may be beneficial by allowing individuals to mitigate conflict with conspecifics and 21 reduce the costs of territoriality. Here we investigated whether North American red squirrels 22 hoc analyses revealed that evidence of social plasticity in this system was primarily due to 32 within-individual changes in behaviour, which we were unable to estimate in the cross-sectional 33data. Our results demonstrate that red squirrels can reduce the costs of territoriality by 34 appropriately adjusting behaviour in response to changes in their social environment. However, 35our results also suggest that estimating plasticity by comparing behaviour among individuals (i.e. 36 cross-sectional analyses) may not always be reliable. Our ability to detect these effects may 37 therefore depend on having data with multiple observations from the same individuals across 38 different social environments. 39 40 Keywords 41 behavioural plasticity, behavioural time budgets, dear enemy, familiarity, red squirrel, social 42 competence, social environment, territoriality, vocalizations 43 44 individual's environment, since high levels of unpredictability are inherent when interacting with 63 other agents that can also exhibit plasticity in behaviour. Examples of social plasticity (changes 64 in behaviour in response to changing social conditions; Sih, Chang, & Wey, 2014;1 Montiglio,

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Specificity of California mouse pup vocalizations in response to olfactory stimuli

Wilson

Wagner

Saltzman

2022

Developmental Psychobiology

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To investigate flexibility in vocal signaling by rodent pups, we examined whether olfactory stimuli influence characteristics of pup calls and how these calls may be affected by sex and litter size in California mice (Peromyscus californicus). Pups were isolated and recorded during a 3-min baseline period followed by a 5-min exposure to bedding containing scent from their home cage, scent from the home cage of an unfamiliar family, coyote urine, or no scent (control). Latency to call, call rate, and call characteristics (duration, frequency, and amplitude) were compared between the baseline and scentexposure periods and among olfactory conditions. Compared with the control condition, pups from two-pup litters called more quietly when exposed to odor from a predator, whereas pups from three-pup litters called more loudly. Additionally, pups showed nonsignificant tendencies to reduce call rates in response to odors from their home cage and to increase call rates when exposed to predator urine. Last, males produced higher-frequency calls and more ultrasonic vocalizations than females. These results indicate that pup calling behavior in this species can be influenced by acute olfactory stimuli as well as litter size and sex. The flexibility of pup calling in response to these three variables potentially increases the communication value of pup calls and helps shape the parents' responses.

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Nest attendance of lactating red squirrels (Tamiasciurus hudsonicus): influences of biological and environmental correlates

Cited by 17 publications

References 50 publications

Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal

Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal

Red squirrels mitigate costs of territory defence through social plasticity

Specificity of California mouse pup vocalizations in response to olfactory stimuli

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