Individual Daily and Seasonal Activity Patterns in Fox Squirrels (Sciurus niger) Quantified by Temperature-Sensitive Data Loggers

Wassmer, Thomas; Refinetti, Roberto

doi:10.3389/fevo.2019.00179

Cited by 9 publications

(9 citation statements)

References 66 publications

(100 reference statements)

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“…This might be due to the differences in the amount of free and workdays participants had in a week. It supports the findings of previous studies that have shown that the interindividual variability in circadian rhythms is greater than the intraindividual variability, both in humans and animals (see, for example, Sharma, 1996 ; Selmaoui and Touitou, 2003 ; Refinetti and Piccione, 2005 ; Romeijn and Van Someren, 2011 ; Wassmer and Refinetti, 2019 ). Differently from Selmaoui and Touitou (2003) , we tested participants in their natural environments and not in the laboratory which adds to the validity and generalizability of the findings as participants were able to follow their normal work/university and sleep routines during the study period.…”

Section: Study 1: Intraindividual Variability In Mid-sleep On Free and Workdays Across 2 Weekssupporting

confidence: 91%

“…Differently from humans, however, there are many studies that have investigated the inter-and intraindividual variability of circadian rhythms in non-human animals. These studies have consistently shown that the interindividual variability of circadian markers is greater than the intraindividual variability (see for example Sharma, 1996;Refinetti and Piccione, 2005;Romeijn and Van Someren, 2011;Wassmer and Refinetti, 2019).…”

Section: Intraindividual Variability and Temporal Stability Of Chronomentioning

confidence: 97%

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Intraindividual Variability and Temporal Stability of Mid-Sleep on Free and Workdays

et al. 2020

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People differ in their sleep timings that are often referred to as a chronotype and can be operationalized as mid-sleep (midpoint between sleep onset and wake-up). The aims of the present studies were to examine intraindividual variability and longer-term temporal stability of mid-sleep on free and workdays, while also considering the effect of age. We used data from a 2-week experience sampling study of British university students (Study 1) and from a panel study of Estonian adults who filled in the Munich Chronotype Questionnaire twice up to 5 years apart (Study 2). Results of Study 1 showed that roughly 50% of the variance in daily mid-sleep scores across the 14-day period was attributed to intraindividual variability as indicated by the intraclass correlation coefficient. However, when the effect of free versus workdays was considered, the intraindividual variability in daily mid-sleep across 2 weeks was 0.71 the size of the interindividual variability. In Study 2, mid-sleep on free and workdays showed good levels of temporal stability—the retest correlations of mid-sleep on free and workdays were 0.66 and 0.58 when measured twice over a period of 0-1 to 5 years. The retest stability of mid-sleep scores on both free and workdays sharply increased from young adulthood and reached their peak when participants were in late 40 to early 50 years of age, indicating that age influences the stability of mid-sleep. Future long-term longitudinal studies are necessary to explore how age-related life circumstances and other possible factors may influence the intraindividual variability and temporal stability of mid-sleep.

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Section: Study 1: Intraindividual Variability In Mid-sleep On Free and Workdays Across 2 Weekssupporting

confidence: 91%

Section: Intraindividual Variability and Temporal Stability Of Chronomentioning

confidence: 97%

Intraindividual Variability and Temporal Stability of Mid-Sleep on Free and Workdays

et al. 2020

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“…foraging, grooming, etc. ), heart rates and body temperatures of recognizable individuals over time (Le Grand et al., 2019; Wassmer & Refinetti, 2019). The value of this added information is especially evident when combined with tracking technology that offers high‐resolution spatial data (Kays et al., 2015; Krause et al., 2013).…”

Section: New Opportunities and Unanswered Questions: A Path Forwardmentioning

confidence: 99%

Observing the unwatchable: Integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data

Smith

Pinter‐Wollman

2020

Journal of Animal Ecology

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1. In the 4.5 decades since Altmann (1974) published her seminal paper on the methods for the observational study of behaviour, automated detection and analysis of social interaction networks have fundamentally transformed the ways that ecologists study social behaviour. 2. Methodological developments for collecting data remotely on social behaviour involve indirect inference of associations, direct recordings of interactions and machine vision. 3. These recent technological advances are improving the scale and resolution with which we can dissect interactions among animals. They are also revealing new intricacies of animal social interactions at spatial and temporal resolutions as well as in ecological contexts that have been hidden from humans, making the unwatchable seeable. 4. We first outline how these technological applications are permitting researchers to collect exquisitely detailed information with little observer bias. We further recognize new emerging challenges from these new reality-mining approaches. 5. While technological advances in automating data collection and its analysis are moving at an unprecedented rate, we urge ecologists to thoughtfully combine these new tools with classic behavioural and ecological monitoring methods to place our understanding of animal social networks within fundamental biological contexts.

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“…While there is some evidence both for [33] and against [34] activity compensation in humans, asking this question about non-human animals in the wild has been difficult so far. Additionally, we can test whether animals in nature have significantly idiosyncratic (individual-specific) activity patterns, a question so far addressed mainly in humans [35] and lab animals [2] (but see [36,37]). Once again, we can draw parallels to research on sleep, where consistent variation among individuals is seen in their preferred times of sleeping and awakening, also referred to as an individual’s chronotype [38,39].…”

Section: Introductionmentioning

confidence: 99%

Accelerometer-based predictions of behaviour elucidate factors affecting the daily activity patterns of spotted hyenas

Minasandra,

Jensen,

Gersick

et al. 2023

R. Soc. Open Sci.

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Animal activity patterns are highly variable and influenced by internal and external factors, including social processes. Quantifying activity patterns in natural settings can be challenging, as it is difficult to monitor animals over long time periods. Here, we developed and validated a machine-learning-based classifier to identify behavioural states from accelerometer data of wild spotted hyenas (Crocuta crocuta) , social carnivores that live in large fission–fusion societies. By combining this classifier with continuous collar-based accelerometer data from five hyenas, we generated a complete record of activity patterns over more than one month. We used these continuous behavioural sequences to investigate how past activity, individual idiosyncrasies, and social synchronization influence hyena activity patterns. We found that hyenas exhibit characteristic crepuscular-nocturnal daily activity patterns. Time spent active was independent of activity level on previous days, suggesting that hyenas do not show activity compensation. We also found limited evidence for an effect of individual identity on activity, and showed that pairs of hyenas who synchronized their activity patterns must have spent more time together. This study sheds light on the patterns and drivers of activity in spotted hyena societies, and also provides a useful tool for quantifying behavioural sequences from accelerometer data.

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Individual Daily and Seasonal Activity Patterns in Fox Squirrels (Sciurus niger) Quantified by Temperature-Sensitive Data Loggers

Cited by 9 publications

References 66 publications

Intraindividual Variability and Temporal Stability of Mid-Sleep on Free and Workdays

Intraindividual Variability and Temporal Stability of Mid-Sleep on Free and Workdays

Observing the unwatchable: Integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data

Accelerometer-based predictions of behaviour elucidate factors affecting the daily activity patterns of spotted hyenas

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