Circadian activity predicts breeding phenology in the Asian burying beetle Nicrophorus nepalensis
Climate change continues to alter breeding phenology in a range of plant and animal species across the globe. Traditional methods for assessing when organisms reproduce often rely on time-intensive field observations or destructive sampling, creating an urgent need for efficient, non-invasive approa...
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| Format: | Article |
| Language: | English |
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The Royal Society
2025-06-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.250624 |
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| author | Hao Chen Dustin R. Rubenstein Guan-Shuo Mai Chung-Fan Chang Sheng-Feng Shen |
| author_facet | Hao Chen Dustin R. Rubenstein Guan-Shuo Mai Chung-Fan Chang Sheng-Feng Shen |
| author_sort | Hao Chen |
| collection | DOAJ |
| description | Climate change continues to alter breeding phenology in a range of plant and animal species across the globe. Traditional methods for assessing when organisms reproduce often rely on time-intensive field observations or destructive sampling, creating an urgent need for efficient, non-invasive approaches to assess reproductive timing. Here, we examined three populations of the Asian burying beetle Nicrophorus nepalensis from subtropical Okinawa, Japan (500 m) and Taiwan (1100–3200 m) that were reared under contrasting photoperiods in order to develop a predictive framework linking circadian activity to breeding phenology. Using automated activity monitors, we quantified adult circadian rhythms and used machine learning to predict breeding phenology (seasonal versus year-round breeding) from behaviour alone. Our model achieved 95% accuracy under long-day conditions using just three behavioural features. Notably, it maintained 76% accuracy under short-day conditions when both types are reproductively active, revealing persistent behavioural differences between breeding strategies. These results demonstrate how integrating behavioural monitoring with machine learning can provide a rapid, scalable method for tracking population responses to climate change. This approach also offers novel insights into species’ adaptive responses to shifting seasonal cues across different elevational gradients in the beetles’ native range. |
| format | Article |
| id | doaj-art-c4275a99a56f4b13b3f82941a9b0c08c |
| institution | Matheson Library |
| issn | 2054-5703 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj-art-c4275a99a56f4b13b3f82941a9b0c08c2025-06-25T09:25:22ZengThe Royal SocietyRoyal Society Open Science2054-57032025-06-0112610.1098/rsos.250624Circadian activity predicts breeding phenology in the Asian burying beetle Nicrophorus nepalensisHao Chen0Dustin R. Rubenstein1Guan-Shuo Mai2Chung-Fan Chang3Sheng-Feng Shen4Biodiversity Research Center, Academia Sinica, Taipei City, TaiwanDepartment of Ecology, Evolution & Environmental Biology, Columbia University in the City of New York, New York, USABiodiversity Research Center, Academia Sinica, Taipei City, TaiwanBiodiversity Research Center, Academia Sinica, Taipei City, TaiwanBiodiversity Research Center, Academia Sinica, Taipei City, TaiwanClimate change continues to alter breeding phenology in a range of plant and animal species across the globe. Traditional methods for assessing when organisms reproduce often rely on time-intensive field observations or destructive sampling, creating an urgent need for efficient, non-invasive approaches to assess reproductive timing. Here, we examined three populations of the Asian burying beetle Nicrophorus nepalensis from subtropical Okinawa, Japan (500 m) and Taiwan (1100–3200 m) that were reared under contrasting photoperiods in order to develop a predictive framework linking circadian activity to breeding phenology. Using automated activity monitors, we quantified adult circadian rhythms and used machine learning to predict breeding phenology (seasonal versus year-round breeding) from behaviour alone. Our model achieved 95% accuracy under long-day conditions using just three behavioural features. Notably, it maintained 76% accuracy under short-day conditions when both types are reproductively active, revealing persistent behavioural differences between breeding strategies. These results demonstrate how integrating behavioural monitoring with machine learning can provide a rapid, scalable method for tracking population responses to climate change. This approach also offers novel insights into species’ adaptive responses to shifting seasonal cues across different elevational gradients in the beetles’ native range.https://royalsocietypublishing.org/doi/10.1098/rsos.250624circadian activitybreeding phenologymachine learningbehavioural monitoringburying beetle |
| spellingShingle | Hao Chen Dustin R. Rubenstein Guan-Shuo Mai Chung-Fan Chang Sheng-Feng Shen Circadian activity predicts breeding phenology in the Asian burying beetle Nicrophorus nepalensis Royal Society Open Science circadian activity breeding phenology machine learning behavioural monitoring burying beetle |
| title | Circadian activity predicts breeding phenology in the Asian burying beetle Nicrophorus nepalensis |
| title_full | Circadian activity predicts breeding phenology in the Asian burying beetle Nicrophorus nepalensis |
| title_fullStr | Circadian activity predicts breeding phenology in the Asian burying beetle Nicrophorus nepalensis |
| title_full_unstemmed | Circadian activity predicts breeding phenology in the Asian burying beetle Nicrophorus nepalensis |
| title_short | Circadian activity predicts breeding phenology in the Asian burying beetle Nicrophorus nepalensis |
| title_sort | circadian activity predicts breeding phenology in the asian burying beetle nicrophorus nepalensis |
| topic | circadian activity breeding phenology machine learning behavioural monitoring burying beetle |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.250624 |
| work_keys_str_mv | AT haochen circadianactivitypredictsbreedingphenologyintheasianburyingbeetlenicrophorusnepalensis AT dustinrrubenstein circadianactivitypredictsbreedingphenologyintheasianburyingbeetlenicrophorusnepalensis AT guanshuomai circadianactivitypredictsbreedingphenologyintheasianburyingbeetlenicrophorusnepalensis AT chungfanchang circadianactivitypredictsbreedingphenologyintheasianburyingbeetlenicrophorusnepalensis AT shengfengshen circadianactivitypredictsbreedingphenologyintheasianburyingbeetlenicrophorusnepalensis |