Longstanding theory predicts that strategic flexibility in when and how to use social information can help individuals make adaptive decisions, especially when environments are temporally or spatially variable. A short-term increase in reliance on social information under these conditions has been experimentally shown in primates, including humans, but whether this occurs in other taxa is unknown. We asked whether migration between spatially variable environments affected social information use with a large-scale cultural diffusion experiment with wild great tits (Parus major) in captivity, a small passerine bird that can socially learn novel behaviors. We simulated an immigration event where knowledgeable birds were exchanged between groups with opposing preferences for a socially learned foraging puzzle, living in similar or different environments. We found evidence that both immigrants and residents were influenced by social information and attended to the rewards that others received. Our analysis supported the use of a payoff-biased social learning by immigrants when both resources and habitat features were spatially variable. In contrast, immigrants relied more-so on individual learning when payoffs or the environment were unchanged. In summary, our results suggest that great tits assess the payoffs others receive and are more influenced by socially observed differences in payoffs when environmental cues differ in their new environment. Our results provide experimental support for the hypothesis that spatial variability is a strong driver for the evolution of social learning strategies.
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http://dx.doi.org/10.1371/journal.pbio.3002699 | DOI Listing |
Sci Rep
January 2025
Westchase Software, Houston, TX, 77063, USA.
It is well known that the sedimentary rock record is both incomplete and biased by spatially highly variable rates of sedimentation. Without absolute age constraints of sufficient resolution, the temporal correlation of spatially disjunct records is therefore problematic and uncertain, but these effects have rarely been analysed quantitatively using signal processing methods. Here we use a computational process model to illustrate and analyse how spatial and temporal geochemical records can be biased by the inherent, heterogenous processes of marine sedimentation and preservation.
View Article and Find Full Text PDFPharmacol Res
January 2025
Department of Cardiology, Changzheng Hospital, The Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China. Electronic address:
G protein-coupled receptors (GPCRs) represent the largest family of membrane receptors and are highly effective targets for therapeutic drugs. GPCRs couple different downstream effectors, including G proteins (such as Gi/o, Gs, G12, and Gq) and β-arrestins (such as β-arrestin 1 and β-arrestin 2) to mediate diverse cellular and physiological responses. Biased signaling allows for the specific activation of certain pathways from the full range of receptors' signaling capabilities.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Punjab Agricultural University, Ludhiana, 141004, Punjab, India.
Groundwater is a crucial global water resource; however, it faces the threat of depletion and quality degradation due to intensive agriculture and excessive fertilizer use. In India, groundwater assessments focus mainly on exploitation levels and often neglect quality. This study integrates groundwater quality with exploitation data to evaluate groundwater resources in Punjab, India.
View Article and Find Full Text PDFSci Rep
January 2025
Office of Protected Resources, NOAA Fisheries, Silver Spring, MD, 20910, USA.
Vessel strikes are a critical threat to endangered North Atlantic right whales (Eubalaena glacialis), significantly contributing to their elevated mortality. Accurate estimates of these mortality rates are essential for developing effective management strategies to aid in the species' recovery. This study enhances existing vessel strike models by incorporating detailed regional data on vessel traffic characteristics as well as whale distribution and behavior.
View Article and Find Full Text PDFThe increasing availability of coarse-scale climate simulations and the need for ready-to-use high-resolution variables drive the climate community to the challenge of reducing computational resources and time for downscaling purposes. To this end, statistical downscaling is gaining interest as a potential strategy for integrating high-resolution climate information obtained through dynamical downscaling over limited years, providing a clear understanding of the gains and losses in combining dynamical and statistical downscaling. In this regard, several questions can be raised: (i) what is the performance of statistical downscaling, assuming dynamical downscaling as a reference over a shared time window; (ii) how much the performance of statistical downscaling is affected by changes in the number of years available for training; (iii) how does the climate normal considered for the training affect the predictions.
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