The Philosophy of Outliers: Reintegrating Rare Events Into Biological Science

Biological outliers: essential elements to understand the causes and consequences of reductions in maximum photochemical efficiency of PSII in plants

MAIN CONCLUSION

By studying Cistus albidus shrubs in their natural habitat, we show that biological outliers can help us to understand the causes and consequences of maximum photochemical efficiency decreases in plants, thus reinforcing the importance of integrating these often-neglected data into scientific practice. Outliers are individuals with exceptional traits that are often excluded of data analysis. However, this may result in very important mistakes not accurately capturing the true trajectory of the population, thereby limiting our understanding of a given biological process. Here, we studied the role of biological outliers in understanding the causes and consequences of maximum photochemical efficiency decreases in plants, using the semi-deciduous shrub C. albidus growing in a Mediterranean-type ecosystem. We assessed interindividual variability in winter, spring and summer maximum PSII photochemical efficiency in a population of C. albidus growing under Mediterranean conditions. A strong correlation was observed between maximum PSII photochemical efficiency (Fv/Fm ratio) and leaf water desiccation. While decreases in maximum PSII photochemical efficiency did not result in any damage at the organ level during winter, reductions in the Fv/Fm ratio were associated to leaf mortality during summer. However, all plants could recover after rainfalls, thus maximum PSII photochemical efficiency decreases did not result in an increased mortality at the organism level, despite extreme water deficit and temperatures exceeding 40ºC during the summer. We conclude that, once methodological outliers are excluded, not only biological outliers must not be excluded from data analysis, but focusing on them is crucial to understand the causes and consequences of maximum PSII photochemical efficiency decreases in plants.

A new approach to geostatistical synthesis of historical records reveals capuchin spatial responses to climate and demographic change

Recent proliferation of GPS technology has transformed animal movement research. Yet, time-series data from this recent technology rarely span beyond a decade, constraining longitudinal research. Long-term field sites hold valuable historic animal location records, including hand-drawn maps and semantic descriptions. Here, we introduce a generalised workflow for converting such records into reliable location data to estimate home ranges, using 30 years of sleep-site data from 11 white-faced capuchin (Cebus imitator) groups in Costa Rica. Our findings illustrate that historic sleep locations can reliably recover home range size and geometry. We showcase the opportunity our approach presents to resolve open questions that can only be addressed with very long-term data, examining how home ranges are affected by climate cycles and demographic change. We urge researchers to translate historical records into usable movement data before this knowledge is lost; it is essential to understanding how animals are responding to our changing world.

Looking beyond the mean: quantile regression for comparative physiologists

Statistical analyses that physiologists use to test hypotheses predominantly centre on means, but the tail ends of the response distribution can behave quite differently and underpin important scientific phenomena. We demonstrate that quantile regression (QR) offers a way to bypass some limitations of least squares regression (LSR) by building a picture of independent variable effects across the whole distribution of a dependent variable. We used LSR and QR with simulated and real datasets. With simulated data, LSR showed no change in the mean response but missed significant effects in the tails of the distribution found using QR. With real data, LSR showed a significant change in the mean response but missed a lack of response in the upper quantiles which was biologically revealing. Together, this highlights that QR can help to ask and answer more questions about variation in nature.

Fantastic beasts and how to study them: rethinking experimental animal behavior

Humans have been trying to understand animal behavior at least since recorded history. Recent rapid development of new technologies has allowed us to make significant progress in understanding the physiological and molecular mechanisms underlying behavior, a key goal of neuroethology. However, there is a tradeoff when studying animal behavior and its underlying biological mechanisms: common behavior protocols in the laboratory are designed to be replicable and controlled, but they often fail to encompass the variability and breadth of natural behavior. This Commentary proposes a framework of 10 key questions that aim to guide researchers in incorporating a rich natural context into their experimental design or in choosing a new animal study system. The 10 questions cover overarching experimental considerations that can provide a template for interspecies comparisons, enable us to develop studies in new model organisms and unlock new experiments in our quest to understand behavior.

A Preliminary Investigation of Prenatal Anxiety Sensitivity and Postpartum Distress

INTRODUCTION

Distress during pregnancy and postpartum is common and contributes to poor infant and maternal outcomes, such as developmental delays and mental health disorders, respectively. Anxiety sensitivity, or fear of the symptoms of anxiety (eg, palpitations, confusion), is a risk factor known to increase distress across psychological and health-related conditions. Given the physiologic and emotional changes that occur during the perinatal period, anxiety sensitivity may be a salient risk factor for maternal distress. In this pilot study, we aimed to understand the unique role of prenatal anxiety sensitivity in postpartum psychological and parenting distress.

METHODS

Twenty-eight pregnant women (mean age, 30.86 years) were recruited from the community in a Southeastern metropolitan area of the United States. Participants completed self-report measures during their third trimester of pregnancy and again within 10 weeks postpartum. The Depression Anxiety and Stress Scales-21 and the Parenting Distress subscale of the Parenting Stress Index-4-Short Form were the primary postpartum outcome measures.

RESULTS

Prenatal anxiety sensitivity was elevated in this sample relative to convenience samples. Prenatal anxiety sensitivity uniquely contributed to postpartum psychological (b, 1.01; P < .001) and parenting distress (b, 0.62; P = .008), after accounting for age, gravidity, and gestation.

DISCUSSION

Albeit preliminary, results suggest prenatal anxiety sensitivity may be an important and malleable risk factor associated with several mental health concerns common in the perinatal period. Anxiety sensitivity may be targeted with brief interventions to prevent or reduce postpartum distress. Reducing prenatal anxiety sensitivity has the potential prevent the onset or worsening of psychological disorders among women and, in turn, may improve infant and child outcomes. Future studies should replicate these findings in a larger sample.

Automatic recording of rare behaviors of wild animals using video bio-loggers with on-board light-weight outlier detector

Rare behaviors displayed by wild animals can generate new hypotheses; however, observing such behaviors may be challenging. While recent technological advancements, such as bio-loggers, may assist in documenting rare behaviors, the limited running time of battery-powered bio-loggers is insufficient to record rare behaviors when employing high-cost sensors (e.g. video cameras). In this study, we propose an artificial intelligence (AI)-enabled bio-logger that automatically detects outlier readings from always-on low-cost sensors, e.g. accelerometers, indicative of rare behaviors in target animals, without supervision by researchers, subsequently activating high-cost sensors to record only these behaviors. We implemented an on-board outlier detector via knowledge distillation by building a lightweight outlier classifier supervised by a high-cost outlier behavior detector trained in an unsupervised manner. The efficacy of AI bio-loggers has been demonstrated on seabirds, where videos and sensor data captured by the bio-loggers have enabled the identification of some rare behaviors, facilitating analyses of their frequency, and potential factors underlying these behaviors. This approach offers a means of documenting previously overlooked rare behaviors, augmenting our understanding of animal behavior.