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1. Clustering Honeybees by Its Daily Activity [Clustering Honeybees by Its Daily Activity]

   https://doi.org/10.5220/0007387505980604  

   Acuna, Edgar; Palomino, Velcy; Agosto, José; Mégret, Rémi; Giray, Tugrul; Prado, Alberto; Alaux, Cédric; Conte, Yves (February 2019, Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods)

   In this work, we analyze the activity of bees starting at 6 days old. The data was collected at the INRA (France) during 2014 and 2016. The activity is counted according to whether the bees enter or leave the hive. After data wrangling, we decided to analyze data corresponding to a period of 10 days. We use clustering method to determine bees with similar activity and to estimate the time during the day when the bees are most active. To achieve our objective, the data was analyzed in three different time periods in a day. One considering the daily activity during in two periods: morning and afternoon, then looking at activities in periods of 3 hours from 8:00am to 8:00pm and, finally looking at the activities hourly from 8:00am to 8:00pm. Our study found two clusters of bees and in one of them clearly the bees activity increased at the day 5. The smaller cluster included the most active bees representing about 24 percent of the total bees under study. Also, the highest activity of the bees was registered between 2:00pm until 3:00pm. A Chi-square test shows that there is a combined effect Treatment× Colony on the clusters formation. 

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2. The role of temperature on the development of circadian rhythms in honey bee workers

   https://doi.org/10.7717/peerj.17086  

   Giannoni-Guzmán, Manuel A; Perez_Claudio, Eddie; Aleman-Rios, Janpierre; Diaz_Hernandez, Gabriel; Perez_Torres, Melina; Melendez_Moreno, Alexander; Loubriel, Darimar; Moore, Darrell; Giray, Tugrul; Agosto-Rivera, Jose L (January 2024, PeerJ)

   Circadian rhythms in honey bees are involved in various processes that impact colony survival. For example, young nurses take care of the brood constantly throughout the day and lack circadian rhythms. At the same time, foragers use the circadian clock to remember and predict food availability in subsequent days. Previous studies exploring the ontogeny of circadian rhythms of workers showed that the onset of rhythms is faster in the colony environment (~2 days) than if workers were immediately isolated after eclosion (7–9 days). However, which specific environmental factors influenced the early development of worker circadian rhythms remained unknown. We hypothesized that brood nest temperature plays a key role in the development of circadian rhythmicity in young workers. Our results show that young workers kept at brood nest-like temperatures (33–35 °C) in the laboratory develop circadian rhythms faster and in greater proportion than bees kept at lower temperatures (24–26 °C). In addition, we examined if the effect of colony temperature during the first 48 h after emergence is sufficient to increase the rate and proportion of development of circadian rhythmicity. We observed that twice as many individuals exposed to 35 °C during the first 48 h developed circadian rhythms compared to individuals kept at 25 °C, suggesting a critical developmental period where brood nest temperatures are important for the development of the circadian system. Together, our findings show that temperature, which is socially regulated inside the hive, is a key factor that influences the ontogeny of circadian rhythmicity of workers. 

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3. Ecosystem-Scale Rainfall Manipulation in a Piñon-Juniper Forest at the Sevilleta National Wildlife Refuge, New Mexico: Water Potential Data (2006-2013)

   https://doi.org/10.6073/pasta/4c7b3696c3101375fbde251622748738  

   Pockman, William; McDowell, Nathan (January 2016, Environmental Data Initiative)

   Climate models predict that water limited regions around the world will become drier and warmer in the near future, including southwestern North America. We developed a large-scale experimental system that allows testing of the ecosystem impacts of precipitation changes. Four treatments were applied to 1600 m2 plots (40 m × 40 m), each with three replicates in a piñon pine (Pinus edulis) and juniper (Juniper monosperma) ecosystem. These species have extensive root systems, requiring large-scale manipulation to effectively alter soil water availability.  Treatments consisted of: 1) irrigation plots that receive supplemental water additions, 2) drought plots that receive 55% of ambient rainfall, 3) cover-control plots that receive ambient precipitation, but allow determination of treatment infrastructure artifacts, and 4) ambient control plots. Our drought structures effectively reduced soil water potential and volumetric water content compared to the ambient, cover-control, and water addition plots. Drought and cover control plots experienced an average increase in maximum soil and air temperature at ground level of 1-4° C during the growing season compared to ambient plots, and concurrent short-term diurnal increases in maximum air temperature were also observed directly above and below plastic structures. Our drought and irrigation treatments significantly influenced tree predawn water potential, sap-flow, and net photosynthesis, with drought treatment trees exhibiting significant decreases in physiological function compared to ambient and irrigated trees.  Supplemental irrigation resulted in a significant increase in both plant water potential and xylem sap-flow compared to trees in the other treatments. This experimental design effectively allows manipulation of plant water stress at the ecosystem scale, permits a wide range of drought conditions, and provides prolonged drought conditions comparable to historical droughts in the past – drought events for which wide-spread mortality in both these species was observed.  Water potential measurements were used to monitor the water stress of the two target species across the four treatment regimes.  Sampling for water potentials occurred twice daily.  One set of samples was collected hours before dawn and another set was collected at mid-day.  The predawn readings provided the “least-stressed†tree water content values as they were collected after the trees had returned to equilibrium over the evening and had yet to start transpiring.  The mid-day values, collected after tree-level respiration had been occurring for hours and when the daily temperatures were highest, represented the opposite “most-stressed†scenario. To gauge the effect of the irrigation treatment on the water content of the trees, we sampled water potentials just before and just after irrigation events.    

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4. Enhancing Streamflow Forecast and Extracting Insights Using Long‐Short Term Memory Networks With Data Integration at Continental Scales

   https://doi.org/10.1029/2019WR026793  

   Feng, Dapeng; Fang, Kuai; Shen, Chaopeng (September 2020, Water Resources Research)

   Abstract Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short‐term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental‐scale median Nash‐Sutcliffe Efficiency coefficient value of 0.86. Integrating moving‐average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow‐dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation‐energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater‐dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high‐aridity basins with 1‐day flash peaks. Despite this limitation, there is much promise for a deep‐learning‐based forecast paradigm due to its performance, automation, efficiency, and flexibility. 

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5. Spatiotemporal Patterns of COVID-19 Impact on Human Activities and Environment in Mainland China Using Nighttime Light and Air Quality Data

   https://doi.org/10.3390/rs12101576  

   Liu, Qian; Sha, Dexuan; Liu, Wei; Houser, Paul; Zhang, Luyao; Hou, Ruizhi; Lan, Hai; Flynn, Colin; Lu, Mingyue; Hu, Tao; et al (May 2020, Remote Sensing)

   null (Ed.)

   The sudden outbreak of the COVID-19 pandemic has brought drastic changes to people’s daily lives, work, and the surrounding environment. Investigations into these changes are very important for decision makers to implement policies on economic loss assessments and stimulation packages, city reopening, resilience of the environment, and arrangement of medical resources. In order to analyze the impact of COVID-19 on people’s lives, activities, and the natural environment, this paper investigates the spatial and temporal characteristics of Nighttime Light (NTL) radiance and Air Quality Index (AQI) before and during the pandemic in mainland China. The monthly mean NTL radiance, and daily and monthly mean AQI are calculated over mainland China and compared before and during the pandemic. Our results show that the monthly average NTL brightness is much lower during the quarantine period than before. This study categorizes NTL into three classes: residential area, transportation, and public facilities and commercial centers, with NTL radiance ranges of 5–20, 20–40 and greater than 40 (nW· cm − 2 · sr − 1 ), respectively. We found that the Number of Pixels (NOP) with NTL detection increased in the residential area and decreased in the commercial centers for most of the provinces after the shutdown, while transportation and public facilities generally stayed the same. More specifically, we examined these factors in Wuhan, where the first confirmed cases were reported, and where the earliest quarantine measures were taken. Observations and analysis of pixels associated with commercial centers were observed to have lower NTL radiance values, indicating a dimming behavior, while residential area pixels recorded increased levels of brightness after the beginning of the lockdown. The study also discovered a significant decreasing trend in the daily average AQI for mainland China from January to March 2020, with cleaner air in most provinces during February and March, compared to January 2020. In conclusion, the outbreak and spread of COVID-19 has had a crucial impact on people’s daily lives and activity ranges through the increased implementation of lockdown and quarantine policies. On the other hand, the air quality of mainland China has improved with the reduction in non-essential industries and motor vehicle usage. This evidence demonstrates that the Chinese government has executed very stringent quarantine policies to deal with the pandemic. The decisive response to control the spread of COVID-19 provides a reference for other parts of the world. 

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