Cold Nights Research Articles

Artificial intelligence reveals past climate extremes by reconstructing historical records

The understanding of recent climate extremes and the characterization of climate risk require examining these extremes within a historical context. However, the existing datasets of observed extremes generally exhibit spatial gaps and inaccuracies due to inadequate spatial extrapolation. This problem arises from traditional statistical methods used to account for the lack of measurements, particularly prevalent before the mid-20th century. In this work, we use artificial intelligence to reconstruct observations of European climate extremes (warm and cold days and nights) by leveraging Earth system model data from CMIP6 through transfer learning. Our method surpasses conventional statistical techniques and diffusion models, showcasing its ability to reconstruct past extreme events and reveal spatial trends across an extensive time span (1901-2018) that is not covered by most reanalysis datasets. Providing our dataset to the climate community will improve the characterization of climate extremes, resulting in better risk management and policies.

Dynamical downscaling and data assimilation for a cold-air outbreak in the European Alps during the Year Without a Summer of 1816

Abstract. The “Year Without a Summer” in 1816 was characterized by extraordinarily cold and wet periods in central Europe, and it was associated with severe crop failures, famine, and socio-economic disruptions. From a modern perspective, and beyond its tragic consequences, the summer of 1816 represents a rare opportunity to analyze the adverse weather (and its impacts) after a major volcanic eruption. However, given the distant past, obtaining the high-resolution data needed for such studies is a challenge. In our approach, we use dynamical downscaling, in combination with 3D variational data assimilation of early instrumental observations, for assessing a cold-air outbreak in early June 1816. We find that the cold spell is well represented in the coarse-resolution 20th Century Reanalysis product which is used for initializing the regional Weather Research and Forecasting Model. Our downscaling simulations (including a 19th century land use scheme) reproduce and explain meteorological processes well at regional to local scales, such as a foehn wind situation over the Alps with much lower temperatures on its northern side. Simulated weather variables, such as cloud cover or rainy days, are simulated in good agreement with (eye) observations and (independent) measurements, with small differences between the simulations with and without data assimilation. However, validations with partly independent station data show that simulations with assimilated pressure and temperature measurements are closer to the observations, e.g., regarding temperatures during the coldest night, for which snowfall as low as the Swiss Plateau was reported, followed by a rapid pressure increase thereafter. General improvements from data assimilation are also evident in simple quantitative analyses of temperature and pressure. In turn, data assimilation requires careful selection, preprocessing, and bias-adjustment of the underlying observations. Our findings underline the great value of digitizing efforts of early instrumental data and provide novel opportunities to learn from extreme weather and climate events as far back as 200 years or more.

Analyzing the Spatiotemporal Changes in Climatic Extremes in Cold and Mountainous Environment: Insights from the Himalayan Mountains of Pakistan

This study assessed the past changes in extreme precipitation and temperature events across the Himalayan Mountains of Pakistan. This cold and mountainous environmental region has witnessed a significant increase in climate-related disasters over the past few decades. Spatiotemporal changes in extreme temperature and precipitation events were analyzed using 24 indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). For this study, in situ data of 16 national meteorological stations were obtained from the Pakistan Meteorological Department (PMD) for the past three decades (1991–2020). The significance of the trends was assessed using the modified Mann–Kendall (MMK) test, and the Theil–Sen (TS) slope estimator was used to estimate the slope of the trend. The results showed that there has been a consistent decline in the total precipitation amount across the Himalayan Mountains of Pakistan. The trend exhibited a decrease in the annual average precipitation at a rate of −6.56 mm/year. Simultaneously, there was an increasing trend in the annual average minimum and maximum temperatures at rates of 0.02 °C/year and 0.07 °C/year, respectively. The frequencies of consecutive wet days (CWDs) and maximum 5-day precipitation (RX5day) have decreased significantly, with decreasing rates of −0.40 days/year and −1.18 mm/year, respectively. The amount of precipitation during very wet days (R95p) and extremely wet days was decreased by −19.20 and −13.60 mm/decade, respectively. The warm spell duration (WSDI) and the frequency of warm days (TX90p) across the Himalayan Range both increased by 1.5 and 1.4 days/decade. The number of cold days (TX10p) and cold nights (TN10p) decreased by 2.9 and 3.4 days/decade. The average temperature of the hottest nights (TXn) and the diurnal temperature range (DTR) were increased by 0.10 and 0.30 °C/decade. The results indicated an increasing tendency of dry and warm weather in the Himalayan region of Pakistan, which could have adverse consequences for water resources, agriculture, and disaster management in the country. Therefore, it is essential to prioritize the implementation of localized adaptation techniques in order to enhance sustainable climate resilience and effectively address the emerging climate challenges faced by these mountainous regions.

Comparison of 2-m surface temperature data between reanalysis and observations over the Arabian Peninsula

The 2-m temperature data is a significant indicator for studying the weather extremes and the exchange of water and energy fluxes between the surface and atmosphere. This study compared three reanalysis datasets, i.e., ERA5, ERA5-Land, and MERRA-2, with observations from in-situ sources from 1990 to 2022 using various statistical error metrics and extreme temperature indices over the Arabian Peninsula (AP) region. We selected these reanalysis datasets due to the continuous improvements and higher spatiotemporal output better capturing the temperature variability. The spatiotemporal climatology shows lower temperatures in winter (<15 °C) and maximum in summer (>35 °C); however, the reanalysis data show more deviations in temperature during the cold season than in the warm season. The reanalysis data underestimated the frequency of the cold (<10 °C) and hot (>30 °C) days across the four regions, except ERA5-Land, which closely followed observed data. The strength of the correlation shows better performance (>0.90) in the cold extremes (cold nights and cold days) frequency than the hot extremes. On an interannual scale, reanalysis products exhibit strong correlations (>0.90) with in-situ data across most regions, particularly in winter and autumn, moderate in spring, and weaker in summer. The reanalysis data shows negative biases in the inland regions and positive biases in the coastal areas with consistent root mean square differences (RMSD) spatiotemporally. The differences in performance are due to the topography and poor representation of the energy fluxes, especially in MERRA-2 as well as missing data in observations. This study recommends ERA5-Land as the first choice for extreme weather simulations in the region, followed by MERRA-2 and ERA5 on the same scale, but proper attention is needed when using reanalysis data for cold and hot extremes.

Impact of hot and cold nights on pneumonia hospitalisations in children under five years: Evidence from low-, middle-, and high-income countries

BackgroundStudies have shown that abnormal temperature at night is a risk factor for respiratory health. However, there is limited evidence on the impact of hot and cold nights on cause-specific diseases such as pneumonia, which is a leading cause of morbidity and mortality in children. MethodsWe collected daily data on pneumonia hospitalisations in children under five years from 2011 to 2017 in three low-, middle- and high-income countries (Bangladesh, China, and Australia). The intensity of hot and cold nights was measured by excess temperature. A space–time-stratified case-crossover analysis was used to estimate the association between hot and cold nights and childhood pneumonia hospitalisations. We further estimated the fraction of childhood pneumonia hospitalisations attributable to hot and cold nights. ResultsBoth hot and cold nights were associated with an increased risk of hospitalisations for childhood pneumonia in low-, middle-, and high-income countries, with a greater disease burden from hot nights. Specifically, the fraction of childhood pneumonia attributable to hot nights was the largest in Australia [21.2%, 95% confidence interval (CI): 11.8%-28.1%], followed by Bangladesh (15.2%, 95% CI: 4.1%–23.8%) and China (2.7%, 95% CI: 0.4%-4.7%). Additionally, the fraction of childhood pneumonia attributable to cold nights was 1.3% (95% CI: 0.4%-2.0%) in Bangladesh and 0.4% (95% CI: 0.1%-0.7%) in China. ConclusionThis multi-country study suggests that hot and cold nights are not only associated with a higher risk of pneumonia hospitalisations in children but also responsible for substantial fraction of hospitalisations, with a greater impact from hot nights.

Near future variations in temperature extremes in northeastern Iran under CMIP6 projections.

Extreme air temperatures which are of significance in plant growth are influenced by climate change. The aim of this study was to assess the climate change effects on temperature extreme indices (TEIs) in northeastern Iran based on the CMIP6 projections. For this purpose, five extreme indices including maximum of maximum temperature (TXx), minimum of minimum temperature (TNn), cold nights (TN10p), warm nights (TN90p), and summer days (SU25) which are effective on plant growth were evaluated. The projections of the three Earth system models including ACCESS-CM2, MIROC6, and MRI-ESM2-0 were assessed under the three scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5 for the period 2026-2050 compared to the historical period (1989-2014). The models' projections were evaluated by statistical tests on the changes in the average and trend of data. Results showed that the MRI-ESM2-0 model revealed the best efficiency compared to the other models. The projections of all models and scenarios indicated a significant increase in the average of TXx and SU25 indices over the study area at the confidence level of 95% by 1.6 to 2.4°C, as well as 15 to 18days under the three scenarios. Also, the results exhibited a significant increasing trend in TN90p and a decreasing one in TN10p overall province atconfidence level of 95%. These changes will lead to an increase in evapotranspiration of the plants and an increase in agricultural water demand, and as a result, a decrease in the yield of some main products like wheat and saffron which are the main products in the study area.

A comparison of climate drivers’ impacts on silage maize yield shock in Germany

Extreme weather events have become more frequent and severe with ongoing climate change, with a huge implication for the agricultural sector and detrimental effects on crop yield. In this study, we compare several combinations of climate indices and utilized the Least Absolute Shrinkage and Selection Operator (LASSO) to explain the probabilities of substantial drops in silage maize yield (here defined as “yield shock” by using a 15th percentile as threshold) in Germany between 1999 and 2020. We compare the variable importance and the predictability skill of six combinations of climate indices using the Matthews Correlation Coefficient (MCC). Finally, we delve into year-to-year predictions by comparing them against the historical series and examining the variables contributing to high and low predicted yield shock probabilities. We find that cold conditions during April and hot and/or dry conditions during July increase the chance of silage maize yield shock. Moreover, a combination of simple variables (e.g. total precipitation) and complex variables (e.g. cumulative cold under cold nights) enhances predictive accuracy. Lastly, we find that the years with higher predicted yield shock probabilities are characterized mainly by relatively hotter and drier conditions during July compared to years with lower yield shock probabilities. Our findings enhance our understanding of how weather impacts maize crop yield shocks and underscore the importance of considering complex variables and using effective selection methods, particularly when addressing climate-related events.

Potential processes leading to winter reddening of young Douglas-fir Pseudotsuga menziesii Mirb. Franco. in Europe

Key messageWinter reddening of young Douglas-fir (Pseudotsuga menziesii Mirb. Franco), triggered by large thermal fluctuations in late winter, is a critical problem for European forestry. A literature review identified certain climatic conditions that are characteristic of ‘reddening’ years, including warm daily temperatures, high daily temperature amplitude, low relative humidity, moderate wind speeds, as well as the occurrence of freeze-thaw cycles with cold night temperatures.By describing the triggering environmental and stand factors, we propose three hypotheses for the physiological processes leading to winter reddening, namely (i) hydraulic failure due to winter drought stress, (ii) photo-oxidative stress in shade-acclimated trees, and (iii) early cold deacclimation during warm periods.i) Low soil temperature, by reducing root water uptake, combined with anticyclonic conditions, by increasing water losses, can induce hydraulic failure in the xylem. Hydraulic failure may be further accelerated by night frosts.ii) Winter reddening can occur when low temperature and high irradiance coincide, disrupting photostasis. Overwhelming of winter photo-protection may lead to photodamage and subsequent reddening.iii) Warm periods, by inducing cold deacclimation, make trees susceptible to frost damage.Finally, the three processes may interact under atypical anticyclonic conditions in late winter (e.g. cold or dry soils, warm days, high irradiance and/or freezing nights). Indeed, trees under water stress would develop a higher sensitivity to freezing night and photooxidative stress. We therefore proposed mitigation actions to avoid exposing trees to stressful conditions based on e.g. stand characteristics, understorey vegetation and planting.

High thermal quality rookeries facilitate high thermoregulatory accuracy in pregnant female rattlesnakes

Temperature is a primary factor influencing organismal development, and the fluctuating daily and seasonal thermal regimes of temperate climates may challenge the ability of viviparous reptiles to optimize body temperatures during gestation. Testing how viviparous reptiles navigate highly variable thermal conditions (e.g., relatively cold nights and/or highly fluctuating temperatures) is a powerful way to understand how they use microhabitats for thermoregulatory benefits. We assessed the thermal ecology of pregnant and non-pregnant female Prairie Rattlesnakes (Crotalus viridis) inhabiting a high-elevation, montane shrubland in northwest Colorado throughout their short summer active season, addressing the thermal consequences of microhabitat selection with a focus on thermoregulation of pregnant females at communal rookery sites. We deployed operative temperature models to collect data on the thermal quality of microhabitats used by the snakes, and calculated thermoregulatory accuracy of the snakes by comparing their field-active body temperatures with preferred body temperatures of snakes placed in a thermal gradient. Pregnant females inhabited rocky, hilltop rookeries that had higher thermal quality due to higher and less variable nighttime temperatures compared to microhabitats in the surrounding prairie. Pregnant females therefore thermoregulated more accurately than non-pregnant females. The difference was most pronounced during the night, when pregnant females at rookeries maintained higher body temperatures than non-pregnant snakes in the prairie. Our results support the hypothesis that one major reason female rattlesnakes at high latitudes and/or high elevations forgo migration and gestate at communal, rocky, hilltop rookeries is that, relative to prairie microhabitats, they provide better conditions for thermoregulation during pregnancy.

Assessing the impacts of temperature extremes on agriculture yield and projecting future extremes using machine learning and deep learning approaches with CMIP6 data

Climate change, particularly extreme weather events, has significantly affected various sectors, including agriculture, human health, water resources, sea levels, and ecosystems. It is anticipated that the intensity, duration, and frequency of these extremes will escalate in the future. This study aims to discover the association between temperature extremes and agricultural yield and to project these extremes using machine learning (ML) and deep learning (DL) models with CMIP6 (Coupled Model Intercomparison Project Phase 6) data under two SSPs (Shared Socioeconomic Pathways). A bi-wavelet coherence technique is employed to investigate the association, providing detailed information in both the frequency and time domains for the period of 1980–2014. Various ML and DL models are trained and tested for the periods of 1985–2004 and 2005–2014, respectively, with gradient boosting machine chosen for projecting temperature extremes based on its superior performance. Mann-Kendall test is used for trend analysis in the projected temperature extremes. The results indicate strong negative and positive associations between TN10p (Cold nights) and TN90p (Warm nights), respectively, with wheat production. Additionally, there is a long-term negative association of CSDI (Cold Spell Duration Indicator) and strong positive association of WSDI (Warm Spell Duration Indicator) with rice yield. Projected results show an increase and decrease under SSP2-4.5 and SSP5-8.5, respectively, in DTR (Diurnal Temperature Range) at most stations. TN10p will increase in the future at most stations, with exceptions such as Muree station where it decreases during 2025–2049 and then increases under both SSPs. Projections show that TXn (annual or monthly minimum value of daily maximum temp) will increase in the future, with Muree station exhibiting the lowest value close to zero, while the average maximum value is around 20 °C at Khanpur station. Trend analysis reveals significantly increasing trend in TR20 (Tropical nights) and decreasing trend in CSDI in future durations under both SSPs. These findings hold implications for policymakers and stakeholders in various departments, including agriculture, health, and water resources management.

Quantifying future climate extreme indices: implications for sustainable urban development in West Africa, with a focus on the greater Accra region

Climate change leading to Climate extremes in the twenty-first century is more evident in megacities across the world, especially in West Africa. The Greater Accra region is one of the most populated regions in West Africa. As a result, the region has become more susceptible to climate extremes such as floods, heatwaves, and droughts. The study employed the Coupled Model Intercomparison Project 6 models in simulating climate extreme indices under the Shared Socioeconomic Pathway scenarios (SSPs) over West Africa between 1979 and 2059 as exemplified by the Greater Accra region. The study observed a generally weak drought in the historical period and expected to intensify especially under SSP585 in Greater Accra. For instance, continuous dry days (CDD) reveal an increasing trend under the SSPs. Similarly, the overall projected trend of CDD over West Africa reveals an increase signifying a more frequent and longer drought in the future. The flood indices revealed a surge in the intensity and duration of extreme precipitation events under the SSPs in the region. For instance, R99pTOT and Rx5days are expected to significantly increase under the SSPs with intensification under the SSP245, SSP370, and SSP585. A similar trend has been projected across West Africa, especially along the Guinean coast. The study foresees a gradual and intensifying rise in heatwave indices over the Greater Accra region. The warming and cooling indices reveal an increasing and decreasing trend respectively in the historical period as well as under the SSPs particularly within urban centers like Accra and Tema. Most West African countries are projected to observe more frequent warm days and nights with cold nights and days becoming less frequent. Expected effects of future climate extreme indices pose potential threats to the water, food, and energy systems as well as trigger recurrent floods and droughts over Greater Accra. The findings of the study are expected to inform climate policies and the nationally determined contribution of the Paris Agreement as well as address the sustainable development goal 11 (Sustainable cities) and 13 (Climate action) in West Africa.

Research on the Value and Language Features of Chinese Language and Literature Texts Based on Text Mining Technology

The linguistic characteristics of a literary work are the way of thinking embodied in the author’s use of language. From the textual value of Chinese language literature, this paper analyzes the spiritual connotation of Chinese language literature from two dimensions: reading and education. Based on the web crawler technology, we obtain the text data of Chinese language literature from three writers, Bajin, Yu Zheng and Qiong Yao, preprocess the data through data cleaning, Chinese word segmentation, de-duplication, etc., and extract the feature values of the text by using the TF-IDF algorithm. Then the text documents are mapped onto vectors using the VSM model, and the parameters of the LDA topic model are estimated by the Gibbs sampling algorithm in order to better obtain the topic changes of the Chinese language literature texts. This paper carries out linguistic feature verification from the lexical and similarity features of Chinese language literary texts. It is found that the difference in lexical density between Ba Jin’s Cold Night and Resting Garden is only 2.1 percentage points, and the frequency of the verb “to say” is 1,213 times and 735 times respectively. The average sentence lengths of Yu Zheng and Qiong Yao fluctuate within the range of [18.49,34.27], and Qiong Yao’s works have a higher thematic concentration than Zheng Zheng’s works. Analyzing the linguistic features of Chinese language literary texts based on text mining techniques helps to understand the authors’ language usage methods and helps to promote innovative expression paths in literary texts.

Study of Cold Wave and Cold Stress in the Four Metropolitan Cities of India for the Period 1985–2020

Cold waves, cold nights and warm nights are major threats to human beings during winter due to climate change in different parts of India. The analysis of these has been studied for four major metropolitan cities (Chennai, Mumbai, Kolkata, and Delhi) of India during the period 1985–2020. The authors have used the 90th and 10th percentile threshold to identify the cold nights, warm nights and cold waves during the winter season. The degree of discomfort and cold stress category are identified using the Humidity Index (HD), and the Universal Thermal Climate Index (UTCI). The results indicate that the cold night event in Mumbai is ~0.36% higher than both Kolkata and Chennai cities but it is ~0.42% higher than Delhi. The number of cold wave events in Delhi is 53.5% higher than in Kolkata during the period of the study. It is also observed from the study of UTCI that the possibility of slight cold stress in the Delhi region during cold nights is 59.36% more than in other metropolitan cities. The study indicates that in the winter season, the climate of Delhi is more dynamic but for Kolkata it is relatively less dynamic.

Early frost detection in wheat using machine learning from vertical temperature distributions

Frost damage significantly reduces global wheat production. Temperature development in wheat crops is a complex and dynamic process. During frost events, a vertical temperature gradient develops from soil to canopy due to the heat loss from the soil and canopy boundary. Understanding these temperature gradients is essential for improving frost management strategies in wheat crops. We hypothesise that the relationship between the temperatures of the canopy, plant and ground can be an early indicator of frost. We collected infrared thermal (IRT) images from field-grown wheat crops and extracted the temperatures from the canopy, plant and ground layers. We analysed these temperatures and applied four machine learning (ML) models to detect coldness scales leading to frost nights with different degrees of severity. We implemented a gated recurrent unit, convolutional neural network, random forest and support vector machines to evaluate the classification. Our study shows that in these three layers, temperatures have a relationship that can be used to determine frost early. The patterns of these three temperatures on a frost night differ from a cold no-frost winter night. On a no-frost night we observed that the canopy is the coldest, plant is warm, and the soil is warmest, and these three temperatures did not converge. On the other hand, on a frost night, before the frost event, the canopy and plant temperatures converged as the cold air penetrated through the canopy. These patterns in temperature distribution were translated into an ML problem to detect frost early. We classified coldness scales based on the temperatures conducive to frost formation of a certain severity degree. Our results show that the ML models can determine the coldness scales automatically with 93%–98% accuracy across the four models. The study presents a strong foundation for the development of early frost detection systems.

Hydraulic Analysis and Cold-Air Pool Interaction of the Raco Gap Wind

Abstract Raco is a local wind occurring in central Chile where the Maipo River Canyon exits into the Santiago valley. The intensification of the easterly down-canyon flow starts at any time during some cold season nights, accompanied by increases in temperature and drops in humidity. The hypothesis of the raco being a gap wind controlled by the narrowest section in the 12-km canyon exit corridor is tested with data from two events in July 2018 and July 2019. The data are analyzed in the framework of hydraulic theory, and a subcritical-to-supercritical transition is documented to occur at the narrows of the gap where the Froude number is close to unity, confirmed by radiosondes launched in the narrows in 2019. For the raco flow, the sum of potential and kinetic energy is conserved upstream of the narrows, while the acceleration occurring farther downstream loses a large fraction of energy to frictional dissipation. The raco events occur under the influence of regional subsidence, but a differential nocturnal warming of the in-canyon air mass is responsible for a pressure gradient driving the raco. In the 2019 case, a ceilometer mounted on an instrumented pickup truck documented the structure and movement of the interface between the raco air and the cold-air pool (CAP) existing over the valley to the west. Together with a radiosonde launched near the CAP–raco surface front, the observations reveal the intense shear-driven mixing taking place at the interface and the factors supporting the establishment of a stationary front.

Research on the Cold Storage Characteristics of Ice Storage Photovoltaic Cold Storage

The ice-on-coil storage tank is one of the core devices in the latent heat cold storage system. The main objective of this study is to couple the solar photovoltaic cold storage with Cold Thermal Energy Storage technology. The internal ice-melting coil energy storage system used the water as a heat transfer fluid for adopting a day and night cold storage control strategy. The experiments were conducted for several days under the conditions of photovoltaic-driven cold storage with and without load for a continuous cold storage. The reasons for the occurrence of ice layers during ice accumulation and melting, as well as the operational economy of the system were analyzed. Moreover, the characteristics of the cold storage tank were summarized. Consequently, when the external ice thickness of the coil was within the range of 27 to 32mm, the ice storage rate reached 35.82% and achieved the optimal refrigeration efficiency in both the coil and the cold storage. The daily ice production in the cold storage tank decreased by an average of 22.06% during continuous operation with load compared to the unloaded operation. However, the daily refrigeration capacity increased by 45.774%. In addition, when cold thermal energy storage was coupled with solar photovoltaic technology, the refrigeration capacity decreased by 7.15% compared to using Cold Thermal Energy Storage technology alone, which resulting in an annual electricity cost saving of 30.20%.

Sleep and Thermoregulation in Birds: Cold Exposure Reduces Brain Temperature but Has Little Influence on Sleep Time and Sleep Architecture in Jackdaws (Coloeus monedula).

Birds have an electrophysiological sleep state that resembles mammalian rapid-eye-movement (REM) sleep. However, whether its regulation and function are similar is unclear. In the current experiment, we studied REM sleep regulation in jackdaws (Coloeus monedula) by exposing the birds to low ambient temperature, a procedure that selectively suppresses REM sleep in mammals. Eight jackdaws were equipped with electrodes to record brain activity and neck muscle activity and a thermistor to record cortical brain temperature. Recordings covered a three-day period starting with a 24 h baseline day at an ambient temperature of 21 °C, followed by a 12 h cold night at 4 °C, after which the ambient temperature was restored to 21 °C for the remaining recovery period. Cold exposure at night caused a significant drop in brain temperature of 1.4 °C compared to the baseline night. However, throughout the cold night, jackdaws expressed NREM sleep and REM sleep levels that were not significantly different from the baseline. Also, EEG spectral power during NREM sleep was unaffected by cold exposure. Thus, while cold exposure had a clear effect on brain temperature in jackdaws, it did not have the same REM sleep suppressing effect reported for mammals. These findings suggest that the REM-sleep-like state in birds, unlike REM sleep in mammals, is protected against the influence of low temperature.

Diurnal activity in an insectivorous bat during migration period

Abstract Diurnal flight activity in otherwise strictly nocturnal bats has typically been linked to random disturbance from day roosts, an urgent need to balance food shortage caused by adverse weather during nighttime, or the absence of diurnal predators. However, migration may be another reason why bats fly during daylight, at least in some areas. Using community-science data collection, we obtained more than 500 records of over 15,000 bats displaying diurnal flight activity, suggesting that it is relatively common in Central Europe. The vast majority of sightings were of common noctules (Nyctalus noctula), with most records concentrated in spring and autumn. The seasonal dynamics of diurnal flights exactly coincided with migratory periods, and directional movements in autumn—when diurnal activity was most frequent and included highest numbers of observed bats—suggest that the behavior may ultimately be linked to migration ecology. The highest frequency of diurnal flights in autumn coincided with highest body mass in the studied territory, thereby refuting the hypothesis of early roost emergence due to poor body condition or decreased predation risk related to increased maneuverability. A shift from strictly nocturnal to partly diurnal activity may balance increased energetic demands imposed by migration, which is temporally synchronized with periods of cold nights when prey density is limited. Common noctule diurnal activity during the migratory period may be beneficial as they can acquire energy by foraging on daily abundant prey while saving nighttime for long endurance flights—alternatively, they may forage on the way to their migratory destination, thereby saving time. Predation risk from diurnal predators may be significantly decreased by choosing high flight altitudes, as observed particularly during autumn. We suggest that observations on the geographic distribution of diurnally flying noctules may help identify migration corridors.

Habitat loss and degradation reduce the abundance of the glossy grass skink, Pseudemoia rawlinsoni

Context Habitat loss and degradation are major drivers of biodiversity loss worldwide. In particular, wetland environments are being removed and degraded faster than any other terrestrial habitat on earth. The loss and degradation of wetlands has been particularly pronounced in south-eastern Australia. Aims Here we investigated the impact of habitat loss and degradation on the Data Deficient glossy grass skink (Pseudemoia rawlinsoni), a species that predominantly favours wetland vegetation in south-eastern Australia. Methods We established artificial cover-object (roofing tiles) survey grids in paired remnant and disturbed sites at six locations across Victoria, Australia, and surveyed for skinks between November 2021 and April 2022. Key results Sites at which glossy grass skinks occur are characterised by tall dense vegetation, with a high cover of matted biomass. Thermal profiles within these complex vegetation structures remain much cooler during hot days, and warmer during cold nights, than external temperatures. Nearby disturbed sites (i.e. grazed or mowed areas within dispersal distance of remnant sites) are generally devoid of skinks, have very low and structurally simple (open) vegetation, and have thermal regimes that offer lizards no respite from high summer temperatures. We found that roofing tiles are an effective way to survey for glossy grass skinks; even on cool cloudy days, the temperature of tiles, and the lizards sheltering beneath them, are often much higher than ambient temperatures. Conclusions These findings implicate habitat loss and degradation as having a substantial negative impact on glossy grass skink presence and abundance; skinks largely avoid disturbed areas, even at sites immediately adjacent to remnant habitat. This may be driven not simply by the removal of tall and dense vegetation structures, but the consequent loss of the optimal thermal buffer afforded by such structures. Implications Our study emphasises the threat that habitat loss and degradation pose to wetland species in Australia, and throughout the world.

Phytoplasma-Induced Leaf Reddening as a Monitoring Symptom of Apple Proliferation Disease with Regard to the Development of Remote Sensing Strategies

Apple proliferation (AP) is an economically important disease in many apple-growing regions caused by ‘Candidatus Phytoplasma mali’ which is spread by migrating psyllid vectors on a regional scale. As infected trees in orchards are the only inoculum source, the early eradication of those trees is one of the most efficient strategies to prevent further spread of AP. Remote sensing is a promising rapid and cost-effective tool to identify infected trees on a regional scale. AP-induced premature leaf reddening was evaluated as a reliable symptom for remote sensing by monitoring more than 20,000 trees in 68 different orchards with 20 representative cultivars from 2019 to 2022 in a highly AP-affected region in Southwest Germany. Specific AP symptoms were almost 100% correlated with molecular detection of ‘Ca. P. mali’ and these specific symptoms were almost 100% correlated with leaf reddening. ‘Ca. P. mali’ was detected in 71–97% of trees which showed partial or entire reddening without any other AP symptom. Experimental and field data showed that reddening was induced by cold night and warm day temperatures (about 5 °C vs. 20 °C) in September. Quantification of the phytoplasma by real-time PCR showed no correlation with the intensity of reddening in the leaf. PCR-RFLP subtyping revealed no influence of different ‘Ca. P. mali’ strains on the symptom expression. In conclusion, leaf reddening in late September/early October was a reliable symptom useful for remote sensing of AP.