Temporal Patterns Of Occurrence Research Articles

Temporal Characterization of Fire Accidents in China

Based on the fire accident statistics, this study uses the seasonal index method to comprehensively analyze the temporal pattern of fire occurrence in China from 2010 to 2015. The study scientifically reflects that the number of fire occurrences has an extremely close relationship with time, and summarizes the temporal pattern and characteristics of fire occurrences. This study concludes that the high incidence of large fire accidents in China occurs in the time period of 3:00 a.m. to 5:00 a.m. The probability distribution model of the average total number of accidents in each time period was established and the Kolmogorov test was carried out, which shows that the time characteristics of fire accidents in China can be understood with the probability distribution model as a whole. The results of the analysis of the time characteristics of the fire accidents can provide decision-making information for combating fire accidents.

Wildfires and social media discourse: exploring mental health and emotional wellbeing through Twitter.

The rise in global temperatures due to climate change has escalated the frequency and intensity of wildfires worldwide. Beyond their direct impact on physical health, these wildfires can significantly impact mental health. Conventional mental health studies predominantly rely on surveys, often constrained by limited sample sizes, high costs, and time constraints. As a result, there is an increasing interest in accessing social media data to study the effects of wildfires on mental health. In this study, we focused on Twitter users affected by the California Tubbs Fire in 2017 to extract data signals related to emotional well-being and mental health. Our analysis aimed to investigate tweets posted during the Tubbs Fire disaster to gain deeper insights into their impact on individuals. Data were collected from October 8 to October 31, 2017, encompassing the peak activity period. Various analytical methods were employed to explore word usage, sentiment, temporal patterns of word occurrence, and emerging topics associated with the unfolding crisis. The findings show increased user engagement on wildfire-related Tweets, particularly during nighttime and early morning, especially at the onset of wildfire incidents. Subsequent exploration of emotional categories using Linguistic Inquiry and Word Count (LIWC) revealed a substantial presence of negative emotions at 43.0%, juxtaposed with simultaneous positivity in 23.1% of tweets. This dual emotional expression suggests a nuanced and complex landscape, unveiling concerns and community support within conversations. Stress concerns were notably expressed in 36.3% of the tweets. The main discussion topics were air quality, emotional exhaustion, and criticism of the president's response to the wildfire emergency. Social media data, particularly the data collected from Twitter during wildfires, provides an opportunity to evaluate the psychological impact on affected communities immediately. This data can be used by public health authorities to launch targeted media campaigns in areas and hours where users are more active. Such campaigns can raise awareness about mental health during disasters and connect individuals with relevant resources. The effectiveness of these campaigns can be enhanced by tailoring outreach efforts based on prevalent issues highlighted by users. This ensures that individuals receive prompt support and mitigates the psychological impacts of wildfire disasters.

Host density and anthropogenic stress are drivers of variability in dark spot disease in Siderastrea siderea across the Florida Reef Tract

Dark spot disease (DSD) was first reported within Florida's coral reefs in the 1990s but factors affecting its spatial distribution have not been well studied. We used a 14-yr (2005–2019) coral monitoring data set, utilizing 2242 surveys collected along Florida's coral reefs (about 530 linear km) to explore the spatial and temporal patterns of DSD occurrence. We built predictive statistical models to test for correlations between a suite of environmental and human impact factors and the occurrence of DSD in the reef coral, Siderastrea siderea. DSD in S. siderea is a chronic disease which occurred in all 14 yrs of the study. Annual DSD prevalence ranged from 0.45% to 4.4% and the proportion of survey sites that had DSD ranged from 4.8% to 30.9%. During the study period, DSD became more widespread across Florida's coral reefs and affected a higher proportion of S. siderea populations. Spatial variations in DSD correlated with environmental and human factors which together explained 64.4% of the underlying variability. The most influential factors were concentration of silica in the surface waters (a proxy for freshwater input), the total number of coral hosts, and distance to septic areas. DSD occurred in all regions, but the highest cumulative prevalence occurred in the upper Keys on reefs around major urban centers with links to coastal water discharges. Our results support the hypothesis that coastal water quality is a key component of DSD disease dynamics in Florida and provides motivation for addressing land–sea connections to ameliorate disease occurrence in the region.

Geospatial Mapping of Malaria in the Lowland Forest Ecological Zone of Ondo State, Nigeria

Purpose: This study analyzed the spatial and temporal patterns of malaria occurrence in Ondo state, Nigeria, and to identify the major factors influencing the disease transmission and control.
 Methodology: The research collects primary data from questionnaires administered to hospital staff and workers in areas with high malaria cases while secondary data were sourced from the State’s ministry of health malaria cases gazette. The data was sorted, processed, and analyzed using Microsoft Excel and ArcGIS 10.2 software to produce maps and graphs showing the malaria occurrence and trends in the study area.
 Findings: The study analyzed the malaria occurrence and trends in Ondo State, Nigeria, from 2011 to 2018, using questionnaires and secondary data. The result revealed that mosquito, blood group, poverty, and communicability were significant factors for malaria incidence (p < 0.001), while seasonality, time of day, and immunity status were non-significant (p > 0.05). Result shows that malaria could be transmitted from mother to child during childbirth (p=0.008), but mortality was non-significant in the three Local Government Areas (LGAs) studied (p = 0.166). In addition, malaria cases and attack rate declined significantly from 2015 to 2018, due to the improved health facilities and preventive measures implemented by the State government and health authorities 
 Unique contributor to theory, policy and practice: The study contributes to the existing knowledge and literature on malaria epidemiology and control in Nigeria, by providing a comprehensive and updated spatial and temporal analysis of the disease occurrence and factors in Ondo state. The study also provides useful recommendations for the state ministry of health and government to conduct further studies and develop strategies to interrupt malaria transmission in low susceptible settings, and to achieve the goal of malaria elimination in the state in line with global setting.

Spatial and Temporal Patterns of Land Subsidence and Sinkhole Occurrence in the Konya Endorheic Basin, Turkey

The endorheic Konya Basin is a vast aggradational plain in Central Anatolia, Türkiye. It occupies a significant portion of Konya Province, covering approximately 50,000 km2. The basin is subjected to intense groundwater withdrawal and extensive agricultural activities with excessive irrigation. These activities have led to human-induced hazards, such as sinkholes and regional land subsidence. Although sinkhole occurrence mainly occurs in the Karapınar area, land subsidence is primarily observed in the central sector of Konya city, with 2 million inhabitants, as well as in various parts of the basin. This study focuses on determining the extent and rate of land subsidence throughout the basin, understanding sinkhole formation, and unraveling their relationship with anthropogenic activities. For this purpose, Interferometric Synthetic Aperture Radar (InSAR) analysis of Sentinel-1 data from 2014 to 2022 was conducted to identify and assess land subsidence. We also used the land cover data and groundwater-level information to better understand the spatial and temporal patterns of land subsidence and sinkhole occurrence. Additionally, the land cover data were used to resolve spatial–temporal variations in the cultivated area and urbanization, which are the main factors governing groundwater exploitation in the region. Our study identified widespread subsidence zones with rates as high as 90 mm/y. Groundwater overexploitation to sustain extensive agricultural operations is the main cause of the high rate of land subsidence. Additionally, it was discovered that the number of sinkholes has substantially increased due to anthropogenic influences, currently amounting to as many as 660.

Testing the power-law hypothesis of the interconflict interval

War is an extreme form of collective human behaviour characterized by coordinated violence. We show that this nature of war is substantiated in the temporal patterns of conflict occurrence that obey power law. The focal metric is the interconflict interval (ICI), the interval between the end of a conflict in a dyad (i.e. a pair of states) and the start of the subsequent conflict in the same dyad. Using elaborate statistical tests, we confirmed that ICI samples compiled from the history of interstate conflicts from 1816 to 2014 followed a power-law distribution. We then demonstrate that the power-law properties of ICIs can be explained by a hypothetical model assuming an information-theoretic formulation of the Clausewitz thesis on war: the use of force is a means of interstate communication. Our findings help us to understand the nature of wars between regular states, the significance of which has increased since the Russian invasion of Ukraine in 2022.

The environmental impact of the feral pigeon (Columba Livia f. Domestica) in the historic city centre of Sopron

The presence of different bird species in inhabited areas is becoming increasingly common. The problem with these species is that they are more likely to spread pathogens, contaminate public and private land with their droppings, cause economic damage or even frighten the public. One of the most notable conflict species causing environmental pressures in populated areas is the feral pigeon (Columba livia f. domestica), whose populations are increasing worldwide. Adequate practices and methods are available to reduce the problem. However, their applicability and effectiveness may vary from one locality to another due to the different characteristics of the localities and the different causes of the pigeons' presence. Therefore, it is essential to think in terms of town-specific solutions, the preparation of which requires, among other things, an assessment of the temporal and spatial pattern of occurrence of the species causing the conflict, an understanding of the extent of damage and an overview of the population's level of information. In our study, we investigated the environmental pressures of the feral pigeon in the historical city centre of Sopron, using a combination of the three elements mentioned above. For this purpose, we carried out a monthly visual population assessment combined with a photographic technique at sample points for one year. We conducted a field visit to the area to draw up a damage map of the study area and a spatial localisation of existing control methods. Questionnaire surveys complemented this to assess the public's awareness of the issue. In light of the results, we drew up a map of the pigeon conflict in the city centre of Sopron and identified possible solutions.

Bacterial culture and antimicrobial susceptibility results from bovine milk samples submitted to four veterinary diagnostic laboratories in Australia from 2015 to 2019.

A 5-year retrospective study was conducted to describe the mastitis-causing organisms isolated from bovine milk samples submitted to four veterinary diagnostic laboratories in Australia. The aim of this study was to identify temporal, geographical, and seasonal patterns of occurrence for the organisms and report the in vitro susceptibility of the most common mastitis-causing pathogens. In total, 22,102 milk samples were submitted between 2015 and 2019. The results were reported as positive growth for at least one significant organism (n = 11,407; 51.6%), no growth (n = 5,782; 26.2%), and mixed/contaminated growth (n = 4,913; 22.2%). Culture results for no growth, gram-negative bacteria, and eukaryotic organisms were combined for each region, and they were accounted for between 23 and 46% of submissions. These results represent a subset of mastitis cases for which the antibiotic treatment may not be warranted. A total of 11,907 isolates were cultured from 11,407 milk samples. The most common isolated organisms were Streptococcus uberis [41.3%; 95% confidence interval (CI): 40.4-42.1%] and Staphylococcus aureus (23.6%; 95% CI: 22.8-24.3%). For S. uberis and S. aureus, there was an association between a positive culture result and the dairy region. All regions except for the Sub-tropical Dairy region were more likely to culture S. uberis compared to the reference, Dairy NSW (P < 0.001). Similarly, for S. aureus, a positive culture result was more likely in all other dairy regions compared to Dairy NSW (P < 0.001). The LISA cluster analysis identified differences between High-High (hotspot) postcodes for S. aureus and S. uberis throughout all the analyzed dairy regions. These results highlight the need for further investigations into specific risk factors, such as environmental factors and herd-level predictors, which may have influenced the observed regional variations. Common mastitis-causing pathogens showed overall good susceptibility to a range of antimicrobials used in the treatment of mastitis. On-going surveillance of mastitis-causing pathogens and their antimicrobial susceptibilities will facilitate targeted mastitis control and treatment programs.

Soil moisture observations and machine learning reveal preferential flow mechanisms in the Qilian Mountains

The complexity of the spatial distribution and temporal occurrence of preferential flow (PF) makes it challenging to understand the mechanisms of PF. This study aims to identify the spatial and temporal patterns of PF occurrence using machine learning (Classification and Regression Trees and Random Forests) in the Qilian Mountains, Northwest China. Our results show that detected PF events transport much more rainfall down to the subsoil than non-PF events. Different vegetation types exhibit variations in the main soil layers where PF occurs, which is closely related to the distribution of roots. The PF proportion varies significantly both vertically and horizontally. Based on the Random Forests, we found that the spatial distribution of the PF proportion is mainly controlled by the saturated hydraulic conductivity and residual soil moisture, which cannot be identified by conventional correlation analysis methods. With these soil properties, the spatial distribution of the PF proportion can be estimated with reasonable performance. Using the Classification and Regression Trees method, we identified the temporal occurrence pattern of the PF for different vegetation types and all observation stations. Results indicate that the dominant factors controlling the temporal occurrence of the PF varied for different vegetation types. The thresholds at which these factors initiate the PF also varied. Finally, we found that the PF occurs particularly under wet conditions (except for hydrophobic soils), under denser vegetation, and under conditions of high rainfall amount and intensity, regardless of vegetation type. Our study confirms that both site factors (e.g., soil properties and vegetation) and temporal factors (e.g., initial soil moisture and rainfall characteristics) control the occurrence of the PF in mountainous regions such as the Qilian Mountains and that the Classification and Regression Trees has great potential to study the temporal occurrence of the PF.

Contribution of remote sensing to wildfire trend and dynamic analysis in two of Ghana’s ecological zones: Guinea-savanna and Forest-savanna mosaic

BackgroundTwo of Ghana’s ecological zones—Guinea-savanna zone (GSZ) and Forest-savanna mosaic zone (FSZ)—are practically homologous in terms of structure and floristic composition, with some differences. The various sub-ecosystems that make up these areas are being depleted and losing their natural values due to various threats. There is little understanding about the fire trends in these areas due to a lack of data and poor accessibility to existing fire statistics. This study aimed to contribute to the understanding of the trends of area burned and active fire in the Guinea-savanna and Forest-savanna mosaic zones in order to inform policy-makers about sustainable management options. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) daily active fire (MDC14ML) and burned-area (MCD64A1) products to characterize the fire regime in terms of seasonality, intensity, density, burned area, frequency, and trends during the study period of 2001 to 2021.ResultsThis study indicated that fire activity started in October and peaked in December (GSZ) and January (FSZ). The mean proportion burned was approximately 39.95% (burned area of 2659.31 km2; FSZ) and 60.05% (burned area of 3996.63 km2: GSZ), while the frequency was approximately 42.87% (1759.95 of active fires; FSZ) and 57.13% (2345.26 of active fires: GSZ). In 2018, GSZ recorded the largest burned area (19 811.2 km2, which represents an average of 825.5 km2 of the total area burned from 2001 to 2021) with 4719 active points detected. FSZ recorded its greatest burned area in 2015 (8727.4 km2; which represents an average of 363.6 km2 of the total area burned from 2001 to 2021) with 5587 active points recorded. In addition, it was found that specific times of the day (1000 h to 1420 h) recorded the majority of burned areas. In overview, between 2001 and 2021, burned areas increased by an average of 1.4 km2 (FSZ) and 4.6 km2 (GSZ), and the number of active fires increased by an average of 4.7 (FSZ) and 4.4 (GSZ) active fires per km2.ConclusionsIn conclusion, burned areas and active fires are increasing in both ecological zones. This study demonstrated the relevance of remote sensing to describe spatial and temporal patterns of fire occurrence in Ghana and highlighted the need for fire control and fuel management by the policies and institutions (e.g., Ghana National Fire and Rescue Service) in these important and vulnerable zones (GSZ and FSZ). This is especially true in the Forest-savanna mosaic zone, which is increasingly affected by the disasters of wildfires and records more active fires than GSZ, indicating that this zone is becoming more and more vulnerable. Therefore, rigorous continuous monitoring is essential, and collaboration between organizations fighting for the conservation of natural resources in the field is strongly recommended.

Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring

AbstractDescribing spatial and temporal occurrence patterns of wild animal populations is important for understanding their evolutionary trajectories, population connectivity, and ecological niche specialization, with relevance for effective management. Throughout the world, blue whales produce stereotyped songs that enable identification of separate acoustic populations. We harnessed continuous acoustic recordings from five hydrophones deployed in the South Taranaki Bight (STB) region of Aotearoa New Zealand from January 2016 to February 2018. We examined hourly presence of songs from three different blue whale populations to investigate their contrasting ecological use of New Zealand waters. The New Zealand song was detected year-round with a seasonal cycle in intensity (peak February–July), demonstrating the importance of the region to the New Zealand population as both a foraging ground and potential breeding area. The Antarctic song was present in two distinct peaks each year (June–July; September–October) and predominantly at the offshore recording locations, suggesting northbound and southbound migration between feeding and wintering grounds. The Australian song was only detected during a 10-day period in January 2017, implying a rare vagrant occurrence. We therefore infer that the STB region is the primary niche of the New Zealand population, a migratory corridor for the Antarctic population, and outside the typical range of the Australian population.

Variability of Annual and Monthly Streamflow Droughts over the Southeastern United States

Understanding the patterns of streamflow drought frequency and intensity is critical in defining potential environmental and societal impacts on processes associated with surface water resources; however, analysis of these processes is often limited to the availability of data. The objective of this study is to quantify the annual and monthly variability of low flow river conditions over the Southeastern United States (US) using National Water Model (NWM) retrospective simulations (v2.1), which provide streamflow estimates at a high spatial density. The data were used to calculate sums of outflow deficit volumes at annual and monthly scales, from which the autocorrelation functions (ACF), partial autocorrelation functions (PACF) and the Hurst exponent (H) were calculated to quantify low flow patterns. The ACF/PACF approach is used for examining the seasonal and multiannual variation of extreme events, while the Hurst exponent in turn allows for classification of “process memory”, distinguishing multi-seasonal processes from white noise processes. The results showed diverse spatial and temporal patterns of low flow occurrence across the Southeast US study area, with some locations indicating a strong seasonal dependence. These locations are characterized by a longer temporal cycle, whereby low flows were arranged in series of several to dozens of years, after which they did not occur for a period of similar length. In these rivers, H was in the range 0.8 (+/−0.15), which implies a stronger relation with groundwater during dry periods. In other river segments within the study region the probability of low flows appeared random, determined by H oscillating around the values for white noise (0.5 +/−0.15). The initial assessment of spatial clusters of the low flow parameters suggests no strict relationships, although a link to geologic characteristics and aquifer depth was noticed. At monthly scales, low flow occurrence followed precipitation patterns, with streamflow droughts first occurring in the Carolinas and along the Gulf Coast around May and then progressing upstream, reaching maxima around October for central parts of Mississippi, Alabama and Georgia. The relations for both annual and monthly scales are better represented with PACF, for which statistically significant lags were found in around 75% of stream nodes, while ACF explains on average only 20% of cases, indicating that streamflow droughts in the region occur in regular patterns (e.g., seasonal). This repeatability is of greater importance to defining patterns of extreme hydrologic events than the occurrence of high magnitude random events. The results of the research provide useful information about the spatial and temporal patterns of low flow occurrence across the Southeast US, and verify that the NWM retrospective data are able to differentiate the time processes for the occurrence of low flows.

Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments

Abstract. Landslides and flash floods are geomorphic hazards (GHs) that often co-occur and interact. They generally occur very quickly, leading to catastrophic socioeconomic impacts. Understanding the temporal patterns of occurrence of GH events is essential for hazard assessment, early warning, and disaster risk reduction strategies. However, temporal information is often poorly constrained, especially in frequently cloud-covered tropical regions, where optical-based satellite data are insufficient. Here we present a regionally applicable methodology to accurately estimate GH event timing that requires no prior knowledge of the GH event timing, using synthetic aperture radar (SAR) remote sensing. SAR can penetrate through clouds and therefore provides an ideal tool for constraining GH event timing. We use the open-access Copernicus Sentinel-1 (S1) SAR satellite that provides global coverage, high spatial resolution (∼10–15 m), and a high repeat time (6–12 d) from 2016 to 2020. We investigate the amplitude, detrended amplitude, spatial amplitude correlation, coherence, and detrended coherence time series in their suitability to constrain GH event timing. We apply the methodology on four recent large GH events located in Uganda, Rwanda, Burundi, and the Democratic Republic of the Congo (DRC) containing a total of about 2500 manually mapped landslides and flash flood features located in several contrasting landscape types. The amplitude and detrended amplitude time series in our methodology do not prove to be effective in accurate GH event timing estimation, with estimated timing accuracies ranging from a 13 to 1000 d difference. A clear increase in accuracy is obtained from spatial amplitude correlation (SAC) with estimated timing accuracies ranging from a 1 to 85 d difference. However, the most accurate results are achieved with coherence and detrended coherence with estimated timing accuracies ranging from a 1 to 47 d difference. The amplitude time series reflect the influence of seasonal dynamics, which cause the timing estimations to be further away from the actual GH event occurrence compared to the other data products. Timing estimations are generally closer to the actual GH event occurrence for GH events within homogenous densely vegetated landscape and further for GH events within complex cultivated heterogenous landscapes. We believe that the complexity of the different contrasting landscapes we study is an added value for the transferability of the methodology, and together with the open-access and global coverage of S1 data it has the potential to be widely applicable.

Temporal pattern and risk factors for occurrence of Canine Rabies in Chennai

Rabies is one of the most important zoonoses resulting in a high case fatality rate in humans. Most of the human Rabies cases are due to dog bites which can be prevented by effective vaccination in dogs. Globally, epidemiological studies on understanding the seasonality and risk factors for occurrence in canines are limited. The present study aimed to understand the temporal pattern of Rabies occurrence in Chennai city of Tamil Nadu, India, and address the suggestive clinical signs for better clinical ante-mortem rabies diagnosis. Data of 598 suspected canine hippocampus brain smear samples with Seller's staining and/or FAT percent positivity of 71.57% (428/598) from March 2010 to February 2019 were included in this study. Cross-correlation between rabies cases and meteorological factors showed that maximum temperature (lag 15), morning relative humidity (lag 0 and lag 5) and evening relative humidity (lag 4) were significantly associated with rabies cases. Auto-Regressive Integrated Moving Average (ARIMA) model with exogenous variables (significant lags of meteorological variables) was used to fit the time series of canine Rabies in Chennai. In logistic regression analysis, the following risk factors were found to be playing a significant role in Rabies positivity viz., behavioural changes in dogs (P < 0.001), free-roaming, unprovoked biting, hyper salivation (P < 0.05), dog bite history and drop jaw (P < 0.01). Hence, the study results highlight the need for continuous surveillance of canine Rabies for devising and implementing future preventive strategies and is helpful to establish the above-identified risk factors as a criterion to help in clinical rabies diagnosis.

Effects of free-ranging livestock on occurrence and interspecific interactions of a mammalian community.

Mammalian communities inhabiting temperate grasslands are of conservation concern globally, especially in Central Asia, where livestock numbers have dramatically increased in recent decades, leading to overgrazing and land-use change. Yet, how this pervasive presence of livestock herds affects the community of wild mammals remains largely unstudied. We used systematic camera trapping at 216 sites across remote, mountainous areas of the Mongolian Altai Mountains to assess the spatial and temporal patterns of occurrence and the interspecific relationships within a mammalian community that includes different categories of livestock. By adopting a recently proposed multispecies occupancy model that incorporates interspecific correlation in occupancy, we found several statistically strong correlations in occupancy among species pairs, with the majority involving livestock. The sign of such associations was markedly species-dependent, with larger wild species of conservation concern, namely, snow leopard and Siberian ibex, avoiding livestock presence. As predicted, we found evidence of a positive correlation in occupancy between predators and their respective main prey. Contrary to our expectations, a number of intraguild species pairs also showed positive co-occurrence, with no evidence of spatiotemporal niche partitioning. Overall, our study suggests that livestock encroaching into protected areas influences the whole local community of wild mammals. Though pastoralism has coexisted with wildlife for millennia in central Asian grasslands, our findings suggest that policies and practices to decrease the pressure of livestock husbandry on wildlife are needed, with special attention on large species, such as the snow leopard and its wild prey, which seem to be particularly sensitive to this pervasive livestock presence.

UVB Irradiation-Induced Transcriptional Changes in Lignin- and Flavonoid Biosynthesis and Indole/Tryptophan-Auxin-Responsive Genes in Rice Seedlings.

Global warming accelerates the destruction of the ozone layer, increasing the amount of UVB reaching the Earth’s surface, which in turn alters plant growth and development. The effects of UVB-induced alterations of plant secondary and cell wall metabolism were previously documented; however, there is little knowledge of its effects on rice seedlings during the developmental phase of leaves. In this study, we examined secondary metabolic responses to UVB stress using a transcriptomic approach, focusing on the biosynthetic pathways for lignin, flavonoid, and indole/tryptophan-auxin responses. As new leaves emerged, they were irradiated with UVB for 5 days (for 3 h/day−1). The genes encoding the enzymes related to lignin (4CL, CAD, and POD) and flavonoid biosynthesis (CHS, CHI, and FLS) were highly expressed on day 1 (younger leaves) and day 5 (older leaves) after UVB irradiation. The expression of the genes encoding the enzymes related to tryptophan biosynthesis (AS, PRT, PRAI, IGPS, and TS) increased on day 3 of UVB irradiation, and the level of tryptophan increased and showed the same temporal pattern of occurrence as the expression of the cognate gene. Interestingly, the genes encoding BBX4 and BBX11, negative regulators of UVB signaling, and SAUR27 and SAUR55, auxin response enzymes, were downregulated on day 3 of UVB irradiation. When these results are taken together, they suggest that secondary metabolic pathways in rice seedlings are influenced by the interaction between UVB irradiation and the leaf developmental stage. Thus, the strategies of protection against, adaptation to, and mitigation of UVB might be delicately regulated, and, in this context, our data provide valuable information to understand UVB-induced secondary metabolism in rice seedlings.

Emoji use in China: popularity patterns and changes due to COVID-19.

Emojis are small pictograms that are frequently embedded within micro-texts to more directly express emotional meanings. To understand the changes in the emoji usage of internet users during the COVID-19 outbreak, we analysed a large dataset collected from Weibo, the most popular Twitter-like social media platform in China, from December 1, 2019, to March 20, 2020. The data contained 38,183,194 microblog posts published by 2,239,472 unique users in Wuhan. We calculated the basic statistics of users’ usage of emojis, topics, and sentiments and analysed the temporal patterns of emoji occurrence. After examining the emoji co-occurrence structure, we finally explored other factors that may affect individual emoji usage. We found that the COVID-19 outbreak greatly changed the pattern of emoji usage; i.e., both the proportion of posts containing emojis and the ratio of users using emojis declined substantially, while the number of posts remained the same. The daily proportion of Happy emojis significantly declined to approximately 32%, but the proportions of Sad- and Encouraging-related emojis rose to 24% and 34%, respectively. Despite a significant decrease in the number of nodes and edges in the emoji co-occurrence network, the average degree of the network increased from 34 to 39.8, indicating that the diversity of emoji usage increased. Most interestingly, we found that male users were more inclined towards using regular textual language with fewer emojis after the pandemic, suggesting that during public crises, male groups appeared to control their emotional display. In summary, the COVID-19 pandemic remarkably impacted individual sentiments, and the normal pattern of emoji usage tends to change significantly following a public emergency.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10489-022-03195-y.

160 glacial lake outburst floods (GLOFs) across the Tropical Andes since the Little Ice Age

Assessing the extent to which glacial lake outburst floods (GLOFs) are increasing in frequency in modern times and whether their incidence is driven by anthropogenic climate change requires historical context. However, progress on this issue is hampered by incomplete GLOF inventories, especially in remote mountain regions. Here, we exploit high-resolution, multi-temporal satellite and aerial imagery, and documentary data to identify GLOF events across the glacierized Cordilleras of Peru and Bolivia, using a set of diagnostic geomorphic features. A total of 160 GLOFs from 151 individual sites are characterised and analysed, tripling the number of previously reported events. We provide statistics on location, magnitude, timing and characteristics of these events with implications for regional GLOF hazard identification and assessment. Furthermore, we describe several cases in detail and document a wide range of process chains associated with Andean GLOFs. • Our inventory consists of 160 glacial lake outburst floods (GLOFs). • About two thirds of these have not been identified previously in the literature. • We analyze spatial and temporal patterns of GLOF occurrence. • We characterize pre-conditions, triggers and mechanisms of GLOFs.

Soundscapes predict species occurrence in tropical forests

Accurate occurrence data is necessary for the conservation of keystone or endangered species, but acquiring it is usually slow, laborious and costly. Automated acoustic monitoring offers a scalable alternative to manual surveys but identifying species vocalisations requires large manually annotated training datasets, and is not always possible (e.g. for lesser studied or silent species). A new approach is needed that rapidly predicts species occurrence using smaller and more coarsely labelled audio datasets. We investigated whether local soundscapes could be used to infer the presence of 32 avifaunal and seven herpetofaunal species in 20 min recordings across a tropical forest degradation gradient in Sabah, Malaysia. Using acoustic features derived from a convolutional neural network (CNN), we characterised species indicative soundscapes by training our models on a temporally coarse labelled point‐count dataset. Soundscapes successfully predicted the occurrence of 34 out of the 39 species across the two taxonomic groups, with area under the curve (AUC) metrics from 0.53 up to 0.87. The highest accuracies were achieved for species with strong temporal occurrence patterns. Soundscapes were a better predictor of species occurrence than above‐ground carbon density – a metric often used to quantify habitat quality across forest degradation gradients. Our results demonstrate that soundscapes can be used to efficiently predict the occurrence of a wide variety of species and provide a new direction for data driven large‐scale assessments of habitat suitability.

Spatio-temporal occurrence patterns of epibiota along the leaves of the seagrass Cymodocea nodosa in the Northern Adriatic Sea

ABSTRACT We investigated the epibiotic community along Cymodocea nodosa leaves in a seagrass meadow in the Northern Adriatic Sea. In accordance with previous studies, we found that autotrophic organisms dominated throughout the study period (May–October 2014) and showed a clear temporal occurrence pattern with highest occurrence frequencies during summertime. Low relative occurrence of autotrophs was observed in spring, coinciding with higher values for sessile heterotrophs. In both autotrophs and heterotrophs, we observed a general trend towards increased epibiota cover on old leaves and towards the apical (i.e. older) parts of leaves, even though this pattern does not hold true for all species or sampling times. Most heterotrophs were rarely observed, but serpulid polychaetes, the foraminiferan Tretomphaloides concinnus, the hydrozoan Clytia linearis as well as gastropod clutches occurred frequently on all parts of the leaves and showed occurrence patterns putatively reflecting spatial competition with autotrophs and predator avoidance. Hitherto, few data have been available on the epifaunal diversity and community structure on C. nodosa. Thus, this study provides important baseline data for future studies investigating the impact of natural and anthropogenic stressors on epibiota communities associated with this ecologically important Mediterranean seagrass species.