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Currently, the generation of electrical energy in Cuba is supported by oil and natural gas. These sources, as it is known, are directly linked to large emissions of pollutants that are released into the environment. Therefore, it is necessary to search for new energy options that are directed towards sustainable development, allowing the preservation of natural ecosystems. Owing to the location and geographical characteristics of Cuba, it is necessary to assess the energy possibilities of the seas that surround it and to search for the most feasible areas to obtain energy from the sea temperature. This renewable energy source, in addition to being used to generate electricity, can also be used in derived technologies, such as desalination, refrigeration, and aquaculture. Hence, a dataset is presented with the calculation of the thermal efficiency for the exploitation of thermal energy from the sea, which is based on the thermal gradient between the sea potential temperatures between the shore and the level of depth being analyzed. Outputs of 27 years of daily data from the Copernicus Marine Environmental Monitoring Service (CMEMS) GLOBAL_MULTIYEAR_PHY_001_030 product with a spatial resolution of 1/12° were used. The calculation was made using a Python script of the daily thermal efficiency at depths of 763, 902, and 1062 m, as these are the levels that are traditionally studied for the exploitation of sea thermal energy. In this way, 27 files of each level were generated for a total of 81 files in text format separated by commas. Each file is presented with the date, level, coordinates, and thermal efficiency. The dataset is available from the Science Data Bank repository ( https://doi.org/10.57760/sciencedb.10037).

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In this study, we examined the relationship between sea surface temperature (SST) and phytoplankton abundance in coastal regions of the Brazilian South Atlantic: São Paulo, Paraná, and Santa Catarina, and the Protection Area of Southern right whales (Eubalaena australis) in Santa Catarina (APA), a conservation zone established along 130 km of coastline. Using SST and chlorophyll-a (Chl-a) data from 2002 to 2023, we found significant differences in SST between the regions, with São Paulo having the highest SST, followed by Paraná and Santa Catarina. All locations showed a consistent increase in SST over the years, with North Santa Catarina, APA and São Paulo experiencing the lowest rate of increase. Correlation analyses between SST and Chl-a revealed a stronger inverse relationship in North Santa Catarina and APA, indicating an increased response of Chl-a to SST variations in this region. The presence of protected area appears to play an essential role in reducing the negative impacts of increasing SST. Specifically, while there is a wealth of research on the consequences of global warming on diverse coastal and oceanic areas, heterogeneity among different settings persists and the causes for this necessitating attention. Our findings have implications for both localized scientific approaches and broader climate policies, emphasizing the importance of considering coastal ecosystem resilience to climate change in future conservation and adaptation strategies.

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Infrared thermography has been investigated in recent studies to monitor body surface temperature and correlate it with animal welfare and performance factors. In this context, this study proposes the use of the thermal signature method as a feature extractor from the temperature matrix obtained from regions of the body surface of laying hens (face, eye, wattle, comb, leg, and foot) to enable the construction of a computational model for heat stress level classification. In an experiment conducted in climate-controlled chambers, 192 laying hens, 34 weeks old, from two different strains (Dekalb White and Dekalb Brown) were divided into groups and housed under conditions of heat stress (35 °C and 60% humidity) and thermal comfort (26 °C and 60% humidity). Weekly, individual thermal images of the hens were collected using a thermographic camera, along with their respective rectal temperatures. Surface temperatures of the six featherless image areas of the hens' bodies were cut out. Rectal temperature was used to label each infrared thermography data as "Danger" or "Normal", and five different classifier models (Random Forest, Random Tree, Multilayer Perceptron, K-Nearest Neighbors, and Logistic Regression) for rectal temperature class were generated using the respective thermal signatures. No differences between the strains were observed in the thermal signature of surface temperature and rectal temperature. It was evidenced that the rectal temperature and the thermal signature express heat stress and comfort conditions. The Random Forest model for the face area of the laying hen achieved the highest performance (89.0%). For the wattle area, a Random Forest model also demonstrated high performance (88.3%), indicating the significance of this area in strains where it is more developed. These findings validate the method of extracting characteristics from infrared thermography. When combined with machine learning, this method has proven promising for generating classifier models of thermal stress levels in laying hen production environments.

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Most of the responses present in animals when exposed to stressors are mediated by the autonomic nervous system. The sympathetic nervous system, known as the one responsible for the "fight or flight" reaction, triggers cardiovascular changes such as tachycardia or vasomotor alterations to restore homeostasis. Increase in body temperature in stressed animals also activates peripheral compensatory mechanisms such as cutaneous vasodilation to increase heat exchange. Since changes in skin blood flow influence the amount of heat dissipation, infrared thermography is suggested as a tool that can detect said changes. The present review aims to analyze the application of infrared thermography as a method to assess stress-related autonomic activity, and their association with the cardiovascular and heart rate variability in domestic animals.

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A surface urban heat island (SUHI) is a phenomenon whereby temperatures in urban areas are significantly higher than that of surrounding rural and natural areas due to replacing natural and semi-natural areas with impervious surfaces. The phenomenon is evaluated through the SUHI intensity, which is the difference in temperatures between urban and non-urban areas. In this study, we assessed the spatial and temporal dynamics of SUHI in two urban areas of the French Guiana, namely Ile de Cayenne and Saint-Laurent du Maroni, for the year 2020 using MODIS-based gap-filled LST data. Our results show that the north and southwest of Ile de Cayenne, where there is a high concentration of build-up areas, were experiencing SUHI compared to the rest of the region. Furthermore, the northeast and west of Saint-Laurent du Maroni were also hotspots of the SUHI phenomenon. We further observed that the peak of high SUHI intensity could reach 5 °C for both Ile de Cayenne and Saint-Laurent du Maroni during the dry season when the temperature is high with limited rainfall. This study sets the stage for future SUHI studies in French Guiana and aims to contribute to the knowledge needed by decision-makers to achieve sustainable urbanization.

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Dairy cows in pasture-based systems are more susceptible to heat stress. Holstein cows have the black or red phenotypes, the latter having lower absorbance of solar radiation. Therefore, the study's objective was to evaluate whether cows with red (R) coats are more resistant than black (B) cows to hot weather in a subtropical climate. R and B lactating Holstein cows were evaluated during the cold and hot seasons for internal and surface temperature and sweating rate. In the cold season, body temperature (n = 9/group) did not differ between groups, but the average superficial temperature (n = 13/group) was lower in R cows (B: 30.9 ± 0.3 °C; RW: 29.6 ± 0.3 °C; p = 0.02). In the hot season, under mild to moderate heat stress, mean body temperature (n = 9/group) of R cows was lower (B: 38.75 ± 0.01 °C; R: 38.62 ± 0.1 °C; p=<0.0001), whereas no difference was observed in superficial temperature (n = 17/group). The maximum internal temperature and sweating rate (n = 11/group), measured in the hot season, and the number of evaluations in hyperthermia in both seasons did not differ. Therefore, there were differences in thermoregulation between phenotypes under mild to moderate heat stress conditions. However, considering that only discrete differences were observed, the red and white coat is unlikely to benefit the Holstein cow's welfare under mild to moderate thermal stress.

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In mammals, progesterone and estrogens affect the stress response. The study aimed to compare the physiological and behavioral responses to the social isolation of ewes during the estrus or luteal phase. Estrous and diestrous ewes (n = 10 and 8 respectively) were individually isolated in a novel place for 10 min. Ewes' behavior was recorded during the test. Cortisol, blood proteins and glucose concentrations, and the skin surface temperature were determined before and after the test. Cortisol increased immediately after the test ended (P = 0.02). Serum total protein (P = 0.02), globulin (P < 0.0001), and plasma glucose (P = 0.006) concentrations were greater in estrus than in the luteal phase. The abdominal skin surface temperature was greater during estrus than during the luteal phase (P = 0.02). Ewes in estrus spent more time standing up immobile than ewes in the luteal phase (P = 0.05). In conclusion, the physiological and behavioral responses changed according to the phase of the estrous cycle. These results highlight the need for future studies focusing on how reproductive status influences the stress response to different management practices in sheep.

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Body temperature is one of the key vital signs for determining a disease's severity, as it reflects the thermal energy generated by an individual's metabolism. Since the first study on the relationship between body temperature and diseases by Carl Reinhold August Wunderlich at the end of the 19th century, various forms of thermometers have been developed to measure body temperature. Traditionally, methods for measuring temperature can be invasive, semi-invasive, and non-invasive. In recent years, great technological advances have reduced the cost of thermographic cameras, which allowed extending their use. Thermal cameras capture the infrared radiation of the electromagnetic spectrum and process the images to represent the temperature of the object under study through a range of colors, where each color and its hue indicate a previously established temperature. Currently, cameras have a sensitivity that allows them to detect changes in temperature as small as 0.01 °C. Along with its use in other areas of medicine, thermography has been used at the ocular level for more than 50 years. In healthy subjects, the literature reports that the average corneal temperature ranges from 32.9 to 36 °C. One of the possible sources of variability in normal values is age, and other possible sources of variation are gender and external temperature. In addition to the evaluation of healthy subjects, thermography has been used to evaluate its usefulness in various eye diseases, such as Graves' orbitopathy, and tear duct obstruction for orbital diseases. The ocular surface is the most studied area. Ocular surface temperature is influenced by multiple conditions, one of the most studied being dry eye; other diseases studied include allergic conjunctivitis and pterygium as well as systemic diseases such as carotid artery stenosis. Among the corneal diseases studied are keratoconus, infectious keratitis, corneal graft rejection, the use of scleral or soft contact lenses, and the response to refractive or cataract surgery. Other diseases where thermographic features have been reported are glaucoma, diabetic retinopathy, age-related macular degeneration, retinal vascular occlusions, intraocular tumors as well as scleritis, and other inflammatory eye diseases.

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There is worldwide concern about how climate change -which involves rising temperatures- may increase the risk of contracting and developing diseases, reducing the quality of life. This study provides new research that takes into account parameters such as land surface temperature (LST), surface urban heat island (SUHI), urban hotspot (UHS), air pollution (SO2, NO2, CO, O3 and aerosols), the normalized difference vegetation index (NDVI), the normalized difference building index (NDBI) and the proportion of vegetation (PV) that allows evaluating environmental quality and establishes mitigation measures in future urban developments that could improve the quality of life of a given population. With the help of Sentinel 3 and 5P satellite images, we studied these variables in the context of Granada (Spain) during the year 2021 to assess how they may affect the risk of developing diseases (stomach, colorectal, lung, prostate and bladder cancer, dementia, cerebrovascular disease, liver disease and suicide). The results, corroborated by the statistical analysis using the Data Panel technique, indicate that the variables LST, SUHI and daytime UHS, NO2, SO2 and NDBI have important positive correlations above 99% (p value: 0.000) with an excess risk of developing these diseases. Hence, the importance of this study for the formulation of healthy policies in cities and future research that minimizes the excess risk of diseases.

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The objective was to assess the physiological and behavioral variables of pasture-raised Holstein heifers on the basis of their coat color. Eight heifers were used, four with black coat, and four with white coat. In the morning and afternoon periods, their physiological variables were measured, namely respiratory rate (RR, mov min. -1), rectal temperature (TR, ºC), and coat surface temperature (TS, ºC). The behavior analysis considered the site (sun or shade), position (standing or lying down), and activities (idleness, rumination, grazing, and others). The data of the physiological variables were assessed through analysis of variance, and significance, through Tukey's test at 5%, while behavioral data were assessed using the Chi-squared test (SAS Software). There was a difference for all of the heifers' physiological variables as a function of coat color (p < 0.0001) and period (p < 0.0001), with black ones showing greater results than white ones as to all physiological variables in the afternoon period (p < 0.0001). Grazing behavior is reduced during the hottest hours of the day. We conclude that solar radiation changes the physiological variables and grazing behavior of pasture-raised Holstein heifers.(AU)

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We identified anomalously warm sea surface temperature (SST) events during 1980-2019 near the major upwelling center at Punta Lavapié in the central Chile-Peru Current System, using the European Centre for Medium-Range Weather Forecasts reanalysis and focusing on time scales of 10 days to 6 months. Extreme warm SST anomalies on these time scales mostly occurred in the austral summer, December through February, and had spatial scales of 1000s of km. By compositing over the 37 most extreme warm events, we estimated terms in a heat budget for the ocean surface mixed layer at the times of strongest warming preceding the events. The net surface heat flux anomaly is too small to explain the anomalous warming, even when allowing for uncertainty in mixed-layer depth. The composite mean anomaly of wind stress, from satellite ocean vector wind swath data, during the 37 anomalous warming periods has a spatial pattern similar to the resulting warm SST anomalies, analogous to previous studies in the California Current System. The weakened surface wind stress suggests reduced entrainment of cold water from below the mixed layer. Within 100-200 km of the coast, the typical upwelling-favorable wind stress curl decreases, suggesting reduced upwelling of cold water. In a 1000-km area of anomalous warming offshore, the typical downwelling-favorable wind stress curl also decreases, implying reduced downward Ekman pumping, which would allow mixed-layer shoaling and amplify the effect of the positive climatological summertime net surface heat flux.

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The objective was to evaluate the differences between hair lambs, born from single or twin births, regarding the latency periods for standing up and suckling, the vitality, glycemic, cortisol, and triiodothyronine concentrations, as well as the phenotypic characteristics related to the maintenance of homeothermy in the immediate postpartum. Single (n = 10) or twin (n = 12) Morada Nova lambs were evaluated after birth, during the first successful suckling (M0 = Timepoint 0), and at regular intervals of 20 min (M20, M40, M60). Lambs from single births had higher birth weight (3.09 vs 2.58 kg; P ≤ 0.05) and higher serum triiodothyronine concentration (267 vs 209 ng/dL; P ≤ 0.05) compared to twin lambs. There was a positive correlation between weight and blood glucose (0.57; P ≤ 0.05) for both single and twin lambs. The type of birth did not affect vitality, which was negatively associated with cortisol concentration (-0.53; P ≤ 0.05). Twin lambs had higher internal and ocular temperatures (39.29 vs 38.67 °C and 38.84 vs 38.13 °C; P ≤ 0.05, respectively). Body surface temperatures increased over time in both groups (P ≤ 0.05). An increase in the temperature of the hips region (ysingle = 27.88 + 0.019*time; R2 = 0.96; P = 0.019 and ytwin = 28.74 + 0.019*time; R2 = 0.94; P = 0.029) was observed for both single and twin lambs, which coincides with the region of brown adipose tissue deposition. The lowest absolute thermal variabilities between twin and single lambs in M0 and M60 were recorded in the midloin and integral dorsal area. The parturition type did not influence the latencies to stand up (P = 0.908) and for the first suckling (P = 0.888), and the vitality score (P = 0.353). Thus, single and twin lambs do not differ in neonatal behavior, but they presented specific metabolic strategies to regulate body temperature over time. Midloin and integral dorsal areas are anatomical regions suggested for use in serial thermographic monitoring. Infrared thermography may be an important complementary resource in neonatal care.

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Forest biodiversity conservation and species distribution modeling greatly benefit from broad-scale forest maps depicting tree species or forest types rather than just presence and absence of forest, or coarse classifications. Ideally, such maps would stem from satellite image classification based on abundant field data for both model training and accuracy assessments, but such field data do not exist in many parts of the globe. However, different forest types and tree species differ in their vegetation phenology, offering an opportunity to map and characterize forests based on the seasonal dynamic of vegetation indices and auxiliary data. Our goal was to map and characterize forests based on both land surface phenology and climate patterns, defined here as forest phenoclusters. We applied our methodology in Argentina (2.8 million km2 ), which has a wide variety of forests, from rainforests to cold-temperate forests. We calculated phenology measures after fitting a harmonic curve of the enhanced vegetation index (EVI) time series derived from 30-m Sentinel 2 and Landsat 8 data from 2018-2019. For climate, we calculated land surface temperature (LST) from Band 10 of the thermal infrared sensor (TIRS) of Landsat 8, and precipitation from Worldclim (BIO12). We performed stratified X-means cluster classifications followed by hierarchical clustering. The resulting clusters separated well into 54 forest phenoclusters with unique combinations of vegetation phenology and climate characteristics. The EVI 90th percentile was more important than our climate and other phenology measures in providing separability among different forest phenoclusters. Our results highlight the potential of combining remotely sensed phenology measures and climate data to improve broad-scale forest mapping for different management and conservation goals, capturing functional rather than structural or compositional characteristics between and within tree species. Our approach results in classifications that go beyond simple forest-nonforest in areas where the lack of detailed ecological field data precludes tree species-level classifications, yet conservation needs are high. Our map of forest phenoclusters is a valuable tool for the assessment of natural resources, and the management of the environment at scales relevant for conservation actions.

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Oceanic heat anomalies affect climate in remote regions through the atmospheric cycle. South America (SA) was the first region found associated with EI Niño, which affects the fishery, agriculture, forestry, and livestock industry of SA. As approximately 60% of the total water is used for agriculture, climate changes in SA caused by ocean anomalies have led to the variability of available water, especially for irrigation water. Where the precipitation is low and/or the temperature is high, the availability and quality of water resources are under pressure. For instance, droughts associated with La Niña severely limited water supply and irrigation requirements between 25°S - 40°S in west-central Argentina and central Chile. In order to study the relationship between ocean variability and the climate of SA, 19 teleconnection indices (TI) related to Ocean abnormity are considered. The 19 indices are: the sea surface temperature (SST) and their anomaly in 4 Niño regions (SST1 + 2, SST3, SST3.4, SST4, ANOM1 + 2, ANOM3, ANOM3.4, ANOM4), Southern Oscillation Index (SOI), Oceanic Niño Index (ONI), Outgoing Longwave Radiation (OLR), Arctic Oscillation (AO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Pacific-North America (PNA), Atlantic Multi-decadal Oscillation (AMO), West and East of Indian Ocean Dipole (IODW, IODE), and the difference between IODW and IODE (IODd). High-resolution gridded climate data (1982-2016) from the Global Precipitation Climatology Centre (GPCC), the Climate Prediction Center (CPC), and the National Centers for Environmental Prediction (NCEP) are applied for correlation analyses. The results show that the 89.4% area of South American climate has a significant correlation with the SST in Niño region 1 + 2, the mean correlation coefficient is 0.55 for NCEP precipitation and 0.54 for CPC temperature. The lag duration for the remote correlation is around 2-3 months. It is the first attempt to analyze the correlation relationship based on 19 TIs, which can provide comprehensive insight into the climate of SA at a high-resolution scale. These findings are helpful for identifying the sensitive factors that affect climate in SA, for projecting the climate variables of SA, and for managing the irrigation water resources of SA.

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Infrared thermography is a non-invasive diagnostic tool for early detection of subclinical mastitis in pasture-raised dairy cows. The study was conducted on eight dairy farms, in Sena Madureira, Acre, in the Western Amazon, during the rainy season. One hundred and thirteen lactating crossbred cows were monitored, with measurements performed once day before milking, from 02:00 to 06:00 in the morning. The California Mastitis Test (CMT) was subsequently applied. Data processing was performed in the R programming language and evaluated by linear models. Our results show that cow udder surface temperature by infrared thermography showed significant differences (p<0.05) between the months of the year. Cow udder temperatures of thermographic images were significantly different (p<0.05) from CMT results for animals with subclinical mastitis. CMT results showed that 45 cows (39.8%) in January, 52 cows (46%) in February and 57 cows (50.4%) in March had subclinical mastitis. Infrared thermography is a technique that can be used for the early detection of subclinical mastitis in dairy herds in a grazing system, as it detected an increase in the udder surface temperature of the same cows that were positive for CMT.

Avaliou-se a termografia infravermelha como ferramenta diagnóstica, não invasiva, para detecção precoce da mastite subclínica em vacas leiteiras criadas a pasto. O estudo foi conduzido em oito propriedades leiteiras, em Sena Madureira, Acre, na Amazônia Ocidental, durante o inverno amazônico. Foram monitoradas 113 vacas mestiças em lactação, com mensurações realizadas uma vez por dia, sempre antes da realizadas das 2h às 6h da manhã. Em seguida, realizou-se exame de Califórnia Mastitis Test-CMT. O ordenha, processamento dos dados foi feito em linguagem de programação R, os quais foram avaliados por modelos lineares. Os resultados mostram que a temperatura da superfície do úbere das vacas avaliadas com a técnica da termografia infravermelha teve diferenças significativas (P<0,05) entre os meses do ano. Constatou-se alteração na na temperatura do úbere das vacas, captada por meio das imagens termográficas, e foi i significativo (P<0,05) o resultado do teste California Mastistis Test (CMT) para os animais que se encontravam com mastite subclínica. Com base nos resultados do CMT, foi possível constatar que 45 vacas (39,8%) em janeiro, 52 vacas (46%) em fevereiro e 57 vacas (50,4%) em março se encontravam com mastite subclínica. Conclui-se que a termografia infravermelha é uma técnica que pode ser utilizada para a detecção precoce de mastite subclínica em rebanho leiteiro em sistema de pastejo, pois foi capaz de detectar aumento na temperatura superficial do úbere das vacas, que resultaram positivas no teste de CMT.

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The determination of the surface energy balance fluxes (SEBFs) and evapotranspiration (ET) is fundamental in environmental studies involving the effects of land use change on the water requirement of crops. SEBFs and ET have been estimated by remote sensing techniques, but with the operation of new sensors, some variables need to be parameterized to improve their accuracy. Thus, the objective of this study is to evaluate the performance of algorithms used to calculate surface albedo and surface temperature on the estimation of SEBFs and ET in the Cerrado-Pantanal transition region of Mato Grosso, Brazil. Surface reflectance images of the Operational Land Imager (OLI) and brightness temperature (Tb) of the Thermal Infrared Sensor (TIRS) of the Landsat 8, and surface reflectance images of the MODIS MOD09A1 product from 2013 to 2016 were combined to estimate SEBF and ET by the surface energy balance algorithm for land (SEBAL), which were validated with measurements from two flux towers. The surface temperature (Ts) was recovered by different models from the Tb and by parameters calculated in the atmospheric correction parameter calculator (ATMCORR). A model of surface albedo (asup) with surface reflectance OLI Landsat 8 developed in this study performed better than the conventional model (acon) SEBFs and ET in the Cerrado-Pantanal transition region estimated with asup combined with Ts and Tb performed better than estimates with acon. Among all the evaluated combinations, SEBAL performed better when combining asup with the model developed in this study and the surface temperature recovered by the Barsi model (Tsbarsi). This demonstrates the importance of an asup model based on surface reflectance and atmospheric surface temperature correction in estimating SEBFs and ET by SEBAL.

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Amazonia experienced unusually devastating fires in August 2019, leading to huge regional and global environmental and economic losses. The increase in fires has been largely attributed to anthropogenic deforestation, but anomalous climate conditions could also have contributed. This study investigates the climate influence on Amazonia fires in August 2019 and underlying mechanisms, based on statistical correlation and multiple linear regression analyses of 2001-2019 satellite-based fire products and multiple observational or reanalyzed climate datasets. Positive fire anomalies in August 2019 were mainly located in southern Amazonia. These anomalies were mainly driven by low precipitation and relative humidity, which increased fuel dryness and contributed to 38.9 ± 9.5% of the 2019 anomaly in pyrogenic carbon emissions over the southern Amazonia. The dry conditions were associated with southerly wind anomalies over southern Amazonia that suppressed the climatological southward transport of water vapor originating from the Atlantic. The southerly wind anomalies were caused by the combination of a Gill-type cyclonic response to the warmer North Atlantic sea surface temperature (SST), and enhancement of the Walker and Hadley circulations over South America due to the colder SST in the eastern Pacific, and a mid-latitude wave train triggered by the warmer condition in the western Indian Ocean. Our study highlights, for the first time, the important role of Indian Ocean SST for fires in Amazonia. It also reveals how cold SST anomalies in the tropical eastern Pacific link the warm phase of the El Niño-Southern Oscillation (ENSO) in the preceding December-January to the dry-season fires in Amazonia. Our findings can develop theoretical basis of global tropical SST-based fire prediction, and have potential to improve prediction skill of extreme fires in Amazonia and thus to take steps to mitigate their impacts which is urgency given that dry conditions led to the extreme fires are becoming common in Amazonia.

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While weather stations generally capture near-surface ambient air temperature (Ta) at a high temporal resolution to calculate daily values (i.e., daily minimum, mean, and maximum Ta), their fixed locations can limit their spatial coverage and resolution even in densely populated urban areas. As a result, data from weather stations alone may be inadequate for Ta-related epidemiology particularly when the stations are not located in the areas of interest for human exposure assessment. To address this limitation in the Megalopolis of Central Mexico (MCM), we developed the first spatiotemporally resolved hybrid satellite-based land use regression Ta model for the region, home to nearly 30 million people and includes Mexico City and seven more metropolitan areas. Our model predicted daily minimum, mean, and maximum Ta for the years 2003-2019. We used data from 120 weather stations and Land Surface Temperature (LST) data from NASA's MODIS instruments on the Aqua and Terra satellites on a 1 × 1 km grid. We generated a satellite-hybrid mixed-effects model for each year, regressing Ta measurements against land use terms, day-specific random intercepts, and fixed and random LST slopes. We assessed model performance using 10-fold cross-validation at withheld stations. Across all years, the root-mean-square error ranged from 0.92 to 1.92 K and the R 2 ranged from .78 to .95. To demonstrate the utility of our model for health research, we evaluated the total number of days in the year 2010 when residents ≥65 years old were exposed to Ta extremes (above 30°C or below 5°C). Our model provides much needed high-quality Ta estimates for epidemiology studies in the MCM region.

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The objective was to compare effects of immunological, chemical and surgical castrations on testicular characteristics, scrotal surface temperatures, sperm quality, and serum testosterone concentrations in Bos indicus bulls. Eighty Nelore bulls (∼20 mo) were grazed on pasture, fed a supplement and slaughtered at ∼480 kg. Bulls were allocated into four groups (n = 20/group). The control group (CON) was non-castrated and there were three castration methods: immune (IMM - Bopriva®), injected on D-30, D30 and D60; chemical (CHE), an intratesticular injection of 40% CaCl2 + 0.5% dimethylsulfoxide on D0; and surgical (SUR) on D0. The CHE group were surgically castrated on D60, due to testicular swelling and necrosis of scrotal skin. Most scrotal surface temperatures (infrared thermography) were significantly higher on D15 in CHE and SUR compared to CON, due to inflammation. All bulls were subjected to a breeding soundness evaluation on D-7 and slaughtered on D220. Scrotal circumference and testicular volume did not differ among groups (P > 0.05) at D0, but at D15, both end points were highest (P < 0.05) in the CHE group (due to swelling), although both end points were smaller for IMM versus CON (P < 0.05) at D60 and D150. Sperm motility in the IMM group was ∼50 and 10% of that in the CON group on D60 and D150, respectively. For the IMM group, serum testosterone concentrations were similar on D0 and D15 (but ∼50% of CHE or SUR on D0, attributed to the first treatment on D-30), and had decreased ∼70% on D60 and D150, whereas in the CHE and SUR groups, there were ∼80-90% decreases in testosterone after D0. In conclusion, immunological castration was a viable alternative to surgical castration, as it supressed testosterone production and spermatogenesis, with the benefits of being much less invasive, with better animal welfare and less stress.

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Understanding the drivers of geographical variation in species distributions, and the resulting community structure, constitutes one of the grandest challenges in ecology. Geographical patterns of species richness and composition have been relatively well studied. Less is known about how the entire set of trophic and non-trophic ecological interactions, and the complex networks that they create by gluing species together in complex communities, change across geographical extents. Here, we compiled data of species composition and three types of ecological interactions occurring between species in rocky intertidal communities across a large spatial extent (~970 km of shoreline) of central Chile, and analyzed the geographical variability in these multiplex networks (i.e., comprising several interaction types) of ecological interactions. We calculated nine network summary statistics common across interaction types, and additional network attributes specific to each of the different types of interactions. We then investigated potential environmental drivers of this multivariate network organization. These included variation in sea surface temperature and coastal upwelling, the main drivers of productivity in nearshore waters. Our results suggest that structural properties of multiplex ecological networks are affected by local species richness and modulated by factors influencing productivity and environmental predictability. Our results show that non-trophic negative interactions are more sensitive to spatially structured temporal environmental variation than feeding relationships, with non-trophic positive interactions being the least labile to it. We also show that environmental effects are partly mediated through changes in species richness and partly through direct influences on species interactions, probably associated to changes in environmental predictability and to bottom-up nutrient availability. Our findings highlight the need for a comprehensive picture of ecological interactions and their geographical variability if we are to predict potential effects of environmental changes on ecological communities.

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