Temporal Data Series Research Articles

Broken expectations: Population decline of a key grouper species within a 30-year-old no-take MPA in the Southwestern Atlantic

Understanding the impacts of human actions on marine species and developing mitigation strategies are crucial conservation challenges. Many fish species, especially marine top predators, are already highly depleted and rely on population monitoring and conservation strategies for their maintenance. In this context, Marine Protected Areas can help recover target species and provide crucial information about population trends when human activities are restricted or managed. Here, we used the longest and most complete temporal series of reef fish data in the Southwestern Atlantic, comprising 16 years of underwater visual census sampling. We evaluated temporal trends in the density and biomass of the dusky grouper (Epinephelus marginatus), both inside and outside a no-take and no-entry Marine Reserve. Our findings reveal a consistent decline in density and biomass inside and outside the MPA over time, reaching alarmingly low densities and experiencing a steeper biomass decline in the last five years. Our results contradict global expectations of positive population recovery trends within MPAs. We highlight the adverse impacts of human activities on the dusky grouper population, including illegal fishing and habitat degradation. Additionally, we address the limited understanding of species' life stages, underscoring that their occurrence outside protected boundaries may render the entire population vulnerable to human pressures. Thus, to guarantee population recovery, stakeholders and researchers must take immediate action to increase knowledge of all life stages and improve ongoing strategies.

Using index and physically-based models to evaluate the intrinsic groundwater vulnerability to non-point source pollutants in an agricultural area in Sardinia (Italy)

This research aims at studying the intrinsic vulnerability of groundwater to diffuse environmental pollutants in the Muravera coastal agricultural area of Sardinia, Italy. The area faces contamination risks arising from agricultural practices, especially the use of fertilizers, pesticides, and various chemicals that can seep into the groundwater. The study examined the interplay among hydrological elements, including soil characteristics, groundwater depth, climate conditions, land use, and aquifer properties. To do that, the outcomes of FLOWS 1D physically-based agrohydrological model were analyzed in parallel with those of the overlay-and-index model SINTACS, in a sort of reciprocal benchmarking. By using FLOWS, water movement and solute transport in the unsaturated zone were simulated by, respectively, solving the Richard Equation (RE) and the Advection-Dispersion equation (ADE). As such, this model allowed to account for the role of soil hydraulic and hydro-dispersive properties variability in determining the travel times of a conservative solute through the soil profile to the groundwater. For FLOWS simulations, a complete dataset was used as input, including soil horizons, soil physical and hydraulic properties of 36 soil profiles, average annual depth to groundwater table at each soil profile (ranging from 1 to 50 meters), and climatic temporal series data on rainfall and evapotranspiration. Detailed analyses of travel times for the movement of 25, 50, 75, and 100% of the solute mass to reach groundwater were conducted, revealing that the depth to groundwater predominantly influences vulnerability. This result was coherent with SINTACS vulnerability map due to the large impact of the depth to groundwater on SINTACS analysis.

Benthic foraminiferal patchiness – revisited

Abstract. Many benthic organisms show aggregated distribution patterns due to the spatial heterogeneity of niches or food availability. In particular, high-abundance patches of benthic foraminifera have been reported that extend from centimetres to metres in diameter in salt marshes or shallow waters. The dimensions of spatial variations of shelf or deep-sea foraminiferal abundances have not yet been identified. Therefore, we studied the distribution of Globobulimina turgida dwelling in the 0–3 cm surface sediment at 118 m water depth in the Alsbäck Deep, Gullmar Fjord, Sweden. Standing stock data from 58 randomly replicated samples depicted a log-normal distribution of G. turgida with weak evidence for an aggregated distribution on a decimetre scale. A model simulation with different patch sizes, outlines, and impedances yielded no significant correlation with the observed variability of G. turgida standing stocks. Instead, a perfect match with a random log-normal distribution of population densities was obtained. The data–model comparison revealed that foraminiferal populations in the Gullmar Fjord were not moulded by any underlying spatial structure beyond 10 cm diameter. Log-normal population densities also characterise data from contiguous, gridded, or random sample replicates reported in the literature. Here, a centimetre-scale heterogeneity was found and interpreted to be a result of asexual reproduction events and restricted mobility of juveniles. Standing stocks of G. turgida from the Alsbäck Deep temporal data series from 1994 to 2021 showed two distinct cohorts of samples of either high or low densities. These cohorts are considered to represent two distinct ecological settings: hypoxic and well-ventilated conditions in the Gullmar Fjord. Environmental forcing is therefore considered to impact the population structure of benthic foraminifera rather than their reproduction dynamics.

Calcium phosphate nanoparticles improve growth parameters and mitigate stress associated with climatic variability in avocado fruit

The avocado cv. Hass is one of the most dynamic fruits in the world and is of particular significance in tropical areas, where climate variability phenomena have a high impact on productivity and sustainability. Nanotechnology-based tools could be an alternative to mitigate and/or adapt plants to these phenomena. Our approach was based on identifying changes in temperature and precipitation associated with climate variability in avocado areas in Colombia and proposing mitigation strategies based on the use of nanotechnology. This study had two objectives: (i) to identify variations in temperature and precipitation in avocado-producing areas in Colombia and (ii) to evaluate the effect of calcium phosphate nanoparticles (nano CP) as an alternative to reduce stress in avocados under simulate climatic variability condition. Climatic clusters were determined based on the spatial K-means method and with the climatic temporal series data (1981–2020), a time series analysis we carried out. Later changes in each cluster were simulated in growth chambers, evaluating physiological and developmental responses in avocado seedlings subjected to nanoCaP after adjusting the application form and dose. XRD diffraction shows that the calcium phosphate phases obtained by solution combustion correspond to a mixture of hydroxyapatite and witocklite nanoparticles with irregular morphologies and particle sizes of 100 nm. Three clusters explained ∼90% of the climate variation, with increases and decreases in temperature and precipitation in the range of 1-1.4 °C and 4.1–7.3% respectively. The best-fitted time series models were of stationary autoregressive integrated moving averages (SARIMA). The avocado seedlings had differential responses (P<0.05) depending on the clusters, with a decrease in physiological behavior and development between 10 and 35%. Additionally, the nanoCaP reduced the climatic stress (P< 0.05) in a range between 10 and 22.5%. This study identified the negative effect of climate variability on avocado seedlings and how nanoCaP can mitigate these phenomena.

The Hydrological Cycle of the Lower Amazon in Brazil Determines the Variation in Local Fishing Patterns

Fishery catches on the Lower Amazon River were analyzed in relation to the hydrological cycle, temporal shifts in fishing effort, and the use of nets or lines in lakes and rivers. The study was based on a temporal series of catch data collected between January 1993 and June 2011. The variables analyzed were the total catch, use of gillnets or lines, the environment targeted (lakes or rivers), and fishing effort. Temporal trends in the data series were analyzed using the Mann–Kendall test. An Analysis of Covariance (ANCOVA) was used to evaluate whether gillnet and line catches had independent effects on the catch data. Duncan’s test was applied to identify the groups (months) of means that were significantly different from each other. The majority of the catch landings were taken with gillnets (98.65%) in a lake environment (64.98%). The temporal series showed a significant decline over time in the gillnet catches from both lakes and rivers, as well as in fishing effort. The influence of the annual flood cycle was reflected in the catches and the flood pulse regulated fishing productivity patterns in the region. The fishers who adapt their activities to this flood pulse have good traditional knowledge of the environment. The results of the study also indicated that any changes in this dynamic system may impact traditional local fisheries and affect the economic wellbeing of local fisher populations.

A Novel BILSTM-CNN Method for Pattern Recognition in Real Time Under Triple Physical Loads in Lower Extremity Exoskeleton

Pattern recognition is of great importance in compliant control of lower extremity exoskeleton. We propose a novel method based on the BILSTM-CNN model for pattern recognition in real time under triple physical loads on different terrains. BILSTM is skilled in dealing with temporal series data while CNN is proficient in coping with spatial series data. Five patterns include level ground walking (LW), stair ascending (SA), stair descending (SD), ramp ascending (RA), and ramp descending (RD). Their accuracies in multigrade loads of 0kg, 20kg, and 40kg reach 98.81%, 98.24%, and 97.04% respectively. Moreover, compared with the universal methods of LSTM, CNN, and BP, the hybrid method has competitive advantage in evaluation indexes of accuracy, precision, recall, and F1 score. In addition to five steady patterns, eight pattern transitions are identified between two neighboring states, such as LW to SA, LW to SD, LW to RA, LW to RD, SA to LW, SD to LW, RA to LW, and RD to LW. For pattern transitions, prediction time (Pre-T) of next pattern in multilevel loads of 0kg, 20kg, and 40kg are 190-620 ms, 180-420 ms, and 50-90 ms respectively prior to the step into that pattern. Pattern transition time (Aug-In) in multilevel loads of 0kg, 20kg, and 40kg are 50-190 ms, 30-310 ms, and 0-280 ms respectively before Pre-T of next pattern. Eventually, the experimental results indicate the proposed method has excellent performance on pattern recognition and pattern transition.

Evaluation of GLDAS soil moisture product over Kermanshah province, Iran

Abstract Land surface modelling and data assimilation are advanced techniques for generating optimal fields of land surface states and fluxes. In this study, the Global Land Data Assimilation System (GLDAS) data were utilized to investigate the soil moisture variations and droughts in Kermanshah province, northwest Iran. The GLDAS soil moisture data were employed in various depths and compared with observed monthly soil moisture. The monthly and annual moisture data were processed in the Geographic Information System (GIS) environment. To compute the Standardized Precipitation Index, SPI, precipitation data from 2000 to 2014 were used, and the relationship between drought and soil moisture variation was studied. The moisture data from GLDAS had a significant correlation with the most severe wet and dry seasons. The minimum and maximum values of the SPI were determined as −2.077 and 0.931 in 2004 and 2009, respectively, which corresponded to the highest and lowest normalized soil moisture of −1.93 and 1.41. The results showed that GLDAS data can be used to reconstruct spatial and temporal moisture data series.

Urban expansion of Korhogo City (Côte d’Ivoire) using gis and nocturnal remote sensing

Temporal data series of stable Artificial Lights At Night (ALAN) obtained from sources such as DMSP/OLS and VIIRS/DNB provide valuable insights into the dynamics of urban expansion. This study introduces a novel methodology for characterizing urban boundaries, which combines textural analysis utilizing the Co-occurrence matrix and urban surface delineation employing the Wombling contour detection algorithm. Applying this method to the city of Korhogo in northern Côte d'Ivoire, the findings reveal an irregular and gradual evolution of urban surfaces between 1992 and 2012, with a rate of change of 35 km2. However, starting from 2012, a rapid urbanization process is observed, continuing until 2020, characterized by an evolution rate of approximately 45 km2. Considering the significant urban expansion witnessed in the city of Korhogo, it is imperative to implement appropriate urban management strategies and measures for ecosystem protection.

A proper orthogonal decomposition analysis upon aerodynamic structures under clean and rough conditions

A transitional flow regime is known to induce complex flow structures upon aerodynamic geometries such as airfoils, and the dynamics of laminar separation bubbles (LSBs) pose a relevant research field. In addition to being affected by the Reynolds value itself, LSBs are shown to be equally sensitive to the surface roughness of the airfoils. The study analyses wind tunnel-derived surface-pressure distribution datasets obtained for a particular airfoil of the standard family developed by the National Advisory Committee for Aeronautics (NACA), namely, the NACA0021, subjected to the range 0.8×104&amp;lt;Re&amp;lt;1.6×104 at different angles of attack under two flow configurations that correspond to a clean and roughened surface. The analysis is undertaken via the proper orthogonal decomposition (POD) technique. The results show that the decomposition of the temporal series of surface-pressure data and the processing of the most energetic POD modes recovers the position of the LSBs, properly capturing the closure point of the separation bubbles and, hence, the turbulence transition location. Some of the most energetic POD modes observed are closely related, in terms of shape, to the POD modes present at the reattachment point on a 5:1 rectangular cylinder. This could indicate there is a recognizable pattern in coherent structures of pressure fluctuations when it comes to a reattached flow. Therefore, a principal component analysis such as the POD presented in this study can be used to determine the reattachment position of the flow or the transition point in the presence of a LSB.

Multifractal Characteristics on Temporal Maximum of Air Pollution Series

Presenting and describing a temporal series of air pollution data with longer time lengths provides more concise information and is, in fact, one of the simplest techniques of data reduction in a time series. However, this process can result in the loss of important information related to data features. Thus, the purpose of this study is to determine the type of data characteristics that might be lost when describing data with different time lengths corresponding to a process of data reduction. In parallel, this study proposes the application of a multifractal technique to investigate the properties on an air pollution series with different time lengths. A case study has been carried out using an air pollution index data in Klang, Malaysia. Results show that hourly air pollution series contain the most informative knowledge regarding the behaviors and characteristics of air pollution, particularly in terms of the strength of multifractality, long-term persistent correlations, and heterogeneity of variations. On the other hand, the statistical findings found that data reduction corresponding to a longer time length will change the multifractal properties of the original data.

Satellite-Based Monitoring of Primary Production in a Mediterranean Islet Post Black Rat Eradication

Invasive rodents have a detrimental impact on terrestrial ecosystem functioning, this is often exacerbated on small islands. Rat eradication campaigns are often used to deal with this environmental perturbation given their classification as invasive species. Studies assessing the effects of rodent control at ecosystem scale are scarce and thus little is known about the subsequent functional response of vegetation subsequent to rat control. In this work, we use remote sensing to assess the effects of black rat (Rattus rattus) eradication on Mediterranean vegetation productivity in the Sa Dragonera Islet, Mallorca (Spain). Rats feed on seeds, sprouts, and leaves of woody vegetation and hence we expect primary production to increase nine years after the rodenticide campaign. The Break Detection approach for additive season and trend (BFAST method) was adopted to examine changes in vegetation density before and after the eradication campaign in Sa Dragonera Islet (Balearic Islands), using a temporal series of monthly NDVI data extracted from Landsat imagery. The same temporal trends were examined for a control zone where no rat eradication took place, in order to control for weather-driven changes. The results of this study revealed changes across the 21-year monthly NDVI time series. However, the dates, magnitude, and trend of these changes could not be explicitly attributed to the action of rats, when compared to the historical changes on the islet and the changes found to co-occur within the control zone. These finding could, perhaps, be explained by the high resilience of Mediterranean shrubs to browsing including that of rat invasion. However, the results from the study appear to show that rat damage on specific plant species, with little contribution to global NDVI values, would be overshadowed by the effects of broader environmental factors in this remote sensing approach. The results suggest that the current passive restoration scheme imposed following eradication is not sufficient for effective ecosystem restoration.

Strong El Niño reduces fruit production of Brazil-nut trees in the eastern Amazon

ABSTRACT The Brazil-nut tree (Bertholletia excelsa) is native to the Amazon rainforest, and its fruit production varies naturally with climatic conditions. Our aim was to evaluate the temporal variation in Brazil-nut production associated with climatic variables, including the strong El Niño of 2015/2016. The study was carried out in two 9-ha permanent plots in the northeastern Brazilian Amazon from 2007 to 2018: one in forest (12-year monitoring) and the other in savannah/forest transition (eight years). Overall, we monitored fruit production of 205 trees with diameter at breast height ≥ 50 cm. Annual fruit production was related to temporal series (2005-2018) of climatic data (the Oceanic Niño Index; and precipitation and air temperature from two local meteorological stations). Average fruit production per tree in 2017 was eight times lower than in 2015 and two times lower than the general average for both sites, and was significantly associated to the El Niño of 2015/2016, that increased average maximum monthly temperature and reduced the precipitation in the region, extending the dry season from three to six months. Years with higher and lower fruit production per tree coincided in both sites. Annual fruit production was significantly and negatively correlated with thermal anomalies that occurred in the third semester prior to harvest monitoring. Years with higher production were related with predominance of neutrality or the La Niña phenomenon at the global scale, and higher rainfall at the local scale. The relationship of fruit production with climate was independent of the local habitat.

Bridging cell-scale simulations and radiologic images to explain short-time intratumoral oxygen fluctuations.

Radiologic images provide a way to monitor tumor development and its response to therapies in a longitudinal and minimally invasive fashion. However, they operate on a macroscopic scale (average value per voxel) and are not able to capture microscopic scale (cell-level) phenomena. Nevertheless, to examine the causes of frequent fast fluctuations in tissue oxygenation, models simulating individual cells' behavior are needed. Here, we provide a link between the average data values recorded for radiologic images and the cellular and vascular architecture of the corresponding tissues. Using hybrid agent-based modeling, we generate a set of tissue morphologies capable of reproducing oxygenation levels observed in radiologic images. We then use these in silico tissues to investigate whether oxygen fluctuations can be explained by changes in vascular oxygen supply or by modulations in cellular oxygen absorption. Our studies show that intravascular changes in oxygen supply reproduce the observed fluctuations in tissue oxygenation in all considered regions of interest. However, larger-magnitude fluctuations cannot be recreated by modifications in cellular absorption of oxygen in a biologically feasible manner. Additionally, we develop a procedure to identify plausible tissue morphologies for a given temporal series of average data from radiology images. In future applications, this approach can be used to generate a set of tissues comparable with radiology images and to simulate tumor responses to various anti-cancer treatments at the tissue-scale level.

Data-driven transition matrix estimation in probabilistic learning models for autonomous driving

A novel approach is presented for learning probabilistic transition matrices from temporal data series as switching models based on Generalized States (GS). An observed data sequence is analyzed by a reference filter whose errors are clustered. Each cluster is associated with a dynamic flow model, which described as a parametric linear attractor. The set of linear attractors define the Hierarchical Generalized Dynamic Bayesian Network (H-GDBN), which encodes a learned model of the obtained sequence. A Markov Jump Particle Filter (MJPF) uses H-GDBN’s probabilistic information to make inferences at a multilevel scale and facilitates the detection of abnormalities. This paper shows how transition matrices can be obtained as an integral part of the clustering step by employing the advantages of GSs, enabling a unique optimal criterion for learning flow models at discrete and continuous levels of H-GDBN. For evaluating the proposed method, odometry and proprioceptive control data from an autonomous vehicle are employed to learn H-GDBNs. Learned H-GDBN are used to detect abnormalities when vehicle encounter any abnormal situation. Performance evaluation based on ROC curves is provided to select the optimal transition matrix.

Determinantes del capital de trabajo y ciclo de conversión de efectivo en empresas chilenas

El propósito de la presente investigación fue identificar y medir el grado de rela-ción de los principales determinantes del capital de trabajo y del ciclo de conversión de efectivo de un grupo significativo de grandes empresas Chile. La investigación es de tipo descriptiva y correlacional, con series de datos temporales, en el período 2010-2019. La muestra está compuesta por 82 empresas de distintos sectores económicos, excluyendo instituciones financieras y la información fue obtenida de la base de datos Economática. Se utilizan modelos de regresión con datos de panel, con variables cuantitativas tradicionales, como el tamaño de la empresa y la tangibilidad de los activos y variables cualitativas, tales como las etapas del ciclo de vida de la empresa. Los resultados permitieron conocer los determinantes del Capital de trabajo y del Ciclo de conversión de efectivo. Además, analizamos otras variables financieras de la empresa, tales como: ventas, márgenes de utilidad, deuda y valor libro de acción, entre otras. Los resultados obtenidos se alinean con la teoría financiera, tales como la relación directa entre las necesidades de capital de trabajo con el tamaño de las ventas y el ciclo de conversión del efectivo, junto a otros resultados, como es la relación inversa de la tangibilidad de los activos con el capital de trabajo neto. Además de las relaciones significativas encontradas, el principal aporte de este estudio consiste en la incorporación de variables cualitativas del ciclo de vida de la empresa. Estas entregan resultados disímiles en los modelos del capital de trabajo neto y el ciclo de conversión del efectivo, lo que invita a revisar la metodología de obtención de las etapas del ciclo de vida de las empresas (Introducción, Crecimiento, Madurez, Declive, Reestructuración y Liquidación)

Métodos de ajuste de Weibull de dos parámetros en series de viento y estimación del Potencial Eolo-eléctrico.

El presente estudio tiene el objetivo de comparar cuatro métodos de interpolación de la función de densidad de probabilidad con distribución de Weibull. Este proceso es fundamental para el análisis del recurso eólico a través de la estimación del potencial Eolo-eléctrico. La información del trabajo corresponde a series temporales de viento de 7 estaciones meteorológicas situadas en las Provincias de Galápagos, Carchi, Tungurahua, Bolívar y Loja. Las series fueron procesadas a través de los métodos de linealización, empírico, momentos y estimador por máxima verosimilitud (MLE). La comparación se basó en los gráficos de interpolación con las frecuencias relativas de las series de viento; y el análisis de los residuos obtenidos de la regresión. Además, se realizó el contraste de: la media de viento, el error cuadrático medio (ECM) y la correlación, entre los métodos en estudio. Este análisis es la base para el calculó del potencial Eolo-eléctrico propuesto por Jijón et al. [1], basado en las medias de viento de cada método. En este trabajo, se pudo observar que el método de MLE fue el que menor ECM y correlación más alta presentó en la mayoría de las bases analizadas; y el método de linealización fue el que tuvo la media de viento más baja. En consecuencia, el potencial Eolo-eléctrico demostró la alta sensibilidad basado en las diferencias en las medias de velocidad de viento, estimadas por cada método. Por las limitaciones identificadas en cada método analizado, se recomienda que este tipo de análisis sea replicable para el estudio de emplazamientos eólicos.

Temporal Series Crop Classification Study in Rural China Based on Sentinel-1 SAR Data

Crop classification is one of the focused topics in remote sensing study nowadays. Optical imageries, although providing much information, are often contaminated with cloud or other weather effects while SAR imageries are more resilient to those. Temporal series data are often used to improve classification accuracy, especially in crop classification. This article investigates the usage of temporal series SAR imagery on crop classification in vast rural areas of China. The selected area of interest has a complicated, heavily mixed agriculture as well as lots of nonagricultural landcovers. Total ten classes are considered, six of them being crop types. A pixel-based classifier using subspace k th nearest neighbor (KNN) algorithm is applied to open source Sentinel-1 polarized SAR data. Discussion includes analyzing temporal features of the SAR observation, time domain smoothing, feature engineering, different classification algorithms, and selection of temporal series. The study results in an overall accuracy of 98.2% for the ten classes in fivefold cross validation, indicating a propitious application for agricultural monitoring using SAR data.

Spatial Downscaling of MODIS Land Surface Temperature Based on a Geographically and Temporally Weighted Autoregressive Model

Land surface temperature (LST) is a key parameter in numerous environmental studies. However, currently, there is no satellite sensor that can completely provide LST data with both high spatial and high temporal resolutions simultaneously. LST downscaling is regarded as an effective remedy for improving the temporal and spatial resolutions of LST data. In this article, a geographically and temporally weighted autoregressive (GTWAR) model of LST downscaling is that comprehensively considers the spatial heterogeneity, spatial autoregression, and temporality of LST is newly proposed. The normalized difference water index, the normalized difference built-up index, and the normalized difference vegetation index were selected as explanatory variables to downscale the moderate resolution imaging spectroradiometer (MODIS) LST from 1000 to 100 m, while the Landsat 8 LST was selected as the reference data. Compared with the thermal data sharpening (TsHARP), the geographically weighted regression (GWR), the geographically weighted autoregressive (GWAR) and the geographically and temporally weighted regression (GTWR) downscaling methods, the proposed method was superior based on quantitative indices, with the lowest root mean square error (Zhangye: 1.57 °C, Beijing: 1.22 °C) and mean absolute error (Zhangye: 1.06 °C, Beijing: 0.85 °C). The downscaling model of GTWAR will facilitate improvements in the accuracy of downscaling for temporal series of LST data.

Research Article Temporal data series and logistic models reveal the dynamics of SARS-CoV-2 spike protein D614G variant in the COVID-19 pandemic

The COVID-19 pandemic started in Wuhan, China, and caused the worldwide spread of the RNA virus SARS-CoV-2, the causative agent of COVID-19. Because of its mutational rate, wide geographical distribution, and host response variance this coronavirus is currently evolving into an array of strains with increasing genetic diversity. Most variants apparently have neutral effects for disease spread and symptoms severity. However, in the viral Spike protein, which is responsible for host cell attachment and invasion, an emergent variant, containing the amino acid substitution D to G in position 614 (D614G), was suggested to increase viral infection capability. To test whether this variant has epidemiological impact, the temporal distributions of the SARS-CoV-2 samples bearing D or G at position 614 were compared in the USA, Asia and Europe. The epidemiological curves were compared at early and late epidemic stages. At early stages, where containment measures were still not fully implemented, the viral variants are supposed to be unconstrained and its growth curves might approximate the free viral dynamics. Our analysis shows that the D614G prevalence and the growth rates of COVID-19 epidemic curves are correlated in the USA, Asia and Europe. Our results suggest a selective sweep that can be explained, at least in part, by a propagation advantage of this variant, in other words, that the molecular level effects of D614G have sufficient impact on population transmission dynamics as to be detected by differences in rate coefficients of epidemic growth curves.

Respostas das geleiras do Monte Melimoyu, Chile, às tendências mais secas e quentes da Patagônia Norte nas últimas décadas

Neste trabalho foram investigadas variações de área nas geleiras no Monte Melimoyu (44ºS, 72ºO) no período de 1970-2017, e as possíveis relações com a variabilidade climática entre 1950 e 2017. Dados ópticos do Sentinel-2A, de elevação do Modelo Digital de Elevação (MDE) ASTER-GDEM v.2, e de área das geleiras do inventário GLIMS foram utilizados na análise em SIG. As séries temporais de precipitação do Explorador Climático - Chile, e dados de reanálise de temperatura da universidade de Delaware foram utilizados para a análise climática. A área das geleiras diminuiu de 80,97 km² em 1970 para 52,14 km² em 2017, representando 35,6% de área, com destaques para a retração de geleiras no Oeste, Sudoeste, Norte e Nordeste, com perdas entre 65% e 44% de área total, e variações de elevação da linha de frente entre 74 m e 570 m. A precipitação anual média aproximada foi de 2.359 mm no período de 1950 a 2017 e mostra tendência de diminuição de aproximadamente -18 mm/ano, enquanto que os dados de temperatura mostram média anual de 9,87°C e uma tendência contínua de aumento na região, de 0,04ºC entre 1948 e 2017. Os contrastes entre as geleiras em perda de área e variação de elevação da linha de frente estão relacionados às diferenças de área e geomorfometria de cada geleira. Essas geleiras são semelhantes em tamanho, altitude e declividade do setor frontal, e as variações identificadas podem estar relacionadas às tendências mais quentes e secas para o período analisado.