A SPECIES DISTRIBUTION MODEL OF THE ANTARCTIC MINKE WHALE (BALAENOPTERA BONAERENSIS)

The Antarctic minke whale (Balaenoptera bonaerensis) is regarded a Southern Hemisphere endemic found throughout the Southern Hemisphere, generally south of 60°S in austral summer. Here they have been routinely observed in highest densities adjacent to and inside the sea ice edge, and where they feed predominantly on krill. Detecting abundance trends regarding this species by employing visual monitoring is problematic. Partly this is because the whales are frequently sighted within sea ice where navigational safety concerns prevent ships from surveying. In this respect species-habitat models are increasingly recognized as valuable tools to predict the probability of cetacean presence, relative abundance or density throughout an area of interest and to gain insight into the ecological processes affecting these patterns. The objective of this study was to provide this background information for the above research needs and in a broader context use species distribution models (SDMs) to establish a current habitat suitability description for the species and to identify the main environmental covariates related to its distribution. We used filtered 464 occurrences to generate the SDMs. We selected eight predictor variables with reduced collinearity for constructing the models: mean annuals of the surface temperature (ºC), salinity (PSS), current velocity (m/s), sea ice concentration (fraction, %), chlorophyll-a concentration (mg/m³), primary productivity (g/m3/day), cloud cover (%), and bathymetry (m). Six modeling algorithms were test and the Bayesian additive regression trees (BART) model demonstrated the best preformance. Based on variable importance, those that best explained the environmental requirements of the species, were: sea ice concentration, chlorophyll-a concentration and topography of the sea floor (bathymetry), explaining in sum around 62% of the variance. Using the BART model, habitat preferences have been interpreted from patterns in partial dependence plots. Areas where the AMW have particularly high likelihood of occurrence are East Antarctica, NE of the Weddell Sea, areas around the northern tip of the Antarctica Peninsula, areas bordering the Scotia–Weddell Confluence. Given the association of AMWs with sea ice the pagophilic character of their biology makes them particularly vulnerable to climate change and a perfect biological indicator for tracking these changes.

A SPECIES DISTRIBUTION MODEL OF THE ANTARCTIC MINKE WHALE (BALAENOPTERA BONAERENSIS)

The Antarctic minke whale (Balaenoptera bonaerensis) is regarded a Southern Hemisphere endemic found throughout the Southern Hemisphere, generally south of 60 degrees S in austral summer. Here they have been routinely observed in highest densities adjacent to and inside the sea ice edge, and where they feed predominantly on krill. Detecting abundance trends regarding this species by employing visual monitoring is problematic. Partly this is because the whales are frequently sighted within sea ice where navigational safety concerns prevent ships from surveying. In this respect species-habitat models are increasingly recognized as valuable tools to predict the probability of cetacean presence, relative abundance or density throughout an area of interest and to gain insight into the ecological processes affecting these patterns. The objective of this study was to provide this background information for the above research needs and in a broader context use species distribution models (SDMs) to establish a current habitat suitability description for the species and to identify the main environmental covariates related to its distribution. We used filtered 464 occurrences to generate the SDMs. We selected eight predictor variables with reduced collinearity for constructing the models: mean annuals of the surface temperature (degrees C), salinity (PSS), current velocity (m/s), sea ice concentration (fraction, %), chlorophyll-a concentration (mg/cub. m), primary productivity (g/cub.m/day), cloud cover (%), and bathymetry (m). Six modeling algorithms were test and the Bayesian additive regression trees (BART) model demonstrated the best preformance. Based on variable importance, those that best explained the environmental requirements of the species, were: sea ice concentration, chlorophyll-a concentration and topography of the sea floor (bathymetry), explaining in sum around 62% of the variance. Using the BART model, habitat preferences have been interpreted from patterns in partial dependence plots. Areas where the AMW have particularly high likelihood of occurrence are East Antarctica, NE of the Weddell Sea, areas around the northern tip of the Antarctica Peninsula, areas bordering the Scotia-Weddell Confluence. Given the association of AMWs with sea ice, the pagophilic character of their biology makes them particularly vulnerable to climate change and a perfect biological indicator for tracking these changes.

Determinação da Concentração de Clorofila-a por Sensoriamento Remoto no Reservatório de Chapéu d’Úvas (Mg), Brasil

Na atualidade, um grande impacto nos reservatórios de água doce é a eutrofização, que afeta diretamente o tratamento e uso da água para abastecimento público, navegação, fauna e flora aquática e impacto visual. A clorofila-a é um dos indicadores de estado trófico da água e pode ser determinada utilizando sensoriamento remoto. Desta forma, este estudo objetivou determinar a concentração de clorofila-a na barragem de Chapéu d’Uvas em Juiz de Fora (Brasil), um dos principais mananciais de abastecimento público da cidade. Através de um modelo utilizando imagens do satélite Sentinel-2 foi avaliado o comportamento espaço-temporal da concentração do componente, foi correlacionado com dados de pluviosidade, temperatura, evaporação e uso e ocupação do solo em torno da barragem. Também foi aplicado um modelo para determinar o índice de estado trófico, que apresenta o grau de trofia que o corpo hídrico se encontra, classificando Chapéu d’Uvas como estado mesotrófico. Os resultados se mostraram satisfatórios para a espacialização e análise temporal da concentração de clorofila-a, a correlação com os dados de evaporação nos permitiu observar uma compatibilidade direta com a concentração do componente e verificou-se valores de clorofila-a acima da média do reservatório nas regiões de entradas de água pelos riachos e próximos as margens com menos cobertura de vegetação.Determination of Chlorophyll-a Concentration from Remote Sensing in Chapéu d’Úvas Reservoir (State of Minas Gerais, Brazil) A B S T R A C TCurrently, one of the great impacts on the freshwater reservoirs is eutrophication, which directly affects the treatment and use of water for public water supply, navigation, aquatic fauna and flora and visual impact. Chlorophyll-a is one of the water trophic state indicators and it can be determined using remote sensing. Thus, this study aimed to determine chlorophyll-a concentration in Chapéu d’Uvas dam, in Juiz de Fora (Brazil), one of the main water sources of public water supply for the city. From a model that uses Sentinel-2 satellite images, the spatial-temporal behavior of that component concentration was evaluated and correlated with data regarding rainfall, temperature, evaporation, and soil use and occupation around the dam. A model was also applied to determine trophic state index, which presents the body of water trophic state, classifying Chapéu d’Uvas as mesotrophic state. The results were satisfactory regarding spatialization and temporal analysis of chlorophyll-a concentration. The correlation with evaporation data permitted us to observe a direct correspondence with the component concentration. Chlorophyll-a values higher than the reservoir average were found in creek inlets and near the shore with lower vegetal cover.Keywords: eutrophication, Sentinel-2, quality of water, inland waters, trophic state

Features of the Distribution of Chlorophyll-a Concentration along the Western Coast of the Novaya Zemlya Archipelago in Spring

In spring 2016, the thermohaline characteristics of water masses and the distribution of chlorophyll-a concentration in the pelagic zone of the eastern part of the Barents Sea were studied. For the first time, in the conditions of an abnormally warm year and the absence of ice cover, a complex of hydrobiological works was carried out on a section crossing the Barents Sea from south to north along the western coast of the Novaya Zemlya archipelago. High concentrations of chlorophyll-a > 1 ˂ 6 mg/m3 at all stations of the transect indicate a stage of spring bloom in the successional cycle of microalgae. Significant differences in the content of chlorophyll-a in waters of various origins were revealed. The highest concentrations of chlorophyll-a corresponded to Arctic surface water (5.56 mg/m3). Slightly lower values were observed in the transformed Atlantic waters of the Novozemelskoe and Kolguevo–Pechorskoe currents (3.53 ± 0.97–3.71 ± 1.04 mg/m3), and the lowest was in the Barents waters (1.24 ± 0.84–1.45 ± 1.13 mg/m3).

Modeling the Habitat Distribution of Acanthopagrus schlegelii in the Coastal Waters of the Eastern Taiwan Strait Using MAXENT with Fishery and Remote Sensing Data

Black sea bream, Acanthopagrus schlegelii, is among the most commercially valuable species in the coastal fishery industry and marine ecosystems. Catch data comprising capture locations for the gillnet fisheries, remotely sensed environmental data (i.e., sea surface temperature, chlorophyll-a concentration, and current velocity), and topography (bathymetry) from 2015 to 2018 were used to construct a spatial habitat distribution of black sea bream. This species is concentrated in coastal waters (<3 nm) from December to April (spawning season). The maximum entropy (MaxEnt) method and corresponding habitat suitability index among seasons were used to clarify the species’ spatial distribution and identify the seasonal variations in habitat selection. The patterns corresponded closely to the changes in oceanographic conditions, and the species exhibited synchronous trends with the marine environment’s seasonal dynamics. Chlorophyll-a concentration and bathymetry substantially influenced (80.1–92.9%) black sea bream’s habitat selection. By applying the MaxEnt model, the optimal habitats were identified with four variables including depth and satellite-derived temperature, current velocity and chlorophyll-a concentration, which provides a foundation for the scientific assessment and management of black sea bream in coastal waters of the Eastern Taiwan Strait.

Impact Assessment of PSBB on Variations of NO2, CO, and Chlorophyll-a Concentration in Java Island during the COVID-19 Pandemic based on Multi-satellite Imagery Data

The enactment of the PSBB (Large-scale Social Restriction) on April to May 2020 due to COVID-19 pandemic, has reduced residents’ activities, especially in Java Island, the most populous area in Indonesia with high anthropological activities. This policy may affect the conditions of atmospheric and water environment around the island of Java. NO₂ and CO concentrations in atmosphere along with chlorophyll-a concentrationin the ocean are environmental indicators that can be observed through satellite imagery. Satellite data TROPOMI and CMEMS were used to investigate the variation of those concentrations in the atmosphere and ocean during PSBB. Oceanographic and atmospheric daily data of each parameter over the Java Island region were retrieved and merged into gridded data with 1 km spatial resolution to be compared. Variations of chlorophyll-a concentration within the same month over the previous years were also taken into account. NO₂, CO, and chlorophyll-a concentration were fluctuating during the PSBB period, but tended to decrease in the early phase of PSBB.The higher concentration of chlorophyll-a concentrated in the northern coastal areas of Java Island. In Jakarta and Surabaya, chlorophyll-a concentrations value during April-May 2020 were decreased by 48% and 27% compared to the previous three years’ averages. Human activities were thought to be the main factor influencing variations in the concentration of NO₂, CO, and chlorophyll-a during PSBB enactment

The Role of Environmental Drivers in Humpback Whale Distribution, Movement and Behavior: A Review

Humpback whales, Megaptera novaeangliae, are a highly migratory species exposed to a wide range of environmental factors during their lifetime. The spatial and temporal characteristics of such factors play a significant role in determining suitable habitats for breeding, feeding and resting. The existing studies of the relationship between oceanic conditions and humpback whale ecology provide the basis for understanding impacts on this species. Here we have determined the most relevant environmental drivers identified in peer-reviewed literature published over the last four decades, and assessed the methods used to identify relationships. A total of 148 studies were extracted through an online literature search. These studies used a combined estimated 105,000 humpback whale observations over 1,216 accumulated study years investigating the relationship between humpback whales and environmental drivers in both Northern and Southern Hemispheres. Studies focusing on humpback whales in feeding areas found preferences for areas of upwelling, high chlorophyll-a concentration and frontal areas with changes in temperature, depth and currents, where prey can be found in high concentration. Preferred calving grounds were identified as shallow, warm and with slow water movement to aid the survival of calves. The few studies of migration routes have found preferences for shallow waters close to shorelines with moderate temperature and chlorophyll-a concentration. Extracting information and understanding the influence of key drivers of humpback whale behavioral modes are important for conservation, particularly in regard to expected changes of environmental conditions under climate change.