Combination Of Environmental Variables Research Articles

Patterns of the relationship between the Secchi disk depth and the optical characteristics of water in the Neva Estuary (Baltic Sea): the influence of environmental variables

The Secchi disc depth (Dsd) measurement is widely used to monitor eutrophication and the quality of the aquatic environment. This study aimed to investigate the relationship between Dsd and various factors, including the coefficient of attenuation of photosynthetically active radiation [Kd (PAR)], the depth of the euphotic zone (Deu), PAR at the Secchi disk depth (Esd) and the absorption coefficient of PAR (F) in the Neva Estuary, one of largest estuaries of the Baltic Sea. Environmental variables impacting these indices were identified using data collected from midsummer 2012 to 2020. The Dsd values in the estuary ranged from 0.3 to 4.0 m, with an average value of 1.8 m, while the Deu/Dsd ratio ranged from 1.5 to 6.0 with an average value of 2.8. These values were significantly lower than those observed in the open waters of the Baltic Sea. The highest Deu/Dsd ratio was observed in turbid waters characterized by high Kd(PAR) and low Dsd. Contrary to expectations, Dsd did not exhibit a significant relationship with the concentration of chlorophyll a, raising doubts about the utility of historical Dsd data for reconstructing phytoplankton development in the estuary. Principal component analysis did not identify the primary environmental variables strongly affecting the optical characteristics of water. However, recursive partitioning of the dataset using analysis of variance (CART approach) revealed that the concentration of suspended mineral matter (SMM) was the primary predictor of Deu/Dsd, Kd(PAR), and F. This SMM was associated with the frequent resuspension of bottom sediments during windy weather and construction activities in the estuary. Concentrations of suspended organic matter and the depth of the water area were found to be less significant as environmental variables. Furthermore, the CART approach demonstrated that different combinations of environmental variables in estuarine waters could result in similar optical indicator values. To reliably interpret the data and determine the optical characteristics of water in estuaries from Dsd, more complex models incorporating machine learning and neural connections are required. Additionally, reference determinations of Esd in various regions with specific sets of environmental variables would be valuable for comparative analyses and better understanding of estuarine systems.

A Novel Fusion-Based Methodology for Drought Forecasting

Accurate drought forecasting is necessary for effective agricultural and water resource management and for early risk warning. Various machine learning models have been developed for drought forecasting. This work developed and tested a fusion-based ensemble model, namely, the stacking (ST) model, that integrates extreme gradient boosting (XGBoost), random forecast (RF), and light gradient boosting machine (LightGBM) for drought forecasting. Additionally, the ST model employs the SHapley Additive exPlanations (SHAP) algorithm to interpret the relationship between variables and forecasting results. Multi-source data that encompass meteorological, vegetation, anthropogenic, landcover, climate teleconnection patterns, and topological characteristics were incorporated in the proposed ST model. The ST model forecasts the one-month lead standardized precipitation evapotranspiration index (SPEI) at a 12 month scale. The proposed ST model was applied and tested in the German federal states of Brandenburg and Berlin. The results show that the ST model outperformed the reference persistence model, XGBboost, RF, and LightGBM, achieving an average coefficient of determination (R2) value of 0.845 in each month in 2018. The spatiotemporal Moran’s I method indicates that the ST model captures non-stationarity in modeling the statistical association between predictors and the meteorological drought index and outperforms the other three models (i.e., XGBoost, RF, and LightGBM). Global sensitivity analysis indicates that the ST model is influenced by a combination of environmental variables, with the most sensitive being the preceding drought indices. The accuracy and versatility of the ST model indicate that this is a promising approach for forecasting drought and other environmental phenomena.

Simulating of human physiological supersystems: interactions of cardiovascular, thermoregulatory and respiratory systems

Special quantitative model of the human thermoregulatory system (MTS) functioning with cardiovascular and lung systems is created. These systems form a human physiological supersystem (HPSS). MTS describes thermoregulatory responses to alterations of both external environmental physical characteristics and internal biological characteristics. Algorithms provide designing of scenarios including simulation of either short-time or long-time (hours or days) observations. Input data include different combinations of environmental variables (air or water temperature, air humidity, wind or water flow speed, light intensity, infrared radiation) for a naked or wear human, as well as for given dynamics of biological characteristics (rate of heat production including its components associated with metabolism and ATP molecules leasing during mental and physical activities). Human body is presented by a core, blood, and a skin compartments. Skin and lung evaporation are under hypothalamic control based on afferent impulse patterns from internal, and skin heat and cold receptors. Dynamic output data include blood, hypothalamic, and skin temperatures, hemodynamic parameters like heart rate, cardiac output, regional blood flows, vascular resistances, blood pressures, and regional blood volumes. Serotonin and melatonin concentrations modulating biological heat production rate are associated with a day/night light intensity. Currently, the PCbased simulator is autonomous software to be used both for educational purposes and for providing of special computer research. In a near future, this simulator has to be widened by models of kidneys, and a mechanism of liverpancreas interaction.

Deterministic Processes Shape Abundant and Rare Bacterial Communities in Drinking Water

The deep mechanisms shaping bacterial assembly are a crucial challenge in drinking water ecosystem. However, much less is known about seasonal diversity distributions and assembly mechanisms of abundant and rare bacteria in drinking water. The combination of environmental variables and high-throughput 16S rRNA gene sequencing was conducted to examine the composition, assembly, co-occurrence patterns of abundant and rare bacteria from five drinking water sites across four seasons in one year in China. The results indicated that abundant taxa were mainly composed of Rhizobiales_UG1, Sphingomonadales_UG1, and Comamonadaceae, while rare taxa were Sphingomonadales_UG1, Rhizobiales_UG2, and Rhizobiales_UG1. The richness of rare bacteria was greater than that of abundant ones, and the richness had no differences among seasons. The beta diversity was significantly discrepant in abundant and rare communities and among seasons. Deterministic mechanism accounted for a larger contribution to abundant taxa than rare taxa. Furthermore, water temperature had higher effects on abundant microbiome than rare ones. Co-occurrence network analysis indicated that abundant taxa that occupied frequently in central positions had stronger effect on co-occurrence network. In our study, these results collectively suggested that rare bacteria respond to environmental variables with an analogical pattern to abundant counterparts (similar community assembly), but their ecological diversities, driving forces, and co-occurrence patterns were not equivalent in drinking water.

All that changes is not shift: methodological choices influence niche shift detection in freshwater invasive species

Species distribution models are often used to predict the potential distributions of invasive species outside their native ranges and rely on the assumption of realized niche conservatism. Analyses observed that freshwater invasive species often show high degrees of niche expansion, suggesting limited reliability of species distribution models. However, observed niche shifts can arise because of both actual niche shifts, determined by biological factors, and apparent shifts, due to methodological issues. We compared metrics of niche dynamics calculated using different sets of variables to identify factors that could influence the rate of niche shifts. We collected presence‐only data for 40 freshwater invasive animal species, then measured niche shift dynamics using 14 different combinations of environmental variables. Shifts were assessed measuring niche overlap, expansion and unfilling, and testing for niche conservatism. We then built generalized linear mixed models relating niche shifts to methodological choices and biological features. Our results showed that methodological choices strongly affected all the considered niche dynamics metrics, while the effects of biological features were less prominent. Moreover, different niche dynamic measures sometimes provided contradictory assessments of niche conservatism. Niche analyses are powerful tools to predict areas at risk of invasion, but inappropriate methodological choices can lead to apparent niche shifts, questioning niche model reliability and biological interpretation. The high rate of niche expansion observed in freshwater invasive species highlights the importance of delineating objective criteria to determine the set of variables to be used in niche dynamic assessments.

Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features.

The soil organic matter (SOM) content is a key factor affecting the function and health of soil ecosystems. For measurements of land reclamation and soil fertility, SOM monitoring using visible and near-infrared spectroscopy (Vis-NIR) is one approach to quantifying soil quality, and Vis-NIR is important for monitoring the SOM content in a broad and nondestructive manner. To investigate the influence of environmental factors and Vis-NIR spectroscopy in estimating SOM, 249 soil samples were collected from the Werigan–Kuqa oasis in Xinjiang, China, and their spectral reflectance, SOM content and soil salinity were measured. To classify and improve the prediction accuracy, we also take into account the soil salinity content as a variable indicator. Relevant environmental variables were extracted using remote sensing datasets (land-use/land-cover (LULC), digital elevation model (DEM), World Reference Base for Soil Resources (WRB), and soil texture). On the basis of Savitzky–Golay (S-G) smoothing and first derivative (FD) preprocessing of the original spectrum, three clusters were obtained by K-means clustering through the use of Vis-NIR and used as spectral classification variables. Using Vis-NIR as Model 1, Vis-NIR combined with spectral classification as Model 2, environmental variables as Model 3, and the combination of all the above variables (Vis-NIR, spectral classification, environmental variables, and soil salinity) as Model 4, a SOM content estimation model was constructed using partial least squares regression (PLSR). Using the 249 soil samples, the modeling set contained 166 samples and the validation set contained 83 samples. The results showed that Model 2 (validation r2 = 0.78) was better than Model 1 (validation r2 = 0.76). The prediction accuracy for Model 4 (validation r2 = 0.85) was better than Model 2 (validation r2 = 0.78). Among these, Model 3 was the worst (validation r2 = 0.39). Therefore, the combination of environmental variables with Vis-NIR spectroscopy to estimate SOM content is an important method and has important implications for improving the accuracy of SOM predictions in arid regions.

An overview of how heat stress impacts dairy cattle fertility

Heat stress disrupts dairy cattle fertility by disturbing reproductive tract activities, changing hormonal balance, lowering the quality of a cell in an ovary which may undergo meiotic division to form an ovum, and consequently reducing embryo formation and survival. This review aimed to combine the earlier studies on how heat stress affects dairy cattle fertility. Any dairy herd's performance is affected by its fertility, which is one of the key factors. It is a broad term influenced by many factors, including genetics, food, hormones, pathophysiology, management, and climatic circumstances. The main natural physical environmental factors affecting dairy systems are air temperature, relative humidity, solar radiation, atmospheric pressure, and wind speed. These environmental factors combine to induce heat stress in animals, defined as any combination of environmental variables that causes the temperature to rise above the animal’s thermoneutral zone temperature range. Reduced or exaggerated release of these reproductive hormones can seriously affect the fertility of dairy cattle. Heat stress impact on dairy cattle fertility can be mitigated using relevant scientific procedures, such as physical environmental modifications, food management, and the genetic development of heat-resistant dairy cattle.

Remote Sensing of Autumn Phenology by Including Surface Soil Temperature: Algorithm Development, Calibration, and Validation

Phenology exercises a critical control on annual terrestrial ecosystem carbon uptake and indicates interaction between climate and vegetation. Solely vegetation index (VIs) is insufficient to accurately detect the end of growing season (EOS). Soil temperature (Ts) plays a modulating role in soil microbial functioning and plant growth, while its impact on EOS remains largely unknown. Hence, we compared the potential between Ts and air temperature (Ta) as indicators of EOS by using flux data from 14 deciduous broadleaf forests (DBF), 24 evergreen needleleaf forests (ENF), 7 mixed forests, and 23 none-forests (NF) over Northern temperate and boreal regions (30°-60°N) for 2001-2014. The widely used NDVI-based double logistic approach failed to capture EOS variability for these ecosystems, and we derived a new EOS algorithm with a soil temperature-based scaler, which improved the EOS modeling for all plant functional types. We found that Ts at different depths showed varied abilities for EOS modeling, and Ts at the 0-10 cm depth provided the best estimates of EOS in terms of both numbers of significant sites and the correlation coefficients (R). Estimated EOS occurred earlier by on average 2.9 days than the current MODIS phenology product for ∼56.5% pixels, especially for the ENF ecosystems (∼5.5 days). Our study suggests the usefulness of surface soil temperature for autumn leaf senescence phenology modeling, and that combination of environmental variables with the current modeling strategy can improve our understanding of autumn phenology with future climate change.

Modelling the impacts of climate change on skipjack tuna (Katsuwonus pelamis) in the Mozambique Channel

AbstractSkipjack tuna play a significant role in global marine fisheries and are of particular interest for socio‐economy in the tropical waters of the Mozambique Channel. However, human‐induced climate change has been leading to a reduction and reallocation of biomass, along with other ecological changes, thereby creating a feedback loop with negative socio‐economic consequences for fisheries‐reliant coastal communities. The objective of this study was to predict the potential skipjack tuna fishing grounds by 2050 and 2100. To that end, skipjack tuna catch data were collected from Spanish purse seine fleets, and subsequently, generalized additive models were used to model these data against a combination of environmental variables and future pathway projections from BIO‐ORACLE models under optimistic (RCP2.6) and pessimistic (RCP8.5) scenarios. Both optimistic and pessimistic scenarios by 2050 predicted that the potential fishing grounds will relocate southward from tropical to more temperate waters, with moderate shifts in the potential fishing grounds of purse seines to the latitude >16°S, whereas the pessimistic scenario predicted higher displacement catches of purse seines in the southernmost part (>24°S) and moderate to high catches in northern (>20°S) of the Mozambique Channel by the end of the century. Despite the degree of uncertainty surrounding the climate change impacts on skipjack tuna, we argue that fisheries stakeholders, administrators and regional tuna fisheries management organizations should work towards building resilience and ensuring sustainability while reducing or mitigating vulnerability and climate change impacts on local and regional communities and their livelihoods.

Biodiversity and ecosystem functions depend on environmental conditions and resources rather than the geodiversity of a tropical biodiversity hotspot

Biodiversity and ecosystem functions are highly threatened by global change. It has been proposed that geodiversity can be used as an easy-to-measure surrogate of biodiversity to guide conservation management. However, so far, there is mixed evidence to what extent geodiversity can predict biodiversity and ecosystem functions at the regional scale relevant for conservation planning. Here, we analyse how geodiversity computed as a compound index is suited to predict the diversity of four taxa and associated ecosystem functions in a tropical mountain hotspot of biodiversity and compare the results with the predictive power of environmental conditions and resources (climate, habitat, soil). We show that combinations of these environmental variables better explain species diversity and ecosystem functions than a geodiversity index and identified climate variables as more important predictors than habitat and soil variables, although the best predictors differ between taxa and functions. We conclude that a compound geodiversity index cannot be used as a single surrogate predictor for species diversity and ecosystem functions in tropical mountain rain forest ecosystems and is thus little suited to facilitate conservation management at the regional scale. Instead, both the selection and the combination of environmental variables are essential to guide conservation efforts to safeguard biodiversity and ecosystem functions.

A non-intrusive data-driven model for detailed occupants’ activities classification in residential buildings using environmental and energy usage data

Recently, in many existing buildings, detailed energy consumption data and various indoor/outdoor environmental data could be collected by BEMS (Building energy management system) or monitoring systems. Since the characteristics of the occupants (e.g., behavior and status) affect the indoor environment, it is possible to predict occupant information by developing an inverse model that utilizes environmental data. In this study, we proposed a method for detecting detailed occupants activities based on environmental data (e.g., temperature, relative humidity, CO2, concentrations, and dust concentrations) and energy consumption data (e.g., related to heating and cooling, lighting, and electronic appliances) which can be monitored in buildings. To this end, the occupants’ characteristics (e.g., what the occupants do for 24 h and how often they do it) were investigated through experiments in which subjects live in a test-bed similar to a residential environment. In this study, we identified that seven specific activities of the occupants (sleeping, resting, working, cooking, eating, exercising, and away) accounted for 98% of the total behavior. Furthermore, the major environmental factors affecting the classification model were identified. The occupants' activities detection accuracy of the three classification algorithms including Random forest (RF), K-nearest neighbor (KNN), and Support vector machine (SVM) was compared and analyzed. The best performance of the model for the occupants' activities was achieved by RF. And the combination of environmental variables and energy use variables of the equipment led to improved accuracy of the average f1-score of 0.94. Detection for detailed activities of occupants was made possible by the development of a classification model using machine learning algorithms, which is expected to be further used for automatic control systems based on the prediction of occupants’ activities and status.

A fine‐scale analysis reveals microgeographic hotspots maximizing infection rate between a parasite and its fish host

Abstract For parasites, finding their hosts in vast and heterogeneous environments is a task that can be complex. Some parasite species rely on elaborate strategies to increase encounter rate with their hosts (e.g. behavioural modification of host), but others do not. For these parasites, a key issue is to reveal the processes that enable them to successfully find their hosts and complete their life cycles. Here, we tested the hypothesis that infective larvae of the freshwater ectoparasite Tracheliastes polycolpus are not homogeneously distributed along the river and preferentially occur in very specific microhabitats that maximize encounter rate, and hence infection rate, with their host fish. To do this, we combined an in situ experiment (caging) with an empirical survey carried out on the same sites to identify potential ‘hotspots’ of infection at the microgeographic scale and their environmental characteristics. Experimental and empirical results demonstrated that infections were not evenly distributed among microhabitats, and that infections were spatially aggregated in hotspots at a very fine spatial grain. We further found that certain combinations of environmental variables were consistently and nonlinearly associated with higher infection rate for both caged and wild‐caught fish. Microhabitats characterized by very low or high stream velocities, associated with medium or very small substrate, respectively, and a deep water column were strongly and repeatedly associated with higher infection rates. These microhabitats could concentrate parasites and/or promote physical contact with the hosts. We conclude that the characteristics of some microhabitats could facilitate contact between hosts and parasites and explain how some parasites manage to find their hosts in complex environments. A free Plain Language Summary can be found within the Supporting Information of this article.

Prediction Potential of Remote Sensing-Related Variables in the Topsoil Organic Carbon Density of Liaohekou Coastal Wetlands, Northeast China

Wetland ecosystems contain large amounts of soil organic carbon. Their natural environment is often both at the junction of land and water with good conditions for carbon sequestration. Therefore, the study of accurate prediction of soil organic carbon (SOC) density in coastal wetland ecosystems of flat terrain areas is the key to understanding their carbon cycling. This study used remote sensing data to study SOC density potentials of coastal wetland ecosystems in Northeast China. Eleven environmental variables including normalized difference vegetation index (NDVI), difference vegetation index (DVI), soil adjusted vegetation index (SAVI), renormalization difference vegetation index (RDVI), ratio vegetation index (RVI), topographic wetness index (TWI), elevation, slope aspect (SA), slope gradient (SG), mean annual temperature (MAT), and mean annual precipitation (MAP) were selected to predict SOC density. A total of 193 soil samples (0–30 cm) were divided into two parts, 70% of the sampling sites data were used to construct the boosted regression tree (BRT) model containing three different combinations of environmental variables, and the remaining 30% were used to test the predictive performance of the model. The results show that the full variable model is better than the other two models. Adding remote sensing-related variables significantly improved the model prediction. This study revealed that SAVI, NDVI and DVI were the main environmental factors affecting the spatial variation of topsoil SOC density of coastal wetlands in flat terrain areas. The mean (±SD) SOC density of full variable models was 18.78 (±1.95) kg m−2, which gradually decreased from northeast to southwest. We suggest that remote sensing-related environmental variables should be selected as the main environmental variables when predicting topsoil SOC density of coastal wetland ecosystems in flat terrain areas. Accurate prediction of topsoil SOC density distribution will help to formulate soil management policies and enhance soil carbon sequestration.

Using environmental variables and Fourier Transform Infrared Spectroscopy to predict soil organic carbon

Soil Organic Carbon (SOC) content is a key element for soil fertility and productivity, nutrient availability and potentially represents a measurement of the sink for greenhouse gas abatement. Improving our knowledge on the spatial distribution of SOC is hence essential for sustainable nutrient management and carbon storage capacity. The objective of this study was to evaluate the performance of six tree-based machine-learning models when using environmental variables (i.e., remote sensing and terrain attributes - scenario 1), Fourier Transform Infrared Spectroscopy (FTIR) data (scenario 2) and combination of environmental variables and FTIR data (scenario 3) as predictors in prediction of SOC content. The models included Random Forest, Cubist, Conditional Inference Forest, Conditional Inference Trees, Extreme Gradient Boosting and Classification, Regression Trees. Furthermore, we explored if the Bat optimization algorithm can improve the prediction accuracy of the models. The study was conducted across a 7000 ha field in the Miandoab County, Northern Iran, with a total of 80 soil samples collected systematically in a regular grid (700 × 1000 m). According to Leave-One-Out Cross-Validation, the best prediction performance was achieved by the Cubist+Bat model when environmental variables and FTIR spectra (scenario 3) were used (Coefficient of determination = 0.73, Concordance Correlation Coefficient = 0.77, Root Mean Square Error = 0.36, Mean Absolute Error = 0.31, Median Absolute Error = 0.28). FTIR data had the highest influence on the prediction accuracy of SOC. Therefore, it can be concluded that the combination of environmental variables and FTIR data with Cubist+Bat model as a precise approach to monitor SOC in semi-arid soils of Iran. The final Digital Soil Map (DSM) of SOC revealed that improvements in prediction might be possible with the collection of more soil samples in areas where the land use and topography changed over short spatial scales.

The importance of adjusting contaminant concentrations using environmental data: A retrospective study of 25 years data in Baltic blue mussels

To improve the statistical power of detecting changes in contaminant concentrations over time, it is critical to reduce both the within- and between-year variability by adjusting the data for relevant confounding variables. In this study, we present a method for handling multiple confounding variables in contaminant monitoring. We evaluate the highly variable temporal trends of Polycyclic Aromatic Hydrocarbons (PAHs) in blue mussels from the central Baltic Sea during the period 1987–2016 (data from 25 years during this period) using various regression analyses. As potential explanatory variables related to PAH exposure, we use mussel size and retrospective analyses of mussel δ15N and δ13C (representing large scale biogeochemical changes as a result of e.g. eutrophication and terrestrial inputs). Environmental data from concurrent monitoring programmes (seasonal data on Chlorophyll-a, salinity and temperature in the water column, bioturbation of sediment dwelling fauna) were included as variables related to feeding conditions. The concentrations of high-molecular-weight and low-molecular-weight PAHs in blue mussel were statistically linked to different combinations of environmental variables. Adjustment using these predictors decreased the coefficient of variation in all 15 PAHs tested and improved the statistical power to detect changes. Moreover, the adjustment also resulted in a significant downward trend for fluoranthene that could not be detected initially. For another PAH, benzo(g,h,i)perylene, adjustment which reduced variation resulted in the loss of an apparent downward trend over time. Hence, our study highlights the importance of using auxilliary data to reduce variability caused by environmental factors with general effects on physiology when assessing contaminant time trends. Furthermore, it illustrates the importance of extensive and well designed monitoring programmes to provide relevant data.

The effects of Bidens alba invasion on soil bacterial communities across different coastal ecosystem land-use types in southern China.

Environments in both biotic and abiotic ecosystems have been affected by the colonization of non-native flora. In this study, we examined the effect of Bidens alba invasion on different land-use types along a coastline in southern China. Bacterial communities in each site were determined using 16S rDNA sequencing, and soil physicochemical properties were analyzed using standard methods. Although our results indicated that B. alba invasion did not have a significant effect on the alpha diversity of bacteria, it caused significant differences in soil bacterial community composition between invaded and uninvaded soil across different land-use types. Beta diversity and several physicochemical properties in forest, orchard and waterfront environments were recorded to be more susceptible to B. alba invasion. A high proportion of the variation of bacterial communities can be explained by a combination of environmental variables, indicating that environmental selection rather than plant invasion is a more effective process in coastal microbial assemblages. By comparing topological roles of shared OTUs among invaded and uninvaded soil, keystone taxa in invaded soil were identified. Acidobacteria was the major phyla involved in the invasive process which could be driven by environmental selection. How key phyla react in our experiment should be verified by further studies.

Distribution, abundance, and habitat characteristics of the congregating firefly, Pteroptyx Olivier (Coleoptera: Lampyridae) in Thailand

Pteroptyx fireflies represent an important economic insect for ecotourism. Two methods, visual counting and sweep net collection, were used to survey Pteroptyx species at 23 study sites along the coast of Thailand. P. malaccae and P. valida were the most common species across all sites with significant differences in abundance among study sites. In contrast, P. asymmetria was found only in the southern region, and P. tener was found only at one site in Surat Thani province. Pteroptyx fireflies displayed in trees whose canopies were opened to view as opposed to those blocked by other vegetation. Fireflies most often perched in the top section. Pteroptyx horizontal distribution was affected by ambient wind direction and speed. In addition, most display trees were located at prominent spots on either the inside bends or the outside bends of mangrove rivers. The principal component regression analysis was performed to determine which environmental factors might influence Pteroptyx species abundances and revealed that three Pteroptyx species appear to be influenced by different combinations of environmental variables.

Lagrangian Analysis of Tropical Cyclone Genesis Simulated by General Circulation Models Compared with Observations

AbstractAs a contribution to understanding the genesis of tropical cyclones (TCs), we compared TC genesis processes simulated by the Seoul National University Atmosphere Model version 0 with a Unified Convection Scheme (SAM0) and the Community Atmosphere Model version 5 (CAM5) with those from the ERA-Interim reanalysis (ERA-Interim, hereafter ERAI) and best track observations. In contrast to previous studies that estimated the TC genesis potential using the Eulerian mean environmental conditions, we calculated the probability of a pre-existing weak cyclonic embryo vortex (EV) developing into a TC by analyzing changes in the environmental conditions along the EV trajectories. Our analysis indicates that the spatial distribution and annual cycles of TCs obtained from the SAM0 and ERAI are similar to those obtained from the best track observation data. With the exception of the mesoscale convective organization and associated variables, most environmental variables along the trajectories of DEVs (EVs developing into TCs) showed monotonic variations. When EVs were born, environmental conditions of DEVs were significantly different from those of nondeveloping EVs, allowing for the prediction of TC genesis. In general, TC genesis probability increased as the environment became more cyclonic, moist, unstable, and with a weaker wind shear. Rapidly strengthening EVs were more likely to develop into TCs. SAM0 and ERAI have the same combination of environmental variables with the best prediction skill for TC genesis—absolute vorticity at 850 hPa, column saturation deficit, sea surface temperature, vertical shear of horizontal winds between 200 and 850 hPa, and latitude—with similar sensitivities to individual environmental variables, indicating that SAM0 well simulates the observed TC genesis processes.

Microclimate modelling of beach sand temperatures reveals high spatial and temporal variation at sea turtle rookeries.

The continual development of ecological models and availability of high-resolution gridded climate surfaces have stimulated studies that link climate variables to functional traits of organisms. A primary constraint of these studies is the ability to reliably predict the microclimate that an organism experiences using macroscale climate inputs. This is particularly important in regions where access to empirical information is limited. Here, we contrast correlative models based on both ambient and sea surface temperatures to mechanistic modelling approaches to predict beach sand temperatures at depths relevant to sea turtle nesting. We show that mechanistic models are congruent with correlative models at predicting sand temperatures. We used these predictions to explore thermal variation across 46 mainland and island beaches that span the geographical range of sea turtle nesting in Western Australia. Using high resolution gridded climate surfaces and site-specific soil reflectance, we predict almost 9°C variation in average annual temperatures between beaches, and nearly 10°C variation in average temperatures during turtle nesting seasons. Validation of models demonstrated that predictions were typically within 2°C of observations and, although most sites had high correlations (r2>0.7), predictive capacity varied between sites. An advantage of the mechanistic model demonstrated here is that it can be used to explore the impacts of climate change on sea turtle nesting beach temperatures as, unlike correlative models, it can be forced with novel combinations of environmental variables.

Large-scale distribution of a deep-sea megafauna community along Mediterranean trawlable grounds

The large-scale distribution pattern of megafauna communities along the Mediterranean middle slope was explored. The study was conducted between 500 and 800 m depth where deep-water fishery occurs. Although community studies carried out deeper than 500 m are partly available for some geographic areas, few large-scale comparative studies have been carried out. Within the framework of the MEDITS survey programme, we compared the megafauna community structure in ten geographical sub-areas (GSAs) along the Mediterranean coasts. Additionally, the spatial distribution of fishing was analysed using vessel monitoring by satellite information. Overall, the community showed a significant difference between sub-areas, with a decreasing eastward pattern in abundance and biomass. Longitude was the main factor explaining variation among sub-areas (by generalized additive models). However, we found a region which did not follow the general pattern. GSA 6 (northern Spain) showed significantly lower abundance and a different composition structure to the adjacent areas. The decrease in community descriptors (i.e. abundance and biomass) in this area is probably a symptom of population changes induced by intense fishery exploitation. Overall, a combination of environmental variables and human-induced impacts appears to influence the bentho-pelagic communities along the slope areas of the Mediterranean.