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411 results for “spatial variation”

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zenodo36/100

Fig. 1 in Spatial and temporal variation of benthic fish assemblages during the extreme drought of 1997-98 (El Niño) in the middle rio Negro, Amazonia, Brazil

Fig. 1. Study area in middle rio Negro basin (a) and detail of the sampling sites (b).

opencc-by-4.0Sep 2004View details →

zenodo36/100

Fig. 2 in Spatial and temporal variation of benthic fish assemblages during the extreme drought of 1997-98 (El Niño) in the middle rio Negro, Amazonia, Brazil

Fig. 2. River level during annual flood cycles. Large arrows denote dates of sampling expeditions.

opencc-by-4.0Sep 2004View details →

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Spatial and temporal variation in phenotypes and fitness in response to developmental thermal environments

1) Phenotypic variation within populations is influenced by the environment via plasticity and natural selection. How phenotypes respond to the environment can vary among traits, populations, and life stages in ways that can influence fitness. 2) Plastic responses during early development are particularly important because they can affect components of fitness throughout an individual's life. Consequently, how natural selection shapes developmental plasticity could be influenced by fitness consequences across different life stages. Moreover, spatial variation in selection pressures could generate differences in plastic responses among populations. 3) To gain insight into sources of variation in phenotypes and survival, we used a laboratory egg incubation experiment using brown anole lizards (Anolis sagrei) from mainland (ancestral) and island (descendent) populations, combined with a mark-release-recapture experiment in the field. Our study was designed to (i) quantify the effects developmental temperature on embryo development and offspring morphology, (ii) assess how developmental temperature influences offspring survival across different life stages, and (iii) quantify how thermal reaction norms vary among ancestral and descendant populations. 4) Developmental temperature influenced offspring morphology, but thermal reaction norms of embryos showed little variation among populations. Developmental temperature influenced offspring survival, but the patterns differed between embryo and hatchling stages; the optimal temperature for embryos was about 5ºC lower than that for hatchlings. High temperatures were thermally stressful to embryos, but they reduced incubation duration and led to early hatching. In turn, earlier hatching increased the probability of survival to adulthood. Moreover, the effect of temperature on hatchling survival was most pronounced for offspring that hatched late in the season. 5) The difference in optimal developmental temperatures between life stages may be driven by physiological tolerance for embryos and by ecological factors for hatchlings. Moreover, the fitness consequences of the developmental environment depend upon the phenology of hatching. Overall, these results highlight how the developmental environment can differentially effect fitness across life stages and show that temporal thermal heterogeneity can influence survival of embryos, but the consequences on post-hatching stages may vary at different times of the season.

opencc-zeroSep 2021View details →

dryad36/100

Behavioral responses of a large, heat-sensitive mammal to climatic variation at multiple spatial scales

1. Climate warming creates energetic challenges for endothermic species by increasing metabolic and hydric costs of thermoregulation. Although endotherms can invoke an array of behavioral and physiological strategies for maintaining homeostasis, the relative effectiveness of those strategies in a climate that is becoming both warmer and drier is not well understood. 2. In accordance with the heat dissipation limit theory, which suggests that allocation of energy to growth and reproduction by endotherms is constrained by the ability to dissipate heat, we expected that patterns of habitat use by large, heat-sensitive mammals across multiple scales are critical for behavioral thermoregulation during periods of potential heat stress and that they must invest a large portion of time to maintain heat balance. 3. To test our predictions, we evaluated mechanisms underpinning the effectiveness of bed sites for ameliorating daytime heat loads and potential heat stress across the landscape while accounting for other factors known to affect behavior. We integrated detailed data on microclimate and animal attributes of moose Alces alces, into a biophysical model to quantify costs of thermoregulation at fine and coarse spatial scales. 4. During summer, moose spent an average of 67.8% of daylight hours bedded, and selected bed sites and home ranges that reduced risk of experiencing heat stress. For most of the day, shade could effectively mitigate the risk of experiencing heat stress up to 10°C, but at warmer temperatures (up to 20℃) wet soil was necessary to maintain homeostasis via conductive heat loss. Consistent selection across spatial scales for locations that reduced heat load underscores the importance of the thermal environment as a driver of behavior in this heat-sensitive mammal. 5. Moose in North America have long been characterized as riparian-obligate species because of their dependence on woody plant species for food. Nevertheless, the importance of dissipating endogenous heat loads conductively through wet soil suggests riparian habitats also are critical thermal refuges for moose. Such refuges may be especially important in the face of a warming climate in which both high environmental temperatures and drier conditions will likely exacerbate limits to heat dissipation, especially for large, heat-sensitive animals.

opencc-zeroDec 2022View details →

zenodo36/100

Biotic and abiotic factors controlling spatial variation of mean carbon turnover time in forest soil

Data description This dataset is associated with the paper "Biotic and abiotic factors controlling spatial variation of mean carbon turnover time in forest soil". This dataset includes the soil organic carbon turnover time (τsoc) based on radiocarbon signals at the global and regional scales. Global synthesis The analysis of global soil radiocarbon data was done using the International Soil Radiocarbon Database (ISRaD v.1.0; Lawrence et al., 2020). ISRaD is an open source data with the records of 8 biomes (i.e., forest, grassland, cropland, shrubland, savanna, tundra, permafrost, and others). Since we focus on the turnover time of SOC (τsoc) based on radiocarbon occurring in the natural forest ecosystem, so we limited our study to data from soil depth within 200 cm in the forest ecosystem. We built a database of radiocarbon-based soil turnover time (τsoc) of 1897 soil samples from 245 forest locations worldwide. It covers a wide geographical range (35.65 oS ‒ 68.8 oN; 159.64 °W – 173.57 °E) and a broad nature climate zone (-5.2 oC to 40.0 oC; 58.66 mm to 6900 mm) over the half a century (1958 – 2017). Where forest age is missing, we derived it from The global forest age dataset (GFAD v 1.0; Poulter et al., 2018). The GFAD database represents the distribution of forest stand age during 2000 – 2010 years. Regional analysis  Soil sampling in forests across the Eastern Asian Monsoon region We sampled soils from twelve permanent forest plots in five mountains in the Eastern Asian Monsoon region (Table 1, Figure 1, and S1). Five of the twelve forest plots are members of the Smithsonian Forest Global Earth Observatory network (ForestGEO, https://forestgeo.si.edu/; Anderson-Teixeira et al., 2018; Chu et al., 2019). The other seven forest plots are members of China's National Ecosystem Research Network (CNERN, http://www.cern.ac.cn). In the Eastern Asian Monsoon region, more than half of the total annual rainfall occurs in the summer season (i.e., June, July, and August) (Tardif et al., 2020; Tian et al., 2003). We estimated the τsoc by the radiocarbon dating analysis of up to 100 cm of soil depth in each forest plot. For each plot, we separated the whole soil increment into the surface (0 – 30 cm) and deep (30 – 100 cm) layers to test the radiocarbon signal due to the high financial cost. Details of the location, climate, and vegetation for each sampling site are provided in Table 1 and Supplementary Text 1. Nine soil cores (2.5 cm in diameter) were collected in each forest plot, and a depth of 10 cm separated each soil column from 0 to 100 cm. In total, 108 soil profiles were sampled across the 12 forest plots. The accumulated aboveground litter was collected and measured in an area of 50 cm × 50 cm in each forest plot, with three replicates adjacent to each soil profile. Fine roots (< 2 mm in diameter) were manually picked from soil samples. The litter and root samples were dried at 65 °C for 48 hours using an oven and then weighed for dry mass. The elevation and other geographic information of each forest plot were measured during the soil sampling.

opencc-by-4.0Feb 2023View details →

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Dataset of ``Plasma Distribution Solver: A Model for Field-Aligned Plasma Profiles Based on Spatial Variation of Velocity Distribution Functions"

This dataset contains the plasma distribution data in the Jupiter–Io system, calculated from the Plasma Distribution Solver and used for figures in the paper “Plasma Distribution Solver: A model for field-aligned plasma profiles based on spatial variation of velocity distribution functions” by K. Saito et al. (2023).   The contents of files ‘all_Case_1.csv’ and ‘all_Case_2.csv’ are as follows: <ul> <li>Position along the magnetic field line (0 at the magnetic equator) [m] (column 1)</li> <li>Distance from the Jovian center [km] (column 2)</li> <li>Magnetic latitude [rad]([degree]) (column 3(4))</li> <li>Magnetic flux density [T] (column 5)</li> <li>The initial condition of electrostatic potential [V] (column 6)</li> <li>The result of electrostatic potential [V] (column 7)</li> <li>Number density profiles [m-3] (columns 8-17)</li> <li>Charge density profiles obtained from the integration of velocity distribution functions [C m-3] (column 18)</li> <li>Charge density profiles obtained from Poisson’s equation [C m-3] (column 19)</li> <li>Convergence value (column 20)</li> <li>Particle flux density [m-2 s-1] (columns 21-30)</li> <li>Mean flow velocity parallel to the field line [m s-1] (columns 31-40)</li> <li>Plasma pressure perpendicular to the field line [Pa] (columns 41-50)</li> <li>Plasma pressure parallel to the field line [Pa] (columns 51-60)</li> <li>Plasma dynamic pressure [Pa] (columns 61-70)</li> <li>Perpendicular temperature [J] (columns 71-80)</li> <li>Parallel temperature [J] (columns 81-90)</li> <li>Alfvén speed considering the displacement current term in Ampère’s law [m s-1] (column 91)</li> <li>Alfvén speed per the speed of light (column 92)</li> <li>Ion inertial length using averaged mass [m] (column 93)</li> <li>Electron inertial length [m] (column 94)</li> <li>Ion Larmor radius using averaged mass [m] (column 95)</li> <li>Ion acoustic gyroradius using averaged mass [m] (column 96)</li> <li>Electron Larmor radius [m] (column 97)</li> <li>Current density [A m-2] (column 98)</li> </ul> The Python codes ‘plot_all.py,’ ‘plot_plasma_beta_comparison.py,’ and ‘plot_Alfven_speed_comparison.py’ can plot Figures 5, 6, 7, and 9 of the paper using the above CSV files.   The files ‘boundary_conditions_Case_1.csv’ and ‘boundary_conditions_Case_2.csv’ contain the boundary conditions for Cases 1 and 2.   The zip files ‘probability_density_function_Case_1_H_Io.zip’ and ‘probability_density_function_Case_1_H_Jupiter_North.zip’ are zipped CSV files with the same name. The contents of these files are as follows: <ul> <li>Magnetic latitude [degree] (column 1)</li> <li>Perpendicular velocity at the particle position [m s-1] (column 2)</li> <li>Parallel velocity at the particle position [m s-1] (column 3)</li> <li>Perpendicular velocity at the boundary [m s-1] (column 4)</li> <li>Parallel velocity at the boundary [m s-1] (column 5)</li> <li>Probability density function [s3 m-3] (column 6)</li> <li>Differential flux per number density [cm-2 s-1 sr-1 keV-1] (column 7)</li> </ul> The Python code ‘plot_velocity_distribution_function.py’ can plot Figure 8 of the paper using this CSV file.

opencc-by-4.0Jul 2023View details →

dryad36/100

Spatial and temporal variation in toxicity and inorganic composition of hydraulic fracturing flowback and produced water

Hydraulic fracturing for oil and gas extraction produces large volumes of wastewater, termed flowback and produced water (FPW), that are highly saline and contain a variety of organic and inorganic contaminants. In the present study, FPW samples from ten hydraulically fractured wells, across two geologic formations were collected at various timepoints. Samples were analyzed to determine spatial and temporal variation in their inorganic composition. Results indicate that FPW composition varied both between formations and within a single formation, with large compositional changes occurring over short distances. Temporally, all wells showed a time-dependent increase in inorganic elements, with total dissolved solids increasing by up to 200,000 mg/L over time, primarily due to elements associated with salinity (Cl, Na, Ca, Mg, K). Toxicological analysis of a subset of the FPW samples showed median lethal concentrations (LC50) of FPW to the aquatic invertebrate Daphnia magna were highly variable, with the LC50 values ranging from 1.16% to 13.7% FPW. Acute toxicity of FPW significantly correlated with salinity, indicating salinity is a primary driver of FPW toxicity, however organic components also contributed to toxicity. This study provides insight into spatiotemporal variability of FPW composition and illustrates the difficulty in predicting aquatic risk associated with FPW.

opencc-zeroSep 2023View details →

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