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225 results for “Environmental variables”
Dynamic weakening in sandstone-derived fault gouges during simulated small-magnitude earthquakes under variable loading and environmental conditions
<p><span>Faults exhibit dynamic weakening during large displacements (>1 m) at seismic slip velocities (>0.1 m/s), but the role of this weakening in small-displacement induced earthquakes (M 3–4), such as those in the Groningen Gas Field (the Netherlands), remains unclear. We conducted seismic slip-pulse experiments on Slochteren sandstone gouges (SSG) using a rotary-shear apparatus to investigate their dynamic behavior. Pre-sheared gouge layers, confined between ~1.5 mm thick sandstone host blocks, were subjected to slip pulses at initial effective normal stresses of 4.9–16.6 MPa and pore fluid pressures of 0.1 and 1 MPa under undrained conditions. Slip pulses reached peak velocities of 1.8 m/s, accelerations up to 42 m/s², and displacements of 7.5–15 cm, using either dry Argon or water as pore fluid at ambient temperatures. Water-saturated gouges showed rapid weakening from a peak friction of ~0.7 to ~0.3, with early dilatancy followed by slower ongoing dilation. In contrast, Argon-filled samples exhibited only subtle weakening. Our findings confirm that water-saturated SSG weakens substantially during slip, with minimal dependence on normal stress, slip acceleration, or displacement, while dry samples do not. Microstructural analysis indicates no systematic relationship between PSZ width and frictional work or power input densities, suggesting that wear or heat production alone does not govern PSZ growth. Instead, thermal pore fluid pressurization, potentially involving water phase transitions at asperity scales, may drive weakening in short-displacement, induced seismic events.</span></p>
Data from: Environmentally induced changes in correlated responses to selection reveal variable pleiotropy across a complex genetic network
Selection in novel environments can lead to a coordinated evolutionary response across a suite of characters. Environmental conditions can also potentially induce changes in the genetic architecture of complex traits, which in turn could alter the pattern of the multivariate response to selection. We describe a factorial selection experiment using the nematode Caenorhabditis remanei in which two different stress-related phenotypes (heat and oxidative stress resistance) were selected under three different environmental conditions. The pattern of covariation in the evolutionary response between phenotypes or across environments differed depending on the environment in which selection occurred, including asymmetrical responses to selection in some cases. These results indicate that variation in pleiotropy across the stress response network is highly sensitive to the external environment. Our findings highlight the complexity of the interaction between genes and environment that influences the ability of organisms to acclimate to novel environments. They also make clear the need to identify the underlying genetic basis of genetic correlations in order understand how patterns of pleiotropy are distributed across complex genetic networks.
Figure 7 from: Borisov RR, Chertoprud ES, Palatov DM, Novichkova AA (2021) Variability in macrozoobenthic assemblages along a gradient of environmental conditions in the stream water of karst caves (Lower Shakuranskaya Cave, western Caucasus). Subterranean Biology 39: 107-127. https://doi.org/10.3897/subtbiol.39.65733
Figure 7 dbRDA ordination for the model of the investigated cave sites (based on Bray–Curtis similarity) factored with distance ranges: 1 – 0–60 m, 2 – 280–380 m, 3 – 460–650 m.
Figure 6 from: Borisov RR, Chertoprud ES, Palatov DM, Novichkova AA (2021) Variability in macrozoobenthic assemblages along a gradient of environmental conditions in the stream water of karst caves (Lower Shakuranskaya Cave, western Caucasus). Subterranean Biology 39: 107-127. https://doi.org/10.3897/subtbiol.39.65733
Figure 6 Changes in the biomass of stygobionts according to the distance from the cave entrance (2018–2019).
Figure 8 from: Borisov RR, Chertoprud ES, Palatov DM, Novichkova AA (2021) Variability in macrozoobenthic assemblages along a gradient of environmental conditions in the stream water of karst caves (Lower Shakuranskaya Cave, western Caucasus). Subterranean Biology 39: 107-127. https://doi.org/10.3897/subtbiol.39.65733
Figure 8 dbRDA ordination for the investigated cave sites during the research in autumn 2019 (based on Bray–Curtis similarity) factored with luminosity ranges: 0 – 0 lx, 1 – 0.07–2.7 lx, 2 – 13.17 lx, 3 – 555 lx.
Figure 3 from: Borisov RR, Chertoprud ES, Palatov DM, Novichkova AA (2021) Variability in macrozoobenthic assemblages along a gradient of environmental conditions in the stream water of karst caves (Lower Shakuranskaya Cave, western Caucasus). Subterranean Biology 39: 107-127. https://doi.org/10.3897/subtbiol.39.65733
Figure 3 Changes in the number of stygoxenes, stygophiles and stygobionts according to the distance from the cave entrance (2018–2019).
Figure 4 from: Borisov RR, Chertoprud ES, Palatov DM, Novichkova AA (2021) Variability in macrozoobenthic assemblages along a gradient of environmental conditions in the stream water of karst caves (Lower Shakuranskaya Cave, western Caucasus). Subterranean Biology 39: 107-127. https://doi.org/10.3897/subtbiol.39.65733
Figure 4 The most numerous stygobionts in the Lower Shakuranskaya Cave AXiphocaridinella osterloffi (Juzbaš'jan, 1941) BPontohoratia birsteini (Starobogatov, 1962).
Figure 1 from: Borisov RR, Chertoprud ES, Palatov DM, Novichkova AA (2021) Variability in macrozoobenthic assemblages along a gradient of environmental conditions in the stream water of karst caves (Lower Shakuranskaya Cave, western Caucasus). Subterranean Biology 39: 107-127. https://doi.org/10.3897/subtbiol.39.65733
Figure 1 A Location of the Lower Shakuranskaya Cave on the map of Abkhazia B sampling stations location scheme in the Lower Shakuranskaya Cave in 2018–2019. In numbers – stations, sampled in February 2018, May and October, 2019; in letters – additional stations, sampled in October, 2019 (for each station indicated numbers of stygobionts, stygophiles and stygoxenes). Views on the cave stream C at station 2 (ecotone zone) D at station 4 E at station 5.
Figure 2 from: Borisov RR, Chertoprud ES, Palatov DM, Novichkova AA (2021) Variability in macrozoobenthic assemblages along a gradient of environmental conditions in the stream water of karst caves (Lower Shakuranskaya Cave, western Caucasus). Subterranean Biology 39: 107-127. https://doi.org/10.3897/subtbiol.39.65733
Figure 2 The variation in the values of Shannon diversity index along the distance from the cave entrance (2018–2019).
Figure 5 from: Borisov RR, Chertoprud ES, Palatov DM, Novichkova AA (2021) Variability in macrozoobenthic assemblages along a gradient of environmental conditions in the stream water of karst caves (Lower Shakuranskaya Cave, western Caucasus). Subterranean Biology 39: 107-127. https://doi.org/10.3897/subtbiol.39.65733
Figure 5 Changes in the biomass of stygoxenes, stygophiles and stygobionts according to the distance from the cave entrance (2018–2019).
Figure 10 from: Borisov RR, Chertoprud ES, Palatov DM, Novichkova AA (2021) Variability in macrozoobenthic assemblages along a gradient of environmental conditions in the stream water of karst caves (Lower Shakuranskaya Cave, western Caucasus). Subterranean Biology 39: 107-127. https://doi.org/10.3897/subtbiol.39.65733
Figure 10 The CCA ordination of hydrobionts species from Nizhnyaya Shakuranskaya Cave. Black points – stygobionts, blue points – stygophiles, green points – stygoxenes. Abbreviations: A gut – A. guttatus, B gem – B. gemellus, C hor – C. horatieformis, C schak – C. schakuranica, C shad – C. shadini, C sp – Caucasopsis sp., Ch abch – C. abchazica, Cn sp – Cnetha sp., E sp – Elmis sp., D sub – D. submaculata, D tau – D. taurocaucasica, Dend sp – Dendrocoelum sp., E ljov – E. cf. ljovuschkini, Es sp – Eisenia sp., E palp – E. palpatus, E zimm – E. zimmermanni, G kom – G. cf. komareki, H gor – H. gordioides, H sang – H. sanguisuga, L col – L. colchicus, L inc – L. incanus, L sp – Leuctra sp., M sp – Macropelopia sp., N abl – N. cf. ablaskiri, N iner – N. inermis, N magn – N. magnus, N mart – N. martynovia, O sp – Odeles sp., P birst – P. birsteini, P lat – P. latissima, Par sp – Parametriocnemus sp., P wern – P. werneri, R som – R. somcheticus, Rh sp – Rhynchelmis sp., S cach – S. cachetica, S clav – S. clavatus, Sty sp – Stylodrilus sp., T cauc – T. caucasica, T valv – T. valvatus, X falc – X. falcirostris, X ost – X. osterloffi, Z yak – Z. yakovi.
Figure 9 from: Borisov RR, Chertoprud ES, Palatov DM, Novichkova AA (2021) Variability in macrozoobenthic assemblages along a gradient of environmental conditions in the stream water of karst caves (Lower Shakuranskaya Cave, western Caucasus). Subterranean Biology 39: 107-127. https://doi.org/10.3897/subtbiol.39.65733
Figure 9 The two-dimensional nMDS ordination of the investigated cave sites, based on Bray–Curtis similarities (stress = 0.09) and factored with luminosity: 0 – 0 lx, 1 – 0.07–2.7 lx, 2 – 13.17 lx, 3 – 555 lx. Dots are labeled: first number – season/year of research: 1 – winter 2018, 2 – spring 2018, 3 – autumn 2019; second number – no of sampling station.
Environmental drivers of plant distributions at global and regional scales: occurrence data with associated environmental variables of plant families/genera/species
<p>How environmental factors drive plant distribution across globe is one of the most fundamental questions in ecology. Plant distributions may be shaped by various environmental factors, such as climate, topography and edaphic factors. Nevertheless, it is not clear about the relative importance of different environmental factors in driving plant distribution across spatial scales and among plant groups. This study aimed to disentangle how plant–environment relationships vary with latitude and among plant taxa including angiosperms, gymnosperms, pteridophytes and bryophytes.</p> <p><b><span>Location</span></b>: Global</p> <p><b><span>Main taxa</span></b>: Plants</p> <p><b><span>Results</span></b>: Our analyses revealed the primacy of climatic variability (temperature seasonality and isothermality) on plant distribution at the global scale. The relative contribution of temperature seasonality and isothermality peaked in tropical areas, whereas solar radiation and annual mean temperature had stronger influence at high-latitude areas. We also found wide-range plant groups tend to occur at area with higher temperature variability (isothermality and temperature seasonality) and flatter terrain (low slope). Climate extremes (low temperature and low solar radiation) determined plant distribution range and limits across latitude. Soil and topography had diverse thought less important effects (related to climate) on broad-scale plant distribution patterns.</p> <p><b><span>Main Conclusions</span></b>: Our study highlights the significance of climate variability for global plant distributions and climate extremes at higher latitude areas. Environmental effects of plant distributions vary across latitude. The findings imply that our understandings on environmental factors affecting plant distributions rely on the geographical scales that we focus on, suggesting that different geographcial and local ecological processes should be integrated to explain multi-scale distribution patterns.</p>
Fig. 6 in Nematode morphometry and biomass patterns in relation to community characteristics and environmental variables in the Mekong Delta, Vietnam
Fig. 6. Variation in the morphology of nematodes in the Mekong Delta, Vietnam. mean (± SD) nematode length in the Mekong Delta (a); mean (± SD) nematode length along the Co Chien estuary (b); mean (± SD) nematode width in the Mekong Delta (c); mean (± SD) nematode width along the Co Chien estuary (d); ratio L/W (the mouth stations [e], along estuary ECC [f]), individual biomass (μg) (the mouth stations [g], along estuary ECC [h]) and total biomass (μg 10 cm−2) (the mouth stations [i], along estuary ECC [k]).
Fig. 2 in Influence of environmental variables and anthropogenic perturbations on stream fish assemblages, Upper Paraná River, Central Brazil
Fig. 2. Ordination of the co-structure between (a) fish assemblages (arrows) and stream sites (squares) and (b) fish species and environmental variables resulting from the co-inertia analysis. Only species that most contributes to each axis are displayed. Black and white squares represent stream sites sampled in the wet and dry season, respectively. Codes correspond to names listed in Tables 1 and 2. Small boxes indicate the graphic scale.
FIGURE 1. A in The larvae of caddisfly species (Insecta, Trichoptera) in northern Thai streams and their relationships to environmental variables
FIGURE 1. A map of Thailand highlighting the northern part with the sampling sites S1–S7.
Data from: Environmental variability counteracts priority effects to facilitate species coexistence: evidence from nectar microbes
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Data from: Environmental determinism, and not interspecific competition, drive morphological variability in Australasian warblers (Acanthizidae)
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Data from: Variable environmental effects on a multi-component sexually selected trait
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Data from: Estimating fish abundance and biomass from eDNA concentrations: variability among capture methods and environmental conditions
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.