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3,583 results for “elevations”
Data for the article: Trophic positions of soil microarthropods in forests increase with elevation, but energy channels remain unchanged
<p><span>Mountain forests are at risk as the consequences of climate change will likely lead to altered tree species boundaries. Characterizing food webs along elevational gradients in primary forests may help to predict potential consequences of such changes. The soil food web provides nutrients to plants through decomposition of dead organic matter. Changes in the soil food web with elevation are poorly characterized and most studies focus on community composition of microbes and individual arthropod taxa. </span><span><span> </span></span><span>Here, for the first time we studied trophic variations in two of the species rich microarthropod taxa, Collembola and Oribatida, along an elevational gradient of primary forest in Northeast China, Changbai Mountain. </span><span>Samples were taken at seven elevations of 150 m elevational difference between 800 and 1700 m. At each elevation eight subplots were established and Collembola and Oribatida were extracted from litter samples. </span><span><span> </span></span><span>We applied three state-of-the-art methods to elucidate trophic positions and basal resource use of both taxa at community level: b<span>ulk stable isotope analysis of nitrogen and carbon (SI<sub>bulk</sub>), compound-specific stable isotope analysis of amino acids (CSSIA-AA) and dietary routing through neutral lipid fatty acids (NLFA).</span></span><span><span> </span></span><span>Δ</span><sup><span>15</span></sup><span>N<sub>bulk</sub> and trophic position calculated using CSSIA-AA (TP<sub>CSSIA</sub>) both increased with elevation in both taxa. Stable isotope mixing models</span><span> using δ<sup>13</sup>C of essential amino acids indicated saprotrophic fungi as most important resource at most elevations for both taxa. Also, proportions of marker NLFAs changed little across elevations in both taxa, however, in Collembola the contribution of bacterial markers was generally higher than in Oribatida. Δ</span><sup><span>13</span></sup><span>C<sub>bulk </sub>did not respond linearly to the elevational gradient; however, changes with elevations differed between Collembola and Oribatida. A strong linear relationship between </span><span>δ</span><sup><span>15</span></sup><span>N of phenylalanine in consumers and</span><span> δ</span><sup><span>15</span></sup><span>N of litter indicated litter as basal resource of both taxa. </span><span>Overall, the results suggest that food web functioning likely changes with changing forest types along elevational gradients with microarthropods switching from feeding at the base of the food web to feeding at higher trophic levels potentially compromising their role in litter decomposition and nutrient cycling.</span></p>
FIG. 2 in Elevation of Divergent Color Polymorphic and Monomorphic Lizard Lineages (Squamata: Agamidae) to Species Level
FIG. 2. Genetic divergence between Ctenophorus modestus (northern Flinders Ranges [NFR], southern Flinders Ranges [SFR], Olary Ranges [OR]) and C. decresii (mainland south [MS] which encompasses the Mount Lofty Ranges and Fleurieu Peninsula, and Kangaroo Island [KI]) and specimens examined for morphological analyses. (A) Results of a Bayesian analysis of ancestry in the program STRUCTURE (K ¼ 2). Each vertical bar represents an individual and individuals are ordered by population and latitude (from north to south). The proportion of white and gray represents the proportion of C. modestus and C. decresii ancestry in each individual, respectively. (B) Two-dimensional principal coordinate plot (PCoA) showing pairwise genetic distances between individuals: Ctenophorus modestus (squares), C. decresii (circles). 95% confidence ellipses of each population are shown. Both A and B were constructed from a genomic SNP dataset of 1333 SNPs (n ¼ 148). (C) Map showing geographic locations of samples of C. modestus and C. decresii shown in A and B.
FIG. 6 in Elevation of Divergent Color Polymorphic and Monomorphic Lizard Lineages (Squamata: Agamidae) to Species Level
FIG. 6. Geographic distribution of Ctenophorus modestus (squares) and C. decresii (circles) based on specimens in Australian Museums from the Atlas of Living Australia (data: https://doi.org/10.26197/ 5d91626857226); coordinates which were likely inaccurate (e.g., unsuitable habitat) were removed. Protected areas described in the Collaborative Australian Protected Areas Database (CAPAD, 2018) are shown as gray areas. The extent of occurrence is shown for C. modestus (49,102 km2; orange lines) and C. decresii (6,604 km2 total [mainland: 5,772 km2; Kangaroo Island: 832 km2], blue lines).
FIG. 1 in Elevation of Divergent Color Polymorphic and Monomorphic Lizard Lineages (Squamata: Agamidae) to Species Level
FIG. 1. (A) The blue mainland male throat of C. decresii sensu stricto and (B) the blue reticulated with yellow throat found on Kangaroo Island. The four male throat morphs of Ctenophorus modestus: (D) orange, (E) yellow, (F) orange-yellow (yellow with an orange central patch), and (G) gray. Male dorsolateral patterning and coloration differ where (C) C. decresii sensu stricto has a more ''pinched'' or broken lateral stripe with a greater extent of bordering orange or yellow coloration, and (H) C. modestus has a relatively straight edged lateral stripe with cream and orange coloration terminating at the shoulder.
FIG. 4 in Elevation of Divergent Color Polymorphic and Monomorphic Lizard Lineages (Squamata: Agamidae) to Species Level
FIG. 4. Males (A, C) and females (B, D) of Ctenophorus decresii (A, B) from Palmer and Mengler's Hill Lookout, respectively, and C. modestus (C, D) from Telowie Gorge, all from South Australia, Australia (photos: copyright Adam Elliott).
FIG. 3 in Elevation of Divergent Color Polymorphic and Monomorphic Lizard Lineages (Squamata: Agamidae) to Species Level
FIG. 3. (A) Map showing localities of examined museum specimens: Ctenophorus modestus (squares); C. decresii sensu stricto (circles); C. fionni (triangles); and C. vadnappa (plus [þ]). Specimens without GPS coordinates are excluded. (B–C) Linear discriminant analyses of (A) males and (B) females based on 23 variables: Ctenophorus modestus (squares); C. decresii (circles); C. fionni (triangles); and C. vadnappa (plus [þ]). 95% confidence ellipses are shown. The lectotypes (males) and paralectotypes (females) of C. modestus and C. decresii are indicated with arrows.
FIG. 5 in Elevation of Divergent Color Polymorphic and Monomorphic Lizard Lineages (Squamata: Agamidae) to Species Level
FIG. 5. (A) Dorsal (left) and ventral (right) photographs of the lectotype of Ctenophorus decresii (Duméril and Bibron, 1837; MNHN 6545). This specimen is an adult male collected from Kangaroo Island, South Australia, in January 1803 (photos: Nicolas Vidal, Muséum national d'Histoire naturelle Paris). (B) Dorsal (left) and ventral (right) photographs of the lectotype of Ctenophorus modestus (Ahl, 1926; ZMB 54516). This specimen is a subadult male from ''Australien'' without an exact locality or reference to a collector or donor (photos: Frank Tillack, Museum für Naturkunde Berlin).
PI Data: Multispectral and Thermal Surface Imagery and Surface Elevation Mosaics (CAMSPEC-AIR)
<p>This data set contains high-resolution image products (orthomosaics) acquired from midsized uncrewed aerial systems that have been processed for value-added quality. The instrument itself, a multispectral imager, the Altum by Micasense, captures six spectral bands (red, green blue, NIR, red edge, and LWIR/thermal1) as radiance, which is converted to reflectance. The code used to develop these images first uses tools from the Micasense Python library2 to apply dark level corrections, row gradient corrections, and radiometric corrections. Next, it uses the processing API from Agisoft Metashape software to align and mosaic the processed imagery, following the processes developed by the USGS' structure from motion workflow documentation.3 Captures at different altitudes (recorded in MSL) produce an orthomosaic, a tif image containing information related to the six spectral bands, and a digital elevation model (DEM), a tif image containing information related to the elevation of the surveyed terraine. Metadata included in every image can be used to extract lat, lon, and reflectance values.</p> <p> </p> <p>Cite as: Tagestad, J., Nelson, K., Goldberger, L., Gonzalez-Hirshfeld, I. PI Data: Multispectral and Thermal Surface Imagery and Surface Elevation Mosaics (CAMSPEC-AIR). (02/04/2023 – 02/08/2023) ARM Data Center [Dataset]. DOI: 10.5439/1969041. <a href="http://arm.gov/capabilities/instruments/camspec-air">https://arm.gov/capabilities/instruments/camspec-air</a> , 2023.</p>
PI Data: Multispectral and Thermal Surface Imagery and Surface Elevation Mosaics (CAMSPEC-AIR)
<p>This data set contains high-resolution image products (orthomosaics) acquired from midsized uncrewed aerial systems that have been processed for value-added quality. The instrument itself, a multispectral imager, the Altum by Micasense, captures six spectral bands (red, green blue, NIR, red edge, and LWIR/thermal1) as radiance, which is converted to reflectance. The code used to develop these images first uses tools from the Micasense Python library2 to apply dark level corrections, row gradient corrections, and radiometric corrections. Next, it uses the processing API from Agisoft Metashape software to align and mosaic the processed imagery, following the processes developed by the USGS' structure from motion workflow documentation.3 Captures at different altitudes (recorded in MSL) produce an orthomosaic, a tif image containing information related to the six spectral bands, and a digital elevation model (DEM), a tif image containing information related to the elevation of the surveyed terraine. Metadata included in every image can be used to extract lat, lon, and reflectance values.</p> <p> </p> <p>Cite as: Tagestad, J., Nelson, K., Goldberger, L., Gonzalez-Hirshfeld, I. PI Data: Multispectral and Thermal Surface Imagery and Surface Elevation Mosaics (CAMSPEC-AIR). (07/09/2022 – 07/18/2022) ARM Data Center [Dataset]. DOI: 10.5439/1962600. https://arm.gov/capabilities/instruments/camspec-air , 2023.</p>
FIG. 1 in Climate-Mediated Competition in a High-Elevation Salamander Community
FIG. 1. (a) Cross-section diagram of the experimental mesocosm with numerical labels indicating surface and underground layers: (1) misting nozzle, (2) surface atmosphere, (3) soil layer, (4) first interstitial space, (5) burrow, (6) second interstitial space, and (0) bulkhead. Arrows indicate directional water flow. (b) Lateral view of a constructed mesocosm (without lid and heat cables). Subsurface spaces and burrows are visible below the soil/litter layer with cover objects.
FIG. 2 in Climate-Mediated Competition in a High-Elevation Salamander Community
FIG. 2. Mean ± SE change in mass for P. cinereus (PCIN; triangles) and P. shenandoah (PSHE; squares) under cool (blue lines) and warm (red lines) treatments in interspecific and intraspecific pairings.
FIGURE 6 in Climatic conditions may structure the distribution of Syzygiella rubricaulis (Nees) Steph., a disjunct and high elevation species
FIGURE 6. Box plots comparing the three classes of clusters 1 (G1), 2 (G2), and 3 (G3) referring to the three most important environmental variables: minimum temperature of coldest month (bio 6), annual precipitation (bio 12), precipitation of wettest month (bio 13), and water vapor pressure.
FIGURE 5 in Climatic conditions may structure the distribution of Syzygiella rubricaulis (Nees) Steph., a disjunct and high elevation species
FIGURE 5. Principal component analysis based on 21 Worldclim dataset environmental variables extracted from the 164 spatially unique records. The environmental variables included: temperature variables: annual mean temperature (bio 1), mean diurnal temperature range (bio 2), isothermality (bio 3), temperature seasonality (bio 4), maximum temperature of warmest month (bio 5), minimum temperature of coldest month (bio 6), temperature annual range (bio 7), mean temperature of wettest quarter (bio 8), mean temperature of driest quarter (bio 9), mean temperature of warmest quarter (bio 10), mean temperature of coldest quarter (bio 11); and precipitation variables: annual precipitation (bio 12), precipitation of wettest month (bio 13), precipitation of driest month (bio 14), precipitation seasonality (bio 15), precipitation of wettest quarter (bio 16), precipitation of driest quarter (bio 17), precipitation of warmest quarter (bio 18), precipitation of coldest quarter (bio 19); as well as water vapor pressure (water_vapo), and solar radiation (solar_radi).
FIGURE 4 in Climatic conditions may structure the distribution of Syzygiella rubricaulis (Nees) Steph., a disjunct and high elevation species
FIGURE 4. Distribution of 164 spatial unique records classified into three cluster groups according to their environmental similarity: Green clade (circles), Black clade (triangles), and Red clade (diamons). The green area presents aggregated Neotropical ecoregions (Atlantic Forests; Central Andean Yungas; Choco-Darien Moist Forests; Greater Antillean Pine Forests; Guyana Highlands Moist Forests; Northern Andean Montane Forests; Northern Andean Páramo; Southwestern Amazonian Moist Forests; Talamancan-Isthmian Pacific Forests) mapped by WWF (terrestrial-ecoregions-of-the-world).
FIGURE 1 in Climatic conditions may structure the distribution of Syzygiella rubricaulis (Nees) Steph., a disjunct and high elevation species
FIGURE 1. Populations of Syzygiella rubricaulis in Podocarpus National Park, Ecuador (A) and Páramo San José, Venezuela (B); method of collection (C); habit (D); male and female shoots (E). [Photos: A-D from D.P. Costa; E from A. Maciel-Silva].
Data for: Biodiversity and elevation gradients across worldwide Mountains
<div> <em>Aim:</em> Identifying macroecological patterns and biases in species distribution is a challenging but essential task in biodiversity-oriented studies. Despite the extensive attempts to find consistent species richness elevation (SRE) patterns, the topic remains controversial owing to widespread conflicting, idiosyncratic, and non-generalizable underlying mechanisms. We compiled a database to perform a meta-analytical review to answer why patterns of species-richness in elevation gradients remain elusive, a long-standing, central but contentious macroecological and biogeographical question.</div> <div> <em>Location</em>: Global elevation gradients.</div> <div> <em>Taxon</em>: Major terrestrial taxa (invertebrates, vertebrates, and plants).</div> <div> <em>Methods</em>: We tested the effect of elevation on species richness using multilevel mixed-effects meta-analytical models. Data from 127 studies spawning almost one century of research were integrated to test the effect of elevation across distinct 1) SRE models, 2) quality of primary data (e.g., mountain sampling coverage), 3) biogeographic realms, 4) studied taxa, and 5) organism mobility.</div> <div> <em>Results</em>: The linear negative pattern showed the strongest model fit followed by the hump-shaped and the linear positive models. We observed that studies with higher sampling sizes showed a consistent decrease in the strength of SRE patterns. Further, the larger the mountain coverage and sampled range, the stronger the detection of some SRE patterns. Overall, the elevational effect on species richness was consistent across biogeographical realms, taxonomic groups, and organism mobility.</div> <div> <em>Main conclusions</em>: This study indicates a bias in the detection of SRE patterns, driven mostly by mountain comprehensiveness, namely the number of sampling units, sampled range, and mountain coverage. These results call attention to the evidence that undersampled elevation gradients may bias our understanding on the complex relationships between elevation and biodiversity, thus impairing a broad understanding on the ecology, evolution, biogeography, and conservation of mountain biota.</div>
Fig. 6 in Profiling alkaloids in Aconitum pendulum N. Busch collected from different elevations of Qinghai province using widely targeted metabolomics
Fig. 6. Comparison of the peak areas of various classes of significantly different abundant metabolites among HZX, MYG, ZKW, GLM, YSZ, and GNG. Bars represent the peak areas of the significantly differentially abundant metabolites.
Fig. 3 in Profiling alkaloids in Aconitum pendulum N. Busch collected from different elevations of Qinghai province using widely targeted metabolomics
Fig. 3. Differential metabolite analysis OPLS-DA plots of MYG, ZKW, GLM, YSZ, and GNG compared to HZX.
Fig. 7 in Profiling alkaloids in Aconitum pendulum N. Busch collected from different elevations of Qinghai province using widely targeted metabolomics
Fig. 7. Correlation analysis between the environmental parameters and differentially abundant alkaloids (a), and. between differentially abundant alkaloids and anti-inflammatory activity (b). The red block indicates a positive correlation; the green block indicates a negative correlation; * indicates significant correlation; ** indicates extremely significant correlation.. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5 in Profiling alkaloids in Aconitum pendulum N. Busch collected from different elevations of Qinghai province using widely targeted metabolomics
Fig. 5. Venn diagram illustrating shared or unique metabolite contents that differed significantly among the different comparison groups.
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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.
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