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60 results for “natural variability”
Disentangling the effects of eutrophication and natural variability on macrobenthic communities across French coastal lagoons
<p>We present here the raw data and scripts to reproduce the results presented in the preprint "Disentangling the effects of eutrophication and natural variability on macrobenthic communities across French coastal lagoons" available on BioRxiv. Before using the scripts and associated data, we recommend reading the "readme" word document also available, which details the information available in the different data sheets. </p> <p>Preprint abstract : </p> <p>Coastal lagoons are transitional ecosystems that host a unique diversity of species and support many ecosystem services. Owing to their position at the interface between land and sea, they are also subject to increasing human impacts, which alter their ecological functioning. Because coastal lagoons are naturally highly variable in their environmental conditions, disentangling the effects of anthropogenic disturbances like eutrophication from those of natural variability is a challenging, yet necessary issue to address. Here, we analyze a dataset composed of macrobenthic invertebrate abundances and environmental variables (hydro-morphology, water, sediment and macrophytes) gathered across 29 Mediterranean coastal lagoons located in France, to characterize the main drivers of community composition and structure. Using correlograms, linear models and variance partitioning, we found that lagoon hydro-morphology (connection to the sea and lagoon surface), which affects the level of environmental variability (salinity and temperature), as well as lagoon-scale benthic habitat diversity (using macrophyte morphotypes) seemed to regulate macrofauna distribution, while eutrophication and associated stressors like low dissolved oxygen, acted upon the existing communities, mainly by reducing species richness and diversity. Furthermore, M-AMBI, a multivariate index composed of species richness, Shannon diversity and AMBI (AZTI's Marine Biotic Index) and currently used to evaluate the ecological state of French coastal lagoons, was more sensitive to eutrophication (18%) than to natural variability (9%), with nonetheless 49% of its variability explained jointly by both. To improve the robustness of benthic indicators like M-AMBI and increase the effectiveness of lagoon benthic habitat management, we call for a revision of the ecological groups at the base of the AMBI index and of the current lagoon typology which could be inspired by the lagoon-sea connection levels used in this study. </p>
VisMetHack2022: Visualizing winds and surface variables from the ECMWF IFS 1-km nature run
<p><strong>Overview</strong></p> <p>This data collection was contributed to the <a href="https://events.ecmwf.int/event/305/">Visualisation Hackathon 2022</a> (#VisMetHack2022), in conjunction with the Using <a href="https://events.ecmwf.int/event/296/">ECMWF's Forecasts (UEF2022</a>) workshop.</p> <p>The European Center for Medium-Range Weather Forecasts (ECMWF) and the Oak Ridge National Laboratory (ORNL) are pleased to announce access to the data collection from global 1-km nature run (NR) simulations using the Integrated Forecast System (IFS) with explicit convection. We invite you to join us in exploring this precursor to a digital twin of the earth!</p> <p>The NR simulations reveal unprecedented detail of the earth’s atmosphere [1], and the then outgoing Editor-in-Chief of AGU JAMES commended the project as one of “stunning ambitions,” enabled by computational capacity at scale [2]. The project also won the <em>2020 HPCwire Readers Choice Award </em>for Best Use of HPC in Physical Sciences. </p> <p>A set of two NR seasonal simulations have been completed, one corresponding to the northern hemispheric winter months (NDJF) and the other for the North Atlantic tropical cyclone season (ASO). The project used the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF). The simulations were facilitated with an INCITE award from the US Department of Energy Office of Science. </p> <p>For the first seasonal run of four months (NDJF), the hydrostatic IFS model was initialized at 00Z on 1 November 2018. The NR for the TC season (AS) was initialized at 00Z on 1 August 2019. The NR simulations were constrained only by sea surface temperatures (SST) at the lower boundary. The IFS output was saved every 3 hours.</p> <p>After feedback and interest from the scientific community, the simulations were rerun for four specific extreme events, with output every 15 minutes. The special cases include a tropical cycle and three severe storm events over the continental USA.</p> <p><strong>NR Data for visualizing winds</strong></p> <p>A small subset from the 1-km IFS NR collection is make available for #VisMetHack22. This subset is extracted from the tropical cyclone area in the North Atlantic from the ASO simulations. The 912 model time steps correspond to 97935 to 111600 in minutes since the NR reference time 2019-08-01 00:00:00. The time increment is 15 minutes, corresponding to the output frequency.</p> <p>The following variables are provided for #VisMetHack2022:<br> </p> <table> <tbody> <tr> <td> <p>Short Name</p> </td> <td> <p>Parameter ID</p> </td> <td> <p>Units</p> </td> <td> <p>Long Name</p> </td> </tr> <tr> <td> <p>10u</p> </td> <td> <p>165</p> </td> <td> <p>m/s</p> </td> <td> <p>10 metre U wind component</p> </td> </tr> <tr> <td> <p>10v</p> </td> <td> <p>166</p> </td> <td> <p>m/s</p> </td> <td> <p>10 metre V wind component</p> </td> </tr> <tr> <td> <p>2t</p> </td> <td> <p>167</p> </td> <td> <p>K</p> </td> <td> <p>2 metre temperature</p> </td> </tr> <tr> <td> <p>i10fg</p> </td> <td> <p>228029</p> </td> <td> <p>m/s</p> </td> <td> <p>Instantaneous 10 metre wind gust</p> </td> </tr> <tr> <td> <p>msl</p> </td> <td> <p>151</p> </td> <td> <p>Pa</p> </td> <td> <p>Mean sea level pressure</p> </td> </tr> <tr> <td> <p>xtprate</p> </td> <td> <p>99999</p> </td> <td> <p>kg m**-2 s**-1</p> </td> <td> <p>Total instantaneous precipitation rate. Summation of convective and large scale rain and snowfall rates. </p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Data processing</strong></p> <ol> <li> <p>The native model output was in the form of data objects consisting of GRIB1/2 16-bit AEC compressed messages. The messages were extracted from the FDB database instances into one or more files.</p> </li> <li> <p>The files containing the GRIB messages were interpolated to 0.02 x 0.02 a regular latitude-longitude grid using ECMWF Meteorological Interpolation and Regridding (MIR), and then written out to files as GRIB messages.</p> </li> <li> <p>The MIR output files were extracted to the area of interest (AOI) from global fields, and converted to Netcdf-4 (NC).</p> </li> <li> <p>The metadata in NC4 files were selectively edited or added.</p> </li> <li> <p>Finally, the NC4 files were compressed to reduce volume using the ncks utility from Netcdf Operators (NCO), with lossless L1 compression.</p> </li> <li> <p>The variable ‘xtprate’ was calculated by a summation of instantaneous and large scape rainfall and snowfall rates.</p> </li> </ol> <p><strong>Contact</strong></p> <p>Valentine Anantharaj <<a href="mailto:vga@ornl.gov">vga@ornl.gov</a>> or <vga1.ornl@gmail.com> </p> <p>Samuel Hatfield <<a href="mailto:Samuel.Hatfield@ecmwf.int">Samuel.Hatfield@ecmwf.int</a>></p> <p> </p> <p><strong>Citation and references</strong></p> <p>Please cite the following manuscript as well as the DOI provided by Zenodo:</p> <p>[1] Wedi, N. P., Polichtchouk, I., Dueben, P., Anantharaj, V. G., Bauer, P., Boussetta, S., et al. (2020). A baseline for global weather and climate simulations at 1 km resolution. Journal of Advances in Modeling Earth Systems, 12, e2020MS002192. <a href="https://doi.org/10.1029/2020MS002192">https://doi.org/10.1029/2020MS002192</a></p> <p>[2] Anantharaj, V., Hatfield, S. and Vukovic, Milana (2022). VisMetHack2022: Visualizing winds and surface variables from the ECMWF IFS 1-km nature run. https://doi.org/10.5281/zenodo.6633929</p> <p><strong>Acknowledgements</strong></p> <p>This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725.</p> <p>ECMWF also benefited from collaborations funded via ESCAPE-2 (No. 800897), MAESTRO (No. 801101), EuroEXA (No. 754337), and ESiWACE-2 (No. 823988) projects funded by the European Union's Horizon 2020 future and emerging technologies and the research and innovation programmes. </p>
Figs. 1–12 in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)
Figs. 1–12. Diplomaragna terricolor (Attems, 1899). Variation in structure of the posterior angiocoxal process of posterior gonopod. 1 — from Anuchinsky District, Primorsky Krai; 2 — from Mt. Ko, Khabarovsky Krai; 3 — from Vladivostok, Okeanskaya Station, Primorsky Krai; (1st copy); 4 — from Vladivostok, Okeanskaya Station, Primorsky Krai; (2st copy); 5 — from Kedrovaya Pad Nature Reserve, Primorsky Krai; 6 — from Popova Island, Primorsky Krai; (1st copy); 7 — from Ussuriysky Nature Reserve, Primorsky Krai; 8 — from Lazovsky Nature Reserve, Primorsky Krai; 9 — from Sikhote-Alin Nature Biosphere Reserve (floodplain terrace, Yasnaya River), Primorsky Krai; 10 — from Chuguevsky District, Verkhneussuriysky Research Station, Primorsky Krai; 11 — from Popova Island, Primorsky Krai; (2st copy); 12 — from Sikhote-Alin Nature Biosphere Reserve, Blagodatnoe, Ozerny Stream, Primorsky Krai; a — apical outgrowth; b — finger-shaped process; m — mesal protrusion. Scale in mm (0.5)
Figs. 13–16 in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)
Figs. 13–16. Diplomaragna terricolor (Attems, 1899). Posterior angiocoxal processes of posterior gonopods. 13 — from Mt. Ko, Khabarovsky Krai; 14 — from Vladivostok, Okeanskaya Station, Primorsky Krai; 15 — from Lazovsky Nature Reserve, Primorsky Krai; 16 — from Sikhote-Alin Nature Biosphere Reserve, Blagodatnoe, Ozerny Stream, Primorsky Krai; a — apical outgrowth; b — finger-shaped process; m — mesal protrusion. Scales: 100 μm
Figs. 21–24 in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)
Figs. 21–24. Diplomaragna terricolor (Attems, 1899). Coxae of male leg pair 11, front view. 21 — from Mt. Ko, Khabarovsky Krai; 22 — from Vladivostok, Okeanskaya Station, Primorsky Krai; 23 — from Lazovsky Nature Reserve, Primorsky Krai; 24 — from SikhoteAlin Nature Biosphere Reserve, Blagodatnoe, Ozerny Stream, Primorsky Krai; p — coxal process. Scales: 100 μm Рис. 21–24. Diplomaragna terricolor (Attems, 1899). Коксы 11-й пары ног самца, виΑ спереΑи: 21 — с горы Ко в Хабаровском крае; 22 — из ВΛаΑивостока, станция Океанская; 23 — из Αазовского заповеΑника в Приморском крае; 24 — из СихотэАΛинского заповеΑника (урочище БΛагоΑатное, кΛюч Озёрный) в Приморском крае; р — коксаΛьный отросток. Масштабы: 100 μm
Figs. 17–20 in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)
Figs. 17–20. Diplomaragna terricolor (Attems, 1899). Gonopods, caudal view. 17 — from Mt. Ko, Khabarovsky Krai; 18 — from Vladivostok, Okeanskaya Station, Primorsky Krai; 19 — from Lazovsky Nature Reserve, Primorsky Krai; 20 — from Sikhote-Alin Nature Biosphere Reserve, Blagodatnoe, Ozerny Stream, Primorsky Krai; a — apical outgrowth; b — finger-shaped process; m — mesal protrusion. Scales: 100 μm
Map. Species diversity of Diplopoda within the Sikhote-Alin State Nature Biosphere Reserve. The numbers of the natural landmarks (1–18) are explained in the "Material and Methods section" Карта. ВиΑовое разнообразие Αвупарноногих многоножек в Сихотэ-АΛинском заповеΑнике. Номера урочищ (1–18) поясняются в разΑеΛе «МатериаΛ и метоΑы» in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)
Map. Species diversity of Diplopoda within the Sikhote-Alin State Nature Biosphere Reserve. The numbers of the natural landmarks (1–18) are explained in the "Material and Methods section" Карта. ВиΑовое разнообразие Αвупарноногих многоножек в Сихотэ-АΛинском заповеΑнике. Номера урочищ (1–18) поясняются в разΑеΛе «МатериаΛ и метоΑы»
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019) in Variability of the gene cyt b in the Korean field mouse Apodemus peninsulae Thomas, 1906 - a reservoir host of AMRV in the Khasansky District of Primorsky Krai
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019)
Fig. 3 in Temporal variability of fish larvae assemblages: influence of natural and anthropogenic disturbances
Fig. 3. Principal Components Analysis of the environmental variables' matrixes recorded in the upper Uruguay River between October 2001 and March 2004. Sampling sites: LIG: Ligeiro, ULIG: Uruguay-Ligeiro, CH: Chapecó and UCH: Uruguay-Chapecó. Reproductive Periods: RP1: First Reproductive Period, RP2: Second Reproductive Period and RP3: Third Reproductive Period.
Fig. 2 in Temporal variability of fish larvae assemblages: influence of natural and anthropogenic disturbances
Fig. 2. Abundance of fish larvae in different stages of development recorded in the sampling sites of the upper Uruguay River from October 2001 to March 2004. Larval development stages: LY = Larval Yolk; PF = Pre-flexion; FL = Flexion and FP = Post-flexion. Sampling sites: LIG: Ligeiro, ULIG: Uruguay-Ligeiro, CH: Chapecó and UCH: Uruguay- Chapecó. Reproductive Periods: RP1: First Reproductive Period, RP2: Second Reproductive Period and RP3: Third Reproductive Period.
Fig. 1 in Temporal variability of fish larvae assemblages: influence of natural and anthropogenic disturbances
Fig. 1. Location of the sampling sites in the upper Uruguay River in southern Brazil. Samplings sites: LIG: Ligeiro, ULIG: Uruguay-Ligeiro, CH: Chapecó, UCH: Uruguay-Chapecó.
Plant Community Formation and Species Distribution Patterns in relation to environmental variables in Endiras Natural Forest, Fogera District, South Gondar Zone, Ethiopia
<p>We need to deposit the data for the manuscript entitled plant community formation and species distribution patterns in relation to environmental variables in Endiras forest, Fogera District, South Gondar Zone, Ethiopia so as to be cited easily </p>
Low temperature magnetic properties of variably oxidized natural and synthetic siderite Dekkers et al., 2023, G-cubed
<p>The data magnetometric measurements performed on natural and synthetic iron carbonates presented in Dekkers et al. (2023) submitted to G-cubed. Details on the samples and methods can be found in the manuscript and the Additional Supporting Information</p> <p> </p>
Including intraspecific trait variability to avoid distortion of functional diversity and ecological inference: lessons from natural assemblages
<p>1. Functional diversity assessments are crucial and increasingly used for understanding ecological processes and managing ecosystems. The functional diversity of a community is assessed by sampling traits at one or more scales (individuals, populations, species) and calculating a summary index of the variation in trait values. However, it remains unclear how the scale at which traits are sampled and the indices used to estimate functional diversity may alter the patterns observed and inferences about ecological processes.</p> <p>2. For 40 plant and 61 ant communities, we assess functional diversity using six methods – encompassing various mean-based and probabilistic methods – chosen to reflect common scenarios where different levels of detail are available in trait data. We test whether including trait variability at different scales (from individuals to species) alter functional diversity values calculated using volume-based and dissimilarity-based indices, Functional Richness (FRic) and Rao, respectively. We further test whether such effects alter the functional diversity patterns observed across communities and their relationships with environmental drivers such as abiotic gradients and occurrences of invasive species.</p> <p>3. Intraspecific trait variability strongly determined FRic and Rao. Methods using only species' mean trait values to calculate FRic (convex hulls) and Rao (Gower-based dissimilarity) distorted the patterns observed when intraspecific trait variability was considered. These distortions generated Type I and Type II errors for the effects of environmental factors structuring the plant and ant communities.</p> <p>4. The high sensitivity of FRic to individuals with extreme trait values was revealed in comparisons of different probabilistic methods including among-individual and among-population trait variability in functional diversity. By contrast, values and ecological patterns in Rao were consistent among methods including different scales of intraspecific trait variability.</p> <p>5. Decisions about where traits are sampled and how trait variability is included in functional diversity can drastically change the patterns observed and conclusions about ecological processes. We recommend sampling the traits of multiple individuals per species and capturing their intraspecific trait variability using probabilistic methods. We discuss how intraspecific trait variability can be reasonably estimated and included in functional diversity in the common circumstance where only limited trait data are available.</p>
Recent natural variability in global warming weakened phenological mismatch and selection on seasonal timing in great tits (Parus major)
<p></p><p> Climate change has led to phenological shifts in many species, but with large variation in magnitude among species and trophic levels. The poster child example of the resulting phenological mismatches between the phenology of predators and their prey is the great tit (Parus major), where this mismatch led to directional selection for earlier seasonal breeding. Natural climate variability can obscure the impacts of climate change over certain periods, weakening phenological mismatching and selection. Here, we show that selection on seasonal timing indeed weakened significantly over the past two decades as increases in late spring temperatures have slowed down. Consequently, there has been no further advancement in the date of peak caterpillar food abundance, while great tit phenology has continued to advance, thereby weakening the phenological mismatch. We thus show that the relationships between temperature, phenologies of prey and predator, and selection on predator phenology are robust, also in times of a slowdown of warming. Using projected temperatures from a large ensemble of climate simulations that take natural climate variability into account, we show that prey phenology is again projected to advance faster than great tit phenology in the coming decades, and therefore that long-term global warming will intensify phenological mismatches. </p><p></p>
Supplementary data for 'Ferrofluid impregnation efficiency and its spatial variability in natural and synthetic porous media: Implications for magnetic pore fabric studies'
<p>Supplementary data for the manuscript 'Ferrofluid impregnation efficiency and its spatial variability in natural and synthetic porous media: Implications for magnetic pore fabric studies'</p>
Plant community formation and species distribution pattern in relation to environmental variables in Endiras Natural Forest, northwest Ethiopia
<p><span>The study was conducted in Endiras Forest, Northwest Ethiopia, so as to evaluate the effects of environmental variables on the patterns of plant community formation. A systematic random sampling technique was used to collect vegetation data from 56 (20 m x 20 m) plots laid at 100 m intervals on ten transects. In each plot, the species encountered and its percent cover abundance were recorded, which was later transformed into a modified Braun-Blanquet scale. The composite soil samples collected from 15 cm x 15 cm subplots were examined for 13 soil parameters. Communities were determined using cluster analysis. The Shannon-Wiener index was employed to quantify species diversity. The relationships between species and environmental variables were evaluated using Canonical Correspondence Analysis (CCA). Seventy three woody plant species, distributed in 38 families, were documented. Fabaceae was found to be the most species-rich family (21.9%). Five communities were generated from the cluster analyses that vary in diversity. Nine environmental variables were found to be significant in determining patterns of community formation (Pr< 0.05). Organic matter, pH, and altitude, highly correlated with CCA axis 1, are largely shaped the species distribution patterns. Various patterns of community formation demonstrate the need to design different conservation measures.</span></p>
Plant community formation and species distribution pattern in relation to environmental variables in Endiras Natural Forest, northwest Ethiopia
<p><span>The study was conducted in Endiras Forest, Northwest Ethiopia, so as to evaluate the effects of environmental variables on the patterns of plant community formation. A systematic random sampling technique was used to collect vegetation data from 56 (20 m x 20 m) plots laid at 100 m intervals on ten transects. In each plot, the species encountered and its percent cover abundance were recorded. The composite soil samples collected from 15 cm x 15 cm subplots were examined for 13 soil parameters. Communities were determined using cluster analysis. The Shannon-Wiener index was employed to quantify species diversity. The relationships between species and environmental variables were evaluated using CCA. Seventy three woody plant species, distributed in 38 families, were documented. Fabaceae was found to be the most species-rich family (21.9%). Five communities were generated from the cluster analysis. Nine environmental variables were found to be significant in determining patterns of community formation (Pr< 0.05). Organic matter, pH, and altitude are largely shaped the species distribution patterns. Various patterns of community formation demonstrate the need to design different conservation measures.</span></p>
Data for "Why Climate Sensitivity may be Constrained by Observable Natural Variability"
<p>Surface temperature and top-of-atmosphere radiation data from a 700-yr CO<sub>2</sub>-doubling experiment performed with the fully-coupled CESM1. Given on the CAM5 native grid (1 degree nominal resolution) as annual means. </p> <p>Data include annual TS (radiative surface temperature; in K), FLNT (net longwave radiation out at top-of-atmosphere), FSNT (net shortwave radiation in at top-of-atmosphere), FLNTC (net clear-sky longwave radiation out at top-of-atmosphere) and FSNTC (net clear-sky shortwave radiation out at top-of-atmosphere).</p>
Reconstruction from Multi-Decadal Variability of Amundsen Sea Low Controlled by Natural Tropical and Anthropogenic Drivers
<p>This archive contains the sea-level pressure, sea surface temperature and sea ice concentration fields from the <em>unforced</em> reconstruction in the Southern Hemisphere and tropical regions over 1871–2000 CE presented in the following study:</p> <p>Dalaiden, Q., Abram, N.J., Goosse, H., Holland, P., O’Connor G.K. and Topál, D., Multi-Decadal Variability of Amundsen Sea Low Controlled by Natural Tropical and Anthropogenic Drivers, Geophysical Research Letters, https://doi.org/10.1029/2024GL109137 (2024).</p> <p>In addition to the <em>unforced </em>reconstruction, the total variability (i.e., unforced and forced variability) of the mean sea-level pressure over the Amundsen Sea Low area is also provided. </p> <p>Each NetCDF file corresponds to one variable. All fields are anomalies relative to the 1961–1990 CE period. The variables included in the archive are:</p> <p>SLP: Sea-level pressure</p> <p>mean_ASL: mean sea-level pressure over the Amundsen Sea Low area</p> <p>IPO: Interdecadal Pacific Oscillation Index</p> <p>SST: Sea surface temperature</p> <p>SIC: Sea ice concentration</p> <p>An estimation of the uncertainty is provided for each variable, except for the total variability of the mean sea-level pressure over the Amundsen Sea Low area.</p> <p>Please contact Quentin Dalaiden (quentin.dalaiden@uclouvain.be; quentin.dalaiden@gmail.com) for more information.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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