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638 results for “biomes”
Data from: Seasonal patterns in species diversity across biomes
A conspicuous Season-Diversity Relationship (SDR) can be seen in seasonal environments, often with a defined peak in active species diversity in the growing season. We ask is this a general pattern and are other patterns are possible? In addition, we ask what is the ultimate cause of this pattern and can we understand it using existing ecological theory? To accomplish this task, we assembled a global database on changes in species diversity through time in seasonal environments for different taxa and habitats and also conducted a modeling study in an attempt to replicate observed patterns. Our global database includes terrestrial and aquatic habitats, temperate, tropical, and polar environments, and taxa from disparate groups including vertebrates, insects, and plankton. We constructed nine alternative models that vary in assumptions on type of seasonal forcing, responses to that forcing, species niches, and types of species interactions. We found that most guilds of species exhibit a repeatable Season-Diversity Relationship (SDR) across years. For north temperate ecosystems, active species diversity generally peaks mid-year. The peak for a guild is generally more pronounced in terrestrial habitats than aquatic habitats and more pronounced in temperate and polar regions than the tropics. We now have evidence that at least several different habitat and taxa types are likely to have multiple peaks in diversity in a year, for example guilds of both aquatic microbes and desert vertebrates can show a bimodal or multimodal SDR. We compared all nine candidate models in their ability to explain the patterns and match their assumptions to the data. Some performed considerably better than others in being able to match the different patterns. We conclude that a model that includes both temperature niches and environmental feedbacks is necessary to explain the different season-diversity relationships. We use such a model to make predictions on how the SDR could be impacted by climate change. More effort should be put into documenting and understanding baseline seasonal patterns in diversity in order to predict future responses to global change.
Figure 1 in Small mammals and microhabitat selection in forest fragments in the transition zone between Atlantic Forest and Pampa biome
Figure 1. Location of the study area in South America and Rio Grande do Sul State, Brazil. Atlantic Forest and Pampa biome extents in the Rio Grande do Sul. Regional landscape with forest fragments, main roads, and matrix extent. Numbers 1 and 2 indicate the studied fragments (Fragment 1 – 28°08′38″S, 54°45′36″W, 30 ha, Fragment 2 – 28°07′33″S, 54°44′57″W, 20 ha).
Figure 2 in Small mammals and microhabitat selection in forest fragments in the transition zone between Atlantic Forest and Pampa biome
Figure 2. Number of individuals captured for the four most abundant species of small mammals in two forest fragments of Atlantic Forest between Autumn 2015 and Spring 2016 in Cerro Largo, Rio Grande do Sul, Brazil. (A) Akodon montensis; (B) Oligoryzomys nigripes; (C) Sooretamys angouya; (D) Didelphis albiventris. F1 = Fragment 1; F2 = Fragment 2.
Fig. 4 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil
Fig. 4. Spatial distribution of the species found in the Santa Alice rural districts.
Fig. 5 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil
Fig. 5. Semi-variograms of Shannon and Simpson diversity indices.
Open data for "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate"
<p>______________________________________________________<br> This page contains public-domain data required to reconstruct simulation results in the manuscript "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate," submitted by the following author.</p> <p>Author: Hisashi SATO (JAMSTEC) <br> email : hsatoscb_(at)_gmail.com</p> <p>______________________________________________________<br> 1. Folder "Code"<br> Detailed descriptions are available on the code. </p> <p>1-1. MachineLearningComparison.R<br> Machine learning programs using random forest (RF), naive Bayes classifier (NV), and support vector machine (SVM) algorithms.</p> <p>1-2. Analyse_MapSimilarity.R<br> Calculate coincidences of simulated potential natural vegetation (PNV) maps simulated by different models.</p> <p>1-3. Visualize_VCE.R<br> Generating VCE (Visualize Climate Image) for training CNN models.</p> <p>1-4. Visualize_Maps.R<br> Visualizing global PNV maps.</p> <p>1-5. Visualize_ClimateHistgrams.R<br> Visualizing histograms of climate datasets.</p> <p>______________________________________________________<br> 2. Folder "Input"</p> <p>2-1. Unified_BIOCLIM_WorldClim.csv<br> Input data for the current climate.<br> This file contains the following variables.<br> lon Longitude at the center of the grid<br> lat Latitude at the center of the grid<br> bio1~19 Average climate indices from BIOCLIM (AveI)<br> CDD~WSDI Extreme climate indices (CEI)<br> c1~c16 Fraction of PNV from MODIS data<br> tavg01~tavg12 Monthly mean air temperature from January to December (Ave)<br> prec01~prec12 Monthly precipitation from January to December (Ave)</p> <p>2-2. Unified_BIOCLIM_WorldClimFutureRCP85.csv<br> Input data for future climate (@RCP8.5)<br> Including variables are the same as Unified_BIOCLIM_WorldClim.csv</p> <p>2-3. BIOCLIM_RefNo.csv<br> This CSV file contains the following information for each grid.<br> lat: Latitude at the center of the grid<br> lon: Longitude at the center of the grid<br> latNo: Latitude number corresponding to the image file name<br> lonNo: Longitude number corresponding to the image file name<br> lineNo: No use. Don't mind.<br> vegNo: Most dominant PNV based on the Unified_BIOCLIM_WorldClim.csv</p> <p>______________________________________________________<br> 3. Folder "Output"</p> <p>3-1. PNV_sim<br> 3-2. PNV_sim_RCP85.csv<br> Current and future PNV maps from various models. These files are the main output files from the code MachineLearningComparison.R. For PNV maps from CNN models (m4p1~6) were supplemented. Detailed methods to build CNN models, please refer to the following manuscript.<br> Sato, H. & T. Ise (2022). "Predicting global terrestrial biomes with the LeNet convolutional neural network." Geoscientific Model Development 15(7): 3121-3132.</p> <p>Labels indicate combinations of machine-learning-algorithm and dataset for training the model. For example, In case of "m1p1", that column shows the simulation result of models trained with randomForest (RF) algorithm and Ave dataset.<br> m1: randomForest (RF)<br> m2: Support vector machine (SVM)<br> m3: Naive Bayes (NB)<br> m4: Convolutional Neural Network (CNN), which is NOT analysed in this code<br> p1: Ave<br> p2: Ave + CEI<br> p3: Ave + CEIpart<br> p4: AveI <br> p5: AveI + CEI<br> p6: AveI + CEIpart</p>
Biomes dataset
<p>This dataset contains images of 4 biomes. Namely, forest, glacier, mountain and sea.</p>
Biomed 101 and Interleukin-2 in Treating Patients With Kidney Cancer
ClinicalTrials.gov study NCT00004890. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Study to Assess the Effect of Doctor's Biome Medical Food in Individuals With Clostridium Difficile Infection
ClinicalTrials.gov study NCT06367504. IPD Sharing: NO. Countries: 1. Publications: 0.
A Registry Study of Biomarkers in Ischemic Heart Disease ( BIOMS-IHD )
ClinicalTrials.gov study NCT05965882. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Effect of GI Biome #7 on Gut Microbiome and Health of the Elderly
ClinicalTrials.gov study NCT05735418. IPD Sharing: NO. Countries: 1. Publications: 0.
Impact of Antibiotic Treatment and Extraction on the Oral Micro Biome
ClinicalTrials.gov study NCT02951676. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
My Baby Biome: Infant Stool Samples for Microbiome Health (MBB)
ClinicalTrials.gov study NCT05472688. IPD Sharing: NO. Countries: 1. Publications: 0.
Different Dialysis Modalities and Diet on Gastrointestinal Biome and Azotaemic Toxins
ClinicalTrials.gov study NCT04714853. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Pilot Study for Colorectal Cancer and Advanced Adenoma Detection With the Mainz Biomed Colorectal Cancer Test
ClinicalTrials.gov study NCT06864338. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Seasonal patterns in species diversity across biomes
Open the record for dataset details and reuse information.
Anthropogenic Biomes of the World, Version 2: 2000
The Anthropogenic Biomes of the World, Version 2: 2000 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa 2000. Potential natural vegetation biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.
Carbon Dynamics in Seagrass and Coral Reef Biomes in the Florida Keys
Measurements made under the High Resolution Assessment of Carbon Dynamics in Seagrass and Coral Reef Biomes, in the Florida Keys.
Anthropogenic Biomes of the World, Version 2: 1900
The Anthropogenic Biomes of the World, Version 2: 1900 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa 1900. Potential natural vegetation biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.
Anthropogenic Biomes of the World, Version 2: 1700
The Anthropogenic Biomes of the World, Version 2: 1700 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa 1700. Potential natural vegetation biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.
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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.