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93 results for “Environmental Predictability”
Supporting data for 'Hourly prediction of phytoplankton biomass and its environmental controls in lowland rivers'
<p>This dataset is used in the manuscript 'Hourly prediction of phytoplankton biomass and its environmental controls in lowland rivers' published in Water Resources Research. The dataset contains hourly observation of water quality in the lower Thames catchment, UK and were made available by the Environment Agency, UK. </p>
Data for: Machine learning for predicting environmental mobility based on retention behaviour
<p>This repository contains the data and supplementary information for the paper: "Machine learning for predicting environmental mobility based on retention behaviour".</p>
Code and data for manuscript: Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir.
<p>This is the source code and data required to reproduce data analysis and figures from the manuscript, "Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir". </p>
Data from: Improving inferences and predictions of species environmental responses with occupancy data
<p>Occupancy models represent a useful tool to estimate species distribution throughout the landscape. Among them, MacKenzie et al.'s model (2002, MC), is frequently used to infer species environmental responses. However, the assumption that detection probability is homogeneous or fully explained by covariates may limit its performance. Species should be more easily observed at sites with a higher number of individuals. We simulated data following Royle and Nichols (2003) occupancy model (RN) that accounts for abundance-driven heterogeneous detection and two variants with overdispersion in the detection probability and local abundances. Then, we compared the performance of the MC model against that of RN.</p> <p> In addition to model misspecifications, insufficient information in data (i.e. infrequent detections) can limit our ability to detect existing effects with affordable sampling designs. To deal with this source of error, we extended RN approach to a community-level joint species model (RN-JSM), where species responses and detectability depended on their traits and phylogeny. Then, we tested RN-JSM performance in simulated and out-of-sample field data.</p> <p>High abundance-driven heterogeneity in detection (i.e. common and secretive species) limited the ability of the MC model to quantify covariate effects; especially, when the number of visits was low. Both models (MC and RN), often failed to detect existing effects when data were overdispersed. Moreover, the RN model consistently lacked sufficient power when analyzing data from uncommon species (even when simulations and model specifications perfectly matched). This problem was solved by our RN-JSM, which yielded more precise and accurate estimates of species environmental responses. Increased accuracy in rare species held when the RN-JSM was tested with real and out-of-sample datasets.</p> <p>In the light of our results, we propose: (i) for common and secretive species analyze occupancy data with the RN model and prioritize revisiting sites; (ii) for species that may have overdispersed detectability or local abundances (e.g. with correlated behaviors or occurring in clusters), apply RN extensions that account for this extra variation (e.g. Poisson-beta or zero-inflated models). Finally, (iii) for uncommon species (mean abundances < 1), whenever possible, gather data at the community level and apply joint-species modeling techniques.</p>
Microclimate simulation output: "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens"
<p>The following microclimate simulation dataset supports the paper "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens" by Mathias Schaefer, published in Urban Ecosystems (2022).</p> <p>"T0Simulation_11082020_output" contains data about the status quo simulation of the area of interest (500 m x 500 m x 60 m), whereas "T1Simulation_11082020_output" shows the results of the Green Infrastructure scenario described in the research article above. Please ensure enough memory space on your device, as both files have a size of approximately 25 GB (unzipped).</p> <p>The output files can be visualized with the ENVI-met Leonardo extension. The ENVI-met LITE-version is freely available and can be downloaded at the <a href="https://envi-met.info/doku.php?id=files:download">ENVI-met homepage</a>. Alternatively, the included .NETCDF files can be imported as a multidimensional raster dataset in ArcGIS Pro.</p> <p>Files in the folder "atmosphere" represent meteorological parameters such as potential air temperature [°C], relative humidity [%], or wind speed [m/s]. Air pollution calculations like particulate matter concentrations [µg/m³] can be found in the folder "pollutants". The folder "buildings" contains building data for 3D visualizations of surface temperatures [°C].</p>
Fig. 2 in Environmental conditions predict helminth prevalence in red foxes in Western Australia
Fig. 2. Prevalence of Uncinaria stenocephala and Dipylidium caninum from red foxes at each sampling location.
Fig. 1 in Environmental conditions predict helminth prevalence in red foxes in Western Australia
Fig. 1. Prevalence of helminths in red foxes (n=147) from sampling locations throughout southwest Western Australia. Numbers in parentheses indicate sample size at each location.
Figure 6. Response curve showing how each environmental variable affected the Maxent prediction. Variables A, B in PhylOgeOgraphy and pOtential distributiOn OF Sturnira lilium and S. giannae (ChirOptera: PhyllOstOmidae) With range eXtensiOn FOr S. giannae in the CerradO and Pantanal biOmes
Figure 6. Response curve showing how each environmental variable affected the Maxent prediction. Variables A, B, and C were the ones that most affected the potential distribution of S. giannae, and D, E, and F most affected the potential distribution of S. lilium. The curves show the average response of the 10 replicate Maxent runs (red) and the standard deviation (blue).
Fig. 3 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 3. Cumulative distribution plots divided by year for (A) T. alpinus and (B) T. speciosus. For each species 2013 is shown in red, 2014 in teal, 2015 in pink. The x-axis represents each host individual, ordered from least to most flea infested, and the y-axis shows the cumulative proportion of total flea counts. The dotted line indicates individuals in the 90th percentile of flea abundances, illustrating that the top 10% most infected chipmunks usually account for close to 50% of all counted fleas. The proportion of individuals without fleas in each year is represented graphically as the proportion at which each colored line departs from the x-axis. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 4. Relationships between fecal glucocorticoid metabolite levels, sex, and flea abundance for (A) T. alpinus and (B) T. speciosus. Points show the mean ± S.E. number of fleas counted for female (white) and male (black) individuals within FGM categories (FGM values were rounded to the nearest 10). Lines of best fit (based on all raw data points) ± 95% confidence intervals are overlaid for each sex.
Fig. 2 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 2. Patterns of flea abundance across years, hosts, and flea species. Overall average flea abundances (A–B) and abundances of each flea species (C–D) in each year for T. alpinus (A, C) and T. speciosus (B, D). Abundances of each flea species on hosts of each sex (Males: closed circles, Females: open circles) on T. alpinus (E) and T. speciosus (F).
Fig. 5 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 5. Relationships between flea abundances and (A) the second principal component of temperature data; or (B) elevation for T. alpinus (white) and T. speciosus (black). Points show the mean ± S.E. number of fleas counted for a given study site in a given year. For each study site in each year, a mean ± S.E. temperature or elevation value is shown. Lines of best fit (based on all raw data points) ± 95% confidence intervals are overlaid for each species.
Fig. 1 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 1. Map showing study sites. Sites (see Supplementary Data S1 for more information) located in and around Yosemite National Park (green) were visited either in all three years (2013, 2014, and 2015; black), in two of the years (yellow), or in only one year (red). Yellow and black lines show significant roadways in the area. Lakes are shown in blue, including Mono Lake at top right. Inset shows Yosemite National Park (green) on a map of California. Site codes: AL: Arrowhead Lake; CL: Cathedral Lake (upper); GA: Glen Aulin; GL: Gaylor Lakes; HC: Hoffmann Creek; MA: Mammoth Lakes; ML: May Lake; PC: Porcupine Creek; SL: Saddlebag Lake; SLN: Saddlebag Lake, north-side (Greenstone and Steelhead Lakes); TM: Tuolumne Meadows. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Predicting the risk of Alaria alata infestation in wild boar on the basis of environmental factors
Fig. 2. The prevalence of A. alata in wild boar in provinces in Poland calculated from literature values and data from the present study (A) and predicted by percentage of areas covered by WETLANDS (B) (for detailed information, see: Methods). The figure shows prevalence values for a given province and confidence intervals (lower; upper).
Prediction of Humpback Whale Sighting Zones based on Environmental Factors using Tree-based Algorithms
<p>This is the datased used in the paper: Prediction of Humpback Whale Sighting Zones based on Environmental Factors using Tree-based Algorithms</p>
The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK"
<p>The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK".</p> <p>Data collector: Runda Zheng</p>
Fig. 3 in Environmental factors predicting fish community structure in two neotropical rivers in Brazil
Fig. 3. Scatterplot of canonical correspondence analysis (CCA) for the fish communities of the Jogui and Iguatemi Rivers.
Fig. 2 in Environmental factors predicting fish community structure in two neotropical rivers in Brazil
Fig. 2. Similarity dendrogram of fish communities in Jogui River (above) and Iguatemi Rivers (below).
Fig. 4 in Environmental factors predicting fish community structure in two neotropical rivers in Brazil
Fig. 4. Altitudinal distributions of the main fish species in the Jogui (A) and Iguatemi (B) rivers. Black dots represent sampling sites. Horizontal black lines represent species distribution range.
Transgenerational effect on sexual reproduction in rotifer populations in relation to the environmental predictability of their habitats
<p>Understanding the processes that enable adaptation of organisms to time-varying environments is critically relevant in evolutionary ecology. A way to cope with environmental fluctuations where predictable conditions affect several generations of individuals is through non-genetic transgenerational effects. The phenotype of ancestors affects the phenotype of their descendants matching it with the expected environment of the latter. Facultatively sexual rotifers inhabiting water bodies that cover a wide gradient of environmental predictability in Eastern Spain are a good study model for this topic. In their life cycle sex is linked to diapausing-egg production that enables survival between growing seasons. In several rotifer species, sexual reproduction is inhibited in several generations after diapausing-egg hatching. We hypothesized that in ponds where the growing season length is more predictable, rotifer clones proliferate asexually longer, hence allowing a fuller exploitation of the growing season and therefore maximize diapausing-egg production by the end of the season. We tested this prediction by estimating the proportion of sexual females produced by eight clones of the rotifer <em>Brachionus plicatilis</em> inhabiting eight ponds (8x8= 64 clones) from our study system. Here, we present the raw data gathered from the experiment. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.