Find research datasets worth reusing

opencc-zeroJan 2022View details →

Data and code from: Traits and phylogenies modulate the environmental responses of wood-inhabiting fungal communities across spatial scales

Identifying the spatial scales at which community assembly processes operate is fundamental for gaining a mechanistic understanding of the drivers shaping ecological communities. In this study, we examined whether and how traits and phylogenetic relationships structure fungal community assembly across spatial scales. We applied joint species distribution modelling to a European-scale dataset on 215 wood-inhabiting fungal species, which includes data on traits, phylogeny and environmental variables measured at the local (log-level) and regional (site-level) scales. At the local scale, wood-inhabiting fungal communities were mostly structured by deadwood decay stage, and the trait and phylogenetic patterns along this environmental gradient suggested the lack of diversifying selection. At regional scales, fungal communities and their trait distributions were influenced by climatic and connectivity-related variables. The fungal climatic niches were not phylogenetically structured, suggesting that diversifying selection or stabilizing selection for climatic niches has played a strong role in wood-inhabiting communities. In contrast, we found a strong phylogenetic signal in the responses to connectivity-related variables, revealing phylogenetic homogenization in small and isolated forests. Altogether, our results show that species-level traits and phylogenies modulate the responses of wood-inhabiting fungi to environmental processes acting at different scales. This result suggests that the evolutionary histories of fungal traits diverge along different environmental axes.

opencc-zeroFeb 2022View details →

Open data for spatial public health research

Background Preventive and health-promoting policies can guide (place and space-specific) factors influencing human health, such as the physical and social environment. Required is data that can lead to a more nuanced decision-making process and identify both, existing and future challenges. Along with the rise of new technologies, and thus the multiple opportunities to use and process data, new options have emerged to measure and monitor factors that affect health. Thus, in recent years, several gateways for open data (including governmental and geospatial data) became available. At present, an increasing number of research institutions as well as (state and private) companies and citizens' initiatives provide data. However, there is a lack of overviews covering the range of such offerings regarding health. In particular, for geographically differentiated analyses, there are challenges related to data availability at different spatial levels and the growing number of data providers. Objectives To provide an overview of open data resources available in the context of space and health to date. It also describes the technical and legal conditions for using open data Results An up-to-date summary of results including information on relevant data access and terms of use is provided along with a web visualization. All data is available for further use under an open license.

opencc-zeroMar 2022View details →

Data for: Modeling of spatial pattern and influencing factors of cultivated land quality based on spatial-temporal big data (PONE-D-21-21084R1)

The quality of cultivated land determines the production capacity of cultivated land and the level of regional development, and also directly affects the food security and ecological safety of the country. This paper starts from the perspective of spatial pattern of cultivated land quality and uses spatial autocorrelation analysis to study the spatial aggregation characteristics and differences of cultivated land quality in Henan Province at the county level scale, and also uses bivariate spatial autocorrelation to analyze the influence of neighboring influences on the quality of cultivated land in the target area. The spatial autoregressive model was used to further analyze the driving factors affecting the quality of cultivated land, and the influence of cultivated land area index was coupled in the process of rating analysis, which was finally used as a basis to propose more precise measures for the protection of cultivated land zoning. The results show that: (1) The quality of cultivated land in Henan Province has a strong spatial correlation (global Moran's I≈0.710) and shows an obvious aggregation pattern in spatial distribution; positive correlation types (high-high and low-low) are concentrated in north-central and western mountainous areas of Henan Province, respectively; negative correlation types are discrete. The negative correlation types are distributed in a discrete manner. (2) The bivariate spatial autocorrelation results show that Slope (Moran's I≈-0.505), Irrigation guarantee rate (IGR, 0.354), Urbanization rate (-0.255), Total agricultural machinery power (TAMP, 0.331) and Pesticide use (0.214) are the main influencing factors. (3) According to the absolute values of the regression coefficients, it can be seen that the magnitude of the influence of different factors on the quality of cultivated land is: Slope (0.089) >IGR (0.025) > Urbanization rate (0.002) > TAMP (0.001) > Pesticide use (1.96e-006). (4) Based on the spatial pattern presented by the spatial autocorrelation results, we proposed corresponding protection zoning measures to provide more scientific reference decisions and technical support for the implementation of refined cultivated land management in Henan Province.

opencc-zeroMar 2022View details →

Data from: Trait hierarchies are stronger than trait dissimilarities in structuring spatial co-occurrence patterns of common tree species in a subtropical forest

1. The dissimilarity and hierarchy of trait values that characterize niche and fitness differences, respectively, have been increasingly applied to infer mechanisms driving community assembly and to explain species co-occurrence patterns. Here, we predict that limiting similarity should result in the spatial segregation of functionally similar species, while functionally similar species will be more likely to co-occur either due to environmental filtering or competitive exclusion of inferior competitors (hereafter hierarchical competition). 2. We used a fully mapped 50-ha subtropical forest plot in southern China to explore how pairwise spatial associations between saplings and between adult trees were influenced by trait dissimilarity and hierarchy in order to gain insight into assembly mechanisms. We assessed pairwise spatial associations using two summary statistics of spatial point patterns at different spatial scales and compared the effects of trait dissimilarity and trait hierarchy of different functional traits on the interspecific spatial associations. These comparisons allow us to disentangle the effects of limiting similarity, environmental filtering and hierarchical competition on species co-occurrence. 3. We found that trait dissimilarity was generally negatively related with interspecific spatial associations for both saplings and adult trees across spatial scales, meaning that species with similar trait values were more likely to co-occur and thus supporting environmental filtering or hierarchical competition. We further found that trait hierarchy outweighed trait dissimilarity in structuring pairwise spatial associations, suggesting that hierarchical competition played a more important role in structuring our forest community than environmental filtering across life stages. 4. This study employed a novel method, by offering the integration of pairwise spatial association and trait dissimilarity as well as trait hierarchy, to disentangle the relative importance of multiple assembly mechanisms in structuring co-occurrence patterns, especially the mechanisms of environmental filtering and hierarchical competition, which lead to indistinguishable co-occurrence patterns. This study also reinforced the importance of trait hierarchy rather than trait dissimilarity in driving neighborhood competition.

opencc-by-4.0Mar 2022View details →

Supplementary data for 'Ferrofluid impregnation efficiency and its spatial variability in natural and synthetic porous media: Implications for magnetic pore fabric studies'

Supplementary data for the manuscript 'Ferrofluid impregnation efficiency and its spatial variability in natural and synthetic porous media: Implications for magnetic pore fabric studies'

opencc-by-4.0Apr 2022View details →

Data from: Even low light pollution levels affect the spatial distribution and timing of activity of a "light tolerant" bat species

Abstract of the related publication : By disrupting nocturnal landscapes worldwide, light pollution caused by Artificial Light At Night (ALAN) is recognised as a major threat to biodiversity. As even low light intensities might affect some taxa, concerns are arising about biological responses to widespread low light levels. We used data from a French citizen science bat monitoring program (1,894 full-nights monitored on 1,055 sites) to explore the landscape-scale effects of light on an open-space-foraging bat species, the Serotine bat (Eptesicus serotinus). We assessed this species' abundance and timing of night-time activity (median time of activity) at foraging sites. ALAN, and to a lesser extent moonlight, reduced E. serotinus abundance. ALAN delayed activity, and this delay was amplified during overcast nights. On the contrary, where there was no ALAN, the higher the cloud cover, the earlier the activity occurred. Cloud cover likely darkened the night sky in rural locations, whereas it amplified skyglow in light-polluted places, increasing ALAN effects on bats. Interestingly, moonlight also delayed activity but this effect was weakened where there was ALAN. Our study shows that even fine variations of light levels could affect the spatiotemporal distribution of a common species usually considered to be “light tolerant”, with potential cascading effects on individual fitness and population dynamics. It stresses how urgent it is to preserve and restore dark areas to protect biodiversity from light pollution while working on light intensity and directivity where ALAN is needed.

opencc-zeroDec 2021View details →

Data from: Prey encounters and spatial memory influence use of foraging patches in a marine central place forager

Given the patchiness and long-term predictability of marine resources, memory of high-quality foraging grounds is expected to provide fitness advantages for central place foragers. However, it remains challenging to characterise how marine predators integrate memory with recent prey encounters to adjust fine-scale movement and use of foraging patches. Here, we used two months of movement data from harbour seals (Phoca vitulina) to quantify the repeatability in foraging patches as a proxy for memory. We then integrated these data into analyses of fine-scale movement and underwater behaviour to test how both spatial memory and prey encounter rates influenced the seals' Area Restricted Search (ARS) behaviour. Specifically, we used one month's GPS data from 29 individuals to build spatial memory maps of searched areas, and archived accelerometry data from a subset of five individuals to detect prey catch attempts, a proxy for prey encounters. Individuals were highly consistent in the areas they visited over two consecutive months. Hidden Markov Models showed that both spatial memory and prey encounters increased the probability of seals initiating ARS. These results provide evidence that predators use memory to adjust their fine scale movement and this ability should be accounted for in movement models.

opencc-zeroApr 2022View details →

Data from: Repeatability of adaptive radiation depends on spatial scale: regional versus global replicates of stickleback in lake versus stream habitats

The repeatability of adaptive radiation is expected to be scale dependent, with determinism decreasing as greater spatial separation among "replicates" leads to their increased genetic and ecological independence. Threespine stickleback (Gasterosteus aculeatus) provide an opportunity to test whether this expectation holds for the early stages of adaptive radiation -their diversification in freshwater ecosystems has been replicated many times. To better understand the repeatability of that adaptive radiation, we examined the influence of geographic scale on levels of parallel evolution by quantifying phenotypic and genetic divergence between lake and stream stickleback pairs sampled at regional (Vancouver Island) and global (North America and Europe) scales. We measured phenotypes known to show lake-stream divergence and used reduced representation genome-wide sequencing to estimate genetic divergence. We assessed the scale-dependence of parallel evolution by comparing effect sizes from multivariate models and also the direction and magnitude of lake-stream divergence vectors. At the phenotypic level, parallelism was greater at the regional than the global scale. At the genetic level, putative selected loci showed greater lake-stream parallelism at the regional than the global scale. Generally, the level of parallel evolution was low at both scales, except for some key univariate traits. Divergence vectors were often orthogonal, highlighting possible ecological and genetic constraints on parallel evolution at both scales. Overall, our results confirm that the repeatability of adaptive radiation decreases at increasing spatial scales. We suggest that greater environmental heterogeneity at larger scales imposes different selection regimes, thus generating lower repeatability of adaptive radiation at larger spatial scales.

opencc-zeroApr 2022View details →

Data from: Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture-recapture movement model

Animal movement is a fundamental ecological process affecting the survival and reproduction of individuals, the structure of populations, and the dynamics of communities. Methods to quantify animal movement and spatiotemporal abundances, however, are generally separate and thus omit linkages between individual-level and population-level processes. We describe an integrated spatial capture-recapture (SCR) movement model to jointly estimate (1) the number and distribution of individuals in a defined spatial region and (2) movement of those individuals through time. We applied our model to a study of polar bears (Ursus maritimus) in a 28,125 km2 survey area of the eastern Chukchi Sea, USA in 2015 that incorporated capture-recapture and telemetry data. In simulation studies, the model provided unbiased estimates of movement, abundance, and detection parameters using a bivariate normal random walk and correlated random walk movement process. Our case study provided detailed evidence of directional movement persistence for both male and female bears, where individuals regularly traversed areas larger than the survey area during the 36-day study period. Scaling from individual- to population-level inferences, we found that densities varied from < 0.75 bears/625 km2 grid cell/day in nearshore cells to 1.6–2.5 bears/grid cell/day for cells surrounded by sea ice. Daily abundance estimates ranged from 53–69 bears, with no trend across days. The cumulative number of unique bears that used the survey area increased through time due to movements into and out of the area, resulting in an estimated 171 individuals using the survey area during the study (95% credible interval 124–250). Abundance estimates were similar to a previous multi-year integrated population model using capture-recapture and telemetry data (2008–2016; Regehr et al. 2018). Overall, the SCR-movement model successfully quantified both individual- and population-level space use, including the effects of landscape characteristics on movement, abundance, and detection, while linking the movement and abundance processes to directly estimate density within a prescribed spatial region and temporal period. Integrated SCR-movement models provide a generalizable approach to incorporate greater movement realism into population dynamics and link movement to emergent properties including spatiotemporal densities and abundances.

Supplementary code and data for: Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE

This repository contains code and data to reproduce the figures in the manuscript:  Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE. Please refer to the readme in the repository.

opencc-zeroMay 2022View details →

Data for: Spatial structure within root systems moderates stability of Arbuscular Mycorrhizal mutualism and plant-soil feedbacks

The persistence of mutualisms is paradoxical, as there are fitness incentives for exploitation. This is particularly true for plant-microbe mutualisms like arbuscular mycorrhizae (AM), which are promiscuously horizontally-transmitted. Preferential allocation by hosts to the best mutualist can stabilize horizontal mutualisms, however, preferential allocation is imperfect, with its fidelity likely depending upon the spatial structure of symbionts in plant roots. In this study, we tested AM mutualisms' dependence on two dimensions of spatial structure: the initial spatial association of fungi and the ease of fungal dispersal, through three complementary experiments. We found that fitness of the beneficial AM fungus increased when fungi were initially separate, while initial spatial mixing benefited the fitness of the non-beneficial fungus. These effects were strongest when dispersal was limited, and hosts could discriminate. Additionally, we found that changes in AM fungal proportional abundance induced by spatial structure in roots of a preferentially allocating host produced positive feedbacks on plant growth, showing that interactions between spatial structure and host choice can determine the direction of plant-soil feedbacks. Our results suggest that symbiont spatial structure within plant roots may act as an important modifier of plant preferential allocation and the dynamics of mycorrhizal mutualisms, with potentially cascading effects on plant-plant interactions.

Data from: Spatial release from masking in crocodilians

Dataset, codes, videos, and audio signals used in the study "Spatial release from masking in crocodilians". All supplementary figures are also available.

opencc-zeroMay 2022View details →

Data from: Positron-emitting radiotracers spatially resolve unexpected biogeochemical relationships linked with methane oxidation in Arctic soils

Arctic soils are marked by cryoturbic features, which impact soil-atmosphere methane (CH4) dynamics vital to global climate regulation. Cryoturbic diapirism alters C/N chemistry within frost boils by introducing soluble organic carbon and nutrients, potentially influencing microbial CH4 oxidation. CH4 oxidation in soils, however, requires a spatio-temporal convergence of ecological factors to occur. Spatial delineation of microbial activity with respect to these key microbial and biogeochemical factors at relevant scales is experimentally challenging in inherently complex and heterogeneous natural soil matrices. This work aims to overcome this barrier by spatially linking microbial CH4 oxidation with C/N chemistry and metagenomic characteristics. This is achieved by using positron-emitting radiotracers to visualize millimeter-scale active CH4 uptake areas in Arctic soils with and without diapirism. X-ray absorption spectroscopic speciation of active and inactive areas shows CH4 uptake spatially associates with greater proportions of inorganic N in diapiric frost boils. Metagenomic analyses reveal Ralstonia pickettii associates with CH4 uptake across soils along with pertinent CH4 and inorganic N metabolism associated genes. This study highlights the critical relationship between CH4 and N cycles in Arctic soils, with potential implications for better understanding future climate. Furthermore, our experimental framework presents a novel, widely applicable strategy for unraveling ecological relationships underlying greenhouse gas dynamics under global change.

Data and code for "High-speed 3D imaging flow cytometry with optofluidic spatial transformation"

Data and codes used in Ugawa & Ota, "High-speed 3D imaging flow cytometry with optofluidic spatial transformation".

Raw Data - Part 2 : Spatial multi-omic map of human myocardial infarction

We provide here the raw data of  snATAC-seq and snRNA-seq for the manuscript: Kuppe, Ramirez Flores, Li et al. "Spatial multi-omic map of human myocardial infarction", 2022

Auxiliary Euro-Calliope datasets: Spatial data to represent a European energy system model at several spatial resolutions

Main output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow. This output provides similar data to <a href="https://doi.org/10.5281/zenodo.3246302">https://doi.org/10.5281/zenodo.3246302</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with two key differences: <ol> <li>The spatial extent has been expanded to include Iceland.</li> <li>Two new spatial resolutions have been added: `ehighways` and `ehighways_disaggregated`.</li> </ol> `ehighways` defines 98 regions based on the result of work undertaken in the European Commission Seventh Framework Programme project e-HIGHWAY 2050 [1]. The regions cover 35 European countries; 19 are described at a national resolution and the rest at a subnational resolution. Those at a subnational resolution are aggregated from NUTS3-2006 statistical units. `ehighways_disaggregated` provides the data at the resolution of statistical units in Europe, which is then aggregated to produce the data at the `ehighways` resolution. The mapping from statistical units to ehighways regions is defined in `./ehighways/statistical_units_to_ehighways_regions.csv`. `./ehighways/units.png` shows a map of the resulting 98 `ehighways` regions. The region colours are used to help differentiate regions and have no other meaning. This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>. [1] Anderski, T., Surmann, Y., Stemmer, S., Grisey, N., Momot, E., Leger, A.-C., Betraoui, B., and van Roy, P. (2014). European cluster model of the Pan-European transmission grid (e-HIGHWAY 2050)

opencc-by-4.0Oct 2022View details →

Field data synthesis accompanying "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"

Synthesis of fuel load and fuel consumption field measurements accompanying the publication:"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"Dave van Wees1, Guido R. van der Werf1, James T. Randerson2, Brendan M. Rogers3, Yang Chen2, Sander Veraverbeke1, Louis Giglio4, and Douglas C. Morton51Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands 2Department of Earth System Science, University of California, Irvine, CA 92697, USA 3Woodwell Climate Research Center, Falmouth, MA 02540, USA 4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA 5Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USADOI: https://doi.org/10.5194/gmd-15-8411-2022 Units are g C / m2

opencc-by-4.0Jun 2022View details →

Raw Data - Part 4 : Spatial multi-omic map of human myocardial infarction ---- Raw image

We provide here the raw image for the visium data for the manuscript: Kuppe, Ramirez Flores, Li et al. "Spatial multi-omic map of human myocardial infarction", 2022

opencc-zeroJul 2022View details →

Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation: supporting data and outputs

1. Tropical forests are subject to diverse deforestation pressures while their conservation is essential to achieve global climate goals. Predicting the location of deforestation is challenging due to the complexity of the natural and human systems involved but accurate and timely forecasts could enable effective planning and on-the-ground enforcement practices to curb deforestation rates. New computer vision technologies based on deep learning can be applied to the increasing volume of Earth observation data to generate novel insights and make predictions with unprecedented accuracy. 2. Here, we demonstrate the ability of deep convolutional neural networks (CNNs) to learn spatiotemporal patterns of deforestation from a limited set of freely available global data layers, including multispectral satellite imagery, the Hansen maps of annual forest change (2001-2020) and the ALOS PALSAR digital surface model, to forecast deforestation (2021). We designed four model architectures, based on 2D CNNs, 3D CNNs, and Convolutional Long Short-Term Memory (ConvLSTM) Recurrent Neural Networks (RNNs), to produce spatial maps that indicate the risk to each forested pixel (~30 m) in the landscape of becoming deforested within the next year. They were trained and tested on data from two ~80,000 km2 tropical forest regions in the Southern Peruvian Amazon. 3. The networks could predict the location of future forest loss to a high degree of accuracy (F1 = 0.58-0.71). Our best performing model (3D CNN) had the highest pixel-wise accuracy (F1 = 0.71) when validated on 2020 forest loss (2014-2019 training). Visual interpretation of the mapped forecasts indicated that the network could automatically discern the drivers of forest loss from the input data. For example, pixels around new access routes (e.g. roads) were assigned high risk whereas this was not the case for recent, concentrated natural loss events (e.g. remote landslides). 4. CNNs can harness limited time-series data to predict near-future deforestation patterns, an important step in harnessing the growing volume of satellite remote sensing data to curb global deforestation. The modelling framework can be readily applied to any tropical forest location and used by governments and conservation organisations to prevent deforestation and plan protected areas.