Find research datasets worth reusing

opencc-by-4.0May 2020View details →

Data from: Spatio-temporal trends in richness and persistence of bacterial communities in decline-phase water vole populations

ABSTRACT Understanding the driving forces that control vole population dynamics requires identifying bacterial parasites hosted by the voles and describing their dynamics at the community level. To this end, we used high-throughput DNA sequencing to identify bacterial parasites in cyclic populations of montane water voles that exhibited a population outbreak and decline in 2014-2018. An unexpectedly large number of 155 Operational Taxonomic Units (OTUs) representing at least 13 genera in 11 families was detected. Individual bacterial richness was higher during declines, and vole body condition was lower. Richness as estimated by Chao2 at the local population scale did not exhibit clear seasonal or cycle phase-related patterns, but at the vole meta-population scale, exhibited seasonal and phase-related patterns. Moreover, bacterial OTUs that were detected in the low density phase were geographically widespread and detected earlier in the outbreak; some were associated with each other. Our results demonstrate the complexity of bacterial community patterns with regard to host density variations, and indicate that investigations about how parasites interact with host populations must be conducted at several temporal and spatial scales: multiple times per year over multiple years, and at both local and long-distance dispersal scales for the host(s) under consideration. FILE DESCRIPTION: Trapping, physical, and demographic data for the 1376 Arvicola terrestris included in sequencing runs 1 to 8 This XLSX file contains the following information concerning the 1376 animals included in the eight sequencing runs: location, session numbering, animal_id,         trap_name, capture_date, species, sex (1=male, 2=female), weight (g), length_body (mm),  length_tail (mm, testes (0=abdominal, 1=scrotal), testes_length (mm), testes_width (mm), nipples (0=small, 1=lactating), vagina (0=not perforate, 1=perforate), pub_symph (0=closed, 1=open), uterus_scars (#), embryos(#), lens_weight (g), lens_weight2 (g) and sequencing labels File name: Animal_details.xlsx   Information concerning the Arvicola terrestris samples and the positive and negative controls multiplexed in the 16Sv4 MiSeq sequencing runs 1 to 8 This XLSX file contains the Run IDs, Sample IDs, Sample types, Dates & Site names, DNA extraction kit, PCR IDs, PCR replicate numbers, numbers of reads before and after filtering and the fastq file names for the 6615 PCR products multiplexed in the eight different Illumina MiSeq runs. File name: Sample_and_sequencing_informations.xlsx   MiSeq raw sequences of the 16Sv4 rRNA gene from Arvicola terrestris samples (Run1) This ZIP file contains the Run1 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each Arvicola terrestris sample using the MiSeq platform. The 1570 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx. File name: MiSeq_Reads_16S_Arvicola_terrestris_Run1.zip   MiSeq raw sequences of the 16Sv4 rRNA gene from Arvicola terrestris samples (Run2) This ZIP file contains the Run2 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each Arvicola terrestris sample using the MiSeq platform. The 1668 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx. File name: MiSeq_Reads_16S_Arvicola_terrestris_Run2.zip   MiSeq raw sequences of the 16Sv4 rRNA gene from Arvicola terrestris samples (Run3) This ZIP file contains the Run3 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each Arvicola terrestris sample using the MiSeq platform. The 1646 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx. File name: MiSeq_Reads_16S_Arvicola_terrestris_Run3.zip   MiSeq raw sequences of the 16Sv4 rRNA gene from Arvicola terrestris samples (Run4) This ZIP file contains the Run4 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each Arvicola terrestris sample using the MiSeq platform. The 1712 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx. File name: MiSeq_Reads_16S_Arvicola_terrestris_Run4.zip   MiSeq raw sequences of the 16Sv4 rRNA gene from Arvicola terrestris samples (Run5) This ZIP file contains the Run5 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each Arvicola terrestris sample using the MiSeq platform. The 1680 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx. File name: MiSeq_Reads_16S_Arvicola_terrestris_Run5.zip   MiSeq raw sequences of the 16Sv4 rRNA gene from Arvicola terrestris samples (Run6) This ZIP file contains the Run6 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each Arvicola terrestris sample using the MiSeq platform. The 1704 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx. File name: MiSeq_Reads_16S_Arvicola_terrestris_Run6.zip   MiSeq raw sequences of the 16Sv4 rRNA gene from Arvicola terrestris samples (Run7) This ZIP file contains the Run7 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each Arvicola terrestris sample using the MiSeq platform. The 1620 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx. File name: MiSeq_Reads_16S_Arvicola_terrestris_Run7.zip   MiSeq raw sequences of the 16Sv4 rRNA gene from Arvicola terrestris samples (Run8) This ZIP file contains the Run8 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each Arvicola terrestris sample using the MiSeq platform. The 1630 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx. File name: MiSeq_Reads_16S_Arvicola_terrestris_Run8.zip   Raw abundance table of the 16v4 rRNA gene from Arvicola terrestris samples before data filtering (Run1 to 8) This CSV file contains the number of reads for each distinct variant (OTU) and each of the 6615 PCR products, including the Arvicola terrestris samples and the controls, sequenced in the MiSeq runs 1 to 8 before the data filtering. File name: Read_abundance_table_before_filtering.csv   Abundance table of the 16v4 rRNA gene from Arvicola terrestris samples after data filtering (Run1 to 8) This CSV file contains the number of reads for each distinct variant (OTU) and each Arvicola terrestris sample sequenced in the MiSeq runs 1 to 8 after the data filtering. File name: Read_abundance_table_after_filtering.csv

opencc-by-4.0Jul 2020View details →

The influence of human disturbances on the spatio-temporal habitat selection patterns of roe deer near Trento (Italy)

This is the dataset used for the MSc. thesis of Matthijs Hinkamp for the master Earth Sciences (Environmental Management track) at the University of Amsterdam. In this thesis the Individual Movement Sequence Analysis Method (IM-SAM) was applied to analyse the influence of human disturbances on the sequential habitat use of roe deer in northern Italy. While it is known that in this area the roe deer populations are affected by anthropogenic pressures, the actual spatial and temporal implications of such pressures are unknown. The input for IM-SAM consisted of habitat sequences, which were obtained through tracking data from the Fondazione Edmund Mach, land use and land cover maps and temporal information. Based on exploratory dissimilarity trees for the real behavioural sequences, several simulation profiles were established, each with a different habitat use pattern. The results show that most roe deer prefer isolated forest areas, but alternating patterns are definitely present. Several co variables were assessed: the influence of the hunting season, increased pressure during weekends, differences between protected and unprotected areas and changes in the average NDVI. It seems that roe deer are affected by the hunting pressure in September. This month shows an increase in the prevalence of alternating profiles, which indicates that roe deer tend to change their habitat use during periods of the day when hunting takes place. It cannot be concluded that habitat use patterns are altered during the weekends and the differences between sequences in protected and unprotected areas seem to be relatively small. However, some results indicate that alternating habitat use is a strategy for some roe deer in protected areas. When looking at the NDVI values, it becomes clear that the average NDVI is higher overall for the alternating profiles, but this just underlines the general conclusions drawn about the habitat use strategies in September.

opencc-zeroOct 2020View details →

Data from: Trait matching and phenological overlap increase the spatio-temporal stability and functionality of plant-pollinator interactions

Morphology and phenology influence plant-pollinator network structure, but whether they generate more stable pairwise interactions with higher pollination success is unknown. Here we evaluate the importance of morphological trait matching, phenological overlap and specialisation for the spatio-temporal stability (measured as variability) of plant-pollinator interactions and for pollination success, while controlling for species abundance. To this end, we combined a six-year plant-pollinator interaction dataset, with information on species traits, phenologies, specialisation, abundance and pollination success, into structural equation models. Interactions among abundant plants and pollinators with well-matched traits and phenologies formed the stable and functional backbone of the pollination network, whereas poorly-matched interactions were variable in time and had lower pollination success. We conclude that phenological overlap could be more useful for predicting changes in species interactions than species abundances, and that non-random extinction of species with well-matched traits could decrease the stability of interactions within communities and reduce their functioning.

opencc-zeroDec 2020View details →

Spatio-temporal persistence of zooplankton communities in the Gulf of Alaska

Spatial structuring of mid-trophic level forage communities in the Gulf of Alaska (GoA) is poorly understood, even though it has clear implications for the health of fisheries and marine wildlife populations. Here, we test the hypothesis that summertime (May-August) mesozooplankton communities are spatially-persistent across years of varying ocean conditions, including during the marine heatwave of 2014-2016. We use spatial ordinations and hierarchical clustering of Continuous Plankton Recorder (CPR) sampling over 17 years (2000-2016) to (1) characterize typical zooplankton communities in different regions of the GoA, and (2) investigate spatial structuring relative to variation in ocean temperatures and circulation. Five regional communities were identified, each representing distinct variation in the abundance of 18 primary zooplankton taxa: a distinct cluster of coastal taxa on the continental shelf north of Vancouver Island; a second cluster in the western GoA associated with strong currents and cold water east of Unimak Pass; a shelf break cluster rich in euphausiids found at both the eastern and western margins of the GoA; a broad offshore cluster of abundant pelagic zooplankton in the southern GoA gyre associated with stable temperature and current conditions; and a final offshore cluster exhibiting low zooplankton abundance concentrated along the northeastern arm of the subarctic gyre where ocean conditions are dominated by eddy activity. When comparing years of anomalous warm and cold sea surface temperatures, we observed change in the spatial structure in coastal communities, but little change (i.e., spatial persistence) in the northwestern GoA basin. Whereas previous studies have shown within-region variability in zooplankton communities in response to ocean climate, we highlight both consistency and change in regional communities, with interannual variability in shelf communities and persistence in community structure offshore. These results suggest greater variability in coastal food webs than in the central portion of the GoA, which may be important to energy exchange from lower to upper trophic levels in the mesoscale biomes of this ecosystem.

opencc-zeroDec 2017View details →

Data from: Spatio-temporally explicit model averaging for forecasting of Alaskan groundfish catch

(1) Fisheries management is dominated by the need to forecast catch and abundance of commercially and ecologically important species. The influence of spatial information and environmental factors on forecasting error is not often considered. We propose a forecasting method called spatio-temporally explicit model averaging (STEMA) to combine spatial and temporal information through model averaging. (2) We examine the performance of STEMA against two popular forecasting models and a modern spatial prediction model: the autoregressive integrated moving averages (ARIMA) model, the Bayesian hierarchical model, and the varying coefficient model. We focus on applying the methods to four species of Alaskan groundfish for which only catch data are available. (3) Our method reduces forecasting errors significantly for most of the tested models when compared to ARIMAX, Bayesian, and varying coefficient methods. We also consider the effect of sea surface temperature (SST) on the forecasting of catch, as multiple studies reveal a potential influence of water temperature on the survival and growth of juvenile groundfish. For most of the preferred models, inclusion of SST in the model improved forecasting of catch. (4) It is advisable to consider both spatial information and relevant environmental factors in forecasting models to obtain more accurate projections of population abundance. The STEMA method is capable of accounting for spatial information in forecasting and can be applied to various types of data because of its flexible varying coefficient model structure. It is therefore a suitable forecasting method for application to many fields including ecology, epidemiology, and climatology.

opencc-zeroDec 2014View details →

Data from: Spatio-temporal dynamics of impulse responses to figure motion in optic flow neurons

White noise techniques have been used widely to investigate sensory systems in both vertebrates and invertebrates. White noise stimuli are powerful in their ability to rapidly generate data that help the experimenter decipher the spatio-temporal dynamics of neural and behavioral responses. One type of white noise stimuli, maximal length shift register sequences (m-sequences), have recently become particularly popular for extracting response kernels in insect motion vision. We here use such m-sequences to extract the impulse responses to figure motion in hoverfly lobula plate tangential cells (LPTCs). Figure motion is behaviorally important and many visually guided animals orient towards salient features in the surround. We show that LPTCs respond robustly to figure motion in the receptive field. The impulse response is scaled down in amplitude when the figure size is reduced, but its time course remains unaltered. However, a low contrast stimulus generates a slower response with a significantly longer time-to-peak and half-width. Impulse responses in females have a slower time-to-peak than males, but are otherwise similar. Finally we show that the shapes of the impulse response to a figure and a widefield stimulus are very similar, suggesting that the figure response could be coded by the same input as the widefield response.

opencc-zeroJan 2024View details →

Diversifying in the mountains: spatio-temporal diversification of frogs in the Western Ghats biodiversity hotspot

Mountain ranges are hotspots of biodiversity. However, the mechanisms that generate biodiversity patterns in different mountainous regions and taxa are not apparent. The Western Ghats (WG) escarpment in India is a globally recognised biodiversity hotspot with high species richness and endemism. Most studies have either invoked paleoclimatic conditions or climatic stability in the southern WG refugium to explain this high diversity and endemism. However, the factors driving macroevolutionary change remain unexplored for most taxa. Here, we generated the most comprehensive dated phylogeny to date for ranoid frogs in the WG and tested the role of paleoclimatic events or climatic stability in influencing frog diversification. We found that the diversity of different ranoid frog clades in the WG either accumulated at a constant rate through time or underwent a decrease in speciation rates around 3–2.5 Ma during the Pleistocene glaciation cycles. We also find no significant difference in diversification rate estimates across elevational gradients and the three broad biogeographic zones in the WG (northern, central, and southern WG). However, time-for-speciation explained regional species richness within clades, wherein older lineages have more extant species diversity. Overall, we find that global paleoclimatic events have had little impact on WG frog diversification throughout most of its early history until the Quaternary and that the WG may have been climatically stable allowing lineages to accumulate and persist over evolutionary time.

opencc-zeroFeb 2024View details →

Data for: Body mass mediates spatio-temporal responses of mammals to human frequentation across Italian protected areas

Protected areas (PAs) networks are a pivotal tool to fight biodiversity loss, yet they often need to balance the mission of nature conservation with the socio-economic need of giving opportunity for outdoor recreation. Recreation in natural areas is important for human health in an urbanised society, but can prompt behavioural modifications in wild animals. Rarely, however, have these responses being studied across multiple PAs and using standardized methods. We deployed a systematic camera trapping protocol at over 200 sites to sample medium and large mammals in four PAs within the European Natura 2000 network to assess their spatio-temporal responses to human frequentation, proximity to towns, amount of open habitat, and topographical variables. By applying multi-species and single-species models on the number of diurnal, crepuscular, and nocturnal detections, and a multi-species model on nocturnality index, we estimated both species-specific and meta-community level effects, finding that increased nocturnality appeared the main strategy that the mammal meta-community used to cope with human disturbance. However, responses in the diurnal, crepuscular, and nocturnal site use were mediated by species' body mass, with larger species exhibiting avoidance of humans and smaller species more opportunistic behaviours. Our results show the effectiveness of standardised sampling and provide insights for planning the expansion of PA networks as foreseen by the Kunming-Montreal biodiversity agreement.

opencc-by-4.0Apr 2024View details →

DArTseq genetic dataset associated with the article "Hybrids as mirrors of the past: genomic footprints reveal spatio-temporal dynamics and extinction risk of alpine extremophytes in the mountains of Central Asia"

Description: This file stores genetic information on the single nucleotide polymorphism markers (SNPs) in the examined alkali grasses (Poaceae: Puccinellia). The dataset was generetad by Genome-Wide Restriction Fragment Analysis via the DArTseq platform (Diversity Arrays Technology Pty Ltd, Canberra, Australia), which combines complexity reduction methods, fragment size selection, and high-throughput sequencing, optimised for a target organism. The file contains raw data.   Usage notes: We used R (version 4.2.2, 2022-10-31; https://www.R-project.org/) and RStudio (version 2022.07.2+576 "Spotted Wakerobin" Release (e7373ef832b49b2a9b88162cfe7eac5f22c40b34, 2022-09-06; http://www.rstudio.com/) on Windows 8.1 to handle this file. We used the dartR R-package (version 2.7.2) with necessary dependencies to import, proccess and analyse this data file as an object of a class genlight (dartR) in the R environment. You may also handle the file as an object of a class genlight using the adegenet and ade4 R-packages. To learn more about installation procedure and how to use of the R-packages visit: https://cran.r-project.org/web/packages/available_packages_by_name.html.

Strategic Integration of Urban Excess Heat Sources in District Heating: A Spatio-Temporal Optimization Paradigm

This data repository supports the techno-economic and geospatial optimization analysis of district heating systems (DHS) in Stockholm, with a particular focus on integrating Urban Excess Heat (UEH) sources. The dataset includes both input data and results, organized by the key analytical tools employed in the study: GIS, OSeMOSYS, and the Hotmaps Dispatch Model. The folder structure is organized as follows: /Data │ ├── Input_Data/ │   ├── GIS/ │   ├── OSeMOSYS/ │   ├── Hotmaps_Dispatch/ │ └── Results/     ├── GIS/     │     ├── Optimized DHS network extensions for UEH sources     │     ├── Long-term capacity and emissions projections for DHS           ├── Dispatch outputs for different electricity price scenarios <h2>Dataset Details</h2> 1. Input Data Folder: This folder includes all data used as inputs across the three primary tools:    - GIS: Contains geospatial data on the DHS network paths, source locations, and spatial configurations for network optimization.    - OSeMOSYS: Provides techno-economic parameters for heat generation technologies, including capital and operational costs, fuel price projections, and emissions factors.    - Hotmaps Dispatch: Includes detailed demand profiles, projected electricity prices, and the supply temperature requirements essential for dispatch simulations. 2. Results Folder: This folder contains the results generated by each tool after processing the input data:    - GIS: Outputs optimized paths for network expansion and geospatial mapping of UEH sources.    -OSeMOSYS: Contains projections for DHS technology adoption, emissions reduction, and cost optimization over a 23-year model period.    - Hotmaps Dispatch: Stores dispatch model results under various electricity pricing and temperature scenarios, capturing the optimal deployment of DHS resources. <h2>Summary of Tools Used</h2> - GIS (Geospatial Information System): Used to locate UEH sources and map their proximity to the existing DHS network, guiding the optimization of network expansions. - OSeMOSYS (Open Source Energy Modelling System): Performs long-term optimization for the DHS, factoring in the addition of UEH sources, electricity price scenarios, and emissions constraints. - Hotmaps Dispatch Model: Simulates operational dispatch of heat generation technologies in response to varying electricity prices and network temperatures, providing insights into real-time heat supply optimization. This repository thus provides a comprehensive dataset to support the techno-economic and spatial analysis of DHS, facilitating the integration of UEH sources in urban heating systems through scenario-based optimizations.

Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria

Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria

Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria

Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria

Dataset on Spatial Analysis and Clustering of Deforestation in the Amazon Biome: Spatio-Temporal Patterns and Priority Areas

The dataset was developed with the aim of facilitating the development of a methodology to identify and evaluate deforestation patterns and trends in the Amazon. This innovative method combines deforestation alerts from the Real-Time Deforestation Detection System (DETER) with detailed information on various land categories, including environmental protection areas, settlements, rural properties, undesignated public forests, indigenous lands, and conservation units. The integration of this robust data allowed for the precise identification of areas at risk of deforestation, significantly strengthening monitoring and control activities aimed at combating deforestation in the Amazon region.   Spatial resolution The data are available with a spatial resolution of 25 x 25 km (625 km²) and cover the Amazon biome.   Temporal resolution  Period of observed data: 2017 and 2021   Coordinate reference system  Geographic Coordinate System with Datum SIRGAS 2000 (EPSG:5880)   Data format Data is provided as Shapefile.   Dataset usage  It is free to use, but please make sure to cite the repository and our paper properly if you use this dataset.   Publication & further information For additional scenario information, please contact Francisco Gilney Silva Bezerra (franciscogilney@gmail.com).

opencc-by-4.0Dec 2021View details →

Self-Limiting Earthquake Dynamics and Spatio-Temporal Clustering of Seismicity Enabled by Off-Fault Plasticity

Earthquakes are among nature’s deadliest and costliest hazards. Physics-based simulations are essential for overcoming the lack of data and elucidating the complex patterns of earthquakes. Enabled by a novel numerical scheme, this work discovers a new mechanism for regulating earthquake dynamics that emerges due to the co-evolution of fault slip and fault zone plasticity. It enables transition from periodic events to fully irregular sequences of earthquakes. The impact of plasticity on earthquake source characteristics goes beyond its limited contribution to the overall energy budget, emphasized in earlier studies, to underscore its crucial role on the redistribution of stresses that self-limits earthquake growth and leads to clustering of seismicity. This work highlights the need for characterizing the fault zone mechanical response beyond their elastic properties to better inform seismic hazard models.

opencc-zeroFeb 2022View details →

Understanding complex spatial dynamics from mechanistic models through spatio-temporal point processes

Landscape heterogeneity affects population dynamics, which determine species persistence, diversity and interactions. These relationships can be accurately represented by advanced spatially-explicit models (SEMs) allowing for high levels of detail and precision. However, such approaches are characterised by high computational complexity, high amount of data and memory requirements, and spatio-temporal outputs may be difficult to analyse. A possibility to deal with this complexity is to aggregate outputs over time or space, but then interesting information may be masked and lost, such as local spatio-temporal relationships or patterns. An alternative solution is given by meta-models and meta-analysis, where simplified mathematical relationships are used to structure and summarise the complex transformations from inputs to outputs. Here, we propose an original approach to analyse SEM outputs. By developing a meta-modelling approach based on spatio-temporal point processes (STPPs), we characterise spatio-temporal population dynamics and landscape heterogeneity relationships in agricultural contexts. A landscape generator and a spatially-explicit population model simulate hierarchically the pest-predator dynamics of codling moth and ground beetles in apple orchards over heterogeneous agricultural landscapes. Spatio-temporally explicit outputs are simplified to marked point patterns of key events, such as local proliferation or introduction events. Then, we construct and estimate regression equations for multi-type STPPs composed of event occurrence intensity and magnitudes. Results provide local insights into spatio-temporal dynamics of pest-predator systems. We are able to differentiate the contributions of different driver categories ( i.e., spatio-temporal, spatial, population dynamics). We highlight changes in the effects on occurrence intensity and magnitude when considering drivers at global or local scale. This approach leads to novel findings in agroecology where, for example, we show that the organisation of cultivated patches and semi-natural elements play different roles for pest regulation depending on the scale considered. It aids to formulate guidelines for biological control strategies at global and local scale.

opencc-by-4.0Jul 2022View details →

Data from: Learning to predict spatio-temporal movement dynamics from weather radar networks

This dataset contains the following: <ul> <li>data: <ul> <li>preprocessed: hourly European weather radar data (here: radar) and aggregated simulation outputs (here: abm), combined with ERA5 reanalysis data and Voronoi tessellation details</li> <li>shapes: geographical shapes used for plotting</li> </ul> </li> <li>results: trained models and corresponding results for both simulated data (here: abm) and weather radar data (here: radar).</li> <li>figures: final figures presented in our paper "Learning to predict spatio-temporal movement dynamics from weather radar networks" to summarize the results</li> </ul> The corresponding code used to train and evaluate models is archived here: <a href="https://doi.org/10.5281/zenodo.6921595">10.5281/zenodo.6921595</a>.

opencc-zeroAug 2022View details →

Predicting spatio-temporal population patterns of Borrelia burgdorferi, the Lyme disease pathogen

The causative bacterium of Lyme disease, Borrelia burgdorferi, expanded from an undetected human pathogen into the etiologic agent of the most common vector-borne disease in the United States over the last several decades. Systematic field collections of the tick vector reveal increases in the geographic range and population size of B. burgdorferi that coincided with increases in human Lyme disease incidence across New York State. Here we investigate the impact of environmental features on the population dynamics of B. burgdorferi. Analytical models developed using field collections of nearly 19,000 nymphal Ixodes scapularis and spatially- and temporally-explicit environmental features accurately explained the variation of B. burgdorferi population sizes across space and time. Importantly, the model identified environmental features that can be used to predict the biogeographical patterns of B. burgdorferi-infected ticks into future years and in previously unsampled areas. Forecasting the distribution and abundance of a pathogen at fine geographic scales offers a powerful strategy to mitigate a serious public health threat.

opencc-zeroOct 2022View details →

Spatio-temporal variation in deep soil water use patterns of overstory and understory layers in subtropical plantations predict community assembly

1. Deep soil water utilization allows plants to cope with drought stress. However, little is known about the roles of the understory layers in driving spatio-temporal variations of deep soil water in forests and how the patterns of deep soil water use among life forms contribute to community assembly processes. 2. We assessed the spatio-temporal patterns and determinants of deep water utilization of tree, shrub and herb layers in subtropical coniferous plantations and investigated associations between deep water use parameters and dominance and richness of understory vegetation. 3. We found that the understory layer had a higher reliance on deep soil water in the dry season, a larger seasonal plasticity of deep soil water uptake, but lower spatial variability in deep soil water utilization than the tree layer. We showed that greater reliance of the tree layer on deep soil water was associated with decreased shrub layer diversity, whereas greater reliance of the shrub layer on deep water was associated with increased herb layer diversity. 4. Synthesis. Our results highlight the roles of understory layers in driving the temporal dynamics of deep soil water in forests and improve our understanding of how deep soil water use patterns amongst life forms shape community assembly in forests.

opencc-by-4.0Oct 2022View details →

Raster-based dataset for spatio-temporal analysis of forest fires in the Amazon rainforest from 2001 to 2020

Forest fire incidents are becoming increasingly common around the world, posing a threat to the environment, economy, and social life. These wildfires are further expected to rise in their frequency and intensity, considering the global climate change and human activities. A variety of attributes must be studied in order to analyse relationships between the probable causes of fire and the characteristics of wildfire incidents, and inform decision-making. Such attributes are available or easily collectable in various regions around the world, but they are not readily available in the South American Amazon. The Amazon rainforest covers such a large area that acquiring a useful dataset necessitates extensive effort and computer intensive pre-processing. The associated study to this dataset investigates potential data sources for the Amazon, establishes a methodological baseline, and prepares a dataset of covariates thought to be contributing to the wildfire ignition process. The dataset is intended to be used for forest fire studies, specifically spatio-temporal and statistical analysis of wildfires. The study provides three sets of (i) raw data (acquired data with a global extent), (ii) pre-processed data (source data transformed to the same projection system and same file format), and (iii) working data (cropped to Amazon region extent with spatial resolution of 500 meters and monthly temporal resolution, to enable the scientific community to work with various possibilities of forest-fire analysis, and to further encourage research in study areas in the other parts of the world.