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Spatio-temporal dynamics of attacks around deaths of wolves: A statistical assessment of lethal control efficiency in France
<h1>Description</h1> <p>This repository contains the supplementary materials (<em>Supplementary_information.pdf</em>, <em>Supplementary_figures.pdf</em>, <em>Supplementary_tables.pdf</em>) of the manuscript: "<strong>Spatio-temporal dynamics of attacks around deaths of wolves: A statistical assessment of lethal control efficiency in France</strong>". This repository also provides the R codes and datasets necessary to run the analyses described in the manuscript. </p> <h1>Datasets</h1> <p>We provide the spatially anonymized R datasets to respect confidentiality. Therefore, the preliminary preparation of the data is not provided in the public codes. These datasets, all geolocated and necessary to the analyses, are:</p> <ul> <li><em>Attack_sf.RData</em>: 19,302 analyzed wolf attacks on sheep <br> - ID: unique ID of the attack <br> - DATE: date of the attack <br> - PASTURE: the related pasture ID from "Pasture_sf" where the attack is located <br> - STATUS: column resulting from the preparation and the attribution of attacks to pastures (part 2.2.4 of the manuscript); not shown here to respect confidentiality </li> <li><em>Pasture_sf.RData</em>: 4987 analyzed pastures grazed by sheep <br> - ID: unique ID of the pasture <br> - CODE: Official code in the pastoral census <br> - FLOCK_SIZE: maximum annual number of sheep grazing in the pasture <br> - USED_MONTHS: months for which the pasture is grazed by sheep </li> <li><em>Removal_sf.RData</em>: 232 analyzed single or multiple wolf removals <br> - ID: unique ID of the removal <br> - OVERLAP: are they single removal ("single" in the manuscript => "NO" here), or not ("multiple" in the manuscrit, here "SIMULTANEOUS" for removals occurring during the exact same operation or "NON-SIMULTANEOUS" if not). <br> - DATE_MIN: date of the single removal or date of the first removal of a group <br> - DATE_MAX: date of the single removal or date of the last removal of a group <br> - CLASS: administrative type of the removal according to definitions from 2.1 part of the manuscript <br> - SEX: sex of the removed wolves if known <br> - AGE: class age of the removed wolves if known <br> - BREEDER: breeding status of the removed female wolves, "Yes" for female breeder, "No" for female non-breeder. Males are "No" by default, when necropsied; dead individuals with NA were not found. <br> - SEASON: season of the removal, as defined in part 2.3.4 of the manuscript <br> - MASSIF: mountain range attributed to the removal, as defined in part 2.3.4 of the manuscript </li> <li><em>Mountain_range_buff_sf.RData</em>: one row for each mountain range, corresponding to the buffered mountain ranges where removal control events could be sampled, as defined in part 2.3.3 of the manuscript </li> <li><em>Area_to_exclude_sf.RData</em>: one row for each mountain range, corresponding to the area too close from other mountain ranges, terrestrial and maritime limits, where removal control events could not be sampled, as defined in part 2.3.3 of the manuscript </li> <li><em>Overlapping_removal_sf.RData</em> corresponds to the spatial dataset necessary to run the supplementary figures about overlapping removals (S9) </li> </ul> <p>You can also find the object called <em>Subset_lt.RData</em>, which gives the ID of removals for each dataset (single/multiple removals) or subsets of single removals. </p> <p>The other RData resulted from the analysis. How to read their names: </p> <ul> <li>First part: <br> - <em>Buffer</em>: they link the attacks to removals or of control events. <br> - <em>Kernel</em>: they give the results of the kernel density estimation (z), according to the spatial (y) and temporal (x) coordinates, for each dataset.<br> - <em>Int</em>: they give, for each distance and time, the total amount of attack intensities before (INT_BEF) and after (INT_AFT) the combined locations and days of removals or control events. 1 unit of distance = 50 meters, 1 unit of time = 1 day.<br> - <em>Trend</em>: they give, for each distance and time, the trends in % of the attack intensities when comparing before and after, with their uncertainties and significance. Files with <em>spatshift</em> are designed for figures about spatial shift (unnested scales), contrary to files without <em>spatshift</em> (nested scales). </li> <li>Middle parts: (noted with an X after) <br> - <em>obs</em>: results from removals <br> - <em>jack</em>: results from jackknife samples of the removals <br> - <em>ctl</em>: results related to control sets <br> - <em>cor</em>: results corrected for livestock presence<br> - <em>raw</em>: results uncorrected for livestock presence<br> - <em>sim</em>: results for simulated attacks according to livestock presence </li> </ul> <p>Lengths of lists or sublists correspond to: </p> <p>- 20 elements: the two main datasets (single/multiple) and the 18 subsets of single removals. <br>- 100 elements: the 100 control sets. <br>- 1000 elements: the 1000 simulations of attacks (for the livestock presence correction). <br>- Other lengths and <em>jack</em> within the name: the jackknife samples. </p> <h1>Structure of the repository</h1> <h2><strong>Code 1</strong>: file <em>Buffer.R</em> </h2> <p>We keep only removals within geographic zones for the analysis (<em>Removal_analyzed_sf</em>), and sample their control events (100 simulations = control sets, <em>Removal_ctl_sf_lt</em>).</p> <p>We start by delimiting the spatio-temporal buffer for each row of the removal and control datasets. <br> - We identify the attacks from <em>Attack_sf.RData</em> within each buffer, thanks to the function <em>Buffer_fn</em>, giving the data frames <em>Buffer_X_df</em> (one row per attack) <br> - We select the pastures from <em>Pasture_sf.RData</em> within each buffer, thanks to the function <em>Buffer_pasture_fn</em>, giving the data frames <em>Buffer_X_sf</em> (one row per removal or control event) </p> <p>We calculate the spatial correction: <br> - We spatially slice each buffer into 200 rings with the function <em>Ring_fn</em>, giving the data frame <em>Ring_sf</em> (one row per ring) <br> - We add the total pastoral area of the ring of the attack ("SPATIAL_WEIGHT") with the function <em>Spatial_correction_fn</em>, for each attack of each buffer, within <em>Buffer_X_df</em> </p> <p>We calculate the pastoral correction: <br> - We create the pastoral matrix for each removal or control event with the function <em>Pastoral_matrix_fn</em>, giving a matrix of 200 rows (one for each ring) and 180 columns (one for each day, 90 days before the removal date and 90 day after the removal date), with the total pastoral area in use by sheep for each corresponding cell of the matrix (one element per removal, <em>Pastoral_X_mx_lt.RData</em>) <br> - We simulate, for each removal or control event, the random distribution of the attacks from <em>Buffer_X_df.RData</em> according to <em>Pastoral_X_mx_lt.RData</em> with the function <em>Buffer_sim_fn</em>. The process is done 1000 times (one element per simulation, <em>Buffer_X_sim_lt.RData</em>). </p> <h2>Code 2: file <em>Kernel.R</em> where we estimate the attack intensities </h2> <p>We classified the removals into 2 main datasets and 18 subsets, according to part 2.3.4 of the manuscript (<em>Subset_lt.RData</em>) (one element per set).<br>We compute the jackknife samples for each dataset or subset (<em>Removal_id_jack_lt</em>). <br>We perform the kernel estimations with the function <em>Kernel_fn</em> (<em>Kernel_X_lt</em>). <br>We sum the intensities of attacks before and after the removals or control events, with the function <em>Intensity_fn</em>, giving <em>Int_X_df_lt.RData</em>. </p> <h2>Code 3: file <em>Trend.R</em> where we calculate the trends of attack intensities after removals </h2> <p> We focus on the nested trends first: <br> - We calculate them (<em>Trend_X_df</em>) with function <em>Trend_fn </em><br> - We set the result significance test by observing the overlapping of confidence intervals of removals and control sets (<em>Trend_X_comparison_df</em>) </p> <p>We focus on the spatial shifts (trends for each specific distance):<br> - We calculate them (<em>Trend_X_spatshift_df</em>) with function <em>Trend_fn</em> <br> - We set the result significance test by observing the overlapping of confidence intervals of removals and control sets (<em>Trend_X_comparison_spatshift_df</em>) </p> <h2>- Code 4: file <em>Functions.R</em> where can be found all custom functions called in the other analysis codes </h2> <h2>- Code 5: file <em>Figures.R</em> produces part of the figures from the manuscript</h2> <p>Detailed comments are included in each code.</p> <h1>Support</h1> <p>If you have any question or request, do not hesitate to contact us at: oksana.grente@gmail.com</p> <h1>Authors and acknowledgment</h1> <p>Grente Oksana (CEFE, CNRS), Opitz Thomas (INRAE), Duchamp Christophe (OFB), Drouet-Hoguet Nolwenn (OFB), Chamaillé-Jammes Simon (CEFE, CNRS) and Gimenez Olivier (CEFE, CNRS). </p> <h1>License</h1> <p>GNU GENERAL PUBLIC LICENSE 3.0</p> <p> </p>
Divergent response and adaptation of specific leaf area to environmental change at different spatio-temporal scales jointly improve plant survival
<p><span>Specific leaf area (SLA) is one of the most important plant functional traits. </span><span>It </span><span>integrates multiple functions and reflects strategies of plants to obtain resources. </span><span>How plants employ different strategies (e.g., through SLA) to respond to dynamic environmental conditions remains poorly understood.This study aimed to </span><span>explore the s</span><span>patial variation in SLA and divergent adaptation of plants by changing SLA </span><span>through the lens of biogeographic patterns, evolutionary history, and short-term responses. SLA data for 5424 plant species from 76 natural communities in China were systematically measured and integrated with meta-analysis of field experiments (i.e., global warming, drought, and nitrogen addition). The mean value of SLA across all species was 21.8 m2 kg–1, ranging from 0.9 to 110.2 m2 kg–1. SLA differed among different ecosystems, temperature zones, vegetation types, and functional groups. Phylogeny had a weak effect on SLA, but plant species evolved toward higher SLA. Furthermore, plants responded nonlinearly to environmental change by changing SLA. Unexpectedly, radiation was one of the main factors determining the spatial variation in SLA on a large scale. Conversely, short-term manipulative experiments showed that SLA increased with increased resource availability and tended to stabilize with treatment duration. However, different species exhibited varying response patterns. Overall, long-term adaptation of plants to environmental gradients and its short-term response to resource pulses through variation in SLA jointly improve plant adaptability to a changing environment. Overall SLA-environment relationships should be emphasized as a multidimensional strategy for elucidating environmental change in future research. </span></p>
Data in support of "Antarctic Bottom Water sensitivity to spatio-temporal variations in Antarctic meltwater fluxes"
<p> <em>This data contains the unprocessed output from the five equilibrium simulations used in the paper "Antarctic Bottom Water sensitivity to spatio-temporal variations in Antarctic meltwater fluxes". Simulations were run using the ocean and sea-ice components of CESM1, and the atmosphere was data-driven, and based on ERA5 reanalysis from 1958 until 1980. All data is in netcdf, and includes the description of flag values, and units. The five simulations differ in the freshwater flux scheme as described below:</em></p> <p><br> <em>Simulation UNIF - files: ga1.f09_g16.308<br> Forced with freshwater fluxes from the Antarctic Ice Sheet (AIS), uniformly distributed around the Antarctic coast. Total freshwater flux from AIS: 2075 Gt/yr<br> <br> Simulation BM - files: ga1.f09_g16.208<br> Forced with asymmetric zonal freshwater fluxes from the AIS.<br> Total freshwater flux from AIS: 2075 Gt/yr<br> <br> Simulation VARI - files: ga1.f09_g16.108<br> Forced with asymmetric zonal and meridional freshwater fluxes from the AIS. Total freshwater flux from AIS: 2075 Gt/yr<br> <br> Simulation CV - files: ga1.f09_g16.408<br> Forced with asymmetric meridional freshwater fluxes from the AIS, to mimic iceberg melting. Total freshwater flux from AIS: 934 Gt/yr<br> <br> Simulation VARI120% - files: ga1.f09_g16.x18<br> Same as VARI, but the total freshwater fluxes were increased by 20%. Total freshwater flux from AIS: 2490 Gt/yr</em><br> </p> <p> </p> <p>[30S 90S] , [180W 180E]</p> <p> </p> <p><strong>Variables:</strong> Ocean Temperature [Celius], Salinity [PSU], Sea ice Fraction [fraction], Salt flux from sea ice [kg/m2/s], and ocean overturning [SV]</p> <p><em>For more information, check:<a href="https://doi.org/10.1002/essoar.10512610.1">https://doi.org/10.1002/essoar.10512610.1</a></em></p>
Propagating spatio-temporal activity patterns across macaque motor cortex carry kinematic information
<p>Propagating spatio-temporal neural patterns are widely evident across sensory, motor and association cortical areas. However, it remains unclear whether any characteristics of neural propagation carry information about specific behavioral details. Here, we provide the first evidence for a link between the direction of cortical propagation and specific behavioral features of an upcoming movement on a trial-by-trial basis. We recorded local field potentials (LFPs) from multi-electrode arrays implanted in the primary motor cortex of two rhesus macaque monkeys while they performed a 2-D reach task. Propagating patterns were extracted from the information-rich high-gamma band (200–400Hz) envelopes in the LFP amplitude. We found that the exact direction of propagating patterns varied systematically according to initial movement direction, enabling kinematic predictions. Furthermore, characteristics of these propagation patterns provided additional predictive capability beyond the LFP amplitude themselves, which suggests the value of including mesoscopic spatio-temporal characteristics in refining brain-machine interfaces.</p>
Raw GBIF data used in the article "Climate-induced range shifts drive adaptive response via spatio-temporal sieving of alleles"
<p>This table contains the raw (unfiltered) species occurrence data for Dianthus sylvestris complied from GBIF. A filtered subset of this data was combined with other data sources to define the input for the species distribution models used in the article "Climate-induced range shifts drive adaptive response via spatio-temporal sieving of alleles".</p>
Workflow-Based Spatio-Temporal Data Analytics
<p>In the biodiversity domain, researchers often have to combine a large variety of heterogeneous spatio-temporal data sources. For example, the loss of biodiversity can be quantified by analyzing occurrence observations of various species across time. To find the root cause of that loss, occurrence data may need to be combined with satellite images to find possible correlations with climate variables. To facilitate an exploratory approach for this combination of data sources, it is essential to provide researchers with workflow-based tools such that each step during the formulation of a research hypothesis can be tracked. In this presentation, we will discuss Geo Engine, a workflow-based analysis platform for spatio-temporal data analytics, and its place within FAIR data spaces.</p>
Data for: Leveraging spatio-temporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models
<p>Joint modeling of correlated multi-environment and multi-harvest data of perennial crop species may offer advantages in prediction schemes and a better understanding of the underlying dynamics in space and time. The goal of the present study was to investigate the relevance of incorporating the longitudinal dimension of within-season multiple measurements of forage perennial ryegrass traits in a reaction norm model setup that additionally accounts for genotype-environment interactions (G×E). Genetic parameters and accuracy of genomic breeding value (gEBV) predictions were investigated by fitting three random regression models (gRRM) using Legendre polynomial functions to the data. Genomic DNA sequencing of family pools of diploid perennial ryegrass was performed using DNA nanoball-based technology and yielded 56,645 single nucleotide polymorphisms which were used to calculate the allele frequency-based genomic relationship matrix. Biomass yield's estimated additive genetic variance and heritability values were higher in later harvests. The additive genetic correlations were moderate to low in early measurements and peaked at intermediates, with fairly stable values across the environmental gradient, except for the initial harvest data collection. This led to the conclusion that complex (G×E) arises from spatial and temporal dimensions in the early season, with lower re-ranking trends thereafter. In general, modeling the temporal dimension with a second-order orthogonal polynomial improved the accuracy of gEBV prediction for nutritive quality traits, but no gain in prediction accuracy was detected for dry matter yield. This study leverages the flexibility and usefulness of gRRM models for perennial ryegrass breeding and can be readily extended to other multi-harvest crops.</p>
Analysing spatio-temporal patterns of non-native fish in a biodiversity hotspot across decades
<p><strong>Aim</strong>: Analysing the spatio-temporal patterns and dynamics of non-native species is essential to understanding the mechanisms underlying successful invasions and developing effective management strategies. Yet, such analyses generally neglect the influence of receiving ecosystem types and non-native species sources (i.e. alien species, non-natives originating outside the concerned region; translocated species, nonnatives introduced to locations outside their historical range within the concerned region). </p> <p><strong>Location</strong>: Yunnan, China.</p> <p><strong>Methods</strong>: We analysed long-term (1950–2022) spatio-temporal patterns and potential underlying dynamics of non-native fishes in a biodiversity hotspot (Yunnan, China), paying special attention to waterbody types receiving non-native species and comparing alien and translocated species. We did this through compiling a highly comprehensive occurrence dataset of native and non-native fishes.</p> <p><strong>Results</strong>: We recorded 783 native species and 94 non-native species (49 alien species and 45 translocated species), which mainly belonged to the order Cypriniformes (52 species) and were introduced via purposes for advancing aquaculture. Most frequently encountered non-native species were either intentionally introduced aquaculture species or small-bodied fish unintentionally introduced via aquaculture activities. The richness and spatial ranges of non-native fishes increased consistently since the 1950s and demonstrated a pronounced change after the 2000s, with densely populated areas and the middle to lower reaches of large rivers being more profoundly affected. The number of records of translocated species exceeded the number of records of alien species after the 2000s. Lakes and reservoirs are hotspots for both alien and translocated species introductions, and watersheds with large areas in Yunnan (e.g. the Jinsha-Yangtze and Lancang-Mekong basins) contained more non-native fish.</p> <p><strong>Main Conclusions</strong>: Our study highlights the need to consider invasion sensitivities of receiving ecosystems and pay special attention to intra-regional species translocations when developing prevention and management strategies against invasions of alien species, particularly in important biodiversity hotspots around the world.</p>
Spatio-temporal electroencephalographic power distribution in experimental pigs receiving propofol
<p>Electroencephalographic (EEG) power recorded from surface electrodes (right side).</p><p>- First column: propofol dose administered;</p><p>- Second column: region assessed;</p><p>- From the third column on: the EEG power for each frequency.</p><p>Each pig has a dedicated worksheet.</p>
Spatio-temporal dynamics of insect communities in constructed and natural tidal marshes with distinct landscape positions
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Spatio-temporal variation in deep soil water use patterns of overstory and understory layers in subtropical plantations predict community assembly
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Data from:Spatio-temporal networks: reachability, centrality and robustness
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Multimodal data helps in identifying spatio-temporal patterns and habitat associations of <em>Aquila chrysaetos</em> (Golden Eagle) in Finland
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Code from: When and where do waterbirds need water? Inferring candidate restoration areas from spatio-temporal variation in surface water availability
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Data from: Spatio-temporal dynamics in syntopy are driven by variability in rangeland conditions
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Data from: Trait matching and phenological overlap increase the spatio-temporal stability and functionality of plant-pollinator interactions
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Diversifying in the mountains: spatio-temporal diversification of frogs in the Western Ghats biodiversity hotspot
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Analysing spatio-temporal patterns of non-native fish in a biodiversity hotspot across decades
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Data for: Body mass mediates spatio-temporal responses of mammals to human frequentation across Italian protected areas
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Spatio-temporal dynamics of impulse responses to figure motion in optic flow neurons
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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.
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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.