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304 results for “scale pattern”
Data from: Pattern and process in hominin brain size evolution are scale-dependent
A large brain is a defining feature of modern humans, yet there is no consensus regarding the patterns, rates, and processes involved in hominin brain size evolution. We use a reliable proxy for brain size in fossils, endocranial volume (ECV), to better understand how brain size evolved at both clade- and lineage-level scales. For the hominin clade overall, the dominant signal is consistent with a gradual increase in brain size. This gradual trend appears to have been generated primarily by processes operating within hypothesized lineages – 64% or 88% depending on whether one uses a more or less speciose taxonomy, respectively. These processes were supplemented by the appearance in the fossil record of larger-brained Homo species and the subsequent disappearance of smaller-brained Australopithecus and Paranthropus taxa. When the estimated rate of within-lineage ECV increase is compared to an exponential model that operationalizes generation-scale evolutionary processes, it suggests that the observed data were the result of episodes of directional selection interspersed with periods of stasis and/or drift; all of this occurs on too fine a time scale to be resolved by the current human fossil record, thus producing apparent gradual trends within lineages. Our findings provide a quantitative basis for developing and testing scale-explicit hypotheses about the factors that led brain size to increase during hominin evolution.
Data from: The role of climate, water and biotic interactions in shaping biodiversity patterns in arid environments across spatial scales
Aim: Desert ecosystems, with their harsh environmental conditions, hold the key to understanding the responses of biodiversity to climate change. As desert community structure is influenced by processes acting at different spatial scales, studies combining multiple scales are essential for understanding the conservation requirements of desert biota. We investigated the role of environmental variables and biotic interactions in shaping broad and fine-scale patterns of diversity and distribution of bats in arid environments to understand how the expansion of nondesert species can affect the long-term conservation of desert biodiversity. Location: Levant, Eastern Mediterranean. Methods: We combine species distribution modelling and niche overlap statistics with a statistical model selection approach to integrate interspecific interactions into broadscale distribution models and fine-scale analysis of ecological requirements. We focus on competition between desert bats and mesic species that recently expanded their distribution into arid environment following anthropogenic land-use changes. Results: We show that both climate and water availability limit bat distributions and diversity across spatial scales. The broadscale distribution of bats was determined by proximity to water and high temperatures, although the latter did not affect the distribution of mesic species. At the fine-scale, high levels of bat activity and diversity were associated with increased water availability and warmer periods. Desert species were strongly associated with warmer and drier desert types. Range and niche overlap were high among potential competitors, but coexistence was facilitated through fine-scale spatial partitioning of water resources. Main conclusions: Adaptations to drier and warmer conditions allow desert-obligate species to prevail in more arid environments. However, this competitive advantage may disappear as anthropogenic activities encroach further into desert habitats. We conclude that reduced water availability in arid environments under future climate change projections pose a major threat to desert wildlife because it can affect survival and reproductive success and may increase competition over remaining water resources.
Data from: Mast seeding patterns are asynchronous at a continental scale
<p>Resource pulses are rare events with a short duration and high magnitude that drive the dynamics of both plant and animal populations and communities. Mast seeding is perhaps the most common type of resource pulse that occurs in terrestrial ecosystems, is characterized by the synchronous and highly variable production of seed crops by a population of perennial plants, is widespread both taxonomically and geographically, and is often associated with nutrient scarcity. The rare production of abundant seed crops (mast events) that are orders of magnitude greater than crops during low seed years leads to high reproductive success in seed consumers and has cascading impacts in ecosystems. Although it has been suggested that mast seeding is potentially synchronized at continental scales, studies are largely constrained to local areas covering tens to hundreds of kilometres. Furthermore, summer temperature, which acts as a cue for mast seeding, shows patterns at continental scales manifested as a juxtaposition of positive and negative anomalies that have been linked to irruptive movements of boreal seed-eating birds. Here, we show a breakdown in synchrony of mast seeding patterns across space, leading to asynchrony at the continental scale. In an analysis of synchrony for a transcontinental North America tree species spanning distances of greater than 5,200 km, we found that mast seeding patterns were significantly asynchronous at distances of greater than 2,000 km apart (all <i>P</i> < 0.05). Other studies have shown declines in synchrony across distance, but not asynchrony. Spatiotemporal variation in summer temperatures at the continental scale drives patterns of synchrony in mast seeding, and we anticipate that this affects the spatial dynamics of numerous seed-eating communities, from insects to small mammals to the large-scale migration patterns of boreal seed-eating birds.</p>
Figure 1. - Representative specimens of the nine Epicephala species in Japan. Wing pattern of Epicephalaparasitica is sexually dimorphic, so specimens of both sexes are shown for this species. A Epicephalaanthophilia (Amami Island, Kagoshima, ♀, holotype) B Epicephalabipollenella (Henoko, Okinawa, ♀) C Epicephalalanceolatella (Cape Hedo, Okinawa, ♀, holotype) D Epicephalaperplexa (Cape Hedo, Okinawa, ♀, holotype) E Epicephalaobovatella (Tomogashima, Wakayama, ♂, paratype) F Epicephalacorruptrix (Takae, Okinawa, ♀, holotype) G Epicephalavitisidaea (Yona, Okinawa, ♀) H Epicephalaparasitica (Yonaguni Island, Okinawa, ♀, holotype) I Epicephalaparasitica (Hateruma Island, Okinawa, ♂) J Epicephalanudilingua (Watarase-yusuichi, Tochigi, ♀, holotype). Scale bar: 5 mm.
Figure 1. - Representative specimens of the nine Epicephala species in Japan. Wing pattern of Epicephalaparasitica is sexually dimorphic, so specimens of both sexes are shown for this species. A Epicephalaanthophilia (Amami Island, Kagoshima, ♀, holotype) B Epicephalabipollenella (Henoko, Okinawa, ♀) C Epicephalalanceolatella (Cape Hedo, Okinawa, ♀, holotype) D Epicephalaperplexa (Cape Hedo, Okinawa, ♀, holotype) E Epicephalaobovatella (Tomogashima, Wakayama, ♂, paratype) F Epicephalacorruptrix (Takae, Okinawa, ♀, holotype) G Epicephalavitisidaea (Yona, Okinawa, ♀) H Epicephalaparasitica (Yonaguni Island, Okinawa, ♀, holotype) I Epicephalaparasitica (Hateruma Island, Okinawa, ♂) J Epicephalanudilingua (Watarase-yusuichi, Tochigi, ♀, holotype). Scale bar: 5 mm.
Data from a flexible framework to assess patterns and drivers of beta diversity across spatial scales
<p><span>The patterns and underlying ecological (e.g., environmental filtering) and </span><span>historical</span><span> (e.g., priority effects) drivers of beta diversity are scale-dependent but generally difficult to distinguish</span> <span>and rarely explored with a sufficiently broad range of spatial scales. We propose a general scale-explicit framework to assess and contrast the patterns and drivers of beta diversity across hierarchical spatial scales ranging from within fine-scale ecoregion-scale to among broad-scale ecoregion-scale. By applying this framework to aquatic macroinvertebrate datasets, we show that beta diversity generally increases with spatial extent.</span> <span>With an increasing spatial extent, beta diversity shifts from being more influenced by environmental filtering to being more influenced by recent historical factors (i.e., past beta diversity). Such recent historical effects may result from past environmental variation rather than priority effects.</span><span> We also found that the small-scale and large-scale environmental drivers act differently on beta diversity across spatial extents. Our research reveals a complex spatial-scale dependence in beta diversity patterns and their drivers and provides a more holistic understanding of beta diversity dynamics. Our framework represents a flexible way to unravel the internal structure of beta diversity across scales by partitioning of entire beta diversity variation into scale-specific differences and may have broad application in community ecology, landscape planning and biodiversity conservation.</span></p>
Seismicity patterns and multi-scale imaging of Krafla (N-E) Iceland with local earthquake tomography: Raw event waveforms for all events used in the inversion and manual picks for temporary network
<p>This Data and Software were used in the submitted paper "Seismicity patterns and multi-scale imaging at Krafla (N-E Iceland) wih local earthquake tomography" by Glück et al.<br>The data and software provided here are used to compute the velocity models with TomoTV.<br>The raw data (.mseed format) can be visualised with the Python package Pyrocko/Snuffler, which was also used for the arrival time picking.<br>For the temporary network the manual picks are provided along with the code to prepare the manual picks as the input files for a localisation with NonLinLoc by weighting and quality checking the data. This resulting localsitations and the weighted traveltimes are then used for the LET.<br>The same workflow was used for the picks from the permanent network.</p> <p>Data:<br>- Raw data (\WaveformsPermanentStations): 7s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/ for the years 2021 and 2022.<br>- Raw data (\WaveformsNodes): 5s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/2022 recorded with the temporary network of 98 temporary nodes in June and July 2022.<br>- Pickfile (ManualPicks_100Nodes_Kafla2022.txt): Manual picks of the events listed in the ISOR catalogue for the evenst recorded with the temporary network.<br>- Station file (Station_file.txt): The station file includes the coordinates (Lat, Lon, Elevation) of the permanent stations (StationID starting with K...) and of the temporary nodes (StationID starting with N...).</p> <p>Software (Hyp_format.py):<br>- Weighting: The picks are weighted according to their Signal-to-Noise ratio (described in more detail in Section 2.3 in the main text of the paper)<br>- Writing the inputfile for NonLinLoc (with the selecting the mode option "PorS" in line 118), including all picks, also for those stations where not both phases were picked. The file "endfile.txt" is needed to write the picks to the NonLinLoc input format.<br>- Quality check of the picks: Computing a modified Wadati diagram from the traveltime differences of P and S phases for all the events available (with the selecting the mode option "PandS" in line 118)<br>- Python packages needed: numpy, scipy, matplotlib, pandas, obspy</p>
Fine-scale spatial patterns of wildlife disease are common and understudied
<p>1. All parasites are heterogeneous in space, yet little is known about the prevalence and scale of this spatial variation, particularly in wild animal systems. To address this question, we sought to identify and examine spatial dependence of wildlife disease across a wide range of systems.</p> <p>2. Conducting a broad literature search, we collated 31 such datasets featuring 89 replicates and 71 unique host-parasite combinations, only 51% of which had previously been used to test spatial hypotheses. We analysed these datasets for spatial dependence within a standardised modelling framework using Bayesian linear models, and we then meta-analysed the results to identify generalised determinants of the scale and magnitude of spatial autocorrelation.</p> <p>3. We detected spatial autocorrelation in 48/89 model replicates (54%) across 21/31 datasets (68%), spread across parasites of all groups. Even some very small study areas (under 0.01km2) exhibited substantial spatial variation.</p> <p>4. Despite the common manifestation of spatial variation, our meta-analysis was unable to identify host-, parasite-, or sampling-level determinants of this heterogeneity across systems. Parasites of all transmission modes had easily detectable spatial patterns, implying that structured contact networks and susceptibility effects are potentially as important in spatially structuring disease as are environmental drivers of transmission efficiency.</p> <p>5. Our findings demonstrate that fine-scale spatial patterns of infection manifest frequently and across a range of wild animal systems, and many studies are able to investigate them – whether or not the original aim of the study was to examine spatially varying processes. Given the widespread nature of these findings, studies should more frequently record and analyse spatial data, facilitating development and testing of spatial hypotheses in disease ecology. Ultimately, this may pave the way for an a priori predictive framework for spatial variation in novel host-parasite systems.</p>
Dissociable Multi-scale Patterns of Development in Personalized Brain Networks
<p>The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derived personalized functional networks across 29 scales in a large sample of youths (n=693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling systematically adhered to and strengthened a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results demonstrate that the development of functional brain networks align with and refine a hierarchy linked to cognition</p>
Data for the paper "Bridging scales in a multiscale pattern-forming system""
<p>Raw data used for Fig. 2 of the paper "Bridging scales in a multiscale pattern-forming system" by Laeschkir Würthner et al.</p>
Unsupervised detection of Large-scale Weather Patterns in the Northern Hemisphere via Markov State Modelling: from Blockings to Teleconnections
<p><em><span>Data Source</span></em></p> <p>This dataset is derived from the <em><span>NCEP-NCAR Reanalysis 1</span> data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at <a href="https://psl.noaa.gov/">https://psl.noaa.gov</a></em>., a robust atmospheric dataset that includes a wide range of climatic measurements essential for comprehensive climate analysis. The original data can be accessed at the NOAA Physical Sciences Laboratory website: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html.</p> <p> </p>
Data from: Unravelling large-scale patterns and drivers of biodiversity in dry rivers
<p>We conducted a coordinated experiment and a metabarcoding approach on environmental DNA targeting multiple taxa (i.e. Archaea, Bacteria, Fungi, Algae, Protozoa, Nematoda, Arthropoda, and Streptophyta). Dry sediments were collected from 84 non-perennial rivers across 19 countries on four continents to investigate biodiversity patterns and drivers.</p>
Data from: Individual Movement - Sequence Analysis Method (IM-SAM): characterising spatio-temporal patterns of animal trajectories across scales and landscapes
<p>Dataset included in Zenodo supports the analyses performed in "<em>Individual Movement - Sequence Analysis Methods (IM-SAM) characterising spatio-temporal patterns of animal trajectories across scales and landscapes.</em>"</p> <p>The dataset includes one RDS file, that can be easily loaded into R using the readRDS function. The RDS file consists out of a list including two objects per animal:</p> <ul> <li>Object 1 contains a data frame with the real and simulated sequences for an animal. e.g., ls[[1]][[1]] </li> <li>Object 2 contains the home range in raster format of an animal. e.g., ls[[1]][[2]]</li> </ul> <p>The data frames in object 1 contain real habitat use sequences and corresponding simulated habitat use sequences generated in the home range of the specific individual (900 simulated sequences: 6 habitat selection rules x 3 selection coefficients x 50 repetitions). Open and closed habitats are respectively encoded by 0 and 1. The first 96 columns of each row in a data frame represent a 16-day habitat use sequence, with a fixed 4-hour relocation interval (0, 4, 8, 12, 16 and 20h). Column names are named as follows: Day_1_0h, Day_1_4h,..., Day_16_20h. In the next columns we provide the selection coefficients (columns 97-99), the habitat selection rules (or pattern, columns 100-102) and the number of missing values (mvs, columns, 103-104) for each of the real and simulated sequences. Note that simulated sequences have no missing values (i.e. values are always 0.00) and for real sequences there is no selection coefficient or habitat selection rule (i.e. values are always xxx).</p> <p>Rownames of simulated sequences are composed out of the habitat selection rule (c, o, a24, a33, a42 and u), the selection coefficient (5, 10, 50) and the replicate (1 to 50), separated by dashes. For example, the first simulated sequence in the first data frame (ls[[1]][[1]][1,]) is described as a24_10_1. The rownames of real sequences instead are composed out of the individuals' identifier, the biweekly period (1 to 23) and the year. For example, the first real sequence in the first data frame (ls[[1]][[1]][901,]) is described as 1_5_2006.</p> <p><br> </p>
Thermal regimes, but not mean temperatures, drive patterns of rapid climate adaptation at a continent-scale: evidence from the introduced European earwig across North America
<p>Full data set + R script</p>
Data for "Effects of forest dieback on deadwood patterns: large scale trends from a cross-analysis of European databases"
<p><strong><span>Aims</span></strong></p> <p><span>We carried out an opportunistic correlative study between past crown conditions and current deadwood volumes.</span></p> <p><span>Our aim was to mobilise available data on site factors and long-term monitoring of crown vitality indicators in Europe to investigate the influence of current and recent local defoliation levels on plot-level deadwood volume.</span></p> <p><span>For a subset of level I, 16*16-km monitoring plots located throughout Europe, we benefitted from data on both (i) deadwood measurements carried out within the framework of the Forest Focus Biosoil Project </span><span>(Galluzzi et al., 2019)</span><span>, pre-processed into a consistent and harmonized deadwood dataset by </span><span>Puletti et al. (2019)</span><span>, and (ii) defoliation assessments provided yearly since 1989 by the International Co-operative Program on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests), the most comprehensive European monitoring network for the large-scale assessment of forest ecosystem health </span><span>(Vitale et al., 2014)</span><span>. </span></p> <p><span>Biosoil data on deadwood and ICP data on defoliation have never been crossed before.</span></p> <p><span>We used defoliation level as a proxy for the severity of stand dieback. Deadwood patterns can be addressed through deadwood profiles, which subdivide local deadwood stocks into classes based on size, position and decay stage.</span></p> <p><a name="_Toc175840512"></a><a name="_Toc116027761"></a><span><strong><span>ICP database and defoliation protocol</span></strong></span></p> <p><span>The International Cooperative Program to assess and monitor air pollution effects on the forest (ICP Forests) is responsible for an extensive level I monitoring system of forest sites </span><span>(Hauβmann & Fischer, 2004)</span><span>, which has been in operation since 1986. This large-scale level I network is made up of dense, spatially representative sampling points placed throughout European forests on a 16 × 16 km virtual grid, and is dedicated to monitoring forest conditions. The sampling points cover most European forested areas and encompasses ca. 6000 monitoring plots in 42 countries. In each plot, a visual evaluation of defoliation and discoloration of tree crowns is performed annually to survey forest health status (<a href="http://icp-forests.net/page/largescale-forest-condition">http://icp-forests.net/page/largescale-forest-condition</a>). Data management is presently carried out at the Programme Co-ordinating Centre (PCC) of ICP Forests in Eberswalde, Germany, and all data are available upon request. Since 1989, a standardized procedure for “annual surveys of crown condition’’ has been applied to 24 selected dominant and co-dominant trees with a minimum height of 60 cm and showing no significant mechanical damage. The defoliation and discoloration level of each tree crown is visually assessed on a sliding scale of 5% increments as the percentage of needle/leaf loss in the assessable crown as compared to a reference tree with full foliage. Mean defoliation at the plot scale was defined as the proportion of “damaged” trees i.e., with a defoliation rate of more than 25%, and used as a proxy for plot decline level. In the ICP database, the factors associated with observed defoliation related to natural disturbances or management (i.e., vertebrate or insect herbivory, fungal or fire damage, drought impacts, signs of removal of coarse woody debris, past landscape) were not recorded in a sufficiently standardized way to be used as covariates in our models. Similarly, plot-level living tree density and above-ground biomass for standing living trees (expressed in kg.ha<sup>−1</sup>), presumably surveyed in subplot 2, were not available.</span></p> <p><a name="_Toc175840513"></a><a name="_Toc116027762"></a><span><strong><span>Biosoil database and deadwood protocol</span></strong></span></p> <p><a name="_Toc116027763"></a><span>In the framework of the large collaborative European Forest Focus BioSoil-Biodiversity project</span><span>, a system of circular concentric subplots was built around certain ICP level I plots to collect additional data on stand structure and biodiversity between 2005 and 2008 (Figure 1). </span><span><span>The individual countries were responsible for selecting the ICP level I plots to be included in the BioSoil project </span></span><span><span>(Galluzzi et al., 2019)</span></span><span><span>. Overall, a total of 3243 geocoded Level I plots were considered in 19 European countries </span></span><span><span>(Puletti et al., 2017)</span></span><span><span>: Austria, Belgium (Flanders only), Cyprus, the Czech Republic, Denmark, Finland, France, Germany (eight federal states only), Hungary, Ireland, Italy, Latvia, Lithuania, Poland, Slovakia, Slovenia, Spain, Sweden and the United Kingdom (Figure 1). BioSoil project results are recorded in the multi-dimensional LI-BioDiv geodatabase that contains raw data on forest structure and vegetation records used to calculate simple plot-level structural and compositional forest variables (i.e., biomass, deadwood volume, plant alpha-diversity; </span></span><span><span>Bastrup-Birk et al. 2007; Hiederer & Durant 2010)</span></span><span><span>. At each plot, deadwood was quantified on an area of 400 m<sup>2</sup> (BioSoil subplots 1 and 2, radius of 11.28 m; </span></span><span><span>Puletti et al., 2017)</span></span><span><span>. The deadwood survey included coarse woody debris (including lying dead trees), snags (including standing dead trees) and stumps more than 10 cm in diameter. Only snags and stumps more than 130 cm in height were considered. Diameter, length or height, tree species and decay stage (5 classes) were recorded for each deadwood piece. The raw ICP deadwood data were processed by </span></span><span><span>Puletti et al. (2017, 2019)</span></span><span><span> into a consistent and harmonized pan-European deadwood dataset, which we used in this study. The dataset provides total deadwood volume and the volume of several deadwood types for each plot. Further details can be found in the ICP Forests manual (</span></span><a href="http://icp-forests.net/page/icp-forests-manual"><span><span>http://icp-forests.net/page/icp-forests-manual</span></span></a><span><span>), </span></span><span><span>Puletti et al. (2019)</span></span><span><span> and </span></span><span><span>Augustynczik et al. (2024)</span></span><span><span>.</span></span></p> <p><span><span>In our study, we considered the following response variables</span></span><span>: (i) total deadwood volume, (ii) </span><span>standing deadwood (snags) volume, (iii) volume of ground-lying deadwood, (iv) </span><span>fresh deadwood volume </span><span>(= Vm3_dec1_Biosoil + Vm3_dec2_Biosoil), and (v) decayed deadwood volume = (= Vm3_dec4_Biosoil + Vm3_dec5_Biosoil).</span></p> <p><span>A few environmental covariates were collected from the Biosoil data: (i) management intensity (grouped into two classes: recently harvested, i.e., with management evidence within the last 10 years; and not recently harvested, i.e., unmanaged (no management evidence) or managed a long time ago (management evidence but more than 10 years previously), (ii) average stand age (separated into 3 classes: mature [>100 yrs], mid-aged [41-100 yrs], young [1-40 yrs]), (iii) elevation (above sea level, a.s.l.), a continuous quantitative variable, (iv) dominant tree genus, and (v) forest type, depending on the dominant tree species: coniferous, deciduous or mixed.</span></p> <p><a name="_Toc175840514"></a><a name="_Toc116027764"></a><span><strong><span>Database joint</span></strong></span><span><strong><span>: <a name="_Toc116027765"></a>plot matching in time series</span></strong></span></p> <p><span>After harmonizing plot names and coordinates in the two datasets (ICP-defoliation and Biosoil-deadwood), only plots with matched data in both datasets were selected. Plots with a maximum of one year’s discontinuity in the data were retained, and the missing values were reconstructed from the average values in contiguous years. Plots with discontinuities in defoliation measurements of more than 2 years were deleted. We matched defoliation measurements for the Biosoil-ICP datasets from 1989 to 2007 and finally obtained 2,070 five-year, 1,804 ten-year and 1,399 fifteen-year time series. This approach made it possible to define three 10-year time series [1995-2005, 1996-2006, 1997-2007] with plots in 17 countries, from five plots in Ireland and nine in the United Kingdom, to 337 plots in Finland and 461 in France.</span></p> <p><a name="_Toc175840515"></a><a name="_Toc116027766"></a><span><strong><span>Calculation of global defoliation metrics</span></strong></span></p> <p><span>We calculated 16 univariate metrics to summarize changes in defoliation throughout the 10-year period prior to the Biosoil deadwood measurements. Some of the selected parameters describe the immediate possible effects of defoliation severity in the recent past on a given year: (i) defoliation level of the previous year (n-1), (ii) defoliation level of the year before the previous year (n-2), (iii) defoliation level of the year two years before the previous year (n-3). Other defoliation metrics relate to the cumulative effects of defoliation levels in the near or the distant past: (i) average defoliation level over the last two years, (ii) average defoliation level over the last three years, (iii) average defoliation level over the last five years, (iv) average defoliation level over the first five years of the 10-year time series, and (v) time elapsed since last peak defoliation. Several other parameters depict general trends in the level of defoliation over the 10-year time series: for cumulative metrics: (i) arithmetic mean of annual defoliation level; (ii) geometric mean of annual defoliation level; (iii) Area Under the defoliation time Curve (AUC), i.e., the cumulative sum of defoliation levels; and for the overall trend: (iv) the estimated slope of the linear regression line for defoliation level over time. Finally, some of the metrics reflect defoliation severity and repetition along the 10-year time series, and their potentially time-lagged effects: (i) maximum defoliation level; (ii) total number of years elapsed after the dieback peak level, whether successive or not; (iii) the number of peaks, consecutive or discontinuous, i.e., the number of severe defoliation events and defoliation frequency; and (iv) duration of the longest peak, i.e., the longest continuous time during which the level of defoliation was greater than the relative threshold.</span></p> <p><span>A peak in defoliation was defined as a year in which the level of defoliation exceeded a relative threshold, i.e., the third quartile value. In our 10-year time series, the peak value was 25% and above. <span><span> </span></span></span></p>
Cortex cis-regulatory switches establish scale colour identity and pattern diversity in Heliconius
<p></p><p>In Heliconius butterflies, wing pattern diversity is controlled by a few genes of large effect that regulate colour pattern switches between morphs and species across a large mimetic radiation. One of these genes, cortex, has been repeatedly associated with colour pattern evolution in butterflies. Here we carried out CRISPR knock-outs in multiple Heliconius species and show that cortex is a major determinant of scale cell identity. Chromatin accessibility profiling and introgression scans identified cis-regulatory regions associated with discrete phenotypic switches. CRISPR perturbation of these regions in black hindwing genotypes recreated a yellow bar, revealing their spatially limited activity. In the H. melpomene/timareta lineage, the candidate CRE from yellow-barred phenotype morphs is interrupted by a transposable element, suggesting that cis-regulatory structural variation underlies these mimetic adaptations. Our work shows that cortex functionally controls scale colour fate and that its cis-regulatory regions control a phenotypic switch in a modular and pattern-specific fashion.</p><p></p>
Figure 2 in Variation in elasmoid fish scale patterns is informative with regard to taxon and swimming mode
Figure 2. Landmark definitions.
Data and codes to replicate the analysis in: The spatial ecology of conflicts: Unravelling patterns of wildlife damage at multiple scales
<p><span><span>Human encroachment into natural habitats is typically followed by conflicts derived from wildlife damages to agriculture and livestock. Spatial risk modelling is a useful tool to gain understanding of wildlife damage and mitigate conflicts. Although resource selection is a hierarchical process operating at multiple scales, risk models usually fail to address more than one scale, which can result in the misidentification of the underlying processes. Here, we addressed the multi-scale nature of wildlife damage occurrence by considering ecological and management correlates interacting from household to landscape scales. We studied brown bear (<i>Ursus arctos</i>) damage to apiaries in the North-eastern Carpathians as our model system. Using generalized additive models, we found that brown bear tendency to avoid humans and the habitat preferences of bears and beekeepers determine the risk of bear damage at multiple scales. Damage risk at fine scales increased when the broad landscape context also favoured damages. Furthermore, integrated-scale risk maps resulted in more accurate predictions than single-scale models. Our results suggest that principles of resource selection by animals can be used to understand the occurrence of damages and help mitigate conflicts in a proactive and preventive manner. </span></span></p>
Meso-scale patterns of shallow convection in the trades: supplemental material
<p>Supplemental material to the dissertation: Meso-scale patterns of shallow convection in the trades</p> <ol> <li><em>ICON_EUREC4A_LES.mov</em><br> Animation of actual (GOES-16 ABI) and synthetic satellite images for the simulated period. GOES-16 ABI; ICON 624m; ICON 312m (from left to right)<br> </li> </ol>
Dataset 2 for Large‐ and small‐scale geographic structures affecting genetic patterns across populations of an Alpine butterfly
<p>Understanding factors influencing patterns of genetic diversity and the population genetic structure of species is of particular importance in the current era of global climate change and habitat loss. These factors include the evolutionary history of a species as well as heterogeneity in the environment it occupies, which in turn can change across time. Most studies investigating spatio-temporal genetic patterns have focused on patterns across wide geographical areas rather than local variation, but the latter can nevertheless be important particularly in topographically complex areas. Here we consider these issues in the Sooty Copper butterfly (<i>Lycaena tityrus</i>) from the European Alps, using genome-wide SNPs identified through RADseq. We found strong genetic differentiation within the Alps with four genetic clusters, indicating western, central, and eastern refuges, and a strong reduction of genetic diversity from west to east. This reduction in diversity may suggest that the southwestern refuge was the largest one in comparison to other refuges. Also, the high genetic diversity in the West may result from (1) admixture of different western refuges, (2) more recent demographic changes, or (3) introgression of lowland <i>L. tityrus</i> populations. At small spatial scales, populations were structured by several landscape features and especially by high mountain ridges and large river valleys. We detected 36 outlier loci likely under altitudinal selection, including several loci related to membranes and cellular processes. We suggest that efforts to preserve alpine <i>L. tityrus </i>should focus on the genetically diverse populations in the western Alps, and that the dolomite populations should be treated as genetically distinct management units, since they appear to be currently more threatened than others. This study demonstrates the usefulness of SNP-based approaches for understanding patterns of genetic diversity, gene flow and selection in a region that is expected to be particularly vulnerable to climate change.</p>
Large-scale survey of excitatory synapses reveals sublamina-specific and asymmetric synapse disassembly patterns in a neurodegenerative circuit
<p>Dataset associated with the manuscript titled "<strong>Large-scale survey of excitatory synapses reveals sublamina-specific and asymmetric synapse disassembly patterns in a neurodegenerative circuit"</strong></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.