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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Kirschel, Alexander; Hadjikyriakou, Thomas; Skarlatos, Dimitrios; Kassinis, Nikolaos; +1 Authors

    The global breeding population of Eleonora’s Falcon (Falco eleonorae) is distributed from the Canary Islands in the west, across the Mediterranean Sea, to Cyprus in the east. The remoteness of nesting colonies, which are predominantly located on sea cliffs and islets, renders breeding success estimation a challenging task, requiring a composite approach to assess each of the breeding stages. Early estimates of the breeding success of Eleonora’s Falcon suggested that the Akrotiri colony in Cyprus had the lowest breeding success among all the colonies throughout the species’ breeding range, at a level seemingly unsustainable, suggesting the colony might have been in danger of gradual extinction. Here we use a diversity of survey methods including boat, ground, and aerial surveys, with the incorporation of photography and photogrammetry, to reassess the breeding success and the effect of nest characteristics on the Eleonora’s Falcon breeding population in Cyprus. During a 6-yr study, we found that Cyprus hosts ~138 ± 8 breeding pairs and that breeding success equals 1.54 ± 0.85 fledglings per breeding pair, and thus is considerably higher than previous estimates. In addition, by analyzing temporal variation in breeding and nest characteristics, we found that early breeding and reuse of nests positively influence breeding success, but physical nest characteristics have a limited effect on colony productivity. The range of survey methods employed, as well as the array of photography techniques utilized, enhanced the efficiency and accuracy of this study, allowing us to overcome the challenge of inaccessibility of nesting cliffs. The raw data used in statistical analyses are all provided along with the R code. The data have all been combined here into one dataset though analyses were performed on subsets of the data as described in the manuscript. The script to produce the digital surface model is provided but we do not provide exact coordinates because of sensitivity of falcon nest sites to disturbance. The dataset is raw survey data from monitoring Eleonora's falcon nest sites using a variety of methods described in the paper. Also included in separate sheets are the code used to analyse the data - R code for statistical analyses and python code to produce a digital surface model of the nesting cliffs.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODO; DRYADarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO; DRYAD
    Dataset . 2021
    License: CC 0
    Data sources: ZENODO; Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODO; DRYADarrow_drop_down
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      ZENODO; DRYAD
      Dataset . 2021
      License: CC 0
      Data sources: ZENODO; Datacite
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    Authors: Chiquillo, Kelcie; Barber, Paul; Vasquez, Marlen; Cruz-Rivera, Edwin; +3 Authors

    The nature and strength of interactions between native and invasive species can determine invasion success. Species interactions can drive, prevent or facilitate invasion, making understanding the nature and outcome of these interactions critical. We conducted mesocosm experiments to test the outcome of interactions between Halophila stipulacea, a seagrass that invaded the Mediterranean and Caribbean Seas, and native seagrasses (Cymodocea nodosa and Syringodium filiforme, respectively) to elucidate mechanisms explaining the successful invasions. Mesocosms contained intact cores with species grown either mixed or alone. Overall, in both locations, there was a pattern of the invasive growing faster with the native than when alone, while also negatively affecting the native, with similar patterns for shoot density, aboveground and belowground biomass. In the Caribbean, H. stipulacea increased by 5.6 ± 1.0 SE shoots in 6 weeks when grown with the native while, when alone, there was a net loss of −0.8 ± 1.6 SE shoots. The opposite pattern occurred for S. filiforme, although these differences were not significant. While the pattern in the Mediterranean was the same as the Caribbean, with the invasive grown with the native increasing shoots more than when it grew alone, these differences for shoots were not significant. However, when measured as aboveground biomass, H. stipulacea had negative effects on the native C. nodosa. Our results suggest that a seagrass that invaded two seas may drive its own success by both negatively affecting native seagrasses and benefiting from that negative interaction. This is a novel example of a native seagrass species facilitating the success of an invasive at its own cost, providing one possible mechanism for the widespread success of this invasive species.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; ZENODOarrow_drop_down
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    DRYAD; ZENODO
    Dataset . 2022
    License: CC 0
    Data sources: ZENODO; Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; ZENODOarrow_drop_down
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      DRYAD; ZENODO
      Dataset . 2022
      License: CC 0
      Data sources: ZENODO; Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    Black and white photograph, showing a winter’s day at a deserted sea shore in Cyprus - Μαυρόασπρη κάρτ ποστάλ που απεικονίζει μια χειμωνιάτικη μέρα σε μια ερημική ακτή στην Κύπρο.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ LEKYTHOSarrow_drop_down
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    LEKYTHOS
    Image . 2016
    Data sources: LEKYTHOS
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      LEKYTHOS
      Image . 2016
      Data sources: LEKYTHOS
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    Authors: Boyer, Matthew; Quéléver, Lauriane; Beck, Ivo; Laurila, Tiia; +3 Authors

    This dataset contains ambient concentrations of aerosol precursor vapors measured in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The timeseries includes a full year of sulfuric acid (SA), methanesulfonic acid (MSA), and iodic acid (IA) concentrations retrieved at a time resolution of 5 minutes between October 2019 and September 2020. The data were collected using a nitrate chemical ionization mass spectrometer (NO3-CIMS) as described by Jokinen et al. (2012). The instrument was located in the Swiss container, which was placed on the starboard side of Polarstern's bow on the D-deck during the campaign (Shupe et al., 2022). The concentration retrievals were obtained by integrating peaks from the high-resolution mass spectra for each compound of interest (either as a deprotonated ion or as its corresponding cluster with nitrate), normalizing the result with the sum of charger ions (NO3-, HNO3NO3-, (HNO3)2NO3-), and multiplying by the calibration factor (6×109 molec·cm-3) obtained from a dedicated calibration using SA. Since the instrument calibration was only performed using SA, the concentrations of MSA and IA are low limit estimations. SA was determined by peaks at mass to charge ratios (m/z) of 96.9601 Th (HSO4-) and 159.9557 Th (H2SO4NO3-), MSA was determined by m/z peaks at 94.9808 Th (CH3SO3-) and 157.9765 Th (CH3SO3HNO3-), and IA was determined by m/z peaks at 174.8898 Th (IO3-) and 237.8854 Th (HIO3NO3-). Zero measurements were performed periodically by placing a filter on the inlet of the instrument to determine the detection limit for each individual species. The detection limits were calculated as μ + 3 × σ, where µ is the average concentration and σ is the standard deviation, both of which were evaluated during filter measurements. The resulting detection limits are 8.8e4, 1.5e5, and 5.5e4 molec·cm-3 for SA, MSA, and IA, respectively. The dataset includes flags to specify the data that are below the detection limit. The influence of local pollution from the research vessel and other logistic activities was identified by applying a pollution detection algorithm (Beck et al., 2022) to particle number concentrations from a condensation particle counter (CPC3025, TSI) that was also located in the Swiss container. Periods that were potentially affected by primary pollution are flagged in the dataset. The columns in the data file include the date and time in Coordinated Universal Time (UTC); the concentration of SA, MSA, and IA in molec·cm-3; a detection limit flag for each individual species (1 = below detection limit); and a local pollution flag where the data may have influence from the vessel and logistical activities (1 = pollution was detected).

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    PANGAEA
    Dataset . 2023
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2023
      Data sources: B2FIND
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    Authors: Kostaki, Evangelia-Georgia; Karamitros, Timokratis; Stefanou, Garyfallia; Mamais, Ioannis; +4 Authors

    Hepatitis B virus (HBV) infection constitutes a global public health problem. In order to establish how HBV was disseminated across different geographic regions, we estimated the levels of regional clustering for genotypes D and A. We used 916 HBV-D and 493 HBV-A full-length sequences to reconstruct their global phylogeny. Phylogeographic analysis was conducted by reconstruction of ancestral states using the criterion of parsimony. The putative origin of genotype D was in North Africa/Middle East. HBV-D sequences form low levels of regional clustering for the Middle East and Southern Europe. In contrast, HBV-A sequences form two major clusters, the first including sequences mostly from sub-Saharan Africa, and the second including sequences mostly from Western and Central Europe. Conclusion: We observed considerable differences in the global dissemination patterns of HBV-D and HBV-A and different levels of monophyletic clustering in relation to the regions of prevalence of each genotype. HBV_Genotype_A_sequence alignmentHBV genotype A full-length genomic sequence alignment after the exclusion of multiple sequences per patient. Sequence identifiers include accesion number of geographic area of samplingHBV_Genotype D_sequence_alignmentHBV genotype D full-length genomic sequence alignment after the exclusion of multiple sequences per patient. Sequence identifiers include accesion number of geographic area of sampling

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODO; NARCIS; DRYA...arrow_drop_down
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    ZENODO; NARCIS; DRYAD
    Dataset . 2019
    License: CC 0
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DANS-EASY
    Dataset . 2018
    Data sources: B2FIND
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODO; NARCIS; DRYA...arrow_drop_down
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      ZENODO; NARCIS; DRYAD
      Dataset . 2019
      License: CC 0
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      DANS-EASY
      Dataset . 2018
      Data sources: B2FIND
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    Authors: Kirschel, Alexander; Seddon, Nathalie; Tobias, Joseph;

    A long-held view in evolutionary biology is that character displacement generates divergent phenotypes in closely related coexisting species to avoid the costs of hybridisation or ecological competition, whereas an alternative possibility is that signals of dominance or aggression may instead converge to facilitate coexistence among ecological competitors. Although this counter-intuitive process⎯termed convergent agonistic character displacement⎯is supported by recent theoretical and empirical studies, the extent to which it drives spatial patterns of trait evolution at continental scales remains unclear. By modeling variation in song structure of two ecologically similar species of Hypocnemis antbird across Western Amazonia, we show that their territorial signals converge such that trait similarity peaks in the sympatric zone, where intense interspecific territoriality between these taxa has previously been demonstrated. We also use remote sensing data to show that signal convergence is not explained by environmental gradients and is thus unlikely to evolve by sensory drive (i.e. acoustic adaptation to the sound transmission properties of habitats). Our results suggest that agonistic character displacement driven by interspecific competition can generate spatial patterns opposite to those predicted by classic character displacement theory, and highlight the potential role of social selection in shaping geographical variation in signal phenotypes of ecological competitors. Raw_Data_Kirschel_etal_ProcBRecordist information and accession numbers for each recording in sound archives, as well as values extracted from recordings and used in analyses, and environmental data extracted from recording localities.

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    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
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    DRYAD; ZENODO; NARCIS
    Dataset . 2019
    License: CC 0
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      DANS-EASY
      Dataset . 2019
      Data sources: B2FIND
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      DRYAD; ZENODO; NARCIS
      Dataset . 2019
      License: CC 0
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    Authors: Lowet, Eric; Roberts, Mark; Hadjipapas, Avgis; Peter, Alina; +2 Authors

    monkey_LFP_V1_stimulus_contrast_example_dataExample LFP data from one contact point of a laminar probe inserted in macaque V1 (superficial cortex)PING_HH_increasing_driveSimulaiton of a Hodgkin-Huxley pramidal-interneuron gamma (PING) network with different input drive conditions (corresponds to Figure 1).ring_PING_HH_dataSimulation of a ring-shaped PING network with spatial-defined connectivity and input drive to E-cells Fig3 = the network used for figure 3 in the main manuscript highEE= high interconnecitons strength between E-cells highII = high interconnection strength between I-cells noII = no interconnection strength between I-cells noEE= no interconnecitons strength between E-cells noiselevel1 = low noise level in the input AMPA train to E-cell noiselevel2 = higher noise level in the input AMPA train to E-celltwo_interacting_PING_HH_dataIt includes the simulation of two interacting Hodgkin-Huxley pryramidal-interneuron gamma (PING) networks with different coupling conditions and input drive conditions. The coupling value is indicated in the folder name and the mean excitatory drive to each network is saved as input variables in the folder.phase_oscillator_lattice_data_part1simulation data from lattice phase-oscillator model part 1phase_oscillator_data_part1.zipoverviewIf any questions arise, please contact e.lowet@fcdonders.ru.nl The sharing folder consists of: 1. monkey_LFP_V1_stimulus_contrast_example_data (Fig.1A-B) Example monkey data (one contact of a laminar probe inserted in parafoveal V1 , see Roberts et al.,2013 in Neuron) with 8 different contrast conditons. A square-wave grating is shown with different constrasts that stimulated the V1 receptive field. The monkey is engaged in a passive fixation task. 2. PING_HH_increasing_drive Here a single PING- network receive different level of excitatory input. This corresponds to Fig.1 C-F. This simulation to show that a PING network react with increasing gamma frequency with increasing input drive. The relates to the experimental observation in the monkey experiment where it is known that visual contrast increase the input drive to V1. 8 input level conditions are inlcuded here. The neuronal spiking data (spikes) as well as different network signals (signals) are included. 3. two_interacting_PING_HH_data This relates to Fig.2. Here two interacting PING networks are simulated. The coupling strength as well as the input level difference is manipulated systematically to be able to reconstruct the Arnold tongue. In each folder the spikes, network signals as well the inputs to the both PING networks are included. The coupling values are in the folder names. 4. ring_PING_HH_data Here different simulaiton with the ring-PING network is included. Simulations realted to Fig.3 as well as Suppl.Fig 1-2 are included. Only the relevant spiking data are included. 5. phase_oscillator_lattice_data It includes the simulation output data from the lattice phase-oscillator model for each of 80 input natural contrast images used. The natural contrast images were used to set the intrinisc freuqency of the phase-osicllators. This relates to Fig.7-8. 6. code_phase_oscillator_ring_network.m (MATLAB code) This simulation code corresponds to Fig.6. The simulation code reproduces the output data of the ring-phase-oscillator model. 7. code_izhi_ring_network.m(MATLAB code) This simulation code corresponds to Suppl.Fig.3. The simulation code reproduces the output data of the ring-PING network with Izhikevih-type neurons.read_phaseoscillator_dataread_HH_datacode_izhi_ring_networkMatlab Code of a ring-shaped pryramidal-interneuron gamma (PING) network with Izhikevich-type model neurons (Suppl. Fig.3).code_phase_oscillator_ring_networkMatlab Code of a ring-shaped phase-oscillator model (Fig.6)phase_oscillator_lattice_data_part2simulation data from lattice phase-oscillator model part 2phase_oscillator_data_part2.zipphase_oscillator_lattice_data_part3simulation data from lattice phase-oscillator model part 3phase_oscillator_data_part3.zipphase_oscillator_lattice_data_part4simulation data from lattice phase-oscillator model part 4phase_oscillator_data_part4.zipphase_oscillator_lattice_data_part5simulation data from lattice phase-oscillator model part 5phase_oscillator_data_part5.zipphase_oscillator_lattice_data_part6simulation data from lattice phase-oscillator model part 6phase_oscillator_data_part6.zipphase_oscillator_lattice_data_part7simulation data from lattice phase-oscillator model part 7phase_oscillator_data_part7.zipphase_oscillator_lattice_data_part8simulation data from lattice phase-oscillator model part 8phase_oscillator_data_part8.zipphase_oscillator_lattice_data_part9simulation data from lattice phase-oscillator model part 9phase_oscillator_data_part9.zipphase_oscillator_laatice_data_part10simulation data from lattice phase-oscillator model part 10phase_oscillator_data_part10.zip Fine-scale temporal organization of cortical activity in the gamma range (~25–80Hz) may play a significant role in information processing, for example by neural grouping (‘binding’) and phase coding. Recent experimental studies have shown that the precise frequency of gamma oscillations varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common frequency. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent frequency modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different oscillation frequencies at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and frequency differences, and quantified the stimulus-related information represented by gamma phase and frequency. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled oscillators, where input drive determines the intrinsic (natural) frequency of oscillators. The gamma phase-locking, the precise phase relation and the emergent (measurable) frequencies were determined by two principal factors: the detuning (intrinsic frequency difference, i.e. local input difference) and the coupling strength. In addition to frequency coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes or slower oscillation phase codes, may resolve conflicting experimental observations on gamma phase coding. Our modeling results offer clear testable experimental predictions. We conclude that input-dependency of gamma frequencies could be essential rather than detrimental for meaningful gamma-mediated temporal organization of cortical activity.

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    ZENODO; DRYAD; NARCIS
    Dataset . 2015
    License: CC 0
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    DANS-EASY
    Dataset . 2015
    Data sources: B2FIND
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      ZENODO; DRYAD; NARCIS
      Dataset . 2015
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      DANS-EASY
      Dataset . 2015
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    Authors: Noguerales, Víctor; Meramveliotakis, Emmanouil; Castro-Insua, Adrián; Andújar, Carmelo; +7 Authors

    Raster layers This ZIP folder contains the TIF files corresponding to the digital elevation model (“01_DEM” subfolder), six WorldClim layers (“02_WorldClim” subfolder), four ENVIREM layers (“03_ENVIREM” subfolder) and the spatial distribution of Quercus alnifolia patches (“04_Quercus_alnifolia” subfolder). This latter subfolder also contains input files (ASCII format) for CIRCUITSCAPE corresponding to the isolation-by-resistance (IBR) scenarios defined by (i) the distribution of Quercus alnifolia forest patches assuming increasing resistance values (from 5 to 1,000,000) for non-Quercus cells, (ii) the topographic complexity as estimated by the terrain roughness index (TRI) and (iii) a “flat” scenario (NULL) whose cells have a fixed resistance (=1) value. Geographic coordinates of Quercus alnifolia sampling sites are also provided in the format required by CIRCUITSCAPE. The WorldClim and ENVIREM layers were processed and interpolated at 90 meters resolution as detailed in Supplemental Information. File: 0A_Raster_layers.zip Community tables Filtered community tables (taxa in rows, sites in columns) used for downstream analyses, at both ASV and OTU levels. Sampling site codes as in Table S1 in Supplemental Information. File: 0B_Community_tables.zip DNA sequences Files in FASTA format containing DNA sequences of (a) fully-filtered ASVs derived from metabarcoding data and (b) Sanger sequences of ‘voucher’ specimens. File: 0C_DNAsequences.zip GLMM input file Input file for Generalized Linear Mixed Models (GLMM), which were used to analyse the relationship between ASV- and OTU-based richness (RICH) or local contribution to beta diversity (LCBD) per site and the topoclimatic variables (ENVPC1 and ENVPC2) as predictors, with latitude and longitude as covariates and forest habitat type as a random effect. Sampling site codes as in Table S1 in Supplemental Information. File: 01_GLMM.zip PCNM input files This ZIP folder contains the topographic weighted distance matrices (SPATWD) used as input files in Principal Coordinates of Neighbour Matrices (PCNM) analyses to generate spatial predictors, at across-habitats and within-habitat scales. Sampling site codes as in Table S1 in Supplemental Information. File: 02_PCNM.zip dbRDA input files This ZIP folder contains the input files used to perform Distance-Based Redundancy Analysis (dbRDA), at both across-habitats and within-habitat scales, including (a) the community dissimilarity matrices based on the Simpson dissimilarity index calculated at both ASV and OTU level, and (b) tables of spatial (SPAPCNMi) and environmental predictors (habitat type, ENVPC1 and ENVPC2). Sampling site codes as in Table S1 in Supplemental Information. File: 03_dbRDA.zip mvGLM input files This ZIP folder contains the input files used to perform Multivariate Generalized Linear Models (mvGLMs), at both across-habitats and within-habitat scales, including (a) community tables (presence/absence) for both ASV and OTU level, and (b) spatial (SPAPCNMi) and environmental predictors (habitat type, ENVPC1 and ENVPC2). Sampling site codes as in Table S1 in Supplemental Information. File: 04_mvGLM.zip MMRR and distance decay input files This ZIP folder contains the input files used to perform Multivariate Matrix Regression with Randomization (MMRR), including (a) community dissimilarity matrices based on the Simpson dissimilarity index calculated at both ASV and OTU level for Quercus alnifolia sampling sites, and (b) distance matrices based on (i) resistance due to habitat fragmentation (FRAIBR) assuming increasing resistance values (from 5 to 1,000,000) for non-Quercus cells, (ii) resistance due to topographic complexity (TRIIBR), (iii) resistance due to a “flat landscape” (NULLIBR), (iv) weighted topographic (SPATWD) distances and (v) topoclimatic (ENVPC1-2) distances. Sampling site codes as in Table S1 in Supplemental Information. File: 05_MMRR-DistanceDecay.zip Disentangling the relative role of environmental filtering and spatial processes in driving metacommunity structure across mountainous regions remains challenging, as the way we quantify spatial connectivity in topographically and environmentally heterogeneous landscapes can influence our perception of which process predominates. More empirical datasets are required to account for taxon- and context-dependency but relevant research in understudied areas is often compromised by the taxonomic impediment. We here employed haplotype-level community DNA metabarcoding, enabled by stringent filtering of Amplicon Sequence Variants (ASVs), to characterize metacommunity structure of soil microarthropod assemblages across a mosaic of five forest habitats on the Troodos mountain range in Cyprus. We found similar β diversity patterns at ASV and species (OTU, Operational Taxonomic Unit) levels, which pointed to a primary role of habitat filtering resulting in the existence of largely distinct metacommunities linked to different forest types. Within-habitat turnover was correlated to topoclimatic heterogeneity, again emphasizing the role of environmental filtering. However, when integrating landscape matrix information for the highly fragmented Quercus alnifolia habitat, we also detected a major role of spatial isolation determined by patch connectivity, indicating that stochastic and niche-based processes synergistically govern community assembly. Alpha diversity patterns varied between ASV and OTU levels, with OTU richness decreasing with elevation and ASV richness following a longitudinal gradient, potentially reflecting a decline of genetic diversity eastwards due to historical pressures. Our study demonstrates the utility of haplotype-level community metabarcoding for characterising metacommunity structure of complex assemblages and improving our understanding of biodiversity dynamics across mountainous landscapes worldwide.

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    ZENODO; DRYAD
    Dataset . 2021
    License: CC 0
    Data sources: Datacite; ZENODO
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      ZENODO; DRYAD
      Dataset . 2021
      License: CC 0
      Data sources: Datacite; ZENODO
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    Black and white photograph, showing a winter’s day at a deserted sea shore in Cyprus - Μαυρόασπρη κάρτ ποστάλ που απεικονίζει μια χειμωνιάτικη μέρα σε μια ερημική ακτή στην Κύπρο.

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    LEKYTHOS
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    Data sources: LEKYTHOS
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      LEKYTHOS
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      Data sources: LEKYTHOS
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    Authors: Neocleous, Vassos; Fanis, Pavlos; Toumba, Meropi; Gorka, Barbara; +13 Authors

    Genetic Analysis Genomic DNA was extracted from peripheral blood using the Gentra Puregene Kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. The DNA purity was measured using the Nanodrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). Prior to library preparation for whole exome sequencing (WES) genomic DNA was quantified using the Qubit dsDNA BR Assay Kit (Invitrogen, Life Technologies, Eugene, OR, USA) on a Qubit® 2.0 Fluorometer (Invitrogen, Life Technologies, Eugene, OR, USA). WES was performed by using the TruSeq Exome Kit (Illumina Inc., San Diego, CA, USA) with paired-end 150 bp reads. NGS was performed using the NextSeq 500/550 High Output Kit v2.5 (150 Cycles) on an NextSeq500 system (Illumina Inc., San Diego, CA, USA). The FastQC quality control tool (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) was used to evaluate the quality of the WES procedure. The mean target coverage of the whole exome was 62.13X. Specifically, 10X coverage was reached for 92.34% of the nucleotides, 20X coverage for 86.03% of the nucleotides and 30X coverage for 76.96% of the nucleotides, indicating that the WES reaction was of sufficiently high quality for subsequent analysis. Variant Analysis The fastq data obtained by WES were processed using an in-house bioinformatics pipeline. Briefly, all variants were inputted into the VarApp Browser and filtered. VarApp is a graphical user interface, which supports GEMINI (18). Variants in selected genes involved in pubertal onset and were mutations have been reported for precocious puberty were further analyzed using the Qualimap v2.2.1 tool to calculate the target coverage. Mean target coverage was 60X of the selected genes (Supplementary Table 1). Variants in these genes were additionally filtered using the VarApp Browser for minor allele frequencies of less than 1% in public databases such as 1000 genomes, ExAC browser and Exome Sequencing Project (ESP). Moreover, variants were filtered and selected according to their impact such as frameshift, splice acceptor, splice donor, start lost, stop gained, stop lost, inframe deletion, inframe insertion, missense, protein altering and splice region. In addition, variants were filtered by the VarApp Browser for their pathogenicity by two in silico tools, SIFT and Polyphen2. Population-specific data from an in-house WES library composed of 43 randomly selected samples of Cypriot origin were used to evaluate the potential disease-causing variants. All variants identified were confirmed by Sanger sequencing. Finally, the variants were categorized for their pathogenicity using the standards and guidelines of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Background Central precocious puberty (CPP) due to premature activation of GnRH secretion results in early epiphyseal fusion and to a significant compromise in the achieved final adult height. CPP is usually idiopathic and is disproportionally observed in girls compared to boys. Currently, only few genetic determinants of children with CPP have been described and the role they exert on the development of the disorder. In this original study rare variants in MKRN3, DLK1, KISS1, KISS1R and MAGEL2 genes are reported in patients with CPP. Methods Fifty-four index females and 2 index males with CPP underwent whole exome sequencing (WES) by Next Generation Sequencing (NGS). The identified rare variants were initially examined by in silico computational algorithms and confirmed by Sanger sequencing. Additionally, a genetic network for the MKRN3 gene mimicking a holistic regulatory depiction of the crosstalk between MKRN3 and other genes is designed. Results Three previously described pathogenic MKRN3 variants in the coding region of the gene occurred in 12 index females with CPP. With the p.Gly312Asp pathogenic variant of the MKRN3 gene being the most prevalent and exclusively found among the Cypriot CPP cohort, it is projected to be the result of founder effect phenomenon. In seven additional CPP patients from the same cohort several other likely and rare pathogenic upstream variants in the MKRN3 gene were also observed. In addition to the MKRN3 variants, a total of 16 other rare variants in DLK1, KISS1 and MAGEL2 were also identified in other CPP patients from the same cohort. Interestingly, the frequent variant rs10407968 (p.Gly8Ter) of the KISS1R gene appeared to be less frequent in the cohort of patients with CPP. Conclusion The results of the present study denote the key role of the imprinted MKRN3 gene in puberty. Additionally, pathogenic variants can also exist in the noncoding region of the MKRN3 gene such as the proximal promoter and 5’-UTR region and which can also be considered as contributing factors to CPP. Overall, the results of present study have emphasised the necessity of the allied genetic and clinical approach which is necessary for the management and treatment of CPP.

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    DRYAD; ZENODO
    Dataset . 2021
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      DRYAD; ZENODO
      Dataset . 2021
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Kirschel, Alexander; Hadjikyriakou, Thomas; Skarlatos, Dimitrios; Kassinis, Nikolaos; +1 Authors

    The global breeding population of Eleonora’s Falcon (Falco eleonorae) is distributed from the Canary Islands in the west, across the Mediterranean Sea, to Cyprus in the east. The remoteness of nesting colonies, which are predominantly located on sea cliffs and islets, renders breeding success estimation a challenging task, requiring a composite approach to assess each of the breeding stages. Early estimates of the breeding success of Eleonora’s Falcon suggested that the Akrotiri colony in Cyprus had the lowest breeding success among all the colonies throughout the species’ breeding range, at a level seemingly unsustainable, suggesting the colony might have been in danger of gradual extinction. Here we use a diversity of survey methods including boat, ground, and aerial surveys, with the incorporation of photography and photogrammetry, to reassess the breeding success and the effect of nest characteristics on the Eleonora’s Falcon breeding population in Cyprus. During a 6-yr study, we found that Cyprus hosts ~138 ± 8 breeding pairs and that breeding success equals 1.54 ± 0.85 fledglings per breeding pair, and thus is considerably higher than previous estimates. In addition, by analyzing temporal variation in breeding and nest characteristics, we found that early breeding and reuse of nests positively influence breeding success, but physical nest characteristics have a limited effect on colony productivity. The range of survey methods employed, as well as the array of photography techniques utilized, enhanced the efficiency and accuracy of this study, allowing us to overcome the challenge of inaccessibility of nesting cliffs. The raw data used in statistical analyses are all provided along with the R code. The data have all been combined here into one dataset though analyses were performed on subsets of the data as described in the manuscript. The script to produce the digital surface model is provided but we do not provide exact coordinates because of sensitivity of falcon nest sites to disturbance. The dataset is raw survey data from monitoring Eleonora's falcon nest sites using a variety of methods described in the paper. Also included in separate sheets are the code used to analyse the data - R code for statistical analyses and python code to produce a digital surface model of the nesting cliffs.

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    ZENODO; DRYAD
    Dataset . 2021
    License: CC 0
    Data sources: ZENODO; Datacite
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      ZENODO; DRYAD
      Dataset . 2021
      License: CC 0
      Data sources: ZENODO; Datacite
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    Authors: Chiquillo, Kelcie; Barber, Paul; Vasquez, Marlen; Cruz-Rivera, Edwin; +3 Authors

    The nature and strength of interactions between native and invasive species can determine invasion success. Species interactions can drive, prevent or facilitate invasion, making understanding the nature and outcome of these interactions critical. We conducted mesocosm experiments to test the outcome of interactions between Halophila stipulacea, a seagrass that invaded the Mediterranean and Caribbean Seas, and native seagrasses (Cymodocea nodosa and Syringodium filiforme, respectively) to elucidate mechanisms explaining the successful invasions. Mesocosms contained intact cores with species grown either mixed or alone. Overall, in both locations, there was a pattern of the invasive growing faster with the native than when alone, while also negatively affecting the native, with similar patterns for shoot density, aboveground and belowground biomass. In the Caribbean, H. stipulacea increased by 5.6 ± 1.0 SE shoots in 6 weeks when grown with the native while, when alone, there was a net loss of −0.8 ± 1.6 SE shoots. The opposite pattern occurred for S. filiforme, although these differences were not significant. While the pattern in the Mediterranean was the same as the Caribbean, with the invasive grown with the native increasing shoots more than when it grew alone, these differences for shoots were not significant. However, when measured as aboveground biomass, H. stipulacea had negative effects on the native C. nodosa. Our results suggest that a seagrass that invaded two seas may drive its own success by both negatively affecting native seagrasses and benefiting from that negative interaction. This is a novel example of a native seagrass species facilitating the success of an invasive at its own cost, providing one possible mechanism for the widespread success of this invasive species.

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    DRYAD; ZENODO
    Dataset . 2022
    License: CC 0
    Data sources: ZENODO; Datacite
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      DRYAD; ZENODO
      Dataset . 2022
      License: CC 0
      Data sources: ZENODO; Datacite
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    Black and white photograph, showing a winter’s day at a deserted sea shore in Cyprus - Μαυρόασπρη κάρτ ποστάλ που απεικονίζει μια χειμωνιάτικη μέρα σε μια ερημική ακτή στην Κύπρο.

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    LEKYTHOS
    Image . 2016
    Data sources: LEKYTHOS
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      LEKYTHOS
      Image . 2016
      Data sources: LEKYTHOS
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    Authors: Boyer, Matthew; Quéléver, Lauriane; Beck, Ivo; Laurila, Tiia; +3 Authors

    This dataset contains ambient concentrations of aerosol precursor vapors measured in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The timeseries includes a full year of sulfuric acid (SA), methanesulfonic acid (MSA), and iodic acid (IA) concentrations retrieved at a time resolution of 5 minutes between October 2019 and September 2020. The data were collected using a nitrate chemical ionization mass spectrometer (NO3-CIMS) as described by Jokinen et al. (2012). The instrument was located in the Swiss container, which was placed on the starboard side of Polarstern's bow on the D-deck during the campaign (Shupe et al., 2022). The concentration retrievals were obtained by integrating peaks from the high-resolution mass spectra for each compound of interest (either as a deprotonated ion or as its corresponding cluster with nitrate), normalizing the result with the sum of charger ions (NO3-, HNO3NO3-, (HNO3)2NO3-), and multiplying by the calibration factor (6×109 molec·cm-3) obtained from a dedicated calibration using SA. Since the instrument calibration was only performed using SA, the concentrations of MSA and IA are low limit estimations. SA was determined by peaks at mass to charge ratios (m/z) of 96.9601 Th (HSO4-) and 159.9557 Th (H2SO4NO3-), MSA was determined by m/z peaks at 94.9808 Th (CH3SO3-) and 157.9765 Th (CH3SO3HNO3-), and IA was determined by m/z peaks at 174.8898 Th (IO3-) and 237.8854 Th (HIO3NO3-). Zero measurements were performed periodically by placing a filter on the inlet of the instrument to determine the detection limit for each individual species. The detection limits were calculated as μ + 3 × σ, where µ is the average concentration and σ is the standard deviation, both of which were evaluated during filter measurements. The resulting detection limits are 8.8e4, 1.5e5, and 5.5e4 molec·cm-3 for SA, MSA, and IA, respectively. The dataset includes flags to specify the data that are below the detection limit. The influence of local pollution from the research vessel and other logistic activities was identified by applying a pollution detection algorithm (Beck et al., 2022) to particle number concentrations from a condensation particle counter (CPC3025, TSI) that was also located in the Swiss container. Periods that were potentially affected by primary pollution are flagged in the dataset. The columns in the data file include the date and time in Coordinated Universal Time (UTC); the concentration of SA, MSA, and IA in molec·cm-3; a detection limit flag for each individual species (1 = below detection limit); and a local pollution flag where the data may have influence from the vessel and logistical activities (1 = pollution was detected).

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    PANGAEA
    Dataset . 2023
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2023
      Data sources: B2FIND
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    Authors: Kostaki, Evangelia-Georgia; Karamitros, Timokratis; Stefanou, Garyfallia; Mamais, Ioannis; +4 Authors

    Hepatitis B virus (HBV) infection constitutes a global public health problem. In order to establish how HBV was disseminated across different geographic regions, we estimated the levels of regional clustering for genotypes D and A. We used 916 HBV-D and 493 HBV-A full-length sequences to reconstruct their global phylogeny. Phylogeographic analysis was conducted by reconstruction of ancestral states using the criterion of parsimony. The putative origin of genotype D was in North Africa/Middle East. HBV-D sequences form low levels of regional clustering for the Middle East and Southern Europe. In contrast, HBV-A sequences form two major clusters, the first including sequences mostly from sub-Saharan Africa, and the second including sequences mostly from Western and Central Europe. Conclusion: We observed considerable differences in the global dissemination patterns of HBV-D and HBV-A and different levels of monophyletic clustering in relation to the regions of prevalence of each genotype. HBV_Genotype_A_sequence alignmentHBV genotype A full-length genomic sequence alignment after the exclusion of multiple sequences per patient. Sequence identifiers include accesion number of geographic area of samplingHBV_Genotype D_sequence_alignmentHBV genotype D full-length genomic sequence alignment after the exclusion of multiple sequences per patient. Sequence identifiers include accesion number of geographic area of sampling

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    ZENODO; NARCIS; DRYAD
    Dataset . 2019
    License: CC 0
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    DANS-EASY
    Dataset . 2018
    Data sources: B2FIND
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      ZENODO; NARCIS; DRYAD
      Dataset . 2019
      License: CC 0
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      DANS-EASY
      Dataset . 2018
      Data sources: B2FIND
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    Authors: Kirschel, Alexander; Seddon, Nathalie; Tobias, Joseph;

    A long-held view in evolutionary biology is that character displacement generates divergent phenotypes in closely related coexisting species to avoid the costs of hybridisation or ecological competition, whereas an alternative possibility is that signals of dominance or aggression may instead converge to facilitate coexistence among ecological competitors. Although this counter-intuitive process⎯termed convergent agonistic character displacement⎯is supported by recent theoretical and empirical studies, the extent to which it drives spatial patterns of trait evolution at continental scales remains unclear. By modeling variation in song structure of two ecologically similar species of Hypocnemis antbird across Western Amazonia, we show that their territorial signals converge such that trait similarity peaks in the sympatric zone, where intense interspecific territoriality between these taxa has previously been demonstrated. We also use remote sensing data to show that signal convergence is not explained by environmental gradients and is thus unlikely to evolve by sensory drive (i.e. acoustic adaptation to the sound transmission properties of habitats). Our results suggest that agonistic character displacement driven by interspecific competition can generate spatial patterns opposite to those predicted by classic character displacement theory, and highlight the potential role of social selection in shaping geographical variation in signal phenotypes of ecological competitors. Raw_Data_Kirschel_etal_ProcBRecordist information and accession numbers for each recording in sound archives, as well as values extracted from recordings and used in analyses, and environmental data extracted from recording localities.

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    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
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    DRYAD; ZENODO; NARCIS
    Dataset . 2019
    License: CC 0
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      DANS-EASY
      Dataset . 2019
      Data sources: B2FIND
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      DRYAD; ZENODO; NARCIS
      Dataset . 2019
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