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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.

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.

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.

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).

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.

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

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.

# Varnava et al. 2023. Assessing the biodiversity and the impact of pollinators on carob production *** ## Description of the data and file structure The dataset contains 5 CSV files with the data in the Varnava et al. 2023 paper on "Assessing the biodiversity and the impact of pollinators on carob production " **insect_presence_DRYAD** *** The dataset contains data on the presence (1) or absence (0) of Apis mellifera, wild bees and wasps. The experiment was carried out in three regions in 2016 (S_Plateau, NW_Coast and S. Coast), with two of the regions (S_Plateau and NW_Coast) sampled again in 2017. In each region, four carob groves (plots) were selected, and within each plot insects visiting flowers were recorded on four female and one male carob trees. For each tree, observations were made on each of four sides (N,S,E,W) for 2 minutes per side. The presence of insects belonging to the following groups, *A. mellifera*, Wild_bees, Wasps, were recorded with 1 signifying the presence of at least one individual of the group and 0 no presence. The dataset can also be used for estimating the probability of sighting of each group at any sampling point for each year (Fig. 2). The dataframe contains the following variables: * Date: Sampling date * Week: Week of sampling beginning from the first week of flowering * Region: Where the plot was located * Plot: Number of the plot * Tree: Number of the tree * Tree_Side: Side of the tree where sampling took place: 1=south, 2=west, 3=north and 4=east. * Tree_Sex: Male or female * Apis_mellifera: 0 if absent, 1 if present (at least one individual) * Wild_bees: 0 if absent, 1 if present (at least one individual) * Wasps_total:0 if absent, 1 if present (at least one individual) **pod_number_analysis** *** The pod_number_analysis dataset contains the data on the production of pods from groups of inflorescences in the open and wind pollination treatments. The experiment was carried out in three different regions, with one carob grove per region, with 20 trees per carob grove. In each tree, there were two branches, with one open and one wind pollination treatment per branch. The variables of the dataframe are as follows: * Region: The region of the carob grove. * Tree_no: The number of the individual tree in each orchard * Branch: The number of the two branches per tree on which the wind and open pollination treatments were applied - each treatment was applied on both branches (for reach branch, 1 group of inflorescences bagged = wind pollination, and one group of inflorescences left free = open pollination). * Treatment: The assigned treatment - wind or open pollination. * inter_R_T: A new variable created to ease the use of tree as a random effect variable as trees in different regions had the same number, which would make the software think that these were the same trees. * Inflorescences_no_beg: The number of inflorescences assigned to each treatment per branch. Pods_no: The number of pods produced by all inflorescences in a group. **pods_weight_data_DRYAD** *** The dataset contains data on the number, weight and length of seeds per pod produced from carob trees in the open and wind-pollination treatments. The experiment was carried out in three different regions, with one carob grove per region, with 20 trees per orchard. In each tree, there were two branches, with one open and one wind-pollination treatment per branch. The data were derived from teh same experiment as the ones contained in the dataframe "pod_number analysis". However, some trees did not produce pods, and therefore the number of trees in the "pod_weight_data_DRYAD" dataset is lower than 20. The variables of the dataframe are as follows: * Region: The region of the grove. * Tree_no: The number of the individual tree in each grove. * Branch: The two branches per tree on which the wind and open pollination treatments were applied - each treatment was applied on both branches. * Treatment: The assigned treatment - open or wind-pollination * pod_weight_gr: The weight of each individual pod in gr * pod_length_cm: The length of each individual pod in cm * seeds_per_pod: The number of seeds produced per pod * Inter_R_Tree: Variable created to assign an individual label to each tree * Inter_R_Tree_B: Variable created to assign an individual label to each branch * Inter_mesh_bag: Variable created to assign an individual label to each inflorescence group **pollen_trans_DRYAD** *** The dataset contains the results of an experiment on pollen transfer via wind. Glycerol traps were placed on one male and four female trees, with four traps per tree. The distance of each female tree from the male was recorded. 24 h later, the traps were collected and the presence of pollen was recorded. * Date_in: Date of trap placement * Date_out: Date of trap removal * Tree_no: Tree number (1-5) * Tree_sex: Male or Female * glycerol_trap_no: Individual trap number * Pollen_presence: 0 if absent, 1 if present * Distance_m: Distance in meters of the female tree from the male tree (set to 0 for male tree). **heatmap_DRYAD** *** The dataset contains data on the species of wild bees and wasps recorded from each carob grove. The data were collected over five weekly visits to each carob grove per year during blooming. The dataset contains the following variables: * Species: Each of the 14 species of wild bees / wasps that were recorded during the study. * One column for each carob grove and sampling year (resulting in a total of 21 carob_grove_year columns). For each carob_grove_year column, the integer value shows the number of weeks (out of a total of five) each insect species was recorded as present in each grove. ## Sharing/Access information The data are available through Dryad. As the current climate crisis intensifies, drought-resistant crops are becoming more important due to their ability to withstand the increasingly hotter and drier summers. Such crops are valuable for pollinators as they provide food resources for wild and managed species. The carob tree (Ceratonia siliqua L.) represents an example of a heat- and drought-resistant crop, able to grow in dry areas with practically no inputs. The current study assessed over two growing seasons the diversity of wild bees and other pollinators relying on carob flowers, as well as the contribution of animal pollination to carob production. Carob flowers were subjected to two treatments: Open pollination, where inflorescences were left untreated, and wind pollination, where inflorescences were bagged in a mesh during blooming. Weekly observations during blooming showed that Apis mellifera was the most frequent floral visitor followed by wild bees and wasps. Carob flowers were visited by at least 10 different wild bee species. Open-pollinated flowers produced significantly more pods, with the benefit ranging from 4 to 16 times higher production, depending on the region. Open pollination led to pods with greater weight, length and number of seeds compared to pods derived from wind pollination. The results of the current study highlight the importance of animal pollination to carob production, as well as the significance of carob trees to wild bee conservation. The information on how the data were collected is presented in the accompanying manuscript. This is the curated dataset that was used for analyses and graph preparation.  OpenOffice can be used to open the CSV and Notepad files. 

In the framework of the EU-FP7 BACCHUS (impact of Biogenic versus Anthropogenic emissions on Clouds and Climate: towards a Holistic UnderStanding) project, an intensive field campaign was performed in Cyprus (March 2015). Remotely piloted aircraft system (RPAS), ground-based instruments, and remote-sensing observations were operating in parallel to provide an integrated characterization of aerosol–cloud interactions. Remotely piloted aircraft (RPA) were equipped with a five-hole probe, pyranometers, pressure, temperature and humidity sensors, and measured vertical wind at cloud base and cloud optical properties of a stratocumulus layer. Ground-based measurements of dry aerosol size distributions and cloud condensation nuclei spectra, and RPA observations of updraft and meteorological state parameters are used here to initialize an aerosol–cloud parcel model (ACPM) and compare the in situ observations of cloud optical properties measured by the RPA to those simulated in the ACPM. Two different cases are studied with the ACPM, including an adiabatic case and an entrainment case, in which the in-cloud temperature profile from RPA is taken into account. Adiabatic ACPM simulation yields cloud droplet number concentrations at cloud base (approximately 400 cm−3) that are similar to those derived from a Hoppel minimum analysis. Cloud optical properties have been inferred using the transmitted fraction of shortwave radiation profile measured by downwelling and upwelling pyranometers mounted on a RPA, and the observed transmitted fraction of solar radiation is then compared to simulations from the ACPM. ACPM simulations and RPA observations shows better agreement when associated with entrainment compared to that of an adiabatic case. The mean difference between observed and adiabatic profiles of transmitted fraction of solar radiation is 0.12, while this difference is only 0.03 between observed and entrainment profiles. A sensitivity calculation is then conducted to quantify the relative impacts of 2-fold changes in aerosol concentration, and updraft to highlight the importance of accounting for the impact of entrainment in deriving cloud optical properties, as well as the ability of RPAs to leverage ground-based observations for studying aerosol–cloud interactions.