dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016). We included four meteorological variables (annual mean air temperature, vapor pressure deficit, shortwave radiation and annual total precipitation) to quantify the contributions of physiological changes. To avoid the impacts of longterm changes on IAV, we used detrended meteorological variables, LAI and GPP, to develop the RF model using the "randomForest" package in R modified by  Liaw and Wiener (2002). We constructed the RF model at each EC site and each pixel and randomly chose 70% of the data for training and the remaining 30% of the data for validation, and repeated this process 200 times. The simulated GPP by the RF model (GPP ALL ) were the mean values of all validated data in each year. Next, to quantify the contributions of physiological and structural changes to IAV in GPP, we conducted two model experiments by holding meteorological variables and LAI constant to simulate GPP, indicated as GPP MET0 and GPP LAI0 , respectively.
Data Availability Statement The TRENDY v10 ensemble can be accessed with requests from the data hub of the Global Carbon Budget (https://globalcarbonbudgetdata. org/index. html). The GPP data from flux sites are available with requests from FLUXNET2015 data set (https://fluxnet. org/data/fluxnet2015-dataset/), Integrated Carbon Observation System 19449224, 2023, 4, Downloaded from https://agupubs. onlinelibrary. wiley. com/doi/10. 1029/2023GB007696 by Cochrane France, Wiley Online Library on [03/05/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License.
To understand the wind speeds seabirds are able to fly in and those they avoid, we analyzed 1,663 foraging trips from 18 species of seabirds (Table  S1 ), representing 326,960 hours of flight time. We compared the wind speeds each bird encountered at every hourly segment (hereafter a step) along its foraging track with wind speeds at 30 alternative steps that were available to the bird in space and time but were not used. Following this step-selection approach (Figure  S1 ), our dataset had a stratified structure, where each stratum was composed of one observed step and its 30 alternative steps. We chose not to proceed with the conditional logistic regressions commonly used to estimate stepselection functions  12  , as this disregards the outliers encountered by the birds in flight. Instead, we used null modeling to compare the used and the maximum available wind speed within each step, and thereby identify the rare wind-avoidance events (STAR Methods).
The interspecific variation in these speeds, itself predicted by wing loading, highlights that the definition of "extreme" wind speeds should be considered relative to a given flight morphology, being linked to both flight speed and flight style. We collected bio-logging data recorded using Platform Transmitter Terminals (PTTs) or GPS loggers for 18 seabird species (Table  S1 ). These datasets were identified by searching Movebank (www. movebank. org) and the Seabird Tracking Database (www. seabirdtracking. org). The species included in this study represent a range of seabird flight styles (Table  S1 ), including dynamic soaring (gaining energy by repeatedly crossing the vertical gradient of wind velocity over the ocean  22  ; nine species of albatross, petrel, and shearwater), wind soaring (regularly alternating flapping and gliding bouts to take advantage of the wind  23  ; five species of gannet and booby), thermal soaring (soaring in circles on rising air columns and gliding down in the direction of travel  24  ; two species of frigatebird), and obligate flapping fliers (two species of tropicbird).
As such, our dataset had a stratified structure, where 30 alternative steps were matched to one observed step per stratum. For each point in the dataset, we extracted eastward (u) and northward (v) components of wind (at 10 m above surface) from the European Center for Medium-Range Weather Forecast (ECMWF; www. ecmwf. int) ERA5 re-analysis database (temporal and spatial resolution of 1 hour and 30 km, respectively). Annotations were done using the ENV-Data track annotation service  27  provided by Movebank. We selected bi-linear interpolation for all variables and calculated wind speed using the u and v components of the wind. For each species, we obtained wing loading and aspect ratio from the literature (Table  S1 ). Preliminary inspection of the largest dataset (Wandering albatross) revealed that the response to extreme winds appeared similar for this species irrespective of whether the step length was set to 1, 2, 4, or 6 hours.
Wind conditions at species-specific breeding distributions To understand seabirds' wind selectivity with regards to the general conditions in their breeding range, we downloaded the eastward and northward components of the wind for the species-specific breeding distributions and seasons. Wind data were obtained for a five year period (2017-2021) from ERA5 for each species breeding range, as given in the BirdLife International database (http://datazone. birdlife. org). We then calculated the maximum and median wind speeds within each species' breeding distribution and season.
Determinants of exposure to strong wind We used linear models to investigate whether morphology determined the exposure of seabird species to strong wind conditions. Due to the positive correlation between wing loading and aspect ratio (r = 0. 60, p < 0. 05), we included wing loading as the sole predictor in our linear models. We extracted the maximum wind speed experienced by each species population from the annotated biologging dataset (one-hourly resolution). Each population (i. e. , species-colony combination) was considered as one sampling unit. We predicted maximum wind as a function of wing loading and checked the model residuals for spatial and temporal auto-correlation that could be related to colony location and timing of breeding (estimated as the median month of the breeding season) following Korner et al.
28. The amount of data varied for each species. To test whether the maximum wind speed experienced by different species was affected by the amount of flight data in our dataset, we estimated the correlation between these. We also investigated whether maximum wind speed experienced by each species was determined by the wind conditions during the breeding season as a whole. For this, we ran linear models predicting maximum encountered wind with the maximum and median wind speed at the breeding distribution. (indicated by an albatross silhouette) was identified to be avoiding strong winds at 2014-11-22 23:53:27 UTC. However, it also briefly flew into the eye of a weaker storm around 2014-11-18 21:00 UTC. This instance was not identified as wind-avoidance in our study, suggesting that this behavior may be more common than our analysis suggests.
Acknowledgments We thank  L. Pearmain  and  B. Clark  for assistance with searching and extraction of data from  BirdLife International 's Seabird Tracking Database and  S. Davidson  for assistance with locating relevant Movebank studies. We also thank  B. Garde  for his input throughout the project. ELCS was supported by a  European Research Council  starter grant ( 715874 ), under the  European Union 's  Horizon 2020 research and innovation program. EN was supported by the  German Academic Exchange Service (DAAD) Postdoctoral Researchers International Mobility Experience (PRIME)  fellowship and a  Max Planck sabbatical fellowship  to ELCS. Data collection from Nazca boobies was supported by the  U. S. National Science Foundation  under Grant No. DEB 1354473  to DJA. Data collection from Masked boobies was supported by  Consejo Nacional de Ciencia y Tecnologia (INAPI-CONACyT)  Grant.
In other words, properly stacking SDMs and considering the biotic interactions among species  24, 27  will yield more realistic estimates of spatial patterns in alpha diversity that relate to environmental gradients  28. This said, no modelling techniques are free from the uncertainty coming from biases in the input data, like uneven sampling effort  [29] [30] [31] [32] [33] [34] [35]  or spatial positioning errors  [36] [37] [38]. Here, an integration of species distributions and community-level biodiversity modelling can be performed under the Spatially-Explicit Species Assemblage Modelling framework (SESAM  39  ), in which species associations and biotic interactions are explicitly considered  40. The increasing availability of spatially explicit open-access databases on species distribution and community composition (e.
Moreover, the striking geographic bias in the accessibility to resources and in scientific data processing among different regions across the globe can only increase gaps in the data. Altogether, bias in data quality represents a key issue in current macroecological and biogeographical research  97  , and hinders realizing the full potential of using large-scale databases in biodiversity modelling (ref. 59, 96, 98, 99  , see Fig. 1 ). Examples exist where smooth geographical biases could be controlled during modelling procedures, in case some in-situ data have still been sampled even in remote areas 100 , but spatial lack of information and stronger bias is generally expected to severely hinder final results 101. Spatial bias has been shown to increase uncertainty in the data  91  , and has strong effects on the outcomes of the whole modelling process  78, 102, 103.
For example, species-people correlations, in which more populated regions show higher biodiversity simply because they are more thoroughly surveyed, are now well known  104. Ensuring an adequate sampling design is of utmost importance to avoid the generation of truncated species response curves  31, 105. For instance, datasets biased towards widespread environmental conditions across the study area  [106] [107] [108]  hampered the characterization of species responses to the effects of land transformation or the rarest climate conditions in highly dynamic landscapes such as the Brazilian Atlantic Forest 109. Moreover, the autecology of species and related eco-geographic characteristics such as species traits 110 , range size 111 and species niche breadths (i. e. , generalist vs specialist 112 ), among other factors, can ultimately influence the performance of species-related models  35.
Recent methodological advances have been proposed to limit spatial bias in data distribution. These can apply either when sampling new species data, or by resampling available data inside a strongly biased dataset  30, 43, 113  , and restricting analyses to the geographical regions holding enough data coverage 114. For instance, Hattab et al. 115  developed a scheme that, by ensuring a systematic sampling of field observations within the environmental conditions available across the study area, can aid in limiting potential shortcomings when modelling species distribution while being not in equilibrium with the contemporary environment (e. g. , the case of a recent introduction of an invasive alien species). Likewise,  Lembrechts et al. 116  developed a new framework to design standardized microclimate networks able to capture the largest variation in microclimate at regional or national extents  117, 118.
Another important effect of sampling bias is that it creates information gaps  129, 130. This could be solved with recourse to citizen science, although it is well known that such information is even more biased (i) spatially, e. g. , with a higher amount of data near roads, cities, research centres, in peculiar ecosystems or regions and, more globally, in the northern hemisphere, but also (ii) taxonomically, toward certain charismatic groups, e. g. , vertebrates in terrestrial ecosystems  94, 96, 131. In order to solve sampling completeness issues, new tools are now available based on diversity estimates and further fine-tuning of datasets, before they are used for further analysis. As an example, Lobo et al. 132 propose a tool to estimate the degree of completeness in biodiversity surveys in each territorial unit, when the number of records (including repeated species) is available, as a surrogate of sampling effort.
Yet, quantifying sources of error is essential for proper descriptive or mechanistic modelling of species distributions  143. Making use of simulated or in-silico datasets-the so-called 'virtual ecologist' approach 143 -allows to generate distribution data with known ecological characteristics  76  , considering that virtual species are better at rejecting candidate models than they are at supporting them  [143] [144] [145] [146] [147]. The use of virtual species is burgeoning in ecology to build toolkits implementing in-silico analytical experiments simulating natural processes, thanks to the complete control on the configurations of factors constraining the distributions of species  19. Moreover, virtual species allow creating simulated data for benchmarking models of different complexity.
152, 155, 156  ). We also provide an example in R in Figs. 2  and 3 , with the complete code in Appendix 1 or in the following GitHub repository: https://github. com/ ducciorocchini/Virtual_species_SDM/(see also ref. 20  for a similar example). The concept of virtual species is not the only example of virtual individuals/surfaces, since it has been widely used in disciplines other than ecology-e. g. , in geology, virtual globes have been used for geophysical modelling  157. Passing from species to assemblages, virtual communities can be simulated (Figs. 2  and 3 ) to understand what should be an effective sampling effort to predict the distribution of species assemblage, for instance when stacking separate species distribution models  146. This is generally done by simulating virtual species in a community given a certain virtual species richness, and then manipulating this artificial set by changing different sampling parameters such as sample size, sampling strategy or different species distribution modelling algorithms such as Generalized Linear Models, Generalized Additive Models, MaxEnt, Boosted Regression Trees or Random Forests 146.
Furthermore, a collinearity test is performed and the correlated variables are removed (f). Once the statistical model has been calibrated, the climatic variables for the prediction are selected (g) and-among them-those which are correlated are deleted (h). Eventually, multiple virtual species distributions are combined together in colorist R package to map community distribution (i). Results are shown in Fig. 3. The complete code to generate virtual species and final maps is available in both Appendix 1 and at the following GitHub repository link: https://github. com/ducciorocchini/ Virtual_species_SDM/. of sampling effort can then be included as covariates in the analysis  [190] [191] [192]. Similarly, estimates of completeness (e. g. , ref. 132  ) or multivariate estimates of data-driven uncertainty, such as the previously cited maps of ignorance approach  75, 101  , can be used as ancillary predictors in SDMs, or as spatially-explicit error terms in regression-based modelling techniques 186.
ACKNOWLEDGEMENTS We are grateful to the handling Editor and to three anonymous reviewers for precious suggestions on a previous draft of the paper. Arianna Ferrara  partially edited Fig. 1 of this manuscript. This study has received funding from the project  SHOWCASE (SHOWCASing synergies between agriculture, biodiversity and ecosystems services to help farmers capitalising on native biodiversity) within the European Union 's  Horizon 2020 Researcher and Innovation Programme  under grant agreement No. 862480. D. R. was partially funded by a research project implemented under the  National Recovery and Resilience Plan (NRRP) ,  Mission 4 Component 2 Investment   1. 4 -Call  for tender No. 3138 of 16 December 2021, rectified by Decree n. 3175 of 18 December 2021 of  Italian Ministry of University  and Research funded by the  European Union  -NextGener-ationEU.
Project code  CN_00000033 , Concession Decree No. 1034 of 17 June 2022 adopted by the  Italian Ministry of University and Research ,  CUP J33C22001190001 , Project title " National Biodiversity Future Center -NBFC ". D. R. was also patially funded by the  Horizon Europe  projects Earthbridge and B 3. F. M. S. gratefully acknowledges financial support by the  Rita-Levi Montalcini  (2019) programme, funded by the  Italian Ministry of University. E. T. is supported by the  Estonian Research Council  (grant code  MOBJD1030 ).
To provide a concise presentation of this work, we first presented our five initial studies, followed by a mini meta-analysis  (Goh, Hall & Rosenthal, 2016). At a second time, we presented a preregistered sixth and final study, carried out to ultimately attempt to replicate the previous results by overcoming some of the limitations common to the previous five studies. All studies were conducted in accordance with the Helsinki Declaration  (WMO, 1964)  and its later amendments, the ethical principles of the French Code of Ethics for Psychologists (CNCDP, 2021), and the 2016 APA Ethical Principles of Psychologists and Code of Conduct (APA, 2017). All statistical analyses were made using JAMOVI, and significance threshold a was set at 0. 05. Datasets and the materials used for all studies can be found on the following OSF repository: https://osf.
15 (or Cohen's d = 0. 84). Because the online questionnaire methodology generates much more noise than in the carefully controlled laboratory conditions of the original experiment, we expected the effect size to be lower than this one, around g 2 = 0. 10, which corresponds to a moderate effect (Cohen's d = 0. 66) according to  Cohen (1988). Given this effect size, with parameters set for twotailed independent samples t-tests, a power of 0. 95 and a = 0. 05, minimum required sample size was 61 per condition (N = 122) for each study. Only participants with missing data were removed from the sample. Datasets can be found on the OSF repository.
To mini meta-analyze our results  (Goh, Hall & Rosenthal, 2016) , we aggregated the databases from the five studies. It yielded an N = 1,041 sample (M age = 27. 29, SD = 12. 16, 16% male). A mixed model was then computed with study label as random factor, uncanny condition as fixed factor, and GCBS score as the dependent variable, according to the following equation: GCBS ~1 + (1|Study label) + Uncanny condition. The model (AIC = 2343. 02, r 2 conditional = 0. 07) does not support the effect of uncanny induction on adherence to CBs, F(1, 1035. 44) = 0. 32, p = 0. 57. The effect size was not significantly different from a null effect, with b = 0. 03, 95% CI [À0. 06 to 0. 12], and r 2 marginal <0. 01 or d = 0. 04 (see Fig. 1 ). This result remained unchanged when the mini metaanalysis took into account familiarity level, negative affect, priming method (painting, text), and survey form (paper-and-pencil vs.
Sample and data screening The minimum required sample size was estimated following the same procedure and the same parameters as in Studies 1 to 5, except that independent samples t-tests were planned as onetailed. Thus, the minimum required sample size after exclusions was 51 per condition (N = 204). In order to anticipate the exclusion of participants, our targeted sample size was rounded up to 250 participants. A total of 284 participants provided a complete response to the questionnaire. They were recruited from a company panel and compensated for their participation. Following preregistered exclusion criteria, 18 participants were excluded from the database. More precisely, we excluded participants who failed the attention check item (n = 4; Aust, Diedenhofen, Ullrich & Musch, 2013), who answered "a lot" to the question "Have you been disturbed or distracted by your environment during the study?" (n = 2), and who did not follow the instructions for the biographical recall task (i.
e. , participants who had written a recall obviously unrelated to feelings of uncanniness, or meaningless; this criterion was independently evaluated by two authors, and disagreements were resolved after consultation between the two authors; n = 12). Finally, 266 participants were retained for the analyses (N control = 71, N painting = 74, N text = 70, N autobiographical recall = 51; 53% male; M age = 43. 15, SD age = 13. 55). The dataset can be found on the OSF repository.
S: susceptible; E: exposed (latent); I: infectious; R: removed. N sp : total number of spores dispersed; Sp ext : number of spores from outside the system; Sp soil : number of spores from the soil; Sp canopy : number of spores produced by lesions. See Table  1  for parameter abbreviations. spores survives and is remobilized in the next cropping season (see below). Our model is formalized as a discrete-time analogue of a continuous-time logistic model. All abbreviations of the model parameters and state variables are given in Tables  1  and 2. The model is implemented in Python 3 and available as open source software on GitHub (https://zenodo. org/record/7139051#. Y_N1FB-ZPIV;  Sanner et al. 2022 ). The main outputs can be reproduced with Jupyter notebook tutorials.
Simulations Each simulation corresponds to one cropping season, with the initial conditions reproducing equilibrium across several cropping seasons for pure wheat. To test the robustness of the effect of the companion characteristics on epidemics, we tested different weather conditions. For STB, rainfall data (but not temperature) were used for the dispersal of pycnidiospores, based on daily monitoring at the meteorological station of INRA-Grignon (78850 France). To analyze the effect of rainfall on STB dynamics, we used the year 1997 as the reference for the average rainfall year, 2000 as the high rainfall year, and 1995 as the low rainfall year. For WLR, we used the date of the onset of epidemics (t inf ) to simulate the favorable, average, and unfavorable conditions (800, 1,000, and 1,200 dd, respectively;  Garin et al.
Reliable neutron star mass measurements are key to determining the equation of state of cold nuclear matter, but such measurements are rare. Black widows and redbacks are compact binaries consisting of millisecond pulsars and semi-degenerate companion stars. Spectroscopy of the optically bright companions can determine their radial velocities, providing inclination-dependent pulsar mass estimates. Although inclinations can be inferred from subtle features in optical light curves, such estimates may be systematically biased due to incomplete heating models and poorly understood variability. Using data from the Fermi Large Area Telescope, we have searched for gamma-ray eclipses from 49 spider systems, discovering significant eclipses in 7 systems, including the prototypical black widow PSR B1957+20.
Dividing these by the pulsar radial velocities (measured by pulsar timing) provides binary https://doi. org/10. 1038/s41550-022-01874-x Monte Carlo simulations' in the Methods). Given the number of pulsars included in our search, this is around a factor of ten lower than expected for the largest outlier (1/n psr ≈ 0. 02). If this is indeed an eclipsing system, another 7 yr of accumulated Fermi-LAT data will be required to reach the same significance as the eclipse in PSR J1555-2908. We also searched for gamma-ray eclipses from seven likely redback systems that were first identified from the discovery of periodic optical and/or X-ray sources within pulsar-like Fermi-LAT sources, but either currently lack radio or gamma-ray pulsation detections to confirm their nature or have only recently been detected as pulsars.