dataesr/scientific-paragraphs-categorization huggingface dataset:
We used a threshold of two weeks to reduce any effects of advancing season on space use and to minimise the likelihood of including data for the same individual when its breeding stage or status had changed (e. g. , as it transitioned from early to late chick-rearing). Tracking data were originally collected at varying sampling intervals (Fig. S1 ), so to improve comparability, we regularized data to a 10 min interval via linear interpolation using the R package adehabitatLT  (Calenge, 2006). Although many year-datasets were of a higher temporal resolution, we used a standard 10 min interval to ensure that most datasets (102 of 105 [98 %]) had a ratio of interpolated to raw points below 2:1. European shag (Gulosus aristotelis) and pelagic cormorant (Urile pelagicus) perform short foraging trips and therefore interpolation to 10 min would result in too few location estimates (<10) to run kernel density estimation for >5 % of all foraging trips; to avoid losing these shorter-duration trips, we instead interpolated data for these species to 5 min intervals.
This process of selecting a trip per individual was re-iterated in each analysis to maximize usage of available information while accounting for potential pseudoreplication (see Overlap analyses section for details;  Lascelles et al. , 2016). We then generated multi-year reference distributions (hereafter the 'reference distribution') for each species by averaging together single-year distributions across years (range 3 to 6 single years). We averaged single-year distributions (i. e. , took the mean of grid cell probability densities), rather than combine all trip UDs from across the multi-year dataset, to avoid years with higher sample sizes contributing disproportionately to the shape of the reference distribution (Fig. 1 ).
, 2009). We used the mean VI overlap values to validate whether the relative differences between species were sensitive to the chosen index. The trip UDs contributing to each single-year distribution were randomly re-sampled across 100 iterations (i. e. , one trip selected per individual per iteration), to ensure that different trips from a given individual would be included, and pairwise annual overlap calculated. For datasets with fewer than 50 unique combinations of trip UDs, unique sets of UDs were determined for each iteration (e. g. , with only 1 trip per bird, only 1 iteration was run). We then calculated the mean overlap for each pairwise comparison across iterations (Fig. S4 ), and plotted mean annual overlap by species, family and foraging habitat to illustrate intraand inter-specific variation in consistency.
We classified species as foraging in predominantly 'shelf', 'oceanic' or 'mixed' habitat by inspecting the distribution of foraging trips of each population overlaid on bathymetry (see Supplement for details and Table  S1  for classification). We fitted a linear mixed-effects model using the R package lme4  (Bates et al. , 2015)  to evaluate which predictor variables explained significant amounts of variation in mean overlap among years, with variables either representing (1) experimental design choices (i. e. , smoothing parameter definition, time lags), or (2) biological factors (habitat type, latitude, taxonomy). As we lacked sufficient data to properly control for phylogeny in this analysis, the purpose was not to predict distributional consistency across seabirds in general, but to explain the variation in our dataset.
Single-year vs. multi-year sampling To test whether sampling across years provides more robust estimates of population distributions than sampling in a single year, we compared the spatial coverage of reference distributions (i. e. distributions of each population estimated using data from all individuals and years) with samples of equal size (i. e. , the same number of individuals) drawn from varying numbers of years (Table  2 ). For this analysis, we limited comparisons to a maximum of three years to ensure comparability across species. We iteratively re-sampled n trip UDs from one, two, or three years, and averaged them together to form a sample distribution. The number n was determined by the third-highest number of individuals tracked in any given year (m) for each species, and was set at n = m -2 to ensure that many more than m combinations of trip UDs were possible.
In N tracks vs. N years, both the number of trips and number of years was varied when forming sample distributions, which were again compared to the reference distribution (based on data from all birds and years). In all three analyses, spatial distributions (single-year, sample, and reference) were estimated by randomly selecting a single foraging trip from each individual bird, running kernel density estimation with the selected trips, and repeating this process over 100 iterations. Visualize the contribution of changing the number of birds and number of years to capturing the full (reference) distribution population across years.
Dots represent the mean overlap across 100 iterations of re-sampling for each pairwise year comparison, in each of which a single foraging trip was selected per individual to derive each singleyear population distribution. Boxes represent the distribution of overlaps calculated across all pairwise combinations of years. The numbers above each box indicate the number of years of tracking data from each species and population. Values for little penguin and Australasian gannet are based on tracking data from two populations (each with same number of years tracked). (B) Estimates of annual spatial consistency aggregated by family and predominant foraging habitat of each species. Box height in (B) relates to the variation in annual consistency in at-sea distributions within and between species in each family, with the number of species indicated above the box.
Additionally, for many species, the relationship between sample size and coverage appeared to begin levelling off, indicating that the sample sizes in our datasets captured a large portion of the population-level spatial distribution (Figs. S10-S31 ). Increasing the number of years in which the population was tracked added spatial information for most species (in addition to that gained by tracking more birds), but lacked a marked gain in some (Figs. S10-S31 ). As indicated by the estimates of average spatial overlap between years (Fig. 2A ), the relative importance of sampling across years differed among species, with the benefit of multiyear samples most apparent for common diving petrels, and negligible for Northern gannets (M. bassanus), highlighted here to show the variation among species (Fig.
Effects of study duration Using GPS-tracking data, we estimated the spatial consistency of 23 seabird species during the chick-rearing period to assess the relevance of sampling across years for identifying important sites for conservation at the population level. Most species showed a similarly high degree of average consistency in their distributions between years. We also found that the number of individuals tracked had a large effect on the estimation of population distributions, supporting previous findings in seabirds  (Gutowsky et al. , 2015; Soanes et al. , 2013; Thaxter et al. , 2017). By contrast, the time lag between years, taxonomic family, and latitude explained only small amounts of variation in annual consistency among the populations studied here. We found that tracking seabirds in multiple years generally improved estimates of population-level distributions, although in most cases there was only a marginal loss of information about space use by the population if tracking data were only available for one year.
Temporal and spatial scale The datasets we analyzed were collected in 3 to 6 different years, sometimes in sequential years and in other cases collected >10 years apart (Table  1 , Fig. S4 ). Climatic cycles, such as the El Niño-Southern Oscillation, often operate at a decadal scale and are known to affect the spatial distributions of marine predators and their prey  (Ballance et al. , 2006; Philander, 1983). Therefore, it is possible that the sample of years for which we had tracking data for our study populations were Fig. 4. Sampling effects on estimation of population-level distributions for three species of seabirds (left to right: common diving-petrel, grey-headed albatross, Northern gannet). By re-sampling the number of tracks (i. e. , one per bird) and the number of years from which tracks were selected, the functional relationship between sample size and annual sampling was estimated for each species.
, 2014; Evans et al. , 2021; Osborne et al. , 2020). Indeed, the dataset we analyzed from common diving petrels was used to illustrate the effects of a marine heatwave on breeding success and at-sea distributions, explaining the low consistency and large effect of adding years of data we report for this species  (Fromant et al. , 2021). This specific case illustrates how tracking data collected over just a few years can provide important information about the areas used during stressful climatic events, when effective protection may be particularly important for populations  (Bogdanova et al. , 2014). In contrast, for black-legged kittiwakes we found only small changes in space use across four years for a population in England, during the same period in which a population in the Gulf of Alaska shifted their distribution to largely new areas in response to a local heatwave  (Osborne et al.
, 2021; Mott and Clarke, 2018). Therefore, it is also important to include under-studied taxa (e. g. , storm-petrels and terns) and regions (e. g. , the tropics) in future analyses of spatial consistency in seabirds. The combination of datasets we analyzed contained insufficient species contrasts within and across taxonomic families to properly account for phylogenetic effects, limiting our ability to robustly identify general predictors of annual consistency in seabirds. As multi-year tracking data become available for more species, life-history classes and stages in the annual cycle, it will become possible to examine the effects of ecological drivers and seasonality in movement patterns in conjunction with intrinsic factors such as sex, age, status and breeding population  (Carneiro et al.
Acknowledgements We thank all the fieldworkers involved in tracking data collection and collation. We recognize the many USFWS volunteers who contributed assistance in the field to monitor the North Pacific albatrosses, including but not limited to:  Hoku Cody ,  Megan Dalton ,  Ann Humphrey ,  Hope Ronco , and  Amanda Gladics. We thank  Elizabeth Mackley ,  Chris Bell ,  Mara Nydegger ,  Alice MacMillan ,  Guy Anderson ,  David Aitken ,  Ruth Brown ,  AJ Bellamy ,  Katherine Snell ,  Emily Scragg  and  Tessa Coledale  for data collection. Some of the fieldwork was undertaken with permission from the  Government of Anguilla. We thank  Sir Hew Hamilton-Dalrymple  and the  Scottish Seabird Centre ,  North Berwick  for access to  Bass Rock. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  766417.
This communication reflects only the authors' views, and the  Research Executive Agency of the European Union  is not responsible for any use that may be made of the information it contains. Funding and project support includes: FCT-Portugal through projects  UIDB/04292/2020  and  UIDP/ 04292/2020 , granted to  MARE  and through the project  LA/P/0069/ 2020  granted to the  Associate Laboratory ARNET ;  Falklands Islands Government ; the Ecosystems component of the  British Antarctic Survey Polar Science for Planet Earth Programme , funded by the  Natural Environment Research Council (NERC) ;  Sea World Research and Rescue Foundation Inc. ;  Holsworth Wildlife Research Endowment ; Winifred Violet Scott Trust;  USFWS ,  NOAA ,  BOEM  ( M14AC00026 ), and  Papahānaumokuākea Marine National Monument; UK-SCaPE programme delivering National Capability  ( NE/R016429/1 ) funded by  NERC ;  French Polar Institute  (IPEV n • 109, resp  H Weimerskirch );  Réserve Naturelle Nationale des Terres Australes Françaises  and the  Zones Atelier Antarctique (LTSER France ,  CNRS-INEE) ;  GulfWatch Alaska ;  Natural Sciences and Engineering Research Council of Canada ;  Canada Research Chair in Arctic Ecology; BirdLife Australia, Deakin University; UK Darwin Plus  ( DPLUS0007 );  Leverhulme Early Career research grant  ('The curious case of asynchronous breeding in tropical seabirds').
Data availability All non-positional data derivatives produced in this study are either available in the Supplementary Materials or are available in the associated Zenodo repository (https://doi. org/10. 5281/zenodo. 7692668). The tracking data used in these analyses may be downloaded via the Seabird Tracking Database (www. seabirdtracking. org), under the following accession codes: 1343, 1344, 1345, 1347, 1440, 1628, 460, 462, 1387,  1885, 1341, 1342, 1472, 1653, 1660, 1222, 1857, 1858, 1449, 1819,  927, 928, 930, 1144, 1145, 1147, 1148, 936, 938, 937, 618, 619, 620,  1383, 1382, 1173, 1174, or requested directly from the authors.
The near-infrared photoluminescence signal was collected with the same objective, then separated from the pump using a dichroic mirror (DMLP950R from Thorlabs), and finally directed with a multimode optical fiber toward an InGaAs single photon detector ID230 from ID Quantique. The TCSPC histograms were built with a PicoHarp 300 module from Pi-coQuant, using the trigger signal from the laser diode driver and the data sent by the ID230 detector. Transient Absorption: Transient absorption measurements were performed by splitting the 800 nm fundamental of a 5 kHz, 60 fs Ti:sapphire laser into pump and probe branches. The pump branch was directed through an OPA to generate 700 nm pump pulses and chopped to 2. 5 kHz; the probe branch was optically delayed using a variable delay stage with retroreflector and then focused into a 5 mm thick sapphire plate to generate a supercontinuum in the near-infrared.
Acknowledgements The project was supported by  ERC  grants blackQD (Grant no. 756225 ),  Race Forward  (Grant no. 771688 ), and  AQDtive  (Grant no. 101086358 ). The authors acknowledge the use of clean-room facilities from the  "Centrale de Proximité Paris-Centre ". This work was supported by  French  state funds managed by the  Agence Nationale de la Recherche  through the grant  Copin  ( ANR-19-CE24-0022 ),  Frontal  ( ANR-19-CE09-0017 ),  Graskop  ( ANR-19-CE09-0026 ),  NITQuantum  ( ANR-20-ASTR-0008 ),  BrightSpark Foundation  ( ANR-21-CE24-0012 ),  MixDFerro  ( ANR-21-CE09-0029 ), and  Quicktera  ( ANR-22-CE09-0018 ). This project received financial support from the  CNRS  through the  MITI interdisciplinary programs and CNRS UChicago program. Work performed at the  Center of Nanoscale Materials , a  US Department of Energy Office of Science User Facility , was supported by the  U.
These predictive relationships were then used to upscale the observations across the global extent of forest coverage (Fig. 2 ). To evaluate model robustness, we tested its performance on an independent validation dataset containing 3,895 sites across the globe  40  , resulting in an R 2 of 0. 47 (see Methods section 'Cross-validation using external data'). In addition, we compared our model output with annual land cover maps from the European Space Agency's Climate Change Initiative (ESA CCI LC)  41. Across all leaf types, our model showed high correlations with the ESA CCI LC outputs, with an R 2 BC of 0. 61. Within each leaf type class, our model explained 78% of the variation in the proportion of broadleaved evergreen trees, 31% of broadleaved deciduous tree proportions, 64% of needle-leaved evergreen tree proportions and 19% of needle-leaved deciduous tree proportions (R 2 ).
These relationships form the foundation of dynamic global vegetation models  31, [33] [34] [35] [36] [37]. Yet, the relative importance of various environmental characteristics on leaf habit and form remains to be determined. Furthermore, until now, these vegetation models have lacked the ground data needed to build tree-density-based 'bottom-up' models. Such models are crucial for validating satellite-derived trends on a global scale and for providing a comprehensive, high-resolution depiction of forest leaf-type variation across environmental gradients. Here we analyse the global distribution of needle-leaved, broadleaved, evergreen and deciduous tree species, by integrating ground-sourced information from 9,781 standardized forest inventory plots in the Global Forest Biodiversity initiative (GFBi)  38  database with species-level leaf habit (evergreen vs deciduous) and leaf form (broadleaf vs needle-leaf) information accessed from the TRY plant trait database  39  (Fig.
1a ). The 9,781 forest inventory plots represented a subsample of the full GFBi data of >1 million records to ensure an equal representation of different forest biomes across the globe and avoid modelling bias caused by uneven spatial sampling (see Methods). Using information on both the occurrences of individual trees per plot and the basal-area weighted occurrences of each individual, we calculated plot-level leaf-type proportions (1) on the basis of the leaf type of each individual tree (tree-based leaf type) and (  2 ) by weighting each tree by its basal area (area-based leaf type) (see Methods section 'Tree leaf-type data'). The first estimate allowed us to map the tree densities of each leaf type, while the second estimate allowed us to map the leaf types represented by the largest trees in a plot.
Incorporating local point observations into the model training was not feasible because the ground-based forest inventory data we used did not include field-measured environmental and soil characteristics. Although we examined additional point-level observations from the World Soil Information Service (WOSIS) dataset (see next paragraph), these observations often did not align spatially with the forest inventory plots, resulting in a reduced sample size (>80% reduction) and geographic bias (Fig. 1  and Supplementary Fig. 14 ). Moreover, training the model using point observations while relying on soil layers for model prediction could introduce further uncertainties and biases. To avoid these limitations, we conducted both model training and predictions using the same soil layers. Nevertheless, we conducted analyses to determine the agreement between results based on global soil layers and point-level soil observations.
We matched our forest inventory dataset  55  with the WOSIS dataset  56  , which contains local point observations of soil features. These analyses indicated that: (1) model predictions remained consistent when using point observations instead of global layers of soil characteristics (97-99% similarity in predictions), (2) the global soil layers exhibited good agreement with point observations for most soil variables, particularly the four most important variables (R 2 = 0. 42-0. 62; Fig. 3c  and Supplementary Fig. 13 ) and (3) the inferred importance of soil features in leaf type variation remained similar (<5% difference) when using point observations instead of soil layers as predictors (Supplementary Fig. 14 ). These analyses underscore the crucial role of soil features in determining global leaf-type variation.
Data collection Forest inventory data. To obtain empirical information on tree occurrences, we extracted a total of 1. 1 million forest inventory plots with more than 50 million individual occurrence records, covering all continents except Antarctica from the GFBi dataset  38. To avoid modelling bias caused by uneven spatial sampling across biomes, we downsampled the dataset so that the relative proportion of plots in each biome in the dataset approximately matched the proportion of forested area within each biome  65. This was done by retaining all tropical forest plots in GFBi (n = 11,367) and randomly downsampling the remaining biomes in proportion to this number. Individuals with stem diameters <10 cm were excluded as the focus was on adult trees, and only plots with ≥10 adult individuals were included in the final analysis.
For each plot, the dataset contains information on the location (coordinates), the year(s) when the inventory took place, stem diameter at breast height (DBH) and species identity of each individual. For plots with time series data, only the most recent observation year was included in the analysis. The average year of observation across all plots was 2005. This resulted in a total of 9,781 plots with a median size of ∼500 m 2 and 817,091 individual tree records, with 20. 3% of the plots in the boreal biome (vs 21% of forested land globally), 20. 4% in temperate biomes (vs 22%), 54% in tropical biomes (vs 50%) and 3% in Mediterranean woodland, tundra, xeric shrubland or mangrove biomes (vs 7% globally). Tree leaf-type data. Information on leaf habit (evergreen vs deciduous) and leaf type (broadleaved vs needle-leaved) was extracted from the TRY plant trait database  39.
For each species and genus, we assigned the most common leaf habit across all TRY observations, treating leaf habit as a binary variable of whether it is evergreen or not. Species names were standardized using the Taxonstand R package  66. We then assigned leaf-type information to the individuals included in the GFBi dataset using species-level information or genus-level information in case species-level information was not available. Plots in which <50% of all individuals had a leaf-type record in TRY were excluded. Out of 10,274 species recorded globally in the GFBi dataset, leaf-type records could be assigned to 8,642 tropical tree species, 453 temperate tree species, 46 boreal tree species and 1,124 tree species in dry areas. Next, we calculated the proportion of each leaf-type combination (evergreen broadleaved, deciduous broadleaved, needle-leaved evergreen or needle-leaved deciduous) within a plot, either by dividing the sum of individuals featuring the respective leaf type by the total number of individuals within the plot (individual-based leaf type) or by dividing the basal area featuring the respective leaf type by the total basal area of all trees in the plot (area-based leaf type).
Thus, by including numerous variables, even if correlated, we can improve our predictive power of the model to accurately quantify current carbon. To run the models, we set the output mode to 'MULTIPROBABIL-ITY' and randomly sampled 10 individuals from each plot 100 times, weighting individuals by their basal area to model area-based leaf types. The following equation was used to transform DBH to basal area: A = πDBH 2 4. To model individual-based leaf types, individuals were sampled without weighting them. This resulted in 100 training datasets, each containing 98,330 rows. After extracting pixel values from 58 environmental covariates, we then modelled leaf types for each training dataset with a random forest model, using the 58 environmental covariates. The correlation between our point-level response variable (leaf type) and spatially contiguous covariates then allowed us to map the global distribution of leaf types.
Importantly, as suggested in ref. 74, equation (  1 ) is estimated using out-of-fit cross-validated data, where the predicted values are estimated by omitting the corresponding observed values from the training data, with the resulting pseudo-R 2 used to assess our four-element model output. To create the final maps (Fig. 2  and Supplementary Fig. 5 ), we used the random forest model for each training dataset with the optimal suite of hyperparameters based on the R 2 BC from the grid search. Extrapolation of our predictions across global forest areas resulted in 100 four-band global layers, with each band representing the global probability of one forest leaf type. We averaged the predictions from these 100 model outputs by taking the mean for the final map. We calculated the 95% confidence intervals across the 100 model layers  76  to represent sampling uncertainty.