dataesr/scientific-paragraphs-categorization huggingface dataset:
0 package in R (https://CRAN. R-project. org/ package=GeoLight) to analyze light data using the threshold method  (27). Light-level geolocation data of the loggers used in Germany were analyzed using the freely available R packages SGAT [Solar/ Satellite Geolocation for Animal Tracking; GitHub Repository (http://github. com/swotherspoon/sgat)] and "BAStag" (basic data processing for light based geolocation archival tags; R package).
Genetics The aim of the genetic part of the research program was to determine the genetic structure of European breeding populations [see  (14)  for additional details] and to assign autumn migrants captured on the western flyway (southwest France) and the eastern flyway (Kuwait and Israel) to genetic breeding groups/areas. We used a shotgun sequencing approach on an ion PGM (Personal Genome Machine) platform (Life Technologies) to develop 24-microsatellite loci from muscle tissue obtained from one specimen found dead in Kuwait in September 2014. We extracted DNA from the tail feather collected on all individuals captured on breeding sites or along migration flyways. A total of 1127 samples, including 143 duplicates, were genotyped for the 24 loci. The resulting breeding dataset consisted of 575 individuals sampled at 26 breeding sites for population-level analyses.
We carried out further analyses without three loci that displayed a high frequency of null alleles and deviated from the Hardy-Weinberg equilibrium (HWE). We performed a randomized G test with 1000 replicates to test for genetic differentiation among sites. We used two methods to uncover genetic population structure. First, the Bayesian clustering program STRUCTURE (28) allowed the assignment of the 575 breeding individuals to K populations by minimizing deviations from the HWE. We obtained the optimal number of clusters K from K, based on the rate of change in the log probability of data in successive K values  (29). Second, we applied a discriminant analysis of principal components (DAPC)  (30)  to the breeding dataset. DAPC is free from population genetic assumptions with inferences made on allelic similarity.
It summarizes genetic variability of individuals within groups while optimizing group discrimination. We used sampling sites as the grouping variable. We carried out analyses out using the ADEGENET 2. 0. 1 package in R 3. 3. 1. We retained the first 115 principal components in the data transformation step, corresponding to 84. 2% of genetic variance, and we saved three discriminant functions for further analyses. We reached consensus on population structure based on results from these two methods. The magnitude and direction of contemporary gene flow occurring between the consensus populations were estimated using the program BAYESASS 3. 0. 1  (31). We then performed a cross-validation of the population structure. We randomly split the full breeding dataset (575 individuals) into a training and a validation dataset by randomly assigning 70% of individuals from each sampling site to the training set (402 individuals) and the remaining 30% (173 individuals) to the validation set.
The training set defined the genetic makeup of the clustering to be tested, and we assigned individuals from the validation set to one of these populations by the program GENECLASS (32) using the Bayesian method described in  (28). We repeated the process 10 times. The cross-validation on the three populations supported the strength of the northern and eastern clusters with an average of 85 and 79% of individuals, respectively, correctly assigned (fig. S2 ). Moderate correct assignment to the western population (52%) with a large contribution of the eastern population (36%) suggests low differentiation between these populations. We finally assigned the 396 individuals captured during migration along the western flyway (in southwest France) and the eastern flyway (Kuwait and Israel) to one of the clusters defined previously by the program GENECLASS using a Bayesian method  (33).
Demographic parameters Demographic data are based on the Norwegian ortolan bunting population monitored during the breeding period (May and June of each year) in central Hedmark County (60°29′ to 60°53′N and 11°40′ to 12°18′E) and Akershus County between 1996 and 2005 [e. g. ,  (15, 16) ]. This is the last breeding population in Norway, which decreased from approximately 250 singing males in the 1990s to only 14 males in 2016. Juvenile and adult male birds were captured and fitted with a metal ring and colored rings, allowing individual identification of adult males  (15). In each year, field observers recorded recaptures (mainly visual) of already marked birds, including information on their breeding status (paired or unpaired). We did not estimate survival for females because the sample size was too small.
We analyzed 419 adult male capture-recapture histories using multievent capture-recapture models, with the program E-SURGE 1. 9. 0  (34). We estimated three basic types of parameters: the initial state probabilities (from the -vector, following E-SURGE's notations), the between-state transition probabilities (probabilities of transition between states and survival from the  and  matrices respectively), and the event probabilities P (from the B matrix). We subdivided the B matrix into two matrices, the P matrix for recapture probabilities and the A matrix for probabilities to confirm the uncertain breeding states when detected. We modeled the betweenstate transitions () conditional on survival (). We estimated parameters simultaneously by standard maximum likelihood procedures, and models were ranked using Akaike's information criterion (AIC) adjusted for small sample size AICc  (35).
e. , A(f )]. We began the model selection by testing temporal and state effects and their interactions on P; then, we used the most parsimonious P model to model  and, finally, . Because there is currently no general procedure for goodness-offit (GOF) tests of multievent models, we estimated the GOF of a simplified dataset with known breeding states. We considered the Jolly-Move (JM) model as the starting general model  (36). We used the program U-CARE 2. 3. 2  (37)  to perform GOF tests and assess data conformity to JM. GOF results indicated that data conformed to the JM model (global test, P value = 0. 565 and c-hat = 0. 96). On the basis of the model selection procedure described in methods, the most parsimonious model was (i)  (f ) P(f + t) A(f), in which survival was constant over time and similar among breeding statuses (table  S3 ).
Acknowledgments: We thank  A. Al-Sirhan  for help in organizing the field work in Kuwait. We also thank  E. Gugueva  and  N. Kolyakina  for help in organizing and realizing the field work in  Volga-Ahtubinskaya flood plain Natural Park, Russia. We are indebted to all workers who helped collecting samples and data in the field, including D. Zhurauliou in Belarus;  J. Honkala  and  T. Jaakkonen  in Finland;  A. Audevard ,  S. Blache ,  L. Courmont ,  J. Fonderflick ,  J. Gonin , and  P. Fiquet  in France;  B. Fontaine ,  M. Ghislain ,  J. Laignel ,  A. -C. Monnet ,  R. Provost ,  M. Zucca ,  V. Romanov ,  Y. Bykov , and  O. Zhuravleva  in Russia; and  C. Grande ,  M. Illa , and  F. E. Navarrete in Spain. We thank  W. Creswell  for comments on the manuscript. Funding: This research was funded by  Conseil Général des Landes ,  Région Aquitaine ,  Région Nouvelle Aquitaine, Fédération Départementale des Chasseurs des Landes  ( FDC40 ),  Association Départementale des Chasses Traditionnelles à la Matole (ADCTM) , and the  French Ministry  in charge of the environment.
Funds for the genetic study were obtained from the  Labex BCDiv at MNHN (CNRS). Environment and Climate Change Canada  supported the stable isotope analyses through an operating grant to K. A. H. The Swiss federal office for environment contributed financial support for the development of the SOI data loggers (UTF-Nr. 254 ,  332 ,  363 ,  400 ). The German research was promoted by  German Federal Environmental Foundation (DBU). H. S. is funded by the  Deutsche Forschungsgemeinschaft (DFG ;  German Research Foundation )-SCHM 2647/1-2. The  Polish National Science Centre (NCN ) partially promoted the research in Poland. M. S. was funded by grant no. N N304 0198 40. The  Kvismare Bird Observatory  supported the work of J. S. Author contributions: F. J. conceived and coordinated the researches.
1) Introduction Characterizing soil variability especially over large areas, remains a crucial challenge to foster sustainable management of agronomic and environmental issues and help stakeholders to design regional projects  (Chaney et al. , 2016). At the regional as well as country level, soil maps are often available at coarse spatial resolution  (Bui and Moran, 2001)  which limits their ability to depict accurate soil information. For instance, the finest soils maps covering France were elaborated by administrative region at 1:250,000 scale, via a set of polygons, called Soil Map Units (SMU) with crisp boundaries. The delineation of SMU is based on soil survey programmes involving pedologists' expertise. In a coarse scale map, each polygon includes one or several Soil Type Unit (STU), which are not explicitly mapped, but their proportions and their environmental conditions, as well as soil characteristics, are provided in a detailed database (Le  Bris et al.
, 2013). To improve soil variability knowledge and overcome the limitation of a coarse mapping scale, several methods have emerged in the field of Digital Soil Mapping (DSM). These methods offer useful tools to predict soil spatial pattern from scarce or limited soil datasets by exploiting the availability of model based methods and an extensive array of spatialise (and more often than not gridded) environmental variables. In recent decades, DSM techniques have been increasingly used to downscale soil information and improve their spatial resolution. Depending on the quality of data and the complexity of soil cover,  Minasny and McBratney (2010)  supply a workflow that outlines different models that can be explored. In general, two main pathways can be distinguished: point based DSM approaches and map disaggregation approaches  (Odgers et al.
The DSMART algorithm is based on a calibration dataset derived from a random selection of a fixed number of sampling points within each soil polygon. Each sampling point is then assigned to one soil type following a weighted random allocation procedure based on the proportions informed in the soil map database. The same procedure was applied by  Chaney et al. (2016)  to spatially disaggregate the soil map of the contiguous United States at a 30m spatial resolution using petascale High Performance Computer (HPC). Because integration of pedological knowledge has been recognized as an effective way to improve digital soil mapping approaches  (Cook et al. , 1996; Walter et al. , 2006; Stoorvogel et al. , 2017; Machado et al. , 2018; Møller et al. , 2019) ,  Vincent et al. (2018)  have applied the DSMART algorithm with additional expert soil landscape rules describing soil distribution in the local context of the Brittany region (France).
By adding supplement sampling points to the calibration dataset selected according to soil parent material, soil redoximorphic conditions and topographic features, and by integrating soil landscape relationships in the DSMART sample allocation scheme, the authors obtained a coherent soil spatial distribution observing soil organisation along hillslopes and occurrence of intensely waterlogged soils in the stream neighbourhood, as observed in Brittany. This study aimed to test the hypothesis that adding soil landscape relationships in the disaggregation procedure improved the accuracy of produced disaggregated soil maps. This involves assessing the contribution of soil landscape relationships implemented in the DSMART algorithm by  Vincent et al. (2018). To achieve this objective, we compared disaggregated soil maps either derived from the original DSMART algorithm, the DSMART algorithm with extra soil observations and the DSMART algorithm fed by soil landscape relationships over an area of 6,775 km² in the eastern part of Brittany, France.
2. 1) Study area The Ille et Vilaine department covers an area of 6,775 km² and is located at the eastern part of 2. 2. 1) Regional soil database at 1:250 000 scale In Brittany, soils are represented through a regional geographic database called "Référentiel Régional Pédologique (RRP)" available at 1:250,000 scale  (INRA Infosol, 2014 ). This regional database identifies soils within Soil Map Units (SMUs), each containing one to several soil types called Soil Type Units (STUs). STUs are defined as areas with homogeneous soil forming factors, such as morphology, geology, and climate. In the study area, 96 SMU and 171 STU have been distinguished and represented by a spatial coverage of 479 polygons. In the regional database, SMUs were spatially delimited with crisp boundaries, while STUs were not explicitly mapped, but their proportion in each SMU as well as associated environmental and soil characteristics were accurately described in a semantic database  (Le Bris et al.
2. 2. 2) Soil validation data To assess the quality of disaggregated soil maps, three validation datasets were used (Fig. 1 ):  135 soil profiles chosen following a stratified random sampling design and specifically described and sampled from March to May 2017 for independent validation purposes in the framework of the Soilserv research project  (Ellili et al. , 2019, submitted).  755 legacy soil profiles collected between 2005 and 2008 during the "Sols de Bretagne" programme  (INRA Infosol, 2014). These profiles were sampled to characterize hydromorphic soil conditions and soil landscape heterogeneity. Existing detailed soil maps (1:25,000) covering 87,150 ha, surveyed according to  Rivière et al. (1992)  and revised later to adapt to the STU typologies developed in the RRP (Le  Bris et al.
All soil environmental covariates were converted to raster format at 50 m spatial resolution. The open source DSMART algorithm  (Odgers et al. , 2014)  was applied to spatially disaggregate the existing legacy soil map at 1:250,000 scale. DSMART algorithm uses machine learning classification trees implemented in C5. 0  (Quinlan, 1993)  to build a decision tree from a target variable (STU) and the environmental covariates supplied. The DSMART algorithm was written in the Python programming language by  Odgers et al. (2014)  and was recently translated in the R programming language. Running DSMART algorithm requires four main steps (Fig. 2 ): 1) Polygon sampling by a random selection of a fixed number of sampling points (n=30) within each polygon. This procedure allowed to select a total of 14,370 sampling points, per iteration, covering the study area and ensured that all polygons were sampled.
2) Soil Type Unit (STU) assignment to each sampling point following a weighted random allocation method. This step was based on the proportion of each STU informed in the RRP database. 3) Decision tree generation: the full set of sampling points were spatially intersected with the selected environmental covariates. This georeferenced dataset was then used as a calibration dataset to build the decision tree allowing the prediction of an STU as a function of environmental covariates. C5. 0 created explicit models, which were applied to the covariates rasters to generate a realisation of STU distribution over the study area at 50 m resolution. These three steps were repeated 100 times to generate 100 realisations of the potential soil type distribution over the study area at 50 m of resolution.
2. 4. 2) Original DSMART algorithm + soil observations (method 2) This disaggregation approach is similar to the original DSMART algorithm. However, the main difference is that 755 additional soil profiles, spatially collocated, were added to the calibration dataset to build decision trees. These soil profiles make it possible to incorporate real field observations with established soil landscape relationships. For each realisation, a calibration dataset (15, 125 samples) including virtual samples randomly selected from polygon units, as well as soil observations were used to model soil type with environmental covariates. The model was then extrapolated over the study area.
2. 4. 3) Original DSMART algorithm + expert rules (Method 3) Including soil landscape relationships in the disaggregation process was explored by  Vincent et al. (2018)  in a specific regional pedoclimatic context in Brittany (France). Expert soil landscape relationships were used to assign STU to sampling points. These relationships were based on expert pedological knowledge, which takes into account soil parental material as well as topography and waterlogging in the UTS allocation procedure. This approach combines two sources of the dataset to calibrate the model. The first one was derived from semantic information for each SMU/STU combination. It consists in attributing a barcode to each SMU/STU combination, derived from a concatenation of four features contained in the RRP database (parent material, SMU identifier, TPI and waterlogging index), and to compare these barcodes to a stack of regional covariates representing the same four features, to assign each pixel of the study area to a suitable STU.
This procedure allowed matching soil exhibiting specific features with their potential spatial distribution. For instance, hydromophic soils occur with slope sequences and valley positions, while well drained soils occur in upslope or middle slope positions. Using a random sampling stratified by SMU's area, a set of sampling points was selected with a proportion of one sample for every 5 hectares and a minimum of five samples per polygon unit. The second dataset was derived from a random sampling of a fixed number of sampling points in each polygon unit. This procedure ensured that all polygons had been sampled. STU allocation was based on the soil map unit proportions. The full set of each realisation (18, 320 samples) combining expert calibration dataset as well as dataset derived from random sampling procedure was spatially intersected with existing environmental covariates and used as a unique calibration dataset to build decision trees.
2. 6) Pair wise comparisons of disaggregated soil maps To compare the soil type rasters derived from the three DSMART based approaches, pairwise comparisons were performed using Vmeasure method implemented as open source software in an R package called Spatial Association Between REgionalisations (SABRE)  (Rosenberg and Hirschberg, 2007). This is a spatial method developed to compare maps in the form of vector objects and it was commonly used in computer science to compare (non spatial) clustering. , the total area of a zone zj is 𝐴𝑗 = ∑ 𝑎 𝑖𝑗 𝑛 𝑖,1 and the total of the domain is 𝐴 = ∑ ∑ 𝑎 𝑖𝑗 𝑛 𝑖=1 𝑚 𝑗=1. The SABRE package calculates a degree of spatial agreement between two regionalizations using an information theoretical measure called the V measure. V measure provides two intermediate metrics: homogeneity and completeness.
3. 1) Disaggregated soil maps Applying DSMART based approaches yielded a set of soil maps and associated probability of occurrence rasters. The original DSMART approach allowed to disaggregate the 96 SMUs into 108 STUs while DSMART with expert rules approach yielded 158 STUs and DSMART with extra soil observations approach yielded 172 STUs with respect to the first most probable STU map. A total of 171 STUs were identified in the Ille et Vilaine department within the RRP database. Unpredicted STUs correspond mainly to rare STUs with low proportions ranging between 2 and 10% within the SMUs containing them. Figure  3  shows the three maps of the 1 st most probable STU derived from each approach as well as the original soil map. Overall, the three most probable STUs maps captured the main pattern of  The quality of maps resulting from DSMART based approaches was quantified via the probabilities of occurrence of each STU predicted and the confusion index maps (Fig.
These areas were predominantly deep loamy soils or developed in alluvial and colluvium deposits. Figure  6  compares the cumulative area of the STUs estimated from the three disaggregated maps and that derived from the regional soil database. For each STU, its relative predicted area was estimated by counting the number of pixels where it was predicted. For the regional soil database, each STU area was computed from total SMU area multiplied by the proportion of the STU. This comparison shows that some STUs were overestimated by the disaggregation approaches when comparing to the soil database. DSMART with extra soil observations and original approaches showed similar cumulative STU areas under the curve whereas DSMART with expert rules had a shape similar to the regional soil database. The most abundant STU in the database (431: Stagnic Fluvisol developed from alluvial and colluvium deposits) was predicted as the most frequent STU by DSMART with extra soil observations and DSMART with expert rules, and it was predicted as the second most abundant STU by the original DSMART algorithm.
3. 3) Validation of disaggregated soil maps The validation procedure was performed for each DSMART based approach applied, considering the three most probable soil types and using both semantic objects (STU or soil group) and spatial neighbourhood (per pixel or 3x3 window of pixels). Considering 755 legacy soil profiles prospected in the framework of "Sols de Bretagne" project, per pixel validation accuracy reached 27%, for original DSMART maps and 34 % for DSMART with expert rules (Table  3 ). A similar comparison using 135 validation sites derived from Soilserv project showed that 18. 1 % of soil profiles match DSMART maps, 19. 8 % match DSMART with expert rules maps and only 16. 9 % match DSMART with extra soil observations maps (Table  3 ). Using a 3 x 3 window of pixels markedly improves the global accuracies, which increased for the two validation datasets (Table  3 ).
DSMART with soil landscape relationships remained the best performing method. When compared to accurate soil maps (1:25,000), the validation procedure showed that DSMART with extra soil observations as well as DSMART with soil landscape expert rules had almost the same performance (37% and 38%) while best accuracy (44%) was observed for Original DSMART maps (44%) (Table  3 ). These scores were clearly improved by considering soil groups and 3x3 pixels neighbourhood. For instance, the accuracy of DSMART with expert rules maps using soil group reached 45. 9% and increased to 62. 1 % when considering 3x3 pixels windows (Table  3 ). and  Møller et al. (2019)  in Denmark (18%) using DSMART algorithm (Table  4 ). In contrast to the latter studies, a large number of STU (171 STU) compose our soil dataset.
4. 2) Legacy soil data Legacy soil data used in this study provide an overall representation of soil over large areas  (1:  250,000 scale). This database was derived from several soil surveys and pedological expert knowledge. SMUs were spatially delineated, and their spatial organisation, as well as STUs features, were described according to available soil data and pedological expertise. STUs and their associated landscape characteristics were identified as accurately as possible using legacy soil profiles collected according to a not probabilistic sampling design between 1968 and 2012. Hence, differences in survey methods covering a large area over a long sampling period could lead to errors in the STU definition or uncertainties in the estimation of their area in a given SMU. Moreover, soil survey intensity was not uniform within SMUs.