dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion We investigated two approaches to estimate a normalized international method of soil aggregate stability index (denoted MWD FW ), using an extensive dataset covering mainland France and by means of i) commonly-measured soil properties (lab-based), and ii) a mid-infrared spectroscopy approaches. From the Results and Discussion we draw the following conclusions: • We found strong positive correlations between aggregate stability measurements, organic carbon and clay content. • Land use was important for the prediction of aggregate stability. • Both approaches to estimate aggregate stability had similar prediction validation statistics, but the lab-based approach had slightly better results compared to the spectroscopic approach. • The results support the use of MIR measurements as a cost and time effective measure for the prediction of soil aggregate stability, without substantial decrease in prediction accuracy compare to pedotransfer function.
When the initial investments for the acquisition of a spectrometer is too high, simple pedotransfer function based on commonly-measured soil properties can be used to estimate aggregate stability. • It is possible to predict aggregate stability at the scale of a country in the context of a wide diversity of soil and different land uses. Since structural stability is highly variable in time and space, we envision building a dataset with repeated measurements over time, for example, by measuring it throughout a year or during high erosive periods (summer or autumn). This might help in both reducing the measurement error and in evaluating the aggregate stability change over time and therefore the soil susceptibility to erosion for a region at a country scale. The methodology developed in this study can be used for this purpose.
Acknowledgements The soil sampling and the analyses of physicochemical properties of soils were supported by the  French Scientific Group of Interest  on soils, GIS Sol, involving the  French Ministry in charge of the Ecological Transition (MTE) , the  French Ministry in charge of the Agriculture and Food (MAA) , the  French Agency for Environment and Energy Management (ADEME) , the  French Biodiversity Agency (OFB) , the  National Institute of Geographic and Forest Information (IGN) , the  French National Research Institute for Sustainable Development , and the  National Institute for Agronomic and Environmental Research (INRAE). We thank all the soil surveyors and technical assistants involved in sampling the sites. This project received funding from  INRAE department Agroecosystem. Spectrum acquisition on RMQS samples was supported by the  MIRSol  project, which was funded by  ADEME  (contract  0675C0102 ).
, 2010; Kerem et al. , 2012; Luna et al. , 2022)  and some areas have been studied more extensively, especially within the western basin  (Mannocci et al. , 2018). Therefore, the ASI project aimed at filling these data gaps. In this study, we aimed to use this visual-acoustic dual platform to estimate the detection probability of small cetaceans in the Mediterranean Sea. Small cetaceans studied here include the bottlenose dolphin (Tursiops truncatus), the long-finned pilot whale (Globicephala melas), the Risso's dolphin, the rough-toothed dolphin, the common dolphin (Delphinus delphis), the striped dolphin (Stenella coeruleoalba) and unidentified dolphins. This diverse community is largely dominated by the striped dolphin (ACCOBAMS, 2021). We only included transects from the ASI survey where visual and acoustic data (based on echolocation clicks) of small cetaceans were collected simultaneously.
2 m/s (6. 2 knots; Figure  1 ). Transects were designed with the Distance software  (Thomas et al. , 2010)  as equal-spaced zig-zag transects to provide uniform coverage probability. The ASI survey used a linetransect sampling protocol and visual observers recorded the location, environmental conditions, group size and perpendicular distance to the animal(s) from the transect line  (Buckland et al. , 2001). Transects were divided into legs with constant environmental conditions, and each leg was finally divided into 5 km segments for analysis.
Visual data collection Visual data were collected during daylight hours, whenever the observation conditions were appropriate (i. e. Beaufort sea state ≤ 4). During the visual effort, two observers were positioned on a 5 m elevated platform to search for cetaceans. Each observer was on either side of the platform: the first observer scanned the starboard side from 340°to 90°and the second observer scanned the port side from 270°to 20°with 0°representing the bow. They observed the respective areas with naked eye and used binoculars to confirm species identity if required. For each visual event, observers provided information to a dedicated data logger using the Logger software package (www. marineconservationresearch. org). Information about species, group size, bearing angle and distance from the boat (between the observer and the centre of the group) were recorded for each visual event.
Bearings and distances were estimated by eye and group sizes were reported as best, high and low estimates of the number of animals for each visual event. In order to improve effectiveness of and consistency between the observers, distance trials and training on the survey protocol were carried out prior to the survey. Additional information was recorded on cue type and animal behaviour. When necessary, the vessel interrupted the transect line to approach animals for species identification or photo-identification before returning to the transect line at the point where it was left. Information on environmental conditions i. e. Beaufort sea state (out of 9), wave and swell heights (m), cloud cover (percentage scale; out of 10), glare, visibility (from 1 low visibility to 3 good visibility), wind direction and speed (m/s) and boat speed (m/s) were logged every hour or whenever conditions changed by the data logger member.
Acoustic data collection The acoustic survey was conducted 24 hours a day with a towed hydrophone array capable of detecting all cetacean species. A 400 m tow cable was used to avoid any boat self-noise; at this distance, any vessel noise, such as propeller cavitation, was imperceptible to both human ear and the detection algorithms described below. Acoustic effort took place when Beaufort sea state was below 5 and local water depths were above 50 m. The hydrophone array was housed in an oil-filled tube and was composed of a pair of hydrophones in a linear configuration. The two hydrophone elements (Magrec HP03), spaced 0. 25 m apart, had a sensitivity of -204 dB re 1V/ μPa with a flat frequency response ( ± 3 dB) from 1 to 100 kHz in the frequency band of most odontocete vocalizations. Preamplifiers with 29 dB gain were used to prevent voltage drop between the array and the research vessel.
The outputs of the pair of hydrophones were digitised at a sample rate of 192 kHz and were monitored in real-time using a click detector in PAMGuard, a passive acoustic monitoring software package  (Gillespie et al. , 2008). Audio recordings were stored as 16-bit wav files. Outputs from the click detector were compressed into binary storage files in PAMGuard and these files were manually analysed post-hoc with a focus on small odontocetes.
, 1978). Most studies focus on understanding clicks produced "on-axis" (Table  1 ), i. e. when the animals are facing the hydrophone where the transmission loss of the signal is minimal, which is not typically applicable to the field conditions when recording free-ranging cetaceans  (Soldevilla et al. , A B D E F C FIGURE 2 Protocol for determining acoustic events using PAMGuard Click detector module. (A) Determination of the number of events. (B) Set a 90°reference line representing the bearing of 90°of the boat. (C) Set a perpendicular line where the first track is crossing the 90°reference line. (D) If the followed track starts before the perpendicular line of the 1 st track, it will be considered as the same event. (E) If we set a perpendicular line on the 2 nd track, the 3rs track starts after the perpendicular line, this would be considered as a distinct event.
Localisation A sequence of clicks can be used to derive an estimation of the perpendicular distance using the Target Motion Analysis (TMA) module's 2D simplex method implemented in PAMGuard. The bearing angle of each detected click was determined using the time delay of arrival of a signal detected by the pair of hydrophones. Convergence of successive bearing lines was used to locate the sound source and allows the estimation of the distance perpendicular from the crossing point. The TMA module attempted to resolve left/right ambiguity using variation in the vessel's course. The number of acoustically active individuals per acoustic event was determined as the number of tracks that occurred at the same time. This method allowed the number of vocalising animals to be counted instead of the number of vocalisations  (Akamatsu et al.
Identification of duplicate events For this study, transects where visual and acoustic data were simultaneously collected were retained to identify duplicate events collected by the visual and acoustic platforms. The MRDS analysis is based on the reliable identification of duplicate events between the two platforms. Two events are considered duplicates when the same group of individuals is detected both visually and acoustically. For this purpose, a two-step decision tree (i. e. a rule-based classification system) was elaborated based on a set of temporal and spatial criteria determining how close in time and space a visual and an acoustic event were recorded. A putative duplicate was a pair of events consisting of a visual and an acoustic event to be tested with the decision tree. A putative duplicate became a potential duplicate then a confirmed duplicate when the first and the second step of the decision tree were respectively passed successfully.
Summary of survey data Transects where acoustic and visual data were simultaneously collected in good conditions (when Beaufort sea state ≤ 4) totalled 6,679 km of effort (Figure  1 ). The lengths and average speed of surveyed transects for each survey block are given in Supplementary Table  2. A total of 108 visual and 122 acoustic events of small cetaceans were identified. Species identification based on the visual survey component is shown in Table  2. The striped dolphin was the most frequently observed species and represented 52. 8% of the total visual events. The second most observed species was the shortbeaked common dolphin (11. 1%) followed by the bottlenose dolphin (4. 6%). The Risso's dolphin (2. 8%), the long-finned pilot whale (0. 9%), and the rough-toothed dolphin (0. 9%) were occasionally observed during the survey.
Estimation of the detection probability A truncation distance was set at 1,500 m, which removed 10 unique visual, 2 unique acoustic and 2 duplicate events, representing 7. 5% of the total events (Figure  5 ). Thus, in total, 67 unique visual, 77 unique acoustic and 28 duplicate events were used to fit the detection function in the MRDS method. Based on the AIC and the goodness of fit, the PI model with a HR key function was the best model (Supplementary Figure  1  and Supplementary Table  3 ). The MR submodel included perpendicular distance, swell height and platform as covariates. The DS submodel with the HR key function included sea state only. However, the model did not appear to fit the data well and overestimated detectability close to the transect line (Figure  6 ). Although the fit of the PI model with a HN key function was not optimal, it had a better fit to the data (Figure  6 ) and could therefore be considered as a more conservative model for estimating detection probabilities.
Discussion The objectives of our study were to develop a method to match visual and acoustic events and to estimate the detection probability of small cetaceans accounting for detection biases. Visual and acoustic data were collected simultaneously using a visualacoustic dual platform in a distance sampling framework. Most studies estimate the detection probability by assuming g(0) is equal to 1 with a single platform  (Barlow, 2006; Øien, 2009; Dick and Hines, 2011; Hildebrand et al. , 2015). To account for detection biases in abundance estimates, g(0) needs to be estimated. The benefit of using a dual visual-acoustic platform is to quantify the proportion of missed events to estimate g(0) and thus to correctly  Perpendicular distance (km) frequency distributions for small cetaceans identified by the R/V Song of the Whale either with the visual or acoustic platforms.
The distance distribution was based on the data and was not an arbitrary threshold. Changing the value of the distribution did not A B D C FIGURE 8 Sensitivity analysis of the two-step decision tree. The variation of the number of duplicates was explored with different values of the distance distribution (A) and the routine speed (B). The vertical yellow line indicates the chosen value for this study. The overall detection probability (solid line) and g(0) (dashed line) were estimated for each value of the distance distribution (C) and the routine speed (D). The green and purple lines represent the models used with a half-normal and a hazard-rate key function respectively. The overall detection probability of the MRDS model was obtained by combining the DS submodel and the g(0) of combined platforms from the MR submodel.
Detectability of small cetaceans 4. 2. 1 Detection process During the survey and the analytical stage, a consistent protocol was implemented to define distinct events. However, the performance of the models tested was not perfect, as either the goodness of fit was good but the model did not seem to fit the data, or the opposite was observed. The detection function using the HR key function was unable to accurately describe the data observed close to the transect line due to a poor fit at low perpendicular distances. This indicates a possible problem associated with the detection process. Accurate distance estimation can be an issue in visual surveys  (Marques et al. , 2006). In this study, radial distances were estimated by naked eyes without tools, which may bias perpendicular distance estimates.
The second assumption is that we counted individuals moving from the front of the boat to the back, and that all the animals in a group were probably not moving in the same way toward the hydrophone, so that only part of the group was detected and counted, the others having moved away or not in the direction of the hydrophone. The third assumption is a mixture of the two, implying that disentangling availability bias and perception bias from the acoustic method is difficult. Due to probably incorrect estimates of the acoustic group size, we did not include this covariate in the model and the remaining unmodelled heterogeneity can lead to a potential overestimation of the detection probability  (Borchers et al. , 2006; Burt et al. , 2014). With further studies of vocalization rates, it may be possible to scale up the underestimation of acoustic group size, but currently we do not have enough meaningful data on vocal rates for small odontocetes in the Mediterranean Sea.
Our dataset was biased in terms of the geographic location of populations, as we collected few data from the tropics and central parts of continents. Tropical regions host a large proportion of avian diversity and are experiencing rapid anthropogenic change, whereas areas inside continents usually undergo stronger climatic changes than areas affected by marine conditions (1). We therefore expect that the heterogeneity in our sample may be lower than the true global heterogeneity. In conclusion, analyses based on 201 wild bird populations from all continents reveal that offspring production has declined during recent decades, but with considerable variation among species and populations. Species with relatively large body mass are the most vulnerable to decline in offspring production. Climate change appears to influence changes in offspring production through complex interactions with ecological and life history traits of species.
For each study population, we also calculated the temporal trend in the number of breeding females present at a study area. We divided study sites into protected and nonprotected; if >50% of a study area was under some forms of legal protection (national parks, nature reserves, etc. ), we classified it as protected, otherwise as nonprotected. Similarly, we divided studies with nests protected against predators and nests nonprotected; if >50% of nests were protected (e. g. , concrete nest-boxes, fences around nests), we classified the population as nest-protected. Finally, we divided studies into those using nest-boxes (if >50% of clutches were laid in nest-boxes) and those using natural nest sites (monitoring open-nesting and cavity-nesting species). Criteria for Data Inclusion. The criteria for a dataset to be included in our project were: 1) a minimum study period of 15 y, during which data were collected for at least 10 breeding seasons; 2) average seasonal number of breeding females was greater than 10; 3) in multi-brooded species, females were individually marked; and 4) research had to be conducted for the whole breeding season to determine accurately the fate of successive broods, and annual offspring production of each breeding female.
2. century concerning long-term changes in offspring production of avian populations, we undertook an exhaustive literature search in Google Scholar using the following key words: breeding, nesting, breeding success, offspring production, population productivity, climate change, and laying dates. Because we wanted to use standardized estimates of changes in offspring production across time (see below), we looked for publications that included original data. From published literature, we collected data on 72 bird populations representing 28 species (SI Appendix, SI Text S2). 2) Our literature search enabled us to identify researchers who potentially had long-term data on annual production of young in the bird populations they studied. Between October 2018 and April 2019, we contacted 313 authors of long-term studies asking them whether they would like to collaborate in our meta-analysis project.
A total of 101 authors shared their data on 86 species and 129 populations (for details on searching process, see SI Appendix, SI Text S3). From both data sources, we collected a total of 201 datasets on 104 bird species (for problems with species number, see SI Appendix, SI Text S4) covering the histories of 745,962 clutches. The data were gathered in both hemispheres between 1970 and 2019 (Fig. 1 ), and different populations were sampled over a period ranging from 15 to 49 breeding seasons (median 26; interquartile range 20 to 35) (SI Appendix, Table  S1 ). Literature sources, including descriptions of field methods used in the studies included in the analyses, can be found in SI Appendix, Table  S4. In addition to the data on offspring production, we collected data on laying dates, clutch size, nest success, breeding density, and proportion of females with second broods or replacement clutches.
These data were not provided by all researchers, and not found in all literature sources. Meteorological, Body Mass, and Migratory Data. Meteorological data for the study areas, including mean monthly temperatures and total monthly precipitation, were provided by authors or, for published data, obtained from the National Oceanic and Atmospheric Administration (http://www. ncdc. noaa. gov/ cdo-web/datatools/findstation) or Tutiempo. net (https://en. tutiempo. net/climate/ europe. html). We used data from meteorological stations closest to study areas with complete records across study years. For each area, we calculated mean temperatures ( o C) and total precipitation (mm) corresponding with each of the five breeding periods: 1) prelaying (a month preceding commencement of egg laying); 2) egg laying; 3) egg laying and incubation; 4) nestling period; and 5) whole breeding season (from the commencement of egg laying until the end of parental care).
Values of the index range from 0 to 50. HFI data were extracted from ref. 51  using QGIS software (version  3. 22. 11). For each study area, we calculated a mean HFI score by overlaying the HFI raster layer with a site raster layer. Because HFI data are not available for islands, we were able to calculate the index for only 114 continental bird populations. Statistical Analysis. Analyses were conducted using R version 4. 2. 2  (52)  and Phylometa version 1. 3  (53). They were carried out in two steps: 1) we estimated grand effect sizes for a group of variables describing changes in species' parameters and 2) using metaregression, we looked for factors explaining heterogeneity in effect sizes (hereafter ESs) from the first step. A list of all variables used in the analysis (including the species' parameters) is provided in SI Appendix, Table  S5.
org (55, 56). Grand ESs of changes in species' parameters were calculated by an iterative procedure with original phylogenetic trees. For meta-regression modeling, we built a 50% majority-rule consensus tree (56) using the R package "ape," version 5. 5  (57). Grand ESs in Table  1  were calculated using Phylometa software. We reran 5,000 iterations, each time using a different phylogenetic tree, and a subset of In all models, phylogenetic correlations between species and identifiers of populations and species were included as random effects. Beta coefficients whose 95% confidence limits do not include zero are shown in bold. The number of species and populations is in square brackets. Models of changes in offspring production and nesting success are visualized in Fig. 4. *At the laying period in the model of clutch size, prelaying period in the model of nest success, and nestling period in the model of offspring production.
Data, Materials, and Software Availability. All data and R scripts relevant to the article are available at Figshare  (61). Other study data are included in the article and/or SI Appendix, Tables  S1-S3. ACKNOWLEDGMENTS. We are grateful to our collaborators who worked with us and collected field data. The list of principal collaborators and institutions is published in SI Appendix, Table  S6. We thank Heather Renner for the information about the data collected by Alaska Maritime National Wildlife Refuge. We sincerely appreciate valuable comments and suggestions made by two reviewers, which helped us in improving the manuscript. This meta-analysis was financed by the grant of the Polish National Science Centre (Narodowe Centrum Nauki) (no. 2017/27/B/NZ8/00465) awarded to Lucyna Hałupka.
In terms of carbon content and abundance of acid sites, DOM appears to be more significant than humic extracts when it comes the impact on soil acid-base properties. This is particularly relevant when solid compost is applied directly to farmland rather than using of HA/FA formulations that are commercially available or prepared by those with technical expertise. After determining the carbon content and the amount of acid groups, the comparison between the extracts was conducted by measuring the extent of the Cd 2+ binding, referred to as cML. This value, expressed in mmol kg compost -1 was calculated by referencing the Cd 2+   binding by each extract to 1 kg of compost. The calculation was based in complexation data at pH 7. 0 and took into account the abundance of the extracts using Equations (  1 ) Figure  3b  compares the abundance of acid sites in humic extracts (M T,HS S ) and DOM (M T,DOM ).