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The categorisation system was developed through an iterative procedure including a careful evaluation at each stage. This was necessary because each of three rounds yielded substantial feedback from the expert taggers, identifying issues to be resolved and proposing improvements to the system. This process led to a much clearer understanding of the structure of sensitive data resources and a wider agreement on definitions to be applied in the tagging process. In addition, the allocation of exactly one tag per category improved during the development for many categories, indicating that the selection process was straightforward for most resources and categories. As a result, the categorisation system could be simplified and the structure improved, appropriately representing a trans-disciplinary effort. This may also be important from the user perspective. At the end of the day, the system should be so intuitive that the users searching for terms would have the same logic as the experts entered the tags.To be beneficial for the domain of LS, the categorisation system and the toolbox requires broad community approval38,39. In the project, we began the approval process with nominated experts from 6 LS RIs, embedded in a larger working group of the H2020-funded project EOSC-Life, covering 13 LS RIs. Though this can be seen as a useful starting point, the toolbox obviously needs community approval at a much larger scale. As the categorisation system is specifying a part of essential metadata for resources about sensitive data, it will be relevant to the FAIR Digital Objects (FDO) Forum for a « resources in the life sciences » FDO. The categorisation system can be used to derive FDO attributes and values for such FDOs. FDOs for the sensitive data itself, when levels of sensitivity and specific access protocols need to be specified is an interesting possible extension, and the categorisation system could support as a backbone information for access governance and technical choices. FDOs are to be “machine actionable”, so desirable mappings between different categorisation systems will be operationalisable. New European projects such as FAIRCORE4EOSC (https://faircore4eosc.eu/), FAIR-IMPACT (https://fair-impact.eu/) and other projects working on pragmatic semantic improvements for FAIR appliance will provide possibilities for registering metadata schemas and mappings that should reuse interdisciplinary approaches in the heterogeneous field of life sciences.The RDA has established and is maintaining a Metadata Standards Catalogue (MSC) (https://rdamsc.bath.ac.uk/mapping-index,5). An appropriate goal for the categorisation system would be to be included in this catalogue, after further refinement and alignment with other vocabularies addressing sensitive data in the life sciences. In any case, the work on the categorisation system can contribute to discussions on methodologies for aligning metadata schemas across scientific domains, while the categorisation system itself can be seen as an important contribution to the process of developing the most useful and appropriate cross-disciplinary terms and categories for describing sensitive data. We keep in mind that similar approaches have been applied via long and iterative processes in other scientific domains, such as understanding and predicting the evolution of climate (essential climate variables, https://public.wmo.int/en/programmes/global-climate-observing-system/essential-climate-variables) and essential biodiversity variables for mapping and monitoring species populations40. There are biases and gaps in the existing system that need to be tackled in the future. The initial content of the toolbox demonstrator, consisting of 110 resources related to sensitive data, has been primarily selected by four RIs with a focus on clinical and biomedical research (BBMRI, EATRIS, ECRIN, Euro-Bioimaging). Other areas and sensitive data types, such as environmental, classified, and proprietary data are under-represented, as are some disciplines, such as zoology, ecology, plant and mycological sciences, and microbiology. This indicates a need for a broader coverage of resources linked to sensitive data in the future work. Another question that needs to be investigated is how interoperable the categorisation system is with other domains outside the LS that systematically deal with sensitive data, for example, the Social Science and Humanities41). In addition, systematic data on the usability/user-friendliness of the toolbox from a broad sample of potential users from the life sciences are needed. Initial and informal evaluation of these aspects by the experts involved so far has been very positive but is clearly limited in scale and needs to be supplemented by more evidence.There are major challenges to the sharing of sensitive data, including interoperability, accessibility, and governance. The primary objective of the toolbox is to improve discoverability of resources and digital objects linked to the sharing and re-use of sensitive data (F in FAIR)4. The systematic application of a standardised typology for resources about sensitive data, as defined by the categorisation system, helps to better structure, and organise the issues and results in metadata enrichment (F4, R1.3 of the FAIR principles in Supplementary, Table S1). The toolbox alone will not be enough for the ‘I’ of the FAIR principles, but it may become a useful backbone for building more interoperable classification systems for sensitive data resources.It is perhaps more common to base findability on a tagging system using keywords (plus title text). That is, for example, how PubMed works—it does not categorise resources, it adds MESH terms to them (https://pubmed.ncbi.nlm.nih.gov/). Another option would have been to try to derive keywords from text or title. In our case, a categorisation system with pre-defined dimensions and pre-listed tags was preferred by the expert group. Keywords, in isolation, suffer from several disadvantages:

(a)

A range of equivalent terms may be used to mean the same thing – making searching for that concept difficult, requiring multiple ‘Or’ statements.

(b)

They may have multiple meanings (polysemy) especially if “drawn from”, or “applied to”, a wide range of scientific disciplines.

(c)

The different aspects of the resource covered by keywords, i.e., the types or dimensions of keyword applied, may be inconsistent and / or incomplete.

The categorisation system, on the other hand, guarantees that all 7 validated dimensions required are used in the tagging process and that the tags selected are standardised and defined. The toolbox categories also aid browsing of results by enabling sequential filtering using the categories and tags.In addition, there is a useful link between developing community approved categories for metadata, in this case for characterising resources dealing with sensitive data, and community understood (but implicit) ontologies used in the same area. Categories and ontologies can complement each other—without a common underlying ontology, metadata terms can be interpreted inconsistently, and without defining metadata categories, ontologies may remain implicit and inconsistent. We found, for example, that discussions on the best categorisation to use for scientific disciplines, or data types, exposed the implicit (and different) ontologies being used by different people and is based on the personal views of those in the group. Those would have been obviously rooted in and / or influenced by the language and working assumptions of their discipline(s), and their roles and experiences, (current and previous). That will be more and more the case with interdisciplinary research development and development in research careers. Developing categories in metadata can therefore play an important role in describing, understanding and, ultimately, harmonising the implicit ontologies scientists use in thinking about the area of sensitive data.In the development of the categorisation system, existing ontologies, classifications, and terminologies were taken into consideration (Table 2). However, many more have relationships to the categorisation system. An example is the Subject Resource Application Ontology (SRAO), an application ontology describing subject areas/academic disciplines used within FAIRsharing records by curators and the user community42. A first crosswalk has demonstrated considerable agreement between the toolbox category “research field” and subsections of SRAO42 and EDAM15. The toolbox has been registered as a resource (database) at FAIRsharing, a curated, informative, and educational resource on data and metadata standards, inter-related to databases and data policies (https://fairsharing.org/3577). It is planned to create a collection group of resources (standards, databases, policies) in FAIRsharing linked to the toolbox and the underlying categorisation system. This will also cover relationships to ontologies and classifications.There is a need to explore the applicability of the toolbox to specific domains. One example could be the European Joint Programme on Rare Diseases (EJP RD), where resources are made progressively FAIR at the record level to support innovative basic, translational and clinical research (https://www.ejprarediseases.org/coordinated-access-data-services/fairification-support/). The goal is to identify, refine and expose core standards for dataset interoperability, asset (data, sample, subject) discovery, and responsible data sharing, concentrating on data level rather than resource level information. Knowledge exchange between EJP RD and the toolbox could be of benefit in exploring the complementary of both approaches in adequately characterising resources linked to sensitive data and thus improving data discoverability.The first pilot study demonstrated major variation in tagging of resources if independent taggers are assessing the same resource (inter-observer variation). The example of BBMRI has shown that this variation can be considerably reduced if adequate training is performed; which in return is resource intense. Thus, to arrive at a valid and reliable tagging process, there is a necessity for adequate training and support to reduce inter-observer variation. Specific training sets and training programs as well as intercalibration tools need to be developed and implemented and approved by the community.Another option to be explored should be AI—or ML-algorithms to support automatic (or at least semi-automatic) tagging of resources. It is not easy to use AI/ML in this field due to the multilingualism and the misinterpretation of terms. Often there are different meanings between scientific disciplines and a common backbone for the application of AI/ML is difficult to achieve. It is necessary to come to a common understanding between people involved in the assessment of resources related to sensitive data in all life sciences. Nevertheless, the toolbox can become of major importance for research and application of AI/ML techniques in this field. It may serve as a resource for AI/ML to better find resources in the field by serving as a kind of gold standard to compare with. Another promising approach would be to consider a knowledge graph as an intelligent representation. For the categorisation system the approach could be used to interlink categories to a resource (e.g., “source related to sensitive data” has “geographical scope”) and to link individual tags between categories if possible (e.g., “clinical research data” result from “clinical research”). This would give a richer representation of the knowledge behind the categorisation system and the option to be integrated in existing approaches (e.g., OpenAIRE, https://www.openaire.eu/). Therefore, we will consider knowledge graphs as an intelligent knowledge representation of the categorisation system in the future.A major challenge will be the transition of the toolbox demonstrator to a mature toolbox and ultimately its maintenance, extension, and sustainability. Development of the toolbox demonstrator has been financed by EOSC-Life, but this project will end in 2023. Discussion on sustainability has been initiated with several life-science infrastructures (e.g., BBMRI, EATRIS, ECRIN and ELIXIR, another European Life-Science Infrastructure). Key aspects of sustainability that need to be considered are maintenance of the toolbox portal and tagging tool and of the toolbox content including expert time for tagging as well as human resources to maintain the system. Different approaches are under evaluation: an organization considering the resource core to its operations and taking full responsibility, or a joint ownership across multiple organisations (e.g., multiple RIs) or a community taking responsibility, either funded by future grants or through in-kind contributions from motivated research parties/individuals. Further costs to be covered will include system maintenance, input from a toolbox manager, tagging of resources by experts, as well as advertisement to the envisioned user groups, hardware costs and costs for debugging and major extension of functionality if needed. More

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Study sites and study designThe study was performed in the frame of a dog ecology research project, with details on the study locations published elsewhere15,42,43. For the current study, five study sites located in Indonesia and Guatemala were included. Site selection was carried out by each country’s research team, taking into consideration rural and urban settings, as well as differing expected number of dogs present at each location. The Indonesian study sites were semi-urban Habi and rural Pogon, in the Sikka regency, at the eastern area of Flores Island (Supplementary Fig. 6). In Guatemala, the study sites were Poptún (urban setting), Sabaneta and La Romana (both rural settings), located in the Guatemalan department of Péten, in the northern part of the country (Supplementary Fig. 7). Data were collected during May to June 2018 in Guatemala and from July to September 2018 in Indonesia.In each location, a 1 km2 area was predefined using Google Earth within which the study took place. The 1 km2 area was chosen because of the research goals of another part of the project, investigating the contact network of the dogs15. Within these areas, the teams visited all dog-owning households. In each household, the study was presented to an adult of the family, who was then asked if they owned a dog and if they were willing to participate in the study. After the dog owner’s oral or written consent was granted, a questionnaire was answered, and the dogs collared. The handling of the dogs was performed by a trained veterinarian or a trained veterinary paramedic of the team.The questionnaire data was collected through interviews with the dog owners. Multiple dogs per household could be included as multiple entries in the questionnaire. The detailed questionnaire contains information on the household location, dog demographics (age, sex, reproductive status) and management (dog’s purpose, origin, confinement, vaccination status, feeding and human-mediated transportation within and outside the pre-determined area).All dogs of a household fulfilling the inclusion criteria were equipped with a geo-referenced contact sensor (GCS) developed by Bonsai Systems (https://www.bonsai-systems.com), containing a GPS module and an Ultra-High-Frequency (UHF) sensor for contact data recording43,44. GCS devices report a 5-m maximum accuracy, a run-time of up to 10 years, can store up to 4 million data points and carry a lithium-polymer-battery (LiPo). For this study, only GPS data were analysed. The GCS were set to record each dog’s geographical position at one-minute intervals. Dogs remained collared for 3 to 5 days with the duration of the data collection being limited by the device’s battery capacity, as batteries were not re-charged or changed during the study. Throughout the time of recording, date, hour, GPS coordinates and signal quality (HDOP) raw data were collected by the GPS module and amassed into the workable databases.Exclusion criteria were dogs of less than four months of age (since they were not big enough to carry a collar), sick dogs and pregnant bitches (to avoid any risk of stress-induced miscarriages). Reasons for non-participation of eligible dogs included dog owner’s absence, dog’s absence, inability to catch the dog, and refusal of participation by the dog owner. In addition, dogs foreseen for slaughtering within the following four days were excluded in Indonesia to ensure data collection for at least four to five days. All dogs included in this study were constantly free roaming or at least part-time (day only, night only and for some hours a day). Human and/or animal ethical approval were obtained depending on the country-specific regulations. All the procedures were carried out in accordance with relevant guidelines. Ethical clearance was granted in Guatemala by the UVG’s International Animal Care and Use Committee [Protocol No. I-2018(3)] and the Community Development Councils of the two rural sites, which included Maya Q’eqchi’ communities45. In Indonesia, the study was approved by the Animal Ethics Commission of the Faculty of Veterinary Medicine, Nusa Cendana University (Protocol KEH/FKH/NPEH/2019/009). In addition, dogs that participated in the study were vaccinated against rabies and/or dewormed to acknowledge the owners for their participation in the study.Data cleaningData were stored in an application developed by Bonsai Systems compatible with Apple operating system (iOS iPhone Operating Systems), downloaded as individual csv file for each unit, and further analysed in R (version 3.6.1)46.The GPS data were cleaned based on three automatised criteria. First, the speed was calculated between any two consecutive GPS fixes, and fixes with speed of  > 20 km/h were excluded, given the implausibility of a dog running at such speed over a one-minute timespan47. It is noteworthy that car travel causes speeds over 20 km/h. However, as we were interested in analysing the dog’s behaviour outside of car transports, removing these fixes was in line with our objectives. Second, the Horizontal Dilution of Precision (HDOP), which is a measure of accuracy48 and automatically recorded by the devices for each GPS fix, was used to exclude fixes with low precision. According to Lewis et al.49, GPS fixes with HDOP higher than five were excluded, which deleted 1.3% of data in Habi, 2.2% in Pogon, 3.3% in Poptún, 1.8% in La Romana and 2.1% in Sabaneta. Third, the angles built by three consecutive fixes were calculated for each dog. When studying animals’ trajectories as their measure of movement, acute inner angles are often connected to error GPS fixes50. The fixes having the 2.5% smallest angles were excluded, to target those fixes with highest risks of being errors, while balancing against the loss of GPS fixes due to the cleaning process. With the exclusion of the smallest angles, 2.6% of data were deleted in Habi, 3% in Pogon, 2.9% in Poptún, 2.6% in La Romana and 2.7% in Sabaneta. After the automatised cleaning was concluded, 18 obvious error GPS fixes (unachievable or inexplicable locations by dogs) still prevailed in the Habi dataset and were manually removed.Habitat resource identification and calculation of terrain slopeTo analyse habitat selection of the collared FRDD, resources were delimited by a 100% Minimum Convex Polygon (MCP) including all cleaned GPS fixes per study site, using QGIS51 (Fig. 1).Figure 1GPS fixes plotted over a Google satellite imagery layer with its respective outlined computed Minimum Convex Polygon (MCP) delimitating the habitat available for the study population in: (a) Habi; (b) Pogon; (c) Poptún; (d) La Romana and (e) Sabaneta. Source QGIS (version 3.4 Madeira, http://qgis.org), map data: Google Satellite.Full size imageResources were defined by taking into consideration the following criteria: resources are (i) likely to impact upon movement patterns of dogs, (ii) identifiable by landscape satellite topography, and (iii) chosen considering information on relevant gathering places for FRDD observed by the field teams. Three resources were disclosed in all study sites: buildings, roads and vegetation coverage. All habitat relevant resources were manually identified within the available area (MCP) in QGIS using satellite imagery. All building-like structures were identified using vector polygons and summed under the layer “buildings”. Roads were identified and manually traced using vector lines in all sites, except in Poptún where the roads were automatically traced using an OpenStreetMap road layer of the area (https://www.openstreetmap.org/export). A buffer vector polygon was generated to encompass the full potential width of the roads, with a 5 m width in Habi and Poptún (semi-urban and urban site) and a 2 m width in Pogon, La Romana and Sabaneta (rural sites). In Habi, a “beach” layer was defined by generating a five-meter buffer from the shoreline in both directions using a vector polygon. The layer “sea” was defined as the vector polygon resulting from the difference between the MCP sea outer limit and the beach buffer polygon. Vegetation coverage was distinct between study sites with sparse vegetation and bushes present in all sites except Pogon, and dense forest-like vegetation present in La Romana and Pogon. These two types of vegetation were defined as “low” and “high vegetation”, respectively. In Habi and La Romana, “low” and “high vegetation”, respectively, were manually identified using vector polygons and summarised under the respective layers. Finally, open field in Habi, high vegetation in Pogon and low vegetation in Poptún, La Romana and Sabaneta were the last vector layers to be established since they represented the difference between all other polygon vector layers and the MCP total area. After all resource vector polygons had been created, an encompassing vector layer was generated by merging all resource polygon vectors for final resource classification (Fig. 2). As part of the resource classification in Habi, the airport terminal and runaway as well as waterways enclosed in the MCP area were identified but excluded from the analysis.Figure 2(a) Habi, (b) Pogon, (c) Poptún, (d) La Romana and (e) Sabaneta Habitat classification vector layers. The different habitat resources, identifiable by colour, were merged to create the comprehensive Habitat classification vector. In the Indonesian sites (a, b) and Guatemalan sites (c–e) buildings are coloured red, vegetation low in Habi, Poptún, La Romana and Sabaneta is coloured light green, vegetation high in Pogon and La Romana dark green, roads black, beach yellow, sea dark blue, airport grey, waterways light blue and open field light orange. The airport area (gray) and waterways (light blue) in Habi were not classified as separate habitat layers and were excluded from further analysis. Source QGIS (version 3.4 Madeira, http://qgis.org), map data: Google Satellite.Full size imageAfter the construction of the habitat resource layers, all GPS fixes were assigned to the respective resource they were located, using the QGIS join attributes by location algorithm. Fixes located exactly on the MCP border in Indonesia were not classified automatically and had to be manually classified to the respective resource.In non-flat topographies (all locations expect Habi) we tested the hypothesis of whether the steepness would influence the dogs’ movement patterns. The degrees of slope were calculated using a 30-m raster-cell resolution (STRM 1-Arc Second Global, downloaded from the United States Geological Survey (USGS) Earth Explorer, https://earthexplorer.usgs.gov/). The slope was assigned by the QGIS join attributes by location algorithm to each GPS fix.Statistical analysisTo quantify habitat selection in each study site, we compared resources used by the dogs with the resources available, according to Freitas et al.52. Adapting the methodology applied by O’Neill et al.18, the observed number of GPS fixes for each dog was used to generate an equivalent number of locations that were randomly distributed within the MCP area using the Random points in layer bound vector tool from QGIS. For example, if dog “D300” had 100 recorded GPS fixes, 100 random points were generated within the MCP of the respective study site and assigned to “D300”. Random points were then assigned to the respective resources and slope of that location, as previously done with the observed GPS fixes. Using this approach, the habitat resources used by each dog could be compared to the available resources in the respective study site, using a regression model.Observation independence is a fundamental presupposition of any regression model. However, the spatial nature of the point-referenced data permits perception of spatial dependence. In our dataset, spatial autocorrelation was proven for all study sites using the Moran’s I test. Therefore, we applied a spatial regression model, which takes into consideration spatial autocorrelation while exploring the effects of the study variables. A mixed effects logistic regression model accounting for spatial autocorrelation was created to quantify the effect of variables on used (i.e. observed GPS fix) versus available (i.e. randomly generated GPS fixes) resources, using the fitme function in the spaMM package in R53,54. The model’s binary outcome variable was defined as either observed (1) or random (0) GPS fix, i.e. the dog being present or absent from a position. The explanatory variable was the resource classification with “buildings”, “roads”, “low vegetation”, “beach”, “sea” and “open field” as levels in Habi; “buildings”, “roads” and “high vegetation” in Pogon; “buildings”, “roads”, “low vegetation” in Poptún and Sabaneta; and “buildings”, “roads”, and “high” and “low vegetation” in La Romana. Different habitat resources were used interchangeably as reference level. In all study sites except Habi, the slope was included as an additional explanatory variable. As observations were not evenly distributed in time, with less observations recorded towards the end of the study, a variable ”hour” was added as an additional continuous fixed effect.Each observed GPS fix was assigned to the hour of its record, with the earliest timestamp registered in each study site being assigned the hour zero. The randomly generated points were randomly assigned to an hour within the determined time continuum of the observed GPS fixes. As our focus was investigating habitat selection at a population-level, we assumed there was no within-dog autocorrelation (space/time) and each dog was independent and exhibited no group behaviour38. Still, to partially account for spatial autocorrelation of each dog’s household, the random effects included in models were defined as each dog’s household geographical location recorded during fieldwork by a GPS device. The restricted maximum likelihood (REML) through Laplace approximations, which can be applied to models with non-Gaussian random effects55, and the Matérn correlation function were used to fit the spatial models with the Matérn family dispersion parameter ν, indicator of strength of decay in the spatial effect, was set at 0.554. More

Study area descriptionYangtze River Basin is situated in central China (Fig. 1). Its geographical coordinates are between 30° 48′ 30″–31° 02′ 30″ N and 112° 48′ 45″–113° 03′ 45″ E. Taizishan is located in the transition zone between the north and south of China, with an altitude of 403–467.4 m. It belongs to the subtropical monsoon humid climate zone and has obvious karst landforms. The farm area is 7576 hectares, the forest coverage rate is 82.0%, and the vegetation is mainly Masson pine, fir, and various broad-leaved tree species. Increased forest coverage reduces sediment production30. The soil is mainly viscous yellow–brown soil and loess parent material. Rain is concentrated in summer, with an average annual rainfall of 1094.6 mm and an average annual temperature of 16.4 °C. Rainfall-related flood risk increased in the Yangtze River Delta in recent years31.The study was based in a Pinus massoniana forest in the Taizishan forest farm of Hubei Province. The Pinus massoniana (Masson pine) is a common species distributed in Central China.Figure 1Geographic location of the study area. Maps were generated using ArcGIS 10.8 for Desktop (http://www.esri.com/software/arcgis/arcgis-for-desktop).Full size imageExperiment designWe chose the Pinus massoniana forest with 47a in the study area as the research object. In the typical Pinus massoniana forest, the separate layers of litter (semi-decomposed and non-decomposed layers) were collected from several 1 m × 1 m quadrat and placed in grid bags. The litter of the semi-decomposed layer have no complete outline, and the color was brown. As the litter leaves of the completely decomposed layer are powdery and are combined with the soil layer, this layer is difficult to collect. Before testing, it was necessary to clean the soil off the pine needles and then allow the litter to dry naturally. The characteristics of the semi-decomposed and non-decomposed litter layers are shown in Table 1. The soil samples need to be dried and screened by 10 mm. When filling the soil trough, every 0.1 m of soil thickness was one layer, for a total of four layers (0.4 m). The characteristics by soil particle sizes are different (Fig. 2). The soil samples were dried naturally, crushed, and then sieved. The soil trough (2 m long, 0.5 m wide and 0.5 m deep) was filled to have a bulk density of 1.53 g·m−3. In this process, an appropriate amount of water was sprinkled on the surface of each soil layer to achieve a soil moisture content consistent with the surrounding, undisturbed, or natural, state. The simulation experiment was conducted in the Jiufeng rainfall laboratory at Beijing Forestry University, China. We used a rainfall simulation system (QYJY-503T, Qingyuan Measurement Technology, Xi’an, China) used a rotary downward spray nozzle. The system is able to simulate a wide range of rainfall intensities (10 to 300 mm h−1) using various water pressure and nozzle sizes controlled by a computer system.Table 1 Characteristics of the non-decomposed and semi-decomposed layers of Pinus massoniana litter.Full size tableFigure 2Soil particle composition of study area soil layers.Full size imageAccording to the results of the field forest investigation, the litter was covered with the experimental treatments shown in Table 2. The treatments mass coverage of non-decomposed litter layer was named as follows: N1 denoted litter mass coverage 0 g·m−2, N2 was ‘the non-decomposed litter mass coverage 100 g·m−2’, N3 was ‘the non-decomposed litter mass coverage 200 g·m−2’, and N4 was ‘the non-decomposed litter mass coverage 400 g·m−2’, N5 was ‘the semi-decomposed litter mass coverage 100 g·m−2’, N6 was ‘the non-decomposed litter mass coverage 100 g·m−2 and the semi-decomposed litter mass coverage 100 g·m−2’, N7 was ‘the non-decomposed litter mass coverage 200 g·m−2 and the semi-decomposed litter mass coverage 100 g·m−2’. N2, N3 and N4 were the undissolved state of litter layer, and N4 (non-decomposed state, ND), N7 (initial stage of litter decomposition, ID), N6 (middle stage of litter decomposition, MD) and N5 (final stage of litter decomposition, FD) respectively represent different stages of litter decomposition.Table 2 The experimental design of this study.Full size tableAccording to the rainfall in the Taizishan area of Hubei Province, erosive rainfall and extreme rainstorms were selected as the research conditions. Summer rainfall events occur mainly in the summer in this area, and a rainfall intensity of 60 mm·h−1 was the most common erosive rainfall intensity. Under extreme weather conditions, the rainfall intensity can reach up to 120 mm·h−1. Our experiments were conducted with 60 and 120 mm·h−1 rain intensities with a rainfall that lasted 1 h. According to the field investigation data of forest land, this area is a low mountain and hilly area with a slope mostly between 5° and 10°. Therefore, 5° and 10° were selected for the slope treatments in this study. The combination of slope and rainfall intensity was named as follows: T1 denoted ‘Slope 5° and rainfall intensity 60 mm·h−1’, T2 was ‘Slope 10° and rainfall intensity 60 mm·h−1’, T3 was ‘Slope 5° and rainfall intensity 120 mm·h−1’, and T4 was ‘Slope 10° and rainfall intensity 120 mm·h−1’. With two rainfall intensities, two slopes, seven litter coverage gradient and two repetitions combined, this study had a total of 56 rainfall events.Experimental procedureBefore the test, the soil samples were wetted for 10 h and then drained for 2 h to eliminate the effect of the initial soil moisture on the soil detachment measurement. When the simulated rainfall started, all the runoff and sediment produced from plot were collected every 5 min in the first 10 min, and then collected once every 10 min during the subsequent 50 min. At the same time, runoff velocity, depth and temperature were measured and vernier calliper (accuracy 0.02 mm) respectively.The overland flow velocity was measured using dying method (KMnO4 solution)32. After judging the flow pattern, we confirmed the correction coefficient K value (in laminar flow state, K = 0.67; transition flow state, K = 0.70; turbulent flow state, K = 0.8). The average velocity of overland flow was obtained by multiplying the correction coefficient K and the instantaneous velocity. Runoff depth was measured using vernier calliper (accuracy 0.02 mm). Runoff temperature was measured using thermometer. When the rainfall experiment finished, the collected runoff samples were measured volumetric cylinder and then settled for at least 12 h. The clear water was decanted, and the samples were put into an oven to dry for 24 h under 105 °C. The sediment sample was dried and weighed with an electronic scale.Calculation of hydrodynamic parametersOverland flow has the characteristics of a thin water layer, large fluctuations of the underlying surface, and unstable flow velocity. At present, most scholars use open-channel flow theory to study overland flow33,34. In open-channel flow theory, the Reynold’s number (Re), Froude constant (Fr), flow index (m), resistance coefficient (f), and soil separation rate (({D}_{r})) are the basic parameters of overland flow dynamics, through Reynold’s number (Re), Froude constant (Fr), flow index (m) can distinguish flow patterns. Re is calculated as:$$Re=Rcdot V/nu ,$$where Re is the Reynolds number of the water flow, which is dimensionless, and can be used to judge the flow state of overland flow. When Re ≤ 500, the flow pattern is laminar; when 500   5000, the flow pattern is turbulent. R is the hydraulic radius (m), which is generally replaced by flow depth as measured by a vernier calliper (accuracy 0.02 mm). (V) is the average velocity (m·s−1); (nu) is the kinematic viscosity coefficient (m2·s−1), and the calculation formula is (nu) = 0.01775·10−4·(1 + 0.0337 t + 0.00021 t2), where t is the test overland flow temperature35.Fr is the Froude constant, which is the ratio of the inertial force to gravity and can be used to distinguish overland flow as rapid flow, slow flow, or critical flow. When Fr  1, the fluid is rapid flow.Fr is calculated as:$$Fr=V/sqrt{gcdot R},$$where (Fr) is the Froude constant of the water flow, which is dimensionless; (V) is the average velocity (m·s−1); g is the acceleration of gravity and has a constant value of 9.8 m·s−2; R is a hydraulic radius (m), and is generally replaced by flow depth as measured by a vernier calliper (accuracy 0.02 mm).Regression fitting is made for runoff depth (h) and single width flow (Q). The runoff depth equation for slope is as follows:$$h=k{q}^{m},$$where q is the single width flow (L·m−1·s−1); h is the depth of water on the slope (m); and m is the flow index, which reflects the turbulent characteristics of the flow state. The larger m is, the more energy the flow consumes in the work of resistance. The comprehensive index (k) reflects the characteristics of the underlying surface and the water viscosity of the slope flow. The larger k is, the stronger the surface material of the slope works on the flow.The resistance of overland flow reflects the inhibition effect of different underlying surface conditions on the velocity of overland flow. The Darcy–Weisbach formula is widely used in research because of its two advantages: applicability and dimensionlessness under laminar and turbulent flow conditions36,37.The resistance coefficient (f) is calculated as follows:$$f=8cdot gcdot Rcdot J/{V}^{2},$$where the resistance coefficient f has no dimension; g is the acceleration of gravity and is always 9.8 m·s−2; R is a hydraulic radius (m), generally replaced by flow depth measured by a vernier calliper (accuracy 0.02 mm); (V) is the average velocity (m·s−1); and J is the hydraulic gradient, which can be converted by the gradient in a uniform flow state and is generally replaced by the sine value of the gradient.Shear stress ((tau)) is the main driving force that affects the stripping of soil particles from the surface soil38. Shear stress is calculated as:$$tau =rcdot gcdot Rcdot J,$$where (tau) is the shear force of runoff (Pa); and r is the density of water and sediment concentration flow (kg·m−3). This study used a muddy water mass and volume ratio in the unseparated state to calculate the density of water and sediment concentration flow.Flow power (W) is the runoff power per unit area of water and refers to the power consumed by the weight of water acting on the riverbed surface to transport runoff and sediment. W is calculated as:$$W=tau cdot V,$$where W is the flow power (N·m−1·s−1); and (tau) is the shear force of runoff (Pa).Soil separation rate (({D}_{r})) refers to the quality of soil in which soil particles are separated from the soil per unit time. The calculation formula is as follows:$${D}_{r}={W}_{d}-{W}_{w}/tcdot A,$$where ({D}_{r}) is the rate of soil separation (kg·m−2·s−1); ({W}_{w}) is the dry weight of soil before the test; ({W}_{d}) is the dry weight of soil after the test, measured by the drying method (kg); t is the scouring time (s); and A is the surface area of the soil sample (m2). More