dataesr/scientific-paragraphs-categorization huggingface dataset:
A common limitation is related to the availability of comparable time series of soil datasets or the existence and availability of biodiversity data with relevant thematic, temporal and spatial extent. Nonetheless, the use of trend analysis corresponds to one of the most valuable tools to identify the determinants of change. Examples of this can be seen through literature (see Further reading for references) using long time series to illustrate the effects of policies, land management, forest fires, amongst others. Figure  2  presents an illustration of a time series of land cover and land use change for a montado landscape in the South of Portugal from which it is possible to calculate long term trends. Such data is of critical importance for understanding changes in ES over time as a result of changes in management and policy implementation.
Issues with data quality for dynamic assessments Ecological modelling and particularly process-based ecological modelling, depend on a vast array of ecological, biophysical and anthropogenic datasets to generate relevant results. Although in recent years, earth observation systems have evolved to the point of delivering continuous (temporally and spatially) data for particular ecosystem components (e. g. forest change and extent, tree density, elevation, human density, economic characteristics, precipitation, etc. ), many of these lack the ability to be compared or used in a modelling environment due to different resolutions and/or methods/sensors. Additionally, there is a clear mismatch between the publication date of the variables to be used in a given assessment (e. g. LUCAS soil data from 2009) and the reference date for the assessment itself (for example using vegetation data from 2016 to assess the effect of soil erosion prevention without considering the 7 years' difference between these datasets).
In several cases, if any modelling approach is to be implemented, these mismatches cannot be simply overcome and often error propagation assessments should be implemented to minimise unwanted effects. Independently of the problems or potential caveats related to particular datasets, the temporal resolution (i. e. the amplitude and frequency of data collection) of a given dataset is an important determining factor for dataset selection in trend analysis. Therefore, future ecosystem service supply studies should include the effects of data quality on their results as it can produce important biases in the overall interpretation and decision-making support.
Different packages and stand-alone software exist to implement these techniques; examples include packages available within the software R, or stand-alone modelling tools such as Maxent or DIVA-GIS. The results of these models are then imported to GIS software to display maps of probability of species occurrence across the landscape of interest. Suitability maps for insect pollinators, regardless of the approach adopted to obtain them, can be interpreted as supply (potential services). Mapping the demand requires information on where crops that need pollination are grown in combination with information on crop dependency on insect pollination. Information on the pollinator-dependency can be obtained through literature and expert knowledge. Crop location, on the other hand, can be obtained through a variety of resources.
Examples of these resources are regional statistics on agricultural land and production, online databases, field samples and models (for instance when looking at fu-ture potential crop distribution). The choice often depends on the extent of the area (e. g. regional vs. national vs. global data), on the crop type (e. g. perennial vs. annual) and on the agricultural practice (e. g. rotational agriculture). Mapping the use can be based on overlay of supply (i. e. the habitat suitability) and demand (i. e. the crop distribution) or based on modelling the impact on yield in the absence of pollination.
Values ascribed to CES can be identified, for example, using the presence of certain products of an area, visible manifestations of CES in the physical landscape, or the number of studies, artistic representations etc. about an ecosystem as proxies. Spatial datasets giving location to certain socially or culturally normative values of the environment (e. g. inventoried cultural heritage or valuable landscapes; green areas of sufficient size and location) can also be used as indicators of areas providing CES. However, if a more detailed and precise picture of CES is to be gained in a specific area, local people must be involved in mapping. Thus, mapping CES is inherently participatory if it is to be done properly.
Bequest Willingness to preserve plants, animals, ecosystems, land-/ seascapes for the experience and use of future generations; moral / ethical perspective or belief. As a result, mapping CES capacity and demand are interwoven. What is considered as potential capacity of an area is dependent on what brings people well-being and what people perceive they need and value in terms of CES. This needs to be understood first. When there is knowledge of this, different datasets can be used to identify where valued environments, features, species, or opportunities for specific experiences having the capacity to provide CES are located (Table 2). Mapping actual demand for CES is frequently done by using participatory mapping methods or indirectly utilising contents of social media. Participatory mapping means involving stakeholders, locals, etc.
Mapping CES using a matrixbased approach A matrix-based approach can be used as a quick and relatively easy way to map supply of or demand for CES. In its most simple form, only land cover data or similar one dataset is sufficient for this purpose. If supply is mapped, experts can be asked to score each land cover type based on its capacity to provide different CES. On the other hand, residents of the study area can be asked to do the same based on how important they personally perceive the different land cover types in terms of CES, i. e. for which land cover types they have demand. As a result, a number of scored matrices are produced in both cases. After some basic statistical operations, such as calculating variance and medians of the given scores, a result matrix is produced. This can be easily transferred to a GIS and combined with the land cover data to produce a map (see Chapter 5.
Future recommendations Given the limited number of MCES maps, there is a need to: • Adapt the current ES methodologies and frameworks that have been developed based on terrestrial ecosystems to the specificities of the marine environment. • Improve the quality and spatial resolution of data and improve data availability; advance initiatives such as the European Marine Knowledge 2020; and feed data into harmonised databases like the EMODNET 8  data portal. • Adopt a holistic view of the ES provision chain focusing on the intermediate steps (from the ES to the benefit). In particular, the valuation of regulating services and the ecological processes supporting provisioning and cultural services should be reinforced. • Communicate the uncertainties in MCES maps. Explain how much of the spatial detail shown on maps is reliable.
Box1. Bridging spatial scales In RegioPower, we focussed on exploring regional biomass provisioning capacities and focus here on the service "timber production". Measured or modelled data, as well as stakeholder experience or expert opinion can serve as the basis for the assessment of this service. We made use of forest inventory data and regional statistics (harvesting, trade) and included empirical data when no specific information could be obtained. Through normalisation, this quantitative information basis can be adjusted for trade-off analyses with other services, such as "Aesthetics" or "Carbon sequestration". Subsequently, with the help of the software GISCAME 3 , the effective capacity of providing services bundles and their balance can be assessed in a spatially explicit manner or as summary information at regional scale.
Limited data It is a general principle that wealthier countries and regions with advanced economies will have higher resolution and more accurate spatial data that can be used to map ES. There will often be readily accessible high resolution climate, topography, soil, biodiversity, land use and land cover data. Data acquisition will be at relatively low cost and data may even be available free of charge when governments commit to open data policies. In locations where there is a lack of ecosystem service data, it will be necessary to fill data gaps with alternative approaches such as remote sensing, participatory mapping, land use proxies and/or use of lower resolution global-scale datasets. Creative ways to fill data gaps are needed when ecosystem service mapping projects have limited resources to collect new data and build new models.
Remote sensing Remotely sensed satellite data can be used to fill data gaps but the data are limited by what can be captured from above the land surface. Remotely-sensed data are often used to derive spatial estimates of many ES such as food production (mapping of crops), surface and ground water (mapping of water bodies and watercourses), vegetation cover and attributes (vigour, biomass), soil condition and erosion, flood control (mapping of floodplain topography) and coastal protection (mapping of mangrove and dune systems). Land cover and land use are common datasets captured by remote sensing and these data can act as proxies for mapping the supply of ES. The land cover/land use approach to mapping ES is a common and very useful technique in the absence of detailed spatial models, data and system understanding.
Global scale data Many global scale datasets are available for mapping ES, although their usefulness will be determined by the scale of mapping required (Chapter 5. 7). Global scale data is typically of coarse resolution (from 1 km resolution and above), so mapping at local scales will often prohibit the use of global scale datasets. Global data will be more useful for mapping at national/regional/continental scales. A list of global datasets useful for mapping ES is provided in Table  1. ent summary data and maps. Figure  2  shows an example of the same data (per capita water availability for human consumption) summarised for 21 different aggregated boundaries in Asia. The choice of boundaries will have a significant impact on the visual interpretation of water shortages in Asia. Ecosystem service maps are vulnerable to MAUP where point-based data (or high-resolution raster data) is aggregated to large spatial units.
Completing metadata and blueprints for ES quantification/ mapping will provide users of the data and maps with a confidence in the pedigree and usefulness of the information. Producing metadata and blueprints as part of a mapping exercise provides a level of stan- dardisation of the data for easy inclusion in catalogues such as the ESP Visualisation Tool (ESP-VT) 1. The ESP-VT is an online web portal and catalogue for uploading, downloading and querying spatial information on ES (Chapter 7. 9). Another important cataloguing tool is the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) Catalogue of Assessments 2. The IPBES Catalogue aims to build a global database of studies of ecosystem service quantification and valuation.
Quantifying uncertainties Regardless of the scope for improvement of ecosystem service models, it is important to realise that uncertainties in ecosystem service maps cannot be completely ruled out. Sensors will never be 100 % accurate and the provision of ES is a complex and multifaceted process where multiple datasets have to be combined, always involving some kind of expert judgement. It is, therefore, important to be transparent on uncertainties in ecosystem service maps. If ES are mapped using complex process-based models (Tier 3 approaches; see Chapter 5. 6. 1), an uncertainty analysis or a Monte Carlo approach can be used. In a Monte Carlo approach, the indicator is calculated several thousand times. Each time, actual input values for calculation are drawn from a probability distribution of each input, resulting in different but realistic representations of the indicator.
This was undertaken by using several national and municipal spatial datasets. At the final stage, the most recent available aerial photographs were used to check the validity of the digitised features. The final outcome of the spatial representation of the GI typology in Järvenpää is presented in Figure  1. spatial patterns of urban ES indicators and their performance 1. GI 1 (http://www. eea. europa. eu/data-and-maps/data/ urban-atlas). Urban ES values for carbon storage and recreation services for the 20 different Urban Atlas land cover classes were derived from empirical studies. For the assessment of recreation, the per capita green space in 63 districts of Leipzig was used as proxy. Population data reflect the district population in 2014. The results are urban ES performance maps based on the different land cover classes.
The Ecosystem Services Partnership Visualisation Tool (ESP-VT) The ESP-VT is an online platform available through esp-mapping. net that systematically organises ES maps and makes them available for the ES community. The ESP-VT consists of: a) a database where all maps and metadata are stored and b) a map and data viewer which is the user interface. The database is structured using an adapted version of the ES mapping blueprint, developed in 2013 as a first attempt to create a checklist for ES maps and models. The database systematically organises the ES maps metadata and the contextual background of the ES maps (e. g. purpose of the study, focal biomes, ES mapped). The ES data are currently organised following the TEEB classification system (see Chapter 2. 4). Within the map and data viewer, the users can: i) search the database for available ES maps and data; ii) view and access maps and associated metadata within the viewer and iii) download the maps or data of interest.
ESP-VT uses and users The ESP-VT is currently used by ES researchers to publish and share ES maps and associated metadata. ES maps resulting from initiatives and projects such as EU MAES 7 or ESMERALDA 8 will be published through the platform. ESP-VT is also planned to store and visual- ESP-VT also serves as an ES map repository that allows researchers to search for relevant ES mapping efforts, methodologies and data used. In the future, with more functionalities added to the ESP-VT, users will be able to perform spatial queries and/or analysis within the maps stored in the database. The ESP-VT is designed to go beyond being a tool just for the scientific community. It can be easily used by practitioners, urban planners and the general public who might require information on how ecosystem benefits are distributed in an area of interest.
EnviroAtlas incorporates many data sources with multi-resolution (i. e. , 1 m and 30 m) land cover data providing fundamental information. The data are updated at 5 year increments, subsequent to US National Land Cover Dataset updates. 6 https://epa. gov/enviroatlas This map shows the kind of data layers that are available in EnviroAtlas. For one of the indicators in the climate stabilisation category, this map shows the amount of carbon stored in the aboveground tree biomass. Like most of the national maps in EnviroAtlas, the data are summarised by medium sized watershed drainage basins known as 12-digit hydrological unit codes (HUCS).
major challenges faced during the ESP-VT development were: a) the heterogeneity among ES mapping approaches; b) increased complexity of the ESP-VT as new functionalities were included. a. The heterogeneity among ES mapping approaches is an aftermath of a plurality in ES classification systems, tools and methods used to produce ES maps, units and visualisation methods. This is related to the different purposes for which ES maps were constructed: to answer different questions; for different users, like ES practitioners, policy makers or the general public (see Chapter 7). Increased complexity of the ESP-VT as new functionalities were included. The database of the ESP-VT is populated with ES maps by ES map-makers. Its contribution to information-sharing is therefore based on the willingness of researchers to share their outputs with the ES community of practice.
To construct a choropleth map, the data for each unit is aggregated into one value. According to their values, the areal units are typically grouped into classes and a colour is assigned to each class. Conservation: The protection, improvement and sustainable use of natural resources for present and future generations. It is used to define the positions of the mapped phenomena in space. Furthermore, it acts as a key to combine and integrate different datasets based on their location. Cost-Benefit Analysis: A technique designed to determine the economic feasibility of a project or plan by quantifying its economic costs and benefits [MA (2005)]. Cultural Ecosystem Service (CES): All the non-material and normally non-consumptive outputs of ecosystems that affect physical and mental states of people.
The notion of stocks and flows pay for clean water). Each of these different at very small spatial scales (such as pollina-ties arising from spatial data handling (dif-The first question is addressed in the scien- with the help of landscape metrics serves society for maps and mapping and thus for is crucial for accounting purposes. The size steps can be quantified using biophysical, tion or biological control). ferent sections under 5. 7). As more efforts tific literature by developing and proposing as a valuable method for communicating mapping ecosystem services as well. In par-of the stock is not necessarily related to the economic or social valuation methods. and research are focused on these areas, it indicators. Ecosystem service indicators are ES-related issues. With regard to the current ticular, there is a demand from policy-mak-magnitude of ES flows, so this challenge A specific challenge is related to the trans-dis-seems likely that datasets generated at dif-used to monitor the state or trends of ecosys- application for ES mapping and assessment ers to map ES (see Chapter 7.
Indirect measurements of ES measure stocks or flows of ES but they are highly useful to quantify global climate reg- ulation as well as all those ES which depend Indirect measurements of ES deliver a bio- directly on the vegetation biomass of ecosys- physical value in physical units but this value tems to regulate or mediate the environment. needs further interpretation, certain assump- Soil protection and water regulation, for ex- tions or data processing, or it needs to be ample, are strongly driven by the presence of combined in a model with other sources of vegetation which can be inferred from earth environmental information before it can be observation datasets. Local climate regulation used to measure an ecosystem service. Indi- can be inferred from spatially and temporally rect measurements of ES deliver a biophysical explicit patterns of surface temperature.
2). ES poten- tials can, for example, give a reference point for planning and scenarios (see Chapter 7. 2). Both the individual patches' land use and land cover and the configuration and ar- rangement of such patches, are important for ES supply. Therefore, the landscape structure with its mosaic of patches should be consid- ered (see Chapter 5. 2). Possible indicators: Land cover can provide an essential database for ES mapping. The CORINE land cover dataset is often used in European studies (see Chapter 3. 5). At na- tional level, land use data from land survey or habitat mapping often are available. Ad- Conditions refer to the integrity and health ditional data need to be integrated in more status of an ecosystem which determine its detailed evaluations (see Example 1). ability to generate ES (see Chapter 3.
http://www. integratedmodelling. org/ The GIS processes are relatively easy to implement, requiring only a medium level of GIS expertise. Makes use of a multitude of GIS datasets combined with both GreenFrame C, D All CES M scientific and local expert scorings. Data on harmful phe-nomena that diminish the CES potential can be included in the analysis. Both quantitative and qualitative data can be used. Gives an overall picture of the relative spatial variation of CES provision potential. Land cover / land use based map-ping C, D Recreation, aesthetics, edu-cation L Gives only a very rough proxy with high uncertainty level. Suits best for quick mapping of specific recreational or experiential activities. Social media based mapping Photo-series analysis content social media analyses of Other C, (F), D C, (F), D Physical and intellectual interactions with biota, ecosystems, and land-/ sea-scapes (including recreation, nature tourism, land-scape aesthetics, cultural heritage and education) CES potentially all cultural heritage, nature tourism, Recreation, M-H M-H It represents a cost-effective way of gathering space-and time-referenced data on observed people's preferences.
It does not directly allow for obtaining information related to the user characteristics (socio-and psycho-cultural). Inherent bias is related with the interpretation of pic-tures. The photo-sharing community may not be repre-sentative of all social groups (the represented population will be dependent on the level of access to information technology, education and age and the user's ability / willingness to correctly geo-tag the photos). Specialised social media communities can produce data on, for example, sites suitable for specific activities but which are not commonly known and do not exist in databases. Communities may not be representative of all social groups (the represented population will be dependent on the level of access to information technology, education and age, and the user's ability / willingness to correctly geo-tag locations.
Ecological processes and ES: knowledge and scale at which they operate structure, land use intensity and land man- agement is poor or lacking. A widely used ecosystem or biome map is provided by the World Wildlife Fund 12. A commonly used land cover and land use dataset is the Glob- al Land Cover (GLC) 2000 map 13 , which The knowledge of ecological and other pro- is also used in the Millennium Ecosystem cesses becomes more limited with increasing Assessment and the IMAGE and GLO- extension of the mapped area. ES that operate BIO-ES models. The TEEB Valuation based on well-known global processes, such Database uses the GlobCover dataset 14. as the hydrological or carbon cycle, are easier This dataset provides a higher-resolution to map globally. Furthermore, global maps alternative to the Global Land Cover, but are more easily generated if an ES can be ag- it also has a lower thematic accuracy.
There gregated across time or space. This is the case are also other databases available targeting for several provisioning services, such as crop, certain ecosystem or land covers, such as timber or livestock production. For these ES, the Global Lakes and Wetlands Database 15 , monetary value maps can also be prepared, as the World Database of Protected Areas 16 , their products are traded on markets. the livestock density database of the Food and Agriculture Organisation of the Unit- ES that operate locally are, however, more ed Nations (FAO) 17 and forest cover data- difficult to map globally. ES such as pest sets 18. Due to the limited data availability, control and air quality regulation are rare- the same datasets are often used for mul- ly considered globally because of the lack of tiple purposes, which can lead to autocor- generalised knowledge and the local scale relations.