Supporting the planning and management of biodiversity through the development of a geospatial decision support system

Today, the issue of biodiversity conservation is, more than ever before, one of primary importance since it has become common knowledge that biodiversity provides many services that are relevant to the sustaining of ecological integrity and, so, for the benefit of humanity. Within this framework, rural areas are particularly exposed to degradation threats and, therefore, biodiversity loss due to their lying between and interlinking with urban and natural areas. The European Commission underlines the relevance of this conservation in its Biodiversity Strategy for 2030, which identifies a series of needs for conserving biodiversity in Europe, among which is the need for more operational tools to support biodiversity management. This paper aims to demonstrate that a geoSpatial decision support system (S‐DSS), developed through focused interdisciplinary research and implemented over a Geospatial Cyberinfrastructure, might provide a powerful web‐based operational tool to encourage both the engagement of a large range of end‐users and stakeholders and the better implementation of the Habitats Directive as indicated by the Biodiversity Strategy for 2030. The platform supports data visualization and on‐the‐fly computer applications for modeling, all via a web browser. The S‐DSS tool brings together knowledge of soil and environmental sciences, biodiversity and information technology. The S‐DSS tool is demonstrated through three use cases in the Campania region (Southern Italy) from different users' perspectives. The connection of data and model aims to provide information that will improve knowledge and awareness of biodiversity. In just a few clicks, the production of maps, results and statistics provides an overview of the biodiversity over a personalized area drawn by the user. The approach is highly transferable to other areas, from other administrative regions to the whole European territory, because it is based on general algorithms that are easily applied elsewhere, providing there is the necessary data availability.


| Background: A journey from potentialities to problems
In a multitude of ways, biodiversity is key to sustaining ecological integrity for the benefit of humanity (Cardinale et al., 2012); it improves agricultural productivity (FAO, 2019) and is fundamental to safeguarding food security and healthy and nutritious diets.Therefore, it is not surprising that over half of the world's gross domestic product depends on nature and the services it provides (World Economic Forum, 2020).Within this context, the conservation of biodiversity is a key issue today since there is a common understanding that nature is important for our physical and mental well-being and for our society's ability to resist climate change, disasters and health threats.
In the EU, biodiversity conservation relies heavily on the Biodiversity Strategy for 2030, which constitutes one of the pillars of the European Green Deal and EU leadership in the field of international action to benefit global public goods and sustainable development.
This strategy identifies the following needs: (i) to extend the network of protected areas (from 26% to at least 30%); (ii) to define, map, monitor and protect all primary and ancient forests; (iii) to create ecological corridors that prevent genetic isolation, allow the migration of species and, therefore, preserve and strengthen the integrity of ecosystems; (iv) to improve awareness, knowledge, education and skills with regard issues relating to the fight against biodiversity loss, and (v) to promote healthy ecosystems, green infrastructure and naturebased solutions through appropriate conscious planning.Some difficulties in the full implementation of the EU Biodiversity Directive (e.g., a lack of cooperation between national, regional and local authorities and stakeholders) are already evident (Hermoso et al., 2022).
Basically, the stakes in the area of biodiversity are high and the above objectives may be very difficult to achieve (Rinaldi, 2021), particularly considering the failure to achieve the Aichi biodiversity targets for 2020.Some of the difficulties involve (i) inadequate planning, enforcement and governance for an effective impact to be achieved (Guidetti et al., 2008;Yates et al., 2019) and (ii) multi-stakeholder governance approaches that are inadequate to achieve environmentally sound management of biodiversity (Apostolopoulou et al., 2012;Dimitrakopoulos et al., 2010;Yates, 2018).

| The rationale: From problems to potential solutions
We believe that all the above difficulties can be partly overcome by making available operational tools that are easily usable, multi-user, completely free and that can provide concrete answers to the topics indicated above.Unfortunately, these tools are not readily available.
Here, we show how this shortcoming can be mitigated by applying interdisciplinary research to the development of operational biodiversity decision support system (DSS) tools for multi-user applications.
This approach obliges various experts (biologists, botanists, ecologists, geologists, pedologists, etc.) to not simply work together but to deliver (free web-based) operational tools to take biodiversity away from one research domain, into multiple research domains and, eventually, to final end-users.The geoSpatial DSS (S-DSS) offer greater opportunities in this context than standard GIS and WEBGIS systems applied to biodiversity.They have the advantage that they manage and analyze complex spatial data structures and provide customized and useful answers, for example, in the form of tables, reports or maps on biodiversity adapted to the specific requests of the end-user in real-time.This overcomes the limitations embedded in the simple displaying of mapping information as produced in standard WEBGIS systems.In addition, a key advantage of such interdisciplinary openaccess based S-DSS is the opportunity to implement new tools that are capable of solving specific problems using the interaction of data and models already present on the platform.Thus, the creation of future tools (for instance an upgrade of the tool presented in this work) has a low cost/benefit ratio.
In this scenario, this contribution aims to demonstrate that a new-generation interdisciplinary S-DSS based on a geospatial information infrastructure can interconnect different areas of knowledge, so providing an operational tool to support both biodiversity management (and planning) and better implementation of the EU Biodiversity Strategy for 2030.This is in line with this special LDD issue which requires papers that focus on (i) the "implementation of S-DSS to address the various sustainable land uses in different sectors and (ii) S-DSS decision support systems helps bridge the gap between scientists working on landscape research and end-users." The paper is organized into the following sections: (i) a short overview of existing S-DSS systems for biodiversity; (ii) the Materials and Methods section, including a description of the following: study site, dataset and the Landsupport platform (dashboard, etc.); (iii) the Results and Discussion section, including new data, models, the biodiversity tool and the potential transferability of the approach to new areas; (iv) use cases that illustrate how the tool supports various users, and (v) conclusions.
Here, it should be emphasized that (as in other S-DSS papers), in Materials and Methods, we deal with data that are already available (and which we only reprocess) and the IT infrastructure, while we place new original data, new models and codes and the biodiversity tool itself in the results section.
More specifically, we employed the three real use cases to demonstrate (i) the production of data, statistics and results as obtained by the tool and (ii) how the tool responds to the needs of different types of stakeholders.This tool has been developed within the EU LANDSUPPORT project and applied, for this specific contribution, to the Campania region (about 13,600 km 2 ) of Southern Italy.A detailed description of the LANDSUPPORT infrastructure is available on www.landsupport.euwhile, here, we just report the main findings and implementations relating to the biodiversity tool.
1.2 | Short overview of existing S-DSS systems for biodiversity S-DSS have been evolving since the mid-1980s (Armstrong et al., 1986), and within just a few years, research produced many contributions (Armstrong, 1993;Densham, 1991;Densham & Armstrong, 1987;Goodchild, 1993).Today, S-DSS have an important, if short, history (Geertman & Stillwell, 2009) with many successes (e.g., https://falling-walls.com/discover/videos/winner-2022-fabio-terribile/),but also many failures, often associated with the development of very complicated systems which are both hard to operate and difficult to modify.Currently, there is limited use of DSS-like systems in biodiversity conservation (e.g., Bottero et al., 2013;Jonsson et al., 2020;Poirazidis et al., 2011) while, due to their great potential, S-DSS are increasingly being used in subjects relating to biodiversity, such as (i) decision-making assistance for land use planning to support farmers' incomes after the making of costly interventions to aid biodiversity (e.g., Sturm et al., 2018) or (ii) management of forests that feature important biological diversity and valuable economical resources for local communities (e.g., Costa Freitas et al., 2019;Marano et al., 2019).
Unfortunately, the currently available tools (S-DSS and WEB-GIS)-as reported in the scientific literature-only respond partially to the numerous requests from biodiversity management (and planning) and those identified by the European Commission Biodiversity Strategy.These systems, when they exist, normally refer to specific, very small geographical areas and it is difficult to find the same conditions or to be able to transfer the systems elsewhere.

| Main environmental features of the Campania Region
Campania is a territory that presents a great variability of landscapes, land use, and spatial pedoclimatic variability (for detailed information see the Data S1).It consists of mountains (35%), hills (50%), and plains (15%), covering an area of more than 13,000 km 2 with a 350 km long coastline.Most mountains have a limestone geology and reach an altitude of 2000 m a.s.l.; there are also a few volcanoes near the coast, such as the well-known Vesuvius (1277 m) and the Phlegrean Fields.
The climate is Mediterranean along the coast and continental inland.From a geological viewpoint, Campania is closely connected to Miocene and Plio-Quaternary tectonics.
The main ways in which land is put to use in the region can be categorized as artificial surfaces (8%), forests (mostly broadleaved) and semi-natural areas (37%) and agricultural areas (55%).The soils of Campania reflect 10 soil systems (Di Gennaro et al., 2002;Mileti et al., 2017)

| Dataset
The dataset connected to the "Biodiversity Tool" and stored in the database (see Figure 1) consists of metadata and georeferenced data from various sources (Table 1).Some of these data were already available, a few required further processing and many new data had to be collected ex novo (see Section 3).The data are ordered on the basis of the following features: (i) themes (soil, geology, etc.), (ii) data type (grid data such as NDVI or vector data such as soil), (iii) spatial resolution, (iv) data details (year, source, etc.), (v) the state of the data, in the sense of whether they are already available or require additional work, and (vi) the output of each specific dataset.
The main themes include geology, land use and soil maps, biodiversity index (e.g., habitat quality), and data from official statistical surveys (ISTAT, administrative limit, etc.).
All raster data were tested for anomalies and up-scaling procedures were performed when necessary (i.e., coarser resolution data for some applications), and only then were they integrated into the database.
Vector data were also (see Table 1) tested for anomalies (e.g., outliers, missing data, correctness of spatial coordinates, etc.) and then loaded onto the database (allowing for SQL queries).
Forest Landscape Integrity (FLI) maps were produced using the Focal Statistics operator ArcGIS-Spatial Analyst (as described athttps://desktop.arcgis.com/en/arcmap/latest/tools/spatial-analysttoolbox/focal-statistics.htm), which makes it possible to calculate a statistic of the values within a specific neighborhood for each pixel of the input map (which in our case is a binary Forest layer).The calculated statistic is the mean value of the surrounding neighborhood circle which may have the following radius: 500 m (corresponding to very high integrity), 1500 m (high integrity) and 5000 m (moderate integrity).
Table 1 shows (in the "state of data" column) that 12 out of 14 data levels are already available.They include the administrative (region, province, and municipality), morphological (Digital Elevation Model), and environmental (protected areas, Natura 2000 network, biodiversity, climate, land use, soil, geology, etc.) levels.

| Sensitivity analysis
In the Cilento and Vallo di Diano National Natural Park, we performed a sensitivity analysis on the "FLI" parameter.More specifically, we analyzed 66 regions of interest (ROIs) which had a circular radius of 250 m and were positioned on a regular grid with sides that were 3 km in length.In addition, we sampled 484 points within each ROI so as to better understand the internal variability of the "FLI" parameter (hFLI).

| The geospatial cyberinfrastructure
The biodiversity Toolbox is located on the LANDSUPPORT dashboard, along with many other tools relating to the environment, agriculture and forestry.The system is built on a geospatial information infrastructure platform (e.g., Yang et al., 2010) which allows the acquisition of static (e.g., Natura 2000) and dynamic data (e.g., temperature and rainfall), their archiving and their management as on-the-fly data elaboration and visualization.
LANDSUPPORT has a 3-tier architecture (as is depicted in Figure 1 and reported in Terribile et al. (2023)): (i) database, (ii) application server, and (iii) graphical user interface (GUI), and is used, as already mentioned, to support many other tools (Bancheri et al., 2022).
The database consists of vector and raster data types (Table 1).
Vector data have three major types of geometries, points, lines and polygons, and are stored in PostgreSQL (open-source license database) and managed (both non-spatial and spatial data) through the PostGIS extension.Raster data, typically composed of pixel arrays (with continuous or discrete values), are managed through the Rasdaman database (Baumann et al., 2021), which allows storage, management and recovery of very large multidimensional arrays.
The data are processed through both static and dynamic models (described below in Section 3.2 and Table 2) to produce different types of output such as pdf reports, tables and interactive maps, and informative Html popups.The graphical interface allows interaction between the user and the tool.

| The dashboard
The layout of the Landsupport dashboard (GUI) is shown in Figure 2 and includes graphical tools, territorial data aggregation processes (visualization and analysis) and the creation of maps and tables that are intuitive and easily navigable.The dashboard comprises three sections: (from right to left) the "analysis tools" box, the "map" box and the "data viewer" box.The "data viewer" section contains the "Layers" sheet (Figure 2a), which allows the user to navigate through the different maps, the "Legend" sheet viewer (Figure 2b), and the "Preferences" sheet (Figure 2c), allowing the user to obtain metadata and change the display of layers.The central "Maps" box (Figure 2d) allows the user to view the maps that he/she has selected from the "layers" sheet.There are two activating buttons for analysis: the "toolbox" (Figure 2e), which allows the user to navigate to other operating tools in the LANDSUPPORT family and to the "results" tab (Figure 2f), where users can find their stored results obtained from the platform (including model type, tool scale and processing status).
In addition, there are some instruments, toward the top section of the dashboard, such as "draw polygon," which enables the user to draw a customized ROI, save it within a public (or personal) space and use it as a biodiversity tool (Figure 2h).The ROI can also be deleted or modified.
Finally, on the lower right-hand side of the GUI, the user will find the biodiversity tool (Figure 2g), which is described below.

| New data (produced for the development of the tool)
The biodiversity tool required both the creation and harmonization of numerous data.Specifically, we classified the data into two main categories: (i) those already available and (ii) new data (Table 1).
The "already available data" were described in Section 2 and are shown in gray in Table 1 while new data are reported here as results from this specific work.The first implementation of new data was in the FLI index map (see Section 2.4).This theme was required to attempt a preliminary quantification of the potential impact of the pressures exerted by urban and agricultural land use on the spatial integrity of forest territories (Cordeiro et al., 2015;Hanski, 2015).To achieve this goal, we represented the FLI index on three separate maps: (i) very high integrity, (ii) high integrity, and (iii) moderate integrity.On all three maps The underlying assumption is that high forest cover integrity may be used as a proxy for the integrity of the biodiversity (Cordeiro et al., 2015).This layer is of particular interest in the planning process (as requested for the biodiversity strategy), such as in the planning of new infrastructure (e.g., forest playgrounds and picnic sites) or other areas which need to be delimited.In such a framework, for instance, the tool allows the identification and quantification of those forest lands with a high integrity value as well as those forest areas that have very low integrity (e.g., forest areas intersected by numerous roads).
In the planning procedure, this classification is important because the two types of forest lands typically require very different planning and management.The system does not suggest a decision, but rather it provides a quantitative objective knowledge basis on which to make an informed decision.
Considering the importance of climate variables for biodiversity, we here report new climatic data obtained after specific new processing in which climatic data were obtained through the Copernicus UERRA reanalysis of data with a native spatial resolution of 5.5 Â 5.5 km.However, since this resolution was not suitable for the aims of the tool, it was improved through a downscaling procedure that employed evidence of the high correlation index between temperature and elevation (DEM).The correlation index between climatic (average, minimum and maximum temperatures) and elevation data were thus estimated for each day of the average year (calculated over the period 2008-2018).The R 2 Pearson correlation index calculated for every day of the year was always very high-for example, the average R 2 for the maximum, average and minimum temperatures, considering 366 days, was 0.82, 0.87, and 0.80, respectively.These data, obtained from the UERRA general climate model, were further corrected (by linear equation) using data measured from 37 ground weather stations in Campania.The correct parameters (intercept and slope) relating to the 366 linear equations (which describe the final relationship between the UERRA model and elevation) have been stored in the database.

| Models
New codes and models were required to produce the biodiversity tool.That is, the biodiversity tool permits several processing procedures to be carried out over the ROI selected by the user.These procedures range from simple map visualization to geospatial operations that allow complex elaborations to be produced after applying specific models.
Hence, implementation of the biodiversity tool involved writing codes for new models or adapting and recompiling existing program codes to fit our Landsupport system.
The models the biodiversity tool relies upon (described in Table 2) can be defined on the basis of the specific theme to be covered, the model functionality and the operating mode (e.g., on-the-fly or offline mode), the required input and finally, the main outputs.Two types of organized models can be classified on the basis of their operating mode: 1. Static models using offline processing: one example of these relates to climatic data (see Section 3.1).For instance, for the case of temperature (average, minimum and maximum), we have precalculated daily-based linear equations parameters.These data are stored in a database (Postgres/PostGIS table) and retrieved as soon as it is required.
2. Dynamic models using on-the-fly calculations over static data: this is the most commonly used model type for the biodiversity tool.

This model type includes the generation of dynamic pdf reports
showing basic statistics of these static data.The nature of these data does not allow them to be pre-calculated because the data will depend on the specific ROI selected within the user's inquiry.
Thus, zonal statistics (min, max, mean, standard deviation, etc.) and clip-on-the-fly models are needed.The climate pdf table is also produced on the basis of this reasoning, starting from the precalculated data discussed above.

| The biodiversity tool
In line with this LDD Special Issue, we here report the main result of this work, that is, the biodiversity tool.This specific tool produces a multidisciplinary environmental description of the area (polygon) selected by the user (ROI), the result of wide interaction with professional end-users and stakeholders including the Italian Institute for Environmental Protection and Research (ISPRA), the Italian Regional Environmental Protection Agency (ARPA), natural parks authorities and tourism agencies.Basically, the user (i) draws and saves their ROI and (ii) clicks on the "biodiversity" icon (Figure 2g) through the model requester (Figure 2h).This operation starts a process that generates a multidisciplinary report (real-time, automatically made on the basis of the request made by the user and pdf file) including the following sections: the abundance and type of each species and provides a link to additional information available on the EEA website.9. "In-depth analysis of protected areas (Parks and reserves)": this section consists of a table and a map where national and regional parks and nature reserves are shown.
10. "In-depth analysis-FLI": in-depth section made up of three tables and three maps.Each table presents the main statistics describing the quantitative occurrence of very high, high, and moderate forest integrity (see Sections 2 and 3.1).The three maps show the presence of such information layers in the ROI.

| Transferability of the approach
One of the main aspects of this work is the easy transferability of the approach.Basically, the tool has been created with flexibility as a necessary aspect of the approach.This aspect opens the tool to further implementations of both models and databases, as well as enabling easy adaptation to new locations and, thus, giving a high transferability value.More specifically, the database structure and the connected models allow the tool to be transferred to other territories and scales, provided specific databases, as shown in Table 1, are made available.
The new data can then be processed using the already-formed models (Table 2), thus greatly lowering the cost of exporting the biodiversity tool to a new area.

| National monitoring reporting
For over a decade, ISPRA has played a central role in reporting, on behalf of the Environmental Ministry (today, the Ministry of Environment and Energy Security) and in close contact with the Italian Regional authorities, on the conservation status of flora and fauna species and habitats of Community importance, as requested by art.17 of the 92/43/EC Habitats Directive.To produce this report, ISPRA requests data from every Italian Regional Environmental Protection Agency (ARPA).ISPRA's report focuses on a range of aspects, including pressure and threats affecting plant and animal species and habitats of community importance.In this use case, the biodiversity tool was applied to the Natura 2000 Sites in the Campania region, simulating how ARPA Campania might use it to evaluate the state of the land degradation threat.In the example below, the user applies the biodiversity tool to a specific area of interest (such as the one shown in Figure 3a) in Campania.Through the tool's "model request" (Figure 3b), the user submits the ROI and automatically obtains the statistics for any Natura 2000 site intersected by the ROI, that is, name, type, code and link to the original database from the European Environmental Agency.In this case (Figure 3c), the results indicate that the chosen ROI falls in an area located between the "Vesuvius and Monte Somma" SPA (code: IT8030037) and the "Vesuvius" SCI Site (code: IT8030036).
Besides this, the user may also obtain detailed information about the current state of land degradation in the ROI (Figure 3d).In our The biodiversity tool also allows the table (Figure 3e) showing land degradation statistics for every Natura 2000 site to be downloaded; thus, making it possible to compare the ROI with each and every Natura 2000 site directly.In our case, both Natura 2000 sites are affected by strong degradation dynamics.More specifically, land degradation is 31% at the "Vesuvius and Monte Somma" (SPA) and 43% at the "Vesuvius" (SCI Area) surface areas.
The tool also allows, in one click, analysis of more aggregated data, such as that for land degradation in 87 of the Natura 2000 sites in Campania.In this case, results show that 42,000 hectares (8.7% of the sample analyzed) are affected by degradation dynamics; 33,000 hectares are improving (6.6%) while 43,000 hectares remain stable (84%).Here, we performed a more detailed analysis to evaluate the level of forest integrity (Forest Landscape High Integrity also named hFLI) in the National Park.Indeed, this parameter greatly affects both the planning and management of forested areas and is influenced by urban and agricultural land use pressures.To better quantify this issue, we performed a sensitivity analysis on the hFLI parameter, as obtained by the tool.
Figure 4a shows the hFLI map of the forested areas of the natural park; the map displays 66 regularly distributed circular ROIs. Figure 4b shows the results from sensitivity analysis reported as a box plot diagram.
The diagram shows the complexity of this forest fragmentation issue in the natural park.We can identify three types of areas: 1 This large variability of hFLI is also reflected at the level of the municipalities in the natural park (Figure 4c).The lowest forest integrity values (municipal average of hFLI) are found on the coast.This is connected with the soil sealing that affects tourist sites and generates patchy urban settlements.Forest integrity, with few exceptions, is best preserved in the central part of the natural park.The most virtuous municipalities are Auletta and Petina.Further information is available in the Table S1).

| Preparation of outdoor activities for schools or local ecotourism agencies
This case refers to the provision of easy, wide dissemination of information on biodiversity to local communities.To offer students or ecotourists, hands-on experience of local biodiversity, schools or tourism operators need to be able to find detailed, organized environmental and biodiversity information quickly.In the case presented here, a high school aims to organize an educational trip to a rural area of the municipality of Agerola (province of Napoli).The teacher planning the excursion wishes to evaluate the environmental context (soils, land use, protected areas, flora and fauna) to assess the area's suitability for the pupils.In addition, the teacher needs to find detailed environmental information as preparatory material to plan the excursion.
On the platform, the teacher, through the model query, can find their ROI (Figure 3j,k) in real-time.Then, in the results section, the biodiversity tool will produce the pdf report for viewing or downloading.The teacher will be able to improve their knowledge, for example of the soils, using maps and detailed tables (Figure 3l,m).Moreover, soil type and properties, as specified in Section 3.3, are only two of the many fields covered in the pdf report (see Data S2).
The three use cases illustrate the great potential of our system.Without using the system, substantial work (e.g., use of complex GIS software) and expertise (e.g., technical experts or scientists) is needed to retrieve large datasets and carry out appropriate data processing to assess the state of biodiversity, the environment and land degradation over time.The biodiversity tool allows anyone to retrieve the above information, for any area of interest, in just a few clicks and without the need to install and operate space-taking, user-unfriendly software.
This makes the biodiversity tool suitable for a large users, ranging from a political decision-maker to the average citizen.Moreover, due to its ease of use, the tool may be used effectively to popularize and disseminate knowledge on local biodiversity around schools or among ecotourists, thereby improving awareness of the importance of preserving the environment and its resources as highlighted by the 2030 Biodiversity Strategy.

| CONCLUSIONS
Despite the well-known evidence that fulfilling the goals of the Habitats Directive and the 2030 Biodiversity Strategy is crucial, also in terms of increasing awareness and knowledge of biodiversity heritage, there is a concerning scarcity of operational tools that help in achieving these goals.While several WEBGIS are available, this is the first S-DSS to the best of our knowledge, which, in addition to the many outputs, produces customized on-the-fly report statistics based on user needs.
In this context, we show that S-DSS developed in geospatial cyberinfrastructure could be very useful and effective.Here, we have demonstrated, through the results and statistics produced from the three real use cases, that such a structured system is easy to use, interconnects environmental data and also improves their of what is normally hard-to-find information that enables the planner to orientate himself and, thus, make an informed decision based on official databases.We have also shown (through the third case study) that the tool may well have an impact on biodiversity awareness, which is a key point of the Biodiversity Strategy for 2030.In our view, this is the way forward to empower both territorial biodiversity management and environmental awareness, especially in rural areas given that they are most vulnerable.In addition, the system enables easy implementation of new products (especially data, models, etc.) to further empower the system.
The tool presented here was implemented for the entire Campania region, yet, the consolidated IT infrastructure and the high level of generalization of the models used make it applicable to other territories.A similar approach may, in fact, be applied to the entire EU territory where similar datasets exist.
Despite the many positive aspects, there are still some critical issues to be addressed, including: 1.The need to have the system used more widely: it would be important to invest more resources in disseminating the system among institutions that deal with the environment (for example ARPA), park authorities (at national, regional and local levels), environmental associations, schools and tourist agencies.
2. The need to equip the tool with additional features, such as further integration with socio-economic data or other locally relevant datasets.
3. Considering the high dissemination and educational potential of the tool, it would help to implement a navigation mode for smartphones which would allow a user to get access to relevant information while in the field.

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I G U R E 1 Geospatial cyberinfrastructure platform scheme.[Colour figure can be viewed at wileyonlinelibrary.com]T A B L E 1 All data employed in the biodiversity tool.

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I G U R E 2 Scheme of the graphical user interface (GUI).[Colour figure can be viewed at wileyonlinelibrary.com]

(
raster), the pixel values ranged from 0 to 1. Pixel values close to zero indicate a very high degree of forest fragmentation whereas Pixel values close to 1 indicate a very low degree of forest fragmentation.
Biodiversity tools can help achieve the objectives of the EU Biodiversity Strategy 2030 and implement the Habitats Directive.The following use cases (Figure 3) demonstrate, through real-life examples, the positive multistakeholder impact of using this innovative tool.These use cases are demonstrated through results and statistics produced by the use of the tool.1.National monitoring reporting: the Habitat Directives report that ISPRA (the Italian Environmental Agency also known as the "Higher Institute for Environmental Protection and Research") is required to compile on behalf of the Ministry of Environment and Energy every 6 years.2. Security Plan revision for the Cilento and Vallo di Diano National Park.3. Preparation of outdoor activities by school teachers or local ecotourism operators.

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I G U R E 3 Use cases of Biodiversity tool.[Colour figure can be viewed at wileyonlinelibrary.com] case, the table shows that, from 2012 to 2018, ca.10% of the ROI (25.5 ha) was degraded while the remaining 90% (ca.250 ha) maintained its condition.
3.5.2| Revision of the plan for Cilento and Vallo di Diano National Natural ParkNational Park Plans (i.e., the key management document of Italian National Parks) must undergo revision every 10 years or less according to art. 12, paragraph 6 of national Law 394/91.To achieve this goal, the Park Authority might apply the biodiversity tool over the entire park area (Figure3f,g) to (i) update the "state of the Park's biodiversity" by obtaining on-the-fly statistics on Natura 2000 areas (Figure3h) and the updated list of all the associated protected animal and plant species (Figure3i) and (ii) update the "state of the environment" of the Park by obtaining detailed statistics on land use, soils and the state of forest fragmentation (FLI).

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I G U R E 4 (a, b) Sensitivity analysis of forest integrity in Cilento and Vallo di Diano National Natural Park.(c) Distribution of the mean of forest integrity at the municipal scale.[Colour figure can be viewed at wileyonlinelibrary.com] accessibility and use.The tool can support both the planning process (e.g., a natural park) and other related activities, such as reporting and natural park plan reviews (as demonstrated in the first and second case studies).The tool provides access to a basket

Table with
Research Centre on the "Earth Critical Zone" for supporting Landscape and Agro-environment management; EEA: European Environment Agency; ISPRA, The Italian Institute for Environmental Protection and Research; ISTAT: Istituto Nazionale di Statistica; SAC, Special Areas of Conservation; SCI, Special Conservation Importance; SPA, Special Protection Area.All models employed in the biodiversity tool.
T A B L E 2Abbreviation: ROI, region of interest.