Ecological Indicators

The monitoring of environmental policies in Europe has taken place since the 1980s and still remains a challenge for decision- and policy-making. For forests, it is concretized through the publication of a State Of Europes Forests every five years, the last report just been released. However, the process lacks a clear analytical framework and appears limited to orient and truly assess sustainable management of European forests. We classified the 34 quantitative sustainable forest management indicators in the Driver-Pressure-State-Impact-Response (DPSIR) framework to analyse gaps in the process. In addition, we classified biodiversity-related indicators in the simpler Pressure-State-Response (PSR) framework. We showed that most of the sustainable forest management indicators assess the state of European forests, but almost half could be classified in another DPSIR category. For biodiversity, most indicators describe pressures, while direct taxonomic state indicators are very few. Our expert-based classification show that sustainable forest management indicators are unbalanced regarding the DPSIR framework. However, completing this framework with other indicators would help to have a better view and more relevant tools for decision-making. The results for biodiversity were comparable, but we showed that some indicators from other criteria than the one dedicated to biodiversity could also help understanding threats and actions concerning it. Such classification helps in the decision process, but is not sufficient to fully support policy initiative. In particular, the next step would be to better understand the links between DPSIR and PSR categories.

Tropical forests are biodiversity hotspots but increasingly fragmented by human activities, multiplying forest edges that alter microclimates and species dynamics. While edge effect on plants is well documented, its impact on fauna remains less ex-plored. Ecoacoustics provides a non-invasive and passive approach to monitor faunal communities, yet the complexity of soundscapes and the opacity of machine learning tools often limit ecological interpretability. Here, we show that acoustic diversity in New Caledonian ultramafic forests exhibits a detectable edge effect, with sound-scapes converging towards homogeneity starting 100 m from the forest edge. Using passive acoustic monitoring and 59 ecoacoustic indices, we combined machine learn-ing, Representational Similarity Analysis, and explainable statistical methods from psychophysics. Results reveal a significant correlation between distance to edge and acoustic diversity, particularly pronounced during day-time. Indices such as NDSI, Hf, and SKEWf contributed most to detecting the effect, whereas some other in-dices added noise rather than informative signal. These findings match the botanical threshold described by Blanchard et al. (2023) and highlight both the promise and limitations of indice-based ecoacoustics. By integrating explicability methods, we offer a transparent framework to disentangle complex ecological signals, advancing ecoacoustics as a pertinent tool for studying forest fragmentation and guiding biodi-versity conservation.

Ecosystems with a naturally accumulated peat layer at the surface and wetlands with or without a peat layer dominated by a vegetation that may produce peat (hereafter peatlands for simplicity) are critical assets in global change due to their large carbon content storage capacity and essential ecosystem services. A Global Peatland Assessment was initiated during the UNFCCC COP22; however, many countries still lack accurate peatland maps. This is particularly concerning in the Western Mediterranean, where these ecosystems are frequently overlooked or misclassified. To help address this issue, we developed predictive distribution models for vascular plant and bryophyte species typical of peatland-type systems in the Western Mediterranean. We identified 28 indicator mire-typical plant taxa and created a database using filtered data from six different data sources, including our own field surveys. Thirteen high-resolution spatial predictor variables, covering topographic, soil, and hydroclimatic factors, were selected to reflect potential relationships with species presence. For each species, a potential distribution model was built using MaxEnt software. The resulting potential-distribution maps were cross-validated, and models with an AUC score of [≥]0.75 were classified into suitable or unsuitable areas. The species richness map aligned well with areas known to support peatlands. The indicator species-based mapping also suggests a broad potential for yet unrecorded peatlands in the Western Mediterranean, including small areas, particularly along the western and southwestern sandy coastlines and in the southern mountains.

Background and aimsTropical rainforests constitute a globally important biome severely threatened by anthropogenic activities. Accounting for the phylogenetic and functional dimensions can provide insights into how anthropogenic activities affect tree species community assembly. Here, we aimed to assess the effects of selective logging and forest regrowth on the tree composition in the Yangambi region (Democratic Republic of the Congo), as compared to reference undisturbed old-growth forest by incorporating phylogenetic and functional information. MethodsWe measured the phylogenetic and functional alpha-diversity and dissimilarity of undisturbed old-growth, disturbed old-growth, and regrowth rainforests by sampling species abundances and key traits related to usefulness (wood density), vegetative (specific leaf area), and reproductive (fruit type) functions. Then, we evaluated the association between phylogenetic and functional dissimilarity to unravel potential drivers underlying changes in the community composition. Key resultsPhylogenetic and functional dissimilarities resulted in a consistent compositional separation of the disturbed old-growth forests and regrowth forests from the undisturbed old-growth forests, with this separation clearly associated with key functional traits. Compared to undisturbed old-growth forests, disturbed old-growth forests subjected to selective logging did not diverge in terms of both phylogenetic and functional diversity and structure. However, regrowth forests displayed increased levels of phylogenetic diversity and comparable functional diversity as undisturbed old-growth forests. ConclusionsSelective logging and forest regrowth have not led to an overdispersion or clustering of phylogenetic lineages nor functional traits in the tropical rainforests in the Congo Basin, rather, these anthropogenic activities brought an altered phylogenetic and functional community composition which may have serious implications for the stability of these ecosystems.

Remote sensing is an invaluable tool to objectively illustrate the rapid decline in habitat extents worldwide. The many operational Earth Observation platforms provide options for the generation of land cover maps, each with unique characteristics, as well as considerable semantic differences in the definition of classes. As a result, differences in baseline estimates are inevitable. Here we compare forest cover and surface water estimates over four time periods spanning three decades (1989-2018) for [~]1.3 million km2 encompassing the Xingu river basin, Brazil, from published, freely accessible remotely sensed classifications. While all datasets showed a decrease in forest extent over time, we found a large range in the total area reported by each product for all time periods. The greatest differences ranged from 9% (year 2000) to 17% of the total area (2014-2018 period). We also show the high sensitivity of forest fragmentation metrics (entropy and foreground area density) to data quality and spatial resolution, with cloud cover and sensor artefacts resulting in errors. We further show the importance of choosing surface water datasets carefully because they differ greatly in location and amount of surface water mapped between sources. In several of the datasets illustrating the land cover following operationalization of the Belo Monte dam, the large reservoirs are notably absent. Freshwater ecosystem health is influenced by the land cover surrounding water bodies (e.g. Riparian zones). Understanding differences between the many remotely sensed baselines is fundamentally important to avoid information misuse, and to objectively choose the most appropriate dataset for conservation, taxonomy or policy-making. The differences in forest cover between the datasets examined here are not a failure of the technology, but due to different interpretations of forest and characteristics of the input data (e.g. spatial resolution). Our findings demonstrate the importance of transparency in the generation of remotely sensed datasets and the need for users to familiarize themselves with the characteristics and limitations of each chosen data set.

ContextAs global urbanization intensifies, Urban Green Spaces (UGS) are pivotal for biodiversity conservation and climate change mitigation. However, comparative assessments of UGS spatial configuration and connectivity across diverse urban landscapes remain limited. ObjectivesThis study aims to assess the spatial arrangement and connectivity of UGS across 28 European capital cities. Additionally, we evaluate how Network Science metrics derived from Graph Theory can complement traditional landscape ecology metrics to provide a more comprehensive understanding of UGS at a large scale. MethodsWe developed a European Urban Vegetation Map using Earth observation data to classify UGS at 10m resolution across the selected capitals. We then analyzed UGS connectivity for each city utilizing 40 traditional landscape metrics and a Graph-Theory-based approach. ResultsWhile traditional landscape metrics effectively quantified fragmentation, they often remain strongly correlated with total vegetation abundance. In contrast, Network Science metrics provided specific insights into UGS functional connectivity, distinguishing the quality of ecological links beyond spatial proximity. This integration allowed us to cluster European capitals into three distinct typologies: unconnected compact cities, large metropolises with complex peri-urban dynamics, and high-connectivity cities with robust networks. These findings demonstrate that graph-based indices effectively complement traditional metrics, highlighting that relying solely on green space percentage is insufficient for assessing the ecological resilience of urban environments. ConclusionsThese results underscore the relevance of Earth observation-based UGS assessment and demonstrate that graph-based landscape connectivity analysis outperforms simple abundance metrics. Therefore, effective assessment requires integrating structural metrics with graph-based connectivity to support resilient urban biodiversity.

While the importance of ecosystem integrity has long been recognised (Leopold, 1949), conservation science has tended to focus on measuring and monitoring species and habitats, avoiding the complexities of working at the ecosystem level. Ecosystems are highly dynamic, defined by both living and non-living components as well as their interactions (CBD, 1992), making it difficult to assess baseline levels of integrity. We present a novel index that represents the integrity of all terrestrial ecosystems globally at 1km2 resolution: the Ecosystem Integrity Index (EII). The index provides a simple, yet scientifically robust, way of measuring, monitoring and reporting on ecosystem integrity. It is formed of three components; structural, compositional and functional integrity, and measured against a natural (current potential) baseline on a scale of 0 to 1. We find that ecosystem integrity is severely impacted in terrestrial areas across the globe with approximately one fifth of all ecosystems and one quarter of all ecoregions having lost, on average, over half of their ecosystem integrity. At a national scale, we estimate similar challenges with 115 nations or territories having lost, on average, over half of their ecosystem integrity. This presents a significant threat for humanity as such levels of degradation are likely to be linked to substantial declines in the ecosystem services on which humanity is reliant. The EII has been developed principally to help national governments measure and report on Goal A of the Kunming-Montreal Global Biodiversity Framework (GBF) (CBD, 2022a), for which it has been listed as a Component Indicator. The EII will also be useful in helping non-state actors measure and report their contributions to the GBF and is listed as an indicator by both the Taskforce for Nature-Related Financial Disclosures (TNFD) (TNFD, 2023) and the Science Based Targets Network (SBTN) (SBTN, 2023). The EII aims to enable these actors to make informed decisions on the conservation, restoration and sustainable use of ecosystems for which they are wholly or partly responsible. We propose that with sufficient effort, ecosystem integrity can be restored and contribute towards the GBFs vision of living in harmony with nature, resulting in the safeguarding of the ecosystem services on which humanity depends.

Biodiversity monitoring is crucial for ecosystem conservation, yet field data collection is limited by costs, time, and extent. Remote sensing represents a convenient approach providing frequent, near-real-time information over wide areas. According to the Spectral Variation Hypothesis (SVH), spectral diversity (SD) is an effective proxy of environmental heterogeneity, which ultimately relates to plant diversity. So far, studies testing the relationship between SD and biodiversity have reported contradictory findings, calling for a thorough investigation of the key factors (e.g., metrics applied, ecosystem type) and the conditions under which such a relationship holds true. This study investigates the applicability of the SVH for plant diversity monitoring at the landscape scale by comparing the performance of three different types of SD metrics. Species richness and functional diversity were calculated for more than 2000 cells forming a grid covering the Czech Republic. Within each cell, we quantified SD using a Landsat-8 "greenest pixel" composite by applying: i) the standard deviation of NDVI, ii) Raos Q entropy index, and iii) richness of "spectral communities". Habitat type (i.e., land cover) was included in the models describing the relationship between SD and ground biodiversity. Both species richness and functional diversity show positive and significant relationships with each SD metric tested. However, SD alone accounts for a small fraction of the deviance explained by the models. Furthermore, the strength of the relationship depends significantly on habitat type and is highest in natural transitional areas. Our results underline that, despite the stability in the significance of the link between SD and plant diversity at this scale, the applicability of SD for biodiversity monitoring is context-dependent and the factors mediating such a relationship must be carefully considered to avoid drawing misleading conclusions. HighlightsO_LIPlant species richness and functional diversity show significant and positive relationships with spectral diversity C_LIO_LISpectral diversity alone explains a small fraction of the total variability in ground biodiversity C_LIO_LISlight differences among the performances of the spectral diversity metrics tested C_LIO_LIThe relationship between spectral and plant diversity is context-dependent C_LI

Primary and old-growth forests are globally valued for their biodiversity, ecosystem services, and cultural significance. The EU Biodiversity Strategy and EU Forest Strategy for 2030 require strict protection of remaining primary and old-growth forests, yet they cover only about 3% of EU forest area and remain highly threatened. The European Commissions guidelines define old-growth forests using three main indicators--native tree species, deadwood, and large/old trees--supported by five complementary indicators. Implementing these indicators for boreal and hemiboreal old-growth forests in northern Europe currently lack science-based operational criteria that meet EU legal standards. We provide recommendations for implementing European Commissions indicators with science-based operational criteria and thresholds to minimize misclassification and ensure cost-effective conservation. Key thresholds include native species dominance, [≥]5% deadwood of the total wood volume, and [≥]20 large/old trees per hectare. Additional guidance is offered for regeneration patterns, structural complexity, microhabitats, and indicator species, emphasizing that all indicators should be applied collectively.

Maintaining appropriate peatland hydration, particularly through the regulation of the depth to the water table (DWT), is crucial for peatland conservation, restoration, and the mitigation of greenhouse gas emissions. In this study, we assess the long-term ecological impact of hydrological changes, primarily induced by drainage, on ombrotrophic peatlands across Europe. Our analysis is based on novel palaeoecological data from seven peat cores collected from sites that have experienced varying degrees of anthropogenic disturbance. We reconstructed historical DWT fluctuations using plant macrofossil and testate amoeba analyses at high resolution. By applying Threshold Indicator Taxa Analysis (TITAN), we identified species-specific and community-level response thresholds to changes in water level. This approach revealed two distinct change points: the first, at approximately 7 cm DWT, corresponds to hydrological conditions favourable for moisture-dependent Sphagnum species, while the second, near 22 cm DWT, is associated with more drought-adapted taxa and signals ecosystem degradation. The interval between these points represents a transition zone between optimal and suboptimal conditions for peatland functioning. An additional TITAN analysis aimed at identifying the timing of the most significant ecological changes indicates that peatland degradation intensified over the past two centuries and has accelerated in recent decades. Our findings also show that even after hydrological restoration, microbial and plant communities often remain distinct from those in undisturbed peatlands. This underscores the importance of preserving sites that still retain near-natural conditions. Based on our results (and consistent with previous studies) we recommend maintaining a DWT of approximately 10 cm below the surface as an optimal target for both peatland conservation and restoration. Such conditions not only support ecological integrity but are also associated with reduced greenhouse gas emissions and higher peat accumulation rates, reinforcing the role of ombrotrophic peatlands as long-term carbon sinks.

The voluntary blue carbon market is severely bottlenecked by outdated methodologies that apply broad, coast-level carbon averages across low-resolution spatial units, systematically failing to account for micro-site ecological realities and critical socio-political constraints. To resolve this structural deficit, this paper introduces the High-Resolution Geographically-Explicit Blue Carbon Assessment (HiGEBCA) pipeline, an innovative geospatial architecture that shifts site intelligence from monolithic raster grids to a topologically verified, hyper-dimensional polygon infrastructure. Operating on 1,601 distinct mangrove features across Colombia, the pipeline mathematically binds 47 ecological attributes to each polygon, integrating Monte Carlo uncertainty propagation, climate-stratified soil organic carbon, and rigorous biodiversity quantification spanning 293 taxon-code pairs. A diagnostic CatBoost machine learning emulator (R{superscript 2} = 0.926) deployed within the pipeline empirically demonstrates that local climate classes and biodiversity metrics drive over 96% of the variance in carbon density, proving that traditional broad biome classifications are inadequate for accurate micro-site valuation. Crucially, the HiGEBCA framework pioneers the integration of operational reality into natural capital assessment. When applied to Colombias theoretical national estate of 276,430 hectares (containing an estimated 478 million tCO2e), the pipeline executes a rigorous REDD+ white space assessment alongside hard mathematical filters for legal land tenure, armed conflict, and regulatory overlap. This strict governance filtration shatters the illusion of massive, easily accessible natural capital, systematically reducing the viable, investment-grade portfolio to a highly de-risked 4,000 to 12,000 hectares. Designed for cross-jurisdictional replication, the HiGEBCA pipeline establishes a new, transparent standard for prioritizing high-integrity blue carbon assets, providing a quantitative mandate for investors seeking to maximize climate impact, capture biodiversity premiums, and definitively mitigate operational risk.

The probability that a propagule reaches a certain location where it can establish and persist is affected by the distance from the source. Fragmented landscapes often promote the isolation of habitats that hinder or impede the movement of species that affect their range distribution, even when environmental conditions are adequate. Studies that assess the connectivity of the landscape are essential to ensure that ecological conservation planning covers all vital processes that occur on a landscape scale. Based on the classification of land cover in the Rio Doce Basin - RDB, we used a habitat / non-habitat approach to assess RDB connectivity for tree species. Based on circuit theory, we built 6 surface models of resistance based on habitat and non-habitat areas. We performed the analysis using the GIS plug-in and Linkage Mapper to generate lest cost path maps. The RDB is very fragmented, but still has functionally connected regions. The west to nothwestern and southeastern portions of the basin are well-connected and demand conservation practices, while the center-north of the basin and the far southwest are regions with greater resistance to connectivity as a result of strong anthropic pressures that reduce forests, requiring intervention through restoration projects avoiding loss due to lack of connectivity. In addition, land reclamation projects in degraded areas must also be organized in the Linhares region, as it is inserted in a region of very high biodiversity with a high level of endemism and a high number of threatened species.

As human activities on our planet persist, causing widespread and irreversible environmental degradation, the need to biomonitor ecosystems has never been more pressing. These circumstances have required a renewal in monitoring techniques, encouraged by necessity to develop more rapid and accurate tools which will support timely observations of ecosystem structure and function. The World Exposition (from now EXPO2015) hosted in Milan from May to October 2015 was a global event that could be categorized as a mega-event, which can be defined as an acute environmental stressor, possibly generating biodiversity alteration and disturbance. During the six months of EXPO2015, exhibitors from more than 135 countries and 22 million visitors insisted on a 1.1 million square meters area. Faced with such a massive event, we explore the potential of DNA metabarcoding using three molecular markers to improve the understanding of anthropogenic impacts in the area, both considering air and water monitoring. Furthermore, we explore the effectiveness of the taxonomy assignment phase considering different taxonomic levels of analysis and the use of data mining approaches to predict sample origin. Unless the degree of taxa identification still remains open, our results showed that DNA metabarcoding is a powerful genomic-based tool to monitor biodiversity at the microscale, allowing us to capture exact fingerprints of specific event sites and to explore in a comprehensive manner the eukaryotic community alteration. With this work, we aim to disentangle and overcome the crucial issues related to the generalization of DNA metabarcoding in order to support future applications.

In forest regions worldwide, industrial forestry has left fragments of natural forests behind. This challenges biodiversity conservation and calls for ecological restoration for sustainable forest management and conservation. The functionality of protected areas need to be improved and forest ecosystems set in a state that better favors biodiversity, resilience and provisioning of ecosystem services. Sweden contributes a substantial share of the European forests, with dominance of non-industrial forest ownership and extensive forestry footprint, and hence with immediate need for advanced conservation and restoration. Protection through voluntary nature conservation agreements and regulated biotope protection areas exists since the 1990s, with schemes involving economic compensation to landowners to facilitate conservation and restoration. Across entire Sweden and all ecoregions, we assessed their accumulated capacity over a 30-year period, including forest types protected, type of restoration management, rotation intervals, and selection of tree species. These nearly 14,000 different areas covering over 70,000ha are small, ranging in size from 5ha but rarely larger than 20ha. Their contribution is important, particularly in south Sweden with low and fragmented forest cover among many different owners. Active restoration dominates over passive set asides, coniferous forest types are less represented than more rare forest types, many different tree species are favored, and different restoration types occur but with few types dominating. In recognizing their critical importance, we find that the practices are narrow and repetitive, and that a greater restoration diversification is needed. The decreasing trend in protection is alarming since these contribute key forest type representativeness and functionality.

Ecological records from before and after the creation of natural parks are valuable for informing conservation and restoration actions. Such records are often unavailable, but high-resolution paleoecological studies may provide useful information. This paper presents a sub-decadal paleoecological reconstruction of vegetation and landscape in a national park in the Pyrenean highlands, established in the 1950s. The park lands were traditionally been used for small-scale cultivation, extensive grazing, forest exploitation and, since 1910, hydroelectricity generation following the damming of numerous glacial lakes. A significant finding is that present-like forests, with negligible changes in composition, have dominated the landscape during the study period. Major vegetation changes involved shifts in forest cover, influenced by both climatic and anthropic factors. Interestingly, the creation of the park in 1955 and the initial restrictions on forest exploitation in 1975 did not significantly affect vegetation cover or composition. Forest expansion did not occur significantly until the 1980s when the park was enlarged, and forest exploitation was further restricted. This expansion peaked in the mid-1990s coinciding with a warming trend and a decrease in fire incidence, before declining due to warmer and drier climates. This decline in forest cover occurred concurrently with the ongoing global forest dieback phenomenon and may be exacerbated by the predicted global warming in this century, which could also increase fire incidence due to the accumulation of dead wood. Under current conservation measures, the main threats are global warming, fire and, on a more local scale, the massification of tourism. Expanding the park and implementing forest restoration actions on degraded terrains surrounding the park could only be beneficial.

Despite their key role in biodiversity conservation, forests of the Congo Basin are increasingly threatened by human activities. Whereas deforestation is very conspicuous, it remains challenging to monitor the impact of forest degradation under a more or less intact canopy. Likewise, the outcomes of forest regeneration following agricultural abandonment remain poorly studied in the Congo Basin. Here, we surveyed 125 vegetation quadrats across 25 forest inventory plots in the Yangambi region, and based on historical land-use maps. We aimed to assess both the impact of anthropogenic disturbance and forest recovery on tree species diversity, tree functional traits, community composition, canopy cover, and forest structure, as compared to undisturbed old-growth forest. We found that undisturbed old-growth forest harboured more tree individuals, particularly individuals with a smaller diameter, as well as species with a higher wood density when compared to both disturbed old-growth and regrowth forest. Moreover, species diversity was highest in disturbed old-growth forest. Tree species composition was significantly different between undisturbed old-growth forest and the other two forest categories. Our findings indicate that anthropogenic disturbance results in a degraded forest structure and may reduce the forests ability to store carbon. Whereas species diversity recovered 60 years after agricultural abandonment, species composition remained different from undisturbed old-growth forest. Our study illustrates the need for more extensive vegetation surveys in the Congo Basin in order to quantify the effects of human disturbance under a closed canopy.

South American wetlands are of global importance, yet limited delineation and monitoring restricts informed decision-making around the drivers of wetland loss. A growing human population and increasing demand for agricultural products has driven wetland loss and degradation in the Neotropics. Understanding of wetland dynamics and land use change can be gained through wetland monitoring. The Neembucu Wetlands Complex is the largest wetland in Paraguay, lying within the Paraguay-Parana-La Plata River system. This study aims to use remotely sensed data to map land cover between 2006 and 2021, quantify wetland change over the 15-year study period and thus identify land cover types vulnerable to change in the Neembucu Wetlands Complex. Forest, dryland vegetation, vegetated wetland and open water were identified using Random Forest supervised classifications trained on visual inspection data and field data. Annual change of -0.34, 4.95, -1.65, 0.40 was observed for forest, dryland, vegetated wetland and open water, respectively. Wetland and forest conversion is attributed to agricultural and urban expansion. With ongoing pressures on wetlands, monitoring will be a key tool for addressing change and advising decision-making around development and conservation of valuable ecosystem goods and services in the Neembucu Wetlands Complex.

Knowledge derived from nucleotide sequence data is increasing in importance in the life sciences, as well as decision making (mainly in biodiversity policy). Metadata standards have been established to facilitate sustainable sequence data management according to the FAIR principles (Findability, Accessibility, Interoperability, Reusability). Here, we review the status of metadata available for raw read Illumina amplicon and whole genome shotgun sequencing data derived from ecological metagenomic material that are accessible at the European Nucleotide Archive (ENA), as well as the compliance of the primary sequence data (fastq files) with data submission requirements. While overall basic metadata, such as geographic coordinates, were retrievable in 98% of the cases for this type of sequence data, interoperability was not always ensured and other (mainly conditionally) mandatory parameters were often not provided at all. Metadata standards, such as the Minimum Information about any(x) Sequence (MIxS), were only infrequently used despite a demonstrated positive impact on metadata quality. Furthermore, the sequence data itself did not meet the prescribed requirements in 31 out of 39 studies that were manually inspected. To tackle the most immediate needs to improve FAIR sequence data management, we provide a list of minimal suggestions to researchers, research institutions, funding agencies, reviewers, publishers, and databases, that we believe might have a potentially large positive impact on sequence data and metadata FAIRness, which is crucial for further research and its derived applications.

Different clustering methods often classify the same dataset differently. Selecting the best clustering solution out of a multitude of alternatives is possible with cluster validation indices. The behavior of validity indices changes with the structure of the sample and the properties of the clustering algorithm. Unique properties of each index cause increasing or decreasing performance in some conditions. Due to the large variety of cluster validation indices, choosing the most suitable index concerning the dataset and clustering algorithms is challenging. We aim to assess different internal clustering validation indices. In the present paper, the validity indices consist of geometric and non-geometric methods. For this purpose, we applied simulated datasets with different noise levels. Each dataset was repeated 20 times. Three clustering algorithms with Jaccard dissimilarity are used, and 27 clustering validation indices are evaluated. The results provide a reliability guideline for the selection cluster validity indices.

Urban liveability is closely related to the landscape features that support human societies. The promotion of liveability influences the well-being of both human and non-human inhabitants of the city, as the two share the same urban habitat. In this paper, we propose a classification of urban landscapes tailored to facilitate the acquisition of interdisciplinary knowledge on the multi-inhabitant liveability of urban landscapes. Our classification represents the layered urban dimensions through four different subclassifications: local and landscape Urban Form, Anthropic Imprint, and Biophysical Conditions. We developed a flexible and scalable methodology that enables us to expand to other cities, by using only opensource geospatial and remote sensing data. We modelled our four subclassifications at 10-metres through a set of automatic pipelines, running Principal Component Analysis as dimensionality reduction and KMeans unsupervised classification. In this paper we explore the application of our methodology in the framework of ECOLOPES, a project that investigates the human and non-human liveability in cities through the computational design of green building envelopes. We apply the methodology to three distinct European cities, i.e. Vienna, Munich, and Genoa. We produced a set of 12 raster maps from 64 variables, with a total of 32 classes and 1264 possible combinations. We analysed inter and intra-urban class frequencies by highlighting the primary spatial signatures over each sub-classification, and between the overall Functional Urban Areas and the core areas. In this paper we discuss how this approach could foster multidisciplinary studies on urban liveability that hold into account not just humans, but all living inhabitants in cities. From this application, we envision a second step where our methodology will be applied to the full extent of European cities.