Urban areas can contain significant levels of biodiversity, contributing to more resilient ecosystems and healthier environments. Urban habitats like green roofs, that convert building surfaces into vegetated areas, can offer environments for organisms, though more information is needed about the biodiversity patterns within and across them. Human residents can help generate valuable urban biodiversity data through monitoring programs while strengthening their own awareness and connection to urban nature. This study explores the role of BioBlitzes in documenting biodiversity on urban green roofs by engaging citizen scientists, biodiversity experts and engineers. Four events were conducted in Lisbon, Portugal on 13 publicly accessible green roofs, yielding 1,123 observations of 345 taxa of plants, fungi, insects, arachnids, and birds. Based on the organisers’ experience and dialogue between citizens and researchers, a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis reflected how BioBlitzes can promote public engagement, highlight the ecological potential of green roofs, and foster interdisciplinary collaborations. Strengths included cost-effective biodiversity assessment, public education, and increased visibility of green roofs as urban habitats. Weaknesses encompassed limited seasonal coverage, and uneven expertise among participants. Opportunities included long-term biodiversity monitoring, early detection of invasive species, and promotion of native flora. Threats involved misinterpretations of the importance of green roofs to biodiversity due to short time sampling, logistical constraints, and vegetation maintenance. These findings demonstrate how BioBlitzes on green roofs contribute to urban biodiversity knowledge, foster engagement of local residents in research, support policies about green infrastructure and nature-based solutions in city planning.

Emerging evidence shows that responses to deforestation can differ both across species and among populations of the same species. The reasons underlying these complex patterns are unclear, and this lack of clarity is a barrier to accurate monitoring and prediction of biodiversity change. Using data from 2,262 bird species across 7,326 sites from all forested continents, we show that between-species and within-species variations in responses to forest cover are mediated by temperature. Populations in warmer macroclimates tend to decline in incidence in deforested landscapes because hotter microclimates push them closer to their species' realized upper thermal tolerance limits. By contrast, populations in cooler macroclimates tend to be less affected by or even benefit from deforestation, as microclimatic temperatures are pushed closer to more optimal temperatures. Our findings offer an empirically grounded framework for biodiversity models that move beyond fixed species responses and incorporate interactions between temperature and land-use change.

The escalating threat of climate-driven wildfires, land abandonment, wildland–urban interface expansion, and inadequate forest management poses an existential challenge to Mediterranean oak ecosystems, for which traditional fire suppression has proven insufficient. This paper presents a combination of integrated fire management (IFM) and closer-to-nature forest management (CTNFM) in a representative mixed Pyrenean oak (Quercus pyrenaica) forest at Quinta da França (QF), in Portugal. It is structured around three main objectives designed to evaluate this pioneer integrated approach: (1) to describe the integration of IFM and CTNFM within an agro-silvo-pastoral landscape; (2) to qualitatively assess its ecological, operational, and socio-economic outcomes; and (3) to quantitatively evaluate the effectiveness of two key nature-based solutions (NbSs), that is, prescribed burning and planned grazing, in reducing wildfire risk and enhancing forest resilience and biodiversity. By strategically combining proactive fuel reduction with biodiversity-oriented silviculture, the QF case provides a replicable model for managing analogous Mediterranean forested areas facing similar risks. This integrated approach supports forest multifunctionality, advancing both prevention and adaptation goals, and directly contributes to the ambitious targets set by the European Union’s New Forest and Biodiversity Strategies for 2030, marking a significant step towards a more sustainable and fire-resilient future for such Mediterranean landscapes.

Biodiversity loss is a global environmental concern, mainly driven by human-induced factors, encompassing both direct and indirect drivers. This study investigates the long-term relationship between either the Human Footprint Index (HFI), which measures the extent of human pressures (i.e., direct drivers), or the Gross Domestic Product (GDP), a measure of economic growth (i.e., indirect driver) and biodiversity change, using bird population trends as indicators. The analysis was based on time-series data for Portugal (2004–2023) aggregated at national and sub-national scales, representative of different socio-economic contexts. Multi-species indices were regressed against either the HFI or GDP using Autoregressive Distributed Lag (ARDL) to identify long-run relationships. Bird population trends varied by species group (common, agricultural, and forest birds) and socio-economic context underscoring the importance of sub-national assessments. The HFI and GDP had varying predictive value across species groups and socio-economic contexts, with the HFI showing greater consistency, particularly as a predictor for agricultural birds. While most models showed a negative association between species abundance and either the HFI or GDP, revealing a signal of socio-economic pressures on bird populations at sub-national scales, some models suggested mixed results, indicating that conservation policies must take local contexts into account.

Mediterranean landscapes are characterized by fine-grained land-cover mosaics of interspersed vegetation types and high wildfire vulnerability, where grazing plays a key role in regulating vegetation structure and composition. This study explores the early effects, over a three-year period, of a transition from extensive commercial cattle grazing to semi-wild horse grazing in two rewilding areas in the Côa Valley region, Portugal. Using grazing exclusion areas as control, we test whether the less intensive regime of semi-wild horse grazing can be used to manage vegetation structure and composition, to mitigate local fire hazard and promote biodiversity. The monitoring scheme followed a paired design, where each survey site of 40 m × 40 m comprises four sampling plots of 10 m × 10 m, including two fenced plots (grazing exclusion) and two plots open to grazing. Effects on vegetation structure were assessed considering grass height, shrub height, shrub cover and aboveground biomass, as well as effects on plant species richness, turnover, and forbs-to-grasses ratio (F:G ratio) and the community-level importance of grasses and forbs. Results showed that grass height had a greater increase in ungrazed plots, suggesting that semi-wild horse grazing helps limit grass height. There were no significant differences in shrub metrics between treatments (i.e. horse grazing vs. no grazing), indicating that horse grazing did not effectively control woody vegetation. While species richness remained stable, species temporal turnover was higher in ungrazed plots. Additionally, the F:G ratio and the importance value of forbs were higher under horse grazing, suggesting potential benefits for anthophilous insects. These findings indicate that semi-wild horse grazing contributes to maintaining open habitats by controlling grass dominance, thereby reducing local fire hazard and potentially fostering habitat and food resources for insects. While this demonstrates the potential of using semi-wild horse grazing in rewilding, the results also suggest that horses alone, particularly at low densities, have limited impact on woody vegetation structure.

Aim: Land-use change is a major threat to biodiversity, yet there remains considerable unexplained variation in how it affects different populations of the same species. Here, we examine how sensitivity to forest cover changes depending on proximity to different limits of a species' range. By comparing responses as species approach their coastal ('hard') and inland ('soft') range limits, we aim to provide insight into the relative influence of mass effects, as compared to abiotic and biotic environmental suitability in shaping population sensitivity. Location: Global. Time Period: 1996-2019. Major Taxa Studied: Birds. Methods: We combined data from several large databases to obtain a dataset of 2543 bird species surveyed across 116 studies, spanning six continents. Using expert-verified range maps, we calculated the position of populations relative to their species' nearest inland ('soft') and coastal ('hard') range limits and categorised the inland limits as equatorward- or poleward- facing. We investigated how distance to range limits and forest cover, derived from a 30 m-resolution global dataset, affect the probability of species' incidence. Results: We found that bird populations are more sensitive to forest cover when located closer to their species' inland ('soft') range limits, whereas this was not the case at coastal ('hard') range limits. The heightened sensitivity to forest cover at soft range limits was similar regardless of whether the range limit faced equatorward or poleward. Main Conclusions: These results highlight how populations close to the soft limits of their species' ranges are at higher risk of extirpation resulting from loss of forest cover. This suggests that environmental conditions (e.g., climate), which become more challenging away from the core of the species' range, drive variability in sensitivity to forest cover.

This study employs state of the art classification techniques, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF), to map shrub coverage in a fire-prone landscape characterized by fine-grain heterogeneous land cover and a Mediterranean climate. Sentinel-2 satellite imagery from 2020 to 2022 were labeled at the pixel level, after intersecting a high-resolution (20 cm pixel) land cover map of 2020 with the Sentinel pixel grid. The goal was to produce an algorithm able to predict shrub cover percentage at the spatial resolution of 10 m x 10 m just informed by satellite imagery. The methodology incorporates K-Fold Cross-Validation (K-fold CV) to strengthen the model structure’s generalization ability to generalize to unseen data by minimizing bias towards specific area. The RF model, identified as the most effective, achieved 73% Precision, 72% Recall, and 72% F-Score for Category 1 (0%-10% shrub coverage), 62% Precision, 70% Recall, and 65% F-Score for Category 2 (>10%-50% shrub coverage), and 89% Precision, 79% Recall, and 84% F-Score for Category 3 (>50% shrub coverage). Our method thus demonstrates significant results for shrub coverage in Category 3, crucial for fire hazard assessments and implementing prevention measures at the landscape scale.

Mediterranean landscapes are shaped by natural disturbances such as herbivory and fire that regulate vegetation structure and fuel loads. As a result of the cessation of traditional agricultural practices, land abandonment is a widespread phenomenon in these landscapes, leading to shrub encroachment and heightened fire hazard. This study reports the effects of grazing by domestic herbivores on vegetation structure in transitional woodland–shrubland systems across three case study areas in Portugal. The effects of low and moderate grazing intensity by cattle and horses on vegetation structure were assessed on three vegetation strata—canopy, shrubs, and grasses—using indicators to evaluate the influence of grazing on both horizontal and vertical vegetation structure. Moderate grazing shaped vertical vegetation structure by reducing shrub and grass height and by browsing and thinning the lower branches, creating a discontinuity between understorey and canopy layers. These effects on vertical fuel continuity are anticipated to limit the upward spread of flames and reduce the potential for crown fires. In contrast, low-intensity grazing showed limited effects on both vertical and horizontal vegetation structure. This work highlights the potential of using domestic herbivores as a tool to manage vegetation structure and its contribution to mitigating local wildfire hazards.

Wildfires pose a growing threat to Mediterranean ecosystems. This study employs advanced classification techniques for shrub fractional cover mapping from satellite imagery in a fire-prone landscape in Quinta da França (QF), Portugal. The study area is characterized by fine-grained heterogeneous land cover and a Mediterranean climate. In this type of landscape, shrub encroachment after land abandonment and wildfires constitutes a threat to ecosystem resilience—in particular, by increasing the susceptibility to more frequent and large fires. High-resolution mapping of shrub cover is, therefore, an important contribution to landscape management for fire prevention. Here, a 20 cm resolution land cover map was used to label 10 m Sentinel-2 pixels according to their shrub cover percentage (three categories: 0%, >0%–50%, and >50%) for training and testing. Three distinct algorithms, namely Support Vector Machine (SVM), Artificial Neural Networks (ANNs), and Random Forest (RF), were tested for this purpose. RF excelled, achieving the highest precision (82%–88%), recall (77%–92%), and F1 score (83%–88%) across all categories (test and validation sets) compared to SVM and ANN, demonstrating its superior ability to accurately predict shrub fractional cover. Analysis of confusion matrices revealed RF’s superior ability to accurately predict shrub fractional cover (higher true positives) with fewer misclassifications (lower false positives and false negatives). McNemar’s test indicated statistically significant differences (p value < 0.05) between all models, consolidating RF’s dominance. The development of shrub fractional cover maps and derived map products is anticipated to leverage key information to support landscape management, such as for the assessment of fire hazard and the more effective planning of preventive actions.

In the Mediterranean basin, the structure and species composition of traditional landscapes have historically been shaped and maintained by human-driven disturbances, such as extensive livestock grazing. The cessation of these activities, which have partially replaced the role of natural disturbances, may lead to vegetation overgrowth and biomass accumulation, with potential adverse impacts on biodiversity, ecosystem functions and services. Recently, the use of livestock for ecosystem management, with the purpose of maintaining grazing disturbance and the associated ecosystem processes, has been gaining traction. Nevertheless, there is still limited evidence on the performance of such grazing interventions. This review assesses the state of the art regarding the use of livestock for ecosystem management in Mediterranean landscapes. It examines the association between the regime and duration of grazing interventions and their reported effects on ecosystems. The list of reviewed interventions (68 interventions, retrieved from 47 studies) covered a diverse range of landcover systems (from grasslands to forests), of grazing regimes (characterized by different levels of grazing intensity and livestock species), and of duration of grazing (from short-term, < 5 years to long-term grazing, > 20 years). Wildfire prevention and biomass control, biodiversity and habitat conservation and the regulation of soil quality are the main reasons for the use of grazing interventions. The results of this review suggest that the use of domestic herbivores in ecosystem management can contribute to wildfire prevention and biomass control, with these positive effects fading away in long-term grazing interventions. Goats seem to perform better than cattle for biomass control. The effects on biodiversity and habitat conservation depend on the grazing regime, with intensive grazing showing negative results, while the effects on soil quality are generally negative but require further assessment, due to data limitations.

Comparing the impacts of future scenarios is essential for developing and guiding the political sustainability agenda. This review-based analysis compares six IPBES scenarios for their impacts on 17 Sustainable Development Goals (SDGs) and 20 biodiversity targets (Aichi targets) for the Europe and Central Asia regions. The comparison is based on a review of 143 modeled scenarios synthesized in a plural cost–benefit approach which provides the distances to multiple policy goals. We confirm and substantiate the claim that transformative change is vital but also point out which directions for political transformation are to be preferred. The hopeful message is that large societal losses might still be avoided, and multiple benefits can be generated over the coming decades and centuries. Yet, policies will need to strongly steer away from scenarios based on regional competition, inequality, and economic optimism.

Urban activities are an important driver of ecosystem services decline. Sustainable urbanisation necessitates anticipating and mitigating these negative socio-ecological impacts, both within and beyond city boundaries. There is a lack of scalable, dynamic models of changes to ecosystems wrought by urban processes. We developed a system dynamics model, ESTIMUM, to predict locations, types, and magnitude of changes in ecosystem services. We tested the model in Lisbon (Portugal) under four specific urban development scenarios – a base case scenario and three local sustainability-driven scenarios – to the year 2050. Our results show that urban sustainability policies focused on reducing impacts within Lisbon can be undermined by increased impacts in the extended regions that supply resources to the city. In particular, carbon sequestration from urban greening pales in comparison to growing greenhouse gases from the consumption of food, energy and construction materials. We also find that policies targeted at these extended environmental impacts can be much more effective than those with a limited focus on the urban form. For example, dietary shifts could support positive changes outside that city to increase global climate regulation by 54% compared to a mere 1% increase through intensive urban greening. This highlights the urgent need for a reframing of urban sustainability in policy and scholarly circles from city-centric focus towards an expanded multi-scalar conceptualisation of urban sustainability that accounts for urban impacts beyond the city boundaries.

Climate change and biodiversity loss are two pressing global environmental challenges that are tightly coupled to urban processes. Cities emit greenhouse gases through the consumption of materials and energy. Urban expansion encroaches on local habitats, while urban land teleconnections simultaneously degrade distant ecosystems. These processes decrease the supply of and increase the demand for ecosystem services inside and outside urban areas. Most cities are in a state of ecosystem services deficit, whereby demand exceeds local supply of ecosystem services. Methods to quantify this deficit by capturing multi-scale and multi-level ecological exchanges are incipient, leaving scholars with a partial understanding of the environmental impacts of cities. This paper deploys a novel method to simulate future urban supplies and demands of two key ecosystem services needed to combat climate change and biodiversity loss - global climate regulation and global habitat maintenance. Applying our model to eight representative European cities, we project growing ecosystems deficits (demand exceeds supply) between 8% and 214% in global climate regulation and 11% and 431% in global habitat maintenance between 2020 and 2050. Variation between cities stems from differing dietary patterns and electricity mixes, which have large implications for ecosystems outside the city. To combat these losses, urban sustainability strategies should complement local restoration with changes to local consumption alongside promoting remote ecological restoration to tackle the multi-level environmental impacts of cities.

The reintroduction of livestock grazing to regulate biomass load is being tested for large-scale restoration in Mediterranean landscapes affected by rural abandonment. Concurrently, there is a need to develop cost-effective methods to monitor such interventions. Here, we investigate if satellite data can be used to monitor the response of vegetation phenology and productivity to grazing disturbance in a heterogenous forest mosaic with herbaceous, shrub, and tree cover. We identify which vegetation seasonal metrics respond most to grazing disturbances and are relevant to monitoring efforts. The study follows a BACI (Before-After-Control-Impact) design applied to a grazing intervention in a Pyrenean oak forest (Quercus pyrenaica) in central Portugal. Using NDVI time-series from Sentinel-2 imagery for the period between June 2016 and June 2021, we observed that each type of vegetation exhibited a distinct phenology curve. Herbaceous vegetation was the most responsive to moderate grazing disturbances with respect to changes in phenology and productivity metrics, namely an anticipation of seasonal events. Results for shrubs and trees suggest a decline in peak productivity in grazed areas but no changes in phenology patterns. The techniques demonstrated in this study are relevant to a broad range of use cases in the large-scale monitoring of fine-grained heterogeneous landscapes.

In Mediterranean landscapes, the encroachment of pyrophytic shrubs is a driver of more frequent and larger wildfires. The high-resolution mapping of vegetation cover is essential for sustainable land planning and the management for wildfire prevention. Here, we propose methods to simplify and automate the segmentation of shrub cover in high-resolution RGB images acquired by UAVs. The main contribution is a systematic exploration of the best practices to train a convolutional neural network (CNN) with a segmentation network architecture (U-Net) to detect shrubs in heterogeneous landscapes. Several semantic segmentation models were trained and tested in partitions of the provided data with alternative methods of data augmentation, patch cropping, rescaling and hyperparameter tuning (the number of filters, dropout rate and batch size). The most effective practices were data augmentation, patch cropping and rescaling. The developed classification model achieved an average F1 score of 0.72 on three separate test datasets even though it was trained on a relatively small training dataset. This study demonstrates the ability of state-of-the-art CNNs to map fine-grained land cover patterns from RGB remote sensing data. Because model performance is affected by the quality of data and labeling, an optimal selection of pre-processing practices is a requisite to improve the results.

Urban ecosystem services (UES) are the benefits supplied by nature to people in urban systems. The supply of UES is threatened because of widespread increasing urbanisation. Modelling scenarios that optimise UES supplies can support sustainable urban planning processes. UES are linked to land use/land cover (LULC) types, which enables the optimisation of UES supply to be based on LULC configurations. However, current modelling approaches are not suitably adapted to the link between UES optimisation and LULC configurations. One possibility to target UES optimal supply is to use mathematical optimisation methods. The objective of this study is to test the combined use of participatory modelling and optimisation models to deliver spatial solutions that maximise UES by optimal urban LULC configurations. An integrated model is built using a multi-objective integer linear programming (MOILP) model along with LULC performance scores to maximise a set of locally supplied UES. This is illustrated with a case study of Lisbon (Portugal) involving the participation of key stakeholders to validate and benchmark the selection of optimisation constraints. Results show land optimally allocated to land cover types with high UES functions combined with a reshuffling and densification of residential land. Thus, the new LULC configuration increased multiple UES supplies while maintaining a level of housing capacity. The model shows clear implications of increasing land cover types whose UES functions are high compared to most other LULC classes. Moreover, incorporating stakeholder participatory modelling offers a transdisciplinary and interdisciplinary scientific contribution intersecting mathematical optimisation, UES, and urban planning.

To account for progress towards conservation targets, monitoring systems should capture not only information on biodiversity but also knowledge on the dynamics of ecological processes and the related effects on human well-being. Protected areas represent complex social-ecological systems with strong human-nature interactions. They are able to provide relevant information about how global and local scale drivers (e.g., climate change, land use change) impact biodiversity and ecosystem services. Here we develop a framework that uses an ecosystem-focused approach to support managers in identifying essential variables in an integrated and scalable approach. We advocate that this approach can complement current essential variable developments, by allowing conservation managers to draw on system-level knowledge and theory of biodiversity and ecosystems to identify locally important variables that meet the local or sub-global needs for conservation data. This requires the development of system narratives and causal diagrams that pinpoints the social-ecological variables that represent the state and drivers of the different components, and their relationships. We describe a scalable framework that builds on system based narratives to describe all system components, the models used to represent them and the data needed. Considering the global distribution of protected areas, with an investment in standards, transparency, and on active data mobilisation strategies for essential variables, these have the potential to be the backbone of global biodiversity monitoring, benefiting countries, biodiversity observation networks and the global biodiversity community.