Wildfire has become an increasing threat to natural ecosystems and human livelihood alike in many parts of the world. Vegetation fuel treatments are considered a viable option for mitigating wildfire risk and damage; yet existing studies have yielded mixed or inconclusive results on fuel treatment effectiveness especially at the landscape level. Using fire behavior simulations and statistical analysis of simulation outputs, we assessed landscape-level effectiveness of prescribed burning (PB) and thinning from below (TFB) relative to their site-level effectiveness in terms of area burned (AB) and total cost of treatment and timber loss (TC) in a forest-dominated ecosystem in the southern United States. We found that effectiveness of a treatment varied with measurement metrics and extent, vegetation characteristics and dynamics, and their interactions with the treatment. PB and TFB were less effective at the landscape level than at the site level where fires burned only inside the treatment area. At both site and landscape levels, the effectiveness of PB and TFB in reducing AB and TC largely depended on the quantity of biomass and fire ignition location. TFB outperformed PB in mitigating both AB and TC with a larger timber volume, a longer delay in fire occurrence after treatment, or a higher uncertainty of fire ignition location. TFB was also more effective than PB in reducing TC at the landscape level. By clarifying the conditions under which a fuel treatment can mitigate the area burned and the total cost, this study advanced knowledge of fuel treatment effectiveness especially at the landscape level. Such knowledge can aid in developing and deploying treatment strategies to minimize fire extent and adverse economic consequences in the study region and beyond.

Purpose This paper aims to assess how the pharmaceutical sector’s stringent quality, safety and efficacy (QSE) standards resist circular economy (CE) adoption. Digital twins (DTWs) are acknowledged for operational efficiency, but their role for enabling CE transition remains under-theorised within rigid, high-compliance regime. This study challenges the conventional multi-level perspective (MLP) and its framing of digital technologies as disruptive niche innovations. We argue that, in hyper-stabilised regimes, DTWs must be reconceptualised as active transition intermediaries. Their primary function is to build digital-circular legitimacy, a novel construct that integrates MLP with Institutional Legitimacy Theory. Design/methodology/approach A systematic literature review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, augmented by bibliometric analysis, identified prevalent DTW functions and CE intersections. We synthesised this analysis with an adapted MLP framework, enriched by Institutional Legitimacy Theory, to develop a conceptual framework. This framework explains how DTWs can enable sustainability transitions within inflexible socio-technical regimes. The review addressed three core questions: current research trends at the DTW–CE intersection; the adaptation of MLP to theorise DTWs as transitional intermediaries; and the key drivers and obstacles influencing DTW adoption for CE in regulated sectors. Findings The analysis reconceptualised DTW as a cross-level transition intermediary rather than a niche technology. The paper introduces the construct of digital-circular legitimacy, elucidating how DTWs generate auditable, real-time evidence to demonstrate that circular practices comply with core institutional (QSE) rules. Three interconnected mechanisms are articulated across MLP levels: (1) operational legitimisation; (2) systemic destabilisation; and (3) strategic alignment. These mechanisms clarify how DTWs can reduce regime rigidity and orchestrate CE transitions in pharmaceutical manufacturing. Originality/value This research contributes to transition theory. First, it introduces the novel concept of digital-circular legitimacy, explaining how DTWs produce trusted, compliance-ready evidence to make circular practices acceptable in rigid regulatory environments. Second, it theoretically repositions the DTW as an active transition intermediary that operates across niche, regime and landscape levels, moving beyond its typical characterisation as a standalone digital tool. Third, it delineates specific mechanisms through which digitalisation systematically reduces institutional rigidity and supports CE transitions in high-compliance sectors. Together, these contributions provide a new theoretical lens for understanding how digital technologies can enable circular transformation in pharmaceuticals and other highly regulated industries.

Live fuel moisture content (LFMC) strongly affects the behavior of wildland fire, resulting in its incorporation into wildfire spread models and danger ratings. In this study, over ten thousand LFMC observations are combined with predictor variables from Landsat imagery and the Weather Research and Forecasting model to train species-specific random forest models that predict the LFMC of four fuel types-chamise, old growth chamise, black sage, and bigpod ceanothus. These models are then utilized to create a historical, 32-year long, LFMC dataset in southern California chaparral. Additionally, the high spatial and temporal sampling frequency of chamise allowed for quantile mapping bias correction to be applied. The final chamise output, which is the most robust, has a mean absolute error of 9.68% and an R2 value of 0.76. The LFMC dataset successfully captures the variability in the annual cycle, the spatial heterogeneity, and the interspecies differences, which makes it applicable for better understanding varying fire season characteristics and landscape level flammability.

Prey species need to adjust their habitat selection either temporally or spatially to reduce predation risk or human disturbance. We tested the risk allocation hypothesis that poses that foraging is higher in low-risk habitats and during low-risk periods for impala (Aepyceros melampus) in the Serengeti Ecosystem. We expected impalas to select ranges that balance long-term predation risk and forage availability and adjust their habitat utilization within their ranges temporally to balance short-term predation risk and energy intake. We modelled multi-scale resource selection for 36 GPS-tracked impala (2016-2018) to disentangle spatial and temporal trade-offs between forage acquisition and predation risk using resource selection functions at the landscape and within-home-range level. We contrasted resource selection inside and outside of Serengeti NP (SNP) for woody cover, forage availability (vegetation heterogeneity, NDVI) and risky places (proximity to water, terrain ruggedness). We also modelled responses to diel and lunar cycles in space use. Impalas attuned their resource selection both towards maximizing forage availability and minimizing risk exposure, especially at the landscape level. While home-ranges were generally placed in more homogeneous woodland close to water sources, impalas preferred to use more heterogeneous, open and rugged patches within their home-ranges. They adjusted their responses outside SNP by placing their home-ranges in less rugged terrain and farther from water sources. Impalas balanced foraging and risk temporally by adjusting their preferences to circalunar and diel patterns. Impala attuned their home-range placement and utilization according to the 'landscape of fear' hypothesis inhabiting a 'squeezed' landscape. Here, prey to a functionally diverse carnivore guild clearly balanced their space use between food acquisition and spatio-temporal risk avoidance. Humans, however, acted as a 'super-predator' eliciting a stronger behavioural response compared to natural predators. Following the risk allocation hypothesis, the presence of humans was of such an intensity and predictability that it changed the anti-predator responses in impala compared to the adjacent natural system with carnivores only. A functional response in habitat selection indicated that impala avoided long-term spatial risk in home-range placement, while within their home-ranges short-term temporal risk mimicked the diel patterns observed within the protected area with natural predators only.

Indonesia’s potato fields are typically small and fragmented, making coarse resolution moisture products prone to spatial mismatch and limiting their usefulness for precision water management. This study developed plot scale, water based suitability information for potato by integrating UAV multispectral imagery with field measurements of soil water availability and plant height response. UAV imagery was processed into four vegetation indices, namely NDWI, SAVI, MSAVI, and SR, followed by geostatistical mapping. Relationships between indices and measured water availability were evaluated using correlation, linear regression, paired t test, and principal component analysis to examine inter index structure and redundancy. NDWI showed the most consistent performance, with a moderate positive correlation with measured water availability (r = 0.47), while SAVI and MSAVI were negatively correlated (r = −0.46) and SR showed the weakest association (r = −0.33). The NDWI based regression for water availability estimation was y = 0.50x + 29.68 with R² = 0.22. The paired t test indicated no significant difference between NDWI based estimates and field measurements, with mean values of 30.09 percent and 30.52 percent, respectively, across 17 observations. Water based land suitability classes were then refined using boundary line analysis linking water availability to plant height response, producing plot scale criteria suitable for precision zoning rather than landscape level evaluation.

ABSTRACT This research provides new directions in conceptualizing emotional geographies at the landscape level using a combination of textual deep learning (DL) and GPT‐based spatial emotion detection and interpolation using 3D empirical Bayesian kriging. The resulting multidimensional raster predicts the prevalence of emotions across the landscape, with each dimension representing a different emotion. The results show the potential for both DL and GPT methods in spatial emotion detection using contextual windows, achieving high accuracy rates in most measures, though indicating the need for improvement in precision (DL) and recall (GPT). The methodology provides further direction forward in developing a geographic information system (GIS) of place that is spatially accurate and sensitive to human experience. It also contributes to the understanding of Holocaust rescue spatially and contextually.

Abstract This study reconstructs the evolution, over the past 35 years, of the plant landscape in the Oreto River Basin (Palermo, NW Sicily), an area of high ecological value included in the Site of Community Importance ITA020012. A vegetation map produced in 1990 was georeferenced and digitalized, then compared with a new land-use map generated from satellite imagery, field-checked, and classified according to the CORINE Land Cover level 3 system. The analyses show that artificial surfaces increased from 8.86% to 15.89% of the Basin, driven by urban sprawl, while agricultural land declined from 38.83% to 29.10%. Pure citrus groves decreased by 14.69 km², whereas olive groves (+ 3.89 km²) and mixed orchards (+ 5.50 km²) expanded, often in response to growing water scarcity. Natural and semi-natural areas show a slight overall increase (from 52.09% to 54.72%), despite a sharp loss of conifer reforestations (–8.17 km²) due to recurrent fires, which fostered the expansion of natural grasslands (+ 13.80 km²). The fragmentation of relict communities of Chrysojasminum fruticans , Ilex aquifolium , Ostrya carpinifolia , and Platanus orientalis , together with the decline of Carex panormitana near urban areas, highlights emerging conservation concerns. Overall, the findings underscore the urgent need for integrated policies to curb uncontrolled urbanization, prevent fires, and enhance both forest biodiversity and the traditional agricultural landscape.

Nest-site selection in birds is a hierarchical process shaped by environmental filters operating across multiple spatial scales. In species that depend on burrows excavated by ecosystem engineers, understanding how these filters interact is essential for effective conservation. We evaluated nest-site selection by the Burrowing owl (Athene cunicularia) within colonies of the Mexican prairie dog (Cynomys mexicanus) in the southern Chihuahuan Desert using a multiscale analytical framework spanning burrow, site, colony, and landscape levels. During the 2010 and 2011 breeding seasons, we located 56 successful nests and paired each with an inactive non-nest burrow within the same colony. Eighteen structural and environmental variables were measured and analyzed using binary logistic regression models, with model selection based on an information-theoretic approach (AICc) and prior screening for predictor collinearity. Nest-site selection was associated with greater internal burrow development and reduced external exposure at the burrow scale, proximity to satellite burrows and low-to-moderate vegetation structure at the site scale, higher densities of active prairie dog burrows at the colony scale, and reduced predation risk and agricultural disturbance at the landscape scale. The integrated multiscale model showed substantially greater support and discriminatory power than single-scale models, indicating that nest-site selection emerges from interactions among spatial scales rather than from isolated factors. These findings support hierarchical habitat-selection theory and underscore the importance of conserving functional Mexican prairie dog colonies and low-disturbance grassland landscapes to maintain suitable breeding habitats for Burrowing owls in the southern Chihuahuan Desert.

Abstract. Forest soils are generally considered a sink for atmospheric methane (CH4), but their uptake rate can vary considerably in space and time. This study investigated the role of topography and vegetation on soil CH4 fluxes and the temporal patterns of spatially upscaled soil CH4 fluxes in a topographically complex cold-temperate mountain forest in central Japan. We measured soil CH4 fluxes nine times during the snow-free season at multiple locations within a 40 ha area in a forested watershed. Non-waterlogged soils were a sink of CH4, while small wetland patches emitted CH4 consistently throughout the study period. We used a machine-learning approach to upscale the measured soil CH4 fluxes to the landscape scale for non-waterlogged soils at each date of measurement, using topographic and vegetation attributes derived from a digital elevation model and aerial images. The accuracy of predicted fluxes varied seasonally, with the highest model performance observed in early autumn (R2=0.67) and the lowest in mid-summer (R2=0.31). Predicted CH4 fluxes varied significantly across topographic positions, with greater uptake on ridges and slopes than on the plain and foot slopes. Topography played a predominant role compared to vegetation in the spatial variability of CH4 fluxes. Predicted CH4 fluxes at the landscape scale in the non-waterlogged area ranged from −0.34 to −0.60 gCH4ha-1h-1 in spring, −0.39 to −1.28 gCH4ha-1h-1 in summer, and −0.48 to −0.89 gCH4ha-1h-1 in autumn. Seasonal fluxes were highly correlated with the 20 d antecedent precipitation index (R2=0.70), revealing the importance of seasonal moisture conditions in regulating CH4 flux dynamics. This study highlighted the importance of topography in controlling soil CH4 fluxes and the efficiency of remote sensing and machine learning approaches to scale field measurements to the landscape level, enabling visualization of spatial patterns of fluxes across the landscape over time, despite high uncertainty on some measurement dates, particularly for low elevation pixels.

Functional diversity (FD) is an essential community property connecting biodiversity, ecosystem functioning, and conservation objectives. In agricultural landscapes, avian communities, which play key functional roles, are facing large‐scale biodiversity erosion, largely due to land‐use changes. However, the long‐term spatiotemporal dynamics of FD and its responses to land use remain poorly understood. This study addressed this gap by analysing bird community data collected from lowland agricultural landscapes at a regional scale between 2007 and 2021. We analysed temporal trends in FD metrics (richness, divergence, evenness), their relationships with species richness, changes in functional composition, patterns of clustering/overdispersion, and examined the multi‐scale effects of land use across a local‐to‐landscape gradient. FD metrics were computed using a probabilistic hypervolume approach and standardised against null expectations. Cause–effect relationships were explored using generalized additive models. Results revealed a decline in FD metrics, despite recent recoveries in richness and divergence. Species richness increased linearly and negatively influenced FD metrics. These findings, coupled with evidence of functional clustering, suggested a loss of functions and originality in the functional space, an increase of redundancy, and a shift towards traits associated with forest‐dwelling, larger‐bodied or omnivorous species. Land use was found to affect FD, likely acting as an environmental filter promoting trait clustering. Built‐up areas exerted a detrimental effect on all FD metrics at smaller scales, while arable land increased FD across all scales. Woody features increased FD primarily at the landscape level, while hedgerows showed a reversed effect on evenness. Meadows and shrubs were important to sustain functional divergence at the local scale. Wetlands increased functional richness and divergence without affecting evenness. These findings raise important questions on the relationships between FD and species richness, emphasise the value of long‐term data, and highlight the benefit of a multi‐scale spatial approach for guiding land‐use planning and biodiversity conservation.

The timing of breeding is an important aspect of any species' realised niche, reflecting adaptations to synchronise with food supplies, dilute predation, avoid competition and exploit seasonal fluctuations in resources. Breeding phenology is typically studied either through long-term monitoring of focal populations (limiting the strength of inferences about species-wide traits and trends) or, when conducted at a landscape level, using remotely visible traits (restricting most studies to plants). For the first time, this study demonstrates landscape-scale measurement of vertebrate breeding phenology using a network of 77 time-lapse cameras to monitor three sympatric penguin species across 37 colonies in the Antarctic Peninsula and Sub-Antarctic islands. Camera temperature loggers showed penguin colony locations are warming up four times faster (0.3°C/year) than the continental average (0.07°C/year), already the second fastest-warming area in the world. We analysed the start of the breeding season of Adélie, Chinstrap and Gentoo penguins at a sub-continental scale between 2012 and 2022. The phenology of all three species advanced at record rates (10.2 ± 2, 10.4 ± 1.5 and 13 ± 4 days/decade, respectively). Different demographic trends as well as intra- and inter-species differences in response to environmental change suggest niche-based response differences between species. Phenological advances are causing niche separation to reduce. In this context, the Gentoo penguins' generalist and resident nature seems better suited to compete for space and resources than krill-specialist Chinstraps and ice-specialist Adélies. Synthesis: A decade of observation of the three pygoscelid penguins shows they are advancing their settlement phenology at record speeds in relation to climate change across the Antarctic Peninsula. These changes are species-dependent, reflecting different vulnerabilities and opportunities depending on their niche and life-history traits. In the long term, the trend towards earlier settlement risks increasing inter-species competition, causing trophic and temporal mismatch, and reshaping community assemblages.

Understanding the scale-dependent mechanisms linking landscape patterns to ecosystem services is crucial for sustainable land management, especially in fragmented hilly regions. This study, conducted in the hilly areas of southern China, aimed to quantitatively unravel these mechanisms at an optimal spatial scale. We first identified 14,400 km2 as the scale where landscape metrics stabilized. Using Spatial Error Models (SEM) to control for spatial autocorrelation, we analysed the distinct effects of landscape configuration on key ecosystem services. At the class level, forest aggregation was a consistent positive driver for multiple services; for example, it maintained a stable, significant positive relationship with carbon sequestration across all study years (P < 0.01). Conversely, farmland edge (total edge) significantly promoted nutrient export (P < 0.001), highlighting a functional contrast with natural landscapes. At the landscape level, total edge exhibited a consistent positive effect on several ecosystem services (P < 0.001), whereas increased landscape evenness was a primary inhibitory factor, showing a significant negative correlation with habitat quality (P < 0.001) and a strengthening negative effect on nutrient retention over time (P < 0.01). These findings provide a scale-specific, quantitative foundation for spatial planning, underscoring the necessity of maintaining forest connectivity and strategically managing agricultural-natural land interfaces to enhance ecosystem services bundles in heterogeneous landscapes.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-34384-7.

Most of the disease associated genetic variants identified in genome wide association studies have been mapped to the non-coding regions of the genome. One of the leading mechanisms by which these variants are thought to affect disease susceptibility is by altering transcription factor (TF) binding. Even though inbred mouse strains have been commonly used to investigate polygenic diseases, less is known on how their genetic differences translate to the level of gene regulation and chromatin landscape. Here, we investigated how genetic variation affects chromatin accessibility in the epididymal white adipose tissue (eWAT) of C57BL/6J and 129S1/SvImJ mice, which are commonly used to study diet-induced obesity, fed either chow or high-fat diet. We show that differences in chromatin accessibility are almost exclusively strain-specific and driven by genetic variation. In addition, we integrate ATAC-seq (chromatin accessibility) and H3K27ac ChIP-seq (active regulatory regions) data to show that tissue-specific TF binding sites are commonly found in the active regulatory regions hosting TF motif altering variants in eWAT. Using footprint analysis, we also show that TF occupancy is consistent with TF binding motif scores at the genetically altered loci. In addition, we validate these findings by extending the analysis to ATAC-seq and H3K27ac ChIP-seq data obtained from the liver. We employ RNA-seq to show that differentially expressed genes are co-located with differentially accessible regions hosting genetic variants. Overall, our findings highlight the connection between differential chromatin accessibility and genetic variation across metabolically central tissues of a mouse model for polygenic obesity.

Since the mid-20th century, the landscapes of Mediterranean mountain regions have undergone a significant transformation, linked to the socioeconomic changes caused by the opening up of these regions to the market economy. This prompted a rural exodus, the abandoning of farmland and the reduction in livestock, so activating various reforestation processes. In parallel, the “green revolution” promoted the modernization of agrifood systems, so contributing to the decline of traditional ways of farming in mountain areas. The farms on which traditional polyculture and agroforestry are still carried out today are important agrobiodiversity reserves. In this research, we monitor the dynamics of land use and cover and the changes in the structure of the agrifood landscapes on the southern slopes of Sierra Nevada (Spain) by comparing maps from 1956, 1984, 2007 and 2020. The results reveal a sharp decline in cultivated land, from 39.19% to 21.54%, and an expansion of natural covers, especially Mediterranean forest, driven by the abandonment of farmland and reforestation policies. Today, the landscape is composed of a more fragmented, less cohesive mosaic of agroecosystems. These changes indicate a reduction in agrobiodiversity at a landscape level, in line with the tendency observed at farm level in the study area.

Large old trees are widely recognized as ecologically important across forest landscapes and concern regarding the decline of these trees is well documented because of their role in maintaining biodiversity for a broad range of organisms. In response to a growing need to inventory such trees, we developed and present the methodology to map and quantify the occurrence of large trees based on height and dbh thresholds using airborne LiDAR data and associated canopy height models. The innovative, succinct, and flexible solution we offer is based on the integration and augmentation of several existing packages within the open‐source statistical software R. We use local tree‐height and crown diameter data to calibrate an algorithm to count individual trees above specified height thresholds, including supercanopy trees. To satisfy large‐tree definitions based on dbh, we used individual‐tree height and dbh data available from existing forest inventory plots to define height–dbh curves for dominant forest community types, which then allowed height thresholds to be used as a surrogate for specified dbh thresholds. We illustrate the use of these methods to efficiently map and quantify large tree distributions within 8 forest communities across a study area consisting of 1.65 million ha of productive, industrially managed forest in New Brunswick, Canada. Spatial maps are presented, along with large‐tree frequency statistics for specific communities, according to the definitions outlined in New Brunswick's provincial forest management guidelines. In excess of 37 million large trees are estimated to be broadly distributed across the study area. The methods developed identify patterns in the distribution of large trees across extensive areas (e.g., in millions of hectares) as one metric for maintenance of biodiversity at the landscape level. The methodology may be readily adapted to alternative forest‐specific definitions of large trees based on tree height or dbh.

Vegetated field margins generally increase plant biodiversity and connectivity in agricultural landscapes. They can deliver ecosystem services, such as providing food and shelter for insects, or maintaining biotic regulation. But they can also represent a risk, for example by hosting competitor plants or cultivated crop pests. In this work, we evaluated the effects of agricultural practices on indicators of three ecosystem services (providing floral resources for pollinators, reducing soil erosion and conserving plant biodiversity), and one ecosystem disservice (competing with the crop by hosting problematic weeds). We used a French nationwide-scale monitoring network, composed of more than 450 fields of cereals, vineyards, and market gardening. Plant sampling and agricultural practices surveys were conducted from 2013 to 2018. We unambiguously found that pesticide use, at either field or municipality levels, or both, had detrimental effects on ecosystem service indicators. Herbicide use and fertilization quantity decreased floral resources, affecting both their quantity and diversity. Pesticide use was also associated with fewer nature-value species and more problematic weeds. Margin management could also sometimes affect the service and disservice indicators. This work not only increases the knowledge on the unintentional negative impacts of agricultural practices on ecosystem service indicators, and then probably on their delivery, but also demonstrates that pesticide reduction is positively associated with proxies for ecosystem services. It also stresses the fact that these practices have to be implemented at both field and municipality levels.

The Ebo forest covers about 140,000 hectares of coastal forest in the Littoral Region of Cameroon. It is one of the most ecologically rich forests in the Gulf of Guinea biodiversity hotspot. This forest, which harbors many globally threatened and endemic plant species, is also home to various emblematic fauna species. Based on a literature review and informal discussions with local communities, this study aims to provide current knowledge on the Ebo primate population, discuss their major threats at landscape level, and present current conservation efforts and perspectives on human-wildlife coexistence. The Ebo forest is rich in primates, featuring four nocturnal species (Galagoides thomasi, Arctocebus calabarensis, Sciurocheirus alleni, and Perodicticus edwardsi) and ten diurnal species, including two African great apes (Gorilla gorilla spp. and Pan troglodytes ellioti), one Drill (Mandrillus leucophaeus leucophaeus), one mangabey (Cercocebus torquatus), one colobus (Piliocolobus preussi), and five guenons (Allochrocebus preussi, Cercopithecus nictitans, C. mona, C. erythrotis, and C. pogonias). The Black and White monkey (Colobus guereza) is nowadays considered locally extirpated, and five other species are becoming rare or very rare. Hunting, wood extraction, slash-and-burn agriculture, and unplanned community resettlement represent the main anthropogenic threats to the Ebo primate community. There is a need for an appropriate integrated conservation strategy on the ground based on biological research and community-led conservation initiatives to safeguard the survival of Ebo primate populations. Additionally, inclusive land-use planning in this key biodiversity hotspot is a way to strategize for the long-term coexistence of humans and the Ebo primate assemblage.

Agri-Environment Measures (AEM) are the primary policy tools under the European Union’s Common Agricultural Policy to combat farmland biodiversity loss, yet their effectiveness is highly variable. This synthesis paper summarizes findings of a doctoral thesis investigating drivers of variation in modelled biodiversity responses to AEM: (1) the use of different land-use intensity (LUI) metrics, (2) the types of AEM and their species-specific scale of effect, (3) landscape structural complexity across different regions. First, virtual species (i.e. species with known species-environment relationships) were used to explore how using alternative LUI metrics in biodiversity models influences the estimated species-AEM relationships. Second, bird observations for the Mulde River Basin in Germany were used to model farmland bird responses to different AEM across scales. These relationships varied across species and AEM type, but were generally strongest at the landscape level as compared to locally. Lastly, landscape-moderated effects of AEM on red-backed shrike (Lanius collurio) occurrences were analyzed across three study regions in Germany, Spain and Czechia. Positive shrike-AEM associations were stronger in structurally simple compared to complex landscapes, but this effect was inconsistent across regions. These findings exemplify species-, scale- and landscape-dependent AEM effects, and support AEM’s spatial targeting and regional tailoring.

Land Cover-Land Use (LC/LU) classification provides data for effective management of environmental and ecological decisions at the landscape scale. In this process, Sentinel-2 Multi Spectral Imager (MSI) satellite images contribute to classification methods by facilitating information extraction with their high spectral resolution. While index-based methods mostly focus on the separation of single classes, landscapes require the separation of multiple classes. This study shows how different spectral indexes derived from Sentinel-2 MSI imagery can be used in large areas with the object-based image classification technique. The Silifke district of Mersin province was selected as a sample area. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Built-up Area Extraction Index (BAEI), Built-up Area Index (BAI), Band Ratio (BR28, BR38), Normalized Built-up Area Index (NBAI), New Building Index (NBI), Urban Index (UI), Normalized Difference Soil Tillage Index (NDTI), Red Edge Based Normalized Difference Vegetation Index (NDVIre) and Normalized Difference Water Index (MNDWI) were used. While no significant results were obtained with BR28, BR38, NBAI, NBI and UI, 0.8815 kappa coefficient of 0.8815 and overall accuracy rate of %94.11 were obtained with other indexes.

Russia’s sustainable construction transition remains fragmented despite policy alignment with international frameworks. This study applies the Multi-Level Perspective (MLP), analysing literature and 40 expert interviews to examine interactions across landscape (macro policies, geopolitical shifts), regime (industry standards, regulations), and niche (innovations, actors) levels. Findings reveal a hybrid pathway characterized by niche innovations and transforming sustainability practices, like green building certification systems, digitalisation, co-creation and urban management, enhancing quality of life, and fostering pro-environmental behaviors aligned with sustainability values and norms. Niche practices shaped regime-level regulations, emphasizing civic engagement and developing strategic city goals for new forms of transformative urban and spatial development. Landscape constraints and challenges (geopolitics, climate, demography, urban economics) create barriers to the broad diffusion of niches, yet techno-institutional synergies are expected to align at the landscape level in future decades to support governmental city planning goals. MLP offers significant potential for analysing transitions in collective sustainability behavior through socio-technical regime interactions. The MLP effectively maps how actor-networks bridge niches and regimes, enabling rules and patterns reconstruction. Russia’s transition adjusts techno-institutional synergies and active stakeholder engagement while addressing challenges to achieve systemic sustainability. Success hinges on leveraging adaptive niche innovations within evolving landscape pressures and governance frameworks.