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Hypothesized relationships of functional groups differentiated along gradients of selected assembly filters a, The Tropical forests biome (T1), with temperature, elevation and water availability gradients. b, The Rivers and streams biome (F1), with stream gradient and temporal flow pattern. c, The Marine pelagic biome (M2), with depth and current gradients. In a, a third filter related to an edaphic environmental gradient differentiates group T1.4 from T1.1, but is not shown here (see Supplementary Information, Appendix 4, for details on the respective functional groups).
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As the United Nations develops a post-2020 global biodiversity framework for the Convention on Biological Diversity, attention is focusing on how new goals and targets for ecosystem conservation might serve its vision of ‘living in harmony with nature’1,2. Advancing dual imperatives to conserve biodiversity and sustain ecosystem services requires r...
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The present investigation had to objective to give a methodologist that can restores the mangroves and with that, there ecosystems services, it favored resilience to climatic change, and it contributed to local development to coast community. The methodologist was creative for experiences to investigators in thematic, specially to multidisciplinary...
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... The IUCN have recently designed a global, hierarchical classification system, the Global Ecosystem Typology 2.0 (GET), to provide a global foundation for ecosystem assessments, sustainable management and conservation (Keith et al. 2020b(Keith et al. , 2022. The IUCN GET describes the first three tiers of the hierarchical system as: 1) 'realms' (terrestrial, freshwater, marine, subterranean and atmosphere); 2) 'biomes' and 3) 'ecosystem functional groups' (EFGs). ...
... EFGs are functionally (and, in practice, structurally) different groups determined by the expression of ecological drivers along temporally variable multidimensional gradients and can be geographically scattered in patches across continents and the world. As an example, within the GET 'T1 Tropical-subtropical forests' biome, the 'T1.1 Tropical subtropical lowland rain forest' and 'T1.2 Tropical subtropical dry forests and thickets', are separated by both contrasting vegetation structure and phenology and differences in drivers, such as water availability, linked to soil and substrate properties (Pennington et al. 2020;Keith et al. 2020aKeith et al. , 2022). EFGs are the hierarchical level that allows correspondence between local and global classification systems (UN 2021) and have recently been compared to Brazilian biome maps by the Brazilian Institute of Geography and Statistics (IBGE 2021). ...
... The IUCN GET tries to address many of the critiques of biome classification noted above, presenting a hierarchical approach to global vegetation delimitation which emphasises the processes that shape ecosystem properties and the interactions between processes and vegetation form. It aims to meet six criteria: 1) incorporate ecosystem functions and ecological processes; 2) encapsulate characteristic biota of ecosystems; 3) conceptual consistency at the global scale; 4) scalability; 5) provide spatially explicit units; and 6) parsimony (Keith et al. 2020b;Keith et al. 2022). There has been some work towards the fifth criterion for the IUCN GET (providing spatially explicit units as a wall-to-wall map at the level of EFGs), for example, by providing indicative maps of the likely occurrence of each EFG (Keith et al. 2020b). ...
Accurate biome delineation is difficult where biomes occupy the same climatic space, as is the case for tropical dry forest and savanna. The resulting confusion limits our ability to understand and manage impacts of global change on these biomes. To address this, we developed an unsupervised, repeatable method to delineate biomes and their component functional ecosystems, based on landscape-level vegetation structure measured using remote sensing and an understanding of the ecology of the region. This approach contrasts with previous definitions, based on climate differences amongst savanna, dry forest and rain forest.
Using the heterogeneous north-east Brazil, where several biomes interdigitate, as a case study, a hierarchical functional ecosystem classification is proposed that aligns with both the IUCN Global Ecosystem Typology (GET) and previous work. Based on fuzzy clustering of remotely sensed vegetation attributes, seven groups were found, identified as rain forest, cerrado (savanna) and five caatinga vegetation groups. These groups broadly align with the literature, for example, sedimentary and arboreal caatinga. These groups align with three ‘Ecosystem Functional Groups’ (EFGs) described by the IUCN GET and, additionally, suggest there is a new, fourth EFG in the region: non-pyric shrublands. Random Forest models showed soil pH was the most important environmental variable distinguishing these vegetation groups.
These results suggest a remotely sensed structure-based approach is an effective method for operationalising the IUCN GET. North-East Brazil – where many EFGs are interdigitated – serves as a challenging case study and, therefore, we hope our approach will have generality for other regions globally.
There are seven vegetation groups in northeast Brazil, including savanna, rain forest and five types of caatinga.
Most of these vegetation groups align with the IUCN Global Ecosystem Typology 2.0, but non-pyric shrubland (caatinga) vegetation may represent a new Ecosystem Functional Group.
Soil pH is the strongest determinant of vegetation distribution in northeast Brazil.
Remote sensing can provide objective, spatially explicit information on vegetation types in the region, largely consistent with previous vegetation classifications.
Accurate biome mapping is vital for management, as biomes differ in ecosystem function and consequently require different management.
... Additionally, a modeler might have knowledge of erosion processes of interest in their region that are poorly represented by RUSLE (e.g., erosion caused by wind, landslides, or streamflow, which causes streambank erosion). If a modeler knowledgeable of wind erosion modeling makes their model interoperable with other ES data 7 While full consensus on such definitions may be desirable, harmonized typologies (e.g., to IUCN's Global Ecosystem Typology, Keith et al., 2022) and crosswalks can be challenging to develop, and a single universal classification may not meet all user needs. However, explicitly noting the typology used and appropriate reuse constraints would allow clearer explanation of model reuse criteria (e.g., use of "model X is appropriate in Dinerstein et al., 2017 Deserts and xeric shrubland biomes" or "dataset Y is appropriately applied in Sayre et al., 2020 Tropical moist world climate regions"). ...
Despite continued, rapid growth in the literature, the fragmentation of information is a major barrier to more timely and credible ecosystem services (ES) assessments. A major reason for this fragmentation is the currently limited state of interoperability of ES data, models, and software. The FAIR Principles, a recent reformulation of long-standing open science goals, highlight the importance of making scientific knowledge Findable, Accessible, Interoperable, and Reusable. Critically, FAIR aims to make science more transparent and transferable by both people and computers. However, it is easier to make data and models findable and accessible through data and code repositories than to achieve interoperability and reusability. Achieving interoperability will require more consistent adherence to current technical best practices and, more critically, to build consensus about and consistently use semantics that can represent ES-relevant phenomena. Building on recent examples from major international initiatives for ES (IPBES, SEEA, GEO BON), we illustrate strategies to address interoperability, discuss their importance, and describe potential gains for individual researchers and practitioners and the field of ES. Although interoperability comes with many challenges, including greater scientific coordination than today's status quo, it is technically achievable and offers potentially transformative advantages to ES assessments needed to mainstream their use by decision makers. Individuals and organizations active in ES research and practice can play critical roles in creating widespread interoperability and reusability of ES science. A representative community of practice targeting interoperability for ES would help advance these goals.
... To ensure ecological accuracy in prompt design, the study utilized the Global Ecosystem Typology (GET) [30] as a classification system for structuring landscape descriptions. The GET was selected due to its extensive scientific validation, expert-reviewed classification structure and global applicability. ...
... The framework began with extracting keywords from the GET [30] to build a scientifically grounded prompt database. GET comprises six hierarchical levels, three of which-realms, biomes, and ecosystem functional groups-were used in this research. ...
... The three lower levels-regional ecosystem subgroups, global ecosystem types, and sub-global ecosystem types-classify ecosystems based on biotic composition. While essential for linking local and global classifications, they are still evolving and contain methodological inconsistencies, particularly in biogeographic boundaries (Level 4), local data integration (Level 5), and national typologies (Level 6) [30]. ...
Generative AI (GenAI), particularly text-to-image (TTI) models, is reshaping landscape representation by transforming textual descriptions into visual outputs. However, these models often reinforce biases embedded in their training datasets, shaping how landscapes are perceived and represented. This research examines the biases in GenAI-generated landscape imagery through the lens of Edward Said’s “imaginative geographies”, focusing on how geographic references, cultural archetypes, and methodological factors influence AI outputs. We employed a structured approach to create prompts based on the Global Ecosystem Typology (GET), using Midjourney V6.1 as the primary tool for image generation. We extracted landscape descriptors from GET classifications and structured them into both simple and detailed prompts. The analysis involved comparing AI-generated images to ecological classifications and reference images to assess biases. The findings reveal the following three key types of biases: (1) geographic biases, where certain locations act as semantic triggers, leading to culturally stereotypical portrayals; (2) representation biases, where landscapes frequently depicted in AI training datasets appear with greater accuracy, while underrepresented landscapes are simplified into essentialized visual tropes; and (3) methodological biases, where prompt complexity influences representational accuracy but does not eliminate pre-existing cultural hierarchies. To address the bias challenges, the research presents the following four key recommendations for future research and practice: (1) incorporating finer-scale ecosystem classifications; (2) diversifying training datasets; (3) engaging local communities in participatory data collection; and (4) refining prompt-writing methodologies. These insights contribute to broader discussions on AI bias, emphasizing the necessity of critically evaluating the role of generative models in shaping landscape imaginaries.
... No attempt is made here toward defining or redefining biomes (Mucina 2019;Keith et al. 2022 (Silveira et al. 2022). This same study also showed that more than 80% of the ecological literature on ecosystem restoration focused on forest ecosystems. ...
Biomism, the pervasive prejudice, discrimination or antagonism against a given biome, highlights critical and overlooked dimensions of human behavior biases that have consequences for real-world conservation. Here, I propose seven ways to end biomism in educational, scientific and conservation arenas, including (1) the recognition and value of all biomes, (2) use of inclusive language that acknowledges diverse perspectives, (3) preventing research prioritization based on colonial legacies, (4) tailoring biome-specific conservation, management and restoration, (5) adapting legislation to embrace all biomes, (6) developing inclusive regulatory measures and (7) equalizing funding opportunities. Recognizing and addressing biases against specific biomes is essential for fostering a more inclusive and equitable approach to conservation arenas and abandoning long-standing prejudices rooted in colonial legacies, aesthetic preferences and utilitarian views of nature.
... A barrier to the development of metrics that directly indicate fauna community condition is the lack of broad-scale typologies of fauna communities at appropriate spatial and ecological resolution. In the terrestrial realm, information used to distinguish ecosystem types tends to focus on soils, terrain, and vegetation (Sattler & Williams 1999;Parkes et al. 2003;Eyre et al. 2015;Keith et al. 2022). At broad scales, vegetationbased ecosystem classifications can be informative proxies for fauna community patterns (Thomson et al. 2009;McAlpine et al. 2016). ...
... Nevertheless, further work to match finer-resolution vegetation types identified under each of the State vegetation mapping exercises to bird communities (in most cases, a many-to-one relationship) could enable at least an initial indication of likely bird community distributions at a fine scale. Such matching may also enable alignment of bird community types with function-based ecosystem classifications, such as the IUCN Global Ecosystem Typology (Keith et al. 2022) The classification of ecological communities requires that continuous gradients and temporally dynamic systems reflective of natural systems are reduced to discrete, fixed classes. As such, the resultant typology will necessarily be a highly simplified and subjective representation of nature, imposed by humans to align with human perceptions. ...
Aim: Increasing interest in holistic measurement of the response of fauna communities to interventions requires suitable community condition metrics. However, the development of such metrics is hindered by the absence of broad-scale typologies at suitable spatial and ecological resolutions. We aimed to derive a preliminary typology of terrestrial bird communities for Australia, based on bird co-occurrence data, and describe and map the likely distribution of each community type across the continent.
Location: Mainland Australia, continental islands
Time period: 1973-2022
Major taxa studied: Aves
Methods: We used fine-scale co-occurrence data from standard 2-ha surveys in BirdLife Australia's citizen-science database. After filtering to reduce bias, we used hierarchical clustering followed by iterative consultation with experts to identify reliably distinct and recognisable terrestrial bird communities across Australia. We used Maxent to model the likely distributions of each community, and developed community descriptions based on each community's composition and distribution.
Results: The resultant typology included 29 reliably distinct and recognisable bird communities with major clusters corresponding with seven broad geographical regions. The distributions of bird communities did not correspond tightly to the boundaries of major vegetation groups, with most communities occurring across multiple vegetation types.
Main Conclusions: Our preliminary typology of bird communities provides a standard classification at a continental scale. It newly defines distinct bird communities as entities for which condition benchmarks can be established to allow assessment of their conservation status and monitoring of change over time. Refinement will enable cryptic communities in areas with sparse data to be identified. The method could be translated to other regions where adequate coverage of data in the form of standardised surveys of fauna are available. Vast biodiversity datasets delivered through citizen science programs provide the opportunity to develop such typologies for fauna communities, as a precursor to developing targeted and informative community condition metrics.
... Moreover, while these archipelagos are relatively similar in terms of habitat composition, the Canary Islands are isolated in featuring extensive seagrass meadows (Barberáet al., 2005;Creed et al., 2016;McKenzie et al., 2020;Schäfer et al., 2021;Bishop et al., 2022a) and being strongly influenced by the Canary Current upwelling system, promoting marine productivity and supporting species diversity (Sambe et al., 2016;Watermeyer et al., 2022). On the other hand, sandy shores are not as ubiquitous in the Azores (Bishop et al., 2022b) while Cabo Verde has relatively few artificial shorelines and submerged structures (Suthers et al., 2022a;Suthers et al., 2022b) and is outside of present-day rhodolith distribution (Fragkopoulou et al., 2021;Keith et al., 2022). ...
The northeast Atlantic Ocean contains multiple habitats considered critical for shark conservation, including nursery areas, migratory corridors and aggregation sites. In this context, updating knowledge on shark diversity and the threats affecting them in this region is essential to defining priorities and implementing the right management and conservation measures. Here, we show that Macaronesian and Cabo Verde marine ecoregions are home to 78 shark species (comprising 26 families), and 56% are threatened with extinction. The Canary Islands revealed the greatest richness (with 56 species), followed by Cabo Verde (53), Madeira (52), and the Azores (45). Cabo Verde presents fewer similarities with the rest of the islands. We also found that: i) Azores share more species with the Canary Islands than Madeira (despite the greater geographical proximity with the latter), and ii) there are no oviparous species in the Cabo Verde archipelago, contrary to the Canary Islands (5), the Azores (4), and Madeira (3). Fishing and habitat degradation are the most relevant anthropogenic pressures for the region, with Cabo Verde having the highest number of endangered species (66%) and a greater magnitude and diversity of threats. As such, this archipelago presents the highest priority area for shark conservation due to the intense industrial fishing in its waters, poor management measures in combination with its greater vulnerability to climate change.
... However, ARIES does not allow users to upload spatial data/polygons of specific boundaries, which is often needed to researchers or practitioners working on specific sites, mostly at small spatial scales. Furthermore, ARIES uses the IUCN Ecosystem Typology classification (Keith et al., 2022) and does not allow as much flexibility with other typologies of ecosystem types. INCA 10 similarly does not allow the desired level of flexibility as its identified users are ''EUROSTAT, Joint Research Centre (JRC) and EU Member States'', which mostly work at national or regional level (Buchhorn et al., 2022). ...
... With a temperate maritime climate, mild temperatures range from 14 • C in winter to 22 • C in summer, accompanied by high humidity and yearround rainfall that sustain lush landscapes (Cropper and Hanna, 2014). The islands' volcanic soil supports diverse flora and fauna, including unique evergreen forests Fig. 1 Keith et al., 2022) and numerous endemic species , particularly arthropods closely tied to native flora (Rego et al., 2019;Tsafack et al., 2022). This distinctive ecosystem faces significant conservation challenges due to high endemism, habitat degradation, and biological invasions Ferreira et al., 2016;Florencio et al., 2021). ...
... Technological knowledge remains a limiting factor, as highlighted by one expert: "Few actors in the industry really know about life cycle or circularity". Innovations such as building information modeling (BIM) offer promising solutions, reducing costs, time, and waste while driving industry transformation [65]. Continued investment in technological literacy will be pivotal for achieving CE-CDW goals. ...
This study investigates the design and implementation of circular economy (CE) strategies for managing construction and demolition waste (CDW) in the Santiago Metropolitan Region of Chile (SMRC). The research aimed to identify key factors influencing the current and future adoption of CE practices for CDW management related to socio-environmental, technical, financial, and strategic-regulatory aspects, employing the Delphi method to gather expert insights. Findings reveal that the lack of knowledge about sustainable practices and the absence of regulatory frameworks for CDW disposal are the most critical barriers to effective CE implementation. The study recommends enhancing public awareness and environmental education through government and school programs, as well as enacting stricter legislation to combat illegal disposal and encourage sustainable practices and valorization of secondary raw materials within companies. Additionally, it emphasizes the importance of designing projects that prioritize waste avoidance and the development of infrastructure, technology, and processes for efficient material separation and recycling. The research also highlights potential challenges such as stagnation in the adoption of sustainable practices, skilled labor shortages, and limited research and innovation. It underscores the need for a comprehensive approach to CDW management that integrates socio-environmental, technical, financial, and regulatory dimensions to promote sustainability at both regional and global levels.
... In this study, the target standard error for the overall accuracy was set to 0.01. Based on experience with similar coastline mapping efforts 28 , errors of commission are more common for muddy, sandy, and artificial coastlines, while biogenic and rocky coastlines are more accurate. Consequently, we conjectured that the producer's accuracy of the artificial, biogenic, sandy, muddy, rocky coastlines would be 0.85, 0.95, 0.85, 0.80 and 0.90 29 . ...
... However, no single classification system has comprehensively covered all aspects in scope or coverage 48,49 . The coastline classification system in this study was developed based on the IUCN Global Ecosystem Typology, a novel framework that integrates all of Earth's ecosystems into a unified theoretical context 28 . To adapt to the demands of global observation using remote sensing technology, this study consolidates supralittoral coastal biome into a biogenic coastline category. ...
The coastline reflects coastal environmental processes and dynamic changes, serving as a fundamental parameter for coast. Although several global coastline datasets have been developed, they mainly focus on coastal morphology, the typology of coastlines are still lacking. We produced a Global CoastLine Dataset (GCL_FCS30) with a detailed classification system. The coastline extraction employed a combined algorithm incorporating the Modified Normalized Difference Water Index and an adaptive threshold segmentation method. The coastline classification was performed a hybrid transect classifier that integrates a random forest algorithm with stable training samples derived from multi-source geophysical data. The GCL_FCS30 offers significant advantages in capturing artificial coastlines, reflecting strong alignment with location validation data. The GCL_FCS30 classification was found to achieve an overall accuracy and Kappa coefficient over 85% and 0.75. Each coastline category accurately covered the majority of the area represented in third-party data and exhibited a high degree of spatial relevance. Therefore, the GCL_FCS30 is the first global coastline category dataset covering the high latitudes in a continuous and smooth line vector format.
