Environmental Evidence最新文献

Background: Coastal ecosystems, including seagrass meadows, saltmarshes, and macroalgae, are crucial in the sequestration and storage of organic carbon. These ecosystems provide essential ecosystem services, such as supporting biodiversity, coastal protection, and water quality enhancement. Despite their significance, they face substantial threats from human activities, including pollution, habitat degradation, and overexploitation, further exacerbated by climate change phenomena like heatwaves and ocean acidification. Efforts to protect, restore, or alleviate pressures on blue carbon ecosystems can yield multifaceted benefits beyond climate mitigation, including preserving biodiversity, enhancing climate resilience, and safeguarding vital services for human well-being. Understanding the factors affecting the biodiversity and carbon capacity i.e. the capacity for carbon uptake, storage and sequestration, of these ecosystems is crucial for effective conservation efforts. The goal of the present study is to assess the available quantitative and qualitative evidence on the impacts of human activities on the biodiversity and carbon storage capacity of blue carbon ecosystems in the North-East Atlantic. Developing a systematic map of the available evidence could significantly enhance our understanding of the pressures faced by blue carbon ecosystems in the North-East Atlantic and facilitate the identification of knowledge clusters and gaps thereby determining the scope and depth of the current knowledge base.

Methods: A systematic map on existing evidence of human impacts on the biodiversity and carbon capacity of blue carbon ecosystems in the North-East Atlantic will be conducted using relevant bibliographic databases and a web-based search engine. All searches will be conducted in English and will gather peer reviewed publications from 1980 to 2024. The resulting literature will be screened by two independent screeners at the level of title and abstract followed by full text against a set of eligibility criteria (i.e. population, intervention, outcome, study type). Metadata will be extracted from studies that meet the eligibility criteria and summarize with heatmaps, bar plots, geographic distribution maps, and tabular summaries.

Background: As people work towards environmental sustainability for urban environments and everyday lives, tensions have been seen in different efforts on food, housing, environmental management, urban planning, and many cross-cutting issues touching on multiple aspects of social-ecological systems. Urban agriculture (UA) as one multifaceted, cross-cutting arena, has had one particular tension regarding relationships with housing and the built environment: its gentrification potential. However, different accounts have provided evidence and theorization of gentrification as a possible outcome of UA activities, as a risk for UA initiatives, and showing still other relationships between UA and gentrification. These different accounts may be partially explained by different theoretical engagements with gentrification, as well as multiple activities constituting a broad notion of urban agriculture. An overview of the scholarly work regarding these two topics can provide a starting point for understanding how they have been approached and theoretically engaged together, and demonstrate gaps in dominant academic discourses.

Methods: This research for a systematic mapping of literature seeks to assess the academic work around relationships between urban agriculture and gentrification. The protocol outlines a comprehensive and reliable search and review strategy based on the core components of urban, agriculture, and gentrification in search strings and inclusion criteria. Texts in English, French, and German will be scanned as historically and currently dominant academic languages, while searching nine bibliographic databases or platforms. The protocol details a data coding strategy for metadata, empirical content, and analytic content. The results are expected to uncover sources of evidence for links between urban agriculture and gentrification, producing interoperable datasets of the evidence base, insights of the overall research landscape, and possibilities to find research gaps.

Artificial intelligence (AI) is increasingly being explored as a tool to optimize and accelerate various stages of evidence synthesis. A persistent challenge in environmental evidence syntheses is that these remain predominantly monolingual (English), leading to biased results and misinforming cross-scale policy decisions. AI offers a promising opportunity to incorporate non-English language evidence in evidence syntheses screening process and help to move beyond the current monolingual focus of evidence syntheses. Using a corpus of Spanish-language peer-reviewed papers on biodiversity conservation interventions, we developed and evaluated text classifiers using supervised machine learning models. Our best-performing model achieved 100% recall meaning no relevant papers (n = 9) were missed and filtered out over 70% (n = 867) of negative documents based only on the title and abstract of each paper. The text was encoded using a pre-trained multilingual model and class-weights were used to deal with a highly imbalanced dataset (0.79%). This research therefore offers an approach to reducing the manual, time-intensive effort required for document screening in evidence syntheses-with minimal risk of missing relevant studies. It highlights the potential of multilingual large language models and class-weights to train a light-weight non-English language classifier that can effectively filter irrelevant texts, using only a small non-English language labelled corpus. Future work could build on our approach to develop a multilingual classifier that enables the inclusion of any non-English scientific literature in evidence syntheses.

The global demand for high-quality, robust and up-to-date evidence to guide decision-making has never been higher. The vast quantity of scientific literature being produced and made accessible presents an unparalleled opportunity for evidence-based decision-making to become a widespread reality. In addition, the world has at its fingertips cutting-edge technologies, such as AI, to make sense of this extensive knowledge base and deliver insights more quickly to decision-makers most in need. AI-powered evidence syntheses promises to be transformative, saving many lives and enhancing livelihoods globally. However, achieving this requires substantial cultural shifts in the evidence community, including amongst both AI developers and users to shape both trustworthy AI and trust in AI. Current efforts to establish best practices are emerging, but progress is hindered by the lack of clear consensus on what constitutes trustworthy AI for evidence synthesis. Philanthropic investments in trustworthy AI systems, alongside robust evaluations of trust in AI for evidence synthesis, must be prioritised to determine the conditions required for an enabling environment. Mainstreaming AI for reliable, faster and cheaper evidence synthesis demands a better understanding of trustworthy AI and trust in these systems. Funders should prioritise aspects of trustworthiness and trust whilst balancing the drive towards ongoing innovation.

Background: Over the last decade, a paradigm shift has been initiated in the field of nature management and conservation with shifting the focus from traditional, more static conservation efforts to dynamic conservation efforts. To promote dynamic restoration efforts, it is essential to provide nature managers with tools to measure the impact and effectiveness of relevant interventions. However, despite increasing practice, quantifying restoration management in a relevant and measurable way remains challenging. Therefore, this systematic map aims to elucidate which metrics are being used to measure the impact of dynamic nature management working with natural processes.

Methods: To assess which metrics are being used to measure this impact, we will perform a systematic map in Web of Science, Scopus and Agricola. In addition, we will search for grey literature through directed visits to organizational websites, search ProQuest for relevant PhD theses on the topic and perform a search in Google Scholar. For the latter, we will only consider the first 200 articles. We will include articles conducted based on research in natural areas within temperate zones, where natural dynamics (e.g., grazing, hydrology, fire) are present, introduced or restored, and are assessed using before/after or control/impact study designs. The selected studies should mention measurements of the natural process restoration outcome related to relevant biodiversity metrics (e.g., richness, diversity, abundance). Literature from review studies will be included to identify other relevant articles. All studies positively assessed as relevant through the criteria above will be subject to critical appraisal. Hereafter, we will use the critical appraisal tool as issued by Environmental Evidence. The data obtained will be used to create an overview of restoration and conservation current practices in order to identify knowledge gaps. We will disseminate our results to nature managers and provide a time- and cost- assessment of each measurement to create a guide on monitoring of dynamic nature management.

Background: Freshwater ecosystems are globally imperiled, with monitored vertebrate populations showing an average 83% decline since 1970. Braiding Traditional Ecological Knowledge (TEK) with Western science is increasingly recognized by global bodies like the IPBES (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services) as essential for achieving the transformative change needed to address this crisis. This systematic map provides a comprehensive, global synthesis of the diverse methodologies used for this purpose by answering the primary question: What is the evidence base for methodologies (approaches, frameworks, or models) that braid the TEK of Indigenous and local communities with Western science in the planning, management, monitoring, or assessment of freshwater social-ecological systems? The resulting synthesis is intended to empower researchers, practitioners, and policymakers to design more effective and equitable management strategies.

Methods: Following Collaboration for Environmental Evidence (CEE) guidelines, our protocol employs a multi-layered search strategy across three core bibliographic databases, targeted grey literature sources (including dissertations and key organizational websites), and a supplementary review-centric snowballing search. Records will be screened for eligibility in a two-stage process (Title/Abstract and Full-text) with robust consistency checking to ensure transparency and minimize bias. Data from included articles will be coded using a detailed protocol designed to answer our secondary questions and build a typology of knowledge braiding methodologies. The systematic map's outputs will include a narrative synthesis identifying knowledge gaps and clusters, a comprehensive public database of included studies, and a suite of interactive data visualizations.

Background: The global agriculture sector is expected to contribute towards carbon net zero by adopting interventions to reduce/offset greenhouse gas emissions and increase carbon sequestration/removal. Many of these interventions require change to land management and agriculturally associated habitats, subsequently impacting biodiversity. This relationship is important as the Convention on Biological Diversity has also pledged to reverse nature decline. To understand this relationship, a systematic map was developed to collate evidence relating to the impacts of carbon footprint reducing interventions on agriculturally associated biodiversity. This systematic map collated studies from temperate farming systems including northern Europe, North America and New Zealand.

Methods: A protocol was published to define the methodology. Potentially relevant articles were identified by searching three academic databases using a predefined search string. Also, nine organisational websites were searched using key words. All potentially relevant articles were exported into EPPI-Reviewer-Web. Following deduplication, the remaining articles were screened at title and abstract level, partially with the aide of machine learning, before full text screening and extraction of metadata.

Review findings: Screening began with 67,617 articles that ended with an evidence base of 820 primary research studies and 82 reviews. The evidence base includes studies from 1978 to April 2024, of which 81% were studies that lasted less than 5 years. Whilst microorganisms (n = 328), arthropods (n = 190), worms (n = 121) and plants (n = 118) were well represented in the evidence base, other groups such as birds (n = 32), gastropods (n = 16), mammals (n = 13), amphibians (n = 1) and reptiles (n = 1) were represented less well. The most studied interventions were to increase soil organic carbon through reduced tillage (n = 227) and cover cropping (n = 136). However, there were less than five studies in total for the following land management objectives: avoiding soil compaction (n = 2), precision farming (n = 2) and renewable energy production. Study authors reported carbon footprint-reducing practices to positively impact biodiversity in 65% of studies, to have mixed effects in 11%, negative in 8% and no effect in 16% of studies. As no critical appraisal was carried out on the included studies, we recommend further study validation and synthesis in order to support these findings.

Conclusions: The evidence base has highlighted evidence clusters and gaps on how farming practices that can reduce the carbon footprint of a farm impacts agriculturally associated biodiversity. There are many areas for further research including studies investigating the long-term relationship of interventions that alter habitats over a long period such as rewetting peat soils and increasing tree cover. Future research sh