Marine ecosystems worldwide face extreme stress from human activities, with the North Sea being particularly affected and experiencing altered processes. To assess anthropogenic drivers for sustainable management, the Millenium Ecosystem Assessment (MEA) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) distinguished five main anthropogenic drivers: direct exploitation of fish and seafood, sea use change, human-driven climate change, pollution, and invasive alien species. However, evidence of the drivers' relevance and their potential effects on species and the environment over time remains scarce. This systematic map provides knowledge on the five main anthropogenic drivers in the North Sea from 1945 to 2020 and identifies potential knowledge gaps in terms of management implications.

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.

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.

Estuarine coastal regions play a critical role in global aquatic ecosystems, providing essential benefits such as diverse marine habitats, support for local economies through fisheries and tourism, and serving as important carbon stocks. Nonetheless, these invaluable, dynamic and complex habitats are under increasing threat from human-induced pressures, including pollution from agricultural runoff to sewage discharge, emphasizing the urgent need for innovative monitoring and mitigation strategies. Traditional biomonitoring methods involve the use of indicator species such as fish and benthic macroinvertebrates; however, these can be limited in their ability to detect pollution at an early stage. As a result, alternative monitoring strategies such as the use of algae have become increasingly popular due to their abundance sensitivity to changes in water quality. Previous research recognizes the capacity of various algae species to accumulate pollutants, thereby serving as reliable indicators of ecological stress and water contamination. Despite the growing acknowledgment of their potential, a comprehensive evaluation of the effectiveness of algae as biomonitors in estuaries remains without a systematic review. This map, therefore, seeks to synthesize existing knowledge on the applicability and reliability of algae for coastal environmental monitoring, aiming to highlight existing knowledge gaps for a future systematic review. By focusing on the utility of algae in estuarine contexts, this study aspires to provide a comprehensive overview of current practices and propose recommendations. Such an endeavor is crucial for directing future research, informing stakeholders, and guiding policy formulation towards more sustainable and effective environmental management of estuaries. This map aims to be a valuable resource for those involved in the management and preservation of estuarine environments, contributing to discussions on sustainable water management and ecological conservation.

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.

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.

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.

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.

1. Evidence synthesists are ultimately responsible for their evidence synthesis, including the decision to use artificial intelligence (AI) and automation, and to ensure adherence to legal and ethical standards. 2. Cochrane, the Campbell Collaboration, JBI and the Collaboration for Environmental Evidence support the aims of the Responsible use of AI in evidence SynthEsis (RAISE) recommendations, which provides a framework for ensuring responsible use of AI and automation across all roles within the evidence synthesis ecosystem. 3. Evidence synthesists developing and publishing syntheses with Cochrane, the Campbell Collaboration, JBI and the Collaboration for Environmental Evidence can use AI and automation as long as they can demonstrate that it will not compromise the methodological rigour or integrity of their synthesis. 4. AI and automation in evidence synthesis should be used with human oversight. 5. Any use of AI or automation that makes or suggests judgements should be fully and transparently reported in the evidence synthesis report. 6. AI tool developers should proactively ensure their AI systems or tools adhere to the RAISE recommendations so we have clear, transparent and publicly available information to inform decisions about whether an AI system or tool could and should be used in evidence synthesis.

As the global climate rapidly warms, one pervasive impact is the "borealisation" of the Arctic. Borealisation occurs when the species, communities and ecological processes of the Arctic transform to resemble that of more boreal lower latitudes. Such change is likely to have profound impacts on the diverse communities and cultures of the Arctic. Some of these impacts are starting to be documented, however this evidence has not been synthesised systematically. This systematic map protocol will therefore address the research question: "What evidence exists on the interlinkages between ecological and societal impacts of borealisation of the Arctic?" Additionally, this systematic map will support two current assessments of the Arctic Council working groups on the societal and ecological impacts of climate change in the Arctic, thus responding to policy relevant questions posed by Arctic governments.

Mapping evidence on a particular research topic among others aims to provide a comprehensive overview of the topic along with a searchable database of relevant literature. When attempting to map large bodies of research, mappers may soon find themselves in a situation where the resources available for the mapping are incommensurate to the number of studies to be handled. This typically requires either a narrower scope of the map or a streamlined mapping process. Grounded in a comparison of five evidence maps on the topic of recovery and reuse of nutrients found in human excreta and domestic wastewater-some of them systematic, some not-the present paper sets out to quantify the potential effect of procedural differences on mapping outcomes. Ultimately, the goal is to discern the factors that matter most for comprehensive and balanced mapping outcomes. This exploration suggests that a good search strategy is key when mapping large bodies of research, especially so when terminology is barely standardized. The paper also sheds light to an issue that could be described as differential search term sensitivity and specificity (compound search terms that are not equally sensitive and specific across all subdomains of the map) and that may deserve more attention in evidence mapping. Drawing from my experiences from compiling the online evidence platform Egestabase, the paper sketches how this issue might be mitigated. In addition, the paper outlines several measures that can help achieve substantial efficiency gains, and offers reflections on how to set priorities and navigate tradeoffs when a standard systematic mapping process appears not to be viable and not strictly necessary.

Exposure to aminoglycosides, a class of potent bactericidal antibiotics naturally produced by soil microorganisms and commonly used in agriculture, has the potential to cause shifts in the population dynamics of microorganisms that impact plant and soil health. In particular, aminoglycoside exposure could result in alterations of the soil and plant root-associated bacterial species diversity and richness due to their potent inhibitory action on microbial growth, the creation of selective conditions for the proliferation of antibiotic-resistant bacteria, or a reduction in the ability to suppress soil pathogens. Previous studies have attempted to understand the relationship between aminoglycoside exposure and the plant-associated microbiota with varying results. Thus, this systematic review aims to survey all relevant published data to answer the question, "What is the impact of aminoglycoside exposure on the soil and plant root-associated microbiota?"

Noise from human activities is a major concern for wildlife, with numerous studies demonstrating significant impacts. In 2020, Sordello and collaborators systematically mapped the literature on the impacts of anthropogenic noise on wildlife up to 2018. Since then, research on this topic has continued to grow steadily. To reflect these developments, we present an updated systematic map encompassing studies published through 2023, exclusively focused on airborne noise.

Uptake of AI tools in knowledge production processes is rapidly growing. In this pilot study, we explore the ability of generative AI tools to reliably extract qualitative data from a limited sample of peer-reviewed documents in the context of community-based fisheries management (CBFM) literature. Specifically, we evaluate the capacity of multiple AI tools to analyse 33 CBFM papers and extract relevant information for a systematic literature review, comparing the results to those of human reviewers. We address how well AI tools can discern the presence of relevant contextual data, whether the outputs of AI tools are comparable to human extractions, and whether the difficulty of question influences the performance of the extraction. While the AI tools we tested (GPT4-Turbo and Elicit) were not reliable in discerning the presence or absence of contextual data, at least one of the AI tools consistently returned responses that were on par with human reviewers. These results highlight the potential utility of AI tools in the extraction phase of evidence synthesis for supporting human-led reviews, while underscoring the ongoing need for human oversight. This exploratory investigation provides initial insights into the current capabilities and limitations of AI in qualitative data extraction within the specific domain of CBFM, laying groundwork for future, more comprehensive evaluations across diverse fields and larger datasets.

Assisted Natural Regeneration (ANR) is an increasingly popular cost-effective approach to restore forests for climate change mitigation and biodiversity conservation. One ANR strategy is the use of perches to attract avian seed dispersers to degraded landscapes for increased seed supply and seedling establishment. This systematic review sought to determine the effectiveness of artificial, semi-natural, and natural perches in promoting natural forest regeneration, specifically in driving four outcomes: seed richness, seed density, seedling richness, and seedling density.

Wild bees have attracted growing attention from both the scientific community and civil society, alongside increasing evidence of biodiversity losses. Declining wild bee populations threaten both the quality and quantity of pollination, which also affect crop production and are therefore critically important for human wellbeing. Landscape homogenisation, land use changes, land use intensity, and climate change are driving the decline. Despite concerns about the wild bee decline, knowledge of wild bee population patterns and long-term trends across Germany remains limited. Here, we present a systematic map, including a newly developed comprehensive database that compiles available data on temporal trends in wild bee communities across Germany. Our goal is to provide an overview of the frequency of wild bee trend studies over time and the land use types and geographical areas they have covered.

Systematic reviews (SRs) in environmental science is challenging due to diverse methodologies, terminologies, and study designs across disciplines. A major limitation is that inconsistent application of eligibility criteria in evidence-screening affects the reproducibility and transparency of SRs. To explore the potential role of Artificial Intelligence (AI) in applying eligibility criteria, we developed and evaluated an AI-assisted evidence-screening framework using a case study SR on the relationship between stream fecal coliform concentrations and land use and land cover (LULC). The SR incorporates publications from hydrology, ecology, public health, landscape, and urban planning, reflecting the interdisciplinary nature of environmental research. We fine-tuned ChatGPT-3.5 Turbo model with expert-reviewed training data for title, abstract, and full-text screening of 120 articles. The AI model demonstrated substantial agreement at title/abstract review and moderate agreement at full-text review with expert reviewers and maintained internal consistency, suggesting its potential for structured screening assistance. The findings provide a structured framework for applying eligibility criteria consistently, improving evidence screening efficiency, reducing labor and costs, and informing large language models (LLMs) integration in environmental SRs. Combining AI with domain knowledge provides an exploratory step to evaluate feasibility of AI-assisted evidence screening, especially for diverse, large volume, and interdisciplinary studies. Additionally, AI-assisted screening has the potential to provide a structured approach for managing disagreement among researchers with diverse domain knowledge, though further validation is needed.

Designing agroecological cropping systems enhancing functional biodiversity and natural pest regulations requires understanding the ecological processes involved, specifically regarding the response of generalist predators. A more precise knowledge of the changes in ground-dwelling communities implied by individual agronomic interventions is needed to make enlightened and consistent choices in the design of such innovative cropping systems. A recent systematic map showed that fertilization, tillage, pesticides use, grazing and mowing are the most studied agronomic interventions regarding their effects on arthropods. The direct and indirect effects of disturbances induced by agronomic interventions on ground-dwelling arthropods in arable fields have been widely investigated, especially for carabids and spiders. However, there is not always a clear pattern outstanding, probably due to antagonistic responses of species with different functional traits. Here, we propose a quantified synthesis on this topic. We will show the impact of the main agronomic interventions in arable fields on the two most studied ground-dwelling predator groups, carabids and spiders, and compare their response (abundance, species richness, taxonomic and functional diversity) in different contexts (crop types and production methods). We will investigate contrasting responses at different taxonomic levels depending on functional traits.

In this paper we show that OpenAI's Large Language Model (LLM) GPT perform remarkably well when used for title and abstract eligibility screening of scientific articles and within a (systematic) literature review workflow. We evaluated GPT on screening data from a systematic review study on electric vehicle charging infrastructure demand with almost 12,000 records using the same eligibility criteria as human screeners. We tested 3 different versions of this model that were tasked to distinguishing between relevant and irrelevant content by responding with a relevance probability between 0 and 1. For the latest GPT-4 model (tested in November 2023) and probability cutoff 0.5 the recall rate is 100%, meaning no relevant papers were missed and using this mode for screening would have saved 50% of the time that would otherwise be spent on manual screening. Experimenting with a higher cut of threshold can save more time. With threshold chosen so that recall is still above 95% for GPT-4 (where up to 5% of relevant papers might be missed), the model could save 75% of the time spent on manual screening. If automation technologies can replicate manual screening by human experts with effectiveness, accuracy, and precision, the work and cost savings are significant. Furthermore, the value of a comprehensive list of relevant literature, rather quickly available at the start of a research project, is hard to understate. However, as this study only evaluated the performance on one systematic review and one prompt, we caution that more test and methodological development is needed, and outline the next steps to properly evaluate rigor and effectiveness of LLMs for eligibility screening.

Over the last decade, pharmaceutical pollution in aquatic ecosystems has emerged as a pressing environmental issue. Recent years have also seen a surge in scientific interest in the use of behavioural endpoints in chemical risk assessment and regulatory activities, underscoring their importance for fitness and survival. In this respect, data on how pharmaceuticals alter the behaviour of aquatic animals appears to have grown rapidly. Despite this, there has been a notable absence of systematic efforts to consolidate and summarise this field of study. To address this, our objectives were twofold: (1) to systematically identify, catalogue, and synthesise primary research articles on the effects of pharmaceuticals on aquatic animal behaviour; and (2) to organise this information into a comprehensive open-access database for scientists, policymakers, and environmental managers.