Environmental Evidence最新文献

{"title":"Mapping large bodies of research in environmental sciences: insights from compiling evidence on the recovery and reuse of nutrients found in human excreta and domestic wastewater.","authors":"Robin Harder","doi":"10.1186/s13750-025-00366-5","DOIUrl":"10.1186/s13750-025-00366-5","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48621,"journal":{"name":"Environmental Evidence","volume":"14 1","pages":"13"},"PeriodicalIF":3.4,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12261714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of aminoglycoside exposure on soil and plant root-associated microbiota: a meta-analysis.","authors":"Jessica L Coates, Ashanti J Lawson, Kathleen Bostick, Mentewab Ayalew","doi":"10.1186/s13750-025-00365-6","DOIUrl":"10.1186/s13750-025-00365-6","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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?\"&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We searched 5 academic databases and 1 specialist organization database for scientific journal publications written in any language. Articles were included based on the criteria described in Coates et al., 2022. Included studies were subject to critical appraisal using the CEE Critical Appraisal Tool Version 0.2 (Prototype) to evaluate their susceptibility to confounding factors, misclassification bias, selection bias, attrition bias, reporting bias and analysis bias. Studies deemed to be high risk based on critical appraisal results were excluded from further analysis. Descriptive data analysis was performed for studies considered low or unclear for risk of bias. Meta-analyses were conducted for antibiotic resistance and microbial diversity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Review findings: &lt;/strong&gt;Out of 8370 screened records, 50 articles fulfilled the search criteria, and from these, 13 studies were included in meta-analysis. Most studies investigated the impact of aminoglycoside exposure on soil microbiota (93%) in a laboratory setting (62%), primarily from the United States (32%), China (24%), France, Switzerland and Germany (8%). A limited number of studies investigated the impact of aminoglycoside exposure on disease suppression, so it was excluded from meta-analysis. Therefore, our synthesis primarily details the impact of aminoglycoside exposure on the microbial diversity and antibiotic resistance of the soil microbiota. Overall, exposure to aminoglycosides did not result in a significant change in the microbial diversity. However, soil use, pH, and type of aminoglycoside used could be potential modifiers. Additionally, we observed an average 7% of the microbial population exhibiting resistance to aminoglycosides, with the relationship between the exposure concentration and the selection concentration emerging as a potential modifier.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Current research is limited by gaps in understanding the relationship between","PeriodicalId":48621,"journal":{"name":"Environmental Evidence","volume":"14 1","pages":"12"},"PeriodicalIF":3.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12243174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144610029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What evidence exists on wild bee trends in Germany? A systematic map.","authors":"Anne-Christine Mupepele, Niels Hellwig, Petra Dieker, Alexandra-Maria Klein","doi":"10.1186/s13750-025-00364-7","DOIUrl":"10.1186/s13750-025-00364-7","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>Our search for data on wild bee trends was conducted in November 2020 and included peer-reviewed literature (from Web of Science databases and Scopus) and grey literature in English and in German. After screening the literature by title and abstract, relevant data were extracted from eligible studies. All eligible studies included data on wild bee taxa from at least two years at the same site within Germany and using the same sampling method. The database consists of data sheets on studies (bibliographic context), on covariates (methodological and spatio-temporal context) and on data (bee species sampled at a specific time on a specific location - exemplarily for two German regions).</p><p><strong>Review findings: </strong>The database contains 382 studies out of 24,486 initial records. Nearly 75% of the full texts screened did not include field data on wild bees from at least two different years and were therefore excluded. Studies date back to the 1880s, with a consistently high number of studies since the 1990s. Most studies were published in German-language journals of entomological societies in Germany. Data originate from different types of land use throughout the country, mostly from southern and north-western Germany and from urban areas.</p><p><strong>Conclusions: </strong>The systematic map shows that there is a lack of long-term monitoring studies. Moreover, there are research gaps in land use categories and federal states, which hinder more regional or land use-specific analyses. We encourage researchers and practitioners to use the database for further analyses on bee trends and their drivers, and the potential success of mitigation measures.</p>","PeriodicalId":48621,"journal":{"name":"Environmental Evidence","volume":"14 1","pages":"11"},"PeriodicalIF":3.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12178071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of perches in promoting bird-mediated seed dispersal for natural forest regeneration: a systematic review.","authors":"Jelaine L Gan, Matthew J Grainger, Mark David Foster Shirley, Sheena Davis, Molly Watson, Shreya Dube, Marion Pfeifer","doi":"10.1186/s13750-025-00363-8","DOIUrl":"10.1186/s13750-025-00363-8","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In September 2023, we searched for studies in eight bibliographic sources, which include an organizational library and a web-based search engine, using a refined search string in English. After deduplication, we conducted double screening at title and abstract, then at full text level to check for eligibility (e.g., compared perches versus control). The final list of studies underwent critical appraisal based on risk of bias and method validity, then data extraction. We used Hedges' g as our effect size and fitted each outcome in a three-level meta-regression model. We also tested the effect of matrix type, bioregion, and precipitation variation as modifiers, and conducted sensitivity analysis based on risk of bias and method validity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Review findings: &lt;/strong&gt;After screening, we accepted a total of 396 studies in 79 articles for the review. The majority of these studies examined seed (49%) and seedling density (28%) outcomes over richness, using mostly natural perches (68%) and, to a lesser frequency, artificial and semi-natural perches. Most studies that included distance to forest edge as a factor reported no effects (n = 68). We then analysed 333 studies in a meta-analysis. Results showed that natural perches had overall positive effects in increasing seed and seedling density and richness, while artificial and semi-natural perches were effective only for seed outcomes. We found high heterogeneity in our models, with perch effectiveness affected by matrix type, bioregion, precipitation variation, method specificity, as well as study quality. In general, perches showed robust positive effects in shrublands and grasslands in tropical, subtropical, and mediterranean biomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The use of perches can be an effective ANR strategy to increase seed rain and seedling establishment in a variety of degraded landscapes. We recommend that natural perches be preserved in the matrix, but in areas lacking these natural features, to explore the use of artificial or semi-natural perches to increase seed rain and follow it up with additional treatments, such as soil amelioration and weeding, to improve seedling establishment. Due to insufficient data, we could not analyse the effect of distance to forest edge as a moderator in our meta-regression models. This gap can be addressed by e","PeriodicalId":48621,"journal":{"name":"Environmental Evidence","volume":"14 1","pages":"10"},"PeriodicalIF":3.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12166613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144295080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating generative AI for qualitative data extraction in community-based fisheries management literature.","authors":"S Spillias, K M Ollerhead, M Andreotta, R Annand-Jones, F Boschetti, J Duggan, D B Karcher, C Paris, R J Shellock, R Trebilco","doi":"10.1186/s13750-025-00362-9","DOIUrl":"10.1186/s13750-025-00362-9","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48621,"journal":{"name":"Environmental Evidence","volume":"14 1","pages":"9"},"PeriodicalIF":3.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12128520/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Verifying authors' claims to have conducted a Systematic Review? A checklist for journal editors and peer reviewers.","authors":"Andrew S Pullin, Biljana Macura","doi":"10.1186/s13750-025-00361-w","DOIUrl":"10.1186/s13750-025-00361-w","url":null,"abstract":"","PeriodicalId":48621,"journal":{"name":"Environmental Evidence","volume":"14 1","pages":"8"},"PeriodicalIF":3.4,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12076875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144026113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Testing the utility of GPT for title and abstract screening in environmental systematic evidence synthesis.","authors":"Björn Nykvist, Biljana Macura, Maria Xylia, Erik Olsson","doi":"10.1186/s13750-025-00360-x","DOIUrl":"https://doi.org/10.1186/s13750-025-00360-x","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48621,"journal":{"name":"Environmental Evidence","volume":"14 1","pages":"7"},"PeriodicalIF":3.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12016299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144023071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the effects of the main agronomic interventions on carabids and spiders in European arable fields: A systematic review protocol.","authors":"Coralie Triquet, Yvonne Fabian, Philippe Jeanneret","doi":"10.1186/s13750-025-00359-4","DOIUrl":"https://doi.org/10.1186/s13750-025-00359-4","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>The evidence will be identified from the recent systematic map on the impacts of agricultural management practices on biodiversity indicator species groups published in 2024. We will select all studies reporting the effect of the most studied agronomic interventions (fertilization, tillage, pesticide application, mowing and grazing) in arable fields (arable crops and temporary grasslands) on carabids and spiders in the map database. A search update will be performed using the search strings used for the systematic map for carabids and spiders, and extracted references will be sorted at title, abstract and full text levels according to the topic of the present work. All selected studies will be critically appraised and a low, medium, or high risk of bias will be assigned to each study. The synthesis of the data extracted from the studies will be first narrative (using qualitative data), and then quantitative for those with adequate data for a meta-analysis.</p>","PeriodicalId":48621,"journal":{"name":"Environmental Evidence","volume":"14 1","pages":"6"},"PeriodicalIF":3.4,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144023409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-assisted evidence screening method for systematic reviews in environmental research: integrating ChatGPT with domain knowledge.","authors":"Chen Zuo, Xiaohao Yang, Josh Errickson, Jiayang Li, Yi Hong, Runzi Wang","doi":"10.1186/s13750-025-00358-5","DOIUrl":"https://doi.org/10.1186/s13750-025-00358-5","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48621,"journal":{"name":"Environmental Evidence","volume":"14 1","pages":"5"},"PeriodicalIF":3.4,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11998256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evidence of the impacts of pharmaceuticals on aquatic animal behaviour (EIPAAB): a systematic map and open access database.","authors":"Jake M Martin, Marcus Michelangeli, Michael G Bertram, Paul J Blanchfield, Jack A Brand, Tomas Brodin, Bryan W Brooks, Daniel Cerveny, Kate N Fergusson, Malgorzata Lagisz, Lea M Lovin, Isaac Y Ligocki, Shinichi Nakagawa, Shiho Ozeki, Natalia Sandoval-Herrera, Kendall R Scarlett, Josefin Sundin, Hung Tan, Eli S J Thoré, Bob B M Wong, Erin S McCallum","doi":"10.1186/s13750-025-00357-6","DOIUrl":"10.1186/s13750-025-00357-6","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We systematically searched two electronic databases (Web of Science and Scopus) and supplemented these with additional article sources. The search string followed a Population-Exposure-Comparison-Outcome framework to capture articles that used an aquatic organism (population) to test the effects of a pharmaceutical (exposure) on behaviour (outcome). Articles were screened in two stages: title and abstract, followed by full-text screening alongside data extraction. Decision trees were designed a priori to appraise eligibility at both stages. Information on study validity was collected but not used as a basis for inclusion. Data synthesis focused on species, compounds, behaviour, and quality themes and was enhanced with additional sources of metadata from online databases (e.g. National Center for Biotechnology Information (NCBI) Taxonomy, PubChem, and IUCN Red List of Threatened Species).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Review findings: &lt;/strong&gt;We screened 5,988 articles, of which 901 were included in the final database, representing 1,739 unique species-by-compound combinations. The database includes data collected over 48 years (1974-2022), with most articles having an environmental focus (510) and fewer relating to medical and basic research topics (233 and 158, respectively). The database includes 173 species (8 phyla and 21 classes). Ray-finned fishes were by far the most common clade (75% of the evidence base), and most studies focused on freshwater compared to marine species (80.4% versus 19.6%). The database includes 426 pharmaceutical compounds; the most common groups were antidepressants (28%), antiepileptics (11%), and anxiolytics (10%). Evidence for the impacts on locomotion and boldness/anxiety behaviours were most commonly assessed. Almost all behaviours were scored in a laboratory setting, with only 0.5% measured under field conditions. Generally, we detected poor reporting and/or compliance with several of our study validity criteria.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our systematic map revealed a rapid increase in","PeriodicalId":48621,"journal":{"name":"Environmental Evidence","volume":"14 1","pages":"4"},"PeriodicalIF":3.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11924672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}