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Climate change remains a primary threat to inland fishes and fisheries. Using topic modeling to examine trends and relationships across 36 years of scientific literature on documented and projected climate impacts to inland fish, we identify ten representative topics within this body of literature: assemblages, climate scenarios, distribution, climate drivers, population growth, invasive species, populations, phenology, physiology, and reproduction. These topics are largely similar to the output from artificial intelligence application (i.e., ChatGPT) search prompts, but with some key differences. The field of climate impacts on fish has seen dramatic growth since the mid-2000s with increasing popularity of topics...
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Inland fishes provide important ecosystem services to communities worldwide and are especially vulnerable to the impacts of climate change. Fish respond to climate change in diverse and nuanced ways which creates challenges for practitioners of fish conservation, climate change adaptation, and management. Although climate change is known to affect fish globally, a comprehensive online, public database of how climate change has impacted inland fishes worldwide and adaptation or management practices that may address these impacts does not exist. We conducted an extensive, systematic primary literature review to identify peer-reviewed journal publications describing projected and documented examples of climate change...
Categories: Data;
Tags: Africa,
Americas,
Asia,
Europe,
Middle East, All tags...
Oceania,
USGS Science Data Catalog (SDC),
USGS:63eff8edd34efa0476b039b7,
climate adaptation,
climate change,
climate change,
database,
fish,
fisheries management,
inland,
inland fishery resources,
utilitiesCommunication, Fewer tags
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This script applies topic modeling to analyze literature trends of climate impacts to inland fish based on the papers within the Fish and Climate Change Database (FiCli, DOI: 10.5066/P9973SMC). Sections 1-8 loaded the .bib file with all of the papers in the database and cleaned the text. This included combining the title/abstract/keywords, removing non-informative words, stemming words, removing punctuation, and forming phrases (ie. climate change to climate_change). Sections 9-10 divided the papers into discrete topics by identifying the ideal number of topics and then using Latent Dirichlet Allocation (LDA) modeling and Gibbs sampling to assign topics to each paper. Sections 11-17 analyzed the topic modeling results...
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