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Record W4380853031 · doi:10.1002/rra.4173

Cumulative effects of dams on migratory fishes across the conterminous United States: Regional patterns in fish responses to river network fragmentation

2023· article· en· W4380853031 on OpenAlexaff
E. M. Dean, Dana M. Infante, Hao Yu, Arthur R. Cooper, Lizhu Wang, Jared Ross

Bibliographic record

VenueRiver Research and Applications · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsWindsor Utilities Commission (Canada)
FundersCollege of Engineering, Michigan State UniversityU.S. Geological SurveyMichigan State University
KeywordsEcoregionFragmentation (computing)HabitatGeographyCumulative effectsEcologyHabitat fragmentationUrbanizationClimate changeEnvironmental scienceFisheryBiology

Abstract

fetched live from OpenAlex

Abstract Globally, dams fragment river networks, threatening migratory fishes which require access to distinct habitats to complete their life cycles. Efforts to understand how cumulative effects of multiple dams affect migratory fishes across large regions, such as a country or continent, could help to identify locations for connectivity‐enhancing actions to conserve migratory fishes. To address this, we evaluated cumulative effects of dams on migratory fishes in rivers across nine ecoregions of the conterminous USA. First, using fish data from thousands of sites ( N = 45,989), we summarized ecoregional patterns in assemblages, quantifying the number of migratory species comprising assemblages, showing the prominence of potamodromous species across the large region as well as differences in migratory life history traits among ecoregions. Next, we compared the importance of a set of river network fragmentation metrics that captured influences of multiple dams in networks versus other anthropogenic landscape stressors and natural landscape factors that impact migratory fishes by ecoregion. We found that migratory fishes were more sensitive to cumulative dam effects than other stressors including urbanization and agriculture in the eastern USA. To further identify specific effects of environmental variables on potamodromous fishes, we conducted Boosted Regression Trees analysis in the eastern ecoregions. Our results suggested that the key natural influences on river fishes included catchment area as well as river baseflow and air temperature, suggesting that migratory fishes may be affected by changing climate. Additionally, we found that downstream dams were more influential than other human stressors to potamodromous fishes, underscoring the importance of enhancing connectivity within river networks to conserve migratory fishes. Collectively, our results provide new insights in identifying threats to migratory fish species across the USA, providing information that can aid in conserving this vulnerable but ecologically and socioeconomically important group of fishes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.366
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2023
Admission routes1
Has abstractyes

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