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Record W3197546080 · doi:10.47886/9781934874561.ch4

Advances in Understanding Landscape Influences on Freshwater Habitats and Biological Assemblages

2019· book-chapter· en· W3197546080 on OpenAlexaboutno aff

Bibliographic record

VenueAmerican Fisheries Society eBooks · 2019
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTributaryOncorhynchusSan JoaquinHabitatPopulationEnvironmental scienceGeographyChinook windDrainage basinFisheryEcologyHydrology (agriculture)Fish <Actinopterygii>BiologyGeology

Abstract

fetched live from OpenAlex

&lt;i&gt;Abstract.&lt;/i&gt;—Climatic variation is a key driver of freshwater physical processes that in turn control stream fish growth and population dynamics at fine spatial scales and species distributions across broad landscapes. A recent downturn in Chinook Salmon &lt;i&gt;Oncorhynchus tshawytscha &lt;/i&gt;returns across the Yukon River basin, Alaska, USA and Yukon Territories, Canada has led to hardship among user groups and increased interest in understanding how freshwater processes affect population persistence within this important commercial, recreational, and subsistence fishery. Here, we present results for the Chena River basin, interior Alaska, where we used field observations and riverscape-scale spatially explicit models to assess the influence of stream temperature on juvenile Chinook Salmon growth potential among years (2003–2015) and across 438 stream kilometers. We ran bioenergetic simulations for warm and cool year scenarios and contrasted temperature model precision and growth among different habitat types (small and large tributaries, main stem, and side channels) based on field estimates of growth, size, diet, and measured stream temperatures. Stream temperature regimes predicted from remotely sensed land surface temperatures were precise during the open water season (&lt;I&gt;R&lt;/I&gt;&lt;sup&gt;2&lt;/sup&gt; &gt; 0.87; root-mean-squared error &lt; 1.1°C), although the relationship was weakest in groundwater-mediated tributary habitats. Field observations revealed salmon were 67% larger by mass (g) in September during a warm year versus a cool year from main-stem sites. Bioenergetic simulations predicted that, on average, growth potential was 42% higher in warm years, although growth potential varied across the riverscape as much as 60% between cool upstream and warm downstream habitats. Climate variability is clearly an important driver of freshwater habitat conditions and has a large role in controlling freshwater growth of juvenile salmon. A better understanding of how climate influences growth conditions in different habitat types and across broad landscapes will be critical for conservation and management of Alaskan Chinook Salmon stocks under an expected warmer and more variable climate.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.729
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.230
Teacher spread0.205 · 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.

Study designObservational
Domainnot available
GenreOther

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

Citations2
Published2019
Admission routes1
Has abstractyes

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