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Resource–consumer diversity: testing the effects of leaf litter species diversity on stream macroinvertebrate communities

2009· article· en· W2082677100 on OpenAlexaff
John S. Kominoski, Catherine M. Pringle

Bibliographic record

VenueFreshwater Biology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFreshwater macroinvertebrate diversity and ecology
Canadian institutionsUniversity of British Columbia
FundersUniversity of GeorgiaNational Science Foundation
KeywordsLitterPlant litterBiologySpecies richnessEcologySpecies diversityDetritusEcosystem

Abstract

fetched live from OpenAlex

Summary 1. Understanding relationships between resource and consumer diversity is essential to predicting how changes in resource diversity might affect several trophic levels and overall ecosystem functioning. 2. We tested for the effects of leaf litter species diversity (i.e. litter mixing) on litter mass remaining and macroinvertebrate communities (taxon diversity, abundance and biomass) during breakdown in a detritus‐based headwater stream (North Carolina, U.S.A.). We used full‐factorial analyses of single‐ and mixed‐species litter from dominant riparian tree species with distinct leaf chemistries [red maple ( Acer rubrum ), tulip poplar ( Liriodendron tulipifera ), chestnut oak ( Quercus prinus ) and rhododendron ( Rhododendron maximum )] to test for additivity (single‐species litter presence/absence effects) and non‐additivity (emergent effects of litter species interactions). 3. Significant non‐additive effects of litter mixing on litter mass remaining were explained by species composition, but not richness, and litter‐mixing effects were variable throughout breakdown. Specifically, small differences in observed versus expected litter mass remaining were measured on day 14; whereas observed litter mass remaining in mixed‐species leaf packs was significantly higher on day 70 and lower on day 118 than expected from data for single‐species leaf packs. 4. Litter mixing had non‐additive effects on macroinvertebrate community structure. The number of species in litter mixtures (two to four), but not litter species composition, was a significant predictor of the dominance of particular macroinvertebrates (i.e. indicator taxa) within mixed‐species packs. 5. In addition, the presence/absence of high‐ ( L. tulipifera ) and low‐quality ( R. maximum ) litter had additive effects on macroinvertebrate taxon richness, abundance and biomass. The presence of L. tulipifera litter had both positive (synergistic) and negative (antagonistic) effects on invertebrate taxon richness, that varied during breakdown but were not related to litter chemistry. In contrast, the presence/absence of L. tulipifera had a negative relationship with total macroinvertebrate biomass (due to low leaf mass remaining when L. tulipifera was present and higher condensed and hydrolysable tannins associated with leaf packs lacking L. tulipifera ). Macroinvertebrate abundance was consistently lower when R. maximum was present, which was partially explained by litter chemistry [e.g., high concentrations of lignin, condensed tannins, hydrolysable tannins and total phenolics and high carbon to nutrient (N and P) ratios]. 6. The bottom‐up effects of litter species diversity on stream macroinvertebrates and litter breakdown are different, which suggests that structural attributes of macroinvertebrate communities may only partially explain the effects of litter‐mixing on organic matter processing in streams. In addition, stream macroinvertebrates colonising decomposing litter are influenced by resource diversity as well as resource availability. Broad‐scale shifts in riparian tree species composition will alter litter inputs to streams, and our results suggest that changes in the diversity and availability of terrestrial litter may alter stream food webs and organic matter processing.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.199
Teacher spread0.174 · 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
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

Citations53
Published2009
Admission routes1
Has abstractyes

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