STRUCTURAL EQUATION MODELLING: A NOVEL STATISTICAL FRAMEWORK FOR EXPLORING THE SPATIAL DISTRIBUTION OF BENTHIC MACROINVERTEBRATES IN RIVERINE ECOSYSTEMS
Bibliographic record
Abstract
ABSTRACT Benthic macroinvertebrates have been used widely as bioindicators to assess the condition of riverine ecosystems. However, understanding and modelling the spatial distribution of benthic macroinvertebrates within these ecosystems remain significant challenges for research and management. Statistical analyses of multivariate data sets offer opportunities to explore the ecological systems controlling the distribution of biota. This article reports a novel statistical analysis of a national‐scale data set from England and Wales using the structural equation modelling (SEM) framework. Relationships between water quality, physical habitat structure and indices reflecting benthic macroinvertebrate community structure were analysed using SEM. On the basis of data from 219 monitoring sites, structural equation models were built. These models explained 87% of the spatial variation in the average score per taxon index and 76% of the spatial variation in the Lotic Invertebrate Index for Flow Evaluation. Significant direct and indirect effects on these indices were exerted by water quality variables, particularly the concentrations of dissolved oxygen, biochemical oxygen demand and orthophosphate. Independent of water quality conditions, both biotic indices were directly affected by variables describing the structure and the degradation of physical habitat. The strengths of the SEM framework include (i) direct evaluation of a priori models against observed data, thereby supporting confirmatory analysis of theoretical models of ecological systems; (ii) specification of latent variables representing unmeasured constructs; and (iii) simultaneous assessment of multiple direct and indirect paths between variables within a model. These strengths define a framework with the potential to be applied widely in the development and testing of hypotheses regarding the processes operating within riverine ecosystems. Copyright © 2012 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".