Joint Biotic and Abiotic Spatial Turnover: A Basis for Modelling Ecosystem Pattern at Landscape Extents
Bibliographic record
Abstract
Ecosystem models are typically built to predict patterns of one or more ecosystem properties, and those properties are often biotic. While some ecosystem models incorporate either biotic and abiotic responses, biotic and abiotic variables are rarely applied jointly as responses in ecosystem models. Here we model continuous spatial turnover among 21 biotic and abiotic properties to explore forest ecosystem patterns across landscapes of Nova Scotia, Canada (55 000 km2) at high (10 x 10 m) resolution. To achieve this objective, we fit generalized dissimilarity models to field collected data on biotic and abiotic response variables and geographic and environmental gradients described by remotely sensed predictor variables. We develop three separate models targeting ecosystem, biotic, and abiotic responses to identify relationships among forest ecosystem properties, across levels of ecological organization. Our final ecosystem, abiotic, and biotic models explained 41.4, 29.03, and 50.9 percent of variance. Vegetation-based predictors were the most significant for our ecosystem and biotic response models, while topographic and hydrological predictors were foremost in our abiotic response model. We show how relationships among biotic and abiotic ecosystem properties collectively give rise to predicted patterns of forest ecosystem heterogeneity across Nova Scotia, with the strongest variations occurring along elevational and north-south gradients. Our emphasis on multiple ecosystem properties, and our simultaneous modelling of both biotic and abiotic responses, including ecosystem structural, compositional, and functional variables, differs from the approaches taken in most spatial ecosystem models. This study provides an analytical road map for scientists and conservation practitioners looking to predict continuous variation in ecosystem makeup and to apply those predictions for mapping emergent spatial ecosystem patterns. Such spatial models of ecosystem pattern are crucial for achieving national and sub-national commitments to global ecosystem conservation targets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".