Evidence for increases in vegetation species richness across UK Environmental Change Network sites linked to changes in air pollution and weather patterns
Bibliographic record
Abstract
We analysed trends in vegetation monitored at regular intervals over the past two decades (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) at the twelve terrestrial Environmental Change Network (ECN) sites. We sought to determine the extent to which flora had changed and link any such changes to potential environmental drivers. We observed significant increases in species richness, both at a whole network level, and when data were analysed within Broad Habitat groupings representing the open uplands, open lowlands and woodlands. We also found comparable increases in an indicator of vegetation response to soil pH, Ellenberg R. Species characteristic of less acid soils tended to show more consistent increases in frequency across sites relative to species with a known tolerance for strongly acidic soils. These changes are, therefore, broadly consistent with a response to increases in soil solution pH observed for the majority of ECN sites that, in turn, are likely to be driven by large reductions in acid deposition in recent decades. Increases in species richness in certain habitat groupings could also be linked to increased soil moisture availability in drier lowland sites that are likely to have been influenced by a trend toward wetter summers in recent years, and possibly also to a reduction in soil nitrogen availability in some upland locations. Changes in site management are also likely to have influenced trends at certain sites, particularly with respect to agricultural practices. Our results are therefore indicative of widescale responses to major regional-scale changes in air pollution and recent weather patterns, modified by local management effects. The relative consistency of management of ECN sites over time is atypical of much of the wider countryside and it is therefore not appropriate to scale up these observations to infer national scale trends. Nevertheless the results provide an important insight into processes that may be operating nationally. It will now be necessary to test for the ubiquity of these changes using appropriate broader spatial scale survey data.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".