Improving biodiversity surrogates for conservation assessment: A test of methods and the value of targeted biological surveys
Bibliographic record
Abstract
Abstract Aim Conservation assessment and planning across extensive regions rely on the use of mapped or modelled surrogates because direct field‐based inventories of biodiversity rarely provide complete spatial coverage. Surrogates are assumed to represent spatial patterns in the distribution of biodiversity, yet the validity of this assumption is rarely evaluated. Here, we use data from new biological surveys targeting poorly known taxonomic groups across sparsely surveyed landscapes to test: (1) the performance of established and novel surrogates; and (2) the value of targeted survey data in further improving surrogate effectiveness. Location Continental Australia. Methods Surrogates were derived from either mapped land classifications (bioregions, vegetation types), or models of spatial turnover in biodiversity composition. Models were derived by linking best‐available biological observations to high‐resolution mapped climate, terrain and soil attributes using generalized dissimilarity modelling (GDM). The performance of surrogates was evaluated using survey data for eight biological groups collected as part of the Bush Blitz programme ( http://bushblitz.org ). For theGDM‐based surrogates, within‐ and cross‐taxon performance was first evaluated for models fitted to biological data available prior to Bush Blitz, and then for models enhanced through the addition of the Bush Blitz data. Results All of the tested surrogates performed significantly better than random across all eight biological groups.GDM‐based surrogates performed over 10% better on average than the best performing combination of mapped land classifications. The addition of Bush Blitz targeted data inGDM‐based surrogates led to further improvements in surrogate performance. Main conclusions Our results support continued investment in targeted biological survey programmes to enhance the performance of surrogates and ensure that surrogates represent a wider breadth of biodiversity. The strong performance of compositional turnover modelling, relative to mapped land classifications, suggests that this surrogate strategy deserves greater consideration in future conservation assessments and has potential for use in continental‐scale monitoring of biodiversity.
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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 | 0.076 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".