Using species distribution models only may underestimate climate change impacts on future marine biodiversity
Bibliographic record
Abstract
In face of global changes, projecting and mapping biodiversity changes are of critical importance to support management and conservation measures of marine ecosystems. Despite the development of a wide variety of ecosystem models capable of integrating an increasing number of ecological processes, most projections of climate-induced changes in marine biodiversity are based on species distribution models (SDMs). These correlative models present a significant advantage when the lack of knowledge on the species physiology is counterbalanced by the availability of relevant environmental variables over the species geographical range. However, correlative SDMs neglect intra- and inter-specific interactions and thereby can lead to biased projections of changes in biodiversity distribution. To evaluate the influence of trophic interactions on projections of species richness and assemblage composition under climate change scenarios, we compared biodiversity projections derived from an ensemble of different SDMs to projections derived from a hybrid model coupling SDMs and a multispecies trophic model in the Mediterranean Sea. Our results show that accounting for trophic interactions modifies projections of future biodiversity in the Mediterranean Sea. Under the RCP8.5 scenario, SDMs tended to overestimate the gains and underestimate the losses of species richness by the end of the 21st century, with marked local differences in projections, both in terms of magnitude and trend, in some biodiversity hotspots. In both SDMs and hybrid approaches, nestedness with gains in species richness was the main pattern driving dissimilarity between present and future fish and macro-invertebrate species assemblages at the Mediterranean basin scale. However, at local scale, we highlighted some differences in the relative contribution of nestedness vs replacement in driving dissimilarity. Our results call for the development of integrated modelling tools that can mechanistically consider multiple biotic and abiotic drivers to improve projections of future marine 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".