Coextirpation of host-affiliate relationships in rivers: the role of climate change, water withdrawal, and host-specificity
Bibliographic record
Abstract
The role of climate-related disturbances on complex host–affiliate relationships remains understudied, largely because affiliate species vary in host use and are often differentially susceptible to disturbance relative to their hosts. Here we report the first set of host–affiliate species–discharge relationships (SDR) in freshwater and examine how anticipated shifts in water availability (flow) will impact coextirpations. We used SDR for freshwater mussels and fish across 11 regions (over 350 rivers) in the continental United States that we coupled to future water availability (2070) to model mussel and fish coextirpations. We also used river-specific host–affiliate matrices (presence–absence) to evaluate how host-specificity (mean number of hosts used by an affiliate) and host-overlap (extent to which affiliates share hosts) relate to extirpation vulnerability. We found that the strength and predictability of SDR models vary geographically and that mussels were more susceptible to flow alterations than fish. These patterns of extirpations were strongest in the southeast where: (1) flow reductions are expected to be greatest; (2) more species are lost per unit flow; (3) and more mussels are expected to be lost per unit of fish. We also found that overall mussel losses associated with reduction in habitat (water availability) were greater than those associated with loss of fish hosts which we assumed to be a function of host redundancy. These findings highlight the utility of SDR as a tool for conservation efforts but they also demonstrate the potential severity of reductions in mussel and fish richness as consequence of climate change and water use. Mussels provide key ecosystem services but face multiple pronged attacks from reductions in flow, habitat, and fish hosts. These losses in biodiversity and ecosystem functions can translate into major effects on food webs and nutrient recycling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".