Infectious disease and the conservation of freshwater fish
Bibliographic record
Abstract
INTRODUCTION Infectious diseases may be an important component of the conservation of freshwater fish. Rates of infectious diseases in freshwater fish are increasing (Johnson & Paull, 2011), and are likely a consequence of the multiple anthropogenic effects that are making freshwater systems the most degraded ecosystems on Earth (Carpenter et al ., 2011). More broadly, emerging diseases across taxa from all habitats tend to be related to environmental changes such as habitat fragmentation, species translocations/invasions, altered food webs, climate change or pollution (Daszak et al ., 2000; Dobson & Foufopoulos, 2001; Kelly et al ., 2009b). Similar processes may be at play in freshwater systems; however, these systems have received comparatively less attention than their terrestrial and marine counterparts (Johnson & Paull, 2011). However, the role of infectious disease in species endangerment and extinction is complex and debatable (Lafferty & Gerber, 2002), and so it is not immediately clear if increasing rates of disease imply that disease is a threat to the conservation of freshwater fish. The primary reason for this is that transmission efficiency of many pathogens is linked to the density of hosts, and so when hosts become rare, it is expected that diseases will fade out and therefore may not necessarily cause extinction of their host (Grenfell & Dobson, 1995; Hudson et al ., 2001). Indeed, a parasite that eliminates its host population also reduces its own fitness to zero, and so it is not clear if diseases are a direct threat to species persistence. Furthermore, fishes have a common life-history characteristic of relatively high fecundity (egg output) but relatively low survival from egg through to reproductive maturity. Such high mortality within the life cycle is often associated with non-disease related factors such as predation. This leads to potentially complex dynamics (Hatcher et al ., 2012) where different mortality processes may interact in compensatory or synergistic ways. For example, if most juvenile fish will die anyway due to predation, does an increase in infection level correspond to an increase in overall mortality? If predators selectively remove infected prey, this may actually counteract disease mortality and lead to healthier populations or alternatively by making prey easier to capture may increase overall mortality (Packer et al ., 2003; Krkošek et al ., 2011a). Such complex ecological dynamics entangle predator–prey, competition, and host–parasite relationships and can thus lead to multiple mortality processes that may exacerbate or dampen the effects of disease on host populations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".