Water, Wildlife and the World: A Global Synthesis of Trends in Wildlife Ecohydrology Research
Bibliographic record
Abstract
ABSTRACT Ecohydrology and hydroecology are interdisciplinary fields that examine the interactions between ecology and hydrology to address critical issues pertaining to both disciplines. However, the study of ‘ecology’ in ecohydrology research has largely focused on vegetation, leading to a gap in knowledge regarding direct relationships between wildlife and hydrology. We followed a scoping review approach and synthesised research from 189 articles to explore the extent to which vertebrate wildlife ecohydrology is being studied and highlight opportunities for future research. Articles published between 1989 and 2023 focused primarily on fish taxa in river ecosystems with few studying marine and terrestrial habitats. Fewer than 2% of vertebrate species studied were mammals or reptiles, although these species tended to be at risk. On average, fish, mammals and birds were studied for > 8 years per study, whereas amphibians were studied for < 5 and reptiles for only 3 years. While wildlife population abundance was frequently associated with water level and discharge, research rarely integrated wildlife variables with hydrological connectivity or soil properties. Future studies should prioritise collaborative and co‐produced research directed towards species‐at‐risk, particularly among birds, mammals, reptiles and amphibians. Wildlife ecohydrology research would also benefit from integrating transformative monitoring tools and techniques, such as the application of environmental DNA (eDNA), and from more explicit consideration of groundwater dynamics. Wildlife ecohydrology research is increasingly important given the pressures placed on wildlife and ecosystems from climate change. Understanding the relationships between hydrology and wildlife will inform management priorities and advance conservation strategies.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.032 | 0.060 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| 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".