Mammal Persistence Along Riparian Forests in Western India Within a Hydropower Reservoir 55 Years Post Construction
Bibliographic record
Abstract
While the negative impacts of dam construction on downstream river stretches and riparian forests are well studied, the status of wildlife presence and persistence in upstream reservoir deltas is virtually unknown. We investigated the drivers of terrestrial mammal occupancy and persistence along riparian forests of Koyna reservoir in western India 55 years after its construction. We adopted a catchment-wide field design grounded in the river continuum concept and sampled different stream orders within the reservoir. Camera traps, nested in an occupancy modeling framework, were deployed across 72 riparian sites and replicated for four seasons across all stream types. We recorded a total of nineteen species of terrestrial mammals during the study period. Multi-season occupancy models revealed three key patterns of mammal persistence: (a) ungulates were more frequently photo-captured in riparian forests; gaur and wild pig had the highest proportions of the total sampled area (0.84 ± 0.12 SE; 0.77 ± 0.07 SE, respectively); (b) small-sized ungulates were more vulnerable to local extinction than large-bodied ungulates; extinction probability was highest for barking deer (0.59 ± 0.07) and lowest for sambar (0.15 ± 0.07); and (c) distance from stream played major roles in determining mammal detection. Riparian forests are fundamentally important to ecosystem functioning and biodiversity conservation, and using the data from this study, managers can plan to sustain high mammal persistence along riparian forests at Koyna reservoir or similar Indian reserves. Further, our robust sampling approach, grounded in the terrestrial-riverine continuum concept, can be applied globally to understand species assemblages, aiding in multi-landscape and wildlife management planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".