Riparian forest paves the way to climate-driven expansion in bats: Evidence from a fine-scale elevational shift
Bibliographic record
Abstract
A well-documented response to climate change is the shift in species distributions, as organisms move away from areas that become unsuitably warm and move to higher latitudes or elevations. Ecological corridors, such as riparian forests, are crucial in facilitating these movements. Bats, as effective environmental indicators, are sensitive to climate change. Among them, Myotis daubentonii —a riparian specialist exhibiting altitudinal sexual segregation—has shown both morphological and distributional responses to warming. In a river system in Central Italy monitored for over two decades, reproductive females were historically confined to elevations below 850 m a.s.l. but are now regularly recorded foraging up to ca. 1050 m a.s.l. as higher altitudes have become climatically suitable. However, it remains unclear whether this shift signifies an actual expansion of the reproductive range or merely an extension of foraging activity into higher elevations. To investigate this, we radiotracked 14 reproductive females captured at high elevation to examine their habitat selection and space use. Our results confirm that maternity roosts have indeed been established well above the previous elevational limit. Riparian forest emerged as a key habitat supporting both roosting and foraging: bats consistently roosted in trees within riparian forests and foraged along verdant riverbanks. Although roost trees did not differ structurally from randomly available trees, selected cavities were higher above the ground, mainly in rot holes, and were associated with sparse understorey and south-eastern exposures. These findings emphasise the importance of cavity type and microhabitat features in roost selection. Overall, our study demonstrates that well-preserved riparian forests act as vital ecological corridors, enabling bats to shift their range in response to climate change. Conservation strategies aiming to enhance the adaptive capacity of bat populations in riverine landscapes should prioritise the protection and restoration of riparian forests. • Climate change is driving upslope colonisation by reproductive Myotis daubentonii. • New maternity roosts found above 950 m a.s.l. confirm range expansion. • Riparian forests are essential for roosting and foraging at range margins. • Roosts are in rot cavities, high above ground, and sun-exposed. • Climate change drives colonisation, but habitat loss may constrain it.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".