MétaCan
Menu
Back to cohort
Record W2724751890 · doi:10.1093/conphys/cox041

Forest disturbance leaves some bats stressed and under the weather

2017· article· en· W2724751890 on OpenAlexaff
Christine L. Madliger

Bibliographic record

VenueConservation Physiology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsBiologyDisturbance (geology)AgroforestryEcologyEnvironmental resource managementEnvironmental science

Abstract

fetched live from OpenAlex

In the face of logging and forest fragmentation, what is a bat to do? Interestingly, the type of roosting site a bat uses may have a big influence on how habitat disturbances affect them. It turns out that bats that roost in caves may have an easier time coping with change. Traditionally, conservation scientists have focused on population size to measure how disturbances affect species, but this type of monitoring can take years to indicate that there is an issue. This lag time is one reason why some conservation scientists are turning to physiology. Hormones, metabolites and body condition are some of the physiological parameters that can change rapidly when the environment changes. This knowledge can allow conservation practitioners to work proactively to protect wildlife. Indeed, Seltmann et al. (2017) and her team used physiology to help pinpoint the bat species most sensitive to logging in the rainforests of Borneo. They examined eight bat species—some that use caves to roost and some that roost in trees or the cavities of trees (foliage)—to determine if the type of roost site could cause some bats to be more susceptible to the impacts of logging. To do this, Seltmann and her team captured bats in patches of recovering, fragmented or actively logged forests. They measured three aspects of the bats’ physiology. First, they weighed the bats, because body mass can be a good indicator of overall condition. Bats that have access to sufficient food tend to have a greater body mass. They also took blood samples to measure two other physiological parameters: white blood cell numbers, which indicate disease susceptibility, and neutrophil to lymphocyte ratios, because they rise when stress hormones go up. Not surprisingly, the researchers found fewer bats in the disturbed sites versus the recovering sites. But forest disturbance only impacted the physiology of some species and barely affected others. Where was the pattern? It seemed that foliage-roosting bats living in disturbed forests weighed less, and some also exhibited weakened immune systems. In contrast, only one species of the cave-roosting bat showed signs of stress. The effects of a disturbed forest seem to differ based on the type of roost that bats use. But why? The team thinks that foliage-roosting bats may be extremely sensitive to forest disturbance because their roosts are directly affected. When they leave their roosts, stress levels can also elevate and lead to disease and/or starvation. Cave-roosting bats in disturbed forests can retreat to undisturbed caves to roost and may already be used to travelling longer distances to find food in the first place. Habitat disturbances can have diverse consequences for wildlife, but physiology can help us to understand which species may be affected and in what way. For tropical bats, managers may want to pay close attention to foliage-roosting species that could be more susceptible to disease outbreaks when human activity encroaches on their habitat. Given that bats provide more than US $1bn worth of pest control globally on corn crops alone, well-informed conservation programs will be crucial for both bats and humans. Illustration by Erin Walsh; Email: ewalsh.sci@gmail.com Editor: Jodie L. Rummer

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.035
GPT teacher head0.237
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractno

Explore more

Same venueConservation PhysiologySame topicBat Biology and Ecology StudiesFrench-language works237,207