ArticleScientific reports2024
Predicting the potential distribution of stingless bee, Tetragonula iridipennis in India using MaxEnt and CMIP6 climate projections.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Prediction of the potentially suitable areas of Elymus dahuricus Turcz in China under climate change based on maxent.Scientific reports · 2025Article
- Projected Distributions of Two Key Vectors of Lumpy Skin Disease,Transboundary and emerging diseases · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Tetragonula iridipennis Smith, commonly known as the stingless bee or 'dammer bee', is a key native species that pollinates a wide variety of horticultural crops, including onions, in India. Climate change significantly affects species distribution and habitat suitability. This study utilized Maximum Entropy Modeling (MaxEnt) to predict the current and future distribution of T. iridipennis in India. By modeling the species' potential distribution using both historical climate data (1970-2000) and future projections for 2050 and 2070 under two socio-economic scenarios, SSP126 (low-emission) and SSP585 (high-emission), the study provided accurate predictions. The area under the receiver operating characteristic curve (AUC) for model training and testing was 0.848 and 0.830, respectively, indicating strong model accuracy. Additionally, the Continuous Boyce Index (CBI) values for training and testing were 0.966 and 0.907, while the True Skill Statistic (TSS) values were 0.510 and 0.484. These metrics confirm that the model effectively distinguishes between suitable and unsuitable habitats for the species. The two most influential variables determining 84.9% of T. iridipennis's potential distribution were temperature seasonality (bio4; 66.2%) and mean temperature of the coldest quarter (bio11; 18.7%). The ideal zone for these variables were 155-170 and 13-28, respectively. The model indicated that the potential distribution of T. iridipennis is concentrated primarily in central and southern peninsular India, with the species' habitat predicted to expand under both SSP126 and SSP585 scenarios. This study provides a detailed overview of the current and potential future habitable areas for T. iridipennis in India, offering insights that could help guide conservation efforts for this important native pollinator.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.