ArticleFrontiers in veterinary science2025
Predicting vector distribution in Europe: at what sample size are species distribution models reliable?
Article in Frontiers in veterinary science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Post-establishment environmental suitability ofOne health (Amsterdam, Netherlands) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Species distribution models can predict the spatial distribution of vector-borne diseases by forming associations between known vector distribution and environmental variables. In response to a changing climate and increasing rates of vector-borne diseases in Europe, model predictions for vector distribution can be used to improve surveillance. However, the field lacks standardisation with little consensus as to what sample size produces reliable models. Objective: Determine the optimum sample size for models developed with the machine learning algorithm, Random Forest, and different sample ratios. Materials and methods: To overcome limitations with real vector data, a simulated vector with a fully known distribution in 10 test sites across Europe was used to randomly generate different samples sizes. The test sites accounted for varying habitat suitability and the vector's relative occurrence area. 9,000 Random Forest models were developed with 24 different sample sizes (between 10-5,000) and three sample ratios with varying proportions of presence and absence data (50:50, 20:80, and 40:60, respectively). Model performance was evaluated using five metrics: percentage correctly classified, sensitivity, specificity, Cohen's Kappa, and Area Under the Curve. The metrics were grouped by sample size and ratio. The optimum sample size was determined when the 25th percentile met thresholds for excellent performance, defined as: 0.605-0.804 for Cohen's Kappa and 0.795-0.894 for the remaining metrics (to three decimal places). Results: For balanced sample ratios, the optimum sample size for reliable models fell within the range of 750-1,000. Estimates increased to 1,100-1,300 for unbalanced samples with a 40:60 ratio of presence and absence data, respectively. Comparatively, unbalanced samples with a 20:80 ratio of presence and absence data did not produce reliable models with any of the sample sizes considered. Conclusion: To our knowledge, this is the first study to use a simulated vector to identify the optimum sample size for Random Forest models at this resolution (≤1 km
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.