ArticlePLoS computational biology2021
Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Arboreal Mammals Are at Risk Under Climate Change: Insights From a Global Systematic Review and Meta-Analysis.Global change biology · 2026Pooled it
- Improving island occupancy predictions using ecologically informed metrics of area and isolation.Ecology · 2026Article
- Prioritizing removals of a highly cryptic invasive species using machine learning and demographic weighting.Ecological applications : a publication of the Ecological Society of America · 2026Article
- Research progress of AI assisted nano-SERS technology in rapid identification of multi species oral pathogenic biofilms.Mikrochimica acta · 2026Review
- Convolutional neural networks outperform other presence-only species distribution modeling algorithms.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Estimating species commonness and prevalence through unsupervised methods.Scientific reports · 2026Article
- Learning the syntax of plant assemblages.Nature plants · 2025Article
- Species habitat modeling based on image semantic segmentation.Scientific reports · 2025Article
- Design of a lightweight recognition network for adult locusts and grasshoppers based on deep learning.iScience · 2025Article
- Ecological drivers of malaria vector habitat and transmission over 1 year of long-lasting insecticidal net intervention in Côte d'Ivoire.Parasites & vectors · 2025Article
- Comparative Analysis of MaxEnt and Deep Learning Approaches for Modeling Humpback Whale Distribution in North Iceland.Ecology and evolution · 2025Article
- Article
- Forecasting the Effects of Global Change on a Bee Biodiversity Hotspot.Ecology and evolution · 2024Article
- Using ensemble modeling to predict the current distribution of Pistacia atlantica Desf. in Algeria.Die Naturwissenschaften · 2024Article
- Deep learning models map rapid plant species changes from citizen science and remote sensing data.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Modelling bluetongue and African horse sickness vector (Culicoides spp.) distribution in the Western Cape in South Africa using random forest machine learning.Parasites & vectors · 2024Article
- Multispecies deep learning using citizen science data produces more informative plant community models.Nature communications · 2024Article
- Assessment of American Bullfrog (Lithobates catesbeianus) spreading in the Republic of Korea using rule learning of elementary cellular automata.Scientific reports · 2024Article
- Machine and deep learning approaches to understand and predict habitat suitability for seabird breeding.Ecology and evolution · 2023Article
- Habitat distribution change of commercial species in the Adriatic Sea during the COVID-19 pandemic.Ecological informatics · 2022Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted "punctual models"). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure ("ablation experiments"). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.
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