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Year: 2019

Key Reference: Botella, C., et al. (2019). Deep learning for species distribution modelling, offering visibility to community scientists.

Relevance: This introduced Convolutional Neural Networks (CNNs) to SDM. Instead of just using climate numbers (e.g., 20°C), these models can "read" satellite imagery or spectrograms (like your acoustic data) as images, capturing complex spatial patterns that traditional models miss.

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Year: 2018–2020

Key Reference: Heberling, J. M., et al. (2021). Data intensive approaches to biology: The case of the Global Biodiversity Information Facility.

Relevance: While GBIF existed before, the late 2010s saw a "Data Explosion." SDMs shifted from modeling 10 or 20 species to "automated pipelines" modeling tens of thousands of species simultaneously, thanks to massive data mobilization and cloud computing.

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Year: 2013

Key Reference: Pollock, L. J., et al. (2014). Understanding co‐occurrence by modelling species‐specific responses to environmental variation and co‐occurring species.

Relevance: Moved SDM from single species to Joint Species Distribution Models. This allows researchers to model how species interact with each other (competition/facilitation) rather than just the climate.

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Year: 2008

Key Reference: Elith, J., et al. (2008). A working guide to boosted regression trees.

Relevance: Popularized Boosted Regression Trees (BRTs), demonstrating that machine learning could outperform traditional regression by automatically handling non-linearities and interactions.

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Key Reference: Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology.

Relevance: This is the most cited paper in SDM history. It provided the first formal conceptual framework, bridging ecological theory with statistical practice.

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BIOCLIM

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Booth, T.H.; McMurtrie, R.E. Climatic change and Pinus radiata plantations in Australia. In Greenhouse: Planning for Climate Change; Pearman, G., Ed.; CSIRO: Melbourne, Australia, 1988; pp. 534–545.