# Deep Multi-Species Embedding

arXiv: https://arxiv.org/abs/1609.09353 · HTML: https://mlsanity.com/paper/1609.09353/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2016.

## Built on by (most cited first, 20 of 20)

- [Spatial Implicit Neural Representations for Global-Scale Species Mapping](https://mlsanity.com/paper/2306.02564/) (2023)
- [One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement](https://mlsanity.com/paper/2206.00517/) (2022)
- [Species Distribution Modeling for Machine Learning Practitioners: A Review](https://mlsanity.com/paper/2107.10400/) (2021)
- [HeuriGym: An Agentic Benchmark for LLM-Crafted Heuristics in Combinatorial Optimization](https://mlsanity.com/paper/2506.07972/) (2025)
- [Active Learning-Based Species Range Estimation](https://mlsanity.com/paper/2311.02061/) (2023)
- [Combining Observational Data and Language for Species Range Estimation](https://mlsanity.com/paper/2410.10931/) (2024)
- [Evaluating Multi-label Classifiers with Noisy Labels](https://mlsanity.com/paper/2102.08427/) (2021)
- [On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning](https://mlsanity.com/paper/2401.02989/) (2024)
- [HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders](https://mlsanity.com/paper/2103.06375/) (2021)
- [Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning](https://mlsanity.com/paper/2108.09523/) (2021)
- [LD-SDM: Language-Driven Hierarchical Species Distribution Modeling](https://mlsanity.com/paper/2312.08334/) (2023)
- [Overview of GeoLifeCLEF 2023: Species Composition Prediction with High Spatial Resolution at Continental Scale Using Remote Sensing](https://mlsanity.com/paper/2509.25816/) (2025)
- [MALPOLON: A Framework for Deep Species Distribution Modeling](https://mlsanity.com/paper/2409.18102/) (2024)
- [Hybrid Spatial Representations for Species Distribution Modeling](https://mlsanity.com/paper/2410.10937/) (2024)
- [Multi-label Learning from Privacy-Label](https://mlsanity.com/paper/2312.13312/) (2023)
- [Determined Multi-Label Learning via Similarity-Based Prompt](https://mlsanity.com/paper/2403.16482/) (2024)
- [Applying the maximum entropy principle to neural networks enhances multi-species distribution models](https://mlsanity.com/paper/2412.19217/) (2024)
- [STELLAR: Spatio-Temporal Environmental Learning with Latent Alignment and Refinement for Long-Tailed Species Distribution Modeling](https://mlsanity.com/paper/2606.08484/) (2026)
- [A Deep Zero-Inflated Model of North Atlantic Right Whale Presence To Support Blue Economy Management in the U.S. East Coast](https://mlsanity.com/paper/2606.14403/) (2026)
- [SAGE: A sampling-aware global evaluation benchmark for species distribution modeling](https://mlsanity.com/paper/2609.31082/) (2026)

## Abstract (arXiv)

Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only target a single species at a time, despite strong ecological evidence that species are not independently distributed. We propose Deep Multi-Species Embedding (DMSE), which jointly embeds vectors corresponding to multiple species as well as vectors representing environmental covariates into a common high-dimensional feature space via a deep neural network. Applied to bird observational data from the citizen science project textiteBird, we demonstrate how the DMSE model discovers inter-species relationships to outperform single-species distribution models (random forests and SVMs) as well as competing multi-label models. Additionally, we demonstrate the benefit of using a deep neural network to extract features within the embedding and show how they improve the predictive performance of species distribution modelling. An important domain contribution of the DMSE model is the ability to discover and describe species interactions while simultaneously learning the shared habitat preferences among species. As an additional contribution, we provide a graphical embedding of hundreds of bird species in the Northeast US.