# CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations

arXiv: https://arxiv.org/abs/2508.06704 · HTML: https://mlsanity.com/paper/2508.06704/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 81 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [On Embeddings for Numerical Features in Tabular Deep Learning](https://mlsanity.com/paper/2203.05556/) (2022)
- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [General Multi-label Image Classification with Transformers](https://mlsanity.com/paper/2011.14027/) (2020)
- [Spatial Implicit Neural Representations for Global-Scale Species Mapping](https://mlsanity.com/paper/2306.02564/) (2023)
- [A deep active learning system for species identification and counting in camera trap images](https://mlsanity.com/paper/1910.09716/) (2019)
- [GeoPlant: Spatial Plant Species Prediction Dataset](https://mlsanity.com/paper/2408.13928/) (2024)
- [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)
- [On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning](https://mlsanity.com/paper/2401.02989/) (2024)
- [Animal Behavior Analysis Methods Using Deep Learning: A Survey](https://mlsanity.com/paper/2405.14002/) (2024)
- [Multi-Scale and Multimodal Species Distribution Modeling](https://mlsanity.com/paper/2411.04016/) (2024)
- [MaskSDM with Shapley values to improve flexibility, robustness, and explainability in species distribution modeling](https://mlsanity.com/paper/2503.13057/) (2025)

## Built on by (most cited first, 1 of 1)

- [SAGE: A sampling-aware global evaluation benchmark for species distribution modeling](https://mlsanity.com/paper/2609.31082/) (2026)

## Abstract (arXiv)

Species distribution models (SDMs) are widely used to predict species' geographic distributions, serving as critical tools for ecological research and conservation planning. Typically, SDMs relate species occurrences to environmental variables representing abiotic factors, such as temperature, precipitation, and soil properties. However, species distributions are also strongly influenced by biotic interactions with other species, which are often overlooked. While some methods partially address this limitation by incorporating biotic interactions, they often assume symmetrical pairwise relationships between species and require consistent co-occurrence data. In practice, species observations are sparse, and the availability of information about the presence or absence of other species varies significantly across locations. To address these challenges, we propose CISO, a deep learning-based method for species distribution modeling Conditioned on Incomplete Species Observations. CISO enables predictions to be conditioned on a flexible number of species observations alongside environmental variables, accommodating the variability and incompleteness of available biotic data. We demonstrate our approach using three datasets representing different species groups: sPlotOpen for plants, SatBird for birds, and a new dataset, SatButterfly, for butterflies. Our results show that including partial biotic information improves predictive performance on spatially separate test sets. When conditioned on a subset of species within the same dataset, CISO outperforms alternative methods in predicting the distribution of the remaining species. Furthermore, we show that combining observations from multiple datasets can improve performance. CISO is a promising ecological tool, capable of incorporating incomplete biotic information and identifying potential interactions between species from disparate taxa.