# Species Distribution Modeling for Machine Learning Practitioners: A Review

arXiv: https://arxiv.org/abs/2107.10400 · HTML: https://mlsanity.com/paper/2107.10400/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 216 references that are in the map.
- First posted on arXiv 3 Jul 2021.

## Builds on (most cited first, 20 of 20)

- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [Recognition in Terra Incognita](https://mlsanity.com/paper/1807.04975/) (2018)
- [Benchmarking Representation Learning for Natural World Image Collections](https://mlsanity.com/paper/2103.16483/) (2021)
- [Continuous Deep Q-Learning with Model-based Acceleration](https://mlsanity.com/paper/1603.00748/) (2016)
- [Presence-Only Geographical Priors for Fine-Grained Image Classification](https://mlsanity.com/paper/1906.05272/) (2019)
- [The Devil is in the Tails: Fine-grained Classification in the Wild](https://mlsanity.com/paper/1709.01450/) (2017)
- [The iWildCam 2020 Competition Dataset](https://mlsanity.com/paper/2004.10340/) (2020)
- [The iWildCam 2021 Competition Dataset](https://mlsanity.com/paper/2105.03494/) (2021)
- [The Herbarium Challenge 2019 Dataset](https://mlsanity.com/paper/1906.05372/) (2019)
- [Deep Multi-Species Embedding](https://mlsanity.com/paper/1609.09353/) (2016)
- [Wildbook: Crowdsourcing, computer vision, and data science for conservation](https://mlsanity.com/paper/1710.08880/) (2017)
- [iCassava 2019 Fine-Grained Visual Categorization Challenge](https://mlsanity.com/paper/1908.02900/) (2019)
- [The iWildCam 2018 Challenge Dataset](https://mlsanity.com/paper/1904.05986/) (2019)
- [The Plant Pathology 2020 challenge dataset to classify foliar disease of apples](https://mlsanity.com/paper/2004.11958/) (2020)
- [Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data](https://mlsanity.com/paper/1803.11266/) (2018)
- [The GeoLifeCLEF 2020 Dataset](https://mlsanity.com/paper/2004.04192/) (2020)
- [The iWildCam 2019 Challenge Dataset](https://mlsanity.com/paper/1907.07617/) (2019)
- [Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder](https://mlsanity.com/paper/1709.05612/) (2017)
- [Evaluation of Deep Species Distribution Models using Environment and Co-occurrences](https://mlsanity.com/paper/1909.08825/) (2019)
- [Preferential sampling for presence/absence data and for fusion of presence/absence data with presence-only data](https://mlsanity.com/paper/1809.01322/) (2018)

## Built on by (most cited first, 21 of 21)

- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [Spatial Implicit Neural Representations for Global-Scale Species Mapping](https://mlsanity.com/paper/2306.02564/) (2023)
- [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)
- [Machine Learning and Deep Learning -- A review for Ecologists](https://mlsanity.com/paper/2204.05023/) (2022)
- [Bird Distribution Modelling using Remote Sensing and Citizen Science data](https://mlsanity.com/paper/2305.01079/) (2023)
- [TIML: Task-Informed Meta-Learning for Agriculture](https://mlsanity.com/paper/2202.02124/) (2022)
- [LD-SDM: Language-Driven Hierarchical Species Distribution Modeling](https://mlsanity.com/paper/2312.08334/) (2023)
- [Generating Binary Species Range Maps](https://mlsanity.com/paper/2408.15956/) (2024)
- [SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data](https://mlsanity.com/paper/2311.00936/) (2023)
- [Multi-Scale and Multimodal Species Distribution Modeling](https://mlsanity.com/paper/2411.04016/) (2024)
- [Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling](https://mlsanity.com/paper/2502.03480/) (2025)
- [Feedforward Few-shot Species Range Estimation](https://mlsanity.com/paper/2502.14977/) (2025)
- [Localized, High-resolution Geographic Representations with Slepian Functions](https://mlsanity.com/paper/2602.00392/) (2026)
- [Conservation Tools: The Next Generation of Engineering--Biology Collaborations](https://mlsanity.com/paper/2301.01103/) (2023)
- [On pseudo-absence generation and machine learning for locust breeding ground prediction in Africa](https://mlsanity.com/paper/2111.03904/) (2021)
- [Predicting Species Occurrence Patterns from Partial Observations](https://mlsanity.com/paper/2403.18028/) (2024)
- [Hybrid Spatial Representations for Species Distribution Modeling](https://mlsanity.com/paper/2410.10937/) (2024)
- [Spatial Clustering of Citizen Science Data Improves Downstream Species Distribution Models](https://mlsanity.com/paper/2412.15559/) (2024)
- [Heterogeneous graph neural networks for species distribution modeling](https://mlsanity.com/paper/2503.11900/) (2025)
- [Investigating Different Geo Priors for Image Classification](https://mlsanity.com/paper/2508.15946/) (2025)

## Abstract (arXiv)

Conservation science depends on an accurate understanding of what's happening in a given ecosystem. How many species live there? What is the makeup of the population? How is that changing over time? Species Distribution Modeling (SDM) seeks to predict the spatial (and sometimes temporal) patterns of species occurrence, i.e. where a species is likely to be found. The last few years have seen a surge of interest in applying powerful machine learning tools to challenging problems in ecology. Despite its considerable importance, SDM has received relatively little attention from the computer science community. Our goal in this work is to provide computer scientists with the necessary background to read the SDM literature and develop ecologically useful ML-based SDM algorithms. In particular, we introduce key SDM concepts and terminology, review standard models, discuss data availability, and highlight technical challenges and pitfalls.