Migrating in a Warming World: A Deep Learning Approach to

What this paper is about

Species distribution models usually describe where an animal lives, which is a poor fit for one that lives in different places depending on the month. The authors built a model that takes time of year as an input and trained it on monarch records across the Americas.

It is then used to project how the seasonal ranges shift under future climate scenarios, for both the breeding grounds and the migratory corridor.

Summarised here from the abstract below. Not the authors’ words.

Abstract

Climate change is driving biodiversity loss, disrupting ecosystem functioning, and altering species distributions. Migratory species, whose range varies across seasons depending on specific climatic conditions, are particularly sensitive to environmental changes and serve as indicators of ecosystem health. However, current species distribution models often fail to capture the temporal dynamics critical for migratory species, limiting their ability to provide accurate future range estimations. In this study, we address this gap by developing a time-aware deep learning species distribution model for the monarch butterfly (Danaus plexippus), an iconic species for biodiversity conservation. Using monarch occurrence records across the Americas gathered from scientific and citizen science sources, we incorporate the effect of monthly climatic variables in a sequential framework. We compare the performance of our concatenated seasonal model to conventional time-static baselines, showing not only better performance in the present, where the models have been trained and validated, but also in the past. Our findings show that climatic factors such as humidity, temperature, precipitation and cloud coverage strongly influence the ecological niche of the monarch butterfly, with notable seasonal and spatial variability. Applying our model under climate change scenarios, we predict a northwestward shift in the monarch range by the end of the XXI century, with expansion in Canada and significant contraction in California and Mexico, key sites for overwintering that also host resident monarch populations. These changes could severely impact the species' migratory cycle and population stability. Using Shapley values, an explainable AI technique, we identify the decrease in precipitation and an increase in temperature as important environmental drivers responsible for the contraction of overwintering sites. By focusing on a species of high ecological relevance through a time-aware modeling approach, this work brings novel insights for the conservation of migratory species in the face of the challenges posed by climate change.

Abstract reproduced from the published paper, which is licensed CC BY. Copyright remains with the authors.

Key terms

Ecosystem healthCitizen ScienceClimate Change ScenariosDeep LearningFuture ProjectionsModel Explainability

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APA

Milkweed USA. (2026). Migrating in a Warming World: A Deep Learning Approach to Predict Pan-American Seasonal Shifts in the Monarch Butterfly Niche (listing). Retrieved DATE, from https://milkweedusa.com/research/migrating-in-a-warming-world-a-deep-learning-approach-2026/

MLA

"Migrating in a Warming World: A Deep Learning Approach to Predict Pan-American Seasonal Shifts in the Monarch Butterfly Niche (listing)." Milkweed USA, 2026, https://milkweedusa.com/research/migrating-in-a-warming-world-a-deep-learning-approach-2026/. Accessed DATE.

Chicago

Milkweed USA. "Migrating in a Warming World: A Deep Learning Approach to Predict Pan-American Seasonal Shifts in the Monarch Butterfly Niche (listing)." Last modified 2026-08-01. Accessed DATE. https://milkweedusa.com/research/migrating-in-a-warming-world-a-deep-learning-approach-2026/.

BibTeX

@misc{milkweedusa_researchmigratinginawarmingw,
  title  = {Migrating in a Warming World: A Deep Learning Approach to Predict Pan-American Seasonal Shifts in the Monarch Butterfly Niche (listing)},
  author = {{Milkweed USA}},
  year   = {2026},
  url    = {https://milkweedusa.com/research/migrating-in-a-warming-world-a-deep-learning-approach-2026/},
  note   = {Accessed DATE}
}