Evaluating Threats to Eastern Migratory Monarch Butterflies (Danaus plexippus) Using Convolutional Neural Network Species Distribution Models

Abstract

Eastern migratory monarch butterflies have declined by over 80% since the 1990s and have a 56–74% chance of extinction by 2080, which has been attributed to climate change and habitat loss. As a native migratory insect widely distributed across North America, the monarch butterfly serves as a valuable bioindicator for environmental change and conservation needs. However, there is still a lack of understanding of the spatiotemporal nature, relative importance, and future risks of individual threats due to the monarch’s multi-generational and vast annual cycle. Given the monarch's distinct ecological niche and sensitivity to environment conditions for migration, this study used convolutional neural network species distribution models (CNN-SDMs), which enhance occurrence predictions by capturing the surrounding environmental neighborhood, to analyze suitability predictors of monarch butterflies and explain their contributions at each step of the migratory route. The models were projected onto future climate and land cover scenarios in 2061–2080. Monthly maximum and minimum temperature ranked highest in feature importance across the migratory cycle, while vegetative land cover became ranked high in importance for the overwintering monarch population and future breeding habitat forecasted to shift northward. This niche-switching suggests that conservation efforts to facilitate the northward expansion of suitable habitat and the cultivation of nectar sources near hibernating colonies will be critical with growing climate impact and emissions. This study was the first to establish a CNN-based predictive spatiotemporal model of monarch butterflies and incorporate a comprehensive set of environmental predictors to evaluate potential threats to the monarch decline.

Introduction

Biodiversity across the globe is declining at an alarming rate, with wildlife populations having declined 73% between 1970 and 2020 and one million species currently at risk of extinction (1, 2). Migratory species are particularly vulnerable due to their reliance on and interactions with multiple habitats throughout their seasonal movements, and over one-fifth are now threatened with extinction (3). Because of their wide dispersal, migratory species provide vital ecosystem services across and also serve as valuable bioindicators for environmental change, including habitat degradation and climatic shifts (4). Thus, effective conservation for migratory species also indirectly benefits countless other species and ecosystems falling in the migratory range (4, 5).

One of the most culturally and ecologically significant migratory species in North America is the eastern migratory monarch butterfly (Danaus plexippus). As a native species across Canada, the United States, and Mexico, monarch butterflies play a crucial role in pollination, the food web, and biodiversity preservation (6). However, the monarch butterfly population has plummeted by over 80% since the 1990s, from 18.19 hectares of occupied overwintering habitat in 1996 to less than two hectares in recent years (7). Due to their persistent decline, migratory monarch butterflies have been listed as endangered and vulnerable in the International Union for Conservation of Nature Red List of Threatened Species in 2022 and 2023, respectively (8). Although climate change and habitat loss have been identified as the primary drivers of the decline, a comprehensive understanding of the spatiotemporal nature, relative importance, and future risks of these threats remains uncertain due to the monarch’s vast geographic distribution and multi-generational annual cycle as highlighted in the systematic review of 115 peer-reviewed literature on the threats to monarch butterfly reproduction and survival from Wilcox et al. (2019) (9). 

The eastern monarch butterfly undergoes a biannual migration between overwintering forests in Mexico and breeding habitat in the United States and Canada through multiple reproduction cycles. Starting in the central highland forests in the States of Michoacán and Mexico, one overwintering generation of monarch butterflies hibernate from early November to late March in a reproductive diapause and a low metabolic state (10). In spring, monarch butterflies begin to migrate northward and enter the southern United States to produce the first breeding generation (11). In summer, this generation reaches the Northeast and Midwest of the United States and parts of southern Canada, producing a second, third, and potentially fourth generation before migrating back to the South in autumn, with a potential fifth generation before cycling back to Mexico before winter (11).

Different climate- and habitat-related threats have been proposed to negatively affect monarch butterflies along their migratory route including (a) weather extremes and severe winter storms, which have led to large-scale mortality events with death rates as high as 70-80% (10); (b) habitat loss of overwintering grounds in Mexico due to deforestation with large portions of core areas in the Monarch Butterfly Biosphere Reserve (MBBR)—44% between 1971 and 1999 and 10% between 2001 and 2009—have already been degraded or eliminated (12, 13); (c) lack of availability of milkweed in breeding areas due to the use of the herbicide glyphosate, also known as Roundup, which has contributed to a 31% decline in non-agricultural milkweed and an 81% decline in agricultural milkweed between 1999 and 2010, or a loss of more than 1 billion milkweed plants (14, 15).

Thus, capturing the monarch’s seasonal migration patterns and corresponding impacts of climate and habitat factors is critical to understanding ecological requirements throughout their annual cycle. To model species distribution for each step of the migration, past studies have used ecological niche models or species distribution models (SDMs), which finds relationships between environmental raster data—values of environmental variables that are stored in gridded pixels each linked to a specific geographical coordinate—and species observation data—presence or absence at a geographic coordinate (16, 17, 18, 15). Recently, a new type of SDMs using convolutional neural networks (CNN-SDMs) has been introduced, which was shown to improve SDM performance compared to other machine learning techniques (19, 20). Since CNNs are typically used for image recognition by extracting features from images, its application to SDMs can help take into account the environmental neighborhood, surpassing the predictive accuracy of deep neural networks, boosted trees, and random forests when predicting the occurrence probabilities of 4,520 plant species in France, with significant gains for rare species (19). Furthermore, CNN-SDMs overcome the constraints of MaxEnt, one of the most popular SDM methods, particularly its limited ability to predict occurrences at finer resolutions and along bodies of water (20). Therefore, given the monarch's distinct ecological niche and sensitivity to environment conditions for migration, CNN-SDMs may enhance occurrence predictions by capturing the local environmental context.

In this study, CNN-SDMs for monarch butterflies were developed for five stages of the migration cycle to investigate the spatiotemporal impact of climate and habitat factors on the monarch decline and evaluate their relative importances. The models were subsequently projected onto future climate and land cover conditions for the years 2061–2080 to assess future risks.

Methods

Species Occurrence Data and Groups