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.
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 [...]
Phytoplankton play a major role in marine ecosystem health. They form the base of aquatic food webs, but under conditions of nutrient loading and high stratification, they can develop into harmful algal blooms that produce toxins harmful to humans and wildlife. Ongoing phytoplankton blooms have been observed in Long Island’s (LI) coastal waters for the past half-century, but there is a lack of a comprehensive view of phytoplankton spatiotemporal distribution and their driving factors due to the analysis of specific sampling sites, species, and years. Thus, this study obtained 20 years of chlorophyll-a, climate, and nutrient remote sensing and in situ data from the ERDDAP data server and the CTDEEP Long Island Sound Water Monitoring Program to establish phytoplankton phenology using the threshold criterion and cumulative sum of anomalies methods and to investigate regional differences, influencing factors, and interannual trends using correlation and linear trend analysis. The phenology of summer-autumn blooms in Long Island Sound (LIS) was associated with high sea surface temperatures (r = -0.46, p < 0.01). In contrast, winter-spring blooms were most strongly correlated with low salinity (r = -0.52, p < 0.01), indicating P-rich Connecticut river discharges as the dominant nutrient source. However, phytoplankton in LI’s southern shores lack access to river outlets, so phytoplankton production was driven by deep winter mixings from low SST that replenish surface water nutrient levels (r = -0.71, p < 0.05). Finally, our results showed strong decreasing interannual trends of autumn chlorophyll-a levels from 2003-2022 (r < -0.66, p < 0.05), potentially due to heightened N-limitation. Hence, the effects of declining phytoplankton productivity on LI’s fisheries and marine ecosystems should be further investigated.
Abstract Phytoplankton play a major role in marine ecosystem health. They form the base of aquatic food webs, but under conditions of nutrient loading and high stratification, they [...]
Evaluating Threats to Eastern Migratory Monarch Butterflies (Danaus plexippus) Using Convolutional Neural Network Species Distribution Models 
