Research themes

The ecological footprint of humans is increasingly apparent: 75% of the land is significantly altered, local crop varieties are disappearing worldwide, species’ fitness and interactions are under pressure to adapt to rapid climate change, among other marked impacts. Through my research, I hope to contribute to understanding and addressing this ecological footprint, by (1) designing practical solutions to reconcile land use incentives and conservation goals,  (2) monitoring the sustainability of different biodiversity outcomes and ecosystem functions at large scales, and (3) estimating the causal impact of policy choices on such outcomes. I combine approaches, data, and knowledge from economics and ecology primarily, but also draw from quantitative tools like remote sensing and machine learning, and qualitative tools like interviews. My current research areas include:

Developing solutions to reconcile urgent land conservation goals in agricultural regions with farmer and agricultural stakeholder incentives

Measuring and studying species and ecosystems at large scales

Estimating the causal impact of land use choices and conservation policies on biodiversity


Gantois, J. (2022). New tree‐level temperature response curves document sensitivity of tree growth to high temperatures across a US‐wide climatic gradient. Global Change Biology, 28(20), 6002-6020.

Graphical abstract: In many ecoregions of the United States, radial tree growth responds non-linearly to temperature. In particular, temperatures above a certain level have a strong negative impact on total annual growth. This breakpoint level is identified in the 1°-40°C range based on model predictive performance. The annual and spring-summer (AMJJA) temperature responses are similar. Excluding drought effects shifts the temperature response up, especially in dry ecoregions, but the non-linearity remains. This flexible temperature model can be used to document the impact of sub-lethal high temperatures on tree growth across large spatiotemporal scales.

Working papers

Monitoring shifts in flowering phenology using satellite imagery and deep learning. (with Mathias Lécuyer)

Shifts in the timing of plant flowering are a key signal of an ecosystem’s response to environmental change. Consequences are far-reaching: the timing of plant flowering affects the synchronization between plants and pollinators; it influences the exposure of crops to weather extremes during flowering, the most sensitive stage of development, with implications for later yield. A consistent and large-scale measure tracking the timing of flowering across years would be of great use for assessing risks of plant-pollinator desynchronization or risks of subpar crop yields. Current efforts, ranging from local to regional scales, do not quite achieve this goal: ground observations, while expanding, remain limited in their spatial and temporal coverage; process-based or statistical models are not flexible enough to capture local acclimation or adaptation to environmental factors; and remotely sensed estimates of plant development stages are often disconnected from the ground. We propose a method that combines spectral reflectance data from satellite imagery, with a deep learning model, to create a large-scale, high-resolution proxy for the timing of flowering. We illustrate this approach with two use cases. First, over cropland in Illinois and Iowa, we construct a proxy measure of the evolution of crop flowering—silking for corn, flowering for soybean—across the growing season. Second, over the Eastern US, we construct a proxy measure of the onset of spring flowering, which reflects the phenology of early-flowering, temperature-sensitive species.