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Pasquarella, V. J.

Publications and source records attributed to Pasquarella, V. J..

2 recordsLinked to original sources

Asymmetric Spring and Autumn Phenology Control Growing Season Length in Temperate Deciduous Forests

Forest phenological responses to climatic variation among species and populations across broad spatial scales remain poorly understood. Here, we quantify four decades of phenological dynamics for 10 major deciduous tree species by compiling a unique dataset that integrates high spatial resolution remote sensing with extensive field inventory data across the Northeastern and Midwestern United States. We found that spring and autumn phenology impose asymmetric controls on growing season length, with spring regulating interannual variation and autumn driving long-term trends. Spring phenology across all species was primarily controlled by temperature, while autumn phenology was influenced by spring phenology and an interacting suite of species-specific environmental factors. Our study demonstrates the promise of combining high resolution remote sensing with forest inventory data to investigate phenological dynamics over large regions. More importantly, our results provide new insights into how species-specific sensitivities to environmental drivers regulate growing season length in temperate forests.

ecology↗

A comparison shopper's guide to forest datasets

Recent advances in remote sensing, data availability, and cloud-based computing have led to a rapid expansion of publicly accessible datasets characterizing forest cover and land use. These datasets are widely used in ecological research, natural resource management, and policymaking. However, the sheer number of available products--and the often-subtle differences among them--pose significant challenges for users seeking the most appropriate dataset for their specific objectives. Here, we evaluate 27 derived products from 12 publicly available sources that quantify tree or forest cover and use across the conterminous United States (CONUS), with temporal coverage ranging from 5 to 30 years. The products include both satellite-based remote sensing data and ground-based national forest inventory data. We ask: How, why, and where do these datasets differ in their estimates of forest extent and change over time? Our analysis reveals that estimates of total forest area at the CONUS scale differ by over 2,000,000 km{superscript 2}, and correlations among forest change estimates vary widely in both direction and statistical significance. To support dataset selection and interpretation, we developed an open-access map comparison tool using Google Earth Engine. Our findings highlight the substantial implications of dataset choice for understanding forest dynamics and underscore the need for careful selection and transparent reporting in forest-related analyses.

ecology↗