About

Paul R Moorcroft is a Professor of Organismic and Evolutionary Biology at Harvard University and an Affiliate in Environmental Science & Engineering. His primary teaching areas include Environmental Science & Engineering. His research focuses on environmental science and engineering, specifically biogeochemical cycles, climate change, and water. He is associated with the Harvard John A. Paulson School of Engineering and Applied Sciences, located at 150 Western Ave, Allston, MA, and also has a presence at 29 Oxford Street, Cambridge, MA. His work involves understanding complex environmental systems and their interactions, contributing to the fields of climate change and water resource management.

Research topics

• Ecology
• Environmental science
• Geography
• Computer Science
• Atmospheric sciences
• Agronomy
• Agroforestry
• Economics
• Botany
• Biology
• Materials science
• Water resource management
• Forestry
• Natural resource economics
• Engineering

Selected publications

• Animal Home Ranges

  Elsevier eBooks · 2026-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Understanding partial migration: linking genetic, developmental, and environmental drivers

  2025-12-02

  preprint

  Partial migration, where some individuals in a population migrate while others remain resident, arises from the dynamic interplay of multiple non-exclusive eco-evolutionary mechanisms. These mechanisms vary because migratory patterns can be shaped by genetics, individual experience, social learning, and fluctuating environmental pressures. Yet there is no clear link between these mechanisms and the behavioral patterns observed across species and environments. As a result, studies often focus on isolated examples without a shared structure for comparison or synthesis. Here, we integrate mechanisms, scales, and patterns to guide the study of partial migration. This synthesis emphasizes three core components: (1) identifying and linking developmental, plastic, and genetic drivers of partial migration, (2) incorporating processes from individual decisions to population-level patterns across spatial and temporal scales, and (3) providing a roadmap to help researchers choose an appropriate framing for their research questions. It also stresses that careful consideration of these scales is essential before developing models or analyses that address eco-evolutionary questions. This holistic approach enables researchers to generate testable hypotheses about the ecological and evolutionary mechanisms governing partial migration, thereby capturing complexity in terrestrial systems more effectively and supporting the selection of appropriate methods.

  PublisherDOI

• Monthly averages of ED2 model simulations initialized with airborne lidar structure, Jan 1981-Dec 2018, Brazilian Amazon

  OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2025-01-01 · 1 citations

  datasetOpen access

  Deforestation and forest degradation (selective logging, fires, fragmentation) have impacted nearly 40% of the original extent of the Brazilian Amazon, and have markedly impacted forest structure across the region. To date, few studies analysed how shifts in forest structure from degradation influence the forest sensitivity to climate extremes, because of the complex interactions between forest structure and micro-environmental conditions. To address this knowledge gap, we carried out a series of simulations across the Brazilian Amazon using the Ecosystem Demography Model (ED2), using observed forest structure derived from 541 airborne lidar transects (375 ha each) and two scenarios representing forest recovery and expansion of degradation to investigate how shifts in forest structure impact ecosystem function under near-average and extreme climate conditions, as part of the manuscript Longo et al 2025 "Degradation and Deforestation Increase the Sensitivity of the Amazon Forest to Climate Extremes". This dataset provides the output results from the ED2 model simulations for the three simulations at monthly time scales, in NetCDF format. For all simulations, we used bias-corrected hourly reanalyses (WFDE5) for most meteorological drivers, except for precipitation, which was obtained from CHIRPS. The meteorological drivers used in the study span 38 years (Jan 1981–Dec 2018). The output results correspond to the last 38 years of simulation (one full cycle of meteorological drivers), in which ED2 simulations used static stand structure (i.e., the forest structure was held constant). The following files are provided:ED2_emean_Global_R004_BrAmaz_s1c0t0l0f0.nc. This corresponds to the Control simulation. The forest structure was obtained from the airborne lidar.ED2_emean_Global_R005_BrAmaz_s1c0t1l1f0.nc. This corresponds to the Degraded simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that expanded deforestation and selective logging across the Amazon.ED2_emean_Global_R006_BrAmaz_s1c0t1l0f0.nc. This corresponds to the Recovery simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that completely halted deforestation and degradation, allowing degraded forests to recover for 38 years.We also provide file ED2_zones_R004_BrAmaz_s1c0t0l0f0.nc, which classifies each grid cell into zones used in the reference manuscript: 1: Southeast. 2: South. 3: West. 4: Central. 5: Northeast. 6: North. 7: Northwest". Index 0 corresponds to grid cells excluded from sub-region analyses because they were dominated by flooded forests, deforestation, and naturally non-forest vegetation.

  PublisherDOI

• Constraining Light‐Driven Plasticity in Leaf Traits With Observations Improves the Prediction of Tropical Forest Demography, Structure, and Biomass Dynamics

  Journal of Geophysical Research Biogeosciences · 2025-05-28 · 1 citations

  articleOpen access

  Abstract Predicting tropical tree demography is a key challenge in understanding the future dynamics of tropical forests. Although demographic processes are known to be regulated by leaf trait diversity, only the effect of inter‐specific trait variation has been evaluated, and it remains unclear as to what degree the intra‐specific trait plasticity across light gradients (hereafter light plasticity) regulates tree demography, and how this will further shape long‐term community and ecosystem dynamics. By combining in situ trait measurements and forest census data with a terrestrial biosphere model, we evaluated the impact of observation‐constrained light plasticity on demography, forest structure, and biomass dynamics in a Panamanian tropical moist forest. Modeled leaf physiological traits vary across and within plant functional types (PFT), which represent the inter‐specific trait variation and the intra‐specific light plasticity, respectively. The simulation using three non‐plastic PFTs underestimated 20‐year average understory growth rates by 41%, leading to a biased forest size structure and leaf area profile, and a 44% underestimate in long‐term biomass. The simulation using three plastic PFTs generated accurate understory growth rates, resulting in a realistic forest structure and a smaller biomass underestimate of 15%. Expanding simulated trait diversity using 18 nonplastic PFTs similarly improved the prediction of demography and biomass. However, only the plasticity‐enabled model predicted realistic long‐term PFT composition and within‐canopy trait profiles. Our results highlight the distinct role of light plasticity in regulating forest dynamics that cannot be replaced by inter‐specific trait diversity. Accurately representing light plasticity is thus crucial for trait‐based prediction of tropical forest dynamics.

  PublisherOA PDFDOI

• Environmental drivers of spatial variation in tropical forest canopy height: Insights from NASA’s GEDI spaceborne LiDAR

  Proceedings of the National Academy of Sciences · 2025-03-03 · 10 citations

  articleOpen accessSenior author

  Forest canopy height is a fundamental ecosystem property-influencing patterns of forest carbon storage and forest ecosystem responses to climate variability and change. Previous studies have analyzed environmental drivers influencing spatial variation in canopy height at landscape-to-regional scales; however, far less is known about the environmental determinants underlying regional and global scale variation in forest canopy height. Using the canopy height metrics products from Global Ecosystem Dynamics Investigation (GEDI), a space-borne Light Detection and Ranging (LiDAR) instrument specifically designed to characterize forest structure, we analyze the environmental correlates of spatial variation of global tropical forest canopy height. Our study demonstrates that climate, topography, and soil properties account for 75% of the variation in tropical forest canopy height. Elevation, dry season length, and solar radiation are the most important drivers in determining canopy height both locally and regionally. These results emphasize the vulnerability of tropical forest structure to ongoing changes in the earth's climate and provide a valuable empirical baseline for tropical forest management.

  PublisherOA PDFDOI

• Legacies of model initialization on predictions of future ecosystem dynamics in California's Sierra Nevada: insights from <scp>GEDI</scp>

  New Phytologist · 2025-11-11 · 1 citations

  articleOpen accessSenior authorCorresponding

  Accurate estimates of aboveground vegetation structure are essential for making reliable predictions of terrestrial ecosystem responses to climate change. However, traditional small-scale ground-based inventory methods cannot easily be scaled up to comprehensive, large-scale estimates of ecosystem structure. We assimilate remotely-sensed Light Detection and Ranging measurements of vegetation structure and corresponding imaging-spectrometry-derived estimates of canopy composition into the ecosystem demography (ED2.2) terrestrial biosphere model across an elevational transect in California's Sierra Nevada. We then used the model to assess: how incorporating observed ecosystem structure and composition influences predictions of ecosystem change over the coming century as compared to simulations initialized with long-term potential vegetation; and how ecosystems are predicted to respond differently to future climate change. Our analyses show multi-decadal impacts of initialization on predictions of ecosystem composition and structure, emphasizing long-term legacies of climate and disturbance history in predictions of ecosystem responses to climate change that are not captured when models are initialized with outputs from long-term historical simulations. The remote sensing-initialized simulations predict increases in aboveground biomass and leaf area index, and pronounced elevation-dependent changes in canopy composition. The differences among initialization methods, climate scenarios, and elevational gradients have important implications for improving ecosystem modeling and informing land management strategies.

  PublisherOA PDFDOI

• Future Predictions of Ecosystem Changes in California&amp;#8217;s Sierra Nevada over the coming century using Remote-Sensing Constrained Terrestrial Biosphere Model Simulations

  2024-03-08

  preprintOpen accessSenior author

  Reliable predictions of ecosystem dynamics and carbon stocks depend on accurate initialization of ecosystem states in process-based model simulations. Unlike traditional potential vegetation simulations which assume that ecosystems equilibrate with long-term climate, observation-initialized simulations integrate the impacts of previous history of disturbance events and human activities on ecosystem structure and composition. However, observation-constrained initialization is challenging at regional scales due to limited availability of spatially-comprehensive measurement data. In this study, we assimilate remote-sensing estimates of canopy structure from Global Ecosystem Dynamics Investigation (GEDI) and canopy composition from AVIRIS imaging spectrometry into Ecosystem Demography version 2 (ED2), a cohort-based Terrestrial Biosphere Model. We drive model simulations with future climate scenarios and rising atmospheric CO2 concentrations to predict ecosystem responses to environmental changes over an elevational transect region in California&amp;#8217;s Sierra Nevada by the end of the century. Our simulations suggest that predictions are significantly impacted by ecosystem initial condition at the multi-decadal (50+ year) scale. The impacts are stronger in dense-canopy forests at mid-to-high elevations than woody savannahs at low elevations. Under a hotter and drier future climate with CO2 enrichment, ecosystems across the elevational transect are predicted to act as a net carbon sink but with marked changes in composition. Aboveground biomass (AGB) is predicted to increase at low elevations due to increasing abundance in both deciduous and coniferous trees. However, at mid-to-high elevations, AGB increases are caused by increasing abundance of coniferous trees but large declines in the abundance of deciduous trees. Our research demonstrates how large-scale remote-sensing data can be assimilated into process-based model simulations to improve future predictions of ecosystem dynamics. &amp;#160;

  PublisherDOI

• The role of memory-based movements in the formation of animal home ranges

  Journal of Mathematical Biology · 2024-04-08 · 2 citations

  articleSenior author
  PublisherDOI

• Constraining uncertainty in terrestrial tropical carbon flux dynamics requires capturing local biogeochemical influences on structure and function

  2024-03-09

  preprintOpen accessSenior author

  Spatial heterogeneity in tropical forest productivity and resulting rates of carbon uptake and storage emerge from variation in ecosystem structure and functional traits reflecting differences in climate, edaphic conditions, evolutionary history, and natural and anthropogenic disturbance histories. Yet, models poorly represent this heterogeneity. Remote sensing data provide landscape-scale measures of tropical forest heterogeneity in structure and functional traits that can be used to advance terrestrial biosphere models. To examine whether forest functional traits related to photosynthetic capacity can be used to improve predictions of tropical biomass dynamics and carbon fluxes, we parameterized the Ecosystem Demography model version 2.2 (ED2.2) using canopy traits derived from visible to shortwave infrared (VSWIR) airborne imaging spectroscopy data across an edaphic gradient in Borneo. We find significant site-level differences in relationships between SLA and foliar nutrient concentrations, suggesting that remotely sensed foliar traits can be used to capture variation in photosynthetic capacity at large, edaphically varying spatial scales. We further show that plant functional types parameterized with site-constrained trait values yield more accurate predictions of canopy demography, forest productivity and above-ground biomass dynamics than simulations that depend solely on parameterization of edaphic conditions. However, the most substantial improvements result from allowing for site-level variation in background disturbance rates in the model. Our study reveals the importance of capturing tropical forest heterogeneity in terrestrial biosphere models, particularly as it relates to nutrient availability and disturbance processes.&amp;#160;

  PublisherDOI

• Mapping fine-scale variation in diverse tropical forests with distinct ecological dynamics requires few leaf traits and structural attributes

  2024-01-30

  preprintOpen accessSenior author

  Remote sensing is a powerful tool for characterizing ecosystems at large scales. However, the relative importance of leaf traits and canopy structure in characterizing the spatial distribution of functionally distinct tropical forests – the most diverse, structurally complex, and heterogeneous ecosystems on Earth – remains under-explored. Using satellite-resolution LiDAR and imaging spectroscopy metrics, we map spatial turnover in tropical forest function, examine the relative importance of leaf traits and canopy structure, and analyze differences in aboveground carbon and demography. We find that leaf phosphorus, LMA, and canopy height are key distinguishing properties of forest types, achieving accuracies of 85-96% and correspond to differences in community growth and mortality rates. Our remotely sensed forest types align with ground-based forest definitions but enable mapping of their entire extent. At 30 m resolution, our method can be used at large scales with spaceborne data to reveal important differences in structure and function across tropical forests.

  PublisherOA PDFDOI

Recent grants

• DISSERTATION RESEARCH: Determining the drought sensitivity of the Amazon forest

  NSF · $15k · 2011–2013

Frequent coauthors

• Marcos Longo

  Brazilian Agricultural Research Corporation

  81 shared

• Eunjee Lee

  54 shared

• Mauricio E. Arias

  University of South Florida

  49 shared

• Fábio Farias Pereira

  Universidade Federal de Alagoas

  42 shared

• John Briscoe

  41 shared

• Fabio Farinosi

  35 shared

• G. C. Hurtt

  University of Maryland, College Park

  33 shared

• R. G. Knox

  Lawrence Berkeley National Laboratory

  33 shared