Research
Our overarching goal is to explore and understand the mechanisms and consequences of landscape spatial patterns, including their mechanisms, consequences, and feedbacks. We are especially interested in the type of landscapes where biological and physical processes strongly interact (e.g., biogeomorphic landscapes). To do so, we use and develop mathematical models, results of which are then compared with data from remote sensing, global databases, or field and experimental measurements.
Here are some themes we currently work on:
Vegetation Spatial Patterns in Global Drylands. 
As environmental changes have pushed ecosystems worldwide towards their limits, there has emerged a growing need to predict responses of ecosystems to external pressures. Prior work suggests that large-scale spatial patterns found in many ecosystems change in a predictable way when approaching a tipping point. Spatial patterns hence can be employed as harbingers of imminent whole ecosystem change. Drylands, as a spatially patchy ecosystem that is globally important yet sensitive to climate change, have been used as pivotal systems in developing such a theory. With increased aridity, the theory predicts that dryland vegetation patterns shift from bare gaps in homogenous vegetation to labyrinthine or striped vegetation cover, then to a spotty pattern, before they catastrophically shift to a bare state. Vegetation patterns conforming to these predictions, however, are limited to a only small area of drylands globally! This is likely because all current dryland pattern formation models neglect a significant component of the system — biological soil crusts (biocrusts).
Biocrusts are defined as an intimate association between soil particles and differing proportions of photoautotrophic (e.g., cyanobacteria, lichens, bryophytes) and heterotrophic (e.g., bacteria, fungi) organisms, which live within, or immediately on top of, the uppermost few millimeters of soil. Biocrusts can cover more surface area than vascular plants and are considered “an organizing principle of drylands”, for their critical role in dynamics of water, energy, and nutrients. Importantly, they alter core processes underlying feedbacks dictating vegetation pattern formation.
Conceptualizing drylands as an integral biocrust-vascular plant complex will require new theories and models that consider inter-specific interactions (e.g., competition, facilitation) in the context of ecosystem spatial self-organization. Funded by NSF-DEB, we (co-PIs: Yufang Jin,Rachata Muneepeerakul,Caroline A. Havrilla, and Yu Zhang) will develop such theories and models, using drylands as a case study. New models will likely reproduce vegetation patterns representative of drylands globally, beyond the few categories of regular patterns predicted by current models. See a recent paper by Dan for results to date.
Effects of Environmental Change on Microbial Spatial Structures in Antarctic Lakes. 
Beneath permanent ice and meters of liquid water in many Antarctic lakes reside structurally complex arrays of spatially self-organized microbial mats. Changing climate has already begun to transform these unique ecosystems in ways not yet understood. Will benthic communities respond to environmental changes linearly and gradually, or abruptly and catastrophically? Using existing datasets on spatial structure of benthic communities from 37 sites on the floor of Antarctic Lake Vanda, collaborating with Dr. Dawn Sumner in the Department of Earth and Planetary Sciences at UC Davis, we will apply recent theories from Spatial Ecology to investigate the mechanisms that give rise to spatial patterns of pinnacles formed by benthic microbes (funded by NSF-OPP). We address two questions: (1) What are the morphological and spatial patterns of pinnacles and how do they vary over developmental stages, along environment gradients, and from 2013 to 2023? And (2) what mechanisms give rise to the geometry of individual pinnacles and their spatial distribution? These questions will be addressed by integrating existing datasets, spatial pattern analyses, Bayesian statistical models, and process-based numerical models. These models will allow us to better predict responses of pinnacles to environmental change.
Geo-evolutionary Feedbacks to Couple Evolution of Landscapes and Plants. By virtue of their niche construction activities, organisms can play a significant role in shaping landscapes (for example, plants modify fluid dynamics and sediment transport). The resultant outcome could change the selective environment, which then influences evolutionary and ecological dynamics of these same organisms. Such feedbacks are described by niche construction theory. Niche construction theory often assumes that all environmental changes are brought about by niche constructions, or it assumes only simple, linear changes. However, landscapes evolve following an independent set of laws governing earth surface processes, some of which are affected by organisms. This is studied by biogeomorphology. Biogeomorphology, however, has mostly assumed plants and their niche construction to be evolutionarily invariant. We are developing theories that integrate geo-evolutionary dynamics, considering feedbacks between landscape changes and evolution of niche constructors. Please refer to this paper and this paper for our recent results so far.
Global Plant Migration under Climate Change. Collaborating with Dr. Francis Moore at UC Davis and Dr. Marc Conte from Fordham University, we evaluate the effect of climate change on ecosystem functioning and biodiversity and the social cost of carbon (funded by NSF-DEB). We build global species distribution models considering species dispersal capacity and topographical barriers of the earth surface to identify regions of biodiversity losses and gains, hotspots of novel species assemblages, and major migration paths under global change. The effect of climate change on ecosystem functioning and biodiversity are then incorporated into the damage functions used by integrated assessment models (IAMs) to determine the marginal damages of CO2 emissions (i.e., social cost of carbon). Please see our most recent results here and here. The map below shows model predicted direction and extent of plant migration by the end of this century globally:
