Forest Advanced Computing and Artificial Intelligence -ECO Focus

About the lab

Our group employs the paradigm of Artificial Intelligence encompassing different state-of-the-art machine learning and statistical methods to study global, regional, and local forest resource management and biodiversity conservation​.
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Featured research (14)

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers.
Planting trillions of trees won’t replace the 10 million hectares of forest ecosystems lost each year, but documenting them could prevent further losses Read more:
Significance Tree diversity is fundamental for forest ecosystem stability and services. However, because of limited available data, estimates of tree diversity at large geographic domains still rely heavily on published lists of species descriptions that are geographically uneven in coverage. These limitations have precluded efforts to generate a global perspective. Here, based on a ground-sourced global database, we estimate the number of tree species at biome, continental, and global scales. We estimated a global tree richness (≈73,300) that is ≈14% higher than numbers known today, with most undiscovered species being rare, continentally endemic, and tropical or subtropical. These results highlight the vulnerability of global tree species diversity to anthropogenic changes.
Mounting evidence suggests that geographic ranges of tree species worldwide are shifting under global environmental change, but little is known about forest migration—the shift in the geographic ranges of forest types—and how it differs from individual tree species migration. Here, based on in situ records of more than 9 million trees from 596,282 sample plots, we quantified and compared the migration patterns of forests and tree species across North America between 1970 and 2019. On average, forests migrated at a mean velocity of 205.2 km per decade, which is twice as fast as species-level migration (95.6 km per decade), and 12 times faster than the average of previous estimates (16.3 km per decade). Our findings suggest that as subtle perturbations in species abundance can aggregate to change an entire forest from one type to another, failing to see the forest for the trees may result in a gross underestimation of the impacts of global change on forest ecosystem functioning and services. With the first forest classification and quantification of forest migration patterns at a continental level, this study provides an urgently needed scientific basis for a new paradigm of adaptive forest management and conservation under a rapid forest migration.
Question Forest ecosystems are the most important global repositories of terrestrial biodiversity. The mixed temperate forests in northeastern China constitute one of the most biodiverse temperate regions globally and provide nearly one‐third of China’s wood supply. We ask what are spatial patterns and potential drivers of tree species diversity in mixed temperate forests. Location Temperate, mixed forests of northeastern China. Methods Using a large set of ground‐source forest inventory data (FIN) and geospatial covariates derived from published raster layers, we compared different machine learning and statistical models to study spatial patterns of tree species diversity and their underpinning drivers? Results The spatial distribution of tree species diversity (species richness and evenness) varied greatly across northeastern China. Tree species diversity varied most with climatic (annual precipitation and annual mean temperature), topographic (elevation and slope), and anthropogenic factors. Anthropogenic factors affected tree species evenness (importance value=13%) more than tree species richness (importance value=9%). Based on these relationships, we mapped spatial patterns of tree diversity throughout the region at a 1 km × 1 km resolution. Conclusions Our findings shed light on the processes behind community assembly and biodiversity patterns in mixed temperate forests in northeastern China, and provide a benchmark for future assessment of biodiversity. Our high‐resolution tree species diversity maps can be useful to landowners and land management agencies in their decision‐making processes about sustainable forest management, biodiversity conservation, and forest restoration—a priority task outlined by the recently implemented 2050 China National Forest Management Plan.

Lab head

Jingjing Liang
  • Department of Forestry and Natural Resources
About Jingjing Liang
  • Jingjing Liang, an Associate Professor of Quantitative Forest Ecology, has cofounded the Global Forest Biodiversity Initiative (GFBI) and led the development of the first global forest inventory database GFBi. Dr. Liang has been working on connecting machine learning and big data in studying fundamental questions in biodiversity and ecosystem processes, ecological and socioeconomic impacts of biological conservation.

Members (5)

Ankita Mitra
  • Purdue University
Akane Abbasi
  • Purdue University
Wook Jin Choi
  • Purdue University
Gabriela Krochmal
  • Purdue University
Aubrey Franks
  • Purdue University

Alumni (1)

Roberto Cazzolla Gatti
  • University of Bologna