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PictureCollaborator Preeti Virkar hand pollinating apple blossoms in Uttarakhand, India. Photo Renu SayalCollaborator Preeti Virkar hand pollinating apple blossoms in Uttarakhand, India. Photo credit: Renu Sayal.
How do biophysical and institutional landscapes shape the potential for a true Pollinator Commons?
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NEW PROJECT! We are delighted to announce we received funding from the NSF Dynamics in Integrated Socio-Environmental Systems program to extend our collaborative project on the Pollinator Commons! This project is in collaboration with colleagues in India and the US, including: the Center for Ecology Research and Development; Dr. Aman Luthra at George Washington University; Dr. Matt Williamson at Boise State University; Dr. Monique Rivera at Cornell; Dr. Ann Fraser; Dr. Preeti Virkar; Kumaon University; and the Sustainable Farming program at Tompkins Cortland Community College. 

Check out the abstract of the new project here: DISES: Do landscapes shape the emergence of institutions governing the pollinator commons?

​Read a bit about our pilot study in India here: Pollinator Landscapes in the Himalayas

Associated papers: 
​Allington, G. R., & Luthra, A. (2024). Geographies of the pollinator commons. Progress in Environmental Geography, 3(1), 40-60. https://doi.org/10.1177/27539687231224457

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Rural land cover change in an era of urbanization: Implications for grassland resilience in Mongolia  

New project (!) funded by NASA LCLUC program, in collaboration with Dr. Qiongyu Huang at the Smithsonian Conservation Biology Institute, Dr. Nicole Motzer at Montana State University and Dr. Tungalag Ulambayar in Mongolia. 

In this project we will be collected household survey data to track how rural out-migration and subsequent changes in rural demographics and labor are impacting traditional livestock management practices across the central Mongolian steppe. These data will be linked to spatio-temporal estimates of grassland condition derived from high resolution imagery from UAVs (drones). 

​MAPPING 
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Classifying Timeseries Trajectories
We're utilizing the full archive of Landsat data, available through Google Earth Engine, to characterize changes in vegetation in northern China over the past 40 years. We then utilize this information to create a new classification based on similarities in timeseries signatures, such as the timing and magnitude of changes. These unique archetypes can be linked to specific policy and management interventions.
Grad student Mia Murray is developing this work to map land cover histories in the Xilingol grasslands of Inner Mongolia 


Establishing new baseline data on historic land cover in northern China
Read more about this project in my tweet thread here!

Most of the information we have about land cover change over time is limited temporally by the availability of satellite data, so most work only extends to the early 1980s. But what if we could go back further??

Grad student Brooke Iacone has georeferenced a set of declassified surveillance imagery from northern China in the late 1960s to give us our first understanding of the scale of grassland degradation during the Cultural Revolution. She is using this information to generate a land cover classification for 1970, 
MODELLING 
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Modeling complex social-ecological systems requires the integration of social, biophysical and spatial data, ideally across multiple time points in order to capture feedbacks and inherent variability. 


METASTUDY
Picturephoto credit: IAN TEH
Ongoing work in the lab is surveying the empirical evidence to date on the impacts of massive afforestation efforts in northern China on biophysical conditions. 
In parallel work, we are building a database of known plantations that we will use to train models to identify and map afforestation across the region at multiple time points, in order to estimate the scale of planting, as well as the ultimate fate of different planting schemes. 

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