The Long Term Ecological Research (LTER) network uncovers unique findings only possible through its long term approach to science. However, many are not aware of its contributions. This team created a video series on the benefits of the LTER network & the scientific insights it can offer.
This project contributes to the larger Building Resilience to Wildfires initiative by presenting a process for creating these potential climate scenarios by stitching together pieces of existing climate model projections. To build each piece of a climate scenario, or each ‘segment’, climate models are searched for pieces that match the specified climate criteria of interest for that very segment. Once all desired segments are built, segments are then stitched together to make one continuous time-series: a ‘climate scenario’.
Regenerative agriculture offers unique environmental benefits that conventional agriculture cannot. This team created targeted papers to certifying bodies and ranchers, and a short film on how regenerative agriculture works for White Buffalo Land Trust.
Adding solar PV to existing wind energy sites can boost profits and minimize the need for new transmission infrastructure. This study used a nonlinear optimization model to size solar PV additions at over 1,300 U.S. wind projects, ranking sites by environmental impact and cost efficiency.
Abandoned or lost fishing gear can be dangerous to sea life, and the Ocean Defender’s Alliance (ODA) is dedicated to removing it. This team created a short film on the collaboration between ODA and local fishers to make a big difference in the marine environment by removing fishing debris.
The ecology of giant kelp is complex and the formation of kelp forests is impacted by many factors, including ocean nutrient concentrations, water depth, sea surface temperature, and seafloor habitat. The Santa Barbara Channel has been home to many long-term research projects that collect data on these factors. Unfortunately, this information is in many formats and data structures making it challenging for researchers to easily incorporate it into their work. This project creates a shortcut for acquiring and using these data by synthesizing a data set of nutrient concentrations, water depth, sea surface temperature, seafloor habitat, kelp area, and kelp biomass.
In this project, we conduct a case study of Zambia to demonstrate a recently developed machine learning pipeline. We use the “Multi-task Observations using Satellite Imagery & Kitchen Sinks” (MOSAIKS) machine learning approach. In this approach, we use processed numerical data from satellite images and agricultural survey data to develop machine learning models that predict various agricultural variables over time.
This project modeled human-black bear conflicts in California, incorporating fire and drought impacts to predict future hotspots. Findings help wildlife managers allocate resources and implement proactive strategies to reduce conflicts, especially in vulnerable communities.
