Jornada scientists often collaborate with other scientists within the LTER network to create research with implications on a global scale! Below is just one recent example of how Jornada Basin LTER knowledge is making a huge impact: Although remote sensing can gauge biomass for the tall trees of forested ecosystems, the technology is still lacking […]
Although many experiments are conducted within the physical boundaries of the Jornada Basin, the Jornada’s influence is much wider reaching! The Jornada Basin LTER program is special because it relies on its own long-term data to inspire new studies–both within the Jornada Basin and across the globe-spanning LTER network. The resulting scientist collaborations and data
Collaboration Beyond the Jornada LTER: How Ocean Variation Creates Rodent Inflation Read More »
Please welcome Cameron Duquette, our newest Jornada postdoctoral researcher! Originally from central Massachusetts, Cameron majored in Wildlife and Conservation Biology at the University of New Hampshire. He then continued his education at Oklahoma State University, where his master’s thesis focused on the effects of the oil and gas industry on Northern Bobwhite habitat selection. Most recently,
Introducing… Cameron! Read More »
The Executive Committee of the Jornada LTER program would like to welcome Dr. Stephanie Bestelmeyer as its newest member. Holding a Ph.D. in Biology from Colorado State University as well as a B.S. in Biology and minor in Teaching from the University of California San Diego, Dr. Bestelmeyer has been working with the Asombro Institute
Welcome Stephanie Bestelmeyer to the Executive Committee Read More »
In a new paper published in Methods in Ecology and Evolution, Jornada LTER scientists, led by Dr. Quiyan Yu, review salient methods in machine learning algorithms and evaluate their effect on successful ecological inference. A number of recommendations emerged, including the removal of spurious (correlated but not functionally important) variables from ML models, and the
Ecological insight through machine learning Read More »