Rodrigo Vargas

Congratulations to our AGU-B DEI Travel Grant winners! Visit their talks/posters and reach out if you know of an opportunity that aligns with their interests!

The result: more realistic sample sets that respect your system’s dependency patterns — great for environmental modelling, simulation, and uncertainty analysis.

📄 Method details:
doi.org/10.1016/j.ca...

Traditional LHS ensures stratified coverage of each variable’s range, but samples are independent — losing spatial/temporal structure.

acLHS preserves those autocorrelations while keeping the stratified, space-filling design you want.

aclhs: Autocorrelated Conditioned Latin Hypercube Sampling Implementation of the autocorrelated conditioned Latin Hypercube Sampling (acLHS) algorithm for 1D (time-series) and 2D (spatial) data. The acLHS algorithm is an extension of the conditioned Latin Hyp...

🚀 Excited to share our new R package aclhs — now on CRAN! @huong-le.bsky.social #VargasLab

It implements autocorrelated conditioned Latin hypercube sampling (acLHS), an improvement over the traditional LHS for data and models with spatial or temporal dependence

🔗 cran.r-project.org/web/packages...

🔗 Más información sobre el Programa Mexicano del Carbono, sus líneas de investigación y convocatorias, disponible en:
👉 pmcarbono.org/pmc

4/4

📖 En esta edición, también se rindió homenaje al Dr. Fernando Paz Pellat, figura clave en la creación y consolidación del PMC, y promotor incansable de la ciencia del carbono en México.
#FernandoPaz #InvestigaciónAmbiental

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💡 Este simposio es un espacio de encuentro anual para la comunidad científica nacional e internacional dedicada al estudio del ciclo del carbono, los ecosistemas y el cambio climático en México.
#CambioClimático #CienciaDelCarbono

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🙌 Con gran satisfacción participamos en el XVI Simposio Internacional del Carbono en México, organizado por el Programa Mexicano del Carbono (#PMC).

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🚀 Nominate an impactful dataset.

⭐️ Deadline October 20th

@agu.org

💡 Why it matters:
Long-term data are irreplaceable for understanding ecosystem resilience, variability, and responses to climate change. Sustained support for long-term ecological research is vital to guide adaptive management and policy decisions.

#DataDriven #SciencePolicy

5/5

📈 What we found:
Even after two decades of measurements, predictions of ecosystem behavior remained highly variable and nonlinear. Short-term datasets often miss emergent and unexpected dynamics that only become evident with long-term observation.

#LTER #Sustainability #EcologicalMonitoring

4/5

🔍 What we did:
We analyzed more than 20 years of aboveground net primary production (ANPP) and precipitation data across three dryland ecosystems, spanning a grassland-to-shrubland transition in central New Mexico. We quantified how model predictive capacity evolves as more data accumulate.

3/5

➡️ Long-term data are essential to guide policies on land management, conservation, and climate adaptation. Stable funding streams are crucial to keep these efforts alive.

#FundingScience #Ecology #ScienceMatters

2/5

🌎 Support for Long-Term Ecological Studies Matters Now More Than Ever

I am excited to share our new paper: “Long-term ecological studies must continue: insights from a dryland transition zone” co-authored with Huong LE, Ph.D. and Scott Collins.

link.springer.com/article/10.1...

1/5

DOE Establishes the Office of Science Advisory Committee The Department of Energy (DOE) today announced the establishment of the Office of Science Advisory Committee (SCAC).

BERAC, along with all other Office of Science advisory committees, will be replaced by the newly established Office of Science Advisory Committee (SCAC). This unified advisory committee will support the entire Office of Science.

www.energy.gov/science/arti...

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DOE Establishes the Office of Science Advisory Committee The Department of Energy (DOE) today announced the establishment of the Office of Science Advisory Committee (SCAC).

It was an honor to serve on the Biological and Environmental Research Advisory Committee (BERAC) at the DOE. During my time, I had the privilege of collaborating with outstanding colleagues and advocating for the remarkable work being conducted at DOE.

www.energy.gov/science/arti...

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Copula‐Based Cosimulation for Simulating Temporal or Spatial Data in Biogeosciences A copula-based cosimulation method is presented to improve simulations The proposed method is flexible and can model complex relationships Simulations better represented statistical features suc...

How can we model temporal or spatial dependence to simulate complex environmental systems?

@huong-le.bsky.social Copula-based Cosimulation as a tool for improving predictions.

agupubs.onlinelibrary.wiley.com/doi/10.1029/...

#EnvironmentalModeling @jgrbiogeo.bsky.social

🌱 What we found:

- Soil respiration temperature thresholds vary across vegetation types and climates.

- Soil moisture strongly interacts with temperature responses.

- A single “universal” temperature response is not enough for accurate carbon-cycle models.

#soilrespiration #drylands

Vegetation type and climate determine temperature thresholds of soil respiration across drylands Soil respiration (SR) is a key component of terrestrial carbon-climate feedbacks, yet its seasonal dynamics in drylands remain poorly understood. In m…

🧪 Excited to share a new paper:

“Vegetation type and climate determine temperature thresholds of soil respiration” in Soil Biology & Biochemistry.

www.sciencedirect.com/science/arti...

Thanks to Maria Almargo and collaborators for this work!

#soilrespiration #drylands #carboncyclescience

Nominate an Impactful Dataset You are invited to nominate a dataset to be included in a commentary about the impact of Earth, space, and environmental data using the dimensions of people, planet, and prosperity. Selected submitter...

⭐️Nominate an Impactful Dataset

@agu.org invites you to nominate an impactful dataset!

📊Submissions will be considered for inclusion in an upcoming featured commentary published in AGU Advances.

Submit nominations by 20 October.

docs.google.com/forms/d/e/1F...

@agubiogeosciences.bsky.social

Climate constrains the enhancement of CO2 fertilization on forest gross primary productivity - IOPscienceSearch Climate constrains the enhancement of CO2 fertilization on forest gross primary productivity, Wei, Xinyuan, Hayes, Daniel J, Schwalm, Christopher R, Fisher, Joshua B, Huntzinger, Deborah N, Ma, Lei, Vargas, Rodrigo, Brunsell, Nathaniel A

☀️New research in Environmental Research Letters

We show how climate limits the CO₂ fertilization effect on forest productivity. Warming may ease this in boreal zones, but tropical forests face a tougher tradeoff.

Read here: iopscience.iop.org/article/10.1...

#ClimateScience #Forests #CarbonCycle

☔

New paper with an incredible group of colleagues ✅

@cleanaircatf.bsky.social

@cleanaircatf.bsky.social
convened a group of forest carbon experts and we reviewed carbon offset protocols for improved forest management projects in North America.

The paper is out today in Earth's Future.

agupubs.onlinelibrary.wiley.com/doi/10.1029/...

AGU joins suit supporting fired federal employees The case brought by federal employee unions and nonprofit organizations asserts that OPM’s directive to fire probationary employees at the National Science Foundation and other agencies was illegal.

news.agu.org/press-releas...

New publication from PhD student Manan Sarupria in #RSE.

A novel spectral unmixing method for high-accuracy land cover quantification.

Read the full paper (free for 50 days!):

authors.elsevier.com/a/1kZmn_o69J...

Great opportunity for early career scientists!!!!

Nominate an ECS by February 7th 😎

@agubiogeosciences.bsky.social

Here's our annual alumni meal photo at #agu24
The group cleaned up nicely, eh? Great to see everyone @jdfuentes.bsky.social @drbenrunkle.bsky.social @youngryel.bsky.social @vargaslab.bsky.social @jbfisher.bsky.social @arman-ahmadi.bsky.social @pattyoikawa.bsky.social @etechlab.bsky.social

Big thanks to everyone who stopped by my poster yesterday at #AGU24!
Almost four straight hours of non-stop talking, and I loved every minute of it. Science is fun, especially when you get to share it with others
#Biogeosciences #VargasLab
@vargaslab.bsky.social
@geolatinas.bsky.social

An autocorrelated conditioned Latin hypercube method for temporal or spatial sampling and predictions A data-driven method is presented for improving sampling designs from times series (1D approach) or spatial arrays (2D approach) of digital informatio…

Hi, meet @huong-le.bsky.social postdoc at #VargasLab

Fantastic work developing algorithms to optimize sampling designs 🤯

For example check out his work on an autocorrelated conditioned Latin hypercube method for temporal or spatial sampling and predictions

www.sciencedirect.com/science/arti...