Konstantinos Kougioumoutzis | kkougiou.com

Downscaling daily solar radiation in the Canary Islands with GAM Researcher in climate science at MBG-CSIC

🆕 BLOG POST
There’s so much more to spatial downscaling today than classic LM or kriging. In my new blog post, I show how flexible and powerful GAMs can be for this task. #rstats #dataviz

➡️ dominicroye.github.io/blog/canary-...

A Few Claude Skills for R Users – R Works The community has come together to create some great Claude Skills that you can try out today.

I rounded up a few Claude Skills for #RStats users.

Huge thanks to the creators who developed them. They share Skills for everything from tidyverse code to brand.yml files to learning while using AI.

Hope the list is useful, and please let me know what I missed! 🧡

rworks.dev/posts/claude...

GitHub - cribbie/negligible: This package contains many functions for conducting negligible effect statistical testing (also called equivalence testing). This package contains many functions for conducting negligible effect statistical testing (also called equivalence testing). - cribbie/negligible

This #rstats #package #negligible examine negligible effect / #equivalent testing in #SEM model after #lavaan, a few of the functions include
1) #neg.semfit (CFI, RMSEA, SRMR)
2) #neg.normal

github.com/cribbie/negl...

supercells helps group raster cells into meaningful spatial units for analysis and ML.

v2 adds many new features.

Docs: jakubnowosad.com/supercells/a...

Please try it and share feedback, suggestions, or use cases -- your input will help shape the release 🙏

#rstats #RSpatial #GISchat

A Niche in the Machine: The Promise of AI Foundation Models for Species Distribution Modeling
doi.org/10.32942/X2V...

Feb. update to the LLM+R guide 💪

8 new packages including:
code review, predictive modeling, speech-to-text, text-to-speech, HuggingFace integration, Gemini CLI companion, a CLI coding agent written in R🤯 ,and more!

available in English 🇱🇷 and Spanish 🇲🇽
luisdva.github.io/llmsr-book/
#rstats

🛖🗺️On the use of neighboring habitats as predictors of species distributions

vist.ly/4s6xc

#Animals #EcologicalNicheModelling #FocalPredictor #MultiScaleProcess #Plants #SpatialScale

Flexible methods for species distribution modeling with small samples vist.ly/4rq4e #SDM #MaxEnt #NicheModels

New research showing a mismatch between projected spruce habitat suitability under climate change and current management practices in Sweden.
www.tandfonline.com/doi/full/10....

Been outside of academia for half a decade now, yet I’m still part of that universe. Life works in mysterious ways

R Coding for Ecology chapter on the cartogram package explores mapping ecological patterns with cartograms -- visualizing sampling bias by resizing regions based on data values.

Chapter: doi.org/10.1007/978-...
Code: github.com/RCodingForEc...

#RStats #GIS #DataViz #LandscapeEcology

When Good Fit Goes Bad: Identifying and Minimising Overfitting in Ecological Niche Models Aim To synthesise current understanding of overfitting as a pervasive and often underdiagnosed problem in correlative ecological niche models (ENMs), and to assess its consequences for model interpr...

Our last contribution is a review on correlative ecological niche models' overfitting. We examined diagnostic approaches and evaluated modelling practices related to sampling bias, predictor choice, study area definition, model complexity and regularisation.
doi.org/10.1111/jbi....

Species distribution modeling with expert elicitation and Bayesian calibration Species distribution models (SDM) are key tools in ecology, conservation, and natural resources management. They are traditionally trained with data on direct species observations. However, if collec....

Ever wondered how to use supra-Bayesian approach in species distribution modeling (SMDs). Well we present a framework for it and exemplify it with real data on fish reproduction area estimation.

nsojournals.onlinelibrary.wiley.com/doi/10.1002/...
1/3

📖 Published!

CISO, a deep learning-based method for species distribution modelling Conditioned on Incomplete Species Observations 🖥️ 🦌 🌍

🔎 Find out more:

🗺️ fastfocal: fast moving-window and point extraction for rasters in R, optimized for large windows and common focal stats via FFT. Built on terra.

cran.r-project.org/package=fast...

#RStats #GIScience #RSpatial

Flexible methods for species distribution modeling with small samples Species distribution models (SDMs) predict where species live or could potentially live and are a key resource for ecological research and conservation decision-making. However, current SDM methods o....

New paper out today 🧵
Flexible methods for species distribution modeling with small samples (Ecography, OA). nsojournals.onlinelibrary.wiley.com/doi/10.1002/...

Check out our new paper in Ecological Modelling!

Divergent responses of endemic and non-endemic plant species to climate change in South American Lomas ecosystems Abstract. The Lomas ecosystem, situated along the arid coast of Peru, is a key refuge for biodiversity, characterized by unique vegetation adapted to dry c

【🎉Latest accepted article】
Divergent responses of endemic and non-endemic plant species to climate change in South American Lomas ecosystems

#Drought | #EndemicSpecies | #MaxEnt | #SpeciesDistributionModeling

@kvanmeerbeek.bsky.social
@kuleuvenplantinst.bsky.social

doi.org/10.1093/jpe/...

#rstats

Soooo if you use #RStats and Claude Code:
R console: install.packages("btw")
Terminal: claude mcp add -s "user" r-btw -- Rscript -e "btw::btw_mcp_server()"
And now Claude Code can answer questions about ANY R package installed on your system.

The Performance and Potential of Deep Learning for Predicting Species Distributions onlinelibrary.wiley.com/doi/10.1111/...

Cascading predictions from common to uncommon species improves species distribution models for plants Species distribution models (SDMs) traditionally rely on abiotic factors like climate and topography to predict plant species distributions. While eff…

Cascading predictions from common to uncommon species improves species distribution models for plants www.sciencedirect.com/science/arti...

Improving multi-species habitat identification through species weighting assignment using joint species distribution model Habitat conservation serves as the cornerstone for fishery resource recovery. Traditionally, multi-species habitat predictions are typically generated…

Improving multi-species habitat identification through species weighting assignment using joint species distribution model www.sciencedirect.com/science/arti...

Two decades of species distribution modeling: A systematic review of methods and applications This systematic review presents a comprehensive synthesis of species distribution modeling (SDM) applied to forest ecosystems in the context of climat…

Two decades of species distribution modeling: A systematic review of methods and applications www.sciencedirect.com/science/arti...

Machine learning and species distribution models for crops: A review The agricultural sector is facing enormous challenges to sustain food security, with rising pressures from population growth, climate change, water sc…

Machine learning and species distribution models for crops: A review www.sciencedirect.com/science/arti...

A Global Atlas of Specific Leaf Area Under Climate Change Motivation Specific leaf area (SLA) is a key plant functional trait linked to plant structural, physiological and resource-use strategies. Despite its importance, spatially continuous data sets capt...

A Global Atlas of Specific Leaf Area Under Climate Change onlinelibrary.wiley.com/doi/10.1111/...

On the use of neighboring habitats as predictors of species distributions Choosing the appropriate scale for measuring environmental predictors is needed for accurately modelling species distributions. This need is becoming increasingly important with the use of high-resol...

🆕 New paper: On the use of neighboring habitats as predictors of species distributions by @fcollart.bsky.social and Pierre-Louis Rey et al. about choosing the appropriate spatial scale at which environmental predictors are measured, critical for high-resolution SDMs. doi.org/10.1002/oik.11963

Foundation for unbiased cross-validation of spatio-temporal models for Species Distribution Modeling Evaluating the predictive performance of species distribution models (SDMs) under realistic deployment scenarios requires careful handling of spatial …

Foundation for unbiased cross-validation of spatio-temporal models for Species Distribution Modeling www.sciencedirect.com/science/arti...

Ecotrends: an R package for estimating habitat suitability trends over time ecotrends is an R package that provides a comprehensive framework for assessing species vulnerability by analysing temporal trends in habitat suitabil…

Ecotrends: an R package for estimating habitat suitability trends over time www.sciencedirect.com/science/arti...

GitHub - brownag/gdalcli: An R Frontend for the GDAL CLI An R Frontend for the GDAL CLI. Contribute to brownag/gdalcli development by creating an account on GitHub.

Introducing gdalcli by Andrew Brown -- an R frontend to GDAL’s unified CLI (≥3.11) 🌐

Compose and execute GDAL workflows with pipe-friendly functions.

Learn more: github.com/brownag/gdal...

#RStats #GDAL #Geospatial #OpenSource #RSpatial