Naturalist. Thrilled by birds, plants & gnocchetti sardi.
PhD stud @LaStatale on community ecology in mountain agro-landscapes.
Intrigued by distribution tails.
Crazy days! While helping w/ 🐦ringing @OssFaunAsinara, my FIRST PAPER went OUT at @RSECJournal! 🤩🎉
with Mattia Brambilla, @DavideScridel et al.💕 we investigated #snowfinch thermoregulatory behaviour and microhabitat selection from space. Get a look! https://t.co/Jpf73ZrMHx
Exciting New Citizen Science Project!
Help us find bird nests in anthropogenic spots — pipes, mailboxes, vents & more.
Join the Urban Cavities Project on iNaturalist!
https://t.co/fvc5GObWfo
📩Ignacy Stadnicki | [email protected]
Is #RegenerativeAgriculture the solution to delivering for both food and nature?
A new report by the British Ecological Society brings together 40 academics, practitioners and farmers across the UK to explore the evidence. 👇
https://t.co/eZLpqWc9C6
Key Takeaways from “Regression Modeling Strategies” by Frank Harrell (@f2harrell)
A must-read for anyone working with predictive modeling. Here’s what you need to know:
Plan your model with clear goals—whether prediction, effect estimation, or hypothesis testing. Avoid arbitrary categorization of continuous variables, as it leads to information loss and reduced statistical power.
Use flexible techniques like restricted cubic splines to relax linearity assumptions while maintaining interpretability. Avoid stepwise selection, which often results in overfitting. Instead, rely on penalized regression methods such as ridge or elastic net.
Handle missing data effectively through multiple imputation rather than case deletion. Reduce dimensionality using redundancy analysis, variable clustering, or principal component analysis to make models more efficient and interpretable.
Validate models rigorously using bootstrap resampling rather than splitting data into arbitrary training and testing sets. Focus on calibration and discrimination metrics like concordance indices to assess predictive performance.
Communicate results clearly with visualization tools such as diagnostic plots and nomograms. Simplify models thoughtfully through approximations rather than haphazardly dropping predictors.
This book combines theoretical depth, practical advice, and reproducible R code, making it essential for statisticians, data scientists, and researchers in fields like biostatistics and machine learning.
Thoughts?
#Statistics #DataScience #Research #Science #Rstats
🚨 PhD siren!🚨 We have an exciting 4 year PhD project on offer using information theory to investigate why different visual systems have evolved in ecologically similar species. Funded by SWBio DTP to start in Sept 2025! Apply here: https://t.co/CmWU6O9mGU
🌲Would you like to study climate effects on forests by remote sensing? We have an open three-year #postdoc#position within #remotesensing of forests! Study the "pulse" of trees using RS and dendrometers.
Learn more and apply here 👉 https://t.co/Jl0OGG4I7k
Some of you have been waiting for this moment.....all lecture recordings including remote sensing and downloads of this years #Animove are online now. 🥳 Happy Learning ! @MPI_animalbehav@safilabmpi https://t.co/YcViN14jCn
The @TheSeabirdGroup has uploaded the 16th International Seabird Group Conference 2024 presentations to its website and YouTube channel. Check out this incredible resource. #CoimbraSeabirds https://t.co/4V1ui7V3Ap
New #rpackage for modelling count data. Usefull for #cameratrap data? 🤔
@MethodsEcolEvol
good: An R package for modelling count data - Agis - Methods in Ecology and Evolution - Wiley Online Library https://t.co/BVPgESbCHH
Decided to call it an early weekend after a series of super intense days. The gods, it seems, approved and granted me spectacular conditions to watch the enormous flocks of Ruff, Dunlin, Golden Plover and Lapwing currently at Blaugerzen. 🤩🤩