Environmental Statistics workshops

Build reproducible maps and spatial analyses of biodiversity and environmental data using R’s terra package. Practical work covers vector and raster data, projections, thematic and interactive maps, spatial queries, geometry operations and layer transformations. Participants calculate distances, buffers and home ranges, examine terrain and spatial autocorrelation, and combine raster and vector information through extraction, interpolation, map algebra and zonal statistics. Demonstrations and supervised exercises support automated workflows for research datasets; participants may bring their own spatial data. 23–27 November 2026, daily 15:00–18:00, Europe/Madrid (CET, UTC+1). Fifteen live hours plus 20 hours of exercises; instructor consultations 09:00–21:00. Spoken Spanish with English slides and scripts; prior R experience required. Sixteen places. Regular fee EUR 485, or EUR 388 for Ambassador Institutions. Live attendance is required for the certificate. Payment follows acceptance; advertised fees include VAT.

Estimate species occurrence while accounting for imperfect detection. Participants prepare repeated survey histories, distinguish occupancy from observation processes and fit single-season models with ecological and survey covariates. Exercises compare two-stage estimation in tsoccu with likelihood-based analysis in unmarked, then develop predictions, uncertainty estimates, diagnostics and reproducible reporting. The final day introduces dynamic, multispecies, spatial and false-positive extensions through ecological applications. Sessions: 1–4 December, 09:00–13:00 daily, Europe/Berlin (CET, UTC+01:00). Live online training; fee €420. No prior occupancy-modelling experience is required; practical work is in R.

Learn regression models that estimate nonlinear relationships from data using splines. Starting from generalised linear models, the course develops fitting and visualisation with mgcv and gratia, explains how smoothing controls overfitting, and covers model checking, selection and inference. Further exercises introduce interacting smooths, hierarchical models with group and individual effects, and posterior simulation, using ecological and environmental examples. Sessions: 7–10 December, 14:00–20:00 daily, Europe/Berlin (CET, UTC+01:00). Live online training; fee €480. R/RStudio fluency is required. Familiarity with generalised linear models and fixed/random effects is useful.

Build and evaluate short-term ecological forecasts by repeatedly linking observations, models and predictions. A biodiversity challenge uses shrub, rabbit and dung-beetle data from Doñana with remote-sensing measurements. Participants process time series and satellite indices, compare autoregressive, hierarchical, correlational and mechanistic models, propagate uncertainty and assess forecasts against benchmarks. Reproducible R workflows lead to a submitted forecast and discussion of how predictions support conservation and adaptive management. Live sessions: 25–29 January, 14:00–18:30 daily, Europe/Madrid (CET, UTC+01:00). Total 27.5 hours: 22.5 live plus five independent. English-language live online course; Fee €236 until 30 November, then €290; Ambassador rates €188.80/€232. VAT included. Capacity: 25. Basic R and an ecology or environmental-science background are required; forecasting experience is unnecessary. Registration requires acceptance; payment follows confirmation. Recordings are provided, but live attendance is required for a certificate.

Build and evaluate correlative species-distribution and ecological-niche models with R. The course connects niche theory with occurrence-data quality, environmental predictors, model fitting and validation, then examines spatial and temporal projections and their limitations. Participants learn to recognize common modelling pitfalls, automate reproducible analyses and apply workflows to example or personal species datasets. 2027-02-01–2027-02-05, 15:00–18:00 daily, Europe/Berlin (CET, UTC+01:00). Online course; fee €480. Basic R knowledge is strongly recommended. Live classes are complemented by about four hours of independent work daily and annotated scripts, with remote support available between 09:00 and 23:00 Berlin time.

This course develops a species-distribution modelling workflow using Bayesian additive regression trees. Participants compare BART with other modelling approaches and use the R packages embarcadero and dbarts to choose environmental predictors, train and evaluate occurrence models, and predict presence or absence in new places or periods. The programme introduces Bayesian regression and boosted trees, then examines predictor effects, interactions, spatial effects and random-intercept BART models, with practical troubleshooting and interpretation. Live online teaching takes place from 15:00 to 18:00 Berlin time (CET, UTC+1) on 9–11 February 2027. The programme also specifies four additional hours of independent practical work using annotated R scripts and online support. Advanced students, researchers and practitioners should already know the basics of species-distribution modelling and be comfortable using RStudio, installing packages and editing simple scripts. A laptop, webcam and reliable internet connection are required. The standalone course fee is EUR 390.