PR stats have some courses soon approaching still with limited availability, Listed below with a short blurb on why they might be useful to Marine Mammal researchers. New short courses listed first. Zero-Inflated Models <https://www.prstats.org/course/zero-inflated-models-zimr01/> https://www.prstats.org/course/zero-inflated-models-zimr01/ A one-day live online course on zero-inflated models in R. Learn to model count data with excess zeros using ZIP, ZINB, and hurdle approaches, plus model diagnostics and interpretation. or marine mammal researchers: many ecological datasets (e.g. acoustic detections, strandings, foraging counts) contain *more zeros than standard count models expect*. This course teaches you how to diagnose zero inflation (vs overdispersion) and apply *zero-inflated, hurdle, and truncated models* in R — giving you more reliable inference about presence/absence vs abundance. With these tools, your models will better reflect real ecological processes, not artefacts of unsuitable assumptions. Tree-Based Models <https://www.prstats.org/course/tree-based-models-tbmr01/> https://www.prstats.org/course/tree-based-models-tbmr01/ Learn decision trees, random forests, and boosted models in R. This one-day live online course covers CART, bagging, boosting, and model interpretation for applied data analysis. For marine mammal researchers: tree-based models let you detect *complex interactions, nonlinear thresholds, and variable importance* in predictors like temperature, prey density, depth, and sea ice — without relying on strict distributional assumptions. They are particularly well suited to messy ecological datasets (with missing values or correlated predictors) and can help you build robust predictive models of habitat use, movement, or risk areas. Using methods like random forests and boosting, you can gain insights into which environmental features drive occurrence or behaviour and generate models with strong predictive performance. Model Selection and Model Simplification <https://www.prstats.org/course/model-selection-and-model-simplification-msms05/> https://www.prstats.org/course/model-selection-and-model-simplification-msms... Model Selection and Simplification in R – a live online course covering model fit, nested model comparison, cross-validation, information criteria (AIC/BIC), and variable selection methods including stepwise, ridge, Lasso, and elastic net. For marine mammal researchers: understanding how to *simplify your statistical models without losing key information*is vital. This course trains you in selecting which predictors matter, avoiding overfitting, and comparing competing models — skills that sharpen habitat modeling, population inference, or behaviour-environment analyses. In short: you’ll gain the judgment and tools to make your models both *robust and interpretable*. Data Visualisation in R using ggplot2 <https://www.prstats.org/course/data-visualisation-in-r-using-ggplot2-dvgg05/> https://www.prstats.org/course/data-visualisation-in-r-using-ggplot2-dvgg05/ Data Visualisation in R using ggplot2 – online course covering scatterplots, histograms, bar plots, boxplots, density plots, line plots, heatmaps, maps, and advanced customisation for publication-quality graphics. For marine mammal researchers: mastering *Data Visualisation in R using ggplot2 (DVGG05)* gives you the tools to turn data into clear, insightful figures — from spatial maps and heatmaps to time series and multivariate plots. You’ll learn how to layer aesthetics, facet data, customise themes, and export publication-quality graphics, improving how you communicate habitat use, movement trends, and environmental associations. Network Analysis for Ecologists <https://www.prstats.org/course/network-analysis-for-ecologists-nwae01/> https://www.prstats.org/course/network-analysis-for-ecologists-nwae01/ Use R to analyse ecological networks. Learn metrics, simulation, and visualisation with igraph. For researchers studying marine mammals, network analysis can reveal hidden structure in social associations, movement networks, or trophic interactions. This course teaches how to construct, analyze, and interpret networks (e.g. weighted, bipartite, temporal), giving you rigorous tools to uncover key individuals, community structure, and connectivity patterns. With these insights, you can more confidently infer social behaviour, habitat use networks, or ecological relationships. Introduction to Spatial Data Analysis <https://www.prstats.org/course/introduction-to-spatial-data-analysis-isda01/> https://www.prstats.org/course/introduction-to-spatial-data-analysis-isda01/ Learn spatial data analysis in R with this 5-day live online course. Covers vector and raster data, CRS, spatial joins, autocorrelation, interpolation, variograms, spatial regression, and reproducible workflows. For marine mammal researchers, *Introduction to Spatial Data Analysis (ISDA01)* gives you a solid foundational toolkit for analyzing spatial patterns in animal occurrence, movement, and habitat use. You will learn how to compute and interpret spatial autocorrelation, variograms, kriging and spatial interpolation, and point process models — all essential for understanding distribution, density, and spatial drivers. These skills let you turn location data into ecological insight and improve inference about spatial processes in marine systems. Spatial Data Visualisation and Mapping using TMAP <https://www.prstats.org/course/spatial-data-visualisation-and-mapping-using-tmap-tmap02/> https://www.prstats.org/course/spatial-data-visualisation-and-mapping-using-... Visualise spatial data in R using the tmap package. Learn to create static and interactive maps, customise layouts, and publish high-quality visualisations. or marine mammal researchers: spatial visualization is key to conveying where animals go, habitat preferences, and range shifts. This tmap course equips you to produce publication-quality static and interactive maps in R, tailor map layouts, choose colour schemes thoughtfully, and integrate spatial and temporal data seamlessly. Your spatial analyses will become clearer, more reproducible, and communicable to colleagues, stakeholders, and the public. Visualizing Spatial Ecological Data <https://www.prstats.org/course/visualizing-spatial-ecological-data-vsed01/> https://www.prstats.org/course/visualizing-spatial-ecological-data-vsed01/ Learn to visualise spatial ecological data in R. Explore remote sensing, species distributions, temporal patterns, and colour-safe scientific graphics. This course gives you the skills to turn spatial and temporal ecological data (e.g. tracking, habitat use, remote sensing) into clear, publication-quality visualisations in R. You’ll learn to portray species distributions, temporal change, and landscape context while avoiding pitfalls like misleading colour scales (including for colourblind accessibility). By mastering these methods, you can more effectively communicate habitat patterns, migrations, density hotspots, and environmental drivers in marine mammal research. Advanced Species Distribution Modelling (SDM’s) and Ecological Niche Modelling (ENM’s) <https://www.prstats.org/course/advanced-species-distribution-modelling-using-r-asdm01/> https://www.prstats.org/course/advanced-species-distribution-modelling-using... Learn advanced SDM and ENM techniques in R. Includes Maxent tuning, MESS and null models, and building mechanistic models and virtual species. For marine mammal researchers: *Advanced Species Distribution Modelling (ASDM01)* equips you with cutting-edge niche modelling tools — from tuning Maxent, designing null and mechanistic models, to building “virtual species” — in order to predict habitat suitability under current and future scenarios. These methods help translate observations into spatially explicit predictions that support conservation planning, identify climate change refugia, or guide surveys for rare species. Please email oliver@prstats.org with any questions. -- Oliver Hooker PhD. PR stats