Integrating multi-scale data with hybrid ecological models to evaluate the role of management, climate, and habitat

Integrating multi-scale data with hybrid ecological models to evaluate the role of management, climate, and habitat

Modeling global ecological connectivity using biologically relevant data to understand expected patterns of species range shifts

Integrating multi-scale data with hybrid ecological models to evaluate the role of management, climate, and habitat

Focusing on randomized paths between multiple locations (or populations, habitats, etc) Note This is simply a tutorial. I’m not (for now) providing a review of the literature surrounding ecological connectivity, or commenting the different meanings of connectivity).

Introduction to EDMs for Forecasting Non-stationary data EDMs are a data-driven solution for uncovering hidden dynamic behavior in natural systems, which are often complex and dynamic (referred to as “non-stationarity” or “non-linearity”).

Introduction linear regression with gradient descent This tutorial is a rough introduction into using gradient descent algorithms to estimate parameters (slope and intercept) for standard linear regressions, as an alternative to ordinary least squares (OLS) regression with a maximum likelihood estimator.

Introduction to machine learning with tidymodels Tidymodels provides a clean, organized, and–most importantly–consistent programming syntax for data pre-processing, model specification, model fitting, model evaluation, and prediction. Anatomy of tidymodels: * a meta-package that installs and load the core packages listed below that you need for modeling and machine learning rsamples: * provides infrastructure for efficient data splitting and resampling parsnip: a tidy, unified interface to models that can be used to try a range of models without getting bogged down in the syntactical minutiae of the underlying packages recipes: * tidy interface to data pre-processing tools for feature engineering workflows: * workflows bundle your pre-processing, modeling, and post-processing together tune: * helps you optimize the hyperparameters of your model and pre-processing steps yardstick: * measures the effectiveness of models using performance metrics dials: * contains tools to create and manage values of tuning parameters and is designed to integrate well with the parsnip package broom: * summarizes key information about models in tidy tibble()s First, lets load the tidymodels meta-package: library(tidymodels) ## Registered S3 method overwritten by 'tune': ## method from ## required_pkgs.