Forecasting

Making gifs in R with `gganimate`

Introduction Package Setup Data Forecasting Generate data to plot Figure Introduction Since I’m writing R code to make certain figures for this website, I thought I could go ahead and annotate some of it in R markdown to serve as blog posts. Package Setup library(tidyverse) ## ── Attaching packages ────────────────────────────────────────────────────────────── tidyverse 1.2.1 ── ## ✔ ggplot2 3.0.0.9000 ✔ purrr 0.2.5 ## ✔ tibble 1.4.2 ✔ dplyr 0.

The intrinsic predictability of ecological time series and its potential to guide forecasting

Successfully predicting the future states of systems that are complex, stochastic and potentially chaotic is a major challenge. Model forecasting error (FE) is the usual measure of success; however, model predictions provide no insights into the …

Ecosystem‐based forecasts of recruitment in two menhaden species

Gulf (*Brevoortia patronus*, Clupeidae) and Atlantic menhaden (*Brevoortia tyrannus*, Clupeidae) support large fisheries that have shown substantial variability over several decades, in part, due to dependence on annual recruitment. Nevertheless, …

Predicting coastal algal blooms in southern California

The irregular appearance of planktonic algae blooms off the coast of southern California has been a source of wonder for over a century. Although large algal blooms can have significant negative impacts on ecosystems and human health, a predictive …

Empirical Dynamic Modeling

Inferring system dynamics from time series

Forecasting

Predicting the future state of ecosystems.

Predicting the future in a nonlinear world

Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling

It is well known that current equilibrium-based models fall short as predictive descriptions of natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For example, model parameters assumed to be fixed constants may …

A nonlinear, low data requirement model for producing spatially explicit fishery forecasts

Spatial variability can confound accurate estimates of catch per unit effort (CPUE), especially in highly migratory species. The incorporation of spatial structure into fishery stock assessment models should ultimately improve forecasts of stock …

Detecting and forecasting complex nonlinear dynamics in spatially structured catch-per-unit-effort time series for North Pacific albacore (Thunnus alalunga)

The presence of complex, nonlinear dynamics in fish populations, and uncertainty in the structure (functional form) of those dynamics, pose challenges to the accuracy of forecasts produced by traditional stock assessment models. We describe two …