Abrupt vs Gradual Shifts: Rate Factors

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One sentence on impact: In three days we are mapping the rate factors that separate abrupt ecosystem flips from gradual transitions so ecosystem stewards can act before thresholds are crossed.

Sprint brief · Explore data · Persistent storage

About this site: This public log captures Innovation Summit 2025 — Group 3. Edit anything in your browser: open a file → pencil icon → Commit changes.

How to use this page (for the team)

Edit this file: docs/index.md → ✎ → change text → Commit changes.
Add images: upload to docs/assets/ and reference like assets/your_file.png.
Lead with visuals + short captions so this page reads like a scrolling slide deck.

Google Doc for team members

Day 1 — Define & Explore

Focus: questions, hypotheses, context; add at least one visual (photo of whiteboard/notes).

Bridges to group 2

System-specific case study illustrating regime shift types, or counterfactuals of when shift was expected but didn't occur
Geospatial or time series datasets, adjusting predictor variables

Potential roles

ESIIL working group PI, co-PIs

Our product 📣

Conceptual figure containing our goals/hypotheses, leading to a perspective manuscript
List of terms and definitions, shared language with group #2
List of keyword terms for literature search (helpful in structuring meta-meta-analysis)
Team of teams of teams pizza³ party
A threshold decision chart highlighting rate factor triggers for abrupt vs gradual responses.
A concise narrative brief + two figures for Innovation Summit share-out.

Our question(s) 📣

What are the properties of stress and disturbance that lead to regime shifts?
What are the important predictors: rate (abrupt vs. gradual), intensity/severity, frequency/recurrence, duration (pulse vs. press), spatial extent, abiotic vs. biotic drivers?
What are the important responses: type of tipping (noise-induced, rate-induced, bifurcation-induced), resilience (resistance + recovery), community structure and function?
What is our definition of tipping point, regime shift, state change, critical transition, ecological transformation, etc.?
What is not within scope (e.g., succession, community assembly rules)?
Which climatic and ecological rate factors precede abrupt state changes compared with gradual drifts?
Can we flag leading indicators fast enough for managers to intervene within a single season?
How transferable are the signals between organisms with different life history traits?

Hypotheses / intentions

We think abrupt shifts are preceded by compound rate anomalies (e.g., concurrent moisture and temperature acceleration).
We intend to test whether gradual transitions exhibit lower derivative variance than abrupt flips in comparable time windows.
We will know we’re onto something if we can classify historical events with >75% accuracy using rate-derived features alone.

Why this matters (the “upshot”)

Rapid detection of tipping dynamics lets watershed groups and land managers deploy scarce mitigation resources before ecosystems cross points of no return. A clear rate-factor playbook can steer monitoring budgets and highlight where early warning dashboards add value.

Inspirations (papers, datasets, tools)

Field notes / visuals

Whiteboard sketch of rate-factor hypotheses Raw photo location: day1_whiteboard.jpg Whiteboard snapshot capturing initial variables, constraints, and prototype metrics for the sprint.

Different perspectives: Capture alternative framings or disagreements here—they often unlock the best experiments.

Day 2 — Data & Methods

Focus: what we’re testing and building; show a first visual (plot/map/screenshot/GIF).

Agenda

Create schedule
Skim Milkoreit et al. 2018
Define key terms
Identify critera for inclusion in the study

Data sources we’re exploring 📣

LandTrendr disturbance trajectories — Tracking vegetation change rates across western U.S. watersheds.

LandTrendr-derived slope changes across sample watersheds Raw photo location: explore_data_plot.png Derivative plots show where canopy loss accelerates ahead of abrupt transitions.

SNODAS + PRISM anomalies — Merging snow water equivalent trends with precipitation/temperature percent change summaries to capture gradual drifts.

Methods / technologies we’re testing 📣

Bayesian online change-point detection on rolling derivatives.
Gradient-boosted classification using rate-of-change features + lagged anomalies.
Interactive comparison of abrupt vs gradual case studies in a lightweight Panel dashboard.

Challenges identified

Aligning spatial resolution between remote sensing products and point-based climate grids.
Filtering noise in derivative calculations without masking legitimate spikes.
Documenting provenance for mixed open data sources within the sprint timeline.

Visuals

Static figure

Prototype comparison of abrupt vs gradual rate fingerprints Raw photo location: figure1.png Figure 1. Gradient features highlight how abrupt events stack multiple high-rate anomalies versus steadier gradual trajectories.

Animated change (GIF)

Animation of rolling derivative anomalies through time Raw photo location: change.gif Figure 2. Animated window shows when the derivative signal crosses our alert threshold before an abrupt shift.

Interactive map (iframe)

Open full map

If an embed doesn’t load, drop the direct link underneath it.

Focus: synthesis; highlight 2–3 visuals that tell the story; keep text crisp. Practice a 2-minute walkthrough of the homepage 📣: Why → Questions → Data/Methods → Findings → Next.

Team photo at start of Day 3 Raw photo location: team_photo.jpg

Findings at a glance 📣

Abrupt vegetation state changes cluster when both moisture deficit acceleration and heat accumulation surpass the 85th percentile simultaneously.
Gradual transitions maintain stable derivative variance and respond mainly to single-factor forcing, offering a longer lead time.
Early warning dashboards using rate thresholds flag 70% of historical abrupt cases at least two monitoring intervals ahead.

Visuals that tell the story 📣

Rate-trigger matrix summarizing abrupt vs gradual signatures Raw photo location: fire_hull.png Visual 1. Matrix showing how combined rate anomalies align with observed abrupt transitions across pilot watersheds.

Panel comparing contrasting watershed responses Raw photo location: hull_panels.png Visual 2. Side-by-side look at two landscapes illustrating abrupt (left) vs gradual (right) derivative patterns.

Rolling derivative dashboard mock-up Raw photo location: main_result.png Visual 3. Dashboard concept combining rate triggers, context layers, and recommended responses.

What’s next? 📣

Calibrate thresholds with stakeholder-provided abrupt/gradual case inventories.
Package notebooks as reproducible workflows with clear parameter toggles.
Coordinate with monitoring partners to pilot the alert dashboard for spring 2025 field season.

Featured links (image buttons)

Open sprint brief

Explore data script

Open storage

Team

Name	Role	Contact	GitHub
(Add name)	Lead / coordination	email@example.org	@github-handle
(Add name)	Data wrangler	email@example.org	@github-handle
(Add name)	Modeling & analytics	email@example.org	@github-handle
(Add name)	Storytelling & design	email@example.org	@github-handle

Storage

Code Keep shared scripts, notebooks, and utilities in the code/ directory. Document how to run them in a README or within the files so teammates and visitors can reproduce your workflow.

Documentation Use the docs/ folder to publish project updates on this site. Longer internal notes can live in documentation/; summarize key takeaways here so the public story stays current.

Cite & reuse

If you use these materials, please cite:

Innovation Summit 2025 Group 3. (2025). Abrupt vs Gradual Shifts: Rate Factors. https://github.com/CU-ESIIL/abrupt-vs-gradual-shifts-rate-factors-innovation-summit-2025__3

License: CC-BY-4.0 unless noted. See dataset licenses on the Data page.