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The problem of pattern and scale in (semi-arid ecosystem) ecology | Does big data help or hurt?

Tyson Lee Swetnam

BIO5 Institute ,University of Arizona

Pattern &

Scale

Problem Statements

Technology has given us the unprecedented ability to study ecosystems across a wider range of scale (in space and time) than at any other time in history.

How do you determine which scale will answer your question?

What question(s) can be asked that couldn't before?

Pattern & Scale

"It is argued that the problem of pattern and scale is the central problem in ecology, unifying population biology and ecosystems science, and marrying basic and applied ecology. Applied challenges, such as the prediction of the ecological causes and consequences of global climate change, require the interfacing of phenomena that occur on very different scales of space, time, and ecological organization." - Simon Levin

Pattern & Scale

"... environmental changes at the global scale lead to changes on individuals, but also impose selective pressures upon populations, and may lead to changes in diversity, at the genetic, phenotypic, and at the species levels.

In essence, the problem is to bridge across very different spatial scales, from one cubic metre of ocean or one square metre of land, to the global scale, a change in linear distance of a factor 10^7.

To paraphrase Levin (1992), ‘the description of pattern is the description of variation, and the quantification of variation requires the determination of scales’ " -- Chave 2013

https://onlinelibrary.wiley.com/doi/full/10.1111/ele.12048

Pattern & Scale

Pattern & Scale

Small Area Design

  • Many plots
  • Repeated measurements
  • Subset of population
  • High Resolution

Large Area Design

  • Single plot
  • Single observation
  • Captures entire population
  • Coarse Resolution

The real world into bits and bytes

Pattern & Scale

Computational Limitations

100 Billion Neurons

1 Trillion Glial cells

10,000 synapses per neuron

1 quadrillion connections

(1,000 Trillion)

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2776484/

Computational Limitations

Oak Ridge National Lab's Summit, its newest Super Computer:

~200 quadrillion (200,000 trillion) calculations per second.

Spatial Limitations

Resolving one kilometer (km) at meter (m) scale results in 1 million pixels / points

A single hectare at cm scale is 100 Megapixels. At mm scale, 10 Gigapixels.

8-bit .TIF = 100 Mb, 16-bit.RAW/DNG = 200 Mb, TIFF CMYK 4x8-bit = 400 Mb

https://toolstud.io/photo/megapixel.php

Temporal limitations

3 minute interval × 365 days × (10,000 × 10,000) pixels [300 Mb] = ~120,000 CPU hours,~200 Gb output

https://grasswiki.osgeo.org/wiki/R.sun

Example

Research

Handheld cameras, sUAS imagery, terrestrial lidar, manned aircraft, and Earth Observation Systems

Sankey et al. 2017 - sUAS lidar & Hyperspectral

Swetnam et al. 2018 - Multi-sensor fusion techniques

Gillan et al. (in review) - rapid change detection

Swetnam et al. 2018

Considerations for achieving cross-platform point cloud data fusion across different dryland ecosystem structural states

https://www.frontiersin.org/articles/10.3389/fpls.2017.02144/full

Swetnam et al. 2018

Swetnam et al. 2018

http://128.196.38.28/mesquite.html

Different data types and sensors for different jobs

Swetnam et al. 2018

Sankey et al. 2017

https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.44

UAV hyperspectral and lidar data and their fusion for arid and semi-arid land vegetation monitoring

Gillan et al. in review

Are you ready? Join CyVerse today!

Earth Observation System (EOS)

AVHRR, LANDSAT (5,7,8), MODIS (Terra, Aqua), VIIRS (NOAA-20), Sentinel-2, Cubesats (Planet 3U Doves)

https://www.planet.com/pulse/firehose/

CAPTURING 50,000,000 KM2 PER DAY

Problem Statements

How to determine which scale will answer your question?

"...there is no single natural scale at which ecological phenomena should be studied; systems generally show characteristic variability on a range of spatial, temporal, and organizational scales. The observer imposes a perceptual bias, a filter through which the system is viewed." - Levin 1992

Problem Statements

How to determine which scale will answer your question?

Species-level

Phenology

Change Detection

Stress

Problem Statements

What new questions can we ask that we couldn't before?

?

G × E = P

Environment

Genotype

Phenotype

Artificial Selection

Climate ready Plants

Natural Selection

Landscape genetics

Genome selection

Genome wide association

Understanding the rules of life:

Predicting Phenotype

Ecosystem forecasting: NEON data could answer these questions.

Data Deluge

If you have an internet connection, and know how to code, you can do transformative research RIGHT NOW!

What are you waiting for?!?

“The meaning of ‘knowing’ has shifted from being able to remember and repeat information to being able to find and use it.”

Herbert Simon, Nobel laureate, 1996

BEWARE!

Digital Amnesia

the Google Effect

  • Over reliance on search query.

(even when you know the correct answer)

  • Less likely to remember if you think it can be looked back up.
  • More likely to remember the location of information, rather than the actual information.

https://cdn.press.kaspersky.com/files/2017/04/Digital-Amnesia-Report.pdf

The problem

Domain Scientists

Developers

#!/bin/bash

$ sudo apt-get install

$ grep FOO "blah" -l \ xargs sed -i

Epigenetics! Abiogenesis!

Regolith! Trinucleotide!

Solution

Domain Scientists

Data Scientists

Developers

import pandas as pd

import numpy as np

iris = pd.read_csv('../input/Iris.csv')

#create a array variable named iris

iris.head()

#display the table

Can you do this for all my data?

Are your data machine readable?

You're stuck in a Red Queen's Race

On being a Data Scientist

Sorry Alice,

On being a Data Scientist

Its not all milk and honey...

NEON

You!

or is it?

NEON Shiny Browser

http://128.196.142.76:3838/NEON-Hosted-Browser/

https://github.com/Danielslee51/NEON-Shiny-Browser

Tomorrow Land

Supercontinuum Lidar

https://www.osapublishing.org/DirectPDFAccess/10FB7405-F6D3-64A7-BD8A3349AAAA5C1B_230309/oe-20-7-7119.pdf?da=1&id=230309&seq=0&mobile=no

https://overview.artbyrens.com/

Gaming Engines as GIS?

Augmented Reality (in the field?)

https://www.magicleap.com/

Acknowledgements

This material is based upon work supported by the U.S. Department of Agriculture, Agricultural Research Service, under Agreement No. 58-2022-5-13. Any opinions, findings, conclusion, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture.

This material is based upon work supported by the National Science Foundation under Award Numbers DBI-0735191 and DBI-1265383, www.cyverse.org; and Jetstream (Award number ACI-144506) cloud resources.

Get started today!

Links my talks: https://tyson-swetnam.github.io/talks/

Join CyVerse: http://www.cyverse.org/

@tswetnam

tyson-swetnam

tswetnam@cyverse.org

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