Ideas to Consider
What do we mean by “Data”?
Both quantitative and qualitative records of observed information
Quantitative Information -- numerical information that
specifies how big, how many, how much, how often
Qualitative Information -- descriptive information about
observed systems and interactions
How and why do we collect data?
Exploration
Measuring and monitoring
Hypothesis testing -- classic "scientific method"
Points
1) Data and observations as documenting the
environment around us
2) Indirect measurement used to infer another
quantity
3) How does the idea of a round Earth motivate
this observation?
Points
4) Observations of geophysical and biological
environment
5) Inference of vertical dimension reflecting time
6) Data collected at various times and various
places
7) How do social views of human role in nature
affect observations?
Points
8) Hypotheses  prediction  verification
9) Remote sensing by observing light that had
passed through the region of interest
10) In situ observations by directly sampling in
the region of interest
11) How important are in situ observations vs.
remote sensing in proving the effect of CFCs?
The Ocean
What are the variables we want to
measure?
•
•
•
•
Temperature, Salinity
Currents
Waves
Tides
• Meteorology
• Chemistry
• Biological productivity
What are the independent variables?
• Location
• Map (Latitude, longitude)
• Altitude
• Time
• Parameter-parameter
relationships
Observations from Ships
• Long history of ship observations back to
beginning of ocean-going ships
• Most observations follow shipping lines and
include only basic measurements
(temperature winds)
• Research expeditions with detailed
observations, but little coverage
Measuring primary production
• Primary production is conversion of inorganic
carbon to organic carbon by photosynthesis
• Can estimate from amount of chlorophyll
either in situ or by satellite
• Need to sample water and determine
“incubation” – the relationship between
chlorophyll and primary production
Points
• Often a combination of measurements is
needed to observe a process of interest
• Remote (satellite) observations need to be
combined with in situ observations and
laboratory measurements
• Observations combine information about
physical, chemical, and biological processes
Observations from Buoys
• Useful for long-term monitoring
• Temporary and permanent deployments
• Example: TOA array and Pacific variability
Observing Networks
• Long-term monitoring of the ocean
environment requires consistent observations
and global coverage
• Satellite and unmanned buoys recently
deployed are a big advance over past
observations
• Observing networks only detect what we
know about. Research experiments need to be
ongoing to understand new issues.
Stationary and Trajectory Observations
• Ocean buoy
– Observations at a
fixed location
– Changes as water
moves past
• ARGOS float
– Observations move
with water
– Location changes as
water moves around
The Atmosphere
What are the variables we want to
measure?
•
•
•
•
Temperature, Humidity, pressure
Winds
Precipitation
Cloudiness
• Chemistry
• Radiation (Infrared, Solar)
• Air quality
• Particulates (dust)
• Ozone
What are the independent variables?
• Location
• Map (Latitude, longitude)
• Altitude
• Time
• Parameter-parameter
relationships
Increment of Observations
• Spatial
• How close are the stations?
• How well do they cover the region?
Lots of stations all in valleys is not as good as a
few stations that are distributed better
• Temporal
• How often are observations taken?
• How fast does the parameter change?
data can be aggregated
• Are observations consistent over time?
data can be “homogenized”
The Vertical Dimension
• To understand weather and the movement of
material in the atmosphere, we need to
observe the vertical structure and winds
• Balloons are released world wide twice per
day
• Radar and other technologies observe the
vertical structure remotely
Observing Networks
• Long-term monitoring of the environment
requires consistent observations and global
coverage
• Satellite and automatic stations recently
deployed are a big advance over past
observations
• Observing networks only detect what we
know about. Research experiments need to be
ongoing to understand new issues.
Observing Networks
• Stations only observe at sites suitable for
locating the instruments or relevant for a
particular observation (e.g. airports)
• Gridding methods can blend model
assumptions and observations to fill in data
voids
Snow Observations
• Old-style Snow course observations
• taken “by hand”
• A few locations
• A few times per year
• Available for a long time (1930s)
• New-style SNOTEL
• automated system
• Lots of locations
• Continuous monitoring
• Large set of variables observed
• Available only recently (10 years)
Biological Data
Compare: Biological Productivity and
Streamflow
We measure chlorophyll
We are interested in productivity:
conversion of CO2
Need to sample water and measure
relationship between
what we measure and
what we want to know
Now we can monitor ocean color
(chlorophyll) or stream stage (height)
and compute productivity or flow
volume
Ecology
Ecology is the scientific study of the interactions
that determine the distribution and
abundance of organisms
These interactions are determined by the
environment
Ecological Data Types: Individuals
How individuals are affected by biological and
physical environment
• Physiology
– reproduction, stress, growth…
• Fitness (relative to other individuals)
• Lifecycles
• Migration and dispersal
Ecological Data Types: Populations
How the population of a species is affected by
biological and physical environment
• Abundance, distribution
• Behavior
• Predator-prey relationships
• parasites, mutualism
• Birth and death rates
• Migration
Ecological Data Types: Communities
The composition and structure of communities
(multiple species and populations)
• Pathways for energy and nutrient transport
• Species diversity
• Biomass production
• Competition
Ecological Data Types:
Independent Variables
The variables we change or use to organize
other observations
•
•
•
•
•
•
•
Temperature
Precipitation
Sunlight
Snowcover
Wind
Abundance of other species
Nutrients in soil or water
Experiment Design in Ecology
Experiments are ways to change the
independent variables. Three types:
1. Laboratory Experiments
Perturbations produced by experimenter in a lab
2. Field Experiments
Perturbations produced by experimenter in the field
3. Natural Experiments
Perturbations produced naturally in the field
Jared Diamond & Ted Case, Community Ecology
Trade-offs in experiment design
1.
2.
3.
4.
5.
6.
Regulation of independent variables
Matching of sites
Maximum spatial and temporal scale
Scope (range of species and manipulations)
Realism
Generality
Trade-offs in experiment design
Lab
Field
Natural
Regulation
Highest
Medium
None
Site Matching
Highest
Medium
Medium/low
Max Scale
Lowest
Lowest
High
Scope
Low
Medium
Medium/High
Realism
Low
Medium
Highest
Generality
None
Low
High
Advantages of Natural Experiments
• Scale
– Can range up to global spatially
– Can range for millions of years using fossil record
• Scope
– Can study manipulations that cannot or should not be
performed in field experiments
• Realism
• Generality
– Can sample a much wider range than other experiments
A fish’s environment
Biotelemetry
• Ultrasonic telemetry (pings)
• Radio telemetry
• Satellite telemetry (Argos)
• Micro data logger (needs
to be retrieved)
Deviation from being really natural?
Data from temperature logger
for an individual sockeye
Adams River Sockeye Thermal Experience 2006
Internal Hourly Temperature oC
20
17
14
11
Lower
Fraser
River
8
5
21-Aug
•
•
•
Shuswap
Lake
31-Aug
10-Sep
20-Sep
30-Sep
10-Oct
20-Oct
Fish temperature increases abruptly on river entry
Avoidance behaviors provide only temporary temperature relief
Fraser R. gradually cools after the August peak temperature
Data from an I-button temperature logger for an individual sockeye approaching the
Fraser R. & during its ~30 day upstream migration
A fish’s physiology
• Lab experiments
Spawning Sockeye
Temperature determines aerobic scope
Topt
Metabolic rate
Tcrit
Aerobic scope
Topt
Tcrit
Temperature (oC)
Topt = max aerobic scope
Temperature (oC)
Tcrit = no aerobic scope
Population response
• Combine Lab and Field data
16%
9
12%
Gates Creek
Sockeye
6
8%
3
4%
0
Temperature frequency
distribution
Aerobic scope
(mg O2 kg-1 min-1)
12
0%
0
5
10
15
20
25
•As little as 6oC between Topt and Tcrit
Temperature (°C)
16%
9
12%
Weaver Creek
Sockeye
6
8%
3
4%
0
Temperature frequency
distribution
Aerobic scope
(mg O2 kg-1 min-1)
12
0%
0
5
10
15
20
16%
9
12%
Chehalis
Coho
6
8%
3
4%
0
0%
0
5
10
15
Temperature (°C)
20
25
Temperature frequency
distribution
12
• Population variability appears to
match their experiences
Hell’s Gate
25
Temperature (°C)
Aerobic scope
(mg O2 kg-1 min-1)
Key points
• Populations vary in their:
- absolute aerobic scope,
- Topt (= max scope)
- Tcrit (= no scope)
Types of Remote Sensing
• Passive
– Sensor receives a signal from the object being observed
– Classic weather satellite
– Landsat, MODIS
• Active
– Sensor “bounces” a signal off the object being observed
– RADAR, SONAR, LIDAR
– SeaWINDS
Why use remote sensing
instead of
in situ observations?
1)Difficult to get there
2)Too long to cover area using in situ
3)Need to monitor a large area continuously
4)Remote sensing technology may be superior at
detecting some things
5)Less invasive than in situ (realism)
Remote Sensing Platforms
• Satellite
– Continuous monitoring
• Ship, Aircraft
– Useful in field projects
– Can provide higher spatial and temporal
resolution (closer to object, smaller area)
• Land
– Continuous monitoring in time at one important
spot
– Cheaper/easier than satellite
Object of Remote Sensing
Atmosphere
• Atmospheric parameters
– clouds, temperature, humidity, precipitation)
• Platforms
– Weather satellites (GOES, TOVS)
– MODIS (Moderate Resolution Imaging
Spectroradiometer)
– CALIPSO (Cloud-Aerosol Lidar and Infrared
Pathfinder Satellite Observation)
Object of Remote Sensing
Ocean
• Ocean parameters
– temperature, winds, currents
– color
• Platforms
– SeaWIFS
– QuikSCAT (Quick Scatterometer) SeaWinds
Object of Remote Sensing
Land
• Parameters
– land use, land cover
– biological productivity
– temperature,
– canopy height)
• Platforms
– LandSAT MODIS (Moderate-resolution Imaging Spectroradiometer)
– Aircraft LIDAR
Object of Remote Sensing
Organisms
• Parameters
– Abundance
– location
• Platforms
– RADAR
– SONAR
Physics of Remote Sensing
How light reacts
to objects
• Transmission
• Reflection
• Scattering
• Absorption
• Emission
RADAR/SONAR
• Active remote sensing emits a signal that
interacts with an object and returns to the
sensor.
• Changes in the origial and reflected signal
provide information about the object
– Location of the object
– Size of the object
– Velocity of object
– Density of object
Satellite Orbits
• Low Earth Orbit
– One satellite can cover
whole Earth
– Crosses each point at the
same time of day
• Geostationary
– Rotates with Earth
– Covers only a fraction
– Can tale many images
per day
RADAR
• Bounces a radio waves off features in the
atmosphere or on the surface
• Used to profile the atmosphere
• Used to map the surface
• Used to locate birds
• May be locate on ground, satellite, ship, or
aircraft
SONAR/Bioacoustics
• Bounces a sound waves off features in the
water
• Used to profile the ocean
• Used to map the ocean floor
• Used to locate fish
• May be locate on ground or ship
LiDAR
• Bounces a laser beam off features in the
atmosphere or on the surface
• Used to profile the atmosphere
• Used to map the surface
• May be locate on ground, satellite, ship, or
aircraft
Statistics and Plotting
•
Types
of
data
Qualitative Data
– categories or attributes
– Can use to group data
– Cannot compute statistics (e.g. average) directly
from category values
– Can convert to quantitative values (eg by
counting) to compute statistics
• Quantitative Data
– Data values are actual numbers with discrete
(integer) or continuous (real number) values
– Compute statistics from quantitative data
Correlation
Two variables are positively correlated if high
values of one are likely to be associated with
high values of the other. They are negatively
correlated if high values of one are likely to be
associated with low values of the other.
Scatter Plot
A graph in which the paired (x,y) sample data
are plotted with a horizontal x axis and a
vertical y axis. Each individual (x,y) pair is
plotted as a single point.
Points are not connected by a line
Regression or Trend
A regression line can be used to statistically
describe the trend of the points in the scatter
plot to help tie the data back to a theoretical
ideal.
This regression line expresses a mathematical
relationship between the independent and
dependent variable.
Correlation Coefficient
The software used to generate the regression
line will provide a number that expresses the
'goodness of fit' of the curve.
The correlation coefficient is usually expressed
as R2 (R-squared), a number between 0 and 1.
A higher R2 value implies a better mathematical
fit to the data sample
Common Errors Involving Correlation
1. Causation: It is wrong to conclude that
implies causality.
correlation
2. Averages: Averages suppress individual
and may inflate the correlation
coefficient.
variation
3. Linearity: There may be some relationship between x
and y even when there is no
significant linear
correlation.
Reconstruction Methods
• Organisms that leave a record of their
environment
– Tree rings
– Corals
– Shells (Geoducks)
• Ice and Sediment layers
• Radiological dating (Carbon-14, Oxygen-18)
• Archaeological sites
Why do we want to reconstruct
the past?
• to put the present in proper historical context
• to better understand current environmental
processes and conditions
• to improve understanding of possible future
environmental issues
Tree Ring Reconstruction
Dendrochronology
the science or technique of dating events, environmental
change, and archaeological artifacts by using the
characteristic patterns of annual growth rings in timber
and tree trunks.
Cross dating
Comparing multiple tree ring sequences to match dates
across different samples
Proxy reconstruction
Relating ring width to a parameter we want to measure
Method
1. Compare current weather conditions to
recent tree ring widths
2. Establish a statistical relationship between
tree ring widths and weather
3. Cross-date multiple tree ring samples
4. Use historic tree ring widths in this
relationship to obtain historic weather
What effects tree growth?
1. Precipitation – in arid places, growth is very
highly correlated to precipitation
2. Fire – Loss of canopy effects individual trees.
Also leaves direct mark in tree that can be
dated
3. Temperature – In very cold places, near tree
lines
4. Snow depth – Snow inhibits tree growth by
covering the tree
Ice Core Reconstructions
• Sample layers in ice cores at North and South
poles
• Can bore over 3 km deep
• Climate records extend back 200,000 years
• Can resolve changes on 10 year intervals
Sediment Reconstruction
• Lake sediment (mud) accumulates
continuously at the bottom of many lakes.
The deeper you go into the mud, the further
you go back in time.
• This mud contains different types of fossils
that can be used to reconstruct changes in
the lake, surrounding terrestrial
environment, and climate.
• Requires collection and preservation of a
“core” of mud, dating of the core, and
physical description, extraction, and analysis
of fossils in the cores.
• The fossil “proxies” include charcoal,
phosphorus, carbon and nitrogen content,
abundance of organic matter
Carbon Dating
• Since living organisms continually exchange carbon with the atmosphere
in the form of carbon dioxide, the ratio of C-14 to C-12 matches the
atmosphere.
• The number of radioactive decays is about 15 decays per minute per gram
of carbon in a living organism.
Carbon Dating
Measure
Radiation
Measure
Fraction
14C /12C