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Global Change and Community Structure
Leader: Peter Groffman
Notes: Jarrett Byrnes
Attendees:
Name
Email
LTER
Michael Pace
[email protected]
VCR
Mark Ohman
[email protected]
CCE
Marcy Litvak
[email protected]
SVE
Sherri Johnson
[email protected]
Andrews
Hugh Ducklow
[email protected]
PAL
Emma RosiMarshall
[email protected]
BES
Emily Stanley
[email protected]
NTL
Sally Holbrook
[email protected]
MCR,SBC
Lihini Aluwihare
[email protected]
CCE
Rhett Jackson
[email protected]
Coweeta
Gaius Shaver
[email protected]
ARC
Michael Nelson
[email protected]
HJA
Dan Reed
[email protected]
SBC
Matt Betts
[email protected]
Andrews
Michael Gooseff
[email protected]
MCM
Topic 1. We ought to be able to say something about these systems and where they
are going. We should be able to make quantitative preductions. Satisfies Policy, etc.
We should be able to do this with the data we have
Can we organize the discussion around that concept?
OR - should we definte what aspects of a system make it very succeptible to change.
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Topic/Concern 1A What is the resolution of prediction? Temptation is to direct
forecast effort to readily measurable quantities. But, in looking at whole
communities as an aggregate - easy to measure - can lose detail. You can lose
detectability of climate signal
Topic 2. What can we say about the effects of climate extremes? There has ben a lot
of attention in various realms. DO LTER sites have the data to put that in context.
Topic 3. Concept of looking at long-term mean and variance. Variance change, Mean
change, Mean and Variance Change, No Change. Interaction of mean and variance
change - variance leads to different outcomes with different means.
Topic 4 Split into broad areas/biomes and talk about predictions there. Start with
stories of where system should go, and then ask how we can evaluate that story.
Possible Products
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Synthesize some data sets
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Necessary first step
Papers
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Everyone likes a paper
Synthesis/Methods Considerations
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Standardization across LTERs - where/when?
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Do we standardize in terms of what we mean by community structure?
What about SVR example? What about looking at accumulations of
patterns. Every system is different but the outcome is cross-comparable
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Different species, different communities, BUT - need a common metric
as an output
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At what level do you aggregate? Trait? Species? Fucntional group?
Frequency of variability
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Relative to lifespan of organisms, etc.
Interactions between variability and organismal biology
Temporal resolution of mean and variance - what is important where?
Formulation of a Question
1.
Is change in average conditions or variability in conditions that is a stronger
driver of community dynamics?
2.
What are the forests/marsh/habitat/ecosystem of the future going to look like?
3.
What can we learn from our predictions that we get right? What can we learn
from our predictions that we get wrong?
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E.g., Looking at predictions of national climate assessment that forests
should be getting better. And in most places we are right. In some we are
not. Why? What is wrong?
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We seem to be coming back to the underlying mechanisms of what we
got right versus wrong.
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The thing about being wrong is that we can categorize the reasons we
were wrong into action items. e.g. Was it due to a driver or due to a
measurement mistake.
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One must be careful about saying "we were wrong" - science
communication issue.
Do we have a set of species that is more succeptible to environmental change
and how does this translate to the whole system?
5.
Do we have better predictive power for the effect of global change on
communities in systems with keystone/foundation/engineering species versus
those that do not?
6.
Have we observed long-term changes in the ratio of generalists:specialists or
other aspects of community structure in terms of all types of interactions (diet,
habitat use, etc.)?
7.
Who are the winners and losers based on the demographic data at each site?
And what binds them together? This can be from either long-term trends or
identifying specific drivers.
8.
At sites where we see the greatest shifts in climate means and/or variance, do
we see the greatest shift towards generalists?
side conversation - John Hobbe has just come out with a paper showing that we
cannot really detect temperature change effects in Toolik lake despite that we see
processes that should be linked to temperature - measurement problem - many
measures are medium-pass filters of a signal. We are measuring temperature at the
wrong frequency.
Well, and temperature can be a crappy measure of how your energy fluxes are
changing.
BUT - the point is that systems themselves are a measure of driver effects, even if
you don't have good measurements of the drivers.
Another example is the ET pattern at Hubbard Brook without a similar trend in P or
temperature
Sometimes the systems are changing more dynamically then the drivers
We are often interested in a key species, that an engineer/keystone is affected which
translates to change to the whole system. Can we winnow down to focusing on
ecosystem engineers
OK, we need to start with either metrics or questions!
Starting with Data
We can say something about marshes We can say something about forests We can
say something about lakes
Where are our systems going? How do we ask this?
We need to think about where are systems going both conceptually and
quantiatively.
But - "The future is very hard to predict, because it hasn't happened yet."
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This is why scenario planning is useful (4 is the optimum useful number) more constructive and you are less likely to look like an idiot
Scenarios are great, but, is it a way to waffle? In some ways, direct predictions - even
with uncertainty - are far more useful.
Although guessing wrong is also problematic.
Scenarios are a way of building predictions, but not testing them.
A story from Coweeta
We had this iconic tree - the Chestnut. Due to blight, it went extinct. You would think
the whole system should shift. But it didn't. We now have a system that doesn't have
Chestnut in it. Period.
But - think at a different trophic level. Hemlock is gone. And it's chaos in terms of
mice, ticks, disease, etc.
Which is perhaps a time to think about our charge - to think about animals!
Can we start with stories and push to metrics
Starting from a different angle: What data do we all have?
We all have data on whether species are specialists or generalists. Can we use this to
look at whether we have long-term changes in generalists:specialists? (Matt Betts)
BUT - you need a network. Maybe focus on producer-consumer.
Winners versus losers
Can we look at each of our sites, identify the winners and losers, and then look for
commonalities between them?
This was tried at NCEAS a few years ago. Reviewers hated winners v. losers. The key
question isn't who won and who lost, but under what conditions did some species
win and some lose.
But, isn't the benefit that we can look at long-term data to have long-term trends in
winners and losers.
Can we look at how drivers link to winner and losers, and then ask if there are traits
(that we can predict a priori) that predict winning v. losing, and then link that to
long-term predictions.
How do we go about this - we like winners and Losers
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Hire a postdoc! LTER synthesis postdoc via the SESYNC postdoc
Synthesis working group - and can ask for a postdoc in there
What species are responding to climate change in your system?
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Antarctica - Penguins. Since 1975, Adelie penguins have declines 75%. Two
sub-antarctic penguins that are migrating in and increasing. Total penguin
decline has actually turned around. Biodiversity has increased from 1 to 3.
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Andrews - Can use thermal niche and diet to look at changes in bird
distribution. In Oregon, it's species that specialize in early successional that are
changing
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Plum Island - Increases in fiddler crabs and blue crabs due to shifts in winter
temperature.
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Baltimore - Urban heat island driving changes in emrald ash-borer (but it's
invasive). Mosquitos increasing, but could be climate or urban heat island
driven. Particularly Asian tiger mosquito.
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North Temperate Lakes - Bass-Walleye tradeoff. Losing walleye, gaining bass,
with thermal habitat as the best predictor. Warming, not a biotic interaction.
Black bass tend to like warmer water, walleye like cooler water
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Antarctica - has a thermal habitat-fish story, too!
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Virginia Coastal Reserve - nesting birds have decreased by ~50% over study
due to mesopredator release. Racoons and foxes increased - somewhat related
to climate change that has increased hedges/shrubs.
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SVR - Consumer data not clear. Some interesting responses from drought...but
still not strong data.
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Coweeta - mean temperature increasing, mean precip is not icnreasing
although precip is. Expected increase in oak and hickory, but instead more red
maple and tulip poplar. This might be affecting some birds.
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MCR - timeseries dominated by effects of single disturbance, but, on longer
timescale is the climate-driven decline in certain species of brnaching corals
(e.g., Acropora) - can get data on this from French Colleagues as well to get 30
years.
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Toolik Lake - fertilizing/warming tundra leads to shrubs, but it takes time. You
have to wait for the plant community. Does community change require a
disturbance to the old community to enable change to come about?
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California Coastal - can see the different timescales of range of variability in
temperature, oxygenation, pH - difficult to parse out just one driver? Overall
message would be one of resilience on longer timescales. System is naturally
forced by huge climate signals - ENSO, PDO, NPO - cannot think of a population
that has disappeared or declined from the entire system due to endemism and
coevolution with large-scale long-term forcing. The planktonic components of
the system look today very much like they did in 1949. One counter-example:
seabirds. While this is only from 30 years of data, it appears that there is a longterm decline in migratory seabirds (e.g., sooty shearwater), but the cause and
where it is located is unclear. Krill increasing - but this may be release from topdown control. Maybe.
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Santa Barbara Coastal - don't see primary producers and inverts tracking cycles
as tightly. Fish do - e.g., with the PDO. There's a difference in mobile v. sessile
tracking signals.
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McMurdo - Physically driven system - lake communities in terms of Chl a
concentrations track ice change in different ways. Clear winners and losers in
the nematode system due to changes in moisture.
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Hubbard Brook - Getting wetter and warmer. Seasonal changes - loss of snow
pack leading to phenological changes. Canopy expansion not keeping up with
loss of snowpack. Marked decline in sugar maple due to loss of Ca from acid
rain effects, replacement by Beech. Species from northern climates moving into
southern areas. Spruce growing like gangbusters, but that's a puzzle. Not
predicted. Good records from other organisms - increases in stream biomass of
algae, but not sure why. Salamanders declined markedly. Most birds have
shown decreases in numbers, although tremendous amount of variability. (data
on small mammals and inverts, check with others on patterns).
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clearnest driver is acid rain
Lost 50% of soil organic matter over 20 years
Not seeing CO2 increases yet - but big increases in vegatation linked to
legacy effects of acid rain
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More research = less clarity! New LTER motto?
Day 2
Comments stemming from Yesterday
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Lots of populations that varied with decadal scale climate models in CCE
We thought about long-term timeseries, but didn't give a lot of thought to
climate variability
Point from the plenary, be careful of long v. short-term timeseries - are
timeseries the best approach, or explicit examination of variability
A central question of the day is differentiating climate variability from climate
change
What we don't know is what happens when climate change combines with
natural climate cycles - is that where we cross a threshold?
Or - do we just lump cycles and long-term change when we do driver analysis?
This way we can use both spatial and temporal variation to understand global
change impacts
Once we establish patterns, we then take apart process - Steve Carpenter fourleg framework: observational data, experiments, models, revision of our
understanding
Strong problem of comingling climate change and climate variability - we can
miscommunicate our results to a broader audience, alter preceptions
13% of SD fishers believe in long-term variability. They all believe in and
understand cycles.
Also the issue of attribution - see the Detection and Attribution framework in
the climate change impact literature
Do we need to consider evolution and adaptive capacity
Evolution occurs on observable timescales
Are our sites big enough to think about evolution?
We also need to consider competition - e.g., while the arctic is getting
shrubbier, why is it not getting shrubbier faster?
Brings up the question of do we only see long-term global change effects
manifesting after a disturbance to the system?
Delay in climate-related effects.