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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. • 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 1. 2. Synthesize some data sets – Necessary first step Papers – Everyone likes a paper Synthesis/Methods Considerations 1. Standardization across LTERs - where/when? – – 2. 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 – Different species, different communities, BUT - need a common metric as an output – At what level do you aggregate? Trait? Species? Fucntional group? Frequency of variability – – – 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? – 4. 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? – We seem to be coming back to the underlying mechanisms of what we got right versus wrong. – 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. – 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." • 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 • • 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? • 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. • 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 • Plum Island - Increases in fiddler crabs and blue crabs due to shifts in winter temperature. • 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. • 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 • Antarctica - has a thermal habitat-fish story, too! • 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. • SVR - Consumer data not clear. Some interesting responses from drought...but still not strong data. • 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. • 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. • 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? • 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. • 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. • 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. • 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). – – – 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 – More research = less clarity! New LTER motto? Day 2 Comments stemming from Yesterday • • • • • • • • • • • • • • • • 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.