New publication: The phenomena of transitional mortality

Using the lovely study system of yellow-cedar, a new paper explores climate-induced mortality of a different sort than the usual threshold type mortality.  Once I started looking at the whole range, it became apparent that it wasn't so much the climate was too warm for yellow-cedar, but rather the transition from cold-to-warm was detrimental.  In fact, very healthy populations are in quite warm environments.  This led to the conclusion, supported by weather station data spanning about 10 degrees of latitude, that mortality is indeed associated with the transition rather than a threshold per se.  

Capture.JPG

 

The fascinating result is that in this case, slower warming (longer time in the transition) may lead to increased mortality.  It can be captured with a simple binomial model, where the odds for a mortality event are commensurate with the time of exposure climatically and the probability of a thaw-freeze event during that time.  This structure matches the observed data pretty well, though limited data in southeast Alaska (primarily due to a lack of weather stations, and non-random location of those stations) means we're still generally in the hypothesis stage and can't make really concrete conclusions yet - need to move into better instrumented areas and retest (it's science - always retest!).  But it's compelling, and this could be a useful concept in other places.

Predicted mortality from the simple binomial model which combines years of exposure and the probability of a thaw-freeze event over that time.

Predicted mortality from the simple binomial model which combines years of exposure and the probability of a thaw-freeze event over that time.

The manuscript will be coming out in Ecosphere:  

Buma B.  Transitional climate mortality:  Slower warming may result in increased climate-induced mortality in some systems.  Ecosphere.  In press.

  

NSF RCN on Coastal Margins Annual Meeting: Jan 30-Feb 3, 2018

The NSF RCN coordinated through our lab, the Alaska Coastal Rainforest Center, and the University of Washington is having its annual meeting this week.  The focus is from an "end user" perspective, so we're taking the aquatic flux information from the first meeting, the terrestrial carbon products that we've been working on, and then focusing on where that material ends up - the ocean.

During this second workshop we will bring together a select group of oceanographers, biogeochemists, biologists, modellers and others interested in processes occurring at the land-sea interface in temperate regions. Although the workshop will focus heavily on the Pacific Coast, our findings are expected to have applications to temperate coastal rainforest domains globally.

Through this multi-disciplinary forum we aim to evaluate the current state of the knowledge of the terrestrial-marine system in the PCTR with respect to five key topics:

  1. Physics – freshwater controls of coastal hydrodynamics;
  2. Biochemistry – micro and macronutrient subsidies and their bioavailability to marine ecosystems; carbonate chemistry;
  3. Food webs – contributions and pathways of freshwater & terrestrial subsidies to marine food webs;
  4. Estuaries – the land-sea interface and role of estuarine ecosystems in modifying terrestrial outputs;
  5. Drivers of change – e.g., land use and climate.

Each topic will be introduced by key speakers, followed by discussion to define scope, discuss the current state of knowledge, distill and summarize data gaps, and identify future research directions. Our goal is to solidify a scientific community and build a research agenda on processes acting across temperate rainforest coastal margins.

We should have at least one manuscript come out of this, and are having an extra day of writing - Saturday.  For more info, send me an email:   bbuma@alaska.edu or see the website (link at top of page).

Mendenhall Fireside Lecture

Edit:  This did not get posted on time, unfortunately.  Thanks to all that attended!  The lecture was live cast via Facebook, and should still be available (as are all the previous ones) on the Mendenhall Glacier Facebook page.

I will be giving the Mendenhall Lecture tonight, at the Glacier Visitor Center.  The talk will be on work in Glacier Bay related to the William S. Cooper successional plots, their inception back in 1916, and what we can learn from the longest running permanent plot network of its kind in the world.  Most of the talk will be about the expedition to find the plots - how old maps, notes, and pictures were utilized, the challenges of adjusting for changing declination and sealevels, and fun stuff like that!

Also note that over the next couple weeks there are some great talks by Liz Graham on pests in Alaskan forests (spruce beetle, spruce aphid, pine beetle) and a rare, but really interesting, alpine tsunami by Rick Edwards.  In the Heen Latinee, where my group has done considerable work, a very large rockfall-turned-debris slide crashed into an alpine lake, sending a huge wall of water splashing out the other side, ripping up the forest for miles down from the lake in a wide swath of disturbance, finally running out into Berners Bay as a large pulse of water.  Cool stuff!  With our lidar biomass maps, we are hoping to calculate the biomass loss from the event - next steps!

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Science and storytelling - A new expedition to document change

Science, especially the science of ecosystems and change, needs symbols.  It needs those symbols to communicate the importance of the change we're quantifying - change which is sometimes incremental but inexorable, adding up to large changes over big areas.  

Species migration is one such incremental thing, hard to communicate the significance but important - at a global scale.

CapeHorn.jpg

In a new project funded by National Geographic, I'll be leading an expedition to Cape Horn, an expedition which is intended to link science and storytelling, ultimately providing us with a single focal point for change, a point which folks can visit virtually.  Together with scientists from the Universidad de Magallanes, Portland State, the University of North Texas, the Pontifical Catholic University of Chile, and the University of Alaska, we're going to find the world's southernmost tree - the ultimate treeline, if you will.  This individual - no doubt stunted but alive - can function as a symbol or signpost, marking the edge of forests as they creep poleward.  We'll establish a strong a quantitative baseline as we can such that future generations can use that focal point as an easily communicable marker of human induced change (there's other science, including using the point as an anchor for regional NDVI work, comparison to other points in Patagonia, and other aspects of course!).

The project is truly focused on storytelling and communicating the science of change - communication of that point (thanks to Google Earth, one will be able to virtually visit) and that landscape.  The region is home to a vibrant ecological and human community, including amazing efforts like the Cape Horn Biosphere reserve, exciting initiatives like the "Tourism with a hand lens" project to explore the fascinating plant life at our feet, and a long cultural legacy of life at the edge of the world.  Thankfully, we'll have a professional writer and photographer along to spread the word about this unique place.

Updates will become more frequent as the expedition nears.  Thanks to National Geographic for their support, and I'm looking forward to a great (no doubt challenging) expedition.  Team members (so far) include Ricardo Rozzi, Juan Armesto, Andres Holz, Glenn Wright, Craig Welch, and myself.

Tierra del Fuego and the Cape.

Tierra del Fuego and the Cape.

Reddit AMA today!

Today I'll be on Reddit talking about the National Geographic sponsored expedition to rediscover the William S. Cooper plots, our success, and how it's now the longest running, time-zero permanent succession plot in the world.

The trip involved navigating by 1916 charts, looking for X's painted on rocks above buried metal markers, metal detectors, old compass bearings and paces, and kayaking in the back of Glacier Bay.

Our first findings were published in Ecology last year, and are available here.  There are various updates about the project here, as well as on my collaborators website:  Sarah Bisbing.

Above - old maps showing the "emergence" of Glacier Bay, from the 1700's via a Russian map to the 1940's.  This rapid emergence of a whole landscape is unique, and the reason why it's such a special place to study ecological communities.

The whole project was a success, and was followed up by a more recent trip where we expanded our data collection efforts to include bacterial and fungal functional diversity, spatial mapping of individual trees, dendrochronology, and broad-scale assessment of tree patterns via stem mapping and remote sensing.  It's a big project, but amazing as well.

All the plot pictures known.

Overall the plots are providing a wealth of information on how plant communities assemble, change, and adapt to rapidly changing climates - Glacier Bay has been undergoing substantial warming for over a century thanks to the Little Ice Age, so it's a great laboratory for how landscapes will change worldwide with anticipated (and observed) warming.

Species richness peaked early, but it really more depends on where you are.  If you're a long ways from seed sources, say because either it's just a long distance or due to rapid climate change, your plants will come in more slowly.  It also turns out that the overall species pool is much more limited - to light seeded species which can then take over.

Species richness peaked early, but it really more depends on where you are.  If you're a long ways from seed sources, say because either it's just a long distance or due to rapid climate change, your plants will come in more slowly.  It also turns out that the overall species pool is much more limited - to light seeded species which can then take over.

Lots of soil data as well.  The first results are below, but work is ongoing on the broader scale patterns (samples collected, in process for publication).

Capture2.JPG

Not a lot of patterns, other than carbon tends to accumulate over time - interestingly, this is independent of the actual species composition.  Two of these plots are dominated by nitrogen fixing species, but the remainder aren't - and some never have been.  This undermines assumptions by some that nitrogen limits early colonization of "late" successional species.  They do just fine, assuming limited competition.

And as always, none of this was or is possible without the whole team of collaborators:  Sarah Bisbing (University of Nevada Reno), John Krapek, Glenn Wright, Greg Wiles, and Allison Bidlack, as well as the help of Glacier Bay National Park, the University of Minnesota archives team, and funding from National Geographic and the University of Alaska.

Opportunities for Rural Alaskan high schoolers

We are partnering with the Rural Alaska Honors Institute (https://www.uaf.edu/rahi/) to get rural high school students into university-level education.  This is a great opportunity - we are looking to hire two students to work all summer studying plant population and community recovery after fires.  You'll gain experience working with scientists in the field - not just plants, but also permafrost scientists, computer modelers, and soil scientists working on bacterial populations.  There's room to design your own project to take back to your village as well.

The position will run from late May to August, 2018.  Positions for 2019 will be made available next year.

If you're interested, contact Brian Buma (bbuma@alaska.edu).  However, many will be familiar with Denise Wartes, who recruits for RAHI - she's going to be contacting high schools from Kake to Akiak.  

RAHI offers other programs as well, like this intensive 6 week program.  

RAHI offers other programs as well, like this intensive 6 week program.  

Physiological sensitivity of yellow-cedar to certain climate conditions appears to be range wide

Guess which site has mass yellow-cedar mortality?

(Mostly) range-wide climate data, from Amphitrite Point in Canada (about 48 degrees N) to Cannery Creek in Alaska (about 60 degrees north).  The sites in red have seen mass mortality as a result of hanging out in a climate transition zone to which yellow-cedar is uniquely maladapted.

(Mostly) range-wide climate data, from Amphitrite Point in Canada (about 48 degrees N) to Cannery Creek in Alaska (about 60 degrees north).  The sites in red have seen mass mortality as a result of hanging out in a climate transition zone to which yellow-cedar is uniquely maladapted.

Yellow-cedar mortality is well described, resulting from a physiological adaptation which takes advantage of historically reliable climatic cues for its phenology - specifically, cedar de-cold hardens early in the spring to take advantage of post-winter nitrogen availability.  Historically, deep snows have protected it from cold snaps and root freezing.  The lack of winter snow resulting from 1) emerging from the Little Ice Age and 2) anthropogenic warming is making those phenological stages vulnerable to freeze damage and mortality results.

Random forest modeling, conducted at the rangewide scale, identifies a distinct zone of mortality - shown here as a relative probability.  Higher values indicate more likely mortality - it's pretty clear that from about -5 to -1 or so is a pretty bad place to be.

Random forest modeling, conducted at the rangewide scale, identifies a distinct zone of mortality - shown here as a relative probability.  Higher values indicate more likely mortality - it's pretty clear that from about -5 to -1 or so is a pretty bad place to be.

Lab and greenhouse experiments have found that -5 C soil temperatures are, more or less, the point at which damage occurs in non-hardened individuals (they are quite cold tolerant earlier in the winter).  This requires a combination of cold air masses and a lack of snow, which generally only occur in areas where the mean winter temperatures are near zero - random forest modeling suggests that the 0 to -5 C mean temperature of the coldest month is the best predictor of where mortality occurs.

 

But climate change is, well, changing.  For more typical climate-induced mortality, like traditional physiological tolerance thresholds, a climate shifts and a whole landscape is changed - everything crosses.  But if the mortality and phenological mismatch is tied to a BAND of climate, like it appears here, then we have some interesting potential implications.

Specifically, this suggests that elevated rates of climate-induced mortality is temporary, assuming the climate keeps changing.  Eventually, as in the figure, you'll come out "on the other side."  Then mortality rates should decline.  Since mortality is usually triggered by proximal events (in this case, low snow + cold snap), it won't happen every year - so faster warming may, surprisingly, result in lower mortality overall because less time is spent in the transitional mortality zone (again, see figure).  

A paper detailing this, using yellow-cedar as a model organism and successfully predicting observed mortality rates based on weather station and climate data, is in review.

The transitional mortality zone hypothesis, which states that increased variability around a specific threshold drives mortality - not necessarily the threshold itself - holds up well in test cases.  One implication is that faster climate change may result in less severe mortality because less time is spent in the highly variable, exposed "danger zone."

The transitional mortality zone hypothesis, which states that increased variability around a specific threshold drives mortality - not necessarily the threshold itself - holds up well in test cases.  One implication is that faster climate change may result in less severe mortality because less time is spent in the highly variable, exposed "danger zone."

Evening lectures at the USFS Mendenhall Glacier Visitors Center

The Mendenhall Fireside Lectures are always a highlight of winter - free and open to all, each Friday.  There are, as always, quite a few good ones lined up.  

A couple of note:  My collaborator and the director of the Heen Latinee Experimental Forest, Rick Edwards, will be reporting on a dramatic happening in our beloved experimental forest, a large rockfall and alpine tsunami which caused some very impressive destruction along the upper reaches of the watershed, and a wall of water which made it down to the ocean.  That is February 9th (6:30 and 8PM).

I will be giving a lecture on work in Glacier Bay and the story of the rediscovery of the Cooper plots - the challenges in finding 100 year old sites in the wilderness, backcountry navigation, and all that.  It will cover work in 2016 and 2017 - as well as into the future.  That will be January 26th, 6:30 and 8PM.  

There are also many interesting talks on mining, transboundary mine issues (a major concern re: pollution, salmon stocks, and social issues), kayaking the Inside Passage, and others.

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Quantifying wind exposure in R

Exposure to storm winds is important for ecological communities - it shapes the pattern of seed dispersal, wind disturbance/blowdown, fire behavior, soil turnover (via tip up mounds), and many other processes.  Quantifying exposure over large areas is tricky, though.  

I put together some code to calculate wind exposure as a function of topographic shading and known storm directions - this is for straight-line winds, primarily over broad extents, and was originally developed in the 1990's for hurricanes.  So it works well on those scales.  This is exceptionally valuable in complex terrain - like Colorado or Alaska.

Top:  Landscape shot.  Middle:  DEM of that landscape.  Bottom:  Calculated wind exposure (red high, blue low).

To calculate exposure, you basically take an angle of wind, and allow that wind to come into your landscape, bend over topographic barriers, and calculate relative exposure.  Areas that are flat have high exposure, of course, as do hills facing the incoming wind direction - if they are not shaded by something upwind.  In real life, of course, you'll have a distribution of wind directions - so calculate exposure from that distribution and weight accordingly (e.g., by relative frequency).  This gives a nice relative score to your landscape.

The code is accomplished via two snippets:


First, the following code quickly calculates - based on user supplied wind direction, deflection angles, and search distances (how far upwind a barrier should matter) - relative exposure:

####################

#    Function

#####################

windout.iter <- function(dem, deflect, angles, max.dist) {
#note that the dem raster must be in planar coordinates

#for smaller datasets:
#dem <- readAll(dem)     #if in memory
res <- res(dem)

# do not ignore the case where x and y resolution are not equal
stopifnot(all.equal(res[1], res[2]))
xr <- res[1]

#number of distances to check, basically goes every other cell for speed.
num.dist <- round(max.dist / xr / 2)
distance <- seq(xr, max.dist, length.out=num.dist)
result <- list()
j <- 1

for (d in deflect) {
    midrow <- cellFromRow(dem,rownr=1)    #note this does the top one
    elev <- extract(dem,midrow)
    coords <- xyFromCell(dem, midrow)

    radangle <- (angles+90) * pi/180  #convert to radians.
    dcosangle <- -cos(radangle) * distance
    dsinangle <- sin(radangle) * distance
    x <- apply(coords[,1,drop=FALSE], 1, function(j) j + dcosangle)
    y <- apply(coords[,2,drop=FALSE], 1, function(j) j + dsinangle)
    xy <- cbind(as.vector(x), as.vector(y))

    comp.elev <- extract(dem, xy)
    comp.elev <- matrix(comp.elev, ncol=num.dist, byrow=TRUE)
    comp.elev <- comp.elev - elev
    comp.elev <- t(t(comp.elev) / distance)
    #notAllNA <- rowSums(is.na(comp.elev)) != num.dist
    ang <- atan(comp.elev) * (180 / pi)

    r <- apply(ang,1,max)

    r <- r<=d
    result[[j]] <- r*1
    j <- j+1
    }

output <-simplify2array(result)
output <- apply(output,1,sum)
output <- output+1
outputs <- list()
outputs[[1]] <- output
outputs[[2]] <- coords
return(outputs)
}
 


This next code then loops through a DEM, first subsetting out an area the size of the max distance (that keeps things fast) and then calculating all wind directions desired and averaging the results.  To do this in practice, one needs to know the distribution of storm-force wind directions, and builds the directions based off that (to weight a given direction, one could either calculate twice or simply duplicate).  The following works at a 5km distance, several deflection angles, and is oriented around S, SE, and SW wind:

########

library(raster)

#load single big DEM
dem <- raster(file.choose())
    plot(dem,maxpixels=10000)
    t <- drawExtent()
    dem <- crop(dem,t)   #If you want to do a focal area

#set projection
proj.def <- "+proj=utm +ellps=WGS84 +zone=8 +units=m"  #Adjust as needed.
dem <- projectRaster(dem, crs=proj.def)

storage <- matrix(nrow=nrow(dem),ncol=ncol(dem))

#set parameters
max.dist <- 5000
deflect <- c(1,3,5,7,9,11,13,14)
angles <- c(135,180,225)

iter <- 1:nrow(dem)    #to 3772 now
r <- res(dem)[1]


for (i in iter) {
    temp.extent <- extent(dem)
    temp.extent@ymax <- temp.extent@ymax-(i*r)    #*res(dem)[1] to avoid top
    temp.extent@ymin <- temp.extent@ymax-(i*r+max.dist+r)

    temp.dem <- crop(dem,temp.extent)
    temp1 <- windout.iter(temp.dem,deflect,angles[1],max.dist)[[1]]
    temp2 <- windout.iter(temp.dem,deflect,angles[2],max.dist)[[1]]
    temp3 <- windout.iter(temp.dem,deflect,angles[3],max.dist)[[1]]

    #temp.coords <- SpatialPoints(temp.coords)
    temp <- apply(cbind(temp1,temp2,temp3),1,mean)
    #t.loc <- cellFromXY(storage,temp.coords)

    storage[i,] <- temp    
    print(i)
    removeTmpFiles(h=0)   #This is needed large processing jobs, which crash otherwise.
    rm(temp.dem)
    gc()
}

gc()
t <- dem   #this creates a place to put the calculated values
t[] <- storage

par(mfrow=c(1,2))   #look at some comparisions
plot(t)
plot(dem)

writeRaster(t,file.choose(),format="GTiff",overwrite=T)


###################################################################3
 


Regeneration densities in climate threatened species suggest glacial migration pace

In our on-going quest to understand how species and communities change in response to warming, we've been tracking migration of a climate threatened conifer.  This process has entailed mapping the range edge - precisely - and then monitoring the production of new recruits.  If those recruits are outside that range edge, then they are pushing the range forward or infilling - migration. 

Climate change in Alaska has been going on some time, since the end of the Little Ice Age, though of course it's accelerating.  This provides a nice opportunity to watch adaptation-in-action, sans models, and provides a good empirical check on migration expectations.  

Yellow-cedar is a great study case - it is, and has been, culturally important for thousands of years among Indigenous cultures, so it's tracked.  It's economically important now.  It's ecologically significant, as it dramatically changes the biogeochemistry of the soil.  And it's unique among a sea of spruce and hemlock (it's a low diversity forest, so cedar is a highlight).  The species should be able to migrate rapidly - the climate is ideal, the plant community is the same as throughout the contiguous range, the topography and edaphic conditions seemingly perfect.

And yet...

Yellow-cedar regeneration densities in understorey plant community associations. (a) Interior subplots. (b) Exterior subplots. Communities are ordered left to right based on soil drainage: communities on left have a higher percentage of well-drained soils, communities on right a higher proportion of poorly drained soils (Martin et al., 1995). Some blueberry (Vaccinium spp.) type communities with similar species composition and soil drainage characteristics were lumped together. In one exterior plot, the dominant plant association was devil’s club—skunk cabbage (Oplopanax horridus—Lysichiton americanum), and this plot was lumped with the blueberry—skunk cabbage (Vaccinium spp. —L. americanum) category due to similar composition and soil drainage. The number of subplots falling in each community type is listed in parentheses.  From Krapek and Buma 2017.

Yellow-cedar regeneration densities in understorey plant community associations. (a) Interior subplots. (b) Exterior subplots. Communities are ordered left to right based on soil drainage: communities on left have a higher percentage of well-drained soils, communities on right a higher proportion of poorly drained soils (Martin et al., 1995). Some blueberry (Vaccinium spp.) type communities with similar species composition and soil drainage characteristics were lumped together. In one exterior plot, the dominant plant association was devil’s club—skunk cabbage (Oplopanax horridus—Lysichiton americanum), and this plot was lumped with the blueberry—skunk cabbage (Vaccinium spp. —L. americanum) category due to similar composition and soil drainage. The number of subplots falling in each community type is listed in parentheses.  From Krapek and Buma 2017.

Regeneration is absolutely minimal outside the existing stands.  There is some regeneration within the individual stands (ranging from a single tree to a few dozen mature individuals), but not a lot - and regeneration outside is constrained to pretty much the blueberry plant/rusty menziesia plant association (Vacc. and Menz.).  It's unclear why - those are productive forests where yellow-cedar isn't expected to actually be competitive, so it's probably less regeneration than it looks like.

Photograph of a typical yellow-cedar stand boundary in the study area. Approximately 200-year-old yellow-cedar (Callitropsis nootkatensis) are located abruptly at the stand edge, with regeneration of other tree species (e.g., western hemlock [Tsuga heterophylla]) outside the boundary, indicating that stands have been in a period of relative stasis for the past many decades to centuries. No obvious yellow-cedar mortality is observed inside the stand boundary.

Photograph of a typical yellow-cedar stand boundary in the study area. Approximately 200-year-old yellow-cedar (Callitropsis nootkatensis) are located abruptly at the stand edge, with regeneration of other tree species (e.g., western hemlock [Tsuga heterophylla]) outside the boundary, indicating that stands have been in a period of relative stasis for the past many decades to centuries. No obvious yellow-cedar mortality is observed inside the stand boundary.

The most likely reasons are either 1) herbivory or 2) a lack of disturbance opportunity.  We need to test both.  The herbivory hypothesis is being informally tested, and currently found lacking, via a few plantations scattered around the area where herbivory is not a factor.  Why not eat nice, fertilized plantation trees?  A lack of disturbance, on the other hand, explains the pattern - rapid migration historically (these stands got there somehow, and they are separated from the main range by 10-20 km) and then suddenly nothing.  That punctuated pattern could be associated with rare, major historical wind disturbance.  These stands are not in particularly storm exposed landscapes, however.  It could also be snow disturbance, as the stands seem to have originated during colder periods.  This could be associated with lower herbivory in the winter too, however - so the work continues!  

Each individual seedling is mapped, understory community marked, and in many cases soil chemistry samples taken.  This allows for precise spatial organization of data, opening up a whole toolbox of spatial statistics for testing community ecology and biological hypotheses.

Each individual seedling is mapped, understory community marked, and in many cases soil chemistry samples taken.  This allows for precise spatial organization of data, opening up a whole toolbox of spatial statistics for testing community ecology and biological hypotheses.