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.

Capture.JPG

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.

Reddit AMA - January 11th!

On January 11th, I will be hosting a Reddit Science AMA (Ask Me Anything) where I will talk and answer any and all questions related to the Glacier Bay/National Geographic expedition and subsequent research.  It's been incredibly fruitful, spawning several side projects already, a cover story for Ecology, and press.  I'm looking forward to sharing the adventure with any and all comers.

 

The Summary Paragraph(s):

I am an ecologist that studies big questions about how landscapes change and what direction they will go in the future.  Are our landscapes, forests, and fields resilient to climate?  Will they adapt, fail, or be replaced?  Fundamentally, how and why do ecosystems grow, change, and end up looking and functioning like they do?  Most of the time, that involves fires, hurricanes, landslides, and other catastrophes, or more sedate change as glaciers retreat and life invades. 

But there’s one fundamental difficulty all of us scientists grapple with – change takes time.  Hundreds of years in many cases.  We’ve come up with a variety of ways to work around that problem – looking at young and old landscapes and comparing them, for example (called a chronosequence).  But nothing substitutes for seeing things with your own eyes, for actual, observational, ground-level data on how a region actually changes and evolves.  It requires just sitting still and watching a system grow, change, and emerge all on its own – long-term research, research which spans multiple generations and lifetimes. 

It turns out that the longest running study along those lines – watching an ecosystem grow and emerge from scratch (so to speak) is located in Glacier Bay National Park, in Alaska, USA.

Via funding from the National Geographic Society, I led a group of researchers to rediscover these plots – which turned out to be a bit of an Indiana Jones endeavor.  The study was initiated in 1916 by William S. Cooper, one of the founding fathers of the science of ecology in the United States.  He went to Glacier Bay and saw a landscape where he could just sit and watch an ecosystem assemble in the wake of warming-induced glacier loss (the warming was from the end of the Little Ice Age).  He visited several times until the 1930’s, then his graduate student took over and visited until the 1980’s.  Cooper even left detailed directions in 1916! 

It seems simple, but reality is of course much more difficult.  Cooper’s directions from 1916 involved orienting by large glacier erratics, visible from shoreline, compass bearings, distances measured in paces or strides, and crosses painted on rocks. It was a literal treasure map.  But shoreline has changed – isostatic rebound from the retreating glaciers has altered sea level and shoreline dramatically.  Vegetation has obscured sightlines.  Cooper utilized compass bearings – but magnetic north isn’t what it used to be (if you weren’t aware, true north is not magnetic north, and magnetic north changes over time!).  Paint has worn off. Soil has built up and buried Cooper’s metal pins. And all of this occurred in the back of Glacier Bay, Alaska, populated by far more bears, wolves, and whales than visitors and where the silence is punctuated by calving glaciers crashing ice in the fjords.

Through a combination of archival data, old field notebooks, hand-drawn sketch maps from the 1916 trip, photographs from the 20’s and 30’s, modern satellite imagery, and a sturdy but temperamental metal detector, four of us set out to re-find the missing plots via kayaks and foot.  After over a week of rain, bear encounters, long kayak traverses, and wandering we were successful.  The plots were found, re-igniting what is now a 101-year observational study, the longest of its kind in the world.  We now have a precise, high resolution record of vegetation change at multiple locations.  These data are being used to test assumptions about our “shortcuts” for monitoring change – like chronosequences.  In 2017 we revisited and expanded the plots, bringing them into the 21st century with ongoing work on things like community change, spatial patterning, bacterial and fungal genetics (led by Dr. Sarah Bisbing), and dendrochronology (led by Dr. Greg Wiles).

This work was featured on National Geographic online (https://news.nationalgeographic.com/2017/05/glacier-bay-plant-succession-study-william-skinner-cooper-buma/), Atlas Obscura (https://www.atlasobscura.com/articles/glacier-bay-william-cooper-100-year-old-plant-succession-study), a variety of newspapers, and featured as the cover story in Ecology (http://onlinelibrary.wiley.com/doi/10.1002/ecy.1848/abstract) and available here.

 

Ad hoc instrumentation methods in ecological studies produce highly biased temperature measurements

In any study which tries to scale grounded field data to a regional perspective, using (as-much-as-possible) unbiased methods is necessary so that data points are comparable across regions.  A good recent paper experimentally looked at the best air temperature sensor design:

Check out the recent paper by Terando et al. It’s open access, available here: http://onlinelibrary.wiley.com/doi/10.1002/ece3.3499/epdf

They experimentally compared 11 different sensor brand and shield combinations to weather station data, and found that quite a few methods had a positive bias. It’s not something we haven’t known is a danger (positive bias due to inadequate/improperly designed shielding), but this is a nice way to standardize methods with some experimental backing behind it.

New publication - How big does your study landscape need to be?

Choosing a landscape extent for study, monitoring, or conservation is difficult once you start considering disturbance processes - it's always possible that a fire, or windstorm, or whatever could come through and drastically change what you're looking at.  This could be fine, but generally when we select landscapes for those purposes we want something representative, something that incorporates disturbances as part of the community and ecosystem ecology of the region (after all, disturbance regimes are a part of the system too!).

This is critical in quantitative ecology at any scale, because few things disrupt a population, community, or ecosystem more than a major fire or a hurricane.  And if we want to truly understand something broader than a single location, we need to know how representative our study system actually is relative to the broader context of the given ecoregion (or system, or whatever).

But how big of a landscape do you need?  The old "rule of thumb" from the 1980's was somewhere between 5-10x the size of the "largest disturbance," but that's really fuzzy.  What you really need is a quantitative analysis of variability in disturbances - how big a landscape such that it doesn't matter where the landscape is, the "disturbance effect" is similar?  This is, in effect, asking how big a landscape extent is required to incorporate that disturbance-driven variability without causing major changes in landscape properties - basically a measure of variance between random landscapes.

So, together with Kurt Riitters at the US Forest Service and Jenn Constanza at NC State, Brian Buma quantified the variability between landscapes at a variety of scales in terms of two disturbance processes - proportion disturbed and contagion (a representation of the shape of disturbances).  This was done for all of North America at a 30m scale, using data from 2000-2014.  Because disturbance regimes vary by region, the data was stratified by ecoregion.

As a first step, we quantified the actual percent disturbed for each ecoregion.  While we used wall-to-wall satellite data, it may also be desirable to set up a series of landscapes that approximate the ecoregion.  In that case, the number of landscapes matters, of course - the more landscapes the better you will be in terms of a representative set (in terms of disturbance area, in this case).  A shows the ecoregions in the boreal denoted by the oblong, northerly circle (large fires); B shows the ecoregions on the North Pacific coast (very low disturbance frequency, small events).  With larger landscapes, one gets a good idea of actual proportion disturbed more rapidly of course.  The actual map (bottom) is the true average, and a nice reference tool when writing proposals - what is the "normal" fraction of disturbed area in my study system?

As a first step, we quantified the actual percent disturbed for each ecoregion.  While we used wall-to-wall satellite data, it may also be desirable to set up a series of landscapes that approximate the ecoregion.  In that case, the number of landscapes matters, of course - the more landscapes the better you will be in terms of a representative set (in terms of disturbance area, in this case).  A shows the ecoregions in the boreal denoted by the oblong, northerly circle (large fires); B shows the ecoregions on the North Pacific coast (very low disturbance frequency, small events).  With larger landscapes, one gets a good idea of actual proportion disturbed more rapidly of course.  The actual map (bottom) is the true average, and a nice reference tool when writing proposals - what is the "normal" fraction of disturbed area in my study system?

The proportion of the landscape disturbed varies quite a bit at small landscape extents - unsurprisingly.  At a small extent, a random landscape might be within a burned area or in a location completely undisturbed.  At broad extents, however, most landscapes had fairly low variability.  In other words, it didn't matter where the disturbance occurred, it was incorporated into the landscape.  

Of course it depends where you are - some boreal ecoregions, with their big fires, still had high variance from landscape to landscape even at our largest extent.  And contagion takes longer to settle down.  But for many ecoregions, there are practical extents such that, at least under current disturbance regimes, you can be fairly confident that any future disturbance will be incorporated just fine, rather than completely shifting your landscape to something non-representative.

We also include a parallel analysis where we focus only on disturbed pixels themselves, useful to those researchers who are going to be looking at disturbance processes - how big an area around the disturbance do you need to get a real image of the broader ecoregion?

These results are critical in scaling and communicating the significance of ecological research that attempts to put these dynamic change processes into their broader ecoregion context. 

The results are in press:  Buma B, Costanza JK, Riitters K.  Determining the size of a complete disturbance landscape:  Multi-scale, continental analysis of forest change.  Environmental Modeling and Assessment.  In press.

If you're concerned about your landscape being disturbed, then setting your landscape size larger than these values (in kilometers squared) will reduce the likelihood of a disruption.  The values represent the minimum landscape size to reduce the standard deviation between landscapes below 10% - in other words, it's fairly unlikely (though not impossible!) that a disturbance will make any given study landscape of that size, or larger, dramatically different from the rest of the ecoregion.   All summary statistics, for all ecoregions, included as Supplementary Data and available here and in the paper.

If you're concerned about your landscape being disturbed, then setting your landscape size larger than these values (in kilometers squared) will reduce the likelihood of a disruption.  The values represent the minimum landscape size to reduce the standard deviation between landscapes below 10% - in other words, it's fairly unlikely (though not impossible!) that a disturbance will make any given study landscape of that size, or larger, dramatically different from the rest of the ecoregion.   All summary statistics, for all ecoregions, included as Supplementary Data and available here and in the paper.

John Krapek's second thesis paper featured on cover of Diversity and Distributions

Congrats to John, whose second paper from his MS thesis was selected as the cover shot for Diversity and Distributions!  

Krapek J, Hennon PE, D’Amore DV, Buma B.  Despite available habitat, migration of climate-threatened tree appears punctuated with past pulse tied to Little Ice Age climate period.  Diversity and Distributions. 23(12): 1381-1392

Available here and on the Publications page.

John Krapek and Alex Bothello mapping individual stems along the northern migration front of yellow-cedar, a species currently dying rapidly due to climate induced changes in snow cover.

John Krapek and Alex Bothello mapping individual stems along the northern migration front of yellow-cedar, a species currently dying rapidly due to climate induced changes in snow cover.