In the field with my wife

My wife is so awesome that she insisted on using two of her floating holidays to help me out in the field in my southern New Hampshire sites this past September. It was great to have Gina in the field for multiple reasons. First, not to get too cheesy, but I really enjoy hanging out with her.  Second, field work goes so much faster with another person. Third, it was interesting to get some new ideas about doing field work from someone who isn't as tied up in the project as myself.

Gina, ready to measure some plants!

One of the best ideas Gina had for me was to draw a rough map for each of my quadrats, showing the general position of each tagged plant in the plot.  At first, this seemed like an awful lot of work, but thinking about it a bit further, and failing to find a few of last seasons marked plants, made me reconsider.  If you have ever tried to find tagged plants you know that sometimes it's very hard.  A year's worth of debris has accumulated, often times hiding tags under a layer of compost.  Some researchers employ tricks and tools such as using a metal detector.  I have not gotten to this point - partly because I cant afford a metal detector.  So, lacking a metal detector, I'll dig around in the duff for upwards of 30 minutes looking for a single tag.  Here's what I imagine a quadrat map might look like.

An example of a 'quadrat map'

The numbers indicate the tag number for each plant.  Clumps of plants with sequential tags might be listed together with a general shape of the clump.  Distinguishing characteristics, such as the presence of a particularly large tree would be noted.  Etcetera.  I really like this idea, and I don't think it will take much time to sketch out such maps in the field when tagging plants.  Next season, as I prepare to make some of my sites longer term monitoring plots (i.e. places to go back to after I finish my PhD) I think I'll start making these quadrat maps.

Happy field workers at the end of the day!

Field Site or Classroom?

The answer to this post's title is - both!

This past week I lead a lab exercise I developed for the course I'm TAing this semester, Ecology Lab, out in one of my Glossy buckthorn field sites.  The primary instructor, Professor Catherine Graham, was away on a conference, and rather than teach one of the old lab activities, I decided to get out into the field with my class of 19 students.  I knew I wanted to do a lab that covered some general information on non-native species and going into this TA from the beginning, I thought it would be fun to incorporate my nearby field sites into the course somehow.  So here was a great opportunity!

I wasn't interested in having my class 'do' my field work for me, so I had to come up with something that made sense to carryout in these plots.  I realized that I could plan an activity that answered some question, or curiosity, of mine about these sites, but that I just haven't had the time to delve into myself. For example, looking around my field sites, I'm often curios about what species are in these plots, beyond simply characterizing them as oak-birch-beech upland forests.  So this last Friday I had the students run belt transects in one of my plots and in a near-by section of woods that has only a small number of buckthorn plants present.

I haven't done to many analyses on the data yet, but looking at overall number of plants counted and relative frequencies and densities of individual plant species, a few things definitely stand out.  The most obvious is that maple leaf viburnum is a very common species in the woods around Stony Brook University.  Low-bush blueberry also seems to be pretty common, though more variable than the viburnum.  Another is that there were fewer plants counted in the buckthorn plot.  There are hundreds of explanations for this and almost no way our data will address which explanations are plausible, but as an observation, I thought it was interesting.  As an assignment, I have my students doing a few analyses and making some interpretations and speculations about our observations - I'm really excited to see what they come up with!

Niche models, the (sub)urban environment, and my uncle's backyard

I use ecological niche models and species distribution models fairly extensively in my research - both my thesis work and side projects.  For some time now my advisor has been involved in projects that link these types of models with population projection models, so when I was thinking about researching the patterns and processes of the invasion of Glossy buckthorn, it made sense to me that I would employ similar techniques.  Additionally, I have only found a dozen or so unique presence records for Glossy buckthorn here on Long Island, so I thought it may be good do a SDM focused just on the island here.  I haven't completed this part of my project, so don't get excited, I don't have any results to share with you yet.  Anyhow, I've spent a fair bit of time gathering more occurrence locations from numerous public lands (parks and what not) in Nassau and Suffolk County, and had been thinking that carrying out an SDM would be pretty straight forward.

SIDE BAR: Looking for invasive species is a bit funny for me.  When I go to a new location that I think is likely to have buckthorn, I'm giddy with anticipation.  I'm excited when I find it and disappointed when I don't.  Now, objectively speaking, if buckthorn is in fact having a strong negative effect on native ecosystems (most likely the case in my New Hampshire populations), then shouldn't I be happy when I don't find it? Yeah, it's complicated I guess...

Ok, back to modeling, and how spending time in the field has affected my thinking.  One thing I tend to think about often when in the field is how Glossy buckthorn got to that particular spot?  Where is the nearest, next-oldest, population?  Glossy buckthorn seeds can be dispersed by any one of the many bird species that eat its fruit, but not too far.  The fruit has a laxative effect (on humans too), so birds don't hold it, or the seeds, for long.  Because nearly all of my field sites are embedded in suburban areas, they all have at least some length of border shared with someones backyard.  At my Long Island sites, I can see backyards from many of my plots.  Initially I hadn't thought much of this, but while visiting my uncle and his family in New Jersey a while I back, I noticed that there in the unmaintained garden next to the patio there was a Glossy buckthorn tree.  Me - "Uncle Joe, did you plant that tree?" Uncle Joe - "Oh no, that came with the house." So there you have it, people in the suburbs may be inclined to plant this tree in there backyard.

What are the implications of this on my models and how I use them?  I'm not sure yet.  On the one hand, it makes me think of possible uses of high-res remote sensing and how it may be used to determine what types of plants are growing in urban and suburban yards.  This technology and its applications are improving rapidly, and we are getting to the point that identification of individual species from satellite imagery may be possible.  (Yeah, that's crazy!) On the other hand, can I use the results of my distribution models to identify distances between populations if plants in your neighbors backyard are actually the source for my populations?  I'm not sure, and will definitely be giving this more thought as the project progresses in the near future.

On how I chose my samples - Part 2

So last I left off on the topic of choosing samples I was discussing how I realized that I really needed to have a good look around me and think about what I was trying to accomplish in order to help me come up with a sampling scheme.  And for the most part, that's just what I did.  During my field visits in the Summer and Fall of 2009, and even somewhat during field visits in 2010, I spent several hours standing in patches of Glossy Buckthorn thinking about how best to select plants to tag and follow.  Sometimes I would call my friend Adam (also studying plant ecology at the time) and bounce ideas off of him (or ask him to generate random numbers for me).  Sometimes I would call my wife and complain about the dense blackberry bushes, multi-flora rose, ticks, etc - ya know, sometimes you just need to whine a bit.

Here are a few of the standout observations that helped me develop my sampling scheme:

• Glossy buckthorn patches vary in density and I wanted to avoid over-representing plants in areas of high density or under-representing plants in areas of low density
• For the purposes of my study I want to follow individuals, therefore areas that are bare of Glossy buckthorn are not good places to look for plants to follow (yeah - this one is obvious)
• Glossy buckthorn grows in varying ecological conditions (under story, gaps, riparian zones, uplands, etc.) and my samples should come from as many of these conditions as is feasible
• The ecological conditions mentioned above are generally clumped. That is, the ecological conditions I'm interested in usually make up their own patch (e.g. a large patch of understory or wetland)

With these observations and thoughts in mind, I decide that the most important aspect of my sampling scheme should be that I select individual plants to follow across different plant densities and in different ecological conditions.  So here's the final sampling scheme I chose:

• Ecological conditions - I selected a specific combination of ecological conditions (e.g. upland-understory forest) and using a GPS outlined the borders of a Glossy buckthorn infestation within the bounds of the borders of the ecological condition.
• Individual plants - Once I had the border of an infestation, I selected 15 to 20 random points within these bounds where I set up 2x2m plots, or quadrats.  Within these plots, I tagged and measured all Glossy buckthorn plants that were taller than 10cm. Plants smaller than 10cm were considered saplings, which were sampled differently.

This type of sample scheme is essentially a stratified-random design, stratified across ecological condition and random within that condition.  So far I think it has worked out pretty well.

Random 2x2m plot in a large forest gap. Orange flags mark the corners of the quadrat.

Fieldwork, ESA, and Moving

The last few weeks have been filled with fieldwork in New Hampshire, travel to Portland, OR for the annual Ecological Society of America (ESA) meeting, and moving from our home out near Stony Brook to a new place in Nassau County (closer to NYC and to my wife's new job) - hence the lack of posts.  Here's a brief post concerning my experiences at the recent ESA meeting.

While in Portland I participated in some audiocasts with a group of friends and colleagues, during which we discussed some conference highlights.  At the end of each day (the meeting was four full days) we gathered and talked about one standout presentation we saw that day.  Give a listen to these audiocasts if you're curious a) what ecologists think and say about each others presentations, b) if you're curious what I thought were a few standout talks, and c) if you want a bit more than an hours worth of entertainment.  The audiocasts can be downloaded from my friend Gabe Yospin's website for our Monday, Wednesday, and (coming soon) Thursday sessions.  Gabe also did solo sessions on Sunday and Tuesday that are worth listening to.

This was my first ESA meeting and I must say, I was impressed.  ESA is a (relatively) large meeting, approximately 4,500 people this year.  There are more than twenty concurrent sessions at a time! Which means during any session there was usually more than one talk I wanted to see.  Also, the conference center was so large that if back-to-back sessions were on opposite ends of the center, it was almost impossible to see both talks in their entirety (had to leave one early and still showed up at the other late).    But these problems aside, I was definitely impressed and inspired by the science reported at this meeting.

Pros and Cons of Local Field Work

My field sites are all within a day's drive of my home. Albeit it's a long day's drive to my sites in northern New Hampshire, but still, far easier to reach than some of my colleagues international field sites.   Three of my field sites are right here on Long Island, less than 30 minutes from my office.  I didn't necessarily pick my field sites out of convenience, but it's a nice bonus.  After college I spent two-plus years working as a Backcountry Caretaker for the Appalachian Mountain Club in the White Mountains of New Hampshire.  This experience, combined with numerous outdoor adventures throughout the northeast with my family during my childhood, instilled in me a deep love and appreciation for northeast deciduous and boreal forests.  So, when I came to grad school to study ecology, I naturally migrated to a project that I could do in this forest type.

Working close to home has many perks.  For example, generally I can look at a weather forecast and pick the nicest days to head out to my sites.  Generally I don't have to go out into the field during a down pour or a 100+ degree day.  Not that I don't spend time in the field during crappy weather, but for my Long Island sites, I can usually avoid it.  Another benefit is that I can go out to my sites just to satisfy my curiosity about field methods that pop into my head while I'm looking at my data.  "I wonder if fruit count on these plants markedly differs in July versus August versus September?" Well, I can just head out and look at my plants.

Counting fruit in July

However, there are a few cons I've experienced with having field sites so close to home.  For example, being so close to the office, I'm available to go to lab meetings, thesis defenses, random talks of interest, etc.  These events seldom take more than a few hours a piece, but it's enough to eat into my field work time.  Also, there's other projects I'm working on that feel more urgent at times - 'I can't go into the field today. I have to finish gathering data for project X!'

For me, and for many others too, being successful at grad school requires good time management.  Field work always requires time management, but having my field sites so nearby requires me to treat field visits like any other day-to-day task I have.  I often talk about my 'field season' as though it's a discreet period of time, but in reality,  it's really a portion of the year during which I shuffle in a few more tasks into my daily life.

Update: Since I wrote this short post, another researcher has also written a great piece on doing local field work. I figured I'd link to it here:
http://dynamicecology.wordpress.com/2013/08/28/in-praise-of-boring-local-field-sites/

On how I chose my samples - Part 1

One of the early challenges I had in doing field work was deciding how to chose plants to measure and tag for long-term monitoring.  At this point, I have read quite a bit about this topic and always take notice of descriptions of this process when they appear in papers. Sadly, such descriptions generally consist of a sentence or two buried somewhere between 'Study Species' and 'Study Site'. I did not have any formal field ecology experience prior to starting graduate school and my research adviser doesn't have any field based projects, so I felt a bit on my own in coming up with a sampling scheme. That's not to say that I didn't get advice from some very smart and experienced folks - just that at the end of the day, I had to cobble together a sampling scheme based on lots of reading, a fair bit of discussion, and a very small amount of experience.

My small bit of experience came by way of working as a Graduate Assistant to the chair of the department here at Stony Brook, Dr. Jessica Gurevtich. Dr. Gurevitch and her colleagues have been monitoring the post-fire recruitment and demography of pitch pine (Pinus rigida) here on Long Island since 1996.  The fire that affected these field sites was in 1995, and growing up near by, I remember it fairly well. Some of the results of this research have been published (Landis et al. 2005; Fang et al. 2006). As Dr. Gurevitch's GA, part of my job was to help out with the Pine Census during the summer of 2009, which that year was organized by my friends Adam and James.  In total, over the years well of 6000 pitch pine trees have been tagged with either plastic bird-band tags or aluminum tags, and once every two years a group of people would go out into the Long Island Pine Barrens, search for these marked plants, and take measurements - such as diameter at ankle height, diameter at breast height (DBH), height, presence/number of cones, etc.  When the census was initially established, 45 square 5x5m plots were distributed across various tree stands within three landscapes.  All pitch pine plants, and those that emerged during the study period, within these 45 plots have been tagged and measured.  It's a lot of work and a lot of data! So when I set to designe my sampling scheme, I had some notion that it would involve square plots and marking all of the Glossy Buckthorn plants inside of those plots with aluminum tags.  


Ok - plots and tags - that made sense.  However, the few papers actually related to Glossy Buckthorn that I had found (nearly all from groups at UNH associated with either Drs. Tom Lee or Robert Eckert) had used transects.  And transects seemed so much easier! Lay down a measuring line, then walk along it.  So, near the end of the summer of 2009, I head up north to the White Mountain National Forest (WMNF), walked into a field of Glossy Buckthorn and laid down a transect.

One end of a transect in WMNF

And this is when I learned that transects are not always the best option.  For one, I wanted to place the transect down in a random spot, and what that meant was that my tape was laid down right next, but not touching, a large patch of buckthorn.  Moving my tape would be biasing my data collection, but not having my tape touching any buckthorn made that transect useless.  Then, I couldn't help but notice that some of my transects went through incredibly dense stands of buckthorn, with trees that had pretty low fruit count, while other transects went through very sparse stands of buckthorn, but each tree had hundreds, if not thousands of fruit.  Hmm ... and this lead to a realization I should've had much earlier - before you decide on a sampling scheme, you have to know what you're planning on doing with the data you collect.  And I don't mean have some vague notion like, 'I'm going to make a demographic model!', I mean, you need to know what values you need to estimate, how much error is acceptable, how you're planning on dealing with outliers and missing data, and there's a whole slew of other things to think about carefully.  So I headed back to Stony Brook and gave this a lot more thought.  And that I'll talk about in the next post.


References:
Landis et al. 2005. Variation in recruitment and early demography in Pinus rigida following crown fire in the pine barrens of Long Island, New York. Journal of Ecology 93(3):607-617.

Fang et al. 2006. Sources of variation in growth, form, and survival in dwarf and normal-stature pitch pines (Pinus rigida, Pinaceae) in long-term transplant experiments. American Journal of Botany 93(8):1125-1133.