Intro to R as GIS

For the past two years I have served as one of two student representatives on the US-IALE executive committee. One of the major things we do in addition to providing a student voice on the ExComm is organize a students-only half-day workshop at our annual meeting. This is offered at no-cost to students attending the conference and we try to do things relevant to the field. In 2015 our chapter hosted the IALE World Congress in Portland, Oregon and with an eye on software that many students are learning to use we (ambitiously) put together an introductory workshop on manipulating and analyzing spatial data using R. We were able to recruit three other people to help develop and deliver the workshop, and managed to cram the whole thing into 4 hours.


Karl providing some guidance during the workshop – equally possible that he’s saying, “I haven’t seen that error before…”

Given that we unleashed a barely controlled firehose of R on the attendees, I think that overall it went okay. Given the material I think it would work better as a 6-8 hour workshop with the option for attendees to bring/use their own data. Maybe this is the way it should be set up from the start, i.e. here is a dataset that I know it works with, now try and do it with your own. I haven’t organized or been a part of delivering many workshops, but I learned a lot and really enjoyed the experience.

If you want to check it out the workshop materials the are freely available here on GitHub.

Effects of diversity, topography, and interannual climate variability on pathogen spillover

Text from an abstract I submitted for the 6th Sudden Oak Death Science Symposium on the initial results from one of my dissertation chapters. Additional analysis is in progress.

Our knowledge of sudden oak death (SOD) disease dynamics indicate that without bay laurel (Umbellularia californica) there is seldom oak (Quercus) infection. This requirement of an alternate host species for disease transmission to oak species is an example of pathogen spillover. We developed a path analysis to test specific hypothesized relationships between physical and ecological factors affecting pathogen spillover. Path analysis enables simultaneous examination of direct and indirect effects from multiple factors, which can enhance our understanding of the multiple influences on pathogen spillover in SOD. We rooted our path model with the topographic wetness index, indicating potential soil wetness and moisture persistence, and examined the direct and indirect effects of species diversity, temperature, precipitation, and bay laurel density on potential inoculum load and infection of oak species.


Path model structure defining relationships between factors influencing pathogen spillover.

We applied 10 years of data from a long-term SOD-monitoring plot network in southeastern Sonoma County, CA. Each of the 200 15-m by 15-m plots was equipped with a temperature logger and plots were visited once per year from 2004 to 2012, and in 2014 to assess P.ramorum/SOD host species for disease symptoms and download temperature data. We inspected oak species for canker symptoms and indexed potential inoculum load by counting symptomatic leaves on each bay laurel stem for 60-seconds. We recorded the abundance of all tree species rooted in each plot during visits in 2005 and 2014 to quantify community diversity. Rainfall was measured at 15 rain gauges installed throughout the study area during this period.


A field crew member taking measurements at one of the plots.

We conducted a piecewise assessment of the path model, enabling us to account for the repeated measures structure of these data. Results from our path model of disease observations aggregated to the plot level revealed that diversity mediates the potential for pathogen spillover through a relatively strong direct negative effect on oak infection. Potential inoculum load on bay laurel had a direct positive effect on oak infection, with its overall influence moderated by temperature, topography, and diversity. Temperature and rainfall had relatively weaker influences on pathogen spillover compared to diversity and inoculum load. The net negative effect of diversity on oak infection is consistent with the dilution effect found in other studies of SOD. Topographic wetness had significant direct influence on diversity and inoculum load, where higher values of the wetness index tended to have lower values for diversity, but higher values for inoculum load. This is consistent with areas where moisture is likely to accumulate and persist providing a more favorable environment for P. ramorum sporulation.

Pathogens Causing Us Pain

Ebola, flu, HIV/AIDS, malaria, are a handful of diseases that most people readily recognize as causing us pain. There are also many microbes in forest ecosystems that cause us “pain.” This pain may be economic, public safety, or otherwise. Large trees suffer mortality from disease more frequently, reducing biological diversity and beauty. While death by tree is less likely than death by ebola, it can still happen. Like human diseases, forest diseases are a natural part of the system, but control and prevention is much more difficult. Trees don’t go to the doctor when they’re sick, and detection is the number one challenge for dealing with any disease.

Video identifying ash dieback

Some diseases are introduced to new places where the local hosts have no resistance, such as sudden oak death

Sudden oak death landscape. Photo by David Rizzo, UC Davis.

Forest diseases can be managed, but it requires concentrated efforts in finding out how the disease is transmitted from one host to the next. And what environmental and human factors are affecting this transmission.

What diseases are impacting the forests where you live?

This was inspired by Richard Cobb and the talk he gave in the NC State FER Seminar series and finished due to participation in the 2015 #SciFund Outreach course

Edge Effects and Connectivity in Landscape Ecology

The the way the landscape is seen from your perspective or mine is likely similar, yet not quite the same, and still our interactions with this landscape are completely different from that of a wolf or a bird or a plant or microbe. This is infinitely fascinating to me.

This semester we have been having paper discussions during our lab meetings, each led by a different member (grad students and postdocs). The first few were tilted toward the human dimension side of our lab, so I was excited to mix things up and lead a discussion about some traditional landscape ecology research. Thinking about the incredible variety of landscapes, how they are connected and divided, how those patterns of connection and division change depending on your perspective is my version of “going back to the bench.” It is one of the major inspirations to me as a scientist. So this week we talked about some ideas that are at the foundation of landscape ecology, particularly edge effects and connectivity.

What are “edge effects” and “connectivity” anyway? The people in our lab group come from a variety of backgrounds, personally and academically. I asked people to provide a definition of “edge effects” from their perspective and this produced two responses. Everyone has at least a little experience with GIS, so one type of “edge effect” brought up was technological where if you are doing a calculation over a gridded surface the values at the edges of the map end up biased because fewer input cells can be used to calculate the values for these cells. The other definition was ecological where an “edge effect” is due to a abrupt transition between environments or landscape characteristics that creates relatively distinct habitat boundaries. This type of edge effect influences the local climate and the species that are likely to occur or occupy the space on either side.

Connectivity is typically in one of two categories, structural or functional, though these are not necessarily mutually exclusive. Structural connectivity is probably the most familiar type to many people. One example are wildlife corridors, which provide a pathway for animals to travel but are not exactly the type of habitat where they would linger. For me, functional connectivity is more easily characterized by thinking about passively dispersed organisms such as wind dispersed pathogens (I study one of these so I might be a little biased). In this example, the pathogen depends on hosts occurring in sufficient frequency and density in order for it to traverse the landscape, and establish and reproduce in a new location. So, a corridor connecting two larger areas may be structural or functional or both in terms of connectivity.

In the paper that we discussed the authors designed a landscape scale experiment to test the effects of connectivity, fragmentation, and edges on the development and spread of a plant disease. The landscape scale experiment itself is admirable because replication at a scale larger than a laboratory or greenhouse is challenging. It is just so big.

The pathogen they were investigating was southern corn leaf blight on sweet corn. They tested whether a structural corridor affected the spread and development of this wind-dispersed pathogen across the landscape. In addition they tested whether there were edge effects on disease development by placing infected plants at varying distances from the edge of the “habitat” patch. The habitat in this case was “regenerating longleaf pine forest” that had been cut into patches with various configurations (I believe for other purposes, but useful for this experiment). They found that connectivity did not have a detectable effect on disease spread or development, but did detect edge effects that were dependent on the configuration of the patch.

While this landscape was supremely useful for doing experiments with this disease system, a substantial drawback was the realism. The immediate question the came to my mind was if there had been functional connectivity in addition to the structural connectivity would they have detected an effect, especially since this is a passively dispersing pathogen? This is an additional experiment that I and others thought would have really improved the study, but that does not take away from the insights that they did gain. And I think this is how science works, in bits and pieces, fits and starts, and eventually we are able to hopefully say at least one thing about a system or process with substantial confidence.