Thursday, May 22, 2014

Collecting Data is the Most Abstract Thing You Can Do.

I borrowed that statement from Tim Allen, Professor Emeritus at the University of Wisconsin.  Tim is a colleague of long standing, and I think he’s one of the best thinkers in ecology.  His books on hierarchy and complexity repay detailed attention.  So why does he say that data collection is so abstract, and what does this have to do with urban long-term ecological research?

At first glance, collecting data seems like a pretty concrete activity, like going to work at the bank, or cutting the grass.  To collect data, you pull on your boots, spray tick repellent on your pants cuffs, and head out the door.  Or you fire up the computer and download the latest census data or a remotely sensed image from the National Agricultural Imagery Program.

But the concreteness is only an illusion.  Of course, there is the obvious abstraction of a statistical design.  Spatial and temporal arrangement of samples, the statistical models that will be used to detect any difference among samples are pretty well in mind as you pull on those boots, or following out footwear metaphor, boot up the computer. 

Erica Tauzer preparing to sample a vacant lot in Baltimore.
Lurking behind the statistical abstraction implied by data, are deeper theoretical and conceptual structures.  Data are as much conceptual as they are empirical.  What kinds of questions does girding for battle with data raise about the conceptual realm?  This is an important area of consideration during BES’s Year of Theory.

Data are first of all, framed.  They are collected within in a specified spatial extent, and represent a specified spatial grain size or temporal window.  The spatial or temporal interval between samples is also a kind of framing.  It is no mistake that the term “framework” is so important in discussions of theory.  That term acknowledges that framing, and specifying the relationships of data in the frame, are key tasks for theory.  In other words, framing specifies the scope and spatial and temporal texture of the area of interest.  The framing tells what the data are "for" or "against."

Data collected are relevant to some model, and that model needs to be specified.  Models indicate the entities or processes of concern, how they are related, what the expected dynamics are, and what the potential outcomes are.  Models thus fill in the details of the working of the system within its specified frame.  Often, multiple models are employed to understand a system, as models work best when they have very specific scopes.  Consequently, complementary models that cover different scopes of the pattern or process of interest must be employed.

Data usually rely on some theoretical structure to determine what measurements are appropriate.   Measurement of temperature as a scientific variable would be useless without the theory of heat to explain what processes temperature can affect.  Further biological models, like that of Q10, expressing the relationship of endothermic versus exothermic metabolism to external temperature add richness to the role of temperature data. 

Theories are also key to comparison.  For example, in Baltimore bird biodiversity has been found to relate to vegetation in neighborhoods and nearby parks, while in Phoenix, bird diversity has been found to relate to wealth of neighborhood residents.  At first these seem to imply perhaps contradictory theories.  However, underwriting both relationships is the response of bird communities to vegetation structure.  It turns out that the social and historical drivers of the bird-vegetation relationship differ between the two cities.  A deeper theoretical structure is implied by the initial incongruity between the data of the two cities.

Another example emerges from the watershed approach used in BES.  Why do we measure the things we do in streams?  The watershed approach frames material fluxes as integrated by water within the boundaries of a catchment.  In BES, as in any urban system, piped water input, and the rerouting of water within the watershed in drains and storm sewers are model details that are required.  So the concreteness of data collection assumes the existence of infrastructure within the watershed.  Of course, it also assumes a patch structure that may influence the processing of materials – their transformation or transport – in the watershed.  Finally, the relevant theory suggests that limiting nutrients will be retained in by the biological processes in the watershed, while those that are not limiting will be passed through at levels reflecting their input and the flow resistance within the watershed.  The chemical forms, sizes of particles, and role in organismal metabolism are all details that determine how a material will behave.  In ecosystems outside of urban areas, these last ideas may be combined in the principle of ecosystem retention.  This emerging theory of urban watershed function explains the different behaviors of materials viewed as contaminants, pollutants by virtue of their excessive concentration, and indicators of human activity.

The frameworks of scientific theories are often depicted as nested hierarchies.  The most general form of content of a theory must contain more specific subtheories or models to translate their abstractions into measurables.  Likewise, even those translating theories and models may need to be further specified for very particular times and places.  Hence, the theories in between the most general and the most specific are of great importance.  They are called “midlevel theories” and are the locus of much interdisciplinary and integrative work.  The models of greatest detail may not translate well across disciplines, while the theories at their most general may offer only metaphorical encouragement for integration across disciplines.  Being attuned to different levels of abstraction is important in managing and linking different kinds of data.

Metacity theory provides an example of nested theories in urban ecology.  The most general level of the metacity states the phenomenon of interest: spatially heterogeneous and changing mosaics of urban systems.  This calls for three more specific kinds of theory: those that deal with the landscape mosaics in which fluxes acn be modeled, those that deal with the choices that people, institutions, and organisms make about where to locate or move in the urban compelx, and finally those that portray the combined outcomes of fluxes and cjoices.  Each of these three mid level theories would be supported by still more specific models.  For example, the flux mosaic might include models of human migration, biogeochemical nutrient flows, energy apportionment, and traffic.  Each of the other mid level theories could similarly be subdivided into more specific models.

Each set of data, and each relationship between one kind of data and another, calls for a statement of the framing assumptions, the model structures, and the more inclusive or general theoretical relationships.  Sorting this out, and articulating these relationships for all our supposedly concrete data sets, is a task for the BES Year of Theory.

Allen, T. F. H. and T. W. Hoekstra. 1992. Toward a unified ecology. Columbia University Press, New York.

Ahl, V. and T. F. H. Allen. 1996. Hierarchy theory: a vision, vocabulary, and epistemology. Columbia University Press, New York.

Pickett, S. T. A., J. Kolasa, and C. Jones. 2007. Ecological understanding: the nature of theory and the theory of nature. 2nd edition. Academic Press, Boston.

Saturday, May 3, 2014

What's An Urban Long-Term Ecological Research Project To Do?

When in 1997 the National Science Foundation (NSF) requested proposals for up to two urban Long-Term Ecological sites to join the network of wild and production ecosystems that had been studied up to that point, it had both long-standing and new goals in mind.  These goals emerged from two main conditions.

Landscape ecologist M.L. Cadenasso, architect Phanat Xanamane, and landscape architect Victoria Marshall (L-R) work on the "periodic table" of urban land covers for Baltimore using the HERCULES methodology.
First, there was the need to understand ecological systems over the long term.  Since 1980, Long-Term Ecological Research “sites,” as they are still most often called, had been funded to conduct research in specific places over the long term.  This was a reaction to the fact that most scientific ecological studies funded before then had been of short duration, generally 1 to 3 years.  That situation limited ecological understanding because many ecological processes take many years or even decades to play out.  Succession, natural disturbance, the accumulation or loss of nutrients, the change in soil and climate, or the effects of colonization of a new species, for example, are processes that require long times to occur, and thus, similarly long times to evaluate.  Of course, simulation modeling can take existing data and, making careful assumptions about dynamics, make reasonable projections through time.  But at some point the validity of such projections is most securely evaluated against real data.  In 1997, the network of 18 sites included such extremes as moist deciduous forest, and a northern hardwoods mountain transect-- both in the eastern US -- temperate coniferous forest in the Pacific Northwest, coastal sites, a high alpine site, a forest and a tundra site in Alaska, a tropical forest, desert grassland, shrub desert, short grass prairie, an agricultural site, northern and southern lakes, and so on.  (See for the complete roster and history of sites.)  The urban sites would add a new kind of ecosystem in which long-term changes were undoubtedly important, to the existing roster of LTER sites.

The second goal was to add sites that explicitly examined the role of people as components of the ecosystems to be studied.  Except for the agricultural site in Michigan, usually people were not thought to be of great significance to the structure and functioning of LTER sites.  But beginning in the early 1980s, when more and more ecologists began to look seriously at the history and distant connections of their sites, the conclusion became clear, that people – both present and absent – could no longer be ignored in understanding the ecology of North America.  So NSF acted on this second goal in choosing a focus on systems where people and their actions could never be ignored – urban ecosystems.

The final goal, according to the 1997 Request for Proposals, was “to enhance the interdisciplinary breadth of the Long-Term Ecological Research (LTER) Network.”  Obviously, to understand urban areas as ecosystems, the skills, talents, theories, and methodologies of experts in various social sciences would have to be integrated with the familiar work of biological ecologists and the physical scientists they were used to working with.  Urban ecological research would necessarily be interdisciplinary.  Not only the technical expertise of social and economic scientists would be required, but also their experience in dealing with social structures and human institutions would be needed for working in urban systems.  It turned out in Baltimore that we also recognized the need to borrow their “social capital” and trust built up over decades of working with communities, organizations, and governments in the Baltimore region.

Putting all this together resulted in seven explicit goals that an urban LTER would have to satisfy.  Five were required of all LTER sites, and had been in place since 1980:

·        Primary Production: pattern and control of primary production,
·        Population Studies: spatial and temporal distribution of populations selected to represent trophic structure,
·        Movement of Organic Matter: pattern and control of organic matter accumulation in surface layers and sediments,
·        Movement of Inorganic Matter: patterns of inorganic inputs and movements of nutrients through soils, groundwater, and surface waters, and
·        Disturbance: patterns and frequency of disturbance to the research site.

But in addition, urban LTERs would have to deal with:

·        Land Use and Land Cover Change: examine the human impact on land use and land-cover change in urban systems and relate these effects to ecosystem dynamics,

·        Human-Environment Interactions: monitor the effects of human-environmental interactions in urban systems, develop appropriate tools (such as GIS) for data collection and analysis of socio-economic and ecosystem data, and develop integrated approaches to linking human and natural systems in an urban ecosystem environment, and

·        School Systems: integrate research with local K-12 educational systems.
The seven core urban LTER action areas and the BES long-term data or programs that contribute to each one.  The five core research areas identified with the origin of the LTER Network in 1980 are shown in brown, and the additional core activities defined in the 1997 NSF call for urban LTER proposals are shown in blue-grey.

So it turns out that the Urban LTERs, our Baltimore Ecosystem Study and the Central Arizona-Phoenix LTER have seven core areas of accomplishment. These seven core requirements can be considered the charter of the Urban LTERs.  Integration of social and biophysical approaches to understand the feedbacks in urban ecosystems as complex, spatially heterogeneous mosaics, is thus a multidimensional pursuit in satisfying the founding charter.