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Human stressors on watersheds: A Scavenger Hunt
INSTRUCTIONS, & SCHEDULE OF WORK
Parts 1 & 2 – try to complete these before arriving at lab, Week 8
Parts 3, 4, & 5 – start work during lab, Week 8; continue as homework Lab period Week 9
– TAs available in Hinds for additional help
FINAL REPORT – due in class, Monday June 1 (start of Week 10)
Part 1. Identify your hometown watershed
Required method:
Go to the EPA (US Environmental Protection Agency) website “Surf your Watershed” – typing that phrase into Google will take you there. Follow the instructions to discover your watershed – search on your zipcode, town or county name. This is your ‘hometown’.. The page will display a map of the watershed (or multiple watersheds) associated with your hometown. If multiple watersheds, choose one to focus on – we recommend choosing the one with the most cultural development (urban area, land use for agriculture, a major ‘polluting industry’ now or in the past) so that you’ll have lots to work with, historically. Another practical consideration for this lab exercise is to choose the watershed that corresponds most closely to a single county (or set of adjacent counties), because most cultural historical data are organized by political units of this scale. Be sure to tell your TA what watershed you have chosen so you do not overlap with others in the lab.
Part 2. Plot the history of (human) population size in your watershed
The US Census Bureau is the most reliable and internally consistent source of information on population sizes. However, Census Bureau data are compiled for cities, counties, and other political units, whose boundaries will rarely coincide with watershed boundaries. You are thus going to have to decide how to proceed in getting a reasonable estimate of population trends in your watershed – use what you learned in Part 1 about your watershed boundaries to decide which political unit (or set of units) to use. We will ask you to describe and justify your reasoning. Using an entire county will probably be most efficient, because most economic data are compiled at that level.
Recommended methods of getting population data:
NOTE: Decade by decade data (“decennial data”, “decadal data”) on population size will be sufficient. You don’t need annual resolution and those data would be very hard to find anyway – the Census Bureau only censuses the US population every 10 years.
Easiest method, if it works: Search Google to see if there’s a wikipedia page about your hometown, county, or major city in your county. These articles usually have a section on
‘population size’ or ‘demographics’, with a table listing population size by year, based almost always on standardized data from the US Census Bureau. The author of the wikipage time-line has usually already done the work of compiling data from the multiple
Census reports needed to cover local history back to the 1800s and 1700s. The wiki page alone is NOT a scholarly source.
Alternate method: Go to the US Census Bureau web site, which organizes data by county and time-interval. Spreadsheets of population data for each county since the first available

2 census are available at http://www.census.gov/population/www/censusdata/pop1790-
1990.html
. These spreadsheets are from a 1996 report, so you will need to update them with data from 2000 and 2010; follow the appropriate link on the above page to do that.
Note that county names are not unique nationwide, so check that the county you are studying is in the correct state.
Part 3. Plot two kinds of key economic information on time-lines in order to develop more specific hypotheses of environmental change linked to human activities
Use already-tabulated quantitative data to create graphs displaying historical changes in (a) total acres in farms (agriculture) and (b) annual value of products in manufacturing. The University of
Virginia http://mapserver.lib.virginia.edu/ provides data of these types at both the state-level and, in many cases, the county-level. On the main UVA page, go directly down to “choose a category” – it will then provide you historical data for all decades available. Total acres in farms is a measure of rural land – over time, quantities will change with European colonization pressure, economic conditions (depressions), urbanization, and suburbanization. Manufacturing value might have any number of trajectories, and sums across many kinds of industry – plotting these data will alert you to significant changes in the economic and likely environmental footprint of your hometown, and can be pursued further in Part 4.
**NOTE: This data set is not perfect there are many gaps and inconsistencies. If the unit you chose does not contain what you think is representative data decide on another metric and explain your reasoning when discussing uncertainties and methods.
**NOTE: Manufacturing dollars on this website changes units around 1910. Going from being reported in dollars to being reported so that the numbers will fit within the box (usually a factor of 1000, sometimes more or less). Explain what you believe is happening to the manufacturing products and if you correct for this error please explain how and why when discussing methods and uncertainties.
Part 4. Original search for data on an environmentally significant economic activity
Most of the historical data provided by the UVA website (Part 3) extend up to 1950 or so, stopping far short of today. In Part 4 you will attempt to bring that environmentally relevant economic history up to date. Using Google, you will find quantitative data for the decades between the 1950s and the 2000s or 2010s, focusing either on some measure of ‘land use’ (e.g., agriculture, forestry, conversion of open lands to housing & malls) or manufacturing (expansion, diversification, conversion, decline of ‘industry’).
**Read all the way through these instructions for Part 4 before starting work!**
Important notes about searching for and selecting data
Getting historical series of quantitative data is much harder than you might think, even with the web. We do not want you spending too much time spinning wheels. We will thus define “today” as anytime between the year 2000 and today – a relatively coarse

3 definition that is comparable to the decadal temporal resolution of the data in Parts 2 and 3.
Also, in many cases quantitative historical data are only available for a larger area than the study area (eg., trends for the state, or for the US, rather than just for the county or watershed of interest) or are only available for an adjacent area. Such data are used as a substitute for more specific data – we flag them in our discussion, considering what error this might introduce into our analysis, just as you identified and discussed gaps in timeseries data in Parts 2 and 3. Thus, if you run across a time-series of data for a larger or nearby area during your search for data about your watershed, make a note of it – continue your search for something better, but you can fall back on the spatially coarser or analogous data. Since we are interested in getting a historical perspective on stress on a watershed, it would be best to select a substitute area that drains into the same larger body of water. For example, if your Illinois watershed drains to Lake Michigan, choose if possible a substitute area that also drains into the Lake.
Ways to find data on the history of economic activities for this lab include wikipedia pages on your town/county (cite the original source of the data in addition to the wiki-page that posts it), professional groups (e.g., reports by unions, by companies, by industry promoters, which will have different biases), and agencies (municipal, state, federal annual reports or summaries).
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Agriculture/Forestry: Searching Google for something like “history of dairy production” comes up with a remarkable number of leads to follow (~10 organizations)… “history of xxx” is, in general, a useful search string. If you have an agricultural school in your state (usually a ‘land-grant’ university), it will probably have a ‘cooperative extension’ that provides advice to farmers; try combining that phrase with ‘dairy’, ‘chickens’, ‘corn’, etc.
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Manufacturing: wiki-pages for counties etc can be boosterish, downplaying a history or continued existence of polluting industry. Try public library web-pages for more balanced information, and for links to local historical societies.
Citing data sources: As a citizen and a scholar, remember that data may be ‘free’ in the sense that they are now widely shared, but data still have a source – an originator of the content – whom must be identified and acknowledged for their intellectual contribution.
You also want to know this source in order to get a sense of data reliability – data about whales from a commercial whaling organization and data from a UN commission may be very different. Content on the web page of a government agency or a professional organization can be assumed to have originated with it, unless noted otherwise (e.g., the page cites the data or figure as having come from some other source). Thus citing the page – www.xxx.org or .gov – will be sufficient for this lab. Wiki-pages are unauthored, but you must still cite the page ( www.xxx
) and the source that is referenced for the data you are using (these sources are usually numbered, with the full citation provided at the end of the wiki-article). If you find data on the web that you want to use but the source is unclear, then cite the page ( www.xxx
) and add “original source uncertain”; treat these data skeptically until you find corroborating evidence from a certain source.
Finally, remember that you are searching for quantitative data that can be plotted.
Data might be provided in the form of a table, like the data used in Parts 2 and 3, but data
(useful numbers) might be sprinkled through a text. Don’t be distressed if the data have many gaps – gaps are endemic to historical information. In professional work we pound away to acquire gap-free and internally consistent data, but at the end of the day we must

4 still, like you, describe and consider incompleteness in weighing the confidence of our interpretation.
Part 5. Develop testable hypotheses of environmental change: Predict the consequences of stressors & their interactions
In this last section, we want you to think about the effects of individual stressors and of interactions among multiple stressors, creating testable hypotheses (predictions) about environmental change.
Cause & effect (Stress & response): Your four plots of potential or suspected stressors on the natural environment – human population size (Part 2), area farmed, manufacturing output (Part 3), and a more specific aspect of land use or industry (Part 4) – constitute a series of hypotheses for environmental change in the watershed and downstream water bodies. Your initial prediction would be that the natural environment changed, and specifically deteriorated, in parallel with change in the stressor – deterioration increased when the population (or economic activity) increased, leveled when it leveled, and declined when it declined. Depending on the kind of human stressor, the environmental impact might have been on water quality, quantity of natural habitat and biodiversity, promotion of aesthetic or recreational species, etc. all of these, and others, are possible responses (see
Figs. 1, 2, & 8 for inspiration).
Multiple stressors : Environmental response to stress can be a function of timing – if a new stress coincides with the application of another stress or arises when other stresses already exist, then the response might be stronger (more negative) than if it occurred alone.
On the other hand, it is possible that one stressor counteracts another – for example, invasion of a water body by a wildly successful non-native filter- feeding clam, capable of clearing vast amounts of phytoplankton from the water column, would counteract nutrient runoff from surrounding land, which would otherwise drive phytoplankton populations up, increasing water turbidity and suppressing native seagrass communities. This is arguably what the Asian mussel M. senhousia and the Zebra mussels are doing today in San Francisco
Bay and the Great Lakes, respectively (although probably at considerable cost to the diversity of native filter-feeding organisms).
Extending time-series to the present day: To create a net-stressors curve for the entire 20 th century and early 21 st
you will need to extend the agriculture and manufacturing curves of Part 3 past the 1950-limit on the U VA website. Unlike gaps in data where you have values on either side, permitting you to interpolate values in between, in this case you only have a value on one side of the gap. Extrapolating trends beyond available data is a perilous business! And so, if in Part 4 you were able to find the same kind of agricultural or industrial data from 1950-2010 as the U VA website provided for earlier periods, please proceed with the instruction for building a net- stressors curve. However, for the other category of U VA data, we recommend that you assume (in the absence of other evidence) that your local manufacturing and/or agricultural trend follows national ones. You can use the attached graphs (from the Bureau of Labor Statistics and the USDA Census of
Agriculture) to fill in manufacturing and agricultural data for the second half of the 20th century.

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NOTE: If the data you located for Part 4 contradicts the national trend (for instance, the number of acres under cultivation for 1950-2010 increases over time, rather than decreases as is observed nationally), you may choose to modify your net-stressor curve appropriately. Make it clear in your answer that you have done this. Realize, however, that the fates of particular industries may not be representative of total manufacturing activity in your region. And so if your own data are quite specific (eg., dairy industry output, number of workers in meat-packing), it's probably better to stick with the national trend for computing the net-stressors curve.