Final Exam Weds April 26 th 1:30 – 3:30 Chem 1400

1. Introduction to Environmental AnalysisEnviron 239Instructor: Prof. W. S. CurrieGSIs: Nate Bosch, Michele TobiasFinal Review

2. Final ExamWeds April 26th 1:30 – 3:30Chem 1400

4. Concluding thoughts and review • Review of main themes and goals for the course • Same slides we saw in the opening lecture • Review of key concepts, topics, take-home messages • Some specific to particular Skills Units • Some cross-cutting themes Disclaimer: Study all SU web pages, lecture notes, readings, and lab concepts – not just this review file!!

5. Course themes: • Quantitative and analytical approaches to understanding patterns and processes in the environment • Our main framework: Coupled human-environment patterns and processes in ecosystems and landscapes • Primarily we will take a natural-science perspective and approach • As opposed to social science, economic, or policy perspective – although linkages to these will be discussed • We will cover fundamental concepts and hands-on skills development, particularly computer skills

6. Skills Units and Exams • SU 1-4: Spatial analysis in ArcView GIS • Midterm #1 • SU 5-9: Empirical versus cause-&-effect modeling • Midterm #2 • SU 10-11: Integrative modeling for applied environmental analysis • Final exam

8. Skills Units and Exams • SU 1-4: Spatial analysis in ArcView GIS • Midterm #1 • SU 5-9: Empirical versus cause-&-effect modeling • Midterm #2 • SU 10-11: Integrative modeling for applied environmental analysis • Final exam

9. Stella software for modeling dynamic systems

10. Skills Units and Exams • SU 1-4: Spatial analysis in ArcView GIS • Midterm #1 • SU 5-9: Empirical versus cause-&-effect modeling • Midterm #2 • SU 10-11: Integrative modeling for applied environmental analysis • Final exam

15. Concluding thoughts and review • Review of main themes and goals for the course • Same slides we saw in the opening lecture • Review of key concepts, topics, take-home messages • Some specific to particular Skills Units • Some cross-cutting themes

16. Review of Key Concepts, Topics, Take-Home Messages Relationships among environmental variables • Spatial analysis • Statistical correlations and regressions • Cause-and-effect modeling of dynamics

17. Review of Key Concepts, Topics, Take-Home Messages What is a GIS and what are its major strengths • GIS combines 3 things: • Database • Tools for analysis (statistical and spatial) • Depicting data and making maps • Ability to do area and distance calculations

18. Components of a GIS or GIS analysis Spatial maps of features Database of feature attributes Statistical and modeling analyses The historical foundation and organizing principle in GIS is the map

19. Review of Key Concepts, Topics, Take-Home Messages What is a GIS and what are its major strengths • Raster versus vector • Features • Attribute tables • Data layers

20. Georeferencing(or geolocation, geocoding) Ways to organize spatial locations of objects or areas (fields) on the Earth’s surface Three main categories we will consider: • “Miscellaneous” • Street addresses, zip codes, and others • Geographic • Latitude, longitude • Projected • 2-d maps and 2-d computerized data layers

22. Projection methods: cylindrical, planar, conic

23. Michigan Georef • Central axis is skewed NW-SE across upper and lower peninsulas of MI • Less precise than MI State Plane, but Georef has one zone • Units are in meters, which allows calculations of distances and areas • Many data layers (“themes”) are available (free!) from the Michigan Geographic Data Library web site • http://www.mcgi.state.mi.us/mgdl/

24. Review of Key Concepts, Topics, Take-Home Messages Classification and depiction of an attribute • Classification based on properties of frequency distribution histogram • Classification into classes based on environmental properties • Land use / land cover, for example

25. Classification: Natural Breaks (Jenk’s) Class breaks are set where there are ‘jumps’ in values Emphasizes natural groups of values – works better for some datasets than others. Works best for datasets with gaps in values or with clusters of values. Mitchell 1999, The ESRI Guide to GIS Analysis, Vol I

26. Classification of cover types in categories Turner, Gardner, O’Neill 2001

27. Could a spatial pattern like this arise through random chance? • Out of 32 polygons: • 15 are light or dark grey; • 7 are dark grey

28. Review of Key Concepts, Topics, Take-Home Messages Where do GIS data layers come from? • Public land surveys in 19th century • Aerial photography • Satellite imagery • Many types of satellite orbits and sensors -- tradeoffs

29. Review of Key Concepts, Topics, Take-Home Messages Metrics used in landscape analysis, landscape ecology • Compositional • Spatial GIS can be used to do both – but its great strength lies in its ability to do calculate the spatial metrics

30. Review of Key Concepts, Topics, Take-Home Messages The concept of SCALE and scale-dependent quantities A key cross-cutting theme throughout the course

31. Fine resolution, or fine scale SCALE Coarse resolution, or coarse scale, or broad scale Turner, Gardner, O’Neill Fig. 2.2

32. Review of Key Concepts, Topics, Take-Home Messages The concept of SCALE and scale-dependent quantities Many metrics are scale-dependent • Average patch size or distance between patches • Edge area / core area ratio • “Stability” of patches of natural forest – need to define spatial and temporal scale of interest Scaling up: Calculating an area-weighted average that can be used to scale up to the landscape • Using environmental heterogeneity in the calculation

33. Review of Key Concepts, Topics, Take-Home Messages Normalizing • Normalizing statistical data in a GIS for depicting results or performing analyses • Normalizing per unit watershed area for watershed budgets Watersheds • How defined • Why useful • Example, linking water quality to LULC and LULC change

34. Review of Key Concepts, Topics, Take-Home Messages Analyzing change in Land Use / Land Cover • The last Skills Unit from the first section of the course • Ties together many key concepts and themes • Compositional changes, spatial changes (fragmentation) • Use of GIS and satellite imagery

35. Foster et al. 1998, Ecosystems 1:96-119

36. Inputs, outputs, and internal functioning Delineated by the boundary System Inputs Internal functioning Outputs Inputs: “sources” Outputs: “sinks”

37. Review of Key Concepts, Topics, Take-Home Messages System: boundaries, parts, interactions among parts, exchange with external environment, initial conditions • As used in watershed budgets • As used in defining pools and fluxes • As used to construct dynamic cause-and-effect models of the environment

38. Review of Key Concepts, Topics, Take-Home Messages Pools and fluxes, Stocks and flows • Units • Residence time • Steady state, equilibrium, dynamic equilibrium

39. Review of Key Concepts, Topics, Take-Home Messages Understanding feedback and cause-and-effect as used in constructing models of dynamic systems • Positive and negative feedback • Causal loop diagrams • Stability

40. Review of Key Concepts, Topics, Take-Home Messages Construction of dynamic models using Stella framework • Stocks, flows, connectors, converters • Reference mode • Steps in constructing, testing, and applying models

41. “First model of Mono Lake”Ford 1999 Fig. 4.5

42. Difference between a pool with a residence time, versus a conveyer with a transit time

43. Stella software for modeling dynamic systems

44. Stability, homeostasis, teleology

49. What we learned about the use of models to aid in decision making • Simulating multiple contrasting scenarios can be useful, but it might it might take some exploration to find the best set of scenarios • Year-to-year randomness can alter the way a resource needs to be managed. • Key state variables (such as adult stock size, or number of migrating smolts) need to be monitored each year so the manager knows what the system is doing • Simulation of one effect alone (e.g. dam construction) does not tell you how that might interact with other effects (e.g. development in the watershed)

50. What we learned about the use of models to aid in decision making (2) • Adjusting parameters to test scenarios seems arbitrary unless you have some data on what the values are and how they change under the different scenarios • It seemed that just about every change we simulated seemed to degrade the resource in some way