1. Emergence of Landscape Ecology Equilibrium View Constant species composition Disturbance & succession = subordinate factors Ecosystems self-contained Internal dynamics shape trajectory No need to look outside boundaries to understand ecosystem dynamics Structure Function ? ? ? ?

2. Emergence of Landscape Ecology Dynamic View Disturbance & ecosystem response = key factors Disturbance counter equilibrium Ecosystems NOT self- contained Multiple scales of processes, outside & inside Essential to examine spatial & temporal context Structure Function

3. Scale What’s the big deal? Seminal pubs –Allen & Starr (1982) – Hierarchy: perspectives for ecological complexity –Delcourt et al. (1983) – Quaternary Science Review 1:153-175 –O’Neill et al. (1986) – A hierarchical concept of ecosystems

4. Ecological Scaling: Scale & Pattern Acts in the “ecological theatre (Hutchinson 1965) are played out across various scales of space & time To understand these dramas, one must select the appropriate scale Temporal Scale Spatial Scale Fine Short Coarse Long Recruitment Treefalls Windthrow Secondary Succession Species Migrations Speciation Extinction Fire

5. Ecological Scaling: Scale & Pattern Different patterns emerge, depending on the scale of investigation American Redstart Least Flycatcher American Redstart Least Flycatcher Local Scale (4 ha plots) Regional Scale (thousands of ha)

6. Ecological Scaling: Components of Scale Grain: minimum resolution of the data –Cell size (raster data) –Min. polygon size (vector data) Extent: scope or domain of the data –Size of landscape or study area

7. Ecological Scale Scale characterized by: –grain: smallest spatial resolution of data e.g., grid cell size, pixel size, quadrat size (resolution) Fine Coarse

8. Ecological Scale Scale characterized by: –extent: size of overall study area (scope or domain of the data) Small Large

9. Ecological Scaling: Components of Scale Minimum Patch Size: min. size considered > resolution of data (defined by grain) –Size of landscape or study area

10. Ecological Scaling: Definitions Ecological scale & cartographic scale are exactly opposite –Ecological scale = size (extent) of landscape –Cartographic scale = ratio of map to real distance

11. Scale in Ecology & Geography ecological vs. cartographic scale EcologyGeography Small (Fine) Fine resolution Small Extent Coarse resolution Large Extent Large (Broad) Coarse resolution Large extent Fine resolution Small extent

12. Scale in Ecology & Geography ecological vs. cartographic scale –e.g., map scale 1:24,000 vs. 1:3,000 fine vs. coarse large vs. small extent

13. 1:24,000 1:200,000

14. Ecological Scaling: Components of Scale Grain and extent are correlated Information content often correlated with grain Grain and extent set lower and upper limits of resolution in the data, respectively.

15. Ecological Scaling: Components of Scale From an organism- centered perspective, grain and extent may be defined as the degree of acuity of a stationary organism with respect to short- and long-range perceptual ability

16. Ecological Scaling: Components of Scale Grain = finest component of environment that can be differentiated up close Extent = range at which a relevant object can be distinguished from a fixed vantage point Fine Coarse Scale Extent Grain

17. Ecological Scaling: Components of Scale From an anthropocentric perspective, grain and extent may be defined on the basis of management objectives Grain = finest unit of mgt (e.g., stand) Extent = total area under management (e.g., forest)

18. Ecological Scaling: Components of Scale In practice, grain and extent often dictated by scale of available spatial data (e.g., imagery), logistics, or technical capabilities

19. Ecological Scaling: Components of Scale Critical that grain and extent be defined for a study and represent ecological phenomenon or organism studied. Otherwise, patterns detected have little meaning and/or conclusions could be wrong

21. Scale: Jargon scale vs. level of organization Space - Time Individual Population Community

25. Ecological Scaling: Implications of Scale As one changes scale, statistical relationships may change: –Magnitude or sign of correlations –Importance of variables –Variance relationships

28. Implications of Changes in Scale Processes and/or patterns may change Hierarchy theory = structural understanding of scale-dependent phenomena Example Abundance of forest insects sampled at different distance Intervals in leaf litter,

29. Implications of Changes in Scale Insects sampled at 10-m intervals for 100 m

30. Implications of Changes in Scale Insects sampled at 2000-m intervals for 20,000 m

31. Identifying the “Right” Scale(s) No clear algorithm for defining Autocorrelation & Independence Life history correlates Dependent on objectives and organisms Multiscale analysis! e.g., Australian leadbeater’s possum

32. Multiscale Analysis Species-specific perception of landscape features : scale-dependent –e.g., mesopredators in Indiana Modeling species distributions in fragmented landscapes

33. Hierarchy Theory Lower levels provide mechanistic explanations Higher levels provide constraints

34. Scale & Hierarchy Theory Hierarchical structure of systems = helps us explain phenomena –Why? : next lower level –So What? : next higher level minimum 3 hierarchical levels needed

35. Constraints (significance) Level of Focus (level of interest) Components (explanation)

36. Constraints Why are long-tailed weasel populations declining in fragmented landscapes? Components Population Community Individual

37. Constraints Why are long-tailed weasel populations declining in fragmented landscapes? Small body size mobility Population Community Individual

38. Predators Competitors Prey dist’n Why are long-tailed weasel populations declining in fragmented landscapes? Components Population Community Individual

39. Scale & Hierarchy Theory Change scale: 1)influential variables might not change, but 2)shift in relative importance likely Example: Predicting rate of decomposition of plant matter Local scale = lignin content & environ. variability Global scale = temperature & precip.