Modeling Complex Interactions of Overlapping River and Road Networks in a Changing Landscape Programmatic overview Structure and Hypothesis Preliminary findings

NSF Emphasis/Problem Integrate different disciplines –DNA, Plate tectonics, Mass extinction's Promote advances in modern technology –Data acquisition and information Remote sensing, DNA…. –Information management Public access to data, monitoring 5 year rule Public relevance –Pure vs applied science –Education

NSF’s Solution RFP’s for Integrated Research Proposals Focused multidisciplinary research teams Environmental Molecular Science Institutes –Chemists & environmental scientists & industry Biocomplexity –Beyond biodiversity; interactions –Complex biological interactions over range of spatial and temporal scales

Successful Biocomplexity proposals must Address the inherent complexity and highly coupled nature of relevant natural and human systems as well as their interactions Describe plans for the work of interdisciplinary teams from the natural, social, mathematical sciences, engineering, and education –Whose coordinated work will enhance theoretical understanding

Projects must include Quantitative approaches or advanced conceptual models Specific plans for education –Graduate students, –Road seminars, multi-campus seminars –K-12 education program

Evaluation Criteria Strength of the collaborations planned and degree of interdisciplinary Effectiveness of the group organization and management plan Value to education in these topical areas Strength of the dissemination plans Extent, effectiveness, and long-term potential of collaborations with industries, national laboratories, and comparable research centers abroad, when appropriate.

NSF BIOCOMPLEXITY IN THE ENVIRONMENT FY 2003 SPECIAL COMPETITION DYNAMICS OF COUPLED NATURAL AND HUMAN SYSTEMS LARGE RESEARCH PROPOSALS 249 Biocomplexity proposals 74 Coupled Natural & Human systems –6 funded (8%) 12PI’s, 5 Universities –1.9M$, 4 yrs, 40K/yr = 8 plus grad students….

Evaluation Criteria Strength of the collaborations planned and degree of interdisciplinary; Different people, universities; UPENN, CSU, USU, UGA, UPR, Strength of the dissemination plans –High school teachers, workshop Extent, effectiveness, and long-term potential of collaborations with industries, national laboratories..USFS, Venezuela,

Modeling Complex Interactions of Overlapping River and Road Networks in a Changing Landscape Dynamics of coupled natural and human systems Three overlapping networks rivers, roads, aquatic food webs Intersect at river/road crossings (nodes) Structure of network are interdependent

Island of many rivers

Island of many roads All roads

PR

Three study watershed Develop models in 2 Test in third Goal= Develop models than can predict what happens if a road is built at a specific location Geomorphic changes Recreation changes Biotic changes

Nodes Order of road Order of stream Road Highway One-lane Trail Sampling at, above & below nodes Aquatic, Recreation, Geomorphologic Sample area issue Sample Design

Climate Topo Dem Landuse UPR Urban Centers Swarm P. Box Stream Network Ramirez, Scatena Road Network Tomlin, Laituri Stream Habitat Scatena Crowl Visitor Loomis UPR Aquatics Crowl Covich

Elevation Climate, geology, landownership

Previous Research and basis for hypothesizes Landuse and channel morphology –10% urbanization rule... Recreation use = f(travel time) –explains < 50% regional variance Roads are corridor for non-natives –nodes have higher biodiversity Habitat and channel morphology

Integrated models better than individual disciplinary models Food web = stream habitat, visitors Visitors = food web, habitat, roads Roads = morphology, population centers –Landuse history…) Stream morphology = geology, hydrology, road and landuse

Recreation/human behavior models Amount and type of human visitation = (road type, travel time,channel morphology) Amount #/weekend, #/year Type; fishing, swimming, boating… Method Visitor use surveys, Multiple regression models, with new measures of habitat

Food web component absolute and relative abundance of shrimp H1: In headwater streams social factors (visitation, harvests) are better predictors of food web structure than habitat H2: Lower elevation streams, physical factors are better predictors… Method Sampling at study nodes.. Developing habitat abundance relationships

Roads & Nodes have higher biodiversity and % non-natives More bamboo more shrimp Olander, Scatena, Silver, 98 Roads are dry streams

River/road node habitat hypotheses H1: At higher elevation, hydraulics & geomorphology control channel and habitat development; roads are less important H2: Lower elevation, alluvial channels have greater diversity of habitat & roads greater influence Method Systematic sampling above, below roads, Bridge scour models, energy models..

Habitat-geomorphic features Relation between habitat structure, geomorphic controls, & energy flows –Habitat structure = velocity, depth, complexity –Geomorphic controls = bedrock, structure –Energy flows; H20, slope, elevation Method Channel surveys, hydraulic analysis (Pike) Energy DEM modeling (Ramirez)

Scatena and Johnson, 2001

Shrimp Biomass vs Pool depth Two headwater streams Scatena and Johnson, 2001

Blanco and Scatena, in review

Main channel of bridge 1000’s of migrating snails

1 m 3 cm 1 cm

Side Channel

Bridge divides flow Side reach Faster velocities Greater predation fewer but larger snails Mail channel small snail migration

Nodes Order of road Order of stream Highway Sampling at, above & below nodes Aquatic, Recreation, Geomorphologic Sample area issue What are Andy and Pete doing in January?

“Energy” vs % boulders

Future… Evolution of bedrock channels –Channel equilibrium ??? –High magnitude storms effects Landscape scale habitat distribution –Drainage area, stream power, energy expenditure, geomorphology… ENVS seminar on roads…