Road and Stream Network Connectivity
in Northeastern Puerto Rico
Kirk Sherrill
Colorado State University
FRWS Department
April 14th, 2006
Overview

I. Introduction

II. Objectives

III. Study Area / Sample Sites

IV. 1st Chapter - Physical Road and
Stream Network Connectivity

V. 2nd Chapter - Connectivity Potential
Road and Stream Networks (RSNCP)
I. Introduction

NSF Biocomplexity Project
– Biocomplexity – “Is a multidisciplinary
approach to understanding our world’s
Environment” Rita Colwell 1999
– Incorporate complexity throughout the
research project
– Investigating River and Road Network
Interactions, across time, and human use,
biota, and geomorphology themes in NE PR
– http://biocomplexity.warnercnr.colostate.edu.

Roads:
I. Introduction
I. Throughout most terrestrial landscapes
- 5,000,000 mile road network in North America
- 250,000,000 vehicles in North America
II. Numerous Environmental Effects
-
Pollution (ie. Road dust, Ambient Noise, Salt, Nitrogen, CO2)
-
Habitat Loss and Degradation (ie. Fragmentation, increased
human access, noxious species, altered disturbance regimes
etc.)
-
Altered Water Processes
I. Road Effects on Water

Roads act as increased sources for water
and sediment movement

Roads act as barriers to water, sediment
and aquatic species movement

Altered stream and sediment flow are the
most important road effects regarding Road
and Stream network connectivity (R/S
Connectivity) (Forman and Alexander 1998, Lugo and Gucinski 2000,
Montgomery 1994, Walker et al. 1996, and Wemple et al. 1996).
R/S Connectivity

Road and Stream Network Connectivity
(R/S Connectivity)
– Degree by which Road and Stream interactions
affect ecosystem processes:

Two Pathways of R/S Connectivity:
– 1. Physical R/S Connectivity - direct contact
between roads and streams
– 2. R/S Connectivity from alteration to
processes due to road presence in the
vicinity of the stream network
II. Objectives

Study two pathways of R/S Connectivity in
NE Puerto Rico
1. Measuring Physical R/S Connectivity
2. Evaluating Road and Stream Network
Connectivity Potential (RSNCP)
III. Study area
Puerto Rico
Puerto Rico

Rio Espiritu (~ 23,500 acres)

Rio Mameyes (~11,000 acres)

Urban and Agriculture in the North

Forest Land Cover in the South

Caribbean National Forest

Sea Level to 3,500 feet
III. Sample Sites

Hierarchical Road and Stream Size Matrix
– 25 River Road Crossing (RRC) Study Sites
– All possible road and stream size combinations
Sample Matrix
Road Size
Stream Size
Primary (P) Secondary (S)
Tertiary (T)
Class 4 (4)
Trail (Tr)
Large (L)
LP
LS
LT
L4
LTr
Medium (M)
MP
MS
MT
M4
MTr
Small (S)
SP
SS
ST
S4
STr
VI. Physical Road and Stream Network
Connectivity: Northeastern Puerto Rico
Kirk Sherrill, A. Pike, M. Laituri, F. Scatena, K. Hein, F. Blanco
Chapter to be submitted to the Journal:
Forest Ecology and Management
Objective

Measure Physical R/S Connectivity by:
I. Performing a localized Bridge Scour Survey
II. Evaluate Steam Network Connectivity For Fish

Determine utility of using Geographic
Information System (GIS) derived data to:
– (I) Model bridge scour
– (II) Identifying road crossings which are fish
barriers
I. Bridge Scour Survey (Johnson et al. 1999)

Amount of or potential
for alteration to
Sediment and Stream
Flow in vicinity of RRC
–
–
–
–
11 Indicator Variables
Rated (1-12)
Weighted
Summed
Final Scour Rating
Indicator Variable
Weight
1. Bank Soil Texture and Coherence *
0.6
2. Average Bank Slope Angle *
0.6
3. Vegetative Bank Protection *
0.8
4. Bank Cutting !
0.7
5. Mass Wasting or Bank Failure *
0.8
6. Amount of Bar Development *
0.6
7. Debris Jam Potential *
0.2
8. Obstructions, Presence of Flow Deflection *
0.2
9. Channel Bed Material φ
1.0
10. Flow Angle of approach to road crossing
structure !
0.5
11. Presence of Blow hole or Scour pool φ
0.8
φ Variables Added to Survey
* Weights from Johnson et al 1999
! Weights different from Johnson et al 1999
Relative Categorical Scour Ratings
Sites
Number of
Sites
Scour Scores
Stable
6
9.50 - 19.75
Moderate
8
19.76 - 30.0
Poor
5
30.01 - 40.25
Unstable
5
40.26 - 50.50
Scour Scores
 Evaluated
Scour Scores relative to:
– Environmental Variables (Land Cover,
Geology, Elevation, Stream Power)
– Road Characteristics (Stream Size, Road
Size, Crossing Type, % Stream
Constriction)
 Model
Scour Scores
I. Scour Rating By Environmental Variables

Two Scales of Study:
1) 250 m circular buffer
2) Upstream contributing area

Variables
–
–
–
–

Land Cover
Underlying Geology
Average Elevation
Average Unit Stream Power
Characteristics of Stable Scour Site?
I. Scour Rating By Stream Power
 Stable
Scour Sites:
Average Unit Stream Power
By Scour Rating
700
 Average Unit Stream
Power: Not Significant
(ANOVA α=0.05, P-Value >.01)
Average unit Stream
Power (W/m)
Stable
600
Moderate
500
Poor
393
400
Unstable
300
200
190
123
100
40
0
Stream Power Data Collected by
(Pike and Scatena in press)
Scour Rating
I. Scour Rating By Average Elevation
Average Elevations Buffer Scale
By Scour Rating
 Significantly Higher
Elevations (ANOVA α=0.05,
500
Average Elevation (m)
Stable Scour Sites:
450
432 *
Stable
Moderate
400
350
Poor
300
Unstable
250
163
200
132
150
105
100
50
0
P-Value <.01)
Scour Rating
Average Elevations Upstream Scale
By Scour Rating
Stable
700
Average Elevation (m)

638 *
Moderate
600
500
Poor
469
Unstable
400
300
278
200
100
0
Scour Rating
337
I. Scour By Land Cover

Land Cover Proportions
(Ramos Gonzalez 2001)
– Forest
– Agriculture
– Urban
I. Scour By Underlying Geology

Geology Proportions
(USGS)
– Extrusive:
Solidified above surface
– Intrusive
Solidified below surface
– Alluvial
Deposited by water
I. Scour By Land Cover & Geology

Stable Scour Sites:
 Buffer Scale 6/6 Stable
Sites


100% Forested Land Cover
100% Extrusive Geology
 Upstream Scale 5/6 Stable
Sites

100% Forested Land Cover
 Upstream Scale 4/6 Stable
Sites

100% Extrusive Geology
I. Scour Rating By Road Characteristics
Stream Size
 Road Size
 Percent Stream Constriction

No Significant Trend with Scour Scores
I. Scour Rating By Crossing Type
Significant
Bridge Scour Score By Crossing Type
(ANOVA
α=0.05, P-Value <.01)
– Bridge Crossings Lower
Scour Scores (more
stable) relative to
Culverts crossings
50
45
Bridge Scour Score

40
Bridge
Culvert
35
30
25
20
15
10
5
0
Crossing Type
I. Modeling Bridge Scour

Best Linear Regression Model
–
–
–
–
Alluvial Geology Buffer scale (+)
Crossing Type (1=Culvert, 2= Bridge) (-)
Stream Size (1=Large, 2=Medium, 3=small) (-)
R2 = 0.65
II. Stream Network Connectivity for Adult Fish

Method
1. GIS Stream Slope Analysis (10m
resolution)
2. Field Data noting potential barrier
crossings

6 Crossing possible barriers
3. Verify Accuracy of Field Data and Stream
Slope Method

Adult Fish Species Richness data (Hein et al in
progress)
II. Stream Network Connectivity

Findings
 GIS Stream Slope correctly
identified 21/24 RRC relative
to 1st natural fish barrier
 2/6 Crossing found to be
acting as Fish Barriers or
Partial Fish barriers
- Both Fish Barriers were
Culverts
Conclusions

GIS can be used to:
– Model Scour
– Locate Crossing location relative to 1st Fish
Barrier

GIS Limitations:
– Important variables require field data
collection
– Extensive Biological Field Data is needed to
Identify Barrier Crossings
Conclusions

Low Physical R/S Connectivity Sites:
– High Proportions Forest and Extrusive
Geology
– Higher Elevations
– Most Importantly Bridge Crossing
 Lower Scour Scores and less likely to
be fish barriers
– Prieta and Bisley sites (Both 100% Forest
and 100% Extrusive, at high elevation but
Culvert Crossings
Poor Scour Scores
Connectivity Potential Between Road and
Stream Networks
Kirk Sherrill, M. Laituri, E. Helmer, K. Hein, F. Blanco, A. Pike
Chapter to be submitted to the Journal:
Ecological Complexity
Objective

Evaluate the Road and Stream Network
Connectivity Potential (RSNCP)
–
R/S Connectivity - from alteration of
processes by roads within the vicinity of the
stream network:
 Function of:
1.
2.
Road Location
Intervening Environmental Variables
Close Road Proximity =
25 M
R/S Connectivity Potential
Distant Road Proximity =
25 M
R/S Connectivity Potential
Rise
Distant Road Proximity and Intact Forest Buffer =
Run
25 M
R/S Connectivity Potential
Rise
Distant Road Proximity, But Sparse Vegetation and Highly Sloped
= R/S Connectivity Potential due to Vicinity
Run
25 M
Objective

Test several variables influencing R/S
Connectivity as it relates to:
–

alteration of sediment and water flow
Underlying Hypothesis:
–
Multi-scale RSNCP indices derived with GIS
can predict important stream biota and
geomorphology response variables
Methods
I. Developed a Multi-Scale RSNCP Database
II. Variable Reduction Process:
– Correlation Analysis
– Variance Inflation Factor (VIF)
– Principal Component Analysis (PCA)
Significant RSNCP Variables (per scale)
III. Linear Regression modeling
– 3 Biota
 Fish,
(per scale)
Decapod, Total Richness
– 4 Geomorphology (Stream Habitat)
 ACMaxDepth, ACWidth, LogD50, Pool Volume
IV. Test models for Autocorrelation
Four Scales of Study
I. 250 Meter Circular Buffer (Buf)
II. Upstream Contributing Area
(Ups)
III. 200 Meter Upstream Stream
Buffer (Stb)
IV. Combination off all scales
(All)
RSNCP Database

Multi-scale Landscape Indices
– Evaluating:
4
Road Characteristics
–
–
–
–
9
(Buf, Ups, & Stb)
River Road Crossings
Road Length in River Buffer (200m)
Road Length by Road Class
Road Length by Hillslope Position
Environmental Variables
–
–
–
–
–
–
–
–
–
(Buf, Ups, & Stb)
Land Cover Proportions (Forest, Agriculture, Urban)
Geology Proportions (Extrusive, Intrusive, Alluvial)
Ownership Proportions (Public, Private)
Average Slope
Mean Elevation
Aspect Proportions
Average Precipitation
Average Riparian Vegetation Patch Size
Riparian Vegetation Proportions (For, Agr, Urb)
Response Variables

Stream Biota Richness Variables (Hein et al in progress)
Fish (Fish and Eel)
Decapod (Shrimp and Crab)
Total Richness (Fish and Decapod)

R/S Connectivity negatively effect Biota
Geomorphology Variables (Pike and Scatena in Press)
Active Channel Max Depth (ACMaxDepth)
Active Channel Width (ACWidth)
Pool Volume
Log Median Active Channel Grain Size (LogD50)
– Expected Relation with R/S Connectivity unknown
Modeling

Using the Significant Explanatory RSNCP
Variables at each scale the response
variables were modeled:
– Without X&Y site coordinates (4 times)
– With X&Y site coordinates (4 times)

Model Selection Procedures:
– Efromyson Stepwise
– Leaps and Bounds
– Best Models maximized R2 and minimized AIC
Biota Models

Best Models across scales:
– R2 from 0.51 - 0.74
– 5/6 Best Models All Scale
Biota Models Discussion

Observed Trends:
1. Extrusive
2. Intrusive
+
-
Biota
=
RSNCP
Biota
=
RSNCP
– Negative Public Ownership relation (Decapod & Total)
Product of Human Usage Harvesting of Biota on Public
lands, not from R/S Connectivity

R2 ranged from ~.50 - .70:
– Variables, Processes and Scales not measured or related to
R/S Connectivity may be unexplained and influencing the
biota response variables
Geomorphology Models

Best Models across scales:
– R2 from 0.22 - 0.86
– 8/8 Best models All scale
Geomorphology Model Discussion

Observed Trends:
– Negative Relation with number of River Road
Crossings
– Negative Relation with Extrusive Geology
– Positive Relation with Public Ownership

Grain Size and Channel Width R2 range 0.72 – 0.86

Pool Vol. and Channel Depth R2 range 0.22 – 0.52
Modeling With X&Y & Autocorrelation



Resulted in Better Model Fit
Incorporated Spatial Trend in the
explanatory and response variables
Strong North-South topographic gradient
effecting:
– Land Cover, Precipitation, Fish Barriers etc
– Y (Latitude) responsible for majority of
increased model fit

Models tested for Autocorrelation using
Lagrange and Moran’s I : Not significant
Conclusions

All Models had at least one road characteristic
variable

Extrusive Geology was the most prevalent
environmental variable: + Biota, and - Geomorphology

Including X&Y increased Model Fit

13 of 14 Best Models used variables with multiple
scales (All scale)

Multi-Scale GIS RSNCP indices can be used to
predict important biota and geomorphology
response variables
Acknowledgements

Funded by NSF River Road Network Biocomplexity Project

Melinda Laituri, Advisor, Dept. of Forest, Rangeland and Watershed
Stewardship, Colorado State University, Fort Collins, CO.

Eileen Helmer, Committee Member, U.S. Forest Service, International
Institute of Tropical Forestry

Jorge Ramirez, Committee Member, Dept. of Civil Engineering,
Colorado State University

Andy Pike and Fred Scatena, Dept. of Earth and Environmental
Sciences, University of Pennsylvania – Geomorphology Data

Katie Hein and Felipe Blanco, Dept. of Aquatic, Watershed and Earth
Resources, Utah State University – Stream Biota Data

Erin Peterson and Pete Barry, Office Mates

NESB - A Wingers 1st Floor!, Fellow Grad Students
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