1. Hawai’i’s fluvial systems: Using GIS to assess
current conditions and identify management
strategies in a changing climate
Ralph W. Tingley III1, Dana M. Infante1 , Richard A. MacKenzie2, Robert
Nishimoto3, James Parham4
1 Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI
2 Institute of Pacific Island Forestry, Pacific Southwest Research Station, Hilo, HI
3 Division of Aquatic Resources, Honolulu, HI
4 Parham and Associates Environmental Consulting, Gallatin, TN

2. Degraded habitat and aquatic biodiversity
• Habitat degradation is the main cause of decline and
extinction of freshwater fishes (Helfman 2007)
– Contributed to 71% of all known fish extinctions
• Other organisms’ declines assumed related to habitat
– In US and Canada, 72% of mussel and 47% of crayfish
taxa imperiled (Abell et al. 2000, Taylor et al. 2000)
– 43% of stoneflies, 36% of amphibians, and 18% of
dragon/damselflies imperiled in U.S. (Stein et al. 2000)

3. www.fishhabitat.org
Mission: To protect, restore and enhance the
nation’s fish and aquatic communities through
partnerships that foster fish habitat conservation
Initial objectives (Partial list)
• Conduct a condition analysis of all fish habitats
• Identify priority fish habitats
Completed for conterminous U.S., Alaska and
Hawai’i in 2010
• Followed a landscape approach

4. The landscape approach
• Natural and
anthropogenic landscape
factors influence habitat Natural Human
factors land uses
• Habitat condition directly
influences stream biota
Habitat
• Landscape disturbances
are assumed to impact
stream biota
Stream
organisms

5. The landscape approach
• For conterminous U.S….
Natural Human
factors land uses
% Network Pasture
Habitat
Stream
organisms
% Network Urban Med
Esselman et al. 2011

6. Assessing the relative risk of degradation
• Catchment landscape factors are good predictors
of aquatic biology and physical habitat (Gergel et al. 2002;
Allan 2004)
Norris et al. (2001))
Danz et al. (2007))
• Useful for assessing large regions across which
site-specific data may be unavailable or
inconsistently collected
• Can compare relative risk of degradation across
stream reaches

7. Hawai’i 2010 initial assessment overview
• Created spatial framework at two scales, local and network
catchment
• Identified variables indicating major sources of disturbance
• Attributed and aggregated data local and network catchments
• Grouped variables and created sub-indices for specific
disturbance categories, relativized and summed into a
cumulative index (follows Danz et al. 2007)
– Urban land cover and population density
– Agriculture
– Barriers to fish movement (fragmentation)
– Point source disturbances
– Length of ditches
– Former plantations
– 303D listed streams

8. Spatial framework and database development
• National Hydrography Dataset (1:24k NHD)
– Stream arcs only
– Canal/ditches and pipelines not included
– New breaks implemented based on
elevation (Parham and Lapp 2006)

9. Spatial framework and database development
• National Hydrography Dataset (1:24k NHD)
– Local and network
catchments delineated
using ArcHydro
– Represents an improvement
to any existing spatial
framework
– Each reach had landscape
data summarized at both
scales
– Data must be continuous Network
and available state-wide NHD 1:24K catchment
Reach Local
catchment

10. Disturbance variables:
Urban and point source inputs of toxics and other pollutants

11. Disturbance variables:
Former plantation land, current agriculture, stream fragmentation,
ditches, 303D listings
• At local and network scale, sub-index scores generated
using Principal Components Analysis (PCA) or additive
methods
• Disturbance scores at local and network catchments
summed to obtain relative condition score

15. Current applications
• Several agencies are using the spatial framework
to organize and attribute data.
• Scores are being incorporated into the Atlas of
Hawaiian Watersheds
• Condition assessment of coastal habitat and
reefs
©2008 Atlas of Hawaiian Watersheds & Their
Aquatic Resources.

16. What’s next for the assessment?
• Streams respond to
disturbances differently
Natural Human
due to differences in factors land uses
natural factors (Melles et al.
2011, Utz et al. 2011)
• Accounting for these Habitat
natural differences
allows for an increased
understanding of relative
condition
Stream
organisms

17. Classification of ecological potential:
Goal and objectives
• Develop an ecological classification of Hawaiian
stream reaches that characterizes their natural
potential for supporting native species
1. Identify biologically relevant landscape-scale
variables
2. Identify biologically relevant climate variables
3. Identify where changes in species composition
occur in relationship to climate and biological
variables

18. Step 1: Identify biologically relevant
landscape variables
• Presence/absence data
Identify initial
natural Compile
is available across
landscape biological data islands
variables
Summarize
local/network
• Only the most
catchments Presence/ influential landscape
absence data
variables will be
Use multivariate included in analysis
approach to
select variables
Output: Biologically
relevant landscape
variables

19. Natural landscape variables and datasets
Variable Description Scale/Res. Scale of summary Source
Drainage area Total area draining to stream reach 1:24k Upstream catchment HFHP modified NHD
Stream order Strahler stream order 1:24k Local reach HFHP modified NHD
Slope Precent slope 4 m grid All reach scales IFSAR elevation data-NOAA
Maximum slope drop Maximum drop in slope downstream of reach 4 m grid Downstream reach IFSAR elevation data-NOAA
Distance Inland Linear distance from marine input 1:24k Downstream reach HFHP modified NHD
Elevation zones Biologically significant breaks in elevation 1:24k Local reach Parham and Lapp 2006
CWRM data/IFSAR elevation data-
Groundwater delivery Mean groundwater delivery to reach Local/upstream catchment
NOAA; In development
*Warm groundwater input Area of catchment with warm groundwater input 10m grid Local/upstream catchment GeothermEx, Inc.
*Soil erodability Mean soil erodability within catchment variable Local/upstream catchment SSURGO
Dataset
*Major land resource areas Terrestrial based landscape descriptions 10m grid Local/upstream catchment USDA/NRCS/NHQ/RAD
*Geologic age Age of hawaiian islands soils/rock variable Local/upstream catchment USGS
*Geologic type Geolocial type of all Hawaiian islands variable Local/upstream catchment USGS
*Soil type Type of Hawaiian soils variable Local/upstream catchment SSURGO
*Datasets aquired through the Hawai'i statewide GIS program

20. Step 2a: Identify biologically relevant
climate variables: linking biology to flow
Identify initial
Compile Identify reaches
natural
biological data with
landscape
flow data
variables
Summarize
Use multivariate
local/network
approach to
catchments Presence/ select variables
absence data
Use multivariate Regional Biologically
approach to species relevant flow
select variables datasets variables
Output: Biologically
relevant landscape
variables

21. Atyoida bisulcata of the Hilo/Hamakua Coast
On-going research of: Richard MacKenzie, Ralph Tingley, Ayron
Strauch, Dana Infante, Greg Bruland, Patra Foulk, Therese Frauendorf
• Extreme precipitation gradient
• 9-15 sites
– Flow data
– Body condition
– Length
– Relative abundance
– Fecundity

22. Atyoida bisulcata of the Hilo/Hamakua Coast
• Variables that show declines
with decreasing annual flow:
– Length
– Body condition
– Relative abundance

23. Step 2b: Identify biologically relevant
climate variables: linking flow to climate
Identify initial Identify
Compile Identify reaches
natural
biological data with climate
landscape
flow data variables
variables
Summarize
Use multivariate Summarize
local/network
approach to local/network
catchments Presence/ select variables catchments
absence data
Use multivariate Regional Biologically
approach to Link climate to
species relevant flow
select variables flow variables
datasets variables
Output: Biologically Output: Biologically
relevant landscape relevant climate
variables variables

24. Climate variables
Variable Description Scale/Res. Scale of summary Source
*Solar radiation Estimated daily solar insulation contours 10m grid Local/network catchment Department of Planning
Mean annual precipitation Mean annual precipitation 250 m grid Local/network catchment 2011 Rainfall Atlas of Hawaii
Mean dry season precipitation April through October 250 m grid Local/network catchment 2011 Rainfall Atlas of Hawaii
Mean wet season precipitation November through March 250 m grid Local/network catchment 2011 Rainfall Atlas of Hawaii
Variance of monthly precipitation Entire water year 250 m grid Local/network catchment 2011 Rainfall Atlas of Hawaii
Variance of monthly precipitation
April though October 250 m grid Local/network catchment 2011 Rainfall Atlas of Hawaii
wet season
Variance of monthly precipitation
November through March 250 m grid Local/network catchment 2011 Rainfall Atlas of Hawaii
dry season
Mean annual air temperature Mean annual air temperature 10 m grid Local/network catchment PRISM data
Daily rainfall metrics Number of consecutive days with no rain
*Datasets aquired through the Hawai'i statewide GIS program
• Initial climate variables are being selected based
on availability and anticipated predictions

25. Step 3: Identify breaks in relevant
variables and classify streams
Biologically relevant Biologically relevant
landscape variables climate variables
Presence/ Regional
absence datasets
Establish breaks through:
1. CART analysis
2. Regional studies
Classification of
ecological
potential

26. Benefit and application of results
• Increased utility of the NFHAP current condition
assessment
– Reference of community structure at reach scale
• Contribution of a new scale of classification to the
Atlas of Hawaiian Watersheds
• Act as a baseline for understanding vulnerability to
changes in climate

27. Species specific vulnerability to climate
change: Atyoida bisulcata

28. Species specific vulnerability to climate
change: Atyoida bisulcata

29. Acknowledgements
• Dr. Christian Giardina, Dr. Gordon Smith, Dr. Ayron
Strauch, Dr. Greg Bruland
• Patra Foulk, Therese Frauendorf
• Arthur Cooper, Dan Wieferich, Jacqui Fenner, Ali David,
Dr. Yin-Phan Tsang
• USDA Forest Service
• Division of Aquatic Resources
• Hawai’i Fish Habitat Partnership
• University of Hawai’i at Manoa
• Kamehameha Schools
• Michigan Sate University
• USGS
• USFWS

30. Mahalo!