Sci-TEK Integrating Traditional Ecological Knowledge and

Sci-TEK
Integrating Traditional Ecological Knowledge and
Geospatial Data for Mapping Local Priorities

Center for Hazards Assessment, Response and Technology (UNO-CHART)
Pontchartrain Institute for Environmental Science (PIES), University of New Orleans
Coastal Protection and Restoration Authority (CPRA), State of Louisiana
SciTEK Team

UNO
 Matt Bethel
 Shirley Laska
 Michelle Esposito
 Corey Miller (now CRCL)
 Lynn Brien (now KSU)
 Kristina Peterson
CPRA
 Honora Buras
 Carol Parsons Richards
Grand Bayou Community
 Rosina Philippe
&
Barataria TEK Experts
Additional Agency
Participants
Presentation Outline

 What is TEK, SciTEK?
 Purpose & Goals of the Project
 What we did
 How the data were used
 How else can the data be used
 What was learned in the process - benefits
 Use of the technique to address other issues
What is Traditional Ecological
Knowledge (TEK)?

“ TEK is a sophisticated knowledge possessed by a group or individual about an
environment as a result of having lived in and observed an environment for
generations. It is both evolving and current, and incorporates an historical,
cultural and spiritual perspective of locals’ existence in that environment.”
Native Women’s Association of Canada
What is SciTEK?

 TEK is gathered from local experts and transcribed.
 TEK transcripts are converted to a coded, spatially
referenced dataset.
 TEK dataset analyzed for issues, factors, priorities,
etc.
 Scientific datasets are chosen or created that reflect
the physical and biological characteristics and social
dynamics described by local experts.
 Combined datasets are used to describe the TEK
analysis in a mapped format.
CPRA’s Impetus for Sci-TEK:
Grand Bayou Project

“I hope that the LCA S&T office
will support research that
focuses on local ecological
knowledge and that such
knowledge can inform scientific
discourse and lead to improved
project planning”
* Page 4, Recommendation by LCA
S&T Program Board Member, Dr.
Conner Bailey
Purpose & Goal of Project

 To expand, test and refine a technique that
incorporates TEK into the restoration tool box.
 Determine resource users’ priorities, concerns, and
their thinking about restoration project techniques
and locations.
 Build relationships between scientists and local
experts with reciprocal knowledge transfer.
 Incorporate lessons learned into the SciTEK
methodology for use in restoration decision-making.
Benefits of Incorporating TEK into
Restoration Decision Making

 Contributes new knowledge from years of on-the-ground
observations.
 Gains support for the projects from local experts and their
communities.
 Reciprocal knowledge transfer (science/TEK).
 Augment the current planning and implementation
process.
Principles of
Community Engagement
 Facilitate collaborative
partnerships in research.

 Respect & build on local
knowledge.
 Openness and honesty.
 Small group processes.
 Promotes a co-learning and
empowering process.
 Consideration of time and cost.
The Study Area

Study Area –
Why Barataria Basin?

Expands on previous Grand Bayou study.
Multiple projects of various types.
Multiple issues and potential conflicts.
Diverse user groups.
History of adapting to changes in resources.
Cultural ties to resource use which provided a good
TEK base.
 Future resource changes and potential tradeoffs.
 Accessible and feasible for fieldwork.

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Determining the Restoration Issues

 Attending public meetings.
 Doing literature and media review.
 Meeting with local officials.
 Meeting with agency practitioners.
 Preliminary conceptualizing of primary issues from these
data before resource user engagement.
Emergent Issues

Barrier
Islands
Resource
Use
Pipeline
Canals
Shoreline
Protection
Ridges
InitialPlanning
4
Diversion
Marsh Creation
Issues
Diversion Operations
Fisheries
Vegetation
Identifying TEK Experts

Verifying and Selecting Experts





Asked initially.
Compiled list.
Identified repeated names.
Asked other community members to review shorter
list.
Location
# of Names
Referred
Names Referred
more than once
Total
Referrals
Lafitte
65
19
163
Plaquemines
85
21
246
150
40
409
Total
Verifying and Selecting Experts

Knowledge Areas of Experts
Yes
No
Shrimping
9
4
Oysters Farming
6
7
Crabbing
3
10
Charter Fishing
3
10
11
2
Hunting (duck, deer, etc.)
9
4
Alligator Hunting
3
10
Trapping
3
10
Navigation
2
11
Recreational Fishing
TEK Data Collection Trips

 Local experts showed us locations that
illustrated their concerns and visions of
restoration.
 Equipment used: Voice recorder,
Trimble (GPS), camera, etc.
 Maps were used as a ‘common
language’ facilitating conversations.
 Salinity meter to compare TEK to
scientific measurement.
Trip Area Coverage

Preliminary TEK Data Analysis

Density map of TEK sites of interest
Collecting Vegetation Data

• Vegetation was frequent topic of discussion
by participating TEK experts.
• Used to help to represent the related TEK in
the mapping and analysis process.
• Scientific data collection enabled knowledge
transfer between participating scientists and
TEK experts.
• Expand and build on previous work related to
Pointe a la Hache Siphon.
Interim Product: West Pointe a la Hache
Turbidity Map and Vegetative Indices
West Pointe a la Hache Siphons

Turbidity maps produced with turbidity
frequency data products from U.S. Army
Corps of Engineers
Mapping relative siphon influence to
marsh areas in combination with
vegetation indices
Interim Product: Davis Pond
Turbidity Map and Vegetative Indices
Davis Pond Freshwater Diversion

Turbidity maps
produced with
turbidity frequency
data products from
U.S. Army Corps of
Engineers
Mapping relative diversion influence to marsh areas in
combination with vegetation indices
Bringing Agency Personnel
Out with TEK Experts

 Reciprocal knowledge transfer done best in person.
 Relationship building for future engagement.
 Refine & demonstrate the collaboration technique
with numerous agency staff.
Field Observations Spark
Discussions

Data Processing (TEK)

 All trip recordings were transcribed.
 Pictures and coordinates were used to contextualize the transcripts.
 Coding was performed in Nvivo 9 transcription software.
 Concepts were divided into 36 categories.
 Further coding was done within the prominent categories.
 Analysis to identify the processes and principles important to harvesters
as they consider how to achieve successful restoration.
 Process converts transcripts of TEK into data that can be analyzed for
mapping.
Validating TEK Analysis:
Land-Based Meetings

 Used maps to facilitate conversation and allowed
experts to draw their visions of restoration.
 Identified areas of consensus among experts.
 Verified priority locations and uncovered the logic.
Restoration Priority Areas Identified at
Land-Based Meetings

Conversion of Coded Data

 Coded transcripts were converted into the
classification parameters (location factors) for each
map layer.
 Each location factor was weighted according to
number of mentions and number of experts who mentioned
it.
 These composite measures were made to determine
which location factors were of the highest priority to
the local experts.
Spatial/Scientific Data to Represent
TEK Priority Conditions

 Through TEK process, ecosystem characteristics important to
users were identified.
 Subsequently, existing or able to be collected spatial/scientific
data of same characteristics were identified:
Land loss
Historical marsh vegetation type
Soil type
Vegetation
Turbidity
Marsh fragmentation
Current and planned coastal protection and restoration project
boundary files
 Remotely sensed image data
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Sci-TEK Spatial Multi-Criteria Decision Analysis
(SMCDA) GIS Mapping Procedure
Complimentary
Scientific/Spatial
Input Data Layers

Parameters for input datasets
defined by TEK
Standardized weighting
based on TEK and merging
datasets
TEK related to
location factors
for emergent
focus issues
SMCDA Model Map indicating
TEK-based priority areas for each
Sci-TEK focus issue
Building a Logic Model

 The following series of slides will explain a few
layers of the marsh creation SMCDA.
 We will show you how quotes from experts become
location factors, are paired with scientific and social
data and are then mapped.
 We will then show you how those layers come
together to make a model that prioritizes areas seen
by the TEK experts as best suited for marsh creation.
Marsh Creation Logic Model
Factor 1: Marsh Fragmentation

 Location Factor One: Areas which have experienced
land loss, but still have remnant marsh to build upon
are preferred for restoration.
 Quote: “Now some of your sections of land that are
still there…try to build land there. But, stuff like this
(pointing to mostly open water) don’t waste your
money on that little bit. This one is gone, that is gone
over there is gone.”
 Scientific Data Sets: Land loss data set from USGS,
marsh fragmentation.
Marsh Creation Location
Factor 1: Remnant Marsh

Marsh Creation Logic Model
Factor 2: Barrier Islands

 Location Factor Two: Key Barrier Islands(speed bumps) that
have experienced land loss, but are important to provide
needed protection.
 Quote: “You have to start on the outside and work your way
in. You have to because if you are working your behind off
on the inside, and your land is still eating away out there you
can’t accomplish anything. Start out there, start protecting
that, then you’re doing something. You can get that protected
and work your way in.”
 Scientific Data Sets: Land/ Water data set from LANDSAT,
Land loss from USGS, Marsh fragmentation.
Marsh Creation Location
Factor 2: Barrier Islands

Marsh Creation Logic Model
Factor 4: Near Other Restoration

 Location Factor Four: Areas near current or planned
restoration projects are more preferable for
restoration.
 Quote: “See if they would build this back up, and
with the siphon, all of this would be ready to hold
that fresh water…I think if they put the siphon back
on and restore the ridge so they can keep some of
that fresh water, and plant vegetation, those are
things you could do to bring it back.
 Scientific Data Sets: current and planned project
layer from CPRA.
Marsh Creation Location Factor 4:
Near Current or Planned Restoration

Summary of TEK Location Factors for the
Marsh Creation/Island Restoration Issue

Location preferences (by weight):
1) Areas where land loss has occurred, but remnant marsh still exists.
2) Barrier islands where land loss has occurred that provide needed
protection.
3) Areas near the barrier islands that would help close the flow of too
much saltwater into the interior of the Basin.
4) Areas near current/planned restoration projects.
5) Poor marsh condition (related to fragmentation).
6) Poor marsh condition related to vegetation condition (related to
greenness and biomass).
7) Restoration near ridges to provide enhanced benefit.
8) Avoid active oyster reef areas.
9) Areas near levee protection projects.
Composite Map: Marsh Creation/Island
Restoration Model of TEK Priorities

Composite Map: Marsh Creation/Island
Restoration Model of TEK Priorities

Composite Map: Marsh Creation Model of TEK
Priorities with Master Plan Projects Overlaid

Shoreline Protection Model
Map of TEK-Based Priorities

Freshwater Introduction Model
Map of TEK-Based Priorities

Uses for Sci-TEK Results

 Refine details of large conceptual footprints of projects in
Barataria Basin.
 Sequencing/prioritizing project implementation.
 Identify additional restoration projects.
 Illustrate areas of consensus and potential conflicts.
 Inform and refine decision criteria that are intended to
represent local priorities and knowledge.
 To engage local experts and residents at every phase of
restoration and protection project implementation.
 The method is being refined for application to SLR issue
Benefits and Products

 Facilitate discussions regarding Pointe a la Hache Siphon
modification within Grand Bayou Community.
 Collaboration between agencies and TEK experts on CWPPRA
project proposal planning.
 Answered local questions about projects.
 Learned about unintended hazards caused by shoreline protection
project construction.
 Technique presented at numerous conferences and in articles,
videos, and newsreels.
 Created products that can be used in other applications: turbidity
maps, vegetation data, additional non-spatial TEK, how-to guide.
 Strong, positive feedback by all project participants and audiences.
Adaptive Management

 The action that links scientists and TEK experts.
Thank You

Questions?
Your thoughts?
Cost Factors of Implementing
SciTEK

 Social Scientist’s hours for identifying expert
harvesters, facilitating and coding the observations
 GIS/Remote Sensing Analyst’s hours
 Number of TEK Experts needed - time and costs
 Scientific/Spatial dataset requirements
 New scientific data acquisition
 Remote sensing image acquisition
 Transcription
 Travel and Equipment Costs
Factors Affecting Time Requirements

 Size and scope of project.
 Size of study area.
 Number of communities impacted.
 Number and variety of TEK experts required.
 Scientific field data collection required.
 Availability of relevant scientific/spatial data
sources.