A presentation of the IFReDI-SciCap Case Study of their high tech and low tech integrated monitoring system for freshwater resources.

1. IFReDI-SciCap Case Study of an
integrated monitoring system
Inland Fisheries Research & Development Ins6tute (IFReDI)
Scien6fic Capacity Development Ini6a6ve (Sci-Cap)

2. troduc)on
alua6ng impacts on the fishery is a key component of monitoring the regions
ers. A complementary system of tools and approaches that are cost-effec6ve
d address these needs is proposed.
dicator development and choice of what to monitor should be based on the
swers to some key ques6ons, rather than on current data collec6on ac6vity
why are we doing this?" not "what data do we have?“
6ng by developing a monitoring strategy is a useful way of
ng this framework.
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3. Balancing Needs and Mi)ga)ng
Challenges
And a=empts to mi>gate the
challenges, including:
• Low quality
• Cost
• Time availability
• Technical challenges
• Accessibility
The Monitoring System uses
a >ered approach to
monitoring that aims to
balance many data needs
and requirements, including:
• Widespread
• Con6nuous
• Accurate
• High quality
• Consistent
• Targeted
• Cost-effec6ve

4. Where to start?
Before selec6ng what and how to monitor we considered:
• The purpose of the monitoring
eg, to assess the state of the environment, to assess management effec6veness, to iden6fy
change (absolute or magnitude)
• Whether there was an opportunity to use the same indicators and
tools to monitor mul>ple targets
• How we could op>mize the costs-benefits
• What our repor>ng needs were and who the audience(s) would be
• How the monitoring needed to feed into policy review and
management
TEGRATION
nsider the links between the different types of monitoring that needed
be undertaken (eg, state of the environment, policy/management
ec6veness, compliance with regula6on, trends).

5. An Integra>ve Approach
Using a integra>ve >ered monitoring system framework
allows you to balance needs and address challenges
The IFReDI-SciCap monitoring system integrates many
of the key components of monitoring systems that
make them efficient and effec>ve, for example:
Ci>zen science – fisheries dependent catch survey
(con6nuous and widespread, low-tech)
Noninvasive biomonitoring: tagging, eDNA etc. reduc
logis6c costs
Integrates verifica>ons of reliability and quality of da
through the selec6ve use of high-tech systems
Uses targeted and overlapping independent studies to
verify key informa6on for quality assurance!

6. ered Monitoring System
Hierarchical structure that balances data needs, logis>cs and costs:
Geographic coverage & con6nuous records with accuracy & depth of informa6on
Key Components:
• Standardiza6on
• Comparable
• Consistency
• Systema6c approach

7. )zen-Science
pproach
Data collec6on can be made more efficient by enlis6ng the help of people
who are working with or near what you want to monitor (eg, fishers). If you
make it easy (or rela6vely easy and not too 6me-consuming) for people to
ollect and record data as they are doing their jobs, it enables you to tap
nto a broader body of human resources, can help with con>nuity and can
educe the chances of geMng gaps in the informa6on.
s cri-cal to get input on design of data collec-on forms from data
lectors and to ensure that they are able to use them correctly and
urately.
Ci>zen-science data can be quan>ta>ve (e.g. taking measures or coun6ng)
or qualita>ve (e.g. giving the impression of something or es6ma6ng) and
o]en it is advisable to balance the use of both.
e: ci6zen-science a cheap way to get a lot of data BUT it is not always the BEST answer, to
ure it is done accurately and effec6vely and the informa6on is reliable it o]en requires costly
ondary evalua6ons. Therefore it is cri6cal to evaluate the context, type of data and need for
uracy and what that would take and then re-asses ci6zen-science against alterna6ves).

8. IFReDI-SciCap Fisher Network
Widespread network of fishers collec)ng
con)nuous catch data
Like the MRC fish monitoring tool it uses fishers to generate fisheries
dependent records – not new
Why a change from business as usual?
• Reinforcement of training
• Standardized methods of data collec6on across mul6tude of
organisa6ons
• Collec6on of metadata to contextualize the results and for verifica
of quality and accuracy
• Follows interna6onal recommenda6ons for observers – independ
assessments
• Uses fisheries independent surveys as spot checks
• Uses cameras for secondary verifica6on of species and other elem
of quality control
• Collects fish fin clippings for ter6ary verifica6on of species ID as ne
• Supplemented by community surveys that contextualize the data
• Also uses more widespread qualita6ve assessment that are verifie
less widespread and frequent overlapping quan6ta6ve assessmen
that are more 6me consuming but more accurate and informa6ve

9. tegra)on of targeted
chnology
n addi6on to the standardized and
imple data collec6on process which
s geographically widespread and
con6nuous the M&E scheme
ncorporates the use of low cost
echnology or equipment to increase
accuracy, reliability and quality by
obtaining secondary data, photos,
geoloca6ons, temperature, depth
measures, turbidity and habitat

10. egra)on of supplemental ac)vi)es
The Fish Catch Monitoring Network is supplemented by other monito
ac6vi6es at different 6mes and geographic scales to address other da
needs and gaps.
• Targeted research efforts – that fill key gaps o]en iden6fied by the
con6nuous M&E
• Uses novel and innova>ve techniques to generate data or verify res
• Uses biomonitoring methods to verify species presence (molecular
iden6fica6on of larvae), to inventory species and establish trends (w
column extrac6on of eDNA), non-invasively tracks key conserva6on
species (molecular graffi6)
Monitoring system helps to target
ques6ons of ecology or infrastructure
impacts which feedback to the monitoring
system – e.g. monitoring impacts of dams
with molecular assessment of migra6on.

11. tegrates context
Habitat assessments
Hydrological indicators
Draws on available remote sensing
e.g. land conversion context
Variability
eling
Species-Habitat
Associa:ons
5 June 2008 Lake area: 3,300 km2
Lake level: 2.2
4 October 2008 Lake area: 12,300 km2
Lake level: 8.6
0 50 10025
Kms

12. egrates Database Design
• Uses cross-checks: mul6ple ways to correct for
errors in the database
• Couples government na6onal sta6s6cs and
community-derived data
• Metadata can also help to prevent duplica6on o
effort and to share knowledge. Shared knowled
can translate to shared commitment.
• Uses crowd-sourcing to help correct for errors
Crowd-sourcing your QC: accept that all data will be inaccurate, all
data collec-on is flawed because you are balancing various trade-off
(informa-on and budget) – sharing with a few people can cause
problems and cri-cisms – sharing with many can create solu-ons

13. ecap:
Don’t necessarily need to invent something new but can reinvent or fix thing
hrough integra6on.
tarted with the needs of the monitoring outputs and what we wanted tool
o evaluate then designing the M&E to op6mize efficiency
Aimed to balance conflic6ng needs
t does not sit in isola6on but relies on other monitoring ac6vi6es
nternally builds mul6-faceted approach to balance the challenges of availab
ools and their cost, effec6veness, efficiency, etc.
emains flexible to integrate new elements to
dress new needs or newly iden6fied needs

14. Take-home
message:
Integrated monitoring
systems using different
scales and elements can
provide good
informa)on in a cost-
effec)ve way!

15. Thanks for Listening
Ques:ons?