informing biodiversity monitoring & reporting designs a coordinated system for biodiversity...
TRANSCRIPT
Informing biodiversity monitoring & reporting designs
• A coordinated system for biodiversity monitoring
• Trustworthy biodiversity measures
• Species occupancy: uses and abuses
• Solutions for standardising and mobilising data
A coordinated system for biodiversity monitoring
Peter Bellingham
Multiple reporting obligations
International• Convention on Biological Diversity
National• NZ Biodiversity Strategy….. ‘maintain and
restore a full range of remaining natural habitats and ecosystems to a healthy functioning state’
Internal• Assessing DOC’s performance with respect to
achieving its stated outcomes
Information on
• Where biodiversity outcomes are being achieved
• How management interventions can be used to improve outcomes
Effective management requires
Biodiversity monitoring in 2000
Networks of biodiversity information with time-series data
Biased assessments No coordination among sites Mostly in managed sites Can’t report losses & gains nationally
Annual mortality and recruitment rates of all trees
3.5
0
35 37
Latitude (degrees)
Pirongia OkatainaPureoraKaimanawa
KawekaTararua
Mt Arthur
KokatahiCraigieburn
CaplesGreenstone
MurchisonsWaitutu
39 41 43 45 47
3.0
2.5
2.0
1.5
1.0
0.5
Mortalityr = -0.57P13 < 0.05
Recruitmentr = -0.61P13 < 0.05
A national monitoring system
For public conservation lands:
1. National and regional reporting of status and trend in
ecological integrity
2. Evaluating the effectiveness of conservation management
and policy
3. Informing prioritisation for resource allocation
4. An early-warning system
Evaluating ecological integrity
Indigenous dominance
‘Are the ecological processes natural?’
Species occupancy
‘Are the species present what you would expect naturally?’
Ecosystem representation
‘Are the full range of ecosystems protected somewhere?’
Biodiversity measuresVegetation1. Distribution and abundance of exotic weeds considered
a threat2. Size-class structure of canopy dominants3. Representation of plant functional types
Animals4. Distribution and abundance of exotic pests considered a
threat5. Assemblages of widespread animal species – Birds
Sampling framework• 8 x 8 km grid
Standardised field surveys• Vegetation• Mammal pests• Birds
5-year rotating-panel design• Unique subset of locations sampled
each year
Building trustworthy biodiversity measures
A proof-of-concept using birds
Catriona MacLeod
Organisation
Citizen scientists
Iwi
Industry
Regional councils
Central government
NZ public
Overseas markets
International policy
Users of monitoring information
Organisation Identify data needs
Provide data
Citizen scientists
Iwi
Industry
Regional councils
Central government
NZ public
Overseas markets
International policy
Users of monitoring information
Organisation Identify data needs
Provide data
Process & report data
Use report
Citizen scientists
Iwi
Industry
Regional councils
Central government
NZ public
Overseas markets
International policy
Users of monitoring information
Iwi
NZ Public
Regional councils
DOC
Citizen scientist
Industry
International
Meeting multiple stakeholders’ expectations
Kakapo Kereru Kaka Tui SkylarkKiwi Magpie Rosella
Iwi
NZ Public
Regional councils
DOC
Citizen scientist
Industry
International
Meeting multiple stakeholders’ expectations
Kakapo Kereru Kaka Tui SkylarkKiwi Magpie Rosella
Iwi
NZ Public
Regional councils
DOC
Citizen scientist
Industry
International
Meeting multiple stakeholders’ expectations
Kakapo Kereru Kaka Tui SkylarkKiwi Magpie Rosella
SPATIAL ZONE OF INFERENCE
POW
ER T
O D
ETEC
T CH
ANG
E
NationalLocal Specific landscape
Weak
Strong
Data sources and use
SPATIAL ZONE OF INFERENCE
POW
ER T
O D
ETEC
T CH
ANG
E
NZ bird atlases: national scale
NationalLocal Specific landscape
Weak
Strong
Museum collections:
national scale
Data sources and use
SPATIAL ZONE OF INFERENCE
POW
ER T
O D
ETEC
T CH
ANG
E
NZ bird atlases: national scale
National
NatureWatch & eBird: Locations of interest to
observer
Local Specific landscape
Weak
Strong
Historic 5MBC database: Specific study sites
Traditional Ecological
Knowledge: taonga species
Museum collections:
national scale
Data sources and use
SPATIAL ZONE OF INFERENCE
POW
ER T
O D
ETEC
T CH
ANG
EDOC BMRS
Tier 1: Publicconservation
lands
NZ bird atlases: national scale
National
NZ Garden bird survey: Urban landscapes
NatureWatch & eBird: Locations of interest to
observer
Local Specific landscape
Weak
Strong
Historic 5MBC database: Specific study sites
DOC BMRS Tier 2:
Managed sites
Traditional Ecological
Knowledge: taonga species
Museum collections:
national scale
Data sources and use
Abundance & distribution
Rare or concentrated
Intermediate Widespread & common
Exte
nt o
f kno
wle
dge
Num
bers
, ran
ges
and
tren
ds
Knowledge development & survey design
Abundance & distribution
Rare or concentrated
Intermediate Widespread & common
Exte
nt o
f kno
wle
dge
Num
bers
, ran
ges
and
tren
ds
Knowledge development & survey design
Generic surveys
Atlases
Site & species surveys
SPATIAL ZONE OF INFERENCE
POW
ER T
O D
ETEC
T CH
ANG
EDOC BMRS
Tier 1: Publicconservation
lands
NZ bird atlases: national scale
National
NZ Garden bird survey: Urban landscapes
NatureWatch & eBird: Locations of interest to
observer
Local Specific landscape
Weak
Strong
Historic 5MBC database: Specific study sites
DOC BMRS Tier 2:
Managed sites
Traditional Ecological
Knowledge: taonga species
Museum collections:
national scale
NatureWatch & eBird: Locations of regional
interest
Improving data sources and use
SPATIAL ZONE OF INFERENCE
POW
ER T
O D
ETEC
T CH
ANG
EDOC BMRS
Tier 1: Publicconservation
lands
NZ bird atlases: national scale
National
NZ Garden bird survey: Urban landscapes
NatureWatch & eBird: Locations of interest to
observer
Local Specific landscape
Weak
Strong
Historic 5MBC database: Specific study sites
DOC BMRS Tier 2:
Managed sites
Traditional Ecological
Knowledge: taonga species
Museum collections:
national scale
NatureWatch & eBird: Locations of regional
interest
NatureWatch & eBird: Locations of regional
interest
Improving data sources and use
Key
step
s fo
r mon
itorin
g de
sign
1. Knowledge focus2. Action focusWhy?
Key
step
s fo
r mon
itorin
g de
sign
1. Knowledge focus2. Action focusWhy?
1. Identify target indicators2. State or dynamic variables?3. Scale you want to inform?
What?
Key
step
s fo
r mon
itorin
g de
sign
1. Knowledge focus2. Action focusWhy?
1. Identify target indicators2. State or dynamic variables?3. Scale you want to inform?
What?
1. Study sites2. Sampling effort/site3. Sampling events4. Sampling method
How?
Key
step
s fo
r mon
itorin
g de
sign
1. Knowledge focus2. Action focusWhy?
1. Identify target indicators2. State or dynamic variables?3. Scale you want to inform?
What?
1. Study sites2. Sampling effort/site3. Sampling events4. Sampling method
How?
1. Database structure & management2. Data analysis skills 3. Audit results4. Report results
Report
GOALS & VALUES OF INTEREST
Rese
arch
aim
s
MECHANISMS TO ENHANCE DATA SOURCES
GOALS & VALUES OF INTEREST
Rese
arch
aim
s
MECHANISMS TO ENHANCE DATA SOURCES
GOALS & VALUES OF INTEREST
TRUSTED & USEFUL INDIVIDUAL INDICATORS
Rese
arch
aim
s
MECHANISMS TO ENHANCE DATA SOURCES
GOALS & VALUES OF INTEREST
TRUSTED & USEFUL INDIVIDUAL INDICATORS
EASILY COMMUNICATED AGGREGATED MEASURES
Rese
arch
aim
s
Process for aggregating
& scaling measures
Trustworthy biodiversity measures to benefit NZ
Process for building
engagement & trust
Process for aggregating
& scaling measures
Trustworthy biodiversity measures to benefit NZ
Process for building
engagement & trust
Process for aggregating
& scaling measures
Ways to improve data
sources & reporting
Trustworthy biodiversity measures to benefit NZ
Critical goals to NZ
Indicator characteristics reflect goals & values
PROCESS FOR AGGREGATING & SCALING MEASURES
Benefits & limitations of harmonised
reporting
Relative value & contributions of different data
sources
Critical goals to NZ
Indicator characteristics reflect goals & values
PROCESS FOR AGGREGATING & SCALING MEASURES
Benefits & limitations of harmonised
reporting
Comparable indicators for different
scales & needs
Relative value & contributions of different data
sources
Critical goals to NZ
Indicator characteristics reflect goals & values
PROCESS FOR AGGREGATING & SCALING MEASURES
Benefits & limitations of harmonised
reporting
Aggregating & scaling measure
for tailored reporting
Comparable indicators for different
scales & needs
Relative value & contributions of different data
sources
Critical goals to NZ
Indicator characteristics reflect goals & values
PROCESS FOR AGGREGATING & SCALING MEASURES
Aggregating & scaling measure
for tailored reporting
Comparable indicators for different
scales & needs
Relative value & contributions of different data
sources
Critical goals to NZ
Indicator characteristics reflect goals & values
Biodiversity values of interest
Range of monitoring & reporting goals
PROCESS FOR BUILDING ENGAGEMENT & TRUST
PROCESS FOR AGGREGATING & SCALING MEASURES
Aggregating & scaling measure
for tailored reporting
Comparable indicators for different
scales & needs
Relative value & contributions of different data
sources
Critical goals to NZ
Indicator characteristics reflect goals & values
Data awareness & sharing barriers
Data credibility & understanding criteria
Biodiversity values of interest
Range of monitoring & reporting goals
PROCESS FOR BUILDING ENGAGEMENT & TRUST
PROCESS FOR AGGREGATING & SCALING MEASURES
Aggregating & scaling measure
for tailored reporting
Comparable indicators for different
scales & needs
Relative value & contributions of different data
sources
Critical goals to NZ
Indicator characteristics reflect goals & values
Individual indicators are useful& trusted
Data awareness & sharing barriers
Data credibility & understanding criteria
Biodiversity values of interest
Range of monitoring & reporting goals
PROCESS FOR BUILDING ENGAGEMENT & TRUST
PROCESS FOR AGGREGATING & SCALING MEASURES
Aggregating & scaling measure
for tailored reporting
Comparable indicators for different
scales & needs
Relative value & contributions of different data
sources
Critical goals to NZ
Indicator characteristics reflect goals & values
Aggregated measures are easily
communicated & understood
Individual indicators are useful& trusted
Data awareness & sharing barriers
Data credibility & understanding criteria
Biodiversity values of interest
Range of monitoring & reporting goals
PROCESS FOR BUILDING ENGAGEMENT & TRUST
PROCESS FOR AGGREGATING & SCALING MEASURES
PROCESS FOR BUILDING ENGAGEMENT & TRUST
PROCESS FOR AGGREGATING & SCALING MEASURES
WAYS TO IMPROVE DATA SOURCES & REPORTING
Aggregating & scaling measure
for tailored reporting
Comparable indicators for different
scales & needs
Relative value & contributions of different data
sources
Communication strategies to cross social boundaries
Mechanisms to collaborate on shared goals
Critical goals to NZ
Indicator characteristics reflect goals & values
Aggregated measures are easily communicated
& understood
Individual indicators are useful& trusted
Data awareness & sharing barriers
Data credibility & understanding criteria
Biodiversity values of interest
Range of monitoring & reporting goals
Aggregating & scaling measure
for tailored reporting
Comparable indicators for different
scales & needs
Cost-effective ways to address gaps & improve data
Relative value & contributions of different data
sources
Communication strategies to cross social boundaries
Mechanisms to collaborate on shared goals
Critical goals to NZ
Indicator characteristics reflect goals & values
Aggregated measures are easily communicated
& understood
Individual indicators are useful& trusted
Data awareness & sharing barriers
Data credibility & understanding criteria
Biodiversity values of interest
Range of monitoring & reporting goals
PROCESS FOR BUILDING ENGAGEMENT & TRUST
PROCESS FOR AGGREGATING & SCALING MEASURES
WAYS TO IMPROVE DATA SOURCES & REPORTING
Aggregating & scaling measure
for tailored reporting
Ways for stakeholders to identify
‘fit-for-purpose’indicators
Comparable indicators for different
scales & needs
Cost-effective ways to address gaps & improve data
Relative value & contributions of different data
sources
Communication strategies to cross social boundaries
Mechanisms to collaborate on shared goals
Critical goals to NZ
Indicator characteristics reflect goals & values
Aggregated measures are easily communicated
& understood
Individual indicators are useful& trusted
Data awareness & sharing barriers
Data credibility & understanding criteria
Biodiversity values of interest
Range of monitoring & reporting goals
PROCESS FOR BUILDING ENGAGEMENT & TRUST
PROCESS FOR AGGREGATING & SCALING MEASURES
WAYS TO IMPROVE DATA SOURCES & REPORTING
Benefits & limitations of harmonised
reporting
Aggregating & scaling measure
for tailored reporting
Ways for stakeholders to identify ‘fit-for-purpose’
indicators
Comparable indicators for different
scales & needs
Cost-effective ways to address gaps & improve data
Relative value & contributions of different data
sources
Communication strategies to cross social boundaries
Mechanisms to collaborate on shared goals
Critical goals to NZ
Indicator characteristics reflect goals & values
Aggregated measures are easily communicated
& understood
Individual indicators are useful& trusted
Data awareness & sharing barriers
Data credibility & understanding criteria
Biodiversity values of interest
Range of monitoring & reporting goals
PROCESS FOR BUILDING ENGAGEMENT & TRUST
PROCESS FOR AGGREGATING & SCALING MEASURES
WAYS TO IMPROVE DATA SOURCES & REPORTING
International
National
Regional
Site/farm
Harmonised system for different needs
Occupancy: Uses and abuses
Andrew GormleyLandcare Research
What is occupancy?• Occupancy is a robust measure of distribution of plants
or animals in the landscape• Key indicator of ecological integrity• Metrics:
1. Probability that a site is occupied2. Proportion of area occupied (PAO)
Presence-only data• Locations of species
– Specimens, sightings etc
Presence dataDate Species Lat Long15/10/13 Kea 43.8 S 172.9 E16/10/13 Kea 43.7 S 172.7 E
Presence-only data• Locations of species
– Specimens, sightings etc
• Draw a shape around points to indicate its distribution
Presence dataDate Species Lat Long15/10/13 Kea 43.8 S 172.9 E16/10/13 Kea 43.7 S 172.7 E
Presence-only data• Locations of species
– Specimens, sightings etc
• Draw a shape around points to indicate its distribution
• Can determine habitat suitability
Habitat suitability
Presence-only data• Locations of species
– Specimens, sightings etc
• Draw a shape around points to indicate its distribution
• Can determine habitat suitability
• Subject to sampling bias• No estimate of uncertainty
Didn’t look here
Presence-absence data• Sample proportion of possible sites• Record where species is present and absent• Occupancy = proportion of sites that are occupied• More reliable estimates of potential distribution
Iratus roseii was at 72 % of sites. Occupancy = 0.72
Presence-Absence Data
Date Species Lat Long Status15/10 I.roseii 43.8 S 172.9 E Present16/10 I.roseii 43.7 S 172.7 E Absent
Measuring occupancy• Can stratify by land cover or other covariates
I. roseii at 89% of forest and 56% of non-forest sitesForest: Present in 8 out of 9Occ. = 0.89
Pasture:Present in 5 out of 9Occ. = 0.56
Issue 1: Imperfect detection
• Species is present but you miss it– Detections might be related to habitat/landcover
Forest: Present in 8 Observed in 3
Pasture:Present in 5Observed in 4
Issue 1: Imperfect detection
• Species is present but you miss it– Sampling and statistical methods available
• Presence-Absence Data w/repeat surveysDate Species Lat Long Survey1 Survey2 Survey315/10/13 Kererū 43.8 S 172.9 E Present Absent Present16/10/13 Kererū 43.7 S 172.7 E Absent Absent Absent
Issue 2: Data from managed areas only
• Managed areas are not representative of entire region– Low due to hence mgmt. of predators (?)– High due to mgmt. of predators (?)
Pest control No control – not measured
Issue 3: Different Data Formats• Presence Data
Date Species Lat Long15/10/13 Kererū 43.8 S 172.9 E16/10/13 Kererū 43.7 S 172.7 E
• Presence-Absence DataDate Species Lat Long Status15/10/13 Kererū 43.8 S 172.9 E Present16/10/13 Kererū 43.7 S 172.7 E Absent
• Presence-Absence Data w/repeat surveysDate Species Lat Long Survey1 Survey2 Survey315/10/13 Kererū 43.8 S 172.9 E Present Absent Present16/10/13 Kererū 43.7 S 172.7 E Absent Absent Absent
• Other data– Survey ID– Person– Method– Time– Weather
Issue 4: Scale of sampling unit
• Occupancy decreases as your sampling unit gets smaller• Issue for pasting together different sources of data.
Issue 4: Scale of sampling unit
• Occupancy decreases as your sampling unit gets smaller• Issue for pasting together different sources of data.
Present in 2 of 9 plots (22%)
Issue 4: Scale of sampling unit
• Occupancy decreases as your sampling unit gets smaller• Issue for pasting together different sources of data.
Present in 2 of 9 plots (22%) Present in 7 of 9 plots (78%)
Issue 4: Scale of sampling unit
• Bird Atlas has 3166 × 10 km2 grid squares– Brown Kiwi in 176: occupancy = 0.06
Issue 4: Scale of sampling unit
• Bird Atlas has 3166 × 10 km2 grid squares– Brown Kiwi in 176: occupancy = 0.06
• If sampling unit was up to 100 km2 (61 squares)– Brown Kiwi in 26: occupancy = 0.43
Issue 5: Data Quality
• Species is misidentified• How to assess quality of the record?• Reliability of observers
Issue 6: What is a presence?
• What constitutes a positive detection?– Specimen?– Sighting?– Sound?
• Other species…– Poo?– Sign?– Remote sensing?
Level of data storage
Site Species StatusAA144 Bellbird PresentAD156 Bellbird Absent… … …
• Site Summary Data
Tier 1 sampling• Conservation lands• National and regional scales• Grid, random start point
Level of data storage
Site Station Species StatusAA144 A Bellbird PresentAA144 D Bellbird PresentAA144 M Bellbird PresentAA144 X Bellbird AbsentAA144 P Bellbird Absent
• Summary Data
Site Species StatusAA144 Bellbird PresentAD156 Bellbird Absent… … …
• Site Summary Data
Level of data storage
• Raw DataSite Station Species StatusAA144 A Bellbird PresentAA144 A Bellbird PresentAA144 A Bellbird PresentAA144 A Bellbird PresentAA144 D Bellbird PresentAA144 M Bellbird PresentAA144 X Bellbird PresentAA144 M Bellbird PresentAA144 P Bellbird Absent
Site Station Species StatusAA144 A Bellbird PresentAA144 D Bellbird PresentAA144 M Bellbird PresentAA144 X Bellbird AbsentAA144 P Bellbird Absent
• Summary Data
Site Species StatusAA144 Bellbird PresentAD156 Bellbird Absent… … …
• Site Summary Data
Have to document how raw data is summarised for analysis
Distribution vs AbundanceDistribution is better!• Estimating abundance is too
hard and/or expensive– Easier to detect species rather
than count individuals
Distribution vs AbundanceDistribution is better!• Estimating abundance is too
hard and/or expensive– Easier to detect species rather
than count individuals
• Distribution is a good surrogate for abundance– As a population increases, so
does its distribution
2010
2013
Distribution vs AbundanceAbundance is better!• Distribution does not provide
enough detail• Abundance may change with
no change in distribution– Species is widespread and then
has localised increases in population
– Species goes into decline but remains widespread
• Distribution will not detect this
Standardising and mobilising data
Goal: solutions for standardisation and mobilisation- ideally through e-federation of distribution data;
What are the barriers to delivering this?
Nick Spencer
Misc data
Specimens
Observations
Databases
Services
Federated Bio-data
Misc data
Specimens
Observations
Databases
Services
Federated Bio-data
GBIF.ORGFree and open access to biodiversity data
Misc data
Specimens
Observations
Databases
Services
Federated Bio-data
Confederated Bio-data
Federated Networked Bio-data
And/Or
Misc data
Specimens
Observations
Databases
Services
Federated Bio-data
Confederated Bio-data
Federated Networked Bio-data
And/Or
GBIF.ORGFree and open access to biodiversity data
National Reporting
National Modelling
International Reporting
Evidential Decision Making
Discovery | Mobilisation | Integration
GBIF.ORGFree and open access to biodiversity data
Botanical Information and Ecology Network
National Vegetation Survey Databank
Barriers
Issues
Consequences
Solutions
Indigenous Vegetation
Tier One MonitoringVegetation Component
Discovery | Mobilisation | Integration
GBIF.ORGFree and open access to biodiversity data
Botanical Information and Ecology Network
National Vegetation Survey Databank
Barriers
Issues
Consequences
Solutions
Indigenous Vegetation
Tier One MonitoringVegetation Component
Issue Consequence SolutionBarrier
Issue Consequence SolutionBarrier
Schemas
Issue Consequence SolutionBarrier
Schemas• Incompatible schemas• Constraints• Missing elements
Issue Consequence SolutionBarrier
Schemas• Incompatible schemas• Constraints• Missing elements
• Restructuring costs• Risk of incorrectly
combining elements• Risk to implying data are
equivalent
Issue Consequence SolutionBarrier
Schemas• Incompatible schemas• Constraints• Missing elements
• Restructuring costs• Risk of incorrectly
combining elements• Risk to implying data are
equivalent
• Standard methods• Standard schema• Be realistic• Don’t underestimate
effort
Schemas
Issue Consequence SolutionBarrier
Geographical
Schemas
Issue Consequence SolutionBarrier
Geographical• Missing or inaccurate
geo-references
• Cultivated specimens
Schemas
Issue Consequence SolutionBarrier
Geographical• Missing or inaccurate
geo-references
• Cultivated specimens
• Unusable data• Distribution and rarity
estimation errors • Effort to resolve issues
Schemas
Issue Consequence SolutionBarrier
Geographical• Missing or inaccurate
geo-references
• Cultivated specimens
• Unusable data• Distribution and rarity
estimation errors • Effort to resolve issues
• Geo-ref’s for locations• Rules for misspellings; valid
coordinates; sensible locations; consistency with location narrative; known distributions; collector routes
Schemas
Geographical
Issue Consequence SolutionBarrier
Attribution
Schemas
Geographical
Issue Consequence SolutionBarrier
Attribution• Tracking data source• Original records• Derived data
Schemas
Geographical
Issue Consequence SolutionBarrier
Attribution• Tracking data source• Original records• Derived data
Without source information integrated data is of dubious quality
Schemas
Geographical
Issue Consequence SolutionBarrier
Attribution• Tracking data source• Original records• Derived data
Without source information integrated data is of dubious quality
• Collect metadata and method information
• Ensure this remains with the data
• Aggregate from source records
Issue Consequence SolutionBarrier
Organism names
Attribution
Schemas
Geographical
Issue Consequence SolutionBarrier
Organism names
• Misspellings• Taxonomic concepts• Taxonomically
homogenous datasets
Attribution
Schemas
Geographical
Issue Consequence SolutionBarrier
Organism names
• Misspellings• Taxonomic concepts• Taxonomically
homogenous datasets
• Inflates species richness• Reduces range estimates• Poor decision about
protection or mitigation
Attribution
Schemas
Geographical
Issue Consequence SolutionBarrier
Organism names
• Misspellings• Taxonomic concepts• Taxonomically
homogenous datasets
• Inflates species richness• Reduces range estimates• Poor decision about
protection or mitigation
• NZOR provides consensus of synonymy, spellings and concepts (but...)
• Must be applied to data to create taxonomically homogenous datasets
Attribution
Schemas
Geographical
Concepts - Nertera dichondrifolia
Until MacMillan (1995) Nertera dichondrifolia regarded as variable species distributed throughout NZ
N. dichondrifolia ?
Nertera Spp
After 1995 N. villosa circumscribed; occurs South of latitude 37o
Concept of N. dichondrifolia narrowed; occurs North of latitude 38o
N. villosa
N. dichondrifolia
Nertera dichondrifolia
Between 37o and 38o latitude uncertainty about which species you have
N. villosa
N. dichondrifolia
?
Issue Consequence SolutionBarrier
Organism names
Attribution
Schemas
Geographical
Species Occupancy
Where do species occur?– If data is poor our ability to answer the other questions well is limited
Data challenges Bird Data with Regional Council data holders
Discovery & Mobilisation• Identifying, • Describing, • Data use agreements, • Technology assistance and challenges
Bio-data Services Stack Terrestrial Work Stream 1