informing biodiversity monitoring & reporting designs

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

035 37

Latitude (degrees)

Pirongia OkatainaPureoraKaimanawa

KawekaTararua

Mt Arthur

KokatahiCraigieburn

CaplesGreenstone

MurchisonsWaitutu

39 41 43 45 47

Mortalityr = -0.57P13 < 0.05Recruitmentr = -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

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

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

NZ Public

Regional councils

Citizen scientist

Industry

International

Meeting multiple stakeholders’ expectations

Kakapo Kereru Kaka Tui SkylarkKiwi Magpie Rosella

NZ Public

Regional councils

Citizen scientist

Industry

International

NZ Public

Regional councils

Citizen scientist

Industry

International

SPATIAL ZONE OF INFERENCE

NationalLocal Specific landscape

Strong

Data sources and use

NZ bird atlases: national scale

NationalLocal Specific landscape

Strong

Museum collections:

national scale

National

NatureWatch & eBird: Locations of interest to

observer

Local Specific landscape

Strong

Historic 5MBC database: Specific study sites

Traditional Ecological

Knowledge: taonga species

Museum collections:

national scale

DOC BMRS Tier 1: Publicconservation

National

NZ Garden bird survey: Urban landscapes

observer

Strong

DOC BMRS Tier 2:

Managed sites

Museum collections:

national scale

Abundance & distribution

Rare or concentrated

Intermediate Widespread & common

Knowledge development & survey design

Abundance & distribution

Rare or concentrated

Intermediate Widespread & common

Knowledge development & survey design

Generic surveys

Atlases

Site & species surveys

National

observer

Strong

DOC BMRS Tier 2:

Managed sites

Museum collections:

national scale

NatureWatch & eBird: Locations of regional

interest

Improving data sources and use

National

observer

Strong

DOC BMRS Tier 2:

Managed sites

Museum collections:

national scale

interest

Improving data sources and use

itorin

1. Knowledge focus2. Action focusWhy?

itorin

1. Identify target indicators2. State or dynamic variables?3. Scale you want to inform?

itorin

1. Study sites2. Sampling effort/site3. Sampling events4. Sampling method

itorin

1. Study sites2. Sampling effort/site3. Sampling events4. Sampling method

1. Database structure & management2. Data analysis skills 3. Audit results4. Report results

Report

GOALS & VALUES OF INTEREST

MECHANISMS TO ENHANCE DATA SOURCES

TRUSTED & USEFUL INDIVIDUAL INDICATORS

EASILY COMMUNICATED AGGREGATED MEASURES

Process for aggregating

& scaling measures

Trustworthy biodiversity measures to benefit NZ

Process for building

engagement & trust

& scaling measures

Process for building

engagement & trust

& scaling measures

Ways to improve data

sources & reporting

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

reporting

Comparable indicators for different

scales & needs

sources

reporting

Aggregating & scaling measure

for tailored reporting

scales & needs

sources

scales & needs

sources

Biodiversity values of interest

Range of monitoring & reporting goals

PROCESS FOR BUILDING ENGAGEMENT & TRUST

scales & needs

sources

Data awareness & sharing barriers

Data credibility & understanding criteria

scales & needs

sources

Individual indicators are useful& trusted

scales & needs

sources

Aggregated measures are easily

communicated & understood

WAYS TO IMPROVE DATA SOURCES & REPORTING

scales & needs

sources

Communication strategies to cross social boundaries

Mechanisms to collaborate on shared goals

Aggregated measures are easily communicated

& understood

scales & needs

Cost-effective ways to address gaps & improve data

sources

& understood

Ways for stakeholders to identify

‘fit-for-purpose’indicators

scales & needs

sources

& understood

reporting

Ways for stakeholders to identify ‘fit-for-purpose’

indicators

scales & needs

sources

& understood

InternationalNational

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

– 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

indicate its distribution• Can determine habitat

suitability

Habitat suitability

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.

Present in 2 of 9 plots (22%)

Present in 2 of 9 plots (22%) Present in 7 of 9 plots (78%)

• 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

Site Station Species StatusAA144 A Bellbird PresentAA144 D Bellbird PresentAA144 M Bellbird PresentAA144 X Bellbird AbsentAA144 P Bellbird Absent

• Summary Data

• 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

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

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

National Reporting

National Modelling

International Reporting

Evidential Decision Making

Discovery | Mobilisation | Integration

Botanical Information and Ecology Network

National Vegetation Survey Databank

Barriers

Issues

Consequences

Solutions

Indigenous Vegetation

Tier One MonitoringVegetation Component

Discovery | Mobilisation | Integration

Botanical Information and Ecology Network

National Vegetation Survey Databank

Barriers

Issues

Consequences

Solutions

Indigenous Vegetation

Tier One MonitoringVegetation Component

Issue Consequence SolutionBarrier

Schemas

Schemas• Incompatible schemas• Constraints• Missing elements

• Restructuring costs• Risk of incorrectly

combining elements• Risk to implying data are

equivalent

• Restructuring costs• Risk of incorrectly

combining elements• Risk to implying data are

equivalent

• Standard methods• Standard schema• Be realistic• Don’t underestimate

effort

Schemas

Geographical

Schemas

Geographical• Missing or inaccurate

geo-references

• Cultivated specimens

Schemas

geo-references

• Unusable data• Distribution and rarity

estimation errors • Effort to resolve issues

Schemas

geo-references

• 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

Attribution

Schemas

Geographical

Attribution• Tracking data source• Original records• Derived data

Schemas

Geographical

Without source information integrated data is of dubious quality

Schemas

Geographical

Without source information integrated data is of dubious quality

• Collect metadata and method information

• Ensure this remains with the data

• Aggregate from source records

Organism names

Attribution

Schemas

Geographical

Organism names • Misspellings• Taxonomic concepts• Taxonomically

homogenous datasets

Attribution

Schemas

Geographical

homogenous datasets

• Inflates species richness• Reduces range estimates• Poor decision about

protection or mitigation

Attribution

Schemas

Geographical

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

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

informing biodiversity monitoring & reporting designs

biodiversity outcomes

species present

coordinated system

suite of species able

mobilising data

ecological integrityevaluating

ecological processes

outcomeseffective management

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