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27 September 2021
Ecosystem Extent and Integrity webinar
Hosted by GEO BON & UN SEEA
National Aeronautics and Space Administration
(c) 2021 California Institute of Technology. Government sponsorship acknowledged.
Ecosystem Extent and
related indicators:
An overview
Gary GELLER
Jet Propulsion Laboratory
California Institute of Technology
gary.n.geller@jpl.nasa.gov
Joe Parks from Wikimedia
2
GEO BON
Wikimedia Avoini
3
GEO BON’s Mission
Improve the acquisition, coordination and delivery
of biodiversity observations and related services
to users including decision makers and the scientific community.
❑ In the GBF context…
• GEO BON supports Parties to track and guide progress to national targets
4
GEO BON Overview
Navarro et al. 2017
Indicators and
other information
5
What Are Essential Biodiversity Variables?
Criteria:
❖ Biological
❖ State variables
❖ Sensitive to change
❖ Scalable
❖ Feasible
❖ Ecosystem agnostic
Set of measurements to capture the major dimensions
of biodiversity and how it is changing
Genetic Composition
Species Populations
Species Traits
Community Composition
Ecosystem Structure
Ecosystem Functions
6
Ecosystem Extent
Wikimedia Apalsola
7
What is Ecosystem Extent?
❑ A dataset that outlines one or more ecosystems of interest
ORNL DAAC from Jorgenson
and Grunblatt, 2013
8
What is Ecosystem Extent?
❑ Hierarchical
ORNL DAAC from Jorgenson
and Grunblatt, 2013
9
Approaches to Define an Ecosystem
❑ By physical structure
• Tree height; scrub; grassland…
• Ex: FAO Land Cover Classification System
❑ By physical environment
• Climate, soils, slope….
• Ex: USGS Global Ecosystem Land Units
❑ By structure, composition, function & processes
• IUCN Ecosystem Typology V2.0
US National Park Service
10
Extent Is Just the Beginning
❑ Pixel-based classification
• Enables derived products…
❑ Total area
❑ Fragmentation (affects condition)
❑ Corridors/connectivity
❑ Location
❑ Habitat
❑ Environmental accounting
Ipe-institutodepesquisasecologicas
googlemaps
Kabir Rasouli et al 2019
11
Relationship to Land Cover
❑ Land Cover includes non-ecosystem classes
❑ Often…the only maps available
❑ Challenges
• Classes
• Update frequency
• Spatial resolution
• Accuracy varies
12
Marine Ecosystems: “Seascapes”
Montes et al 2020, Front Mar Sci
13
Ecosystem Extent in the Context of GBF
2050 Goal Components Headline Indicators Component Indicator
Goal A: The integrity of all
ecosystems is enhanced,
with an increase of at least
15% in the area,
connectivity and integrity of
natural ecosystems…
A.1. Area of natural ecosystems A.0.1. Extent of selected natural and
modified ecosystems…e.g.,:
• Forest, savannahs & grasslands
• Wetlands
• Mangroves
• Saltmarshes
• Coral reef
• Seagrass
• Macroalgae
• Intertidal habitats
A.2. Connectivity of natural
ecosystems
A.0.2. Species Habitat Index
(presented in the species population
webinar)
A.2.1. CMS Connectivity Indicator
(CMS)
A.3. Integrity of natural
ecosystems
A.3.1. Ecosystem Integrity Index
[ ]
Proposed monitoring approach and headline, component and complementary indicators for the post-2020 global biodiversity framework
CBD/WG2020/3/INF/2 (5 August 2021)… Annex I
14
Main Challenges
❑ Most common source: Land Cover maps
❑ Geographic and temporal availability varies
❑ Accuracy varies
❑ Classes
❑ Many datasets available on GEO BON EBV Data Portal
• https://portal.geobon.org/datasets
15
Thank you
Improving capacity for monitoring ecosystem extent
through the integration of global and national spatial
data products
Victor H Gutierrez-Velez, Maria Cecilia Londoño, Jeronimo Rodriguez,
Angela Mejia, Victoria Sarmiento, Wilson Lara, Ivan Gonzalez, Jaime
Burbano
Overview of Ecosystem Extent and Integrity Slides
Overview of Ecosystem Extent and Integrity Slides
Priorities for improving the monitoring of ecosystem
extent through data integration
• To produce methods for data integration, QA/QC, gap identification
and gap filling
• To produce software that facilitates data integration and analysis
• To enable access to data and methods to inform reporting and
decision-making.
Priorities for improving the monitoring of ecosystem
extent through data integration
• To produce methods for data integration, QA/QC, gap identification
and gap filling
• To produce software that facilitates data integration and analysis
• To enable access to data and methods to inform reporting and
decision-making.
6
Year
available
IDEAM
Hansen
Maximum forest extent
IDEAM
Hansen et al (2013)
Overview of Ecosystem Extent and Integrity Slides
Overview of Ecosystem Extent and Integrity Slides
Overview of Ecosystem Extent and Integrity Slides
Overview of Ecosystem Extent and Integrity Slides
Overview of Ecosystem Extent and Integrity Slides
Overview of Ecosystem Extent and Integrity Slides
Map
agreement
(%)
Tree cover Hansen (%)
Map
agreement
(%)
Tree cover Hansen (%)
Maximum common extent
IDEAM-HANSEN
National biotic units Optimal agreement per unit Harmonized map
15
Country wide weighted agreement
Overall agreement Optimum threshold
Wetlands
Pekel et al 2016
Florez et al 2016
Ecosystem and Land
cover products
Multi-temporal spectral metrics
Land
cover
legend
Resolve classification
conflicts (decision rules)
Identification of data gaps
Gap filling
QA/QC
Priorities for improving the monitoring of ecosystem
extent through data integration
• To produce methods for data integration, QA/QC, gap identification
and gap filling
• To produce software that facilitates data integration and analysis
• To enable access to data and methods to inform reporting and
decision-making.
Input data
roi()
Ecosystem distribution
EBV Time series
sequence()
Horizontal structure
Fragmentation
Lara, Gutierrez-Velez, Londoño (in prep.)
Integrated metrics/ indicators
EBVmetric()
Year
Area
(ha)
% live cover
Enthropy
ecoChange package
roi()
Polygon/gadm
(User defined)
https://cran.r-project.org/web/packages/ecochange/ecochange.pdf
0 100 200
Reference
image
Band 1 Band 2
Band 3 Band 4
Band 6
Band 5
Target image
Gutierrez-Velez et al
(submitted)
rastermapr package
Priorities for improving the monitoring of ecosystem
extent through data integration
• To produce methods for data integration, QA/QC, gap identification
and gap filling
• To produce software that facilitates data integration and analysis
• To enable access to data and methods to inform reporting and
decision-making.
1. Front-end
Queries
New
products
Virtual/local machine
2. Back-end
API
Data
42GB
ecoChange
rastermapr
3. Software
Take home messages
• The harmonization of global and national data sets aims to inform
biodiversity monitoring and decision making nationally while ensuring
consistency with global assessments.
• The development of a cloud infrastructure facilitates the use of harmonized
data for characterizing, monitoring and reporting biodiversity conservation
efforts.
• The integration of data, software and infrastructure can enable dynamic data
improvements and timely data use for decision-making.
Improving capacity for monitoring ecosystem
extent and integrity through the integration of
global and national spatial data products
Victor H Gutierrez-Velez, Maria Cecilia Londoño, Jeronimo Rodriguez,
Angela Mejia, Victoria Sarmiento, Wilson Lara, Ivan Gonzalez, Jaime
Burbano
victorhugo@temple.edu
www.bosproject.org
Ecosystems in the post-2020 global
biodiversity framework
Professor Emily Nicholson (Deakin University, Australia)
Ecosystems in the Post-2020 Global Biodiversity Framework
• Strategic Plan for Biodiversity 2011-2020, created in 2010,
include the Aichi Biodiversity Targets
• 20 targets under 5 goals, none met (some partially met)
• Species targets (no extinctions) but no specific ecosystem
target
• Goals of the first draft of the post-2020 global biodiversity
framework
Watson, J.E.M., Keith, D.A., Strassburg, B.B.N., Venter, O., Williams, B.,
Nicholson, E. (2020) Set a global target for ecosystems. Nature 578, 360-362.
Nicholson et al. (2021) Scientific foundations for an ecosystem goal &
indicators for the post-2020 global biodiversity framework. Nature Ecol & Evol
Safeguard
species
Maintain
genetic diversity
Sustain
ecosystems
Figure 1. Ecosystems are central to meeting all three CBD objectives, which
2020 goals: 1) conservation of biodiversity (from genes, species to ecosyste
green); 2) the sustainable use of its components (draft Goal B, in orange); a
Maintain &
enhance Nature’s
contributions to
people
Share benefits
of genetic diversity
fairly & equitably
Why an ecosystem goal?
Big developments in ecosystem conservation science over last decades:
• Accepted definitions of ecosystems and collapse
• Ecosystem: biotic and abiotic components, processes and interactions
within and between them, in a place
• Collapse: endpoint of decline, defining features are lost, replacement
by another ecosystem type
Why an ecosystem goal?
Big developments in ecosystem conservation science over last decades:
• Accepted definitions of ecosystems and collapse
• Ecosystem mapping
https://www.intertidal.app/
https://www.globalmangrovewatch.org
http://www.earthenv.org/cloudforest
Why an ecosystem goal?
Big developments in ecosystem conservation science over last decades:
• Accepted definitions of ecosystems and collapse
• Ecosystem mapping
• Ecosystem classification: https://global-ecosystems.org/
• Ecosystem risk assessment:
• Red List of Ecosystems (>3000 ecosystem assessed) http://iucnrle.org
• Ecosystem accounting: https://seea.un.org/
Why an ecosystem goal?
Big developments in ecosystem conservation science over last decades:
• Accepted definitions of ecosystems and collapse
• Ecosystem mapping
• Ecosystem classification: https://global-ecosystems.org/
• Ecosystem risk assessment: Red List of Ecosystems
>3000 ecosystem assessed http://iucnrle.org
• Ecosystem accounting: https://seea.un.org/
Terrestrial Marine
All ecosystems
Subsets
Underway
Strategic
Core components for an ecosystem goal
Ecosystem area
Ecosystem integrity
Risk of ecosystem collapse
1. Ecosystem area or extent
2. Ecosystem integrity
3. Risk of ecosystem collapse
Core components for an ecosystem goal
Direct drivers of loss (threats)
Land-use/sea-use change
Resource extraction (biotic, abiotic)
Invasive species
Pollution
Climate change
Ecosystem area
Ecosystem integrity
Risk of ecosystem collapse
Decreases
Increases
Targets to achieve an ecosystem goal
Direct drivers of loss (threats)
Land-use/sea-use change
Resource extraction (biotic, abiotic)
Invasive species
Pollution
Climate change
Ecosystem area
Ecosystem integrity
Risk of ecosystem collapse
T1
T3
T6
T7
T8
T9
T5
T2
A T4
B
T2 T3
T3
A T4
Decreases
Increases
T11
T11
Retain ecosystems
Protected areas & OECMs
Sustainable
harvest
Manage invasive species
Reduce pollution
Action on climate change
Actions targets to halt loss of
ecosystem area & integrity
Actions targets to reverse loss of ecosystem area &
integrity: restoration
Restore ecosystems, PAs & OECMS, species
recovery, nature-based solutions
Restore ecosystems, PAs & OECMS, species
recovery, nature-based solutions
Indicators: what do they need to do?
A good indicator set for an ecosystem goal is:
1. Aligned with and cover all goal components
2. Relevant to ecosystems: specific ecosystems, features, collapse
3. Tested & behaves predictably: responses & biases are understood
4. Calculated using available, accessible data: spatial & temporal coverage; open access
Watermeyer et al. (2021), Using decision science to evaluate global biodiversity indices. Conserv Biol, 35: 492-501.
https://doi.org/10.1111/cobi.13574
Nicholson et al. (2021) Scientific foundations for an ecosystem goal & indicators for the post-2020 global biodiversity
framework. Nature Ecology & Evolution (in review)
Direct drivers of loss (threats)
Land-use/sea-use change
Resource extraction (biotic, abiotic)
Invasive species
Pollution
Climate change
Ecosystem area
Ecosystem integrity
Risk of ecosystem collapse
Target Target scope Examples of actions to achieve an ecosystem goal
T1a Retain ecosystem area & integrity Planning, regulation & incentives to address land/sea-use change
T1b Restore ecosystem area & integrity Restoration of abiotic environment/processes (e.g. water, fire regimes) & biotic
components (e.g. direct seeding, planting, rewilding)
T2 Expanded & effective protected areas (PAs) & other effective area-
based conservation measures (OECMs)
Preventing further loss through regulation; increasing integrity & area through
effective PA/OECM management & restoration action
T3 Manage for recovery of wild species In situ management of species, including restoration action,
reintroductions/rewilding & habitat management
T4 Sustainable harvest of biota Effective management of fisheries, bushmeat-hunting, forestry activities
T5 Manage invasive species Prevent new introductions, reduce spread, eradicate or control invasive species
to eliminate or reduce their impacts
T6 Reduce pollution to levels not harmful to biodiversity & ecosystem
functions
Reduce excess nutrients, biocides (pesticides etc), & plastic waste
T7 Increase action on climate change to ensure resilience & minimize
negative impacts on biodiversity
Nature-based solutions & ecosystem management for resilient ecosystems,
disaster-risk reduction & mitigation (eg carbon sequestration)
T8 Ensure benefits through sustainable management of wild species Overlap with T4; management of fisheries, bushmeat-hunting, harvest
T10 Nature-based solutions for ecosystem services Restore and protect ecosystems to support regulating services
A Ecosystem management Fire & water management/regulation (rather than restoration)
B Sustainable harvest of abiotic ecosystem components Water extraction (currently not explicitly included in targets)
T1a
T2
T5
T6
T7
T8
T4
T1b
A T3
B
T1b T2
T2
A T3
Decreases
Increases
T10
T10
Indicator
Collapse
risk Area Integrity
Composition
Integrity
Structure
Integrity
Function
Drivers Marine Freshwater Terrestrial
Ecosystem
relevance
Performance
tested
Global
trend
coverage
Red List Index of Ecosystems + + + + + + x
Change in the extent of water-related ecosystems over
time
+ + + - +
Continuous Global Mangrove Forest Cover + + + + - +
Ecosystem Area Index + + + + + + -
Forest Area as a Proportion of Total Land Area + + x - +
Global Mangrove Watch + + + - +
Trends in Primary Forest Extent + + + - +
Tree Cover Loss (Global Forest Watch) + + x - +
Wetland Extent Trends Index + + + + - +
Bioclimatic Ecosystem Resilience Index + + + + x x +
Biodiversity Habitat Index + + + x x +
Biodiversity Intactness Index + + + x - +
Living Planet Index + + + + x + +
Mean Species Abundance + + + + x x +
Red List Index for species + + + + - + +
Species Habitat Index + + + x x +
Ecosystem Intactness Index + + + x - +
Forest Landscape Integrity Index + + + x x -
Live Cover via Vegetation Continuous Fields + + x - +
Ecosystem Health Index + + + + + + + + -
Live Coral Cove + + + + +
Proportion of land degraded over total land area + + x - +
Vegetation Health Index + + x - +
Water Turbidity & an estimate of Trophic State Index + + + - +
Coral Reef Watch + + + + +
Human Footprint + + x - +
Marine Cumulative Human Impacts + + - - +
Ocean Health Index + + x - +
1. Only one indicator of collapse risk
2. Bias towards terrestrial & forest ecosystems
3. Bias towards composition (vs function)
4. Low relevance to specific ecosystems
5. Trade-off between data coverage & ecosystem relevance
6. Low levels of performance testing
Where to next?
• Post-2020 goals need strong scientific basis: area, integrity & collapse
risk
• Theory of change can identify pathways for impact & gaps
• Indicator set needs work! Need an ongoing process so we are not
constrained by current data & indicators
• Post-2020 goals will flow through SDGs, national & local policy,
influence monitoring frameworks: we need to get them right
Prof Emily Nicholson
Deakin University, Australia
e.nicholson@deakin.edu.au
https://conservationscience.org.au/
https://iucnrle.org/
@n_ylime @redlisteco
Thank you
Andrew Hansen
Montana State University
hansen@montana.edu
The Concept and Monitoring of Ecosystem Integrity
Ecosystem Extent and Integrity Webinar
Webinars on Supporting Implementation of the Post-2020 Global
Biodiversity Framework: Indicators
Monday September 27th
A. 2050 Goals and 2030 Milestones
Goal A
The integrity of all ecosystems is enhanced, with an
increase of at least 15 per cent in the area,
connectivity and integrity of natural ecosystems, …
Introduction
Finalizing a post-2020 GBF requires:
A working definition of ecosystem integrity (EI);
Indicators of ecosystem structure, function, and composition;
The means by which countries globally can measure, monitor, and evaluate
trends in condition of these indicators;
A system to report improvements or degradation in EI.
We offer a schema for using Earth observations to monitor and
evaluate global forest EI.
Topics
Define EI
Define the schema
Draw conclusions
Presentation is based on:
Introduction
What is Ecosystem Integrity?
Oxford Dictionary - The condition of having no part or element taken away or wanting;
undivided or unbroken state; material wholeness, completeness, entirety.
Andreasen et al., 2001; Dale & Beyeler, 2001; Parrish et al., 2003; Wurtzebach & Schultz,
2016 - The ecosystem structure, function, and composition relative to “the natural or
historic range of variation of these characteristics” or are “characteristic of a region.
Schema for Monitoring EI in the Post-2020 GBF
Schema for Monitoring EI in the Post-2020 GBF
Representation of the concept of ecosystem integrity in the
context of the ecosystem and controlling state factors.
EI - a measure of ecosystem
structure, function and
composition relative to the
reference state of these
components being
predominantly determined by
the extant climatic–geophysical
environment (while
acknowledging a backdrop of
climate change.
Selection of Metrics
Ecosystem Component (Level I / Level II)
Potential Indicator
(source)
1. Ecosystem
structure,
function, or
composition
2. Extent and
Spatial
Resolution
3. Temporal
Resolution
4. Aggrega-
tion
5. Credibility
 Availability
6. Refer-
ence
State
Ecosystem Structure
Stand Structure
Forest Structural Condition Index
(Hansen et al. 2019)
Yes Yes Yes Yes Yes No
Landscape Structure
Lost Forest Configuration (Grantham et al. 2020) Yes Yes Yes Yes Yes Yes
Relative Magnitude of Fragmentation1
Yes Yes Yes Yes No No
Ecosystem Function
Productivity
MODIS Net Primary Productivity
(Running et al. 2004)
Yes Yes Yes Yes Yes No
Carbon Storage
Carbon Density
(Spawn et al. 2020)
Yes Yes No Yes Yes No
Natural Disturbance Regime
MODIS Area Burned
(Chuvieco et al. 2018)
Yes Yes Yes Yes Yes No
Ecosystem Composition
Populations
Living Planet Index (Collen et al. 2009) Yes No No No Yes No
Red List
Index2
(Rodrigues et al. 2014)
Yes No No Yes Yes No
Communities
Species Habitat Index by group
(Jetz et al. 2019)
Yes Yes Yes Yes Yes Yes
Biodiversity Intactness Index (BII)
(Tim Newbold et al. 2016)
Yes Yes Yes Yes Yes Yes
Biodiversity Habitat Index (BHI)
(Hoskins et al. 2020)
Yes Yes Yes Yes Yes Yes
Bioclimatic Ecosystem Resilience Index (BERI)
(Ferrier et al. 2020)(This is a combination of
ecosystem structure and composition elements)
Yes Yes Yes Yes Yes Yes
does not meet criteria
meets all except reference state
meets all criteria
Recommended Metrics
Ecosystem
Component /
Indicator
Description Data Inputs Spatial / Temporal
Resolution
Citation and
Data Source
Ecosystem Structure
Forest Structural
Condition Index (FSCI)
Vegetation structure within forest stands. Inputs include
canopy cover, canopy height, and time since disturbance.
….
Landsat
Sentinel-2
ICESAT-2
30 m
2012-2019
Tropical forests
Hansen et al.
20191
Lost Forest
Configuration (LFC)
Index of the current patchiness of forest areas relative to
the natural potential in forests without extensive human
modification. ….
Laestadius et
al. 2011
300m 2019. Plans
for annual
updates.
Grantham et
al. 20202
Ecosystem Function
MODIS Net Primary
Productivity (NPP)
Functional measure of new biomass fixed by green plants
through photosynthesis. ….
MODIS 1 km
2000-2020
Running et
al. 20045
Scurlock and
Olson 2013
MODIS Burned Area Fire history relates directly to the function of a given
ecosystems disturbance regime. ….
MODIS 250 m
2000-2020
Chuvieco et
al. 20186
Ecosystem
Composition
Species Habitat Index
by group
Average decrease in suitable habitat and populations of
amphibian, bird and mammal species and the resulting
change in the ecological integrity of ecosystems. ….
Landsat,
MODIS
1km
2000-2018
Powers &
Jetz 2019,
Jetz et al.
20198
Potential to be readied for use
Ready for use
Reference Conditions
Gradient of methods for establishing reference state
Conclusions
Our schema could allow for consistent, fine-scale, nationally
relevant, global monitoring of the components of EI that would
help facilitate measurable success in reaching the post 2020
biodiversity targets.
We advocate that Parties to the CBD build upon this schema and
operationalize a comprehensive approach for using EO to
monitor indicators of EI.
Catalyzing this opportunity will help nations to better
identify, address, monitor, and ultimately overcome critical
underlying causes of ecosystem and biodiversity loss.
Forest extent and integrity indicators for national
reporting on SDG 15: A case study in Peru,
Colombia, and Ecuador
Webinars on Supporting Implementation of the Post-2020 Global Biodiversity Framework
September 27th, 2021
Patrick.Jantz@nau.edu
NASA Life on Land Project
Science Team: Andy Hansen, Scott Goetz, Patrick Jantz, James Watson, Oscar Venter, Ivan Gonzalez, Jaris Veneros, Jose Aragon
UNDP Team: Jamison Ervin, Anne Virnig, Christina Supples
National Teams: Colombia -Susana Rodríguez-Buritica, Maria Cecilia Londoño, Dolors Armenteras
Ecuador –Nestor Alberto Acosta Buenaño, Monica Andrade, Carlos Montenegro
Peru -Erasmo Otarola, Michael Valqui, James Leslie
NASA HQ:Cindy Schmidt
Grant number. 80NSSC19K0186
Satellite
imagery
High-quality
spatial
datasets
SDG15
subindicators
SDG15
reporting
The goal of the project is to move from…
...to set and achieve ambitious nature-based
goals and targets
Engagement of Key
Partners
• Monthly Project Calls
• ~30 participants up to the Director
level
• Virtual Annual Workshop
• ~50 participants up to the Director
level
• Regional Partnerships
• SERVIR Amazonia, ProAmazonia
(Ecuador), National Adaptation
Plan and Amazonia Resiliente
(Perú), Paramos de Vida
(Colombia), and Amazonia
Sostenible para la paz (Colombia)
IDEAM
(Env. Inst.)
MinAmbiente
(Environment minister)
MAE (Environment
minister)
Humboldt
(Biod. Inst.)
DANE (Stat.
Inst.)
Planifica
(Stat. Inst.)
Colombia Ecuador Perú
Environmental secretariat Environmental Ministry (MinAmbiente) Ministry of Environment (MAE) Ministry of the Environment (MINAM)
Statistic offices Statistic national department (DANE) Census and statistics national
institute (INEC)
Statistic and informatics national institute (INEI)
Environmental agencies Environmental studies institute (IDEAM),
Humboldt’s biodiversity institute (IAVH)
- National service of protected areas (SERNANP)
National aerospatial research and development
commission (CONIDA)
International agencies UNDP Colombia UNDP Ecuador UNDP Perú
MINAM (Environment
minister)
CONIDA
(Spat. Inst.)
INEI (Stat.
Inst.)
SERNAP
(PA Inst.)
SERFOR
(Forest Inst.)
UNDP
Colombia
UNDP
Ecuador UNDP Peru
UNDP –world / NY
ANA
(Water Inst.)
Overview of Ecosystem Extent and Integrity Slides
INDICATOR 15.1.1
• Forest area as a proportion of total land area (by natural forest and
ecosystem type)
• Forest area
• Forest area as a proportion of total land area by ecosystem type
• Natural forest area by ecosystem type
• Natural habitat area as a proportion of ecosystem type
• Land area
INDICATOR 15.1.2
• Proportion of important sites for terrestrial and freshwater
biodiversity that are covered by protected areas, by ecosystem type
• Average proportion of Freshwater Key Biodiversity Areas (KBAs) covered by
protected areas
• Average proportion of ecosystem types covered by protected areas
• Average proportion of high forest structural integrity areas covered by
protected areas
• Human Footprint change in protected areas
• Human Footprint change around protected areas
• Average proportion of Terrestrial Key Biodiversity Areas (KBAs) covered by
protected areas
INDICATOR 15.2.1
• Progress towards sustainable forest management
• Above-ground biomass stock in forest
• Forest area annual net change rate
• Forest area under an independently verified forest management certification
scheme
• Proportion of forest area under a long-term management plan
• Proportional distribution of forest structural integrity condition classes by
ecosystem type
• Forest fragmentation index by ecosystem type (all forest, high FSII forest)
• Forest connectivity index by ecosystem type (all forest, high FSII forest)
• Proportion of forest area within legally established protected areas
INDICATOR 15.5.1
• Red List Index
• Red List Index
• Area of suitable habitats for selected vertebrate species
Overview of Ecosystem Extent and Integrity Slides
Indicador ODS 15.2.1.5 Fragmentación
Limitaciones: Depende del
insumo de capa de bosque.
Requiere mapas binarios que
se derivan de insumos
continuos. No analiza las
condiciones de áreas en no
bosque
Medida de incertidumbre:
Ninguna para reportar
Periodicidad: Según la fuente, 2012-2018 o 2000-
2019
Resolución: 30m o según la fuente
Extensión espacial: Nacional
Agregación: Sí. Cuencas, estados, etc.
Interpretación: Menores valores del índice señalan
mejor condición espacial y de unidad para los pixeles
identificados como bosques. Valores más altos
indican más fragmentación. Valores entre 0 y 100
Metodología:
- Vogt et al. 2007 para el análisis MPSA
- Vogt et al. 2017 para software y cálculo
- Jantz et al. In prep para la
implementación en bosques tropicales
(inlcluído Co, Ec, Pe)
Fuente de datos:
- Hansen et al. 2019: Mapas de bosque
de alta condición estructural [2012-
2018]
- Hansen et al. 2013: Mapas bosque no
bosque anuales [2000 - 2019]
- Conjuntos de datos nacionales (IDEAM,
SERFOR)
Algoritmo:
- GuidosToolbox para uso local
- GEE* para cálculo en la nube
Repositorio:
- UNBiodiversityLab
- GEE
- FigShare, Zenodo, etc *
Índice de fragmentación
Área en bosque bajo la categoría de núcleos
Área en bosque bajo la categoría de perforaciones
Área en bosque bajo la categoría de borde o interfaz
Área total del área de estudio
TIERS
I II III
III
III
III
Project Coordinators
• UNDP Regional and National Coordinators
• Carlos Montenegro
• Claudia Fonseca
• Gabriela Albuja
• Patricia Huerta
• Ph.D. Students
• 1 from each country
• Jaris Veneros
• Jose Aragon
• Ivan Gonzalez
Contact: Patrick.Jantz@nau.edu
Links: https://unbiodiversitylab.org/
https://nbsapforum.net/nasa-forest-integrity-project/nasa-forest-integrity-
project-data-access-instructions
Thank You!!
Accounting for Ecosystem Extent and Integrity
- Overview of SEEA Ecosystem Accounting
Alessandra Alfieri
United Nations Statistics Division
Outline
• Overview of SEEA Ecosystem Accounting (EA) and its relevance to Goal A of
the GBF
• Accounting ecosystem extent in SEEA EA
• Accounting ecosystem condition in SEEA EA
• Conclusions
Standardisation of measurement of the environment
• SEEA Central Framework adopted as statistical standard through an
intergovernmental process (ECOSOC / United Nations Statistical
Commission) in 2013
• SEEA Ecosystem Accounting discussed in March 2021
> chapters 1-7 describing the accounting framework and the physical
accounts adopted as an international statistical standard
> chapters 8-11 recognized as describing internationally recognized
statistical principles and recommendations for the valuation of
ecosystem services and assets in a context that is coherent with the
concepts of System of National Accounts
• SEEA status of implementation 2020:
> 89 countries implementing the SEEA Central Framework
> 34 countries compiling SEEA Ecosystem Accounts
> 27 countries planning to start implementation of the SEEA
SBSTTA-24
• The Subsidiary Body on Scientific, Technical and
Technological Advice (SBSTTA) at its recent meeting in May
2021 :
> “Recognizes the value of aligning national monitoring with
the United Nations System of Environmental-Economic
Accounting statistical standard in order to mainstream
biodiversity in national statistical systems and to
strengthen national information and monitoring systems
and reporting”
Source: Non-Paper on SBSTTA-24 Agenda item 3
https://www.cbd.int/doc/c/13e9/73d6/0de346d7d3433024a3ef1441/sbstta-24-nonpaper-item-03-v1-en.pdf
Goal A (CBD/WG2020/3/3/Add.1 - 11 July 2021)
Goal A. The integrity of all
ecosystems is enhanced, with an
increase of at least 15% in the area,
connectivity and integrity of natural
ecosystems, supporting healthy and
resilient populations of all species, the
rate of extinctions has been reduced at
least tenfold, and the risk of species
extinctions across all taxonomic and
functional groups, is halved, and
genetic diversity of wild and
domesticated species is safeguarded,
with at least 90% of genetic diversity
within all species maintained
A.0.1 Extent of
selected
natural and
modified
ecosystems (i.e.
forest,
savannahs and
grasslands,
wetlands,
mangroves,
saltmarshes,
coral reef,
seagrass,
macroalgae and
intertidal habitats)
By terrestrial
and marine
ecosystem
types
By mountains
UN System of Environmental
Economic Accounting (SEEA):
https://seea.un.org/ecosyste
maccounting
Ecosystem types based on
IUCN categories.
Near ready**
Proposed goal or target Proposed indicators
Proposed
disaggregation
Methodological basis
Global data set for
national
disaggregation
EA1
4
E1
9
EA1
2
ET
4
EA1
1
ET
3
EA
1 ET
1
ET
1
EA
2
EA
3
ET
2
ET
3
ET
3
ET
3
ET
3
EA7
ET
2
EA1
0
ET
6
ET
7
EA8
EA9
ET
4
Ecosystem accounting approach
Filtration
Clean water
Households
Soil depth
Benefit
Forest
2
4
5
1
3
Condition
Service
Asset
Beneficiaries
1
2
3
4
5
Illustration
SEEA EA - Core Accounts
Ecosystem types
• SEEA EA endorses the
IUCN GET as
international
reference
classification
• 6 levels – accounts are
compiled at level of
the Ecosystem
Functional Groups
(e.g. tropical lowland
rainforest)
Realms Biomes
Terrestrial T1 Tropical–subtropical forests
T2 Temperate–boreal forests &
woodlands
T3 Shrublands & shrubby woodlands
T4 Savannas and grasslands
T5 Deserts and semi-deserts
T6 Polar-alpine
T7 Intensive land-use systems
Freshwater F1 Rivers and streams
F2 Lakes
F3 Artificial fresh waters
Marine M1 Marine shelfs
M2 Pelagic ocean waters
M3 Deep sea floors
M4 Anthropogenic marine systems
Subterranean S1 Subterranean lithic
S2 Anthropogenic subterranean voids
Transitional TF1 Palustrine wetlands
FM1 Semi-confined transitional waters
MT1 Shoreline systems
MT2 Supralittoral coastal systems
MT3 Anthropogenic shorelines
MFT1 Brackish tidal systems
SF1 Subterranean freshwaters
SF2 Anthropogenic subterranean
freshwaters
SM1 Subterranean tidal
Ecosystem extent account
Source: SEEA EA
Realm
Biome
F1 … FM1 M1 … MFT1
Selected Ecosystem Functional
Group (EFG) Tropical-subtropical
lowland
rainforests
Tropical-subtropical
dry
forests
and
scrubs
Tropical-subtropical
montane
rainforests
Tropical
heath
forests
Boreal
and
temperate
high
montane
forests
and
woodlands
Deciduous
temperate
forests
…
Temperate
pyric
sclerophyll
forests
and
woodlands
…
…
…
Derivied
semi-natural
pastures
and
old
fields
Permanent
upland
streams
…
Intermittently
closed
and
open
lakes
and
lagoons
Seagrass
meadows
…
Coastal
saltmarshes
and
reedbeds
T1.1 T1.2 T1.3 T1.4 T2.1 T2.2 … T2.6 … … … T7.5 F1.1 … FM1.3 M1.1 … MFT1.3
Opening extent
Additions to extent
Managed expansion
Unmanaged expansion
Reductions in extent
Managed reductions
Unmanaged reductions
Net change in extent
Closing extent
TOTAL
Selected ecosystem types (based on Level 3 - EFG of the IUCN Global Ecosystem Typology)
Terrestrial
T1 Tropical-subtropical
forests
T2 Temperate-boreal
forests and woodlands
… T7
Freshwater Marine
In India, compilation of ecosystem extent
accounts is based on locally relevant
ecosystem type classifications. These have
been mapped to the IUCN GET classification
at the EFG level for the purposes of
international comparability.
Source: Ministry of Statistics and Programme Implementation, 2021.
The values in the cells represent the share
of Indian forests that map to the GET
categories:
• Values of 1 represent a 1-to-1 match.
• Values less than 1 indicate that the
Indian forest type maps to more than
one GET forest type - in proportion to
the values given in the corresponding
cells.
Example: Mapping of Indian forest types to IUCN GET forest
ecosystem functional groups (EFG)
12
Natural areas
Anthropized areas
Artificial surfaces
Cropland
Mosaic in forest area
Managed pasture
Silviculture
Forest tree cover
Wetland
Forest
Barren land
Inland water bodies
Coastal water bodies
Mosaic in non-forest area
13
Ecosystem Extent Accounts
14
The higher absolute totals of natural area reduction were concentrated
on the Amazônia and Cerrado biomes (86,2%) 15
(continues)
….
….
….
….
….
The SEEA Ecosystem Condition Typology (ECT)
Source: SEEA EA
ECT groups and classes
Group A: Abiotic ecosystem characteristics
Class A1. Physical state characteristics: physical descriptors of the abiotic components of the ecosystem (e.g., soil structure, water availability)
Class A2. Chemical state characteristics: chemical composition of abiotic ecosystem compartments (e.g., soil nutrient levels, water quality, air
pollutant concentrations)
Group B: Biotic ecosystem characteristics
Class B1. Compositional state characteristics: composition / diversity of ecological communities at a given location and time (e.g., presence /
abundance of key species, diversity of relevant species groups)
Class B2. Structural state characteristics: aggregate properties (e.g., mass, density) of the whole ecosystem or its main biotic components (e.g.,
total biomass, canopy coverage, annual maximum normalized difference vegetation index (NDVI))
Class B3. Functional state characteristics: summary statistics (e.g., frequency, intensity) of the biological, chemical, and physical interactions
between the main ecosystem compartments (e.g., primary productivity, community age, disturbance frequency)
Group C: Landscape level characteristics
Class C1. Landscape and seascape characteristics: metrics describing mosaics of ecosystem types at coarse (landscape, seascape) spatial
scales (e.g., landscape diversity, connectivity, fragmentation)
Ecosystem condition indicator account
Source: SEEA EA
SEEA Ecosystem Condition
Typology Class
Indicators
Ecosystem type
Variable values Reference level values Indicator values (rescaled)
Descriptor Opening value Closing value
Upper level (e.g.,
natural)
Lower level (e.g.,
collapse) Opening value
Closing
value
Change in
indicator
Physical state
Indicator 1
Indicator 2
Chemical state
Indicator 3
Compositional state
Indicator 4
Indicator 5
Structural state
Indicator 6
Functional state
Indicator 7
Landscape/waterscape
characteristics
Indicator 8
Source: CSIR, 2020
Example: Changes in South African estuarine
ecosystem’s condition
Mexico – Ecosystem Integrity index -2018
Ecosystem type
Opening value
2004
Opening
value 2018
Change
Aquaculture 0.78 0.55 -0.23
Annual cropland 0.34 0.35 0.00
Perennial cropland 0.41 0.41 0.00
Human settlements 0.12 0.10 -0.03
Planted forest 0.55 0.55 0.00
Coniferous forest 0.81 0.83 0.02
Oak forest 0.77 0.78 0.02
Montane cloud forest 0.76 0.78 0.02
Special other woody vegetation types 0.65 0.65 0.00
Special other non-woody vegetation types 0.74 0.72 -0.02
Woody xeric shrubland 0.84 0.85 0.01
Non-woody xeric shrubland 0.88 0.87 -0.01
Other lands 0.81 0.68 -0.13
Grassland 0.47 0.52 0.05
Deciduous tropical forest 0.70 0.73 0.02
Evergreen tropical forest 0.78 0.79 0.01
Semideciduous tropical forest 0.69 0.71 0.01
Woody hydrophytic vegetation 0.81 0.83 0.01
Non-woody hydrophytic vegetation 0.74 0.81 0.07
What is ARIES for SEEA?
• Tool that uses ARIES technology to compile
ecosystem accounts that are consistent with
the SEEA Ecosystem Accounting
• Includes land cover accounts consistent with
the SEEA Central Framework
• Uses same definitions, classifications,
accounting rules as the SEEA
• Can help automate production of maps and
tables
• Provides infrastructure for the SEEA
community to share and reuse interoperable
data and models
Conclusions
• SEEA adopted as statistical standard major milestone
• Makes nature count within economic planning and decision-making
• Standardization is important in order to obtain high-quality, and comparable
statistics
• Provides framework for deriving indicators to support various monitoring and
reporting frameworks such as SDGs, Biodiversity, Climate Change, Green
Economy
• SEEA EA implementation strategy:
• Guidelines and tools are developed to facilitate accounts compilation
• Enhanced collaboration between various communities (statistical, geospatial,
biodiversity, policy makers)
Australia’s National Science Agency
Adding value to monitoring of ecosystem extent and integrity
through derivation of habitat-based biodiversity indicators
Simon Ferrier | 27 September 2021
With thanks to: Chris Ware, Becky Schmidt, Tom Harwood, Andrew Hoskins, Karel Mokany (CSIRO)
Andy Purvis (NHM), Hedley Grantham (WCS)
https://seea.un.org/content/exploring-approaches-constructing-species-accounts-context-seea-eea
https://geobon.org/ebvs/indicators/
Habitat-based biodiversity indicators can play an important role in
large-scaled biodiversity assessment
➢ Habitat-based biodiversity indicators assess how changes
in ecosystem extent and condition (integrity) are
expected to affect retention of species diversity
➢ They therefore offer one simple means of linking the
ecosystem and species components of draft GBF Goal A
➢ Habitat-based indicators employ mapping either of
individual species distributions (e.g. the Species Habitat
Index) or of overall variation in community composition
➢ This presentation focuses on a community-level indicator
generated by CSIRO – the Biodiversity Habitat Index
The Biodiversity Habitat Index (BHI) translates ecosystem extent
& integrity mapping into an indicator of biodiversity retention
Hoskins AJ et al (2020) Environmental Modelling & Software 132: 104806 Ferrier S et al (2020) Ecological Indicators 117: 106554
Extent and local (per grid-cell)
integrity of ecosystems
Biodiversity Habitat Index (BHI)
– expected impact of ecosystem
extent, local integrity [and
connectivity] on regional/global
retention of species diversity
Spatial variation in
community composition
(modelled from data for
>400,000 species globally)
Spatial habitat
connectivity analysis
(with or without effects of
climate change)
The BHI can be scaled either as
“effective proportion of habitat
remaining” or as “proportion of species
expected to persist” (by invoking the
species-area relationship)
Optional incorporation of
connectivity analysis from
CSIRO’s Bioclimatic
Ecosystem Resilience
Index (BERI) indicator
The BHI has been generated globally at 1km grid-resolution across all
terrestrial biomes
Hoskins AJ et al (2020) Environmental Modelling & Software 132: 104806
Di Marco M et al (2019) Global Change Biology 25: 2763-2778
IPBES Regions
Results can be mapped at
raw grid-cell resolution …
… or reported by any
specified set of spatial units
https://ipbes.net/global-assessment
https://epi.yale.edu/ http://chm.aseanbiodiversity.org/
This capability is already being used to assess global and regional
trends in the state of habitat supporting biodiversity …
Mokany K et al (2020) PNAS 117: 9906-9911 http://www.sparc-website.org/
… and to prioritize areas for habitat protection or restoration to
enhance prospects for biodiversity persistence
MODIS Vegetation
Continuous Fields
ESA CCI
Land Cover
Remote sensing time series
LUH2 coarse
resolution
land-use
training data
Environmental
covariates
Responses of
local biodiversity
to land use
Biodiversity Intactness Index (BII) time series
2020
2000
Translation of land
cover into 12 land-use
class probabilities
through statistical
downscaling
https://www.nhm.ac.uk/our-science/our-work/biodiversity/predicts.html
Hoskins AJ et al (2016) Ecology and Evolution 6: 3040-3055
The source of data on local ecosystem
integrity used most extensively in the BHI is
the Biodiversity Intactness Index (BII)
➢ Derived by coupling CSIRO’s downscaled land-use
time series with the Natural History Museum
PREDICTS project’s meta-analysis of land-use
impacts on local biodiversity
➢ Recently updated to provide 1km-resolution
mapping of change for every year from 2000 to
2020 globally
➢ The Natural History Museum have committed to
continuing production of the BII post-2020
The BHI can additionally be derived from other ecosystem integrity inputs
e.g. from the recently developed 300m-resolution Forest Landscape Integrity Index, in a current
collaboration with WCS and University of Queensland
Grantham HS et al (2020) Nature Communications 11: 5978
https://www.forestintegrity.com/
Nepal
The BHI is also derivable at national & subnational scales – including
from UN SEEA-EA ecosystem extent & condition accounts data
https://eea.environment.gov.au/accounts/ecosystem-accounts
https://www.wavespartnership.org/en/planning-tool-peru
Applications from the San Martin Region of Peru …
… to the Murray-Darling Basin of Australia
What can habitat-based biodiversity indicators, such as the BHI,
contribute to post-2020 GBF implementation?
Better linking monitoring of progress towards
achieving Goal A with prioritization of actions
under Targets 1 to 3
Better integrating consideration of multiple
ecosystem-focused (extent, integrity, connectivity)
and species-focused components of Goal A
Better enabling seamless indicator derivation across
scales, employing best-available global, national and
subnational datasets
Australia’s National Science Agency
CSIRO Land & Water
Simon Ferrier
Chief Research Scientist
simon.ferrier@csiro.au
Thank you

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Overview of Ecosystem Extent and Integrity Slides

  • 1. 1 27 September 2021 Ecosystem Extent and Integrity webinar Hosted by GEO BON & UN SEEA National Aeronautics and Space Administration (c) 2021 California Institute of Technology. Government sponsorship acknowledged. Ecosystem Extent and related indicators: An overview Gary GELLER Jet Propulsion Laboratory California Institute of Technology gary.n.geller@jpl.nasa.gov Joe Parks from Wikimedia
  • 3. 3 GEO BON’s Mission Improve the acquisition, coordination and delivery of biodiversity observations and related services to users including decision makers and the scientific community. ❑ In the GBF context… • GEO BON supports Parties to track and guide progress to national targets
  • 4. 4 GEO BON Overview Navarro et al. 2017 Indicators and other information
  • 5. 5 What Are Essential Biodiversity Variables? Criteria: ❖ Biological ❖ State variables ❖ Sensitive to change ❖ Scalable ❖ Feasible ❖ Ecosystem agnostic Set of measurements to capture the major dimensions of biodiversity and how it is changing Genetic Composition Species Populations Species Traits Community Composition Ecosystem Structure Ecosystem Functions
  • 7. 7 What is Ecosystem Extent? ❑ A dataset that outlines one or more ecosystems of interest ORNL DAAC from Jorgenson and Grunblatt, 2013
  • 8. 8 What is Ecosystem Extent? ❑ Hierarchical ORNL DAAC from Jorgenson and Grunblatt, 2013
  • 9. 9 Approaches to Define an Ecosystem ❑ By physical structure • Tree height; scrub; grassland… • Ex: FAO Land Cover Classification System ❑ By physical environment • Climate, soils, slope…. • Ex: USGS Global Ecosystem Land Units ❑ By structure, composition, function & processes • IUCN Ecosystem Typology V2.0 US National Park Service
  • 10. 10 Extent Is Just the Beginning ❑ Pixel-based classification • Enables derived products… ❑ Total area ❑ Fragmentation (affects condition) ❑ Corridors/connectivity ❑ Location ❑ Habitat ❑ Environmental accounting Ipe-institutodepesquisasecologicas googlemaps Kabir Rasouli et al 2019
  • 11. 11 Relationship to Land Cover ❑ Land Cover includes non-ecosystem classes ❑ Often…the only maps available ❑ Challenges • Classes • Update frequency • Spatial resolution • Accuracy varies
  • 12. 12 Marine Ecosystems: “Seascapes” Montes et al 2020, Front Mar Sci
  • 13. 13 Ecosystem Extent in the Context of GBF 2050 Goal Components Headline Indicators Component Indicator Goal A: The integrity of all ecosystems is enhanced, with an increase of at least 15% in the area, connectivity and integrity of natural ecosystems… A.1. Area of natural ecosystems A.0.1. Extent of selected natural and modified ecosystems…e.g.,: • Forest, savannahs & grasslands • Wetlands • Mangroves • Saltmarshes • Coral reef • Seagrass • Macroalgae • Intertidal habitats A.2. Connectivity of natural ecosystems A.0.2. Species Habitat Index (presented in the species population webinar) A.2.1. CMS Connectivity Indicator (CMS) A.3. Integrity of natural ecosystems A.3.1. Ecosystem Integrity Index [ ] Proposed monitoring approach and headline, component and complementary indicators for the post-2020 global biodiversity framework CBD/WG2020/3/INF/2 (5 August 2021)… Annex I
  • 14. 14 Main Challenges ❑ Most common source: Land Cover maps ❑ Geographic and temporal availability varies ❑ Accuracy varies ❑ Classes ❑ Many datasets available on GEO BON EBV Data Portal • https://portal.geobon.org/datasets
  • 16. Improving capacity for monitoring ecosystem extent through the integration of global and national spatial data products Victor H Gutierrez-Velez, Maria Cecilia Londoño, Jeronimo Rodriguez, Angela Mejia, Victoria Sarmiento, Wilson Lara, Ivan Gonzalez, Jaime Burbano
  • 19. Priorities for improving the monitoring of ecosystem extent through data integration • To produce methods for data integration, QA/QC, gap identification and gap filling • To produce software that facilitates data integration and analysis • To enable access to data and methods to inform reporting and decision-making.
  • 20. Priorities for improving the monitoring of ecosystem extent through data integration • To produce methods for data integration, QA/QC, gap identification and gap filling • To produce software that facilitates data integration and analysis • To enable access to data and methods to inform reporting and decision-making.
  • 29. Map agreement (%) Tree cover Hansen (%) Map agreement (%) Tree cover Hansen (%) Maximum common extent IDEAM-HANSEN National biotic units Optimal agreement per unit Harmonized map
  • 30. 15 Country wide weighted agreement Overall agreement Optimum threshold
  • 31. Wetlands Pekel et al 2016 Florez et al 2016
  • 32. Ecosystem and Land cover products Multi-temporal spectral metrics Land cover legend Resolve classification conflicts (decision rules) Identification of data gaps Gap filling QA/QC
  • 33. Priorities for improving the monitoring of ecosystem extent through data integration • To produce methods for data integration, QA/QC, gap identification and gap filling • To produce software that facilitates data integration and analysis • To enable access to data and methods to inform reporting and decision-making.
  • 34. Input data roi() Ecosystem distribution EBV Time series sequence() Horizontal structure Fragmentation Lara, Gutierrez-Velez, Londoño (in prep.) Integrated metrics/ indicators EBVmetric() Year Area (ha) % live cover Enthropy ecoChange package roi() Polygon/gadm (User defined) https://cran.r-project.org/web/packages/ecochange/ecochange.pdf
  • 35. 0 100 200 Reference image Band 1 Band 2 Band 3 Band 4 Band 6 Band 5 Target image Gutierrez-Velez et al (submitted) rastermapr package
  • 36. Priorities for improving the monitoring of ecosystem extent through data integration • To produce methods for data integration, QA/QC, gap identification and gap filling • To produce software that facilitates data integration and analysis • To enable access to data and methods to inform reporting and decision-making.
  • 37. 1. Front-end Queries New products Virtual/local machine 2. Back-end API Data 42GB ecoChange rastermapr 3. Software
  • 38. Take home messages • The harmonization of global and national data sets aims to inform biodiversity monitoring and decision making nationally while ensuring consistency with global assessments. • The development of a cloud infrastructure facilitates the use of harmonized data for characterizing, monitoring and reporting biodiversity conservation efforts. • The integration of data, software and infrastructure can enable dynamic data improvements and timely data use for decision-making.
  • 39. Improving capacity for monitoring ecosystem extent and integrity through the integration of global and national spatial data products Victor H Gutierrez-Velez, Maria Cecilia Londoño, Jeronimo Rodriguez, Angela Mejia, Victoria Sarmiento, Wilson Lara, Ivan Gonzalez, Jaime Burbano victorhugo@temple.edu www.bosproject.org
  • 40. Ecosystems in the post-2020 global biodiversity framework Professor Emily Nicholson (Deakin University, Australia)
  • 41. Ecosystems in the Post-2020 Global Biodiversity Framework • Strategic Plan for Biodiversity 2011-2020, created in 2010, include the Aichi Biodiversity Targets • 20 targets under 5 goals, none met (some partially met) • Species targets (no extinctions) but no specific ecosystem target • Goals of the first draft of the post-2020 global biodiversity framework Watson, J.E.M., Keith, D.A., Strassburg, B.B.N., Venter, O., Williams, B., Nicholson, E. (2020) Set a global target for ecosystems. Nature 578, 360-362. Nicholson et al. (2021) Scientific foundations for an ecosystem goal & indicators for the post-2020 global biodiversity framework. Nature Ecol & Evol Safeguard species Maintain genetic diversity Sustain ecosystems Figure 1. Ecosystems are central to meeting all three CBD objectives, which 2020 goals: 1) conservation of biodiversity (from genes, species to ecosyste green); 2) the sustainable use of its components (draft Goal B, in orange); a Maintain & enhance Nature’s contributions to people Share benefits of genetic diversity fairly & equitably
  • 42. Why an ecosystem goal? Big developments in ecosystem conservation science over last decades: • Accepted definitions of ecosystems and collapse • Ecosystem: biotic and abiotic components, processes and interactions within and between them, in a place • Collapse: endpoint of decline, defining features are lost, replacement by another ecosystem type
  • 43. Why an ecosystem goal? Big developments in ecosystem conservation science over last decades: • Accepted definitions of ecosystems and collapse • Ecosystem mapping https://www.intertidal.app/ https://www.globalmangrovewatch.org http://www.earthenv.org/cloudforest
  • 44. Why an ecosystem goal? Big developments in ecosystem conservation science over last decades: • Accepted definitions of ecosystems and collapse • Ecosystem mapping • Ecosystem classification: https://global-ecosystems.org/ • Ecosystem risk assessment: • Red List of Ecosystems (>3000 ecosystem assessed) http://iucnrle.org • Ecosystem accounting: https://seea.un.org/
  • 45. Why an ecosystem goal? Big developments in ecosystem conservation science over last decades: • Accepted definitions of ecosystems and collapse • Ecosystem mapping • Ecosystem classification: https://global-ecosystems.org/ • Ecosystem risk assessment: Red List of Ecosystems >3000 ecosystem assessed http://iucnrle.org • Ecosystem accounting: https://seea.un.org/ Terrestrial Marine All ecosystems Subsets Underway Strategic
  • 46. Core components for an ecosystem goal Ecosystem area Ecosystem integrity Risk of ecosystem collapse 1. Ecosystem area or extent 2. Ecosystem integrity 3. Risk of ecosystem collapse
  • 47. Core components for an ecosystem goal Direct drivers of loss (threats) Land-use/sea-use change Resource extraction (biotic, abiotic) Invasive species Pollution Climate change Ecosystem area Ecosystem integrity Risk of ecosystem collapse Decreases Increases
  • 48. Targets to achieve an ecosystem goal Direct drivers of loss (threats) Land-use/sea-use change Resource extraction (biotic, abiotic) Invasive species Pollution Climate change Ecosystem area Ecosystem integrity Risk of ecosystem collapse T1 T3 T6 T7 T8 T9 T5 T2 A T4 B T2 T3 T3 A T4 Decreases Increases T11 T11 Retain ecosystems Protected areas & OECMs Sustainable harvest Manage invasive species Reduce pollution Action on climate change Actions targets to halt loss of ecosystem area & integrity Actions targets to reverse loss of ecosystem area & integrity: restoration Restore ecosystems, PAs & OECMS, species recovery, nature-based solutions Restore ecosystems, PAs & OECMS, species recovery, nature-based solutions
  • 49. Indicators: what do they need to do? A good indicator set for an ecosystem goal is: 1. Aligned with and cover all goal components 2. Relevant to ecosystems: specific ecosystems, features, collapse 3. Tested & behaves predictably: responses & biases are understood 4. Calculated using available, accessible data: spatial & temporal coverage; open access Watermeyer et al. (2021), Using decision science to evaluate global biodiversity indices. Conserv Biol, 35: 492-501. https://doi.org/10.1111/cobi.13574 Nicholson et al. (2021) Scientific foundations for an ecosystem goal & indicators for the post-2020 global biodiversity framework. Nature Ecology & Evolution (in review) Direct drivers of loss (threats) Land-use/sea-use change Resource extraction (biotic, abiotic) Invasive species Pollution Climate change Ecosystem area Ecosystem integrity Risk of ecosystem collapse Target Target scope Examples of actions to achieve an ecosystem goal T1a Retain ecosystem area & integrity Planning, regulation & incentives to address land/sea-use change T1b Restore ecosystem area & integrity Restoration of abiotic environment/processes (e.g. water, fire regimes) & biotic components (e.g. direct seeding, planting, rewilding) T2 Expanded & effective protected areas (PAs) & other effective area- based conservation measures (OECMs) Preventing further loss through regulation; increasing integrity & area through effective PA/OECM management & restoration action T3 Manage for recovery of wild species In situ management of species, including restoration action, reintroductions/rewilding & habitat management T4 Sustainable harvest of biota Effective management of fisheries, bushmeat-hunting, forestry activities T5 Manage invasive species Prevent new introductions, reduce spread, eradicate or control invasive species to eliminate or reduce their impacts T6 Reduce pollution to levels not harmful to biodiversity & ecosystem functions Reduce excess nutrients, biocides (pesticides etc), & plastic waste T7 Increase action on climate change to ensure resilience & minimize negative impacts on biodiversity Nature-based solutions & ecosystem management for resilient ecosystems, disaster-risk reduction & mitigation (eg carbon sequestration) T8 Ensure benefits through sustainable management of wild species Overlap with T4; management of fisheries, bushmeat-hunting, harvest T10 Nature-based solutions for ecosystem services Restore and protect ecosystems to support regulating services A Ecosystem management Fire & water management/regulation (rather than restoration) B Sustainable harvest of abiotic ecosystem components Water extraction (currently not explicitly included in targets) T1a T2 T5 T6 T7 T8 T4 T1b A T3 B T1b T2 T2 A T3 Decreases Increases T10 T10
  • 50. Indicator Collapse risk Area Integrity Composition Integrity Structure Integrity Function Drivers Marine Freshwater Terrestrial Ecosystem relevance Performance tested Global trend coverage Red List Index of Ecosystems + + + + + + x Change in the extent of water-related ecosystems over time + + + - + Continuous Global Mangrove Forest Cover + + + + - + Ecosystem Area Index + + + + + + - Forest Area as a Proportion of Total Land Area + + x - + Global Mangrove Watch + + + - + Trends in Primary Forest Extent + + + - + Tree Cover Loss (Global Forest Watch) + + x - + Wetland Extent Trends Index + + + + - + Bioclimatic Ecosystem Resilience Index + + + + x x + Biodiversity Habitat Index + + + x x + Biodiversity Intactness Index + + + x - + Living Planet Index + + + + x + + Mean Species Abundance + + + + x x + Red List Index for species + + + + - + + Species Habitat Index + + + x x + Ecosystem Intactness Index + + + x - + Forest Landscape Integrity Index + + + x x - Live Cover via Vegetation Continuous Fields + + x - + Ecosystem Health Index + + + + + + + + - Live Coral Cove + + + + + Proportion of land degraded over total land area + + x - + Vegetation Health Index + + x - + Water Turbidity & an estimate of Trophic State Index + + + - + Coral Reef Watch + + + + + Human Footprint + + x - + Marine Cumulative Human Impacts + + - - + Ocean Health Index + + x - + 1. Only one indicator of collapse risk 2. Bias towards terrestrial & forest ecosystems 3. Bias towards composition (vs function) 4. Low relevance to specific ecosystems 5. Trade-off between data coverage & ecosystem relevance 6. Low levels of performance testing
  • 51. Where to next? • Post-2020 goals need strong scientific basis: area, integrity & collapse risk • Theory of change can identify pathways for impact & gaps • Indicator set needs work! Need an ongoing process so we are not constrained by current data & indicators • Post-2020 goals will flow through SDGs, national & local policy, influence monitoring frameworks: we need to get them right Prof Emily Nicholson Deakin University, Australia e.nicholson@deakin.edu.au https://conservationscience.org.au/ https://iucnrle.org/ @n_ylime @redlisteco Thank you
  • 52. Andrew Hansen Montana State University hansen@montana.edu The Concept and Monitoring of Ecosystem Integrity Ecosystem Extent and Integrity Webinar Webinars on Supporting Implementation of the Post-2020 Global Biodiversity Framework: Indicators Monday September 27th
  • 53. A. 2050 Goals and 2030 Milestones Goal A The integrity of all ecosystems is enhanced, with an increase of at least 15 per cent in the area, connectivity and integrity of natural ecosystems, … Introduction Finalizing a post-2020 GBF requires: A working definition of ecosystem integrity (EI); Indicators of ecosystem structure, function, and composition; The means by which countries globally can measure, monitor, and evaluate trends in condition of these indicators; A system to report improvements or degradation in EI. We offer a schema for using Earth observations to monitor and evaluate global forest EI.
  • 54. Topics Define EI Define the schema Draw conclusions Presentation is based on: Introduction
  • 55. What is Ecosystem Integrity? Oxford Dictionary - The condition of having no part or element taken away or wanting; undivided or unbroken state; material wholeness, completeness, entirety. Andreasen et al., 2001; Dale & Beyeler, 2001; Parrish et al., 2003; Wurtzebach & Schultz, 2016 - The ecosystem structure, function, and composition relative to “the natural or historic range of variation of these characteristics” or are “characteristic of a region.
  • 56. Schema for Monitoring EI in the Post-2020 GBF
  • 57. Schema for Monitoring EI in the Post-2020 GBF Representation of the concept of ecosystem integrity in the context of the ecosystem and controlling state factors. EI - a measure of ecosystem structure, function and composition relative to the reference state of these components being predominantly determined by the extant climatic–geophysical environment (while acknowledging a backdrop of climate change.
  • 58. Selection of Metrics Ecosystem Component (Level I / Level II) Potential Indicator (source) 1. Ecosystem structure, function, or composition 2. Extent and Spatial Resolution 3. Temporal Resolution 4. Aggrega- tion 5. Credibility Availability 6. Refer- ence State Ecosystem Structure Stand Structure Forest Structural Condition Index (Hansen et al. 2019) Yes Yes Yes Yes Yes No Landscape Structure Lost Forest Configuration (Grantham et al. 2020) Yes Yes Yes Yes Yes Yes Relative Magnitude of Fragmentation1 Yes Yes Yes Yes No No Ecosystem Function Productivity MODIS Net Primary Productivity (Running et al. 2004) Yes Yes Yes Yes Yes No Carbon Storage Carbon Density (Spawn et al. 2020) Yes Yes No Yes Yes No Natural Disturbance Regime MODIS Area Burned (Chuvieco et al. 2018) Yes Yes Yes Yes Yes No Ecosystem Composition Populations Living Planet Index (Collen et al. 2009) Yes No No No Yes No Red List Index2 (Rodrigues et al. 2014) Yes No No Yes Yes No Communities Species Habitat Index by group (Jetz et al. 2019) Yes Yes Yes Yes Yes Yes Biodiversity Intactness Index (BII) (Tim Newbold et al. 2016) Yes Yes Yes Yes Yes Yes Biodiversity Habitat Index (BHI) (Hoskins et al. 2020) Yes Yes Yes Yes Yes Yes Bioclimatic Ecosystem Resilience Index (BERI) (Ferrier et al. 2020)(This is a combination of ecosystem structure and composition elements) Yes Yes Yes Yes Yes Yes does not meet criteria meets all except reference state meets all criteria
  • 59. Recommended Metrics Ecosystem Component / Indicator Description Data Inputs Spatial / Temporal Resolution Citation and Data Source Ecosystem Structure Forest Structural Condition Index (FSCI) Vegetation structure within forest stands. Inputs include canopy cover, canopy height, and time since disturbance. …. Landsat Sentinel-2 ICESAT-2 30 m 2012-2019 Tropical forests Hansen et al. 20191 Lost Forest Configuration (LFC) Index of the current patchiness of forest areas relative to the natural potential in forests without extensive human modification. …. Laestadius et al. 2011 300m 2019. Plans for annual updates. Grantham et al. 20202 Ecosystem Function MODIS Net Primary Productivity (NPP) Functional measure of new biomass fixed by green plants through photosynthesis. …. MODIS 1 km 2000-2020 Running et al. 20045 Scurlock and Olson 2013 MODIS Burned Area Fire history relates directly to the function of a given ecosystems disturbance regime. …. MODIS 250 m 2000-2020 Chuvieco et al. 20186 Ecosystem Composition Species Habitat Index by group Average decrease in suitable habitat and populations of amphibian, bird and mammal species and the resulting change in the ecological integrity of ecosystems. …. Landsat, MODIS 1km 2000-2018 Powers & Jetz 2019, Jetz et al. 20198 Potential to be readied for use Ready for use
  • 60. Reference Conditions Gradient of methods for establishing reference state
  • 61. Conclusions Our schema could allow for consistent, fine-scale, nationally relevant, global monitoring of the components of EI that would help facilitate measurable success in reaching the post 2020 biodiversity targets. We advocate that Parties to the CBD build upon this schema and operationalize a comprehensive approach for using EO to monitor indicators of EI. Catalyzing this opportunity will help nations to better identify, address, monitor, and ultimately overcome critical underlying causes of ecosystem and biodiversity loss.
  • 62. Forest extent and integrity indicators for national reporting on SDG 15: A case study in Peru, Colombia, and Ecuador Webinars on Supporting Implementation of the Post-2020 Global Biodiversity Framework September 27th, 2021 Patrick.Jantz@nau.edu
  • 63. NASA Life on Land Project Science Team: Andy Hansen, Scott Goetz, Patrick Jantz, James Watson, Oscar Venter, Ivan Gonzalez, Jaris Veneros, Jose Aragon UNDP Team: Jamison Ervin, Anne Virnig, Christina Supples National Teams: Colombia -Susana Rodríguez-Buritica, Maria Cecilia Londoño, Dolors Armenteras Ecuador –Nestor Alberto Acosta Buenaño, Monica Andrade, Carlos Montenegro Peru -Erasmo Otarola, Michael Valqui, James Leslie NASA HQ:Cindy Schmidt Grant number. 80NSSC19K0186
  • 64. Satellite imagery High-quality spatial datasets SDG15 subindicators SDG15 reporting The goal of the project is to move from… ...to set and achieve ambitious nature-based goals and targets
  • 65. Engagement of Key Partners • Monthly Project Calls • ~30 participants up to the Director level • Virtual Annual Workshop • ~50 participants up to the Director level • Regional Partnerships • SERVIR Amazonia, ProAmazonia (Ecuador), National Adaptation Plan and Amazonia Resiliente (Perú), Paramos de Vida (Colombia), and Amazonia Sostenible para la paz (Colombia)
  • 66. IDEAM (Env. Inst.) MinAmbiente (Environment minister) MAE (Environment minister) Humboldt (Biod. Inst.) DANE (Stat. Inst.) Planifica (Stat. Inst.) Colombia Ecuador Perú Environmental secretariat Environmental Ministry (MinAmbiente) Ministry of Environment (MAE) Ministry of the Environment (MINAM) Statistic offices Statistic national department (DANE) Census and statistics national institute (INEC) Statistic and informatics national institute (INEI) Environmental agencies Environmental studies institute (IDEAM), Humboldt’s biodiversity institute (IAVH) - National service of protected areas (SERNANP) National aerospatial research and development commission (CONIDA) International agencies UNDP Colombia UNDP Ecuador UNDP Perú MINAM (Environment minister) CONIDA (Spat. Inst.) INEI (Stat. Inst.) SERNAP (PA Inst.) SERFOR (Forest Inst.) UNDP Colombia UNDP Ecuador UNDP Peru UNDP –world / NY ANA (Water Inst.)
  • 68. INDICATOR 15.1.1 • Forest area as a proportion of total land area (by natural forest and ecosystem type) • Forest area • Forest area as a proportion of total land area by ecosystem type • Natural forest area by ecosystem type • Natural habitat area as a proportion of ecosystem type • Land area
  • 69. INDICATOR 15.1.2 • Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type • Average proportion of Freshwater Key Biodiversity Areas (KBAs) covered by protected areas • Average proportion of ecosystem types covered by protected areas • Average proportion of high forest structural integrity areas covered by protected areas • Human Footprint change in protected areas • Human Footprint change around protected areas • Average proportion of Terrestrial Key Biodiversity Areas (KBAs) covered by protected areas
  • 70. INDICATOR 15.2.1 • Progress towards sustainable forest management • Above-ground biomass stock in forest • Forest area annual net change rate • Forest area under an independently verified forest management certification scheme • Proportion of forest area under a long-term management plan • Proportional distribution of forest structural integrity condition classes by ecosystem type • Forest fragmentation index by ecosystem type (all forest, high FSII forest) • Forest connectivity index by ecosystem type (all forest, high FSII forest) • Proportion of forest area within legally established protected areas
  • 71. INDICATOR 15.5.1 • Red List Index • Red List Index • Area of suitable habitats for selected vertebrate species
  • 73. Indicador ODS 15.2.1.5 Fragmentación Limitaciones: Depende del insumo de capa de bosque. Requiere mapas binarios que se derivan de insumos continuos. No analiza las condiciones de áreas en no bosque Medida de incertidumbre: Ninguna para reportar Periodicidad: Según la fuente, 2012-2018 o 2000- 2019 Resolución: 30m o según la fuente Extensión espacial: Nacional Agregación: Sí. Cuencas, estados, etc. Interpretación: Menores valores del índice señalan mejor condición espacial y de unidad para los pixeles identificados como bosques. Valores más altos indican más fragmentación. Valores entre 0 y 100 Metodología: - Vogt et al. 2007 para el análisis MPSA - Vogt et al. 2017 para software y cálculo - Jantz et al. In prep para la implementación en bosques tropicales (inlcluído Co, Ec, Pe) Fuente de datos: - Hansen et al. 2019: Mapas de bosque de alta condición estructural [2012- 2018] - Hansen et al. 2013: Mapas bosque no bosque anuales [2000 - 2019] - Conjuntos de datos nacionales (IDEAM, SERFOR) Algoritmo: - GuidosToolbox para uso local - GEE* para cálculo en la nube Repositorio: - UNBiodiversityLab - GEE - FigShare, Zenodo, etc * Índice de fragmentación Área en bosque bajo la categoría de núcleos Área en bosque bajo la categoría de perforaciones Área en bosque bajo la categoría de borde o interfaz Área total del área de estudio TIERS I II III III III III
  • 74. Project Coordinators • UNDP Regional and National Coordinators • Carlos Montenegro • Claudia Fonseca • Gabriela Albuja • Patricia Huerta • Ph.D. Students • 1 from each country • Jaris Veneros • Jose Aragon • Ivan Gonzalez
  • 76. Accounting for Ecosystem Extent and Integrity - Overview of SEEA Ecosystem Accounting Alessandra Alfieri United Nations Statistics Division
  • 77. Outline • Overview of SEEA Ecosystem Accounting (EA) and its relevance to Goal A of the GBF • Accounting ecosystem extent in SEEA EA • Accounting ecosystem condition in SEEA EA • Conclusions
  • 78. Standardisation of measurement of the environment • SEEA Central Framework adopted as statistical standard through an intergovernmental process (ECOSOC / United Nations Statistical Commission) in 2013 • SEEA Ecosystem Accounting discussed in March 2021 > chapters 1-7 describing the accounting framework and the physical accounts adopted as an international statistical standard > chapters 8-11 recognized as describing internationally recognized statistical principles and recommendations for the valuation of ecosystem services and assets in a context that is coherent with the concepts of System of National Accounts • SEEA status of implementation 2020: > 89 countries implementing the SEEA Central Framework > 34 countries compiling SEEA Ecosystem Accounts > 27 countries planning to start implementation of the SEEA
  • 79. SBSTTA-24 • The Subsidiary Body on Scientific, Technical and Technological Advice (SBSTTA) at its recent meeting in May 2021 : > “Recognizes the value of aligning national monitoring with the United Nations System of Environmental-Economic Accounting statistical standard in order to mainstream biodiversity in national statistical systems and to strengthen national information and monitoring systems and reporting” Source: Non-Paper on SBSTTA-24 Agenda item 3 https://www.cbd.int/doc/c/13e9/73d6/0de346d7d3433024a3ef1441/sbstta-24-nonpaper-item-03-v1-en.pdf
  • 80. Goal A (CBD/WG2020/3/3/Add.1 - 11 July 2021) Goal A. The integrity of all ecosystems is enhanced, with an increase of at least 15% in the area, connectivity and integrity of natural ecosystems, supporting healthy and resilient populations of all species, the rate of extinctions has been reduced at least tenfold, and the risk of species extinctions across all taxonomic and functional groups, is halved, and genetic diversity of wild and domesticated species is safeguarded, with at least 90% of genetic diversity within all species maintained A.0.1 Extent of selected natural and modified ecosystems (i.e. forest, savannahs and grasslands, wetlands, mangroves, saltmarshes, coral reef, seagrass, macroalgae and intertidal habitats) By terrestrial and marine ecosystem types By mountains UN System of Environmental Economic Accounting (SEEA): https://seea.un.org/ecosyste maccounting Ecosystem types based on IUCN categories. Near ready** Proposed goal or target Proposed indicators Proposed disaggregation Methodological basis Global data set for national disaggregation
  • 83. SEEA EA - Core Accounts
  • 84. Ecosystem types • SEEA EA endorses the IUCN GET as international reference classification • 6 levels – accounts are compiled at level of the Ecosystem Functional Groups (e.g. tropical lowland rainforest) Realms Biomes Terrestrial T1 Tropical–subtropical forests T2 Temperate–boreal forests & woodlands T3 Shrublands & shrubby woodlands T4 Savannas and grasslands T5 Deserts and semi-deserts T6 Polar-alpine T7 Intensive land-use systems Freshwater F1 Rivers and streams F2 Lakes F3 Artificial fresh waters Marine M1 Marine shelfs M2 Pelagic ocean waters M3 Deep sea floors M4 Anthropogenic marine systems Subterranean S1 Subterranean lithic S2 Anthropogenic subterranean voids Transitional TF1 Palustrine wetlands FM1 Semi-confined transitional waters MT1 Shoreline systems MT2 Supralittoral coastal systems MT3 Anthropogenic shorelines MFT1 Brackish tidal systems SF1 Subterranean freshwaters SF2 Anthropogenic subterranean freshwaters SM1 Subterranean tidal
  • 85. Ecosystem extent account Source: SEEA EA Realm Biome F1 … FM1 M1 … MFT1 Selected Ecosystem Functional Group (EFG) Tropical-subtropical lowland rainforests Tropical-subtropical dry forests and scrubs Tropical-subtropical montane rainforests Tropical heath forests Boreal and temperate high montane forests and woodlands Deciduous temperate forests … Temperate pyric sclerophyll forests and woodlands … … … Derivied semi-natural pastures and old fields Permanent upland streams … Intermittently closed and open lakes and lagoons Seagrass meadows … Coastal saltmarshes and reedbeds T1.1 T1.2 T1.3 T1.4 T2.1 T2.2 … T2.6 … … … T7.5 F1.1 … FM1.3 M1.1 … MFT1.3 Opening extent Additions to extent Managed expansion Unmanaged expansion Reductions in extent Managed reductions Unmanaged reductions Net change in extent Closing extent TOTAL Selected ecosystem types (based on Level 3 - EFG of the IUCN Global Ecosystem Typology) Terrestrial T1 Tropical-subtropical forests T2 Temperate-boreal forests and woodlands … T7 Freshwater Marine
  • 86. In India, compilation of ecosystem extent accounts is based on locally relevant ecosystem type classifications. These have been mapped to the IUCN GET classification at the EFG level for the purposes of international comparability. Source: Ministry of Statistics and Programme Implementation, 2021. The values in the cells represent the share of Indian forests that map to the GET categories: • Values of 1 represent a 1-to-1 match. • Values less than 1 indicate that the Indian forest type maps to more than one GET forest type - in proportion to the values given in the corresponding cells. Example: Mapping of Indian forest types to IUCN GET forest ecosystem functional groups (EFG)
  • 87. 12 Natural areas Anthropized areas Artificial surfaces Cropland Mosaic in forest area Managed pasture Silviculture Forest tree cover Wetland Forest Barren land Inland water bodies Coastal water bodies Mosaic in non-forest area
  • 89. 14
  • 90. The higher absolute totals of natural area reduction were concentrated on the Amazônia and Cerrado biomes (86,2%) 15
  • 92. The SEEA Ecosystem Condition Typology (ECT) Source: SEEA EA ECT groups and classes Group A: Abiotic ecosystem characteristics Class A1. Physical state characteristics: physical descriptors of the abiotic components of the ecosystem (e.g., soil structure, water availability) Class A2. Chemical state characteristics: chemical composition of abiotic ecosystem compartments (e.g., soil nutrient levels, water quality, air pollutant concentrations) Group B: Biotic ecosystem characteristics Class B1. Compositional state characteristics: composition / diversity of ecological communities at a given location and time (e.g., presence / abundance of key species, diversity of relevant species groups) Class B2. Structural state characteristics: aggregate properties (e.g., mass, density) of the whole ecosystem or its main biotic components (e.g., total biomass, canopy coverage, annual maximum normalized difference vegetation index (NDVI)) Class B3. Functional state characteristics: summary statistics (e.g., frequency, intensity) of the biological, chemical, and physical interactions between the main ecosystem compartments (e.g., primary productivity, community age, disturbance frequency) Group C: Landscape level characteristics Class C1. Landscape and seascape characteristics: metrics describing mosaics of ecosystem types at coarse (landscape, seascape) spatial scales (e.g., landscape diversity, connectivity, fragmentation)
  • 93. Ecosystem condition indicator account Source: SEEA EA SEEA Ecosystem Condition Typology Class Indicators Ecosystem type Variable values Reference level values Indicator values (rescaled) Descriptor Opening value Closing value Upper level (e.g., natural) Lower level (e.g., collapse) Opening value Closing value Change in indicator Physical state Indicator 1 Indicator 2 Chemical state Indicator 3 Compositional state Indicator 4 Indicator 5 Structural state Indicator 6 Functional state Indicator 7 Landscape/waterscape characteristics Indicator 8
  • 94. Source: CSIR, 2020 Example: Changes in South African estuarine ecosystem’s condition
  • 95. Mexico – Ecosystem Integrity index -2018 Ecosystem type Opening value 2004 Opening value 2018 Change Aquaculture 0.78 0.55 -0.23 Annual cropland 0.34 0.35 0.00 Perennial cropland 0.41 0.41 0.00 Human settlements 0.12 0.10 -0.03 Planted forest 0.55 0.55 0.00 Coniferous forest 0.81 0.83 0.02 Oak forest 0.77 0.78 0.02 Montane cloud forest 0.76 0.78 0.02 Special other woody vegetation types 0.65 0.65 0.00 Special other non-woody vegetation types 0.74 0.72 -0.02 Woody xeric shrubland 0.84 0.85 0.01 Non-woody xeric shrubland 0.88 0.87 -0.01 Other lands 0.81 0.68 -0.13 Grassland 0.47 0.52 0.05 Deciduous tropical forest 0.70 0.73 0.02 Evergreen tropical forest 0.78 0.79 0.01 Semideciduous tropical forest 0.69 0.71 0.01 Woody hydrophytic vegetation 0.81 0.83 0.01 Non-woody hydrophytic vegetation 0.74 0.81 0.07
  • 96. What is ARIES for SEEA? • Tool that uses ARIES technology to compile ecosystem accounts that are consistent with the SEEA Ecosystem Accounting • Includes land cover accounts consistent with the SEEA Central Framework • Uses same definitions, classifications, accounting rules as the SEEA • Can help automate production of maps and tables • Provides infrastructure for the SEEA community to share and reuse interoperable data and models
  • 97. Conclusions • SEEA adopted as statistical standard major milestone • Makes nature count within economic planning and decision-making • Standardization is important in order to obtain high-quality, and comparable statistics • Provides framework for deriving indicators to support various monitoring and reporting frameworks such as SDGs, Biodiversity, Climate Change, Green Economy • SEEA EA implementation strategy: • Guidelines and tools are developed to facilitate accounts compilation • Enhanced collaboration between various communities (statistical, geospatial, biodiversity, policy makers)
  • 98. Australia’s National Science Agency Adding value to monitoring of ecosystem extent and integrity through derivation of habitat-based biodiversity indicators Simon Ferrier | 27 September 2021 With thanks to: Chris Ware, Becky Schmidt, Tom Harwood, Andrew Hoskins, Karel Mokany (CSIRO) Andy Purvis (NHM), Hedley Grantham (WCS)
  • 99. https://seea.un.org/content/exploring-approaches-constructing-species-accounts-context-seea-eea https://geobon.org/ebvs/indicators/ Habitat-based biodiversity indicators can play an important role in large-scaled biodiversity assessment ➢ Habitat-based biodiversity indicators assess how changes in ecosystem extent and condition (integrity) are expected to affect retention of species diversity ➢ They therefore offer one simple means of linking the ecosystem and species components of draft GBF Goal A ➢ Habitat-based indicators employ mapping either of individual species distributions (e.g. the Species Habitat Index) or of overall variation in community composition ➢ This presentation focuses on a community-level indicator generated by CSIRO – the Biodiversity Habitat Index
  • 100. The Biodiversity Habitat Index (BHI) translates ecosystem extent & integrity mapping into an indicator of biodiversity retention Hoskins AJ et al (2020) Environmental Modelling & Software 132: 104806 Ferrier S et al (2020) Ecological Indicators 117: 106554 Extent and local (per grid-cell) integrity of ecosystems Biodiversity Habitat Index (BHI) – expected impact of ecosystem extent, local integrity [and connectivity] on regional/global retention of species diversity Spatial variation in community composition (modelled from data for >400,000 species globally) Spatial habitat connectivity analysis (with or without effects of climate change) The BHI can be scaled either as “effective proportion of habitat remaining” or as “proportion of species expected to persist” (by invoking the species-area relationship) Optional incorporation of connectivity analysis from CSIRO’s Bioclimatic Ecosystem Resilience Index (BERI) indicator
  • 101. The BHI has been generated globally at 1km grid-resolution across all terrestrial biomes Hoskins AJ et al (2020) Environmental Modelling & Software 132: 104806 Di Marco M et al (2019) Global Change Biology 25: 2763-2778 IPBES Regions Results can be mapped at raw grid-cell resolution … … or reported by any specified set of spatial units
  • 102. https://ipbes.net/global-assessment https://epi.yale.edu/ http://chm.aseanbiodiversity.org/ This capability is already being used to assess global and regional trends in the state of habitat supporting biodiversity …
  • 103. Mokany K et al (2020) PNAS 117: 9906-9911 http://www.sparc-website.org/ … and to prioritize areas for habitat protection or restoration to enhance prospects for biodiversity persistence
  • 104. MODIS Vegetation Continuous Fields ESA CCI Land Cover Remote sensing time series LUH2 coarse resolution land-use training data Environmental covariates Responses of local biodiversity to land use Biodiversity Intactness Index (BII) time series 2020 2000 Translation of land cover into 12 land-use class probabilities through statistical downscaling https://www.nhm.ac.uk/our-science/our-work/biodiversity/predicts.html Hoskins AJ et al (2016) Ecology and Evolution 6: 3040-3055 The source of data on local ecosystem integrity used most extensively in the BHI is the Biodiversity Intactness Index (BII) ➢ Derived by coupling CSIRO’s downscaled land-use time series with the Natural History Museum PREDICTS project’s meta-analysis of land-use impacts on local biodiversity ➢ Recently updated to provide 1km-resolution mapping of change for every year from 2000 to 2020 globally ➢ The Natural History Museum have committed to continuing production of the BII post-2020
  • 105. The BHI can additionally be derived from other ecosystem integrity inputs e.g. from the recently developed 300m-resolution Forest Landscape Integrity Index, in a current collaboration with WCS and University of Queensland Grantham HS et al (2020) Nature Communications 11: 5978 https://www.forestintegrity.com/ Nepal
  • 106. The BHI is also derivable at national & subnational scales – including from UN SEEA-EA ecosystem extent & condition accounts data https://eea.environment.gov.au/accounts/ecosystem-accounts https://www.wavespartnership.org/en/planning-tool-peru Applications from the San Martin Region of Peru … … to the Murray-Darling Basin of Australia
  • 107. What can habitat-based biodiversity indicators, such as the BHI, contribute to post-2020 GBF implementation? Better linking monitoring of progress towards achieving Goal A with prioritization of actions under Targets 1 to 3 Better integrating consideration of multiple ecosystem-focused (extent, integrity, connectivity) and species-focused components of Goal A Better enabling seamless indicator derivation across scales, employing best-available global, national and subnational datasets
  • 108. Australia’s National Science Agency CSIRO Land & Water Simon Ferrier Chief Research Scientist simon.ferrier@csiro.au Thank you