1. Responding to evolving threats using
innovative tools, technologies and
datasets
Professor Kathy Willis,
Biodiversity Institute, University of Oxford

2. Evolving threats

3. • Global population
Population projection (Lutz & Samir 2010)
most likely to peak
~9B 95%
12B
60%
• People will be richer 20%
8B
and demand higher
quality diet
4B
Livestock consumption (FAO 2009)
2000 2050 2100
Livestock consumption
Developed nations
China
India
Increasing demand
Africa
on land
1970 1980 1990 2000

5. Protected
(12%)
Not protected (88%)
Hwange National Park, Zimbabwe

6. Biodiversity declines
Stokard 2010. Despite progress, biodiversity
declines. Science. 329: 1272-1273.

7. Is all lost for biodiversity?

9. Convention of Biological Diversity
targets (2011)
Target 5
By 2020, the rate of loss of all natural habitats, including forests, is at least halved and
where feasible brought close to zero, and degradation and fragmentation is
significantly reduced.
Target 14
By 2020, ecosystems that provide essential services, including services related to
water, and contribute to health, livelihoods and well-being, are restored and
safeguarded
Target 15
By 2020, ecosystem resilience and the contribution of biodiversity to carbon stocks
has been enhanced, through conservation and restoration

10. Talk outline
What innovative tools, technologies and
datasets do we need to:
1. Identify and reduce loss of natural habitats?
2. Enhance ecosystem resilience?
3. Conserve ecosystems that provide essential services
related to human well-being?

11. What tools are available to Identify and reduce
loss of natural habitats?
Case study:
Determining the ecological value of landscapes
beyond protected areas
Willis, K.J. et al., 2012, Biological
Conservation, 147, 3-12

12. “ Where can we damage? ”
? ?
? ? ?

13. Points arising from workshops with Statoil
1. Need a tool that provides estimation of ecological
value of land outside of protected areas
2. To produce landscape information at a spatial scale
less than 500m;
3. Use existing available web-based databases;
4. Produce simplified displays – preferably maps;
5. Simple user input;
6. Able to assess any region in world;

14. What is the finest spatial resolution (pixel size)?
Global vegetation cover at 300m pixel size resolution
(GLOBCOVER (Bicheron et al. 2009)

15. What data are needed to provide an spatial
distribution of ecological value on a landscape?
Need data on:
1. Key ecological properties of the landscape
(e.g. biodiversity, threatened species)
2. Key features for supporting ecosystem
functions (e.g. connectivity (migration
routes, wetlands) habitat integrity, resilience)
3. Their spatial configuration on the landscape.

16. Biodiversity data
• For most regions in the world will rarely be enough
detailed species data to obtain clear picture
• Necessary to model predictive diversity across
landscape (generalised dissimilarity modelling)
• Can then use combination of point species
occurrences + environmental variables to predict
diversity (spatial heterogeneity) across landscape

17. Biodiversity species
occurrence data
Global Biodiversity (GBIF):
Data Portal (http://data.gbif.org)
that provides access to more than
330 million records of species
occurrence worldwide

18. GBIF network Data Coverage
>330 million occurrence records from >8,500 datasets from
>360 publishers and spanning a wide range of geospatial,
temporal and taxonomic coverages being shared through
distributed network
Last updated: 2

19. Data sources for environmental
variables

20. Beta-diversity for Canadian site measured using
Generalised Dissimilarity modelling
Value provided for every 300m pixel

21. Threatened species data sources
• 2010 IUCN Red List of
Threatened Species
• Assessments for ~56,000
species, of which about 28,000
have spatial data.
• Consider all categories in
concession area except ‘least
concerned’ and ‘extinct’
• More threatened species in
pixel, higher its value

22. Threatened species distribution in Canadian
concession area

23. Fragmentation data
• Spatial continuity of natural vegetation based on the
size (ha) of each continuous patch
• Computer programme FRAGSTATS (McGarigal and
Marks, 1995) defines individual patches and
calculates patch size
• Apply FRAGSTATS to vegetation cover
• Greater the patch size, higher the ecological value

24. Fragmentation map Canadian concession areas

25. Connectivity (1) Migratory routes
Global Register of Migratory Species
• Contains list of 2,880 migratory
vertebrate species in digital format
• Also their threat status according
to the International Red List 2000,
• Digital maps for 545 species
• Sum the number of migratory
ranges occurring in each per pixel
www.groms.de

26. Connectivity (2) – Migration processes
• Prioritize pixels that support migratory processes:
– Rivers, wetlands and lakes (at 300m resolution)
– Adjacent pixels to rivers (so as to allow migratory
corridors)
Data source: HYDROSHEDS (USGS), Global lakes &
wetlands database (WWF)

27. Water bodies and drainage networks for
Canadian concession area
Global Lakes and Wetlands Database,
HYDROSHEDS; 30m pixel resolution

28. Resilience
– Areas of landscape that are particularly resistant
to climate change/disturbance
– Areas of landscape that are able to recover from
disturbance quicker than others

29. Resilience: measured through ability of vegetation to
maintain relatively high levels of productivity despite low levels
of rainfall
Rainfall (mm) in driest month
Scoring Rule:
1, if highest quartile of
productivity & lowest
Annualized NPP quartile of rainfall
0.5, if highest quartile of
productivity & next
lowest quartile of rainfall
0, otherwise
Assessed per vegetation
Vegetation Type
type

30. Resilience, Canadian concession area

31. To summarise
Factors and data sources used in LEFT
Willis, K.J. et al., 2012, Biological
Conservation, 147, 3-12

32. Final index: Local ecological footprint valuation
Species richness
+
Vulnerability
+
Connectivity
+
Fragmentation
+
Resilience
Final index

33. Automation

35. How accurate in comparison to field data?
Cusuco, Honduras
• Montane tropical moist forest
• Surveyed 2004-2010
• Extensive datasets e.g >50,000 records of terrestrial
vertebrates in database

36. Cusuco national park, Honduras
Can LEFT correctly identify which globally threatened terrestrial
vertebrates are present in a study site?
All threatened terrestrial vertebrates Threatened birds
Field data Web data
3 4 10
5 26 17
Threatened mammals
LEFT
1 2 6
correct
LEFT LEFT
omission error commission error Threatened reptiles
(detected by (not detected by
fieldwork, but fieldwork, yet 0 1 0
missed by LEFT) included in LEFT)
Threatened amphibians
1 19 1

37. Cusuco – normalised number of threatened species
Can LEFT correctly identify which
locations in a study site are most
important for threatened species?
Difference map
White = agreement.
Red = LEFT predicts relatively
more threatened species than
field data (commission error)
Blue = LEFT predicts relatively
fewer threatened species than
field data (omission error)

38. Cusuco – beta-diversity using GBIF
Beta-diversity
calculated using
species occurrence
data (birds) in GBIF
Cusuco, Aves Beta-diversity based on GBIF data
n = 405 (67 sites)

39. Cusuco – beta-diversity using field data
Beta-diversity
calculated using
species occurrence
data (birds) from
field data
Cusuco, Aves Beta-diversity based on field data
n = 3297 (116 sites)

40. Summary
• Tool will work anywhere in the world at local-
scale resolution (~ 300m pixel)
• Provides report, maps, files on all values used
to calculated ecological value in ~10 minutes
• Preliminary studies to compare tool output
with high resolution field data indicates that
general ecological trends well represented
• Consistent and quick approach for obtaining
most up-to-date biodiversity information

41. Talk outline
What innovative tools, technologies and
datasets do we need to:
1. Identify and reduce loss of natural habitats?
2. Enhance ecosystem resilience?
3. Conserve ecosystems that provide essential services
related to human well-being?

42. Target 15
“By 2020, ecosystem resilience
and the contribution of
biodiversity to carbon stocks
has been enhanced, through
conservation and restoration

43. ”Resilience is the capacity of a system to absorb
disturbance and still retain its basic function and
structure” (Holling, 1973)
Alternative definition:
‘Resilience is speed
of return to an
equilibrium state
following a
perturbation from
that state’
(Nystrom et al. 2000)

44. What is scientific information is needed to
determine and plan for resilient landscapes?
1. How resilient is the landscape to
environmental perturbations?
– e.g. climate change/land-use change
2. What is the spatial arrangement of resilient
ecosystems across the landscape?

45. How resilient is the landscape to environmental
disturbance?
Recovery rates of tropical forests to disturbance events
L. Cole, S. Bhagwat & K.J
Willis, in prep

46. • Data from 40 individual fossil sedimentary pollen sequences
• Contain records of vegetation dynamics spanning last 10,000 years
• Document a total of 140 disturbance events across 3 continents

47. Classification of disturbance type
Disturbance Disturbance type Proxy
source
NATURAL Climate (C) Oxygen isotopes, fire (low levels, not linked to human
presence), magnetic susceptibility, lithology
Precipitation (CP) Rainfall, monsoon strength variation, climate drying
Sea-level rise (CS) (CD)
Sea level
Large infrequent (LI) Hurricane (LI-H), landslide (LI-L), fire (LI-F), volcano
(volcanic ash) (LI-V)
HUMAN Burning (B) Micro- & macro-charcoal
Forest clearing Temporary, predominantly resulting from shifting
(FC) cultivation (SC), or more permanent, generally selective
clearing, or not described (FC) signified by e.g. fruit
trees, Poaceae, & disturbance indicators/secondary
forest taxa, e.g. Arenga and Macaranga, or magnetic
susceptibility
Agriculture (Ag) Agricultural indicators, e.g. fruit trees - Ficus, crops -
Poaceae
Unclear U Disturbance indicators but type undefined

48. Calculation of resilience
Metric Description Calculation
Recovery Rate (RR) Rate of forest recovery relative to degree of RR = ((Fmax - Fmin)/(Fpre - Fmin))*100/ Trec
disturbance-induced percentage change
Forest % decline (FD) Forest percentage decline relative to baseline Rel.D = ((Fpre - Fmin)/ Fpre)*100
forest cover percentage
Resilience (RS) Change in RR through time (RR1 represents (RS) = RR2 – RR1
oldest sample in study)

49. How quickly have tropical forests recovered
from disturbances in the past?
L. Cole, S. Bhagwat & K.J Willis,
in prep

50. Does geographical location affect recovery rates?
Fastest
recovery
rates in
Central Slowest
America recovery
rates in S.
America

51. Type of disturbance also indicated significant
impact on recovery rates
Forest clearance
through burning
etc. resulted in
slowest recorded
recovery rates
(and greatest L. Cole, S. Bhagwat & K.J Willis,
in prep
variation)

52. • Using long-term datasets it is possible to start
to determine relative recovery rates
• But this still doesn’t give a clear indication of
which areas across a landscape are more
resilient to climatic perturbations
• To do this we need to examine shorter-
term/finer resolution datasets

53. Resilience: measured through ability of vegetation to
maintain relatively high levels of productivity despite low levels
of rainfall
Rainfall (mm) in driest month Scoring Rule:
1, if highest quartile of
productivity & lowest
quartile of rainfall
Annualized NPP 0.5, if highest quartile of
productivity & next
lowest quartile of rainfall
0, otherwise
Assessed per vegetation
type
Vegetation Type K.J. Willis et al., 2012 Biological
Conservation, in press

54. Devising A Global Map of Ecological Resilience: Step 1- NDVI (photosynthetic ‘health’)
• 12 year monthly time-slice
of NDVI (MODIS) (144
layers in total)
• 5km resolution
• Masked for sea-areas/
large terrestrial water
bodies
• Red = high, green = low
• Data are detrended for
seasonality and
transformed to Z-scores in
each pixel.
• Provides an estimate of
amount of variability away
from the mean over the 10
years.
A.W.R. Seddon, P. Long and K.J. Willis
in prep
Red = high; Green = low

55. Devising A Global Map of Ecological Resilience: Step 1- NDVI
• Variance of these Z scores provides a global map of the
variance in productivity for each pixel
• Red = high variance, green = low variance
A.W.R. Seddon, P. Long and K.J. Willis
in prep

56. Towards A Global Map of Ecological Resilience: Step 2- Temperature variance
•12 year monthly time-slices of
mean monthly surface
temperature (MOD-7 profiles)
•5km resolution
• Converted to z scores to
provide a global map of the
variance in temperature for
each pixel at 5 km resolution
• Red = high variance, green =
low variance

57. Towards a Global Map of Ecological Resilience: Step 3
Sensitivity (γ) = Temporal Variance in Productivity
Temporal Variance in Climate
Resilience = 1/γ
(of NDVI (productivity) to climate
variability over a 10 year period)

58. Global 12 year Resilience of NDVI to Climate Variability
• red = low and green = high

59. Talk outline
What innovative tools, technologies and
datasets do we need to:
1. Identify and reduce loss of natural habitats?
2. Enhance and identify ecosystem resilience?
3. Conserve ecosystems that provide essential
services related to human well-being?

60. Target 14
“By 2020, ecosystems that provide
essential services, including
services related to water, and
contribute to health, livelihoods
and well-being, are restored and
safeguarded.”

61. What knowledge do we need?
R.S. de Groot et al. 2010 Ecological
Complexity 7 (2010) 260–272

62. Current landscape planning, management
and decision making tools
ARIES (ARtificial Intelligence InVEST
for Ecosystem Services) (Integrated Valuation of Ecosystem
Services and Tradeoffs)
ESValue

63. ARIES (ARtificial Intelligence for Ecosystem Services)
End-user needs to work with the ARIES team; developed for specific area; one site
output requires 200-300 hours of Senior GIS technician time
InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs)
Time varies depending on the site and the technician’s expertise; one site output
requires 160-280 hours of Senior GIS technician time
ESValue
~ 200 hours for one site; requires GIS expertise, expert knowledge of ecological
relationships plus data from stakeholders
EcoAIM (Ecological Asset Inventory and Management)
>25 hours; involves reviewing, downloading, converting and uploading data by
stakeholder
Current Ecosystem Service Tools:
(http://www.bsr.org/reports/BSR_ESTM_WG_Comp_ES_Tools_Synthesis3.pdf)

64. "a gap in biodiversity market infrastructure
that persists is lack of landscape-scale
ecological monitoring. While site-level
ecological monitoring is not uncommon, the
data is not easily available, much less
complied in a comprehensive way".
Madsen, B., Caroll, N., Kandy, D., Bennett, G (2011)
Update: State of Biodiversity Markets. Washington, DC:
Forest Trends, 2011. http://www.
ecosystemmarketplace.com/reports/2011_update_sbdm.

65. landowner
What data do we need
to provide a tool to
quickly and remotely
determine ecosystem
service provision?

66. What information is required to map pollination services?
GBIF species
Land cover occurrence data
Environmental
co-variables
DISTRIBUTIONS OF
Crops Nesting habitat for P.
POLLINATORS
Pollinator foraging
distance
Pollination Availability of
DEPENDENT pollinators
CROP
Pollination service delivery
P.= pollinator

67. Steps to follow
Distribution Model
+
Landscape + Foraging distance
features
e.g. nesting Landscape
habitat containing
pollinators
x
Crop
dependency
Final pollination service delivery

68. Preliminary pollination service delivery for
Tenerife
Tenerife foraging
Tenerife nesting habitat Tenerife tree crops distance
More service
delivered
Less service
delivered
Tenerife actual pollination
service delivery
Important areas for
pollination services
for tree crops
More service
delivered
Nogues, Long & Willis, Less service
delivered
in prep 0.5 km

69. Responding to evolving threats using
innovative tools, technologies and datasets
• Large scientific biodiversity resource becoming
available through databases, modelling and ecological
knowledge
• Creation of tools to link this information together
requires highly interdisciplinary research community
• … but must also have good knowledge of requirements
of end-user
• The challenge is to bring together these tools,
technologies and datasets but in a framework that is
relevant to both science and stakeholder communities
• This requires pragmatism and a different approach to
funding such work…