EcoMathsThe Numbers of Life (and Death)
Professor Corey J. A. BradshawTHE ENVIRONMENT INSTITUTE, University of Adelaide
South Australian Research & Development Institute
•> 4 million protists
•16600 protozoa
•75000-300000 helminth parasites
•1.5 million fungi
•320000 plants
•4-6 million arthropods
•> 6500 amphibians
•> 30000 fishes
•10000 birds
•> 5000 mammals
99 % of ALL species that have ever existed...
EXTINCTspecies lifespan = 1-10 M years
Ordovician (490-443 MYA)
Devonian (417-354 MYA)
Permian (299-250 MYA)
Triassic (251-200 MYA)
Cretaceous (146-64 MYA)
Anthropoceneextinction rate 100-10000× background
Crutzen 2002 Nature 415:23; Bradshaw & Brook 2009 J Cosmol 2:221-229© T
ianti
an Z
hang
, Goo
d50x
70.o
rg
Brad
shaw
et a
l. 20
09 F
ront
Eco
l Env
iron
7:79
-87
Brad
shaw
et a
l. 20
09 T
rend
s Ec
ol E
vol 2
4:54
1-54
8
Han
sen
et a
l. 20
10 P
NAS
doi:1
0.10
73/p
nas.
0912
6681
07
Bars
on e
t al.
2000
Lan
d Co
ver
Chan
ge in
Aus
tral
ia, B
ur R
ur S
ci
•1,011,000 km2 lost 2000-2005 (3.1 %; 0.6 %/year)•highest in boreal biome (60 %)•humid tropics next (Brazil, Indonesia, Malaysia)•dry tropics next highest (Australia, Brazil,
Argentina)•N.A. greatest proportional lost by continent•Nationally, Brazil, Canada, Indonesia, DR Congo
•21 % of all known mammals•30 % of all known
amphibians•12 % of all known birds•35 % of conifers & cycads•17 % of sharks•27 % of reef-building corals
threatened with extinction
IUCN RED LIST OF THREATENED SPECIES www.iucnredlist.org
•3366 spp
•life history (reproduction, fecundity, body size, habit)
•ecological (range size)
•environment (temperature, precipitation, human density)
• threat ~ X1 + X2 + X3… (Order/Family)
•decline ~ …
Correlates of extinction
Sodhi et al. 2008 PLoS One 3:e1636
Sodhi et al. 2008 PLoS One 3:e1636
Sodhi et al. 2008 PLoS One 3:e1636
range (number of FAO Fishing Areas),
• risk for sharks with small range size
•similar for teleosts with slightly larger ranges
habitat • threat risk for reef sharks• and for pelagic teleosts
environmental temperature regime
• risk for deepwater sharks• risk deepwater teleosts
Field et al. 2009 Advances in Marine Biology 56:275-363
Bradshaw et al. 2008 J Ecol 96:869-883
Bradshaw et al. 2008J Ecol 96:869-883
deforestation, soil erosion, sediment & nutrient loading
destructive fishing practices
overfishing
invasive species and starfish outbreaks
bleaching
Mellin et al. 2010 Glob Ecol Biogeog 19:212
3.0 ± 0.42.2 ± 0.4
1.5 ± 0.3
Reef area Reef isolation
3.1 ± 0.42.0 ± 0.31.7 ± 0.3
Mellin et al. In press Ecology
Mellin et al. In press Ecology
1. habitat destruction
2. over-exploitation
3. introduced species
4. extinction cascades
Diamond 1984 Extinctions Chicago University Press
Evil quartet
Broo
k et
al.
2008
Tre
nds
Ecol
Evo
l 25
:453
-460
1. habitat destruction
2. over-exploitation
3. introduced species
4. extinction cascades
5. climate change
Evil quintet
6. synergies
Evil sextet
Brook et al. 2008 Trends Ecol Evol 25:453-460
© Millennium Ecosystem Assessment
justification to maintain healthy ecosystems is intangible because it seems unrelated to personal well-being
• reduce desertification• maintain soils• crop pollination• seed dispersal• food provision• water purification• fuel provision• fibre provision• climate regulation• flood regulation• disease regulation• waste decomposition/detoxification• nutrient cycling• soil formation• primary production• pharmaceutical sources• cultural appreciation (aesthetic, spiritual, educational, recreational…)
• €50 billion lost/year• land-based ecosystem loss €545 billion by 2010
• > €14 trillion/year lost by 2050
Cost of Policy Inaction (COPI):The case of not meeting the 2010 biodiversity target.
European Commission
€153 billion/year
fisheries: €50 billion/year
Bradshaw et al. 2007 Glob Change Biol 13:2379-2395
1990-2000• ~100,000 people killed• 320 million people displaced• total reported damages > US$1151 billion
•decades of warning
•human population 6.8 B; 9-10 B by 2050
•competition for resources – famine, wars
•loss of basic ecosystem services
•fundamental worldwide shifts in policy required
•identifying relative country degradation
–highlight nations needing assistance
–better-performing nations as model governance structures
City Development Index www.unchs.org
Ecological Footprint www.footprintnetwork.org
Environmental Performance Index epi.yale.edu
Environmental Sustainability Index sedac.ciesin.columbia.edu
Genuine Savings Index worldbank.org
Human Development Index hdr.undp.org
Living Planet Index www.panda.org
Well-Being Index www.well-beingindex.com
Environmental Impact Rank
Böhringer & Joachim 2007 Ecol Econ 63:1-8
•inability to describe complexity of ‘sustainability’
•not comprehensive
•mix environmental, economic and health data
•often subjective combinations, weightings, normalisation
•not available for large sample of nations
•not consistent
•natural forest loss2005-1990 D/ha
•natural habitat conversionhuman-modified landcover/total landcover
•marine captures1990-2005 fish, whales, seals/EEZ km
•fertiliser useNPK/ha arable land
•water pollutionbiochemical oxygen demand/total renewable water resources
•carbon emissionsforestry, land-use change, fossil fuels/km2
•biodiversity threatRed List threatened birds, mammals, amphibians/listed species
Bradshaw et al. 2010 PLoS One 5:e10440
NFL NHC MC FU WP BT CE RANK
128 5 91 1 4 63 1 10.6
23 61 20 17 21 29 5 20.4
- 198 112 20 3 - 7 24.8
128 197 114 11 1 - 8 25.1
87 87 18 21 29 13 6 25.2
128 5 91 1 4 63 1 10.6
Bradshaw et al. 2010 PLoS One 5:e10440
1 Singapore 179 Cape Verde
2 Rep Korea 178 Cent Afr Rep
3 Qatar 177 Swaziland
4 Kuwait 176 Antig & Barb5 Japan 175 Niger6 Thailand 174 Grenada7 Bahrain 173 Samoa8 Malaysia 172 Tonga9 Philippines 171 Djibouti
10 Netherlands 170 Tajikistan11 Denmark 169 Bhutan12 Sri Lanka 168 Chad13 Indonesia 167 Vanuatu14 Israel 166 Mali15 Bangladesh 165 Kazakhstan16 Malta 164 Gabon17 China 163 Turkmenistan18 New Zealand 162 Lesotho19 Iceland 161 Suriname20 Honduras 160 Eritrea
“I anticipate that the anti-science crowd will be screeching and howling with indignation when they read this one.”
“This is such BS, China is WAY worse then the U.S.”
“This researcher is a waste ...”
“This article is crap.”
“Can we really depend on some study when the Chinese could have funded this or maybe some group who was angry at the US and Brazil for whatever? I highly doubt the accuracy of the findings. Looks like the Treehuggers are at it again.”
“Shame on you Australia !!! I guess your dying great Barrior [sic] reef is America's fault too!!!!”
“here we go again. I'm so frickin' sick of these watermelons (green on the outside, red (communist) on the inside) treehuggers. The only f*^king green I care about is made of paper and folds.”
1 Brazil
2 USA
3 China
4 Indonesia
5 Japan6 Mexico
7 India8 Russia9 Australia
10 Peru
11 Argentina12 Canada13 Malaysia14 Myanmar15 Ukraine
16 Thailand17 Philippines
18 France19 South Africa
20 Colombia
POPULATION
WEALTH
GOVERNANCE
+
impa
ct
0 50 100 150 200
0
50
100
150
Governance quality rank
Pro
po
rtio
nal
en
viro
nm
enta
lim
pac
t ra
nk
0 50 100 150
0
50
100
150
Gross National Income rank
Ab
solu
te e
nvi
ron
men
tal
imp
act
ran
k
0 50 100 150 200
0
50
100
150
Total population rank
Pro
po
rtio
nal
en
viro
nm
enta
lim
pac
t ra
nk
0 50 100 150 200
0
50
100
150
Population density rank
0 50 100 150 200
0
50
100
150
Population growth rank
Pro
po
rtio
nal
en
viro
nm
enta
lim
pac
t ra
nk
0 50 100 150
0
50
100
150
Gross National Income rank
A B
C D
- im
pact
+ im
pact
+ people - people
+ growth - growth
- im
pact
poorer wealthier
- quality + quality poorer wealthier
+ density - density
- im
pact
+ im
pact
E F
Bradshaw et al. 2010 PLoS One 5:e10440
Bradshaw et al. 2010 PLoS One 5:e10440
per capita prosperity
envi
ronm
enta
l dam
age
ENVIRONMENTAL
KUZNETS CURVE
Bradshaw et al. 2010 PLoS One 5:e10440
1 10 100
0
50
100
150
l inear
quadratic
intercept
per capita PPP-adjusted GNI
Pro
po
rtio
nal
en
viro
nm
enta
lim
pac
t ra
nk*
1 10 100
0
50
100
150
per capita PPP-adjusted GNI
Ab
solu
te e
nvi
ron
men
tal
imp
act
ran
k*
- im
pact
+ im
pact
- im
pact
+ im
pact
poorer wealthier
poorer wealthier
A
B
Bradshaw et al. 2010 PLoS One 5:e10440
© http://tropicaltoxic.blogspot.com
Does a sick environment make sick people?
•physician-assessed morbidity declines with more green spaces near Dutch patients
Maas et al. 2009 J Epidemiol Comm Health 63:967-973
•dioxin-poisoning accident in Milan – increased circulatory disease, lymphoma, pulmonary disease & diabetes 25 years later
Consonni et al. 2008 Am J Epidemiol 167:847-858
•low water quality, poor sanitation & indoor air pollution from household solid fuels increased child mortality and reduced life expectancy in Mexico
Stevens et al. 2009 Proc Natl Acad Sci USA 105:16860-16865
•malaria-vector mosquito bite rates 278× higher in deforested sites in Amazon
Vittor et al. 2006 Am J Trop Med Hyg 74:3-11
•Anopheline mosquito density after deforestation in 60% of 60 studies over past century; 70 % of cases incidence of malaria
Yasuoka & Levins 2007 Am J Trop Med Hyg 76:450-460
Human health: World Health Organization Global Burden of Disease database
Environment: - Environmental Combination Index (adapted from Yale Env Performance Index)
- Proportional Environmental Impact rank (Bradshaw et al. 2010 PLoS One 5:e10440)- natural habitat conversion proportion (Global Land Cover 2000 dataset)- air/water quality (Yale Environmental Performance Index)- NPK fertiliser use/area arable land (FAOSTAT database)- CO2 emissions (Climate Analysis Indicators tool)
Control: - human population size (United Nations Common Database)
- purchasing-power parity-adjusted GNI (World Resources Institute)- health expenditure (WHO Statistical Information System)
DATA
Human health: WHO Global Burden of Disease database
• Disability-Adjusted Life Years (DALY) - years of life lost due to premature mortality and healthy years of life lost due to disability
• Infant Mortality (male) – 2004 mortality per 1000 live births
• Life Expectancy at birth (male) – 2004
• Diarrhoea deaths among children < 5 years (2000)
• Malaria deaths among children < 5 years (2000)
• Deaths due to Cardiovascular Disease (2002 age-standardised per 10,000)
• Deaths due to Cancers (2002 age-standardised per 10,000)
DATA
http://epi.yale.edu
10 % ECI mINFM 7.0/1000 live births mLE 1.9 years
• extinction must be inferred from record of sightings/collections
• when a species becomes increasingly rare before extinction, might persist unseen for many years
• so the time of last sighting often poor estimate of extinction date
Roberts & Solow 2003 Nature 426:245
present pastx x x xx x x?? xx x x
• optimal linear estimation• joint distribution of k same Weibull form regardless
of parent distribution• estimated extinction time q
• L: symmetric k×k matrix
• n: Estimated shape parameter of joint Weibull distribution of k
Roberts & Solow 2003 Nature 426:245
present pastx x x xx x x
qxx x x
kTTTT ...321
k
iiiTa
1
eeea t 111
ij
ji
ji
i
iiij
,ˆ
ˆ2ˆ2
2
1 11
1log1
1ˆ
k
i i
k
TT
TT
k
CI
11000 12000 13000 14000
YBP
• maximum likelihood to account for radio carbon dating error
• assume true ages U independent/uniformly distributed over (b1,g1) where b1 = extinction date
• PDF of Xj:
Solow et al. 2006 PNAS 103:7351
present pastx x x xx x x
b1 xx x x
jjj UX
11
11
)(
jj
j
xx
xf
11000 12000 13000 14000
YBP
• but... previous sighting rate important• length of period since last sighting informative• given previous sighting rate(n/tn), probability of next
sighting
• where p drops below threshold with increasing T-tn, TE inferred
McInerny et al. 2006 Conserv Biol 20:562
present pastx x x xx x x
TE xx x x
ntT
nt
np
1
5 10 15 20 25 30
10900
11000
11100
11200
11300
11400
11500
samples
Te
• but... TE depends on number of samples in ‘final’ period• declining influence of dates within time since last sighting• sequentially recalculated TE, weighting by cumulative distance
from T1
present pastx x x xx x x
TE xx x xT1
10000 15000 20000 25000 30000 35000 40000
YBP
IS1 IS2 IS3 IS4 IS5 IS6 IS7 IS8 IS9 IS10
Mammoth Equus S.Horse
Bison
C.BearSF.Bear
Neand
extinctions - constrained
P(rand overlap) = 0.09
© Moronail.net
© WWF
www.adelaide.edu.au/directory/corey.bradshaw
ConservationBytes.com
• Barry Brook University of Adelaide• Alan Cooper University of Adelaide• Camille Mellin University of Adelaide/AIMS• Mark Meekan AIMS• Iain Field Macquarie University• Xingli Giam Princeton University• Navjot S. Sodhi National University of Singapore• Tony McMichael Australian National University
© T
ianti
an Z
hang
, Goo
d50x
70.o
rg