![Page 1: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/1.jpg)
Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem
Management
Catherine Graham Stony Brook University
(many contributions – individual slides)
![Page 2: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/2.jpg)
Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem
Management
Catherine Graham Stony Brook University
(many contributions – individual slides)
![Page 3: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/3.jpg)
Improving assessment and modelling of climate change impacts on global terrestrial biodiversity
– McMahon et al. 2011
• Critical challenges were presented at the IPCC Working Group 2 (2007) – still many gaps in knowledge remain.
• “In common with other areas of global change science, the credibility of these predictions is limited by incomplete theoretical understanding, predictive tools that are acknowledged to be imperfect, and insufficient data to test, develop and improve model predictions.”
• What are these gaps? and How is NASA science filling them?
![Page 4: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/4.jpg)
Monitoring programs
Species’ ability to adapt
Range models
Community structure and dynamics
CURRENT BIOLOGY FORCASTING
Integrative models
ECOSYSTEM MANAGEMENT
Modified (slightly) from McMahone et al. 2011 Trends in Ecology and Evolution
![Page 5: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/5.jpg)
Monitoring programs
Species’ ability to adapt
Range models
Community structure and dynamics
CURRENT BIOLOGY FORCASTING
Integrative models
ECOSYSTEM MANAGEMENT
Monitoring programs• Remote-sensing• Biological data•Phenology •Rates
![Page 6: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/6.jpg)
Monitoring programs
Species’ ability to adapt
Range models
Community structure and dynamics
CURRENT BIOLOGY FORCASTING
Integrative models
ECOSYSTEM MANAGEMENT
Species’ ability to adapt•Genetic variation•Phenotypic plasticity•Migration
![Page 7: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/7.jpg)
Monitoring programs
Species’ ability to adapt
Range models
Community structure and dynamics
CURRENT BIOLOGY FORCASTING
Integrative models
ECOSYSTEM MANAGEMENT
Range models (species/functional group)•Correlative•Physiological•Population dynamics
![Page 8: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/8.jpg)
Monitoring programs
Species’ ability to adapt
Range models
Community structure and dynamics
CURRENT BIOLOGY FORCASTING
Integrative models
ECOSYSTEM MANAGEMENT
Community structure and dynamics• Species interactions –(disease, competition)•Food webs
![Page 9: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/9.jpg)
Monitoring programs
Species’ ability to adapt
Range models
Community structure and dynamics
CURRENT BIOLOGY FORCASTING
Integrative models
ECOSYSTEM MANAGEMENT
Integrative models• Biogeochemical models• Extinction risk models• Invasive/disease species spread models• Changes in distribution of species and functional groups • Influence of disturbance (disease/fire) on productivity
![Page 10: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/10.jpg)
Monitoring programs
Species’ ability to adapt
Range models
Community structure and dynamics
CURRENT BIOLOGY FORCASTING
Integrative models
ECOSYSTEM MANAGEMENT
Monitoring programs• Remote-sensing• Biological data•Phenology •Rates
![Page 11: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/11.jpg)
Are ocean deserts getting larger?
Irwin and Olivier. 2009. Geophysical Research Letters.
RS data used:SeaWiFS/AVHRR
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! Survey routes
Study sites
Forested ecoregions
km1000±
Disturbance and bird biodiversity (BBS data)- Forest harvest
Rittenhouse et al. 2010 PLoS
Landsat used to quantify land cover change1985-2006
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Current and past forest disturbances affect progressive similarity of forest birdsProgressive similarity - community similarity for each subsequent year relative to the baseline
All forest birds Midstory and canopy Neotropical migrants GroundTemperate migrants CavityPermanent residents Interior forest
Rittenhouse et al. 2010 PLoS
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Gaps in our knowledge of global ant diversity
Lots of ant data
Not so many data
No-analogueclimates
Jenkins et al. (2011) Diversity
and Distributions.
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Predicted Future Ant Diversity
Generalized Linear ModelClimate: temperature, precipitation, aridityGeography: biogeographic regionInteractions: region * climate
Jenkins et al. (2011) Diversity and Distributions.
No-analogueclimates
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16Nemani et al., 2003, EOM White & Nemani, 2004, CJRS
TOPS: Common Modeling Framework
Monitoring, modeling, and forecasting at multiple scales
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Monitoring programs
Species’ ability to adapt
Range models
Community structure and dynamics
CURRENT BIOLOGY FORCASTING
Integrative models
ECOSYSTEM MANAGEMENT
Species’ ability to adapt•Genetic variation•Phenotypic plasticity•Migration
![Page 18: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/18.jpg)
Genetic and morphological variation across taxa mapped using RS data (MODIS products, Q-scat)
Red – genetic diversity
Blue – morphological diversity
Yellow - bothThomassen et al. 2011
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Monitoring programs
Species’ ability to adapt
Range models
Community structure and dynamics
CURRENT BIOLOGY FORCASTING
Integrative models
ECOSYSTEM MANAGEMENT
Range models (species/functional group)•Correlative•Physiological•Population dynamics
![Page 20: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/20.jpg)
Manderson, Palamara, Kohut , Oliver in press. Marine Ecology Progress Series
Sea surface temp Divergence, HF radar
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Dynamic layers
Climate model
Static layers
Current occurrences
Future projected species habitat (time series of maps)
Current environmental conditions
Projected future conditions1. 2
.3.
4.
2100
2010
SDM
Velasquez, Salaman and Graham
More Andean bird species are predicted to loose habitat than to gain it with climate
COLONIZATIONS LOSSES
RS data used:MODIS productsQ-Scat
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Distribution of Antarctic and sub- Antarctic penguin colonies
Rapid warming
Olivier and colleagues
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Significant Changes in Ideal Breeding
Habitats: 1978-2010
Chinstrap Habitats
Adelie Habitats
Gentoo Habitats
Olivier and colleagues
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Changes in penguin habitat suitability correspond to empirical changes in abundance of penguins at the Palmer Station,
Antarctica
Changes in habitat suitability within 75 km of Palmer Station.
Percent change in population trends from initial sampling (Ducklow et al. 2007)
![Page 25: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/25.jpg)
Can richness be monitored and forecasted?
Coops, Waring, Wulder, Pidgeon and Radeloff. 2010. Journal of biogeography
Based on the annual sum, the minimum, and the seasonal variation in monthly photosynthetically active radiation, fPAR from MODIS
Dynamic Habitat Index
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Woodland bird species richness can be predicted by the Dynamic Habitat Index
![Page 27: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/27.jpg)
Dynamic habitat index can be used to forecast patterns of species richnessof woodland/forest birds.
Coops, Waring, Wulder, Pidgeon and Radeloff. 2010. Journal of biogeography
OBSER VED
PREDICTED
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Broad scale estimates of forest bird species richness are consistent across studies
Models derived from BBSRS data – Lidar canopy
structure predictor variables, mODIS
Goetz et al. (forthcoming) Global Ecology & Biogeography
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Lidar used to map multi-year prevalence / optimal breeding habitat..
Black throatedblue warbler
Goetz et al. (2010) Ecology 91:1569-1576
Hubbard Brook Experimental Forest
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Habitat groupDeciduous, evergreen forest(2001 NLCD)
ConstraintsEdge & area sensitivityForest composition (FIA)Housing density
Intrinsic elementsSnags/logsUnderstory vegetationForage/prey abundance
Main modeling unit; general habitat requirements
Species-specificmodifiers
Habitat needs not mapped at large spatial scales; need to be maintained within each habitat group
Beaudry et al. 2010 Biological Conservation
Building potential habitat models using nested habitat elements
Anna M. Pidgeon, Fred Beaudry, Volker C. Radeloff
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Habitat groupDeciduous, evergreen forest(2001 NLCD)
ConstraintsEdge & area sensitivityForest composition (FIA)Housing density
Intrinsic elementsSnags/logsUnderstory vegetationForage/prey abundance
Main modeling unit; general habitat requirements
Species-specificmodifiers
Habitat needs not mapped at large spatial scales; need to be maintained within each habitat group
Beaudry et al. 2010 Biological Conservation
Building potential habitat models using nested habitat elements
Anna M. Pidgeon, Fred Beaudry, Volker C. Radeloff
![Page 32: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/32.jpg)
Monitoring programs
Species’ ability to adapt
Range models
Community structure and dynamics
CURRENT BIOLOGY FORCASTING
Integrative models
ECOSYSTEM MANAGEMENT
Range models (species/functional group)•Correlative•Physiological•Population dynamics
![Page 33: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/33.jpg)
Linking environmental data to physiological response over large scales
Kearney, Simpson, Raubenheimer and Helmuth 2010, PTRS
• Biophysical (Heat Budget)
Model
• Dynamic Energy Budget
Model
• Growth, reproduction,
size
•Environmental data
• Survival, distribution
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20
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60
70
80
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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
snou
t-ve
nt le
ngth
(cm
)
years
Max of SVL
05
10152025303540
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
wet
mas
s (g)
years
Min of Mass
HeatDeath
ColdDeath
Egg?
More accurate predictions are made when daily remote-sensing data are used in models
0-50% shade, 10cm burrow
0
2000
4000
6000
8000
10000
12000
14000
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5re
serv
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MaxRes
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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
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rve
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MaxRes
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10152025303540
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
wet
mas
s (g)
years
Max of Mass
HeatDeath
ColdDeath
Egg?
monthly data daily datasize
reserve
mass/repro (8 clutches)
size
reserve
mass/repro (11 clutches)
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Monitoring programs
Species’ ability to adapt
Range models
Community structure and dynamics
CURRENT BIOLOGY FORCASTING
Integrative models
ECOSYSTEM MANAGEMENT
Range models (species/functional group)•Correlative•Physiological•Population dynamics
![Page 36: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/36.jpg)
Predicting Extinction Risks under Climate ChangePredicting Extinction Risks under Climate ChangeDynamic
layersClimate model
Static layers
210020
10SDM
2010
2100
Metapopulation model with dynamic spatial structure6.
Demographic model
000
000
000
3
2
1
44332211
S
S
S
SmSmSmSm
5.
Extinction risk assessment7.Synthesis across species to inform IUCN Red List process
8.
Akçakaya & Pearson
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Predicting Extinction Risks under Climate ChangePredicting Extinction Risks under Climate ChangeDynamic
layersClimate model
Static layers
210020
10SDM
Metapopulation model with dynamic spatial structure6.
Demographic model
000
000
000
3
2
1
44332211
S
S
S
SmSmSmSm
5.
Extinction risk assessment7.Synthesis across species to inform IUCN Red List process
8.
Akçakaya & Pearson
2010
2100
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Predicting Extinction Risks under Climate ChangePredicting Extinction Risks under Climate ChangeDynamic
layersClimate model
Static layers
210020
10SDM
2010
2100
Metapopulation model with dynamic spatial structure6.
Demographic model
000
000
000
3
2
1
44332211
S
S
S
SmSmSmSm
5.
Extinction risk assessment7.Synthesis across species to inform IUCN Red List process
8.
Akçakaya & Pearson
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Monitoring programs
Species’ ability to adapt
Range models
Community structure and dynamics
CURRENT BIOLOGY FORCASTING
Integrative models
ECOSYSTEM MANAGEMENT
Community structure and dynamics• Species interactions –(disease, competition)•Food webs•Guild/functional group structure
![Page 40: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/40.jpg)
Phytoplankton diversity from ocean color
• Phytoplankton class-specific approach used in conjunction with SeaWiFS 10-year time series of surface Chl data in the global ocean
• Microphytoplankton (mostly diatoms) are major contributors in temperate-subpolar regions (50%) and coastal upwellings (70%) during the spring-summer season
• Nanophytoplankton (mainly prymnesiophytes) provide substantial ubiquitous contribution (30–60%)
• The contribution of picophytoplankton reaches maximum values (45%) in subtropical oligotrophic gyres
Contribution (%) to total primary production in boreal summer
Stramski and colleagues
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Models accurately predict change of ecosystem engineershindcasts of limits (lines) and observed historical limits (dots), geographic region in grey
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Predicting satellite derived patterns of large-scale disturbances in forests of the Pacific Northwest region response to recent climate variation(Waring, Coops and Running)
Physiologically informed models of 15 species of conifers
Physiological models and remote-sensing provide similar insights into ecosystem function
Stress of species predicted using a physiological informed models corresponds to areas that Disturbance predicted using physiological basis
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Physiological models and RS measures provide the same pattern in Leaf Area Index (correlated maximum growth potential)
![Page 44: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/44.jpg)
Land surface temperature & EVIMildrexler et al. 2009
Proportion of species stressed between 2005-2009 compared to baseline conditions (1950-1975)
~70% variation explained
![Page 45: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/45.jpg)
Monitoring programs
Species’ ability to adapt
Range models
Community structure and dynamics
CURRENT BIOLOGY FORCASTING
Integrative models
ECOSYSTEM MANAGEMENT
Modified (slightly) from McMahone et al. 2011 Trends in Ecology and Evolution
![Page 46: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/46.jpg)
What next?
• Linking RS time-series data biological data to better predict future biological diversity– Key for decision making– Key for inputs into biogeochemical models
• Determining what RS data captures in terms of biological diversity or ecosystem stress
![Page 47: Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem Management Catherine Graham Stony Brook University (many](https://reader035.vdocuments.mx/reader035/viewer/2022062800/56649de95503460f94ae4ba9/html5/thumbnails/47.jpg)