remote sensing for biodiversity conservation, land cover and land use change and carbon/ecosystem...

Remote Sensing for Biodiversity Conservation, Land cover and Land use Change and Carbon/Ecosystem

Management

Catherine Graham Stony Brook University

(many contributions – individual slides)

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?

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

Monitoring programs


Range models



Integrative models


Monitoring programs• Remote-sensing• Biological data•Phenology •Rates

Monitoring programs


Range models



Integrative models


Species’ ability to adapt•Genetic variation•Phenotypic plasticity•Migration

Monitoring programs


Range models



Integrative models


Range models (species/functional group)•Correlative•Physiological•Population dynamics

Monitoring programs


Range models



Integrative models


Community structure and dynamics• Species interactions –(disease, competition)•Food webs

Monitoring programs


Range models



Integrative models


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

Monitoring programs


Range models



Integrative models


Monitoring programs• Remote-sensing• Biological data•Phenology •Rates

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

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

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.

Predicted Future Ant Diversity

Generalized Linear ModelClimate: temperature, precipitation, aridityGeography: biogeographic regionInteractions: region * climate

Jenkins et al. (2011) Diversity and Distributions.

No-analogueclimates

16Nemani et al., 2003, EOM White & Nemani, 2004, CJRS

TOPS: Common Modeling Framework

Monitoring, modeling, and forecasting at multiple scales

Monitoring programs


Range models



Integrative models


Species’ ability to adapt•Genetic variation•Phenotypic plasticity•Migration

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

Monitoring programs


Range models



Integrative models



Manderson, Palamara, Kohut , Oliver in press. Marine Ecology Progress Series

Sea surface temp Divergence, HF radar

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

Distribution of Antarctic and sub- Antarctic penguin colonies

Rapid warming

Olivier and colleagues

Significant Changes in Ideal Breeding

Habitats: 1978-2010

Chinstrap Habitats

Adelie Habitats

Gentoo Habitats

Olivier and colleagues

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)

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

Woodland bird species richness can be predicted by the Dynamic Habitat Index

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

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

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

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

Monitoring programs


Range models



Integrative models



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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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

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wet

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s (g)

years

Max of Mass

HeatDeath

ColdDeath

Egg?

monthly data daily datasize

reserve

mass/repro (8 clutches)

size

reserve

mass/repro (11 clutches)

Monitoring programs


Range models



Integrative models



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

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3

2

1

44332211

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SmSmSmSm

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Extinction risk assessment7.Synthesis across species to inform IUCN Red List process

8.

Akçakaya & Pearson


layersClimate model

Static layers

210020

10SDM


Demographic model

000

000

000

3

2

1

44332211

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S

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SmSmSmSm

5.


8.

Akçakaya & Pearson

2010

2100


layersClimate model

Static layers

210020

10SDM

2010

2100


Demographic model

000

000

000

3

2

1

44332211

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S

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SmSmSmSm

5.


8.

Akçakaya & Pearson

Monitoring programs


Range models



Integrative models


Community structure and dynamics• Species interactions –(disease, competition)•Food webs•Guild/functional group structure

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

Models accurately predict change of ecosystem engineershindcasts of limits (lines) and observed historical limits (dots), geographic region in grey

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

Physiological models and RS measures provide the same pattern in Leaf Area Index (correlated maximum growth potential)

Land surface temperature & EVIMildrexler et al. 2009

Proportion of species stressed between 2005-2009 compared to baseline conditions (1950-1975)

~70% variation explained

Monitoring programs


Range models



Integrative models


Modified (slightly) from McMahone et al. 2011 Trends in Ecology and Evolution

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

remote sensing for biodiversity conservation, land cover and land use change and carbon/ecosystem...

Documents

land use change

land cover change1985

biodiversity conservation

areas of global change

distribution of species

oligotrophic provinces

insufficient data

neighboring provinces