L. Pintea1, S. Jantz2, J.R. Nackoney2, M.C. Hansen2

1the Jane Goodall Institute, Vienna, VA, USA; 2Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA.

The First High Resolution Maps of Chimpanzee Habitat Health in Africa

Overview

• Chimpanzee (Pan troglodytes), is listed as endangered since 1996 (IUCN Red List)

• Habitat destruction and degradation is one of the main threats

• A new Decision Support System (DSS) to annually monitor and forecast chimpanzee habitat health in Africa is proposed.

• The DSS uses a combination of species modeling, 30-meter Landsat satellite imagery, and crowd-sourced field data to systematically monitor habitats at scales locally relevant and consistent across the entire chimpanzee range.

Deforestation 2000-2013 (red)

Developing a Decision Support System (DSS)

• The Jane Goodall Institute's (JGI) 30-year mission focus is to protect 85% of chimpanzees and their habitats in Africa.

• In order to achieve that goal JGI and partners are using Open Standards (OS) for the practice of conservation.

• From the DSS, JGI and partners will be able to have near real-time access to consistentrange-wide information describing current threats and status of chimpanzee habitats and be able to develop and implement conservation strategies and measure conservation success according to Open Standards .

Geographic scopeChimpanzee habitat health DSS covers geographic ranges of all four sub-species of chimpanzees. The feasibility of DSS was assessed in sub-regions (shown in red) that capture a gradient of the habitat types, ranging from the humid tropical forests of Eastern DRC to the dry savanna woodlands of western Tanzania.

• Chimpanzee habitat suitability was modeled for the time periods 2000-2004 and 2005-2010.

• Models were run using the randomForestssoftware package and used a combination of static and dynamic variables .

Habitat suitability modeling

Static variables Dynamic variables

* Indicates important variables for both time periods

* Indicates important variables for both time periods

Elevation* Canopy height

Slope Forest edge density*

Proximity to steep slopes* Percent bare ground

Proximity to water bodies Percent canopy cover*

Proportion forest loss*

Normalized ratio Landsat bands 4,3

Normalized ratio Landsat bands 4,5

Normalized ratio Landsat bands 4,7

Cloud computing enable 30 meter Landsat satellite data to provide a synoptic view of chimpanzee habitats at finer spatial and temporal resolutions, that are locally relevant and consistent across the entire chimpanzee range.

Regional Scale Village Scale

Crowd-sourced information

• The DSS is enhanced by the field data collected by the local communities, rangers and other citizens using mobile smartphones and tablets.

• The crowd-sourced data is used to substantially increase the amount of data available for the development and validation of species distribution, land cover change and habitat health models.

• JGI crowd-sourcing platform uses Android mobile devices and Open Data Kit (ODK), a free and open source app. To learn more see: https://www.youtube.com/watch?v=CNXv8EEs0P8

Innovative and effective• Chimpanzee habitat health DSS leverages the latest advances in remote sensing, ecological modeling,

cloud computing and citizen science to map and monitor chimpanzee habitats at unprecedented spatial and temporal scales while focusing on converting big data into information that can be used by the conservation decision-makers.

Evidence of implementation and impact• Chimpanzee suitability and habitat change layers derived from 2000-2013 Landsat satellite imagery have

been already used to support conservation decisions in Tanzania, Uganda, DRC and Congo. The DSS is in its final stages of the feasibility test. It is expected to be fully operational in 2-3 years.

Applied elsewhere or more broadly• Since the chimpanzee is not only an important keystone species but also an excellent flagship and

umbrella species, an annual chimpanzee habitat health index would support conservation goals of other species within its large 2.5 million sq km range and could be one of the important indicators of overall ecosystem health of tropical forest systems in Africa.

An inspiring solution?

Components that lead to success• Integrating innovative remote sensing and species modeling approaches with citizen

science data; • Using Open Standards for the practice of conservation as the decision making

framework to plan, implement and measure success of conservation strategies and actions.

• Partnerships with IUCN/SSC/Primate Specialist Group and Great Apes Survival Partnership (GRASP)

Enabling factors• Advances in cloud computing , remote sensing science, and mobile mapping

technologies.• Funding support from NASA and other donors that focus on developing operational

decision support systems that convert big data into information useful for conservation decisions.

What makes it work?