A Management Landscape for Jaguars in the Upper Paraná Atlantic Forest
Carlos De Angeloa, Agustín Pavioloa, Thorsten Wiegandb,Rajapandian Kanagarajb, Mario S. Di Bitettia
a National Research Council (CONICET)Instituto de Biología Subtropical, Universidad Nacional de Misiones
Asociación Civil Centro de Investigaciones del Bosque AtlánticoPuerto Iguazú, Argentina. [email protected].
b Department of Ecological Modelling, UFZ Helmholtz Centre for Environmental Research, Leipzig, Germany
The Atlantic Forest of South America
• The Atlantic Forest is a Biodiversity Hotspot
(Olson et al. 2001)
The Atlantic Forest of South America
• The Atlantic Forest is a Biodiversity Hotspot
• One of the most endangeredenvironments of the world
Myers et al. (2000); Mittermeier et al. (2004)
The Upper Paraná Atlantic Forest
• Upper Paraná Atlantic Forest (UPAF):– Largest eco-region of
the Atlantic Forest– Three countries:
Argentina. Brazil & Paraguay
Olson et al. (2001); Di Bitetti et al. (2003)
The Upper Paraná Atlantic Forest
• Upper Paraná Atlantic Forest (UPAF):– Largest eco-region of
the Atlantic Forest– Three countries:
Argentina. Brazil & Paraguay
– Different political and social histories have modified this landscape
Olson et al. (2001); Di Bitetti et al. (2003)
UPAF Conservation & Jaguars
• The Biodiversity Vision
Di Bitetti et al. (2003)
UPAF Conservation & Jaguars
• The Biodiversity Vision– A “Conservation
Landscape”– The jaguar was
used as an “umbrella” species for its design
– Poor information about jaguars in the UPAF in 2003
Di Bitetti et al. (2003)
UPAF Conservation & Jaguars
• The Biodiversity Vision
• The UPAF contains two Jaguar Conservation Units (JCUs nº: 87 & 89)– They were defined in
1999 also with scarce information
Sanderson et al. (2002)
Main Objectives
• To explore the factors that are affecting jaguar persistence in this region.
• To provide a landscape-management tool to validate and support conservation initiatives for the UPAF and for jaguars.
Study Area
• Upper Paraná Atlantic Forest: >250.000 km2
= core area of the UPAF
De Angelo (2009)
Species Data
• Participatory monitoring network
De Angelo et al. (2011a)
Species Data
• Participatory monitoring network
• Direct and indirect evidences of jaguarpresence– Tracks– Fecal samples– Sightings, etc.
De Angelo et al. (2011a)
Species Data
De Angelo et al. (2011a)
Species Data
• >2500 records
De Angelo et al. (2011a)
Species Data
• >2500 records
• Surveyed area represented >90.000 km2 of the study area
De Angelo et al. (2011a)
Species Data
• >2500 records
• Surveyed area represented >90.000 km2 of the study area
• 950 accurately identified as jaguar presence records
De Angelo et al. (2011a)
Species Data
• Data filtered:– Grid with cells of the
size of a female territory (12x12 km)
Species Data
• Data filtered:– Grid with cells of the
size of a female territory (12x12 km)
– Random selection of one register per each cell of the grid
– Only 106 jaguar records were used in our analysis out of 950
Preliminary Habitat Suitability Analysis
• Presence-only habitat suitability analysis: Ecological Niche Factor Analysis (Hirzel et al. 2002)
De Angelo et al. (2011b)
Pseudo-absences• Rule-based random
generation
Engler et al. (2004)
Generalized Linear Models
• Binary response variable: presence/pseudo-absence
• Information-theoretic approach for variables and model selection
• Two main hypotheses about landscape influence on jaguars:
• Landscape Structure and History• Direct Human Impact
(at a landscape scale)
• Landscape structure and history– Forest
• Amount and connectivity of forest (present condition)• Amount of forest 30 years ago• Combination (past and present)
– Abiotic conditions• Rivers• Altitude• Slope• Combination
– Human land uses• Intensive agriculture, small farms, pastures, pine plantations• Combination
– Combined landscape models
Hypotheses
Elevation map
Distance to rivers
Availability of native forest
Intensive agriculture
Amount of native forest
Hypotheses
• Direct human impact– Protection and accessibility
• Protection• Accessibility• Interaction between protection & accessibility
– Rural population• Most recent data (2001)• Average density (1970-2000)
– Combined human impact models
• Final global model: Landscape + Human impact
Human access cost
Protection levels
Rural population density
Results• Best supported hypotheses (GLM models)
– Landscape model including:(AICc = 182.9)
• Present condition of the forest• Amount of forest 30 years ago• Human land uses around each cell (intensive agriculture,
pastures and small farms)
AICc= Akaike Information Criterion
Results• Best supported hypotheses (GLM models)
– Landscape model including:(AICc = 182.9)
• Present condition of the forest• Amount of forest 30 years ago• Human land uses around each cell (intensive agriculture,
pastures and small farms)
– Human impact model including:(AICc = 215.1)
• Combination of protection level and human accessibility• Rural population density (historical average 1970-2000)
– GLOBAL (Landscape + Human Impact)(AICc =176.5)
AICc= Akaike Information Criterion
Results
• Traditional habitat modeling approach– Landscape
(AUC=0.905)
– Human impacts(AUC=0.841)
– Global(AUC=0.915)
AUC= Area Under “Receiver Operating Characteristic” Curve
Results
• Traditional habitat modeling approach– Landscape– Human impacts– Global
• Only 7% of the study area with suitable conditions
Two-dimensional Model
“Sinks” “Refuges”
“Attractive sinks”
Core areas (potential sources)
“Barriers”
“Bar
riers
”
Direct human impactHIGH LOW
Land
scap
e co
nditi
ons G
OO
DBA
D
High poaching pressure.Low prey density
High jaguar mortality
ProtectionInaccessible areas
Low human population density
High proportion of forest cover
Habitat connectivity
Fragmented areasHigh proportion of
anthropic land uses
Naves et al. (2003); Nielsen et al. (2006)
ProtectionInaccessible areas
Low human population density
High poaching pressure.Low prey density
High jaguar mortality
Two-dimensional Model
“Sinks” “Refuges”
“Attractive sinks”
Core areas (potential sources)
“Barriers”
“Bar
riers
”
Direct human impactHIGH LOW
Land
scap
e co
nditi
ons G
OO
DBA
D
MANAGEMENT: protection / mitigation measures
MA
NA
GEM
ENT:
la
ndsc
ape
reco
very
an
d re
stor
atio
n
Fragmented areasHigh proportion of
anthropic land uses
Two-dimensional Model
Presence
Pseudo-absence
Sinks Validation
• Proportion of killed or removed jaguars found in the different habitat categories
0
5
10
15
20
25
Barreras Sumideros Núcleos
Jagu
ares
mue
rtos
Observado Esperado
2 = 0.00005; df = 2; p < 0.001
Delibes et al. (2001); Naves et al. (2003); Battin (2004); Nielsen et al. (2006)
Observed Expected
“Barriers” “Sinks” Core areas
Kill
ed ja
guar
s
Two-dimensional Model
• Spatial population structure of jaguars in the UPAF: – e.g. potential source & sink
populations
• Management priorities:–– Landscape restorationLandscape restoration–– Protection/mitigationProtection/mitigation
“`Sinks”
“Attractive-sinks’
Refuges – “islands”
Core areas
Using the Model for Landscape Management
Presence
Pseudo-absence
Using the Model for Management
Zoom in the “Green Corridor” of Misiones
Increase protectionMitigation actions
Landscape restorationIncrease connectivity
Validation of the Biodiversity Vision
• UPAF conservation– Biodiversity vision
validation
Di Bitetti et al. (2003)
Conservation and Management
• UPAF conservation– Biodiversity vision
validation
• Jaguar conservation– New definition of the
Jaguar Conservation Units
Sanderson et al. (2002); Zeller et al. (2007)
ConclusionsConclusions
• Jaguars demonstrated a complex response to the landscape transformation: – forest, human land uses, protection, human accessibility &
human population density
ConclusionsConclusions
• Jaguars demonstrated a complex response to the landscape transformation
• Historical conditions of the landscape are important for predicting jaguar presence– forest cover 30 years ago & historical human population
density
ConclusionsConclusions
• Jaguars demonstrated a complex response to the landscape transformation:
• Historical conditions of the landscape are important for predicting jaguar presence
• Two-dimensional approach with habitat modeling can increase their application as management tools
•• Thank you to:Thank you to: SCGIS Scholarship Program and all the people of the SCGIS for making this possible. Proyecto Yaguareté collaborators and Brazilian researchers for their help in data collection.
•• Financial supportFinancial support: WWF-Internacional; WWF-Suiza; Species Action Found, WWF USA; Education for Nature, WWF USA; Field Conservation Funds, Lincoln Park Zoo; Kaplan Graduate Awards, Panthera Corporation; Conservation Leadership Program (CLP); CONICET.
•• Institutional supportInstitutional support:: Fundación Vida Silvestre Argentina; Administración de Parque Nacionales;Ministerio de Ecología, Recursos NaturalesRenovables de Misiones.
ACKNOWLEDGMENTS
Contact: [email protected]
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