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Towards a cartography of wildlife moves Elodie Buard PhD began in January 2009 directed by Anne Ruas (COGIT) and Léna Sanders (Géographie-Cités ) « Spatio-temporal dynamics interaction of space and wildlife » Journée cartactive – 5 juin 2009 Outline Context multiple data sources…

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towards a cartography of wildlife moves

Towards a cartography of wildlife moves

Elodie Buard

PhD began in January 2009

directed by Anne Ruas (COGIT)

and Léna Sanders (Géographie-Cités)

« Spatio-temporal dynamics interaction of space and wildlife »

Journée cartactive – 5 juin 2009

outline
Outline
  • Context
  • multiple data sources…
  • …to model the trajectories
  • …and to cartography wildlife presence
  • conclusion
context
Context
  • Hwange national park, Zimbabwe
  • In this park, biologists have noticed (Fritz 2007) :
  • 1- Elephant population increases and moves
  • => modify the space (empty spaces, not enough water)
  • 2- Space is changing naturally by seasons or rainfalls
  • modify wildlife moves

What are the interactions between space and wildlife

at different scales of time and space ?

objectives
Objectives

What are the interactions between space and wildlife

at different scales of time and space ?

  • Identifying wildlife moves
  • at different scales (spatial and temporal)
  • Cartography wildlife moves
    • Animated dynamic maps (video)
    • Or static maps showing moves
objectives5
Objectives

historic data

of wildlife and space

(since 1970)

Cartographic output

Static/dynamic

Calculations

Data mining

multiple sources7
Multiple sources
  • Data source (space) are various
    • Aerial photographs
    • Vegetation maps
    • DTM
multiple sources8
Multiple sources
  • Observed data are various… in spatial scale
multiple sources9
Multiple sources
  • Observed data are various… in temporal scale

Wildlife census on road and water points :

- regular data in time

- but representative in space

multiple sources10
Multiple sources

vegetation :

exhaustive in space

but versionning (only few years)

and varying in the season, in fonction of rainfalls

model the trajectories
Model the Trajectories
  • Identify trajectories of animal or groups
  • Typically:

Trajectories = {moves and stops} during a certain period

model the trajectories13
Model the Trajectories
  • But: we don’t known them directly -> calculations
  • From data census on roads
  • A observer trajectory needs to be done as he is mobile as well!

CAR

ELEPHANT

model the trajectories14
Model the Trajectories
  • Observer follows a path and stops to each observation at a time
cartography of moves
Cartography of moves
  • Bertin: graphic semiology for moves

-> Diffusion of phenomena,

construction of spatial features

-> Moves of individuals

cartography of moves16
Cartography of moves
  • Kraak from Minard
  • Time=3D
  • Stops = nodes
  • Size of troups= size of the line
cartography of wildlife moves
Cartography of wildlife moves
  • GPS tracking not done on herbivores
  • so trajectories are constructed from
    • Calculations and hypothesis
    • Expert knowledge
  • Here cartography of individual moves
  • So far : wildlife presence depending on the observation time
expert knowledge elephant move
Expert knowledge: elephant move
  • Aggregation in dry season
  • Time step: the year
expert knowledge zebra move
Expert knowledge: zebra move
  • 2 time steps together: the week and the year
  • As a result, 2 spatial scales together
expert knowledge giraffe move
Expert knowledge: giraffe move
  • =>definite translation
  • Over 30 years
  • No moves α season => a territory
expert knowledge giraffe move21
Expert knowledge: giraffe move

To simplify the giraffe move, in one map:

The giraffe does not move in relation to rainfall

maps of wildlife presence
Maps of wildlife presence
  • Animated maps, feature by feature
  • Time step: the year
  • Spatial scale: the park (macro scale)
  • Based on water points counting
  • => Inter-year analysis
conclusion of the work
Conclusion of the work
  • Maps showing moves are not necessary animated
  • Animation maps are to be done for roads census as time step is finer
  • Analyse of data imprecision to emphasis the gaps