(FOV) covering a continuous rectangular area of the base of 33 
x 100 cm. Two webcams (Logitech communication STX with 
an optical resolution of 640 x 480 pixels) were mounted at the 
same height, with a FOV covering the base. Therefore the 
dynamics of the thermal environment as well as the animal 
responses of inside the terrarium were recorded. 
2.2 Model the body temperature 
We aimed to predict accurately the body temperature dynamics 
of a lizard in any given thermal environment with a physically 
based model through a rigorous calibration procedure based on 
Monte Carlo simulation techniques. From a physical 
perspective, the energy exchange between a lizard and its 
environment has been described by Porter’s models (Porter and 
Gates, 1969; Porter et al., 1973). This study took this model and 
made some small adjustments. In summary, the total energy 
intake of a lizard in a fixed time interval (4Q,) may be written 
as the sum of six terms: solar radiation, convective heat flow 
(Qcom), infrared radiation (Qongwave)-» conductive heat flow 
(Ocond), energy gain (Omera) by food intake (metabolism) (Q,). 
energy loss through respiration/water evaporation (O aterloss)- 
To further improve the performance of the model and generate 
realistic parameters, we re-estimated the model parameters 
using a Monte Carlo simulation. In a preliminary step, the 9- 
dimensional parameter space was sampled over an equally 
distributed grid. Across each parameter’s range, which was 
assumed to be +10% of its reference value, the body 
temperature model was run at each sample point using the input 
of the actual thermal environment of the animal experiment, 
thereby predicting the range in body temperature over time. 
Meanwhile, the observed lizard body temperature dynamics 
were recorded, and later compared with the predicted values. 
Independent observations (N=31) of lizard’s body temperature 
were collected to validate the body temperature model. For a 
detailed information about the model parameterization please 
refer to (Fei et al, 2012). Finally, the observations were 
compared with model simulations and the root mean square 
error (RMSE) of the temperature prediction was calculated. 
2.3 Predict the thermal habitat occupancy 
The thermal habitat occupancy was predicted through 
simulating the movement of the lizard. Following a set of 
transition rules, a simulated lizard performed behavioral 
thermoregulation as a response to thermal environmental 
changes. By tracking and aggregate these movements, 
predictive thermal habitat occupancy maps can be generated. 
An animal experiment was carried on in a lab and the real 
habitat usage of a lizard was recorded by cameras. The results 
were compared with the simulation for the accuracy assessment. 
Transition rules form the core of a CA algorithm (Chen et al, 
2002). These rules depend on the behavioural traits of the 
modelled species, their response to thermal landscape dynamics, 
and their ability to perceive their environment. The assumption 
made here is that during the day time (from 8:30 to 19:00) the 
lizard will try to maintain its preferred body temperature (7,) for 
as long as possible. When its body temperature falls below (or 
increases above) 7,, it will sense the ambient temperature within 
a certain distance (one cell in our model) and with a chance P it 
will move to the warmest (or coldest) cell in its vicinity.The 
transition rules were defined as bellow: 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
  
Randomly Locate the 
Simulated Lizard 
i 
Get Current Thermal 
Environment 
   
     
    
    
   
Radiation 
Intensity 
  
  
  
Ground 
Surface 
/ Temperature / 
/ Air 
/ NOTICE Body Temperature 
MEM 
  
    
Preferred Body 
He Instant Body 
Temperature 
Teraperature of lizard 
  
  
  
L— wvonipàrison 
       
     
  
To the 
next 
time step 
Moving Probability 
Caleutation 
Make a Move?» 
E red 
—7 
Yes 
Move to an optimal 
neighbor pixel 
Te 
< “Time Or oed 
om 
No 
  
   
   
  
M cr 
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x 
  
Translate individual's 
Path to Microhabitat 
Usage Map 
Figure 1. The flowchart of the CA algorithm 
  
  
  
The moving probability P in each step was defined as: 
1 
P=k(T,-T,) tam asses 
5 
P=1, when( 
  
1 
5-0 5) 
And K was parameterized from observations of the animal 
experiment. the detail of the parameterization process can be 
found at (Fei et al., 2011). 
2.4 Thermal roughness index 
At larger scales, the CA model is computational challenged 
because of the computational load is proportional to the habitat 
size as well as the number of individual animals. As an 
alternative, thermal roughness index is defined to quantified the 
deviations of a real temperature distribution on a surface from 
its average value. Thermal roughness index was proposed in 
this work as a way to predict the occupancy of lizard’s thermal 
habitat. Because of the fact that a reptile regulate its body 
temperature by shuttling between places with different thermal 
properties, it make sense that a surface with a more complex 
thermal conditions is more suitable for the behavioural 
thermoregulation of reptiles. The thermal roughness index is 
defined by the arithmetic average of average land surface 
temperature: 
  
Where T, is the thermal roughness index, n is the number of 
sample points, t bar is the averaged land surface temperature of 
the area of interest. The thermal roughness index maps was 
compared with the simulation results. 
    
    
  
  
  
   
   
    
  
    
    
  
  
   
    
     
   
     
   
  
  
  
  
  
     
     
    
   
  
  
   
   
  
    
    
    
  
     
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