(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
Yes
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.
3.1
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