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TURTLES ARE THE TURTLES
Yassine Gangata, Mayeul Dalleaub, Daniel Davida,
Nicolas Sebastiena, Denis Payeta.
aEA2525-LIM/IREMIA University of La R´eunion
email: {yassine.gangat, daniel.david, nicolas.sebastien, denis.payet}@univ-reunion.fr
bK´elonia, l’observatoire des tortues marines - IFREMER of La R´eunion
EA12-CREGUR, University of La R´eunion - CNRS-CEFE, Montpellier, France
email: mayeuldalleau@kelonia.org
KEYWORDS
Agent-based simulation, NetLogo, Green turtles
ABSTRACT
Green sea turtles Chelonia mydas inhabit tropical and
subtropical oceans worldwide. Living in the marine en-
vironment and laying eggs on the beach, they are mainly
threatened by human activities (poaching, fisheries by-
catch, habitat destruction, etc.). In Reunion Island, the
K´elonia observatory and IFREMER develop various sci-
entific programs to study and protect sea turtles. One of
them consists in studying migrations of green sea turtles
for mating purpose. As existing mathematical models
struggle to take spatial dimension into account, we pro-
pose an agent-based model to study some of the numer-
ous questions regarding green sea turtles migrations.
Coming with high expectations, experts in sea turtles
also provide many heterogeneous but incomplete data.
Considering available or obtainable data in one hand and
the various questions of experts in the other hand, we de-
fined an innovative modelling process in which we simul-
taneously conduct discussion with experts and prototyp-
ing. This paper aims at presenting our simulation model
but also our approach as well as the data-collection and
modelling roadmap it produced.
INTRODUCTION
Environment and biodiversity protection is one of the
major actual issue. At this point, some decision-support
tools are needed to assist decisioners in the process of
biodiversity conservation.
This is where simulation comes into play: firstly we de-
fine the problem with as much detail as possible, then we
build a conceptual model of the real system and eventu-
ally we implement it. The aim is to evaluate the con-
sequences of multiple strategies to preserve threatened
species.
In this paper, we first describe a simulation approach
dedicated to green sea turtle populations. Then we dis-
cuss the design method to develop an Agent-Based Sim-
ulation (ABS) meeting thematician requirements using
a platform called NetLogo. Lastly, we present some in-
teresting considerations resulting from this full process.
THE GREEN TURTLES : A FRAGILE AND
ENDANGERED SPECIES
Chelonia mydas, also known as Green Turtle, inhabits
tropical and subtropical oceans and is classified among
the ”endangered” species of the IUCN (International
Union for Conservation of Nature) Red List of Threat-
ened Species. Like other marine turtles species, survival
of green turtles is mainly dependent of direct and in-
duced anthropogenic threats affecting all life stages. One
of the major threat may certaintly be intentional har-
vest of eggs, juvenile and adults individuals. Habitats
degradation or fisheries bycatch are also known to have
detrimental consequences on green turtle populations.
In order to understand the impact and possibly control
such threats, a deeper understanding of the green turtle
ecology is needed. This is a major concern of researchers
from IFREMER (French Research Institute for Exploita-
tion of the Sea) and K´elonia (the observatory of marine
turtles), who develops scientific programs to study ma-
rine turtles in the South-West Indian Ocean (SWIO).
The usual way to build a model in population ecology is
to take advantage of mathematical approaches in popu-
lation dynamics model such as (Chaloupka 2002) or in
individual based model such as (Mazaris et al. 2006).
Such models gave some precise formalism in population
parameters but some questions like spatialization are left
aside.
Indeed, green turtle is a migratory species that moves
across three habitat types.
This is where our expertise in the field of Agent-Based
Simulation (ABS) offers an original perspective. ABS
enables experts to simulate turtles behavior and ecol-
ogy while taking into account the spatial aspects of the
migration events. Even if our proposal is specifically ap-
plied to the SWIO, the genericity of our model would
allow us to consider any population of green sea turtles
around the world.
The main difficulty of this kind of project lies in the in-
teraction of two specific groups of scientific experts. On
one hand there are experts in the field of complex simula-
tion and ABS. On the other hand, are the thematicians,
experts in another domain of Science, such as marine
turtle ecologist in our case. The latter being usually not
familiar with ABS, ABS experts must then be able to
support them in the development of a simulation that
will help them to improve their understanding of their
system. In order to work hand-in-hand, we used an in-
novative approach described in the next part.
MODELLING PROCESS
In an ABS, agents are entities that interact through
mechanisms of perception and influence within one or
more environments. Those interactions would result in
fluctuations in the variables of agents and environments.
It is obvious that in the development of an ABS model
relative to green turtles, these turtles would be the main
agents. The originality of this article’s title ”Turtles are
the turtles”, lies in the fact that the keyword turtle is
well known in the MAS community as being the histor-
ical word for agents in the NetLogo platform (Wilensky
1999). This glance blended with the need to pinpoint our
objectives has led us to establish a modelling approach,
where conceptual discussion and prototyping using Net-
Logo were done concurrently.
In the SWIO, more than 25 years of study have produced
a large amount of data, sometimes incomplete. However,
despite this long term studies, large gaps still remain for
instance regarding migration or physiology. Behind the
apparent simplicity of green turtle life history, there are
very complex interacting mechanisms driving population
dynamics. The construction of the model itself help in
the comprehension of the biological system.
This is the reason why we conducted modelling and pro-
totyping simultaneously. This allow us to identify prob-
lems that we hoped to answer, thus narrowing down the
amount of data that are really exploitable, to identify
missing data that should be collected.
Due to the fact that we already had some experiences
in development of applications with NetLogo, we were
able to test various hypotheses and focus more on the
thematic conception of the model than the technical side.
It allowed us to close-in on the thematic patterns we
wanted to modellize.
The experience earned after this step (modelling and pro-
totyping) is the first brick to the elaboration of a more
mature conceptual model, which can be afterwards im-
plemented on a poweful simulation platform more effi-
cient in terms of calculation and detailled in terms of
knowledge representation.
GENERAL PRINCIPLE OF THE MODEL
The main question that we choose to deal with was the
evolution of a green turtles population on several gener-
ations. The idea was to ultimately find some conditions
leading to a stabilization, an increase or a decrease in
the number of individuals ; the latter possibly leading to
the extinction of the population.
The particular life cycle of the green turtle brings the
adult to regularly travel long distances between feeding
sites and hatching sites. The feeding sites are generally
coastal areas where food, mainly seagrasses, is present.
In the SWIO, they are mainly located on the east coast
of Africa and around Madagascar. The hatching sites
are beaches where turtles mate and lay their eggs. In
the SWIO, they are mostly located on islands around
Madagascar and in the Mozambique channel.
At this stage of the modelling process, we chose to rep-
resent only adult individuals as agents because the early
phases of their life cycle are barely known to themati-
cians. In addition, only female turtles were taken into
account. If hypothesize a balanced sex-ratio (fifty per-
cent of female), it allows us to double the size of the
population that we can simulate. Moreover, females are
the ones that usually migrate and mate every two to four
years, while males migration is less documented. Repre-
senting only female adult individuals allows us again to
gain more calculation power when we want to simulate
with NetLogo.
IMPLEMENTATION
Following our discussion with thematicians, we came up
with a model that we describe in this section. This model
focuses on population dynamic and will serve as a base
to answer thematical questions.
Our environment: the SWIO Area
The spatial environment is a grid applied on the SWIO,
where feeding and hatching sites are implemented as
patches in NetLogo (database structure related to a
portion of the space). These patches are created ac-
cording to the real world: their geographical coordinates
provided by experts are loaded in the prototype.
•The feeding sites. Patches representing those
sites have a certain quantity of food that regenerates
at a determined rate. However the amount of food
on this site diminishes when eaten by the turtles.
•The hatching sites. Unlike feeding sites, patches
representing the hatching sites have no internal evo-
lution in the current version of our prototype. They
are geographic locations where green turtles come
to lay their eggs.
Our agents: the turtles
In our implementation, our green (real) turtles are
the (agents) turtles and interact with other turtles
through the environment. They have the following prop-
erties.
Figure 1: Prototype’s GUI
•Actions. The main actions that turtles can exe-
cute are: eating (increasing their energy), migrating
and reproducing (both decreasing their energy).
•Spatial knowledge. A green turtle almost always
returns to lay eggs on the island where it was born.
Each turtle is intrinsically linked to the patch rep-
resenting its hatching site. However, turtles have
access to all feeding sites’ patches. Through this,
we are able to test the influence of the fidelity of a
turtle to its feeding site.
•Birth. The number of new turtles to be instan-
tiated is calculated after each clutch, using the sur-
vival rate of juveniles (ranging from 1‰to 1%).
•Death. Aturtle is removed by the system either
if it reaches the maximum age (fixed entry given by
thematicians) or if it has no more energy.
•Life Cycle. The turtles stay on feeding sites
where they eat available food. When a turtle has
accumulated enough energy, it starts a migration to-
ward its hatching site, then mates and lays eggs a
certain number of times. Then it migrates back to
feeding sites where it will rebuild its energy stock
before the next migration cycle.
PROTOTYPE
The Graphical User Interface (GUI) of our prototype
(Figure 1) is classically composed of four main areas of
information that shows (from left to right):
•Variable parameters, to define simulation scenarios
•Control elements, to initialize and launch simula-
tions
•A representation of our environment, to visualize
the movement of the turtles in the SWIO
•Graphical output, to follow the evolution of system’s
indicators requested by thematicians.
The first simulations were just for model calibration.
Most of the parameters have been determined based on
thematicians knowledge (experiments and litterature).
Some were not enough documented and hence went
through a more delicate process; particularly at this
stage, parameters which fell under the energetic aspect
have been determined fairly empirically.
When our parameters were set up, we were able to launch
simulation tests to study the influence of individual vari-
ations on the evolution of global population of green tur-
tles and visually observe their behaviours: changing of
feeding sites, modifying of migration periodicity or du-
ration, etc. even if some parameters are still ignored at
this stage (water currents, air temperature, etc.)
RESULTS
Simulation results
Our prototype allowed us to put in evidence evolution
trends of the population of green turtles on several gen-
erations. Thus, in the upper right corner of the figure 1,
we can notice important oscillations in the number of tur-
tles. Those oscillations are in relation with inter-annual
variations observed in the real world. But this prototype
and the underlying simple model quickly reach certain
limits. For example, it is impossible at this stage to high-
light the intra-annual cycle that corresponds to seasonal
turtles’ reproduction.
We identify some conditions that could lead to the stabi-
lization, increase or decrease of the turtles’ population.
One of the most sensitive parameter is the low proba-
bility of survival from egg to adult. Energy parameters
although played a key role in population trend, eg. food
regeneration rate on feeding sites: if it is not swift enough
or the number of turtles present on the site is too impor-
tant, the turtle population is either greatly reduced or
goes to extinction.
Another important result is that through our prototype
we were able to overview the consequences of a sudden
disappearance of a specific feeding site, thanks to the
spatialization.
Approach results
Even if the simulation results are thematically relevant,
our process method are in fact our main interest, be-
cause without the involvement of this domain’s experts,
we would end up solving wrong or irrelevant issues. This
is why we choose to develop the prototype together with
the modelling stage. As soon as our experts change some-
thing in their conceptual model, we tried to incorporate
the changes into our prototype.
We worked together to build up a roadmap which contain
the to-do list for both simulation and fieldwork. With
this approach, we were able to identify:
•which objectives should be successful or not
•which data will be useful or not
•which data should be collected in order to reach our
goals (eg. energy cost for movement, tracking of
some turtles’ migration path, etc.)
In the forthcoming development, we will first develop a
model that implements the energetic aspect of actions.
Using this base, we will then build two different models:
•The first model will deal with the variability (peri-
odicity and quantity) of turtle’s track on the beach
to test if it does rely upon environmental parameters
(sea surface temperature, ocean currents, etc.).
•The second model will take into account the strategy
of sites selection (both feeding and nesting sites) to
assess long term population viability.
When those models will be validated, we will try to
merge them into one global model in order to find a
temporal pattern and spatial partition. In the outlook
of this project, we are going to take advantage of this
first experience on NetLogo in order to achieve a more
mature conceptual model, whose implementation will be
made on GEAMAS-NG (a complex system’s simulation
platform that we develop in our laboratory).
CONCLUSION
In this paper, we presented a collaborative method and a
NetLogo prototype focused on green turtles in the SWIO,
in order to bring the right support to the project by
clarifying what should be done or not, both in the real
and virtual world (respectively fieldwork’s collect of data
and simulation’s step of development).
Practically, we worked on two description documents:
•the description of the conceptual model
•the description of the computational model.
The conceptual description specifies the model according
to how the experts think turtles shoud be modelled, while
the computational description specifies the model as it is
(or will be) implemented. Our task is to reduce as much
as possible the gap between these two documents (called
implementation gap). This reduction imposes changes on
both documents, and therefore efforts have to be done by
both experts in simulation and experts in fields to:
•Improve the computational description in order to
make it closer to the experts’ expectations
•Re-formulate the conceptual model, or even simplify
it, in order to meet the technical constraints (and
limits) of the computing.
In the end, the smaller this implementation gap, the
more relevant our prototype. Only when this relevancy
will become acceptable, the effort to migrate from ”pro-
totype” to ”end-user tool” should be considered.
Our experience through this project has shown that us-
ing of NetLogo (as prototyping platform) contributes to
significantly reduce the implementation gap. Indeed due
to its simplicity of use, it brought both kind of experts
around a set of common bases. Moreover due to a rapid
development with it, it accelerates interaction between
those communities.
REFERENCES
Chaloupka M., 2002. Stochastic simulation modelling
of southern Great Barrier Reef green turtle population
dynamics.Ecological Modelling, 148(1), 79 – 109.
Mazaris et al., 2006. An individual based model of a sea
turtle population to analyze effects of age dependent
mortality.Ecological Modelling, 198(1-2), 174 – 182.
Wilensky U., 1999. NetLogo.Center for Connected
Learning and Computer-Based Modeling, Northwest-
ern University Evanston, IL.
