Computational Intelligence Applied to Monitor Bird Behaviour

D. F. Larios

, C. Rodríguez², J. Barbancho

, M. Baena

, F. Simón¹,

J. Marín

, C. León

and J. Bustamante

Department of Electronic Technology, University of Seville, Seville, Spain

Department of Wetland Ecology, Doñana Biological Station (EDB-CSIC), Seville, Spain

ICTS, Singular Scientific and Technological Infrastructure, Doñana Biological Station, Seville, Spain

Keywords: Neuronal Network, Computational Intelligence, Data Fusion, Environmental Monitoring, Sensor Networks.

Abstract: The best way to obtain relevant information about the behaviour of animals is direct observation (of

individuals). However, traditional close-up observations can interfere on the behaviour, and taking

biometric measurements requires the capture of individuals, which also causes stress. This paper describes

an automatic motoring system for birds breeding in nest boxes. The main goal is to significantly increase the

amount and quality of data acquired on bird behaviour without stressing the individuals or interfering. This

system is based in an interconnected embedded sensor network, which permits sharing this valuable

information with researchers all over the world through the internet. Each device of the network is a smart

nest-box that allows a cross-validation of sensor information and data quality. This system has been

evaluated for the specific case of a lesser kestrel breeding colony in Southern Spain. The lesser kestrel is an

insectivorous migratory falcon that readily accepts nest-boxes. The system has been named HORUS and the

results obtained from a year experiment demonstrate the efficiency of this approach.

1 INTRODUCTION

For zoologists, one of the most important periods for

a bird is the breeding period, being this period one of

the most frequently studied. Mate acquisition, nest

defence, mate feeding, incubation, and chick rearing

(including provisioning flights) are studied in the

surroundings of the nest. Many of these aspects help

us understand key topics in ecology, such as what

factors influencing lifetime reproductive success

(LRS: Newton, 1992), the parent-offspring conflict

(Trivers, 1974; Schlomer et al., 2010), or evolutive

stable strategies (ESS: Maynard-Smith and Price,

1973) regarding sex roles in reproduction (Kokko

and Wong, 2007).

Classic monitoring methods that require the

capturing of individuals or close-up observations

limit the amount and quality of data that can be

obtained. Therefore, in this paper a remote

monitoring system based on smart nest-boxes is

proposed. These smart nest-boxes allow acquiring

high amount of data without stressing the animals,

gathering long-term and highly reliable information

on the species.

The proposed system, called HORUS, permits

gathering basic information on the identity of

individuals, studying its behaviour and the temporal

changes in individual body mass. All this

information can be made accessible through the

internet to scientist all over the world.

Some of the information recorded by the system

can be used to study, without interference, the

behaviour of species during the breeding period.

One of the most important biometric parameters in

birds is body mass. It allows us to measure the

impact of parental care on breeding individuals.

Manually, it is impossible to develop a continuous

monitoring of this parameter. Capturing causes too

much stress on the individual in its most sensitive

period. Another important parameter is the

measurement of the amount of food brought to the

nest by individuals to feed their offspring.

The proposed system allows us to perform a

continuous monitoring of the reproduction without

stressing the individuals, e.g. obtaining reliable body

mass measurements every time a bird enters or

leaves the nest. The main problem obtaining the

measurements is the movement of the animal, which

produces unstable values. These values have been

calibrated using a neuronal network processing,

F. Larios D., Rodríguez C., Barbancho J., Baena M., Simón F., Marín J., León C. and Bustamante J..

Computational Intelligence Applied to Monitor Bird Behaviour.

DOI: 10.5220/0004068100230032

In Proceedings of the International Conference on Data Communication Networking, e-Business and Optical Communication Systems (DCNET-2012),

pages 23-32

ISBN: 978-989-8565-23-5

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: Picture automatically taken by the outside

camera of the monitored colony with the HORUS external

sensors. From top to down it shows a pigeon, a lesser

kestrel male and a female.

obtaining high accurate measurements.

The rest of the paper is organized as follows:

Section 2 focuses on the lesser kestrel behaviour

study, especially comparing a traditional approach

versus automated data reading. Section 3 briefly

describes the HORUS system infrastructure. A

detailed description of the information treatment

developed in this project can be found in section 4.

The results obtained with our system are shown in

section 5. Finally, section 6 sum-up conclusions and

provides remarks.

2 LESSER KESTREL BREEDING

BEHAVIOR STUDY

The lesser kestrel (Falco naumanni, figure 1) is a

small (body mass around 150 grams) migratory

falcon inhabiting open landscapes (Cramp and

Simmons, 1980). It is a colonial species that breeds

in old buildings, such as churches or castles within

urban areas in Western Europe. The species

experienced a marked decline in its Western

Palearctic breeding range in the middle of the 20th

century (Cramp and Simmons, 1980; Biber, 1990).

Considered previously one of the most abundant

raptors in Europe (Bijleveld, 1974) the lesser kestrel

became extinct in several countries (e.g. Austria,

Hungary, Poland) and practically disappeared in

others (e.g. France, Portugal, Bulgaria).

Mediterranean Spain constitutes its stronghold in

the Western Palearctic (Biber, 1990). However, the

Spanish population also suffered a precipitous

decline, as it dropped from an estimated 20,000–

50,000 pairs in the 1970s (Garzón, 1977) to 4,000–

5,000 breeding pairs in 1988 (González and Merino,

1990). This decline has been attributed to the

reduction in both the extent and quality of foraging

habitats (Peet and Gallo-Orsi, 2000). The species is

also sensitive to climate warming (Rodriguez and

Bustamante, 2003). So it makes for a good model

species to study the impact of global change on an

endangered species.

2.1 Traditional Monitoring

The “Estación Biológica de Doñana” (EDB-CSIC)

has been monitoring lesser kestrel colonies since

1988. It has been recording colony occupancy and

breeding success in terms of number of fledglings

and proportion of successful nests. Regarding

individual monitoring, birds have been marked with

metal and PVC rings with a unique alphanumeric

code that allows identifying individuals by using

telescopes. Biometric measures were taken

sporadically for all marked individuals when

captured. Due to ethical reasons, however, the

number of captures in the nest is limited (the capture

alters breeding behaviour and may jeopardize the

survival of the offspring) and the majority of

resightings were made with telescopes. This causes

high differences in the frequency of recaptures

among individuals mainly due to differences in

detectability. In a classic data base monitoring,

2,135 birds figured as recaptured (including

resightings with telescope). On average they were

captured 3 times on the same breeding season

(range: 1-70). In approximately 45% of cases, body

mass was measured and maximum number of

measurements per bird and year was 4.

Because of that, the pattern of body mass

variation of breeding adults from arrival to the

colony in mid-February to the end of the nestling

period in mid-July is not well known. Accordingly,

we have no information on the pattern of intraday

variation in body mass.

The proposed remote monitoring system aims to

bridge the above detailed logistic and ethic gaps,

thus allowing us to get enough information to

document both patterns.

2.2 Automated Data Reading

Habitat monitoring has evolved greatly evolution

due to the boom of sensor networks technology.

Several consequences have been caused due to

the increase of sensors: Firstly the quality of

information grows in time and on the spatial

domain; secondly the possibility of transmitting the

measured data through the network increases the

need of having high bandwidth communications;

and thirdly, it to the reduction of the cost of the data

DCNET 2012 - International Conference on Data Communication Networking

Figure 2: HORUS Network scheme.

storage makes possible to save huge amounts of

data.

All these consequences imply some negative

effects: an increase of data traffic and increase of

power consumption.

Some authors have taken these effects into

account (Cook, 2007; Sridhar, 2007) and have

expressed the need to employ processing techniques

in order to reduce these handicaps.

There are different approaches to habitat

monitoring. Some of them use wireless sensor

network technology in order to acquire and process

the physical information (García-Sánchez et al,

2010; Handcock et al, 2010; Valente et al, 2011;

Carullo et al, 2009). Others focus on the needed

middleware that allows access to the physical

information (Hwang et al., 2010; Farshchi et al.,

2007).

In our approach, both aspects are considered.

3 HORUS INFRAESTRUCTURE

The proposed infrastructure is a distributed system

as figure 2 depicts. This figure shows the most

important devices of the proposed architecture.

These devices will now bebriefly described.

3.1 Network Infrastructure

The HORUS infrastructure is made up of different

subsystems interconnected through a low data rate

communication network.

This network has been designed considering the

following restrictions:

 The devices that provided information to the

network are deployed in a spread way without

any previous planning.

 The data rate associated with the data sources is

low (< 250 kbps)

 The system could be easily scalable.

The network used in HORUS can be accessible

through different physical media (wireless or wired

based).

Robustness of the network is very important, for

the proposed application: during the breeding period,

it is not possible to realize maintenance tasks,

because it can disturb and stress the colony. All

detected failures would be repaired in winter, after

the birds have left the colony.

3.2 Base Station

The process server is a system that offers the

following services:

 Database server.

 Monitoring and control system.

 Remote control access.

The database stores all the historic sensors

information gathered from the system.

The monitoring control system is a program

responsible for adding additional valuable

information to the sensor measurements, such as

information about the nest sender, a time stamp

Computational Intelligence Applied to Monitor Bird Behaviour

Figure 3: Nest cabinet.

determining the number of loss packets. This system

stores the information in the database.

Remote access control offers the cloud services

for remote users, such as biologists. These services

permit remote access to the sensors database.

3.3 Smart Nest-Boxes

The smart nest-boxes are the main components of

the monitoring systems. It consists of the next two

blocks:

 The nest cabinet.

 The electronic system

The nest cabinet (Figure 3) is divided into two

parts: a corridor and the incubation chamber. This

nest cabinet has a smart design to ensure that the

birds pass the corridor each time they enter or leave

the nest. The advantage of this is to allow the

distribution the sensors in a small area (the corridor)

where the animal is forced to pass and, therefore, it

ensures obtaining the sensor information.

The electronic system (Figure 4) of each smart

nest-box is accomplished with the next subsystems:

3.3.1 Microcontroller Board

This board is based on the ATmega2560, an

economic, low power and robust microcontroller. It

controls and processes the nest’s sensor information.

This board communicates with sensors and other

components, and processes the collected information

that is sent to the process server over the

communication interface.

The program implemented in the microcontroller

performs the following tasks:

 Communicates with the process server over a

communication interface, and synchronize

clock time with this.

Figure 4: Architecture of the electronic system.

 Checks infra-red barriers. Each nest-box has

two infra-red barriers at both extremes of the

corridor. The sequence in which they are

activated indicates whether birds enter or

leave the nest-box.

 Checks if the RFID reader has read a code from

ringed kestrels.

 Obtains the body mass measurement from a

digital balance.

 Reads the temperature and humidity of the nest.

 Controls the RFID reader to identify

individuals.

3.3.2 Sensors Board

A sensor board adapts the logic levels from the nest

sensors to the microcontroller board’s requirements.

All the nest’s sensors are spread onto the

corridor of the nest. Positions of sensors are

designed to ensure that every time the birds pass the

corridor the system registers at least one record per

sensor.

The deployed sensors are:

 A digital balance. It allows a maximum weight

of 600 gr. and an accuracy of 0.01 gr, offering

16 measures per second. It permits getting an

estimate of the body mass of the individuals in

movement. Although the pan is round, it has

been modified to be rectangular in order to fit

the shape of the corridor.

DCNET 2012 - International Conference on Data Communication Networking

(a)

(b)

(c)

(d)

Figure 5: Different weight pattern Y-axis, weight in grams. X-axis, samples.

 An integrated temperature sensor located in the

window. It is calibrated to operate in

environmental temperature range. It is used to

measure the nest temperature.

 An integrated humidity sensor. It is used to

measure the nest humidity.

 Two infra-red barriers, used to trace the

direction of birds’ movements.

 A RFID reader. It communicates via RS-232C

and offers a reading on the unique ID of a

tagged bird, when it is passing through the nest

entrance. This system has mechanisms to avoid

collisions, permitting operation even when

there are several birds around the entrance.

 A Servomechanism. It is used to remotely

capture birds when they enter in the nest-box.

4 TREATMENT OF THE

INFORMATION

As described before, every nest-box provides the

following information:

 Measurements of body mass: The digital

balance used offers 16 measurements per

seconds without calibration and classifies

measurements as stable or unstable.

 IR information: These sensors permit

determining if the birds go into or go out of the

nest.

 RFID information: It permits attributing the

information of other sensors to an individual

bird.

All this information is obtained from the sensors

deployed in every nest-box. The sensors offer

relevant information on the individual breeding at

the colony to the biologists that study them. This

information, except the body mass, cannot be added,

as they inform about discrete events. Therefore, this

information is sent directly to the database without

any local processing or treatment.

On the other hand, the digital balance offers a

high amount of information. Its frequency of

measurement is much higher than the body mass

evolution of the animal. i.e., the animal body mass

evolution has more inertia than the weight provided

by the balance. Due to this, it is possible to perform

a data pre-processing about weight information,

reducing with that the amount of information send to

the central processing.

4.1 Weight Pre-processing

The algorithm described in this paper, is focused on

locally pre-processing the weight information, to

reduce the amount of unnecessary information and

increase its accuracy. It is designed to be executed in

each nest-box, in the microcontroller board. It has

been designed to fulfil the next goals:

 To reduce the amount of useless information in

the database using local pre-processing.

 To increase the accuracy of the measurements,

calibrating the results obtained.

 To increase accuracy of the communication

network, reducing the amount of packet loss,

the delays and the collisions.

 To increase the amount of useful information in

the database, estimating a body mass from

each pattern with non-stable measurements.

 To permit its execution on devices with low

resources.

To increase the accuracy a tare calibration is

necessary. The balance used offers measurements

without a tare calibration. This calibration would be

obtained consulting the body mass measured by the

balance, when there is no animal on the pan i.e.,

when the measured weight is below a certain

Computational Intelligence Applied to Monitor Bird Behaviour

threshold. This threshold can be obtained as a

function of the body mass of the animals to monitor.

In our deployment for the Lesser Kestrel (with a

body mass range of 100-190 g) a threshold of 100

grams of has been used.

In the real deployment we have proven that the

tare does not change significantly during a year.

Therefore, measuring the tare only once per day

offers enough accuracy for the proposed system.

On the other hand, as described before, the

balance offers 16 weight measurements tagging

them every second by itself as stable (i.e.,

measurements that remain a same value during a

long period of time) or unstable. But birds usually

do not pass over the balance slow enough to obtain

stable measurements. This causes the database to

have a high amount of the information as unstable

measures. In the real prototype only about 15.25%

of the measured patterns had a stable measurement,

considering a pattern as the collection of

measurements obtained from the time the bird gets

on the balance (i.e., when the balance acquires a

weight over the threshold) until the animal gets out

of balance (i.e., when the balance acquires during 5

seconds weights below the threshold). Fig. 5 shows

different examples of weight patterns obtained in the

real deployment with these conditions.

This figure shows different real weight patterns

obtained from the same animal in different days.

Only pattern (a) has some stable measurement.

These stable measurements have been compared

with measurements of the animal done manually

capturing the bird. The stable tare measurements are

correct, but not frequent enough to obtain a long

term sequence of body mass temporal change of the

birds at the colony.

To solve this, a computational intelligence

algorithm to estimate the body mass of animals from

the patterns with non-stable weights has been

developed, increasing the amount of useful

information. This neuronal network algorithm is

described below.

Initially, the system has been designed to store,

in the central server, all weight measurements of the

pattern acquired by the balance, stable or unstable,

but it causes high bandwidth consumption in the

communications interface.

To reduce the amount of useless information, the

proposed algorithm only sends one estimated weight

to the database for each measured patterns. If the

pattern has some stable measurements, the estimated

weight sent to the central server will be the average

of the obtained stable measurements. If no

measurements of the pattern are stable, the weight is

estimated through a computational intelligence

algorithm. In both cases, only one selected weight

per pattern is sent to the database. These selected

weights are calibrated with the tare, before sending

them.

The proposed algorithm is summed-up in the

next pseudo-code:

while 1:

wait new(meas_weight);

if meas_weight>=threshold

weight[i]:=meas_weight–tare;

increase I;

if stable(meas_weight)==1

stable :=1;

end if

else

if i!=0

if stable==1

est_weight:=average(

stable_weight);

stable:=0;

else

est_weight:=model(weigth);

end if

send_server(est_weight);

i:=0;

else

new_tare:=meas_weight;

tare:=Iter_RMS(prev_tares,

new_tare);

end if

end while

4.1.1 Applying Machine Learning for

Weight Recognition

For this application, an algorithm has been

evaluated. Initially, an algorithm without machine

learning based on the differences between

consecutives measurements has been considered.

This algorithm considers a weight stable if there are

more than a certain number of measurements of the

same weight. This is similar to the internal algorithm

of the balance for tagging measurements as stable or

unstable, but it is less restrictive: the balance

requires a high number of measurements with the

same value to consider a measurement stable. It

permits the retrieval of some weights from the

unstable patterns, but it fails with complex patterns.

Our proposal of using computational intelligence

(machine learning) increases the percentage of

success.

Machine learning is widely used in pattern

recognition, but its use in animal monitoring is less

widespread. Other supervised learning techniques

apart of the neuronal network have been considered.

DCNET 2012 - International Conference on Data Communication Networking

Non supervised techniques, such as Self-Organized

maps (SOM, Kohonen, 1990) or Support Vector

Machine (SVM, Cortes 1995) were discarded,

because we have some stable measurements that

permit performing training.

One example of the considered supervised

machine learning techniques is the use of Artificial

Neuro-Fuzzy Inference Systems (ANFIS; Jang,

1993). ANFIS has many applications in the

evaluation of complex systems, but it requires a

previous knowledge of the system to design the rules

and the initial system. This system was discarded;

due to the complex forms of the patterns that do not

easily permit acquire this initial system.

Expert systems or case based experts system

were not considered, due to the amount of previous

information gathered from the smart nest-box was

not sufficient for these kinds of systems.

For these reasons, a neuronal network model was

finally chosen. The variables used as inputs of the

model are as follows:

 Max_1: The most repeated weight in a pattern

(the largest if multiple).

 N_1: Number of repetitions of the previous

variable in a pattern.

 Max_2: The second most repeated weight in a

pattern (the largest if multiple).

 N_2: Number of repetitions, in a pattern, of the

previous variable.

 Max_C1: The most consecutively repeated

weight in a pattern.

 NC_1: Number of repetitions of the previous

variable in a pattern,

 Max_C2: The second most repeated weight,

consecutively, in a pattern.

 NC_2: Number of repetitions, in a pattern, of

the previous variable.

 N_EL: Total number of weight measures in a

pattern.

In order to obtain these parameters, a pattern

with at least 5 weight measurements is needed. As

Table 1: Analysis of the database.

Caption

Value

Measurement weight

2583565

Number of pattern

51517

Patterns with stable weights

7856

Average pattern time

23,18 seconds

Days of test

399 days

an value output, the neuronal network model offers a

value, called “Output weight”. This output reflects

the estimated weight of the neuron model and it is

the information sent through the network to the

server database.

The steps execution of this neuronal network

model is summed-up in the next pseudo-code, where

the neuronal network is the execution of a three

layer network.

Neuronal network needs a set of parameters for

its training. These sets have been obtained for each

pattern with stable measures, by executing the

following steps:

Step 1: A variable name “Target weight” was

defined for every pattern. This variable stores the

average value of all stable weights. This is the target

result of the training of the neuronal network.

Step 2: For every pattern, a new pattern has been

created, eliminating all stable measurements.

Step 3: The inputs have been obtained from this

new pattern without stable values.

Step 4: The input values for each pattern were

stored, together with their respective Target weight

into a table, named “Training information”

With these tables two sets of information were

obtained, one for training and the other for

evaluating the accuracy of the system. In total, the

training information table has 1163 sets of values.

50% of these values (randomly selected) were used

for training, and the other 50 % were used for

validation.

5 SIMULATION, TESTS AND

RESULTS

The results obtained with this system can be

classified in two types: analysis of the network

performance and weight estimation accuracy

obtained with the real deployment.

This section summarizes these two types of

results.

5.1 Network Performance

During the first year of the deployment (2010), the

prototype was sending information from all sensors,

even the 16 records per second of the balance, to the

database of the central server. The main

characteristic of the gathered information in the

database is summarized in table 1.

After a year of deployment, the analysis of data

allowed us to detect some network conflicts. For

example, if different nest-boxes are acquiring

weights from individuals at the same time, they are

competing for control of the bus, causing data

collisions and delays in transmitting information.

Computational Intelligence Applied to Monitor Bird Behaviour

Table 2: Cost per message with CC2420 Radio

transceiver.

Caption

Energy (J)

Without data fusion

255.3

With data fusion

0.608

The proposed system allows avoiding these

conflicts, using the proposed data fusion.

In this section we are going to quantify the

advantage of data fusion against the classical

centralized systems. Due to that, in this kind of

applications it is important to reduce the use of

bandwidth as much as possible.

The analysis of database information has been

summed-up in the table 1. It shows that only a

15.25% of the acquired patterns have any stable

measurement.

Knowing that the balance offers 16 Samples Per

Second (SPS), the average payload of the

application layer per pattern of the system without

data fusion can be obtained with the equation 1.

16 · · ·

T raw SPS T Bytes msg

N P N N

(1)

Where

,T raw

is the number of bytes to send

per day at application layer;

is the length of the

pattern in seconds;

Bytes

is the number of bytes to

send. 16 bytes in this case and

msg

is the number

of messages per day.

On the other hand, with the proposed algorithm,

only one message per pattern is sent. In this case, the

payload per pattern can be obtained according to

equation 2.

T raw Bytes msg

N N N

(2)

This shows that the amount of information sent

to the database varies in function of the number of

patterns and the length (in time) of the pattern.

Figure 6 depicts these results.

Concluding, the local processing permits one to

drastically reduce the used throughput of the

network, especially in days with a high number of

patterns.

This data fusion and aggregation scheme is

especially important for its use in low bandwidth

systems, due to it permitting one to save energy.

With the proposed system, only one message per

pattern is sent, instead of 16 measurements per

second during the capture of the pattern. These

results are summed-up in table 2. They consider the

average pattern length of 23.18 seconds, i.e. the

average time while the bird is on the balance.

With these conditions and with the CC2420

radio transceiver, widely used in wireless sensor

network, permit saving 99.76 % of the energy used

in data transmissions, considering a power

consumption of 38mW in transmission mode

(Polastre et al, 2005.

Using all weight patterns obtained in the year

2010, the proposed body mass estimation algorithm

permits the retrieval of around 56.21% of the

patterns without stable measurements.

This is a good result that permits us to obtain an

average of 4 body mass estimations per day and

nest, which is 4 times higher than using only

patterns with stable measurements. It permits to

have a continuous tracing of body mass in

individuals.

As a conclusion, the local processing permits us

to drastically reduce the used throughput of the

network, especially in the days with a high number

of patterns.

5.2 Body Mass Estimation Accuracy

Based on the training and verification set described

in section 4.1.1, some analysis has been done to the

proposed algorithm for body mass estimation.

With the evaluation set, the system offers an

accuracy of 98.7%, i.e., an error in the order of 2

(a)

(b)

Figure 6: Bytes per day send, at application layer, to the central server. (a) Without data fusion. B) With data fusion.

DCNET 2012 - International Conference on Data Communication Networking

Figure 7: Neural network model: importance of the

variables in the calculation of the estimated weights.

Figure 8: Grain elevator used for the prototype

installation.

grams, which is quite small considering the typical

body mass of these animals (150 grams).

This accuracy permits analysis of a long series

for the evaluation of temporal changes in body mass,

and sometimes to determine the body mass of prey,

when birds bring medium-sized animals to the nest

to feed the nestlings.

From the training procedure, an analysis of the

importance of the input parameters in relationship

with the target body mass can be obtained. Figure 7

shows these results. This analysis concludes that the

selected parameters are valid to effectively estimate

the body mass of animals.

5.3 Real Deployment

A prototype, for a real validation of the proposed

system, has been deployed in the grain elevator of

“La Palma del Condado (Huelva Province, SW

Spain” (figure 8). At this site, researchers of the

Estación Biológica de Doñana have been studying

the lesser kestrel colony since 1994. At this colony,

kestrels nested on the windowsills of the grain

elevator that are sheltered and sufficiently enclosed

to make a suitable nesting site.

For the prototype installation we select the

windows on the 6th floor of the building where

smart nest-boxes were installed, and readily

accepted, by kestrels during preliminary checking (3

and 4 nest-boxes during 2008 and 2009,

respectively) and also when the definitive prototype

installation was made in 2010.

Nest-boxes are placed in all the windows along

the sixth floor. They are named “6XY”, where X

refers to the cardinal point and Y is an ordinal

number. Each box has two separate entrances and

two incubation chambers (I. left and D: right) in a

symmetrical distribution. Entrances are placed at the

extremes of the box to avoid potential aggressions

between neighbours, thus maximizing the number of

potential breeding pairs. Nonetheless, the right part

has not been opened yet.

The results provided by the system are still being

analysed by biologists. However, in its current state,

it is possible to obtain some conclusions:

 18 of the 20 installed nests-boxes were used by

breeding kestrels. This leads to the conclusion

that the proposed system effectively allows one

to gather a high amount of information about

the behaviour of breeding individuals without

stressing them. If the nest and its sensors were

hostile, it would not have been chosen by lesser

kestrels breeding pairs.

 A preliminary result of the continuous

weighting of individuals will allow the

researchers to estimate the cost of breeding in

terms of body mass. This cost is directly

associated with the foraging trips to feed the

nestlings.

 The lesser kestrel mainly feeds on insects, but

sometimes can catch slightly bigger prey, such

as small rodents, birds or lizards (with around a

dozen of grams). The proposed system would

permit an analysis of the frequency of big prey

captures.

6 CONCLUSIONS

The main goal of the proposed system is to use

current technological advances in a real-world

application in the area of Biodiversity Conservation

to study how global climate change could affect a

colonial and endangered bird species.

The results obtained conclude that the proposed

system would permit its use in a system with low

resources and with a low bandwidth usage.

The prototype deployed in Spain for evaluation

Computational Intelligence Applied to Monitor Bird Behaviour

with the lesser Kestrel, has been demonstrated to be

a good method for studying these animals. The

proposed system permits us to do this evaluation

without stressing the animals and without the need

of a human observer. As a consequence, this

monitoring does not change the animal behaviour,

offering reliable information to researchers all over

the world that can access the information in real time

through the Internet.

The authors are currently working on several

improvements of the project: Increasing the number

of sensors, such as adding optical barriers over the

balance, with the goal of increasing robustness of the

body mass measurements; Studying the way to

increase the amount of pre-processing, using the rest

of the sensors, and looking for a simplification in the

use of the system information for the final user.

ACKNOWLEDGEMENTS

This work has been supported by the Consejería de

Innovación, Ciencia y Empresa, Junta de Andalucía,

Spain, through the excellence projects HORUS 2006

(reference number P06-RNM-01712), HORUS

2009 (reference number P06-RNM-04588) ARTICA

(reference number P07-TIC-02476), and eSAPIENS

(reference number TIC-5705). The authors would

like to thank the Cámara Agraria de La Palma del

Condado, Consejería de Agricultura y Pesca de la

Junta de Andalucía, Reserva Biológica de Doñana –

ICTS y Espacio Natural Doñana (Almonte, Huelva)

for their collaboration and support.

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