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*** 1. Movement ecology of small wild animals is often reliant on radio‐tracking methods due to the size and weight restrictions of available transmitters. In manual radio telemetry, large errors in spatial position and infrequent relocations prevent the effective analysis of small‐scale movement patterns and dynamic aspects of habitat selection. Automatic radio‐tracking systems present a potential solution for overcoming these drawbacks. However, existing systems use customized electronics and commercial software or exclusively record presence/absence data instead of triangulating the position of tagged individuals. *** 2. We present a low‐cost automatic radio‐tracking system built from consumer electronic devices that can locate the position of radio transmitters under field conditions. We provide information on the hardware components, describe mobile and stationary set‐up options, and offer open‐source software solutions. *** 3. We describe the workflow from hardware setup and antenna calibration, to recording and processing the data and present a proof of concept for forest‐dwelling bats using a mixed forest as study area. With an average bearing error of 6.8° and a linear error of 21 m within a distance ranging from 65 m to 190 m, the accuracy of our system exceeds that of both traditional methods as well as manual telemetry. *** 4. This affordable and easy‐to‐use automatic radio‐tracking system complements existing tools in movement ecology research by combining the advantages of lightweight and cost‐efficient radio telemetry with an automatic tracking set‐up. This article is protected by copyright. All rights reserved.
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Methods Ecol Evol. 2019;00:1–10.  wileyonlinelibrary.com/journal/mee3 
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1 | INTRODUCTION
The analysis of animal movements based on tracking data enables
ecologists to investigate questions related to habitat and resource
utilization (Wyckof f, Sawyer, Albeke, Garman, & Kauffman, 2018),
migration and dispersal (Cagnacci, Boitani, Powell, & Boyce, 2010;
Walton, Samelius, Odden, & Willebrand, 2018) or to build predic-
tive models of animal behaviour (Browning et al., 2018). Recent im-
provement s in tracking technology have increased the number of
locations recorded per animal from a few dozen by manual radio te-
lemetr y to millions of movement steps from GPS tags and satellite
telemetry, leading Kays , Crofoot, Jetz, & Wikelski (2015) to proclaim
Received:15March2019 
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  Accepted:28August2 019
DOI : 10.1111 /20 41-210X.13294
PRACTICAL TOOLS
Introduction of an automatic and open‐source radio‐tracking
system for small animals
Jannis Gottwald1| Ralf Zeidler2| Nicolas Friess1| Marvin Ludwig1|
Christoph Reudenbach1| Thomas Nauss1
1Faculty of Geography, Philipps-University
Marburg, Marburg, Germany
2Freies Ins titut für Datenanalyse – Di pl.-
Phys. Ralf Zeidler, Freiburg, Germany
Correspondence
Ralf Zeidler
Email: ralf.zeidler@radio-tracking.eu
Funding information
Ministry of the Environment, Climate
Protection and the Energ y Sector Baden-
Württemberg, Germany; Hessen State
Ministry for Higher Educ ation, Researc h and
the Ar ts, Germany
Handling Editor: Chris Sutherland
Abstract
1. Movement ecology of small wild animals is often reliant on radio-tracking methods
due to the size and weight restrictions of available transmitters. In manual radio
telemetry, large errors in spatial position and infrequent relocations prevent the ef-
fective analysis of small-scale movement patterns and dynamic aspects of habitat
selection. Automatic radio-tracking systems present a potential solution for over-
coming these drawbacks. However, existing systems use customized electronics and
commercial software or exclusively record presence/absence data instead of trian-
gulating the position of tagged individuals.
2. We present a low-cost automatic radio-tracking system built from consumer elec-
tronic devices that can locate the position of radio transmitters under field condi-
tions. We provide information on the hardware components, describe mobile and
stationary set-up options, and offer open-source software solutions.
3. We describe the workflow from hardware setup and antenna calibration, to record-
ing and processing the data and present a proof of concept for forest-dwelling bats
using a mixed forest as study area. With an average bearing error of 6.8° and a
linear error of 21 m within a distance ranging from 65 m to 190 m, the accuracy of
our system exceeds that of both traditional methods as well as manual telemetry.
4. This affordable and easy-to-use automatic radio-tracking system complements
existing tools in movement ecology research by combining the advantages of
lightweight and cost-efficient radio telemetry with an automatic tracking set-up.
KEYWORDS
automatic radio-tracking, Marburg Open Forest, movement ecology, open-source, radio-
tracking, telemetry, triangulation
This is an op en access article under t he terms of the Creat ive Commons Attributio n License, which permits use, dist ribution and reproduc tion in any medium,
provide d the orig inal work is proper ly cited .
© 2019 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society
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a new golden age of animal tracking. To complement this more finely
resolved movement data, researchers have also developed a variety
of sophisticated analytical techniques such as path segmentation
analysis, step-selection functions and autocorrelated kernel meth-
ods (Fleming et al., 2015; Seidel, Dougherty, Carlson, & Getz, 2018).
Despite numerous advantages, both GPS tracking and satellite te-
lemetr y are still limited in their application to practical conservation
and ecological research. The cost of tags notwithstanding, size and
weight have limited their deployment in the past. New developments
have success fully reduced t he weight of such tag s to ~1 g (e.g. PinPoint
GPS tags, Lotek Wireless, Newmarket, CA). Nevertheless, bat tery
lifetime and recording frequency are inversely related to weight, so
the tags either record with low frequency or have short battery life-
times (e.g. 5 nights for a 4.2 g GPS tag with a 30 -s GPS-fixed sched-
ule; Roeleke, Teige, Hoffmeister, Klingler, & Voigt, 2016). Lightweight
GPS tags also need to be retrieved to access the data (Hallworth &
Marra, 2015), which either directly or indirectly increases most stud-
ies' expenditures in the form of lost material or data (Smith, Har t,
Mazzotti, Basille, & Romagosa, 2018; Tomkiewicz, Fuller, Kie, & Bates,
2010). These limitations aside, such tags are also too heav y for spe-
cies weighing less than 20 g, as their weight should not exceed 5% of
the individual's body mass to which it is attached (Brooks, Bonyongo,
& Harris, 2008). This leaves radio tags, with weights as low as 0.2 g,
as the single option for 5 0% of European passerines (Bau er, 2012) and
80% of European bats (Dietz, Nill, & Helversen, 2016).
Manual radio telemetry has disadvantages including labour in-
tensity, low temporal and spatial resolution (Montgomery, Roloff,
Ver Hoef, & Millspaugh, 2010; Thomas, Holland, & Minot, 2011),
infrequent and irregularly timed locations (Alexander & Maritz,
2015), small sample sizes (usually one frequency at a time; (Kays
et al., 2011)) and areal restrictions due to safet y concerns for field
workers (Smith et al., 2018). The qualit y of the resulting data also
precludes any advanced analytical techniques created for fine-scale
tracking data. Several working groups have designed automatic te-
lemetr y systems to overcome these drawbacks (Kays et al., 2011;
Řeřuchaetal.,2015;Weiseretal.,2016).Regardlessofequipment,
the key feature of modern automatic telemetr y systems is a contin-
uous signal record sent by radio tags using a st ationary automatic
receiver and a combination of either omnidirectional or directional
Yagi-Uda antennae. The former can detect presence and absence,
while the latter can detect the timing and direction of movement
(Crysler, Ronconi, & Taylor, 2016; Falconer, Mitchell, Taylor, &
Tozer, 2016). Existing systems use customized electronic devices
with proprietar y soft ware (Kays et al., 2011; Weiser et al., 2016)
BOX 1 Hardware overview
A Station with four antennae positioned in the cardinal directions and tuned to the regional frequency for wildlife telemetry (around
150.100 MHz in Germany)
B One RTL-SDR dongle per antenna (e.g. Nooelec NESDR SMart SDR, Nooelec, NY, USA) with a frequency range of 25–1,700 MHz and
a maximal sample rate of 2 MHz (quadrature sampling)
C Raspberry Pi 3B single-board computer (Farnell elements14, Leeds, UK) with the Raspbian operating system with a Docker-based
architecture
D High-capacity power supply with voltage regulation to work with the single-board computer, recommended for longer deployment
times. Battery time can be further increased through solar panels and a solar charge regulator.
E Power bank (20 Ah at 3.6 V ), able to supply a station for ~8 hr, recommended for mobile setups
F Mobile WiFi hot spot (Huawei E5330, Shenzhen, China), enables remote access within reach of the station's Raspberry Pi
    
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or monitor presence and absence in large-scale movement studies,
but cannot triangulate the position of a tagged individual (Taylor et
al., 2017). Here, we describe an automatic radio-tracking system
for locating individuals Zeidler, R. (2017) that has a high temporal
and spatial resolution and works with inexpensive consumer elec-
tronics, flexible antenna designs and user-friendly, open-source
software. In addition to a field test of system accuracy, we present
a proof of concept based on forest-dwelling bats that illustrates the
general use of the system under field conditions.
2 | SYSTEM COMPONENTS AND
METHODS
2.1 | Core system
The low-cost, automatic radio-tracking system (Box 1, A, B, C) con-
sists of three basic elements: (a) a receiver chip, (b) antennae and
(c) a single-board computer (e.g. Raspberry Pi). Common DVB-T tel-
evision receivers with RTL2832U chips process the radio signal (e.g.
Nooelec NESDR SMArt SDR, NooElec, NY USA). Inexpensive soft-
ware-defined radios (RTL-SDRs, Laufer, 2015) allow multiple radio
signals to be simultaneously recorded. The RTL-SDRs connect the
single-board computer with the Yagi-Uda antennae.
To calculate the source direction of incoming signals, the antenna
pole at a given station requires an array of at least three directional
antennae (and an equal number of receivers) together with informa-
tion about their orientation. The number of receivers that one com-
puter can monitor depends on the number of available USB ports.
At least two antenna setups with known coordinates must be
available and within the range of the radio source to triangulate the
tag's position. Each station should be connected to the Internet
to guarantee synchronized system times with e.g. a mobile Wi-Fi
hotspot carrying a SIM card (Box 1, F). The Network Time Protocol
synchronizes the station times when they are first operational and
approximately every 5 min thereafter.
The stations are operated using custom software. Operational
hardware settings on the receiving units can be done by remote
access in a user-friendly web-interface. This includes the setting of the
monitored frequency band, activation of receivers as well as settings
to reduce the recording of interference. Once the receivers are acti-
vated, they digitize incoming signals. An algorithm based on liquidSDR
(Gaeddert, 2016) automatically detects peaks in the radio signals along
with timestamps, the frequency relative to a user-defined mid-fre-
quency (Hz), signal bandwidth (Hz), duration of the signal (s) and signal
streng th (dB; For additional information see www.radio-track ing.eu).
2.2 | Transmitter specifications
The system suppor ts any type of radio tag common in wildlife radio te-
lemetr y. Individual tags are identified by their specific frequency. The
number of tags that can be simultaneously monitored depends on the
tag feat ures and the possi ble width of the fre quency band, a s constrained
by the CPU per formance (e.g. 250 kHz for the Raspberry Pi 3 Model B,
1 MHz for the Model A+). With highly stable tag frequencies, tags can
have frequencies as small as 1 kHz apar t. Pulse timing is irrelevant for
signal detection, which enables tags that transfer additional information
(e.g. body temperature by varying time intervals between pulses) to be
deployed. Tags are attached to the animal's skin, fur or feathers using
skin glue that dissolves after a certain time. Alternatively, tags can be
attached by e.g. harnesses and collars. Depending on the size of the tag,
they can be operational between a few days and several months.
2.3 | Principle of bearing calculation and
triangulation
Signal amplification of a direc tional antenna depends on the angle
of the incoming electromagnetic wave. The relation between the
gain of a directional antenna and the angle of arrival can be approxi-
mated using a cosine func tion (Figure 1, Equation 1, Rabinovich &
Alexandrov, 2013), where g(ω) describes the gain or loss relative to
the angle ω in degrees compared to the gain of ω = 0°.
(1)
g(𝜔)=
cos
(
𝜋
90 ×𝜔
)
2
+
1
2
FIGURE 1 Radiation pattern of
directional antennae
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In comparing two antennae of the same design, the absolute
gain in dB can be ignored because the values will be subtracted.
Assuming that the propagation path of the incoming electromag-
netic wave to the antenna is the same for both antennae (see also
calibration), the direction of arrival (ω) of the transmitter signal
is calculated by comparing the relative gains of two neighboring
antennae (Equations 2, 3, 4) with α describing the angle between
the antennae (Figure 2).
To calculate Δg in Equation 4, the dif ference in signal strength
between the two antennae (sl and sr) is normalized with the maxi-
mum signal strength difference Δm (Equation 5).
This can be derived by either simulating the gain pattern of the
antennae or a simple field experiment, in which the signal loss of the
antenna pointing directly at the tag (0°) is compared to the signal loss
of an antenna angled 90° relative to the tag.
Therefore, the direction of arrival is a function of the normal-
ized signal loss between the antennae and the angle bet ween those
antennae:
The tag's position is approximated by finding the point of inter-
section of two lines produced by bearing calculations at two sepa-
rate stations. If more than two stations simultaneously receive the
signal, the centroid of the resulting polygon is calculated.
2.4 | Calibration
Recorded signals may dif fer in strength due to varying sensitivities
of the components (e.g. antennae, cables, plugs, receivers). Since the
(2)
Δ
g(𝜔,𝛼)=
cos
(
𝜋
90 ×𝜔
)
2
cos
(
𝜋
90 ×(𝜔𝛼)
)
2
(3)
Δ
g(𝜔)=cos
(
𝜋
90
×𝜔
)
with 𝛼=90
(4)
𝜔
=
𝜋
90
×arcos (Δg)with 𝛼=90
(5)
Δ
g=
(
slsr
)
Δm
(6)
𝜔
(Δg,𝛼)=
(
1cos
(
𝛼×𝜋
90 ))
×
(
1
𝛼
Δg+1
2)
FIGURE 2 Incoming signal (wavy line), angle ω in degrees
compared to ω = 0°, angle between antennae α
FIGURE 3 Calibration curves of
four HB9CV antennae arranged in an
array with 90° difference between
neighbouring antennae (Station 3–S3). A
radio tag was placed c. 115 m away and
the array was slowly turned on its vertical
axis
    
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bearing calculation relies on an equal net gain at each receiving arm,
each arm must be calibrated. Calibration cur ves can be produced by
mounting a transmitter at a fixed distance to the station and rotating
the station around its vertical axis (Figure 3). Calculating the differ-
ence between each antenna's local maximum and the strongest local
maximum signal returns a correction value for each receiving arm.
Adding the correction value to the recorded signal strength adjusts
every antenna to the same maximum signal strength.
2.5 | Data processing
Different processing workflows were tested to identify relevant set-
tings and boundary conditions for obtaining optimal tracking results.
The bearing calculation requires that each receiving arm reliably
record the signal. An individual antenna may drop a signal when the
angle of the incoming electromagnetic wave strongly deviates from
the angle of possible maximum gain (ω = 0°). Furthermore, very small
or large intersection angles between bearings from two stations may
produce erroneous or no triangulations, if bearings run parallel.
We tested the effect of available antennae on the accuracy of
bearing calculations. The error of each bearing based on two, three
or four receiving antennae was assessed by the difference in the cal-
culated bearing and the angle between a station and the respective
test position.
To assess the influence of intersection angles between bear-
ings, we triangulated points and iteratively increased and decreased
the allowed minimum and maximum intersec tion angle by 10°,
respectively. For each set of triangulation points, we calculated the
position error, which is the mean dist ance bet ween the expected
and measured positions.
Data were processed using r version 3.6.0 (R Core Team, 2019).
The functions are publicly available as an r Shiny analysis tool (https
://github.com/radio track ingeu/ logger_app) or an r package (https ://
github.com/radio track ingeu/ radio track ingeu ).
2.6 | Accuracy study on an empty field
In Januar y 2019, we installed and tested this system on a bare field
free of vegetation to assess the its potential accuracy and evalu-
ate the data processing algorithm. The test setup comprised three
stations, each equipped with four directional antennae (HB9CV,
Telemetrie-Service Dessau) connected to RTL-SDR receivers
(Nooelec NESDR SMArt SDR, NooElec). The system was mounted
on 2.5 m tripods that were installed in an isosceles triangle forma-
tion with 200 m side length. The stations were calibrated against a
transmitter at a fixed distance of 115 m. After calibration, a sight-
ing compassed was used to position each station's antennae in the
cardinal directions. A regular, 50 m-wide test grid was constructed
between the stations and a 400 µW VHF radio-tag with a frequency
of 150.203 kHz and a pulse interval of 0.7 s mounted on a 2 m pole
was placed at each intersection of the test grid (Figure 4). The inter-
sections and the stations were localized with a dif ferential GPS. The
distance of the radio-tracking stations to the test positions ranged
from 65 m to 190 m.
FIGURE 4 Testing scheme. Radio-
tracking stations (S1–S3) were placed in
an isosceles triangle and a radio tag was
placed on each reference position for
2 min (M1–M6)
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2.7 | Usability study of forest‐dwelling bats in a
mixed forest area
Results from an ongoing study that is part of the LOEWE priority pro-
gram Nature 4.0 – Sensing Biodiversity are briefly presented and dis-
cussed to demonstrate the system's capability under field conditions.
In 2019, 15 tracking stations were installed in the Marburg Open
Forest, the open research and education forest of the University of
Marburg, to track bats and songbirds over each breeding season until
2022. Each station is equipp ed with an array of four HB9CV antennae
mounted on 9 m aluminium poles. The stations record movement and
body temperature data of tagged bats, which belong to one of four
forest-dwelling species (Nyctalus leisleri, Myotis daubentonii, Myotis be
chsteinii, Barbastella barbastellus). The temperature sensitive tags (V3,
Telemetrie-Service Dessau, 0.35 g) vary the time interval between
consecutive signals based on the individual's skin temperature.
Exemplary results of bat ac tivit y are shown for 26 June 2019.
The optimal settings as identified in the accuracy test study were
used to triangulate individuals' positions. In order to handle and tag
the bats, a special license was granted by the Nature Conservancy
Department of Central Hessen (‘Obere Naturschutzbehörde
Mittelhessen, Regierungspräsidium Gießen’, v54-19c 2015 h01).
Tags were attached to the skin between the scapula with skin adhe-
sive (Manfred Sauer GmbH, Lobbach Germany) and the weight of the
attached tags was always <5% of the tagged individual's body mass.
3 | RESULTS
3.1 | Results of the accuracy study
Correction values obtained from local maxima in the calibration curves
ranged between 0.07 dB and 2.9 dB with the lowest and highest devia-
tions in maximum received signal s trength for st ations S3 and S2, resp ec-
tively ( Table 1). Thu s, calibrat ion had the stro ngest effec t on S2, improvin g
the bearing error from a median of 11.6° to 5.4° (Figure 5). Calibration
improved the median bearing accuracy by 2° across all stations.
Bearings calculated based on signals recorded by two antennae de-
viated from the real angle by 14.9° (median). Bearing error was reduced
to 6.8° when more than two antennae received a signal (Figure 6).
The position error decreased as the minimum and maximum
permissible intersection angles converged towards 90° (Figure 7).
Minimum and maximum angles of 40° and 140°, respectively, sub-
stantially improved results as well as sharply reducing the number
of triangulated point s. Placing additional limits on the intersection
angle steadily reduced the available data.
Positions were triangulated with calibrated signal strengths and a
minimum of three available antennae. Since a substantial number of
locations were lost due to restrictions to the intersection angle, we tri-
angulated positions with all intersection angles and with intersection
angles re stricted to 40 –140 °. Including a ll possible inters ection angles in
the tria ngulation process r esults in 673 locatio ns and a mean positio ning
error of 25 m. The triangulated points scatter in string-shaped patterns
TABLE 1 Correction values in dB based on calibration curves
obtained in the field
Station
Correction
[dB] 0°
Correction
[dB] 90°
Correction
[dB] 180°
Correction
[dB] 270°
S1 2.2 1.7 00.07
S2 0.26 1.7 02 .9
S3 0.8 00.9 0.98
FIGURE 5 Difference in angle error
before and after calibration. Calibration
substantially reduces the number of
points with a high bearing error (outliers in
the boxplot)
    
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around the reference positions (Figure 8). Restricting the intersection
angles to a minimum of 40 ° and a maximum of 140° reduces this error
to 21 m. However, this results in a substantial loss of triangulated points
(292; Figure 8) with no points for position M5 (Figure 8).
3.2 | Results of the forest usability study
Tracking the movement of M. bechsteinii reveals different areas of
activity throughout the night (Figure 9, left). During 5-min intervals
that night (Figure 9, right), 301 positions were recorded within an
activity area of approximately 50 m2.
Body temperature patterns of four different bat species are
shown for nocturnal activity and resting in the day roost (Figure 10).
For the B. barbastellus as well as for the M. daubentonii, a clear drop
of the body temperature of approximately 7°C was recorded shor tly
before and after sunrise, respectively.
4 | DISCUSSION
The automatic radio-tracking system presented in this paper incorpo-
rates the advantages of lightweight and cost-efficient radio telemetry
into a continuous tracking setup. This enhances the number of tri-
angulated positions without manual telemetry and allows analytical
techniques previously reserved for fine-scale GPS tracks to be used.
These techniques enable researchers to glean important information
about dif ferent behaviour al states of an individual over lar ge trajecto-
ries. The exemplar y 5-min tracking interval, for example, shows a low
displacement in spatial units in relation to the time spent within the
area in question, which may be interpreted as an intensive area-re-
stricted search and, thus, foraging behaviour (Knell & Codling, 2012).
Overall, the accuracy of the radio-tracking system from the
field test compares well to reported manual bearing errors of ex-
perienced field workers (Bartolommei, Francucci, & Pezzo, 2013).
However, this strongly depends on the data processing techniques
used. Antenna calibration reduces the bearing error, confirming both
the underlying theoretic al assumption and the need for calibration
to obtain reasonable results. For more precise results, all bearings
calculated based on fewer than three available antennae should be
excluded from triangulated results. Reducing the intersection angle
improves results, yet also reduces the size of the dataset .
Since incorrect positioning in our field test appears systematic,
errors can be more accurately considered than in manual telemetry
and may be further reduced by, for example, field experiments that
are able to capture this regularity.
FIGURE 6 Absolute deviation of the bearings from the real
angle depending on the number of antennae available. The mean
absolute error is 7° with a standard deviation of 6.
FIGURE 7 Distance error and available
locations depending on the allowed
cutting angle
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The low-cost solution for automatic radio-tracking presented
in this study enables researchers to apply automatic radio-track-
ing techniques in the field while the open-source hardware and
software components allows for active participation in future
development. As the principle of calculating bearing is based on
physical properties shared by most directional antennae, these
algorithms are suitable for triangulating positions with data
gathered by other systems such as SensorGnome (https ://senso
rgnome.org). Further relevant features, such as recording tagged
individuals' body temperature have also been implemented and
tested.
Continuously measuring animal positions and movement with a
long-term antenna setup can greatly contribute to research into animal
behaviour. The movement tracks it generates are comparable to those
generated by satellite and GPS tracking techniques, even below the
canopy in forested areas. This allows researchers to investigate ques-
tions related to small-scale habitat and resource utilization, choice of
breeding sites or migration and dispersal events in organism groups
FIGURE 8 Localization point s with all cutting angles bet ween two antennae allowed (lef t) and with cutting angles restricted to 40–140°
(right). Isolines represent point density increasing from the outside to the inside
FIGURE 9 Tracking data of a Bechstein's bat recorded on the night of 26 June 2019 (left). Yellow crosses indicate permanent radio-
tracking stations. The bounding box (black box) highlights the area of 5 min of relocations shown in detail in the right part of the figure
    
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that movement ecologists cannot yet adequately study due to size re-
strictions. In this vein, this affordable and easy-to-use automatic radio-
tracking system adds a power ful tool to movement ecology research.
ACKNOWLEDGEMENTS
The first permanent setup of the system was implemented at
Marburg Open Forest – the open research and education forest
of Marburg University. The research is funded by the Hessen State
Ministr y for Higher Education, Research and the Arts, Germany,
as part of the LOEWE priority project Nature 4.0 – Sensing
Biodiversity. Radio-tracking.eu is the outcome of a feasibility study
financially supported by the Ministry of the Environment, Climate
Protection and the Energy Sector Baden-Württemberg, Germany.
AUTHORS' CONTRIBUTIONS
R.Z. invented the system and developed the algorithms for bearing
calculation. J.G., C.R., M.L., N.F. and T.N. provided development sup-
port, especially in the development regarding dislocation corrections
and monitoring of body temperature. J.G. and M.L. collected the test
data; bat foraging data were collected by J.G. J.G., M.L . and N.F. led
the writing of the manuscript. All authors interpreted the data and
contributed to the manuscript.
DATA AVAIL ABI LIT Y S TATEM ENT
Example data is provided online on Zenodo (Zenodo https ://doi.
org/10.5281/zenodo.3381909 (Gottwald, 2019)) and can be pro-
cessed using the r package “rad iotracki ngeu (https ://github.com/
radio track ingeu/ radio track ingeu , Zenodo https ://doi.org/10.5281/
zenodo.3381316 (Zeidler, 2019)).
ORCID
Jannis Gottwald https://orcid.org/0000-0001-7763-1415
Ralf Zeidler https://orcid.org/0000-0002-3607-6512
Nicolas Friess https://orcid.org/0000-0003-0517-3798
Marvin Ludwig https://orcid.org/0000-0002-3010-018X
FIGURE 10 24-hr temperature curves of four dif ferent bat species
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Christoph Reudenbach https://orcid.org/0000-0002-7476-3663
Thomas Nauss https://orcid.org/0000-0003-3422-0960
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... A second limitation was the labor-intensive task of tracking finches with portable antennas in variable, but generally harsh, climatic conditions. A potential solution could be the implementation of automated radio tracking, consisting of a system of antennas distributed across the landscape, thus scanning a broader area with less effort (e.g., Cellular Tracking Technologies (CTT), Bridge et al., 2011;Motus et al., 2017); or with an open source telemetry system (Gottwald et al., 2019). Such a network of antennas scattered in the landscape would be particularly useful for determining the movement patterns of nonbreeding finches flocking and moving long distances. ...
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... An alternative method of increasing readings throughout the day is by setting up an ad-hoc wireless telemetry system at a particular habitat which can automatically triangulate the location of tagged individuals. Such a system is suggested by Gottwald et al. (2019) who proposes it as budget friendly option especially when number of personnel is limited. Other future studies should also look at comparing species movement across different habitats or over longer periods of time, in order to increase the scalability of the study. ...
Thesis
Ecological corridors are crucial for the survival of species; they facilitate movement between different areas within a species' home range, whilst aiding genetic exchange across the metapopulation, thus maintaining viable their populations. Despite their importance, maintaining corridors for conservation purposes is no easy task, largely because of conflicts-of-use and related impacts, mostly resulting in habitat loss and fragmentation. This study takes a species-approach towards understanding ecological corridors across specific areas in the Maltese islands. The species selected for this study are the blue rock thrush, Monticola solitarius (Linnaeus 1758), the Old-World swallowtail butterfly, Papilio machaon melitensis (Eller, 1936) and the Mediterranean chameleon, Chamaeleo chamaeleon (Linnaeus 1758). These three faunal species are noteworthy, being of ecological significance, charismatic, and protected under different legal instruments. This study focuses on telemetry, wherein radio tags are affixed to a voucher specimen from each of the aforementioned species to follow their respective movements over an approximate period of two weeks of field monitoring. Appropriate permits were acquired from respective official agencies. The sites of release include Mdina, Comino and Buskett respectively. P. m. melitensis tagged individuals were recorded to undertake a short sea crossing between the islands of Comino and Gozo, subsequently, each making their way to the northern side of Gozo. Each butterfly is estimated to have covered a minimum distance of approximately 16.6 km in 17 days. C. chamaeleon was noted to remain within a woodland habitat without crossing into adjacent garrigue-dominated karst, with the tagged male demonstrating the most vagility from the monitored sample. Not enough data was collected for M. Solitarius to establish movement patterns due to complications which arose during field sessions. Based on present findings, the study proposes a number of recommended measures for ecological connectivity within the local context for the different target species researched.
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Migratory ungulates are often exposed to anthropogenic infrastructure along their migration routes. Understanding the influence of such development on migratory behavior is critical to successful planning and conservation. Impermeable barriers have obvious and detrimental effects to migratory ungulate populations, but the influence of semi-permeable barriers, where the connectivity of migration habitat is maintained but the migration routes are compromised by anthropogenic development, remains unclear. We evaluated the influence of development on the migratory behavior of individual mule deer (Odocoileus hemionus) in western Wyoming, USA. We used fine-scale movement data to evaluate the influence of anthropogenic infrastructure on deer movement rates, stopover use, and fidelity to migration routes for individual animals across multiple seasons and years. Deer avoided human infrastructure when selecting stopover sites. Fidelity to migration routes and stopover areas, as measured by the degree of spatial overlap between years, was not influenced by development, except in one heavily developed area. Our results suggested that deer increased rate of movement, reduced time in stopovers, and shifted stopovers in areas of intense development. In most cases, deer maintained fidelity to migration routes, regardless of development, suggesting that deer mediated exposure to development by altering movement—rates and timing—rather than the routes they traversed. This work adds to a growing number of studies indicating that development can disrupt migratory behavior. Understanding how different types and intensities of development influence migration can help inform land-use planning and conservation of migratory ungulates.
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Background: GPS telemetry has revolutionized the study of animal spatial ecology in the last two decades. Until recently, it has mainly been deployed on large mammals and birds, but the technology is rapidly becoming miniaturized, and applications in diverse taxa are becoming possible. Large constricting snakes are top predators in their ecosystems, and accordingly they are often a management priority, whether their populations are threatened or invasive. Fine-scale GPS tracking datasets could greatly improve our ability to understand and manage these snakes, but the ability of this new technology to deliver high-quality data in this system is unproven. In order to evaluate GPS technology in large constrictors, we GPS-tagged 13 Burmese pythons (Python bivittatus) in Everglades National Park and deployed an additional 7 GPS tags on stationary platforms to evaluate habitat-driven biases in GPS locations. Both python and test platform GPS tags were programmed to attempt a GPS fix every 90 min. Results: While overall fix rates for the tagged pythons were low (18.1%), we were still able to obtain an average of 14.5 locations/animal/week, a large improvement over once-weekly VHF tracking. We found overall accuracy and precision to be very good (mean accuracy = 7.3 m, mean precision = 12.9 m), but a very few imprecise locations were still recorded (0.2% of locations with precision > 1.0 km). We found that dense vegetation did decrease fix rate, but we concluded that the low observed fix rate was also due to python microhabitat selection underground or underwater. Half of our recovered pythons were either missing their tag or the tag had malfunctioned, resulting in no data being recovered. Conclusions: GPS biologging technology is a promising tool for obtaining frequent, accurate, and precise locations of large constricting snakes. We recommend future studies couple GPS telemetry with frequent VHF locations in order to reduce bias and limit the impact of catastrophic failures on data collection, and we recommend improvements to GPS tag design to lessen the frequency of these failures.
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To prevent further global declines in biodiversity, identifying and understanding key habitats is crucial for successful conservation strategies. For example, globally, seabird populations are under threat and animal movement data can identify key at-sea areas and provide valuable information on the state of marine ecosystems. To date, in order to locate these areas, studies have used global positioning system (GPS) to record position and are sometimes combined with time-depth recorder (TDR) devices to identify diving activity associated with foraging, a crucial aspect of at-sea behaviour. However, the use of additional devices such as TDRs can be expensive, logistically difficult and may adversely affect the animal. Alternatively, behaviours may be resolved from measurements derived from the movement data alone. However, this behavioural analysis frequently lacks validation data for locations predicted as foraging (or other behaviours). Here, we address these issues using a combined GPS and TDR dataset from 108 individuals by training deep learning models to predict diving in European shags, common guillemots and razorbills. We validate our predictions using withheld data, producing quantitative assessment of predictive accuracy. The variables used to train these models are those recorded solely by the GPS device: variation in longitude and latitude, altitude and coverage ratio (proportion of possible fixes acquired within a set window of time). Different combinations of these variables were used to explore the qualities of different models, with the optimum models for all species predicting non-diving and diving behaviour correctly over 94% and 80% of the time, respectively. We also demonstrate the superior predictive ability of these supervised deep learning models over other commonly used behavioural prediction methods such as hidden Markov models. Mapping these predictions provides useful insights into the foraging activity of a range of seabird species, highlighting important at sea locations. These models have the potential to be used to analyse historic GPS datasets and further our understanding of how environmental changes have affected these seabirds over time.
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