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ORIGINAL RESEARCH
published: 25 January 2019
doi: 10.3389/fevo.2018.00242
Frontiers in Ecology and Evolution | www.frontiersin.org 1January 2019 | Volume 6 | Article 242
Edited by:
David Jack Coates,
Department of Biodiversity,
Conservation and Attractions (DBCA),
Australia
Reviewed by:
Luke T. B. Hunter,
Panthera Corporation, United States
Bilal Butt,
University of Michigan, United States
*Correspondence:
Florian J. Weise
florian.weise@gmail.com
Specialty section:
This article was submitted to
Conservation,
a section of the journal
Frontiers in Ecology and Evolution
Received: 19 October 2018
Accepted: 31 December 2018
Published: 25 January 2019
Citation:
Weise FJ, Hauptmeier H, Stratford KJ,
Hayward MW, Aal K, Heuer M,
Tomeletso M, Wulf V, Somers MJ and
Stein AB (2019) Lions at the Gates:
Trans-disciplinary Design of an Early
Warning System to Improve
Human-Lion Coexistence.
Front. Ecol. Evol. 6:242.
doi: 10.3389/fevo.2018.00242
Lions at the Gates: Trans-disciplinary
Design of an Early Warning System
to Improve Human-Lion Coexistence
Florian J. Weise 1,2
*, Helmut Hauptmeier 3, Ken J. Stratford 4, Matthew W. Hayward 1, 5,
Konstantin Aal 6, Marcus Heuer 6, Mathata Tomeletso 2, Volker Wulf 6, Michael J. Somers 1
and Andrew B. Stein 2,7
1Eugène Marais Chair of Wildlife Management, Centre for Invasion Biology, Mammal Research Institute, University of
Pretoria, Pretoria, South Africa, 2CLAWS Conservancy, Worcester, MA, United States, 3iSchool—School of Media and
Information, University of Siegen, Siegen, Germany, 4Ongava Research Centre, Klein Windhoek, Windhoek, Namibia,
5School of Environmental and Life Sciences, University of Newcastle, University Drive, Callaghan, NSW, Australia, 6Faculty
III—School of Economic Disciplines, Business Information Systems, University of Siegen, Siegen, Germany, 7Landmark
College, Putney, VT, United States
Across Africa, lions (Panthera leo) are heavily persecuted in anthropogenic landscapes.
Trans-disciplinary research and virtual boundaries (geofences) programmed into
GPS-tracking transmitters offer new opportunities to improve coexistence. During a
24-month pilot study (2016–2018), we alerted communities about approaching lions,
issuing 1,017 alerts to four villages and 19 cattle posts. Alerts reflected geofence
breaches of nine lions (2,941 monitoring days) moving between Botswana’s Okavango
Delta and adjacent agro-pastoral communities. Daily alert system costs per lion were
US$18.54, or $5,460.24 per GPS deployment (n=13). Alert-responsive livestock
owners mainly responded by night-kraaling of cattle (68.9%), significantly reducing their
losses (by $124.61 annually), whereas losses of control group and non-responsive
livestock owners remained high ($317.93 annually). Community satisfaction with alerts
(91.8%) was higher than for compensation of losses (24.3%). Study lions spent
26.3% of time monitored in geofenced community areas, but accounted for 31.0%
of conflict. Manual alert distribution proved challenging, static geofences did not
appropriately reflect human safety or the environment’s strong seasonality that influenced
cattle predation risk, and tracking units with on-board alert functions often failed or
under-recorded geofence breaches by 27.9%. These insufficiencies prompted the design
of a versatile and autonomous lion alert platform with automated, dynamic geofencing.
We co-designed this prototype platform with community input, thereby incorporating
user feedback. We outline a flexible approach that recognizes conflict complexity
and user community heterogeneity. Here, we describe the evolution of an innovative
Information and Communication Technologies-based (ICT) alert system that enables
instant data processing and community participation through interactive interfaces on
different devices. We highlight the importance of a trans-disciplinary co-design and
Weise et al. Lion Early Warning Botswana
development process focussing on community engagement while synthesizing expertise
from ethnography, ecology, and socio-informatics. We discuss the bio-geographic,
social, and technological variables that influence alert system efficacy and outline
opportunities for wider application in promoting coexistence and conservation.
Keywords: Panthera leo, conflict mitigation, geofencing, socio-informatics, alert system, early warning,
coexistence, grounded design
INTRODUCTION
Globally, large predators struggle with the consequences of
human population growth and development. An iconic example
is the African lion (Panthera leo). Lion numbers and distribution
have decreased precipitously over the past century with many
regional populations continuing to decline, putting them at
risk of local extinction (Riggio et al., 2013; Henschel et al.,
2014; Bauer et al., 2015). The situation is exacerbated along
protected area boundaries where human retaliation for livestock
losses and indiscriminate persecution can inflict heavy losses that
reverberate throughout protected areas (e.g., Loveridge et al.,
2016). Edge effects have far-reaching demographic consequences
for lions (Woodroffe and Frank, 2005) and conflict with people
remains the single biggest threat to their persistence. Particularly
worrying is the continent-wide use of poison to control lions
(Ogada, 2014;Supplementary Data 1) as this indiscriminate
method also drives the declines of other endangered biota (Ogada
et al., 2016).
Despite decades of applied conflict management research
(Trinkel and Angelici, 2016; van Eeden et al., 2018), sustainable
coexistence of rural communities with lions has yet to be
achieved in many countries (Bauer et al., 2015). The successful
mitigation of conflict primarily depends on changes in people’s
behaviors and risk management (Reddy et al., 2017), requiring
trans-disciplinary research and conservation approaches that
appropriately reflect the human dimensions of coexistence
(Bennett et al., 2017; Pooley et al., 2017). This, inevitably, entails
the direct involvement of rural communities in the design and
testing of coexistence strategies. Because they bear the risks and
costs of coexistence, Africa’s communities are the key stakeholder
of lion conservation outside protected areas. Mirroring a global
omission in biodiversity conservation (Sterling et al., 2017),
Africa’s communities rarely have direct access to lion monitoring
information and are often marginalized during conservation
development processes.
Recent advances in Global Positioning System (GPS) tracking
technology have revolutionized our understanding of wildlife
movements and ecology (Kays et al., 2015). Beyond the mere
tracking of fauna, innovative integrations of geofences (i.e.,
virtual boundaries that can trigger alerts when transgressed),
automated data processing, and modern communication
networks offer opportunities for use of wildlife tracking
technology in various conservation contexts (Wall et al., 2014).
For example, geofence applications can be designed to reduce
human-induced wildlife mortalities (e.g., Sheppard et al., 2015).
Dynamic geofencing can improve human safety by integrating
near real-time processing of situational awareness, i.e., the
continuous evaluation of relative risk (Zimbelman et al., 2017).
In the case of lions and people (and their livestock), conflict
typically manifests along well-defined land use and land tenure
boundaries such as protected area edges. Simplistically, we
can express conflict (the risk of undesirable interactions) as
the probability of direct contact between these actors at any
point in time. In other words, what is the real-time proximity
of lions to people and livestock? Using proximity-based risk
rules that are linked to geofences may, therefore, provide an
opportunity to reduce the likelihood of conflict between people,
their livestock, and lions. Informing people about the presence
of lions in anthropogenic landscapes (by geofence triggered
early warning) might enable them to exercise the changes in
behavior upon which successful coexistence relies (Reddy et al.,
2017).
The Okavango Delta lion population in northern Botswana
represents one of the last strongholds for the long-term
survival of the species (Riggio et al., 2013; Bauer et al., 2015).
The Delta’s eastern panhandle region constitutes a critical
lion conservation area with on-going conflict (Weise et al.,
2018), widespread persecution (Supplementary Figure 1) and
important dispersal linkages with other lion areas in the Kavango
Zambezi Transfrontier Conservation Area (KAZA-TFCA). In
this anthropogenic landscape with multiple edges, we tested the
efficacy of alerting rural communities about approaching lions to
improve human and livestock safety. Here, we reflect critically
on experiences from the system’s 2-year pilot stage (Figure 1)
with daily online data checks, static risk geofences, subjective
evaluation of geofence breaches and manual alert distribution.
We evaluate pilot study results in terms of conflict, technology
performance, community satisfaction and feedback, financial
costs, and the ecological implications for human and livestock
safety. We provide details of the bio-geographic, social, and
technological variables that influence alert efficacy. Following
identification of the core challenges of effective early warning,
we employed an adaptive, trans-disciplinary research design
(Figure 1) to develop a versatile, autonomous, Information and
Communication Technologies-based (ICT) lion alert system,
capable of delivering near real-time alerts through interactive
community interfaces. The evolution of this prototype platform
was founded on a co-development strategy with maximum
community participation and feedback. Our results highlight
the importance of multi-disciplinary research that synthesizes
ethnography, ecology, and socio-informatics. Finally, we outline
opportunities for the platform’s wider application in wildlife
conservation.
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Weise et al. Lion Early Warning Botswana
FIGURE 1 | Timeline of key research activities and lion alert system elements during the pilot study. Numbers in row “Lion GPS tracking” represent monthly sample
sizes. Numbers in row “Geofences” indicate which virtual boundaries (see Figure 2) triggered alerts.
FIGURE 2 | Design of the lion alert system during the pilot study (May
2016–May 2018) showing the placement of virtual geofences across the study
area in the northern Okavango Delta, Botswana. Geofences 1 and 2 were
programmed into lion GPS tracking units deployed until end of 2017,
Geofence 3 into units deployed from January 2018 onward.
MATERIALS AND METHODS
Study Area
Between 13 May 2016 and 12 May 2018 (the pilot study),
we studied lions, conflict and cattle (Bos taurus) (Figure 1)
across communities living at the boundary of the NG/11
and NG/12 multi-use areas (settlement, agriculture, livestock,
and wildlife tourism) located along the northern edge of
Botswana’s Okavango Delta (Figure 2). The area supports high
levels of biodiversity (Ramberg et al., 2006), forms part of
UNESCO’s 1000th World Heritage Site, Ramsar Site No. 879,
and provides critical linkage habitat with protected areas
in the KAZA-TFCA. The study area comprised five main
villages, 44 remote cattle posts (i.e., small, often seasonal,
homesteads with a cattle night enclosure), and intermittent
settlements (Figure 2) with ∼5,000 resident inhabitants from
three ethnic groups, namely the Hambukushu, the Bayeyi,
and the Basarwa (Mendelsohn and el Obeid, 2004). The main
subsistence activities entail household-specific combinations of
agro-pastoralism with small business; most families subsist on
<$US 500 (hereafter $) monthly income. Increasing conflict with
lions and elephants (Loxodonta africana) significantly impacts
agro-pastoral households (Songhurst, 2017; Weise et al., 2018).
Livestock are mainly a socio-cultural commodity; most cattle
roam freely across unrestricted communal pastures shared with
wildlife, and their management is haphazard, with minimal
day-time herding (<10%) and irregular night-time confinement
(∼40%) in predator-proof enclosures (see Weise et al., 2018
for additional detail). Through herd counts, we estimated a
total standing herd of 16,500 cattle in 2017. The Department
of Wildlife and National Parks (DWNP) compensates livestock
owners for lion-related stock losses at average national cattle
market values (Department of Wildlife National Parks, 2013).
Owners report livestock losses to the nearest DWNP or police
office and compensation is granted following case-specific
evaluation of the supporting evidence (for additional procedural
detail see Songhurst, 2017).
The Okavango Delta experiences annually variable seasonal
flooding (Murray-Hudson, 2009), the extent of which strongly
influences the socio-ecology of lions (Kotze et al., 2018). We
considered three climatic seasons: (1) Wet season (January–
April) with rising flood levels, >80% of annual rainfall, and
surface water availability in seasonal pans in NG/11; (2) Early
dry season (May–August) with a progression from peak Delta
flooding to low flood levels in NG/12, no rains, cold winter
temperatures, and the drying up of seasonal pans in NG/11; and
(3) Late dry season (September–December) with dry seasonal
pans, consistently high mid-day temperatures (>35◦C), minimal
rainfall, and surface water restricted to the last permanent
channels in NG/12. Detailed bio-geographic and socio-cultural
descriptions are available from Mendelsohn and el Obeid (2004),
Pröpper et al. (2015), and Sianga and Fynn (2017). The regional
mix of dry savannah woodlands with wetland habitats provides
critical functional heterogeneity of seasonal habitats for wild and
domestic herbivores (Fynn et al., 2015; Weise et al., 2019).
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Weise et al. Lion Early Warning Botswana
Lion Tracking and Movement Analyses
We tracked nine adult study lions (four females, five males) from
different social groups with combined VHF radio-GPS Iridium
satellite transponders that enabled near real-time transmission of
positional data and that were equipped with on-board geofencing
functions. Tagging of specific lions focused on individuals with
known or suspected conflict histories and was conducted in the
immediate vicinity of communal grazing pastures. All GPS units
were programmed to transmit geofence breach and exit SMS
messages to the researchers. Transponders weighed <1.5% of
adult body weight and were equipped with automated drop-
off mechanisms. Until December 2016, we tracked four lions
with Telonics TGW-4570-3 units (Telonics Inc., Mesa, AZ, USA)
that recorded and relayed five daily locations, however switching
into 2-hourly sampling mode when breaching Geofences 1 or
2 (Figure 2). From December 2016 onwards, we followed lions
using Vectronic Vertex Plus v2.1 units (Vectronic Aerospace
GmbH, Berlin, Germany) that transmitted GPS locations every
2 hours (h) (Figure 1). The six units deployed in December
2016 were programmed to report Geofence 1 breaches. Following
the revision of geofences (see Geofence Placement), the three
units deployed in January 2018 were programmed to report
Geofence 3 breaches (Figures 1,2). At any one time, we
tracked between three and six social groups simultaneously. In
addition, we recorded lion group compositions and identification
details (using whisker spot differentiation) during direct field
monitoring and from high-resolution photographs that were
sourced from wildlife tourism enterprises operating in the study
area.
Because spatial outliers often represented lions entering
community areas, we calculated seasonal home ranges as 100%
Minimum Convex Polygons (MCP) (Mohr, 1947). We computed
range metrics with QGIS 2.18 (QGIS Development Team, 2016)
using the AniMove 1.4.2 (Boccacci et al., 2014) extension. We
calculated home range centroids and percentage overlap with
community areas using QGIS geoprocessing tools. For each lion,
we also calculated the duration (minutes) between consecutive
GPS locations. For any duration <250 min (nominally 4 h), we
calculated the distance between locations using the Haversine
formula (Sinnott, 1984), which compensates for the curvature of
the earth’s surface, and calculated the mean speed for that interval
by dividing distance by duration. Speeds were converted to km/h
for analysis purposes. Each speed calculation was assigned to a
time of day using the midpoint of the associated time interval.
Cattle Tracking
Between January and December 2017, we deployed SPOT
TraceTM GPS tracking units on 42 domestic cattle (forty one
females, one male) from 29 herds (see Weise et al., 2019 for
additional herd and tracker details). Monitored cattle represented
herds from four main villages and 18 cattle posts. Herds were
sampled using a stratified-random approach that acknowledged
each sampling location’s proportional contribution to the study
area’s entire standing herd in 2017. At each location, specific
herds were randomly selected from all local herds. Monitoring
focused on lead animals and had no influence on the herd’s
management. We programmed trackers to record and relay GPS
positions at hourly intervals, or, if trackers had been stationary
for >1 h, at first detection of movement via an in-built motion
sensor.
Geofence Placement
Prior to the pilot study’s start in May 2016, we manually
created static alert boundaries Geofences 1 and 2 (Figure 2) in
Google Earth. Geofence 1 (grazing) reflected the known extent of
livestock grazing areas (2015–2016), whilst Geofence 2 (village)
represented the known subsistence activity area, i.e., the area
where humans might encounter lions on foot. Geofence 1 had
the primary objective of improving livestock safety by allowing
owners to collect cattle from grazing lands as lion approached,
whilst Geofence 2 aimed at improving human safety.
Based on a cumulative MCP that contained all human
settlements and cattle posts, 95% of cattle GPS data recorded
in 2017, and 90% of lion-related livestock depredation locations
(pilot study cases), we created Geofence 3 as a static alert polygon
in December 2017 (Figure 2). We discarded the outermost 5% of
cattle positions and 10% of predation incidents as these reflected
outliers of unguarded herds moving beyond community areas
into tourism concessions. Geofence 3 served as an updated alert
boundary and was programmed into lion GPS units deployed
in January 2018 (Figure 1) with the objective of improving both
human and livestock safety.
Alert Distribution
During the alert pilot study (13 May 2016 – 12 May 2018), we
received geofence breach alerts from lion GPS units via SMS
notification. Regardless of the time of day, we immediately
relayed breach information to the headmen of all villages
and cattle posts within 8.0 km linear distance of Geofence
1 (grazing land) breach locations, and within 5.0 km linear
distance of Geofence 2 (village) breach locations. We informed all
communities within 8.0 km linear distance of Geofence 3 breach
locations, assuming that this window would provide a safety
buffer of at least 2 h, sufficiently long to take precautions.
In agreement with traditional customs, we initially alerted
village and cattle post headmen who had previously agreed
to forward alerts to livestock owners in their communities.
Each recipient then distributed messages further, resulting in a
snowball distribution system. In the course of the pilot study,
we expanded alert distribution to other community members
who requested to receive alerts directly, e.g., conflict-affected
livestock owners (see Conflict Investigations and Lion Conflict
Involvement). Participation was voluntary and there was no
discrimination by ethnicity, gender, age, location, or profession.
We did not distribute accurate lion GPS latitude/longitude
information but informed recipients about the identity of
approaching lions and an approximated distance and direction.
Geographically distinct villages and cattle posts were alerted
separately.
Following alert malfunctions by the collars and the
deployment of new transponders in December 2016, we
also checked lion GPS locations online (via the manufacturer’s
data portal) at least twice per day (usually around sunrise and
sunset) and alerted communities about geofence breaches in case
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Weise et al. Lion Early Warning Botswana
collars did not detect breaches or failed to transmit breach SMSs
(Figure 1). On randomly selected dates, we recorded the work
effort for manual data checks and alert distribution. Additionally,
we recorded all financial costs pertaining to the implementation
of this pilot alert system, including any expenses for GPS units
and data fees, veterinary fees, deployment expeditions, and staff
cost for alerting effort.
Evaluation of Static Geofences
To evaluate the validity of initial geofences (Geofences 1 and 2;
Figure 2), we calculated seasonal cattle-lion encounter risk levels
as well as a human safety buffer calibrated by lion movement
speed.
Seasonal Cattle-Lion Encounter Risk
We mapped all investigated lion-related livestock depredation
incidents and seasonal cattle-lion encounter risk levels into a
1.0 km ×1.0 km grid of the study area. For each lion GPS
location recorded in 2017 (n=13,503), we determined the closest
GPS time match for simultaneously tracked cattle (n=69,793
locations), considering a maximum time difference of 6 h. We
then calculated the separation distance between these locations
using the Haversine formula (Sinnott, 1984). Based on distances,
we assigned a preliminary risk level to all 1.0 km2grid cells that
contained lion locations:
0 - No risk (separation >5.0 km);
1 - Lowest risk (separation <5.0 km and >1.0 km); and
2 - Intermediate risk (separation <1.0 km).
For all data points, we then determined the frequency of any
cell being assigned as intermediate risk, and for multiple counts,
refined the risk level by adding further levels as:
3 - High risk (lion-cattle separation distance <1.0 km occurred 2
or 3 times); and
4 - Vertically high risk (lion-cattle separation distance <1.0 km
occurred >3 times).
Any cells containing investigated lion kills were assigned level 4,
very high risk.
Human Safety
To calculate the human risk area, we created a 5.5 km radius
circular buffer around each human settlement. The value for this
radius is based on the maximum hourly distance traveled by any
lion in this study. We then mapped buffers into a 1.0 km2grid of
the study area, and marked each cell whose centroid overlapped
within any of the buffer zones. The ensemble of marked cells
comprises the human risk area.
Conflict Investigations and Lion Conflict
Involvement
Livestock owners voluntarily reported depredation incidents by
carnivores for further investigation. During investigations, we
recorded attack location (latitude/longitude), date, time, livestock
characteristics and value, evidence of responsible carnivore
species and their numbers, details of protective measures and
herd management, and the owner’s opinions about conflict lion
management, compensation of losses, and the lion alert system.
To allow for a guided process with maximum flexibility, we
employed semi-structured interviews (Brockington and Sullivan,
2003) that were administered through open and closed questions.
All respondents participated voluntarily and anonymously. We
also recorded all livestock loss claims to large carnivores reported
to the local DWNP office. These data have no accurate GPS
reference and are collated at the village level.
To determine the involvement of study lions in investigated
depredation incidents, we cross-referenced each incident
location against all lion GPS data 24 h before and after the event.
We used a proximity-association rule to infer responsibility. We
considered study lions as responsible for an incident if: (1) they
were directly observed at the site; (2) they were located within
250 m of the attack site within 6 h of the estimated attack time; or,
in case positional data were sparse, (3) they were located within
500 m of the attack site within 12h of the estimated attack time.
Community Perceptions and Feedback
For a grounded understanding of human-lion interactions,
researchers need to grasp the complexity of social circumstances
that influence community life, interactions with predators and
conflict (Dickman, 2010; Pooley et al., 2017). During this
study, we maintained a permanent research presence in the
northern Okavango Delta. We conducted several semi-structured
interview surveys (Figure 1) that yielded important insights into
the various dimensions of conflict and social practices of the alert
system’s stakeholders.
In addition to interviews with conflict-affected livestock
owners (n=78; see section Conflict Investigations and Lion
Conflict Involvement), we recorded conflict lion management
suggestions from a randomly sampled control group of livestock
owners (n=53). Again, we asked their opinions about conflict
lion management, the government’s compensation scheme, and
the lion alert system. For 12 months preceding the pilot study,
and during alert distribution, we recorded livestock depredation
by lions in terms of number of livestock lost, percentage
stock loss, and financial value. Furthermore, we interviewed all
senior village headmen (n=12) using open-ended questions.
Regardless of current wildlife legislation and conflict mitigation
activities tested by the researchers, we asked headmen to state
their three main aspirations about how conflict lions should be
managed. For all interviews, we grouped common answers and
ranked them in order of priority, assigning weight scores of 3, 2,
and 1 in declining order.
To determine community perceptions of the pilot alert
system across the wider community, we also conducted a
qualitative study using a participatory action research approach
(Kemmis and McTaggart, 2005) (Figure 1). Social scientists
interviewed a randomly sampled group of local inhabitants,
trying to understand how local residents perceive and deal
with lions, given the presence of a pilot system designed to
improve the terms of coexistence. Semi-structured interviews
(n=36) were conducted in those villages with highest
conflict. For triangulation of responses, we included a diverse
group of people that comprised different ages, both genders,
varying levels of education, and from different villages and
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Weise et al. Lion Early Warning Botswana
cattle posts (Supplementary Data 2). A local assistant translated
questions into one of the three main local languages (Setswana,
Hambukushu, Bayeyi). Interviews focused on the daily life of
participants, the use of digital technologies and their integration
into routines, knowledge, and attitudes toward lions, the lion
alert pilot system, and the participant’s social life in the
community. Researchers documented visits via field notes, video,
and photos, transcribing insights on the day of interviews.
All interview materials were analyzed using qualitative content
analysis (QCA) (Schreier, 2014). Analyses focused on key aspects
relating to the conflict mitigation potential of the pilot lion alert
system. Analyses were corroborated by external researchers who
had not been involved in interviews. We took care to use findings
from different researchers and interview series to ensure a broad
perspective and to offset any observer bias.
Community Co-design Workshops
Following the pilot study, we involved communities in the design
of an autonomous ICT-based lion alert platform (Figure 1),
intended to distribute alerts flexibly and automatically, and
to a larger group of recipients. Similar to social media, the
transformed alert system has a user interface, thus requiring
direct community feedback during the development phase for
effective implementation. The study communities are diverse
in terms of their ethnicity, languages, socio-economic status,
cultural traditions, literacy (Mendelsohn and el Obeid, 2004;
Hanemann, 2005; Songhurst, 2017), and exposure to modern
communications technology (Ertl, 2018). Consequently, a variety
of technological, cultural and individual barriers could inhibit the
efficacy of an ICT-based lion alert distribution platform (Irani
et al., 2010; Mutula et al., 2010; Vitos et al., 2017; Ertl, 2018).
We conducted co-design workshops in Gunotsoga, Eretsha,
and Beetsha, the villages that had experienced highest conflict
and received most alerts (Figure 3). To facilitate maximum
community consultation, workshops followed a participatory
design approach (Schuler and Namioka, 1993). We developed
prototype designs for different telecommunication devices
and purposes using the Axure (2017) tool. Printed and
digital illustrations were used to visualize the existing system
(Supplementary Figure 2) as well as proposed outputs under
an autonomous platform (Supplementary Figures 3, 4). Paper
versions were utilized to engage target groups with limited
experience in technology use (Gubbiotti et al., 1997; Vitos et al.,
2017). Workshop participants (n=35) comprised traditional
village leadership, Village Development Committee chairpersons,
representatives of local farmers’ associations, livestock owners,
and herdsmen, with an age range from 21 to 80 years. These
represented a large degree of diversity in terms of occupation,
location, age, literacy, herd sizes (range: 1–220), husbandry
practices, conflict with lions, and experience with the pilot alert
system (Supplementary Data 3). Workshops lasted between 2.5
and 4 h and participants were divided into two working groups
per village.
In each village, co-designing involved a two-tiered process
with a pre-defined set of workshop implementation guidelines.
Initial workshops focused on assessing and discussing
experiences, benefits, and challenges from the lion alert
FIGURE 3 | Proportional distribution of cattle, livestock losses to lions, lion
alert messages received and investigated (invest.) conflict locations across the
study area in northern Botswana. Except for cattle holdings that were
determined in 2017, data represent the lion alert pilot study from 13 May 2016
until 12 May 2018.
pilot study while also reflecting on system elements and
processes (Supplementary Figure 2). These workshops included
focus group interviews (Byrne and Sahay, 2007; Pruneau
et al., 2018), mapping and creative ideation and discussion
sessions (Gubbiotti et al., 1997; Pruneau et al., 2018), in which
participants expressed their aspirations and expectations toward
alert delivery by an autonomous platform. Following several
days of reflection, follow-up workshops focused on iterating
the existing system and co-designing the functionality of the
autonomous platform, capturing specific user requirements.
Participants tested our preliminary, printed or digital designs
(Supplementary Figures 3, 4), which we modified according to
their feedback.
During this step-wise consultation process, we discussed
all elements pertaining to the data acquisition phase, data
processing, system components, and future alert output and
distribution. We determined literacy levels, the types of
communication technology used, and their integration into
daily routines. Further, we determined user-specific preferences
regarding alert frequencies, alert contents, message formats
(i.e., text, image, sound, or voice message) in relation to
telecommunication devices, and languages. Combining users’
needs and new functions, we designed a feedback portal that
allows for independent user registration and reporting of lion
encounters in community areas, livestock movements and
depredation events.
RESULTS
All means are presented ±one standard error.
Conflict Summary (2015–2018)
Prior to alerting (May 2015–April 2016), depredation by lions
affected 63.7% of livestock owners, with a mean annual loss of
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Weise et al. Lion Early Warning Botswana
TABLE 1 | Reported livestock losses to carnivores with associated compensation value ($US), 13 May 2016–12 May 2018.
Species No. of
incidents
Percentage Cattle Goats Horses Donkeys No. of
livestock
Percentage Compensation
value in $US
Percentage
Lion 255 87.9 282 0 3 6 291 87.1 80,814.00 97.8
Wild Dog 26 9.0 30 0 0 0 30 9.0 1,591.00 1.9
Leopard 8 2.8 5 4 0 0 9 2.7 226.00 0.3
Caracal 1 0.3 0 4 0 0 4 1.2 0.00 0.0
Total 290 100 317 8 3 6 334 100 82,631.00 100
Source: Department of Wildlife and National Parks, Seronga office.
4.1 ±0.7% of stock owned (range: 0–60.0%; n=102). This is
equivalent to a mean of 1.8 ±0.2 livestock per owner (range: 0–
13) and a mean compensation value of $319.12 ±$408.67 per
owner (range: $0–$2,351.00).
During the pilot study (13 May 2016–12 May 2018),
community members directly encountered lions on at least 57
occasions (i.e., those reported to the researchers), including
one near-fatal incident in which a male lion attacked a group
of livestock owners (seriously injuring one person) who had
attempted to kill the animal. In addition, livestock owners
reported 290 incidents of livestock depredation by carnivores
to the DWNP, with an annual compensation value of $41,316
(Table 1). Lions were responsible for >87% of reported predation
incidents and total livestock lost (Table 1). Due to different
compensation valuation rules (Department of Wildlife National
Parks, 2013), lion-related losses amounted to >97% of the total
compensation value (Table 1). Lions predominantly predated on
cattle (96.9% of livestock killed) (Table 1). The associated impact
was not evenly distributed across the study area; Gunotsoga
village incurred disproportionately high losses in relation to
cattle numbers (Figure 3). Depredation was highest during
low-flood months (September–February), comprising 60.8% of
incidents (n=255) and 61.5% of stock losses (n=291)
(Supplementary Data 4). At this time, lions have unrestricted
access to communal grazing pastures and cattle roam further into
core lion habitat by following receding flood waters in NG/12,
thereby increasing depredation risk significantly (Weise et al.,
2019).
Lion Movements and Geofence
Transgressions
Seasonal Home Range Overlap With Community
Areas
Corresponding with the Delta’s flooding regime, lion home range
sizes and percentage overlap with geofenced community areas
were highly variable, exhibiting strong seasonality (Figure 4;
Supplementary Figure 5). Males had significantly larger seasonal
home ranges than females (U=20; Z= −2.76; p=0.006),
whereas females, on average, spent nearly twice as much time
in geofenced community areas (Table 2). This was influenced
by females PleoF003 and PleoF005 raising cubs in community
areas. Seasonal home ranges were largest during the late dry
season (Figure 4) when lion movements are no longer restricted
by seasonal flooding. Home range overlap with community
areas was highest during the wet season (Figure 4) when wild
FIGURE 4 | Comparison of 100% Minimum Convex Polygon lion home range
size (A) and percentage home range overlap with geofenced community areas
(B) by season and genders. Blue asterisks show mean values. Sample sizes
are shown in parentheses. The early dry season lasted from May to August,
the late dry season from September to December, and the wet season from
January to April. 100% MCPs were only calculated for individuals with
sufficient GPS data (>60 days).
lion prey like plains zebra (Equus burchelli) and Cape buffalo
(Syncerus caffer) follow early rains and migrate out of the Delta
into NG/11 dryland grazing areas causing a northward shift in
lion home ranges (panels c and f in Supplementary Figure 5).
Seasonal home range overlap with geofenced community areas
significantly decreased (rho = −0.756; p<0.001, n=25) with
increasing home range centroid distance to the nearest settlement
(Supplementary Figure 6).
Lion Activity
Study lions were predominantly nocturnal, exhibiting peaks
in movements between 16.00 and 05.00 local time (Figure 5).
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Weise et al. Lion Early Warning Botswana
TABLE 2 | Summary of lion monitoring, geofence breaches, and alerts issued during the pilot study.
Lion ID GPS
days
Alerts
issued (incl.
reminders)
Geofence
breaches
Mean
duration
±SE (hours)
Minimum
(hours)
Median
(hours)
Maximum
(hours)
GPS
hours
Percentage
inside
geofence
Percentage
outside
geofence
PleoF001 730 0 1 2.0 ±0.0 2.0 2.0 2.0 17,520 0 100.0
PleoF003 730 367 37 205.2 ±105.4 2.0 22.0 3,866.0 17,520 43.3 56.7
PleoF004 118 0 5 10.4 ±4.3 2.0 8.0 26.0 2,826 2.0 98.0
PleoF005 511 239 51 146.4 ±57.5 2.0 32.0 2,150.0 12,264 60.9 39.1
Females
subtotal
2,089 606 94 160.8 ±51.8 2.0 27.0 3,866.0 50,130 30.2 69.8
PleoM001 73 0 2 9.0 ±1.0 8.0 9.0 10.0 1,726 1.0 99.0
PleoM005 290 33 25 38.9 ±11.2 2.0 8.0 188.0 6,946 14.0 86.0
PleoM006 380 275 22 54.0 ±13.9 2.0 26.0 248.0 9,120 13.0 87.0
PleoM009 98 74 19 66.1 ±44.3 6.0 26.0 862.0 2,354 53.4 46.6
PleoM010 24 10 4 14.0 ±4.7 8.0 10.0 28.0 578 09.7 90.3
Males
subtotal
865 392 72 48.5 ±12.9 2.0 18.0 862.0 20,724 16.8 83.2
All individuals
(n=9)
2,954 998 166 112.1 ±30.1 2.0 24.0 3,866.0 70,854 26.3 73.7
Un-collared
individuals
19 – – – – – – – –
Total 2,954 1,017 166 112.1 ±30.1 2.0 24.0 3,866.0 70,854 26.3 73.7
See Supplementary Data 8 and 9 for additional lion tracking details.
FIGURE 5 | Mean movement speed of lions by hour of day. Speed was
inferred by measuring displacement between successive GPS fixes, adjusted
to hourly intervals.
Correspondingly, 81.4% of lion attacks on livestock with known
time of day (n=97) occurred during night hours, significantly
more than during the day (χ2=38.36; df =1; p<0.001). While
males and females exhibited similar activity rhythms (Figure 5),
males, on average, moved 2.63 ±0.26 times faster during peak
activity hours, and with a maximum speed of 5.47 km/h.
Geofence Transgressions
Study lions spent 26.3% of all time monitored within geofenced
areas (Table 2), with a mean of 21.9 ±8.0% per individual
(range: 0–60.9%; n=9). Geofence transgressions were highly
variable in terms of frequency and duration (Table 2). Four
of the nine study lions only sporadically breached geofences
(cumulative n=12) (Supplementary Figure 5), whereas five
lions accounted for 92.8% of all breaches (Table 2), and with
a bias toward females (χ2=2.91; df =1; p=0.088), two
of which raised seven cubs to >12 months age in community
areas. During cub-rearing, individual transgressions by females
lasted as long as 89.6 days and 161.1 days, respectively, whereas
the median duration of transgressions across all individuals was
24 h (Table 2). Due to cub-rearing, female transgressions, on
average, lasted significantly longer than those of males (T=1.86;
p=0.032).
Lion Conflict Involvement
We investigated 116 (45.5%) of the 255 lion-related livestock
predation incidents reported for compensation (Table 1) and
cross-referenced depredation sites against GPS paths. Spatial
association analyses showed that the nine study lions were
responsible for 36 incidents (31.0%), whilst un-tagged lions
accounted for the majority of investigated losses (69.0%). This
result demonstrates the effect of partial sampling (i.e., low
population representation by tagged study lions) on alert utility.
From a total of 277 lion observations (88 by researchers, 189
from tourism sources with photo evidence), we estimated that
the nine study lions represented 12.3% of all resident individuals
(>24 months age) and 41.2% of known female prides and male
coalitions in the study area.
All study lions that transgressed geofence boundaries were
involved in livestock depredation, but males were significantly
more conflict prone when controlling for monitoring time
(χ2=4.98; df =1; p=0.026). The two females that reared
cubs in community areas were involved in 58.3% (n=21)
of all depredation incidents involving study lions. The four
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Weise et al. Lion Early Warning Botswana
TABLE 3 | Community conflict lion management suggestions during the pilot study.
All respondents
n=90
Community headmen
n=12
Livestock owners
affected by lion conflict
n=78
Statement content Frequency Weight score Frequency Weight score Frequency Weight score
1. Remove lions from community livestock
lands by translocation, fencing or lethal control.
70 192 5 13 65 179
2. Improve livestock husbandry and protection
(herding, kraaling etc.).
23 42 2 3 21 39
3. Increase/improve government compensation
for livestock losses.
17 38 3 7 14 31
4. Collar more lions for alert distribution. 16 36 1 3 15 33
5. Provide alternative cattle water sources
and/or move livestock away from the Delta.
12 24 5 11 7 13
6. No active management of conflict lions. 8 19 3 6 5 13
7. Other (e.g., deterrence). 9 17 3 6 6 11
8. Collect more information for community
education.
6 11 3 6 3 5
9. No comment. 6 – 2 – 4 –
Total 167 377 27 56 140 321
MANAGEMENT CHARACTERISTICS OF RESPONSES
Physical separation of people/livestock from
lions
82 206 10 24 72 182
Reactive/symptomatic strategy 87 230 8 20 79 210
Proactive/protective strategy 60 116 11 23 49 93
Laissez faire/no-interference strategy 14 30 6 12 8 18
Answers represent community expectations recorded between October 2016 and May 2018 and were weighted, assigning scores of 3, 2 and 1 in declining order of priority. If individual
livestock owners were interviewed multiple times, the most recent answers were considered for analyses.
individuals with home range centroids nearer settlements
(<10.0 km distance) and highest home range overlap with
community areas (>33.3%) were significantly more involved in
conflict (χ2=25.31; df =1; p<0.001), accounting for 86.1%
(n=31) of incidents attributable to study lions.
Manual Alert Distribution
Alerts Issued (May 2016–May 2018)
We alerted the community about approaching lions on 188 days,
or 25.8% of study days, representing 366 lion alert days when
considering multiple animals on the same day. In total, we sent
1,017 alert SMS to 66 recipients in four villages and 19 cattle posts
(Table 2;Supplementary Data 5). Alert messages represented
166 geofence breaches (males: 72; females: 94) resulting in 304
original alerts and 694 reminder messages when lions spent
prolonged periods in community areas, as well as nine incidents
of un-tagged lions detected near cattle posts (19 alerts) (Table 2).
Five lions accounted for 97.1% of alerts issued (Table 2),
with females disproportionately represented (χ2=45.88;
df =1; p<0.001). Reflecting the variable movements of
individual study lions (see Lion Movements and Geofence
Transgressions) and the contribution of un-collared individuals
to depredation (69.0%), the distribution and frequency of alerts
(Figure 3;Supplementary Data 6) differed significantly from
that expected by livestock losses (χ2=infinite value; df =4;
p<0.001).
Effort
The mean daily researcher effort for retrieving and checking
location data of 3–6 active collars and associated alert distribution
via SMS messages on 116 randomly selected days was 40.8 ±
1.1 min (range: 14–71 min). The number of active units weakly
correlated with daily effort (rho =0.142).
Community Opinions, Aspirations, and
Actions
Livestock and Conflict Lion Management
QCA of 36 semi-structured interviews revealed that people value
their livestock for reasons other than monetary income. Livestock
are predominantly a cultural commodity, provide food security
(especially in times of drought when crops fail), and are sold
context-dependently, e.g., when owners pay school fees or funeral
costs. Respondents regarded livestock husbandry as a full-time
commitment, yet very few employ herders (Weise et al., 2018).
Based on the narratives of daily life routines, husbandry entails
night-time kraaling, release of herds in the morning hours, and
searches for stray animals.
Interviews with 12 community headmen and 78 depredation-
affected livestock owners yielded 161 conflict lion management
suggestions that reflected eight common themes. The majority
of responses entailed removing lions from community land
by lethal control, translocation or effective fencing, comprising
50.9% of the total priority weight (Table 3). This mirrors results
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Weise et al. Lion Early Warning Botswana
from QCA such that the majority perception of lions was
negative (75.0%, n=27). Together with improvements of general
livestock protection and the state-funded compensation scheme,
expansion of the lion alert system featured among secondary
lion management approaches, ranking fourth in terms of overall
priority weight (Table 3). The alert system’s priority weight
was higher for conflict-affected livestock owners (10.3%) than
community headmen (5.4%). In terms of conflict management
characteristics, most community members expected a form
of physical separation from free-ranging lions (54.6% priority
weight), corresponding with QCA results in that community
members felt that “coexistence with lions was not possible.”
Lion management suggestions were significantly biased toward
symptomatic, reactive mitigation strategies (61.0% priority
weight), followed by proactive, protective strategies (30.8%
priority weight) and no-interference management approaches
(8.0% priority weight) (Table 3) (χ2=121.76; df =2; p<0.001).
Community Satisfaction With Lion Alerts and
Compensation
Livestock owner satisfaction was higher for alerts than the
state-funded damage compensation scheme. Of all 78 livestock
owners interviewed during depredation investigations, 65
commented about their perceptions of the alert system
and compensation scheme. Of these, 56.9% had previously
received compensation for losses, whilst 75.4% had previously
received lion alerts (Supplementary Data 7). Only 24.3% of
compensation recipients (n=9) were satisfied with the
compensation scheme. QCA showed that compensation did not
improve people’s situation. Instead, respondents felt “left alone”
as the DWNP does not have the resources to attend to their
lion conflict reports. The key reasons for dissatisfaction included
“insufficient compensation amounts” (85.7%, n=24) and/or
“delayed compensation payments” (35.7%, n=10).
Conversely, 91.8% of lion alert recipients found messages
beneficial (n=45), whilst 6.1% (n=3) stated that they
“no longer wished to receive them.” QCA corroborated that,
despite prevailing uncertainties about appropriate responses
to approaching lions, the pilot alert system was perceived
as beneficial and respondents requested its “continuation and
expansion.” Of the 65 conflict-affected livestock owners, 90.8%
(n=59) wished to receive lion alerts via SMS in the future
(Supplementary Data 7).
Actions Following Alerts
The 45 livestock owners who perceived alerts as beneficial stated
different benefits and actions. The most common response was
livestock kraaling for increased night-time protection (68.9%),
followed by changes in cattle grazing directions and areas
(15.6%), setting of deterrence fires at homesteads (4.4%) and
active herding of cattle (2.2%). Other stated benefits included
a feeling of increased personal security due to awareness about
lion presence (13.3%) and an improved understanding of lion
movements and ecology (17.8%). However, another 32 alert
recipients stated that they did not take actions because they
“did not know what to do” (59.4%) and/or “feared dangerous
encounters with elephant, buffalo or lions during night hours”
(81.3%). QCA confirmed that community members “rarely
encounter lions,” contributing to an uncertainty of how to
respond when direct interactions occur.
Conflict Mitigation Potential
During the pilot study, mean annual livestock losses of those
owners who acted upon alerts (n=49) significantly decreased in
terms of number of stock lost (U=876.5; Z= −2.29; p=0.021),
compensation value (T= −2.38; p=0.021) and percentage of
stock lost (T= −3.07, p=0.003) (Figure 6). Their mean annual
losses were significantly less than those incurred by a randomly
sampled control group (n=53) in terms of number of stock
lost (U=945.5; Z=2.36; p=0.018), compensation value
(T= −2.34; p=0.010) and percentage stock loss (T=1.91;
p=0.029) (Figure 6), despite 23 control group livestock owners
(43.4%) also regularly receiving lion alerts but choosing not to
act. By comparison, prior to receiving alerts (2015–2016), mean
annual losses did not differ significantly between alert-sensitive
livestock owners and control group owners (number of livestock
lost: U=1209; Z=0.596; p=0.548, compensation value:
T=0.14; p=0.440, percentage stock lost: T=1.424; p=0.078)
(Figure 6). There were no significant changes in the mean annual
number of stock lost (U=1308; Z= −0.60; p=0.541), mean
compensation value (T=0.48; p=0.628) or mean percentage
stock lost (T=0.88; p=0.379) when comparing pre-alert and
pilot study losses incurred by control group livestock owners
(Figure 6). Alert-sensitive livestock owners reduced losses even
though reported lion depredation increased by nearly 300%
during the pilot study: year 1 =74 livestock, year 2 =214
livestock. In financial terms, livestock owners who acted upon
alerts reduced their mean annual loss to lions by $124.61
(59.6%), equivalent of a percentage stock loss reduction of 3.4%
(Figure 6). During the pilot study, the mean annual loss incurred
by alert-sensitive livestock owners was $134.40 less (57.7%) than
that by control group livestock owners (Figure 6).
Financial Cost and GPS Collar
Performance
Financial Cost
Considering all associated expenses, the total cost of GPS-
collaring nine adult lions for inclusion in the alert system was
$70,983.04 (n=13 deployments), with a mean deployment
cost of $5,460.24 ±$493.89 for units equipped with on-board
geofence breach technology (range: $3,821.10–$10,764.40). GPS
tracking technology and associated data fees contributed 46.8%
to the total cost, whilst veterinary expenses accounted for 44.5%
of the total, the remainder accruing from local staff, vehicle, and
expedition expenses. The 13 geofence-enabled units were active
for a total of 3,829 tracking days [including days for first units
prior to commencement of the pilot study (n=888)], giving an
approximate daily alert system cost of $18.54 per lion. Daily cost
would decrease to $4.99 if all units had operated until the end of
their expected lifetimes.
In light of an annual livestock damage of approximately
$40,407 caused by lions (Table 1), and assuming that early
warning would effectively prevent depredation, the observed
daily alerting cost merits the inclusion of only six lions (8.2% of
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Weise et al. Lion Early Warning Botswana
FIGURE 6 | Comparison of annual lion-related livestock losses (A), percentage
stock loss (B), and associated compensation value ($US) (C) for responsive
alert recipients before (A) and during alerts (B) and a control group of
non-responsive recipients before alerts (C) and during alerts (D). Blue asterisks
show mean values. Sample sizes are shown in parentheses. Livestock losses
were recorded from May 2015 until April 2016 for the pre-alert phase and from
November 2016 until October 2017 for the alert phase.
the estimated population), whereas 22 individuals (30.4% of the
population) could be included if GPS units functioned reliably.
Early warning financially breaks even if the inclusion of a specific
lion, on average, can prevent the depredation of 12 livestock per
year, emphasizing that GPS tracking efforts need to be focused on
habitual livestock raiders.
Collar Performance
Lion GPS data
The performance and reliability of GPS tracking units was
highly variable in terms of complete daily tracking data
received (range: 2.3–92.6%; Supplementary Data 8) and the
proportion of scheduled GPS locations received (range: 51.1–
95.9%; Supplementary Data 9). We replaced four active collars
in December 2016. At the end of the pilot study, four units
still transmitted data daily, whereas three units (23.1% of
deployments) malfunctioned between 22 and 71 days after
deployment, and two units were deactivated following lion
deaths.
During the pilot study, GPS units (n=13 deployments)
delivered complete data sets on only 28.8% of all lion tracking
days (n=2,941 with a 24 h cycle), with a mean of 27.9 ±
7.7% per deployment (Supplementary Data 8). Increasing the
GPS sampling frequency from five daily locations (Telonics
units, n=875 tracking days) to 12 (Vectronic units, n=2,066
tracking days) decreased the daily success rate of receiving
complete data from 55.9 to 17.3%, respectively. There was no
significant difference in the mean percentage of complete GPS
tracking days between collars fitted on male (n=7 deployments)
and female (n=6 deployments) lions (U=10; Z= −1.50;
p=0.133). Despite the low number of complete tracking days,
the 13 transmitters delivered 83.3% of the expected 29,609
lion GPS locations (Table 4;Supplementary Data 9). The mean
percentage of scheduled GPS fixes received per lion was 81.2 ±
1.6% (range: 74.8–87.3%, n=9). A randomized sample of 286
GPS data gaps (both collar types combined) showed that GPS fix
and transmission failures resulted in a mean data gap duration
of 6.9 ±0.3 h (range: 0–124 h). The modal duration of GPS
data gaps was 4 h, with maximum durations of 36 h for Telonics
units (five daily locations) and 124 h for Vectronic units (12 daily
locations).
Detecting geofence breaches and alert issuing
Early GPS units (May–December 2016) were programmed
to report both geofence breaches and exit events. These
transponders detected 44 geofence breaches, including 42
Geofence 1 transgressions and two Geofence 2 transgressions.
However, in 22.7% of cases (n=10), breach messages were only
received after lions had already left the geofence again, in 15.9%
of cases (n=7) the units failed to transmit exit messages, and in
11.4% of cases (n=5) the units failed to transmit breach messages
but reported exits. Compared with lion movement paths plotted
from GPS locations, on-board geofence functions under-detected
true Geofence 1 breaches (n=51) by 17.6%, whilst missing
80.0% of true Geofence 2 breaches (n=10) across 6 months
of operation. In combination with delayed breach messages,
the community received alerts about 74.5% of true Geofence 1
breaches (n=38) and 30.0% of true Geofence 2 breaches (n=3),
the latter including one reminder. These irregularities caused
us to return to daily manual data checks and alert distribution.
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Weise et al. Lion Early Warning Botswana
TABLE 4 | Proportions of expected and received GPS fixes from geofence-enabled lion tracking collars.
Gender Unit 1
(expected)
Unit 1
(received)
Percentage Unit 2
(expected)
Unit 2
(received)
Percentage Total
(expected)
Total
(received)
Percentage
Female 2,204 2,111 95.8 19,781 16,427 83.0 21,985 18,538 84.3
Male 2,672 1,663 62.2 4,952 4,455 90.0 7,624 6,118 80.3
All units (n=13) 4,876 3,774 77.4 24,733 20,882 84.4 29,609 24,656 83.3
See Supplementary Data 9 for details of individual lions and deployments.
Moreover, of the nine units deployed subsequently (December
2016 and January 2018), four (44.4%) did not detect any geofence
breaches (although they occurred), whilst three (33.3%) failed
altogether within 11 weeks of deployment.
Seasonal Cattle Depredation Risk and
Human Safety
Based on a GPS distance matrix of lion and cattle GPS locations,
and the locations of investigated lion conflict, Figure 7 shows
the seasonal distribution of lion risk to cattle. Very high risk
was widespread during the early wet season when lions still had
unobstructed access to communal grazing lands before annual
floods arrive, and two females raised cubs in community lands.
Arbitrarily defined Geofence 1 (i.e., the original grazing lands
boundary) contained 88.2 and 91.3% of high and very high
risk cells during the wet and early dry seasons, respectively.
However, as the risk interface progressively changed throughout
the year, moving in relation to receding flood waters, Geofence
1 only contained 59.5% of high and very high risk cells during
the late dry season, revealing the need for dynamic geofencing
that appropriately reflects seasonally changing environmental
conditions and associated predation risk.
Similarly, Figure 8 demonstrates that the arbitrarily drawn
village safety boundary (Geofence 2) only partially protected
human settlements. Geofence 2 contained only 75.4% of the
estimated 1,278 km2risk area and excluded one cattle post while
another was partially excluded. Additionally, 24 cattle posts and
two villages appeared too close to Geofence 2 to permit sufficient
time for adequate precaution. Based on a 1 h human safety
buffer calibrated to the maximum hourly speed recorded for any
lion (5.47 km/h), safety alerts should be triggered further from
village areas and cattle posts. Human settlements change over
time, requiring dynamic geofencing to reflect these changes (e.g.,
adjusted Geofence 3, Figure 2).
Community Co-design Workshops
The communities are technologically marginalized, with sporadic
access to electricity and the Internet. Despite these infrastructural
challenges, mobile phones are ubiquitous in this rural part of
Botswana (as elsewhere in Africa) and are, therefore, an ideal
medium for receiving alerts. All workshop participants (n=35)
owned mobile phones but predominantly used these for basic
functions such as calling relatives. The prevalence of feature
and smart phones varied by village (Figure 9), with an overall
smart phone representation of 23.5%. Participant literacy was
low (62.9%), digital literacy even lower (14.3%), compromising
the delivery of lion alerts via text or image messages only.
Community members speak five different languages, with
Setswana being the only one common to everyone. For future
alerting, desired message formats differed geographically and a
combination of image and voice messages was most popular
(Figure 9). Respondents remarked that feature phones prohibit
the use of imagery to convey all necessary alert information
(lion distance and direction), necessitating combinations with
text and voice formats (Supplementary Figure 3). Participants
also suggested the incorporation of specific sounds and
ringtones (e.g., lion roar) to distinguish alerts from other
messages.
Workshop participants advocated for the development of an
information feedback loop in the form of a community portal
with application interfaces that enable users to report their own
lion observations and depredation incidents, but also to retrieve
lion alert information independently. Respondents commented
that such a portal would improve reporting speed and ease while
relinquishing the reliance on private mobile devices, which may
not be charged or have insufficient credit for reporting. Co-design
workshops also revealed the need for additional digital literacy
training and community mapping of the local environment to
enable accurate reporting of feedback via digital maps (Mapedza
et al., 2003; Vitos et al., 2017). The preferred method of ensuring
personal data security and privacy was password protection,
which participants knew from social media. Meanwhile, 30 min
was the preferred frequency of lion information updates on the
portal. The variety of user-specific requirements and contexts
demonstrates the necessity for a flexible, customized lion alert
platform that provides a range of interactive features.
Autonomous System Design With
Automated Alerts
In combination with the logistical and technological challenges
encountered during the pilot study, community co-design
workshops prompted the development of an autonomous,
automated lion alert platform (Figure 10). This development
provides maximum versatility in terms of data acquisition and
integration, geofence creation, alert distribution, and community
participation. The system’s key features and capabilities are
outlined in Box 1 and Supplementary Figure 4.
The new system is based on the computation of implicit
surfaces for defining dynamic geofences. It automatically
retrieves, and integrates, positional data from any actors
equipped with GPS transponders as well as supporting
environmental information from online databases (e.g., seasonal
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Weise et al. Lion Early Warning Botswana
FIGURE 7 | Seasonal geo-assessment of lion risk to cattle, showing (a) the
wet, (b) the early dry, and (c) the late dry seasons of 2017. Risk levels are
mapped into a 1.0 km ×1.0 km grid across the study area. Each grid cell is
hot-cold color-coded to indicate relative risk level. Yellow (1) and orange (2 or
3) indicate the number of instances in which lions approached to within 1.0 km
of tracked cattle. Red cells are those in which either kills occurred, or there
were >3 records of lions within 1.0km of tracked cattle.
FIGURE 8 | Comparison of Geofence 2 placement with geo-assessment of
lion risk to people. Risk was calibrated to the maximum hourly speed moved
by any lion in this study (5.47 km/h) and mapped into a 1.0 km ×1.0 km grid
across the study area. Red cells indicate risky areas within 1 h distance from
permanent settlements.
FIGURE 9 | Village-specific use of feature and smart phones and expected
lion alert message formats as determined during co-design workshops.
flooding levels). Virtual boundaries can be adjusted either
manually or by machine-learning algorithms that process GPS
and other relevant data in near real-time, predicting risk areas
while automatically adjusting geofences. Conflict is defined
by empirically determined risk thresholds (e.g., see Seasonal
Cattle Depredation Risk and Human Safety). Based on our
results, the literature, and community feedback, we identified
26 key variables (Supplementary Data 10) that influence conflict
likelihood and, therefore, require empirical consideration in
computation of flexible geofences. We developed the prototype
in Java with Java Database Connectivity for a Structured Query
Language. The system is currently implemented on a Windows
2016 server with PostgreSQL. Depending on the type of tracking
technology utilized, it can be compartmentalized into GPS
storage with data processing on any other platform. Autonomous
data integration and processing removes reliance on predefined,
static geofences and on-board alert functionality. The system is
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Weise et al. Lion Early Warning Botswana
FIGURE 10 | Visualization of the autonomous, automated lion alert system with key features and functionalities.
Box 1 | Key features and capabilities of the autonomous lion alert system.
1. Real-time integration of diverse environmental data and community
feedback (e.g., lion encounters, cattle movements, depredation sites);
2. Autonomous, objective geofence computation based on available input
data and a priori threshold setting (e.g., critical distances and areas);
3. Flexible implementation of village-specific geofences;
4. Dynamic geofencing, including static and mobile virtual boundaries, and
possible attractors and deterrents for depredation;
5. Automated, instant alert delivery via multiple media formats to a diversity
of communication technology devices;
6. Integration and configuration of risk evaluation parameters;
7. Interactive community portal with data report and entry interfaces;
8. Independent user registration;
9. User-specific selection of preferred alert message types (text, image,
sound, or combinations thereof) and languages based on literacy levels
and communications technology; and
10. Options for alert delivery via social media platforms (e.g., WhatsApp
or Facebook groups) or supporting devices such as sirens and light
installations.
currently hosted at the Department of Economic Disciplines,
Faculty III of Siegen University, Germany.
The platform enables independent, password-secured
and anonymous user registration via cell phone number
(Supplementary Figure 4A). Subscribers can then select
their preferred alert format for different phone types (e.g.,
Supplementary Figure 3). We designed alert applications for
smart and feature phones, ensuring that feature phones provide
the same functionalities. Registered users receive alerts instantly.
Instead of data processing time, alert frequency is determined by
the sampling regime of GPS transponders and the availability of
corroborating data.
To enable maximum community participation and ownership
as well as system sustainability, we co-designed a suite of
alert portal interfaces (Supplementary Figure 4). These will
be accessible via tablet computers installed at each village’s
administrate office, with guidance provided by specially trained
community members. Interfaces include an overview of current
and past alerts, the reporting of lion encounters or tracks, the
reporting of depredation incidents, information on lions, display
of geofence(s), and personal settings (Supplementary Figure 4).
All features are delivered in Setswana language. Following others
(Medhi et al., 2006; Vitos et al., 2017), we utilized visual
elements to support interface use by illiterate subscribers. Map
interfaces are currently based on distance zones that resemble
active geofences, an approximation that was understood by
workshop participants. In future, digital maps will provide
an intuitive avenue for reporting of relevant information
according to geo-physical landmarks such as trees, lagoons,
and islands that all community members can relate to. For
feature phones, we programmed USSD-based (locally used to
recharge phone credit) and voice-prompted reporting interfaces
that have shown success in other participatory design projects
(Weld et al., 2018). For illiterate users, automated voice prompts
will be used to replace text prompts. Similar speech-based
interfaces have successfully been tested with illiterate target
groups (Raza et al., 2018). The autonomous platform allows
for the instant integration of community feedback into alert
calculations (Figure 10), increasing the representation of un-
tagged lions in alerting and community ownership of the
process.
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Weise et al. Lion Early Warning Botswana
DISCUSSION
Managing undesirable human-wildlife interactions requires
adaptive strategies. In northern Botswana, communities seek
safety from lions, primarily via physical separation and lion
control. However, the continued lethal removal of damage-
causing lions threatens population viability (Loveridge et al.,
2016; Trinkel et al., 2017); fencing is expensive (Packer
et al., 2013), ecologically problematic (Trinkel and Angelici,
2016), and can shift problems elsewhere (Osipova et al.,
2018); while translocations are resource-intensive and have
historically shown low success rates (Stander, 1990). By default,
separation undermines coexistence. Consequently, separation
also compromises the sustainable conservation of large free-
ranging lion populations (Creel et al., 2013) that depend
on connectivity between key ecosystems in human-inhabited
conservation landscapes such as the KAZA-TFCA (Cushman
et al., 2016). Early warning by geofence-triggered alerts
provides an alternative mechanism that facilitates non-lethal
and non-permanent separation at flexible spatio-temporal scales.
Our study demonstrates that alerting rural livestock owners
about approaching lions promotes improvements in livestock
protection that result in significantly reduced losses, thereby
facilitating the behavioral changes that coexistence requires
(Reddy et al., 2017). Despite the immediate benefits of improving
human and livestock safety, the success of early warning depends
on effective system implementation by overcoming a variety of
technological, human, and environmental challenges (Box 2).
The efficacy of lion alerts directly hinges upon user responses.
To enable responses, the system needs to deliver messages to a
variety of communication devices in a timely and understandable
fashion (Medhi et al., 2006; Sherwani et al., 2009). Considering
the user community’s heterogeneity in terms of literacy, attitudes
toward modernization, use of communication technology, and
language and message style preferences, this poses challenges
(Box 2). Careful pre-studies of the socio-cultural contexts, co-
design of the system’s functionalities, and alert distribution
via modern socio-informatics are imperative to engage users
satisfactorily and sustainably. For example, a user-friendly device
for community data collection, the CyberTracker, provides
adaptable, yet easily understandable, interfaces that are designed
to enable geo-referencing of environmental information by
rural inhabitants with different educational backgrounds (Ansell
and Koenig, 2011). Co-design workshops revealed the need
for information feedback loops that enable active participation
in risk management, thus increasing community ownership.
Instead of eroding traditional environmental skills and practices,
an ICT-based alert platform provides an interactive avenue
for their propagation and inclusion in environmental decision
making (also see Ansell and Koenig, 2011). Other, maybe
less apparent, benefits of a participatory strategy include the
community’s perception of “being heard,” improved digital
literacy, and access to WiFi through the installation of
community alert portals in remote villages.
Human-lion conflicts are complex (Dickman, 2010) and early
warning efficacy strongly depends on our ability to quantify risk
and its many drivers (Supplementary Data 10). In the Okavango
Delta, lions and their wild and domestic prey live in a seasonally
changing ecosystem (Murray-Hudson, 2009; Fynn et al., 2015).
Changes in conflict likelihood correspond with seasonality as it
influences lion and cattle movements, associated predation risk,
and lion socio-ecology (Kotze et al., 2018; Weise et al., 2019).
Translating these ecological changes, and their high levels of
spatio-temporal variation, into risk probabilities in near real-
time is a central prerequisite for alert efficacy, one that requires
dynamic geofencing. It also requires intensive monitoring of
different ecological variables (Supplementary Data 10) and can
benefit from machine learning to infer behavioral patterns from
animal GPS data (Valletta et al., 2017). Early warning will be most
effective in situations where boundaries are clearly definable.
Geofence design and breach detection must account for the speed
of actors, likelihood of attack, and GPS fix schedules. With an
ability to travel up to 5.47 km in 1 h, lions could breach geofences
and enter villages without detection, even with short GPS fix
intervals that considerably reduce transponder battery lifetime.
Static geofences programmed into GPS transponders cannot
sufficiently reflect the variability of ecosystems and conflict
complexity. Here, we present an autonomous platform for the
instant computation of dynamic geofences and flexible delivery
to a variety of users.
Human-lion conflicts and their effective mitigation are
scale-dependent (Montgomery et al., 2018a). The conservation
benefit of individual- or group-based conflict mitigation
methods decreases as economic cost increases in relation to
population size (Shivik, 2004) and conflict area. Therefore,
expensive interventions such as alerting are most feasible in
conflict hotspots with highest conservation significance (e.g.,
by maintaining effective connectivity between populations), and
where the majority of actors can be tracked cost-effectively. At
the currently high cost of intensive GPS monitoring (Thomas
et al., 2011), the on-going implementation of an early warning
system requires significant resources, especially in high conflict
zones bordering protected areas where lion mortality is high
(Trinkel et al., 2017) requiring frequent tagging of new actors.
We demonstrate the importance of appropriate population
representation as our study lions accounted for 31% of the
regional conflict. Partial collaring of the lion population resulted
in focal alerting, not always benefiting the communities with
highest losses. Alert systems will be most meaningful where the
movements of actors and the effects of local attractors (e.g.,
cattle post locations or seasonally shifting prey density) can
be combined into risk proxies that are computed as changing
virtual boundaries. Tracking technology failures prevented the
effective monitoring of lions and the detection of geofence
breaches (Box 2), thus decreasing alert efficacy. Autonomous
implementation of geofencing and alert functions through
a stand-alone platform not only provides maximum system
flexibility but also increases alert reliability. With continuous
advances in modern animal tracking systems in terms of unit
size and weight, cost, and independence from on-board battery
supply (Tomkiewicz et al., 2010; Thomas et al., 2011), the
feasibility of GPS-tagging actors increases, thus also improving
the scalability of early warning and its overall cost-efficiency by
reducing labor effort.
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Weise et al. Lion Early Warning Botswana
Box 2 | Opportunities and challenges of lion alerts identied during the pilot study.
Opportunities:
1) Increasing human safety by preventing dangerous encounters, e.g., during subsistence activities;
2) Encouraging livestock protection and husbandry practices (e.g., herding and kraaling);
3) Reducing livestock depredation, thus increasing food and economic security;
4) Improving tolerance of lions in human-dominated landscapes;
5) Increasing awareness and knowledge about local lions and their ecology; and
6) Advancing digital literacy and intra-community communication in partner communities.
Challenges:
Anthropogenic
1) Manual alert distribution depends on researcher/manager effort (and availability) in terms of checking GPS units daily and issuing alerts;
2) Risk of misuse of alert information to persecute lions;
3) Geofence breach messages dispatched by collars may be missed during night hours when breach probability is highest;
4) Alert distribution via community SMS snowball system is subject to intra-community relationships—not all farmers may receive messages from others;
5) System’s effectiveness depends on human response to alerts;
6) Risk of increasing resentment toward and fear of lions via continuous reminders of their presence, i.e., artificial reinforcement of perceived threats;
7) Non-probabilistic, subjective interpretation of breach messages, and lion movement trajectory by researcher/manager is prone to human error, introducing the
risk of informing the wrong communities;
8) Risk of desensitizing people to the relative threat posed by lions via frequent reminders of their presence without direct dangerous interactions;
9) Subjective selection of focal lions by researchers may bias population representation—conflict lions may exhibit strong avoidance of humans, thus being
under-represented in the monitored sample;
10) System components may be difficult to understand for rural communities with little previous exposure to modern communications technology;
Technology
11) Efficient alert distribution via cell-phone network depends on reliable network coverage in remote areas, but also power supply to charge phones, retrieve GPS
data, and distribute alerts—in January 2017, the system collapsed during the rainy season when public power supply and network coverage was insufficient
during a high conflict period;
12) Limited reliability and accuracy of GPS-tracking technology causes delays in distributing alerts, influencing alert frequency, timing, and relevance;
13) On-board geofence functions are static and may not operate reliably or timely;
14) Lifespan of GPS-tracking technology limits system effectiveness and feasibility;
Ecology
15) Impact of tagging only one individual per group: lion mortality, changing group compositions and variable cohesiveness, immigration, and emigration affect
population representation and system effectiveness;
Information
16) System effectiveness is scale-dependent—appropriate population representation (i.e., tagging all adult lions/groups simultaneously) across large areas is difficult
to achieve in terms of financial feasibility and logistics; and
17) Objective, probabilistic establishment of relevant geofences requires a wealth of empirical bio-geographic data that may not always be available, or difficult to
obtain (Supplementary Data 10).
We can use lions and livestock depredation as conflict
proxies for a universal challenge, the increasing interface
between people, livestock and wildlife. Globally, 262 wild
terrestrial vertebrates, including 53 threatened species, interact
detrimentally with people (Torres et al., 2018). Our lion alert
platform is neither species- nor context-specific, lending itself
to various conservation applications (also see Wall et al.,
2014). Amongst others, possible scenarios include elephants
approaching human settlements or crop fields (human and food
security), a rhino leaving the safety of a reserve core management
area (biodiversity security), or a buffalo herd approaching cattle
(disease transmission risk). In each of these cases, the risk of an
undesirable interaction is defined by critical distance thresholds,
expressible as a likelihood of interaction and risk. As more
and more wildlife are tracked via GPS (Kays et al., 2015), and
advances in both animal-borne and non-invasive technologies
provide cheaper and more reliable sensor options, the feasibility
of implementing alert systems in human-dominated wildlife
habitats increases. Our autonomous platform can be tailored to
any risk scenario, given that the relevant vector data are available
and conflict risk can be expressed in an algorithmic manner. In
the case of alerting communities about lions, the new system
permits the evolution from an experienced-based pilot phase into
an experimental phase. Reducing human errors associated with
manual alerting, for example by subjective interpretation of lion
movement trajectories toward specific cattle posts or villages, is
paramount.
An automated, autonomous platform provides a robust and
objective mechanism with maximum versatility for creating
dynamic, ecologically relevant virtual conflict boundaries and
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Weise et al. Lion Early Warning Botswana
maximum flexibility in issuing customized alerts in a timely and
culturally appropriate fashion. Successful conflict mitigation, via
methods such as alerting, is highly context-specific and usually
cannot be inferred from disciplinary knowledge alone. It requires
adaptive co-design that encompasses local knowledge, a detailed
understanding of the affected parties, and relevant research
findings (Pooley et al., 2017; Montgomery et al., 2018b). Here,
we chose a trans- and multi-disciplinary development approach
involving local stakeholders as well as researchers from different
disciplines to progress “from a science for society to a science
with society” (Scholz and Stauffacher, 2009). The inherent
complexity of human-carnivore conflict, both ecologically
(Supplementary Data 10) and socio-culturally (Dickman, 2010),
demands a direct and constructive collaboration between science
and society. This interaction needs to identify and address the
different dimensions of conflict and, therefore, can only be
realized with a multi-disciplinary research and development
strategy (Pooley et al., 2017; Montgomery et al., 2018b). Omission
to do so will inevitably result in unsustainable or ineffective
conflict mitigation efforts, particularly if the safety or livelihood
of rural communities is at stake. System automation does not
provide a panacea, however, as it cannot resolve all the challenges
of early warning (Box 2). For instance, the utility of any early
warning system, autonomous or otherwise, strongly depends on
the rigorous identification and continuous tracking of its key
actors. We demonstrate that this can be costly and difficult to
achieve. Therefore, care should be taken to avoid reliance on
early warning as a stand-alone mitigation method. The prototype
alert platform (Figure 10), alongside other conflict reduction
measures such as vigilant full-time herding (Weise et al., 2019),
will be implemented in 2019, including further refinement of
system processes and monitoring of its efficacy.
Alerting communities about lions devolves important
ecological knowledge and sensitive information to Africa’s key
lion conservation stakeholders, the people that live with lions.
It encourages active risk management, thereby moving beyond
the symptomatic treatment of damage. Recipient communities,
however, are not homogenous. In addition, technological
limitations, the complexity of human-carnivore conflicts, and
the variability of the natural environments in which these occur,
complicate effective early warning, which requires a combination
of expertise that synthesizes ethnography, ecology, livestock
management, conservation psychology, and socio-informatics.
Our development of a versatile, autonomous lion alert platform
emphasizes the critical importance of a trans-disciplinary
approach to mitigating human-lion conflicts.
ETHICS STATEMENT
We conducted research under permit numbers EWT 8/36/4
XXVII (61) and EWT 8/36/4 XXXVIII (63) granted by the
Ministry of Environment, Wildlife and Tourism in Botswana.
We interviewed human subjects and monitored livestock and
lions with ethics approval from the University of Pretoria (no.:
EC170525-120, EC170525-120a) and the Institutional Animal
Care and Use Committee of the University of Massachusetts
(Protocol no.: 2014-0083).
AUTHOR CONTRIBUTIONS
FW, AS, KS, HH, KA, MWH, MH, VW, and MT conceived the
ideas and designed the methodology. FW, HH, KA, MT, and
KS collected the data. FW, KS, HH, KA, and MH analyzed the
data. FW, HH, AS, KS, MWH, and MS wrote the manuscript. All
authors contributed critically to the drafts and gave final approval
for publication.
ACKNOWLEDGMENTS
We thank the Ministry of Environment, Wildlife, and Tourism
in Botswana for granting permission to conduct this study.
We thank G. Bigl and C. Winterbach for constructive ideas
and criticisms, and E. Verreynne for veterinary support.
We thank R. Grinko, V. Wenzelmann, C. Dimbindo, and
E. LeFlore for assistance with field work and co-design
workshops, and the Ecoexist Project for assistance with livestock
tracking. Comments by two reviewers improved the quality
of the paper. We thank G. S. Flaxman, S. Eckhardt, and J.
Walls for logistical support. We thank community members
and leadership. This work was supported by the National
Geographic Big Cats Initiative (grant numbers: B5-15, B10-16,
B6-17), WWF’s INNO fund (grant no.: 17-03), and Stichting
SPOTS, NL, and its supporters. FW was supported by a
post-doctoral fellowship at the University of Pretoria. MS
was supported by a National Research Foundation Incentive
grant. MWH was supported by the Australia-Africa Universities
Network.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fevo.
2018.00242/full#supplementary-material
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Conflict of Interest Statement: The authors declare that the research was
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