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Mapping attack hotspots to mitigate human–carnivore conflict: approaches and applications of spatial predation risk modeling

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Mapping attack hotspots to mitigate human–carnivore conflict: approaches and applications of spatial predation risk modeling

Abstract

A major challenge in carnivore conservation worldwide is identifying priority human–carnivore conflict sites where mitigation efforts would be most effective. Spatial predation risk modeling recently emerged as a tool for predicting and mapping hotspots of livestock depredation using locations where carnivores attacked livestock in the past. This literature review evaluates the approaches and applications of spatial risk modeling for reducing human–carnivore conflict and presents a workflow to help conservation practitioners use this tool. Over the past decade 18 studies were published, most which examined canid and felid (10 and 8 studies on each group, respectively) depredation on cattle (14) and sheep (12). Studies employed correlation modeling, spatial association and/or spatial interpolation to identify high-risk landscape features, and many (but not all) validated models with independent data. The landscape features associated with carnivore attacks related to the species (carnivore and prey), environment, human infrastructure and management interventions. Risk maps from most studies (14) were used to help livestock owners and managers identify top-priority areas for implementing carnivore deterrents, with some efforts achieving >90 % reductions in attacks. Only one study affected policy, highlighting a gap where risk maps could be useful for more clearly communicating information to assist policymakers with large-scale decisions on conflict. Studies were used to develop a six-step workflow on integrating risk modeling into conservation. This review reveals a need for future predation risk modeling to focus more on validating models, accounting for feedbacks and impacting conflict-related policy in order to reliably improve the mitigation of human–carnivore conflict globally.
REVIEW PAPER
Mapping attack hotspots to mitigate human–carnivore
conflict: approaches and applications of spatial predation
risk modeling
Jennifer R. B. Miller
1,2
Received: 12 June 2015 / Revised: 7 August 2015 / Accepted: 12 August 2015
!Springer Science+Business Media Dordrecht 2015
Abstract A major challenge in carnivore conservation worldwide is identifying priority
human–carnivore conflict sites where mitigation efforts would be most effective. Spatial
predation risk modeling recently emerged as a tool for predicting and mapping hotspots of
livestock depredation using locations where carnivores attacked livestock in the past. This
literature review evaluates the approaches and applications of spatial risk modeling for
reducing human–carnivore conflict and presents a workflow to help conservation practi-
tioners use this tool. Over the past decade 18 studies were published, most which examined
canid and felid (10 and 8 studies on each group, respectively) depredation on cattle (14)
and sheep (12). Studies employed correlation modeling, spatial association and/or spatial
interpolation to identify high-risk landscape features, and many (but not all) validated
models with independent data. The landscape features associated with carnivore attacks
related to the species (carnivore and prey), environment, human infrastructure and man-
agement interventions. Risk maps from most studies (14) were used to help livestock
owners and managers identify top-priority areas for implementing carnivore deterrents,
with some efforts achieving [90 % reductions in attacks. Only one study affected policy,
highlighting a gap where risk maps could be useful for more clearly communicating
information to assist policymakers with large-scale decisions on conflict. Studies were used
to develop a six-step workflow on integrating risk modeling into conservation. This review
reveals a need for future predation risk modeling to focus more on validating models,
Communicated by Karen E. Hodges.
Electronic supplementary material The online version of this article (doi:10.1007/s10531-015-0993-6)
contains supplementary material, which is available to authorized users.
&Jennifer R. B. Miller
jennie.r.miller@gmail.com
1
Yale School of Forestry & Environmental Studies, 195 Prospect Street, New Haven, CT 06511,
USA
2
Wildlife Institute of India, Post Box 18, Chandrabani, Dehra Dun, Uttarakhand 248001, India
123
Biodivers Conserv
DOI 10.1007/s10531-015-0993-6
accounting for feedbacks and impacting conflict-related policy in order to reliably improve
the mitigation of human–carnivore conflict globally.
Keywords Attack hazard !Carnivore conservation !Grazing management !
Livestock depredation !Nonlethal carnivore control !Predator–prey interactions
Introduction
Carnivore attacks on livestock cause substantial conflicts between humans and carnivores,
making livestock depredation a major challenge for pastoral communities and conservation
practitioners worldwide (Treves and Karanth 2003; Woodroffe et al. 2005; Inskip and
Zimmermann 2009). Though cost-efficient nonlethal techniques exist for reducing carni-
vore attacks (McManus et al. 2014; Lichtenfeld et al. 2014), these tools are often time-
intensive and difficult to implement across the expansive landscapes where carnivores and
livestock interact (Shivik 2006). As a result, many livestock owners continue to use lethal
measures to reduce carnivore attacks (Ogada et al. 2003; Inskip et al. 2013), contributing to
rapid carnivore population declines and loss of attendant ecosystem service values for
humans (Ripple et al. 2014). However, a spatial statistical approach known as predation
risk modeling that identifies high-priority ‘conflict hotspots’ where carnivores are likely to
attack livestock is rapidly emerging as a tool for informing livestock management and
carnivore conservation. A small yet notable number of modeling studies on carnivore–
livestock predation risk have been published over the past decade, and the accelerating use
of predation risk modeling suggests that the technique may become more widely used for
guiding mitigation of human–carnivore conflict. Thus, there is now a critical need to
evaluate the findings and impacts of predation risk modeling studies and prescribe best
practices for future studies.
Predation risk modeling was originally developed to quantify the ecological interactions
between predators and their natural prey. Models built in a spatially explicit context
(hereafter referred to as spatial risk models) use site-specific data on past species inter-
actions to quantify and map associations between predator–prey encounters and landscape
attributes (Hebblewhite et al. 2005). The concepts underlying spatial risk modeling,
including models involving carnivore–livestock interactions, stem from principles of
spatial ecology, predation risk and optimal foraging theory. How carnivores and livestock
interact spatially is largely shaped by the interplay between predator hunting strategies and
prey avoidance tactics (Sih 1984; Kluever et al. 2009; Laporte et al. 2010). To increase the
likelihood of a successful attack, carnivores tend to search for prey at locations where prey
are in high densities and in an exposed environment (Hopcraft et al. 2005; Balme et al.
2007; Atwood et al. 2009). To reduce vulnerability, prey exhibit antipredator behavior and
select locations that balance a trade-off between maximizing basic biological and social
needs (e.g., nutritional intake, physiological stress, mating) and minimizing exposure to
predators (Brown et al. 1999; Stankowich and Blumstein 2005; Laundre
´et al. 2010).
Though livestock tend to show attenuated antipredator responses and people often restrict
their spatial movements, risk avoidance and optimal foraging choices nonetheless can
affect their resource use decisions. In experiments with livestock, predator cues resulted in
increased vigilance, grouping and movement variation, decreased foraging rates and
association with more open habitats (Kluever et al. 2008,2009; Shrader et al. 2008;
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Laporte et al. 2010). Hence, the sites where livestock are present on a landscape (prey
availability) may differ from locations where carnivores can successfully kill (prey ac-
cessibility) (Hebblewhite et al. 2005; Hopcraft et al. 2005; Trainor and Schmitz 2014). The
spatial nature of carnivore and livestock decisions also means that their interactions change
with environmental attributes across heterogeneous landscapes (Wydeven et al. 2004;
Gorini et al. 2012), forming gradients of predation risk where carnivores are more or less
likely to attack (Brown et al. 1999; Laundre
´et al. 2010).
Spatial risk models quantify the landscape attributes correlated with sites where car-
nivores kill prey and in doing so, reveal the spatial features associated with where car-
nivores are most likely to kill livestock. However, since spatial risk models are (most
often) based on livestock kill data, they describe the landscape attributes associated with
only a subset of overall predation risk: locations where carnivores successfully killed
livestock but not where attacks were unsuccessful or where attacks did not occur. In other
words, spatial risk models quantify the realized predation risk (where direct mortality
occurs) rather than the overall fundamental predation risk (where mortality and indirect
non-consumptive effects may occur) (Lima and Dill 1990; Schmitz et al. 1997; Hebble-
white et al. 2005). This distinction is particularly important to recognize if one wishes to
understand the non-consumptive effects of predators on livestock stress, which may affect
animal foraging, weight gain, reproduction and other factors tied to overall productivity
and financial value (Howery and Deliberto 2004). In this case, telemetry data on carnivore
and livestock movement would be a better measure of fundamental predation risk than
livestock kill sites (Gorini et al. 2012). The literature on predation risk modeling of
carnivore–livestock interactions has not addressed the difference between realized and
fundamental predation risk as clearly as has the literature on wild predator–prey interac-
tions (Hebblewhite et al. 2005; Atwood et al. 2009; DeCesare 2012), though this topic
merits detailed discussion. However, since the non-consumptive effects of carnivores on
livestock have yet to be extensively demonstrated but carnivore depredation on livestock
(mortality) provokes human–carnivore conflict with serious implications for human
livelihoods and carnivore conservation, this review focuses only on spatial predation risk
modeling with livestock kills. The term ‘predation risk’ is used for consistency with current
literature and here refers to the realized predation risk, or the threat of mortality as mea-
sured by livestock kills.
When applied to carnivore–livestock interactions, spatial risk models reveal locations
and associated habitat features where carnivores kill livestock, providing both quantitative
and visual guides for targeting conflict mitigation interventions (Marucco and McIntire
2010; Treves et al. 2011). However, it is not yet clear from published studies whether
predation risk models actually serve as effective conservation tools and at which levels of
decision-making they are being implemented (household, management or policy). Fur-
thermore, case studies are needed to illustrate how risk maps can be practically integrated
into education and intervention efforts and whether the guidance provided by risk models
significantly reduces livestock depredation.
Spatial risk models may be especially appealing to natural resource managers and
conservation practitioners because they offer a potentially low-effort and low-cost tech-
nique for understanding the spatial patterns of livestock depredation using already existing
data. Such models require data on where carnivores attack and kill livestock. Conveniently,
due to the financial impacts of livestock losses on people’s livelihoods, livestock owners in
many regions report attacks to a local government agency or non-profit organization as
evidence for financial compensation or insurance payments (Inskip and Zimmermann
2009; Dickman et al. 2011). These records are often archived in long-term databases and
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contain the same data needed to build spatial risk models. When such databases exist, risk
modeling may be integrated into management as a relatively simple step to quantify
spatiotemporal patterns of human–carnivore conflict. A clear workflow of this process is
necessary to make risk modeling more accessible to practitioners. In addition, because
multiple statistical approaches have been used to produce spatial risk models without clear
rationale for which methods to employ, guidance is needed to elucidate techniques for
modeling carnivore–livestock interaction data.
Here I review the approaches and applications of spatial risk modeling for mitigating
carnivore attacks on livestock. Using the emerging literature on spatial risk modeling of
carnivore–livestock interactions, I critically examine statistical methods and model valida-
tion and evaluate contributions to the management of livestock husbandry and carnivore
populations. Based on lessons learned, I develop guidelines for integrating spatial risk
modeling into the process of managing human–carnivore conflict and provide real-world
examples of how study authors worked with stakeholders, managers and policymakers to use
risk maps during decision-making. Finally, I compare the locations of current studies to the
spatial distribution of potential carnivore–livestock interactions to identify where mitigation
tools might be needed worldwide. Given that reducing livestock depredation is critical for
enabling coexistence between people and carnivores (Treves and Karanth 2003; Ripple et al.
2015), and that mistakes in conflict management can have serious consequences for people’s
livelihood and carnivore survival, this review increases the accessibility of spatial risk
modeling as a tool to enhance the effectiveness of human–carnivore conflict mitigation.
Methods
Literature search
I found peer-reviewed journal articles on spatial risk modeling and livestock depredation by
searching the Web of Science (www.webofknowledge.com) and Google Scholar (www.
scholar.google.com) using the keywords: (‘risk’) AND (‘spatial’ OR ‘map’ OR ‘model’ OR
‘hotspot’) AND (‘livestock’ OR ‘cattle’ OR ‘goat’ OR ‘sheep’ OR ‘buffalo’) AND (‘car-
nivore’ OR ‘predat*’ OR ‘depredation’ OR ‘predator-prey’). Articles were deemed relevant
if they involved spatial modeling and mapping of carnivore depredation on domestic live-
stock. Articles that focused solely on attacks on humans or pets (including dogs) were
excluded because the landscape variables associated with these attacks may differ from
livestock. Articles were also excluded if they did not include a spatially explicit analysis
technique that included mapping risk hotspots. For each paper I recorded the study year,
location and carnivore and livestock species. These data were used to assess temporal and
spatial trends. I also recorded landscape attributes associated with carnivore attacks in the
models and noted the relationship (positive or negative) between each variable and carnivore
risk. These data were examined to identify common landscape attributes associated with
livestock depredation. Finally, I also noted the type of model and the steps of the research
process, which I synthesized into a workflow for creating and applying risk models.
Impacts on conservation and management
None of the studies identified in the literature review contained specific information on
how risk models and maps were applied to mitigate human–carnivore conflict. I therefore
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contacted the corresponding authors of all studies for information on how they commu-
nicated research findings to stakeholders, managers and policymakers and whether results
had been incorporated into conservation or management actions or the policies that control
them.
Spatial analysis of studies
To explore where mitigation tools might be needed worldwide, I compared the spatial
overlap of livestock and depredating carnivores with the locations of studies. I identified
areas of high potential livestock depredation by comparing maps of global livestock
density data (from Gridded Livestock of the World v.2.0; Robinson et al. 2007) to the
global presence of the world’s main carnivore species responsible for depredating livestock
(species list in Table S1 adapted from Breitenmoser et al. 2005; species range maps from
the IUCN Red List of Threatened Species version 2014.2 and Letnic et al. 2012).
Results
Overview of literature
The literature search revealed 18 studies involving spatial risk modeling of carnivore–
livestock interactions (Table S2). The oldest study was published in 2004 and thereafter
one or two articles were consistently published each year, with a noticeable increase to five
articles published in 2014 (Fig. 1). Papers mostly addressed Canids (n =10) and Felids
(n =8), with fewer studies addressing impacts from species in the families Ursids (n =2)
and Hyaenids (n =1; note that some studies included multiple species). Studies especially
focused on wolves (n =6; Canis lupus) and wild dogs (n =2; Cuon alpinus) and on tigers
(n =4; Panthera tigris), leopards (n =4; Panthera pardus), lions (n =2; Panthera leo)
and pumas (n =2; Puma concolor). Less-studied species were caracal (Caracal caracal),
jaguar (Panthera onca), spotted hyena (Crocuta crocuta), black-backed jackal (Canis
mesomelas), American black bear (Ursus americanus) and grizzly bear (Ursus arctos;
n=1 on each species). The majority of studies investigated depredation on cattle
(n =14), sheep (n =12) and goat (n =10) but the literature also reported attacks on
alpaca, buffalo, camel, horse, llama, pig, poultry and rabbit.
Modeling approaches
Most spatial risk modeling studies (13 out of 18; see Table S2 for studies) used cor-
relation modeling approaches, including ensemble modeling (common models included
Maxent and ecological niche factor analysis), generalized linear models, logistic
regression and single-sample discriminant-function analysis (see ‘Discussion’ section
for information on models). Three studies employed spatial association approaches using
Getis–Ord G and G* statistics and Mahalanobis D
2
(k) (Baruch-Mordo et al. 2008; Davie
et al. 2014; Meena et al. 2014). One study used spatial interpolation to extrapolate risk
probabilities at known sites across a larger landscape (Shrader et al. 2008). One study
mapped hotspots by scaling the probability of wolf pack presence by the number of
available livestock pastures rather than explicitly modeling livestock depredation (Mar-
ucco and McIntire 2010).
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Most (11 of the 18) studies conducted validation tests to measure the accuracy of model
predictions, and these studies used a mixture of external and internal validation methods
(see ‘Discussion’ section for details on validation tests). To conduct external validation,
which requires independent data for comparison against model predictions, authors either
retained a portion of the original dataset explicitly for validation testing (Treves et al. 2004;
Marucco and McIntire 2010; Zarco-Gonza
´lez et al. 2013; Abade et al. 2014) or used data
from a later time period (Treves et al. 2011; Behdarvand et al. 2014; Miller et al. 2015).
Cross-validation (e.g., 10-folds or leave-one-out) was commonly used for internal vali-
dation (Treves et al. 2004; Kaartinen et al. 2009; Edge et al. 2011; Soh et al. 2014;
Behdarvand et al. 2014).
Landscape attributes associated with depredation hotspots
Studies commonly found that the risk of a large carnivore attack on livestock was asso-
ciated with several of four main factors: species (carnivores and wild and domestic prey),
environment (e.g., vegetation type and elevation), human infrastructure (e.g., presence of
roads and villages) and management (e.g., park boundaries and preventative husbandry
techniques; Table 1).
Species
The density of carnivores and/or livestock was one of the strongest predictors of livestock
depredation. In reviewed studies that included densities of predators or prey as variables in
risk models, all but one found that higher densities of carnivores and livestock led to
greater predation risk (Baruch-Mordo et al. 2008; Kaartinen et al. 2009; Karanth et al.
2013). Zarco-Gonza
´lez et al. (2013) found a negative relationship because dense livestock
production areas were located away from forested areas and protected with fencing.
Studies also found positive associations between wild prey densities and livestock
depredation, suggesting that greater wild prey availability may lead to increased frequency
of attacks on livestock (Treves et al. 2004; Karanth et al. 2013). However, the connection
between livestock depredation and wild prey densities is currently under debate in the
human–wildlife conflict literature due to its complexity (Gervasi et al. 2013) and is likely
Fig. 1 The accumulative number of spatial risk modeling studies on carnivore–livestock interactions
published through March 2015
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Table 1 Top-ranked landscape attributes used in spatial predation risk models for predicting carnivore
attacks on livestock
Carnivores Livestocks Landscape
attributes
a
Relationship to
predation risk
b
Studies
Puma (Puma
concolor)
Cattle, goat,
sheep
Forest ?Zarco-Gonza
´lez et al. (2013)
Altitude or altitudinal
range
?Kissling et al. (2009) and
Zarco-Gonza
´lez et al.
(2013)
Distance from
paddock to puma
habitat
-Kissling et al. (2009)
Livestock density -Zarco-Gonza
´lez et al. (2013)
Jaguar (Panthera
onca)
Cattle, goat,
sheep
Percent tree cover ?Zarco-Gonza
´lez et al. (2013)
Arid vegetation -
Altitude ?
Percent free grazing
livestock
?
Asiatic lion
(Panthera leo
persica)
Buffalo, camel,
cattle,
donkey, goat,
horse, sheep
Distance to lion
dispersal corridors
-Meena et al. (2014)
Distance to protected
area
-
Tiger (Panthera
tigris)
Cattle, buffalo,
goat, pig
Tree cover ?Soh et al. (2014)
Deciduous forest ?
Distance to very
dense forest
-Miller et al. (2015)
Distance to
moderately dense
forest
-
Distance to scrub ?
Distance to scrub
2
-
Distance to river -Soh et al. (2014)
Aspect -
Protected area
(within or outside)
?when within
Distance to protected
area core
?Miller et al. (2015)
Distance to snare -Soh et al. (2014)
Distance to road ?Miller et al. (2015) and Soh
et al. (2014)
Distance to road
2
-Miller et al. (2015)
Distance to village ?Miller et al. (2015) and Soh
et al. (2014)
Distance to village
2
-Miller et al. (2015)
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Table 1 continued
Carnivores Livestocks Landscape
attributes
a
Relationship to
predation risk
b
Studies
Tiger and leopard
(Panthera
pardus)
(combined)
Cattle, buffalo,
goat, pig
Distance to forest
cover
-Karanth et al. (2012)
Distance to water ?Karanth et al. (2012,
2013)
Distance to protected
area
-
Park wildlife density ?Karanth et al. (2013)
Number of livestock ?
Grazing cows inside
protected area
?Karanth et al. (2012,
2013)
Use of any mitigation
measure
-
Leopard, African
lion (Panthera
leo), spotted
hyaena
(Crocuta
crocuta)
(combined)
Cattle, goat, sheep Percent tree cover -Abade et al. (2014)
Distance to river -
Altitude -
Slope -
Annual precipitation -
Normalized
difference
vegetation index
(NDVI)
?
Black-backed
jackal (Canis
mesomelas),
caracal (Felis
caracal)
(combined)
Goat Visibility ?Shrader et al. (2008)
Wolf (Canis
lupus)
Bison, cattle, duck,
poultry, rabbit,
sheep, white-
tailed deer
Forest ?Kaartinen et al. (2009)
Coniferous forest ?/-Edge et al. (2011) and
Treves et al. (2004)
Distance to forest ?Treves et al. (2011)
Tall vegetation ?Davie et al. (2014)
Shrubland ?
Forbland -
Wetland -Treves et al. (2004) and
Kaartinen et al. (2009)
Open water ?/-Treves et al. (2004)
Distance to waterway ?Behdarvand et al. (2014)
Plantation ?Kaartinen et al. (2009)
Cropland -Treves et al. (2004) and
Edge et al. (2011)
Grass/pasture/
hayfield
?/-Treves et al. (2004,2011)
and Edge et al. (2011)
Dry farms ?Behdarvand et al. (2014)
Number of sheep in
flock
?Kaartinen et al. (2009)
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Table 1 continued
Carnivores Livestocks Landscape attributes
a
Relationship to
predation risk
b
Studies
Pasture size -
Distance to wolf pack -Treves et al. (2011)
Distance to wolf
pack * distance to
forest
-
Wolf density ?Kaartinen et al. (2009)
Denning probability -Behdarvand et al. (2014)
Deer density ?Treves et al. (2004)
Distance to protected
area
?Behdarvand et al. (2014)
Distance from Russian
border
-Kaartinen et al. (2009)
Distance to human
settlement
?/-Behdarvand et al. (2014) and
Davie et al. (2014)
Road density -Treves et al. (2004)
Distance to road ?Behdarvand et al. (2014)
Grizzly bear
(Ursus arctos)
Cattle,
sheep
Percent riparian cover ?Wilson et al. (2005)
Distance to spring,
summer or fall
pastures
-
Unmanaged boneyard
present
?
Beehive present (either
fenced or unfenced)
?
Unfenced beehive *
unmanaged
boneyard
-
Distance to lambing
area
-
Calving area (inside or
outside)
?when inside
American black
bear (Ursus
americanus)
Sheep,
poultry
Sheep and farm density ?Baruch-Mordo et al. (2008)
Note that results from Marucco and McIntire (2010) were omitted from table because depredation risk was
based on assumptions rather than empirical data on livestock kill cases (refer to the study for details)
a
Variables are organized by theme, not ranking—see studies for coefficients and importance to the model
b
?Indicates positive relationship, -indicates negative relationship, ?/-indicates mixed results from
studies; binary variables are explained with text; for non-linear or interaction variables, see specific studies
for details
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more dynamic than a simple inverse or direct relationship and tied to many other factors,
such as the spatial distribution of domestic versus wild prey species and denning or resting
sites for predators (Stahl et al. 2001; Treves et al. 2004; Suryawanshi et al. 2013).
Environment
Studies typically quantified the environment at kill sites in terms of vegetation or habitat
type, percent vegetation cover, distance to vegetation or visibility. The relationship
between these factors largely depended on the hunting mode of the carnivore. Active-
roaming hunters such as wolves, which often kill in flat, open areas conducive to long
pursuits, tended to exhibit higher predation risk in shrubland, field or pasture and at farther
distances from denser forest (Treves et al. 2011; Davie et al. 2014), although not in all
cases (Kaartinen et al. 2009; Edge et al. 2011; Treves et al. 2011). Greater risk from
stalking hunters such as tigers, jaguars and pumas, which commonly use thick vegetation
with heavy cover to ambush prey, was associated with dense forest and low visibility
(Shrader et al. 2008; Kissling et al. 2009; Karanth et al. 2012; Zarco-Gonza
´lez et al. 2013;
Soh et al. 2014; Miller et al. 2015). Water also featured prominently in risk models and was
associated with lower risk of depredation by wolves (Treves et al. 2004,2011) but greater
risk of depredation by leopards (though effects were weak; Karanth et al. 2012,2013).
Finally, elevation was often ranked as an influential variable in systems where a large
topographic gradient exists, such as for jaguar in Mexico and puma in Argentina, with
higher elevations linked to greater predation risk (Kissling et al. 2009; Zarco-Gonza
´lez
et al. 2013).
Human infrastructure
Human infrastructure had mixed associations with predation risk in several carnivore
species. Tigers were more likely to kill livestock farther from roads and villages in China
(linear relationship; Soh et al. 2014) but in India, risk peaked around 1 km from roads and
villages and decreased very near and far from infrastructure (nonlinear relationship; Miller
et al. 2015). Wolves in Mongolia and the US tended to kill livestock away from towns and
roads (linear relationship; Treves et al. 2004; Davie et al. 2014) but in Iran wolf risk
showed a nonlinear relationship to human settlement, spiking very close to settlements but
decreasing at distances greater than 1.0 km. In these cases, the effect of human infras-
tructure may be linked to the density of roads and villages in the area. Neither human
presence (density and distance to camp) nor road (density and distance from) were ranked
as top predictors of attack on livestock for jaguar or puma (Kissling et al. 2009; Zarco-
Gonza
´lez et al. 2013) and human infrastructure was not explicitly explored for other
species in the reviewed studies.
Management
Many studies found strong connections between land-use zoning or preventative husbandry
interventions and risk of livestock depredation. Close proximity between livestock grazing
sites and high-quality carnivore habitat was one of the strongest indicators of attacks by
nearly all carnivores. Livestock depredations more often occurred in livestock paddocks
located near ideal habitat for puma (Kissling et al. 2009) and near dispersal corridors for
Asiatic lion (Meena et al. 2014). Greater predation risk by tigers, Asiatic lions and wolves
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occurred close to protected areas (Karanth et al. 2012,2013; Soh et al. 2014; Meena et al.
2014; Behdarvand et al. 2014; Miller et al. 2015), and risk of attack increased if cattle
grazed within the park (Karanth et al. 2012,2013). Similarly, allowing livestock to freely
graze increased predation risk from jaguar (Zarco-Gonza
´lez et al. 2013). Livestock owners
lost fewer animals to tigers, leopards and wild dogs in India if they generally used miti-
gation measures, although no individual techniques were strongly associated with reduced
depredation (Karanth et al. 2012,2013). Grizzly bears were particularly drawn to certain
attractants and more likely to kill livestock near beehives (no matter whether they were
fenced or not), lambing and calving areas and unmanaged boneyards (Wilson et al. 2005).
Impacts on conservation and management
Authors from 16 of the 18 reviewed studies provided information about how risk models
had impacted conservation, management and policy. The authors of 14 papers shared study
results by actively and regularly engaging with local herders and villagers, government
agencies, conservation organizations and research institutes, and most knew of direct
outcomes on conservation, management and policy resulting from their efforts. The authors
of two studies did not engage extensively in outreach and were not aware of any impacts at
the study site. Overall, authors shared risk model results with three levels of decision-
makers: (1) stakeholders, (2) management and conservation authorities and (3) policy-
makers (Table 2).
At the stakeholder level, authors shared results directly with livestock owners and local
households through educational events and efforts to subsidize and improve the effec-
tiveness of carnivore deterrents (Table 2). In Mexico, Zarco-Gonza
´lez et al. (2013) pre-
sented risk maps to farmers during workshops on how to adapt grazing locations, design
pastures and handle animals to reduce attacks, and how to apply for financial compensation
after an attack (Martha Zarco-Gonza
´lez personal communication). In Tanzania, Abade
et al. (2014) worked with the Ruaha Carnivore Project to share risk maps with local
villagers through Swahili PowerPoint presentations delivered during educational DVD
nights to help households recognize distributions of risk in their areas (Dickman personal
communication). The group also used risk maps to prioritize households for receiving
guard dogs and assistance constructing predator-proof enclosures, the latter which
reportedly reduced carnivore attacks by over 90 %. Maps also provided a baseline for
focusing mitigation efforts and examining stock loss trends over time. In the US, Wilson
et al.’s (2005) efforts to share grizzly bear conflict hotspot maps with livestock owners
through PowerPoint presentations, informal meetings and regular workgroups motivated
the community to help install electric fences around 18 calving areas and 85 beehives,
phase out 120 boneyards through a livestock carcass removal program and provide 100
bear-resistant trashcans to residents in high-risk areas (Wilson personal communication).
These actions reduced bear conflicts by 96 % from 2003 to 2010 (Wilson et al. 2014).
Through active communication with authorities, authors also integrated risk maps into
the decision-making process within government agencies and conservation organizations
(Table 2). In western India, Meena et al. (2014) developed conflict management manuals
on Asiatic lion attacks on livestock which were adapted and then distributed to high-risk
villages by the Gujarat Forest Department during lion conservation awareness programs
(Meena personal communication). In central India, tiger and leopard depredation hotspot
maps developed by Miller et al. (2015) were shared with the management community
through written reports, oral presentations and one-on-one meetings with agency officials
and ultimately used by the Madhya Pradesh Forest Department to strategically build fences
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Table 2 Outreach actions and impacts of risk models and maps on conservation, management and policy
related to human–carnivore conflict at the stakeholder, management/conservation authority and policymaker
level
Levels of
decision-
making
Outreach actions Impact on
conservation/
management
Regions Studies
Stakeholder Worked with households
in highest-priority/risk
areas to strengthen
mitigation efforts
Predator-proofing
enclosures reduced
carnivore attacks by
[90 %; increased
use of guard dogs
Ruaha, Tanzania Abade et al.
(2014)
Increased awareness
about ways of re-
designating and
designing pastures
and handling
livestock to reduce
risk of predation
Mexico Zarco-Gonza
´lez
et al. (2013)
Shared published papers
with local herders
Education Northern Cape,
South Africa
Shrader et al.
(2008)
Continue to measure
livestock loss data at
high-risk households
after initial risk
modeling/mapping
Risk model/map serves
as baseline to
measure change
Ruaha, Tanzania Abade et al.
(2014)
Shared risk maps with
villages using local-
language PowerPoint
presentations and/or
DVD nights
Education Alps ecosystem,
Europe
Marucco and
McIntire
(2010)
Education Ruaha, Tanzania Abade et al.
(2014)
Conducted classes and
workshops for livestock
owners on financial
compensation process
Education Mexico Zarco-Gonza
´lez
et al. (2013)
Distributed awareness
posters or leaflets with
results, especially in
communities with high
attack risk
Education Gujarat, India;
Hamadan, Iran
Meena et al.
(2014) and
Behdarvand
et al. (2014)
Posted interactive risk
map online
Education Wisconsin, USA Treves et al.
(2011)
Management
or
conservation
authority
Formatted results into
awareness materials for
each village (e.g.,
pamphlets) for
distribution by
management agency
Education Gujarat, India Meena et al.
(2014)
Used results to acquire
additional funding for
large-scale carnivore
conservation efforts
EU Life?Project
‘WOLFALPS’
Alps ecosystem,
Europe
Marucco and
McIntire
(2010)
Interactive online risk
map
Education Wisconsin, USA Treves et al.
(2011)
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to exclude livestock grazing from high-risk areas (Jhala personal communication). In the
Italian Alps, maps showing potential livestock depredation (Marucco and McIntire 2010)
led to the EU Life?Project ‘WOLFALPS’, which supports natural wolf recolonization
over the Alps ecosystem through partnerships between agencies and conservation orga-
nizations (www.lifewolfalps.eu; Marucco personal communication). Hotspot maps also
helped identify regions where conflict management required more information: Baruch-
Mordo et al.’s (2008) risk model of black bear–human conflicts across Colorado in the
USA prompted interest from a local agency in the high-risk hotspot of Aspen to collect
finer-scale habitat quality data that revealed greater insights into how to mitigate conflicts
(Baruch-Mordo et al. 2014). Several authors expressed the importance of regular inter-
action with authorities, especially government agencies, for developing trusted relation-
ships that eventually impacted management of human–carnivore conflict.
Though many authors sought to affect conservation at the policy-level, the authors of
only one reviewed study reported success (Table 2). In Hunchun, China, where the
Wildlife Conservation Society and local government authorities are jointly working
towards conserving the Amur tiger, hotspot results from Soh et al. (2014) were included in
a policy document that reportedly contributed to the government agency increasing efforts
to resolve human–tiger conflict (Wildlife Conservation Society personal communication).
Spatial distribution of studies and potential livestock depredation
Mapping these studies revealed a global distribution of research with clusters in several
countries (Fig. 2). Most studies were conducted in North America (n =6 studies; 5 in the
USA and 1 in Mexico) and Asia (n =7; 4 in India and 1 each in China, Iran and
Mongolia). Africa (n =2; 1 each in South Africa and Tanzania), Europe (n =2; 1 each in
Finland and Italy) and South America (n =1 in Argentina) were also represented.
Table 2 continued
Levels of
decision-
making
Outreach actions Impact on
conservation/
management
Regions Studies
Shared results as written
materials and/or
presentations to
government agency and
conservation
organizations
Results included in
management plan
Gujarat, India Meena et al. (2014)
Fencing built to keep
livestock out of high-
risk areas
Madhya Pradesh,
India
Miller et al. (2015)
Education Colorado, USA Baruch-Mordo
et al. (2008)
Education Dornogobi Aimag,
Mongolia
Davie et al. (2014)
Education Karnataka, India Karanth et al.
(2013)
Education Mexico Zarco-Gonza
´lez
et al. (2013)
Education Ruaha, Tanzania Abade et al. (2014)
Policymaker Shared results with
government agency via
policy document
Increased recognition
of the need to address
human–tiger conflict
at the study site
Hunchun, China Soh et al. (2014)
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Comparing study locations against areas of potential livestock losses (livestock densi-
ties and presence of carnivore species) revealed a lack of studies in areas with the greatest
potential human–carnivore conflict (Fig. 2). Eighty-three percent of studies were con-
ducted in areas of low–moderate levels of potential livestock depredation (carnivores
present and 0–300 livestock/km
2
) and the remaining 17 % occurred in areas of high
potential conflict (carnivores present and 300–1000 livestock/km
2
). No studies were
located in regions where conflict-prone carnivores overlapped with the highest densities of
livestock (1000–13,000 livestock/km
2
). The highest levels of potential livestock depre-
dation (carnivores present and [300 livestock/km
2
) were distributed across a contiguous
belt stretching through eastern Europe, western Asia and southern and eastern Asia. High
potential conflict was also apparent in western (Nigeria), eastern (Ethiopia and Kenya) and
southern (South Africa) Africa, eastern South America (Ecuador and Peru), southern
Mexico and the midwestern US. Reviewed studies addressed hotspots in Italy, India,
Mexico and the US but spatial risk modeling has yet to be published from many areas with
high potential rates of livestock depredation. It is also worth noting that several regions
with significantly dense livestock populations ([50 livestock/km
2
) no longer support
conflict-prone carnivores (e.g., parts of western Europe, southeastern Argentina, south-
eastern Australia and eastern southeast Asia; arrows in Fig. 2).
Fig. 2 Global potential livestock depredation hotspots compared to published spatial risk studies. Hotspots
of livestock depredation are likely to occur where high densities of livestock (shown for buffalo, cattle,
sheep, goats and pigs combined) overlap with the presence of the primary carnivores responsible for
livestock depredation worldwide (see Table S1 for species). Arrows are meant to clarify certain regions
where carnivores are absent (see Fig. S1 for separate livestock and carnivore maps). Data sources livestock
densities from FAO’s Gridded Livestock of the World and species ranges from IUCN Red List of
Threatened Species, Version 2014.2 (Letnic et al. 2012)
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Discussion
This literature review demonstrates the utility of spatial predation risk modeling as a
technique for identifying high-risk priority areas in order to maximize the effectiveness and
minimize the costs of conflict mitigation. The number of published spatial risk studies is
currently small yet will continue to increase as conservation practitioners share successes
with colleagues and spatial resources become more freely accessible (Garcı
´a-Rangel and
Pettorelli 2013). It is therefore important to assess the gaps in current risk modeling and
outline the process for integrating hotspot mapping into management, conservation and
policy decision-making.
Guidelines for integrating spatial risk modeling and mapping
into conservation
The reviewed studies collectively indicated that six main steps were necessary for
developing conservation-relevant spatial risk models: (1) identify objectives, (2) collect
data, (3) build risk models, (4) map predation risk, (5) apply results to conservation and
management and (6) account for feedbacks in the system (Fig. 3). Due to the context-
specific nature of human–carnivore conflict, these steps should be considered flexible and
adapted to specific systems, species and conservation goals.
Fig. 3 The six-step process of creating and applying predation risk maps to mitigating human–carnivore
conflict: (1) identify objectives, (2) collect spatial data on predation risk and landscape attributes that may
influence carnivore–livestock interactions, (3) model data using a spatially explicit modeling approach and
validate results, ideally with independent data, (4) use model to map predictions of risk while considering
the purpose, type of media and risk level scheme, (5) apply results using various media formats to
stakeholders, managers and conservation practitioners and policymakers and (6) update models and maps
regularly to account for feedbacks within the system
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Identifying objectives
The modeling process should be closely tied to the local community, carnivore species and
the ultimate objectives of management and conservation (Fig. 3-1). In situations where
existing data collected by agencies or conservation organizations will be used for risk
modeling, the spatial (extent and resolution) and temporal limitations of the data must be
considered when determining how risk models can inform the desired objectives. For
instance, models may focus on the spatial patterns of current or future livestock depre-
dation across an entire country (Marucco and McIntire 2010) or a single park (Abade et al.
2014), or for a carnivore community (Karanth et al. 2013) or a single carnivore species
(Miller et al. 2015).
Collecting data
Modeling carnivore predation risk on livestock requires two types of information (Fig. 3-
2). First, a model requires data on the locations of carnivore–livestock interactions. In
many landscapes globally, conservation schemes exist which enable owners to report
livestock death to authorities for financial compensation or insurance (e.g., Schiess-Meier
et al. 2007; Agarwala et al. 2010). These programs generate databases of kill data that—if
accurate and unbiased—may be appropriate for modeling carnivore attacks. Most of the
reviewed studies used data from livestock kill sites (15 studies) or villager surveys about
livestock mortalities (2 studies). Databases on livestock mortality may contain detailed
information on the livestock (species, sex, age, health), carnivore (species, sex, age), owner
(name, village, income) as well as spatial (habitat type, distance from village) and temporal
(time of attack, date) characteristics. The long-term nature of these databases can also
provide insight into the historical patterns of attacks and changes with management
interventions or natural events (e.g., drought, native prey declines). However, livestock
mortality databases may include strong reporting biases if social tension or spatial isolation
prevent certain livestock owners from reporting losses (Karanth et al. 2012) or if managers
do not consistently record conflict incidences (Baruch-Mordo et al. 2008). It is essential
that researchers understand these biases and limitations when analyzing and interpreting
livestock kill data. Furthermore, risk models generated from kill data represent the odds of
a successful kill given an encounter between carnivore and livestock but do not account for
failed hunting attempts, encounters where hunting did not occur or livestock fear (Heb-
blewhite et al. 2005; Gorini et al. 2012).
Besides kill data, several other types of information may be used to analyze carnivore–
livestock spatial interactions, although these methods are currently underdeveloped. First,
livestock fear may serve as a proxy for the likeliness of a carnivore attack (Brown et al.
1999; Laundre
´et al. 2010). Since prey must choose between allocating precious energy
resources between feeding and anti-predator efforts, the energetic cost involved in staying
aware of predators can make vigilance an indicator of predation risks (Lima and Bednekoff
1999). Vigilance can be quantified through measures like alertness, flight initiation dis-
tance or the willingness to give up food in order to remain on guard, the latter which is also
known as the giving-up-density (Brown 1999; Stankowich and Blumstein 2005; see
Shrader et al. 2008 for an example). However, because prey can misinterpret environ-
mental cues or misjudge carnivore behavior, and livestock in particular are known to show
attenuated responses to carnivore threats (Laporte et al. 2010), fear responses may not
serve as an accurate prediction of the likeliness of livestock depredation (Creel and
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Christianson 2008). Second, data on general carnivore–livestock encounters (defined as a
spatial and/or temporal overlap) may provide insight into a broader spectrum of predation
risk beyond realized risk based on mortality alone (Hebblewhite et al. 2005). Encounters
can include various elements of a hunt (e.g., searching, approaching, watching and
attacking prey; Kluever et al. 2008) and may present the opportunity for a carnivore to
attack livestock, although carnivores may not always attack or with success (only 25 % of
observed leopard attacks resulted in a wild prey kill; Balme et al. 2007). Furthermore,
measuring encounters is time-consuming and expensive, often requiring scientific equip-
ment and monitoring teams, because animals typically interact quickly and within small
areas, making it essential to record encounters and very fine spatial and temporal resolu-
tions (Gorini et al. 2012).
The second type of data required for modeling is information on the landscape where
livestock are killed. Gathering spatial data on the landscape attributes, organismal biology
and land cover at and surrounding livestock kill sites strengthens the ability of risk models
to identify the specifically variables associated with carnivore attacks (Treves et al. 2011;
Garcı
´a-Rangel and Pettorelli 2013). The four categories of factors identified in the
reviewed studies (species, environment, human infrastructure and management) represent a
starting point of biologically meaningful variables for modeling risk, representing both
prey availability (species presence and abundance) and accessibility (vulnerability based
on the surrounding environment). On-the-ground observations can also be highly infor-
mative for understanding the site-specific context, including the heterogeneity that may
exist throughout the landscape and influence species interactions (Gorini et al. 2012). Local
peoples’ experiences can offer long-term insights into livestock husbandry and grazing
patterns, species population trends, socio-economic factors, policy or governance changes,
and other interactions that might affect risk (Treves et al. 2006; Dickman 2010). In
addition, the unit of a variable must be carefully chosen to precisely express biological
impact (Treves et al. 2011). Once the appropriate landscape attributes have been identified,
many remote sensing and geographic information data and software are available (often
free of cost) for accessing spatial data (for a list of sources see Garcı
´a-Rangel and Pettorelli
2013).
Building models
Reviewed studies revealed that three primary approaches are commonly used for mapping
carnivore risk to livestock: correlation modeling, spatial association and spatial interpo-
lation (Fig. 3-3).
Correlation modeling was the most popular method and is often used in the wildlife
biology field for examining animal resource use. Resource selection functions (RSFs) are a
subset of correlation models that estimate the probability of an animal selecting a resource
(e.g., carnivore targeting livestock) and are often applied to generate spatially explicit
predictions of predation risk (Manly et al. 2002; Boyce et al. 2002). RSFs use regression
models to reveal associations between landscape characteristics and selection frequencies
(Lele et al. 2013), such as the tendency of carnivores to kill livestock in certain envi-
ronmental conditions. Many spatial software use correlation modeling to predict gradients
of animal interactions relative to spatially explicit environmental or anthropogenic vari-
ables (e.g., Maxent, open modeller and biomapper; Behdarvand et al. 2014; Zarco-Gon-
za
´lez et al. 2013). Correlation models may be built with presence–absence data (locations
where events did and did not occur) or presence-‘availability’ data following a use-
availability design (locations where events occurred and random locations representing
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where events could have occurred; Johnson et al. 2006). For analyzing livestock depre-
dation hotspots, correlation modeling is recommended if data are available on livestock
kills as well as the surrounding landscape where kills did not or could have occurred but
are not known.
Several studies used spatial association to examine the clustering of kill sites for
identifying significant conflict hotspots. These techniques work well with relatively smaller
datasets and apply well-established statistical approaches for assessing autocorrelation to
obtain insights on biology (Dale and Fortin 2014). However, spatial association strictly
requires independent data samples (i.e., kills that are unrelated to each other), which may
be impossible for ecological studies. The process may also over-emphasize risk hotspots
due to autocorrelation depending on how sampling was distributed (Dale and Fortin 2014).
Spatial association techniques include statistics such as Moran’s I, which reveals spatial
clustering between points to identify associations between response and covariate variables
(Willems and Hill 2009). The Getis–Ord statistics examines concentrations of points with
respect to a central area of interest (Getis and Ord 1992), such as livestock kills located
around a protected area (Meena et al. 2014), and provides information regarding high and
low clusters (hotspots and coldspots; Baruch-Mordo et al. 2008; Dale and Fortin 2014).
Mahalanobis D
2
(k) distance presents an alterative method for cluster analysis that requires
only presence data (e.g., kill site locations), thus alleviating problems associated with
gathering absence data (where kills did not occur) or random data (where kills could have
occurred) as well as assumptions of data normality (Clark et al. 1993). Since these statistics
specifically examine spatial autocorrelation and point clustering, they are useful for
addressing whether areas of high or low livestock depredation densities cluster on the
landscape relative to certain landscape attributes and spatial extents (Bump et al. 2009;
Dale and Fortin 2014). Models assign significance values to neighborhood units (e.g.,
defined village or district area) to provide information about clustering across the study
extent; scores over a certain significance threshold indicate high clustering (e.g., areas
where carnivores tend to depredate) and scores under a certain threshold indicate low
clustering (areas where carnivores do not tend to depredate; Baruch-Mordo et al. 2008;
Meena et al. 2014). Once these statistics reveal clustering patterns, additional analysis is
required to identify spatial associations with landscape attributes (Baruch-Mordo et al.
2008).
Spatial interpolation was used by several studies for quantifying continuous spatial
gradients of environmental factors based on sampled points. Interpolation techniques, such
as kriging or inverse distance weighing, fit a statistical model through spatially separated
data points to characterize the remaining landscape between the known sites (Dale and
Fortin 2014). Spatial interpolation is useful for adapting spatially limited datasets (e.g.,
villages with household socio-economic surveys or specific sites where livestock numbers
were estimated) for use in combination with other spatially explicit data (e.g., GIS data;
Alessa et al. 2008; Bryssinckx et al. 2012) but does not examine correlations between
multiple variables or their associations with species interactions. Models output a spatial
layer of continuous data for a given variable across a fixed area. Therefore, the technique is
most appropriate for initial, descriptive examination of carnivore risk to livestock or for
converting spatially implicit variables but not for complex multivariate analyses of live-
stock depredation hotspots. Additionally, interpolation models are very sensitive to the
sample density, data variation and sampling design such that sampling must adequately
capture variation in the underlying environmental conditions for the model to accurately
represent the study extent (for details see Li and Heap 2011).
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Many reviewed studies did not carry out model validation, the process of comparing
model output against independent data (Fig. 3-3). Measuring a model’s predictive accuracy
is an essential part of confirming its strength as a tool for conservation decision-making
(Sinclair 1991; Schmolke et al. 2010). High accuracy is particularly important when
dealing with human–carnivore conflict (Treves et al. 2011), where inaccurate guidance
could provoke additional livelihood losses and species declines. The most rigorous method
of validating data is to conduct external validation tests using independent data. These data
may have been separated from the data used to train models and reserved for testing
purposes, or may have been collected in the ‘‘future’’ from a time period after the data used
to build the model was collected, or may have been collected outside the study area extent
and time period if the model is applicable elsewhere. When independent data are not
available or the dataset is too small to withhold testing data, internal validation offers an
alternative process of validation. Internal methods, such as k-folds cross-validation, use the
complete dataset for both calibration and testing by partitioning and cross-checking data
multiple times (Boyce et al. 2002). However, these methods commonly produce overly
optimistic assessments because they use the same data for model training and testing
(Dormann et al. 2012).
Mapping predicted risk
Authors used maps as a primary tool when communicating the results of risk modeling.
Maps are considered highly effective tools for community engagement in management due
to their ability to bridge communication challenges through a visual language composed of
colors and symbols (Brown and Raymond 2007; Rambaldi et al. 2006). Especially in the
case of human–carnivore conflict, maps depicting color gradients of risk are more likely to
be more interpretable than model statistics by stakeholders from diverse educational and
cultural backgrounds (Rambaldi et al. 2006). The reviewed studies portrayed risk using a
variety of visualization approaches, ranging from black-and-white to color, from discrete
to continuous gradients, and at different spatial scales and management units (Fig. 4).
Producing a meaningful map of risk is an important step that requires consideration as to
how the map will be ultimately used for conservation (Fig. 3-4). For instance, when color
gradients are used, the color scheme should be carefully chosen to emphasize the most
biologically meaningful levels of risk. Also, maps should account for varying visual and
cultural interpretations of color, such as the effects of red–green color blindness or the
association of color with certain social cues (e.g., traffic lights).
With increasing global access to mobile phones and internet, maps are no longer
restricted to printed media, offering researchers the opportunity to develop engaging
interactive maps that provide people with insights into carnivore risks in their immediate
proximity. The Carnivore Coexistence Lab at the University of Madison-Wisconsin used
the open access platform Leaflet (www.leafletjs.com) to create an interactive online map of
wolf risk to livestock in Wisconsin, where users can type in an address and view to risk
from several years around their property (www.faculty.nelson.wisc.edu/treves/wolves/
interactiveRiskMap.php; Treves et al. 2011). Such creativity in showcasing risk models
and maps should play a greater role to increase access by a broader audience.
Applying results to conservation and management
Studies revealed that risk model output can successfully be applied to conservation and
management at multiple stages of decision-making, from farm-level management to
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region-level policies, through the use of diverse forms of communication (Fig. 3-5).
Organizing meetings or classes created an opportunity to share results directly with
stakeholders who could personally incorporate model outcomes into their decision-making.
Risk models helped identify livestock owners in high-priority areas where implementing
preventative husbandry techniques (predator-proof enclosures, guard dogs and livestock
carcass removal programs) and deterrents (electric fences and predator-proof enclosures
and trash cans) resulted in dramatic reductions in carnivore attacks. Likewise, directly
communicating results to managers helped integrate risk model results into management
plans, village awareness programs and on-the-ground actions for preventing livestock
grazing in high-risk areas. Risk models and maps significantly strengthened outreach
efforts, supporting previous evidence that outreach increases the likeliness of stakeholders
implementing conflict mitigation interventions (Pienaar et al. 2015).
The fact that only one study impacted policy-level decision-making highlights a sig-
nificant gap in current efforts and a critical target for future outreach. One of the greatest
advantages of spatial risk modeling is the visual output, and maps could be showcased in
policy documents and presentations to help policymakers understand patterns of human–
carnivore conflict across constituents’ properties. Such success is currently underway in
Sweden, where risk maps helped the Environmental Protection Agency, Department of
Agriculture and Regional County Administration Boards allocate subsidies for farmers
building fences to deter carnivores from attacking livestock (Karlsson personal commu-
nication). Swedish agencies similarly used risk maps to identify regions with high pre-
dation risk and high sheep farm density where culling carnivores might be more effective
in the short term than fencing subsidies. Though not yet published, the utility of risk maps
for advising policy-level budgetary decisions in Sweden demonstrates the potential impact
that future risk modeling efforts could have on policy decision-making.
Management, conservation and policy decisions occur over a range of scales, spanning
from individual farms to entire states and nations. Carefully thinking through how model
results will be used can help avoid mismatches between the scale of analysis and imple-
mentation (Chapin et al. 2010). Will regional managers use the analysis implement pre-
ventative interventions according to management units (Baruch-Mordo et al. 2008) or
townships (Treves et al. 2011)? Would a policy leader prefer to understand the risks from
recolonizing carnivores to livestock across the entire country or a particular region
(Marucco and McIntire 2010)? Consulting with both stakeholders and decision-makers to
understand how risk models will be used for management and conservation will facilitate
modeling at a practical spatial scale (Treves et al. 2006).
Success in applying risk models and maps to on-the-ground conservation ultimately
depends on the interest of decision-makers. Authorities sometimes feel they intuitively
understand carnivore threats so well that they do not need risk models, yet later see
unexpected patterns and insights emerge from quantitative analysis (Author personal
observation; Treves personal communication). Many authors stressed the importance of
building long-term relationships with management authorities to develop trust, which
created opportunities for presenting risk maps. Especially within government agencies,
shifting constituent concerns and political priorities can also affect managers’ interest in
risk modeling. In 2011 after wolves were delisted from the US Endangered Species Act,
the impetus to manage high-risk hotspots was lost once wolf hunting was legalized as a
management tool (Treves personal communication).
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Keeping risk models relevant: accounting for behavioral feedbacks
The literature review revealed a lack of attention on the behavioral feedbacks that may
arise from the use of spatial risk models in management and conservation. Spatial risk
models represent dynamic systems in which carnivores and livestock (and humans that
manage livestock) continuously respond to one another. Management efforts that consider
carnivore risks when applying interventions, such as in shifting livestock to low-risk
grazing sites, may prompt feedbacks: carnivores could adjust attack patterns to account for
different livestock distributions, which could change risk distributions from the original
modeled predictions. This ‘arms race’ of human–carnivore conflict mitigation—whereby
carnivores adapt behavior to override mitigation strategies that in turn must adapt to deter
carnivore attacks—is the reason why the effective period of most conflict mitigation tools
is short (Shivik 2006).
To account for feedbacks, models must be regularly updated to reflect changes in the
system (Marucco and McIntire 2010; Fig. 3-6). Yet none of the reviewed studies addressed
Fig. 4 Risk maps showing different approaches for illustrating livestock depredation hotspots. aContour
probability plot for grizzly bears in Montana, USA, where more widely spaced lines represent lower levels
of conflict (Wilson et al. 2005). bGetis–Ord hotspots illustrating spatial clustering of livestock kills by
African lion by village area in western India (Meena et al. 2014). cLogistic regression model representing
the relative probability of tiger attacks on livestock in central India (Miller et al. 2015). dPuma risk to
livestock by paddock (livestock pasture) calculated using generalized linear regression (Kissling et al. 2009).
Darker shades (a,d) or warmer colors (b,c) represent greater probability of carnivore attack.
Figures published with permission. (Color figure online)
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changes in predation risk over time or ties to behavioral feedbacks. Regularly revising
spatial risk models with recent field data on carnivore–livestock interactions (e.g., sea-
sonally or annually) will be necessary to ensure that hotspot maps offer up-to-date guid-
ance on carnivore attack patterns. This is an especially critical step if risk maps are
incorporated into management, conservation and policy.
Conclusions
Based on the review, five key issues require consideration to strengthen the utility of spatial
predation risk modeling as a tool for mitigating carnivore attacks on livestock. First, models
should be quantitatively and externally validated with independent data to ensure the integrity
of model output for accurately guiding mitigation interventions. Second, risk modeling
should be integrated into long-term monitoring to assess the impacts of associated mitigation
efforts on livestock depredation. As part of this process, risk models must be updated with
recent data to account for behavioral feedbacks and changes in carnivore–livestock inter-
actions. Third, innovative methods for displaying, sharing and applying results from risk
models should be used to reach new audiences among stakeholders, managers and policy-
makers. Fourth, outreach with policymakers should be prioritized so that risk maps may
inform large-scale decisions on conflict. Finally, there is great opportunity for more risk
modeling across the world, particularly in the areas of high potential livestock depredation
such as eastern Europe, western, southern and eastern Asia western, eastern and southern
Africa and eastern South America. Efforts to establish regular risk modeling efforts in these
regions should be prioritized for financial and logistical support, especially since human–
carnivore conflict is currently contributing to the rapid of greatly endangered carnivores such
as the tiger and the Andean bear (Tremarctos ornatus).
Quantitative, scientific tools are a critical part of wildlife management (Sinclair 1991).
Yet despite the serious ramifications of carnivore attacks for local livelihoods and carni-
vore conservation, surprisingly few quantitative techniques exist to assist stakeholders,
managers and policymakers in understanding and mitigating human–carnivore conflicts
such as livestock depredation (Treves and Karanth 2003; Woodroffe et al. 2005). The
studies in this review demonstrate that spatial predation risk modeling can serve as a
practical tool for guiding on-the-ground decision-making about where to implement pre-
ventative husbandry interventions and carnivore deterrents. Especially in situations where
authorities routinely collect information on livestock mortalities as part of financial
incentive or compensation programs, risk modeling can use existing data to offer additional
insight into the spatiotemporal patterns and socio-ecological drivers of human–carnivore
conflict. As conservation practitioners increasingly recognize and use spatial predation risk
modeling as a valuable tool in the conservation toolkit, the technique will continue to
improve the effectiveness of mitigation efforts for reducing livelihood losses and
strengthening carnivore conservation.
Acknowledgments Oswald Schmitz, Adrian Treves, Y. V. Jhala, Walter Jetz, David Skelly, J.
S. Chauhan, Rakesh Shukla, Mark Hebblewhite, Meghna Agarwala, Anne Trainor, Colin Donihue, Wesley
Hochachka and several anonymous reviewers provided input that greatly improved this manuscript. Thank
you to the authors of the reviewed studies for sharing their experiences in applying risk models to con-
servation. Funding was provided by the American Institute for Indian Studies, American Philosophical
Society, Association of Zoos and Aquariums, John Ball Zoo Society, Yale Tropical Resources Institute
Endowment Fellowship and the United States National Science Foundation.
Biodivers Conserv
123
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1
Online Resource 1
Miller, JRB (2015) Mapping attack hotspots to mitigate human-carnivore conflict: Approaches and applications
of spatial predation risk modeling. Biodivers Conserv 24(12):2887-2911. DOI 10.1007/s10531-015-0993-6.
http://link.springer.com/article/10.1007/s10531-015-0993-6
Table S1 Main mammalian carnivore species in the world responsible for depredating livestock. Adapted from
Breitenmoser et al. 2005
Family
Common Name
Scientific name
Canidae
Golden jackal
Canis aurenus
Coyote
C. latrans
Grey wolf
C. lupus
Dingo
C. l. dingo
Black-backed
jackal
C. mesomelas
Red wolf
C. rufus
African wild dog
Lycaon pictus
Ursidae
Andean bear
Tremarctos
ornatus
American black
bear
Ursus americanus
Brown bear
U. arctos
Asiatic black bear
U. thibetanus
Mustelidae
Wolverine
Gulo gulo
Hyaenidae
Spotted hyaena
Crocuta crocuta
Brown hyaena
Hyaena brunnea
Striped hyaena
H. hyaena
Felidae
Cheetah
Acinonyx jubatus
Caracal
Caracal caracal
Puma
Puma concolor
Asiatic golden cat
Felidae temmincki
Eurasian lynx
Lynx lynx
Bobcat
L. rufus
African/Asiatic lion
Panthera leo
Jaguar
P. onca
Leopard
P. pardus
Tiger
P. tigris
Snow leopard
Uncia uncia
2
Table S2 Published spatial risk modeling studies on carnivore-livestock interactions through March 2015
Country
Continent
Specific
region
Carnivore
Carnivor
e family
alphabeti
cal)
Prey (alphabetical)
Modeling method
Tanzania
Africa
Rungwa-
Ruaha
National Park
Leopard (Panthera
pardus), lion
(Panthera leo),
spotted hyaena
(Crocuta crocuta)
Felidae,
Hyaenidae
Cattle, goat, sheep
Correlation modeling (ensemble modeling
using Maxent, Ecological Niche Factor
Analysis and Support Vector Machines)
USA
North
America
Western
Colorado
American black bear
(Ursus americanus)
Ursus
Alpaca, cattle, goat, llama,
pig, poultry, sheep
Spatial association (Getis-Ord G* clustering)
Iran
Asia
Hamedan
providence
Wolf (Canis lupus)
Canidae
Cattle, goat, sheep
Correlation modeling (Maxent)
Mongolia
Asia
Ikh Nart
Nature
Reserve,
Dornogobi
Aimag
Wolf (Canis lupus)
Canidae
Camel (Camelus bactrianus),
cattle (Bos tarus), goat (Capra
aegragus), horse (Equus ferus
caballus), sheep (Ovis aries)
Spatial association (Mahalanobis D2 (k)
analysis)
USA
North
America
Upper
Peninsula,
Michigan
Wolf (Canis lupus)
Canidae
Cattle, sheep, poultry, rabbit
Correlation modeling (single-sample
discriminant-function analysis)
Finland
Europe
southern and
central
Wolf (Canis lupus)
Canidae
Sheep
Correlation modeling (general additive
models)
India
Asia
Kanha Tiger
Reserve,
Madhya
Pradesh
Leopard (Panthera
pardus), tiger
(Panthera tigris),
wild dog (Cuon
alpinus)
Canidae,
Felidae
Buffalo, cattle, goat
Correlation modeling (logistic regression) to
identify landscape attributes associated with
risk; spatial interpolation to map risk
probabilities
India
Asia
Western
Ghats,
Karnataka
Leopard (Panthera
pardus), tiger
(Panthera
tigris),wild dog
(Cuon alpinus)
Canidae,
Felidae
Buffalo, cattle, goat
Correlation modeling (logistic regression) to
identify landscape attributes associated with
risk; spatial interpolation to map risk
probabilities
Argentina
South
America
Patagonia
Puma (Puma
concolor)
Felidae
Sheep (Ovis aries)
Correlation modeling (generalized linear
models)
Italy
Europe
western,
central and
eastern Alps
Wolf (Canis lupus)
Canidae
Sheep
Spatially explicit, individual-based model*
India
Asia
Gir Protected
Area, Gujarat
Asiatic lion
(Panthera leo
persica)
Felidae
Cattle, buffalo, goat, sheep
Spatial association (Getis-Ord G clustering)
3
India
Asia
Kanha Tiger
Reserve,
Madhya
Pradesh
Leopard (Panthera
pardus), tiger
(Panthera tigris)
Felidae
Cattle, buffalo, goat, pig
Correlation modeling (resource selection
function using logistic regression)
South
Africa
Africa
Riemvasmaak,
Northern Cape
Caracal (Caracal
caracal)
Felidae
Goat
Spatial interpolation (giving-up density)
China
Asia
Hunchun, Jilin
Tiger (Panthera
tigris)
Felidae
Cattle
Correlation modeling (negative binomial zero-
inflated)
USA
North
America
Wisconsin,
Minnesota
Wolf (Canis lupus)
Canidae
Bison, cattle, poultry, sheep
Correlation modeling (single-sample
discriminant-function analysis)
USA
North
America
Wisconsin
Wolf (Canis lupus)
Canidae
Cattle
Correlation modeling (logistic regression)
USA
North
America
Montana
Grizzly bear (Ursus
arctos)
Ursus
Cattle, sheep
Correlation modeling (logistic regression)
Mexico
North
America
Jaguar (Panthera
onca), puma (Puma
concolor)
Felidae
Cattle, goat, sheep
Correlation modeling (ensemble modeling
using Artificial Neural Network,
Environmental Distance, Genetic Algorithm
for Rule-set Production, Support Vector
Machines, Ecological Niche Factor Analysis
and Maxent
*Note: This study is not discussed in the review of modeling methods because it specifically addresses the recolonization of carnivores. To estimate
livestock depredation hotspots, the study multiplied the probability of wolf pack presence by the number of available livestock pastures, which is less
precise than modeling carnivore-livestock interactions more explicitly.
4
Fig S1 Maps compiled to examine global potential livestock depredation in Fig. 2. Top: Livestock density from
the Gridded Livestock of the World v.2.0 (Robinson et al., 2007). Bottom: The world’s primary carnivore
species responsible for depredating livestock (species list in Table S1 adapted from Breitenmoser et al. 2005;
species range maps from the IUCN Red List of Threatened Species v.2014.2 and Letnic et al. 2012)
5
References
Abade L, Macdonald DW, Dickman AJ (2014) Assessing the relative importance of landscape and husbandry
factors in determining large carnivore depredation risk in Tanzania’s Ruaha landscape. Biol Conserv
180:241–248
Baruch-Mordo S, Breck SW, Wilson KR, Theobald DM (2008) Spatiotemporal distribution of black bear–
human conflicts in Colorado, USA. J Wildl Manage 72:1853–1862
Breitenmoser U, Angst C, Landry J-M, et al (2005) In: Wodroffe, R, Thirgood, S, Rabinowitz, A (eds) People
and wildlife: Conflict or Coexistence? Cambridge University Press, Cambridge, pp 49–71
Davie HS, Murdoch JD, Lhagvasuren A, Reading RP (2014) Measuring and mapping the influence of landscape
factors on livestock predation by wolves in Mongolia. J Arid Environ 103:85–91
Edge JL, Beyer DE, Belant JL, et al (2011) Adapting a predictive spatial model for wolf Canis spp. predation on
livestock in the Upper Peninsula, Michigan, USA. Wildlife Biol 17:1–10
Kaartinen S, Luoto M, Kojola I (2009) Carnivore-livestock conflicts: determinants of wolf (Canis lupus)
depredation on sheep farms in Finland. Biodivers Conserv 18:3503–3517
Karanth KK, Gopalaswamy AM, DeFries RS, Ballal N (2012) Assessing patterns of human-wildlife conflicts
and compensation around a central Indian protected area. PLoS One 7:e50433
Karanth KK, Gopalaswamy AM, Prasad PK, Dasgupta S (2013) Patterns of human–wildlife conflicts and
compensation: insights from Western Ghats protected areas. Biol Conserv 166:175–185
Kissling WD, Fernández N, Paruelo JM (2009) Spatial risk assessment of livestock exposure to pumas in
Patagonia, Argentina. Ecography 32:807–817
Letnic M, Ritchie EG, Dickman CR (2012) Top predators as biodiversity regulators: the dingo Canis lupus
dingo as a case study. Biol Rev Camb Philos Soc 87:390–413
Marucco F, McIntire EJB (2010) Predicting spatio-temporal recolonization of large carnivore populations and
livestock depredation risk: Wolves in the Italian Alps. J Appl Ecol 47:789–798
Meena V, Macdonald DW, Montgomery RA (2014) Managing success: Asiatic lion conservation, interface
problems and peoples’ perceptions in the Gir Protected Area. Biol Conserv 174:120–126
Miller JRB, Jhala Y V, Jena J, Schmitz OJ (2015) Landscape-scale accessibility of livestock to tigers:
Implications of spatial grain for modeling predation risk to mitigate human-carnivore conflict. Ecol Evol
5:1354–1367
Robinson TP, Franceschini G, Wint W (2007) The food and agriculture organization’s gridded livestock of the
world. Vet Ital 43:745–751
Shrader AM, Brown JS, Kerley GIH, Kotler BP (2008) Do free-ranging domestic goats show “landscapes of
fear”? Patch use in response to habitat features and predator cues. J Arid Environ 72:1811–1819
6
Soh YH, Carrasco LR, Miquelle DG, et al (2014) Spatial correlates of livestock depredation by Amur tigers in
Hunchun, China: relevance of prey density and implications for protected area management. Biol Conserv
169:117–127
Treves A, Martin KA, Wydeven AP, Wiedenhoeft JE (2011) Forecasting environmental hazards and the
application of risk maps to predator attacks on livestock. Bioscience 61:451–458
Treves A, Naughton-treves L, Harper EK, et al (2004) Predicting human-carnivore conflict: a spatial model
derived from 25 years of data on wolf predation on livestock. Conserv Biol 18:114–125
Wilson SM, Madel M, Mattson D, et al (2006) Landscape conditions predisposing grizzly bears to conflicts on
private agricultural lands in the western USA. Biol Conserv 130:47–59
Zarco-González MM, Monroy-Vilchis O, Alaníz J (2013) Spatial model of livestock predation by jaguar and
puma in Mexico: conservation planning. Biol Conserv 159:80–87

Supplementary resource (1)

... However, where a species utilizes landscapes dominated by monoculture crops with minimal heterogeneity, such as oil palm, model outputs may not produce meaningful results. Another approach is to apply hot spot analysis within a species geographic range (i.e., the extent of occurrence) to map the frequency or weight of where a species is occurring across a landscape [38][39][40][41] . A hot spot is defined here as an area with statistically significant spatial clusters of either high or low values 42 . ...
... A hot spot is defined here as an area with statistically significant spatial clusters of either high or low values 42 . Hot spot analyses are useful in identifying important ecological biogeography patterns and habitat features, as well as anthropogenic characteristics associated with species distributions [38][39][40][41] . ...
... We also look at hot spots on, or near to, a planned highway alignment to understand the potential impact of this infrastructure development project. Lastly, we look at time spent by elephants in different oil palm estates to identify where better management strategies are needed to improve habitat permeability and reduce human-elephant conflicts 40,43,44 . ...
... However, where a species utilizes landscapes dominated by monoculture crops with minimal heterogeneity, such as oil palm, model outputs may not produce meaningful results. Another approach is to apply hot spot analysis within a species geographic range (i.e., the extent of occurrence) to map the frequency or weight of where a species is occurring across a landscape [38][39][40][41] . A hot spot is defined here as an area with statistically significant spatial clusters of either high or low values 42 . ...
... A hot spot is defined here as an area with statistically significant spatial clusters of either high or low values 42 . Hot spot analyses are useful in identifying important ecological biogeography patterns and habitat features, as well as anthropogenic characteristics associated with species distributions [38][39][40][41] . ...
... We also look at hot spots on, or near to, a planned highway alignment to understand the potential impact of this infrastructure development project. Lastly, we look at time spent by elephants in different oil palm estates to identify where better management strategies are needed to improve habitat permeability and reduce human-elephant conflicts 40,43,44 . ...
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In the Kinabatangan floodplain, Sabah, Malaysian Borneo, oil palm and settlements have reduced and fragmented lowland tropical forests, home to around 200 endangered Bornean elephants ( Elephas maximus borneensis ). In this region, elephants range within forests, oil palm and community areas. The degree to which elephants are using these areas remains unclear. We used GPS telemetry data from 2010 to 2020 for 14 collared elephants to map their entire known ranges and highly used areas (hot spots) across four land use categories and estimate time spent within these. The use of land use types across elephants varied significantly. Typically, females had strong fidelity to forests, yet many of these forests are threatened with conversion. For the three males, and several females, they heavily used oil palm estates, and this may be due to decreased landscape permeability or foraging opportunities. At the pooled level, the entire range and hot spot extents, constituted 37% and 34% for protected areas, respectively, 8% and 11% for unprotected forests, 53% and 51% for oil palm estates, and 2% for community areas. Protecting all forested habitats and effectively managing areas outside of protected areas is necessary for the long-term survival of this population.
... In many countries, especially developing countries with abundant wildlife resources, the increasingly intensified HWCs have caused serious problems regarding ecology, food security, social governance, and the sustainable development of farmers' livelihoods as well as other issues [1,3]. HWCs not only pose an enormous obstacle to wildlife conservation but also bring great economic or welfare losses to humans [4][5][6]. For example, in 2005, wild boars led to crop losses of USD 800 million in the United States [7]. ...
... There are many indirect effects of HWCs on human life. For example, (1) increased damage to ecosystems, especially the hunting of wildlife [5,6], occurs because for farmers who experience HWCs, protection is more passive [13,44]. (2) The increase in the labour intensity and risks of agricultural operations makes farmers more psychologically and physically vulnerable [57][58][59]. ...
Article
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Effectively managing and mitigating “human–wildlife conflict” (HWC) by adjusting the use of cultivated land to realise the coexistence of humans and wildlife plays an important role in protecting biodiversity, ensuring food security, improving cultivated land use efficiency, and improving the livelihoods of community residents in nature reserves. Based on the “harmful experience–expectation change–behavioural adjustment” theoretical analysis framework and survey data on 1008 farmers in China’s Giant Panda National Park, this paper uses a binary logit model and a mediating utility model to analyse the effect of HWC experience on the cultivated land use of farmers and the action mechanisms. The results show the following: (1) HWC experience increases the probability that farmers’ cultivated land use behaviour will be adjusted, which will not only increase the probability that farmers’ planting structure of cultivated land will be adjusted but also increase the probability that farmers will abandon cultivated land. (2) Farmers in the national park have a higher probability of adjusting their cultivated land use behaviour after experiencing HWCs than those outside the national park. Farmers in the national park mainly prefer to adjust the planting structure, while farmers outside the park are more inclined to abandon cultivated land. (3) Low-income farmers are more likely to adjust their cultivated land use behaviour after experiencing HWCs than high-income farmers. The low-income group mainly tends to adjust the planting structure of cultivated land, while the high-income group is more inclined to directly abandon cultivated land. (4) The farmers in the group with a high degree of dependence on cultivated land for their livelihood have a higher probability of adjusting their cultivated land use behaviour after experiencing HWC than those in the low-dependency group, and they tend to adjust the planting structure. (5) HWC experience mainly improves farmers’ adjustment of cultivated land use behaviour by improving their awareness of future risks of HWC. In other words, “HWC” will change the behavioural decision-making of farmers and the differences in constraints, such as different regions and different income levels, will lead to differences in “HWC” affecting farmers’ behaviours. The conclusions of this paper not only help in understanding the adjustment mechanism of farmers’ cultivated land use behaviour in the context of HWCs but also provide a decision-making reference for actively changing cultivated land use methods to address HWCs.
... Predictive risk models are multidimensional approaches that explore both ecological and social aspects of human-wildlife competition (Holmern & Røskaft, 2014;Restrepo-Cardona et al., 2019). To build a predictive risk model, information on kill sites and sites without livestock or poultry loss (presence-absence) is needed, along with measurements of spatial-ecological and domestic animal husbandry data (Miller, 2015). ...
... Predictive risk models, including spatial risk assessments, have been successfully used to propose guidelines to prevent carnivore attacks on livestock in high risk areas (Kissling, Fernández, & Paruelo, 2009;Treves, Martin, Wydeven, & Wiedenhoeft, 2011) via advising policymakers and farmers about management of both their land and domestic animals (Miller, 2015). A suite of studies has reported that landscape attributes, such as vegetation, topography and proximity measures, as well as animal husbandry variables are the most predominant predictors in predation risk models. ...
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The Andean temperate forests of Southern Chile are a Global Biodiversity Hotspot where diurnal raptors cohabit with humans in rural socio-ecosystems. Raptors can predate on small domestic animals, which is why farmers associate them to poultry loss and can develop an aversion to diurnal raptors in their territory. At the local level, economic impacts to people can be severe, but, on the other hand, raptor attacks may be overestimated. In Chile, although there has been an increase of farmers reporting this situation, there is no information on raptors predation impact on poultry. There is the need to address this conflict through a multidimensional approach to have a comprehensive insight of coupled people-wildlife systems. Predictive risk models have been used to identify effective management measures to reduce human-wildlife competition by analysing both social and ecological factors of the conflict. This study aims to build a predictive risk model to identify husbandry practices and landscape indicators associated with poultry predation by diurnal raptors in Southern Chile. Questionnaires were used to ask farmers about their poultry production systems, their husbandry practices and about raptor predation impact. Data from questionnaire were used for the model generation, along with landscape features of the areas surrounding the farmers' property. Results show that farmers maintaining an exclusion structure for the chickens, a guard dog and a high proportion of forest in their property can reduce the risk of predation on their birds. These findings can be used to prevent raptor attacks on poultry by encouraging other peasants to implement these management measures. Further research is needed to have a clearer understanding of the predation impact of raptors and the effectiveness of management measures, by also exploring other techniques that have been successful in similar contexts.
... Such hotspots often focused on (de)predation by carnivores. Predation risk is known to be spatially unevenly distributed: habitat and landscape characteristics, as well as anthropogenic factors, can influence the odds of impact and determine the existence of 'predation hotspots' (Miller, 2015;Broekhuis et al., 2017). Spatial predation risk models that correlate landscape attributes with the occurrence of wildlife impacts on human activities (Abade et al., 2014;Miller, 2015) are then used to focus prevention efforts and mitigation measures towards high-risk areas (Zanin et al., 2015). ...
... Predation risk is known to be spatially unevenly distributed: habitat and landscape characteristics, as well as anthropogenic factors, can influence the odds of impact and determine the existence of 'predation hotspots' (Miller, 2015;Broekhuis et al., 2017). Spatial predation risk models that correlate landscape attributes with the occurrence of wildlife impacts on human activities (Abade et al., 2014;Miller, 2015) are then used to focus prevention efforts and mitigation measures towards high-risk areas (Zanin et al., 2015). However, focusing only on impacts such as predation on livestock has led to important misconceptions about conservation conflict "(A) that the level of wildlife damage is directly related to the level of conflict engendered; (B) that the level of conflict elicits a proportionate response; (C) that altering the response to conflicts will have proportionate conservation effects" (Dickman, 2010, p. 459). ...
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Global processes manifesting as activities in local places have led to an increase in documented conservation conflicts. Conservation conflicts are sometimes labelled human-wildlife conflict, focusing only on the direct negative impact of species (usually wildlife) on humans or vice versa. However, many authors now recognize that conservation conflicts arise between people with diverse views, when one party acts against the interests of another. They are thus human-human conflicts and not merely an impact on or from conservation. Conflict is not always directly correlated with impact because perceptions of risk, levels of tolerance and conservation values influence human responses. This review aims to define the concept of ‘conservation conflict hotspots’ and explore its practical applications in conservation. We propose that the interaction of impact, risk perception, level of tolerance in a context of conservation values can be mapped at a local scale, with spatial visualization assisting the prediction, understanding and management of such hotspots. The term conservation value incorporates measures of indigeneity, endemicity and demography along with emotional or cultural attachment to species or places. The umbrella terms of risk perception and tolerance capture many of the aspects of attitude, values and individual demographics that can influence people’s actions, enabling contextualization of relevant social factors at local scales. Spatially mapped layers enable us to plan and target conservation efforts towards human as well as ecological factors. The concept of ‘conservation conflict hotspot’ emphasizes the need for transdisciplinary research to understand underlying drivers of conflict and for dialogical and peace-building approaches to facilitate trust and cooperation amongst actors. We can thus address conflicts and achieve sustainable outcomes.
... Although multiple studies have used distribution models to map predation risk by wildlife species (e.g. Miller, 2015;Rostro-García et al., 2016;Broekhuis et al., 2017), one important but fairly neglected application of these models is to evaluate conflict risks associated with migration corridors. While corridors facilitate individual movements and thus can help maintain connectivity between habitats, these linkages may increase the potential for conflict with human and human-induced mortality risk (Cushman, Compton, & McGarigal, 2010;Cushman et al., 2018). ...
... The positive effects of villages may be attributed to its positive association of available croplands and livestocks, which increases the risk of bear damages (e.g. Miller, 2015;Rostro-García et al., 2016). Another important predictor of brown bear damages risk was distance to conservation areas, suggesting that bears were more likely to have conflict with local people near conservation areas. ...
Article
Multiple studies have used species distribution models to identify human–wildlife conflict drivers. An important application of these models is spatial conflict resolution by accounting for habitat suitability and corridors. We used distribution and connectivity models to identify habitats and corridors for brown bear Ursus arctos in southwestern Iran with high risk of bear damages, and evaluated the effects of landscape composition and configuration on the predicted conflict hotspots. We used 154 locations of bear damage incidents along with a suit of predictors to develop risk models. To prepare predictive variables, we used brown bear occurrence data and a number of covariates to develop a suitability model. We then converted the suitability map into a resistance surface and used a connectivity model to predict corridors. Finally, the bear damages risk map, habitats and corridors were overlaid to prioritize conflict hotspots, corridors and habitats, and conflict-prone corridors. Proportion of suitable habitats, distance to village, density of forest patches, conservation areas and corridor bottlenecks were the main predictors contributing to bear damages risk. A total of 38.73% of habitats, and 6.24% of corridors across the 124 000-km² study area were identified as areas with high risk of bear damages. The risk of bear damage was also spatially associated with forests fragmentation and patchiness of habitat. Our results highlight the importance of landscape configuration and corridors when investigating the spatial patterns of bear damages. Our findings showed how the combination of distribution models and connectivity analysis can guide carnivore conservation planning aiming at reducing the risk of carnivore-inflicted damages.
... There is a general agreement that biodiversity is under threat globally due to human population growth and the exponential use of natural resources (Blount-Hill, 2021;Whittaker et al., 2005;Zarzo-Arias et al., 2021). The human population expansion into the wildlife habitat, agricultural intensification, change in the cropping pattern, and dependence on natural resources are the main causes behind the widespread human-wildlife conflicts (Berihun et al., 2016;Dickman, 2010;Lozano et al., 2019;Miller, 2015;Sharma et al., 2021;Treves & Karanth, 2003). The brown bear (Ursus arctos Linnaeus, 1758) is distributed in most of Europe, Asia, North America, the Middle East, and some parts of North Africa (Servheen et al., 1999;Swenson et al., 2000). ...
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Brown bear-mediated conflicts have caused immense economic loss to the local people living across the distribution range. In India, limited knowledge is available on the Himalayan brown bear (HBB), making human-brown bear conflict (HBC) mitigation more challenging. In this study, we studied HBC in the Lahaul valley using a semi-structured questionnaire survey by interviewing 398 respondents from 37 villages. About 64.8% of respondents reported conflict in two major groups-crop damage (30.6%) and livestock depredations (6.2%), while 28% reported both. Conflict incidences were relatively high in summer and frequently occurred in areas closer to the forest (<500 m) and between the elevations range of 2700 m to 3000 m above sea level (asl). The dependency of locals on forest resources (70%) for their livelihood makes them vulnerable to HBC. The "upper lower" class respondents were most impacted among the various socioeconomic classes. Two of the four clusters were identified as HBC hot spots in Lahaul valley using SaTscan analysis. We also obtained high HBC in cluster II with a 14.35 km radius. We found that anthropogenic food provisioning for HBB, livestock grazing in bear habitats, and poor knowledge of animal behavior among the communities were the major causes of HBC. We suggest horticulture crop waste management, controlled and supervised grazing, ecotourism, the constitution of community watch groups, and others to mitigate HBC. We also recommend notifying a few HBB abundant sites in the valley as protected areas for the long-term viability of the HBB in the landscape.
... Spatial risk mapping is a practical tool for understanding patterns of depredation risk (Treves et al., 2011). Typically, such maps predict marginal predation risk (Miller, 2015) the product of the probability of predator-livestock encounters and the conditional probability of predation given an encounter (Lima and Dill, 1990) -and may thus conflate factors influencing carnivore presence with those influencing livestock vulnerability (Hebblewhite et al., 2005). It may instead be desirable to understand the spatial distribution of depredation while explicitly accounting for carnivore presence. ...
Article
The cycle of livestock depredation and retaliatory killing constitutes a major threat to large carnivores worldwide and imposes considerable hardships on human communities. Mitigation efforts are often undertaken with little knowledge of ecological underpinnings and patterns of depredation, limiting conservationists' ability to develop, prioritize, and evaluate solutions. Carnivore detection and depredation data from interviews in affected communities may help address this gap, but such data are often prone to false-positive uncertainty. To address these challenges in the Pamir Mountains of Tajikistan we collected snow leopard, lynx, wolf, and bear detection and depredation reports from local communities via semi-structured interviews. We used a novel hierarchical multi-species multi-state occupancy model that accounted for potential false-positives to investigate carnivore site use and depredation concurrently with respondents' apparent vulnerability to that risk. Estimated false-positive probabilities were small, but failure to account for them overstated site use probabilities and depredation risk for all species. Although individual vulnerability was low, depredation was nonetheless commonplace. Carnivore site use was driven by clear habitat associations, but we did not identify any clearly important large-scale spatial correlates of depredation risk despite considerable spatial variation in that risk. Respondents who sheltered livestock in household corrals reinforced with wire mesh were less likely to report snow leopard depredations. Reducing depredation and retaliation at adequately large scales in the Pamirs will likely require a portfolio of species-specific strategies, including widespread proactive corral improvements. Our approach expanded inference on the often-cryptic processes surrounding human-carnivore conflict even though structured wildlife data were scarce.
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Caracterización espacial y evaluación económica del conflicto: puma (Puma concolor) y producciones pecuarias en la cuenca del río Yucao, Puerto López, Meta, Colombia Spatial characterization and economic evaluation of the conflict: puma (Puma concolor) and livestock in the Yucao River Basin, Resumen El puma (Puma concolor), felino ampliamente distribuido en Colombia, es afectado por las alteraciones ambientales, estructurales y espaciales de su hábitat, solapando su nicho realizado con los modelos pro-ductivos pecuarios en los departamentos de Meta y Casanare. Objetivo: Evaluar el conflicto humano-felino mediante la caracterización de los eventos de depredación del puma sobre los animales domésticos en producciones pecuarias de la cuenca del río Yucao, Puerto López, Meta. Metodología: Se caracterizó espacialmente el conflicto puma-producciones pecuarias en 13 producciones pecuarias empleando un modelo GAMLSS. También se valoró económicamente las pérdidas de animales domésticos por pumas entre septiembre de 2019 y octubre de 2020. Resultados: Se reportaron en total 73 animales domésticos depredados por pumas. El mayor valor en el índice de uso se reporta en equinos y bovinos H y M <6 meses de edad. El GAMLSS mejor ajustado (GAIC=63,2) estableció que los eventos de depredación se explican por la distancia a bosques, densidad de fuegos, áreas boscosas, y distancia a cuerpos de agua y a vías. La valoración económica del conflicto registrado en los 13 predios fue de $62'700.000 con un costo promedio $4'823.076 para cada predio. Conclusión: Se evidencia la necesidad de implementar medidas anti-depredatorias en los predios para disminuir la intensidad del conflicto, permitiendo a su vez, la generación de escenarios de coexistencia evitando la muerte por retaliación de los pumas y la pérdida de la funcionalidad ecosistémica que brinda esta especie de felino a los ecosistemas de sabanas nativas en la Orinoquia. Palabras clave: Coexistencia, Estrategias anti-depredación, Felinos, Llanos Orientales, Valoración económica. Abstract The puma (Puma concolor), a feline with a wide distribution in Colombia, is affected by the physical alterations it suffers in its habitat, causing its niche to overlap with the models of livestock production in the departments of Meta and Casanare diferente. Objective: To evaluate the human-feline conflict by characterizing the predation events of the puma on domestic animals in livestock production in the Yucao River basin, Puerto López, Meta. Methodology: The puma-livestock production conflict was spatially characterized in 13 livestock productions using a GAMLSS model. Additionally, the losses of domestic animals by pumas between september 2019 and october 2020 were economically valued. Results: A total of 73 domestic animals predated by pumas were reported. The highest value in the use index is reported in horses and bovines H and M <6 months of age. The best adjusted GAMLSS (GAIC=63.2) established that predation events are explained by distance to forests, fire density, forested areas, distance to water bodies, and roads. The economic valuation of the conflict recorded in the 13 properties was $62’700.000 with an average cost of $4’823.076 for each property. Conclusion: There is a need to implement antipredatory measures on the properties to reduce the intensity of the conflict, allowing in turn to generate scenarios of coexistence, avoiding the retaliatory death of pumas and the loss of the ecosystems functionality that this feline species offers to the native savannah ecosystems of the Orinoquia. Keywords: Antipredation estrategies, Coexistence, Economic valuation, Llanos Orientales, Wildcats.
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Large wild herbivores are crucial to ecosystems and human societies. We highlight the 74 largest terrestrial herbi-vore species on Earth (body mass > – 100 kg), the threats they face, their important and often overlooked ecosystem effects, and the conservation efforts needed to save them and their predators from extinction. Large herbivores are generally facing dramatic population declines and range contractions, such that ~60% are threatened with extinction. Nearly all threatened species are in developing countries, where major threats include hunting, land-use change, and resource depression by livestock. Loss of large herbivores can have cascading effects on other species including large carnivores, scavengers, mesoherbivores, small mammals, and ecological processes involving vegetation , hydrology, nutrient cycling, and fire regimes. The rate of large herbivore decline suggests that ever-larger swaths of the world will soon lack many of the vital ecological services these animals provide, resulting in enormous ecological and social costs.
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Innovative conservation tools are greatly needed to reduce livelihood losses and wildlife declines resulting from human–carnivore conflict. Spatial risk modeling is an emerging method for assessing the spatial patterns of predator–prey interactions, with applications for mitigating carnivore attacks on livestock. Large carnivores that ambush prey attack and kill over small areas, requiring models at fine spatial grains to predict livestock depredation hot spots. To detect the best resolution for predicting where carnivores access livestock, we examined the spatial attributes associated with livestock killed by tigers in Kanha Tiger Reserve, India, using risk models generated at 20, 100, and 200-m spatial grains. We analyzed land-use, human presence, and vegetation structure variables at 138 kill sites and 439 random sites to identify key landscape attributes where livestock were vulnerable to tigers. Land-use and human presence variables contributed strongly to predation risk models, with most variables showing high relative importance (≥0.85) at all spatial grains. The risk of a tiger killing livestock increased near dense forests and near the boundary of the park core zone where human presence is restricted. Risk was nonlinearly related to human infrastructure and open vegetation, with the greatest risk occurring 1.2 km from roads, 1.1 km from villages, and 8.0 km from scrubland. Kill sites were characterized by denser, patchier, and more complex vegetation with lower visibility than random sites. Risk maps revealed high-risk hot spots inside of the core zone boundary and in several patches in the human-dominated buffer zone. Validation against known kills revealed predictive accuracy for only the 20 m model, the resolution best representing the kill stage of hunting for large carnivores that ambush prey, like the tiger. Results demonstrate that risk models developed at fine spatial grains can offer accurate guidance on landscape attributes livestock should avoid to minimize human–carnivore conflict.
Book
Nowadays, ecologists worldwide recognize the use of spatial analysis as essential. However, because of the fast-growing range of methods available, even an expert might occasionally find it challenging to choose the most appropriate one. Providing the ecological and statistical foundations needed to make the right decision, this second edition builds and expands upon the previous one by: • Encompassing the basic methods for spatial analysis, for both complete census and sample data • Investigating updated treatments of spatial autocorrelation and spatio-temporal analysis • Introducing detailed explanations of currently developing approaches, including spatial and spatio-temporal graph theory, scan statistics, fibre process analysis, and Hierarchical Bayesian analysis • Offering practical advice for specific circumstances, such as how to analyze forest Permanent Sample Plot data and how to proceed with transect data when portions of the data series are missing. Written for graduates, researchers and professionals, this book will be a valuable source of reference for years to come.
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The number and variety of statistical techniques for spatial analysis of ecological data are burgeoning and many ecologists are unfamiliar with what is available and how the techniques should be used. This book provides an overview of the wide range of spatial statistics available to analyze ecological data, and provides advice and guidance for graduate students and practicing researchers who are either about to embark on spatial analysis in ecological studies or who have started but need guidance to proceed. © M.-J. Fortin and M.R.T. Dale 2005 and Cambridge University Press 2009.