ArticlePDF Available

Spatial patterns of large African cats: a large‐scale study on density, home range size, and home range overlap of lions Panthera leo and leopards Panthera pardus

Authors:

Abstract

Spatial patterns of and competition for resources by territorial carnivores are typically explained by two hypotheses: 1) the territorial defence hypothesis and 2) the searching efficiency hypothesis. According to the territorial defence hypothesis, when food resources are abundant, carnivore densities will be high and home ranges small. In addition, carnivores can maximise their necessary energy intake with minimal territorial defence. At medium resource levels, larger ranges will be needed, and it will become more economically beneficial to defend resources against a lower density of competitors. At low resource levels, carnivore densities will be low and home ranges large, but resources will be too scarce to make it beneficial to defend such large territories. Thus, home range overlap will be minimal at intermediate carnivore densities. According to the searching efficiency hypothesis, there is a cost to knowing a home range. Larger areas are harder to learn and easier to forget, so carnivores constantly need to keep their cognitive map updated by regularly revisiting parts of their home ranges. Consequently, when resources are scarce, carnivores require larger home ranges to acquire sufficient food. These larger home ranges lead to more overlap among individuals' ranges, so that overlap in home ranges is largest when food availability is the lowest. Since conspecific density is low when food availability is low, this hypothesis predicts that overlap is largest when densities are the lowest. We measured home range overlap and used a novel method to compare intraspecific home range overlaps for lions Panthera leo ( n = 149) and leopards Panthera pardus ( n = 111) in Africa. We estimated home range sizes from telemetry location data and gathered carnivore density data from the literature. Our results did not support the territorial defence hypothesis for either species. Lion prides increased their home range overlap at conspecific lower densities whereas leopards did not. Lion pride changes in overlap were primarily due to increases in group size at lower densities. By contrast, the unique dispersal strategies of leopards led to reduced overlap at lower densities. However, when human‐caused mortality was higher, leopards increased their home range overlap. Although lions and leopards are territorial, their territorial behaviour was less important than the acquisition of food in determining their space use. Such information is crucial for the future conservation of these two iconic African carnivores.
PREDICTIVE REVIEW
Spatial patterns of large African cats: a large- scale study on
density, home range size, and home range overlap of lions
Panthera leo and leopards Panthera pardus
Vilis O. NAMS*Department of Plant, Food and Environmental Scienes, Faculty of Agriculture,
Dalhousie University, Truro, NS B2N 5E3, Canada and Wildlife and Reserve Management Research
Group, Department of Zoology & Entomology, Rhodes University, P.O. Box 94, Grahamstown 6140,
South Africa. Email: vilis.nams@dal.ca
Dan M. PARKER Wildlife and Reserve Management Research Group, Department of Zoology &
Entomology, Rhodes University, P.O. Box 94, Grahamstown 6140, South Africa and School of Biology
and Environmental Sciences, University of Mpumalanga, Nelspruit 1200, South Africa.
Email: daniel.parker@ump.ac.za
Florian J. WEISECentre for Wildlife Management, University of Pretoria, Pretoria 0002, South Africa
and CLAWS Conservancy, Pride in Our Prides, Worcester, MA 01608, USA and N/a’an ku sê Research
Programme, P.O. Box 99292, Windhoek, Namibia. Email: florian.weise@gmail.com
Bruce D. PATTERSONNegaunee Integrative Research Center, Field Museum of Natural History,
Chicago, IL 60605, USA. Email: bpatterson@fieldmuseum.org
Ralph BUIJAnimal Ecology Group, Wageningen University & Research, Droevendaalsesteeg 3A, 6708
PB Wageningen, The Netherlands and The Peregrine Fund, 5668 West Flying Hawk Lane, Boise, ID
83709, USA. Email: ralph.buij@gmail.com
Frans G. T. RADLOFFDepartment of Conservation and Marine Sciences, Faculty of Applied Sciences,
Cape Peninsula University of Technology, P.O. Box 652, Cape Town 8000, South Africa.
Email: radlofff@cput.ac.za
Abi Tamim VANAKAshoka Trust for Research in Ecology and the Environment, Bangalore 560064,
India and School of Life Sciences, University of KwaZulu- Natal, Durban 3629, South Africa.
Email: abivanak@gmail.com
Pricelia N. TUMENTADepartment of Forestry, Faculty of Agronomy and Agricultural Sciences,
University of Dschang, P.O. Box 138, Yaounde, Cameroon and Regional Training Centre Specialized
in Agriculture, Forestry- wood and Environment (CRESA Foret Bois), University of Dschang, P.O. Box
138, Yaounde, Cameroon. Email: priceliat@gmail.com
Matt W. HAYWARDConservation Science Research Group, School of Environmental and Life Sciences,
College of Engineering, Science and the Environment, University of Newcastle, Callaghan, NSW 2308,
Australia and Department of Zoology and Entomology, Mammal Research Institute, University of
Pretoria, Pretoria 0002, South Africa. Email: matthew.hayward@newcastle.edu.au
Lourens H. SWANEPOELDepartment of Zoology, University of Venda, Thohoyandou 0950, South
Africa. Email: lourens.swanepoel.univen@gmail.com
Paul J. FUNSTONDepartment of Nature Conservation, Tshwane University of Technology, Private Bag
X680, Pretoria 0001, South Africa and Panthera, New York, NY 10018, USA. Email: pfunston@
panthera.org
Hans BAUERWildlife Conservation Research Unit, Zoology Department, University of Oxford, The
Recanati- Kaplan Centre, Tubney House, Abingdon Road, Tubney, Abingdon OX13 5QL, UK.
Email: hans.bauer@biology.ox.ac.uk
R. John POWERDepartment of Economic Development, Environment, Conservation and Tourism,
North West Provincial Government, Mahikeng 2735, South Africa. Email: jpower@nwpg.gov.za
John O’BRIENWildlife and Reserve Management Research Group, Department of Zoology & Entomology,
Rhodes University, P.O. Box 94, Grahamstown 6140, South Africa. Email: john.obrien@shamwari.com
1
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium,
provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Mammal Review ISSN 0305-1838
bs_bs_banner
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
2
V. O. Nams et al.Spatial patterns of large African cats
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
Timothy G. O’BRIENWildlife Conservation Society, Global Conservation Programs, 2300 Southern
Blvd., Bronx, NY 10460, USA. Email: tobrien@wcs.org
Craig J. TAMBLINGDepartment of Zoology and Entomology, University of Fort Hare, Alice, Eastern
Cape 5700, South Africa and Department of Zoology and Entomology, University of Pretoria, Pretoria
0028, South Africa. Email: cjtambling@gmail.com
Hans H. deIONGHEvolutionary Ecology Group, Department Biology, University of Antwerp,
Universiteitsplein 1, Wilrijk, Building D 132, Antwerpen, Belgium and Institute of Environmental
Sciences, Leiden University, Einsteinweg 2, P.O. Box 9518, 2300 RA Leiden, The Netherlands.
Email: iongh@cml.leidenuniv.nl
Sam M. FERREIRAScientific Services, SANParks, Private Bag x 402, Skukuza 1350, South Africa.
Email: sam.ferreira@sanparks.org
Norman OWEN- SMITH Centre for African Ecology, School of Animal, Plant and Environmental
Sciences, University of the Witwatersrand, Private Bag 3, Wits 2050, South Africa. Email: norman.
owen-smith@wits.ac.za
James W. CAINIIICentre for African Ecology, School of Animal, Plant and Environmental Sciences,
University of the Witwatersrand, Private Bag 3, Wits 2050, South Africa. Email: jwcain@nmsu.edu
Julien FATTEBERTPanthera, New York, NY 10018, USA and Centre for Functional Biodiversity, School of
Life Sciences, University of KwaZulu- Natal, Durban 4000, South Africa. Email: julien.fattebert@gmail.com
Barbara M. CROESInstitute of Environmental Sciences, Leiden University, Einsteinweg 2, P.O. Box
9518, 2300 RA Leiden, The Netherlands. Email: croes.barbara@gmail.com
Goran SPONGForestry and Environmental Resources, College of Natural Resources, NCSU, Raleigh
27695, USA and Molecular Ecology Group, Wildlife, Fish, & Environmental Studies, SLU, 90183 Umeå,
Sweden. Email: goran.spong@slu.se
Andrew J. LOVERIDGEWildlife Conservation Research Unit, Zoology Department, University of
Oxford, The Recanati- Kaplan Centre, Tubney House, Abingdon Road, Tubney, Abingdon OX13 5QL,
UK. Email: tawnycat1@hotmail.com
Ann Marie HOUSERCheetah Conservation Botswana, Private Bag 0457, Gaborone, Botswana.
Email: amhouser3@yahoo.com
Krystyna A. GOLABEKBotswana Predator Conservation Trust, Private Bag 13, Maun, Botswana.
Email: krystyna.jordan@gmail.com
Colleen M. BEGGNiassa Carnivore Project, Private Bag X18, Rondebosch, South Africa.
Email: colleenmbegg@gmail.com
Tanith GRANTWildlife and Reserve Management Research Group, Department of Zoology &
Entomology, Rhodes University, P.O. Box 94, Grahamstown 6140, South Africa. Email: tanithgrant@
gmail.com
Paul TRETHOWANWildlife Conservation Research Unit, Zoology Department, University of Oxford,
The Recanati- Kaplan Centre, Tubney House, Abingdon Road, Tubney, Abingdon OX13 5QL, UK.
Email: pdtrethowan@gmail.com
Charles MUSYOKIKenya Wildlife Service, P.O. Box 40241, 00100 Nairobi, Kenya. Email: musyoki@kws.go.ke
Vera MENGESDepartment Evolutionary Ecology, Leibniz Institute for Zoo and Wildlife Research,
Alfred- Kowalke- Str. 17, D- 10315 Berlin, Germany. Email: vera.menges@gmail.com
Scott CREELDepartment of Ecology, Montana State University, Bozeman, MT 59717, USA.
Email: screel@montana.edu
Guy A. BALMEPanthera, New York, NY 10018, USA and Institute for Communities and Wildlife in
Africa, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa. Email: gbalme@
panthera.org
Ross T. PITMANPanthera, New York, NY 10018, USA and Institute for Communities and Wildlife in
Africa, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa. Email: rpitman@
panthera.org
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
3
Spatial patterns of large African catsV. O. Nams et al.
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
Charlene BISSETTWildlife and Reserve Management Research Group, Department of Zoology &
Entomology, Rhodes University, P.O. Box 94, Grahamstown 6140, South Africa and Scientific Services,
SANParks, Private Bag x 402, Skukuza 1350, South Africa. Email: charlene.bissett@sanparks.org
David JENNYCentre Suisse de Recherches Scientifiques, 17 Rte de Dabou, Abidjan, Ivory Coast
and Zoologisches Institut, Universität Bern, Baltzerstrasse 6, Bern 3012, Switzerland. Email: jenny.d@
compunet.ch
Paul SCHUETTEDepartment of Ecology, Montana State University, Bozeman, MT 59717, USA.
Email: paschuette@alaska.edu
Christopher C. WILMERSEnvironmental Studies Department, University of California, Santa Cruz, CA
95064, USA. Email: cwilmers@ucsc.edu
Luke T. B. HUNTERWildlife Conservation Society, Global Conservation Programs, 2300 Southern Blvd.,
Bronx, NY 10460, USA and School of Biological and Conservation Sciences, University of KwaZulu- Natal,
Westville Campus, Private Bag X54001, Durban 4000, South Africa. Email: luketbhunter@gmail.com
Margaret F. KINNAIRDMpala Research Centre, P.O. Box 555, Nanyuki 10400, Kenya. Email:
mkinnaird@wwfint.org
Keith S. BEGGNiassa Carnivore Project, Private Bag X18, Rondebosch, South Africa. Email: ratel@
iafrica.com
Cailey R. OWENSchool of Life Sciences, University of KwaZulu- Natal, Durban 3629, South Africa.
Email: cailey@sudwalacaves.com
Villiers STEYNDepartment of Nature Conservation, Tshwane University of Technology, Private Bag
X680, Pretoria 0001, South Africa. Email: villiers@absamail.co.za
Dirk BOCKMUEHLDepartment Evolutionary Ecology, Leibniz Institute for Zoo and Wildlife Research,
Alfred- Kowalke- Str. 17, D- 10315 Berlin, Germany. Email: dirkbockmuhl@yahoo.com
Stuart J. MUNRON/a’an ku sê Research Programme, P.O. Box 99292, Windhoek, Namibia.
Email: stuartnaankuse@gmail.com
Gareth K. H. MANNWildlife and Reserve Management Research Group, Department of Zoology &
Entomology, Rhodes University, P.O. Box 94, Grahamstown 6140, South Africa and Panthera, New
York, NY 10018, USA and Department of Biological Sciences, University of Cape Town, Cape Town
7701, South Africa and The Cape Leopard Trust, Cape Town 7806, South Africa. Email: gmann9@
gmail.com
Byron D. duPREEZWildlife Conservation Research Unit, Zoology Department, University of Oxford,
The Recanati- Kaplan Centre, Tubney House, Abingdon Road, Tubney, Abingdon OX13 5QL, UK.
Email: bydupreez@gmail.com
Laurie L. MARKERCheetah Conservation Fund, P.O. Box 1755, Otjiwarongo, Namibia. Email: director@
cheetah.org
Tuqa J. HUQAKenya Wildlife Service, P.O. Box 40241, 00100 Nairobi, Kenya. Email: tjirmo@yahoo.com
Gabriele COZZIBotswana Predator Conservation Trust, Private Bag 13, Maun, Botswana
and Department of Evolutionary Biology and Environmental Studies, Zurich University,
Winterthurerstr. 190, Zürich 8057, Switzerland. Email: gabriele.cozzi@ieu.uzh.ch
Laurence G. FRANKLiving with Lions, Mpala Research Centre, P.O. Box 555, Nanyuki 10400, Kenya
and Museum of Vertebrate Zoology, University of California, Berkeley, CA 94720, USA.
Email: lgfrank@berkeley.edu
Phumuzile NYONIWildlife and Reserve Management Research Group, Department of Zoology &
Entomology, Rhodes University, P.O. Box 94, Grahamstown 6140, South Africa and Debshan Ranch,
PO Box 24, Shagani, Zimbabwe. Email: phumuzilenyoni@gmail.com
Andrew B. STEINCLAWS Conservancy, Pride in Our Prides, Worcester, MA 01608, USA
and Department of Environmental Conservation, University of Massachusetts, Amherst, MA 01003,
USA and Landmark College, Putney, VT 05346, USA. Email: astein33@hotmail.com
Samuel M. KASIKIKenya Wildlife Service, P.O. Box 40241, 00100 Nairobi, Kenya. Email: skasiki@kws.go.ke
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
4
V. O. Nams et al.Spatial patterns of large African cats
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
David W. MACDONALDWildlife Conservation Research Unit, Zoology Department, University of
Oxford, The Recanati- Kaplan Centre, Tubney House, Abingdon Road, Tubney, Abingdon OX13 5QL,
UK. Email: david.macdonald@biology.ox.ac.uk
Quinton E. MARTINSThe Cape Leopard Trust, Cape Town 7806, South Africa and True Wild LLC, Glen
Ellen, CA, USA. Email: mountainleopard1@gmail.com
Rudie J. vanVUURENN/a’an ku sê Research Programme, P.O. Box 99292, Windhoek, Namibia.
Email: naankuse@me.com
Ken J. STRATFORDOngava Research Centre, 102A Nelson Mandela Avenue, Windhoek, Namibia.
Email: ks@ongava.com
Laura R. BIDNERKenya Wildlife Service, P.O. Box 40241, 00100 Nairobi, Kenya. Email: lrbidner@gmail.com
Alayne ORIOL- COTTERILWildlife Conservation Research Unit, Zoology Department, University of
Oxford, The Recanati- Kaplan Centre, Tubney House, Abingdon Road, Tubney, Abingdon OX13 5QL,
UK and Living With Lions, Museum of Vertebrate Zoology, University of California, Berkeley, CA
94720, USA. Email: alayne.cotterill@gmail.com
Nakedi W. MAPUTLADepartment of Zoology and Entomology, Mammal Research Institute, University
of Pretoria, Pretoria 0002, South Africa. Email: nwmaputla@zoology.up.ac.za
Nkabeng MARUPING- MZILENIDepartment of Zoology and Entomology, Mammal Research Institute,
University of Pretoria, Pretoria 0002, South Africa. Email: nkabeng.mzileni@sanparks.org
Tim PARKERWildlife and Reserve Management Research Group, Department of Zoology & Entomology,
Rhodes University, P.O. Box 94, Grahamstown 6140, South Africa. Email: timparker@wol.co.za
Maarten VAN’T ZELFDEEvolutionary Ecology Group, Department Biology, University of Antwerp,
Universiteitsplein 1, Wilrijk, Building D 132, Antwerpen, Belgium. Email: zelfde@cml.leidenuniv.nl
Lynne A. ISBELLMpala Research Centre, P.O. Box 555, Nanyuki 10400, Kenya and Department of
Anthropology, University of California, Davis, CA 95616, USA. Email: laisbell@ucdavis.edu
Otto B. BEUKESDepartment of Conservation and Marine Sciences, Faculty of Applied Sciences, Cape Peninsula
University of Technology, P.O. Box 652, Cape Town 8000, South Africa. Email: otto.beukes@yahoo.com
Maya BEUKESDepartment of Conservation and Marine Sciences, Faculty of Applied Sciences, Cape Peninsula
University of Technology, P.O. Box 652, Cape Town 8000, South Africa. Email: mayabeukes@hotmail.com
ABSTRACT
1. Spatial patterns of and competition for resources by territorial carnivores are
typically explained by two hypotheses: 1) the territorial defence hypothesis
and 2) the searching efficiency hypothesis.
2. According to the territorial defence hypothesis, when food resources are abun-
dant, carnivore densities will be high and home ranges small. In addition,
carnivores can maximise their necessary energy intake with minimal territorial
defence. At medium resource levels, larger ranges will be needed, and it will
become more economically beneficial to defend resources against a lower
density of competitors. At low resource levels, carnivore densities will be low
and home ranges large, but resources will be too scarce to make it beneficial
to defend such large territories. Thus, home range overlap will be minimal
at intermediate carnivore densities.
3. According to the searching efficiency hypothesis, there is a cost to knowing
a home range. Larger areas are harder to learn and easier to forget, so car-
nivores constantly need to keep their cognitive map updated by regularly
revisiting parts of their home ranges. Consequently, when resources are scarce,
carnivores require larger home ranges to acquire sufficient food. These larger
home ranges lead to more overlap among individuals’ ranges, so that overlap
Keywords
African cats, home range overlap, leopards
Panther pardus, lions Panthera leo, movement,
searching efficiency, territorial defence
*Correspondence
Received: 11 January 2022
Accepted: 25 November 2022
Editor: DR
doi: 10.1111/mam.12309
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
5
Spatial patterns of large African catsV. O. Nams et al.
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
in home ranges is largest when food availability is the lowest. Since conspecific
density is low when food availability is low, this hypothesis predicts that
overlap is largest when densities are the lowest.
4. We measured home range overlap and used a novel method to compare
intraspecific home range overlaps for lions Panthera leo (n = 149) and leop-
ards Panthera pardus (n = 111) in Africa. We estimated home range sizes
from telemetry location data and gathered carnivore density data from the
literature.
5. Our results did not support the territorial defence hypothesis for either spe-
cies. Lion prides increased their home range overlap at conspecific lower
densities whereas leopards did not. Lion pride changes in overlap were pri-
marily due to increases in group size at lower densities. By contrast, the
unique dispersal strategies of leopards led to reduced overlap at lower densi-
ties. However, when human- caused mortality was higher, leopards increased
their home range overlap. Although lions and leopards are territorial, their
territorial behaviour was less important than the acquisition of food in de-
termining their space use. Such information is crucial for the future conser-
vation of these two iconic African carnivores.
INTRODUCTION
The home range is the area traversed by an individual
as it fulfils its typical needs of food gathering, mating,
and caring for young (Burt 1943). Home range size, and
the amount of overlap between home ranges of neigh-
bouring individuals or groups, varies according to factors
such as habitat quality and resource availability (Riley
et al. 2003). Understanding the factors that underlie vari-
ation in home range size and overlap for large, terrestrial
carnivores is important for their conservation and man-
agement. For example, understanding spatial patterns can
help researchers to identify key habitat types and dispersal
corridors (Riley et al. 2003, Kaszta et al. 2020). In addi-
tion, for species with larger home ranges that are susceptible
to human– carnivore conflict (Woodroffe & Ginsberg 1998),
understanding spatial patterns can help us to predict and
mitigate conflict.
In the absence of other conspecifics, the area that an
animal uses is determined by available resources (Loveridge
et al. 2009), suitable habitat (Gese et al. 1988), and the
spatial (Geffen et al. 1992) and temporal (Fleming
et al. 2014) distribution of the habitat. However, other
individuals that use the same resources may limit the
quantity and quality of resources and how they are dis-
tributed. Therefore, to limit the impact of other individuals,
individuals of many species defend parts of their home
ranges to exclude competitors – that is, they demonstrate
territorial behaviour (Packer et al. 2005). For terrestrial
carnivores, territoriality affects the total amount of space
used. If space use becomes exclusive (through territory
defence), then more resources become available within
the home range of the animal, and a smaller area is needed.
Thus, space use for terrestrial carnivores is influenced by
both the available resources and the extent of their
territoriality.
Despite substantial variation in the home range sizes
of two of Africa’s largest territorial carnivores, lions Panthera
leo and leopards Panthera pardus (Funston et al. 2003,
Hayward et al. 2009, Loveridge et al. 2009, Balme et al. 2010,
Davidson et al. 2011, Fattebert et al. 2016), it is unclear
whether this variation is due to resource use or territory
defence.
Lions live in groups of varying sizes, depending on
factors such as resource value (Mosser & Packer 2009)
and prey dispersion (Valeix et al. 2012). Females live
in prides consisting of one or more related adults and
their offspring (Packer et al. 1990). Prides are strongly
territorial (Funston et al. 1998, Packer et al. 2005) and
territory size varies with food supply during the dry
season, but not with group size (Mbizah et al. 2019).
However, the spatial behaviour of males varies. For ex-
ample, in the Kruger National Park, South Africa, prides
live separately from male coalitions and are territorial,
but male and female home ranges overlap (Funston
et al. 1998). Contrastingly, in the Serengeti, Tanzania,
some males live in female territories while others are
nomadic, living singly or as coalitions (Borrego
et al. 2018). Female group size is mainly affected by
food and internal competition (Packer et al. 1990).
Similarly, lion home range overlap shows some plasticity.
In Selous Game Reserve, Tanzania, about half of female
home ranges are exclusive (Spong 2002). In Hwange
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
6
V. O. Nams et al.Spatial patterns of large African cats
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
National Park, Zimbabwe, lion density increased after
the termination of lion trophy hunting, but home ranges
decreased, and home range overlap increased for females
but decreased for males (Davidson et al. 2011).
Leopards are also territorial (Fattebert et al. 2016), but
their territories are not exclusive (Stander et al. 1997),
and some animals are transients (Bailey 1993). Furthermore,
females may form matrilinear clusters and tolerate each
other (Fattebert et al. 2016). Males and females react dif-
ferently to changes in food and conspecific density but
typically live singly in their own home ranges, with related
females normally adjacent to one another (Fattebert
et al. 2016). Leopard home range overlap varies throughout
Africa, ranging between 25% and 60% for neighbouring
males (Jenny 1996, Steyn & Funston 2009), although in-
stances of zero home range overlap have also been recorded
(Mizutani & Jewell 1998). Nevertheless, mother– daughter
associations appear to have the highest levels of home
range overlap (Naude et al. 2020), presumably because of
their relatedness. Leopard spatial patterns are also affected
by human- induced mortality. For example, after leopard
trophy hunting was stopped in Phinda Game Reserve,
South Africa, leopard densities increased, home range sizes
decreased, and overlap increased for females but not for
males (Fattebert et al. 2016).
Two hypotheses that may explain lion and leopard spatial
patterns are the territorial defence hypothesis and the
search efficiency hypothesis.
Territorial defence hypothesis
Where food resources are abundant, animal densities are
high and home ranges are small (Kittle et al. 2015). Animals
can maximise energy intake with minimal territorial de-
fence, and, in some cases, competitor density increases so
much that territorial defence is impossible (Carpenter &
MacMillen 1976). Thus, there is high overlap of home
ranges and little sharing of resources (Mcloughlin
et al. 2000). At medium resource levels, larger ranges are
needed to acquire the necessary resources, and it is en-
ergetically more feasible to defend them against a lower
density of competitors. Thus, there is minimal overlap of
home ranges and the sharing of resources. By contrast,
at low resource levels, animal densities are low and home
ranges are large, but the resources available are so scarce
that is it not beneficial to defend such a large territory.
Consequently, there is much overlap of home ranges and
sharing of resources.
The territorial defence hypothesis therefore predicts that
decreased resource quality will result in larger home range
sizes and lower predator densities, and also a - shaped
response in territoriality with increasing resource quality,
and a U- shaped response in home range overlap with
increasing resource quality (Fig. 1a). This hypothesis also
predicts a U- shaped relationship between home range
overlap and both home range size and predator density.
In addition, territorial behaviour affects the statistically
defined (e.g. 50% isopleth) core home range size more
than searching behaviour. Thus, since variation in resources
affects territoriality, the size of the defended core home
range should vary more than that of the entire home
range (95%). Therefore, the shape of the relationship be-
tween overlap and size and density should be different
for the entire home range and core areas: specifically,
overlaps of the core areas should vary more than overlaps
of the home range areas.
Searching efficiency hypothesis
Animals need to know where resources, dangers, and po-
tential mates are within their home ranges (South 1999,
Powell & Mitchell 2012), and therefore visit parts of their
home ranges regularly to update their cognitive map (Powell
& Mitchell 2012). For example, striped skunks Mephitis
mephitis retain search images from feeding sites and apply
those search images when visiting those sites in the future
(Nams 1997). When resources are scarce, animals require
larger home ranges to acquire sufficient food, which leads
to increased overlap between the home ranges of individu-
als. Thus, the searching efficiency hypothesis predicts a
decrease in conspecific density and an increase in home
range overlap at low resource levels (Fig. 1b). The hy-
pothesis also predicts an increase in home range overlap
with increasing home range size and decreasing density.
While the searching efficiency hypothesis does not include
territoriality, this does not mean that it only applies to
non- territorial animals. Rather, it states that searching ef-
ficiency ultimately drives home range size. Consequently,
at certain resource levels and/or home range sizes, some
species will develop territorial behaviours to defend the
available resources. Under this hypothesis, territorial be-
haviour derives from the space use, rather than determines
it. Furthermore, the defended, statistical core home range
would vary similarly to the entire home range. Thus, this
hypothesis also predicts that the shape of relationship
between overlap and size and density is similar for the
entire home range and for core areas.
Testing the hypotheses
We test the territory defence and search efficiency hy-
potheses of space use using a comparative approach with
lions and leopards. Although both species are large ter-
restrial carnivores that live in similar habitats in Africa
(Maputla et al. 2015), and feed on similar prey (Hayward
& Kerley 2008), they have different social systems – lions
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
7
Spatial patterns of large African catsV. O. Nams et al.
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
being social and leopards mostly solitary. Nevertheless,
for both species, densities (Hayward et al. 2007) and home
range sizes (Hayward et al. 2009) vary with resources.
We therefore aimed at comparing home range overlap
across a wide variety of densities and home range sizes.
We also wanted to investigate the factors that may affect
home range overlap, such as group size and
nomadicity.
For the territorial defence hypothesis, we predicted that:
1a) there would be a U- shaped relationship between home
range overlap, and home range size and conspecific density;
and 1b) the shape of the relationship between home range
overlap and size and density would be different for the
entire home range and for the core home range – specifi-
cally, overlaps of core home ranges would vary more than
overlaps of entire home ranges.
For the searching efficiency hypothesis, we predicted
that: 2a) home range overlap would increase with decreas-
ing conspecific density and increasing home range size;
and 2b) the shape of the relationship between overlap
and size and density would be similar for the entire home
range and for the core home range.
Fig. 1. Idealised diagram of (a) territorial defence and (b) searching efficiency hypotheses.
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
8
V. O. Nams et al.Spatial patterns of large African cats
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
METHODS
Movement datasets
We used telemetry movement data from collared lions
and leopards to estimate home range sizes at 45 sites
across Africa, with lion data collected from 26 sites and
leopard data from 27 sites (Appendix S1). Data were from
149 lions (all female) and 111 leopards (48 males, 63
females). We only used data from female lions because
they represent movements of entire prides (Packer
et al. 1990, Loveridge et al. 2009), and because the rela-
tionship of male coalitions to prides varies among sites
(Bouley et al. 2018). The telemetry data for both lions
and leopards were collected by the authors during their
site- specific research projects (Appendix S1). Sampling
areas were designated as separate sites if the movements
of the study animals in each area did not overlap.
Home range size
Our datasets varied immensely in terms of the numbers
of data points, type of sampling (Global Positioning System
and Very High Frequency radio tracking), sampling in-
tervals, location accuracy, temporal variation in sampling
intervals, and correlations among locations. Thus, the
method of home range size estimation needed to be flex-
ible. We used the autocorrelated kernel density estimator
(AKDE; Fleming et al. 2015) using the R package ‘ctmm’
(Calabrese et al. 2016), which fits a continuous- time,
correlated- velocity movement model to describe the move-
ment data. We used model selection to fit the best move-
ment model using the small- sample size corrected Akaike’s
Information Criterion (AICc; Burnham et al. 2011). The
models incorporated various combinations of position
autocorrelation, velocity correlation, and restricted space
use.
The AKDE is a recent method that results in more
accurate home range estimates than previous methods
when velocity and locations are correlated (Noonan
et al. 2019). Previously, home ranges were estimated us-
ing geometric methods such as minimum convex polygons
or some variation of a kernel density estimator (Fleming
et al. 2015). These methods are dependent on sample
sizes, and the kernel density estimator assumes that loca-
tions are independent of each other. If locations are not
independent, the kernel density estimator underestimates
home range size, sometimes severely (Noonan et al. 2019).
AKDE minimises these limitations, in that it is insensi-
tive to sample size and considers spatial and velocity
correlations among locations. If there are no correlations,
then the AKDE converges towards the kernel density
estimator. In effect, the AKDE uses movement data while
the kernel density estimator uses location data.
Consequently, our home range size estimates are larger
than those reported in the literature for study sites that
have used kernel density estimator for correlated data.
Since the AKDE is a newer method that is fundamental
to our study, we give an intuitive explanation of it in
Appendix S2. However, if our model selection showed
that velocities and locations were not correlated, then a
fixed kernel density estimate model was fitted. Entire
home ranges and core home range areas were estimated
using 95% and 50% isopleths.
Nomadicity
We use the term ‘nomadic’ to describe lions and leop-
ards that do not have stable home ranges. The AKDE
estimates variograms, which represent the variability in
distance between two locations, as a function of time
between these locations (Fleming et al. 2014). If an
animal has an established home range, then the vari-
ogram has an asymptote. Thus, we used the slope of
the variogram over the time- scale of data as a measure
of nomadicity. If the slope of the variogram was >0.4
of the home range (selected by visually evaluating a
series of variograms), then we designated that animal
as nomadic, meaning that either the individual was not
monitored for long enough, or was not resident. From
this, we estimated the proportion of nomadic animals
in each study site.
Densities
Predator density estimates (number of adult individu-
als km−2) were obtained from various sources, depending
on the site (Appendix S3). Most sites had a single density
estimate, but some had different estimates for subregions
within the site. If so, then for each individual animal,
conspecific density within the surrounding area was esti-
mated by the density within an area twice the size of the
home range (measured by the minimum convex polygon).
Density was then a mean of the subregion densities, weighed
by the proportion that each subregion overlapped the
polygon. If sites had estimates taken at several times, then
conspecific density estimates for each animal were estimated
for each year of available location data. If the year of
location data fell between the times of density estimates,
we used a linear interpolation. If year of location data
was outside of the range of time of density estimates,
then we used the estimate closest in time. Of the loca-
tions that lay within the times that density data were
collected, over half were within four months of the density
times. Of the locations that lay outside, over half were
within three years.
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
9
Spatial patterns of large African catsV. O. Nams et al.
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
Overlap
Home range overlaps have been estimated in two ways:
the geometric area of overlap (Fieberg & Kochanny 2005,
Steyn & Funston 2009, Fattebert et al. 2016), and the
relationship between home range size and conspecific
density (Fashing & Cords 2000, Efford et al. 2016). It has
been shown that geometric overlap is biased, with the
bias depending on the amount of overlap, the shapes of
home ranges and the proportion of the population being
tracked (Fieberg & Kochanny 2005). Thus, we elected to
use the relationship between home range size and density
(Ov; Fashing & Cords 2000, Efford et al. 2016). If there
is no overlap, and there are no spaces between home
ranges, then home range size and density are inversely
related. If there is some overlap, then home range size is
larger than we would expect from the inverse of density.
Thus, we can estimate overlap by using a modification
of Jetz et al.’s (2004) equation:
where H = home range size; D = density.
This measure of overlap estimates the mean numbers
using each home range. Thus, an overlap of 1 means
that an individual has exclusive use of its home range
and that all space is occupied with non- overlapping
home ranges. We denote this estimate of overlap as
‘density overlap’. This estimate does not require tracking
data from all individuals, only home range size and
density estimates (Appendix S4). To compare relation-
ships between species, we estimated the doubling rate,
which is the proportional change in overlap for each
doubling of home range size.
Group sizes
Group size estimates were obtained from various sources,
depending on the site. Lion pride size was not reported
consistently, with some sources reporting the numbers of
adult females and some the numbers of adults
(Appendix S5), and thus, we tested each. Group size es-
timates were only available on a site basis. Thus, for each
site, overlap was estimated using Jetz et al.’s (2004) overlap
equation, and then group size was compared with overlap,
on a per site basis (Appendix S4). We then statistically
removed the effect of group size from our estimate of
density overlap, to estimate geometrical overlap
(Appendix S4).
Analyses
We fitted a linear and quadratic model between
log(density) and log(home range size). Typical parametric
models assume that the independent variable has no
error, because errors in the independent variable yield
biased estimates of the parameters (Draper &
Smith 2014). Since our analysis required estimates of
these parameters and not just tests for significant rela-
tionships, we used the more general total least squares,
which allows for error in both dependent and independ-
ent variables (van Huffel & Lemmerling 2013). This
analysis uses error estimates for each data point. Sampling
errors in home range size were estimated from the AKDE
algorithm (Fleming et al. 2015). Sampling errors in
density were taken from the literature (Appendix S3).
However, if error estimates were not available for a site,
then we used the overall mean variance for the species.
The appropriate models were chosen using significance
tests rather than model likelihood, because AICc is not
valid for total least squares models.
We also tested whether group size and nomadicity varied
with overlap. These analyses were carried out using means
for each site, not each individual. This process was done
for group size, because the literature estimates of group
sizes were only available for entire sites. The process was
also carried out for nomadicity because it is a population
measure – that is, the proportion of individuals. For each
site, we estimated overlap using Jetz et al.’s (2004) overlap
equation. For tests of the relationships between group size
and nomadicity and overlap, we did not have error esti-
mates for individual data points and thus used the or-
thogonal regression variant of total least squares (van
Huffel & Lemmerling 2013), with bootstrapping to estimate
errors.
RESULTS
Overlap relationships differed between species and between
sexes. Density overlap increased significantly for lions at
larger home ranges and lower densities, but there was no
significant relationship for leopards (Table 1, Figs 2 and 3).
Neither of the species showed significant curvilinearity (lions:
t(148) = 0.01, P = 0.99, leopards: t(134) = 0.000, P = 0.99).
There was no significant difference in the slope between
Ov
=
H
×
D,
Table 1. Parameters for total least squares fit of log(density) vs. log(home
range size), for 95% and 50% home range size isopleths. The values are
as follows: mean ± standard error. Tests for significance were used be-
cause AICc is not valid for total least squares analysis. Effect of home
range size on overlap is 1 + k2
Isopleth Parameter Female lions All leopards
95% Intercept (k1)0.50± 0.11 0.59± 0.17
Slope (k2)−0.71± 0.05** −1.0± 0.092
50% Intercept (k1)0.040± 0.08 −0.13± 0.12
Slope (k2)−0.72± 0.05** −0.93± 0.09
**The slope is significantly different from −1, at α=0.01.
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
10
V. O. Nams et al.Spatial patterns of large African cats
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
sexes of leopards (t(75) = 1.07, P = 0.28). Thus, both sexes
of leopards showed no significant relationship between overlap
with conspecific density and home range size (Table 1).
The partitioning of overlap also differed between species
and sexes. The proportion of nomadic individuals showed
no significant relationship with overlap for either of the
species (lions: t(17) = 0.34, P = 0.73; leopards: t(11) = 0.69,
P = 0.50). Lion pride size, measured by both the number
of adult females (t(21) = 2.2, P = 0.037) and the number
of adults (t(21) = 2.0, P = 0.058), significantly increased
with increasing overlap (Fig. 4).
Doubling rate (proportional change in overlap for each
doubling of home range size) for lions was 1.22; for leop-
ards, it was constant at 1.0. To estimate net overlap, the
effects of increasing group size were removed, with the
lion doubling rate decreasing to 1.12 (Table 2). Lion pride
size (numbers of adult females) increased with overlap
by a doubling rate of 1.36. Thus, removing the effect of
changes in pride size (Appendix S4), and setting the
effects of nomadicity to zero, we found that lion net
overlap increased with home range size by a doubling
rate of 1.12 (Table 2, Fig. 3).
The same analyses were run using home range sizes of
50% isopleths, rather than 95% entire home ranges. All
results were like those of 95% isopleths (Table 1, Fig. 3).
Thus, overlaps of core home range areas responded in
similar ways to changes in size and density, as did overlaps
of entire home ranges.
DISCUSSION
Density overlap changed at varying degrees for both species.
Since the curvilinear term was not significant for either
species, there was no U- shaped response in home range
overlap with respect to home range size and conspecific
density. Thus, prediction 1a was not supported. Lions showed
a significant increase in home range overlap with decreasing
density and increasing home range size, whereas leopards
showed no change in home range overlap (Fig. 3). Therefore,
prediction 2a was supported for lions but not for leopards.
Both species showed similar relationships for the 95% and
50% home range sizes (Fig. 3). Therefore, prediction 1b
was not supported but 2b was supported for both species.
Our results support the searching efficiency hypothesis for
lions, but neither hypothesis for leopards.
Our study would not be a valid test of the territorial
defence hypothesis if our sites did not include the highest
levels of resources. However, we included regions of Africa
with the highest reported densities of leopards (Chase Grey
et al. 2013, Fattebert et al. 2016) and lions (Bauer
et al. 2015), which are therefore likely to have the highest
levels of resources found in nature. In addition, some of
the sites were small, fenced reserves that sometimes enrich
prey to higher levels than found in nature (McEvoy
et al. 2022); thus, our study included the highest levels
of resources. While it is possible that at our small, fenced
sites (n = 7) home ranges were physically constrained,
they constituted a relatively small proportion of all lion
sites.
The overall patterns in lion overlap could be construed
as a result of, within one region, large home ranges over-
lapping more than small home ranges. However, our results
do not compare individuals within study sites. Our results
compare among sites, showing that at smaller home ranges
and higher densities, individuals’ home ranges have less
overlap than at larger home ranges and lower densities.
Sampling issues
It is important to examine how methodological issues may
have affected our results. There is much heterogeneity among
our sites, both in natural conditions and in sampling
Fig. 2. Density vs. home range size for (a) lions Panthera leo and (b)
leopards Panthera pardus. The wide red band is the 95% confidence
band, with the central line being the line of best fit (using total least
squares fit). The solitary green line is the expected relationship if overlap
is constant. The dots represent individual animals. For lions, overlap
increases as home range size increases.
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
11
Spatial patterns of large African catsV. O. Nams et al.
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
methods. The effects of heterogeneous sampling were mini-
mised by our use of AKDE to estimate home range size.
Unlike other techniques, AKDE is insensitive to sample sizes,
sampling frequencies, precision of locations, and correlations
among locations (Fleming et al. 2015, 2019, Noonan
et al. 2019). Thus, accuracies of home range sizes were not
affected by the different types of field protocols and types
of collars among study sites. In fact, the natural heterogeneity
among study sites is a strength of our study. Our hypotheses
about spatial patterns are general ones and should therefore
be tested in sites that vary in densities, resources, and habitats,
like the sites we included. In other words, we asked whether
the effects were larger than background variation. In addi-
tion, the heterogeneity makes our analyses more conservative,
giving more confidence in the significant results.
Our method to estimate overlap is also a strength. Unlike
the method of geometric overlap, accuracy of density overlap
is not affected by the number of individuals at each site.
Precision is affected by the number of individuals, but that
effect is minimised by the large numbers of sites. Our study
also avoids the difficulties encountered in other studies of
these hypotheses (Mcloughlin et al. 2000, López- Bao
et al. 2014), either because the correct resources were not
measured, or because the resource range was not wide enough.
We avoided these difficulties in two ways. First, we did not
measure a specific resource, but compared overlap for dif-
ferent levels of conspecific density. Second, we used data
from sites across Africa, covering the entire range of resources
available for these two terrestrial carnivores.
Partitioning overall overlap
Lion home range overlap increases with home range size,
with a doubling rate of 1.22. The three components of
density overlap are nomadicity, group size, and net overlap.
However, changes in nomadicity are a minor component
Fig. 3. Overlap vs. home range size for (a) lions Panthera leo and (b) leopards Panthera pardus, for 95% and 50% home range isopleths. ‘Lions- Group’
represents overlap with the effects of lion group size removed. The bands are the 95% confidence bands. The horizontal line at 1.0 represents a
baseline of one individual per home range.
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
12
V. O. Nams et al.Spatial patterns of large African cats
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
of density overlap. Non- territorial adult lions are primarily
males in coalitions. However, their behaviour differs among
regions. Only two studies have measured the proportion
of nomadic individuals. In the Serengeti, coalitions of
non- territorial males form into nomadic coalitions. Being
nomadic, they travel widely and thus the proportion of
non- territorial individuals varies from year to year, with
an overall mean average of 0.165 (Borrego et al. 2018).
In Kruger National Park, non- territorial male coalitions
do not become nomadic, but remain close to their natal
territories. They do not travel widely and thus the
proportion of non- territorial individuals varies very little
from year to year, with an overall mean of 0.152 (Funston
et al. 2003). Using our data, we can estimate density
overlap in these two sites using home ranges and densities
(see ‘Results’ and Appendices S1– S4). In the Serengeti,
mean home range size is 455 km2, lion density is
0.100 ind km−2, with a resulting density overlap of 45.5 ind/
home range, and pride size is 6.2 adult females. In Kruger
National Park, mean home range size is 129 km2, density
is 0.0911 ind km−2, with a resulting density overlap of
12 ind/home range, and pride size is 4.0 adult females.
The difference in density overlap from Kruger to Serengeti
is large (3.8×), yet the difference in density of nomadic
animals is small (1.1×). This difference suggests that most
of the density overlap increase over the range of home
range sizes is due to a change in net density overlap, not
due to changes in nomadicity.
Leopards do not show a significant increase in density
overlap with changes in home range size. The precision
of density overlap estimates was similar between species
(see the confidence bands in Fig. 3). Thus, density overlap
for leopards is constant, compared to that in lions.
Nomadicity does not change significantly with overlap.
Thus, group size and net density overlap are either con-
stant, or both vary. Leopards are usually solitary
(Bailey 1993), but females may occasionally be accompanied
by their dependent young (Fattebert et al. 2015). Thus,
overall group size (irrespective of sex) shows little varia-
tion from low to high home range sizes. Therefore, we
can conclude that density overlap is also constant, and
that group size, net density overlap, and nomadicity are
constant with respect to home range size and leopard
density.
Responses to resources
Each species responds to changes in conspecific density
and resources in a different way, but both reduce their
home range size with increasing resources (Hayward
et al. 2009). Lions adjust to resource scarcity by increas-
ing group size and home range overlap (Loveridge
et al. 2009). About half of the change in overlap was
due to changes in pride sizes (Loveridge et al. 2009).
Larger prides often break into smaller hunting groups
that can cover more area, while smaller prides are forced
to travel as one group in defence of attack by neigh-
bouring prides (Packer et al. 1990). Thus, larger prides
can defend larger territories and potentially search for
food more efficiently. However, larger lion prides are
capable of capturing larger prey, effectively increasingly
prey availability in some systems (Loveridge et al. 2006).
Nevertheless, this finding is the opposite of the territo-
rial defence hypothesis, which predicts that at lower
Fig. 4. Pride size vs. home range overlap for lions Panthera leo. The thick
line is the line of best fit (averaging, via AICc, the constant and linear
models), and the band is the 95% confidence band. Each dot represents
mean values for one site. Pride size increases as overlap increases.
Table 2. Effect of home range size on gross and net overlaps, and effect
of gross overlap on group size. Slope measures the rate of change in
linear log regressions. The mean slopes are shown ± the standard errors.
Doubling rate is the amount of change in the dependent variable when
the independent variable doubles
Variable Isopleth Statistics Female lions All leopards
Gross 95% Slope 0.29 ± 0.05** 0 ± 0.092
Overlap1Doubling 1.22 1.00
50% Slope 0.28 ± 0.05** 0.07 ± 0.091
Doubling 1.21 1.00
Group Slope 0.48 ± 0.212,*
Size3Doubling 1.39
Net 95% Slope 0.15 ± 0.09
Overlap4Doubling 1.11 1.00
50% Slope 0.14 ± 0.09
Doubling 1.10 1.00
1Log(Gross Overlap) vs. log(Home Range Size).
2For lions, this refers # of adult females in prides.
3Log(Group Size) vs. log(Gross Overlap).
4Log(Net Overlap) vs. log(Home Range Size).
*Mean significantly different from 0, at α=0.05.
**Mean significantly different from 0 at α=0.01.
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
13
Spatial patterns of large African catsV. O. Nams et al.
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
resource levels territorial behaviour becomes too costly.
Territorial defence drives wolf Canis lupus space use in
North America, where, unlike lions, wolves adjust their
territory size and not their group size in response to
changes in resource quality (Kittle et al. 2015). It may
be that the unique social structure of lions, where males
are frequently not resident with female prides and prac-
tice infanticide, forces females to form larger prides to
protect their cubs and not necessarily to search for food
(McEvoy et al. 2022).
Leopards do not change overlap with changes in con-
specific density or home range size. Density overlap is
3.5, meaning that 3.5 individuals use each home range,
and this is constant throughout the range of densities
and home range sizes. The home range of one male
leopard typically overlaps with the home ranges of two
to five females (Balme & Hunter 2013, Fattebert
et al. 2016).
We suggest that the constant overlap for leopards
occurs for two reasons. First, both dispersal strategies
and human hunting would affect overlap, but in dif-
ferent directions. Dispersal strategies lead to smaller
overlaps at low densities, and dispersal strategies differ
between males and females. Male dispersal is driven
mostly by mate competition (Fattebert et al. 2015, Naude
et al. 2020), and thus young males tend to emigrate.
Female dispersal is affected mostly by philopatry, where,
in favourable conditions, the benefit of daughters stay-
ing outweighs the cost to the mothers (i.e. the resident
fitness hypothesis; Naude et al. 2020). For example, at
high densities, mean overlap between all individuals
(both sexes) was between 18% and 20%, but within
kin- groups it was as high as 60% (Naude et al. 2020).
Thus, at high densities we would expect higher overlap
among females than at low densities. By contrast, human-
caused mortality leads to higher overlaps at low densi-
ties. While leopards are sometimes viewed as being
adaptable and resilient, their world- wide range loss of
~70% is greater than the loss for the world’s other
large carnivores (Jacobson et al. 2016). Leopards are
declining for three reasons: loss of prey, loss of habitat,
and mortalities from humans (Jacobson et al. 2016).
Leopards are heavily persecuted in farmland areas, with
retaliatory killing having an even greater effect on num-
bers than sport hunting (Swanepoel et al. 2015). Even
protected areas do not completely protect leopards –
hunting outside of protected areas decreases leopard
numbers in protected areas, even when there is enough
prey (Balme et al. 2010). In areas where leopards are
heavily persecuted, home ranges are larger and more
unstable, resulting in less territoriality and more overlap
(Fattebert et al. 2016). Such a response to human- induced
mortality has also been reported for cougars Puma
concolor (Maletzke et al. 2014). Thus, those leopard
populations exposed to high persecution should show
lower density and larger overlaps than those in areas
with low pressure.
Second, leopards search more efficiently than lions.
Leopards are more generalised predators (Hayward &
Kerley 2008) and have a smaller range of home range
sizes than lions (a maximum size of 800 km2, as com-
pared to 4800 km2 for lions). At low resource levels,
leopards may not have to increase search areas as much
as lions, leading to smaller home range sizes than lions.
Perhaps at even lower resource levels than observed in
nature, overlap might increase. Lions show a consistent
change in overlap, not just at the extremes – a reanalysis
of our lion overlap relationships but using the same
narrow breadth of ranges of home range sizes as evident
for leopards, did not change the results (details not
shown).
CONCLUSION
Being top predators means that lions and leopards can
play important roles in the structuring of ecosystems, and
in the survival of other species (Ripple & Beschta 2004).
However, top predators are also among the most vulner-
able components of biodiversity in any system. Although
both lions and leopards are territorial, their territorial
behaviour does not appear to drive the scale of space use
in our study. For lions, space use appeared to be driven
by variations in search efficiency, governed by different
aspects of their social behaviour. By contrast, for leopards,
space use seemed to be driven by dispersal strategies, ex-
ternal mortality, and their flexible predatory behaviour.
Thus, even though lions and leopards live in similar habi-
tats, often together, and feed on similar prey items, their
social structures appear to determine how they respond
to variations in resource abundance. Our findings are
significant, because understanding the space use of large
carnivores is crucial for their future conservation (Johansson
et al. 2016). Although numerous site- specific assessments
of these two species have been conducted, our study is
one of the first to bring together data from multiple sites
throughout the African continent, to begin to understand
the drivers behind the use of space in these important
terrestrial carnivores.
FUNDING
We acknowledge the support of the Natural Sciences
and Engineering Research Council of Canada (NSERC;
funding reference number RGPIN- 2015- 05201) and a
Hugh Kelly Fellowship to VON from Rhodes University,
Grahamstown, SA.
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
14
V. O. Nams et al.Spatial patterns of large African cats
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
REFERENCES
Bailey TN (1993) The African Leopard: Ecology and Behavior
of a Solitary Field. Columbia University Press, New York,
New York, USA.
Balme GA, Slotow R, Hunter LTB (2010) Edge effects and
the impact of non- protected areas in carnivore
conservation: leopards in the Phinda– Mkhuze complex,
South Africa. Animal Conservation 13: 315– 323.
Balme GA, Hunter LTB (2013) Why leopards commit
infanticide. Animal Behaviour 86: 791– 799.
Bauer H, Chapron G, Nowell K, Henschel P, Funston PJ,
Hunter LT, Macdonald DW, Packer C (2015) Lion
(Panthera leo) populations are declining rapidly across
Africa, except in intensively managed areas. Proceedings of
the National Academy of Sciences of the United States of
America 112: 14894– 14899.
Borrego N, Ozgul A, Slotow R, Packer C (2018) Lion
population dynamics: do nomadic males matter?
Behavioral Ecology 29: 660– 666.
Bouley P, Poulos M, Branco R, Carter NH (2018) Post- war
recovery of the African lion in response to large- scale
ecosystem restoration. Biological Conservation 227: 233– 242.
Burnham KP, Anderson DR, Huyvaert KP (2011) AIC
model selection and multimodel inference in behavioral
ecology: some background, observations, and comparisons.
Behavioral Ecology and Sociobiology 65: 23– 35.
Burt WH (1943) Territoriality and home range concepts as
applied to mammals. Journal of Mammalogy 24: 346– 352.
Calabrese JM, Fleming CH, Gurarie E (2016) ctmm: an r
package for analyzing animal relocation data as a
continuous- time stochastic process. Methods in Ecology
and Evolution 7: 1124– 1132.
Carpenter FL, MacMillen RE (1976) Threshold model of
feeding territoriality and test with a Hawaiian
honeycreeper. Science 194: 639– 642.
Chase Grey JN, Kent VT, Hill RA (2013) Evidence of a
high density population of harvested leopards in a
montane environment. PLoS One 8: e82832.
Davidson Z, Valeix M, Loveridge AJ, Madzikanda H,
Macdonald DW (2011) Socio- spatial behaviour of an
African lion population following perturbation by sport
hunting. Biological Conservation 144: 114– 121.
Draper NR, Smith H (2014) Applied Regression Analysis. 3rd
ed. Wiley- Interscience, New York, New York, USA.
Efford MG, Dawson DK, Jhala YV, Qureshi Q (2016)
Density- dependent home- range size revealed by spatially
explicit capture– recapture. Ecography 39: 676– 688.
Fashing PJ, Cords M (2000) Diurnal primate densities and
biomass in the Kakamega Forest: an evaluation of census
methods and a comparison with other forests. American
Journal of Primatology 50: 139– 152.
Fattebert J, Balme GA, Dickerson T, Slotow R, Hunter LTB
(2015) Density- dependent natal dispersal patterns in a
leopard population recovering from over- harvest. PLoS
One 10: e0122355.
Fattebert J, Balme GA, Robinson HS, Dickerson T, Slotow
R, Hunter LTB (2016) Population recovery highlights
spatial organization dynamics in adult leopards. Journal of
Zoology 299: 153– 162.
Fieberg J, Kochanny CO (2005) Quantifying home- range
overlap: the importance of the utilization distribution.
The Journal of Wildlife Management 69: 1346– 1359.
Fleming CH, Calabrese JM, Mueller T, Olson KA,
Leimgruber P, Fagan WF (2014) From fine- scale foraging
to home ranges: a semivariance approach to identifying
movement modes across spatiotemporal scales. American
Naturalist 183: E154– E167.
Fleming CH, Fagan WF, Mueller T, Olson KA, Leimgruber
P, Calabrese JM (2015) Rigorous home range estimation
with movement data: a new autocorrelated kernel density
estimator. Ecology 96: 1182– 1188.
Fleming CH, Noonan MJ, Medici EP, Calabrese JM (2019)
Overcoming the challenge of small effective sample sizes
in home- range estimation. Methods in Ecology and
Evolution 10: 1679– 1689.
Funston PJ, Mills MGL, Biggs HC, Richardson PRK (1998)
Hunting by male lions: ecological influences and
socioecological implications. Animal Behaviour 56: 1333– 1345.
Funston PJ, Mills MGL, Richardson PRK, van Jaarsveld AS
(2003) Reduced dispersal and opportunistic territory
acquisition in male lions (Panthera leo). Journal of
Zoology 259: 131– 142.
Geffen E, Hefner R, Macdonald DW, Ucko M (1992)
Habitat selection and home range in the Blanford’s fox,
Vulpes cana: compatibility with the resource dispersion
hypothesis. Oecologia 91: 75– 81.
Gese EM, Rongstad OJ, Mytton WR (1988) Home range
and habitat use of coyotes in southeastern Colorado.
Journal of Wildlife Management 52: 640– 648.
Hayward MW, O’Brien J, Kerley GIH (2007) Carrying
capacity of large African predators: predictions and tests.
Biological Conservation 139: 219– 229.
Hayward MW, Kerley GIH (2008) Prey preferences and
dietary overlap amongst Africa’s large predators. South
African Journal of Wildlife Research 38: 93– 108.
Hayward MW, Hayward GJ, Druce DJ, Kerley GIH (2009)
Do fences constrain predator movements on an
evolutionary scale? Home range, food intake and
movement patterns of large predators reintroduced to
Addo Elephant National Park, South Africa. Biodiversity
and Conservation 18: 887– 904.
van Huffel S, Lemmerling P (2013) Total Least Squares and
Errors- in- Variables Modeling: Analysis, Algorithms and
Applications. Springer Science & Business Media,
Dordrecht, the Netherlands.
Jacobson AP, Gerngross P, Lemeris JR Jr, Schoonover RF,
Anco C, Breitenmoser- Würsten C et al. (2016) Leopard
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
15
Spatial patterns of large African catsV. O. Nams et al.
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
(Panthera pardus) status, distribution, and the research
efforts across its range. PeerJ 4: e1974.
Jenny D (1996) Spatial organization of leopards Panthera
pardus in Taï National Park, Ivory Coast: is rainforest
habitat a ‘tropical haven’? Journal of Zoology 240:
427– 440.
Jetz W, Carbone C, Fulford J, Brown JH (2004) The scaling
of animal space use. Science 306: 266– 268.
Johansson Ö, Rauset GR, Samelius G, McCarthy T, Andrén
H, Tumursukh L, Mishra C (2016) Land sharing is
essential for snow leopard conservation. Biological
Conservation 203: 1– 7.
Kaszta Ż, Cushman SA, Macdonald DW (2020) Prioritizing
habitat core areas and corridors for a large carnivore
across its range. Animal Conservation 23: 607– 616.
Kittle AM, Anderson M, Avgar T, Baker JA, Brown GS,
Hagens J et al. (2015) Wolves adapt territory size, not
pack size to local habitat quality. Journal of Animal
Ecology 84: 1177– 1186.
López- Bao JV, Rodríguez A, Delibes M, Fedriani JM,
Calzada J, Ferreras P, Palomares F (2014) Revisiting
food- based models of territoriality in solitary predators.
Journal of Animal Ecology 83: 934– 942.
Loveridge AJ, Hunt JE, Murindagomo F, Macdonald DW
(2006) Influence of drought on predation of elephant
(Loxodonta africana) calves by lions (Panthera leo) in an
African wooded savannah. Journal of Zoology 270:
523– 530.
Loveridge AJ, Valeix M, Davidson Z, Murindagomo F, Fritz
H, Macdonald DW (2009) Changes in home range size
of African lions in relation to pride size and prey
biomass in a semi- arid savanna. Ecography 32: 953– 962.
Maletzke BT, Wielgus R, Koehler GM, Swanson M, Cooley
H, Alldredge JR (2014) Effects of hunting on cougar
spatial organization. Ecology and Evolution 4: 2178– 2185.
Maputla NW, Maruping NT, Chimimba CT, Ferreira SM
(2015) Spatio- temporal separation between lions and
leopards in the Kruger National Park and the Timbavati
Private Nature Reserve, South Africa. Global Ecology and
Conservation 3: 693– 706.
McEvoy OK, Ferreira SM, Parker DM (2022) The impacts
of management interventions on the sociality of African
lions (Panthera leo): implications for lion conservation.
Ecological Solutions and Evidence 3: e12135.
Mcloughlin PD, Ferguson SH, Messier F (2000) Intraspecific
variation in home range overlap with habitat quality: a
comparison among brown bear populations. Evolutionary
Ecology 14: 39– 60.
Mizutani F, Jewell PA (1998) Home- range and movements
of leopards (Panthera pardus) on a livestock ranch in
Kenya. Journal of Zoology 244: 269– 286.
Mosser A, Packer C (2009) Group territoriality and the
benefits of sociality in the African lion, Panthera leo.
Animal Behaviour 78: 359– 370.
Mbizah MM, Valeix M, Macdonald DW, Loveridge AJ
(2019) Applying the resource dispersion hypothesis to a
fission-fusion society: A case study of the African lion
(Panthera leo). Ecology and Evolution. https://doi.
org/10.1002/ece3.5456
Nams VO (1997) Density- dependent predation by skunks
using olfactory search images. Oecologia 110: 440– 448.
Naude VN, Balme GA, O’Riain J, Hunter LT, Fattebert J,
Dickerson T, Bishop JM (2020) Unsustainable
anthropogenic mortality disrupts natal dispersal and
promotes inbreeding in leopards. Ecology and Evolution
10: 3605– 3619.
Noonan MJ, Tucker MA, Fleming CH, Akre TS, Alberts SC,
Ali AH et al. (2019) A comprehensive analysis of
autocorrelation and bias in home range estimation.
Ecological Monographs 89:1- 21.
Packer C, Scheel D, Pusey AE (1990) Why lions form
groups: food is not enough. American Naturalist 136:
1– 19.
Packer C, Hilborn R, Mosser A, Kissui B, Borner M,
Hopcraft G, Wilmshurst J, Mduma S, Sinclair ARE
(2005) Ecological change, group territoriality, and
population dynamics in Serengeti lions. Science 307:
390– 393.
Powell RA, Mitchell MS (2012) What is a home range?
Journal of Mammalogy 93: 948– 958.
Riley SP, Sauvajot RM, Fuller TK, York EC, Kamradt DA,
Bromley C, Wayne RK (2003) Effects of urbanization and
habitat fragmentation on bobcats and coyotes in southern
California. Conservation Biology 17: 566– 576.
Ripple WJ, Beschta RL (2004) Wolves and the ecology of
fear: can predation risk structure ecosystems? Bioscience
54: 755– 766.
South A (1999) Extrapolating from individual movement
behaviour to population spacing patterns in a ranging
mammal. Ecological Modelling 117: 343– 360.
Spong G (2002) Space use in lions, Panthera leo, in the
Selous Game Reserve: social and ecological factors.
Behavioral Ecology and Sociobiology 52: 303– 307.
Stander PE, Haden PJ, Kaqece I, Ghau I (1997) The ecology
of asociality in Namibian leopards. Journal of Zoology
242: 343– 364.
Steyn V, Funston PJ (2009) Land- use and socio- spatial
organization of female leopards in a semi- arid wooded
savanna, Botswana. South African Journal of Wildlife
Research 39: 126– 132.
Swanepoel LH, Somers MJ, Dalerum F (2015) Functional
responses of retaliatory killing versus recreational sport
hunting of leopards in South Africa. PLoS One 10:
e0125539.
Valeix M, Loveridge AJ, Macdonald DW (2012) Influence of
prey dispersion on territory and group size of African
lions: a test of the resource dispersion hypothesis. Ecology
93: 2490– 2496.
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
16
V. O. Nams et al.Spatial patterns of large African cats
Mammal Review (2023) © 2023 The Authors. Mammal Review published by Mammal Society and John Wiley & Sons Ltd.
Woodroffe R, Ginsberg JR (1998) Edge effects and the
extinction of populations inside protected areas. Science
280: 2126– 2128.
SUPPORTING INFORMATION
Additional supporting information may be found in the
online version of this article at the publisher’s website.
Appendix S1. Site information.
Appendix S2. Intuitive explanation of the autocorrelated
kernel density estimator.
Appendix S3. Sources of density data.
Appendix S4. Mathematical modifications of Jetz et al.’s
(2014) overlap equation.
Appendix S5. Lion pride size data.
13652907, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mam.12309 by Test, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
... Leopards are a solitary, elusive, primarily nocturnal species which makes them challenging to study (Bailey, 1993). Currently, data collection relies upon camera traps (e.g., du Preez et al., 2014;Searle et al., 2021;Strampelli et al., 2018), telemetry studies (Nams et al., 2023) and spoor surveys (Searle et al., 2020;Stander, 1998), which are initially expensive, invasive and time-consuming, respectively. Technology such as ARUs, that can be cheap, have large detection areas, and be deployed for long periods of time without the need for regular servicing may improve leopard monitoring. ...
Article
Full-text available
Conservation requires accurate information about species occupancy, populations and behaviour. However, gathering these data for elusive, solitary species, such as leopards (Panthera pardus), is often challenging. Utilizing novel technologies that augment data collection by exploiting different species' traits could enable monitoring at larger spatiotemporal scales. Here, we conducted the first, large‐scale (~450 km²) paired passive acoustic monitoring (n = 50) and camera trapping survey (n = 50), for large African carnivores, in Nyerere National Park, Tanzania. We tested whether leopards could be individually distinguished by their vocalizations. We identified individual leopards from camera trap images and then extracted their roaring bouts in the concurrent audio. We extracted leopard roar summary features and used 2‐state Gaussian Hidden–Markov Models (HMMs) to model the temporal pattern of individual leopard roars. Using leopard roar summary features, individual vocal discrimination was achieved at a maximum accuracy of 46.6%. When using HMMs to evaluate the temporal pattern of a leopard's roar, individual identification was more successful, with an overall accuracy of 93.1% and macro‐F1 score of 0.78. Our study shows that using multiple modes of technology, which record complementary data, can be used to discover species traits, such as, individual leopards can be identified from their vocalizations. Even though additional equipment, data management and analytical expertise are required, paired surveys are still a promising monitoring methodology which can exploit a wider variety of species traits, to monitor and inform species conservation more efficiently, than single technology studies alone.
... By using mini-GPS for the first time in a small rodent to determine spatial structure, we found that the ranges of bush Karoo rats overlapped more with their kin than with their nonkin neighbours. Large carnivores that display territorial defence have greater overlap in spatial ranges when food availability is low (Nams et al., 2023). In our study, season did not significantly influence range overlap, but we cannot exclude this possibility (see Fig. 4b). ...
Article
Full-text available
Keywords: intraspecific variation in social organization kinship kin selection social organization social structure social system solitary spatial structure Kin selection is important for understanding the evolution of social behaviour in group-living species. Yet, the role of kinship in solitary species has received little attention. We studied how kinship influences intraspecific variation in social organization and spatial structure in a predominantly solitary species, the bush Karoo rat, Otomys unisulcatus, from the Succulent Karoo semidesert of South Africa. We predicted that if social groups occur, they should consist of close kin. We further predicted that the spatial structure is not random, but that close kin live closer to each other. Over 5 years we performed trapping and focal animal observations and fitted mini-GPS dataloggers simultaneously on 125 neighbouring female bush Karoo rats to investigate how their spatial structure was influenced by kinship. Females were mainly solitary, although small social groups also occurred, all consisting of close kin, typically females, such as a mother and her adult daughter or sisters. Although females did have more nonkin than kin neighbours, kin lived closer to each other than nonkin. Daily ranges were larger in the breeding than in the nonbreeding season and overlapped more between kin than nonkin females. We conclude that kinship should be considered when studying solitary species as it might influence variation in social organization and spatial structure.
... Home range size.-We estimated home range size using the autocorrelated kernel density estimator (AKDE) (Fleming et al. 2015;Nams et al. 2023) estimated with the R package "ctmm" (Calabrese et al. 2016), which fits a continuous-time, correlated-velocity movement model to describe the movement data. We used model selection to fit the best movement model, employing the small-sample size corrected Akaike's Information Criterion (AICc). ...
Article
Full-text available
The size of the home range of a mammal is affected by numerous factors. However, in the normally solitary, but polygynous, Leopard (Panthera pardus), home range size and maintenance is complicated by their transitory social grouping behavior, which is dependent on life history stage and/or reproductive status. In addition, the necessity to avoid competition with conspecifics and other large predators (including humans) also impacts upon home range size. We used movement data from 31 sites across Africa, comprising 147 individuals (67 males and 80 females) to estimate the home range sizes of leopards. We found that leopards with larger home ranges, and in areas with more vegetation, spent longer being active and generally traveled faster, and in straighter lines, than leopards with smaller home ranges. We suggest that a combination of bottom-up (i.e., preferred prey availability), top-down (i.e., competition with conspecifics), and reproductive (i.e., access to mates) factors likely drive the variability in Leopard home range sizes across Africa. However, the maintenance of a large home range is energetically expensive for leopards, likely resulting in a complex evolutionary trade-off between the satisfaction of basic requirements and preventing potentially dangerous encounters with conspecifics, other predators, and people.
... Our results only indicate the distribution area of suitable habitat for two species in Xinlong County, which is approximately 2111 km 2 and 5142 km 2 , respectively. Taking into account existing studies, the typical home range size for leopards ranges from 20-200 km 2 , while for wolves, it is generally between 500-8300 km 2 [23][24][25][26][27]. We can predict that the suitable habitats in Xinlong County can support approximately 10-105 leopards and 1-10 wolves, respectively. ...
Preprint
Full-text available
Large terrestrial carnivores play a crucial role in top-down control within terrestrial ecosystems, maintaining ecosystem stability and biodiversity. However, intense interspecific competition often arises among sympatric large carnivores, leading to population reductions or even extinctions. Spatial partitioning through divergent habitat selection helps mitigate such competition. In Xinlong County, Sichuan Province, we used 293 infrared cameras for monitoring from September to May 2016 and March to October 2022. By employing the Generalized Linear Model (GLM) and the Maximum Entropy Model (MaxEnt), we developed an ensemble model predicting the suitable habitat distribution of leopards (Panthera pardus) and wolves (Canis lupus). We analyzed the main environmental factors influencing habitat selection and the fragmentation of suitable habitats. We found that suitable habitat distribution differed significantly between them. Both species preferred areas with gentle slopes close to settlements. While leopards' habitat selection primarily depended on the distance from settlements, the slope was predominant for wolves. Suitable habitats displayed aggregation, yet wolves exhibited higher fragmentation and more complex patch shapes, indicating greater susceptibility to human activities. These results suggest that sympatric large carnivores, such as leopards and wolves, can reduce spatial competition intensity and promote spatial partitioning by selecting divergent suitable habitats, thereby facilitating species coexistence.
Article
Full-text available
African lion (Panthera leo) populations normally consist of several neighbouring prides and multiple adult males or groups of males that interact competitively. In large, open systems, cub defence from infanticidal males and territory defence drive group living in lions. However, in smaller (<1000 km²), fenced wildlife reserves, opportunities for natural immigration and emigration are limited which means that the evolutionary drivers of lion sociality may collapse. Here, we use lion behavioural data collected from 16 wildlife reserves across South Africa to test how management‐induced ecological conditions alter lion social dynamics. The number of lionesses observed together was best predicted by pride size, prey biomass and biome. Lionesses were less likely to group together as pride size increased, but more likely to group together as prey biomass and habitat productivity increased. In addition, adult males were observed more frequently with prides that had young (<12 months) cubs in reserves that had unfamiliar adult males present compared to reserves without any unfamiliar adult males. Our results demonstrate how intraspecific competition between lions drives their sociality, and this may break down in small, fenced wildlife reserves where lions are actively managed. Although small, fenced reserves in South Africa have made a significant contribution to increasing lion numbers on the continent, our work highlights several important ecological implications of active lion management. For wildlife managers, mimicking the outcomes of different levels of intraspecific competition is likely a critical management tool for the persistence of lions in small reserves.
Article
Full-text available
Anthropogenic mortality of wildlife is typically inferred from measures of the absolute decline in population numbers. However, increasing evidence suggests that indirect demographic effects including changes to the age, sex, and social structure of populations, as well as the behavior of survivors, can profoundly impact population health and viability. Specifically, anthropogenic mortality of wildlife (especially when unsustainable) and fragmentation of the spatial distribution of individuals (home‐ranges) could disrupt natal dispersal mechanisms, with long‐term consequences to genetic structure, by compromising outbreeding behavior and gene flow. We investigate this threat in African leopards (Panthera pardus pardus), a polygynous felid with male‐biased natal dispersal. Using a combination of spatial (home‐range) and genetic (21 polymorphic microsatellites) data from 142 adult leopards, we contrast the structure of two South African populations with markedly different histories of anthropogenically linked mortality. Home‐range overlap, parentage assignment, and spatio‐genetic autocorrelation together show that historical exploitation of leopards in a recovering protected area has disrupted and reduced subadult male dispersal, thereby facilitating opportunistic male natal philopatry, with sons establishing territories closer to their mothers and sisters. The resultant kin‐clustering in males of this historically exploited population is comparable to that of females in a well‐protected reserve and has ultimately led to localized inbreeding. Our findings demonstrate novel evidence directly linking unsustainable anthropogenic mortality to inbreeding through disrupted dispersal in a large, solitary felid and expose the genetic consequences underlying this behavioral change. We therefore emphasize the importance of managing and mitigating the effects of unsustainable exploitation on local populations and increasing habitat fragmentation between contiguous protected areas by promoting in situ recovery and providing corridors of suitable habitat that maintain genetic connectivity. Unsustainable anthropogenic mortality of wildlife and fragmentation of the spatial distribution of individuals disrupts natal dispersal mechanisms, with long‐term consequences to genetic structure, by compromising outbreeding behavior and gene flow. Our study investigates this threat in African leopards, demonstrating novel evidence directly linking unsustainable anthropogenic mortality to inbreeding through disrupted dispersal in a large, solitary felid and exposes the genetic consequences underlying this behavioral change.
Article
Full-text available
The relationship between the spatiotemporal distribution of resources and patterns of sociality is widely discussed. While the resource dispersion hypothesis (RDH) was formulated to explain why animals sometimes live in groups from which they derive no obvious benefits, it has also been successfully applied to species that benefit from group living. Some empirical tests have supported the RDH, but others have not, so conclusions remain equivocal and further research is required to determine the extent to which RDH predictions hold in natural systems. Here, we test four predictions of the RDH in an African lion population in the context of their fission–fusion society. We analyzed data on group composition of GPS‐collared lions and patterns of prey availability. Our results supported the first and second predictions of the RDH: Home range size (a) was independent of group size and (b) increased with distance between encounters with prey herds. Nonetheless, the third and fourth RDH predictions were not supported: (c) The measure of resource heterogeneity and (d) resource patch richness measured through prey herd size and body size had no significant effect on lion group size. However, regarding the fourth prediction, we added an adaptation to account for dynamics of fission–fusion society and found that the frequency of pride fission increased as group size increased. Our data set restricted us from going on to explore the effect of fission–fusion dynamics on the relationship between group size and patch richness. However, this should be investigated in future studies as including fission–fusion dynamics provides a more nuanced, realistic appreciation of lion society. Our study emphasizes the importance of understanding the complexity of a species' behavioral ecology within the framework of resource dispersion. Whatever larger theoretical framework may emerge to explain lion society, incorporating fission–fusion dynamics should allow the RDH to be refined and improved. Our study is one of the few that have tested and found support for two of the four predictions of the resource dispersion hypothesis. However, regarding the fourth prediction, we added an adaptation to account for dynamics of fission–fusion society and found that the frequency of pride fission increased as group size increased. Our study emphasizes the importance of understanding the complexity of a species' behavioral ecology within the framework of resource dispersion and incorporating fission–fusion dynamics should allow the RDH to be refined and improved.
Article
Full-text available
Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated‐Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross‐validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half‐sample cross‐validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation (N^area) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID‐based estimates by a mean factor of 2. The median number of cross‐validated locations included in the hold‐out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing N^area. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small N^area. While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an N^area >1,000, where 30% had an N^area <30. In this frequently encountered scenario of small N^area, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.
Article
Full-text available
Key population processes are sometimes driven by male dynamics, but these drivers are often overlooked because of the scale over which they operate. Lions (Panthera leo) provide an ideal case study for investigating factors governing male dynamics and their influence on population sustainability. Lions display sexually selected infanticide, and resident males must defend their offspring from nomads that may have dispersed over long distances; factors affecting male–male competition over large spatial scales can have population wide consequences. We report here on the first systematic analysis of long-term individual-based data of male lions in the Serengeti National Park, Tanzania. From 1974 to 2012, we observed 471 coalitions (796 males) in our study area. We investigate factors affecting male immigration and the impacts on the resident population. The yearly number of nomadic males entering the study population affected cub survival and mating access. Success rates of nomadic males gaining tenure with a pride increased with age and coalition size. We observed a significant decline in male immigration, which resulted in lowered levels of male replacement in the study population, reduced infanticide, and greater cub survival. The decline in incoming males likely resulted from increased anthropogenic pressures in surrounding areas. Conversely, the core study population was largely buffered from anthropogenic threats and likely served as a source to neighboring sinks. Reduced infanticide in the core population might have compensated for rising lion mortalities in surrounding areas, but as human-wildlife conflicts intensify with the rapidly growing human population, compensatory mechanisms may become overwhelmed.
Article
With increasing loss and fragmentation of habitats driving the emerging global extinction crisis, paired with limited resources for conservation, there is an immense need to identify and prioritize the most important areas for conservation actions. The goal of this study was to measure, map and rank core areas and corridors for mainland clouded leopard (a forest indicator species) across its entire range in Southeast Asia. We used an empirically based landscape resistance model developed from range‐wide camera survey data, cumulative resistant kernel analysis to define core areas and least‐cost network analysis to identify corridors for long‐distance dispersal. We then ranked core areas based on their strength and size, and corridors based on their strength and the strength of core areas they connect. We found that the most important core areas and corridors are concentrated in Southeast Asia, largely in Myanmar, Laos and Malaysia. Myanmar contains nearly the entirety of the first and third highest ranked core areas, as well as the most important network of corridors in SE Asia. Almost the entire territory of Laos constitutes one large potential core area, ranked as the second most important across the clouded leopard’s range. A large number (22) of very small (<8000 km ² ) and fairly isolated core areas are in China. Only 24% of clouded leopard core areas and 17% of corridors are protected. This is the first example of using empirical models to prioritize conservation actions across the full range of a large carnivore. Our analysis identifies the location, size and connectivity of the most important remaining habitats of the clouded leopard across its range, which could provide quantitative guidance in the efforts to maximize the efficacy of regional conservation initiatives to conserve this species and the ecosystems it inhabits.
Article
Technological advances have steadily increased the detail of animal tracking datasets, yet fundamental data limitations exist for many species that cause substantial biases in home‐range estimation. Specifically, the effective sample size of a range estimate is proportional to the number of observed range crossings, not the number of sampled locations. Currently, the most accurate home‐range estimators condition on an autocorrelation model, for which the standard estimation frame‐works are based on likelihood functions, even though these methods are known to underestimate variance—and therefore ranging area—when effective sample sizes are small. Residual maximum likelihood (REML) is a widely used method for reducing bias in maximum‐likelihood (ML) variance estimation at small sample sizes. Unfortunately, we find that REML is too unstable for practical application to continuous‐time movement models. When the effective sample size N is decreased to N ≤ (10), which is common in tracking applications, REML undergoes a sudden divergence in variance estimation. To avoid this issue, while retaining REML’s first‐order bias correction, we derive a family of estimators that leverage REML to make a perturbative correction to ML. We also derive AIC values for REML and our estimators, including cases where model structures differ, which is not generally understood to be possible. Using both simulated data and GPS data from lowland tapir ( Tapirus terrestris ), we show how our perturbative estimators are more accurate than traditional ML and REML methods. Specifically, when (5) home‐range crossings are observed, REML is unreliable by orders of magnitude, ML home ranges are ~30% underestimated, and our perturbative estimators yield home ranges that are only ~10% underestimated. A parametric bootstrap can then reduce the ML and perturbative home‐range underestimation to ~10% and ~3%, respectively. Home‐range estimation is one of the primary reasons for collecting animal tracking data, and small effective sample sizes are a more common problem than is currently realized. The methods introduced here allow for more accurate movement‐model and home‐range estimation at small effective sample sizes, and thus fill an important role for animal movement analysis. Given REML’s widespread use, our methods may also be useful in other contexts where effective sample sizes are small.
Article
We present data from the first, long-term study underway of a recovering population of indigenous, free-ranging Panthera leo in Gorongosa National Park (GNP), Mozambique. GNP is undergoing post-war recovery and large-scale ecological restoration under a 25-year private-governmental partnership – the “Gorongosa Project (GP),” – offering a rare opportunity to elucidate the long-term recovery dynamics of a population of lion in response to strategic conservation interventions. GNP forms a core part of the greater Gorongosa-Marromeu Lion Conservation Unit which is designated as a “potential lion stronghold.” Within the Park we established an intensive study area of 1100 km2 encompassing prime areas of herbivore productivity. Between 2012 and 2016, 104 lions were documented and 6 prides and 7 males or coalitions in our study area were satellite-collared and intensively monitored. We describe seasonal male and female home-ranges, prey utilization, estimated versus predicted lion densities in relation to recovering herbivore biomass, and anthropogenic factors limiting the population's full recovery potential. The dominant factor observed to be negatively impacting the population was top-down and anthropogenic in the form of by-catch by wire snares and steel-jaw traps set by bushmeat hunters. These findings have since resulted in tangible and measurable interventions to reduce these impacts and resultant future datasets will elucidate detailed demography and how management interventions impacted the trajectory of large-carnivore recovery.