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Research
Cite this article: Sheehy E, Sutherland C,
O’Reilly C, Lambin X. 2018 The enemy of my
enemy is my friend: native pine marten
recovery reverses the decline of the red squirrel
by suppressing grey squirrel populations.
Proc. R. Soc. B 285: 20172603.
http://dx.doi.org/10.1098/rspb.2017.2603
Received: 28 September 2017
Accepted: 12 February 2018
Subject Category:
Ecology
Subject Areas:
ecology
Keywords:
occupancy modelling, spatial capture–
recapture, apparent competition, predator-
mediated competition, pest-regulating
ecosystem service, species interactions
Author for correspondence:
Xavier Lambin
e-mail: x.lambin@abdn.ac.uk
Electronic supplementary material is available
online at https://dx.doi.org/10.6084/m9.
figshare.c.4014001.
The enemy of my enemy is my friend:
native pine marten recovery reverses the
decline of the red squirrel by suppressing
grey squirrel populations
Emma Sheehy1,3, Chris Sutherland2, Catherine O’Reilly3and Xavier Lambin1
1
School of Biological Sciences, University of Aberdeen, Zoology building, Tillydrone Avenue,
Aberdeen AB24 2TZ, UK
2
Department of Environmental Conservation, University of Massachusetts-Amherst, Amherst, MA, USA
3
Department of Science, Waterford Institute of Technology, Waterford, Ireland
ES, 0000-0001-8749-2893; CS, 0000-0003-2073-1751; XL, 0000-0003-4643-2653
Shared enemies may instigate or modify competitive interactions between
species. The dis-equilibrium caused by non-native species introductions
has revealed that the outcome of such indirect interactions can often be
dramatic. However, studies of enemy-mediated competition mostly consider
the impact of a single enemy, despite species being embedded in complex
networks of interactions. Here, we demonstrate that native red and invasive
grey squirrels in Britain, two terrestrial species linked by resource and
disease-mediated apparent competition, are also now linked by a second
enemy-mediated relationship involving a shared native predator recovering
from historical persecution, the European pine marten. Through combin-
ing spatial capture–recapture techniques to estimate pine marten density,
and squirrel site-occupancy data, we find that the impact of exposure to
predation is highly asymmetrical, with non-native grey squirrel occupancy
strongly negatively affected by exposure to pine martens. By contrast,
exposure to pine marten predation has an indirect positive effect on
red squirrel populations. Pine marten predation thus reverses the well-
documented outcome of resource and apparent competition between red
and grey squirrels.
1. Introduction
Understanding the mechanisms through which species interact and the conse-
quences of perturbations to those interactions is a fundamental goal of ecology.
Species interactions are typically not pairwise, but rather are embedded in com-
plex interaction networks. For instance, two-species interactions may be
mediated, in part or wholly, by the presence of a third species, as in predator-
or pathogen-mediated apparent competition [1]. The potential outcomes of
such indirect interactions include complete competitor exclusion and fugitive
coexistence where inferior competitors thrive temporarily in the absence of a
shared enemy [2]. Which outcomes emerge depends on the nature of trophic
interactions, including how the natural enemy affects, and responds to, the
competing prey/host species, and on the ability of prey to occupy refuges
that are temporarily or permanently unoccupied by their enemies.
The introduction of non-native species has provided some dramatic
examples of indirect interactions leading to species extirpation. For example,
pathogen-mediated apparent competition between invasive grey squirrels,
Sciurus carolinensis, and native red squirrels, Sciurus vulgaris, in Britain where
the process of replacement via exploitative competition is vastly expedited in
&2018 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution
License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original
author and source are credited.
the presence of squirrelpox virus (SQPV), a disease which is
usually lethal to red squirrels but asymptomatic in grey
squirrels which act as reservoir [3].
The occurrence of indirect interactions makes predicting
the consequences of novel species interactions on biological
networks challenging, especially in an applied context. This
is illustrated by the many failures to achieve biological con-
trol of target invasive species through the introduction of
non-native generalist predators which have resulted in un-
intended, often disastrous, direct and indirect impacts on
native species ill-adapted to coexist with a novel predator
[4]. Whether such dramatic impacts will emerge following
the reinsertion of native predators on natural food webs
which have been invaded by non-native prey is unknown.
Although in decline globally, hitherto persecuted native
predator populations are recovering in Europe in response
to large-scale conservation initiatives [5]. Predator recoveries
take place in often profoundly modified landscapes used by
native prey species with which they share a coevolutionary
history, and by multiple non-native species that have invaded
in their absence. While the eventual outcome of this modicum
of ecosystem restoration is uncertain, a broad prediction is that
the outcome of novel interactions between recovering native
predators and non-native prey are more likely to lead to
non-native species extirpation than interactions involving
native prey species. More refined predictions are hampered
by the contextual network of interactions such that so-called
‘contingent’ rather than general theories are required to
characterize circumstances where both direct and enemy-
mediated interactions between species occur [1]. Specifically,
predicting whether native predator recovery will lead to the
extirpation of some non-native species or their fugitive coexis-
tence with native guilds in a given context is, as yet, beyond
the predictive ability of community ecology.
Despite the obvious relevance of indirect interactions for
species and ecosystem conservation in the face of ever-
increasing invasion pressure, empirically demonstrating the
influence of enemy-mediated interactions is challenging,
especially when dealing with low density and difficult to
study mammalian predators. Performing manipulative
experiments at relevant spatial scales is particularly difficult,
and consequently, the evidence is often correlative. Yet, the
standard of evidence required to shape policy is high,
especially when some of the species involved are at the
centre of conflicts. For instance, the controversy as to whether
the dingo, Canis dingo, reduces the abundance or occupancy
of introduced red foxes, Vulpes vulpes, and feral cats, Felis
catus, and hence benefits endangered marsupials in the
vulnerable prey size range in Australia, hinges on the need
to account for imperfect detectability rather than using
uncorrected indices of abundance [6].
Here, we investigate how the recovery of a native preda-
tor, the European pine marten, Martes martes, influences
native red and non-native grey squirrels in Britain, two
species that interact through exploitative competition and a
form of disease-mediated apparent competition [3,7]. We
ask whether these two species are linked by a second
enemy-mediated relationship involving a shared predator,
recovering from historical persecution, and we expect that
the impact of the predator may be asymmetrical, due to a
lack of similar predators in the invasive species’ native
range [8]. Sheehy & Lawton [9] reported a strong positive cor-
relation between sightings of pine marten and red squirrel,
and a negative correlation between pine marten and grey
squirrel detection rates. While this evidence has profound
implications both in the conservation of native red squirrels
and the management of grey squirrels, the study focused
on pairwise correlations of species incidences, which pre-
cludes investigation of the potential for interacting
relationships between native and non-native species. More-
over, Sheehy & Lawton [9] concluded that pine marten
density, rather than presence alone, may influence grey squir-
rels, although estimates of marten density are mostly lacking.
Here, we tested the hitherto untested hypothesis that preda-
tor density is mediating competition between a non-native
and native prey species using estimates of red and grey
squirrel occupancy, and pine marten density and space use.
2. Material and methods
(a) Survey design
Our survey took place between January and May 2016 in three
regions of Scotland that differed in time since pine marten reco-
lonization. Northernmost is the Highlands (HI: 24.7 W, 57.3 N
around 150 km
2
), which was recolonized more than 45 years
ago [10] and is beyond the current invasion range of the grey
squirrel. Central Scotland (CS: 24.4 W, 56.1 N, around
900 km
2
), 100 km south of the HI region, was recolonized by
pine martens approximately 8 – 14 years ago, and grey squirrels
have been present since at least 1945 [11]. The Scottish Borders
(BO: 23.2 W, 55.7 N, around 600 km
2
), 65 km southeast of the
CS region, is in the early stage of pine marten recolonization
(oldest contemporary record is less than 5 years old) and grey
squirrels have been present since at least 1980 [12]. Grey squirrel
control is ongoing in CS and BO: on average, 14 and 252 grey
squirrels per year have been removed by culling since 2014,
respectively (Scottish Wildlife Trust 2017, unpublished data).
This take represents a small fraction of the standing grey squirrel
population (0.2 and 5.1 individuals (ind) km
22
in CS and BO,
respectively) when compared to reported densities of greater
than 100 and 50 grey squirrels per km
2
in broadleaf and conifer-
ous habitat, respectively [13]. Moreover, the SQPV is endemic in
BO [14] and was first detected in CS in 2017 (Scottish Wildlife
Trust 2017, unpublished data).
In CS and BO, we used stratified random sampling to select
10 and seven 2 2 km grid cells, respectively, giving preference
to those containing greater than 30% ‘broadleaved/mixed
mainly broadleaved’ habitat (electronic supplementary material,
table S1). This focused the study in areas where grey squirrels
were expected to have a competitive advantage over red squirrels
[13] and, therefore, where species responses were not limited by
habitat quality or availability. Each grid cell contained a mini-
mum of approximately 70 hectares (ha) of suitable squirrel
habitat, i.e. mature woodland (min ¼68 ha, max ¼400 ha,
mean ¼182 ha; electronic supplementary material, table S1)
and between five and 21 sampling locations (hereafter ‘sites’)
(mean ¼11.63) where multi-species feeders were permanently
deployed a mean within-grid distance of 325 m apart (min ¼
267, max ¼391). Mean home ranges of red and grey squirrels
in these habitats are less than 5 ha [15,16], and hence each
sampling site within a grid cell was considered independent at
the scale of the squirrel species. In HI, where grey squirrels are
absent, two grid cells were selected opportunistically based on
site accessibility.
(b) Multi-species sampling
Multi-species feeders were used to detect red and grey squirrels
and pine martens. Detectors (15 15 15 cm wooden boxes
rspb.royalsocietypublishing.org Proc. R. Soc. B 285: 20172603
2
with a wire mesh front and a liftable lid) were attached to trees at
a height of 1.5 m at 223 sampling locations and were baited with
a mixture of nuts and seeds that are attractive to martens and
squirrels. On the underside of each lid, three 2 cm 0.5 cm
glue strips made from Big Cheesewglue traps were fixed to a
pressure sensitive double-sided adhesive tab (3MTM). To access
the bait, squirrels and martens must climb a tree and lift the
lid of the feeder with their head, resulting in hair samples
being deposited on the glue strips, which can be used to identify
squirrel species, and from DNA extracted from hair samples,
individual martens. Each site was visited four times between Jan-
uary and May 2016 at approximately four-week intervals, with
one additional visit to sites in the HI region in June. During
each visit, hair samples were collected, new adhesives were
deployed and feeders were re-baited.
Bushnell ‘Natureview’ (model 119739) trail cameras
were rotated between sites and visits throughout the study
period such that during any sampling interval, a subset of sites
(n¼19) had a camera and a feeder deployed. Cameras were
positioned on an adjacent tree facing the feeder, and pro-
grammed to capture three images per trigger (at 0.6 s interval
speed), followed by a 1 min trigger delay.
(c) Genetic identification of marten individuals
Hair samples were dried and stored at 2208C to preserve DNA,
and subsequently identified to species level, i.e. red squirrel, grey
squirrel, pine marten, based on colour, shape and size using a
dissection microscope at 40magnification. Pine marten hair
samples (greater than or equal to 10 where possible) were recov-
ered from adhesives using 1– 2 drops of xylene to soften the
glue and transferred to 1.5 ml microfuge tubes using forceps
that were heated and cooled between samples to avoid cross-
contamination. Because more than one pine marten could visit
a feeder between visits, hairs clumped together on a single
adhesive strip were collected as unique samples.
To identify individual pine martens, DNA was extracted and
real-time quantitative PCR assays for species and sex determi-
nation were carried out as described in [17]. Microsatellite
amplification and allele scoring across six markers (Mel1, Ma2,
Mvi1341, Gg7, Mar21 and Mar53) were performed as described
in [18]. In samples where greater than two alleles per locus
were observed (n¼4 samples), we assumed that this was a
result of DNA amplification from more than one individual,
and therefore samples were excluded from further analysis.
Sample matching, calculation of allele frequencies and probabil-
ities of identity were carried out using GENECAP [19]. For full
details of microsatellite loci and primers used, see [18].
(d) Spatial capture–recapture
Pine marten home ranges are larger than squirrels’ and therefore,
it is possible to detect hair from the same individual at multiple
sites. The identification of unique individuals from hair samples
generates individual spatial encounter histories (y
ijk
) document-
ing whether each observed individual (i) was observed at each
site ( j) during each occasion (k). Such data lend themselves natu-
rally to analysis using spatial capture– recapture methods [20]
that model encounter probabilities as an explicit function of the
distance between site locations (x
j
) and individual activity
centres (s
i
):
pijk ¼p0exp dðsi,xjÞ2
2
s
2
!
,
where d(s
i
,x
j
) is the distance between an individual’s activity
centre and a detector, p
0
is the probability of encounter when
that distance is 0, and
s
is the spatial scale parameter that defines
the distance over which the detection decreases. Here, to account
for hypothesized variation in encounter probabilities, all
combinations of sex- and region-specific detection parameters
(p
0
and
s
) were compared.
Activity centres, which are not directly observed, are esti-
mated using the spatial pattern of observations. The estimation
of a spatial detection function that accounts for imperfect and
spatially heterogeneous encounter rates allows inferences to be
made about the total number of activity centres (individuals)
within an area of interest, S, i.e. we estimate density. Sis rep-
resented as the centre points of a discretized area that
encompasses the sampling sites. This area must be large
enough to contain the activity centres of all detected individuals;
here a 4 km buffer was used which is larger than the radius of a
pine marten home range (male and female home ranges 2.23 km
2
and 1.49 km
2
, respectively) [21]. The resolution of the state space
pixels must be fine enough to approximate continuous space but
coarse enough to be computationally tractable; here the area was
divided into 100 100 m pixels which is at least an order of
magnitude smaller than a typical pine marten home range.
Grid cells that were entirely water or non-forested habitat were
removed assuming that density in these areas was 0. We tested
for between-region differences in density by fitting models that
assumed either constant density or region-specific density.
All combinations of encounter and density models were con-
sidered resulting in 32 candidate models. Models were ranked
and weighted according to AIC [22]. In cases where no single
model is overwhelmingly preferred, inference is based on
model-averaged predictions using AIC weights. Models were
fitted in R (R Core Team 2012) using the package oSCR [23].
(e) Pine marten connectivity metrics
To compute pine marten connectivity, we use two results from
the SCR analysis. First, the estimated spatial encounter model,
which is a kernel that describes how space use declines with dis-
tance from the activity centre, and second, the realized density
estimates for each pixel on the landscape. Following Sutherland
et al. [24], we centre the kernel on each pixel and weight it by
the realized density, and then compute the cumulative utilization
of all pixels by all other pixels on the landscape. This produces a
joint description of the population level relative frequency of
pixel use across the landscape—or density weighted connectivity.
The resulting derived measures are spatially explicit surfaces
(100 m 100 m) of pine marten density (DENS) and density
weighted connectivity (DWC, figure 1). The former describes
the distribution of activity centres, whereas the latter describes
how intensely pine martens use parts of the landscape, and
hence, is a candidate measure of exposure to pine marten
impacts, whether mediated by consumptive or non-consumptive
influences (predation hereafter). The interest was whether either
of these measures explained variation in red or grey squirrel
occurrence. Because each site was taken to represent an indepen-
dent sampling unit, the average density and density weighted
connectivity values within a 125 m buffer around each sampling
site, i.e. larger than a typical squirrel home range radius of 100 m
(as per [13]), were calculated.
(f ) Squirrel occupancy modelling
Like SCR, occupancy models are hierarchical models that jointly
model the ecological process (occupancy,
c
) and the encounter
process (detection probability, p) that accounts for imperfect
detection through repeated visits. Using the detection–non detec-
tion squirrel data, we fit a series of single species occupancy
models to investigate factors influencing squirrel occupancy, and
in particular, to test hypotheses about the relationship between
pine martens and red and grey squirrels. During the sampling
period, we assumed negligible birth, recruitment, mortality and
dispersal, i.e. closure was assumed. Thus, repeated visits are
rspb.royalsocietypublishing.org Proc. R. Soc. B 285: 20172603
3
informative about detectability of the underlying occupancy state,
detection probabilities can be estimated, and inference can be
made about true occurrence and its determinants [25].
We modelled variation in detection as a function of two
visit-specific covariates: VISIT, categorical covariate allowing
detectability to vary by sampling occasion, and METHOD, a
binary covariate indicating whether a camera and a feeder was
deployed (1), or just a feeder (0). We also modelled between-
region variation in detection using a site level categorical vari-
able, REGION (noting that red squirrels occurred in all three
regions, whereas grey squirrels are only present in CS and BO).
We also modelled variation in detection as a function of forest
attributes: per cent forest cover (COVER) and per cent forest
cover that is broadleaved or mixed mainly broadleaved (BL),
and pine marten metrics: pine marten density (DENS) and con-
nectivity (DWC), all continuous site-level covariates summarized
within a 100 m buffer of a site (electronic supplementary material,
table S2).
We modelled variation in occupancy also as a function of per
cent forest cover (COVER), per cent forest cover that is broad-
leaved or mixed mainly broadleaved (BL), and by region
(REGION). Attempting to capture the competitive interaction
of grey squirrels on reds, we included a measure of grey squirrel
feeder use in the surrounding area (GS: the ratio of used/avail-
able feeders within a 500 m buffer of each feeder) in the red
squirrel analysis. Finally, to test hypotheses about the effect of
pine marten density and connectivity on squirrel occurrence,
we also modelled occupancy as a function of the two covariates
derived from the SCR analysis: DENS and DWC, respectively.
DENS and DWC were never included in the same model.
We adopted a two-step approach to model selection. First, all
combinations of the detection models, with no interactions, were
fitted with two global models for the state process, i.e. a DENS
and a DWC version of the most complex occupancy model.
This resulted in 96 candidate detection models for each species
(electronic supplementary material, file S3). Next, using the
detection model(s) with most support, all biologically plausible
combinations of occupancy models were fitted for each species.
Models included no more than one two-way interaction resulting
in 61 candidate occupancy models for grey squirrels and, with
the addition of GS covariate, 132 candidate occupancy models
for red squirrels (electronic supplementary material, file S4).
As with the SCR analysis, models were ranked and weighted
according to AIC and in cases where no single model was over-
whelmingly preferred, inference was based on model averaging
the predictions of parameters of interest using AIC weights
from all models. Models were fitted in R (R Core Team 2012)
using the package UNMARKED [26]. We considered a model
to be a competitor for drawing inference if parameters in the
top model were not simply a subset of those in the competing
models, as per Anderson & Burnham [27], resulting in one top
detection model for each squirrel species.
3. Results
In total, there were 725 species detections at feeders (115 red
squirrel, 101 grey squirrel and 509 pine marten) and 85 at trail
cameras (26 red squirrel, 14 grey squirrel and 45 pine marten).
Regional summaries of detections are provided in the elec-
tronic supplementary material, table S5. A total of 388 pine
marten hair samples were successfully sex-typed and geno-
typed to individual level (76.5% of all samples), from which
42 unique genotypes were identified (19 female and 23
male). All loci were polymorphic with three to four alleles
per locus, the probability of identity (PI
HW
) was less than
0.0001, and the sibling probability of identity (PI
sibs
)was
0.0143. On average, across the study period, individuals in
BO, CS and HI were encountered 12.40, 11.00 and 7.09
times at an average of 6.80, 7.10 and 4.00 feeders, respect-
ively, and 41 out of the 42 animals were encountered more
than once.
(a) Spatial capture–recapture
Pine marten density (ind km
22
of woodland), space use (km)
and detection varied among the three regions. Density varied
between region (cumulative AIC weight, or relative variable
importance, of a region effect,
v
þ
DREGION
¼0.82, table 1),
and by sex (estimated probability of being a male,
f
male
¼
0.36 +0.13, table 1). Female marten density was about twice
that of males (table 1). Densities were similar in the HI and
CS regions and markedly lower in BO (table 1). Space
use also differed both by region (
v
þ
s
REGION
¼1.00) and
sex (
v
þ
s
sex
¼1.00), and was negatively correlated with den-
sity (table 1). Males moved more than twice as far as females,
and spatial scale of movement was highest in the BO where
density was lowest (
s
BO,female
¼1.10 +0.11,
s
BO,male
¼2.7 +
0.24) compared to CS (
s
CS,female
¼0.55 +0.06,
s
CS,male
¼
1.32 +0.12) and HI (
s
HI,female
¼0.74 +0.12,
s
HI,male
¼
1.78 +0.35) regions. Finally, there was some support for
variation in baseline detection probability by region
(
v
þ
pREGION
¼0.57) and by sex (
v
þ
psex
¼0.75, table 1). The
probability of detecting a marten at its activity centre was
highest in CS and lowest in HI (table 1).
Borders
5 km 5 km
3.464
0
5 km
DWC
Central Highland
Figure 1. Pine marten density weighted connectivity surface for the Borders, Central and Highland study regions of Scotland with locations of multi-species detectors.
(Online version in colour.)
rspb.royalsocietypublishing.org Proc. R. Soc. B 285: 20172603
4
Based on the AIC ranked model set, we model-averaged
predictions of density and density weighted connectivity sur-
faces, which were then used in the squirrel occupancy
analyses. The range of estimated pixel-specific realized marten
density (DENS), which was converted to individuals per
square km, was 0.00– 2.48 in BO, 0.00–5.86 in CS and 0.00 –
4.72 in HI. The range of estimated pixel-specific density
weighted connectivity, which is a relative measure of utilization,
was 0.06–2.08 in BO, 0.12–2.46 in CS and 1.17–3.06 in the HI.
(b) Squirrel occupancy modelling
In the first step of our sequential modelling approach, a single
preferred detection model was identified for each squirrel
species in turn. For grey squirrels, the AIC-top detection
model allowed for differences between regions (
v
þpREGION
¼
1.00) and visits (
v
þpVISIT
¼1.00), and an effect of pine
marten connectivity (
v
þpDWC
¼0.83, p(REGION þDWC þ
VISIT), electronic supplementary material, table S6). For red
squirrels, the AIC-top detection model also allowed for
variation between regions (
v
þpREGION
¼1.00) and visits (
v
þ
-
pVISIT
¼1.00), and an effect of pine marten connectivity
(
v
þpDWC
¼1.00) but also allowed for differences in detect-
ability depending on whether a camera was deployed or not
(
v
þpMETHOD
¼1.00, p(REGION þDWC þMETHOD þ
VISIT), electronic supplementary material, table S7). We note
that although several models performed similarly, i.e. had
less than two
D
AIC units, the AIC-top model was nested
within all the models within four AIC units and thus
contained uninformative parameters [28].
Grey squirrel detection probability was positively related
to pine marten connectivity (
b
DWC
¼0.74 +0.26), was higher
in CS than in BO (contrast relative to BO:
b
CS
¼0.74 +0.26),
and increased through time by visit (contrasts of visit 2–4
relative to 1 from the top model:
b
VISIT2
¼1.36 +0.46,
b
VISIT3
¼1.708 +0.47,
b
VISIT4
¼3.185 +0.56; electronic sup-
plementary material, table S6). For red squirrels, detection
was negatively related to pine marten connectivity
(
b
DWC
¼22.64 +0.42) (electronic supplementary material,
table S8), was also higher in CS than in the BO (contrast rela-
tive to BO:
b
CS
¼6.22 +0.92), and also increased through
time across visits (contrasts of visit 2– 4 relative to 1 from
the top model:
b
VISIT2
¼1.40 +0.54,
b
VISIT3
¼2.86 +0.58,
b
VISIT4
¼2.91 +0.58,
b
VISIT5
¼3.59 +0.75; electronic supple-
mentary material, table S8). Red squirrel detection was also
method-specific such that detection probability is much redu-
ced when deploying feeders alone (
b
HAIR
¼26.59 +1.48;
electronic supplementary material, table S7).
There was strong support for a negative relationship
between grey squirrel occupancy and pine marten DWC,
while no such relationship was evident for red squirrel occu-
pancy (electronic supplementary material, tables S6 and S7).
For grey squirrels, pine marten connectivity negatively affected
grey squirrel occupancy: the slope of this relationship in BO was
b
BO,DWC
¼20.96 +0.37, and was stronger in CS (estimated
difference in slopes from the top model,
b
CS,DWC
¼20.70 +
0.63, electronic supplementary material, table S6). Occupancy
also varied by region (
v
þ
c
REGION
¼1.00) and with the pro-
portion of broadleaf cover (
v
þ
c
BL
¼0.83), and there was
substantial support for an interaction, i.e. that the slopes of
the broadleaf relationship were different between the regions
(
v
þ
c
REGION:BL
¼0.76, electronic supplementary material,
table S6). All else equal, grey squirrel occupancy was lower in
CS than in BO (from the top model with BO as the reference
level:
b
CS
¼21.282 +0.7) and was positively related to BL
(top model:
b
BL
¼0.56 +0.57, electronic supplementary
material, table S6).
For red squirrels, the opposite effect of pine marten connec-
tivity was observed, i.e. DWC positively affected red squirrel
occupancy: the common slope of this relationship is
b
DWC
¼
0.81 +0.32 (electronic supplementary material, table S7). Occu-
pancy was also positively related to the proportion of total
cover (
v
þ
c
COVER
¼0.91, top model:
b
COVER
¼3.21 +1.46)
and negatively associated with the per cent of broadleaf cover
(
v
þ
c
BL
¼0.85, top model:
b
BL
¼21.18 +0.47, electronic sup-
plementary material, table S7). Model-averaged predictions of
the occupancy connectivity relationships on the probability
scale for both species are shown in figure 2 using partial
regression holding all other covariates at region-specific average
(median) conditions.
4. Discussion
We found unequivocal support for the hypothesis that a reco-
vering native predator density is modifying competition
between two closely related native and non-native prey
species. As with the virus that links the two species, the
impact of exposure to a native predator was highly asym-
metrical, but in the opposite direction to that of disease.
Table 1. Model-averaged parameter estimates for male and female pine martens in the Borders, Central Scotland and the Highlands. Density is the estimated
number of activity centres (individuals) per square km, detection ( p) is the estimated probability of observing an individual at its activity centre, and sigma (
s
)
is the estimated spatial scale parameter that defines the distance over which the detection decreases.
density (km
2
) detection ( p) sigma (
s
)
sex region ^
u
se(^
u
)^
u
se(^
u
)^
u
se(^
u
)
female Borders 0.062 0.025 0.565 0.080 1.104 0.108
Central 0.137 0.046 0.630 0.095 0.551 0.062
Highlands 0.119 0.046 0.510 0.098 0.740 0.122
male Borders 0.035 0.014 0.417 0.093 2.651 0.235
Central 0.076 0.022 0.485 0.102 1.322 0.115
Highlands 0.066 0.024 0.366 0.084 1.775 0.346
rspb.royalsocietypublishing.org Proc. R. Soc. B 285: 20172603
5
Non-native grey squirrel presence was strongly negati-
vely affected by connectivity to individual pine martens,
whereas the presence of the native red squirrel was seemingly
positively affected.
(a) Study design
Our evidence is correlative rather than experimental but
derived from a structured survey considering key covariates,
and the sampling methods used account for variation in
detectability of the focal species. Central to the design was
the stratified sampling in three regions, each with a contrast-
ing time since recolonization by pine martens, and thus
differing lengths of time for which squirrel populations
have been exposed to pine marten predation. It is important
to note that our inferences are not solely based on between-
region differences in squirrel occupancy, as our analysis
included a region-specific intercept to account for this, but
rather on the large between- and intra-region variation in
pine marten density reflected in marten connectivity and its
relationship with squirrel occupancy.
Our study meets the high standard of statistical rigour
required of studies seeking to inform policy on controversial
wildlife management issues. Through embracing probabilis-
tic methods to account for variability in detection
probability in field data, we elevated the robustness of evi-
dence relative to previous studies using naive occupancy
and indices of abundance [9]. For instance, had we not
used trail cameras in addition to feeders, red squirrel prob-
ability of detection would have been artificially high which,
if unaccounted for, would have led to low naive occupancy
estimates and thus a spurious negative effect of pine martens
on red squirrels.
(b) Marten density versus connectivity to martens
While there is no expectation that the abundance of pine
martens, a generalist predator, should be strongly affected
by the abundance of any single prey species, variation in
predator abundance is likely to regulate the abundance of
prey populations [29]. In describing correlative patterns in
detection rates of pine marten, and red (positive) and grey
(negative) squirrels, Sheehy & Lawton [9] concluded that
inference based on detection rates was overly simplistic and
that marten abundance likely influences the interactions
between the three species. Here we directly address this
issue using recently developed spatial capture– recapture
methods to generate spatially explicit estimates of marten
density and density weighted connectivity [24]. The impor-
tant distinction here is that density surfaces describe the
spatial distribution of activity centres, ignoring available
information about how individuals use space. By contrast,
density weighted connectivity incorporates information
about marten space use and describes how connected any
location on the landscape is to the population of marten in
the area, i.e. for squirrels, it is a measure of overall exposure
to predation. Although only recently proposed [24], density
weighted connectivity provides a more intuitive measure
for how frequently species interact than a description of
where activity centres are located.
In direct support of the hypothesis that pine martens have
the potential to naturally control populations of invasive grey
squirrels [9], our analyses demonstrate that exposure to pine
marten (i.e. connectivity) clearly suppresses grey squirrel
populations in Scotland. Moreover, we provide equally com-
pelling evidence that this negative effect is not realized in red
squirrels. In fact, we found a positive effect of pine marten
connectivity on red squirrels in all three regions. This positive
effect is likely a consequence of the suppression of grey squir-
rel populations by pine martens as demonstrated in BO and
CS, and is suggestive of predator modified competition.
Although the lack of any interactions between region and
pine marten, DWC suggests the positive relationship extends
to the HI region, where grey squirrels are absent, this likely
reflects a lack of statistical power in HI (36 detectors,
1.0
0.8
0.6
0.4
0.2
0
0 0.5 1.0 1.5 2.0 2.5 3.0
pine marten connectivity
0 0.5 1.0 1.5 2.0
Highlands
Central
Borders
2.5 3.0
pine marten connectivity
occupancy
1.0
0.8
0.6
0.4
0.2
0
Figure 2. Model-averaged predictions of the relationships between occupancy and pine marten density weighted connectivity for grey squirrels and red squirrels in
the Borders, Central and Highland regions of Scotland.
rspb.royalsocietypublishing.org Proc. R. Soc. B 285: 20172603
6
compared to 80 and 107 in BO and CS, respectively), thus the
common relationship is dominated by BO and CS.
(c) Hint at proximal cause for differential impact
Our findings that grey and red squirrel detectability increased
and decreased with pine marten DWC (
b
DWC
¼0.74 +0.26
and
b
DWC
¼22.64 +0.42, respectively) provide some indi-
cation as to the proximal basis for the asymmetrical impact
of pine martens on red and grey squirrels. The detection com-
ponent of our occupancy models imply that a considerable
behavioural difference exists between red and grey squirrels,
such that red squirrels, despite being present, were less likely
to visit feeders in sites with high pine marten connectivity,
i.e. they respond in accordance with exposure to predation
risk. Conversely, grey squirrels, given they were present,
were more likely to use feeders in sites with high pine
marten connectivity, suggesting a maladaptive response to
a predator with which they share no coevolutionary history.
Sheehy et al. [30] reported that grey squirrels occurred in
15.6% of pine marten scats in Ireland compared to 2.5%
occurrence of red squirrels. This difference in the frequency
of occurrence between red and grey squirrels may also reflect
a higher vulnerability of grey squirrels to direct predation by
pine martens as a result of predator naivety; however esti-
mates of predation rates in areas where grey squirrels are
available as prey items are lacking.
(d) Nature of predation impact: extirpation versus
fugitive coexistence
Occupancy models predict near zero occupancy probabilities
of grey squirrel presence in those portions of landscapes with
the highest pine marten connectivity and severely depressed,
but above zero, occupancy probabilities elsewhere. Whether
the recovery of the pine marten will lead to the eventual extir-
pation of grey squirrels, and of the SQPV that it transmits to
red squirrels, or to a situation of coexistence with grey squir-
rels persisting fugitively at low density in parts of the
landscape not used by pine martens cannot be determined
from the snapshot data gathered here.
In our survey, areas with the highest pine marten connec-
tivity values were encountered in the CS region where
martens have been present for 8– 14 years. However, even
in this region where the relatively slow growing marten
population had time to increase, there remained many por-
tions of the landscape where marten density is presently
too low for region-wide grey squirrel extinction to occur.
However, while grey squirrel occupancy was positively
related to broadleaf forest cover in the BO region most
recently colonized by pine martens, this was not the case in
CS. A tentative interpretation of the interaction between
REGION and broadleaf forest cover in models predicting
grey squirrel occupancy influence is that, over time, pine mar-
tens suppress grey squirrel numbers even in their preferred
habitats.
(e) Implications for disease-mediated apparent
competition
Despite the uncertainty on whether the return of the pine
marten will ultimately lead to the extirpation or suppression
of grey squirrel abundance, it will likely profoundly alter the
overall competitive interaction between the squirrel species
through its impact on SQPV dynamics. General understand-
ing of directly transmitted diseases, specific models of SQPV
spatial dynamics [14] and field evidence that grey squirrel
control can lead to a reduction in disease prevalence in
residual grey populations [31] all suggest that pine-marten-
induced reductions in grey squirrel density will affect SQPV
transmission. Depending on the size of grey squirrel refuges
from pine marten predation in, for example, urban areas,
their connectivity through habitat corridors and chance
events, pine marten predation on grey squirrels might
either reduce or even eradicate the pathogen in local grey
squirrel populations. Establishing whether this expected
reduction is sufficient to preclude the transmission of SQPV
to immunologically susceptible red squirrel populations [32]
is an urgent applied research priority. Central to current
red squirrel management strategies is the creation of refuges
from grey squirrel-mediated infection with control buffers
where intensive grey squirrel culling is instigated. Siting
such refuges in areas where pine martens have fully
recovered might be one means to make such long-term
management feasible.
(f ) Management implications
Our evidence that, in addition to their intrinsic value, pine
martens provide an ecosystem service by suppressing
invasive grey squirrel populations, which has important
management implications. Substantial funds are spent
annually in the UK to control grey squirrels, for the benefit
of red squirrel conservation, and particularly in the southern
half of Britain, to limit the damage to growing deciduous
timber caused by grey squirrels [33]. The pine marten is
already heavily suppressing grey squirrel populations
where they are well established, and presumably this
influence will spread spatially over time.
The pine marten’s range in the UK is currently expanding
southwards through Scotland and into the north of England
but this is a slow, gradual process, commensurate with the
low fecundity of this species. This range expansion will
take place in areas where conflicts surrounding predators
can be locally acute and associated with the illegal persecu-
tion of protected predator species [34]. It is as yet unknown
whether the pine marten, as a recovering predator, is a
victim of such persecution and what contribution this
makes to slowing down the spread of the species.
There have been several reintroductions of pine martens
in Britain, including translocations in the BO region of our
survey, and official reintroductions in southwest Scotland,
and a population reinforcement in Wales. Our analyses
suggest that the impact of these reintroductions on grey
squirrels will only become substantial when pine marten
density has risen. Such densities are to be expected in estab-
lished populations and behind the frontier of naturally
expanding ranges.
Most of the residual range of the red squirrel in northern
Britain has been recolonized by the pine marten, and our
findings provide confidence that the return of the pine
marten will not have a detrimental impact on red squirrels,
even where pine martens reach average natural densities of
0.19 ind km
22
of woodland as seen in the Highlands of Scot-
land. Indeed, a detrimental impact would not be the expected
outcome of interactions between two species that share a
rspb.royalsocietypublishing.org Proc. R. Soc. B 285: 20172603
7
coevolutionary history. By contrast, it is not unlikely that
other prey species may be affected by the return of a long
absent native predator. Evidence from Fennoscandia suggests
that the pine marten is likely to affect the reproductive suc-
cess of some ground nesting bird species in years of low
vole abundance, with transient impacts on abundance (e.g.
[35]). Future investigations as to the impact of pine martens
on the capercaillie, Tetrao urogallus, a species endangered in
Scotland’s fragmented forest as a result of climate change
[36] and increasing predation by generalists, ought to adopt
the rigorous density estimates we strove for here rather
than using uncalibrated indices (e.g. [37]).
5. Conclusion
Hitherto, both resource and disease-mediated apparent com-
petition between red and grey squirrels have been well
documented. Our results demonstrate how the addition of
a second, indirect, interaction involving a recovering predator
reverses the expected outcome of resource and disease-
mediated competition. Where the native predator recovery
is more advanced, the native squirrel species now occupies
a greater portion of the landscape than non-native grey
squirrels which are predicted to only persist in landscape
refugia where the predators are scarce. As such we present
a rare demonstration of predator-mediated competition
in terrestrial mammals also linked by exploitative and
disease-mediated apparent competition.
Data accessibility. The datasets supporting this article have been
uploaded to Dryad: http://dx.doi.org/10.5061/dryad.478kp63 [38].
Authors’ contributions. E.S. and X.L. jointly conceived of and designed the
study. E.S. coordinated the study, collected field data and carried out
analysis; C.S. carried out analysis; C.O.R. carried out the molecular
laboratory work and analysis; E.S., C.S. and X.L. drafted the manu-
script; all authors gave final approval for publication.
Competing interests. We declare we have no competing interests.
Funding. The research leading to these results has received funding
from the Irish Research Council and from the People Programme
(Marie Curie Actions) of the European Union’s Seventh Framework
Programme (FP7/2007– 2013) under REA grant agreement no.
291760 ELEVATE, and Forestry Commission Scotland.
Acknowledgements. We thank Forest Enterprise Scotland and Trees for
Life for support. Thanks also to the Scottish Wildlife Trust, E. Schulte,
B. Priestly, K. Kortland, A. Jarrott, D. Anderson, T. Lightly,
I. Cepukaite, S. Eastwood, M. Oliver, L. Currie, R. Greenwood,
P. Whyatt, M. Hawkins, G. Neill, S. Willis, G. Stewart, I. Wilkinson,
T. Ferrie, Luss Estates, Penicuik Estate, East and West Dunbartonshire
Councils, Ballikinrain School and Dawyck Botanic Gardens.
References
1. Holt RA, Lawton J. 1994 The ecological
consequences of shared natural enemies. Annu. Rev.
Ecol. Syst. 25, 495– 520. (doi:10.1146/annurev.es.
25.110194.002431)
2. Bonsall M, Hassell M. 1997 Apparent competition
structures ecological assemblages. Nature 388, 371.
(doi:10.1038/41084)
3. Tompkins DM, Sainsbury AW, Nettleton P, Buxton D,
Gurnell J. 2002 Parapoxvirus causes a deleterious
disease in red squirrels associated with UK
population declines. Proc. R. Soc. Lond. B 269,
529–533. (doi:10.1098/rspb.2001.1897)
4. Colautti RI, Ricciardi A, Grigorovich IA, MacIsaac HJ.
2004 Is invasion success explained by the enemy
release hypothesis? Ecol. Lett. 7, 721–733. (doi:10.
1111/j.1461-0248.2004.00616.x)
5. Chapron G et al. 2014 Recovery of large carnivores
in Europe’s modern human-dominated landscapes.
Science 346, 1517– 1519. (doi:10.1126/science.
1257553)
6. Hayward MW, Marlow N. 2014 Will dingoes really
conserve wildlife and can our methods tell?
J. Appl. Ecol. 51, 835– 838. (doi:10.1111/1365-
2664.12250)
7. Gurnell J, Lurz P, Wauters L. 2015 Years of
interactions and conflict in Europe: competition
between Eurasian red squirrels and North American
grey squirrels. In Red squirrels: ecology, conservation
& management in Europe (eds C Shuttleworth,
PWW Lurz, MW Hayward), pp. 19– 37.
Woodbridge, UK: European Squirrel Initiative.
8. Sheehy E, Lawton C. 2015 Predators of red and grey
squirrels in their natural and introduced ranges. In
(eds C Shuttleworth, PWW Lurz, M Hayward) Red
squirrels: ecology, conservation & management in
Europe, pp. 83–96. Woodbridge, UK: European
Squirrel Initiative.
9. Sheehy E, Lawton C. 2014 Population crash in an
invasive species following the recovery of a native
predator: the case of the American grey squirrel and
the European pine marten in Ireland. Biodivers.
Conserv. 23, 753– 774. (doi:10.1007/s10531-014-
0632-7)
10. Croose E, Birks J, Schofield HW. 2013 Expansion zone
survey of pine marten (Martes martes)distribution in
scotland. Inverness, UK: Scottish Natural Heritage.
11. Shorten M. 1957 Squirrels in England, Wales and
Scotland, 1955. J. Anim. Ecol. 26, 287– 294.
(doi:10.2307/1748)
12. Bryce J. 1997 Changes in the distributions of red
and grey squirrels in Scotland. Mammal Rev. 27,
171–176. (doi:10.1111/j.1365-2907.1997.tb00446.x)
13. Kenward R, Hodder K, Rose R, Walls C, Parish T,
Holm J, Morris P, Walls S, Doyle F. 1998
Comparative demography of red squirrels (Sciurus
vulgaris) and grey squirrels (Sciurus carolinensis)
in deciduous and conifer woodland. J. Zool. 244,
7– 21. (doi:10.1111/j.1469-7998.1998.tb00002.x)
14. White A, Lurz PW, Bryce J, Tonkin M, Ramoo K,
Bamforth L, Jarrott A, Boots M. 2016 Modelling
disease spread in real landscapes: squirrelpox spread
in Southern Scotland as a case study. Hystrix
Ital. J. Mammal. 27, hystrix-27.1-11657. (doi:10.
4404/hystrix-27.1-11657)
15. Wauters L, Dhondt AA. 1992 Spacing behaviour of
red squirrels, Sciurus vulgaris: variation between
habitats and the sexes. Anim. Behav. 43, 297– 311.
(doi:10.1016/S0003-3472(05)80225-8)
16. Koprowski JL. 1994 Sciurus carolinensis.Mamm.
Species 480,1–9.
17. Mullins J, Statham MJ, Roche T, Turner PD, O’Reilly
C. 2010 Remotely plucked hair genotyping: a
reliable and non-invasive method for censusing pine
marten (Martes martes, L. 1758) populations.
Eur. J. Wildl. Res. 56, 443– 453. (doi:10.1007/
s10344-009-0332-x)
18. O’Mahony DT, Powell C, Power J, Hanniffy R,
Marnell F, Turner P, O’Reilly C. 2017 Non-invasively
determined multi-site variation in pine marten
Martes martes density, a recovering carnivore in
Europe. Eur. J. Wildl. Res. 63, 48. (doi:10.1007/
s10344-017-1108-3)
19. Wilberg MJ, Dreher BP. 2004 GENECAP: a
program for analysis of multilocus genotype
data for non-invasive sampling and capture–
recapture population estimation. Mol. Ecol. Resour.
4, 783–785. (doi:10.1111/j.1471-8286.2004.
00797.x)
20. Efford M. 2004 Density estimation in live-trapping
studies. Oikos 106, 598– 610. (doi:10.1111/j.0030-
1299.2004.13043.x)
21. Zalewski A, Je˛drzejewski W, Je˛drzejewska B. 1995
Pine marten home ranges, numbers and predation
on vertebrates in a deciduous forest (Białowiez
˙a
National Park, Poland). Annales Zoologici Fennici 32,
131–144.
22. Burnham KP, Anderson D. 2003 Model selection and
multi-model inference.A Pratical informatio-theoric
approch. Berlin, Germany: Springer.
23. Sutherland C, Royle J, Linden D. 2016 oSCR: Multi-
session sex-structured spatial capture –recapture
models. R package version 0.30.0.
rspb.royalsocietypublishing.org Proc. R. Soc. B 285: 20172603
8
24. Sutherland C, Fuller AK, Royle JA. 2015 Modelling
non-Euclidean movement and landscape
connectivity in highly structured ecological
networks. Methods Ecol. Evol. 6, 169–177. (doi:10.
1111/2041-210X.12316)
25. MacKenzie D, Nichols J, Royle J, Pollock K, Bailey L,
Hines J. 2006 Occupancy estimation and modelling.
Burlington, MA: Academic Press.
26. Fiske I, Chandler R. 2011 unmarked: an R package
for fitting hierarchical models of wildlife occurrence
and abundance. J. Stat. Softw. 43, 1– 23. (doi:10.
18637/jss.v043.i10)
27. Anderson DR, Burnham KP. 2002 Avoiding
pitfalls when using information-theoretic methods.
J. Wildl. Manage. 66, 912–918. (doi:10.2307/
3803155)
28. Arnold TW. 2010 Uninformative parameters and
model selection using Akaike’s information criterion.
J. Wildl. Manage. 74, 1175–1178. (doi:10.1111/j.
1937-2817.2010.tb01236.x)
29. Sinclair ARE, Pech RP, Dickman CR, Hik D, Mahon P,
Newsome AE. 1998 Predicting effects of predation
on conservation of endangered prey. Conserv. Biol.
12, 564– 575. (doi:10.1046/j.1523-1739.1998.
97030.x)
30. Sheehy E, O’Meara DB, O’Reilly C, Smart A, Lawton
C. 2014 A non-invasive approach to determining
pine marten abundance and predation. Eur. J. Wildl.
Res. 60, 223– 236. (doi:10.1007/s10344-013-0771-2)
31. Schuchert P, Shuttleworth CM, McInnes CJ, Everest
DJ, Rushton SP. 2014 Landscape scale impacts of
culling upon a European grey squirrel population:
can trapping reduce population size and decrease
the threat of squirrelpox virus infection for the
native red squirrel? Biol. Invasions 16, 2381– 2391.
(doi:10.1007/s10530-014-0671-8)
32. Chantrey J, Dale TD, Read JM, White S, Whitfield F,
Jones D, McInnes CJ, Begon M. 2014 European red
squirrel population dynamics driven by squirrelpox
at a gray squirrel invasion interface. Ecol. Evol. 4,
3788–3799. (doi:10.1002/ece3.1216)
33. Williams F et al. 2010 The economic cost of invasive
non-native species on Great Britain. Wallingford, UK:
CABI.
34. Whitfield DP, Fielding AH. 2017 Analyses of the
fates of satellite tracked golden eagles in Scotland.
Scottish Natural Heritage Commissioned Report No.
982. Edinburgh, UK: Scottish Natural Heritage.
35. Kurki S, Helle P, Linde
´n H, Nikula A. 1997 Breeding
success of black grouse and capercaillie in
relation to mammalian predator densities on two
spatial scales. Oikos 79, 301– 310. (doi:10.2307/
3546014)
36. Moss R, Oswald J, Baines D. 2001 Climate change
and breeding success: decline of the capercaillie in
Scotland. J. Anim. Ecol. 70, 47 – 61. (doi:10.1046/j.
1365-2656.2001.00473.x)
37. Baines D, Aebischer NJ, Macleod A. 2016 Increased
mammalian predators and climate change predict
declines in breeding success and density of
capercaillie Tetrao urogallus, an old stand specialist,
in fragmented Scottish forests. Biodivers. Conserv.
25, 2171–2186. (doi:10.1007/s10531-016-1185-8)
38. Sheehy E, Sutherland C, O’Reilly C, Lambin X. 2018
Data from: The enemy of my enemy is my friend:
native pine marten recovery reverses the decline of
the red squirrel by suppressing grey squirrel
populations. Dryad Digital Repository. (http://dx.doi.
org/10.5061/dryad.478kp63)
rspb.royalsocietypublishing.org Proc. R. Soc. B 285: 20172603
9
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