ORIGINAL PAPER
Niche overlap of mountain hare subspecies
and the vulnerability of their ranges to invasion
by the European hare; the (bad) luck of the Irish
Anthony Caravaggi . Katie Leach . Francesco Santilli .
Jukka Rintala . Pekka Helle . Juha Tiainen . Francesco Bisi .
Adriano Martinoli . W. Ian Montgomery . Neil Reid
Received: 13 February 2016 / Accepted: 7 November 2016 / Published online: 17 November 2016
 The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract Niche conservatism is the tendency of
related species to retain ancestral tolerances after
geographic separation. We used Ecological Niche
Modelling and Principal Components Analysis of
bioclimatic and habitat variables to describe the extent
of the species niche, and degrees of bioclimatic–
habitat niche conservatism within the mountain hare
(L. timidus) clade. Mountain hare niche space was
contrasted with that of the European hare (L.
europaeus), to shed light on species interactions in
contact zones throughout Europe. All five subspecies
of mountain hare had quantifiably distinct niches.
Fennoscandian (L.t. sylvaticus, L.t. timidus) and
highland (L.t. scoticus, L.t. varronis) subspecies,
however, were most similar, exhibiting greatest
apparent niche conservatism. They inhabit tundra,
boreal forest and uplands, and, hence are presumed
most similar to the ancestral form. The Irish hare was
distinct, being consistently distinguished from other
mountain hares in both 2D and nth dimensional (4D)
niche space. The ecological distinctiveness of the Irish
hare provides further evidence that it is an Evolution-
arily Significant Unit, particularly vulnerable to
Electronic supplementary material The online version of
this article (doi:10.1007/s10530-016-1330-z) contains supple-
mentary material, which is available to authorized users.
A. Caravaggi  K. Leach  W. I. Montgomery  N. Reid
Quercus, School of Biological Sciences, Queen’s
University Belfast, Belfast BT9 7BL, UK
A. Caravaggi (&)  K. Leach  W. I. Montgomery 
N. Reid
School of Biological Sciences, Queen’s University
Belfast, Belfast BT9 7BL, UK
e-mail: acaravaggi01@qub.ac.uk
F. Santilli
Department of Veterinary Sciences, Universita` di Pisa,
Pisa, PI, Italy
J. Rintala  J. Tiainen
Natural Resources Institute Finland, P.O. Box 2,
Viikinkaari 4, 00791 Helsinki, Finland
P. Helle
Natural Resources Institute Finland, University of Oulu,
Paavo Havaksen tie 3, 90014 Oulu, Finland
F. Bisi  A. Martinoli
Department of Theoretical and Applied Sciences, Insubria
University, Via J. H. Dunant 3, 21100 Varese, Italy
W. I. Montgomery  N. Reid
Institute of Global Food Security (IGFS), Queen’s
University Belfast, Belfast BT9 5BN, UK
123
Biol Invasions (2017) 19:655–674
DOI 10.1007/s10530-016-1330-z
displacement by introduced European hares with
which it competes and hybridises. Projections under
global climate change suggest that, by 2070, biocli-
matic space for invasive European hares in Ireland will
expand (by 79%) but contract for endemic Irish hares
(by 75%), further facilitating their replacement. The
near complete species replacement of the heath hare
(L.t. sylvaticus) in southern Sweden, where the
European hare has also been introduced, may suggest
a similar fate may be in store for the Irish hare.
Keywords Environmental Niche Modelling 
Invasion biology  Lepus  Niche conservatism 
Principal Components Analysis  Species Distribution
Model
Introduction
The controversial concept, ‘niche conservatism’, is the
tendency of emergent species to retain their ancestral
ecological traits such that closely related species may
be more ecologically similar than would be expected
based on their phylogenetic divergence (Wiens et al.
2010). A niche comprises a multivariate set of abiotic
and biotic conditions which facilitate the persistence
of a species, and to which it is suitably adapted
(Hutchinson 1957). However, studies investigating the
relationships between a species’ distribution and niche
frequently fail to appropriately define their terms
(Sobero`n 2007). The fundamental niche is uncon-
strained by limiting biotic factors such as ecological
competition, predation, dispersal ability, and environ-
mental conditions (Hutchinson 1957; Wiens and
Graham 2005). The realised niche is described as the
fundamental niche constrained by limiting factors, i.e.
the space occupied by, and the resources available to,
an organism (Hutchinson 1957; Sobero`n 2007). There
are, however, issues inherent in the utilisation of these
terms due to considerations of spatial resolution and
biotic interactions (Arau´jo and Guisan 2006). Popu-
lation viability depends on a degree of environmental
stability or adaptive predictability, and hence, climate
and habitat are key factors (Sobero´n and Peterson
2005; Ja¨ka¨la¨niemi 2011). In the absence of gene flow,
populations may diverge genetically while occupying
similar habitat to ancestral species, and hence, the
species niche is conserved (Peterson et al. 1999; Wiens
2004). Niche adaptation may, therefore, be spatiotem-
porally stable and can be conserved during allopatric
speciation via geographic isolation.
Environmental Niche Modelling (ENM) is increas-
ingly used to estimate environmental suitability as a
function of geospatial species occurrence relative to
environmental variables (Phillips et al. 2006), thus
capturing a species’ niche space. Here, we investigate
species occurrences and overlap in potentia, with
dispersal constrained by climatic and habitat variables
(hereafter, referred to simply as the species ‘niche’).
We aimed to describe the degree of niche conser-
vatism within the mountain hare clade in Europe,
capturing the niche of each subspecies. These are
contrasted against the niche of the European hare, thus
shedding light on observed differences in species
interactions post-contact. We pay particular attention
to the Irish hare due to its ecological equivalency to the
European hare (i.e. temperate, lowland, grazing
habits). We hypothesise that, given prolonged, post-
glacial isolation and ecological expansion: (1) the
niche of the Irish hare is more ecologically distinct
from other mountain hare subspecies than those
subspecies are from one another and, (2) the niches
of the Irish and European hare are more similar to each
other than are those of other mountain hare subspecies,
relative to the European hare (making the Irish hare
more vulnerable to the impact of European hare
invasion). We also model the predicted shift in the
bioclimatic space suitable for Irish and European hares
in Ireland, under projected global climate change. We
hypothesised that (3) the bioclimatic space available
for the Irish hare is likely to become increasingly
unsuitable, and, (4) the bioclimatic space available for
the European hare is likely to become increasingly
suitable under warming temperatures due to their
contrasting origins and differentially-adapted physi-
ology (the former having Arctic, and the latter Middle
Eastern, ancestry). Thus, we expect that the trajectory
of global climate change is likely to be beneficial to the
invader and detrimental to the native species.
In the Order Lagomorpha, the genus Lepus (hares
and jackrabbits) is represented in Europe by five extant
species, two of which, the mountain hare (Lepus
timidus, Linnaeus, 1758) and the European hare (L.
europaeus, Pallas 1837), are widely distributed. These
species are readily distinguished phenotypically, by
ear and limb length (all shorter in mountain hares,
apart from the hind feet), head shape (convex in
656 A. Caravaggi et al.
123
European hares), strongly contrasting black ear-tips
(present in European hares), white-striped muzzle
(present in European hares), ventral tail surface (black
in European hares), body mass (lower in mountain
hares) and pelage colour (darker and more uniform in
mountain hares; Flux and Angerman 1990; Caravaggi
et al. 2016). The European hare is a highly successful
invasive species that has been introduced to a large
number of countries worldwide (sensu Flux and
Angerman, 1990). It is typically parapatric with the
mountain hare, being separated by elevation or habitat,
with narrow contact zones suggesting each species has
a distinct niche separated by, for example, differences
in habitat or climate (Amori et al. 2008). The invasion
dynamics of the European hare and its interaction with
native mountain hare populations are poorly under-
stood (Thulin 2003; Reid 2011). Some contact zones
between the species are largely stable (e.g. in the Alps
and Scottish Highlands), though it is predicted that
such elevationally-defined contact zones will shift
upwards due to the effects of global climate change
(Leach et al. 2015a). Other, more recently established
contact zones are highly unstable with the larger
European hare outcompeting and displacing the
smaller mountain hare. Indeed, European hares have
displaced mountain hares over much of southern
Sweden (Jansson and Pehrson 2007) and part of
southern Finland (Leva¨nen et al. 2015) during the
twentith century, and part of Ireland in the last few
decades (Reid and Montgomery 2007; Reid 2011;
Caravaggi et al. 2015, 2016). Thus, post-introduction
sympatry is a typically transient phenomenon (Thulin
2003).
The mountain hare is a circumpolar, arcto-alpine
species complex, distributed from Ireland in the west,
to Japan and Kamchatka in the east, and from the Alps
in the south, to 75N (Flux and Angerman 1990; Smith
and Johnson 2008b). There are five extant European
mountain hare subspecies differentiated by morpho-
physiological characteristics, behaviour, and ecology
(Angerbjo¨rn and Flux 1995). There is generally low
genetic differentiation between subspecies, indicative
of a post-glacial panmictic European population,
which subsequently underwent fragmentation, isola-
tion, and divergence (Hamill et al. 2006). The most
widespread subspecies, recognised as the typical form
(and thus presumed similar to the ancestral type), is the
northern hare (L. timidus timidus, Linnaeus 1758)
which inhabits tundra (in the north) and boreal forest
(further south) in the Arctic and Fennoscandia
(Angerbjo¨rn and Flux 1995). Its diet varies seasonally,
with hard woody material being consumed in winter,
and grasses, sedges, and herbs, in late summer and
autumn (Flux and Angerman 1990; Helle 1995). The
heath hare (L.t. sylvaticus, Nilsson 1831) occurs in
southern Sweden and Gotland (Winiger 2014). The
subspecific status of this taxon is debated, with some
regarding it as a synonym of L.t. timidus. However,
many others recognise it as a distinct subspecies based
on winter pelage, which is blue-grey rather than white
(Lindstro¨m 1980; Suchentrunk et al. 1999; Thulin
et al. 2003; Winiger 2014). Both the northern and
heath hares are thus Fennoscandian mountain hare
subspecies and geographically distinct from three
isolated mountain hare populations, two of which are
true highland mountain hares: the Scottish hare (L.t.
scoticus, Hizheimer 1906) and the Alpine hare (L.t.
varronis, Miller 1901). The former is widespread
throughout montane habitats in Scotland, occurring up
to 1300 m asl (Newey et al. 2011). The latter is
generally found on forested slopes (Bisi et al. 2013;
Rehnus et al. 2013) up to 3500 m asl (Thulin 2003;
Rehnus et al. 2013), throughout the Alps (Angerbjo¨rn
and Flux 1995). The highland subspecies browse hard,
woody plant material e.g. heather Calluna vulgaris
(Flux and Angerman 1990). The Irish hare (L.t.
hibernicus, Bell 1837) is endemic to the island of
Ireland, where it has been isolated for
30,000–60,000 years (Hughes et al. 2006). One esti-
mate placed the divergence of Irish hares from other
mountain hares (specifically, Russian L.t. timidus) at
ca. 360,000 years before present (Hughes et al. 2006).
This subspecies possesses a comparatively high num-
ber of unique genetic forms (mitochondrial haplo-
types) not shared by any other subspecies outside
Ireland (Hughes et al. 2006). It exhibits considerable
ecological plasticity, being found at all altitudes in
Ireland, but is most common in the lowlands (Whelan
1985; Reid et al. 2007). In contrast to other mountain
hares it feeds predominantly on soft, mostly agricul-
tural grasses, e.g. ryegrass Lolium perenne (Strevens
and Rochford 2004). Nearly all mountain hare popu-
lations exhibit winter whitening as camouflage during
winter snow cover (e.g. Hewson 1958), with one
exception. The Irish hare has largely lost the trait, save
for minimal whitening of the ear margins and feet
(Flux and Angerman 1990). Such is the genetic,
phenotypic, behavioural and ecological distinctiveness
Niche overlap of mountain hare subspecies and the vulnerability 657
123
of the Irish hare, that some contend it may warrant full
species status (Hughes et al. 2006). It is as divergent
from other mountain hare subspecies as the mountain
hare is from other species such as the Arctic (L.
arcticus, Ross 1819) or Alaskan (L. othus, Merriam
1900) hares (Paulo Prodo¨hl pers. comm.), whose
taxonomic status and phylogenetic relationships with
the mountain hare have been the subject of debate (e.g.
Wu et al. 2005; MacDonald and Cook 2010).
Methods
Data sources and preparation
A total of 238,813 records of mountain hare sub-
species and European hare found in Europe were
obtained from a large number of sources, principally
biodiversity data record centres, academics and ecol-
ogists (Tables S1, S2 in Supporting Information). Data
were collected via a variety of methods, combinations
of which differed between and within regions, coun-
tries and organisations, e.g. scientific surveys, hunting
bags, opportunistic sightings by the public, ecological
surveys, road casualties. Hereafter, we adopt the terms
‘‘(sub-)species’’ to refer to the mountain hare (includ-
ing all subspecies) and the European hare, or ‘‘sub-
species’’ when referring to the mountain hare only.
Records were extracted during 2013–2014 and were
sub-sampled by date (post-1950, to ensure consistency
with current bioclimatic datasets), and geospatial
accuracy (B1 km resolution). Furthermore, while
there may be difficulties inherent in discriminating
between sympatric species, we were unable to quan-
tify observer bias. Duplicate records were removed, as
were those considered erroneous based on known
distributions of each (sub-)species (i.e. falling beyond
the boundary of the International Union for Conser-
vation of Nature range polygon; Smith and Johnston
2008a, b). Species-specific records that occurred
within the known range of that species were, therefore,
considered ‘true’ and retained, while those that
occurred outside the known range were considered
‘false’ and removed. The range polygons for each
mountain hare subspecies were extracted from the
parent IUCN range polygon and sub-divided into
geographically isolated populations i.e. Ireland, Scot-
land, and the Alps, whilst the Fennoscandian mountain
hare subspecies ranges were delineated according to
Bergengren (1969). Due to a lack of sufficiently
precise data in northern Fennoscandia and much of
central Europe, Northern and European hares
appeared erroneously ‘absent’ from parts of their
known range. Furthermore, data exhibited consider-
able sample bias (Yackulic et al. 2013), with large
numbers of records occurring around urban centres,
particularly in the UK and Sweden. There are a
number of methods available for accounting for
sample bias (see Fourcade et al. 2014), including the
utilisation of target background points (Phillips et al.
2009) or bias grids (Elith et al. 2010). Target-
backgrounds are defined as background points drawn
from occurrences of a focal class (e.g. lagomorphs,
herbivorous mammals). Thus, background data will
exhibit similar spatial bias to that of the modelled
species (Phillips et al. 2009). Similarly, a bias grid is a
surface scaled to represent survey effort (Elith et al.
2010), a quantity unknown for almost all ([99.9%) of
our data. However, data manipulation (i.e. removing
data in over-sampled regions) may be effective in
reducing or removing bias (Phillips et al. 2009). Thus,
in order to reduce sample selection bias, presence
records were thinned using OccurrenceThinner ver-
sion 1.04 downloaded from www.phycoweb.net/
software. OccurrenceThinner uses probability algo-
rithms to remove occurrence records based on an
associated kernel density grid. The probability that an
occurrence will be removed is proportional to occur-
rence density described by the kernel density grid
(Verbruggen et al. 2013). Due to the extremely high
density of occurrences in some regions (e.g. urban
areas in the UK and southern Sweden), data were
sequentially thinned to appropriate densities which
were informed a priori by densities of records else-
where in the species range. A priori thinning aimed to
equalise the densities of occurrence records on a
landscape scale, and, hence, produce ecologically
relevant models. A total of 9075 records were used in
modelling (see Table S2 for species specific pre- and
post-thinning occurrence counts, Fig S1 for occur-
rence distribution maps). 10,000 background data
points (i.e. pseudo-absences) were generated ran-
domly within the range of each individual (sub-)spe-
cies, analogous to the Restricted Background
approach detailed in Fourcade et al. (2014).
Climate data were downloaded from WorldClim
(www.worldclim.org) at 30 arc-second (ca. 1 km2)
resolution. Species records were associated with mean
658 A. Caravaggi et al.
123
data from 1950 to 2000 for current models only. Three
raw-format (mean temperature, precipitation season-
ality and temperature seasonality) and three composite
(Hilliness Index, Normalised Difference Vegetation
Index (NDVI), and water balance) environmental
variables were used (Table S3). Eight land cover
variables (coniferous forest, crops, mixed forest,
moorland and heathland, pasture, peat bog, scrub and
sparse vegetation; see Table S3 for vector filenames)
were obtained from the CORINE Land Cover 2006
(EEA 2010).
Shapefile and raster creation and manipulation were
carried out using ArcGIS 10.2.2 (ESRI 2011).
Environmental Niche Modelling
MAXENT is a popular presence-only modelling tool
(Phillips et al. 2006, 2010), which uses a maximum
entropy approach, i.e. the probability distribution
which best represents the data is the one with the
largest entropy. Despite its widespread use, MAXENT
has been criticised due to its vulnerability to overfit-
ting and the use of logistic output to estimate absolute
occurrence probabilities (e.g. Royle et al. 2012). Such
limitations may be mitigated against by careful a priori
data manipulation, to closer approximate the assump-
tions of the model, e.g. that occurrence data represent
unbiased independent samples, constant probability of
detection, and that detectability is independent of
model variables (Yackulic et al. 2013). Indeed,
MAXENT has been shown to consistently outperform
other comparable modelling techniques (e.g. Wisz
et al. 2008; Tarkesh and Jetschke 2012). While
MAXENT is relatively robust against collinear variables
(Rodriguez-Robles et al. 2010; Kuemmerle et al.
2012), several climatic variables exhibited strong
collinearity; exploratory models suggested a strong
cumulative influence. Variables with the greatest
permutation importance, i.e. mean temperature (col-
linear with minimum temperature, maximum temper-
ature) and annual water balance (collinear with
minimum precipitation, maximum precipitation, mean
precipitation), were retained. Climatic and environ-
mental variables with a mean permutation importance
of\ 2 (complex cultivation, human influence index,
inland marsh, natural grassland, radiation, snow,
urban, number of months with positive water balance)
were also removed. ENMs were run using linear,
quadratic, product and threshold features with
clamping and extrapolation disabled, for 50 replicates.
Presence records were split randomly into a 75%
training set and a 25% test set, with cross-validation.
Models for the Irish and European hare were
projected under global climate change at time-slices
for the current period (2010–2014), 2050s and 2070s.
IPCC Fifth Assessment Report Coupled Model Inter-
comparison Project Phase 5 (CMIP5) future climatic
data for the Representative Concentration Pathway
(RCP) 8.5 for 2050 (averaged across 2041–2060) and
2070 (average for 2061–2080) were downloaded from
WorldClim at 1 km2 grid cell resolution. RCP 8.5
indicates a mean average global temperature increase
of 2 C by the 2050s and 3.7 C by the 2070s. All
variables were averaged across five Global Circulation
Models (GCMs), CNRM-CM5, GFDL-CM3, GISS-
E2-R, Had-GEM-ES and MIROC-ESM-CHEM, thus
reducing model error (sensu Pierce et al. 2009).
Originally described as ‘‘extreme climate change’’,
this climate scenario now appears to best fit observed
climatological trends (following Leach et al. 2015a;
Table S3). Changes in predicted range extent were
calculated using Max SSS, i.e. the sum of test
specificity plus sensitivity, which is effective when
using presence only data, and is not affected by
pseudo-absences (Liu et al. 2013). A major caveat of
this approach is that CORINE habitat variables were
kept constant when projecting into future time-slices
as no robust predictions are available for how land
cover will respond under future climatic conditions.
However, this approach is consistent with most studies
that project species ranges into future conditions (e.g.
Acevedo et al. 2012).
Model evaluation
Models were evaluated using the Area Under the
Curve (AUC; Fielding and Bell 1997) of the Receiver
Operating Characteristic (ROC) curve, a model-accu-
racy assessment measure that is independent of
prevalence (McPherson et al. 2004). The classification
of AUC values follows a commonly-used, yet arbi-
trary ranking system based on suggestions by Swets
(1988), Greiner et al. (2000). Values between 0.9 and
1.0 are considered excellent, 0.9–0.8 good, 0.7 and 0.8
average and\0.7 poor. However, where ROC curves
are constructed from presence-only data, the maxi-
mum possible AUC is\1 (Wiley et al. 2003), and it is
not possible to determine optimal performance
Niche overlap of mountain hare subspecies and the vulnerability 659
123
(Phillips et al. 2006). Nevertheless, relative perfor-
mance may still be inferred, given that an AUC of 0.5
describes random prediction (Phillips et al. 2006). We
also tested the omission rate (proportion of true
occurrences misidentified), sensitivity (proportion of
presences which are correctly predicted), specificity
(proportion of absences which are correctly pre-
dicted), proportion correct (proportion of the presence
and absence records correctly identified), and True
Skill Statistic (TSS), calculated using SDMTools
package (Van der Wal et al. 2012) in R (version
3.2.2). TSS is a prevalence-independent metric
derived from threshold sensitivity and specificity.
Values range from -1 to ?1 and test the agreement
between the expected and observed distribution, and
whether the outcome could be predicted due to chance
(Allouche et al. 2006). A value [0.4 was taken as
indicating that the model was a good fit (Landis and
Koch 1977; sensu Leach et al. 2015a).
Niche overlap and equivalency
The similarity of (sub-)species continuous-surface
probability models (i.e. geographic niche overlap)
were evaluated using the niche overlap metric,
I (Warren et al. 2008). This method calculates pairwise
overlap between models, producing values between 0
(no overlap between niche models) and 1 (identical
niche models). Warren’s I is based on the probability
(px,i, py,i) of a species (X or Y) occurring in a given cell
(i); defined by the ENM. In contrast to Schoener’s D
(Schoener 1968), another commonly-used metric,
Warren’s I treats px and py as probability distributions
with no biological assumptions, and, hence, is more
appropriate for presence-only analyses (Warren et al.
2008). Niche overlap metrics were calculated for
contemporary and future climate-projected models
using the R package fuzzysim (Barbosa 2015).
Niche equivalency tests were used to assess
whether paired-species ENM overlap values (I) were
significantly different from a one-tailed normalized
null distribution of comparative overlap values. Null
distributions were generated by comparing ENMs of
two focal species to random subsets drawn from
pooled presences, where the number of extracted (i.e.
‘null’) presences were equal to the number of observed
presences for each species. This was repeated 100
times for each species pair (Warren et al. 2008).
Ecological niches were said to be non-equivalent if
paired-species overlap values were significantly lower
than those of the null distribution (P B 0.05). Niche
equivalency tests were carried out using ENMTools
(Warren et al. 2010) and using only contemporary
data.
Ecological distance
Principal Component Analysis of occurrence records
and associated data was used to reduce bioclimatic and
habitat variables associated with all species records to
four hypothetical axes with eigenvalues[1, describ-
ing ecological niche space, using core R functions. A
multifactorial General Linear Model (GLM) was used
to establish differences in Principal Components (PC1
through PC4) between each (sub-)species with Bon-
ferroni pairwise post hoc test for multiple comparisons
used to identify niche space differences. Biplots of
paired Principal Component Axes were used to plot
the proximity of each (sub-)species in 2D space. For
each pairwise plot the mean Mahalanobis distance (De
Maesschalck et al. 2000) was calculated between: (1)
all pairwise comparisons of mountain hare subspecies
excluding the focal subspecies (i.e. the Irish hare); and
(2) all pairwise combinations including the focal
subspecies. Mahalanobis distances were calculated
using the R package StatMatch (D’Orazio 2015). The
n-dimensional Euclidean distance between each pair
of (sub-)species was also calculated across all four
Principal Components simultaneously, thus deriving a
single measure of distance between (sub-)species in
multidimensional (4D) niche space. Euclidean dis-
tances were calculated using the R package pdist
(Wong 2013). A t test was used to test for significance
of differences between the two groups (mountain hares
including and excluding the Irish hare).
Results
Model evaluation
All (sub-)species continuous-suitability ENMs per-
formed well (AUC[ 0.7, TSS[ 0.4; Table 1). Tem-
perature seasonality (43.1%) and mean annual
temperature (26.3%) had the greatest mean contribu-
tion across all (sub-)species models (Table 2), but
their contribution to individual (sub-)species ENMs
varied substantially. For example, temperature
660 A. Caravaggi et al.
123
seasonality was the single most important variable for
the Irish hare (97.2%), yet was relatively unimportant
for the northern hare (0.5%).
The predicted probabilities of mountain hare (sub-
)species presence closely approximated the actual
range extent of each (sub-)species (Fig. 1). Niche
space for the European hare was predicted northward
beyond its northern (invasive) range edge in Sweden
extending west into southern Norway, southward
beyond its southerly (natural) range edge in north-
eastern Iberia and in all directions around its current
invasive range in Northern Ireland (Fig. 1f).
Ecological (dis)similarities
Geographic niche overlap measures derived from
continuous-surface probability models described
potential overlap between six (sub-)species pairs
(I C 0.4; Table 3). Almost all pairwise comparisons
between hare (sub-)species and ENMs generated using
randomly selected background points did not differ
from null distributions, and, hence, their niches can be
said to be similar. Only four pairwise comparisons
between were found to be significantly different (i.e.
less similar than expected by chance; P B 0.05),
though the relationship was unidirectional rather than
reciprocal: the Alpine hare was distinct from the
Scottish hare and the European hare; the Irish hare was
distinct from the European hare; and the Northern hare
was distinct from the Irish hare (Table 3). Thus, the
ecological niche of the Alpine hare, for example, was
more distinct from that of the European hare than
would have been expected by chance, but not vice
versa. Our results suggest that while the ecological
niches of hare (sub-)species in Europe are similar, they
are not identical.
Ecological niche space from occurrence point data
was described by Principal Component Axis 1 (PC1)
capturing 24% of bioclimatic and habitat variation,
describing mean annual temperature (0.82; linear
combination coefficient, or loading), Normalized
Difference Vegetation Index (0.89), pasture (0.55),
precipitation seasonality (-0.59), and temperature
seasonality (-0.72), PC2 captured 17% of the varia-
tion, describing annual water balance (0.80), hilliness
(0.80), and sparse vegetation (0.59), PC3 captured 8%
of variation, describing coniferous forest (0.79) and
scrub (0.53), and PC4 also captured 8% of variation,
describing peat bogs (0.84; Table 4).
All Principal Component values varied signifi-
cantly between (sub-)species (Table S4). The biplot of
PC1 and PC2 (accounting for 41% of cumulative
variation) suggested that the niches of Fennoscandian
mountain hare subspecies were more similar to one
another than they were to any other hare (sub-)species
(Fig. 2). Both highland mountain hare subspecies
were also more similar to one another than they were
to any other hare (sub-)species. The Scottish hare
occupied a similar, yet slightly more productive
environment, suggested by a more positive value on
Table 1 Environmental Niche Model evaluation metrics for six European hare (sub-)species, using 75% training and 25% test data
(50 replications)
(Sub-)species Data AUC Omission rate Sensitivity Specificity Proportion correct TSS
Alpine hare Training 0.74 0.19 0.81 0.66 0.66 0.47
Test 0.74 0.19 0.81 0.66 0.66 0.47
Heath hare Training 0.73 0.20 0.80 0.67 0.67 0.47
Test 0.73 0.21 0.79 0.67 0.67 0.47
Irish hare Training 0.72 0.27 0.73 0.70 0.70 0.43
Test 0.73 0.24 0.76 0.70 0.70 0.46
Northern hare Training 0.74 0.20 0.80 0.68 0.68 0.48
Test 0.74 0.21 0.79 0.68 0.68 0.47
Scottish hare Training 0.73 0.27 0.73 0.72 0.72 0.45
Test 0.73 0.26 0.74 0.72 0.72 0.46
European hare Training 0.74 0.16 0.84 0.64 0.64 0.48
Test 0.73 0.17 0.83 0.64 0.64 0.47
AUC Area Under the Curve of the Receiver Operating Characteristic curve, TSS True Skill Statistic
Niche overlap of mountain hare subspecies and the vulnerability 661
123
PC1, indicating higher NDVI. Variation in Irish hare
niche space not only did not overlap with any other
mountain hare subspecies, but its centroid was further
away from other mountain hare subspecies than it was
from the European hare, which was associated with
agricultural crops. The Irish hare was associated with
temperate, highly productive pastures (Fig. 2).
Other pairwise comparisons between remaining
Principal Components showed fewer distinct differ-
ences (Fig. S2), as they accounted for less variation,
Table 2 Comparison of environmental response curves for each variables used in Environmental Niche Modelling and their esti-
mated relative contribution to of each model. Variables are ranked in descending order of their averaged contribution across all six
(sub-) species. x-axis = metrics of the focal variable; y-axis = probability of suitable conditions
Variable Alpine
hare
Heath
hare
Irish
hare
Northern
hare
Scottish
hare
European
hare
x̅
Temperature 
seasonality
36.1% 18.9% 97.2% 0.5% 79.5% 26.6% 43.1%
Mean 
temperature
17.5% 49.7% 0.0% 51.6% 15.8% 23.2% 26.3%
NDVI
6.4% 2.8% 0. 4% 29.1% 2.9% 13.9% 9.3%
Hilliness
Index
29.6% 4.1% 0.1% 0.9% 0.1% 7.1% 7.0%
Water
balance
2.7% 10.5% 0.1% 5.0% 0.4% 3.5% 3.7%
Precipitation
seasonality
2.0% 0.9% 1.9% 1.3% 0.0% 11.9% 3.0%
Pasture
0.4% 5.4% 0.1% 0.7% 0.0% 2.6% 1.5%
Peat bog
1.9% 1.5% 0.1% 1.3% 0.0% 2.9% 1.3%
Crops
1.3% 0.2% 0.1% 3.4% 0.2% 2.3% 1.3%
Sparse
vegetation
0.6% 3.0% 0.0% 1.9% 0.0% 1.0% 1.1%
Coniferous
forest
0.3% 1.7% 0.1% 1.1% 0.1% 1.7% 0.8%
Moorland &
heathland
0.9% 0.8% 0.0% 0.4% 0.6% 1.1% 0.6%
Mixed
Forest
0.1% 0.3% 0.0% 0.6% 0.4% 1.9% 0.6%
Scrub
0.1% 0.2% 0.0% 2.1% 0.0% 0.4% 0.5%
662 A. Caravaggi et al.
123
(a) (b)
(c) (d)
(e) (f)
Fig. 1 Predicted bioclimatic and habitat suitability from
Environmental Niche Models of a Alpine hare, b Heath hare,
c Irish hare, (d) Northern hare, e Scottish hare, and f European
hare. Shaded areas indicate the (sub-) species range extent as
derived from IUCN polygons or known distributions (Bergen-
gren 1969; Winiger 2014; Caravaggi et al. 2015)
Niche overlap of mountain hare subspecies and the vulnerability 663
123
yet almost all pairwise post hoc tests between (sub-
)species were significant (Fig. S3). The Irish hare was
most similar to the heath hare and European hare on
PC1, the European hare on PC2 (Fig. 2) and the
Scottish hare on PC3 and PC4 (Fig. S2). The European
hare did not differ significantly from the Alpine hare
on PC1, the heath hare on PC2 (Fig. 2) and the
northern hare on PC4 (Fig. S2).
Ecological distance
All pairwise mountain hare subspecies comparisons
including the Irish hare had significantly longer
Mahalanobis distances on each Principal Component
biplot, than all pairwise comparisons excluding the
Irish hare, except between PC2 and PC3 (Fig. 3c). The
Irish hare was on average 13% further away from other
mountain hares than they were to one another across
all four Principal Component Axes. Pairwise Maha-
lanobis distances were greatest on those axes explain-
ing the greatest percentage variation in bioclimatic and
habitat variables. Mahalanobis distances on Principal
Component biplots did not differ significantly for
other mountain hare subspecies (Fig. 3b, d, e) with the
exception of the Alpine hare, which had significantly
shorter distances than all pairwise combinations
excluding itself on PC1:PC2, and significantly longer
distances on PC2:PC3 and PC3:PC4 (Fig. 3a). The
Table 3 Niche overlap/equivalency measures (Warren’s I) derived from continuous-suitability Environmental Niche Models.
Histograms show null distributions, where ENMs of focal (sub-)species occurrences were compared to random subsets drawn from
pooled occurrences of each (sub-)species pair. The relative position of overlap metrics against null distributions are indicated by
vertical black lines. Paired species overlap values which differ significantly from related null distributions (i.e. demonstrate non-
equivalence; P B 0.05) are highlighted
Hare (sub-)species Niche overlap (I)
a b a→b b→a
Alpine Heath 0.111 0.226
Irish 0.107 0.128
Northern 0.249 0.266
Scottish 0.186 0.220
European 0.592 0.588
Heath Irish 0.076 0.064
Northern 0.480 0.412
Scottish 0.145 0.134
European 0.498 0.518
Irish Northern 0.070 0.089
Scottish 0.409 0.454
European 0.244 0.277
Northern Scottish 0.184 0.175
European 0.444 0.533
Scottish European 0.435 0.410
664 A. Caravaggi et al.
123
Alpine hare was on average 7% further away from
other mountain hares than they were to one another
across all four Principal Component axes. Conse-
quently, when using a single metric for the mean nth-
dimensional (4D) Euclidean distance between species
pairs, those mountain hare pairs involving the Irish
hare were significantly further away from other
mountain hares than pairs excluding the Irish hare
were from each other (tdf=5 = 3.66, p = 0.015;
Fig. 4).
Predicted range shifts
Geographic niche overlap metrics derived from con-
tinuous-surface probability models suggest that niche
overlap between the European hare and several
mountain hare subspecies will increase in coming
decades (Table 5). Most notably, overlap with the
heath hare is predicted to be almost complete by 2070
(I = 0.91). Geographic niche overlap between the
European hare with the Scottish (I = 0.81) and
Northern (I = 0.73) mountain hares increases by
2050 but is largely stable thereafter (Table 5).
Projections of predicted probability of occurrence
for Irish and European hares in Ireland under future
climate change suggest major changes in the likely
distribution of their respective envelopes over the next
half century. ENMs for the Irish hare predicted the
current range to cover the whole of Ireland at
83,497 km2 (Fig. 5a). By 2050, the suitable biocli-
matic envelope was predicted to contract westward
with the most suitable habitat remaining in the north-
west, the total range extent declining to 35,461 km2
(Fig. 5b), and declining further to 21,107 km2 by 2070
(Fig. 5c). Thus, ENMs predicted a 75% contraction in
the species suitable bioclimatic envelope over the next
50 years. Conversely, the current bioclimatic envel-
ope of the European hare was predicted to be restricted
mostly to Northern Ireland at 12,417 km2 (Fig. 5d).
By 2050, it expanded south and westward to
53,874 km2 (Fig. 5e), expanding further to
66,312 km2 or 79% of the island by 2070 (Fig. 5f).
Discussion
All five European subspecies of mountain hare were
found to have quantifiably distinct niches. Fennoscan-
dian subspecies (the northern and heath hares) were
more similar to each other than any other subspecies
while the highland subspecies (the Scottish and Alpine
hares) were also more similar to each other than any
other subspecies. The Irish hare was not only distinct,
but had zero overlap with other subspecies and was
consistently distinguished from other mountain hares
in both 2D and nth dimensional (4D) space. Moreover,
the niche space of the Irish hare was more similar to
that of the European hare than any other mountain
Table 4 Principal
Component Axes loadings
capturing variation in
bioclimatic and habitat
variables used in
Environmental Niche
Modelling throughout
Europe. Loadings which
explain the greatest
proportion (i.e. are the
largest contributors) of each
Principal Component are
highlighted in bold. The
percentage variation
explained by each Principal
Component is also given.
CORINE vector filenames
are given in Table S3
Variable Principal Component Axes
PC1 (24%) PC2 (17%) PC3 (8%) PC4 (8%)
Mean temperature 0.82 -0.21 -0.19 -0.18
NDVI 0.89 -0.02 -0.02 -0.15
Pasture 0.55 -0.08 -0.04 0.01
Water balance 0.25 0.80 -0.07 0.13
Hilliness Index -0.01 0.80 0.14 -0.15
Sparse vegetation -0.35 0.59 -0.33 -0.15
Coniferous forest -0.23 -0.04 0.79 -0.20
Scrub -0.11 -0.05 0.53 0.17
Peat bog -0.10 -0.10 0.03 0.84
Crops 0.18 20.47 20.43 -0.40
Moorland and heathland -0.08 0.43 -0.30 0.40
Mixed forest -0.16 -0.09 0.01 -0.10
Precipitation seasonality 20.59 -0.29 0.10 0.03
Temperature seasonality 20.72 20.48 0.22 -0.13
Niche overlap of mountain hare subspecies and the vulnerability 665
123
hare. Thus, all subspecies had separate niches, but the
Irish hare stands out as being uniquely different, with
little or no commonality to other mountain hares.
During the Last Glacial Maximum (LGM), ances-
tral mountain hares likely maintained a panmictic
European population which inhabited highlands,
boreal forest, and tundra, in snowy, cold conditions
(Angerbjo¨rn and Flux 1995). Northern, Scottish and
Alpine hares appear, therefore, to exhibit niche
conservatism, retaining their ancestral ecological traits
and environmental distributions, such that they are
ecologically similar despite their geographic isolation.
The extent of their distributions may have been
constrained by interspecific interactions with the
European hare that invaded lowland areas after the
LGM. Moreover, agricultural intensification and
changes in land management are likely to have further
reinforced elevational and latitudinal separation.
Contact zones between European hares and moun-
tain hare subspecies in the Scottish highlands and the
Alps are largely stable. This stability can be largely
attributed to differences in habitat and dietary prefer-
ences. Scottish hares are most abundant on moorlands,
where up to 90% of their diet can be comprised of
heather (Hewson 1962). The Alpine hare is found on
forested slopes (Rehnus et al. 2013) at altitudes of up
to 3,000 m asl (Bisi et al. 2015) throughout the Alps
where it exhibits a flexible foraging strategy, but
positively selects forest habitats for protection and
cover (Rehnus et al. 2013). These preferences stand in
contrast to the lowland habitat and soft agricultural
grasses and herbs preferred by the European hare
Fig. 2 Principal Component (PC) scores (±1SD) for six (sub-)
species of hare in Europe. Environmental Niche Modelling
variables and the direction of effect within PCs are aligned with
respective axes. Frequency histograms represent sample sizes
across each Principal Component. Box plots along top-x and
right-y axes describe the spread of PC scores for each species
666 A. Caravaggi et al.
123
(Reid and Montgomery 2007; Schai-Braun et al.
2015), thus limiting the potential for interspecific
competition. It has been suggested, however, that
rapid climatic changes will facilitate the expansion of
European hare populations at the expense of mountain
hares (Leach et al. 2015a). This is supported by our
models and niche overlap metrics which describe an
increasing probability of geographic overlap and,
hence, interspecific interaction between European
hares and neighbouring mountain hare subspecies in
coming decades. It is important to remember, how-
ever, that our models do not account for land use
change and, indeed, the habitat vacated by mountain
hare subspecies may not be favourable for the
expansion of the European hare (sensu Bisi et al.
2015).
In contrast, the heath hare and, most notably the
Irish hare, are adapted to a lowland ecology in the
absence of contact with the European hare. The heath
hare was isolated at the most southerly tip of the
Scandinavian Peninsula, far from Russian European
hares, and buffered by an expansive northern hare
population to the north, while the Irish hare was
isolated on an island. Thus, in contrast to other
mountain hare subspecies, the niches of the heath and
Irish hares were, and indeed, are particularly
Fig. 3 Mahalanobis distances (±1SD) across paired Principal
Components (e.g. Axis 1 against 2 i.e. 1:2) for all pairwise
combinations of mountain hare subspecies excluding (white),
and pairwise combinations of mountain hare subspecies
including (grey): a Alpine hare; b Heath hare; c Irish hare;
d Northern hare; e Scottish hare. Significant differences are
shown above the bars where *p\ 0.05 and **p\ 0.01.
Cumulative variance explained across each pair of Principal
Components is shown below the x-axis
Niche overlap of mountain hare subspecies and the vulnerability 667
123
Scottish hare
x pairwise species combinations
Including L. t. hibernicusExcluding L. t. hibernicus
x  
E
uc
lid
ea
n 
di
st
an
ce
3.0
3.5
4.0
4.5
5.0
5.5
6.0
Excluding
Irish hare
Including
Irish hare
*
Northern hare
Irish hare Scottish hare
Heath hare
Alpine hare
x Euclidean distance
2 3 4 5 6 7 8
Pa
ir
w
is
e 
sp
ec
ie
s c
om
bi
na
tio
ns Northern hare
Alpine hare
Alpine hare
Scottish hare
Scottish hare
Heath hare
Heath hare
Heath hare
(a)
(b)
Fig. 4 nth-dimensional
(4D) Euclidean distances
(± 1SD) across four
Principal Component scores
for a all pairwise
combinations of mountain
hare subspecies and b the
mean Euclidean distances
between all pairwise
combinations of mountain
hare subspecies excluding
(white) and including (grey)
Irish hare. Significant
difference is shown on the
bar plot, where *p\ 0.05
Table 5 Geographic niche overlap measures (Warren’s I) of hare (sub-)species under future climate change (2050s on the hori-
zontal, 2070s on the vertical), derived from continuous-suitability Environmental Niche Models
Hare (sub-)species
Alpine Heath Irish Northern Scottish European
Alpine - 0.61 0.32 0.73 0.89 0.55
2070s
Heath 0.61 - 0.31 0.89 0.90 0.91
Irish 0.30 0.29 - 0.27 0.29 0.31
Northern 0.76 0.86 0.25 - 0.91 0.72
Scottish 0.83 0.91 0.26 0.92 - 0.81
European 0.61 0.87 0.31 0.73 0.81 -
2050s
668 A. Caravaggi et al.
123
vulnerable to invasion by the ecologically similar
European hare, with their populations susceptible to
collapse upon contact with the latter after anthro-
pogenic introductions (Thulin 2003; Reid and Mont-
gomery 2007; Caravaggi et al. 2015). After the
introduction of the European hare into southern
Sweden, it expanded northwards, rapidly outcompet-
ing and hybridising with the heath hare (Suchentrunk
et al. 1999; Thulin et al. 2003; Winiger 2014) which is
now said to be virtually extinct (Carl-Gustaf Thulin
pers. comm.), and, to a lesser extent, the Northern hare
(Thulin et al. 2003). The continued northward expan-
sion of the European hare into Sweden has only been
slowed by the southern extent of the snowline and
lower temperatures (here, the Northern hare was
associated with a high degree of seasonality in both
temperature and precipitation, and coniferous forest in
our ENMs); conditions to which the European hare is
not well adapted. However, our models and niche
overlap metrics suggest that a rapidly changing
climate will facilitate the continued expansion of the
European hare northward in Sweden. It is highly likely
that this will be to the detriment of the native Northern
hare. A similar pattern of species replacement and
northern range restriction may be underway in
Finland, following the natural expansion of the
European hare from Russia (sensu Tiainen and
Pankakoski 1995; Syrja¨la¨ et al. 2005; Leva¨nen et al.
2015).
The Irish hare occupies temperate lowlands, where
it is most abundant in agricultural pastures (Whelan
1985; Reid et al. 2006, 2007) and feeds predominately
on grasses (Strevens and Rochford 2004). Our ENMs
predicted Irish hare range extent almost exclusively
using temperature seasonality (a measure of climatic
stability). Seasonal temperatures in Ireland are rela-
tively stable due the maritime influence of the warm
Atlantic Conveyor, from a mean daily temperature of
19 C in the summer to 2.5 C in winter (www.met.
ie). In contrast, Great Britain and central or northern
Europe experience greater seasonal variation, the lat-
ter experiencing freezing winters with long periods of
snow cover and hot summers, which are rare in Ire-
land. Thus, when interpreting the results of the Irish
hare model, one must acknowledge that while the
modelled niche space is necessarily restricted to con-
ditions within Ireland, the potential niche of the Irish
hare must be broader given its observed ecological
plasticity and adaptability. It is impossible to quantify,
however, the degree to which its true niche has been
truncated, if indeed that is the case. Moreover, while
the Irish hare has evolved to exploit a wide range of
habitats and food items, adapted over thousands of
years, this does not necessarily equate to short-term
adaptive potential. Our analyses provide additional
well-quantified ecological evidence, to add to
observed genetic, phenotypic and behavioural differ-
ences, by which the Irish hare might be judged to
warrant full species status (e.g. Arribas and Carranza
2004). No single line of evidence is sufficient to
resolve the issue of specific-status, but taken together,
the evidence for the Irish hare being at the very least an
Irish hare
(a) Current (2010-14) (b) 2050s (c) 2070s
European hare
(d) (e) (f)
Fig. 5 Projected change in
the bioclimatic suitability of
Ireland for a–c Irish hare and
d–f European hare from the
current period (2010–2014)
to the 2050s and 2070s.
Current (sub-)species ranges
are indicated by black
polygons
Niche overlap of mountain hare subspecies and the vulnerability 669
123
Evolutionarily Significant Unit (i.e. a set of popula-
tions which are genetically and morphologically dis-
tinct from similar species; Ryder 1986), if not a full
species, originally described by Bell (1837) as L.
hibernicus, becomes increasingly persuasive.
Fourteen historical introductions of European hares
occurred throughout Ireland between 1848 and 1890
(Reid 2011), with most failing to become established
(Reid and Montgomery 2007). Our ENMs predict that
most of Ireland is (and presumably was) unsuitable for
the European hare, providing a potential explanation
as to why most introductions failed. At present, there is
a relatively range-restricted population of introduced
European hares in Northern Ireland (Caravaggi et al.
2015), the only region of Ireland currently predicted
by our ENMs as being suitable for the species. Their
range expanded three-fold between 2005 and
2012/2013 (Caravaggi et al. 2015) with a core range
populated solely by the invader being established
recently (Caravaggi et al. 2016). Moreover, there is a
high degree of multi-generational, bidirectional
hybridisation in areas of sympatry (Hughes et al.
2009; Prodo¨hl et al. 2013). Certainly, the Irish hare
shares much in common with the European hare,
though the latter has a greater association with arable
land, typical of its more easterly and central European
distribution (Smith et al. 2005). Indeed, previous
studies have demonstrated that both species exhibit
comparable niche breadths and almost complete niche
overlap in Ireland (Reid and Montgomery 2007;
Caravaggi et al. 2015). Niche overlap metrics in the
present study are, on the face of it, contradictory.
However, it must be remembered that the European
hare niche described herein is reflective of its entire
range, whereas niche similarities described by previ-
ous studies were confined to Ireland. Niche overlap
estimates derived from ENMs are associated with
geographic overlap of generated probability surfaces
(Warren et al. 2008). Our estimates, therefore, must be
placed in context of the small range of the European
hare in Ireland at present, and future projections of
European hare range increase and Irish hare range
decrease. Climatological projections under future
climate change suggest Ireland is likely to get warmer
and drier, though with heavier winter rainfall (Holden
and Brereton 2003), favouring an increase in arable
agriculture (Holden et al. 2003). ENMs suggest such
changes will result in a dramatic reduction in the
suitable bioclimatic envelope for the Irish hare. As
such, while the Irish hare exhibits considerable
ecological plasticity and adaptability, the species
may struggle under increased climate instability.
Remaining suitable areas are likely to be in the cool,
wet west of Ireland in habitats suboptimal for the
European hare, such as peat bogs or the north-west
uplands. Conversely, the bioclimatic envelope suit-
able for the European hare is likely to expand in
Ireland in future, with conditions and any increase in
arable cropland becoming increasingly favourable.
Indeed, it has been suggested that invasive interactions
may be more likely in areas with greater than average
climatic instability (Leach et al. 2015b). The European
hare may, therefore pose a direct threat to the
ecological integrity of the Irish hare in the short-term
(i.e. over the next 30 years or so) only, after which
areas of sympatry are likely to be temporally transient.
Our ENMs predict that the west of Ireland will remain
suitable for the Irish hare and unsuitable for the
European hare by 2070. It should be cautioned,
however, that no mountain in Ireland is high enough
to have a permanent snowline, nor any habitat
suboptimal for the European hare expansive enough,
in terms of individual patch size, to provide refuge for
the Irish hare, should post-European hare introduction
population dynamics mirror those of Sweden. Conse-
quently, only occupied offshore islands in the north-
west are likely to provide refuge for the Irish hare in
the long-term.
Control of invasive species is frequently recom-
mended and increasingly common (e.g. Courchamp
et al. 2003). Most successful eradications have
occurred on islands, where recolonisation is less
likely (e.g. Imber et al. 2000; Howald et al. 2007).
Invaders may be removed via biological (e.g. the
introduction of a predator, competitor or pathogen),
physical (e.g. shooting, trapping) or chemical (poi-
soning) methods (Courchamp et al. 2003). The use
of any one or combination of control methods
requires careful evaluation due to the potential for
significant unintended consequences on non-target
species and the wider ecosystem. Control or erad-
ication of range-restricted non-native species is
eminently feasible given the application of appro-
priate techniques, and observation of control/re-
moval criteria (e.g. Bomford and O’Brien 1995).
Once invasive species become widespread, however,
eradication may be impractical and applied man-
agement becomes increasingly difficult and
670 A. Caravaggi et al.
123
expensive. This is certainly the case with regards to
the European hare populations in Sweden. The
European hare was introduced to Sweden in the late
19th century (Lonnberg 1905), and rapidly expanded
across southern Sweden, completely displacing the
heath hare from [9,000 km2 by 1999, and estab-
lishing a considerable zone of sympatry which also
extends into the southerly extent of the northern
hare (sensu Bergengren 1969; Jansson and Pehrson
2007). The situation in Scotland is more nuanced, as
the European hare is considered a priority species in
Great Britain, despite its alien origin, as is the native
mountain hare. Our niche overlap projections sug-
gest that interspecific competition between European
and Scottish hares will become increasingly com-
mon. We may expect, therefore, the Scottish hare to
come under increasing pressure from what we can
reasonably assume to be an ecologically dominant
competitor, leading to spatial displacement. It must
be remembered, however, that our projections did
not account for changes in land use. Given the
different habitat preferences of Scottish and Euro-
pean hares (e.g. Hewson 1962, Schai-Braun et al.
2015), competition and displacement may be less
severe than our models suggest and may be
mediated by habitat management to the benefit of
the Scottish hare (e.g. the maintenance of heather
moorland and other upland habitats).
Given that the current European hare population in
Ireland may have been introduced as recently as the
1970s (Caravaggi et al. 2015) with its extent and
numbers expanding rapidly, policy makers and con-
servationists in Ireland would do well to take heed of
the Irish hare’s likely future prospects by using
Sweden as a case study example. The authorities of
both political jurisdictions of Ireland are signatories to
the Convention on Biological Diversity (UNEP 1992),
the Bern Convention (1979), the European Habitats
Directive (EEC 43/1992), and the EU Regulation
1143/2014 on Invasive Alien Species (Official Journal
of the European Union [OJ] 2014), and hence, are
obliged to address invasive species issues. Lessons
from the heath and Northern hare in Sweden highlight
the precarious, unstable nature of mountain and
European hare interspecific population dynamics,
and suggest that continued inaction from authorities
in Northern Ireland will only serve to facilitate the
continued expansion of the invader, to the detriment of
the endemic.
Acknowledgements This project was funded by the Natural
Heritage Research Partnership (NHRP) (Project QU12-07
Account Number R3326BSC) between the Northern Ireland
Environment Agency (NIEA) and Quercus, Queen’s University
Belfast (QUB). Species presence data and permission for use in
this publication were obtained from a large number of biological
record centres and academics which are listed in full in Table S1
in Supporting Information. We are grateful to the Editor and
reviewers for their instructive guidance and comments which
substantially improved the manuscript. AC was the primary
author, KL created several climatic variables, developed the
averaged future climatic data and contributed to the manuscript,
FS, JR, PH, JT, FB and AM provided species presence records
and contributed to the manuscript, while WIM and NR
supervised the work, conceived the idea, and edited the
manuscript.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unre-
stricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Com-
mons license, and indicate if changes were made.
References
Acevedo P, Jime´nez-Valverde A, Melo-Ferreira J, Real R, Alves
PC (2012) Parapatric species and the implications for cli-
mate change studies: a case study on hares in Europe. Glob
Change Biol 18:1509–1519
Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy
of species distribution models: prevalence, kappa and the
true skill statistic (TSS). J Appl Ecol 43:1223–1232
Amori G, Contoli L, Nappi A (2008) Mammalia II. Erinaceo-
morpha, Soricomorpha, Lagomorpha, Rodentia. Fauna
d’Italia, vol XLIV. Edizioni Calderini de Il Sole 24 ORE
Business Media Srl, Milano
Angerbjo¨rn A, Flux JEC (1995) Lepus timidus. Mamm Species
495:1–11
Arau´jo MB, Guisan A (2006) Five (or so) challenges for species
distribution modelling. Glob Ecol Biogeogr 33:1677–1688
Arribas O, Carranza S (2004) Morphological and genetic evi-
dence of the full species status of Iberolacerta cyreni
martinezricai (Arribas, 1996). Zootaxa 634:1–24
Barbosa AM (2015) fuzzySim: applying fuzzy logic to binary
similarity indices in ecology. Methods Ecol Evol
6:853–858
Bergengren A (1969) On genetics, evolution and history of
distribution of the heath-hare, a distinct population of the
arctic hare, Lepus timidus. Svenaka jagareforbundet,
Stockholm
Bern (1979) Convention on the conservation of European
wildlife and natural habitats. Eur Treaty Ser 104:1–16
Bisi F, Nodari M, Oliveira NMDS, Ossi F, Masseroni E, Pre-
atoni DG, Wauters LA, Martinoli A (2013) Habitat selec-
tion and activity patterns in Alpine mountain hare (Lepus
timidus varronis). Mamm Biol 78:28–33
Niche overlap of mountain hare subspecies and the vulnerability 671
123
Bisi F, Wauters LA, Preatoni DG, Martinoli A (2015) Inter-
specific competition mediated by climate change: which
interaction between brown and mountain hare in the Alps?
Mamm Biol 80:424–430
Bomford M, O’Brien P (1995) Eradication or control for ver-
tebrate pests? Wildl Soc Bull 23:249–255
Caravaggi A, Montgomery WI, Reid N (2015) Range expansion
and comparative habitat use of insular, congeneric lago-
morphs: invasive European hares Lepus europaeus and
endemic Irish hares Lepus timidus hibernicus. Biol Inva-
sions 17:687–698
Caravaggi A, Zaccaroni M, Riga F, Schai-Braun SC, Dick JTA,
Montgomery WI, Reid N (2016) An invasive-native
mammalian species replacement process captured by
camera trap survey Random Encounter Models. Remote
Sens Ecol Conserv 2:45–58
Courchamp F, Chapuis J-L, Pascal M (2003) Mammal invaders
on islands: impact, control and control impact. Biol Rev
78:347–383
D’Orazio M (2015) StatMatch: statistical matching. R package
version 1.2.3. http://CRAN.R-project.org/package=
StatMatch. Accessed 3 Aug 2015
De Maesschalck R, Jouan-Rimbaud D, Massart D (2000) The
Mahalanobis distance. Chemometr Intell Lab 50:1–18
EEA (2010) Corine Land Cover 2006 raster data. European
Environment Agency. http://www.eea.europa.eu/data-and-
maps/data/corine-land-cover-2006-raster-3. Accessed 17
Jan 2015
EEC (1992) Habitats Directive. J Eur Comm L 206/7:1–44
Elith J, Kearney M, Phillips S (2010) The art of modeling range-
shifting species. Methods Ecol Evol 1:330–342
ESRI (2011) ArcGIS desktop: release 10. Environmental Sys-
tems Research Institute, Redlands
Fielding AH, Bell JF (1997) A review of methods for the
assessment of prediction errors in conservation presence/
absence models. Environ Conserv 24:38–49
Flux JEC, Angerman R (1990) The hares and jackrabbits. In:
Chapman J, Flux JEC (eds) Rabbits, hares and pikas: status
survey and conservation action plan. IUCN, Gland,
pp 61–94
Fourcade Y, Engler JO, Ro¨dder D, Secondi J (2014) Mapping
species distributions with MAXENT using a geographi-
cally biased sample of presence data: a performance
assessment of methods for correcting sampling bias. PloS
ONE 9:e97122
Greiner M, Pfeiffer D, Smith RD (2000) Principles and practical
application of the receiver-operating characteristic analysis
for diagnostic tests. Prev Vet Med 45:23–41
Hamill RM, Doyle D, Duke EJ (2006) Spatial patterns of genetic
diversity across European subspecies of the mountain hare,
Lepus timidus L. Heredity 97:355–365
Helle E (1995) Metsa¨ja¨nis (Lepus timidus). In: Linde´n H, Hario
M, Wikman M (eds) Riistan ja¨ljille, Riista - ja kalatalouden
tutkimuslaitos. Edita, Helsinki, pp 18–21 (Finnish with
English summary)
Hewson R (1958) Moults and winter whitening in the mountain
hare Lepus timidus scoticus, Hilzheimer. Proc Zool Soc
Lond 131:99–108
Hewson R (1962) Food and feeding habits of the mountain hare
Lepus timidus scoticus, Hilzheimer. Proc Zool Soc Lond
139:515–526
Holden N, Brereton AJ (2003) Impact of climate change on Irish
agriculture. Pages. In: Sweeney J, Brereton T, Byrne C,
Charlton R, Emblow C, Fealy R, Holden N, Jones M,
Donnelly A, Moore S, Purser P, Byrne K, Farrell E, Mayes
E, Minchin D, Wilson J (eds) Climate change: scenarios
and impacts for Ireland. Environmental Protection Agency,
Wexford, pp 33–81
Holden N, Brereton AJ, Fealy R, Sweeney J (2003) Possible
change in Irish climate and its impact on barley and potato
yields. Agric For Meterol 116:181–196
Howald G, Donlan CJ, Galva´n JP, Russell JC, Parkes J, Sama-
niego A, Wang Y, Veitch D, Genovesi P, Pascal M,
Saunders A, Tershy B (2007) Invasive rodent eradication
on islands. Conserv Biol 21:1258–1268
Hughes M, Montgomery WI, Prodo¨hl P (2006) Population
genetic structure and systematics of the Irish Hare. Report
prepared by the Natural Heritage Research Partnership,
Quercus for the Northern Ireland Environment & Heritage
Service, Northern Ireland, UK
Hughes M, Reid N, Montgomery I, Prodo¨hl P (2009) Verifica-
tion of hybridisation between introduced European and
native Irish hares. Report prepared by the Natural Heritage
Research Partnership, Quercus for the Northern Ireland
Environment Agency, Northern Ireland, UK
Hutchinson G (1957) A treatise on limnology. Wiley, New York
Imber M, Harrison M, Harrison J (2000) Interactions between
petrels, rats and rabbits on Whale Island, and effects of rat
and rabbit eradication. N Z J Ecol 24:153–160
Ja¨ka¨la¨niemi A (2011) Narrow climate and habitat envelope
affect the survival of relict populations of a northern Arnica
angustifolia. Environ Exp Bot 72:415–421
Jansson G, Pehrson A˚ (2007) The recent expansion of the brown
hare (Lepus europaeus) in Sweden with possible implica-
tions to the mountain hare (L. timidus). Eur J Wildl Res
53:125–130
Kuemmerle T, Hickler T, Olofsson J, Schurgers G, Radeloff VC
(2012) Reconstructing range dynamics and range frag-
mentation of european bison for the last 8000 years. Divers
Distrib 18:47–59
Landis JR, Koch GG (1977) The measurement of observer
agreement for categorical data. Biometrics 33:159–174
Leach K, Kelly R, Cameron A, Montgomery WI, Reid N
(2015a) Expertly validated models and phylogenetically-
controlled analysis suggests responses to climate change
are related to species traits in the order Lagomorpha. PLoS
ONE 10:e0122267
Leach K, Montgomery WI, Reid N (2015b) Biogeography,
macroecology and species’ traits mediate competitive inter-
actions in the order Lagomorpha. Mamm Rev 45:88–102
Leva¨nen R, Kunnasranta M, Pohjoisma¨ki J (2015) Abundance
and distribution of hare hybrids in Finland. In: Angerbjo¨rn
A, Dale´n L, Elmhagen B, Werdelin L (eds) Proceedings of
the 7th European congress of mammalogy. Stockholm
University, Stockholm, p 54
Lindstro¨m E (1980) The red fox in a small game community of
the south taiga region in Sweden. In: Zimen E (ed) The red
fox: symposium on behaviour and ecology. Springer,
Dordrecht, pp 177–184
Liu C, White M, Newell G (2013) Selecting thresholds for the
prediction of species occurrence with presence-only data.
J Biogeogr 40:778–789
672 A. Caravaggi et al.
123
Lonnberg E (1905) On hybrid hares between Lepus timidus L.
and Lepus europaeus Pall. from Southern Sweden. Proc
Zool Soc Lond 1:278–287
MacDonald SO, Cook JA (2010) Recent mammals of Alaska.
University of Alaska Press, Fairbanks, pp 123–125
McPherson JM, Jetz W, Rogers DJ (2004) The effects of spe-
cies’ range sizes on the accuracy of distribution models:
ecological phenomenon or statistical artefact? J Appl Ecol
41:811–823
Newey S, Potts J, Baines D, Castillo U, Duncan M, Harrison A,
Ramsay S, Thirgood S, Iason G (2011) Development of a
reliable method for estimating mountain hare numbers.
SNH Commissioned Report, vol 444, pp 1–29
OJ (2014) Regulation (EU) No 1143/2014 of the European
Parliament and of the Council of 22 October 2014 on the
prevention and management of the introduction and spread
of invasive alien species. J Eur Union 35–55
Peterson A, Sobero´n J, Sa´nchez-Cordero V (1999) Conser-
vatism of ecological niches in evolutionary time. Science
285:1265–1267
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum
entropy modeling of species geographic distributions. Ecol
Model 190:231–259
Phillips SJ, Dudı´k M, Elith J, Graham CH, Lehmann A,
Leathwick J, Ferrier S (2009) Sample selection bias and
presence-only distribution models: implications for
background and pseudo-absence data. Ecol Appl 19:
181–197
Phillips SJ, Dudik M, Schapire R (2010) Maxent Software, ver.
3.3.3e. https://www.cs.princeton.edu/*schapire/maxent/.
Accessed 3 Feb 2014
Pierce DW, Barnett TP, Santer BD, Gleckler PJ (2009) Selecting
global climate models for regional climate change studies.
PNAS 106:8441–8446
Prodo¨hl PA, Hughes MA, Hynes RA, Montgomery WI, Reid N
(2013) Molecular evidence for bidirectional hybridisation
between the endemic Lepus timidus hibernicus and the
invasive Lepus europaeus in Ireland. In: Montgomery WI
(ed) Proceedings of the 11th international mammalogical
congress, p 77
Rehnus M, Marconi L, Hackla`nder K, Filli F (2013) Seasonal
changes in habitat use and feeding strategy of the mountain
hare (Lepus timidus) in the Central Alps. Ital J Mammal
24:161–165
Reid N (2011) European hare (Lepus europaeus) invasion
ecology: implication for the conservation of the endemic
Irish hare (Lepus timidus hibernicus). Biol Invasions
13:559–569
Reid N, Montgomery WI (2007) Is naturalisation of the brown
hare in Ireland a threat to the endemic Irish hare? Biol
Environ 107:129–138
Reid N, Dingerkus K, Montgomery WI, Marnell F, Lynn D,
Kingston N, Mcdonald RA (2006) Status of hares in Ire-
land. Irish Wildlife Manuals 30. National Parks and
Wildlife Service, Department of Environment, Heritage
and Local Government, Dublin
Reid N, Sweeney O, Wilson C, Preston SJ, Montgomery WI,
McDonald RA (2007) Developments in hare survey
methodology—AS applied to the NI Irish hare survey
2007. Report prepared by Quercus for the Environment and
Heritage Service (DOE, N.I.)
Rodriguez-Robles JA, Jezkova T, Leal M (2010) Climatic sta-
bility and genetic divergence in the tropical insular lizard
Anolis krugi, the Puerto Rican ‘Lagartijo Jardinero de la
Montana˜’. Mol Ecol 19:1860–1876
Royle JA, Chandler RB, Yackulic C, Nichols JD (2012) Like-
lihood analysis of species occurrence probability from
presence-only data for modelling species distributions.
Methods Ecol Evol 3:545–554
Ryder OA (1986) Species conservation and systematics: the
dilemma of subspecies. Trends Ecol Evol 1:9–10
Schai-Braun SC, Reichlin TS, Ruf T, Klansek E, Tataruch F,
Arnold W, Hackla¨nder K (2015) The European hare (Lepus
europaeus): a picky herbivore searching for plant parts rich
in fat. PLoS ONE 10:e0134278
Schoener TW (1968) Anolis lizards of Bimini: resource parti-
tioning in a complex fauna. Ecology 49:704–726
Smith AT, Johnston CH (2008a) Lepus europaeus. The IUCN
red list of threatened species 2008: e.T41280A10430693.
doi:10.2305/IUCN.UK.2008.RLTS.T41280A10430693.
en. Accessed 18 Dec 2013
Smith AT, Johnston CH (2008b) Lepus timidus. The IUCN Red
List of Threatened Species 2008: e.T11791A3306541.
doi:10.2305/IUCN.UK.2008.RLTS.T11791A3306541.en.
Accessed 18 Dec 2013
Smith RK, Jennings NV, Harris D (2005) A quantitative analysis
of the abundance and demography of European hares Le-
pus europaeus in relation to habitat type, intensity of
agriculture and climate. Mamm Rev 35:1–24
Sobero´n J, Peterson AT (2005) Interpretation of models of
fundamental ecological niches and species’ distributional
areas. Biodivers Inform 2:1–10
Strevens TC, Rochford JM (2004) The diet and impact of the
Irish hare (Lepus timidus hibernicus, Bell 1837) in a young
plantation. Biol Environ 104:89–94
Suchentrunk F, Polster K, Giacometti M, Ratti P, Thulin C-G,
Ruhle C, Vasilev AG, Slotta-Bachmayr L (1999) Spatial
partitioning of allozyme variability in European mountain
hares (Lepus timidus): gene pool divergence across a dis-
junct distributional range? Mamm Biol 64:308–318
Swets JA (1988) Measuring the accuracy of diagnostic systems.
Science 240:1285–1293
Syrja¨la¨ P, Nylund M, Heinikainen S (2005) European brown
hare syndrome in free-living mountain hares (Lepus timi-
dus) and European brown hares (Lepus europaeus) in
Finland 1990–2002. J Wildl Dis 41:42–47
Tarkesh M, Jetschke G (2012) Comparison of six correlative
models in predictive vegetation mapping on a local scale.
Environ Ecol Stat 19:437–457
Thulin C-G (2003) The distribution of mountain hares Lepus
timidus in Europe: a challenge from brown hares L. euro-
paeus? Mamm Rev 33:29–42
Thulin C-G, Jaarola M, Tegelstro¨m H (2003) The occurrence of
mountain hare mitochondrial DNA in wild brown hares.
Mol Ecol 6:463–467
Tiainen J, Pankakoski E (1995) Rusakko (Lepus europaeus). In:
Linde´n H, Hario M, Wikman M (eds) Riistan ja¨ljille, Riista
- ja kalatalouden tutkimuslaitos. Edita, Helsinki, pp 22–25
( Finnish with English summary)
UNEP (1992) The convention on biological diversity. United
Nations Environmental Program. https://www.cbd.int/.
Accessed 14 Feb 2015
Niche overlap of mountain hare subspecies and the vulnerability 673
123
Van der Wal J, Falconi L, Januchowski D, Shoo L, Storlie C
(2012) SDMTools: Species Distribution Modelling Tools:
tools for processing data associated with species distribu-
tion modelling exercises. R package version 1.1-13. http://
cran.r-project.org/web/packages/. Accessed 6 Aug 2015
Verbruggen H, Tyberghein L, Belton GGS, Mineur F, Jueter-
bock A, Hoarau G, Gurgel CFD, De Clerck O (2013)
Improving transferability of introduced species’ distribu-
tion models: new tools to forecast the spread of a highly
invasive seaweed. PLoS ONE 8:e68337
Warren DL, Glor RE, Turelli M (2008) Environmental niche
equivalency versus conservatism: quantitative approaches
to niche evolution. Evolution 62:2868–2883
Warren DL, Glor RE, Turelli M (2010) ENMTools: a toolbox
for comparative studies of environmental niche models.
Ecography 33:607–611
Whelan J (1985) The population and distribution of the moun-
tain hare (Lepus timidus L.) on farmland. Irish Nat J
21:532–534
Wiens JJ (2004) Speciation and ecology revisited: phylogenetic
niche conservatism and the origin of species. Evolution
58:193–197
Wiens JJ, Graham CH (2005) Niche conservatism: integrating
evolution, ecology, and conservation biology. Ann Rev
Ecol Evol Syst 36:519–539
Wiens JJ, Ackerly DD, Allen AP, Anacker BL, Buckley LB,
Cornell HV, Damschen EI, Davies TJ, Grytnes JA, Har-
rison SP, Hawkins BA, Holt RD, McCain CM, Stephens
PR (2010) Niche conservatism as an emerging principle in
ecology and conservation biology. Ecol Lett 13:1310–1324
Wiley EO, McNyset KM, Peterson AT, Robins CR, Stewart AM
(2003) Niche modeling and geographic range predictions
in the marine environment using a machine-learning
algorithm. Oceanography 16:120–127
Winiger A (2014) The apparent population crash in heath- hares
Lepus timidus sylvaticus of southern Sweden—do complex
ecological processes leave detectable fingerprints in long-
term hunting bag records? Unpublished masters thesis,
Swedish University of Agricultural Sciences
Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan
A, Elith J, Dudı´k M, Ferrier S, Huettmann S, Leathwick JR,
Lehmann A, Lohmann L, Loiselle BA, Manion G, Moritz
C, Nakamura M, Nakazawa Y, Overton JM, Phillips SJ,
Richardson KS, Scachetti-Pereira R, Schapire RE, Sobero´n
J, Williams SE, Zimmermann NE (2008) Effects of sample
size on the performance of species distribution models.
Divers Distrib 14:763–773
Wong J (2013) pdist: partitioned distance function. R package
version 1.2. http://CRAN.R-project.org/package=pdist.
Accessed 6 Aug 2015
Wu CH, Wu JP, Bunch TD, Li QW, Wang YX, Zhang YP
(2005) Molecular phylogenetics and biogeography of
Lepus in Eastern Asia based on mitochondrial DNA
sequences. Mol Phylogenet Evol 37:45–61
Yackulic CB, Chandler R, Zipkin EF, Royle JA, Nichols JD,
Campbell Grant EH, Veran S (2013) Presence-only mod-
elling using MAXENT: when can we trust the inferences?
Methods Ecol Evol 4:236–243
674 A. Caravaggi et al.
123