B I O L O G I C A L C O N S E RVAT I O N
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available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/biocon
Will dragonblood survive the next period of climate
change? Current and future potential distribution
of Dracaena cinnabari (Socotra, Yemen)
Fabio Attorrea,*, Fabio Francesconia, Nadim Talebb, Paul Scholteb,c,
Ahmed Saedb, Marco Alfod, Franco Brunoa
a
Dipartimento di Biologia Vegetale, Università di Roma ‘‘La Sapienza’’, Piazzale Aldo Moro 5, 00185 Roma, Italy
Socotra Archipelago Conservation and Development Programme, P.O. Box 16494, Sana’a, Yemen
c
Institute of Environmental Sciences, Leiden University, P.O. Box 9518, 2300 RA Leiden, The Netherlands
d
Dipartimento di Statistica, Università di Roma ‘‘La Sapienza’’, Piazzale Aldo Moro 5, 00185 Roma, Italy
b
A R T I C L E I N F O
A B S T R A C T
Article history:
The potential impact of climate change on Dracaena cinnabari, a spectacular relict of the
Received 11 November 2006
Mio-Pliocene Laurasian subtropical forest in Socotra (Yemen), was analysed. Current distri-
Received in revised form
bution, abundance and vertical structure of D. cinnabari populations were assessed with 74
7 May 2007
plots in nine remnant areas. A deterministic regression tree analysis model was used to
Accepted 17 May 2007
examine environmental variables related to the current species distribution. Using this
Available online 20 July 2007
model, a current potential map and a predicted potential map for the 2080 climatic scenario were generated. D. cinnabari has an altitudinal range from 323 to 1483 m a.s.l., with a
Keywords:
mean annual temperature of 19.8–28.6 C and an annual precipitation of 207–569 mm. The
Bioclimatic model
current distribution and abundance of D. cinnabari is correlated to three factors: moisture
Climate change
index (i.e. the ratio between the annual precipitation and potential evapotranspiration),
Dracaena cinnabari
mean annual temperature and slope. According to this model, D. cinnabari occupies only
Regression tree analysis
5% of its current potential habitat. This potential habitat is expected to be reduced with
Socotra island
45% by 2080 because of a predicted increased aridity. Only two out of the nine remnant
Yemen
areas should be considered as potential refugia. The boundaries of the strictly protected
Skund Nature Sanctuary, where no (road) infrastructure is allowed, should be extended
to include both areas. The construction of new roads leading towards these areas, thereby
increasing permanent settlements and grazing pressure, should also be discouraged.
2007 Elsevier Ltd. All rights reserved.
1.
Introduction
Separated from present Dhofar, Oman, in the late Cretaceous
(Fleitmann et al., 2004), Socotra is characterised by a high level
of endemism (37%) comparable with other oceanic islands
such as Mauritius, Galapagos and the Canary islands (Miller
and Morris, 2004). Socotra is also characterised by many tree
species, which are threatened, by cutting for fuel and timber
(Maerua angolensis var. socotrana, Ziziphus spina-christi, Dirachma socotrana, Avicennia marina), the production of fodder (Dendrosycios socotrana, ‘tree cucumber’), the collection of resins
(Boswellia spp., frankincense and Dracaena cinnabari, dragon
blood) as well as increased (goat) grazing. In addition, climate
change can negatively influence the survival of these species
* Corresponding author: Tel.: +39 06 49912445; fax: +39 06 49912901.
E-mail address: [email protected] (F. Attorre).
0006-3207/$ - see front matter 2007 Elsevier Ltd. All rights reserved.
doi:10.1016/j.biocon.2007.05.009
B I O L O G I C A L C O N S E RVAT I O N
(Parmesan and Yohe, 2003; Root et al., 2003). Global warming is
widely seen as a serious threat to biodiversity of hotspots because it may exacerbate the vulnerability of restricted-range
endemic species (Midgley et al., 2002; Thomas et al., 2004; Malcolm et al., 2006; Thuiller et al., 2006). Concern over the impact
of climate change on biodiversity has led to the development
of species-climate envelope models to forecast the risk of species extinction under different climate change scenarios
(Huntley et al., 1995; Iverson and Prasad, 1998; Shafer et al.,
2001; Bakkenes et al., 2002; Berry et al., 2002; Iverson and Prasad, 2002; Pearson et al., 2002; Thuiller, 2003; Araújo et al.,
2004; Skov and Svenning, 2004; Thuiller et al., 2005). Many
uncertainties associated to forecasting the future distribution
of species have been pointed out and they should be carefully
analysed before being used for the elaboration of biodiversity
conservation strategies (Pearson and Dawson, 2003; Hampe,
2004; Segurado and Araùjo, 2004; Thuiller, 2004; Thuiller
et al., 2004). In this paper, we investigated the applicability
of a climate-species model based on the regression tree analysis to analyse the present and future potential distribution of
arguably the most prominent endemic of Socotra, D. cinnabari.
In addition the possibility of using this approach to support
actions for its conservation will be discussed.
2.
Material and methods
2.1.
Study area
Socotra, part of the Yemen Republic, covers 360,000 ha and is
situated in the northern part of the Indian Ocean, 250 km East
1 3 8 ( 2 0 0 7 ) 4 3 0 –4 3 9
431
of the Somali coast and 380 km South of the Arabian peninsula (Fig. 1).
The island is characterised by an undulating plateau ranging between 300 and 900 m composed of a thin stratum of
Cretaceous and Tertiary limestone that overlies an igneous
and metamorphic basement, which is exposed in three areas
(Beydoun and Bichan, 1970). The most scenic of these are the
peaks of the Haggeher mountains which rise up to 1500 m,
the summit of the island. In the coastal plains and inner
depressions, Quaternary and recent deposits of marine, fluvial and continental origin overlie the older substratum. In
those areas the decomposition of granites has led to the formation of rich, red and fertile soils (Popov, 1957).
The climate is characterised by a SW summer monsoon
that blows from June to September bringing hot, dry, strong
winds with occasional rainfall in the higher mountains, while
the NE winter monsoon (November–January) is less pronounced and coincides with the main rainy season.
2.2.
Dracaena cinnabari
D. cinnabari is arguably the main flagship species of Socotra. It
is one of the six arboreal species (dragonblood tree) of the
genus (Marrero et al., 1998). The others are D. serrulata (SW
Arabia), D. ombet and D. schizantha (eastern Africa), D. draco
(Macaronesian islands and Morocco) and D. tamaranae (Gran
Canaria – Canary Islands). These species are considered remnants of the Mio-Pliocene Laurasian subtropical forests, now
almost extinct because of the climate changes in the late Pliocene, causing the desertification of north-Africa (Quetzel,
Fig. 1 – Study area.
432
B I O L O G I C A L C O N S E RVAT I O N
1978; Mies, 1996). D. cinnabari is localised in the Haggeher
mountains and adjacent limestone plateaux in the centreeast of Socotra where it often forms monospecific stands.
The species is an evergreen tree with a typical umbrellashaped crown due to a ‘dracoid’ ramification of branches.
Adolt and Pavlis (2004) used this characteristic to estimate
its age, concluding that, because of the current lack of regeneration, populations of D. cinnabari will face an intensive disintegration within 30–77 years.
2.3.
Data collection
In 2005, 74 plots of 100 · 100 m, identified by GPS coordinates,
were sampled (Fig. 2). The number of trees (N) and diameter at
breast height of each tree (D) were measured; each tree was
classified in one of four structural classes (Fig. 3) (cf Petroncini, 2000). The presence of dead trees and of trees with a partially damaged crown were also recorded.
Density (De), dominance (Do) and vertical evenness (VE)
were calculated with the following formula:
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2.4.
Environmental data
Raster environmental layers used in this study were: mean
temperature (C), precipitation (mm), moisture index, altitude, aspect, slope, distance from the coast and lithology,
the latter indicating nutrient and water availability.
As elevation data we used the 3 arc-seconds (90 m) spatial
resolution ‘hole-filled’ SRTM Digital Elevation Model. Secondary data such as slope, aspect and distance from the coast
were derived from the DEM using the Spatial Analyst module
of ESRI ArcGIS 9.0 software. Climatic data were interpolated
from a network of 10 meteorological stations using universal
kriging, with a trend function defined on the basis of a set of
covariates (DEM, slope, aspect, distance from the coast). This
interpolation method produces reliable climatic and bioclimatic raster maps at a regional scale when complex topographical effects are present (Attorre et al., 2007). In
addition a moisture index was used (UNEP, 1992), which is
based on:
Mi ¼ P=ETp
where P is the mean annual precipitation, ETp is the potential
evapotranspiration.
ETp has been calculated following Jensen and Haise (1963).
De ¼ N=Area
P
ðBAÞ=Area
P
VE ¼ ð log Pi ÞPi = log 4
Do ¼
where N is the number of stems, BA is the basal area = P(D/
200)2, D is the diameter at breast height and Pi is the proportion of the i class.
The value of VE ranges between 0 for an area characterised
by only one class and 1 where all four classes are equally
distributed.
Then an importance value (IV) for each area is calculated
IV ¼ 100ððDe=Demax Þ þ ðDo=Domax Þ þ ðVE=VEmax ÞÞ:
IV ranges between 0 and 300, increasing with density, dominance and vertical stratification of the tree population.
ETp ¼ ðRS=2450Þð0:025T þ 0:08Þ
where RS is the annual potential solar radiation (kJ), T is the
mean annual temperature.
The annual potential solar radiation was calculated using
a module implemented in the software GIS GRASS (Hofierka
and Súri, 2002).
2.5.
Climatic scenario
Future projections for 2080 (average for the years 2070–2099)
were derived using one general circulation model experiment
Fig. 2 – Location of sampled areas.
B I O L O G I C A L C O N S E RVAT I O N
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433
Fig. 3 – Dracaena cinnabari class age. 1: plant with a single rosette and without a trunk. 2: plant with one rosette and a trunk. 3:
plant with more than one rosette and a crown with a diameter smaller than 2.5 m. 4: plant with a crown larger than 2.5 m
(from ‘‘Age monitoring form of Dracaena cinnabari’’ elaborated by the Royal Botanic Garden of Edinburgh, modified).
(HadCM3, Mitchell et al., 2004). This GCM projected climate
under an extreme scenario: the Intergovernmental Panel on
Climate Change Special Report on Emissions Scenarios (IPCC
SRES) A1F1 storyline (Nakicenovic and Swart, 2000). This scenario describes a future world of rapid economic growth, a
global population that peaks by the mid-century and then declines, and the rapid introduction of new and more efficient
technologies. Amongst all IPCC scenarios, this one assumes
the greatest intensity of energy use, showing the extreme effects of climate change on biodiversity. Expected changes in
annual mean temperature and precipitation under the scenario was calculated and applied to the values measured at
the meteorological stations. The projected climatic raster
maps were generated by the same interpolation method.
2.6.
Statistical analyses
Because of limited presence data, predictive distribution
models have seldom been applied to rare species (Engler
et al., 2004). Our data consist of 74 plots with D. cinnabari
and approximately 17,000 plots without. Using only presence
data would have led to over-optimistic potential distribution
predictions. On the other hand, comparing the 74 presence
plots with the remaining 17,000 plots would have produced
an under-estimation of potential distribution. Profile techniques (Robertson et al., 2001) were used to produce a map
of environmental suitability to be matched with the observed
presence data to depict potential distribution. Without multispecies data, so called pseudo-absences (Zaniewski et al.,
2002) may be drawn at random, with the risk of being conservative, generating absences in areas that may be favourable to
the analysed species. To counter such under-estimations,
Engler et al. (2004) proposed to generate pseudo-absences
data using weights given by a prior environmental factor
analysis (EFA), drawing them from areas with EFA predictions
below a given threshold This produces, however a predictive
model for environmental suitability rather than for observed
presence and abundance values. To reduce selection bias of
absence plots, we applied a case-control study (Rothman,
1986) and drew 74 absence plots excluding areas below
300 m a.s.l., an ecological threshold for D. cinnabari, and used
these plots as controls. Altitude was subsequently disregarded for species distribution modelling.
To quantify the relationship between environmental factors and species distribution, a regression tree analysis
(RTA) (Iverson and Prasad, 1998, 2002; Iverson et al., 1999)
was performed using S-PLUS (Statistical Sciences, 1993) defining species IV as response influenced by six predictor variables: moisture index, annual average temperature, annual
average temperature of the hottest month, annual precipitation, slope and lithology. The resulting model was used to
generate current predicted IV, subsequently used as output
to ArcGIS for mapping. The model was calibrated using a random sample of 70% the data and evaluated with the remaining 30%. The evaluation consisted of a comparison between
current predicted and observed IV values for the whole data
set as well as for the evaluation data set only using linear correlation and a measure of the classification accuracy. The latter was based on PM/(PM + MO) * 100, where PM is the number
of plots characterised by the presence of D. cinnabari correctly
predicted by the model, while MO is the number of plots with
the species that were not predicted by the model.
The regression tree was used to generate predictive maps
of current distributions, as well as of potential future distribution under a scenario of a changed climate by replacing the
climate-related variables in our predictor variable set with
those obtained by using the HadCM3 model. The previously
calculated regression tree was used with the new predictive
variable and the data output to ArcGIS.
3.
Results
3.1.
Current distribution
D. cinnabari, absent from the West, has a fragmented distribution in the central and eastern part of the island where it was
sampled in nine areas (Fig. 2). In the 74 sample plots, ranging
between 323 m at Homhil and 1483 m in Skund, a total of 4516
individuals were recorded (Table 1). The intervals for the
434
S-E
W
W-S
S-W
S–W
W-S
S-W
S-W
201
1219
5
11
Skund
95
5
Serahon
156
5
Kleem
541
8
Katan
359
9
Haggeher
Deidho
Homhil
1345
12
Firmhin
255
11
Diksam
IV = Importance Value, VE = Vertical evenness, DO = Dominance, DE = Density, ALT = Altitude (m a.s.l.), SLO = Slope (degree), ASP = Aspect, PRE = Annual precipitation (mm), MAT = Mean annual
temperature (C), Mi = moisture Index, Geo = Geology. For the numeric variables mean and range are reported.
Granite
Limestone
Limestone
Limestone
Limestone
Granite
Limestone
Limestone
Limestone
0.13
(0.11–0.14)
0.20
(0.16–0,21)
0.15
(0.14–0.17)
0.21
(0.19–0.24)
0.14
(0.12–0.16)
0.14
(0.13–0.16)
0.12
(–)
0.10
(0.09–0.12)
0.33
(0.28–0.40)
27.3
(26.2–28.1)
23.9
(22.4–25.8)
25.8
(24.9–26.3)
24.0
(23.2–25.0)
26.6
(25.9–27.7)
26.2
(25.6–26.6)
27.1
(26.9–27.4)
28.3
(27.8–28.6)
20.4
(19.8–22.2)
253.6
(234–278)
385.2
(304–420)
310.0
(255–340)
365.2
(321–398)
265.6
(234–289)
277.4
(262–300)
242.3
(236–249)
218.6
(207–231)
527.8
(409–569)
S-W
10.1
(1.9–20.0)
5.4
(1.8–13.7)
7.9
(3.7–11.7)
20.0
(15.1–26.3)
16.8
(1.5–35.1)
11.2
(4.5–19.0)
7.6
(5.2–10.5)
7.0
(4.9–12.4)
25.7
(12–34.8)
532.9
(465–567)
890.7
(598–1031)
639.8
(493–749)
684.7
(623–771)
363.9
(323–412)
474.6
(456–489)
457.3
(432–485)
426.5
(395–455)
1339.2
(949–1483)
345
Difshe
8
79.7
(22.2–111.6)
48.8
(22.5–78.4)
126.3
(69.3–204.3)
117.3
(63.7–159.5)
133.4
(64.1–182.7)
113.4
(83.2–152.7)
48.1
(32.8–60.0)
71.1
(32.9–95.4)
189.7
(119.3–247.7)
18.9
(0.0–45.7)
12.6
(0.0–39–6)
19.8
(0.0–38.7)
65.7
(24.1–100.0)
33.6
(0.0–58.4)
45.9
(22.1–62.3)
0.0
(0.0–0.1)
0.0
(–)
78.0
(57.1–99.2)
35.9
(14.6–60.4)
25.5
(14.4–65.1)
54.4
(26.3–72.3)
28.3
(12.1–50.6)
61.7
(35.1–88.4)
51.5
(18.8–83.4)
36.2
(24.343.2)
24.6
(12.1–37.2)
55.8
(10.6–100.0)
24.9
(7.6–37.9)
10.7
(7.6–17.2)
52.2
(23.7–100.0)
23.4
(18.6–31.7)
38.2
(14.1–52.0)
16.1
(7.0–29.6)
11.9
(8.5–17.1)
46.5
(20.8–61.7)
56.0
(12.6–73.7)
SLO
DO
VE
IV
N trees
N plots
Area
Table 1 – Structural and environmental characteristics of the sampled areas
DE
ALT
ASP
PRE
MAT
Mi
GEO
B I O L O G I C A L C O N S E RVAT I O N
1 3 8 ( 2 0 0 7 ) 4 3 0 –4 3 9
annual average temperature and the annual precipitation are
respectively 19.8–28.6 C and 207–569 mm. Only two areas
(Skund and Haggeher Deidho) are semi-arid with an average
moisture index above 2.0. The other areas are classified within the arid region (Mi 6 2.0). The aspect of the sampled areas
is mainly south-western. Two areas (Skund and Haggeher
Deidho) are characterised by a granite substratum, while the
others lay on limestone plateaus and hills.
Skund, located on the peak of the Haggeher mountains,
has the highest mean Importance Value (190) due to the
noticeable vertical stratification of its tree population
(VE = 78). The second area, according to mean IV, is Homhil,
which is characterised by trees of great dimensions (diameter
at breast height above 200 cm). The third area is Firmhin,
characterised by a very high value of density and dominance,
classified as forest in a recent land-cover map (Kràl and Pavliš, 2006). It has, however, one of the lowest vertical evenness
values (20) because of the low number of young trees. In contrast, Haggeher Deidho, located in the eastern part of the Haggeher mountains and characterised by steep escarpments
(with an average slope of 20) presents a high number of trees
belonging to the first and second structural class, together
with seedlings. The remaining areas (Difshe, Serahon, Diksam, Kleem) show IVs below 100 and are characterised by
scattered old trees, with frequent damage caused by natural
events (strong winds or lightening) or by the exploitation of
resin (Miller and Morris, 2004).
3.2.
Potential distribution
The tree diagram output from RTA analysis depicts, in a hierarchical way, the interaction between environmental variables and species distribution (Fig. 4). The length of the tree
branches is proportional to the variance explained by splitting
step. The terminal nodes indicate the average IV for the relatively homogeneous subset.
The moisture index, related to the potentially amount of
precipitation available, was the most important factor
explaining the distribution of D. cinnabari. In particular, nonzero predicted IV occurred only on the right branch, with a
moisture index value above 0.1. This implies that the coastal
Fig. 4 – Regression tree for Dracaena cinnabari using a nonuniform tree of four nodes. Average predicted IV for a
particular branch are shown at the relative termination
node.
B I O L O G I C A L C O N S E RVAT I O N
and alluvial valleys (characterised by a hyper-arid climate –
Mi < 0.05) and most of the arid areas (0.2 > Mi < 0.05) can be
considered unsuitable for the species. For the rest of the island, only areas with a mean annual temperature (MAT) of
less than 22 C show a high predicted IV value (184), while
in the hotter areas high IV could be predicted only in steep
escarpments (slope > 8). The significant correlation between
the predicted and measured IV for the whole data set
(R = 0.75, P = 0.01) and the high classification accuracy for both
the whole data set (92.8%) and the test set (85.2%) showed the
model’s good prediction capacity.
The regression tree enabled the production of a predictive
map of current potential IV distribution (Fig. 5a). Since areas
with IV below 30 are characterised by few and scattered trees,
of marginal importance for the survival of the species; only
areas with IV values greater than 30 were mapped. According
to this map, D. cinnabari occupies only 5.5% of its potential
distribution.
Replacing the climate-variables with those of the HadCM3
model, a potential future distribution map under a scenario of
a changed climate was produced (Fig. 5b). According to the climatic model, D. cinnabari is predicted to loose about 45% of its
current potential distribution by 2080 because of increasing
aridity. Although no changes in precipitation are predicted,
1 3 8 ( 2 0 0 7 ) 4 3 0 –4 3 9
435
the increasing temperatures will cause a strong increase of
the potential evapotranspiration extending the hyper-arid
area to the detriment of the arid and semi-arid ones (Fig. 6a
and b).
4.
Discussion
4.1.
Regression tree model
RTA is a useful tool to analyse the spatial distribution of quantitative parameters of plant species with respect to environmental variables at a continental scale (Iverson and Prasad,
1998, 2002; Iverson et al., 1999). This study demonstrates the
possibility to use this method for restricted-range endemic
species as well. The model could be improved by introducing
data on the role of biotic interactions, seed dispersal and germination. A physiologically based model could produce even
better results (Pearson and Dawson, 2003), but the necessary
knowledge of autoecology and of historical changes is lacking. A recent observation (June 2006) confirmed indirectly
our approach when two mature trees were recorded on the
mountain behind Dihamri that was indicated as potentially
suitable for the species, but had been overlooked by local
community members with whom the field work was planned.
Fig. 5 – Current-predicted IV modelled by RTA (a). Future-predicted IV based upon climate change projections of HadCM3
model (b).
436
B I O L O G I C A L C O N S E RVAT I O N
1 3 8 ( 2 0 0 7 ) 4 3 0 –4 3 9
Fig. 6 – Current (a) and future (b) distribution of climatic regions based on the classification of moisture index.
This finding suggests the use of the model to delimit further
field work to search for this species.
4.2.
mountain areas, the amount of moisture available due to atmospheric humidity seems to be comparable with that of precipitation (Mies and Beyhl, 1996).
Current distribution
4.3.
The study suggests that the original distribution of D. cinnabari has been significantly reduced in the past. We hypothesise that a combination of factors may have contributed to its
reduction: human activities, soil erosion, increased aridity
and biotic interactions. It was also shown that current pattern
of distribution, though fragmented, may be primarily explained in terms of response to climatic constraints. We
therefore hypothesise that, within its realised ecological
niche, D. cinnabari is able to colonise suitable areas if the present climatic conditions will remain stable and if a reduction of
grazing and human pressure will occur. At present, the capacity of regeneration for the species is limited to areas characterised by steep escarpments and availability of water due
to the precipitation regime and atmospheric humidity. These
areas are favourable for the establishing of seedlings because
of their inaccessibility to livestock. Moreover, D. cinnabari can
grow on exposed rock substrata thanks to small pockets of
fine soil between crevices.
The umbrella type crown is considered an adaptation to collect water from atmospheric humidity falling from the
branches (Beyhl, 1996). With its predominantly south-western
aspect, D. cinnabari can survive the summer season, intercepting the humidity brought by the SW monsoon. Particularly in
Climate change implications for conservation
An analysis of age structure and growth of D. cinnabari in two
(Firmhin and Homhil) of the nine sampled areas showed that:
‘‘with 95% of probability, stands here will be in the stage of
intensive disintegration within 30–77 years’’ (Adolt and Pavlis,
2004). Moreover, D. cinnabari populations have a very high
Table 2 – Comparison between average observed, current
potential and future potential importance value of the
sampled areas
Area
Difsa
Dixam
Firmhin
Haggeher
Deidho
Homhil
Kleem
Serahon
Serbah
Skund
Observed Current-predicted Future-predicted
IV
IV
IV
79.7
48.8
126.3
117.3
71.5
63.0
115.0
115.4
33.0
96.1
67.3
113.5
117.5
48.1
71.1
113.4
189.7
91.1
55.2
44.1
101.8
181.5
55.1
18.3
4.6
49.6
147.8
B I O L O G I C A L C O N S E RVAT I O N
1 3 8 ( 2 0 0 7 ) 4 3 0 –4 3 9
437
Fig. 7 – Skund and Haggeher Deidho sampled areas superimposed on the zoning map.
structural homogeneity since about 75% of the sampled individuals belong to the fourth class with a diameter at breast
height between 25 and 50 cm. We hypothesise that D. cinnabari is only able to regenerate extensively with adequate rainfall conditions after prolonged droughts when most livestock
are killed. Because of increasing access to fodder, major drops
in livestock pressure are no longer expected and regeneration
of D. cinnabari may in future be limited to inaccessible places
such as cliffs.
The predicted climate change may lead to a 45% loss of
Draceana potential distribution area by 2080. Because of these
potentially dramatic effects, mitigating conservation actions
are urgently needed. However, considering the scarce knowledge on the required management, especially related to
grazing pressure, our analysis was restricted to the protection
of priority areas for the conservation of the species under
the effects of climate change. The averages of observed,
current-predicted and future-predicted IV for the sampled
areas were compared (Table 2). Only three areas show future-predicted IV values high enough to be considered as potential refugia for the species: Skund (148), Haggeher Deidho
(114) and Diksam (96). However, because of intensive grazing,
Diksam has nowadays one of the lowest vertical stratification
values with few signs of regeneration. For this reason only
the adoption of strong conservation recommendations and
the implementation of restoration activities could guarantee
the survival of D. cinnabari in this area. Of the two other areas,
only Skund is within the boundary of a Nature Sanctuary
(Fig. 7), with the strictest protection regime, i.e. where no
new (road) infrastructure is allowed. To conserve D. cinnabari,
an important flagship and umbrella species (Miller and Morris, 2004), the present limits of the Nature Sanctuary should
be extended to encompass Haggeher Deidho. Moreover, efforts should be made to avoid new road construction leading
towards the vicinity of these areas as they will induce permanent settlements, and limit the seasonal migration of livestock and human population, further increasing pressures
on D. cinnabari.
Acknowledgements
This study was undertaken in the framework of the Socotra
Conservation and Development Programme, financed by the
Governments of Italy and Yemen and the United Nations
Development Programme and partly supported by IUCN, Project 76273-070/BL 23311. Special thanks to Alfredo Guillet for
the useful suggestions, Edoardo Scepi for his support during
the field surveys and to An Bolen for the revision of meteorological data. We are also grateful to the referees for their comments on the manuscript.
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