SUPPORTING INFORMATION
APPENDIX S1: Additional methodological information about data collection, habitat
variable selection, data analysis and impact of forest management.
APPENDIX S2: Relationship between factors used in the MMI and geographic database
sources.
Table S1: Habitat cover classification used to build the land cover factor.
APPENDIX S3: Detail of the field survey effort throughout the study area.
Table S2: Detail of sampling effort per mountain between 1994 and 2008.
Figure S1: Distribution of the survey throughout the study area between 1994 and 2008.
APPENDIX S4: Analysis of residuals from the multimodel inference.
Figure S2: Spatial distribution of the residuals from the multimodel inference.
Figure S3: Boxplot of the residuals from the multimodel inference approach for the 100
presence points used to calibrate the models and grouped by mountain.
Data collection
In 1994, an action plan was implemented to investigate the presence of the Orsini’s viper in six
previously known locations and to explore other areas in search of previously unreported
populations. The six locations were: Lure (hereafter preferred to as LURE), Préalpes de Grasse
(PRGR), Ventoux (VENT), Cassine (CASS), Grand Coyer (GRCO) and Verdon (VERD).
Between 1994 and 2008, numerous surveys, representing 2,239 hours spread over 12,248 ha,
were conducted throughout the region. A total of 742 hours of field surveys was spent on already
known populations, and 1,497 hours in the search of previously unreported populations (see
details in Appendix S2: Table S2 and Figure S1). These surveys focused mainly on areas that
were thought to be suitable: that is, located within the known altitudinal range of the species
throughout its French and Italian distribution (900 to 2400 m above sea level, Nilson & Andren,
2001), in grasslands or open heath (Penloup et al., 1999). For each field survey, we recorded the
limits of the surveyed area, the time of the field search, the number of observers, the date, and a
short description of the weather conditions (sunny, partially or totally cloudy, stormy or rainy).
Because of the large survey area, most of the sites were only visited once. However, areas of
higher interest were visited several times, until either the species was observed or until the search
was abandoned. We obtained 164 indices of presence (referred to as presence points in the rest of
the document), i.e. direct observations of a viper or a slough (when the identification of the
species was possible), recorded with a geographic precision of 50 meters or less. The observation
dataset was then randomly divided into two subsets: one consisting of 100 presence points to
compile a training dataset and the other consisting of the 64 remaining presence points to
compile a test dataset. We also selected 5,000 random points within the whole study area
representing pseudo-absence points, required to calibrate the model and to evaluate the accuracy
of the model’s outputs through the Area Under the Receiver Operating Characteristic (ROC)
Curve (AUC) (Elith, 2002). The pseudo-absence points were weighted in order to get a balanced
ratio of presence vs. pseudo-absence points for the species modelling procedure.
Habitat variables
Based on a deductive approach, we chose a subset of 19 predictor variables among
environmental factors believed to be potential causal, driving forces for the distribution of the
species at the scale of our study (Dettki et al., 2003; Guisan & Zimmermann, 2000). The selected
predictors and their respective source and original resolutions are shown in Table 1. When
required, we refined the map for an environmental factor to match the finest resolution. To limit
the collinearity within factors (Barbosa et al., 2003), we used a principal components analysis
and selected a subset of non-collinear variables. Whenever possible, data was transformed
(square-root, square, logarithm) in order to match a Gaussian distribution. We did not account
for food and shelter availability, local vegetation structure, predators and competitors; although
we recognize such factors can have significant effects on the true presence or abundance of the
species (Santos et al., 2006). Accounting for such factors always represents challenges that are
difficult, if not impossible, to tackle at the scale of our study because they have to be assessed
directly in the field, which makes the prediction of current and future spatial distribution
impossible. This critical point is further developed in our discussion.
Data analysis
Identifying landscape determinants of species presence and predicting habitat suitability—
Among the species-distribution models commonly used, generalized additive models (GAM)
have proven to be one of the best compromises between interpretability and predictability
(Guisan & Thuiller, 2005). To measure the actual power of each variable, we used multimodal
inference based on the all-subsets selection of GAM. This method has been proven to be more
robust and useful than stepwise regression (Burnham & Anderson, 2002; Link & Barker, 2006)
and allows the measurement of the weight of evidence with which each explanatory variable
explains the response variable (Burnham & Anderson, 2002). With eight predictors, there are
256 (28) possible models in an all-subsets selection. Therefore, we estimated a small-sample
(second order) bias adjustment of AIC (AICc) for each sub-model. The estimation of the weight
of evidence of each predictor (wpi) to explain the habitat suitability was calculated as the sum of
the AICs weights (wi) of the models in which the predictor appeared. The model was fitted on
the training dataset (100 presence points) and its predictions (a weighted average of all fitted
models) were tested on the test dataset (64 presence points), assuming the two fractions to be
quasi-independent.
To estimate the real power of our findings, we used a stratified permutation test et al.,
2006). This was created by doing a random permutation of each predictor separately within the
dataset, recalculating wpi, and repeating this procedure 100 times for each predictor. The absolute
weight of evidence (Δwp) was then calculated by subtracting the median value of the 100
randomized wpi from the original wpi. Only predictors with an Δwp higher than zero were
considered to have explanatory power on local species occurrence.
Evaluating model accuracy and producing a habitat-suitability map— The predictive capacity of
the model was assessed by comparing its predictions with a sub-set of independent presence
points ( test dataset) using the area under the curve (AUC) of a receiver-operating characteristics
(ROC) plot (Elith, 2002). This area provides a measure of discrimination ability varying
generally from 0.5 (for a model with discrimination ability no better than random) to 1.0 (for a
model with perfect discriminatory ability) (Elith, 2002). We then calculated a threshold to
optimize the separation of the true presences (test data) and false presences (pseudo-absence
data) in a contingency table, and the outputs of the model were transformed into binary results
for presences and absences. The final model gave the habitat-suitability index of the species as a
function of environmental factors and allowed us to determine the geographical extent of
apparently suitable areas. We projected the model over the whole study area, using zeros where
the index was below the selected threshold, and using the value of the index where it was above
the threshold. We obtained a map of distinct areas, or polygons, of apparently suitable habitat
(referred to as patches in the rest of the document) spread throughout the region. Every unique
patch was surrounded by apparently unsuitable habitat and isolated from other patches.
Inferring species abundance from the habitat-suitability index— As the frequency of distribution
of presence pixels (i.e. indicating observation of the species) along the habitat-suitability gradient
was rarely uniform, the habitat-quality value did not directly reflect the probability of presence
or the abundance of the species at a particular pixel. We therefore fitted a probability density
function of a Beta distribution on the frequency distribution of all Orsini’s viper presence points
(N = 164). This function was then used to convert the habitat-suitability index into a potentialabundance index, which would highly facilitate biological interpretations. Assuming the density
of probability is proportional to local potential abundance of snakes (i.e. carrying capacity), we
were able to evaluate the potential number of individuals in a given patch. Overall potential
number of individuals Ni on a given patch i is given by:
K
N i   p k .D
k 1
K is the number of pixels. pk is the value of probability density on a pixel k and is deduced from
the fitted function. D is the maximal possible number of individuals on a single pixel and was
deduced from capture–recapture studies on four French populations (Baron 1997, Lyet
unpublished data).
Evaluating population fragmentation— Because natural barriers such as unsuitable habitat
reduce or prevent the movement of individuals, geographical distance rarely reflects the actual
framework of exchanges between populations. To better represent this framework, we thus used
a ‘friction map’ (which represents the cost of moving through the landscape) to evaluate the
effective distance between individuals, and hence the fragmentation level of a given population
(Ray 2005). Assuming that this cost was inversely proportional to habitat suitability, we used the
probability density function to build our friction map: Cost = (1 – scaled probability density
value) × 10] for cumulative frequency superior to 0.001, and Cost = 100 below this threshold
(ArcGIS 9.2 software, ESRI 2008). We generated random pseudo-presence points (one point per
50 ha) within the observed distribution of the Orsini’s viper obtained through our modelling
procedure and then computed the matrix of pairwise effective distances or cost-distances using a
least-cost path algorithm (PATHMATRIX Ray, 2005). This analysis was performed at mountain
scale; that is, across points close enough to be potentially connected.
We used the minimum cost-distance between the closest pair of genetically isolated group
of individuals or deme (Ferchaud et al., 2011) as a threshold for connectivity/isolation: two
points less distant than this threshold were assigned to the same deme, and conversely, more
distant points were assigned to distinct deme. We used a single linkage cluster analysis with the
predefined threshold as the cut height in order to evaluate the number of isolated deme for each
mountain massif.
Evaluating the potential impact of forest cutting
In order to evaluate the areas where forest cutting would be a relevant management strategy to
enhance the suitability of a given site or to reconnect different isolated patches, we simulated
extensive forest cutting by manipulating the existing vegetation-cover map. All the forested
pixels (levels 4 and 5) were converted to grassland & shrubs pixels (level 3). We then ran the
model again (see above) in order to obtain a new habitat-suitability map with this hypothetical
converted vegetation. We then compared average value of predicted habitat suitability on each
patch before and after the hypothetical treatment. Mean habitat-suitability values were calculated
over all pixels within a patch. Tests were performed using a two-way ANOVA on paired samples
with mean habitat suitability as the dependent variable, and populations and forest cutting as
factors. We also evaluated, using the fragmentation analysis described above, whether forest
cutting enhanced connectivity between populations by comparing before-cutting and aftercutting numbers of demes within each mountain previously identified.
References
Barbosa, M., Real, R., Olivero, J. & Vargas, M. (2003) Otter (Lutra lutra) distribution modeling
at two resolution scales suited to conservation planning in the Iberian Peninsula.
Biological Conservation, 114, 377-387.
Baron, J.-P. (1997) Demographie et dynamique d'une population francaise de Vipera ursinii
ursinii (Bonaparte, 1835). Unpublished Ph.D. thesis, University of Paris (E.N.S.).
Brook, B.W., Traill, L.W. & Bradshaw C.J.A. (2006) Minimum viable population sizes and
global extinction risk are unrelated. Ecology Letters, 9, 375-382.
Burnham, K.P. & Anderson, D.R. (2002) Model selection and multimodal inference: a practical
information-theoretic approach. New York: Springer-Verlag.
Dettki, H., Löfstrand, R. & Edenius, L. (2003) Modeling Habitat Suitability for Moose in Coastal
Northern Sweden: Empirical vs Process-oriented Approaches. Ambio, 32, 549-556.
Elith, J. (2002) Quantitative methods for modeling species habitat: comparative performance and
an application to Australian plants. Quantitative Methods for Conservation Biology (ed.
by S. Ferson and M. Burgman), pp. 39-58. Springer-Verlag Publications, New York.
ESRI (2008) Arcview GIS, version 9.2. Environmental Systems Research Institute Inc.
Redlands, CA.
Ferchaud, A.L., Lyet, A., Cheylan, M., Arnal, V., Baron, J.P., Mongelard, C. & Ursenbacher, S.
(2011) High genetic differentiation among French populations of the Orsini's meadow
viper based on mitochondrial and microsatellite data: Implications for conservation
management. Journal of Heredity, 102, 67-78.
Guisan, A. & Thuiller, W. (2005) Predictive species distribution: offering more than simple
habitat models. Ecology Letters, 8, 993-1009.
Guisan, A. & Zimmermann, N.E. (2000) Predictive habitat distribution models in ecology.
Ecological Modelling, 135, 147-186.
Link, W. A. & Barker, R. J. (2006). Model weights and the foundations of multimodel inference.
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Nilson, G. & Andrén, C. (2001) The meadow and steppe vipers of Europe and Asia. The vipera
(Acridophaga) ursinii complex. Acta zoologica Academiae Scientiarum Hungaricae, 47,
87-267.
Penloup, A., Orsini, P. & Cheylan, M. (1999) Orsini’s viper Vipera ursinii in France: present
status and proposals for a conservation plan. Current Studies in Herpetology:
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Herpetologica (ed. by C. Miaud, and R. Guyétant), pp. 363-369. Sociatas Europea
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Ray, N. (2005) Pathmatrix: a geographical information system tool to compute effective
distances among samples. Molecular Ecology Notes, 5,177-180.
Santos, X., Brito, J.C., Sillero, N., Pleguezuelos, J.M., Llorente, G.A., Fahd, S. & Parellada, X.
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Table S1: This table shows the habitat cover classification available in the European Corine Land
Cover database (CLC2006, EEA 2007) and in the national vegetation inventory (Cartographie
Forestière de l'IFN v1, IFN 2010). We used most detailed classification from both table
(uncrossed cells) to build a combined CLC2006/IFN classification. The table shows the
correspondences between every class of the combined classification and the classes of the CLC
and VEG factors as used in our models.
CLASSIFICATION
CLASSIFICATION
COMDINED
HABITAT TYPE
VEGETATION
CLC2006
IFN 2006
CLASSIFICATION
(CLC FACTOR.)
DENSITY (VEG
USED IN MODELS
Sea Water
Other
FACTOR)
Sea Water
5 = water bodies
NA
Continental water
Continental water
5 = water bodies
NA
Inland humid area
Inland humid area
4 = wetlands
NA
Maritime humid
Maritime humid
4 = wetlands
NA
area
area
Urban area
Urban area
1 = artificial areas
1 = no vegetation
Industrial and
Industrial and
1 = artificial areas
1 = no vegetation
commercial area
commercial area
Landmine Dump
Landmine Dump
1 = artificial areas
1 = no vegetation
Workings
Workings
Garden and
Garden and
1 = artificial areas
4 = open forest
recreation park
recreation park
Arable land
Arable land
2 = agricultural
3 = grasslands &
areas
shrublands
Permanent culture
Permanent culture
2 = agricultural
3 = grasslands &
areas
shrublands
4 = open forest
Heterogeneous
Heterogeneous
2 = agricultural
agriculture area
agriculture area
areas
Sparse vegetation
Sparse vegetation
3 = natural and
2 = sparse short
semi-natural areas
vegetation
3 = natural and
3 = grasslands &
semi-natural areas
shrublands
3 = natural and
3 = grasslands &
semi-natural areas
shrublands
3 = natural and
4 = open forest
Meadow
Scrubland
Forest
Meadow
Scrubland
Open forest
Scrubland
Open forest*
semi-natural areas
Young poplar
plantation
Deciduous forest
Coniferous forest
Mixed forest
Mixed deciduous
forest and young
poplar plantation
Mixed coniferous
forest and young
poplar plantation
Dense forest**
3 = natural and
semi-natural areas
5 = dense forest
poplar plantation
* Open forest refers to areas where canopy cover average is comprised between 10 and 40%.
**Dense forest refers to areas where canopy cover average is 40% or higher.
References
European Environmental Agency. CLC2006 technical guidelines, EEA Technical report No
17/2007, ISSN 1725-2237 (http://www.eea.europa.eu/publications/COR0-landcover).
Inventaire forestier national (2010) Description générale des données cartographiques produites
de 1986 à 2006 (version 1) (http://www.ifn.fr/spip/spip.php?rubrique202&rub=cat).
Table S2: This table presents the list of the nine mountains where the Orsini’s was found
(Ventoux, Lure, Cheval Blanc, Grand Coyer, Malay, Préalpes de Grasse, Blayeul) or where it
was present in the past (Cassine, Verdon) but was not detected during our study, with the total
area surveyed and effort of field survey on every mountain.
Survey effort
Mountain
Area of suitable habitat (ha)
Area covered (ha)
Time (h)
VENT
66
66 (100%)
42 (0 63)
LURE
566
566 (100%)
195 (0 34)
CHBL
2445
1140 (47%)
186 (0 16)
GRCO
6576
1228 (17%)
283 (0 23)
MALA
235
60 (26%)
8 (0 13)
PRGR
6235
316 (5%)
29 (0 09)
CASS
24
24 (100%)
16 (0 67)
VERD
4668
744 (16%)
177 (0 24)
BLAY
325
240 (74%)
64 (0 27)
FIGURE CAPTION
Figure S1. This map shows the distribution of the field survey effort spent in the search of
previously unreported populations throughout the study area between 1994 and 2008. This effort
represent 2,239 hours spread over 12,248 ha (black areas). A total of 742 hours of field surveys
was spent on the already known populations, of which names are shown on the map. Light green
area represents the area of suitable habitat predicted by our multimodel inference approach.
Figure S2. This map shows the spatial distribution of the residuals from the multimodel inference
approach. Residuals are plotted for the 5000 pseudo-absence points and the 100 presence points
used to calibrate the models.
Figure S3. Boxplot of the residuals from the multimodel inference approach for the 100 presence
points used to calibrate the models and grouped by mountain. Three mountains (CASS, VERD
and BLAY) are not displayed because no presence data were available.
Figure S1
Figure S2
Figure S3