P Royet
email: paul.royet@cemagref.fr

L Peyras

New French Guidelines for Structural Safety of Embankment Dams in a Semi-probabilistic Format

Both in France and in most countries, hydraulic works (dams and levees) have remained excluded from semi-probabilistic methods and regulations and their design was, until recently, of a deterministic nature, without formal rules. In this context, the French Committee on Large Dams -FRCOLDinitiated, with a panel of experts from the French engineering industry and related government agencies, a research project to define a semi-probabilistic limit-state method for the design of embankment hydraulic works. This paper accounts for the work done over two years, presents the more important provisions emerging from this project and describes the benefits of a limit-state approach. It is arranged according to the Eurocode format: design situations and associated load combinations, geological and geotechnical model, internal hydraulic model, limit states and corresponding conditions.

Introduction

Until now, there was no truly standard method in France for justifying the stability of embankment dams. French engineers made use of internationally recognised publications like those issued by the US Bureau of Reclamation [START_REF] Usbr | Design of small dams[END_REF], the US Army Corps of Engineers [START_REF]General Design and Construction Considerations for Earth and Rock-fill Dams[END_REF] or the French Committee on Large Dams [START_REF] Peyras | French Recommendations for Limit-State Analytical Review of Gravity Dam Stability[END_REF]. Safety in these guidelines is taken into account in a deterministic manner. The gradual involvement of Eurocodes in construction computation did not significantly help develop the principles of hydraulic work design. Indeed, although in theory nothing stands in the way of using Eurocodes for construction computation, Eurocode 0 (EN 1990) provides that, for the design of special construction works (nuclear installations, dams, etc.), other provisions than those in EN 1990 to 1999 might be necessary. Consequently, Eurocodes are not integrated computation rules for hydraulic works, and the project designer will have to seek professional guidelines for their design. In practice, it is noted that, today, none of the French engineering consulting agencies uses Eurocodes for dam design.

In this context, FRCOLD decided to draw up its own guidelines on the stability analysis of embankment dams and levees, with a clearly operational focus. This task was undertaken in 2007 and achieved beginning of 2010 by a working group from FRCOLD representing the very best of French engineering skills and national bodies involved in hydraulics engineering (see acknowledgment). This work had a double objective: to harmonise French practice and adopt a limit-state analytical format. The outcome is a set of French guidelines for the structural analysis of embankment dams and dikes [START_REF] Frcold | Recommandations pour la justification des barrages et des digues en remblai[END_REF].

Embankment Hydraulic Works in Relation to Eurocodes

General Rules for Design per Eurocodes Although Eurocode 0 (EN 1990) tends to exclude dams from its scope, Eurocode 7 [START_REF] Frank | Designer's guide to EN 1997-1 Eurocode 7: Geotechnical design -General rules[END_REF] indicates that the provisions in this section apply to small dams and infrastructure embankments. "Small dam" is not defined in Eurocode 7; however, so as to lay down a commonly accepted definition, the International Commission on Large Dams considers a dam as "large" when it is more than 15 m high above its foundation. The design procedure provided by Eurocodes includes the following steps: -Defining design situations; -Defining limit-states to be taken into account; -Assessing values for loads and defining load combinations; -Assessing values for soil properties; -Designing limit-states based on limit-state conditions. Such steps are also included in deterministic practices pertaining to embankment hydraulic work design although they are not necessarily properly standardized in the computation notes and practices are relatively heterogeneous.

Eurocodes bring in the concept of characteristic value, which does not usually appear in deterministic computation notes for dams: the G k characteristic value of a G load (respectively, R k for R strength) is a cautious measurement of load intensity (of strength intensity, respectively). In Eurocodes, such cautiousness in measuring the parameters is taken into account by a 95% fractile (or 5%, depending on the positive or adverse nature) of the considered load or strength probability distribution. Using statistical methods may only be performed when data come from sufficiently homogenous identified populations or when enough feedback is available. To this end, the spatial variability of parameters on the volume of soil ruling the limit-state, the test data scattering and the statistical uncertainty related to the number of tests should all be taken into account.

In a number of fields, including hydraulic works, it is only seldom possible and relevant to resort to statistics. Cautious measurement therefore should rely on an expert assessment produced from available test results or from guideline values found in the literature. The characteristic value is then a cautious and proficient assessment of the material load or strength causing limit-states to appear.

Semi-Probabilistic Limit-State Method for Embankment Dams and Levees Developed by FRCOLD

In a conventional manner, the project team reviewed design situations, loads and values representative thereof, strengths, limit-states and limit-state conditions.

Design Situations

The Eurocode 0 advises to classify the design situations within three categories as follows: permanent, transient, accidental. On the same basis but using a slightly different terminology and formally considering how important floodrelated specific situations are, we suggest the following classification for design situations:

-Normal operating situation: Normal Water Level for dams;

-Transient or unusual situations: end of construction, rapid emptying and operational basis earthquake (OBE) situations;

-Accidental situations, including: maximum credible earthquake (MCE), accidental situations related to external hazards (impact from fast gravity hazard, avalanche, etc.).

A specific category of design situation was introduced to meet the specific requirements of embankment hydraulic work design: flood situations. Those include:

-Unusual flood situation: specific to dams for flood control and to flood protection dikes. It relates to protection goals for challenges assigned to the relevant work. It typically has a 10 to 100 years return period; -Exceptional flood situation: pertains to all types of dams and relates to floods that increase the reservoir level up to the Maximum Headwater Level (MHL). This situation return period is in the range of 100 to 1,000 years for small levees and dams and goes up to 10,000 years for large embankment dams;

-Extreme flood situation: pertains to all dams and dikes and relates to reaching a level above which the work might suffer from major damages that may quickly lead to break. We suggest that the related return period should be an order of magnitude over the exceptional flood situation. The last category of design situations correspond to failures of elements or components directly involved in the safety of the dam such as: failure of one or more valves of a spillway, failure of an overflow spillway by partial or complete obstruction due to ice jams; disruption of the drainage system (including if a drain pump), failure of a conduit, failure of waterproofing (core, upstream face, contact between the gallery and the upstream face perimeter or diaphragm wall) etc. These failures can lead to water levels potentially worse than the previous situations. The determination of rare or accidental situations related to failures of the security features derives from the risk analysis study, which is in the French regulation mandatory for dams of classes A and B and dikes of classes A, B and C. The risk analysis studies will be used to estimate the probabilities of failure of the security features (means of drainage, flood evacuation devices, etc.) combined with the water level in the reservoir. It evaluates overall probability of occurrence attached to a scenario combining the simultaneous failure of a component and a water level. Depending on the likelihood of the situation examined, it may be considered as rare situation (probability greater than 10 -3 to 10 -4 per year) or as accidental (probability less than 10 -4 per year).

Load Representative Values

Typically, loads have been classified within three categories:

-Permanent loads which are the embankment dead load, the ancillary works and equipment weight; -Variable loads: action of water, snow, wind, road capacity; -Seismic accidental load defined according to the design earthquake and that may be backed up with a specific assessment of the action of water. Computation intensities for permanent loads are obtained from their characteristic value; those for variable loads of water are directly assessed in the design situation (pursuant to AN 4.1. provision in the French national appendix to Eurocode 7); finally, computation intensities for accidental loads are agreed from a unique rated value.

Strength Properties

The procedure we recommend for formalizing geotechnical data includes the following three steps.

1. The geological model for foundation intends to provide information on the foundation level, its bearing capacity, its sealing, and to assess the stability of the abutments, the risks of differential settlements and erosion of the foundation.

2. Then, the geometrical model draws up a streamlined representation of the work and foundation geometry. Most frequently, such representation consists in wisely selecting cross sections of the work.

3. The geotechnical model for foundation and embankment which comes to determining the characteristic values of geotechnical parameters. It defines a representation framework for foundation and work strength properties so as to assess as accurately as possible the behavior and security thereof in relation to the different limit-states. Creating a geotechnical model requires the following:

-To identify the failure mechanisms and their related limit-states and to combine with suitable behavior laws and limit-state models;

-To identify materials contained in the embankment and the foundations; -To best assess, using characteristic values, the properties of materials contained in the embankment or the foundations involved in rheological laws and limit-state conditions.

Limit-States

In a conventional manner, the developed semi-probabilistic method differentiates the SLS (Serviceability Limit-States) and the ULS (Ultimate Limit-States). SLS are associated with the serviceability of the dam and availability thereof during its working life. The main structural elements of the dam, ensuring its availability, are defined according to the following practicalities: sealing, filtration, discharge, security monitoring and environmental protection. ULS are associated with losses of static balances or failure modes of embankment hydraulic works, summarized below in table 1:

Internal erosion Erosion by overflowing Scouring

The FRCOLD guidelines for embankment dams and levees provide detailed recommendations for the following ULS:  Overall stability -Sliding;  Lack of bearing capacity -punching;  Hydraulic uplift;  Scouring. ULS related to seismic situations will be treated in a specific document, to be issued end of year 2010. And ULS related to erosion will be treated in another document to be issued end of year 2011.

Overall stability (Sliding) Limit-State

Physical Models for Sliding Limit-State Such limit-state may be approached through various physical models based on slices computations implementing relatively advanced assumptions. The models mainly used in standard engineering practices are the following: Fellenius, Bishop, Janbu, Morgenstern-Price. Many authors discussed these subjects, including [START_REF] Aryal | Slope stability evaluations by limit equilibrium and finite element method[END_REF] who provides an update on the various methods and [START_REF] Zhu | A concise algorithm for computing the factor of safety using the Morgenstern-Price method[END_REF] who goes through the Morgenstern-Price algorithm. The slices computation methods consist in cutting the slope along an assumed sliding surface (Figure 1). The resulting shape is then cut into slices. The slice balance is studied following various inter-slice force assumptions. Lastly, some of these methods attempt to control force balance (Janbu), others abide by the balance of moments (Fellenius, Bishop), and the most comprehensive methods control both types of balances (Morgenstern-Price).

Selecting a Set of Partial Factors

The selection of partial factors was based on approach 3 of the Eurocode 7 and inflected by hydraulic work design practice which brings up security criteria differentiation depending on the design situation [START_REF]General Design and Construction Considerations for Earth and Rock-fill Dams[END_REF], [START_REF] Rolley | Technique des barrages en aménagement rural[END_REF]. Such partial factors are applied to characteristic values specific to material strength properties. The limit state condition is written as an inequality which compares, first the ratio of resisting forces (or their moment) on driving forces (or their moment) of other hand the coefficient model. The mathematical expression of the limit state condition depends on the model adopted; it involves the characteristic values of strength properties weighted by their partial factor and representative values of actions corresponding to the reviewed design situation. For example, with Fellenius method (which is not the most recommended method) and an effective stress calculation, the limit state condition is written as in Equation (1):

 W N i N = W .cos  i i i i i u =  .h h i i i w i                      i i i i m i i i mw i i W b u W b c          sin ' tan cos cos cos ' ' tan mc' >  d (1)

Calibration of model factor

One major step in our work was to find a solution to the model factor calibration issue. We used the conventional approach and the calibration principle consisted in searching for the best equivalence between the semi-probabilistic method security levels and those arising from deterministic practices [START_REF] Peyras | Study on a semi-probabilistic method for embankment hydraulic works -Application to sliding mechanism[END_REF]. Therefore, the  d model factors calibration was performed in reference to control standard practices so as to remain as close as possible to current sizing. The agreed set of partial factors was based on the approach 3 of Eurocode 7, and inflected by hydraulic work design practice. Computations were performed for several test works. The final selection of suggested model factors included the required consensus to be reached within the industry and specific to each standardization process.

The set of partial and model factors agreed for the FRCOLD semi-probabilistic method is outlined in Table 2. The above rules are modulated in some cases:

-It is sometimes useful or necessary to conduct a more comprehensive model, for example by finite element method, as an alternative or in complement to the model for calculating the equilibrium limit. The finite element calculations can provide a vision of deformations and local safety factors. The stability criteria may remain similar to those for the limit equilibrium calculations: the calculations can be performed with the characteristic values of the criterion of plasticity and stiffness module, bearing the same partial factors applied to the soils properties and presented in Table 2.

-For the situation at the end of construction, in addition to calculating effective stresses, it is possible to conduct a calculation in terms of total stresses.

-For dams with plastic clay materials, special precautions must be taken and a specific section is devoted to those cases.

Uplift Limit-State

The hydraulic uplift limit state at the downstream toe must be considered when the dam or the dike has been built on a foundation with a geological stratification consisting in a low permeability soil layer overlying a more permeable layer of soil. This can lead to water pressure under this layer of soil that could destabilize it. The stability criterion is expressed by comparing weight of the layer of low permeability soil and water pressure under this layer. This is controlled by adequate drainage, which is drilling for example in the impermeable layer downstream toe (relief wells). This is the solution for dams. Where this is not possible, the limit state condition is established by considering:

-Water pressure acting under the soil layer, u; -The total stress given by the weight of the soil layer,  v .

Cohesion is neglected. The limit state condition is as follows (Equation 2), with the set of partial and model factors of table 3:

  u u >  m  v (2)

Bearing Capacity of the Foundation

The concept of bearing capacity of the foundation refers to a criterion of shear failure of the foundation for punching failures. These are failures of the foundation soil characterized by the fact that the embankment collapsed while undergoing traction. The failure of the foundation is general since the entire width of the embankment is concerned. The failure pattern of the foundation soil is similar to that which occurs under a shallow foundation and can therefore be studied as such.

The worst case is generally the end of construction (short term situation). The stability check should therefore assume the embankment being built instantly, without dissipation of pore pressures in the foundation. Only new dams or projects of heightening of ancient dams are concerned. The vertical stress q under an embankment of height H and weight density  can be approximated by: q =  H (3) Mandel & Salençon have proposed a solution in which the limit pressure on a soil with a cohesion c u can be written: q max = c u N c (4) with Nc: coefficient function of B/D, where B is the average width of the embankment at half height and D the thickness of compressible foundation. Nc is determined from charts but can be approximated by 4 + 0.5 B/D. The limit-state condition can be expressed by Equation 5, with the set of partial factors of table 4. The value of 1.4 for  m is the value recommended for c u in Eurocode 7 and the value of 1.2 for model factor is consistent with the value adopted for overall stability limit-state in case of transient situations.

q max / m >  d q (5)

Conclusion

This paper accounts for the work done as part of an FRCOLD project intended to develop a semi-probabilistic method for designing embankment hydraulic work stability. The resulting outputs, to be released in 2010 with the support of the FRCOLD, are of various kinds. Developing a semi-probabilistic method requires accurately and formally structured designs, which accounts for a scientific challenge in itself. The implemented procedure successively discussed: loads, strengths, design situations and load combinations, limit-states and conditions thereof. The Eurocode format proved well-fitted to these works after a number of adjustments. On that account, a specific category of design situations -flood situations -was put forward. Moreover, we suggest that variable actions of water should be directly assessed in the studied design situation. Finally, there are mostly not enough available data to enable any statistical appraisal of characteristic values for strength properties, which is therefore performed through an expert assessment considering simple statistical computations, identification tests, literature data and feedback on similar materials. One major step in this work was to find a solution to the model factor calibration issue. We used the conventional approach and the calibration principle consisted in searching for the best equivalence between the semi-probabilistic method security levels and those arising from deterministic practices. Therefore, the  d model factors calibration was performed in reference to control standard practices so as to remain as close as possible to current sizings. The agreed set of partial factors was based on the approach 3 of Eurocode 7, and inflected by hydraulic work design practice. Computations were performed for several test works. The final selection of suggested model factors included the required consensus to be reached within the industry and specific to each standardization process.

The suggested application allows us to bring interesting findings into general use, which we observed on the various applications carried out during calibration work. The first application pertains to comparing the deterministic practice with the FRCOLD semi-probabilistic method. It shows similar security levels between both methods, which makes sense considering the FRCOLD semi-probabilistic method calibration against the deterministic practice.

The second application relates to comparing the Eurocode 7 with the FRCOLD semi-probabilistic method. It appears that the approach 2 of Eurocode 7 may not be used directly for embankment hydraulic works considering the significant discrepancies in security levels against building industry practices. This conclusion may also be extended to designing normal water level situations through the approach 3 of Eurocode 7.

Ongoing work

As pointed out previously, ultimate limit states related to seismic situations will be treated in a specific document, to be issued end of year 2010, with a consistent methodology. And ULS related to erosion will be treated in another document to be issued end of year 2011, as a deliverable of a national research project on internal erosion (ERINOH project). All those documents will form, in a near future, a global and complete framework for justification of dams and dykes of any types in the French context, and also with possible applications in other countries.

Figure 1 :

 1 Figure 1: Physical Model for Sliding Limit-State (slices methods)

Figure 2 :

 2 Figure 2: Physical Model for Uplift Limit-State

TABLE 1 :

 1 MAIN ULTIMATE LIMIT STATES

	Scale	Type		ULS
	Intergranular and	Shear		Overall stability -Sliding
	hydrostatic			Seismic stability without
	forces at global			pressure rise
	scale			Lack of bearing capacity
				-punching
		Soil heave		Hydraulic uplift
	Hydrodynamic	Static	or	Boiling
	forces at global	dynamic		Liquefaction
	scale	liquefaction
	Hydrodynamic	Erosion
	forces at local
	scale

TABLE 2 :

 2 SET OF PARTIAL AND MODEL FACTORS FOR OVERALL STABILITY LIMIT-STATE

	Design	Partial factor	Partial factor	Model
	situations	applied to	applied to	factor  d
		cohesion ( c' )	unit weight
		and to the	(  ) and to
		tangent of angle	soil strength
		of friction	( R;e )
		( tan ' )
	Normal	1.25	1	1.2
	operating
	Transient or	1.1	1	1.2
	unusual
	Exceptional	1.1	1	1.2
	flood (MHL)
	Extreme	1	1	1.1
	flood
	Accidental	1	1	1.1

TABLE 3 :

 3 SET OF PARTIAL FACTORS FOT UPLIFT LIMIT-STATE

	Downstream of the
	embankment
	Impervious layer	 v
	Permeable layer	u

TABLE 4 :

 4 SET OF PARTIAL FACTORS FOT BEARING CAPACITY OF FOUNDATION

	Design situations	Partial factor ( m ) applied to undrained cohesion	Model factor  d
	Transient or unusual	1.4	1.2

Acknowledgements

Members of the working group were: Paul Royet (coordinator), Laurent Peyras (secretary), Stéphan Aigouy, Patrice Anthiniac, Jean-Robert Courivaud, Bernard Couturier, Luc Deroo, Laurence Duchesne, Edouard Durand, Jean-Jacques Fry, Mathieu Galiana, Daniel Loudière, Jean-Pierre Magnan, Sébastien Mercklé, Bachir Touileb, Caroline Varon, Eric Vuillermet.


Luque

Mathilde Redon
email: mathilde.redon@cemagref.fr

Sandra Luque

Presence-only modelling for indicator species distribution: biodiversity monitoring in the French Alps

Keywords: Maxent, distribution modelling, conservation planning, biodiversity indicators, cavity-nesting owls, French Alps MOTS CLES Maxent, modélisation de la répartition d'espèces, planification de

The problem in biodiversity monitoring and conservation is that usually exist vast gaps in available information on the spatial distribution of biodiversity that poses a major challenge for the development of biodiversity indicators and regional conservation planning. Within this context, models that establish relationships between environmental variables and species occurrence have been developed to predict species distribution over large areas. We present an example using two indicator bird species, Tengmalm owl (Aegolius funereus) and Pygmy owl (Glaucidium passerinum). Maximum entropy (Maxent), a presence-only modelling approach, is used to model the distribution of these two species within a large study area in the French Alps. Despite biased sampling design, this method performs very well in predicting spatial distribution of the two owl species and brings useful information to help decision-making concerning the protection of valuable habitats. Maximum d'entropie (Maxent), une méthode de modélisation basée uniquement sur des données de présence, a été utilisée pour modéliser la distribution de ces deux espèces de chouettes sur une grande zone d'étude dans les Alpes françaises. Malgré un échantillonnage biaisé, cette méthode donne un très bon résultat pour la répartition potentielle des deux espèces et apporte des informations très utiles pour aider à la prise de décision concernant la protection des habitats à forte valeur écologique.

RESUME

Le problème actuel dans les domaines de la gestion et de la conservation de la biodiversité est que les informations disponibles sur la distribution spatiale de la biodiversité sont souvent incomplètes. Dans ce contexte, des modèles qui établissent des relations entre des variables environnementales et des observations d'espèces ont été développés pour prédire la distribution des espèces à grande échelle. Nous présentons un exemple basé sur deux espèces emblématiques : la chouette de Tengmalm (Aegolius funereus) et la chevêchette d'Europe (Glaucidium passerinum). Le

Introduction

Improving knowledge on the distribution of indicator and emblematic but locally poorly known species is of great importance for managers as well as for naturalists [START_REF] Anonyme ; Pierre D'entremont | La Chevêchette d'Europe, Association Le Petit Entremont[END_REF]. These species can be used as a surrogate for biodiversity monitoring and conservation [START_REF] Lindenmayer | Indicators of Biodiversity for Ecologically Sustainable Forest Management[END_REF]. Still vast gaps in available information on the spatial distribution of biodiversity exist, that poses a major challenge for the development of relevant biodiversity indicators for regional conservation and forest management planning. In addition, the development of spatial knowledge on the habitat requirements and ecology of these species would facilitate conservation of a great number of related species.

Models that establish relationships between environmental variables and species occurrence have been developed and are widely used with many applications in conservation and management-related fields [START_REF] Cowley | Habitat-based statistical models for predicting the spatial distribution of butterflies and day-flying moths in a fragmented landscape[END_REF][START_REF] Elith | Novel methods improve prediction of species" distributions from occurrence data[END_REF][START_REF] Gibson | Spatial prediction of rufous bristlebird habitat in a coastal heathland: a GIS-based approach[END_REF][START_REF] Pearce | An evaluation of alternative algorithms for fitting species distribution models using logistic regression[END_REF][START_REF] Stockwell | Effects of sample size on accuracy of species distribution models[END_REF]. They can help to guide additional field work, by identifying unknown population locations. It also supports management decisions with regard to biodiversity, to determine suitable sites for reintroductions or to assist selection of protected areas [START_REF] Anonyme ; Pierre D'entremont | La Chevêchette d'Europe, Association Le Petit Entremont[END_REF]. First, these models were mainly developed for presence-absence data modelling. However, absence data are often lacking or biased and a new generation of models adapted to presence-only data modelling have been proposed [START_REF] Anonyme ; Pierre D'entremont | La Chevêchette d'Europe, Association Le Petit Entremont[END_REF][START_REF] Hirzel | Ecological-niche Factor Analysis : How to compute habitat-suitability maps without absence data[END_REF][START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF]. An important number of such methods exist and differ from their data requirements, statistical models used, output formats, performance in diverse situations [START_REF] Elith | Novel methods improve prediction of species" distributions from occurrence data[END_REF][START_REF] Guisan | Predictive habitat distribution models in ecology[END_REF]. Much of them are based on the ecological niche theory [START_REF] Hirzel | Habitat suitability modelling and niche theory[END_REF][START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF]. Ecological-niche based models generally define a function that links the fitness of individuals to their environment [START_REF] Hirzel | Habitat suitability modelling and niche theory[END_REF].Thus, theoretically, if we know precisely the habitat characteristics of a species, it is possible to rebuild its ecological niche from the environmental variables describing its habitat.

In this study, we have chosen to use the Maximum Entropy modelling approach, which is a relatively recent method developed by [START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF]. Maxent is a presence-only modelling approach with a proved good potential to predict wildlife distribution. Despite biased sampling design, this method performs very well in predicting spatial distribution of species data [START_REF] Elith | Novel methods improve prediction of species" distributions from occurrence data[END_REF][START_REF] Pearson | Predicting species distributions from small numbers of occurrence records : a test case using cryptic geckos in Madagascar[END_REF].

Herein we aim to predict the distribution of two owls" species, Tengmalm owl (Aegolius funereus) and Pygmy owl (Glaucidium passerinum), in the French Alps. These species have specific habitat needs and their presence reflects those of numerous other forest-dwelling species. They are considered as relicts from Ice Age and need quite cold areas [START_REF] Lpo | [END_REF], which make them good candidates to develop further studies in relation to global climate changes.

In addition, their distributions are poorly known and their protection statuses are not well defined [START_REF] Loose | La Chevêchette d'Europe (Glaucidium passerinum) sur la Réserve naturelle des Hauts-plateaux du Vercors[END_REF]. It is also important to denote that Pygmy owl populations in the Vercors Mountains area (Alps range) represent the occidental limit of the European range of the species [START_REF] Anonyme ; Pierre D'entremont | La Chevêchette d'Europe, Association Le Petit Entremont[END_REF]. It represents also an additional stake to learn more about distribution and habitat structure of this species at the limit of its range. Requirements and distribution of Tengmalm owl are less well known because this species is nocturnal and discreet [START_REF] Hakkarainen | Survival of male Tengmalm"s owls increases with cover of old forest in their territory[END_REF]; therefore census data are difficult to gather. Modelling its potential distribution will allow to improve knowledge on its habitat and ecological needs. Several local surveys efforts took place in order to develop a census of the populations within the "Vercors" region. But these works are limited to very small areas, and a distribution model which covers the entire mountain region would be very useful to help to define adequate surveys efforts for the future while at the same time will provide an overview of the likely distribution of the two species.

Material and methods

Case study area and species occurrence data

This work was conducted within the Vercors" Natural Regional Park (VNRP), located at the frontier between northern and southern French Alps (Figure 1; http://parc-du-vercors.fr). It covers 206 000 hectares with 139 000 hectares of forests. Approximately a half of these forests are Public (State and municipalities forests) and the rest is in the hands of private stakeholders. The main tree species are Silver Fir (Abies alba), Norway Spruce (Picea abies) and European Beech (Fagus sylvatica).

We used Tengmalm owl (Aegolius funereus) and Pygmy owl (Glaucidium passerinum) point counts data from several surveys conducted in the "Hauts Plateaux du Vercors" Natural Reserve (HPVNR), which is located within the VNRP. The Reserve is mainly composed of three main forest types: i) mixed uneven-aged beech/spruce/fir forests, ii) pure quite sparse even-aged or uneven-aged spruce forests and at high elevation, iii) pure sparse naturally even-aged Mountain Pine forests. The bigger State forest in the Reserve has recently been classified as an Integral Biological Reserve (IBR).

All local surveys have take place in this State forest and mainly in the IBR. The two owls" species are from the North European boreal forests and the cold sparse spruce Reserve forests look-like their original habitat. Therefore, local people generally thought that the range of the two species is limited to these particular forests in the Vercors.

In this part of the Alps, Pygmy owl depends on cavities carved by the Great spotted woodpecker (Dendrocopus major) and Tengmalm owl by the Black Woodpecker (Dryocopus martius) for breeding.

These cavity providers favour respectively spruce and beech trees to breed. It implies that the presence of the woodpeckers and their host trees are likely to be important habitat variables for the two owls.

Figure 1. Study area localisation

The point counts data come from the observation network of the National Forest Office and the "Ligue de Protection des Oiseaux" (Bird protection organisation), an NGO which aims to improve knowledge on the local fauna species. These data are a combination of visual and eared bird contacts in addition to nests locations. Each contact point is located with a Global Positioning System (GPS). The reliability of these data is very heterogeneous because each data source has its own sampling design and its own database system. We therefore harmonize data before integration into a common database. It is important to denote that despite the low precision of visual and eared occurrence data we include them into our database since these are owl"s activity centres within their territory. Gathering of all available point counts gives 95 points for Pygmy owl and 76 for Tengmalm owl.

The resulted dataset is composed of presence points represented as latitude/longitude coordinates, and then no absence points are considered. This is a common issue when someone works with wildlife surveys data [START_REF] Anderson | Evaluating predictive models of species' distributions: criteria for selecting optimal models[END_REF][START_REF] Chefaoui | Assessing the effects of pseudo-absences on predictive distribution model performance[END_REF]. Therefore the interest of models like Maxent, as aforementioned, is the use of presence data only for the computation of the habitat modelling.

Modelling algorithm

Maxent was first developed to make predictions or inferences from incomplete information in many different fields [START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF]. Since recently it has been widely used as a general approach for presence-only modelling of species distribution. It estimates the less constrained distribution of training points compare to random background locations with environmental data layers defining constrains [START_REF] Anonyme ; Pierre D'entremont | La Chevêchette d'Europe, Association Le Petit Entremont[END_REF]. The results show how well the model fits the location data as compared to a random distribution [START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF][START_REF] Phillips | A Maximum Entropy Approach to Species Distribution Modeling[END_REF].

An increasing number of comparisons with others modelling methods show that Maxent performs better than much of them (Ortega-Huerta and [START_REF] Ortega-Huerta | Modeling ecological niches and predicting geographic distributions: a test of six presence-only methods[END_REF][START_REF] Ward | Modelling the potential geographic distribution of invasive ant species in New Zealand[END_REF][START_REF] Bing-Yan | The Potential Geographic Distribution of Radopholus similis in China[END_REF]. This performance is due to the numerous advantages of the method. For example, it can be run with very few occurrence data [START_REF] Hernandez | The effect of sample size and species characteristics on performance of different species distribution modeling methods[END_REF][START_REF] Pearson | Predicting species distributions from small numbers of occurrence records : a test case using cryptic geckos in Madagascar[END_REF], it manages with different kind of environmental data, as continuous and discrete datasets, without any transformation (Ortega-Huerta and [START_REF] Ortega-Huerta | Modeling ecological niches and predicting geographic distributions: a test of six presence-only methods[END_REF] and it includes a parameter to avoid over-fitting [START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF]. The strategy to manage with lack of absence data, based on random pseudo-absence, performs better than other ways such as those of ENFA [START_REF] Hirzel | Ecological-niche Factor Analysis : How to compute habitat-suitability maps without absence data[END_REF] or BIOCLIM [START_REF] Busby | BIOCLIM: A bioclimate analysis and prediction system[END_REF] [START_REF] Wisz | Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data[END_REF]. It also shows percentage variable contributions to the final model and response curves for each variable, which allows targeting those which can be deleted to improve model performance and to detect variable correlations [START_REF] Anonyme ; Pierre D'entremont | La Chevêchette d'Europe, Association Le Petit Entremont[END_REF]. The output is continuous that allows a great flexibility in threshold choice and results representation. Resulting maps can be very easily exported in a GIS database [START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF]. Outputs are also easy to interpret from an ecological point of view.

Maxent presents also some drawbacks, the main is linked to the exponential model for probability which can give very large predicted values for environmental conditions outside the range present in the study area. The method can have therefore some difficulties to extrapolate to other study area [START_REF] Peterson | Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent[END_REF][START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF].

GIS environmental data and model implementation

We used a set of environmental data based on the knowledge of the species ecology and factors affecting distribution of the species within the entire study area (Array 1). These data are represented as raster layers with a 50 m resolution, which is those of the most restrictive raster included in the analysis. We used ArcGIS 9.3 to prepare the different data layers.

Implementation of Maxent requires that rasters were perfectly overlapping and have exactly the same number of cells. A single raster mask delimiting the study area was therefore used to assure that all raster layers have the same dimensions. For the two species, we used 25% randomly selected occurrence data for crossvalidation, leaving the remaining 75 % for analysis, as done by [START_REF] Ward | Modelling the potential geographic distribution of invasive ant species in New Zealand[END_REF].

We implemented the model with freeware Maxent developed by (Phillips et al., 2005). It is friendly use, as species occurrence training and test files and environmental data layers are automatically recognize by the application.

We used simultaneously continuous and discrete data. We let almost all default parameters, but we set the regularization value to 1 for the two species. To evaluate the relative contribution of each variable to the model we first chose to see the jackknife test of variable importance. It shows how each variable contributes to the model by testing variation of model gain with and without including each variable separately. We also analyzed the response curves which show the response of each variable to presence probability.

We first include all the environmental variables in the model. We then delete those which did not show any significant contribution to the model.

Six variables were finally selected for Pygmy owl: elevation, topography, land cover, mean annual temperature, forest / non-forest map and presence of Norway Spruce; and five for Tengmalm owl: Land cover, elevation, slope, forest/non-forest map and European Beech presence. Fifty model replicates were run for each species and we select the best among the 50. The contribution of each variable can be visualized through jakknife tests of variable importance and calculation of variables percentage contributions. Maxent provides three output formats. We select the logistic output as generally recommended. The result is a continuous value between 0 and 100. Each resulting raster pixel contains a value reflecting how well the predictive conditions for each pixel are.

GIS layer

We then export results into ArcGIS 9.3 in order to apply a threshold value to produce the occurrence map. Applying a threshold is the last step of many species modelling approaches. It is necessary to transform the probability map in presence/absence data. Many methods exist to determine the presence threshold. Objective thresholds seem to be more effective than subjective ones [START_REF] Liu | Selecting thresholds of occurrence in the prediction of species distributions[END_REF]. We used 10 percentile training presence (threshold 0.345 for Pygmy owl and 0.259 for Tengmalm owl) as suggested by [START_REF] Phillips | Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation[END_REF]. This threshold value provides a better ecologically significant result when compared with more restricted thresholds values. Therefore, the use of only one threshold value gives a very narrow overview of the species distribution. In addition, for conservation purpose it is more useful to have a presence gradient which is more realistic and easier to validate with expert knowledge. Hence, we produce output maps with four ranges of presence probability: 0-25% as unsuitable habitat, 25-50% as acceptable habitat, 50-75% as quite suitable and > 75% as suitable habitat.

Model evaluation

To evaluate model results, the best method would have been to use an independent data set. However, for the two owl"s species, observation data are spatially aggregated and it would have had no sense to use data located in the same place than the training data to evaluate the performance of the model.

We test models performance with several other tools. Maxent calculates the AUC (Area Under the receiver operating Curve) for each run. It is a standard, threshold-independent method for model evaluation. This method was initially developed for presence-absence data. In Maxent, absence data are replaced by random points [START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF]. AUC tests if a prediction is better than random for any possible presence threshold. It varies between 0.5 when the result is not better than a random selection and 1 when the result is significantly better than random.

Another evaluation method lies in the analysis of the two types of prediction errors provided by all presence/absence models: false negatives (omission error) and false positives (commission error) [START_REF] Ward | Modelling the potential geographic distribution of invasive ant species in New Zealand[END_REF]. Maxent algorithm calculates an omission rate for training and test data. Omission rate indicates the percentage of test localities that falls into pixels not predicted as suitable for the species [START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF]. It should be low for a good model performance. With presence-only modelling, is it very difficult to calculate commission errors as no absence date is available and commission error often increases with omission decreasing [START_REF] Hernandez | The effect of sample size and species characteristics on performance of different species distribution modeling methods[END_REF]. We therefore use different evaluation tools to overcome this difficulty.

AUC and omission rate were nevertheless used to select the best model among fifty replicates for the two species. Models with the lowest training and test omission rates and with the highest AUC were chosen, as generally done by Maxent users. We also verify if all training points where predicted with a high probability.

In addition, expert knowledge was included in the validation process. We compare models results with a mental map drawn by an expert of the two owl species on the Vercors Mountains. We also gather a group of forest managers with good naturalist skills to validate the results.

Results and discussion

Despite the aggregation patterns of the training data, Maxent modelling method has successfully predicted owl"s presence on a large range in the Vercors Mountains (Figure 2). Results show that the approach has a good capability to capture the intrinsic species-habitat relationships and to reconstruct the ecological niche even in sites were any data were available.

Moreover, this method performed very well in predicting potential spatial distribution of the two owl species. Test omission rates are null at minimum training presence threshold for training and test datasets (rate = 0.000 for Pygmy owl and Tengmalm owl) and low at 10 percentile training presence threshold (Tengmalm owl: 0.093 for training data and 0.000 for test data and Pygmy owl: 0.095 for training data and 0.000 for test data). For the two species, models show an AUC value very close to 1 for test data ( 0.992 for Tengmalm owl and 0.996 for Pygmy owl), which generally proves good model performance. However, when species have a narrow range (or training data are spatially aggregated), AUC is often overestimated [START_REF] Phillips | Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation[END_REF], which is certainly the case here. The high AUC values are therefore not sufficient to evaluate model performance in this study. Mean training data predictive rate is 0.65 (SD = 0.21) for Pygmy owl and 0.63 (SD = 0.20) for Tengmalm owl. The model predictive capacity seems to be good then based on the well predicted calibration points. In addition, for the two species, the resulting presence/absence maps overlap very well with the expert mental map and are in accordance with manager"s knowledge. For Pygmy owl, the environmental variable with highest gain when used in isolation is elevation (Figure 3a), which therefore seems to contain the most useful information by itself. It is supported by local expert knowledge and literature where this species is linked with a quite narrow altitudinal range (between 1100 and 2200 meters). Mean annual temperature brings also high gain to the model. These two variables could be considered as very correlated, as temperature generally decreases with elevation, but omission of one of the two variables decreases model performance. This is not the case for Tengmalm owl, where removal of mean annual temperature increases elevation gain as it captures the gain deleted by removal of temperature. The environmental variable that contains the most information that is not present in the other variable is topography (called "altitopo"), because it decreases the gain the most when is omitted (Figure 3a). As for mean annual temperature, this variable seems to be quite correlated with elevation, but removal of one of them decreases also model performance.

The environmental variable with highest gain for Tengmalm owl is elevation which is also the variable that decreases the most the gain when it is omitted (Figure 3b). Elevation brings therefore the most useful information by itself and contains the most information that is not represented by the other variables. The linkage of the two owl"s species with elevation can be due to the fact that they are dependent of particular tree species to breed, these trees themselves growing in limited altitudinal ranges. They also need quite cold conditions, that are often linked with high elevation or soil depressions, which are frequent in limestone soils as present in the Vercors Mountains. For the two species, the resulting distribution is wider than would be expected by local knowledge (see Figure 2). This result is not surprising because some observations have been done in Vercors forests outside of the HPVNR and in an adjacent mountain area with a different type of forest habitats (i.e. more humid, more productive and with closed canopy conditions).

The distribution maps bring new information on these poorly known species. For example, they are certainly present in some of the most productive Vercors forests, where their conservation will therefore become a new stake for forest management. In addition, the grain and extent of the resulting presence/absence maps allow their use at different scales. They can be used at management units scale (few hectares) to better integrate owl"s conservation in forest planning as well at entire forests scale to avoid suitable sites when extension of road network is planed, for example.

However, it is important to note that species could not be present in a site even if they are predicted. Other factors, not taken into account in the analysis, can explain species absence. They can be for example: predator presence (notably Tawny owl (Strix aluco) for the two owls and European pine marten (Martes martes) for Tengmalm owl), sites far from existing population and not yet colonized and a lack of prey resources (little mammals, passerine birds, etc.). The integration of a quantitative analysis of landscape heterogeneity, in terms of structure and composition, is underway to better take into account matrix characteristics.

Conclusion

Combination of GIS tools with species distribution modelling algorithm shows to have a good potential for species monitoring. In this study GIS was first used as a platform to gather, homogenise and prepare data layers before running the model. These steps are determinant to assure model quality and, in this study, the model algorithm Maxent performed very well in predicting potential spatial distribution of the two owls" species. GIS is then used to facilitate results interpretation and to create maps of presence probability useful for forest managers and naturalists.

This kind of modelling process would be useful to follow the evolution of their spatial distribution in years to come. Furthermore, the tools developed can be applied in assessing biodiversity value of both managed and protected forest areas to help decision-making concerning the protection of valuable habitats. As GIS tools are commonly used in management planning and Maxent algorithm is free and quite simple to implement, this kind of method can be easily adapted and implemented by local managers interested in species monitoring. Sites of predicted presence would for example guide naturalists" future work in order to identify other suitable areas where the bird distribution is unknown while at the same time facilitate selection of areas with high ecological value. As numerous public forests are managed for wood production in the Vercors, these maps would allow to better integrating biodiversity conservation into management planning. In addition, as these species are linked to cold habitats, they could serve as good indicators of climate change with further work including temporal analysis. Distribution modelling of these species is among the first attempts to model suitable habitat distribution of cavity-nesting owl species in France. We hope it will launch the use of such methods, which aim to improve species ecological knowledge and facilitate species censuses and conservation.

Figure 2 .

 2 Figure 2 . Results of Maxent model on Vercors Mountains a) Tengmalm owl and b) Pygmy owl, with four ranges of presence probability.

Figure 3 .

 3 Figure 3. Jakknife of regularized training gain a) Pygmy owl and b) Tengmalm owl.


S Luque

A landscape approach to sustainable forest management: the challenge to adaptive management and the maintenance of biodiversity value The challenge to adaptive management and the maintenance of biodiversity value

The challenge to adaptive management and the maintenance of biodiversity value

S.


V Jousseaume

J Cuzzocrea

N Bernier

V T Renard

Few Graphene layer/Carbon-Nanotube composite Grown at CMOS-compatible Temperature

We investigate the growth of the recently demonstrated composite material composed of vertically aligned carbon nanotubes capped by few graphene layers. We show that the carbon nanotubes grow epitaxially under the few graphene layers. By using a catalyst and gaseous carbon precursor different from those used originally we establish that such unconventional growth mode is not specific to a precise choice of catalyst-precursor couple. Furthermore, the composite can be grown using catalyst and temperatures compatible with CMOS processing (T < 450 • C).

The limitations of copper interconnects are driving research towards alternative materials and technologies for the next-generation of Integrated Circuits (ICs). Carbon nanomaterials, with their many attractive properties, are emerging as the frontrunners to potentially replace copper for interconnects and passive devices in ICs, including vias, through-silicon vias (TSVs) and horizontal wires [1][START_REF] Nihei | Proc. IEEE Int.Interconnect Technol. Conf. Jun. 2005[END_REF][START_REF] Awano | [END_REF][4]. Indeed, low-dimensional allotropes of carbon (in particular, carbon nanotubes (CNTs) and graphene) have extraordinary physical properties because of their unique structure. Contrary to copper, CNTs can accept a high current density and can have an extremely high thermal conductivity [5,6]. Graphene has the potential for very high electrical conductivity [7]. Beside these advantages for interconnects technology, Field Effect Transistors have been demonstrated on CNTs and graphene. [8,9] It could be very interesting to combine these advances in interconnects and transistor technologies in an all carbon electronics. The succes of such approach could strongly depend on the ability to assemble them in working devices. Recently, Fujitsu Corp. has demonstrated the possibility of building carbon interconnect structures combining CNTs and few graphene layers [10](these structures will we reffered to as "composite" in the following). This approach of binding CNTs and few-layer graphene during the growth itself maybe a powerfull method to produce CNT/graphene hybrid systems.

Here, we show that such composite is composed of CNTs epitaxially grown bellow the few graphene layers. We demontrate that the composite can be obtained independently of the choice of the precursor and catalyst indicating that this unusal growth mode is in fact quite general. Furthermore, we achieved growth using catalyst and temperatures compatible with Back End of Line (BEOL) processing. This opens new possibilities concerning its integration in next generations of interconnects.

Figure 1 shows Scanning Electron Microscope (SEM) * vincent.jousseaume@cea.fr of the product of Chemical Vapour Deposition (CVD) on a 6 nm thick nickel film. The growth was performed at 550 • C in an Applied Material 200 mm DXZ chamber using propylene (C 3 H 6 ) as carbon source. The Ni film was deposited by Physical Vapour Deposition on a 10 nm TiN layer deposited by CVD on a standard silicon 200 mm wafer. Depending on the pretreatment (initial step before the introduction of propylene) very different result are obtained. On one hand, with a pre-treatment consisting of an argon flow during 15 minutes, carbon nanotubes are obtained (Figure 1a). The nanotubes are multiwall with catalyst at the tip and about 10 nm in diameter (not shown). On the other hand, in absence of pre-treatment a complex nanostructure composed of a forest of nanotubes with a thin layer bonded on top of it is formed (Figure 1b).

A similar nanostructure was observed by Kondo and colleagues by CVD at 510 • C using cobalt as catalyst and acetylene (C 2 H 2 ) as carbon precursor. [10] They proposed the following mechanism for the growth of this composite. When a substrate with a catalyst film is heated in presence of carbon precursor, the catalytic film first decomposes the source gas while remaining in its thin-film structure. This leads to the formation of few graphene layers on the catalyst. Second, the catalyst film breaks into particles. Third, multi-wall CNTs are synthesized from catalyst particles by the tip growth mode.

Multi-and single graphene layers are quite well known to form on Nickel films. [11][12][13][14][15] Carbon nanotubes are also well known to grow from Nickel nanoparticles. [16][17][18] It is therefore surprising that the composite has not yet been reported on Ni. This fact, together with our observations already indirectly confirms the scenario proposed by Kondo and colleagues. [10] Indeed, in the CNT community, very thin Ni layers (1 nm or less) are dewetted into small droplets that subsequently catalyze the nanotube growth. The thinner the film the smaller the particles and therefore the thinner the nanotubes. This is important because the goal here is to synthesize the thinnest nanotubes which are more likelly to show interesting effects such as quantum confinement. [19] On the contrary, in the graphene community relativelly large Ni thicknesses (typically larger than 100 nm) are used since one wants to avoid a dicontinuous graphene layer due to dewetting. We used a catalyst layer of intermediate thickness (few nanometre) which may represent a better compromise between the necessity to keep the catalyst film intact during multilayer-graphene growth and the necessary dewetting after some time to start nanotube growth. This is comfirmed by the observation that when the Nickel film is intentionnaly dewetted using argon pretreatment only nanotubes are formed. This is also confirmed by the SEM images taken at the initial stages of the growth. After 5 minutes of growth under the same conditions as in Figure 1a there is an extended carbon film at the surface of the sample. In some places the catalyst has already dewetted and the CNTs growth has started. This is evidenced by bumps at the surface and cracks from wich it is possible to see nanotubes below the film (See Figure 2). Therefore our observation clearly establishes that the few graphene layers form first.

Figure 3 presents SEM and High Resolution Transmission Microscope (HRTEM) image of the junction between a nanotube and the graphene layers.The composite has been collected on an amorphous carbon TEM grid for HRTEM investigations. The nanotube are epitaxial under less than 10 layers of graphene. In the previous study, [10] there were indications that the nanotube orig- The scale bar is 600 nm for both images inated from holes in the first graphene layer forming a trumpet like structure (Fig. 3c). Figure 3a clearly illustrates that in our case closed nanotubes are in fact expitaxially formed under the graphene layers (Sketched in Fig. 3d). The shape of this interface may therefore depend on the experimental conditions. This will have to be deeply investigated since it can have important consequenses concerning electrical contact between the nanotube and the graphene layers. Indeed, the contact resistance can be expected to be larger than in a configuration where the nanotube gradually transforms into a flat graphene sheet. Still, the contact electrical resistance should be reasonnable given that the graphene orientation in the nanotube and the multilayer graphene should match due to their epitaxial relation. [20] This will have to be studied by electrical measurements. The fact that the nanotube is closed by a relativelly important number of graphene layers suggests that NTCs grow from the root and not from their tip. Indeed, growth stops whenever the catalyst is completelly encaplusated. [17] The reduction of their diameter towards the substrate can be attributed to the loss of catalysing material durning the catalyst elongation/contraction process occuring during CNT growth. [17] Some key points in this mechanism remain unclear. In particular, why does the catalyst film brakes into particles after some time? In the context of CNTs, it is established that Nickel atoms exhibit fast self-diffusivity in presence of graphene interface. [17,18] At the initial stage of CNT formation, graphene layers grow at the catalyst step edges and catalyst atoms rapidly move toward the graphene free region. This causes an elongation of the catalyst until the energy gained when binding the graphitic fibre to the Ni surface no longer compensate the increase in the Ni surface energy. At this moment, the catalyst collapses into a more or less round particle. [17] We believe an analogous rapid diffusion of nickel atoms occurs in our case leading to the formation of catalyst particles, although we do not have direct evidences. Another key question concerns the way the organic precursor is provided to the catalyst after the graphitic film is formed. Indeed, graphene is a good barrier to gas diffusion [21] and nanotube CVD growth stops when the catalyst particle is encaplusated. [17] These questions will have to be investigated by in-situ measurements to fully determine the detailed growth process.

We have also investigated the possibility to grow such type of composite at lower temperatures. This point is critical in the case of the co-integration of these carbon structures with CMOS architecture at the interconnection levels since the maximum temperature acceptable during the device fabrication is 450 • C. As can be seen on Figure 4, composites are also obtained at temperatures as low as 400 • C. Note that at these temperatures, dewetting does not occur as easily as at 550 • C and the initial pretreatment with argon is necessary to achieve the composite. This is not surprising since Ni self-diffusion should be slower at lower temperatures. As expected, the lower the temperature, the shorter the CNTs. It appears that the quality of the multi-layer graphene film degrades with decresing temperature as seen from the number of visible defects on the SEM pictures.

VTR acknowledges the support from Nanoscience fundation of Grenoble.

FIG. 1 .

 1 FIG. 1. Result of the CDV growth at 550 • C during 30 minutes. a) Prior to growth, Argon was flown in the chamber during 30 minutes. Then propylene was introduced at 30 Torr. b) Propylene was introduced in the chamber without argon pretreatment. The scale bar is 1 µm in both images.

FIG. 2 .

 2 FIG. 2. Initial stage of the composite growth. The images are taken after growth in the same condition as in Fig. 1a but the growth lasted 5 minutes instead of 30 minutes. a) Top view of the sample showing a bump below which CNT growth has started. b) Side view of such bump illustrating the presence of nanotubes.

FIG. 3

 3 FIG. 3. a) Connexion between nanotubes and multilayergraphene viewed from below by SEM. Ni catalyst can be seen in the NTCs. The scale bar is 300nm. b) HRTEM image of the carbon-nanotube/multilayer-graphene interface. The dotted lines are guides for the eyes to localize the nanotube tip and the multilayer graphene sheet. The scale bar is 5 nm. c) and d) Sketch of the two possible arrangement for the CNT/multilayer-graphene interface. The red particles represent the catalyst. Our result indicate that the interface corresponds to the configuration depicted in d) and that the nanotubes grow from their root.

FIG. 4 .

 4 FIG. 4. Impact of the deposition temperature on the composite growth. a) Growth at 450 • C. b) growth at 400 • C. In both cases a 15 minutes pretreatment with Argon was made prior the growth under 15 Torr of C3H6 during 15 minutes. The scale bar is 600 nm for both images