Tom Dupré

La Tour

Yves Grenier

Alexandre Gramfort

PARAMETRIC ESTIMATION OF SPECTRUM DRIVEN BY AN EXOGENOUS SIGNAL

Keywords: linear auto-regressive models, nonstationary, spectrum estimation, electrophysiology, phaseamplitude coupling

In this paper, we introduce new parametric generative driven auto-regressive (DAR) models. DAR models provide a nonlinear and non-stationary spectral estimation of a signal, conditionally to another exogenous signal. We detail how inference can be done efficiently while guaranteeing model stability. We show how model comparison and hyper-parameter selection can be done using likelihood estimates. We also point out the limits of DAR models when the exogenous signal contains too high frequencies. Finally, we illustrate how DAR models can be applied on neuro-physiologic signals to characterize phase-amplitude coupling.

INTRODUCTION

Auto-regressive (AR) models are stochastic signal models that have proved their usefulness in many applications: from speech and audio applications, econometrics or even physiology and biological signal processing. One of their advantages is the existence of fast inference algorithms [START_REF] Durbin | The fitting of time-series models[END_REF] and to provide a compact representation of the spectral content of a signal (e.g. using 10 to 16 parameters for coding 32 ms of speech).

Standard AR models are so-called stationary, meaning that the statistics of the signal are assumed to be stable over time. When working with such models, the spectrum is therefore not a function of time. In many applications, this modeling assumption is not adapted to describe the interesting dynamics of the physical system observed. This is for example the case in the field of econometrics [START_REF] Tong | Threshold autoregression, limit cycles and cyclical data[END_REF] where time-varying or nonlinear AR models were first studied, but it is also the case for physiological signals as it will be illustrated below with a phenomena known as phase-amplitude coupling [START_REF] Ryan T Canolty | High gamma power is phase-locked to theta oscillations in human neocortex[END_REF]. The type of signal we propose to study here is illustrated in Fig. 1.

Related non-linear auto-regressive models Various nonlinear auto-regressive models have been proposed in audio signal processing or econometrics. Models with a conditional heteroskedasticity (ARCH [START_REF] Robert F Engle | Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation[END_REF], GARCH [START_REF] Bollerslev | Generalized autoregressive conditional heteroskedasticity[END_REF]) are extremely popular in econometrics, since they are able to model a varying amplitude for the entire spectrum. However, these models do not model variations in the spectrum itself. To do so, a simple way is to define several regimes: at each time, the instantaneous model is a simple linear AR model, yet the AR coefficients change depending on a non-linear function of the past of the signal. The first models based on this idea are the SETAR models [START_REF] Tong | Threshold autoregression, limit cycles and cyclical data[END_REF], which estimate several AR models depending on the amplitude of the signal with respect to some thresholds. To get a smoother transition between regimes, SE-TAR models have inspired other models like EXPAR [START_REF] Haggan | Modelling nonlinear random vibrations using an amplitude-dependent autoregressive time series model[END_REF] or STAR [START_REF] Sik | On estimating thresholds in autoregressive models[END_REF], in which the AR coefficients change continuously depending on a non-linear function of the past of the signal. FAR models [START_REF] Chen | Functional-coefficient autoregressive models[END_REF] are even more general, since they estimate the different regimes independently from each other. Motivations behind these models are shared with the DAR models presented in this paper. However, DAR models do not require to infer the driving behavior from the signal itself but rely on the prior knowledge of an exogenous driving signal. As we demonstrate below, this increases both speed and robustness of the estimation, while we guarantee the local stability of the models thanks to a parametrization using parcor coefficients.

DRIVEN AUTO REGRESSIVE MODELS

Models definition

An auto-regressive (AR) model specifies that a signal y depends linearly on its own p past values, where p is the order of the model:

∀t ∈ [p + 1, T ] y(t) + p i=1 a i y(t -i) = ε(t) (1)
where T is the length of the signal and ε is the innovation (or residual), modeled with a Gaussian white noise: ε(t) ∼ N (0, σ(t) 2 ). To extend this AR model to a non-stationary model, one can assume [START_REF] Grenier | Estimating an AR Model with Exogenous Driver[END_REF][START_REF] Grenier | Time-dependent ARMA modeling of nonstationary signals[END_REF] that the AR coefficients a i are driven by a polynomial function of a given exogenous signal x, here called the driver:

∀i ∈ [1, p] a i (t) = m j=0 a ij x(t) j = A i X(t) (2)
where x(t) j is x(t) to the power j. The choice of the polynomial basis is discussed in section 3.2. Note that A i is a vector, as the upper-case letter suggests, composed of the scalars a ij . We note Âi the estimated value, and A i the transpose.

As we parametrize the AR coefficients, the estimated models are not guaranteed to be stable. To examine stability, the standard technique is to consider the parcor coefficients k i , introduced in the fast Levinson-Durbin algorithm [START_REF] Durbin | The fitting of time-series models[END_REF] that estimates the a i coefficients recursively from previous orders: the a i at order p (noted a (p) i in Eq. 3) can be computed from k p and the AR coefficients at previous order a (p-1) i :

a (p) p = k p ; ∀i ∈ [1, p-1], a (p) i = a (p-1) i +k p a (p-1) p-i (3)
From this so-called lattice representation, we can define a second non-stationary parametrization, where the k i (t) are directly driven by the exogenous signal instead of the a i (t):

∀i ∈ [1, p] k i (t) = m j=0 k ij x(t) j = K i X(t) (4)
This parametrization is used as an intermediate step during model estimation. The lattice representation brings a simple condition (necessary and sufficient) for stability:

-1 < k i < 1 [START_REF] Benesty | Springer handbook of speech processing[END_REF]. To enforce this condition, we use the log-area ratios (LAR) γ i [START_REF] Wakita | Direct estimation of the vocal tract shape by inverse filtering of acoustic speech waveforms[END_REF], as suggested in [START_REF] Grenier | Autoregressive models with time-dependent log area ratios[END_REF]:

γ i = log 1 + k i 1 -k i ⇐⇒ k i = e γi -1 e γi + 1 (5)
We use the LAR coefficients γ i (t) in order to force the k i (t) to be within ] -1, 1[, which enforces the stability of the instantaneous model for all t. Our third and final non-stationary parametrization is:

∀i ∈ [1, p] γ i (t) = m j=0 γ ij x(t) j = Γ i X(t) (6)
We call this model a driven auto-regressive (DAR) model.

Model estimation

For DAR models, as the innovation is assumed to be Gaussian white noise, the likelihood L is obtained via:

L = T t=p+1 1 2πσ(t) 2 exp - ε(t) 2 2σ(t) 2 (7)
-2 log(L) = T log(2π) + T t=p+1 ε(t) 2 σ(t) 2 + 2 T t=p+1 log(σ(t))
To estimate a DAR model, we maximize the likelihood iteratively from order 1 to order p, by adding each time a lattice cell. First, we assume that the innovation variance is constant σ(t) 2 = σ 2 and equal to the signal's empirical variance. So for each lattice cell i from i = 1 to p, we estimate the DAR coefficients Γ i ∈ R m+1 by minimizing a least square criterion over the forward residual:

J 1 (Γ i ) = T t=i+1 ε + i (t) 2 σ(t) 2 (8)
where the forward and backward residuals at the output of the i-th lattice cell are given by:

∀i ∈ [1, p] ∀t ∈ [i + 1, T ] ε + i (t) = ε + i-1 (t) + k i (t)ε - i-1 (t -1) ε - i (t) = ε - i-1 (t -1) + k i (t)ε + i-1 (t) (9)
and where we initialize with ε + 0 (t) = ε - 0 (t) = y(t). We minimize J 1 (Γ i ) with a Newton-Raphson procedure since the gradient and Hessian can be computed easily. To start with a good initialization, we approximate the problem with a twostep algorithm. In the first step, we solve the linear problem argmin Ki J 1 (K i ) using ( 4), which leads to a normal equation:

T t=i+1 ε - i-1 (t -1) 2 σ(t) 2 X(t)X(t) Ki = - T t=i+1 1 σ(t) 2 ε + i-1 (t)ε - i-1 (t -1)X(t) (10)
In the second step, we estimate Γ i with a regression over the trajectories γ i (t) and ki (t): After clipping ki (t) = K i X(t) inside [-1 + η, 1 -η] (e.g. η = 10 -6 ), we measure the quality of the approximation with a second least-squares criterion:

J 2 (Γ i ) = T t=i+1 Γ i X(t) -log 1 + ki (t) 1 -ki (t) 2 (11)
Again this leads to a normal equation:

T t=i+1 X(t)X(t) Γi = T t=i+1 X(t) log 1 + ki (t) 1 -ki (t)
These two steps are used to initialize the Newton-Raphson procedure, and largely speed-up the optimization. We iterate this over each lattice cell i: For i > 1, the residuals ε + i (t) and ε - i (t) are obtained with (9) using the ki (t) derived from γi (t) = Γ i X(t) using [START_REF] Bollerslev | Generalized autoregressive conditional heteroskedasticity[END_REF]. We then obtain the DAR coefficients Γ i for every order i from 1 to p.

Innovation variance estimation

During inference of the DAR coefficients Γ i , we first assumed a fixed innovation variance σ 2 . We propose to model it as driven by x, as in [START_REF] Grenier | Estimating an AR Model with Exogenous Driver[END_REF]:

∀t ∈ [p + 1, T ] log(σ(t)) = m j=0 b j x(t) j = B X(t) (12)
The vector B ∈ R m+1 is estimated from the residual ε + p (t) by maximum likelihood also using a Newton-Raphson procedure. We then iterate between the estimation of the Γ i and of B. We observed in our experiments that two iterations are generally sufficient.

Power spectral density

After model estimation, we can compute the conditional power spectral density (PSD) of the model S y : For a given driver's value x 0 , we compute the AR coefficients a i (x 0 ) from the DAR coefficients Γ i using ( 6), ( 5) and ( 3), along with the innovation's standard deviation σ(x 0 ) using ( 12). Since AR models with time-varying coefficients are locally stationary [START_REF] Dahlhaus | On the kullback-leibler information divergence of locally stationary processes[END_REF], we can compute the PSD for this driver's value :

S y (x 0 )(f ) = p i=0 a i (x 0 ) σ(x 0 ) e -j2πf i -2 (13)
where j 2 = -1 and a 0 (x 0 ) = 1. Fig. 2 shows an example of S y (x) for the observed range of value of the driver x (for robustness, we only use the value of x between the 5 and 95 percentiles of all x values) (the signal used in this estimation will be detailed in section 4). Using the fast Fourier transform (FFT) in ( 13), we estimate the PSD for a range of frequency f ∈ [0, f s /2]. As model estimation is also very fast, we obtain the PSD very quickly: With (p, m) = (10, 2), on a signal with T = 10 6 time points, the entire DAR estimation lasts 0.22 s.

MODEL AND HYPER-PARAMETER SELECTION

BIC selection

DAR models are probabilistic, thus we can define a likelihood function L (7), and perform model and hyper-parameter selection by maximizing it. To compare models with different number of hyper-parameters (i.e. degrees of freedom), we can also use the well known bayesian information criterion (BIC) [START_REF] Schwarz | Estimating the dimension of a model[END_REF], which reads BIC = -2 log(L) + d log(T ), where d is the number of degrees of freedom. In DAR models, we have d = (p + 1)(m + 1) . As model estimation is very fast, we can easily estimate on a full grid of hyper-parameters (p and m) and keep the model that leads to the lowest BIC.

Simulations Using simulated data, we tested the hyperparameters selection based on the BIC. We generated a driving signal x for T = 10 4 time points, by re-sampling a Gaussian white noise of length T f x /f s (where f s = 10 3 Hz is the In dashed black, we plot the PSD estimated with a Welch method over the entire signal.

Note that the PSD is mostly flat since the signal was globally whitened as preprocessing, cf. 4 .

sampling frequency). We then drew a set of LAR coefficients γ i (t) using random walks: starting from a real number

γ i (0) ∈] -1, 1[, we computed γ i (t + 1) = γ i (t) + i (t) with i (t) ∼ N (0, 0.3).
Note that the LAR coefficients do not need to be in ] -1, 1[ to make the system stable (cf. section 2.1). However, to avoid having very large coefficients during a long period of time, we rescaled the trajectory to be inside [-4, 4]. To make the LAR coefficients dependent of the driver x, we projected them on the basis

X(t) = [1 x(t) ... x(t) m ] : Γi = argmin Γi∈R m+1 T t=1 (Γ i X(t) -γ i (t)) 2 (14)
With the obtained DAR coefficients Γi , we computed the instantaneous LAR coefficients γi (t), and generated a signal y(t) by feeding the corresponding lattice filters defined by ki (t) with a Gaussian white noise. To focus on the spectral fluctuations, we used a constant innovation gain: σ(t) = σ.

We then fitted several DAR models on y, with p ∈ [1, 20] and m ∈ [0, 3], and compared the BIC of each fit. We tested if the BIC selects the correct hyper-parameters, and we tested it for different driver's frequency f x . The results are presented in Fig. 3 : Interestingly, the BIC selection of m is correct in most cases, provided that the driver's frequency is not too high (f x < 50 Hz). When the driver's frequency is too low, the BIC sometimes overestimate m since the time length is too short to see many driver's oscillations. The BIC selects p correctly in most cases at ±2 (not shown), for all driver's frequencies.

Comparison with STAR models

The choice of a polynomial basis in ( 6) and ( 12) is motivated by simplicity, and practice also shows that a low order (≤ 3) is enough as stated by a BIC selection. For the sake of comparison, we also estimated a multi-regime LSTAR [START_REF] Dick Van Dijk | Smooth transition autoregressive models-a survey of recent developments[END_REF]: We then estimated some DAR models on these signals, and we selected p and m that minimized the BIC. The graphs show the proportion of each m selected. The hyper-parameter m is correctly estimated in most cases if the driver's frequency is not too high (f x < 50 Hz).

∀i ∈ [1, p] a i (t) = m j=0 a ij F j (x(t)) (15)
which is parametrized with sigmoid functions:

∀j ∈ [1, m] F j (x(t)) = (1 + e -γj (x(t)-cj ) ) -1 (16)
and with F 0 (x(t)) = 1. Note that to fairly compare the models, we use the same exogenous signal x to drive the AR coefficients, when standard LSTAR models use the delayed signal y(t -d), which results in a worse fit (cf. Fig 4). We also extended them to model the innovation variance σ(t) 2 with sigmoids:

∀t ∈ [p + 1, T ] log(σ(t)) = m j=0 b j F j (x(t)) (17)
The results are presented in Fig. 4, and show that the polynomial and the sigmoid parametrizations achieve comparable performances. However, the optimization of the LSTAR is much slower, since there is no good initialization procedure for the transitions parameters γ j and c j : the non-linear optimization is slow and may contain several local minima, and the cost of an initial grid-search is large, or even prohibitive as soon as m ≥ 2. On the contrary, the DAR models estimation is very fast and does not suffer from multiple minima. Fig. 5. The driver x is extracted from the raw signal z with a band-pass filter. The remaining high frequencies are whitened to form the modeled signal y.

APPLICATION TO NEUROSCIENCE

In neuroscience, phase-amplitude coupling (PAC) refers to the interaction between the phase of a slow neural oscillation and the amplitude of high frequencies within the same signal or at a distinct brain location. High frequencies driven by slow signal fluctuations have been observed in animal [START_REF] Bragin | Gamma (40-100 hz) oscillation in the hippocampus of the behaving rat[END_REF] and human [START_REF] Ryan T Canolty | High gamma power is phase-locked to theta oscillations in human neocortex[END_REF] studies and have been reported as a key element for neural communication during complex cognitive processes [START_REF] Hyafil | Neural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions[END_REF][START_REF] John | The theta-gamma neural code[END_REF]. Yet, PAC is usually measured through empirical and somehow ad hoc metrics [START_REF] Ryan T Canolty | High gamma power is phase-locked to theta oscillations in human neocortex[END_REF][START_REF] Adriano | Measuring phase-amplitude coupling between neuronal oscillations of different frequencies[END_REF]. To give a proper model to PAC, we applied DAR models on a human electro-corticogram (ECoG) channel from [START_REF] Ryan T Canolty | High gamma power is phase-locked to theta oscillations in human neocortex[END_REF] (730 seconds at 333 Hz).

After decimation and removal of the electrical network noise, we extracted the driver x with a zero-phase bandpass filter (f x = 3.8 Hz, ∆f x = 1 Hz). We removed a wider bandpass signal around it (∆f = 4 Hz) to prevent spurious correlations between x and y, and we filled the gap with white noise filtered with the same filter. We then whitened y with a simple AR model, by applying the inverse AR filter to the signal. This whitening step is not necessary, yet it reduces the need of a high order p in DAR, which reduces both the computational cost and the variance of the model. After an exhaustive grid search with p ∈ [1, 50] and m ∈ [0, 4], the hyper-parameters (p, m) = (10, 2) are selected by BIC, hence revealing the phase-amplitude coupling present in the data.

Fig. 5 shows a portion of the signals, while Fig. 2 shows the PSD (in dB) depending on the driver's value, estimated through a DAR model. PAC can be identified in the difference of S y (x) as the driver x varies: the PSD has more power for negative driver's values than for positive driver's values, and PSD shapes are also different.

CONCLUSION

In this paper, we introduced driven auto-regressive (DAR) models, to provide a parametric spectrum estimation of a signal, conditionally to another exogenous driving signal. The chosen parametrization guarantees local stability, and allows fast inference and easy hyper-parameter selection. We also showed how DAR models can be used to characterize phaseamplitude coupling on neuro-physiologic signals.

Fig. 1 .

 1 Fig. 1. Simulated signal with time-varying spectrum driven by exogenous signal.

Fig. 2 .

 2 Fig. 2. Power spectral density (PSD) evaluated through a DAR model, for different driver's values x.In dashed black, we plot the PSD estimated with a Welch method over the entire signal. Note that the PSD is mostly flat since the signal was globally whitened as preprocessing, cf. 4 .

Fig. 3 .

 3 Fig.3. Selection of hyper-parameter m in simulated data with respect to the driver's frequency. We simulated 100 signals from 100 DAR models with p = 10 and m = 0 (left), m = 1 (middle), m = 2 (right). We then estimated some DAR models on these signals, and we selected p and m that minimized the BIC. The graphs show the proportion of each m selected. The hyper-parameter m is correctly estimated in most cases if the driver's frequency is not too high (f x < 50 Hz).

ACKNOWLEDGMENTS

This work was supported by the ERC Starting Grant SLAB ERC-YStG-676943.


G Migliori

A Siemiginowska

M Sobolewska

A Loh

S Corbel

L Ostorero

L Stawarz

FIRST DETECTION IN GAMMA-RAYS OF A YOUNG RADIO GALAXY: FERMI-LAT OBSERVATIONS OF THE COMPACT SYMMETRIC OBJECT PKS 1718-649

Keywords: galaxies, active -galaxies, individual (PKS 1718-649) -galaxies, jets -radiation mechanisms, non-thermal -gamma-rays, galaxies

We report the γ-ray detection of a young radio galaxy, PKS 1718-649, belonging to the class of Compact Symmetric Objects (CSOs), with the Large Area Telescope (LAT) on board the Fermi satellite. The third Fermi Gamma-ray LAT catalog (3FGL) includes an unassociated γ-ray source, 3FGL J1728.0-6446, located close to PKS 1718-649. Using the latest Pass 8 calibration, we confirm that the best fit 1σ position of the γ-ray source is compatible with the radio location of PKS 1718-649. Cross-matching of the γ-ray source position with the positions of blazar sources from several catalogs yields negative results. Thus, we conclude that PKS 1718-649 is the most likely counterpart to the unassociated LAT source. We obtain a detection test statistics TS∼ 36 (>5σ) with a best fit photon spectral index Γ =2.9±0.3 and a 0.1-100 GeV photon flux density F 0.1-100GeV =(11.5±0.3)×10 -9 ph cm -2 s -1 . We argue that the linear size (∼2 pc), the kinematic age (∼100 years), and the source distance (z = 0.014) make PKS 1718-649 an ideal candidate for γ-ray detection in the framework of the model proposing that the most compact and the youngest CSOs can efficiently produce GeV radiation via inverse-Compton scattering of the ambient photon fields by the radio lobe non-thermal electrons. Thus, our detection of the source in γ-rays establishes young radio galaxies as a distinct class of extragalactic high-energy emitters, and yields a unique insight on the physical conditions in compact radio lobes interacting with the interstellar medium of the host galaxy.

INTRODUCTION

The radio loud active galactic nuclei (RL AGN) constitute nearly 60% of all the γ-ray sources detected in the first four years of all-sky-survey of the Large Area Telescope (LAT) on board the Fermi satellite, and listed in the Third Fermi Gamma-ray LAT catalog (3FGL; Acero et al. 2015). The overwhelming majority (98%) of the RL AGN in the 3FGL have been classified as blazars, i.e. AGN with relativistic jets oriented close to the line of sight of the observer (see the Third LAT AGN Catalog, 3LAC; Ackerman et al. 2015). The remaining 2% of the LAT AGN includes mostly radio galaxies whose jets point away from the observer, the so-called misaligned AGN (MAGN, Abdo et al. 2010a), and a few AGN of other types (see Massaro, Thompson & Ferrara 2016 for a review). About one third of the 3FGL sources is still unidentified (1010 over 3033) and it is among them that we may expect to discover new classes of γ-ray emitters.

In blazars, Doppler boosted γ-ray emission is produced in a compact, relativistically moving jet region close to giulia.migliori@cea.fr 1 Laboratoire AIM (CEA/IRFU -CNRS/INSU -Université Paris Diderot), CEA DSM/SAp, F-91191 Gif-sur-Yvette, France.

2 Harvard-Smithsonian Center for Astrophysics, 60 Garden St., Cambridge, MA 02138, USA.

3 Nicolaus Copernicus Astronomical Center, Bartycka 18, 00-716 Warsaw, Poland.

4 Station de Radioastronomie de Nançay, Observatoire de Paris, CNRS/INSU, Univ. Orléans, F-18330 Nançay, France.

5 Dipartimento di Fisica, Università degli Studi di Torino and Istituto Nazionale di Fisica Nucleare (INFN), Via P. Giuria 1, 10125 Torino, Italy.

6 Astronomical Observatory, Jagiellonian University, ul. Orla 171, 30-244 Kraków, Poland.

the AGN. The origin of the emission detected by Fermi-LAT in MAGN is less clear (Abdo et al. 2010a, Kataoka et al. 2011), and models of stratified jets or extended emitting regions have been considered. So far, given the Point Spread Function (PSF) of Fermi-LAT, Centaurus A remains the only radio galaxy in the 3FGL associated with a γ-ray component that clearly extends beyond the central nuclear region and contributes more than ∼50% of the total γ-ray flux (Abdo et al. 2010b). This extended γ-ray emission spatially coincides with the giant radio lobes of Centaurus A and it is consistent with inverse-Comptonization (IC) of the relic cosmic microwave background (CMB) radiation. In Centaurus A, the detection and imaging of the diffuse γ-ray emission, which is isotropic and not boosted by relativistic effects, is likely possible because of the source physical extension (∼ 600 kpc) and proximity (distance of ∼ 3.6 Mpc).

Compact radio sources with radio structures fully contained within the central regions of their host galaxies (< 1 kpc) are thought to be the progenitors of the large-scale radio galaxies (lobes' linear sizes of 10s-100s kpc). The initial stage of the radio source growth is represented by Compact Symmetric Objects (CSOs) with sub-kpc-scale structures, symmetric radio morphologies, total radio emission dominated by the mini-lobes, and kinematical ages smaller than a few thousand years (review by [START_REF] Orienti | [END_REF]. CSOs belong to the spectral class of gigahertz-peaked spectrum (GPS) radio galaxies, characterized by a stable, convex radio spectrum peaking at GHz frequencies (O'Dea 1998 for a review). Some theoretical models predict that CSOs should be relatively strong γ-ray emitters as their compact radio lobes contain copious amount of highly relativistic par-ticles and are embedded in an environment rich in lowenergy photons (see Stawarz at al. 2008, Ostorero et al. 2010). During the initial phase of expansion, the lobes could be the sites of production of bremsstrahlung γray emission potentially detectable by Fermi-LAT (Kino et al. 2009). The radio lobes are expanding at subrelativistic velocities, therefore the γ-ray emission is expected to be isotropic and steady on short timescales (up to months/years). However, detections of the γ-ray emission from young radio sources have been so far elusive even with Fermi (Migliori et al. 2014[START_REF] D'ammando | on behalf of the Fermi Large Area Telescope Collaboration[END_REF].

In this paper, we report for the first time the detection with Fermi-LAT of γ-ray emission from the nearby (z = 0.014) GPS radio galaxy PKS 1718-649 (Tingay et al. 2015 and references therein). The double-lobed, compact (∼2 pc) radio morphology of PKS 1718-649 and its estimated age (t age ∼ 100 years, Giroletti & Polatidis 2009) make this source one of the youngest in the class of CSOs. Throughout the paper we assume a ΛCDM cosmology with H 0 = 70 km s -1 Mpc -1 , Ω M =0.3 and Ω Λ =0.7.

OBSERVATIONS

PKS 1718-649 belongs to the CSO sample selected for a γ-ray study with Fermi-LAT (Migliori 2016). This initial analysis of five year accumulation of the Fermi-LAT data resulted in a marginal detection (≈4σ) of a faint γ-ray source at the radio position of PKS 1718-649, F 0.1-100GeV =(1.5±0.7)×10 -8 ph cm -2 s -1 . The 3FGL contains an unassociated γ-ray source (3FGL J1728.0-6446) detected at 4.4σ level with a slightly different location, for which PKS 1718-649 lies just outside the 95% uncertainty radius (Figure 1, left panel, Table 2). We thus performed a new analysis to assess the significance, flux, and location of the γ-ray source, exploiting the improved data quality and statistics of the new Pass 8 data release.

We analyzed seven years of the Fermi-LAT data (observations from August 8, 2008to August 8, 2015). We selected events of the Pass 8 (P8R2) Source class (Atwood et al. 2013) and used the adequate instrument response functions for the analysis (P8R2 V6) and the Science Tools software package version v10r0p5. The standard event selections were applied: we used FRONT and BACK events and applied a zenith angle cut at 90 • to eliminate Earth limb events. We made use of both the binned and unbinned analyses. Given the long computational times due to the large dataset, we limited the unbinned analysis to consistency checks and, most importantly, to secure the source localization.

We first considered the 0.2-100 GeV band to minimize the systematic errors and study the background contamination (Ackermann et al. 2012). Next, we increased the photon statistics by lowering the minimum energy threshold to 0.1 GeV. In the latter case, we excluded the data with the lowest quality of the reconstructed direction7 (evtype=56) and applied a zenith angle cut at 80 • , to minimize the contribution of the background γ-rays from the Earth's limb. We selected a circular region of interest (RoI) of 15 • radius, centered on the radio posi-tion of PKS 1718-649.

The Fermi source model that we adopted to calculate the binned likelihood includes all the 3FGL point-like and diffuse sources within the RoI. Additionally, we included the 3FGL sources falling between 15 • and 25 • . In fact, due to the energy-dependent size of the Fermi-LAT PSF, these sources can contribute to the total counts observed within the RoI. The 3FGL only accounts for sources detected within the first four years of observations. Therefore, we carefully applied this initial model to the seven-year dataset and, whenever necessary, we improved it by, e.g., adding sources that were detected in the following three years. In order to assess the fit quality, we inspected the residual maps obtained by comparing the Fermi-LAT count map with a count map created from the best-fit model. For example, we added a variable radio source, PKS 1824-582, located ∼10.3 • from the RoI center (flaring in April 2014, ATel #6067, ATel #6076). The photon spectral indices, Γ, of the faintest sources within 7 • from the RoI center were fixed to the values reported in the 3FGL. For sources located 7 • away from the RoI center, all the spectral parameters were initially fixed to the respective 3FGL values. We opportunely modified these values, whenever the inspection of the residuals revealed a significant variation of a source (e.g., 3FGL J1703.6-6211, ATel #7330).

The emission model also accounts for the Galactic and extragalactic (and instrumental) diffuse backgrounds. We used the 'mapcube' file gll iem v06.fits and the iso P8R2 V6 v06.txt table to describe the emission from the Milky Way and the isotropic component, respectively.

To test the hypothesis that the γ-ray source reported in the 3FGL is associated with PKS 1718-649, in the model we substituted 3FGL J1728.0-6446 with a source located at the radio position of PKS 1718-649, assuming a power-law spectrum model (F = KE -Γ ). We performed a fit of the data using the binned maximum likelihood (gtlike). The temporal behavior of the source was investigated with a lightcurve with one-year time resolution, and in an incremental way, i.e. by progressively summing the years of observation. Quoted errors are at 1σ statistical significance, if not otherwise specified.

RESULTS

The results of the binned likelihood analysis performed on the seven-year dataset for the two low-energy cutoffs are shown in Table 1. The binned analysis of the 0.2-100 GeV dataset yielded γ-ray emission detected at the PKS 1718-649 position with a test significance 8 TS=18.5 (σ ∼4.3), which improves over the TS=14 of our previous five-years-data analysis (Migliori 2016). We obtained an integrated source photon flux density above 0.2 GeV F 0.2-100GeV =(2.1±0.8)×10 -9 ph cm -2 s -1 and a best fit photon spectral index Γ =2.6±0.3.

The results improved when the analysis was extended to the 0.1-100 GeV band. We obtained a TS∼36 detection ( 5σ) at the location of PKS 1718-649. The best fit photon spectral index, Γ =2.9±0.3, is steeper than that measured at ≥ 0.2 GeV, but the two values are consistent within the uncertainties. As a check, we restricted the analysis to the two best quality quartiles of the data in the PSF type partition (event types PSF2 and PSF3), i.e. the data with the best quality of the reconstructed direction of the photons. We used the Python package SummedLikelihood to combine the likelihoods of the separately analyzed (PSF2 and PSF3 type) datasets. Despite the significant cut in event number, we detected a source at the position of PKS 1718-649 with a TS∼24 and spectral parameters consistent within the uncertainties with the 0.1-100 GeV best fit values.

The source is not detected at a significance level exceeding TS=10 on yearly timescales. The incremental time analysis shows that the source begins to be significantly detected over 4 years. The TS value progressively increases over 5, 6 and 7 years, whereas the measured flux does not vary significantly (Table 1).

Gamma-ray source Association

In order to optimize the localization of the γ-ray source, we ran the gtfindsrc tool, which calculates the best TS for different positions given an initial guess (in this case PKS 1718-649 coordinates) until the convergence tolerance for a positional fit is reached. The best fit position and the corresponding 68% and 95% error radii are reported in Table 2. PKS 1718-649 lies within the 68% confidence radius, at a distance of 0.13 • from the best fit γ-ray position (Figure 1, right panel). We evaluated the significance of a γ-ray source located at the position of 3FGL J1728.0-6446 using the 0.1-100 GeV and the 0.2-100 GeV datasets, and we obtained lower TS values (< 4σ) than for PKS 1718-649. Finally, we fit to the data a model with two distinct emitters located at the positions of PKS 1718-649 and 3FGL J1728.0-6446: the γ-ray emission in excess over the background is attributed to the former, whereas the latter has TS∼0.

We searched for other counterparts to the detected γray source in catalogs and surveys of blazars and extragalactic radio sources, including the Roma BZCAT (Massaro et al. 2009), a catalog of γ-ray candidates among WISE sources (D'Abrusco et al. 2014), the Combined Radio All-Sky Targeted Eight GHz Survey (CRATES, Healey et al. 2009), the Parkes Catalog (Wright & Otrupcek 1990), and the Parkes-MIT-NRAO survey (PMN, Gregory et al. 1994), and found no match within the gtfindsrc 95% error circle. At low radio frequencies (843 MHz), a query to the Sydney University Molonglo Sky Survey (SUMSS, Mauch et al. 2003) returned 15 radio sources within the error circle. However, PKS 1718-649 is: 1) the brightest source at 843 MHz, with a flux density F 843MHz = (3.7±0.1) Jy; 2) the only source reported in the high radio frequency catalogs, with a detection at 5.0 and 8.4 GHz, and a counterpart in the Two Micron All Sky Survey (2MASS, Skrutskie et al. 2006). For the remaining 14 SUMSS sources, the flux faintness (only three of them have 843 MHz fluxes in the 100-200 mJy range), non-detection at the higher frequencies, and lack of a 2MASS counterpart strongly disfavor the identification with an extragalactic, flat spectrum radio source. We concluded that there are no evident blazar candidates in the 95% error circle of the γ-ray source.

DISCUSSION AND CONCLUSIONS

Our analysis of the seven-year Fermi-LAT data resulted in a >5σ detection of the γ-ray source 3FGL J1728.0-6446, which had no association in the 3FGL catalog. Based on our revised best fit γ-ray position and the analysis of nearby sources, we concluded that this source is most likely the γ-ray counterpart to PKS 1718-649, a young radio galaxy classified as CSO. This represents the first significant (>5σ) γ-ray detection of a bona fide CSO, and it may provide us with important insights on the nature of the high-energy emission observed in young radio sources.

We compared the γ-ray properties of PKS 1718-649 with those of blazars and MAGN from the clean 3LAC sample. We considered only blazars with known redshift, and classified as either Flat Spectrum Radio Quasars (FSRQs) or BL Lac objects. The MAGN sample includes steep spectrum radio quasars (SSRQs), Fanaroff-Riley (FR) type I and type II radio galaxies [START_REF] Fanaroff | [END_REF] detected by Fermi-LAT9 , and Tol1326-379, the only FR 0 radio galaxy10 associated with a γ-ray source (Grandi et al. 2015). Figure 2 shows the position occupied by PKS 1718-649 in the radio (1.4 GHz rest frame) vs. γ-ray (integrated above 1 GeV) luminosity plot (left panel). The 1.4 GHz luminosity of PKS 1718-649 was calculated from the ATCA flux, F 1.4GHz =3.98 Jy (Maccagni et al. 2014). The source is located in the low radio and γ-ray luminosity region occupied by MAGN.

The position of PKS 1718-649 in the Γ vs. L γ plot also supports a non-blazar origin of its γ-ray emission: the source occupies the upper left corner, well separated from the blazar sources (Figure 2, right panel). It is interesting to note that its γ-ray luminosity and photon index are similar to those characterizing the giant lobes of Centaurus A (labelled in Figure 2). Stawarz at al. (2008) proposed a dynamical-radiative model of young radio sources, where the γ-ray emission is produced via inverse-Comptonization of circumnuclear (IR-to-UV) photon fields off relativistic electrons in compact, expanding lobes. The level of the nonthermal high-energy emission predicted by the model depends on several source parameters, such as the kinetic jet power, the accretion disk luminosity, and the lobes' compactness: sources with linear sizes <100 pc and jet powers of 10 46 erg s -1 could reach γ-ray luminosities of the order of 10 44 erg s -1 or larger (see also [START_REF] Orienti | Fermi Symposium proceedings -eConf C110509[END_REF][START_REF] D'ammando | on behalf of the Fermi Large Area Telescope Collaboration[END_REF]. The CSO PKS 1718-649 represents an ideal candidate to test this model, due to its linear size of only ∼ 2 pc and proximity (luminosity distance of 60.4 Mpc). The symmetric radio morphology of PKS 1718-649 suggests that we are observing the source at a large inclination angle, ruling out a γ-ray contribution of the boosted jet emission. The steady flux detected with Fermi-LAT is consistent with the γ-ray emission being isotropic and produced in the radio lobes expanding at a sub-relativistic velocity (Giroletti & Polatidis 2009 report a hot spot expansion velocity of ∼0.07c).

A detection in γ-rays may help us establishing the nature of the X-ray emission of CSOs [START_REF] Tengstrand | [END_REF], Siemiginowska et al. 2016). At the angular resolution of the current X-ray observatories, this X-ray emission is usually spatially unresolved and could be a superposition of several distinct components, including that of the disk-corona system and IC emission of the infrared photons of the putative torus off the lobes' electrons (Ostorero et al. 2010). However, a proper test of the above models requires a comparison of the predicted broad-band spectral energy distribution with the available multiwavelength data (see Ostorero et al. 2010, Migliori et al. 2012). This is beyond the scope of this Letter and will be discussed in a forthcoming paper.

Is PKS 1718-649 an isolated case or could we expect an increase of the detections of CSOs in γ-rays? So far, γ-ray searches of young radio sources have not provided other significant detections (Migliori et al. 2014[START_REF] D'ammando | on behalf of the Fermi Large Area Telescope Collaboration[END_REF]). The 3FGL contains three γray sources tentatively identified as Compact Steep Spectrum (CSS) radio sources (i.e. sources which likely are more evolved than CSOs): 3C 84 (Kataoka et al. 2010;Dutson et al. 2014), 3C 286, and 4C +39.23B (whose γ-ray association is however doubtful). These are recurrent or restarted radio sources with complex radio morphologies and multiple pairs of lobes on various linear scales. A CSO classification has been proposed for three other γ-ray sources: 4C+55.17 (z=0.896, McConville et al. 2011), PMN J1603-4904 (z=0.18, Müller et al. 2014[START_REF] Ojha | The Astronomer's Telegram[END_REF], PKS 1413+135 (z=0.247, Gugliucci et al. 2005. They show the evidence of a CSO-like morphology of the inner radio structures and the absence of the γ-ray and radio variability typically observed in blazars. Differently from PKS 1718-649, all these sources are located at relatively high redshifts, and display high γ-rays fluxes with hard spectra. If their γ-ray emission is produced in the lobes and is thus isotropic, the mechanism producing it must be extremely efficient.

Other 'γ-ray emitting CSOs' might be hiding among the large number of unidentified 3FGL γ-ray sources. If this were the case, it would be crucial to define an efficient strategy to unveil them. The example of PKS 1718-649 suggests that a detection in γ-rays is the most feasible for the most compact and nearby CSOs. On the other hand, no detection was reported for another very compact and nearby (z=0.076) CSO, OQ 208 [START_REF] Orienti | Fermi Symposium proceedings -eConf C110509[END_REF][START_REF] D'ammando | on behalf of the Fermi Large Area Telescope Collaboration[END_REF], implying that additional key-parameters may play a role. However, given its redshift, OQ 208 would appear a factor of ∼30 fainter than PKS 1718-649, if its intrinsic γ-ray luminosity were the same as of PKS 1718-649.

We are grateful to the anonymous referee for comments which helped to improve the paper. The authors thank P. Grandi for sharing the data of Columns: 1 -Energy band selected for the analysis in GeV; 2 -Observing time in Modified Julian Day; 3 -Test statistic value; 4 -Gamma-ray photon spectral index, (f) indicates fixed Γ; 5 -Fermi-LAT photon flux in the selected energy band in units of ×10 -9 ph cm -2 s -1 ; 6 -Logarithm of the flux density at 1 GeV in erg cm -2 s -1 . Notes: a & b -semi-major and semi-minor axes in degrees of the ellipse uncertainty region at 68% and 95% confidence level respectively, followed by the position angle of the 95% confidence region (reported in the 3FGL); c & d -uncertainty radius of the best fit gtfindsrc position at 68% and 95% confidence level in degrees; e -uncertainty on the radio position in arcseconds (Johnston et al. 1995).

  Figure 2, and J. Ballet, F. D'Ammando and F. Massaro for useful suggestions. G. M. and S. C. acknowledge the financial support from the UnivEarthS Labex program of Sorbonne Paris Cité (ANR10LABX0023 and ANR11IDEX000502). L. S. was supported by Polish NSC grant DEC-2012/04/A/ST9/00083. L.O. acknowledges the grants: INFN InDark, MIUR PRIN2012 "Fisica Astroparticellare Teorica", and "Origin and Detection of Galactic and Extragalactic Cosmic Rays" from UniTo and Compagnia di San Paolo. This research is funded in part by NASA contract NAS8-39073. Partial support was provided by the Chandra grants GO4-15099X and GO0-11133X.

Fig. 1 .

 1 Fig. 1.-Left: Fermi-LAT 0.1-100 GeV count map of the sky centered on the radio position of PKS 1718-649 (yellow cross). The pixel size is 0.2 degrees/pixel. The magenta ellipse gives the uncertainty position (95% confidence level) of 3FGL J1728.0-6446 reported in the 3FGL. Field γ-ray sources in the 3FGL are indicated with green circles. Right: Zoom on the PKS 1718-649 region at >200 MeV energies. The pixel size is 0.1 degrees/pixel. The cyan circles correspond to the gtfindsrc best fit position (68% and 95% confidence levels).

  Fig. 2.-Left: Radio luminosity of the 3LAC FR I (solid, green triangles) and FR II (solid, magenta squares) radio galaxies, BL Lacs (open, open triangles), FSRQs (open, black squares), and SSRQs (cyan, solid triangles) plotted as a function of the γ-ray luminosity between 1 GeV and 100 GeV. We included Tol1326-379 (solid, yellow diamond), classified as FR 0 (Grandi et al. 2015), and the γ-ray detected lobes of Centaurus A (the south lobe with measured γ-ray and 1.4 GHz fluxes, see Abdo et al. 2010b and Hardcastle et al. 2009, respectively). The position of PKS 1718-649 is indicated by the solid, red circle. Right: γ-ray spectral index versus 1-100 GeV luminosity. PKS 1718-649 is located in the MAGN region of the diagram, with values of Γ and Lγ similar to those of Centaurus A. (Sample data: courtesy of P. Grandi.)

http://fermi.gsfc.nasa.gov/ssc/data/analysis/ documentation/Cicerone/Cicerone_Data/LAT_DP.html.

The test statistic is the logarithmic ratio of the likelihood of a source being at a given position in a grid to the likelihood of the model without the source, TS=2log(likelihoodsrc/likelihood null )(Mattox et al. 1996).

For completeness we added 3C 111(Grandi et al. 2012), and Cen B(Katsuta et al. 2013), excluded from the clean 3LAC sample because of their low Galactic latitudes, and 3C 120[START_REF] Tanaka | The Astronomer's Telegram[END_REF], Casadio et al. 2015).

FR 0 have radio linear sizes LS 10 kpc, radio core powers similar to those of FR Is, and a strong deficit of the corresponding extended radio emission(Baldi, Capetti & Giovannini 2015).


Steven M Goodman

Yasser Anbdou

Yannie Andriamiarantsoa

Brian L Fisher

Owen Griffiths

Brad Keitt

Jean-Jacques Jacques Rafanomezantsoa

Eric T Rajoelison

Jean-Claude Claude Rakotonirina

Luc Ranaivoarisoa

Eugenia Aff Calciscopulasum

Patrick Ranirison

Voahangy Soarimalala

Michaël L Tantely

Pablo Tortosa

Achille P Raselimanana

Results of a biological inventory of the Nosy Ankao island group, Parc National de Loky-Manambato, northeastern Madagascar

  L'archive ouverte pluridisciplinaire

 et al. et al. et al., et al., et al. et al., et al. et al., et al. et al. et al., 2006et al.

Methods

Context of the 2016 biological inventory and study sites

Study sites

Villages on Nosy Ankao Islands (geographical coordinates based on centroid positions)

Weather data

Surface area sampling

General botanical collections

Data analysis

Floristic richness

Geographic range of plant species Plant life-forms

Inventory methods

Non-selective method

Selective method

Sample processing and data analysis

), which has up-to-date summaries of what is known about the taxonomy of Malagasy ants,

Inventory methods

Capture

Inventory techniques et al et al

Direct observations

Live traps

Specimens

Results

Weather data

Vascular plants

General aspects

Global vegetational characteristics

Floristic diversity

Life-forms spectrum of the vegetation

Faunistic similarities between sites

Abundance of introduced species

Plagiolepis alluaudi and Pheidole megacephala Tetramorium delagoense

Comparison of the ant fauna of the Nosy Ankao island group to the mainland sites of Daraina and Analamera

Malagidris Tanipone

Larval breeding sites

Anopheles arabiensis

Laliostoma labrosum Zonosaurus madagascariensis

Snails

Tropidophora thesauri Edentulina minor E. arenicola Gulella microdon

Ants

Ant diversity on islands in the Nosy Ankao group

Comparing the ant diversity on islands in the Nosy Ankao group to that of Daraina and Analamera

Reptiles and amphibians

Faunistic and biogeographic

Questions of biosecurity

  Figure 1.

  Figure 4.

  Figure 5.

  Figure 6.

  Figure 8.

  Figure 9.

  Figure 10.

  Figure 11.

  Figure 12.

  Figure 14.

Figure 20 .

 20 Figure 20.Capurodendron greveanum and Eugenia aff. calciscopulasum

Figure 24 .

 24 Figure 24.Mystroxylon aethiopicum and Diospyros erythrosperma

  Figure 27

Figure 29 .

 29 Figure 29.Mimusops coriacea and Ficus tiliifolia

  Figure 30.

  Figure 39.

  Figure 40. Sterna fuscata

Table 1 .

 1

	Name	Study group	Home institution
	Yasser Anbdou
	Ants

Table 2 .

 2

		Nosy Ankao	Nosy Manampao	Nosy Ratsy	Nosiborona
						7.1
		Yes		Some		Yes	Some
		Yes		1		2
		Yes		Yes
		Yes		Yes		Yes	Yes
	1
	2
	Table 3.
	Taxonomic group	Nosy Ankao	Nosy Manampao	Nosy Ratsy	Nosiborona
	Ants
	Birds

Table 4 . Floristic group Group Capurodendron greveanum and Eugenia aff. calciscopulasum Group Mystroxylon aethiopicum and Diospyros erythrosperma Group Mimusops coriacea and Ficus tiliifolia

 4

	3	2
	Tr 10, Tr 11, Tr 12

Table 5 .

 5

	Island	Elevation	Latitude south Longitude east	Period	Habitat type	Distance to nearest point on
		20 m
		22 m
					forest

Possible Plasmodium infection of mosquitoes

				Plasmodium
			et al
			P. falciparum and P. vivax, both
	Reptiles	and	amphibians	(Achille	P.
	of preimaginal stages		et al
					Handling of mosquitoes
				et al.
					et al
					Aedes	Skusea,
	Capture of adult stages			Anopheles gambiae
					et al
					et al.

Table 6 .

 6

		Minimum	Maximum
	Date	temperature	temperature
			33	1
			32
			33	1
			33	0
		23	33	0
				0
			33	0
			31
			32	0
			33

Table 7 Family

 7

		Genus and species	Nosy Ankao	Nosy Ratsy	Nosy Manampao	Nosiborona
	Acanthaceae	Hypoestes strigata Benoist
	Amaranthaceae Lagrezia ambrensis
	Anacardiaceae
		Poupartia gummifera
	Annonaceae	Monanthotaxis pilosa
	Apocynaceae	Cynanchum arenarium
		Landolphia gummifera
		Gonocrypta s
		Pervillea
		Secamone aff.
		Secamone capitata
	Bignoniaceae	Rhodocolea racemosa
	Boraginaceae	Cordia myxa L.
	Capparaceae	Crataeva excels
	Celastraceae	Brexia aff. humbertii
		Brexia madagascariensis
		Loeseneriella rubiginosa
		Maytenus undata
		Mystroxylon aethiopicum
		Loes.
	Combretaceae Combretum coccineum
	Commelinaceae Commelina aff. madagascarica
	Convolvulaceae Ipomoea rubens
	Cyperaceae	Cyperus betafensis
		Cyperus longifolius
	Asparagaceae

lanceolata Figure 13. Family Genus and species Nosy Ankao Nosy Ratsy Nosy Manampao Nosiborona

	Table 7
	Ebenaceae	Diospyros erythrosperma Diospyros aff. ferrea	Structural groups
		Diospyros pervilleana
		Diospyros platycalyx
		Diospyros pruinosa
		Diospyros quercina
	Floristic groups	Diospyros squamosa
	Erythroxylaceae Erythroxylum retusum
		Erythroxylum	sphaeranthus
		Erythroxylum platyclados
		Erythroxylum rignyanum
	Euphorbiaceae Euphorbia tetraptera
		Margaritaria anomala
		Thecacoris aff. perrieri Leandri
	Fabaceae	Albizia arenicola	.
		Dichrostachys richardiana
		Senna occidentalis
	Loganiaceae	Strychnos
	Lythraceae	Capuronia benoistii
	Malvaceae	Grewia aff. barorum
		Grewia betulaefolia
		Hibiscus megistanthus
	Meliaceae	Malleastrum boivinianum
	Moraceae	Ficus lutea
		Ficus tiliifolia
	Myrtaceae	Eugenia aff. calciscopulorum
		Eugenia aff. oligantha
	Ochnaceae	Ochna ciliata Lam.
	Oleaceae	Jasminum greveanum
		Noronhia seyrigii
	Orchidaceae	Angraecum aff. magdalenae
	Primulaceae	Oncostemum
		Oncostemum
	Rhamnaceae	Scutia myrtina (
	Rhizophoraceae Cassipourea microphylla
	Rubiaceae	Coffea boiviniana
		Coptosperma
		Empogona ovalifolia
		Ixora aff. ripicola
		Ixora emirnensis
		Pyrostria sambavensis
		Tarenna grevei
		Tricalysia ambrensis
	Rutaceae	Vepris aff. arenicola
	Salicaceae	Casearia nigrescens
	Sapindaceae	Allophylus cobbe
		Macphersonia gracilis
		hildebrandtii
	Sapotaceae	Capurodendron greveanum
		Mimusops coriacea
		Sideroxylon aff. saxorum
	Vitaceae	Cissus microdonta

Table 8 . Group Family Taxa Indicator Group designation Island 1 Sapotaceae Capurodendron

 8

greveanum Capurodendron greveanum and Eugenia aff. calciscopulasum Myrtaceae Eugenia aff. calciscopulasum 2 Celastraceae Mystroxylon aethiopicum 100.0 Mystroxylon aethiopicum and Diospyros erythrosperma Ebenaceae Diospyros erythrosperma 3 Sapotaceae Mimusops coriacea Mimusops coriacea and Ficus tiliifolia Moraceae Ficus tiliifolia

Table 9 . Transects Floristic group Structural group Vegetation group

 9

	1	A

Table 10 . Group Name of vegetational group Structural form Island

 10

	Figure 17.
			Macphersonia gracilis	hildebrandtii
	Dracaena	lanceolata	Maytenus undata
	Strychnos		Vepris aff. arenicola	Eugenia aff.
	Diospyros pervilleana

Capurodendron greveanum and Eugenia aff. calciscopulasum Mystroxylon aethiopicum and Diospyros erythrosperma Mimusops coriacea and Ficus tiliifolia Figure 15. Figure 16. Capurodendron greveanum and Eugenia aff. calciscopulasum Capurodendron greveanum. forest. calciscopulasum Figure 18. Figure 19. Capurodendron greveanum Eugenia aff. calciscopulasum. Structural variant 2: Low-growing forest with open canopy --Vepris aff. arenicola and Maytenus undata. Capurodendron greveanum and Diospyros erythrosperma Group and (Group II) Floristic characteristics of Group II h

Table 11 .

 11

		Status	Dry forest Nosy Ankao	Nosy Manampao	Nosy Ratsy	Nosiborona	Dune forest Nosy Ankao
			w	G	w	G	w	G	W	G	W	G
	Subfamily Amblyoponinae
	Mystrium oberthueri	E
	Xymmer
	Subfamily Dolichoderinae
	Tapinoma	E
	Subfamily Dorylinae
	Lioponera
	Tanipone subpilosa
	Tanipone
	Subfamily Formicinae
	Camponotus alamaina
	Camponotus	E
	Camponotus grandidieri	E
	Camponotus hova
	Camponotus repens
	Nylanderia madagascariensis	E
	Paratrechina longicornis
	Plagiolepis
	Plagiolepis	E
	Plagiolepis alluaudi
	Plagiolepis madecassa
	Tapinolepis
	Subfamily Myrmicinae
	Carebara	E
	Carebara grandidieri	E
	Carebara
	Crematogaster hafahafa	E
	Crematogaster maina	E
	Crematogaster tricolor	E
	Crematogaster tavaratra
	Crematogaster rasoherinae
	Malagidris jugum
	Monomorium termitobium	E
		E
	Monomorium platynode	E
	Monomorium micrommaton	E
	Monomorium lepidium	E
	Monomorium hanneli	E
	Nesomyrmex gracilis	E
	Pheidole
	Pheidole	E
	Pheidole megacephala
	Strumigenys inatos	E
	Strumigenys fanano	E
	Strumigenys sphera	E
	Strumigenys olsoni	E
	Strumigenys maxillaris
	Strumigenys emmae
	Terataner	E
	Terataner alluaudi
	Tetramorium ryanphelanae	E
	Tetramorium sericeiventre
	Tetramorium quasirum	E
	Tetramorium delagoense
	Tetramorium trafo	E
	Tetramorium scytalum
	Tetramorium
	Subfamily Ponerinae
	Anochetus madagascariensis
	Anochetus grandidieri

Table 12 .

 12

		Dry forest Nosy Ankao	Nosy Manampao	Nosy Ratsy	Dune forest Nosy Ankao	Nosiborona
	Dry forest Nosy Ankao
	Nosy Manampao	0.10
	Nosy Ratsy	0.13	0.21
	Dune forest Nosy Ankao	0.20	0.07	0.12
	Nosiborona

Table 13 .

 13

	Taxa	Dry forest Nosy Ankao	Nosy Manampao	Nosy Ratsy	Dune forest Nosy Ankao	Nosiborona
	Subfamily Formicinae
	Paratrechina longicornis
	Pheidole megacephala		1.00		0.20	0.20
	Plagiolepis alluaudi				0.20
	Subfamily Myrmicinae
	Tetramorium delagoense			0.32		1.00
	Tetramorium sericeiventre					0.20
	Tetramorium scytalum			0.32
	Strumigenys emmae
	Strumigenys maxillaris	0.20
	Subfamily Ponerinae
	Leptogenys maxillosa

Table 14 .

 14

			Nosy Ankao island group	Analabe	Anala
		Status	A	M	R	B
	Subfamily Amblyoponinae
	Mystrium oberthueri	E
	Xymmer
	Subfamily Dolichoderinae
	Tapinoma	E
	Subfamily Dorylinae
	Lioponera
	Tanipone subpilosa
	Tanipone
	Subfamily Formicinae
	Camponotus alamaina
	Camponotus	E
	Camponotus grandidieri	E
	Camponotus hova
	Camponotus repens
	Nylanderia madagascariensis	E
	Paratrechina longicornis
	Plagiolepis
	Plagiolepis	E
	Plagiolepis alluaudi
	Plagiolepis madecassa
	Tapinolepis
	Subfamily Myrmicinae
	Carebara	E
	Carebara grandidieri	E
	Carebara
	Crematogaster hafahafa	E
	Crematogaster maina	E
	Crematogaster tricolor	E
	Crematogaster tavaratra

Table 14 .

 14

Table 15 .

 15

	et al

Reptiles and amphibians Figure 34.

Table 17 .

 17

							Island
	Taxa	IUCN status	Habitat and biotope	Mode of displacement	Relative abundance	Nosy Ankao	Nosy Manampao	Nosy Ratsy	Nosiborona 6 ha
						365 ha	29 ha	6 ha
	AMPHIBIANS
	Mantellidae
	Laliostoma labrosum		Forest and		SA
	Total amphibian: 1								1
	REPTILES
	Gekkonidae
	Ebenavia inunguis		Forest
	Geckolepis maculata		Forest
	Hemidactylus frenatus
	Paroedura lohatsara		Forest
			Forest
	Phelsuma abbotti				SA
			forest
	Phelsuma aff.		Forest		SA
	dorsivittata
	Uroplatus ebenaui		Forest
	Gerrhosauridae
	Zonosaurus		Forest and		A
	madagascariensis
	Scincidae
	Amphiglossus		Forest
	Cryptoblepharus				A
	boutonii
	Madascincus aff.		Forest		A
	miafana
	Paracontias aff.		Forest and
	hildebrandti
	Trachylepis elegans
	Trachylepis gravenhorstii		Forest and		SA
	Chamaeleonidae
	Brookesia aff. tristis
	Furcifer petteri		Forest
	Furcifer oustaleti		Forest and		SA
	Boidae
	Acrantophis		Forest and		SA
	madagascariensis
	Lamprophiidae
	Liophidium aff.		Forest and		SA
	torquatum
	Lycodryas aff. inopinae		Forest
	Madagascarophis		Forest, forest		SA
	colubrinus
	Mimophis mahfalensis		Forest, forest		A
	Typhlopidae
	Madatyphlops		Forest		SA
	microcephalus
	Xenotyphlops aff.		Forest and
	grandidieri
	Total reptiles: 25					18	11	4	1
	Total amphibians and					18	11	4	2
	reptiles: 26

Figure 35. Figure 36.

	Figure 38.		Xenotyphlops aff.
	grandidieri
			Paracontias aff.
					hildebrandti, Cryptoblepharus boutonii, Brookesia aff.
					tristis, and Xenotyphlops aff. grandidieri
						Madatyphlops microcephalus,
					Amphiglossus	Madascincus aff. miafana
	Table 18.
	Characteristics	Pitfall line 1	Pitfall line 2	Pitfall Line 3	Total
	Habitat type
	Ground cover
	Taxa
	Amphiglossus					2	2
	Madascincus aff. miafana			7		21
	Trachylepis elegans			2	1	3	6
	Madatyphlops microcephalus		1			1
	Liophidium aff. torquatum		1	1		2
				10		13	32
	Figure 37. lohatsara Uroplatus ebenaui	Furcifer petteri	11 10 110 9.1	11 10 110 8.2	11 10 110 11.8	33 Paroedura 10 330 9.7

Amphiglossus aff. miafana Brookesia aff. tristis Phelsuma aff. dorsivittata Lycodryas aff. inopinae hildebrandti Mimusops coriacea and Ficus tiliifolia formation (see Trachylepis elegans and T. gravenhorstii Phelsuma aff. dorsivittata, Paracontias aff.

Table 19 .

 19

	Characteristics	Pitfall line 1		Pitfall line 2	Total
	Habitat type
	Ground cover
	Taxa
	Paracontias aff. hildebrandti			1		1
	Trachylepis gravenhorstii			3		11
	Liophidium aff. torquatum	2				2
		11		3		14
		11		11		22
						5
						110
		20.0		5.5		12.7
	Table 20.
	Characteristics	Pitfall line 1		Pitfall line 2	Total
	Habitat type
	Ground cover
	Zonosaurus madagascariensis	3		2	5
	Madagascarophis colubrinus			1	1
	Liophidium aff. torquatum	1			1
				3	7
		11		11	22
					5
					110
		7.3		5.5	6.4
	Table 21.
				Mode of capture
	Taxa	Activity period	Direct observation	Systematic searching	Pitfall traps
	Laliostoma labrosum
	Ebenavia inunguis
	Geckolepis maculata
	Hemidactylus frenatus
	Paroedura lohatsara
	Phelsuma abbotti
	Phelsuma aff. dorsivittata
	Uroplatus ebenaui
	Zonosaurus madagascariensis
	Amphiglossus
	Cryptoblepharus boutonii
	Madascincus aff. miafana
	Paracontias aff. hildebrandti
	Trachylepis elegans
	Trachylepis gravenhorstii
	Brookesia aff. tristis
	Furcifer petteri
	Furcifer oustaleti
	Acrantophis madagascariensis
	Liophidium aff. torquatum
	Lycodryas aff. inopinae
	Madagascarophis colubrinus
	Mimophis mahfalensis
	Madatyphlops microcephalus
	Xenotyphlops aff. grandidieri
	Total		18	8

Table 22 .

 22

		Nosy Ankao	Nosy Manampao	Nosy Ratsy
	Taxa
	Ardea cinerea
	Bubulcus ibis
	Butorides striata
	Egretta dimorpha
	Buteo brachypterus
	Falco newtoni
	Turnix nigricollis
	Dryolimnas cuvieri
	Nesoenas picturata
	Centropus toulou
	Caprimulgus madagascariensis
	Cypsiurus parvus
	Eurystomus glaucurus
	Upupa marginata
	Hypsipetes madagascariensis
	Cisticola cherina
	Terpsiphone mutata
	Nectarinia souimanga
	Nectarinia notata
	Zosterops maderaspatana
	Corvus albus
	Acridotheres tristis
	Foudia madagascariensis
	Total species	25	6	4

Table 23 .

 23

		Nosy Ankao	Nosy Manampao	Nosy Ratsy
		100
		10
		1000
	Reptile captures
	Zonosaurus madagascariensis	0	0	1
	Small mammal captures
	Rattus rattus		11	0
	Rattus norvegicus	1 1	0	0
		6.3	2.9	0.0

Table 24 .

 24 Rattus rattus

Position of trap Localization for those on ground Localization for those off ground On ground

		Off ground	In litter	Next to fallen and rotten wood	On roots	Other	Branch or trunk > 10 cm	Branch or trunk < 10 cm	Other
				1		10		20
	Sex and age
	10	10			2		7	3
							13	2
		1					1
							2

Table 25 .

 25 Rattus rattus

	Position of trap	Localization for those on ground	Localization for those off ground
	On	Off	In litter	Next to	On	Other (mostly	Branch or	Branch	Other
	ground	ground		fallen and	roots		trunk	or trunk
				rotten			> 10 cm	< 10 cm
				wood
			1		1	70	3	1
	Sex and age
	7					7
	3				1	2
	1					1

Conservation priorities and recommendations Some important generalities associated with invasive invertebrates

					et al., 2012
					Tenrec ecaudatus,	2011; Feare et al.
	Setifer setosus, and Microgale brevicaudata	P. megacephala
					Rattus
	C. equisetifolia	R. rattus	Mystrium, Lioponera, Tanipone,
					C. equisetifolia	Leptogenys, and Proceratium
			et al	Acranthophis P. megacephala madagascariensis, and Rattus
	Manambato (Goodman et al.,	Xymmer and Malagidris.
			Tenrec and Setifer	et al
	Other introduced mammals
			-
				voalavo arabo Suncus murinus	of P. megacephala Nosy Manampao Introduced ants
				Leucaena leucocephala	Paratrechina longicornis, Plagiolepis alluaudi, Pheidole megacephala, Strumigenys
					emmae, Tetramorium scytalum, and Leptogenys
					Rattus	maxillosa
					R.
	rattus	-			Rattus
				cat scats
	Introduced ants		Introduced rodents
				Rattus	Introduced snails
	geminata	Rattus feed Pheidole megacephala and Solenopsis	Rattus rattus immaculata norvegicus	Lisachatina R.
	for Rattus Pheidole megacephala Rattus
			Rattus
	Introduced birds		Introduced rodents
	et al			Rattus
	megacephala Introduced birds tristis	P. Acridotheres	rattus.	Rattus
	Rattus, season.	P. megacephala Acridotheres tristis	Ficus
		Paratrechina longicornis, Plagiolepis alluaudi, Upupa marginata, for
	Strumigenys maxillaris, S. emmae, Tetramorium
	sericeiventre, and T. delagoense
	Rattus			Nosy Ankao Introduced plants state.
				Zonosaurus madagascariensis,
					trees, Casuarina equisetifolia

Acknowledgements