High-resolution imaging of basin-bounding normal faults in the Southern Apennines seismic belt (Italy) by traveltime and frequency-domain full-waveform tomography

We apply a two-step seismic imaging flow by combined first-arrival traveltime and frequency-domain waveform tomographies to dense wide aperture data collected in the Val d’Agri basin (southern Italy). A large wavelength Vp model determined by first-arrival traveltime tomography is used as a starting model for waveform tomography. The multiscale waveform tomography consisting of successive inversion of increasing frequencies allows to progressively reconstruct the short wavelengths of the velocity model, providing valuable information on the Quaternary basin and on range-bounding normal-faulting systems

Top 13 AICc models competing for first ranking model.

<p>Beta coefficients ± standard errors for each covariate are listed. The presence of categorical variables Habitat type and Area in the models is indicated with the • symbol.</p

Proportion of observations per time of day spent active fitted with a line function using the activity package in R [33] to determine proportion of time spent active.

<p>Vertical lines indicate sunrise and sunset at approximately 05.30 and 17.50 year-round.</p

Distinct habitat types found at the study site.

<p>Adapted from Nijman [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151732#pone.0151732.ref017" target="_blank">17</a>].</p

Beta coefficients ± standard errors for the categorical) in the top 13 AICc models competing for first ranking model.

<p>Variables Habitat type (1 = Shrubland & Degraded forest; 2 = Teak stands; 3 = Tall forest; 4 = Community forest, reference category, not included) and Area (1 = Gunung besar; 2 = Payung-payung; 3 = Kumalasa, reference category, not included.</p

Parameter estimates for <i>S</i>. <i>blouchi</i> included in the REM.

<p>Parameter estimates for <i>S</i>. <i>blouchi</i> included in the REM.</p

Camera trapping sampling precision for <i>S</i>. <i>blouchi</i>.

<p>Sampling precision was expressed as CV of <i>S</i>. <i>blouchi</i> camera trap rate with cumulative sampling effort (number of camera trap days) in the protected areas.</p

Model-averaged predicted occupancy (Ψ) in relation to (a) The distance from the nearest border (95% CI = 0.194. -1.040), (b) Litter depth (95% CI = 0.454, -0.146), (c) Altitude (95% CI = 0.179, -0.716) and (d) Tree density (95% CI = 0.304, -0.121).

<p>Predictor variables were log transformed.</p