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Thanks to the installation of a temporary seismic network, a microseismicity study has been conducted in the Sulmona area (Abruzzo, Italy) with the aim of increasing the knowledge of seismogenic potential of existing active faults. In this work the first 7 months (from 27 May to 31 December 2009) of recorded data have been analysed over a total period of acquisition of about 30 months. Using a semi-automatic procedure, more than 800 local earthquakes have been detected, which highlights the previously unknown background seismicity. About 70% of these events have been relocated using a 1-D velocity model estimated specifically for the Sulmona area. The integration of temporary network data with all the other data available in the region enables us to obtain a statistically more robust data set of earthquake locations. Both the final hypocentral solutions and phase pickings are released as a supplement; an appendix also describes phase readings' quality with respect to weighting schemes used by location algorithms. Local magnitude values of the newly detected events range between -1.5 and 3.7 and the completeness magnitude for the Sulmona area during the study period is about 1.1. Duration magnitude coefficients have been estimated as well for comparison/integration purposes. The local Gutenberg--Richter relationship, estimated from the microseismic data, features a low b value, tentatively suggesting that the Sulmona area may be currently undergoing high-stress conditions, in agreement with other recent studies. The time--space distribution of the seismic activity with respect to the known active faults as well the seismogenic layer thickness are preliminarily investigated. [ABSTRACT FROM AUTHOR]

Abstract: When dealing with small populations of elusive species, capture–recapture methods suffer from sampling and analytical limitations, making abundance assessment particularly challenging. We present an empirical and theoretical evaluation of multiple data source sampling as a flexible and effective way to improve the performance of capture–recapture models for abundance estimation of small populations. We integrated three data sources to estimate the size of the relict Apennine brown bear (Ursus arctos marsicanus) population in central Italy, and supported our results with simulations to assess the robustness of multiple data source capture–recapture models to violations of main assumptions. During May–August 2008, we non-invasively sampled bears using systematic hair traps on a grid of 41 5×5km cells, moving trap locations between five sampling sessions. We also live-trapped, ear-tagged, and genotyped 17 bears (2004–2008), and integrated resights of marked bears and family units (July–September 2008) into a multiple data source capture–recapture dataset. Population size was estimated at 40 (95% CI=37–52) bears, with a corresponding closure-corrected density of 32 bears/1000km2 (95% CI=28–36). Given the average capture probability we obtained with all data sources combined (), simulations suggested that the expected degree of correlation among data sources did not seriously affect model performance, with expected level of bias <5%. Our results refine previous simulation work on larger populations, cautioning on the combined effect of lack of independence and low capture probability in application of multiple data source sampling to very small populations (N <100). [Copyright &y& Elsevier]