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The vulnerability of automatic speaker verification (ASV) systems to spoofing is widely acknowledged. Recent years have seen an intensification in research efforts to develop spoofing countermeasures, also known as presentation attack detection (PAD) systems. Much of this work has involved the exploration of features that discriminate reliably between bona fide and spoofed speech. While there are grounds to use different front-ends for ASV and PAD systems (they are different tasks) the use of a single front-end has obvious benefits, not least convenience and computational efficiency, especially when ASV and PAD are combined. This paper investigates the performance of a variety of different features used previously for both ASV and PAD and assesses their performance when combined for both tasks. The paper also presents a Gaussian back-end fusion approach to system combination. In contrast to cascaded architectures, it relies upon the modelling of the two-dimensional score distribution stemming from the combination of ASV and PAD in parallel. This approach to combination is shown to generalise particularly well across independent ASVspoof 2017 v2.0 development and evaluation datasets.