## Introduction

Reliable biological inferences about the processes driving survival of individuals in a population depend on the proper formulation of stochastic process models that are confronted with capture–recapture/resighting data. Such models translate fundamental biological questions into testable hypotheses that further our understanding of the system of interest (Cohen 2004; Gimenez *et al*. 2007). When such models are inappropriately formulated, bias caused by structural errors can lead to unreliable statistical inferences (Pradel & Sanz-Aguilar 2012).

For capture–recapture/resighting data, formulation of an appropriate stochastic process model requires consideration of the structure of the data collected (e.g. discrete vs. continuous sampling events), the type of data collected (e.g. recapture, resighting or dead recovery) and the biological characteristics of the study system (e.g. open vs. closed populations). For example, multiple models have been developed to estimate survival from open populations when using discrete-resighting data (Hightower, Jackson & Pollock 2001; McClintock & White 2009; Johnson *et al*. 2010) or discrete-recapture data (Lebreton *et al*. 1992). The recent expansion of continuous-resighting telemetry methods (e.g. acoustic receivers, PIT tag antennae; Heupel & Simpfendorfer 2002; Barbour & Adams 2012) has created a class of ecological data not well suited for standard statistical methods when fates are unknown (Kie *et al*. 2010). Without an investigation of proper model formulation, the information contained in this data will not be fully harnessed, and statistical inferences may be weak or misleading (Strong *et al*. 1999).

Several previous survival studies using continuous-resighting data collapsed continuous resightings into discrete-time intervals and applied existing discrete-time models. For example, Heupel & Simpfendorfer (2002) applied Hightower, Jackson & Pollock (2001)'s discrete-time model to continuous-resighting data by collapsing resightings into weekly sampling bins. Similarly, Adams *et al*. (2006) collapsed continuous-resighting data into weekly intervals and estimated apparent survival with the discrete Cormack–Jolly–Seber (CJS) model. During a multiyear study, Cameron *et al*. (1999) collapsed 4 months (November through February) of continuous resightings into a single encounter occasion labelled as January 1st each year and then estimated annual survival with a discrete multistate model. Hewitt *et al*. (2010) took a similar approach, but used a discrete CJS model.

The use of continuous data in discrete-time models violates the assumption that sampling occasions are instantaneous with respect to the interval between periods (e.g. a cohort is marked in a single day, a prolonged period of time elapses [e.g. a month], then a subsequent capture–recapture event occurs over a single day; Pollock *et al*. 1990). Some studies have recognized and accounted for this issue (Barbour, Boucek & Adams 2012a; Bowerman & Budy 2012; Ruiz-Gutiérrez *et al*. 2012; Mintzer *et al*. 2013), but it is unknown how violating this assumption biases survival probabilities in studies that have not. Here, we explore this issue by simulating a population of marked individuals that are resighted on a relatively continuous (daily) basis and collapsing these ‘continuous’ resightings into discrete-time bins. We then estimate the known survival values with two survival estimation models to determine whether a model currently exists that is appropriate for estimating survival from continuous resightings.