The estimation and comparison of demographic rates in animal and plant populations (Lebreton et al. 1992, Gregg and Kery 2006) is currently based on following marked individuals over time and possibly space and analyzing the resulting data with adequate statistical models. Such models, in which individuals may move among states (such as geographical sites or breeding statuses) over discrete time periods, must account for the non-exhaustive detection of individuals. These so-called capture–recapture models are still evolving (Pradel et al. 2005). In their current form, they are based on two key assumptions. 1) The individuals are assumed to be independent, 2) within the history of each individual, the successive pairs of each release associated with its subsequent reencounter (if any) are assumed to be independent (Burnham 1991).

While fitting multi-state capture–recapture models can now be carried out without much difficulty with a variety of computer software applications (M-SURGE (Choquet et al. 2004), E-SURGE (Choquet et al. 2009) or MARK (White and Burnham 1999) for a frequentist approach, MARK for Bayesian inference), the examination of the above mentioned assumptions can be done in an optimal way (Pradel et al. 2003) only with program U-CARE. And yet this step is critical. For instance, if a major structural effect such as the presence of transient individuals on the study site is overlooked, survival will be severely underestimated. More generally, spurious effects will often be detected if no model fits the data. This is best seen when considering how model selection proceeds.

In the frequentist paradigm, model selection is generally based on the Akaike information criterion (AIC, (Burnham and Anderson 2002)). The AIC is equal to the deviance plus two times the number of estimable parameters. In presence of lack of fit, the deviance tends to be inflated, thus leading to the selection of over-parameterized models and potentially to erroneous biological conclusions. Moreover, the estimate of precision of the maximum likelihood estimators (MLE) will be overly optimistic. The consequences of lack-of-fit are thus too deleterious to be ignored.

A preliminary assessment of goodness-of-fit (GOF) is thus a crucial prerequisite, just as in any statistical analysis (D'Agostino and Stephens 1986). Unlike in regression, residuals are not easily available in CR to check the validity of a model. Furthermore, the omnibus approach to goodness-of-fit testing that consists of comparing expected vs observed sufficient statistics is impractical due to the sparseness of the data. A specific approach was developed (Pradel et al. 2003). By making this approach available in an easy-to-use software application, we aim at encouraging practitioners to assess the validity of their assumptions. Sound model selection, i.e. preceded by appropriate GOF assessment, is indeed becoming more and more common in the literature (Henaux et al. 2007, Jenouvrier et al. 2008) but it is still far from being systematic. We describe here the main features of program U-CARE. More details on tests and tools can be found in the user's manual (Choquet et al. 2005).

U-CARE (<http://purl.oclc.org/NET/U-CARE>) is a free, easy-to-use, stand-alone, menu-driven computer application for Windows, with a set of options for managing data and GOF tests capabilities for both single-state and multi-state capture-recapture models (Fig. 1). The two kinds of test are presented as the sum of components examining different aspects of the data through a range of contingency tables. This structure guides the choice of an appropriate model. For instance, if the only significant subcomponent of the multistate goodness-of-fit test is TEST 3G.SR, a two age-class structure for survival should be used (Pradel et al. 1995), while, if this is TEST WhereBeforeWhereAfter (WBWA), a memory model is recommended (Pradel et al. 2003). Due to historical reasons, biological relevance is particularly furthered for single-state data through the computation of directional tests. We briefly examine hereafter the following four aspects of data analysis for which U-CARE can be very useful. 1) How to prepare the data? 2) How to detect overdispersion? 3) How to correct for overdispersion? 4) How to conduct more specific tests?