Given marbled murrelets (Brachyramphus marmoratus) can be extremely cryptic in forest environments, audio-visual surveys are difficult, time consuming, and expensive to conduct. Passive acoustic moni...

Neonicotinoids are expected to play a significant role in the decline of bird populations through their adverse effects on food resources (primarily i…

Voorlopig geen spieringvisserij in Waddenzee.

Boerenzwaluwen komen synchroon terug naar de stal.

What hope is there for science reform, if we can't agree on what to reform? Right now, principles are more important than practices.

Most models of ecological and eco-evolutionary processes involve creating trajectories of something, be it population densities, average trait values, or environmental states, over time. This requ...

Bayesian cross-validation (CV) is a popular method for predictive model assessment that is simple to implement and broadly applicable. A wide range of CV schemes is available for time series applications, including generic leave-one-out (LOO) and K-fold methods, as well as specialized approaches intended to deal with serial dependence such as leave-future-out (LFO), h-block, and hv-block. Existing large-sample results show that both specialized and generic methods are applicable to models of serially-dependent data. However, large sample consistency results overlook the impact of sampling variability on accuracy in finite samples. Moreover, the accuracy of a CV scheme depends on many aspects of the procedure. We show that poor design choices can lead to elevated rates of adverse selection. In this paper, we consider the problem of identifying the regression component of an important class of models of data with serial dependence, autoregressions of order p with q exogenous regressors (ARX(p,q)), under the logarithmic scoring rule. We show that when serial dependence is present, scores computed using the joint (multivariate) density have lower variance and better model selection accuracy than the popular pointwise estimator. In addition, we present a detailed case study of the special case of ARX models with fixed autoregressive structure and variance. For this class, we derive the finite-sample distribution of the CV estimators and the model selection statistic. We conclude with recommendations for practitioners.