Temporal disaggregation through interval-integrated B-splines for the integrated analysis of trapping counts in ecology

Abstract

O_LIPassive trapping techniques such as pitfall and malaise traps probably constitute the most widely used methods for standardised surveys of invertebrate populations worldwide. These methods typically yield aggregated count data over multi-day trapping periods, often spanning several weeks, during which species activity (i.e. phenology) can vary. The analysis of trapping data collected over temporally misaligned sampling intervals is challenging, hampering the integrated analysis of historically available trapping datasets. C_LIO_LIWe introduce a temporal disaggregation approach using interval-integrated B-splines to analyse data collected over misaligned sampling intervals while accounting for phenological influences. We present computationally efficient Taylor series approximations for integrating exponentiated B-splines over sampling intervals. We further tailor our approach to typical trapping datasets by providing several extensions, including joint species distribution modelling. C_LIO_LIThrough simulations and cross-validation, we demonstrate that our approach of temporal disaggregation outperforms naive approaches and provides improved inference on phenology and other parameters of interest, such as inter-annual trends. The first-order Taylor approximation, which can be fit using regular software routines, properly accounts for heterogeneity in sampling duration and timing, while the second-order Taylor approximation and the exact model additionally allow for improved estimation of phenological patterns. C_LIO_LIBy applying this model to a large pitfall trapping dataset, spanning almost 50 years and over 10,000 trapping events in the Belgian province of Limburg, we illustrate how this approach can be used to reveal phenological, spatiotemporal and co-distributional patterns for 331 spider species. C_LIO_LIThe interval-integrated B-splines approach we present provides a convenient way to infer phenology and other ecological parameters from temporally aggregated count data obtained over misaligned sampling intervals, facilitating the integrated analysis of heterogeneously collected datasets to infer biodiversity trends. C_LI