Rodriguez, L. F. | Search bioRxiv

bioRxiv2026

Malaise trap samples of 1000 individuals per week suggest 4 million insects per hectare in the boreal zone

A basic question in ecological research and biodiversity monitoring concerns the estimation of species abundances from trap catches. As a case in point, a Malaise trap can yield thousands of arthropod individuals, but how this count should be converted to numbers of individuals per unit area has remained an open question. Here, we supplement observational data with an experimental approach targeted at quantifying catchability. We released marked insects in a boreal forest and examined their capture rate by a grid of Malaise traps around the release location. We estimated insect movement rates, mortality rates, and Malaise trapping capture rates by fitting a joint species movement model to these data. As a methodological novelty, we show how to convert the movement model parameters to the expected number of captured individuals, given their actual population density. Our results show that multiplying the sample content by 30 000 yields a rough estimate of the number of individuals per hectare. This conversion factor depends on the species, generally decreasing with increasing body size. We apply the estimated conversion factors to conclude that typical boreal forest contains some four million insect individuals per hectare, out of which around half belong to Diptera. SIGNIFICANCE STATEMENTTraditionally, the Malaise trap method has been used for assessing the state of the local communities and to estimate population abundances. However, a topical question is: how does the number of individuals observed in a sample relate to the true density of individuals in the surrounding community? To answer this question, we implement a movement model parametrized by a carefully designed mark-recapture experiment, in which we are able to obtain taxon-specific conversion factors for different groups of insects. We found that different insect groups come with different conversion factors, causing a mismatch between sample contents and true community composition. Thus, treating the sample contents as a direct representation of the reference community will be misleading.

ecology↗

bioRxiv2026

R-package Jsmm: Joint species movement modelling of mark-recapture data

O_LIWith small-bodied species, it is difficult to directly track individual movements, leaving mark-recapture as the most feasible method for collecting movement data. Markrecapture data are challenging to analyse because they are indirect: many individuals are never seen after release, and for recaptured individuals there is no information on the movements between release and recapture locations. This makes it difficult to apply many statistical approaches that have been developed for continuous movement data. Among the statistical methods targeted specifically to mark-recapture data, most are focused on the estimation of population sizes or vital parameters rather than the estimation of movement behaviours. C_LIO_LIWe present the R-package Jsmm that expands and implements the earlier published Joint Species Movement Modelling (JSMM) framework with Bayesian inference. Jsmm estimates parameters related to habitat selection (behaviour at edges between habitat types), diffusion (random component of movement), advection (directional component of movement) and reaction (mortality rate), and their dependence on spatial, temporal or spatiotemporal covariates. Jsmm implements both instantaneous capture process and cumulative capture process, enabling its applications to a broad range of studies. If applying Jsmm to data on multiple species, it can estimate how species-specific parameters depend on species traits and/or phylogenetic relationships. C_LIO_LIWe use real and simulated case studies to demonstrate the workflow of Jsmm: (1) defining the model through importing the spatial domain, the spatiotemporal covariates, and the capture-recapture data; (2) fitting the model with Bayesian inference and evaluating model fit through posterior predictive checks; and (3) using the fitted model for inference and/or prediction. The simulated example validates the technical implementation by showing that the estimated parameters match with the assumed values. The real data example on moth light-trapping illustrates the practical utility of the package. C_LIO_LIThe R-package Jsmm offers a flexible resource for analysing capture-recapture data in a model-based framework that explicitly accounts for the spatiotemporal study design of where and when captures are attempted. By analysing data jointly on multiple species, the approach facilitates analyses of sparse datasets where the low number of recaptures would not allow fitting species-specific models separately for each species. C_LI

ecology↗