Multinomial analysis of behavior: statistical methods

Abstract

Behavioral ecologists frequently use observational methods, such as instantaneous scan sampling, to record the behavior of animals at discrete moments in time. We develop and apply multilevel, multinomial logistic regression models for analyzing such data. These statistical methods correspond to the multinomial character of the response variable while also accounting for the repeated observations of individuals that characterize behavioral datasets. Correlated random effects potentially reveal individual-level trade-offs across behaviors, allowing for models that reveal the extent to which individuals who regularly engage in one behavior also exhibit relatively more or less of another behavior. Using an example dataset, we demonstrate the estimation of these models using Hamiltonian Monte Carlo algorithms, as implemented in the RStan package in the R statistical environment. The supplemental files include a coding script and data that demonstrate auxiliary functions to prepare the data, estimate the models, summarize the posterior samples, and generate figures that display model predictions. We discuss possible extensions to our approach, including models with random slopes to allow individual-level behavioral strategies to vary over time and the need for models that account for temporal autocorrelation. These models can potentially be applied to a broad class of statistical analyses by behavioral ecologists, focusing on other polytomous response variables, such as behavior, habitat choice, or emotional states.Electronic supplementary materialThe online version of this article (doi:10.1007/s00265-017-2363-8) contains supplementary material, which is available to authorized users.

Small mammal controls on the climate‐driven range shift of woody plant species

“…These included a highly preferred seed (white pine Pinus strobus ) and a less preferred seed (balsam fir, Abies balsamea ). Small mammal preference for these seeds is well documented in the literature (Abbott and Hart 1960, Lobo ) and was also confirmed through a pilot study conducted in July . For experiments involving large seeds, we also included a familiar oak species that is present in the area (red oak Quercus rubra ) and consumed by small mammals.…”

“…Seeds were offered in equal quantities (i.e. the mass of seeds in each cup was the same) (Richardson et al , Berl et al ). Cups were not replenished within an experiment, which was run for a maximum of three days (i.e.…”

“…it terminated earlier if all seeds were consumed during the first two days). Experiments were conducted during September and October and May 2018. Both these periods represent times of natural availability of seeds (during the fall and soon after snow melting).…”

“…As a consequence, we cannot assume interactions between species in areas of current co‐occurrence will represent those taking place under novel conditions. This uncertainty is particularly true for rodents, where local populations can develop specific enzymes required to overcome plant defense mechanisms (Vander Wall ), and where seed selection and cache management strategies depend on the relative availability of different species (Lichti et al ). Further, small mammal community structure (i.e.…”

“…Previous studies have investigated the potential role of predators (Hillyer and Silman , Urli et al 2016) as well as the role of rodents on non‐native plants (Shahid et al 2009, Chiuffo et al 2018, Wróbel and Zwolak ); however, we are not aware of studies focusing on climate change and small mammal seed predators. Further, the role of small mammals is especially critical with respect to range expansion because, in addition to being seed predators, small mammals also function as dispersers by caching seeds some distance away from the parent tree (Vander Wall , Lichti et al ), which could lead to accelerated expansion (Jansen et al 2004).…”