Biotic predictors with phenological information improve range estimates for migrating monarch butterflies in Mexico

Abstract

Although long‐standing theory suggests that biotic variables are only relevant at local scales for explaining the patterns of species' distributions, recent studies have demonstrated improvements to species distribution models (SDMs) by incorporating predictor variables informed by biotic interactions. However, some key methodological questions remain, such as which kinds of interactions are permitted to include in these models, how to incorporate the effects of multiple interacting species, and how to account for interactions that may have a temporal dependence. We addressed these questions in an effort to model the distribution of the monarch butterfly Danaus plexippus during its fall migration (September–November) through Mexico, a region with new monitoring data and uncertain range limits even for this well‐studied insect. We estimated species richness of selected nectar plants (Asclepias spp.) and roosting trees (various highland species) for use as biotic variables in our models. To account for flowering phenology, we additionally estimated nectar plant richness of flowering species per month. We evaluated three types of models: climatic variables only (abiotic), plant richness estimates only (biotic) and combined (abiotic and biotic). We selected models with AICc and additionally determined if they performed better than random on spatially withheld data. We found that the combined models accounting for phenology performed best for all three months, and better than random for discriminatory ability but not omission rate. These combined models also produced the most ecologically realistic spatial patterns, but the modeled response for nectar plant richness matched ecological predictions for November only. These results represent the first model‐based monarch distributional estimates for the Mexican migration route and should provide foundations for future conservation work. More generally, the study demonstrates the potential benefits of using SDM‐derived richness estimates and phenological information for biotic factors affecting species distributions.

ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions

“…In particular for studies that transfer to new areas or time periods, model selection using spatial cross‐validation (see below) or fully withheld test data from another region should result in models with better transferability (Soley‐Guardia et al., 2019). When users prefer to select models with AICc, which comes with a statistical caveat for Maxent (Warren & Seifert, 2011), ENMeval 2.0 can additionally quantify how well such models predict withheld data compared with null models (Kass et al., 2020). Lastly, AICc can also be considered along with other performance metrics (Galante et al., 2018), and ENMeval 2.0 both provides more than the earlier version and allows users to add others.…”

“…Null models can account for features of the system—such as spatial autocorrelation or unequal environmental representation—that often lead to incorrect statistical conclusions, especially with background or pseudo‐absence data (Bohl et al., 2019). Without needing to quantify these features explicitly, the approach implemented here evaluates both empirical and null models with the same withheld data (instead of random data), and has been shown to produce more statistically reliable results than earlier ones (Bohl et al., 2019; Kass et al., 2020).…”

Lineage‐level distribution models lead to more realistic climate change predictions for a threatened crayfish

“…We then ran a series of null model simulations ( n = 100) using the complexity settings of each selected model to determine if the empirical models predicted validation data better than models built with random occurrences (results for simulations with 1000 iterations, which do not change our conclusions, are available in Figure S9). To do so, we used a recently described null model approach for SDMs available in ENMeval 1.9.0 that evaluates null model performance against the same validation data as the empirical model, making the null and empirical results directly comparable (Bohl et al., 2019; Kass, Anderson, et al., 2020). Next, we examined the permutation importance calculated by Maxent and marginal response curves for the variables of each selected model.…”

A review of the methods for studying biotic interactions in phenological analyses

“…Similarly, Kass et al. (2020) used presence of resource and refugia providing plants at a monthly resolution, aiming to capture the importance of plant phenology on the distribution of the monarch butterfly during autumn migration, with coexistence records serving as a proxy to infer suitable habitat for the butterfly.…”