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

Citation

Kass, Jamie M. et al. (2020), Biotic predictors with phenological information improve range estimates for migrating monarch butterflies in Mexico , Dryad, Dataset, https://doi.org/10.5061/dryad.18931zcs4

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 L. 1758) 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.

Methods

Please find below the DOI for the GBIF occurrence data for species of Asclepias and trees downloaded on June 04, 2014 for use in the analyses in this paper.

https://doi.org/10.15468/dl.fdzhj0

The monarch butterfly occurrence data used for analyses in this paper is from two sources. One is the citizen science database Journey North (https://journeynorth.org/), accessed in March 2018, which is an open data source. Please find the details of the time periods of this dataset used for analysis in the paper. The other is from the Mexican federal agency Comisión Nacional de Áreas Naturales Protegidas (CONANP) and represents field data for the fall season in 2015 (the only processed data available at the time of the study)--this data is currently proprietary.