DOI: 10.5061/dryad.cvdncjt76
title: Evolutionary history and precipitation seasonality shape niche overlap in Neotropical bat-plant pollination networks

description: Species interactions are one dimension of the niche, and niche overlap
 arises when two species share an interaction partner. In pollination
 systems, environmental and biotic factors impact the niche overlap. Here
 we explored the effects of climate seasonality, plant and bat richness,
 morphological traits, and phylogenetic distance in shaping the niche
 overlap of Neotropical bat-plant pollination networks. For that, we used a
 dataset of 22 bat-plant pollination networks in the Neotropical region. We
 measured niche overlap in bats and plants with the Morisita-Horn index,
 ĈH, and then, we used a SAR model to test the relationships between niche
 overlap and the abiotic and biotic factors. We found a lower niche overlap
 among bats in communities composed of phylogenetically distant bat
 species. Moreover, plant and bat overlap were lower in regions with higher
 precipitation seasonality. Our results indicate that climate seasonality
 and bat evolutionary history drive niche overlap in Neotropical bat-plant
 pollination interactions. These findings suggest that a higher
 precipitation seasonality may promote the emergence of temporal modules
 reducing niche overlap, probably as a consequence of seasonal species
 phenologies.  Furthermore, the method used to record the
 interactions impacts the degree of niche overlap. Interactions recorded
 with pollen samples tend to have higher niche overlap than direct
 observations. The uncoupled responses of morphological traits and
 phylogenetic distances in bat niche overlap suggest an effect of
 historical processes independently of the morphological traits. Our study
 reinforced the importance of evolutionary history and ecological processes
 in imprinting patterns of interaction niche overlap.
description: 1. Bat-plant pollination
 studies We obtained published and unpublished lists of
 pollination interactions between bat and plant species at the community
 level, performing a search on Google Scholar. For that, we  combined the
 following keywords: “bat”, “Carolliinae”, “Glossophaginae”,
 “Lonchophyllinae”, “Phyllostominae”, “Stenodermatinae” with “community”,
 “flower”, “pollination”, “trophic”, and “chiropterophily”. The results in
 Google Scholar included published papers, book chapters, theses,
 dissertations, and undergraduate monographs. Each dataset describes the
 plant species used as nectar resources by bat species in a given locality.
 Interactions between bats and plants were summarized in adjacency
 matrices, with plants in columns and bats in rows. The interaction between
 a pair of species was represented by 1, while the absence of interaction
 was 0. The interaction occurrence was described by direct observations
 (n=5 studies; 23%) or pollen present in the fur or fecal samples of each
 bat species (n=17 studies; 77%). As some species had outdated taxonomic
 names, we made taxonomic adjustments. For that, we followed the
 classifications of Burgin,
 Colella, Kahn, & Upham (2018) for bats and Flora do Brasil
 (Jardim Botânico do Rio do
 Janeiro, 2020) for plants.
 When a species was not
 found in Flora do Brasil (2020), we checked in Tropicos v3.2.3
 (https://tropicos.org).  2. Niche overlap
 To measure the niche overlap,
 we used the Morisita-Horn index (ĈH) (Horn, 1966). We quantified niche
 overlap for bats and plants separately. ĈH describes the mean similarity in interaction
 patterns between species within the same group (i.e.: plants or bats)
 (Horn,
 1966). To this end, ĈH
 considers the proportion of species used as a resource by all pairs of
 species, determining the mean degree of similarity in their resource use
 within each guild. The index varies from 0 (low niche overlap) to 1 (high
 niche overlap) (Dormann et
 al., 2017). We performed
 ĈH calculations using the function “grouplevel” of the bipartite v2.08
 package (Dormann et al.,
 2017) in R 3.3.3
 (R Core Team,
 2017).
   3. Drivers of niche overlap
 Climate seasonality was
 described by the standard deviation of monthly temperature and the
 coefficient of variation of monthly precipitation. We obtained the
 temperature seasonality (BIO4) and precipitation seasonality (BIO15) from
 the WorldClim v2.0 database (worldclim.org) at 5 arc-min resolution
 (Fick and Hijmans,
 2017). Most of the studies
 (n=20; 91%) included in the analyses have a sampling effort equal to or
 great than 12 months, suggesting that different seasons were recorded in
 our dataset.   We considered bat richness as the number of bat
 species with nectar consumption records in each community. We included bat
 richness as a predictor variable in models where we analyzed the drivers
 of niche overlap of bats. We used the same procedure when analyzing the
 drivers of the overlap of plants. Hence, we defined plant richness as the
 number of plant species with records of bat visits in each study.
 Functional dispersion (FDis) (Laliberté and Legendre, 2010) described the
 distance in trait filling among species, using only presence-absence data.
 FDis describes the mean distance of each species to the community’s
 average trait. Therefore, high values of FDis indicate communities
 composed of species that are functionally distinct from each other
 (Laliberté and Legendre,
 2010).
 We
 selected traits reflecting morphological adaptations that optimize
 resource exploitation in bat-plant pollination interactions
 (Fleming et al.,
 2009). For bats, we used
 the forearm length, the greatest length of the skull, the breadth of the
 braincase, and length of the maxillary tooth row. For plants, we chose
 traits related to the most common characteristics associated with bat
 pollination (Fleming et
 al., 2009). We used plant
 habit (tree, shrub, herb, liana, or epiphyte), flower accessibility (low,
 medium, or high), and resource quantity. We classified flower shapes
 according to their nectar accessibility. Flowers with low accessibility
 were tubular-shaped; flowers with medium accessibility were  bell-, flag-,
 funnel-, or cup-shaped, and flowers with high accessibility were open-,
 dish- or brush-shaped (Ramírez, 2003). Morphological trait characteristics were
 obtained from the literature. We excluded those species from the analyses
 because we did not find trait data for 50 plant species (23%). We used the
 “dbFD” function of the FD v1.0.12 package (Laliberté and Legendre,
 2010) in R to quantify
 functional dispersion. Phylogenetic distances among species were
 described by MPD (Webb,
 2000). This index
 represents the mean phylogenetic nodal distance between each species pair
 in the community while evaluating phylogenetic diversity
 (Webb,
 2000). Therefore, higher
 values of MPD indicate a community composed of species that are more
 phylogenetically distant from each other. To calculate MPD, we used the
 phylogenetic hypothesis of Rojas, Warsi, & Dávalos
 (2016) for bats and
 Magallón, Gómez-Acevedo,
 Sánchez-Reyes, & Hernández-Hernández (2015) for plants. Three bat species (7%) occurring in
 two communities were missing in the phylogeny: Leptonycteris nivalis,
 Lonchophylla inexpectata and Xeronycteris vieirai. When the genus was
 monophyletic, missing species were added to the genus node (L. nivalis and
 L. inexpectata). When the missing species were monospecific, missing
 species were added to the subfamily node (Xeronycteris vieirai –
 Lonchophyllinae, Gardner,
 2007).
 Species additions were performed in the web
 service of SUNPLIN (http://bioinfo.inf.ufg.br/sunplin/,
 Martins, Carmo, Longo,
 Rosa, & Rangel, 2013). To overcome phylogenetic uncertainty
 associated with species additions, we built 1,000 phylogenetic hypotheses
 using the branch-based insertion method (Martins et al., 2013), randomly adding missing species with different
 branch lengths in specific nodes, as described above (phylogenies
 available in Appendix S3). We repeated the analyses using all phylogenetic
 hypotheses to check the robustness of our results. The plant phylogeny was
 constructed with Phylocom 4.2 (Webb et al., 2008) using the tree of Magallón et al. (2015) as the
 backbone tree (see Appendix S3, Fig. S3.2). We performed phylogenetic MPD
 calculations using the “mpd” function of the picante v1.7 package
 (Kembel et al.,
 2010) in
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description: Our data are CSV files. Thus, MS Excel software, Google
 Browser's extension, OpenOffice, or another software that reads
 spreadsheets is required. Files can also be opened with R, R Studio, or
 another statistics software.