DOI: 10.5061/dryad.0rxwdbs29
title: Molecular food webs of bat-plant interactions during an extreme El Nino event

description: Interaction network structure reflects the ecological mechanisms acting
 within biological communities, which are affected by environmental
 conditions. In tropical forests, higher precipitation usually increases
 fruit production, which may lead frugivores to increase specialization,
 resulting in more modular and less nested animal-plant networks. In these
 ecosystems, El Niño is a major driver of precipitation, however, we still
 lack knowledge of how species interactions change under this influence. To
 understand bat-plant network structure during an extreme ENSO event, we
 determined the links between frugivorous bat species and the plants they
 consume by DNA barcoding seeds and pulp in bat faeces. These interactions
 were recorded in the dry forest and rainforest of Costa Rica, during the
 dry and the wet seasons of an extreme El Niño year. From these we
 constructed seasonal and whole-year bat-plant networks and analyzed their
 structures and dissimilarities. In general, networks had low nestedness,
 high modularity, and were dominated by one large compartment which
 included most species and interactions. Contrary to our expectations,
 networks were less nested and more modular in drier conditions, both in
 the comparison between forest types and between seasons. We suggest that
 increased competition, when resources are scarce during drier seasons and
 habitats, lead to higher resource partitioning among bats and thus higher
 modularity. Moreover, we have found similar network structures between dry
 and rainforests during El Niño and non El Niño years. Finally, most
 interaction dissimilarity among networks occurred due to interaction
 rewiring among species, potentially driven by seasonal changes in resource
 availability.
description: <strong>Materials and Methods</strong>
 <em>Study sites</em> Fieldwork was
 conducted at two forest sites in Costa Rica that show contrasting
 seasonality and precipitation: the Atlantic rainforest of La Selva
 Biological Station (10°25′19” N, 84°00′54” W) and the Pacific dry forest
 at Sector Santa Rosa of Área de Conservación Guanacaste (ACG) (10°48’53”
 N, 85˚36’54” W) (Figure 1). La Selva Biological Station covers 1,611 ha of
 lowland wet tropical forest between 35 to 137 m on the Caribbean slope of
 the Cordillera Central mountain range. It has a mean annual temperature of
 25˚ C with a mean annual precipitation of 3,962 mm (Sigel, Sherry,
 & Young, 2006). La Selva Biological Station has a mild dry season
 that ranges from January to April and a wet season that goes from May
 until December (Sanford et al. 1994). Sector Santa Rosa (of ACG) covers
 &gt;38,000 ha of tropical dry forest ranging from 0 m to 300 m, and is
 part of Área de Conservación Guanacaste (Asensio, Schaffner, &
 Aureli, 2015). Sector Santa Rosa (of ACG) has a mean annual temperature of
 25˚C with a mean annual precipitation of 1,575 mm. The dry season ranges
 from December to April, while the wet season extends from May to November,
 where 85%-97% of the precipitation falls (Castro et al. 2018) (Figure 1).
 The annual precipitation is higher at La Selva Biological Station (range
 2,809-6,164 mm) than at Área de Conservación Guanacaste (range 880-3,030
 mm, six-month dry season) (Gillespie, Grijalva, & Farris,
 2009).   <em>Bat
 sampling</em> We captured bats using four to six
 mist nets (6m – 12m) opened along trails and near watercourses in the
 study area from 18h – 22h. In addition, a canopy net and harp trap were
 used in 2009 but these had low capture rates and so were not used in 2015.
 Sampling took place in the wet season May-July (Santa Rosa of ACG) (2009),
 and in the wet season July-August (Sector Santa Rosa of ACG) and
 September-October (La Selva) (2015), and in the dry season during
 January-February (Sector Santa Rosa of ACG) and March-April (La Selva)
 (2015). Sampling and bat identification during the non El Niño year was
 conducted as described in Clare et al. (2019). Sampling effort using mist
 nets was equal to approximately 2,250 m<sup>2</sup> hours
 within each season during the El Niño year, and approximately the same
 during the non El Niño year. We collected wing punches for another study
 and these also served to avoid recaptures in order to maintain the
 independence of the data in our analysis. We measured the forearm length
 with callipers (0.1 mm precision) and identified species following Reid
 (1997), Timm & LaVal (1998), and LaVal & Rodriguez-Herrera
 (2002). Bats were held in cloth bags for a maximum of two hours for the
 collection of faecal samples. All samples were frozen after collection
 (-20° C).   <em>DNA
 extraction, PCR and sequencing</em> For this
 study, we focused on nectar and fruit eating species, which produced
 faecal samples consisting of either seeds or digested fruit pulp. For DNA
 extraction, PCR and sequencing of the samples we followed standard
 protocols for plants and all work was conducted by the Canadian Centre for
 DNA barcoding (CCDB) following these procedures (Ivanova, Kuzmina,
 & Fazekas, 2011). In brief, dried plant material from faeces
 (fruit pulp or seed) was placed in a sterile strip-tube with pre-aliquoted
 sterile stainless steel beads and the tissue was ground using a Tissue
 Lyser (Qiagen, USA). The ground material was incubated with 2x CTAB buffer
 at 65°C for 1 hour and DNA extraction was performed using a semi-automated
 glass fiber filtration method (Ivanova, Fazekas, & Hebert, 2008;
 Fazekas et al. 2012). Following established methods, we amplified a 552 bp
 fragment of the 5’ end of the large subunit of RuBisCO
 (<em>rbc</em>L) and the ~350 bp second nuclear encoded
 internal transcribed spacer (ITS2) flanking by the partial 5.6S and 26S
 ribosomal genes. Sanger sequencing was performed using a ABI 3730xl
 capillary sequencer (Ivanova, DeWaard, Hajibabaei, & Hebert, 2005;
 Ivanova & Grainger,<em> </em>2006; Kuzmina &
 Ivanova, 2011a; Kuzmina & Ivanova, 2011b; Fazekas et al. 2012).
 Although plant DNA barcoding yields lower species resolution compared to
 fungi and animals (Hollingsworth, Graham, & Little, 2011),
 generally it provides robust results for identification of vascular plants
 at the genus level (Kress et al. 2009; Parmentier et al. 2013; Braukmann,
 Kuzmina, Sills, Zakharov, & Hebert, 2017). For the samples from
 the non-El Niño year, plant specimens collected on the sites were
 identified using <em>rbcL </em>and <em>matK</em>
 and the supplementary non-coding plastid region
 <em>trnH-psbA</em> (see Clare et al. 2019 for full methods).
     <em>Identification of
 plant DNA sequences from bat faecal samples </em>
 We initially filtered all sequences for quality and excluded low
 quality sequences where the PHRED score was &lt;30 as indexed on the
 Barcode of Life Data Management System (BOLD) (Ratnasingham &
 Hebert, 2007). At least one genetic marker was recovered for each faecal
 sample, thus no samples were excluded at this stage. We compared the
 obtained <em>rbc</em>L and ITS2 sequences with the reference
 libraries of GenBank and BOLD using the BLAST algorithm with default
 search parameters (Altschul, Gish, Miller, Myers, & Limpman, 1990)
 in GenBank and the combined BLAST and Hidden Markov Model methods
 implemented by the BOLD server (Ratnasingham & Hebert, 2007). For
 each reference database (BOLD, GenBank), we assigned query sequences to
 taxa based on highest percentage similarity, and considered a threshold of
 ≥97% to be a reliable assignment (Lamb, Winsley, Piper, Freidrich,
 & Siciliano, 2016). When there was an agreement between
 species-level matches for both markers
 (<em>rbc</em>L<em> </em>and ITS2) in both
 databases, with at least one match &gt;97%, we assigned a species
 name. In cases where the query matched with equal similarity to multiple
 taxa in the same genus, we assigned the taxon to the level of genus only,
 and similarly we used the same approach to assign query sequences to the
 level of the family. Where <em>rbc</em>L and ITS2 sequences
 matched different species from different genera, both at &gt;97%, we
 concluded that two taxa were present in the sample and assigned to both
 genera. Query sequences that did not show significant similarity to a
 reference were excluded from the analysis, with only one faecal sample
 removed due to this procedure. To corroborate species
 assignments, for each candidate genus match, we reconstructed a gene
 phylogeny in which we included our query sequences together with all
 available reference sequences from species of the same genus present in
 BOLD that are also known to occur in Costa Rica. Sequences from
 <em>rbc</em>L and ITS2 of each plant genus were aligned with
 ClustalW (Larkin et al. 2007) in BioEdit v7.2.5 (Hall, 1999). For each
 alignment we ran a model selection test to check which would be the best
 method to build the phylogenetic tree based on the lowest BIC value. We
 ran model selection and built the phylogenetic trees using MEGA 6.06
 (Tamura, Stecher, Peterson, Filipski, & Kumar, 2013). These
 phylogenies (not shown) recovered paraphyletic groupings for some species,
 perhaps through a lack of reference material, and therefore such species
 assignments were considered unreliable. To address this impact of
 taxonomic resolution, we reduced all data to genus-level designations and
 repeated our analyses for both species and genera data to check for
 consistency of results (see Supporting Information).
 The identification of plant DNA sequences from bat faecal samples
 during the non El Niño year relied on GenBank and BOLD, with the exception
 of the <em>trn</em>H-<em>psb</em>A region which
 was not searchable within BOLD (see Clare et al. 2019 for more details)
 for our purposes we used the assignments as given in Clare et al.
 (2019).   <em>Network
 matrices</em> We compiled the inferred
 interactions into weighted matrices where each cell value represented the
 number of observed interactions between each bat-plant taxon pair. We
 considered one realized interaction when the DNA of a plant taxon was
 detected in the faeces of one individual bat. We constructed matrices for
 (1) each forest site in which we pooled data from both seasons during the
 El Niño year (‘La Selva’ and ‘Santa Rosa’), and (2) for each forest site
 in which we separated the data collected for dry and wet season during the
 non El Niño and El Niño years (‘wet’ versus ‘dry’ for each site). As
 species usually distribute their interactions unevenly among partners
 (Ings et al 2009), weighted analysis might uncover preferences, and even
 modules, that are invisible to binary analysis. Thus, we additionally
 applied weighted metrics to characterize network topologies. All
 statistical analysis and network drawings were performed using R, version
 3.3.2 (R Development Core Team, 2017).