DOI: 10.5061/dryad.sbcc2fr45
title: Climate seasonality drives ant-plant-herbivore interactions via plant phenology in an extrafloral nectary-bearing plant community

description: Interactions between ants and plants bearing extrafloral nectaries (EFNs)
 are among the most common mutualisms in Neotropical regions. Plants
 secrete extrafloral nectar, a carbohydrate-rich food that attracts ants,
 which in return protect plants against herbivores. This ant-plant
 mutualism is subjected to temporal variation, in which abiotic factors can
 drive the establishment and frequency of such mutualistic interaction.
 However, studies investigating how abiotic factors (e.g., climate)
 directly and indirectly influence ant-plant-herbivore interactions are
 incipient. In this study, we investigated direct and indirect (via plant
 phenology) effects of temperature and rainfall on ant-plant-herbivore
 interactions. To address these goals, we estimated six plant phenophases
 (newly flushed leaves, fully-expanded leaves, deciduousness, floral buds,
 flowers, and fruits) monthly, the activity of EFNs and abundance of ants
 and herbivores in 18 EFN-bearing plant species growing in a markedly
 seasonal region (the Brazilian Cerrado) during a complete growing season.
 Our results showed that (i) there were marked seasonal patterns in all
 plant phenophases, EFN activity, and the abundance of ants and herbivores;
 (ii) the peak of EFN activity and ant and herbivore abundance
 simultaneously occurred at the beginning of the rainy season, when new
 leaves flushed; and (iii) rainfall directly and indirectly (via changes in
 theproduction of new leaves) influenced EFN activity and this in turn
 provoked changes in ant abundance (but not on herbivores). Synthesis:
 Overall, our results build toward a better understanding of how climate
 drives seasonal patterns in ant-plant-herbivore interactions, explicitly
 considering plant phenology over time.
description: Study area We performed this study in the
 Ecological Reserve of the Clube de Caça e Pesca Itororó de Uberlândia
 (CCPIU: 48º18’01”W; 18º59’05”S, Uberlândia, Brazil). The study area had
 ~200 ha of cerrado sensu stricto vegetation (Oliveira & Marquis,
 2002). Open areas consisted of shrubs and small trees, whereas more
 enclosed areas consisted of trees reaching up to 15 meters in height
 (Del-Claro, Rodriguez-Morales, Calixto, Martins, &
 Torezan-Silingardi, 2019). The climate is markedly seasonal with the rainy
 season from October to March, and the dry season from April to September.
 The annual mean temperature varies from 18 to 28 ºC and the rainfall from
 800 to 2,000 mm (Alvares, Stape, Sentelhas, De Moraes Gonçalves, &
 Sparovek, 2013). Data collection
 For this study, we systematically established ten 50 m transects
 at least 50 m apart from each other. Transects started perpendicular to a
 reserve trail (2.5 m wide) going through the vegetation. In order to
 diminish edge effects, we set transects separated at least 10 m from the
 trail. All transects were parallel to each other and soil characteristics,
 light availability and humidity of each transect were similar.
 We selected the 18 most common and abundant plant species
 (Appolinario & Schiavini, 2002; Lange, Calixto, &
 Del-Claro, 2017; Lange & Del-Claro, 2014) bearing EFNs in our
 field site: <i>Banisteriopsis malifolia </i>(Nees &
 Mart.) B. Gates<i> </i>(Malpighiaceae),
 <i>Banisteriopsis stellaris </i>(Griseb.) B. Gates<i>
 </i>(Malpighiaceae), <i>Bauhinia rufa </i>(Bong.)
 Steud<i>. </i>(Fabaceae), <i>Bionia coriacea
 </i>(Nees & Mart.) Benth.<i> </i>(Fabaceae),
 <i>Caryocar brasiliense </i>Cambess.<i>
 </i>(Caryocaraceae), <i>Eriotheca gracilipes
 </i>(K.Schum.) A.Robyns (Malvaceae), <i>Heteropterys
 pteropetala </i>A. Juss.<i> </i>(Malpighiaceae),
 <i>Lafoensia pacari </i>A.St.-Hil.<i>
 </i>(Lythraceae), <i>Manihot caerulescens
 </i>Pohl<i> </i>(Euphorbiaceae), <i>Ouratea
 hexasperma </i>(A.St.-Hil.) Baill.(Ochnaceae), <i>Ouratea
 spectabilis </i>(Mart.) Engl.<i> </i>(Ochnaceae),
 <i>Qualea grandiflora</i> Mart.<i>
 </i>(Vochysiaceae), <i>Qualea multiflora
 </i>Mart.<i> </i>(Vochysiaceae), <i>Qualea
 parviflora </i>Mart.<i> </i>(Vochysiaceae),
 <i>Senna rugosa </i>(G.Don) H.S.Irwin &
 Barneby<i> </i>(Fabaceae),<i>Senna velutina
 </i>(Vogel) H.S.Irwin & Barneby<i>
 </i>(Fabaceae), <i>Smilax polyantha </i>Griseb.<i>
 </i>(Smilacaceae), and S<i>tryphnodendron
 polyphyllum</i> Mart. (Fabaceae). In total, we tagged 180 plant
 individuals equally divided among the 10 transects. Each transect included
 one individual of each species. We selected these individuals based on
 similar phenological characteristics within each species (e.g., total
 height and number of branches). Each month, we
 collected data of plant phenology for each tagged individual of all
 species from March 2017 to February 2018. In particular, we evaluated six
 phenophases: newly flushed leaves, fully-expanded leaves, deciduousness,
 floral buds, flowers, and fruits. For each phenophase, we used the
 following semi-quantitative scale: 0 = absence of plant phenological
 event; 1 = sporadic event (1-25% of the branches); 2 = infrequent event
 (26-50% of the branches); 3 = frequent event (51-75% of the branches); and
 4 = very frequent event (76-100% of branches) (Fournier, 1974). For each
 plant, we also evaluated EFN activity by estimating the number of plants
 with active EFN monthly. We considered that EFNs were active when they
 were bright and light in colour, and generally producing extrafloral
 nectar. By contrast, we considered that EFNs were inactive when they were
 dark with a necrotic aspect and did not produce nectar (Calixto, Lange,
 & Del-Claro, 2015). The main reason to choose EFN activity instead
 other EFN characteristics (e.g., volume, concentration) was that the
 circular statistics used (see details in <i>Statistical
 analyses</i>) only work with presence or absence of specific event
 over time. Finally, to quantify the number of ants and herbivores we
 conducted field observations on two consecutive days per month during the
 same period as the plant phenology observations. Each day we censused half
 of transects (five transects) in both daytime (8:00 a.m. to 12:00 p.m.)
 and nighttime (7:00 p.m. to 11:00 p.m.). In each plant individual, we
 first collected ants and other insects without touching the plant. Next,
 using a white inverted umbrella placed under the plant, we shook the
 branches and collected those insects who fell on the umbrella. We
 subsequently stored all insects in 70% ethanol. Then we identified insect
 herbivores by using insect field guides. At night, we used flashlights
 covered with red cellophane to lessen the deterrence of insects.
 Climatic data We obtained climatic data
 (namely daily averages of rainfall, humidity, and temperature) of our
 fieldwork season from Climate Station of the Instituto de Geografia da
 Universidade Federal de Uberlândia. For statistical analyses, we
 calculated mean humidity and temperature, and total rainfall for each
 month. Statistical analyses We
 performed all statistical analyses in R software 4.0.0 (R Core Team,
 2020). Packages used are detailed in each analysis below.
 First, we investigated whether there was seasonal variation
 (i.e., peaks in activity or abundance) in plant phenophases (newly flushed
 leaves, fully-expanded leaves, deciduousness, floral buds, flowers, and
 fruits), EFN activity, ant and herbivore abundance throughout the year.
 For this, we used circular statistical analysis (Morellato, Alberti,
 & Hudson, 2010; Novaes et al., 2020). Circular statistics evaluate
 recurring biological events associated to climatic factors, where specific
 units of time (e.g., months) are converted in angles (Morellato et al.,
 2010; Novaes et al., 2020). Basically, it uses the frequency or abundance
 of a specific event over time, identifying peaks of activity (events
 concentrated in specific times). We converted months into angles, and used
 the abundance of each factor to calculate the mean vector (µ), mean vector
 length (r), median, standard deviation, Rayleigh test Z, and Rayleigh test
 p. Rayleigh test Z with significant values (p &lt;0.05) and mean
 vectors (r) close to 1 indicate seasonality of the data, that is,
 phenological activity was concentrated around one single date or mean
 angle (Morellato et al., 2010; Novaes et al., 2020). We conducted circular
 analyses with the package ‘circular’ (Agostinelli & Lund, 2017).
 We performed the Watson’s goodness of fit to examine unimodality before
 conducting circular analyses. In the case of EFN activity and abundance of
 ants and herbivores, we used the total number of plants with active EFNs,
 ants and herbivores per month for analysis at community level.
 Second, we investigated whether the peak in the activity of EFNs,
 and the abundance of ants and herbivores simultaneously occurred in the
 same period and when plants produce new leaves. To corroborate our
 hypothesis, the peak (mean vector) of EFNs, ants and herbivores should
 occur at the same time (or shortly after the appearance of leaf flushing),
 always before any other phenophase, and should not overlap with other
 phenophase mean vector. Since there is a sequence of plant phenological
 events over time, we expected that EFNs peak when newly flushed leaves
 appear, and not during other plant phenological events. For this, we used
 two statistical approaches: circular statistics and overlap analysis. With
 these two statistical approaches, we were able to test for differences
 between peak activity of plant phenological events (e.g., EFN activity and
 newly flushed leaves) and also to analyze the overlap of events occurrence
 over time. We firstly carried out Watson’s two-sample test of homogeneity
 to test whether peaks were different, and compared the mean vector in the
 circular diagrams of EFNs activity with newly flushed leaves,
 fully-expanded leaves, deciduousness, floral buds, flowers, fruits, ants
 and herbivores. We secondly conducted niche overlap analyses by using
 temporal abundance matrices, which were analysed with the coefficient of
 overlapping (Ridout & Linkie, 2009) and niche overlap null models.
 The coefficient of overlapping uses Kernel density estimates fitted to the
 data to approximate two density functions, that is, it represents
 quantitative measure of overlap (0 = no overlap, 1 = identical peak). We
 used the estimator Dhat1, since it is the best option for small samples
 sizes (Ridout & Linkie, 2009). In the case of niche overlap null
 model, we used two randomization algorithms (1000 simulated null
 assemblies), RA3 and RA4, and the Pianka’s metric with the package
 “EcoSimR” (Gotelli, Hart, & Ellison, 2015). RA3 rearranges the
 rows and RA4 rearranges the non-zero row values; both retain “niche
 breadth” of each species. Finally, we investigated
 whether climatic variables directly and indirectly influenced the
 ant-plant-herbivore interaction. For this, we carried out a Structural
 Equation Modelling (SEM) based on monthly data at plant individual level
 using ‘lavaan’ package (Rosseel, 2012). SEM is a multivariate statistical
 analysis that estimate direct and indirect relationships among variables.
 Specifically, we investigated direct associations among climatic factors
 (temperature and rainfall), newly flushed leaves, EFN activity and the
 abundance of ants and herbivores, as well as the indirect associations
 between climate and the abundance of ants (via newly flushed leaves and
 EFN activity) and herbivores (via newly flushed leaves, EFN activity and
 ants). We only chose newly flushed leaves as plant phenophase for these
 analyses because their peak simultaneously occurred with the peak in ant
 and herbivore abundance (see <i>Results</i>). The hierarchical
 order of the variables included in the SEM (i.e., EFNs &gt; Ants
 &gt; Herbivores) was chosen because EFNs attract ants and these in
 turn prey on herbivores. We estimated direct effects in the SEM as
 standardized partial regression coefficients, whereas we obtained indirect
 effects by combining the specified coefficients for direct effects on both
 the predictor and the response. We assessed the significance of direct and
 indirect coefficients with z-tests. We evaluated the goodness of fit of
 the general model using the Chi-square statistic, Comparative Fit Index
 (CFI), the Root Mean Square Error of Approximation (RMSEA), and the
 Standardized Root Mean Square Residual(SRMR).