DOI: 10.5061/dryad.xwdbrv1cd
title: Data from: Population-level plant pollination mode is influenced by Quaternary climate and pollinators

description: Patterns in ecology are the products of current factors interacting with
 history. Nevertheless, few studies have attempted to disentangle
 the contribution of historical and current factors, such
 as climate change and pollinator identity and behaviour, on plant
 reproduction. Here, we attempted to separate the relative
 importance of current and historical processes on geographical patterns
 of the mating system of the tree species Curatella americana
 (Dilleniaceae). Specifically, we asked: 1) How do Quaternary and
 current climate affect plant mating system? 2) How does
 current pollinator abundance and diversity relate to plant mating
 system? 3) How does mating system relate to fruit/seed quantity
 and quality in Curatella americana? We recorded
 pollinators (richness, frequency and body size) and performed
 pollination tests in ten populations of C. americana spread over
 3,000 km in the Brazilian savannah. The frequency of self-pollination
 in the absence of pollinators was strongly influenced by
 historical climatic instability and not by present-day
 pollinators. In contrast, seed set from hand-cross and natural pollination
 were affected by pollinators (especially large bees) and
 temperature, indicating the importance of current factors on
 out-cross pollination. Two populations at the Southern edge of the
 species’ distribution showed high level of hand-cross-pollination
 and high flower visitation by large bees, but also a high level
 of autogamy resulting from recent colonization. Our results indicate
 that historical instability in climate has favoured 
 autogamy, most likely as a reproductive insurance strategy
 facilitating colonization and population maintenance over time, while
 pollinators are currently modulating the level of
 cross-pollination.
description: Study sites and species We studied ten
 populations of Curatella americana in three disjunct areas of savannah.
 Vegetation physiognomies are very similar among sites, but in general
 plant species diversity decreases northwards (Ratter et al. 2003,
 Bridgewater et al. 2004). We observed animal pollinators and performed
 experiments on C. americana at all the studied sites. The species flowers
 from June to September in Central Brazil, mid-August to early October
 in Pará state, and October and November at Roraima state. Flowers are
 white, pentamerous and grouped into dense inflorescences, and each flower
 stays receptive for three to five hours for one single day (see Rech et
 al. 2018 for more details).  Mating system
 In order to study the reproductive system of C. americana in situ
 we applied the following pollination tests: hand-cross-pollination,
 hand-self-pollination, autogamous self-pollination and natural
 pollination. All pollination tests were performed with flowers previously
 bagged using cloth insect exclusion bags, except for natural pollination,
 which involved counting and tagging flowers exposed to flower visitors. In
 order to mitigate possible differences related to resource allocation we
 always performed the pollination tests on the same branch (considered as
 a functional unit). The number of tested flowers was always higher than 20
 flowers per individual and a mean of 15 different individuals per test per
 population. In two of the studied areas (Nova Xavantina and Caldas Novas)
 we chose 12 individuals and compared the fruit weight from self (n = 107)
 and cross (n = 102) pollinated flowers, which may represent seed quality
 (Coomes & Grubb 2003).<br> <br> Flower visitation and
 pollination For all populations we recorded daily
 flower visitors (species richness and abundance) from anthesis until the
 end of visitation. In order to quantify visitation, we counted all visits
 to an observable (and counted) set of flowers for ten minutes each half an
 hour for at least 20 hours (120 x ten minute sessions) in each population.
 All the visitors touching anthers and/or stigmas were considered and
 scored as potential pollinators. After observing behaviour, flight
 distance and pollinator size, we grouped the pollinators into two
 categories: 1) Large-sized bees, and 2) Others, which includes bees the
 same size or smaller than Apis mellifera, beetles, flies and wasps. We
 separated pollinators according to size because flight range correlates
 with body size (Gathmann & Tscharntke 2002, Araújo et al. 2004,
 Greenleaf et al. 2007). Based on this premise, we expected a higher level
 of cross-pollination by large-sized bees. Statistical
 analysis To test for differences in fruit set related
 to the mating system and the regions, we used a Generalised Linear Mixed
 Model assuming a binomial distribution. The fixed factors were region,
 pollination experiment treatment, and the interaction between them. The
 random factors were the individuals nested within sites and these nested
 within regions. Our response variable was the production of a fruit from
 each flower. We performed the models with all fixed factor combinations
 and only a fixed intercept (Null Model), always keeping the random factor.
 For the fruit weight comparison we used pollination treatment (self- and
 cross-pollination) as predictors and generated models using individuals as
 random factors. All the alternative models were built removing factors or
 interactions between factors from the full model. A null model using
 only<br> 178 the intercept was also considered. In order to compare
 the generated models we used the Akaike Information Criterion – AIC
 (Burnham & Anderson 2004). All tests and models were performed in
 the R environment (R Core Team 2018). For each studied site, we modelled
 the climate changes since Last Glacial Maximum (LGM) by estimating the
 mean annual temperature (MAT_LGM) and annual precipitation (MAP_LGM) at
 each location for 21ky, according to the Community Climate System
 Model (CCSM) (Gent et al. 2011). We also extracted the current values of
 temperature (MAT_Current) and precipitation (MAP_Current) from the Global
 Climate Data (Worldclim 1.4 -http://www.worldclim.org/). For each site, we
 calculated the anomalies and velocities of change in temperature
 (MAT_Velocity_21) and precipitation (MAP_Velocity_21), as the
 long-term average over the last 21ky. Both climate anomaly and velocity
 are measures of climate stability (or climate change), but they are
 calculated in two different ways. Whereas climate anomaly simply is the
 difference in climatic conditions between two time periods (today and
 21,000 years ago), climate velocity integrates macroclimatic shifts (i.e.
 anomalies) with local spatial topoclimate gradients. Velocity is
 calculated by dividing the rate of climate change through time (i.e.
 anomaly) by the local rate of climate change across space (Sandel et al.
 2011). All calculations are based on a 2.5 minutes geographical
 resolution. We then estimated the effect of climate and pollinator
 activity on pollination mode. Due to the modest sample size of populations
 (n = 10) and some predictor variables being strongly correlated (i.e. r ≥
 0.6; Table S2), we took the following modelling approach. First, we
 modelled the effect of climate on pollination mode using current and past
 climate predictors, identifying minimum adequate models (MAMs) using the
 approach outlined in Diniz-Filho et al. (2008). As the temperature and
 precipitation anomalies used as a measure of past climate stability were
 strongly correlated, we modelled the effect of temperature and
 precipitation anomaly separately. The effect of past climate stability was
 also tested using modelled temperature and precipitation velocity instead
 of anomaly, giving qualitatively the same results (not shown). Second, we
 tested whether the four pollinator variables (pollinator richness,
 visitation frequency, and proportion of large bee visitation calculated
 both with and without the exotic honey bee) were significantly related to
 pollination mode. To do this we used single correlation tests using
 traditional non-spatial correlation analysis and correcting the degrees of
 freedom using Dutilleul’s (1993) method (Table 1), followed by models
 testing whether each of these pollinator activity variables may have other
 or additional effects from climate. We examined this by again following
 the approach of Diniz-Filho et al. (2008) to identify MAMs, but this time
 only considering climate variables included in the above-identified MAMs
 and each of the four pollinator variables. For all analyses, MAP, MAP
 anomaly, MAP velocity and MAT velocity were Log10-transformed, pollination
 visitation frequency was square root transformed, and all
 proportional measures (i.e., pollination mode variables and large bee
 predictors) were arcsine-square root transformed. All other variables were
 left untransformed. All analyses were conducted using the software Spatial
 Analysis in Macroecology, SAM 4.0 (Rangel et al. 2010)