DOI: 10.5061/dryad.70rxwdc8c
title: Data from: Seasonality and morphological variation shape intraspecific seed dispersal networks in gopher tortoises

description: Intraspecific variation within animal seed disperser populations can
 influence the structure and function of plant-animal networks, with
 important consequences for vegetation dynamics and consumer behavior.
 However, such variation remains poorly studied. We used an
 individual-based bipartite network approach to examine how morphological
 traits (e.g., carapace size) and seasonality (wet vs. dry season) shape
 seed dispersal interactions of gopher tortoises (Gopherus polyphemus)
 inhabiting South Florida’s hyper-diverse pine rockland ecosystem. Over 1.5
 years, we dissected fecal samples from 14 radio-tracked tortoises and
 identified seeds from 55 plant species. The annual tortoise–plant network
 was significantly modular and specialized but not nested, indicating that
 distinct subsets of tortoises and plants interacted more frequently with
 each other than expected by chance. Larger individuals were more
 generalized and dispersed a greater diversity of seeds than smaller
 tortoises. Seasonal subnetworks also displayed high modularity but
 differed markedly in structure, with 61% interaction dissimilarity driven
 primarily by interaction rewiring (89%) rather than species turnover.
 During the wet season, both tortoises and fleshy-fruited plant species
 exhibited reduced specialization and increased partner diversity,
 reflecting the influence of fruiting phenology on network structure.
 Together, our findings demonstrate that morphological variation and
 seasonal resource availability jointly shape seed dispersal networks.
 These results underscore the importance of preserving intraspecific
 variation in disperser populations and highlight the value of
 individual-based network analyses for understanding the ecological and
 evolutionary dynamics of seed dispersal under environmental change.
description: # Data from: Seasonality and morphological variation shape intraspecific
 seed dispersal networks in gopher tortoises Dataset DOI:
 [10.5061/dryad.70rxwdc8c](10.5061/dryad.70rxwdc8c) ## Description of the
 data and file structure This dataset was collected as part of a 1.5-year
 field study examining how intraspecific variation and seasonal dynamics
 shape seed dispersal networks in gopher tortoises (*Gopherus polyphemus*)
 inhabiting South Florida’s pine rockland ecosystem. Fourteen radio-tracked
 tortoises were monitored via telemetry across three spatially distinct
 sites in the Richmond Tract of Miami-Dade County, Florida. Fecal samples
 were opportunistically collected and dissected to identify seeds consumed
 and dispersed by each individual tortoise. For each sample, the date,
 tortoise ID, and GPS location were recorded. Identified seeds were
 categorized by plant species and dispersal syndrome (e.g., endozoochorous
 vs. other). Morphological traits—including straight-line carapace length,
 carapace width, gape size, and body mass—were measured for each tortoise.
 These data were used to construct individual-based annual and seasonal
 bipartite seed dispersal networks, assess network-level and node-level
 properties (e.g., modularity, specialization, partner diversity), and
 model the influence of tortoise morphology and seasonal fruiting phenology
 on interaction structure. The dataset underpins analyses presented in the
 associated manuscript published in *Oikos*. ### Files and variables ####
 File: Figueroa_et_al._2025_Oikos_Data.xlsx **Description:** This Excel
 workbook contains data collected from 14 radio-tracked gopher tortoises
 (*Gopherus polyphemus*) in the South Florida pine rockland ecosystem over
 a 1.5-year period. Fecal samples were dissected to identify ingested seeds
 and used to construct individual-based annual and seasonal bipartite seed
 dispersal networks. Morphological traits were also measured for each
 individual. This dataset underlies all network, seasonal, and trait-based
 analyses presented in the associated *Oikos* manuscript. ####
 **Variables** • In the sheets `"All"`, `"Dry"`,
 `"Wet"`, and `"All.Modules"`, the column
 `"species"` contains the scientific name of each plant species
 detected in at least one fecal sample from the tortoises. • In the sheets
 `"Dry.syndrome"` and `"Wet.syndrome"`, the column
 `"syndrome"` indicates the dispersal syndrome of each plant
 species detected in tortoise diets. Categories include: *anemochory*,
 *autochory*, *ballistochory*, *ectozoochory*, *endozoochory*, *foliage is
 fruit*, and *myrmecochory*, based on classifications from Ridley (1930),
 Janzen (1984), and Van der Pijl (1982). • In all sheets listed above, all
 columns (except the first column) contain the **proportion of fecal
 samples** from each tortoise (identified by ID in the column header) that
 included seeds of that plant species or syndrome. Values range from 0 to
 1. • In `"plant.trait.analysis"`, the column
 `"syndrome"` is classified as either `"endozoochory"`
 (i.e., seeds enveloped in fleshy fruit) or `"other"`. • In
 `"plant.trait.analysis"` and
 `"seasonal.trait.analysis"`, the column `"season"`
 denotes when the sample was collected and is categorized as either
 `"Dry"` or `"Wet"`. • In
 `"all.trait.analysis"`, the column `"samples"`
 indicates the total number of samples collected for each tortoise
 individual. • In all sheets where they appear:  – `"ind"` is the
 unique tortoise identifier  – `"site"` denotes the site where
 the tortoise was located: E (East), S (South), or W (West)  –
 `"cl"` and `"cw"` are straight-line **carapace
 length** and **carapace width**, respectively, measured in **centimeters
 (cm)**  – `"gape"` is the **gape width** measured in
 **millimeters (mm)**  – `"mass"` is the average **body mass** in
 **grams (g)**  – `"sex"` indicates adult sex as `"M"`
 (male) or `"F"` (female)  – `"age.class"` categorizes
 individuals as `"M"` (adult male), `"F"` (adult
 female), or `"J"` (juvenile of undetermined sex) #### **Network
 Metrics (produced via** **`specieslevel()`** **in the** **`bipartite`**
 **R package)** • `d` (Blüthgen’s d′): A measure of interaction
 specialization that quantifies how selectively a node interacts relative
 to the availability of potential partners. * Values range from 0
 (completely generalized) to 1 (completely specialized). * Accounts for
 partner availability and interaction frequency. • `norm.deg` (Normalized
 degree): The proportion of all possible plant species that a tortoise
 interacts with. * Calculated as: `(number of partners used) / (total
 number of available partners)` * Values range from 0 to 1. Does not
 account for interaction strength. • `partner.diversity`: A Shannon
 diversity index reflecting the diversity and evenness of plant species
 interacted with. * Calculated as: `-Σ (p_i * log(p_i))` where `p_i` is the
 proportion of interactions with plant species *i*. * Higher values
 indicate more evenly distributed interactions. • `species.strength`: Sum
 of the dependency values that all partners place on a given node. *
 Interpreted as the importance of the node (e.g., a tortoise or plant) in
 the network. * Higher values indicate greater centrality or influence in
 the interaction web. • In `"all.trait.analysis"`, the column
 `"module"` indicates the modular group each tortoise was
 assigned to based on the `computeModules()` function in the `bipartite`
 package. #### Missing Values: No special indicators such as
 `"NA"` or blank cells are used. Absence of interaction is coded
 as `0`. #### Literature Cited: Janzen, D. H. 1984. Dispersal of Small
 Seeds by Big Herbivores: Foliage is the Fruit. *The American Naturalist*
 123:338–353. Ridley, H. N. 1930. *The Dispersal of Plants Throughout the
 World*. L. Reeve & Company, Limited. Van der Pijl, L. 1982.
 *Principles of Dispersal in Higher Plants*. Springer. ## Code/software The
 dataset was analyzed using **R version 3.3.0**, an open-source statistical
 computing environment. Several free R packages were used to process the
 data, calculate individual- and network-level metrics, and generate the
 outputs reported in the associated manuscript. **Required software and
 packages:** * **R (v3.3.0)** –
 [https://www.r-project.org](https://www.r-project.org) * **Packages:** *
 `bipartite` – for constructing bipartite seed dispersal networks and
 calculating network and species-level indices (e.g., `specieslevel()`,
 `networklevel()`, `computeModules()`). * `readxl` – for importing Excel
 data. * `tidyverse` – for data wrangling (`dplyr`, `tidyr`) and reshaping.
 * `betalinkr` – for quantifying temporal change in individual-based
 networks using beta diversity metrics (`beta.temp()`). * `ggplot2` – for
 data visualization. **Workflow summary:** 1. **Data import and cleaning:**
 Seed interaction matrices were imported from `.xlsx` files using `readxl`,
 and reshaped using `tidyverse` tools. 2. **Network construction and
 analysis:** * Annual and seasonal bipartite matrices were analyzed using
 `bipartite::specieslevel()` and `bipartite::networklevel()` to calculate
 individual-level metrics (e.g., `d'`, `normalized degree`, `species
 strength`, `partner diversity`). * Modularity structure was assessed using
 `bipartite::computeModules()`. 3. **Trait analyses:** Morphological traits
 of tortoises were merged with network metrics and analyzed to identify
 trait-network correlations. 4. **Temporal comparison of networks:** The
 `betalinkr` package was used to quantify turnover in network composition
 and structure between wet and dry seasons using `beta.temp()`. **Code
 Summary:** This R script: Oikos_GT_Network_Code.R conducts a comprehensive
 analysis of seed dispersal networks constructed from fecal samples of
 radio-tracked gopher tortoises (*Gopherus polyphemus*) inhabiting the pine
 rocklands of South Florida. The workflow includes: **1. Data Import and
 Preparation** * Fecal sample data were imported from .xlsx files using
 readxl and converted into bipartite matrices representing individual
 tortoises (rows) and plant species or syndromes (columns). * Annual,
 seasonal (dry/wet), and syndrome-level matrices were prepared and
 converted to integer-weighted matrices for downstream network analysis. *
 Color-coded web visualizations were generated using bipartite::plotweb()
 to depict interaction structure across seasons and individuals. **2.
 Network Metrics and Visualization** * Node-level metrics including
 Blüthgen’s d′, normalized degree, species strength, and partner diversity
 were calculated using bipartite::specieslevel() for each matrix (annual
 and seasonal). * Network-level metrics such as modularity, nestedness
 (wNODF), specialization (H2′), interaction evenness, linkage density, and
 weighted connectance were quantified via bipartite::networklevel(). * Null
 model comparisons (1,000 iterations using vaznull) were used to assess
 significance of observed network metrics. **3. Modularity and Community
 Structure** * Module affiliation was calculated using
 bipartite::computeModules() and visualized with plotModuleWeb() for the
 overall and seasonal networks. * Individuals were assigned to modular
 groups and analyzed for trait-level correlates. **4. Temporal
 Dissimilarity in Interactions** * Seasonal turnover in interaction
 structure was quantified using betalinkr::betalinkr() to partition
 dissimilarity into species turnover (β<sub>ST</sub>) and
 shared species rewiring (β<sub>OS</sub>). **5. Trait-Based
 Bayesian Modeling** * Generalized linear multilevel models (GLMMs) were
 fitted using brms (with cmdstanr backend) to test the influence of
 tortoise morphological traits (e.g., carapace length, width, gape size,
 mass) on d′ and partner diversity. * Seasonal effects on specialization
 metrics were tested for both tortoise and plant communities. * Interaction
 plots and marginal effects were generated with tidybayes, marginaleffects,
 and ggplot2. **6. Null Model Comparisons at Node-Level** * Confidence
 intervals (95% CI) and standard deviations for each node-level metric were
 calculated from null distributions for each individual or species. These
 null values were extracted and organized by node identity to statistically
 compare observed versus expected specialization patterns. 
 
 ## Access information This dataset is original and was not derived from
 any other publicly accessible source. It is not available elsewhere and is
 exclusively archived on Dryad. No external licenses apply.