DOI: 10.5061/dryad.0cfxpnwdx
title: Pollinator interactions of native and introduced plants in smallholder tropical orchards across a gradient of anthropogenic landscapes

description: Anthropogenic habitats commonly favor introduced species. However, there
 is little information regarding the responses of pollinators to native and
 introduced plants across anthropogenic landscapes. The main goal of the
 study is to investigate pollinator interactions of native and introduced
 plants across smallholder tropical orchards in Thailand, as these
 plantations are essential to local food security. We compiled data from
 four recent studies on plant-pollinator networks in 65 smallholder
 tropical orchards across Thailand. Using network analyses, we compared
 species degree (i.e., the number of pollinator species visiting a plant)
 and specialization (d′) between native and introduced plant species, with
 the latter further categorized into non-naturalized, naturalized, and
 invasive groups. Analyses were conducted using linear
 mixed-effects models, treating individual plant species as the unit of
 analysis and incorporating orchard identity nested within study as a
 random effect. Additionally, we examined whether variation in surrounding
 anthropogenic landscapes (i.e., the proportion of agricultural and
 urbanized areas) and the proportional representation of introduced plant
 species influenced the structure of plant–pollinator networks. Overall,
 specialization (d′) of the native plants was significantly greater than
 that of the introduced plants, while pollinators exhibited no preference
 towards naturalized or invasive plants over non-naturalized ones. We found
 a decrease in species degree of native plants as well as connectance and
 linkage density with increasing proportion of anthropogenic areas, likely
 due to limited floral resources and nesting availability. However, we also
 found an increase in network specialization (H2′) with a higher proportion
 of urban area. Understanding the impact of land-use changes with the
 presence of introduced plant species is important for predicting community
 stability of plant-pollinator interactions as well as for habitat
 management strategies. Surrounding natural habitats and important food
 plants should be preserved and promoted to maintain pollinator
 interactions in human-dominated areas.
description: # Pollinator interactions of native and introduced plants in smallholder
 tropical orchards across a gradient of anthropogenic landscapes Dataset
 DOI: [10.5061/dryad.0cfxpnwdx](10.5061/dryad.0cfxpnwdx) ## Description of
 the data and file structure This project analyzes how plant-pollinator
 interaction networks vary across different plant categories and landscapes
 in tropical orchards. We focus on both species-level and network-level
 metrics to assess the ecological roles of native vs. introduced plants,
 human food plants, and the impacts of land-use change. ### Files and
 variables #### File: Dataset.zip **Description:** Compiled CSV datasets of
 plant–pollinator interactions across 96 orchard sites. Used as input for
 metric calculations. #### File:
 SP_and_NW_level-Pollination_network_each_site.R **Description:** R script
 to calculate species-level and network-level pollination metrics per site.
 #### File: SP_level.zip **Description:** Contains all scripts and data for
 species-level analyses, including group comparisons and land-use effects.
 **File: NW_level.zip** **Description:** Contains all scripts and data for
 network-level analyses, focusing on land-use impacts on connectance,
 linkage density, and H2′. ## Code/software ### `SP and NW
 level-Pollination network each site.R` This script calculates both
 species-level and network-level pollination metrics for each orchard site
 using binary plant-pollinator interaction matrices. #### **Species-Level
 Metrics** * Computed using `specieslevel()` from the `bipartite` package.
 **Network-Level Metrics** Computed for each site using `networklevel()`: *
 Connectance * Nestedness * Linkage density * H2′ (network-level
 specialization) #### **Workflow Summary** * Loads 1–96 site-specific CSV
 files from `Dataset.zip`, each representing a unique plant-pollinator
 interaction matrix. * For each site: * Reads the matrix. * Calculates
 species- and network-level metrics. ### **SP level.zip** ###
 `LMM-comparison plant groups.R` This script performs comparative
 statistical analysis of species-level pollination network metrics across
 five plant group contrasts using linear mixed-effects models (LMMs). ####
 **Plant Group Comparisons** 1. Native vs. Introduced 2. Non-naturalized
 vs. Naturalized 3. Naturalized vs. Invasive 4. Human-food vs.
 Non-human-food 5. Native human-food vs. Introduced human-food ####
 **Metrics Analyzed** * Species Degree (log-transformed) * Specialization
 (d′) (square-root transformed) #### **Workflow Summary** * Loads
 preprocessed datasets from `LMM-plant groups` folder. * Transforms metrics
 to improve normality based on Shapiro tests. * Fits LMMs for each
 comparison with `Study_ID / Orchard_ID` as nested random effects. *
 Extracts model summaries and calculates: * Marginal R² (variance explained
 by fixed effects) * Conditional R² (variance explained by fixed + random
 effects) * Applies Benjamini–Hochberg correction within each metric for
 multiple testing control. ### `LMM-SP level-Land use.R` This script
 investigates the effects of land-use change on species-level pollination
 network metrics — species degree and specialization (d′) — for native and
 introduced plants, using linear mixed-effects models (LMMs). #### **Key
 Objectives** * Assess how increasing proportions of agricultural (AG) and
 urbanized (UR) landscapes around orchards influence plant species’
 interaction metrics. * Evaluate native and introduced plants separately to
 detect differential land-use responses. #### **Dataset** * Input:
 `LMM-Dataset-Native-Intro-Land use-Final.csv` * Transformed metrics: *
 `log_degree` = log(species_degree) * `sqrt_dprime` = sqrt(d′) * Nested
 random effects: `Orchard_ID` within `Study_ID` to account for spatial and
 study-level variation. #### **Modeling Workflow** * Separate models for
 native and introduced plant groups: * Full model: AG + UR * Reduced
 models: AG-only, UR-only, and null models * Model selection via
 likelihood-ratio tests using `anova()` from `lme4`. * R² values computed
 using `r.squaredGLMM()` from the `MuMIn` package. ### **NW level.zip** ###
 `LMM-NW level-Landuse.R` This script analyzes how landscape composition
 (agricultural and urbanized areas) and the proportion of introduced plants
 influence network-level pollination metrics in smallholder tropical
 orchards using linear mixed-effects models (LMMs). #### **Objective** To
 examine the effects of: * `% Agricultural area (AG)` * `% Urbanized area
 (UR)` * `% Introduced plant species (INTRO)` * And their interactions on
 three key network-level metrics: 1. Connectance 2. Linkage Density
 (log-transformed) 3. Specialization (H2′) #### **Dataset** * Input:
 `Dataset-NW level-Land use-Final.csv` * Random effect: `Study_ID` to
 account for variation across studies. #### **Modeling Workflow** *
 Stepwise model selection via likelihood-ratio tests (LRT) using `anova()`
 to compare nested LMMs. * For each metric, models start from full
 interaction models (AG × INTRO, UR × INTRO), with stepwise simplification.
 * Model assumptions assessed using `check_model()`. * Goodness-of-fit
 measured via AIC, logLik, R² marginal (fixed effects), and R² conditional
 (fixed + random effects) using `MuMIn::r.squaredGLMM()`.