DOI: 10.5061/dryad.zkh1893fk
title: Data and code from: Breakdown in seasonal dynamics of subtropical ant communities with land-cover change

description: Concerns about widespread human-induced declines in insect populations are
 mounting, yet little is known about how land-use change modifies the
 dynamics of insect communities, particularly in understudied regions.
 Here, we examine how the seasonal activity patterns of ants—key drivers of
 terrestrial ecosystem functioning—vary with anthropogenic land-cover
 change on a subtropical island landscape, and whether differences in
 temperature or species composition can explain observed patterns. Using
 trap captures sampled biweekly over two years from a biodiversity
 monitoring network covering Okinawa Island, Japan, we processed 1.2
 million individuals and reconstructed activity patterns within and across
 habitat types. Forest communities exhibited greater temporal variability
 of activity than those in more developed areas. Using time-series
 decomposition to deconstruct this pattern, we found that sites with
 greater human development exhibited ant communities with diminished
 seasonality, reduced synchrony, and higher stochasticity compared to sites
 with greater forest cover. Our results cannot be explained by variation in
 regional or site temperature patterns, or by differences in species
 richness or composition among sites. Our study raises the possibility that
 disruptions to natural seasonal patterns of functionally key insect
 communities may comprise an important and underappreciated consequence of
 global environmental change that must be better understood across Earth’s
 biomes.
description: The ant activity data used in the analysis was collected with
 Sea, Land, and Air Malaise (SLAM) traps on Okinawa Island in Japan from
 2016-2018, and was processed using the code provided in the Zenodo archive
 (see README for links and the paper for references). Other datasets come
 from the Japan Meterological Agency (JMA), <em>in situ</em>
 climate variables for sampling stations measured on-site, and land-cover
 data for Okinawa developed by our team. The data used in the analysis is
 included in the data upload (with JMA and land-cover data files in a
 Zenodo supplemental information under the CC-By 4 license), and all the
 analysis code is included in the R package provided in a separate Zenodo
 software archive. NOTE: All datasets (from Dryad and
 Zenodo) must be put into a single folder called `/data` within the main
 directory of the R analysis package before running any code. Please
 consult the README for further details on the data and code.
description: All analysis were conducted in R.
description: # Data and code for: Breakdown in seasonal dynamics of subtropical ant
 communities with land-cover change [Access these datasets on
 Dryad](https://doi.org/10.5061/dryad.zkh1893fk) This archive contains the
 data files used in the analyses for "Breakdown in seasonal dynamics
 of subtropical ant communities with land-cover change", published in
 Proceedings of the Royal Society B: Biological Sciences (DOI:
 10.1098/rspb.2023.1185). **NOTE**: All datasets (from Dryad and Zenodo)
 must be put into a single folder called `/data` within the main directory
 of the R analysis package before running any code. `activity_orig.rds`: A
 tibble with the ant activity data (before processing), collected by the
 [Okinawa Environmental Observation Network](https://okeon.unit.oist.jp/)
 (OKEON) with Sea, Land, and Air Malaise (SLAM) traps on Okinawa Island in
 Japan from 2016-2018 at 24 sites sampling different environments across
 the island (data downloaded from OKEON database in November 2022). The
 field "count" refers to how many individuals were collected
 during a collection event, but the interpretation of this count is a
 quantity called "activity", further described in the paper.
 `microclim_data.rds`: Tibbles with *in situ* climate measurements and
 associated metadata per site with temporal resolution of 15 minutes. These
 *in situ* climate data (site air (1.5 m above-ground) and soil (10 cm
 below-ground) temperatures) were used to determine if site differences in
 climate contributed to observed community temporal variability patterns.
 Temperatures are in Celcius. Some values for soil\_temperature in the
 original dataset are NA due to occasional equipment malfunction, but the
 final table `mc.summary` (created in `tempvar_analysis.Rmd`, see below)
 averages these values over 2-week time bins and ignores these NA values.
 Thus, the final table used for analysis does not contain NAs.
 `samples_FY2016.csv` and `samples_FY2017.csv`: Tables describing the exact
 dates that collection events occurred. These data were used to attribute
 the sampling events by time bin. Standardized time bin attribution was
 necessary because some sampling events began or ended on different days.
 `okinawa_ant_invasive_status.csv`: Table describing invasive status per
 species collected, decided by expert opinion by co-authors M. Yoshimura,
 F. H. Garcia, and G. Fischer. [Other datasets on
 Zenodo](https://doi.org/10.5281/zenodo.8371770) Several other data files
 are included in a Zenodo supplemental information archive with the CC-By 4
 license. `lc.rds`: Raster data describing land-cover classifications for
 Okinawa Island, developed by co-author K. L. Dudley and originally
 described in Ross et al. 2018 (reference below). The land-cover data was
 used to characterize each sampling site. We did this by calculating
 land-cover proportions within a 1-km buffer, then reducing dimensionality
 with a PCA to derive two principal component axes: PC1 describing the
 developed-forested gradient, and PC2 describing the rural-urban gradient.
 These axes (PC1 and PC2) were used as predictor variables in our models of
 temporal variability. In this dataset, `jma_data.rds` and `jma_meta.rds`:
 Tibbles with Japan Meteorological Agency (JMA) regional climate data with
 temporal resolution of 1 day. The downloaded data was processed to retain
 only dates between 2016-03-22 and 2018-04-01 for the analysis, Japanese
 station site names were removed while English ones were kept, and several
 column names were changed for ease of analysis. Like the *in situ* site
 climate data, the JMA regional climate data was used to determine if site
 differences in climate (here, coarser spatial scale) contributed to
 observed community temporal variability patterns. Temperatures are in
 Celcius and precipitation is in millimeters. Similar to the *in situ*
 climate dataset, some values in the original dataset are NA but the final
 table `jma.summary` used for analysis (created in `tempvar_analysis.Rmd`,
 see below) averages over 2-week time bins and ignores these NA values.
 Thus, the final table used for analysis does not contain NAs. ##
 Description of the data and file structure R object files with extension
 .rds can read in `R` using `readRDS()`, and .Rdata files can be read with
 `load()`. **NOTE**: All datasets (from Dryad and Zenodo) must be put into
 a single folder called `/data` within the main directory of the R analysis
 package before running any code. ## Code/Software [Analysis
 code](https://doi.org/10.5281/zenodo.7981051) The analysis code for the
 paper is found here structured as a simple R package. Run the file
 `okiAntsTempVar.Rproj` to open an R Studio session to easily access the
 code. ### Main analysis script `tempvar_analysis.Rmd`: A documented R
 Markdown script file with the code for the main analysis. This script run
 all analyses presented in the paper, creates and saves all figures to .pdf
 or .png (some of which were then edited in Adobe Illustrator), saves the
 model results table to a .csv file, and also saves a copy of the R
 environment as a .Rdata file after the analysis is complete. This .Rmd has
 parameters in the YAML header (between the `---` lines at the top), and
 these are changed to rerun different iterations in
 `analysis/run_sensitivity_analysis.R` (explained below) to perform a
 sensitivity analysis (Fig. S6 in the paper). All custom package functions
 used in this .Rmd are found in the scripts below that specify them. ###
 Scripts found in `/R` `okeon_activ.R`: Functions for processing the ant
 activity data. `okeon_bd.R`: Functions for calculating community temporal
 beta diversity ("compositional diversity" in the paper) using
 the R package `adespatial`. `okeon_cv.R`: Functions for calculating
 community temporal coefficient of variation ("functional
 diversity" in the paper). `okeon_div.R`: Functions for calculating
 species richness metrics, including extrapolations, using the R package
 `iNEXT`. `okeon_gis.R`: Function for making spatial objects for the site
 points using the R package `sf`. `okeon_jma.R`: Functions for processing
 the JMA climate data. `okeon_lc.R`: Functions for processing the land
 cover data. `okeon_mods.R`: Functions for building models, calculating
 AICc, and performing model selection. `okeon_rar.R`: Functions for
 performing different rarefaction analyses on the ant activity data
 (sampling site-level and land-cover group-level). `okeon_ts.R`: Functions
 for implementing time-series regression models to decompose the ant
 activity time-series data. `okeon_util.R`: Utility functions to abbreviate
 text strings for species and site names. `utils-pipe.R`: ROxygen code to
 allow the use of the R package `magrittr` "pipe" command. ###
 Scripts found in `/analysis` `run_sensitivity_analysis.R`: This code
 iterates different parameterizations for the main analysis script
 `tempvar_analysis.Rmd` by changing the count threshold and trap/site
 level. The count threshold (parameter `threshold.val`) governs the maximum
 ant count value retained in the dataset, and all values above this are
 constrained to the threshold (the default is 500). The trap/site level
 (parameter `threshold.lvl`) governs the spatial level at which the
 threshold is applied (either by trap or by site, where there are 3 traps
 per site). The purpose of these iterations is to conduct a sensitivity
 analysis for the effects of these parameter changes on the results, and
 the outcomes are shown in Fig. S6 (none were found to be extreme). ##
 References Ross, S. R. J., Friedman, N. R., Dudley, K. L., Yoshimura, M.,
 Yoshida, T., & Economo, E. P. (2018). Listening to ecosystems:
 data-rich acoustic monitoring through landscape-scale sensor networks.
 Ecological Research, 33, 135-147.