DOI: 10.6084/m9.figshare.25857385
title: 3D Structure - Hornbill Seed Dispersal

description: # Predicting spatial patterns of seed dispersal by hornbills based on selection for attributes of vegetation structure<br>Nicholas J. Russo<br>University of California, Los Angelesnickrusso@ucla.edu<br>This repository contains the data and code necessary to reproduce the results of:<b>Russo, N.J.</b>, D.L. Nshom, A. Ferraz, N. Barbier, M. Wikelski, M.J. Noonan, E.M. Ordway, S. Saatchi, and T.B. Smith. 2024. Three-dimensional vegetation structure drives patterns of seed dispersal by African hornbills. <i>Journal of Animal Ecology</i> 93:1935-1946. https://doi.org/10.1111/1365-2656.14202<br>### Scripts-`Hornbill_iSSA_share.R`: script to conduct iSSA for each hornbill individually and compare between species, and generate Figure 2 of manuscript-`Hornbill_GLMM_population-level.R`: script to apply GLMM to hornbill data and generate population-level estimates for all covariates included in individual iSSAs, and saving SSF objects for the seed dispersal simulations as `ssf.mango.RData` and `ssf.kata.RData`-`Hornbill_swamp-temp_interaction_share.R`: script to apply GLMM to hornbill data for each of four temperature classes and show how selection for swamp habitat varies according to each temperature class, and generate Figure 3 of manuscript-`Hornbill_ODBA_GLMMs_share.R`: script to apply GLMM to hornbill acceleration data, showing how spatial covariates and weather covariates influence Overall Dynamic Body Acceleration (ODBA)-`seedSim_Staudtia_kamerunensis_share.R`: script to simulate spatial patterns of seed dispersal for a Staudtia kamerunensis tree and generate rasters featured in Figures 5A and 5B of manuscript-`seedSim_Xylopia_hypolampra_share.R`: script to simulate spatial patterns of seed dispersal for a Xylopia hypolampra tree and generate rasters featured in Figures 5C and 5D of manuscript-`seedSim_Maesopsis_eminii_share.R`: script to simulate spatial patterns of seed dispersal for a Maesopsis eminii tree and generate rasters featured in Figures 5E and 5F of manuscript<br>### Data folder# Environmental Layers- `ch.tif`: Canopy Height, 10 m resolution- `vc.tif`: Vertical Complexity Index, 10 m resolution- `d50.tif`: Distance to gap of size &gt;= 50 m, 10 m resolution- `d500.tif`: Distance to gap of size &gt;=500 m, 10 m resolution- `swamp.tif`: Landscape classified as either swamp (1) or other habitat (0)<br># Hornbill Tracking and Covariates- `hornbills_weatherSelect_10steps.RData`: Hornbill GPS locations with 10 random steps per movement step, extracted spatial covariates, and merged weather data- `bch_acc_weather.RData`: Black-casqued hornbill acceleration summary covariates with GPS locations, extracted spatial covariates, and merged weather data`wth_acc_weather.RData`: White-thighed hornbill acceleration summary covariates with GPS locations, extracted spatial covariates, and merged weather data- `ssf.mango.RData`: SSF object based on black-casqued hornbill population-level coefficients of selection for spatial covariates, needed for seed dispersal simulations- `ssf.kata.RData`: SSF object based on white-thighed hornbill population-level coefficients of selection for spatial covariates, needed for seed dispersal simulations- `black.casqued.RData`: Black-casqued hornbill random step data frame of class "random_steps/steps_xyt/steps_xy/tbl_df/tbl/data.frame", needed only to set the starting location for redistribution kernel- `white.thighed.RData`: White-thighed hornbill random step data frame of class "random_steps/steps_xyt/steps_xy/tbl_df/tbl/data.frame", needed only to set the starting location for redistribution kernelR Session Info (version 4.3.1):<pre>attached base packages:<br>[1] parallel stats graphics grDevices utils datasets methods base <br><br>other attached packages:<br> [1] colorRamps_2.3.1 spatstat_3.0-6 spatstat.linnet_3.1-1 spatstat.model_3.2-4 <br> [5] rpart_4.1.19 spatstat.explore_3.2-1 spatstat.random_3.1-5 spatstat.geom_3.2-4 <br> [9] spatstat.data_3.0-1 circular_0.4-95 pbs_1.1 animove_2023.0 <br>[13] move2_0.2.2 ggeffects_1.4.0 plyr_1.8.8 MuMIn_1.47.5 <br>[17] moveACC_0.1 multcomp_1.4-25 TH.data_1.1-2 mvtnorm_1.2-2 <br>[21] lme4_1.1-34 Matrix_1.6-0 mgcv_1.9-0 nlme_3.1-162 <br>[25] rgeos_0.6-4 rgdal_1.6-7 viridis_0.6.4 viridisLite_0.4.2 <br>[29] ggpubr_0.6.0 AICcmodavg_2.3-2 survival_3.5-5 car_3.1-2 <br>[33] carData_3.0-5 glmmTMB_1.1.7 ctmm_1.1.1 moveVis_0.10.6 <br>[37] amt_0.2.1.0 move_4.2.4 raster_3.6-23 geosphere_1.5-18 <br>[41] adehabitatHR_0.4.21 adehabitatLT_0.3.27 CircStats_0.2-6 boot_1.3-28.1 <br>[45] MASS_7.3-60 adehabitatMA_0.3.16 ade4_1.7-22 sp_2.0-0 <br>[49] terra_1.7-39 sf_1.0-14 magrittr_2.0.3 lubridate_1.9.2 <br>[53] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2 purrr_1.0.1 <br>[57] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.3 <br>[61] tidyverse_2.0.0 <br><br>loaded via a namespace (and not attached):<br> [1] splines_4.3.1 polyclip_1.10-4 datawizard_0.9.1 lifecycle_1.0.3 <br> [5] Rdpack_2.4 rstatix_0.7.2 lattice_0.21-8 vroom_1.6.3 <br> [9] insight_0.19.8 backports_1.4.1 plotly_4.10.2 gifski_1.12.0-1 <br> [13] spatstat.sparse_3.0-2 cowplot_1.1.1 pbapply_1.7-2 DBI_1.1.3 <br> [17] minqa_1.2.5 pkgload_1.3.2.1 abind_1.4-5 expm_0.999-7 <br> [21] sandwich_3.0-2 spatstat.utils_3.0-3 units_0.8-2 goftest_1.2-3 <br> [25] fitdistrplus_1.1-11 codetools_0.2-19 xml2_1.3.5 tidyselect_1.2.0 <br> [29] basemaps_0.0.5 farver_2.1.1 stats4_4.3.1 jsonlite_1.8.7 <br> [33] polycor_0.8-1 e1071_1.7-13 emmeans_1.9.0 av_0.8.3 <br> [37] tools_4.3.1 Rcpp_1.0.11 glue_1.6.2 gridExtra_2.3 <br> [41] admisc_0.35 msm_1.7.1 slippymath_0.3.1 withr_2.5.0 <br> [45] numDeriv_2016.8-1.1 fastmap_1.1.1 fansi_1.0.4 digest_0.6.33 <br> [49] timechange_0.2.0 R6_2.5.1 estimability_1.4.1 colorspace_2.1-0 <br> [53] tensor_1.5 utf8_1.2.3 generics_0.1.3 data.table_1.14.8 <br> [57] class_7.3-22 httr_1.4.7 htmlwidgets_1.6.2 pkgconfig_2.0.3 <br> [61] gtable_0.3.3 htmltools_0.5.5 TMB_1.9.4 scales_1.2.1 <br> [65] rstudioapi_0.15.0 tzdb_0.4.0 coda_0.19-4 checkmate_2.2.0 <br> [69] curl_5.0.2 nloptr_2.0.3 proxy_0.4-27 cachem_1.0.8 <br> [73] zoo_1.8-12 KernSmooth_2.23-22 pillar_1.9.0 grid_4.3.1 <br> [77] vctrs_0.6.3 VGAM_1.1-8 xtable_1.8-4 magick_2.7.4 <br> [81] cli_3.6.1 compiler_4.3.1 rlang_1.1.1 crayon_1.5.2 <br> [85] ggsignif_0.6.4 labeling_0.4.2 classInt_0.4-9 stringi_1.7.12 <br> [89] deldir_1.0-9 assertthat_0.2.1 munsell_0.5.0 lazyeval_0.2.2 <br> [93] hms_1.1.3 unmarked_1.3.2 bit64_4.0.5 ltm_1.2-0 <br> [97] haven_2.5.3 rbibutils_2.2.13 broom_1.0.5 memoise_2.0.1 <br>[101] lwgeom_0.2-13 bit_4.0.5</pre><br>