DOI: 10.5061/dryad.ghx3ffc0n
title: Data from: The interplay of traits, phylogeny, and abundance shapes spatial mammal diversity patterns

description: Terrestrial mammal communities are crucial indicators of the health and
 sustainability of tropical ecosystems. Consequently, understanding the
 mechanisms of community assembly and the population dynamics that
 determine the structure and composition of these communities across
 spatial and temporal scales is crucial, yet these factors are often
 overlooked when identifying conservation hotspots. This study aims to
 evaluate the relative importance of environmental filtering processes in
 shaping mammal communities at different spatial scales and understand how
 these processes are influenced by species traits, phylogenetic
 relationships, and abundances. Ultimately, high mammal diversity areas
 reflecting underlying assembly mechanisms are identified as conservation
 hotspots. We analyzed the occurrence data of 44 terrestrial mammal
 species, collected over six years from 936 camera trap stations in the
 Indo-Burma biodiversity hotspot, including Upper Myanmar and
 Xishuangbanna, China, using community hierarchical models. Climate and
 habitat factors predominantly drive species richness, while traits and
 phylogenetic relatedness together shape distinct community patterns.
 Large-bodied mammals with omnivorous guilds prefer stable temperature
 niches, while reduced precipitation tends to filter communities with
 wide-ranging mammals. Anthropogenic disturbances and reduced forest cover
 alter community structures, diminishing the prevalence of carnivorous
 traits. Phylogenetically clustered niches were observed in the Cervidae
 and Felidae families, likely driven by shared evolutionary adaptations for
 temperature tolerance in ungulates and disturbance tolerance in
 carnivores. Mammal diversity hotspots reflecting distribution and
 abundance patterns highlight the northwest and southeast ranges of Myanmar
 as key areas for fostering ecological connectivity with the Far Eastern
 Himalaya and Dawna-Tanintharyi landscapes. Ultimately, this study
 presents a novel framework for improving climate- and habitat-based
 biodiversity models by integrating trait- and phylogeny-driven assembly
 patterns, while also accounting for population abundances in suitable
 habitats to define ecologically representative and effective conservation
 areas.
description: # Data from: The interplay of traits, phylogeny, and abundance shapes
 spatial mammal diversity patterns  This dataset accompanies the
 publication: Than, K. Z., Hughes, A. C., Wang, L., Zaw, Z., & Quan,
 R.-C. (2025). The interplay of Traits, Phylogeny, and Abundance shapes
 Spatial Mammal Diversity Patterns. Global Ecology and Biogeography. **1.
 Study Overview** The dataset contains camera-trap detection data,
 environmental and trait covariates, and R scripts used to model mammal
 richness, occupancy, abundance, and community assembly across Myanmar and
 southern Yunnan, China. **Objectives:** 1\. Evaluate the relative
 importance of ecological assembly processes that sustain terrestrial
 mammal communities across tropical forest landscapes. 2\. Examine trait-
 and phylogeny-based community responses to environmental and climatic
 gradients. 3\. Generate baseline diversity maps that reflect assembly
 processes, species’ ecological niches, and population sizes to inform
 regional conservation planning. **2. Data Summary and File Structure** All
 datasets are provided in comma-separated (.csv) format, and all analytical
 codes are in R script format (.R). **File 1 –**
 **Supplementary_data1.csv** **: Sampling Parameters and Weighting**
 **Description:** Metadata for each 5 × 5 km sampling grid, including the
 number of camera traps, sampling occasions, and the weighting factors used
 to standardize sampling efforts (numbers of camera traps and numbers of
 sampling occasions) in the Multi-species N-mixture model. **Main
 columns:** Grid_ID – Unique grid identifier (character) No_Camera – Number
 of camera traps deployed (integer) No_Season – Number of independent
 sampling occasions (integer) SWeight – Camera density correction factor
 (numeric) CWeight – Camera-day correction factor (numeric) **File 2
 –** **Supplementary_data2.csv** **: Detection Metadata** **Description:**
 Detection records for all species captured by camera traps across six
 sampling sites. Each row represents an independent detection event. **Main
 columns:** Grid_ID – 5 × 5 km grid identifier (character) Camera_ID –
 Camera trap identifier (character) Season – Sampling occasion identifier
 (character) Latin_Name – Scientific name (based on IUCN taxonomy)
 (character) Individual – Unique detection event code (integer) Site_ID –
 Sampling site name identifier (character) **File 3 –**
 **Supplementary_data3.csv** **: Species Presence–Absence Matrix**
 Description: Binary (0/1) presence–absence matrix for 44 mammal species
 across all sampling grids, derived from the detection metadata. **Notes:**
 1 = detected in at least one occasion; 0 = not detected. Used as input for
 HMSC modeling. **File 4 –** **Supplementary_data4.csv** **: Environmental
 and Habitat Covariates** Description: Scaled covariate dataset used for
 HMSC modeling, representing habitat, disturbance, and climate factors.
 **Main variables:** TC – Percent forest cover within grid (Hansen et al.,
 2013) FFI – Forest fragmentation index (Ma et al., 2023) Canopyht – Forest
 canopy height (Lang et al., 2023) DEM – Elevation (Caglar et al., 2018)
 CBdst – Euclidean distance to the nearest disturbance sources (Esri, 2020;
 [https://livingatlas.arcgis.com/landcover/](https://livingatlas.arcgis.com/landcover/)) Bio3 – Isothermality in % (WorldClim; Fick and Hijmans, 2017) Bio19 – Precipitation of the coldest quarter in mm (WorldClim; Fick and Hijmans, 2017) **File 5 –** **Supplementary_data5.csv** **: Trait and Phylogenetic Data** **Description:** Scaled trait dataset for 44 mammal species derived primarily from the PanTHERIA database, with missing values supplemented from the Animal Diversity Web (Myers et al., 2024). Includes life-history traits used in trait–environment and phylogenetic correlation analyses. **Main traits:** Latin_Name – Scientific name (based on IUCN taxonomy) (character) BM – Adult body mass (log-transformed) (continuous) TL – Primary diet category (herbivore, carnivore, omnivore) (categorical) HR – Mean home-range size (km²) (continuous) **3. Description of Code Files** Supplementary Code 1 – Phylogeny Extraction: Extracts species-level subtree from the global mammal phylogeny. (R: ape, phyloOrchard) Supplementary Code 2 – Trait Data Extraction: Extracts and scales species-level trait data. (R: dplyr, tidyr) Supplementary Code 3 – HMSC Model: Runs the spatial hierarchical community model (HMSC). (R: HMSC, R2jags, ape) Supplementary Code 4 – Model Evaluation: Assesses model explanatory and predictive performance. (R: HMSC, caret) Supplementary Code 5 – Spatial Predictions: Generates gridded occupancy estimates over the prediction surface based on the best-fitted HMSC model. (R: raster, sf) Supplementary Code 6 – Multi-species N-mixture Model: Fits MNM and performs bootstrapping for abundance estimation. (R: jagsUI, R2jags, multcompView, caret, clusterGeneration) Supplementary Code 7 – Diversity Indices: Calculates Shannon and Simpson diversity indices. (R: iNEXT, vegan)  **4. Relationships Among Files** Supplementary Data 2 (detection metadata) is processed into Supplementary Data 3 (presence–absence matrix). Supplementary Data 1 (sampling effort) and Supplementary Data 4 (environmental covariates) are used as predictors in the HMSC and MNM models. Supplementary Data 5 (traits and phylogeny) is integrated into the HMSC model for trait-based and phylogenetic analyses. Supplementary Codes 1–7 correspond sequentially to the analytical workflow described in the manuscript. **5. Contact** For questions or data requests, please contact:  Kay Zin Than (PhD) Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences Email: [kayzin@xtbg.ac.cn](mailto:kayzin@xtbg.ac.cn), [kayzin.pma@gmail.com](mailto:kayzin.pma@gmail.com) Last updated: November 2025 ## Code/software **R version:** 4.2.0 or higher **Required R packages:** HMSC, ape, phytools, R2jags, jagsUI, dplyr, tidyr, raster, sf, iNEXT, vegan, caret, multcompView, clusterGeneration All scripts are annotated and reproducible using standard package functions. Ensure that working directory paths are set before running.