This file contains the data from Muylaert et al. 2022. Present and future distribution of bat hosts of sarbecoviruses: implications for conservation and public health. Proceedings of the Royal Society B. and guidelines for the use of the workflow.
Please read the following guidelines:
muylaert_et_al_data.zip
.dynamic
repository GitHub
and find the distribution_models
folder.muylaert_et_al_data
to the distribution_models
folder.distribution_models
R
project.Please find the content description for each folder below:
_env_27km_ss6
contains all environmental layers (*.tif)
for the present, already scaled._env_fut_27km_ss6
contains all environmental layers
(*.tif) for the future, already scaled, for all global circulation
models, periods and scenarios.dynamic_copies
is an auxiliary data folder for when the
user decides to update the species list used (*.xlsx and *.csv
files).dynamic_master
contains the text files for the master
dataset with IUCN-intersected
and non-intersected occurrences iwthin our working extent.hotspots
contains data for species presence and future
projections.IUCN_assessment_list_chiroptera
IUCN assessments for the Order
Chiroptera (*.xlsx).iucn_shapefile
contains the IUCN ranges dataset for the
Class Mammalia (terrestrial only).olson
contains the shapefile files for all terrestrial
ecosystems of the world (Olson
et al. 2001).rasters_temp_forest
contains the rasters used for
hotspot calculation (*.tif).results_40o_ss6_maxent_15rep
ENMTML data sctructure for
non-intersected occurrences (text files and *.tif).results_iucni_40o_ss6_maxent_15rep
ENMTML data sctructure for
IUCN-intersected occurrences (text files and *.tif).The reference for workflow table used is Zurell et al. 2020. A standard protocol for reporting species distribution models. Ecography 43, 1261–1277.
Section | Subsection | Element | Value |
---|---|---|---|
Overview | Authorship | Study title | Present and future distribution of bat hosts of sarbecoviruses: implications for conservation and public health. |
Overview | Authorship | Author names | Renata L. Muylaert; Tigga Kingston; Jinhong Luo; Maurício Humberto Vancine; Nikolas Galli; Colin J. Carlson; Reju Sam John; Maria Cristina Rulli; David T. S. Hayman. |
Overview | Authorship | Contact | R.deLaraMuylaert@massey.ac.nz |
Overview | Authorship | Study link | https://doi.org/10.1098/rspb.rspb.2022.0397 |
Overview | Model objective | Model objective | Forecasting and transfer. |
Overview | Model objective | Target output | Continuous occurrence probabilities and binary maps of potential presence. |
Overview | Focal Taxon | Focal Taxon | Bats hosts of sarbecoviruses. |
Overview | Location | Location | World. |
Overview | Scale of Analysis | Spatial extent | -30, 160, -30, 70 (xmin, xmax, ymin, ymax) |
Overview | Scale of Analysis | Spatial resolution | 0.25 dd |
Overview | Scale of Analysis | Temporal extent | Near-current and Future (2021-2100). |
Overview | Scale of Analysis | Temporal resolution | Near-current, 2021-2040, 2041-2060, 2061-2080, 2081-2100. |
Overview | Scale of Analysis | Boundary | Terrestrial areas of the world. |
Overview | Biodiversity data | Observation type | Human observation of occurrences. |
Overview | Biodiversity data | Response data type | Presence. |
Overview | Predictors | Predictor types | Bioclimatic; karst; forest cover. |
Overview | Hypotheses | Hypotheses | Implications for the conservation and public health through evaluation of species distribution change in response to climatic, karst, and forest cover. |
Overview | Assumptions | Model assumptions | Bats occur within their bioregions where they were detected, and around their highest density of occurrence points (MSDMs). Bat distribution is driven bioclimatic covariates, karst and native forest cover. Accessibility bias partially drives observed occurrences. Sampling bias is minimized by filtering, spatial thinning and minimal occurrences for inclusion criteria (N=40). |
Overview | Algorithms | Modelling techniques | Maxent through the ENMTML R package. |
Overview | Algorithms | Model complexity | Six follwoing covariates were used bio 1, bio 4, bio 12, bio 15, karstm, primf tif files. |
Overview | Algorithms | Model averaging | True skill statistics-weighted (TSS-weighted) averaging. |
Overview | Workflow | Model workflow | ENMTML workflow. |
Overview | Software | Software | R 4. |
Overview | Software | Code availability | https://github.com/renatamuy/dynamic |
Overview | Software | Data availability | Dryad. |
Data | Biodiversity data | Taxon names | Aselliscus stoliczkanus Hipposideros armiger Hipposideros galeritus Hipposideros larvatus Hipposideros pomona (gentilis) Hipposideros pratti Hipposideros ruber Miniopterus schreibersii Chaerephon plicatus Tadarida teniotis Rhinolophus acuminatus Rhinolophus affinis Rhinolophus blasii Rhinolophus blythi Rhinolophus cornutus Rhinolophus creaghi Rhinolophus euryale Rhinolophus ferrumequinum Rhinolophus hipposideros Rhinolophus luctus Rhinolophus macrotis Rhinolophus malayanus Rhinolophus marshalli Rhinolophus mehelyi Rhinolophus monoceros Rhinolophus pearsonii Rhinolophus rex Rhinolophus shameli Rhinolophus siamensis Rhinolophus sinicus Rhinolophus stheno Rhinolophus thomasi Nyctalus leisleri Plecotus auritus |
Data | Biodiversity data | Taxonomic reference system | Wilson D, Mittermeier R, editors. Handbook of the Mammals of the World. Barcelona: Springer; 2019. |
Data | Biodiversity data | Ecological level | assemblage-level, species-level. |
Data | Biodiversity data | Data sources | Darkcides v1, Global Biodiversity Information Facility (GBIF), Berkeley Ecoinformatics Engine (Ecoengine), Vertnet, Integrated Digitized Biocollections (IDigBio), iNaturalist, Obis, Vertnet, and data compiled for previous publications Darkcides v01, Rulli et al. (2020), Luo et al. (2013) |
Data | Biodiversity data | Sampling design | ENMTML workflow. |
Data | Biodiversity data | Clipping | Terrestrial areas of the world. |
Data | Biodiversity data | Scaling | None. |
Data | Biodiversity data | Cleaning | Data cleaning: Temporal range from 1970-2020. Cleaning process through CooordinateCleaner package including species with at least 40 occurrence points. |
Data | Biodiversity data | Absence data | None. |
Data | Biodiversity data | Background data | pres_abs_ratio = 1 |
Data | Biodiversity data | Errors and biases | Errors and biases: Sampling rates estimates through sampbias R package. |
Data | Data partitioning | Training data | 75:25 training:test. |
Data | Data partitioning | Validation data | 75:25 training:test. |
Data | Data partitioning | Test data | Ratio of 75:25 training:test cross-validation splits with 10 repeats. |
Data | Predictor variables | Predictor variables | Bioclimatic variables, Karst composite layer, Primary forest cover. |
Data | Predictor variables | Data sources | Table S3. |
Data | Predictor variables | Spatial extent | -30, 160, -30, 70 (xmin, xmax, ymin, ymax)’ |
Data | Predictor variables | Spatial resolution | 0.25 dd. |
Data | Predictor variables | Coordinate reference system | WGS84. |
Data | Predictor variables | Temporal extent | Bioclimatic variables cover 1970-2000 for near-current conditions. Future projection periods: 2020-2040, 2040-2060, 2060-2080, 2080-2100. |
Data | Predictor variables | Temporal resolution | Future projection periods: 2020-2040, 2040-2060, 2060-2080, 2080-2100. |
Data | Predictor variables | Data processing | Covariates resampled to 0.25 dd. |
Data | Predictor variables | Errors and biases | Assessed via sampbias R package. |
Data | Predictor variables | Dimension reduction | None. |
Data | Transfer data | Data sources | |
Data | Transfer data | Spatial extent | World. |
Data | Transfer data | Spatial resolution | 0.25 dd |
Data | Transfer data | Temporal extent | 1970-present |
Data | Transfer data | Temporal resolution | Yearly |
Data | Transfer data | Models and scenarios | Future bioclimati data downloaded from Worldclim (CMIP6). |
Data | Transfer data | Data processing | Future-occurrence predictions were made for each species and then ensembled per period per GCM and SSP. |
Data | Transfer data | Quantification of Novelty | NA |
Model | Variable pre-selection | Variable pre-selection | Relevance for our conceptual model of important native habitats for the selected species. |
Model | Multicollinearity | Multicollinearity | All bioclimatic covariates, karst layer and forest layer were pre-selected and then filtered after correlation analysis (0.7 cutoff value). |
Model | Model settings | Model settings (fitting) | MXS’ and ‘MXD’ algorithms. |
Model | Model settings | Model settings (extrapolation) | Extrapolations over near-current accessible areas assuming MSDM ‘OBR’ for the present. |
Model | Model estimates | Coefficients | NA |
Model | Model estimates | Parameter uncertainty | NA |
Model | Model estimates | Variable importance | Correlative. |
Model | Model selection - model averaging - ensembles | Model selection | NA |
Model | Model selection - model averaging - ensembles | Model averaging | NA |
Model | Model selection - model averaging - ensembles | Model ensembles | Weighted averaging of the algorithms through TSS. |
Model | Analysis and Correction of non-independence | Spatial autocorrelation | NA |
Model | Analysis and Correction of non-independence | Temporal autocorrelation | NA |
Model | Analysis and Correction of non-independence | Nested data | NA |
Model | Threshold selection | Threshold selection | We used the sensitivity‐specificity sum maximisation (max TSS) approach to select the optimal suitability threshold. |
Assessment | Performance statistics | Performance on training data | NA |
Assessment | Performance statistics | Performance on validation data | NA |
Assessment | Performance statistics | Performance on test data | True skill statistics (TSS). |
Assessment | Plausibility check | Response shapes | NA |
Assessment | Plausibility check | Expert judgement | IUCN range polygons and the Handbook of the Mammals of the World. |
Prediction | Prediction output | Prediction unit | Continuous suitability and estimated richness for hotspots inference (sum of final binary maps). |
Prediction | Prediction output | Post-processing | Area calculation through raster R package. |
Prediction | Uncertainty quantification | Algorithmic uncertainty | Ensemble over two algorithms and 10 repeats. |
Prediction | Uncertainty quantification | Input data uncertainty | Sampling bias adjusted map in Figure 2. 2 SSPs and 2 GCMs for future scenarios. |
Prediction | Uncertainty quantification | Parameter uncertainty | Table S2 for parameters used in sampbias. |
Prediction | Uncertainty quantification | Scenario uncertainty | SSP-2.45 and SSP-5.85 scenario evaluation. |
Prediction | Uncertainty quantification | Novel environments | NA |