DOI: 10.5061/dryad.gqnk98t0k
title: Data and code from: Demography meets climate change: Life history challenges for a Neotropical viviparous lizard

description: This repository contains a comprehensive demographic dataset and
 analytical code for Notomabuya frenata, a Neotropical viviparous lizard,
 collected over a 15-year period (December 2005 to January 2021) in the
 Cerrado biome (Reserva Ecológica do IBGE, Federal District, Brazil). The
 data were compiled to model population dynamics and viability under
 current and future climate change scenarios. Dataset structure and
 contents: The repository is organized into four main categories:
 Mark-recapture data: Raw and processed field records from standardized
 pitfall trapping campaigns. Variables include individual identification
 (toe-clipping), morphology (snout-vent length, tail length, body mass),
 sex, capture history, and microsite descriptions (plot/trap). Reproductive
 traits: Morphological data obtained from the dissection of preserved
 museum specimens (University of Brasília Herpetological Collection -
 CHUNB), providing clutch sizes and female body sizes used to parameterize
 fecundity models. Environmental and spatial data: Georeferenced occurrence
 records, Minimum Convex Polygon (MCP) shapefiles defining the study area,
 and processed raster data (NetCDF/RDS) containing historical (2005–2020)
 and projected (2021–2100) climatic variables (maximum temperature and
 precipitation) sourced from WorldClim v2.1 under SSP2-4.5 and SSP5-8.5
 scenarios. Code and model outputs: A complete suite of R scripts to
 replicate the data cleaning, vital rate estimation (growth, survival,
 fecundity), and Integral Projection Model (IPM) construction. Fitted model
 objects (.rds) are provided to facilitate the reproduction of the
 demographic projections without re-running computationally intensive
 steps. Reuse potential: These data are suitable for comparative
 life-history studies of tropical ectotherms, analysis of fire-prone
 ecosystem dynamics, and methodological development of spatially explicit
 demographic models. The code provides a framework for implementing IPMs
 with environmental covariates in R. Legal and ethical considerations:
 Fieldwork procedures were approved by the Animal Use Ethics Committee of
 the University of Brasília (CEUA-UnB process no. 33,786/2016).
 Reproductive data were derived from specimens collected during a fauna
 rescue operation (Serra da Mesa hydroelectric dam); no animals were
 euthanized specifically for this study.
description: We sampled <em>Notomabuya frenata</em> from December
 2005 to January 2021 in the Reserva Ecológica do Roncador, Distrito
 Federal, Brazil, using pitfall traps arranged in five subplots with
 different fire regimes. Lizards were captured, measured (SVL, tail length,
 and body mass), sexed, and permanently marked before release. Reproductive
 parameters were assessed using museum specimens collected during the
 filling of the Serra da Mesa reservoir (Goiás, Brazil). Environmental data
 (temperature and precipitation) was obtained from WorldClim for past and
 future projections (SSP2-4.5, SSP5-8.5).  Data was
 processed in R (v4.3.3). Missing SVL values were imputed using
 the <em>missForest</em> package. Growth was modeled with the
 Von Bertalanffy equation and analyzed via Non-Linear Mixed Effects Models
 (NLME). Survival was estimated using a Gompertz mortality model
 in <em>BaSTA</em> and a Cormack-Jolly-Seber model
 (<em>marked</em> package), relating monthly survival to
 environmental predictors through model selection based on
 AIC. Reproduction was analyzed using Bayesian models
 (<em>brms</em> package), relating litter size to female body
 size. Population dynamic was modeled with an Integral Projection Model
 (IPM), estimating growth, survival, and fecundity under different
 environmental scenarios from 2005 to 2100. Population growth rate (λ) was
 computed as the dominant eigenvalue of the transition matrix, and temporal
 trends were analyzed via time series decomposition.
description: # Data and code from: Demography meets climate change: Life history
 challenges for a Neotropical viviparous lizard Dataset DOI:
 [10.5061/dryad.gqnk98t0k](10.5061/dryad.gqnk98t0k) ## Description of the
 data and file structure This dataset was collected to investigate the
 demographic parameters and population dynamics of *Notomabuya frenata*, a
 lizard species from the Cerrado biome in Brazil. The study aimed to assess
 growth, survival, reproduction, and population viability under different
 environmental conditions and climate change scenarios. Field data was
 collected from December 2005 to January 2021 in the **Reserva Ecológica do
 IBGE** (Distrito Federal, Brazil) using standardized pitfall trap
 sampling. Additional reproductive data was obtained from museum specimens
 collected during the filling of the Serra da Mesa reservoir (Goiás,
 Brazil). Environmental predictors (temperature and precipitation) were
 sourced from the WorldClim database for past conditions (2005–2020) and
 future projections (2021–2100) under different climate scenarios. The
 dataset includes measurements of individual lizards (snout-vent length,
 tail length, body mass, sex), capture-recapture histories, reproductive
 parameters, and environmental data. These data were used to model growth
 (Von Bertalanffy equation), survival (Gompertz mortality model,
 Cormack-Jolly-Seber model), reproduction (Bayesian regression models), and
 population dynamics (Integral Projection Model) to estimate population
 trends over time and construct a demographic distribution model. The
 repository is organized into four main categories: (1) Raw Data (capture
 records and museum measurements); (2) Processed Data (cleaned, imputed,
 and spatially explicit climatic datasets); (3) Model Outputs (fitted model
 objects in .rds format); and (4) R Scripts (code for data inspection,
 analysis, and figure generation). Missing values are coded as `NA`. ###
 Note on Language and Script Compatibility The column headers and
 categorical data values within the files `data_tab_crescimento.csv`,
 `Fecundidade_N_frenata.txt`,
 `Demografia_N_frenata_corrigido_atejaneiro.xls`, and `nfrenata5.csv` have
 been maintained in their original Portuguese language (e.g.,
 "cauda" instead of "tail", "s/n" instead of
 "y/n"). This preservation is necessary to ensure full
 compatibility and reproducibility with the provided R scripts, which
 explicitly reference these variable names and specific string values. To
 facilitate reuse, comprehensive English descriptions and translation keys
 for all non-English variables and categorical levels are provided in the
 **Variable Descriptions** section of this README. ### Files and variables
 #### File: 01_InspectData.R **Description:** This R script performs the
 initial inspection, cleaning, and imputation of the raw mark-recapture
 data for *Notomabuya frenata*. The workflow includes: 1. Loading raw data
 and correcting basic formatting (dates, species names). 2. Identifying and
 correcting inconsistencies in campaign numbers, plot/trap assignments, and
 biological plausibility of measurements (mass, SVL, tail length). 3.
 Detecting outliers using Z-distribution and Huber and Van der Veeken
 (2008) methods. 4. Imputing missing body mass and Snout-Vent Length (SVL)
 values using the Random Forest algorithm (package `missForest`) to
 maximize data usage. 5. Generating the final cleaned dataset
 (`nfrenata5.csv`) and a growth increment table (`tab.crescimento`) for
 subsequent demographic analyses. #### File: 02_PreliminaryAnalyses.R
 **Description:** This script conducts exploratory data analyses and
 generates visualizations from the cleaned dataset (`nfrenata5.csv`). The
 workflow includes: 1. **Capture Dynamics:** Analysis of temporal variation
 in capture and recapture rates, including the frequency of captures per
 individual. 2. **Population Structure & Seasonality:** Assessment of
 juvenile and adult detection patterns over months and their relationship
 with precipitation (seasonality). 3. **Body Size Analysis:** Investigation
 of Snout-Vent Length (SVL) variations across sex and time. 4. **Figure
 Generation:** Production of **Figure 1** (Scatter plot of SVL over time
 with rainy season overlays and sexual maturity thresholds). 5. **Data
 Preparation:** Calculation of the mean SVL for each sampling campaign. It
 uses the `missForest` package to impute missing mean SVL values for months
 with no captures, exporting the file `crc_medio_camp.csv` for subsequent
 environment-dependent modeling. #### File: 03_AmbVarSelection.R
 **Description:** This script processes environmental data and selects the
 climatic predictors for demographic modeling. The workflow includes: 1.
 **Climate Data Extraction:** Loads high-resolution climate rasters
 (WorldClim 2.1) for Minimum Temperature, Maximum Temperature, and
 Precipitation, cropping them to the study area extent. 2. **Collinearity
 Analysis:** Calculates Variance Inflation Factors (VIF) to detect and
 remove highly correlated environmental variables (e.g., removing Minimum
 Temperature to avoid multicollinearity). 3. **Data Integration &
 Imputation:** Merges the climate data with the mean Snout-Vent Length
 (SVL) data and uses the `Amelia` package to impute any remaining missing
 values in the time series. 4. **Variable Selection:** Performs variable
 importance analyses using Random Forest (`Boruta`), stepwise regression,
 and AIC-based model selection (`MuMIn`) to identify which environmental
 variables best explain variations in mean body size. 5. **Output
 Generation:** Exports the final dataset `crc_amb_worldclim.csv` containing
 the selected environmental covariates and mean body size for each time
 step. **Note:** Users must update the file paths for the climatic raster
 files (TIFFs) to match their local directory structure. #### File:
 crc_amb_worldclim.csv **Description:** A dataset combining the time series
 of mean body size (SVL) of the population with the selected local climatic
 variables for each month **Variable List:** * **Row ID**: Row number
 (automatically generated). * **year**: Year of the record. * **month.n**:
 Sequential month number (1 to 182, representing the timeline from Dec 2005
 to Jan 2021). * **crc**: Mean Snout-Vent Length (SVL) of the population
 for that month. Unit: millimeters (mm). * **tmax**: Mean Maximum
 Temperature for the study area. Unit: degrees Celsius (°C). * **prec**:
 Mean Precipitation for the study area. Unit: millimeters (mm). #### File:
 04_WorldClimData.R **Description:** This script extracts and processes
 spatially explicit climate data (Maximum Temperature) required for the
 demographic projections. The workflow includes: 1. **Occurrence Mapping:**
 Visualizes species occurrence points (`N_frenata_occurclean.xlsx`) on a
 map of South America to define the study area extent. 2. **Climate Data
 Retrieval:** * Loads historical WorldClim data (2005-2020) from local TIFF
 files. * Downloads future climate projections (CMIP6, model MPI-ESM1-2-HR)
 for two Shared Socioeconomic Pathways: **SSP2-4.5** (optimistic) and
 **SSP5-8.5** (pessimistic) covering the period 2021-2100. 3. **Data
 Processing:** Crops the climate layers to the species' distribution
 buffer, extracts pixel-level temperature values, and organizes them into
 long-format data frames containing coordinates (lat/lon), year, month, and
 maximum temperature. 4. **Output:** Saves the processed spatiotemporal
 climate datasets as `.rds` files
 (`amb_worldclim_245_20052100_BufferCrop.rds`and
 `amb_worldclim_585_20052100_BufferCrop.rds`) for use in spatial IPMs. ####
 File: 05_Growth_Sex_FabensVB.R **Description:** This script analyzes the
 growth patterns of *Notomabuya frenata* considering sexual dimorphism. It
 fits sex-specific Von Bertalanffy Growth Models (VBGM) using Fabens'
 method implemented via Non-linear Mixed-Effects (NLME). The workflow
 includes: 1. **Data Classification:** Categorizes individuals as Male,
 Female, or Juvenile based on their entire capture history to maximize
 sample size for each sex. 2. **Statistical Testing:** Performs T-tests,
 Shapiro-Wilk, and Wilcoxon tests to assess differences in body size
 between sexes. 3. **Growth Modeling:** Fits separate NLME models for
 females and males to estimate growth parameters (K and L∞​) while
 accounting for individual heterogeneity. 4. **Visualization:** Generates
 **Figure 2**, plotting the estimated growth curves and confidence
 intervals for both sexes against age. 5. **Output:** Exports the growth
 increment dataset (`data_tab_crescimento.csv`) and saves the fitted model
 objects (e.g., `growth_fem.rds`). #### File: 06_Survival_BaSTA.R
 **Description:** This script estimates age-specific survival and mortality
 rates for *Notomabuya frenata* using the **BaSTA**(Bayesian Survival
 Trajectory Analysis) package. The workflow includes: 1. **Data
 Filtering:** Selects adult individuals (Males and Females) and removes
 outliers, similar to the growth analysis scripts. 2. **Input
 Preparation:** * Converts capture histories into the specific matrix
 format required by BaSTA. * Estimates birth times (Tbirth​) by
 back-calculating from the estimated age at first capture (derived from the
 growth models in `05_Growth_Sex_FabensVB.R`). * Constructs a covariate
 matrix to test the effect of **Sex** (Female vs. Male) on mortality. 3.
 **Bayesian Modeling:** Runs the BaSTA model (CMR configuration) with
 50,000 MCMC iterations to estimate Gompertz mortality parameters. 4.
 **Visualization:** * Generates diagnostic plots (trace plots, posterior
 densities) to check convergence. * Produces **Figure 3**, which compares
 the age-specific mortality and survival curves for Males and Females. 5.
 **Output:** Saves the BaSTA model output (e.g., `outBaSTA_sex.rds` and
 `outBaSTA_Fem.rds`). Note: This script assumes the objects `data_validF`
 and `data_validM` from the growth analysis script are available in the R
 environment. #### File: 07_Survival_CJS.R **Description:** This script
 fits Cormack-Jolly-Seber (CJS) models to estimate survival (Φ) and
 recapture (p) probabilities as functions of environmental and biological
 covariates. The analysis is performed using the `marked` package. The
 workflow includes: 1. **Capture History Construction:** Converts the
 individual capture data into a monthly capture history matrix (0/1
 format), filling in gaps for months without sampling campaigns to ensure a
 continuous time series (182 months). 2. **Covariate Integration:**
 Linkages the capture history with time-varying covariates: *
 **Environmental:** Mean Maximum Temperature (`tmax`) and Precipitation
 (`prec`) from `crc_amb_worldclim.csv`. * **Biological:** Mean population
 SVL (`crc`) from `crc_medio_camp.csv`. 3. **Model Selection:** Defines and
 runs a set of candidate CJS models testing different combinations of
 covariates on Φand p. 4. **Visualization:** * **Figure 4a:** Plots the
 estimated survival probability over time alongside maximum temperature. *
 **Figure 4b:** Visualizes the correlation between maximum temperature and
 survival. * **Figure S1:** Decomposes the survival time series into
 seasonal, trend, and residual components. 5. **Output:** Saves the CJS
 model results object (e.g., `surv_VarAmb_WC.rds`). #### File:
 08_FecundityCRC.R **Description:** This script analyzes the relationship
 between female body size (SVL) and fecundity (litter size) using Bayesian
 Generalized Linear Models (GLM) with a Poisson distribution (implemented
 via `brms`). The workflow includes: 1. **Data Loading:** Imports the
 fecundity dataset containing measurements of female SVL and their
 corresponding litter sizes. 2. **Model Fitting:** Fits and compares four
 candidate models to describe the size-fecundity relationship: * Null model
 (Intercept only). * Linear model (`neggs ~ crc`). * Non-linear model with
 smoothed splines (`neggs ~ s(crc)`). * Quadratic model (`neggs ~ crc +
 crc^2`). 3. **Model Selection:** Uses Leave-One-Out Cross-Validation
 (`loo`) to select the model with the best predictive performance. 4.
 **Visualization:** Generates **Figure S2**, plotting the observed data
 against the predicted relationship (with credible intervals) derived from
 the selected Bayesian model. 5. **Output:** Saves the selected fecundity
 model object (e.g., `mnb_Nfrenata.rds`). #### File: 09_Occurrence_data.R
 **Description:** This script processes raw species occurrence records to
 define the spatial extent of the study. The workflow includes: 1. **Data
 Cleaning:** Imports raw occurrence data, corrects coordinate formatting
 (decimal separators), standardizes scientific names, and filters records
 for *Notomabuya frenata*. 2. **Duplicate Removal:** Identifies and removes
 duplicate geographical coordinates to create a unique set of occurrence
 points. 3. **Data Export:** Saves the cleaned occurrence dataset as
 `N_frenata_occurclean.xlsx`. 4. **Spatial Visualization:** * Loads the
 South American continent shapefile. * Generates a Minimum Convex Polygon
 (MCP) representing the species' extent of occurrence (Note: The MCP
 generation code is present but commented out; the script reads the
 resulting shapefile `n_frenata_mcp_sf.shp`for plotting). * Produces
 **Figure S1** (referred to as "Figure 0" in the code),
 calculating the Minimum Convex Polygon (MCP) and mapping the occurrence
 points. #### File: 10_IPM_2005_2020.R **Description:** This script builds
 spatially explicit Integral Projection Models (IPMs) to estimate
 population growth rates (λ) across the species' distribution for the
 historical period (2005–2020). It serves as the core integration step,
 linking vital rates to environmental drivers. The workflow includes: 1.
 **Vital Rate Integration:** Loads parameters from the previously fitted
 growth (`growth_fem.rds`), age-dependent survival (`outBaSTA_sex.rds`),
 and environment-dependent survival (`surv_VarAmb_WC.rds`) models. 2.
 **Kernel Construction:** Defines the projection kernels (P for
 survival/growth and F for fecundity) and constructs specific kernels for
 each grid cell and month based on local climatic conditions (Temperature).
 3. **Population Analysis:** * Calculates the asymptotic population growth
 rate (λ) for each location and time step. * Performs perturbation analyses
 (Sensitivity and Elasticity) to quantify the relative importance of
 different demographic processes. * Computes the geometric mean of λ to
 assess overall population stability. 4. **Visualization:** * **Figures 5a
 & 5b:** Maps the projected mean and standard deviation of population
 growth (λ) for the 2005-2020 period. * **Figure S3:** Plots the time
 series of population growth (λ) with seasonal overlays. * **Figure S4:**
 Decomposes the λ time series into seasonal, trend, and residual
 components. * Generates kernel visualizations (P, F, and K matrices). 5.
 **Output:** Saves the computed metrics and kernel objects (e.g.,
 `lambda_2005.rds` to `lambda_2020.rds`, `MeanKernel_20052020.rds`). ####
 File: 10_IPM_ssp245_2021_2100.R **Description:** This script projects the
 population dynamics of *Notomabuya frenata* under the **SSP2-4.5** climate
 change scenario (an "optimistic" or intermediate greenhouse gas
 emission pathway) for the period 2021–2100. The workflow essentially
 mirrors the historical analysis but applies future climate projections: 1.
 **Model Initialization:** Loads the fitted vital rate models (Growth,
 Survival, Fecundity) and defines the integration parameters (kernels). 2.
 **Future Climate Integration:** Loads the spatially explicit climate
 projections (`amb_worldclim_245_20052100_BufferCrop.rds`) and filters for
 the future time slices. 3. **Kernel Construction & Projection:** *
 Constructs IPM kernels for every grid cell across the species'
 distribution for four future periods: **2021–2040**, **2041–2060**,
 **2061–2080**, and **2081–2100**. * Calculates the asymptotic population
 growth rate (λ) for each location and time step. 4. **Visualization:** *
 **Figures 6a & 6b:** Generates maps showing the projected mean λ and
 its standard deviation across the distribution for the four future
 periods. 5. **Output:** Saves the computed λ metrics for each future time
 slice (e.g., `lambda_ssp245_2021_2040.rds`, etc.). #### File:
 10_IPM_ssp585_2021_2100.R **Description:** This script projects the
 population dynamics of *Notomabuya frenata* under the **SSP5-8.5** climate
 change scenario (a "pessimistic" or high greenhouse gas emission
 pathway) for the period 2021–2100. The workflow is identical to the
 SSP2-4.5 analysis but uses the high-emission climate projections: 1.
 **Model Initialization:** Loads the fitted vital rate models (Growth,
 Survival, Fecundity) and integration parameters. 2. **Future Climate
 Integration:** Loads the spatially explicit climate projections
 (`amb_worldclim_585_20052100_BufferCrop.rds`) corresponding to the
 SSP5-8.5 scenario. 3. **Kernel Construction & Projection:** *
 Constructs IPM kernels for every grid cell across the species'
 distribution for four future periods: **2021–2040**, **2041–2060**,
 **2061–2080**, and **2081–2100**. * Calculates the asymptotic population
 growth rate (λ) for each location and time step. 4. **Visualization:** *
 **Figures 7a & 7b:** Generates maps showing the projected mean λ and
 its standard deviation across the distribution for the four future periods
 under this high-emission scenario. 5. **Output:** Saves the computed λ
 metrics for each future time slice (e.g., `lambda_ssp585_2021_2040.rds`,
 etc.). #### File: 11_LambdaBoxplots.R **Description:** This script
 performs a comparative analysis of population growth rates (λ) across
 different time periods and climate scenarios. It uses the outputs
 generated by the IPM scripts (`10_IPM...`) to visualize demographic
 trends. The workflow includes: 1. **Data Integration:** Combines the
 historical population growth metrics (2005–2020) with projections from the
 **SSP2-4.5** (optimistic) and **SSP5-8.5** (pessimistic) scenarios
 (2021–2100). 2. **Visualization of Trends:** * **Figure 8a:** Generates a
 boxplot comparing the distribution of λ in the historical period versus
 future periods under the **SSP2-4.5** scenario. * **Figure 8b:** Generates
 a boxplot comparing the distribution of λ in the historical period versus
 future periods under the **SSP5-8.5** scenario. * **Figure 9:** Compares
 the two future scenarios side-by-side to highlight the potential impact of
 higher greenhouse gas emissions on population viability. 3. **Output:**
 Exports the final publication-ready figures in PDF format. #### File:
 crc_medio_camp.csv **Description:** Monthly mean snout-vent length ####
 File: data_tab_crescimento.csv **Description:** A derived dataset
 containing growth increments for recaptured individuals, used as input for
 the growth models. **Variable List:** * **identidade**: Unique identifier
 for the individual. * **delta.camp**: Time interval between the initial
 capture and the recapture (in months). * **crc.t0**: Snout-Vent Length
 (SVL) at the initial capture (Lt​). Unit: mm. * **crc**: Snout-Vent Length
 (SVL) at the recapture (Lt+Δt​). Unit: mm. * **delta.crc**: Change in SVL
 during the interval (ΔL). Unit: mm. * **sexo**: Sex of the individual
 assigned for the growth analysis. * Key: F = Female, M = Male, J =
 Juvenile (or unsexed). #### File: Fecundidade_N_frenata.txt
 **Description:** Raw dataset containing morphological and reproductive
 data obtained from the dissection of preserved museum specimens
 (Herpetological Collection of the University of Brasília - CHUNB). These
 specimens were collected during the fauna rescue of the Serra da Mesa
 hydroelectric power plant reservoir and were used to parameterize the
 size-fecundity relationship. **Variable List:** * **numero**: Specimen
 voucher number/ID in the scientific collection. * **data**: Date of
 collection (Format: DD.MM.YY). * **dia**: Day of collection. * **mes**:
 Month of collection. * **ano**: Year of collection. * **local**: Sampling
 locality (e.g., "S._Mesa" = Serra da Mesa). * **especie**:
 Scientific name of the species. * **sexo**: Sex of the specimen (f =
 Female). * **condicao_reprodutiva**: Text description of the reproductive
 status (e.g., "5_embrioes" = 5 embryos, "4_fetos" = 4
 fetuses). * **embrioes**: Count of embryos found. * **fetos_filhotes**:
 Count of fetuses or near-term offspring found. * **tamanho_ninhada**:
 Total litter size (number of eggs/embryos). Unit: Count. * **condicao**:
 Qualitative condition of the specimen or reproductive tract (e.g.,
 "b", "c"). *Key: "Internal reference code"*.
 * **crc**: Snout-Vent Length (SVL). Unit: millimeters (mm) #### File:
 Demografia_N_frenata_corrigido_atejaneiro.xls **Description:** The raw,
 long-term mark-recapture dataset collected from December 2005 to January
 2021. It contains individual capture records, morphological measurements,
 and microsite data for *Notomabuya frenata*. **Variable List:** *
 **campanha**: Sequential campaign number. * **mês**: Month of capture. *
 **data**: Date of capture. *Note: In the raw CSV/Excel, this may appear as
 a serial number (e.g., 38688) or formatted date.* * **ano**: Year of
 capture. * **parcela**: ID of the sampling plot (e.g., "BT",
 "Q", "BP", "C"). * **armadilha**: Trap
 number within the plot. * **dedos**: Raw toe-clipping code representing
 the marked digits (e.g., "40_20_6_5"). * **identidade**: Unique
 alphanumeric identifier assigned to the individual. * **massa**: Body
 mass. Unit: grams (g). * **crc**: Snout-Vent Length (SVL). Unit:
 millimeters (mm). * **recaptura**: Recapture status for that event. Key:
 "s" = Yes (Sim), "n" = No (Não). * **cc**: Tail length
 (Comprimento da Cauda). Unit: millimeters (mm). * **bc**: Length of the
 tail base (measured from the cloaca to the proximal limit of the
 regenerated portion). Unit: millimeters (mm). * **cauda**: Tail condition.
 Key: "inteira" = Intact, "regenerada" = Regenerated,
 "quebrada" = Broken. * **sexo**: Sex of the individual. Key:
 "F" = Female, "M" = Male, "J" = Juvenile. *
 **morto**: Mortality status during capture. Key: "s" = Dead
 (Sim), "n" = Alive (Não). * **observacao**: Field notes and
 observations (e.g., "sem_pata_anterior" = missing forelimb). *
 **month.n**: Sequential month number calculated from the start of the
 study. #### File: N_frenata_occurclean.xlsx **Description:** A processed
 dataset containing unique, georeferenced occurrence records of *Notomabuya
 frenata*. This file is the output of the data cleaning script
 (`09_Occurrence_data.R`) and serves as the input for defining the study
 area and extracting climatic variables in the WorldClim processing script
 (`04_WorldClimData.R`). **Variable List:** * **lon**: Longitude of the
 occurrence record. Unit: Decimal degrees (WGS84). * **lat**: Latitude of
 the occurrence record. Unit: Decimal degrees (WGS84). #### File:
 nfrenata5.csv **Description:** The cleaned and processed mark-recapture
 dataset used for all demographic analyses. This dataset is derived from
 the raw data (`Demografia_N_frenata_corrigido_atejaneiro.xls`) after
 cleaning, error correction, and imputation of missing values (mass and
 SVL) as performed by the script `01_InspectData.R`. **Variable List:** *
 **nrow**: Row index. * **campanha**: Sequential sampling campaign number.
 * **mês**: Month of capture (Numeric, 1-12). * **data**: Date of capture
 (Format: YYYY-MM-DD). * **ano**: Year of capture. * **parcela**: Sampling
 plot ID (e.g., BT, Q, BP, BM, C). * **armadilha**: Trap number within the
 plot. * **dedos**: Toe-clipping code representing the marked digits. *
 **identidade**: Unique alphanumeric identifier for each individual. *
 **massa**: Body mass. Contains imputed values for missing entries. Unit:
 grams (g). * **crc**: Snout-Vent Length (SVL). Contains imputed values for
 missing entries. Unit: millimeters (mm). * **recaptura**: Recapture
 status. Key: "s" = Yes (recapture), "n" = No (first
 capture). * **cc**: Tail length. Unit: millimeters (mm). * **bc**: Length
 of the tail base (cloaca to regeneration start). Unit: millimeters (mm). *
 **cauda**: Tail condition. Key: "inteira" = Intact,
 "regenerada" = Regenerated. * **sexo**: Sex of the individual.
 Key: "F" = Female, "M" = Male, "J" =
 Juvenile/Unsexed. * **morto**: Mortality status during capture. Key:
 "s" = Dead, "n" = Alive. * **observacao**: Field
 observations (e.g., physical injuries). * **month.n**: Sequential month
 number from the start of the study (1 to 182). * **especie**: Scientific
 name (*Notomabuya frenata*). * **year**, **month**, **day**: Date
 components extracted from the 'data' column. #### **File:**
 `growth_fem.rds` * **Description:** The Non-linear Mixed-Effects (NLME)
 model object describing the Von Bertalanffy growth curve for females. Used
 to estimate body size transitions. #### **File:**
 `MeanKernel_20052020.rds` * **Description:** The mean Integral Projection
 Model (IPM) kernel representing the average demographic transitions over
 the historical period (2005-2020). #### **File:** `mnb_Nfrenata.rds` *
 **Description:** The Bayesian Generalized Linear Model (Poisson) object
 describing the relationship between female body size (SVL) and fecundity
 (litter size). #### **File:** `outBaSTA_sex.rds` * **Description:** The
 Bayesian Survival Trajectory Analysis (BaSTA) model output containing
 age-specific mortality estimates for males and females. #### **File:**
 `surv_VarAmb_WC.rds` * **Description:** The Cormack-Jolly-Seber (CJS)
 model object estimating monthly survival probabilities as a function of
 maximum temperature. #### File: WC_2005_2020.zip **Description:** A
 compressed archive containing the raw raster files (GeoTIFF format) of the
 climatic variables used to characterize the environmental conditions of
 the study area during the historical period. These data were sourced from
 WorldClim v2.1 (downscaled CRU-TS 4.06) at a spatial resolution of 10
 minutes (~340 km²). **Contents:** The archive includes monthly regional
 layers for the years 2005 to 2020 for the following variables (referenced
 in script `03_AmbVarSelection.R` ): * **Maximum Temperature (tmax):**
 Monthly averages of maximum air temperature (°C). * **Minimum Temperature
 (tmin):** Monthly averages of minimum air temperature (°C). *
 **Precipitation (prec):** Total monthly precipitation (mm). **Note:**
 These files are required to run the scripts `03_AmbVarSelection.R` and
 `04_WorldClimData.R`, which extract local climate values for the
 species' occurrence points and study area buffer. ## Code/software
 The dataset can be processed and analyzed using **R (v4.3.3)**. ## Access
 information Climatic data were derived from the WorldClim database.