DOI: 10.5061/dryad.vx0k6dk1j
title: Reptile diversity patterns under climate and land use change scenarios in a subtropical montane landscape in Mexico

description: Aim: Mountainous regions are rich in reptile biodiversity but face threats
 from climate and land use changes. Understanding how these factors affect
 reptile diversity in these regions can highlight key conservation hotspots
 that require effective conservation actions. Here, we explored reptile
 taxonomic and functional diversity patterns along the Sierra Madre del Sur
 (SMS) region in southeast Mexico, and potential changes in future years
 caused by different climate and land use change scenarios. Location:
 Sierra Madre del Sur, México. Taxon: Reptiles.
 Methods: We used species distribution models and information on
 species traits to estimate taxonomic and functional diversity throughout
 the SMS region under current and future climate and land use change
 scenarios. Results: Greater taxonomic and functional diversity
 was observed in both temperate and tropical forests. Taxonomic diversity
 was higher in more drier areas at high elevations while higher functional
 diversity was observed in wetter areas at intermediate to high elevations.
 Lower diversity for both dimensions were associated with anthropic land
 uses. In future scenarios, both dimensions of diversity are expected to
 increase in temperate forest in highlands of central Oaxaca and decrease
 in the southcentral portion of the SMS, particularly for the worst
 scenarios due to increased deforestation rates. Main conclusions: Higher
 taxonomic diversity in more drier areas at high elevations could be due to
 historical and evolutionary factors, while higher functional diversity in
 wetter areas at intermediate to high elevations may be explained by a
 higher environmental heterogeneity in forests within these conditions.
 Larger diversity losses in the southcentral portion of the SMS are
 probably due to larger predicted deforestation rates in those areas. Our
 results are valuable not just for informing conservation actions, such as
 the creation of protected natural areas but also to understand the
 underlying processes behind the patterns of reptile diversity.
description: # Reptile diversity patterns under climate and land use change scenarios
 in a subtropical montane landscape in Mexico
 [https://doi.org/10.5061/dryad.vx0k6dk1j](https://doi.org/10.5061/dryad.vx0k6dk1j) We used species distribution models and species trait information to estimate taxonomic and functional diversity across the Sierra Madre del Sur region, Mexico, under current (2015) and future (2050 and 2070) climate and land use change scenarios, at a ~5 km2 resolution. We obtained occurrence data from literature reviews and biological collections databases, and used Maxent to create species distribution models and generate presence/absence matrices. We compiled information for eight functional traits related to various aspects of species' morphology, habitat use, life history, and trophic niche from the literature. Using the presence-absence matrices and the compiled functional traits database, we: 1\) Calculated and mapped taxonomic and functional diversity across the Sierra Madre del Sur region for seven scenarios: a current scenario for 2015, two Representative Concentration Pathway (RCP) 2.6 scenarios for 2050 and 2070, two RCP 4.5 scenarios for 2050 and 2070, and two RCP 8.5 scenarios for 2050 and 2070. Since taxonomic and functional diversity are sometimes correlated, we also calculated the standardized effect sizes of functional diversity for the seven scenarios. 2\) Modelled the relationships between taxonomic, functional diversity, functional diversity standardized effect sizes, and climatic, topographic, and land use/cover variables using generalized additive models (GAM). 3\) Compared mean taxonomic and functional diversity between the seven scenarios using generalized additive models (GAM). ## Description of the data and file structure 1\) For the calculation of taxonomic and functional diversity, we have submitted the presence/absence matrices for each scenario: a current scenario (SpeciesPAM_Current-2015.csv), two future RCP 2.6 scenarios (SpeciesPAM_RCP2.6-2050.csv, SpeciesPAM_RCP2.6-2070.csv), two RCP 4.5 scenarios (SpeciesPAM_RCP4.5-2050.csv, SpeciesPAM_RCP4.5-2070.csv), and two RCP 8.5 scenarios (SpeciesPAM_RCP8.5-2050.csv, SpeciesPAM_RCP8.5-2070.csv). The functional traits database can be accessed in the published paper: [https://doi.org/10.1111/jbi.15017](https://doi.org/10.1111/jbi.15017). 2\) To explore the relationships between taxonomic diversity, functional diversity, functional diversity standardized effect size (SES), and climatic, topographic, and land use/cover variables, we have submitted a dataframe containing the calculated taxonomic and functional diversity information for the current scenario, along with the corresponding predictor variables (Environment_GAM.csv). 3\) To compare taxonomic and functional diversity across scenarios, we have submitted a dataframe with the calculated taxonomic and functional diversity information for all scenarios, along with a categorical variable named "Scenarios" corresponding to the name of each scenario (Scenarios_GAM.csv). We have submitted the R script to perform all these analyses (R_code.R). ### Files and variables #### Files: SpeciesPAM\_Current-2015 **Description:** Presence-absence matrix used for the calculation of taxonomic and functional diversity, as well as functional diversity standardized effect size (SES), for the current scenario (2015). ##### Variables * *ID*: ID from each community. Here, each community represent a ~5 km2 pixel within the Sierra Madre del Sur region area.  * *x*: Longitude in decimal degrees for each community. * *y*: Latitude in decimal degrees for each community. * The rest of the columns represent the presence (1) or absence (0) of each species for each community.  #### Files: SpeciesPAM\_RCP2.6-2050 **Description:** Presence-absence matrix used for the calculation of taxonomic and functional diversity, as well as functional diversity standardized effect size (SES), for the RCP 2.6 scenario (2050). ##### Variables * *ID*: ID from each community. Here, each community represent a ~5 km2 pixel within the Sierra Madre del Sur region area.  * *x*: Longitude in decimal degrees for each community. * *y*: Latitude in decimal degrees for each community. * The rest of the columns represent the presence (1) or absence (0) of each species for each community.  #### Files: SpeciesPAM\_RCP2.6-2070 **Description:** Presence-absence matrix used for the calculation of taxonomic and functional diversity, as well as functional diversity standardized effect size (SES), for the RCP 2.6 scenario (2070). ##### Variables * *ID*: ID from each community. Here, each community represent a ~5 km2 pixel within the Sierra Madre del Sur region area.  * *x*: Longitude in decimal degrees for each community. * *y*: Latitude in decimal degrees for each community. * The rest of the columns represent the presence (1) or absence (0) of each species for each community. #### Files: SpeciesPAM\_RCP4.5-2050 **Description:** Presence-absence matrix used for the calculation of taxonomic and functional diversity, as well as functional diversity standardized effect size (SES), for the RCP 4.5 scenario (2050). ##### Variables * *ID*: ID from each community. Here, each community represent a ~5 km2 pixel within the Sierra Madre del Sur region area.  * *x*: Longitude in decimal degrees for each community. * *y*: Latitude in decimal degrees for each community. * The rest of the columns represent the presence (1) or absence (0) of each species for each community. #### Files: SpeciesPAM\_RCP4.5-2070 **Description:** Presence-absence matrix used for the calculation of taxonomic and functional diversity, as well as functional diversity standardized effect size (SES), for the RCP 4.5 scenario (2070). ##### Variables * *ID*: ID from each community. Here, each community represent a ~5 km2 pixel within the Sierra Madre del Sur region area.  * *x*: Longitude in decimal degrees for each community. * *y*: Latitude in decimal degrees for each community. * The rest of the columns represent the presence (1) or absence (0) of each species for each community. #### Files: SpeciesPAM\_RCP8.5-2050 **Description:** Presence-absence matrix used for the calculation of taxonomic and functional diversity, as well as functional diversity standardized effect size (SES), for the RCP 8.5 scenario (2050). ##### Variable * *ID*: ID from each community. Here, each community represent a ~5 km2 pixel within the Sierra Madre del Sur region area.  * *x*: Longitude in decimal degrees for each community. * *y*: Latitude in decimal degrees for each community. * The rest of the columns represent the presence (1) or absence (0) of each species for each community. #### Files: SpeciesPAM\_RCP8.5-2070 **Description:** Presence-absence matrix used for the calculation of taxonomic and functional diversity, as well as functional diversity standardized effect size (SES), for the RCP 8.5 scenario (2070). ##### Variable * *ID*: ID from each community. Here, each community represent a ~5 km2 pixel within the Sierra Madre del Sur region area.  * *x*: Longitude in decimal degrees for each community. * *y*: Latitude in decimal degrees for each community. * The rest of the columns represent the presence (1) or absence (0) of each species for each community. #### File: Environment\_GAM.csv **Description:** Dataframe used to perform generalized additive models to explore the relationship between taxonomic diversity, functional diversity, functional diversity standardized effect size (SES), and climatic, topographic and land use/cover variables. ##### Variables * *ID*: ID from each community. Here, each community represent a ~5 km2 pixel within the Sierra Madre del Sur region area.  * *x*: Longitude in decimal degrees for each community. * *y*: Latitude in decimal degrees for each community. * *TD*: Taxonomic diversity. For taxonomic diversity, we calculated species richness (number of species). * *FD*: Functional dispersion index. For functional diversity, we calculated the functional dispersion index. This index is unitless and range from 0 to 1.  * *SESFD*: Functional dispersion standardized effect sizes. Standardized effect sizes are unitless. * *Columns from Bio1 through Bio19*: Bioclimatic variables from CHELSA climatology. Definitions and units for each variable can be consulted from [https://chelsa-climate.org/](https://chelsa-climate.org/). * *Elev*: Mean elevation in meters. * *Tpi*: Topographic position index. This index is unitless and ranges from negative to positive values. Positive and negative values correspond to ridges and valleys, while values of zero correspond to generally flat areas * *LUC*: Land use/cover categories: 1 = Temperate forest, 2 = Cloud forest, 3 = Hydrophilic vegetation, 4 = Shrubland, 5 = Evergreen forest, 6 = Deciduous forest, 7 = Natural grassland, 8 = Other type of vegetation, 9 = Non-natural pasture, 10 = Rainfed agriculture, 11 = Irrigation agriculture, 12 = Urban area, 13 = Area with non-vegetation, 14 = Body of water. #### File: Scenarios\_GAM.csv **Description:** Dataframe used to perform generalized additive models to compare taxonomic and functional diversity between the seven scenarios (Current, RCP 2.6-2050, RCP 2.6-2070, RCP 4.5-2050, RCP 4.5-2070, RCP 8.5-2050, RCP 8.5-2070). ##### Variables * *ID*: ID from each community. Here, each community represent a ~5 km2 pixel within the Sierra Madre del Sur region area.Longitude * *x*: Longitude in decimal degrees for each community. * *y*: Latitude in decimal degrees for each community. * *TD*: Taxonomic diversity. For taxonomic diversity, we calculated species richness (number of species). * *FD*: Functional dispersion index. For functional diversity, we calculated the functional dispersion index. This index is unitless and range from 0 to 1.  * Scenario: Scenarios names as categories: Current, RCP 2.6-2050, RCP 2.6-2070, RCP 4.5-2050, RCP 4.5-2070, RCP 8.5-2050, and RCP 8.5-2070. ## Information sources * Species occurence data was obtained from GBIF ([https://www.gbif.org/](https://www.gbif.org/)), Colección Nacional de Anfibios y Reptiles (IBdata; [https://www.ibdata.abaco2.org/web/web-content/admin-queryfilter/queryfilter.php](https://www.ibdata.abaco2.org/web/web-content/admin-queryfilter/queryfilter.php)), Portal de Datos Abiertos UNAM ([https://datosabiertos.unam.mx/biodiversidad/](https://datosabiertos.unam.mx/biodiversidad/)), and Red Mundial de Información sobre Biodiversidad (REMIB; [http://www.conabio.gob.mx/remib/doctos/remib_esp.html](http://www.conabio.gob.mx/remib/doctos/remib_esp.html)) * Trait data information for each species was obtained by reviewing several bibliographic references and trait databases (see published paper: [https://doi.org/10.1111/jbi.15017](https://doi.org/10.1111/jbi.15017)). * Climatic variables were obtained from [https://chelsa-climate.org/](https://chelsa-climate.org/). * Topographic variables were obteined from [https://www.earthenv.org/](https://www.earthenv.org/). * Land use/cover variable was obtained from Mendoza-Ponce et al. (2018; 2020; see published paper: [https://doi.org/10.1111/jbi.15017](https://doi.org/10.1111/jbi.15017)). ## Code/software All analyses were performed in RStudio 1.4.1106 (R Core Team, 2021).  We are including a R script file (R_code.R) with all codes used to performed these analyses. The script include three parts: 1. Script for computing species distribution models. In this case, we are not including csv files for the occurence data of each species, as some may be threatened.   2. Script for calculating taxonomic diversity, functional diversity and functional diversity standardized effect sizes. 3. Script for computing generalized additive models to: * Model the relationships between taxonomic, functional diversity, functional diversity standardized effect sizes, and climatic, topographic, and land use/cover variables. * Compare mean taxonomic and functional diversity between seven climate and land use change scenarios.