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Museum specimens and citizen science initiatives are valuable sources of information on how anthropogenic activities affect biodiversity and how species respond to rapid global change. Although tropical regions harbor most of the planet’s biodiversity, investigations on species’ phenological changes are heavily biased towards temperate regions. Such unevenness in phenological research is also taxonomically biased, with reptiles being the least studied group among tetrapod species regarding animal phenology. Herein we used long-term time series data to investigate environmentally driven changes in the activity pattern of tropical forest snakes. We gathered natural history collection and citizen science data for 25 snake species (five venomous and 20 non-venomous) from an Atlantic Forest region in southeastern Brazil. Using circular mixed-effects models, we investigate whether snake activity patterns followed the variation in environmental variables over a decade. Our results show that the activity pattern of Atlantic Forest snakes was seasonal and largely driven by average temperature and relative humidity. Since snakes are ectothermic animals, they are particularly sensitive to temperature variations, especially at small scales. Moreover, relative humidity can affect snake seasonal activities through physiological constraints and/or prey availability. Most specimens were registered during the rainy season, with highly venomous snakes (lanceheads and coral snakes) emerging as the most abundant taxa. We highlight the importance of citizen science and natural history collections in better understanding biodiversity. Furthermore, our data obtained from local collectors underscores the need environmental education programs and collaboration between researchers and local decision-makers to raise awareness and reduce conflicts between people and snakes in the region. Methods All analyses in this study were performed in the software R v. 4.0.2. Raw data and R-code to replicate the findings of this study are as follow: 1. Rscript_SnakeSeasonalActivity.R 2.rawdata.csv 3.INMET_SE_MG_A510_VICOSA_2009_2018.csv 4.SnakeBiteData.csv The R-script is commented by the authorsand contains all code used to analyze data tables, get summary stats, perform the models,and create the figures in the manuscript. Acknowledgements We thank the environmental police officers, firefighters, security guards, and other local collectors who brought snakes to MZUFV along the years. JJMG is supported by a PhD scholarship from Coordenacão de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). MRM acknowledges support from São Paulo Research Foundation (FAPESP) for grants proc. 2021/11840-6 and proc. 2022/12231-6.

More than two million species have been described so far, but our knowledge on most taxa remains scarce or inexistent, and the available biodiversity data is often taxonomically, phylogenetically, and spatially biased. Unevenness in research effort across species or regions can interact with data biases and compromise our ability to properly study and conserve biodiversity. Herein, we assess the influence of biological, conservation, geographic, and socioeconomic correlates of reptile research effort globally and across six biogeographic realms. We combine bibliometric data from the Scopus database with trait-based approaches and provide research effort information for 10,531 reptile species, modelling it as a function of 10 putative correlates of species-level variation in research effort through negative binomial generalised mixed effect models. We show that reptile research effort is highly skewed toward certain taxa and regions, such as turtles, crocodiles, tuatara, viperids, pythons, and some anguimorph lizards, as well as for temperate compared to tropical regions. Our findings indicate that greater research attention is directed towards large-sized and early described reptile species, particularly those whose geographic range overlap with biodiversity institutions. Although we demonstrate that biological and socioeconomic factors more strongly affect reptile research effort variation, geography and conservation-related factors also matter. Global patterns are mostly consistent, but variation across realms were observed and likely reflects differences in socioeconomic attributes as well as in the amount of species to be studied in each realm. Directing researchers and citizen scientists’ attention toward understudied taxa will contribute to alleviate this biased biodiversity knowledge, although the sheer amount of species in tropical regions inevitably makes it a long-term solution. Performing comparative studies across species with similar levels of research attention could represent a more immediate and feasible alternative. Methods All analyses in this study were performed in the software R v. 4.0.2. Raw data and R-code to replicate the findings of this study are as follow: 1. Guedesetal_ReptileResearchEffort.R 2.ResearchEffortData.csv 3.ReptSpatialData.csv 4.Subset100Supertrees_7102taxaReptiles.Rdata 5.gridcells_110km.zip The R-script (the second version)is commented by the authorsand contains all code used to analyze data tablesand create the figures in the manuscript. Acknowledgements This study was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, as a D.Sc. scholarship provided to JJMG. Work by J.A.F.D.-F. is supported by CNPq productivity fellowships and has been developed associated with the National Institutes for Science and Technology (INCT) in Ecology, Evolution and Biodiversity Conservation, supported by MCTIC/CNPq (proc. 465610/2014-5) and FAPEG (proc. 201810267000023).

Ecological Niche Modelling of Tropical Dry Forest Species Raw data and code base for the research approach on climate change-driven biotic changes in tropical dry forests. Briefly, the code reproduces the traditional Ecological Niche Modelling (ENM) and the Ensemble of Small Models (ESM) framework; perform assemblage-level analysis in the resulting outputs; and then illustrate the building of figures used in the main manuscript. The modelling framework was built using the directory structure informed in the README.pdf file. The R-code provided have steps designed to replicate the directory structure as informed, but understanding it is a good starting point to navigate the output produced. The files provided below were designed for illustrative purposes and to avoid the need to re-run all ENM and ESM models in order to replicate the major findings of the manuscript (the complete set of outputs surpass 600GB and 1 million of output files). There are five zip files whose content we described below: Occurrences Folder.zip: it represents the "Occurrence Folder" directory, as illustrated in the README.pdf. This zip includes three files representing the occurrence data for the plant species used in the modelling framework. Datasets.zip: it represents the "Datasets" folder directory, as illustrated in the README.pdf. This zip one the 'PlantLifeForms.txt' file, which informs plant growth form for 2656 tropical dry forest species. This is the only file that will not be produced by R-scripts provided. The other 94 csv files in this zip are provided for illustrative purposes to avoid the need of the user to re-run all ENM and ESM models. Among these csv files, we provide (i) species-level outputs (measures of species range shift), (ii) assemblage-level outputs (measures of richness, beta-diversity, ecological generalism, and woodiness of plant assemblages), (iii) summary statistics (ENM-ESM model performance metrics, kruskal wallis tests, spatial correlations), and (iv) processed tables used to build the main figures in the manuscript. Shapefiles.zip: it represents the "Shapefiles" folder directory, as illustrated in the README.pdf. There are five shapefiles in this zip. (i) Caatinga_wgs84, spatial polygon illustrating the study area. (ii) cea_grid_cells, 10x10 km equal-area grid cells created for the assemblage-level analysis. (iii) ne_110m_admin_0_countries, Natural Earth Vector draws boundaries of countries (see https://www.naturalearthdata.com/downloads). (iv) ne_110m_graticules_20, Natural Earth Vector draws graticules of the globe (see https://www.naturalearthdata.com/downloads). (v) wwf_realm, the WWF biogeographical realms, modified from Dinerstein et al. (2017, see https://ecoregions.appspot.com/). AggregatedUncertainty.zip: it represents the "AggregatedUncertainty" folder directory, as illustrated in the README.pdf. Aggregated uncertainty is a combined measure of variation in species habitat suitability across five generalized circulation models (GCM). This zip file contains 16 raster files in format tif, with each raster representing a combination between SSP scenario (ssp245 or ssp585) and extrapolation constraints (MOP00, MOP70, MOP80, or MOP90). RData.zip: it represents the "RData" folder directory, which is designed to store RData files produced during the running of the R-scripts. It currently includes two files to facilitate the plotting of main figures. Predictors Folder.zip: it includes the raster files produced for the folder "Predictors Folder". There are three subfolders: PCA, WorldClim, SoilGrids. All rasters were generated at the spatial resolution of 5 arc-minutes. Projection_PCA.zip: it includes the raster files produced for the folder "Projection_PCA". There are 20 subfolders, each named according to a combination of GCM (5 options) and SSP scenario (2 options). All rasters were generated at the spatial resolution of 5 arc-minutes. R-code: Moura_etal_JEcol2023_Script1_ENM_DataAnalysis.R: perform data cleanning, prepare predictor layers, run traditional ecological niche models, get ensemble models across GCMs, apply extrapolation constraints, and compute species geographical range shifts. Moura_etal_JEcol2023_Script2_ESM_DataAnalysis.R: run ensemble of small models for rare species, get ensemble models across GCMs, apply extrapolation constraints, and compute species geographical range shifts. Moura_etal_JEcol2023_Script3_AssemblageLevelAnalysis.R: build species-level tables to classify plants with respect to ecological generalism and woodiness, compute aggregated model uncertainty, extract presence-absence matrices, compute assemblage-level metrics, and assess changes in biodiversity patterns across regions subject to homogenization or heterogenization. Moura_etal_JEcol2023_Script4_Figures_MainText.R: build figures 1 to 6 reported in the main text. AdaptedRFunctions.R: auxiliary script containing functions modified from existing R-packages. References Moura, M.R., Nascimento, F.A.O., Paolucci, L.N., Silva, D.P., & Santos, B.A. (2023). Pervasive impacts of climate change on the woodiness and ecological generalism of dry forest plant assemblages. Journal of Ecology, doi: 10.1111/1365-2745.14139. Correspondence to: mariormoura@gmail.com, The RCode.1 (Moura_etal_JEcol2023_Script1_ENM_DataAnalysis.R) has been revised to eliminate invalid subdirectories, and compressed zip files now contain rasters of predictor layers for the current and future time.

Ecological Niche Modelling ofTropical Dry Forest Species Raw data and code base for the research approach on climate change-driven biotic changes in tropical dry forests. Briefly, the code reproduces the traditional Ecological Niche Modelling (ENM) andthe Ensemble of Small Models (ESM) framework; perform assemblage-level analysis in the resulting outputs; and then illustrate the building of figures used in the main manuscript. The modelling framework was built using the directory structure informed in the README.pdf file. The R-code provided have steps designed to replicate the directory structure as informed, but understanding it is a good starting point to navigate the output produced. The files provided below were designed for illustrative purposes and to avoid the need to re-run all ENM and ESM models in order to replicate the major findings of the manuscript (the complete set of outputs surpass 600GB and 1 million of output files).There are five zip files whose content we described below: Occurrences Folder.zip: it represents the "Occurrence Folder" directory, as illustrated in the README.pdf. This zip includes three files representing the occurrence data for the plant species used in the modelling framework. Datasets.zip: it represents the "Datasets" folder directory, as illustrated in the README.pdf. This zip one the 'PlantLifeForms.txt' file, which informs plant growth form for 2656 tropical dry forest species. This is the only file that will not be produced by R-scripts provided. The other 94 csv files in this zip are provided for illustrative purposes to avoid the need of the user to re-run all ENM and ESM models. Among these csv files, we provide (i) species-level outputs (measures of species range shift), (ii) assemblage-level outputs (measures of richness, beta-diversity, ecological generalism, and woodiness of plant assemblages), (iii) summary statistics (ENM-ESM model performance metrics, kruskal wallis tests, spatial correlations), and (iv) processed tables used to build the main figures in the manuscript. Shapefiles.zip: it represents the "Shapefiles" folder directory, as illustrated in the README.pdf. There are five shapefiles in this zip. (i) Caatinga_wgs84, spatial polygon illustrating the study area. (ii) cea_grid_cells, 10x10 km equal-area grid cells created for the assemblage-level analysis. (iii) ne_110m_admin_0_countries, Natural Earth Vector draws boundaries of countries (see https://www.naturalearthdata.com/downloads). (iv) ne_110m_graticules_20, Natural Earth Vector draws graticules of the globe (see https://www.naturalearthdata.com/downloads). (v) wwf_realm, the WWF biogeographical realms, modified from Dinerstein et al. (2017, see https://ecoregions.appspot.com/). AggregatedUncertainty.zip: it represents the "AggregatedUncertainty" folder directory, as illustrated in the README.pdf. Aggregated uncertainty is a combined measure of variation in species habitat suitability across five generalized circulation models (GCM). This zip file contains 16 raster files in format tif, with each raster representing a combination between SSP scenario (ssp245 or ssp585) and extrapolation constraints (MOP00, MOP70, MOP80, or MOP90). RData.zip: it represents the "RData" folder directory, which is designed to store RData files produced during the running of the R-scripts. It currently includes two files to facilitate the plotting of main figures. R-code: Moura_etal_JEcol2023_Script1_ENM_DataAnalysis.R: perform data cleanning, prepare predictor layers, run traditional ecological niche models, get ensemble models across GCMs, apply extrapolation constraints, and compute species geographical range shifts. Moura_etal_JEcol2023_Script2_ESM_DataAnalysis.R: run ensemble of small models for rare species, get ensemble models across GCMs, apply extrapolation constraints, and compute species geographical range shifts. Moura_etal_JEcol2023_Script3_AssemblageLevelAnalysis.R: build species-level tables to classify plants with respect to ecological generalism and woodiness, compute aggregated model uncertainty, extract presence-absence matrices, compute assemblage-level metrics, and assess changes in biodiversity patterns across regions subject to homogenization or heterogenization. Moura_etal_JEcol2023_Script4_Figures_MainText.R: build figures 1 to 6 reported in the main text. AdaptedRFunctions.R: auxiliary script containing functions modified from existing R-packages. References Moura, M.R., Nascimento, F.A.O., Paolucci, L.N., Silva, D.P., & Santos, B.A. (2023). Pervasive impacts of climate change on the woodiness and ecological generalism of dry forest plant assemblages. Journal of Ecology, in press. Correspondence to: mariormoura@gmail.com