Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species’ ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT), to a spatial Bayesian SDM method (fitted using R-INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1–3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10–12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account for spatial autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit Bayesian SDMs may be the better choice when modelling the spatial distribution of target species.

I&M Staff in conjunction with a University of Tennessee graduate student developed maximum entropy (specifically the program MaxEnt) distribution models to predict wetland occurrence. Because MaxEnt is able to account for interactions among landscape features and climate variables, it is an ideal modeling approach for predicting the probability of finding a wetland across the park’s complex topography. Model outputs were based on 24 environmental variables (e.g., slope, microtopography, rainfall, etc.) at a 30x30 m grid cell resolution.The corresponding Integration of Resource Management Applications (IRMA) NPS Data Store reference is Great Smoky Mountains National Park Wetlands MaxEnt Model.

The methods for compiling the occurrence data, performing species distribution modeling, and conducting GapAnalysis for these taxa are described in full in Khoury et al. (2019a, b). In short, occurrence data were drawn from biodiversity and conservation repository databases and from the authors’ own botanical explorations. Duplicates and non-wild records were removed, taxonomic names were standardized, and records were classified as H (reference) or G (ex situ sample) as per the definitions described in this paper (see Input data). Species distributions were produced with the MaxEnt algorithm (Phillips et al. 2017) using 26 climatic and topographic predictors (Jarvis et al, 2008, Fick and Hijmans 2017) at a spatial resolution of 2.5 arc-minutes, employing a subset of variables as well as number and location of pseudo-absences specific to each taxon. The occurrence data, models, and full results are available at Khoury et al. (2019c, d). A GapAnalysis R tutorial for three of the&nbs...

This dataset is based on CMIP6 climate model data and historical grassland fire samples, constructed using multiple regression prediction models and maximum entropy models (MaxEnt), aiming to evaluate the spatial distribution characteristics of grassland fire susceptibility in Qinghai Province under different climate scenarios (SSP119, SSP245, SSP585) and different periods (short-term: 2025-2049, medium-term: 2050-2074, long-term: 2075-2100). Climate variable data includes temperature, precipitation, and wind speed, which are downscaled to a resolution of 1km × 1km using bias correction and bilinear interpolation methods. The fire susceptibility is constructed using a maximum entropy model, and the model input variables also include NDVI, human footprint, slope, etc DEM、 Factors such as relative humidity and distance from residential areas, roads, and rivers. The data output format is GeoTIFF (. tif), where each. tif file represents the spatial probability distribution of fire susceptibility in a certain scenario and period, with a numerical range of 0-1.

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

ArcGIS post-processing of MaxEnt response curves to make data and computed probability of occurence spatially explicit.Contains GDB. Need to download all files to display correctly (with ArcMap 10.5)

The Tropical Andes Biodiversity Hotspot holds a remarkable number of species at risk of extinction due to anthropogenic habitat loss, hunting and climate change. One of these species, the Critically Endangered yellow-tailed woolly monkey (Lagothrix flavicauda), was recently sighted in JunÃn region, 206 kilometres south of its previously known distribution. The range extension, combined with continued habitat loss, calls for a re-evaluation of the species’ distribution and available suitable habitat. Here, we present novel data from surveys at 53 sites in the regions of JunÃn, Cerro de Pasco, Ayacucho and Cusco. We encountered L. flavicauda at 9 sites, all in JunÃn, and the congeneric L. l. tschudii at 20 sites, but never in sympatry. Using these new localities along with all previous geographic localities for the species, we made predictive Species Distribution Models based on Ecological Niche Modelling using a generalized linear model and maximum entropy. Each model incorporated biocli..., Presence data was collected through a literature search of all L. flavicauda localities since 2010. Prediction results are from 1) a generalized linear model produced in R and 2) MaxEnt Programming Software (v. 3.4.4).,

VanCampernolle et al 2019Folder includes species locality data and current and future environmental data used for Maxent species distribution models. The 'Calibration fish localities' file includes individual georeferenced species localities used for Maxent model calibration (training). The 'Validation fish localities' file includes individual georeferenced species localities used for Maxent model validation (testing). The 'Currrent_environment' folder includes GIS habitat data for Maxent modeling of contemporary species distributions. The 'Future_environment' folder includes GIS habitat data for Maxent projection of future species distributions. The 'Future_environment' folder contains subfolders with all Global Climate Model (GCM) scenarios used for future Maxent projections.

IntroductionClimate change forms one of the most dangerous problems that disturb the earth today. It not only devastates the environment but also affects the biodiversity of living organisms, including fungi. Macrophomina phaseolina (Tassi) Goid. is one of the most pervasive and destructive soil-borne fungus that threatens food security, so predicting its current and future distribution will aid in following its emergence in new regions and taking precautionary measures to control it.MethodsThroughout this work, there are about 324 records of M. phaseolina were used to model its global prevalence using 19 environmental covariates under several climate change scenarios for analysis. Maximum Entropy (MaxEnt) model was used to predict the spatial distribution of this fungus throughout the world while algorithms of DIVA-GIS were chosen to confirm the predicted model.ResultsBased on the Jackknife test, minimum temperature of coldest month (bio_6) represented the most effective bioclimatological parameter to fungus distribution with a 52.5% contribution. Two representative concentration pathways (RCPs) 2.6 and 8.5 of global climate model (GCM) code MG, were used to forecast the global spreading of the fungus in 2050 and 2070. The area under curve (AUC) and true skill statistics (TSS) were assigned to evaluate the resulted models with values equal to 0.902 ± 0.009 and 0.8, respectively. These values indicated a satisfactory significant correlation between the models and the ecology of the fungus. Two-dimensional niche analysis illustrated that the fungus could adapt to a wide range of temperatures (9 °C to 28 °C), and its annual rainfall ranges from 0 mm to 2000 mm. In the future, Africa will become the low habitat suitability for the fungus while Europe will become a good place for its distribution.DiscussionThe MaxEnt model is potentially useful for predicting the future distribution of M. phaseolina under changing climate, but the results need further intensive evaluation including more ecological parameters other than bioclimatological data.

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Amphidecta calliomma is a butterfly species that occurs in Colombia, Bolivia, Peru, Venezuela, Ecuador, Panama and Brazil (in the states of Mato Grosso, Mato Grosso do Sul, Rondônia and Pará). Here, we present a new occurrence of A. calliomma in the Carajás National Forest (Pará, eastern Amazon), expanding the known distribution of the species. We also provide Species Distribution Model comparing the contribution of the new occurrence to species area of occurrence projections, supporting future field research. The projections reveal an expansion of area of occurrence for A. calliomma located mainly in the southeast portion of Amazon Forest. Despite its wide distribution, the small number of records of A. calliomma may indicate that the species has a low detectability in surveys. This study provides support for new surveys and reduces the knowledge gap about A. calliomma, thus supporting its conservation. Methods Sampling From 05 to 14 November 2019, we conducted a campaign to collect frugivorous butterflies in the Carajás National Forest (southwestern Pará state, Brazil). Butterflies were collected using Van Someren-Rydon traps baited with a mixture of banana and beer (instead of sugarcane), which was fermented for 48 hours, following methodologies adapted from Uehara-Prado et al. (2005) and Freitas et al. (2014). The individuals captured in the traps were collected (SISBIO license number: 68977-1) and identified based on literature resources and with the help of the website “Butterflies of America” (https://www.butterfliesofamerica.com/L/Nymphalidae.htm, accessed in November 2020) (Warren et al., 2013). After identification and preparation, the specimen of A. calliomma was incorporated into the entomological collection of the Museu Paraense Emílio Goeldi (MPEG.HLE 04045043) (MPEG, Pará, Brazil). Occurrence records In addition to field collection, we retrieved data from Global Biodiversity Information Facility (GBIF; www.gbif.org, accessed in November 2022; DOI: https://doi.org/10.15468/dl.kgbph8) and SpeciesLink (https://specieslink.net/, accessed in November 2022) and from published articles, totaling 52 records. We also removed duplicate and non-georeferenced data. We removed inconsistencies using a conservative pipeline (Gomes et al., 2018). Thus, our final database totaled 16 occurrence records (11 from the digital databases, 4 from articles and 1 occurrence from our field collections) (Supporting Information Table 1). Climate information We downloaded climate data with a resolution of 10 arc-minutes (~ 18 km x 18 km) from the WorldClim database version 2.1 (www.worldclim.org, accessed in November 2022). We focused on non-correlated climate data, based on ecological relevance. Butterflies are highly sensitive to climate as warm temperatures can stimulate their flight muscles efficiency and wind is a key component for flying animals and precipitation affects species richness (Turner et al. 1987; Checa et al. 2019). We downloaded and tested for correlation (coefficient threshold |ρ| < 0.7) seven historical climate variables: precipitation, water vapor pressure, solar radiation, wind speed, maximum temperature, minimum temperature and average temperature. Species Distribution Model We used an algorithm based on maximum entropy (MaxEnt) to produce models of species potential distribution to estimate A. calliomma area of occurrence (AOO) (Phillips et al., 2004; IUCN, 2022). We followed Gomes et al. (2019) and used background information to calibrate MaxEnt predictions based on data of tree species from Amazon forest since most of the occurrences of the A. calliomma are located in this biome. Background data is a sample from the study area used to characterize its environmental conditions (Phillips et al., 2009). Distribution modelling methods using background data generally outperformed those using presence-absence or pseudo-absence information, especially when modelling mobile species (Fernandez et al., 2022). Also, background information methods are more flexible, producing more realistic and less over-fitted predictions (Peterson et al., 2011). Since A. calliomma has little occurrence information available, we used a more flexible approach to understand the general distribution pattern of the species. We used product, threshold and hinge features of MaxEnt (Boucher-Lalonde et al., 2012; Merow et al., 2013). To evaluate the models, we used a null model approach (Raes & Steege, 2007). We tested the predictive performance of the A. calliomma models as estimated by the area under the ROC curve (AUC) against the predictive performance of 99 null models generated using the same number of occurrences of A. calliomma generated randomly. If the AUC of the models scores higher than the 95th best null models, this means that the chance of a model generated randomly showing a better performance is less than five percent. The models were converted in binary maps by using the 10th percentile training presence threshold, which omits the regions with environmental suitability lower than the lowest 10% of occurrence records (Gomes et al., 2018). We then clipped the binary maps by using the extent of occupancy (EOO) of the species plus a buffer of 300 km, based on the notion that the EOO is restricted by dispersal capabilities (Gaston, 2009; De Ro et al., 2021). We estimated A. calliomma AOO using the new occurrence sampled and comparing with the AOO estimation with no new occurrence. All calculations and analyses were performed with R version 3.6.3, including the R packages raster (Hijmans & van Etten, 2016), rgdal (Bivand, Keitt, & Rowlingson, 2017), gstat (Pebesma & Heuvelink, 2016), dismo (Hijmans et al., 2016), rJava (Urbanek, 2017) and SDMTools (VanDerWal et al., 2019).

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Besides being central for understanding both global biodiversity patterns and associated anthropogenic impacts, species range maps are currently only available for a small subset of global biodiversity. Here, we provide a set of assembled spatial data for terrestrial vascular plants listed at the global IUCN red list. The dataset consists of pre-defined native regions for 47,675 species, density of available native occurrence records for 30,906 species, and standardized, large-scale Maxent predictions for 27,208 species, highlighting environmentally suitable areas within species’ native regions. The data was generated in an automated approach consisting of data scraping and filtering, variable selection, model calibration and model selection. Generated Maxent predictions were validated by comparing a subset to available expert-drawn range maps from IUCN (n = 4,257), as well as by qualitatively inspecting predictions for randomly selected species. We expect this data to serve as a substitute whenever expert-drawn species range maps are not available for conducting large-scale analyses on biodiversity patterns and associated anthropogenic impacts.

Methods The dataset includes spatial data for 47,675 species at different levels of detail. In total, range estimates (i.e. relative environmental suitability within native regions) have been predicted for 27,208 species using maximum entropy modelling (Maxent), for 30,906 species native occurrence records from the global biodiversity information facility (GBIF) are provided, and for 47,675 species the spatial extent of its native regions, retrieved from Plants of the World online and based on the world geographical scheme for recording plant distributions (WGSRPD), is provided.

This dataset includes sample collection data of existing Phoebe zhennan in China, as well as historical climate data and future climate data (with a resolution of 2.5 minutes and using the BCC-CSM2-MR GCM model) collected by Worldclim, along with geographical elevation data. These data are used to predict the potential distribution range of Phoebe zhennan in the future.

Climate change has significantly impacted the distribution patterns of medicinal plants, highlighting the need for accurate models to predict future habitat shifts. In this study, the Maximum Entropy model to analyze the habitat distribution of Pulsatilla chinensis (Bunge) Regel under current conditions and two future climate scenarios (SSP245 and SSP585). Based on 105 occurrence records and 12 environmental variables, precipitation of the wettest quarter, isothermality, average November temperature, and the standard deviation of temperature seasonality were identified as key factors influencing the habitat suitability for P. chinensis. The reliability of the model was supported by a mean area under the curve (AUC) value of 0.916 and a True Skill Statistic (TSS) value of 0.608. The results indicated that although the total suitable habitat for P. chinensis expanded under both scenarios, the highly suitable area contracted significantly under SSP585 compared to SSP245. This suggests the importance of incorporating climate change considerations into P. chinensis management strategies to address potential challenges arising from future ecosystem dynamics.

Maxent software (http://www.cs.princeton.edu/~schapire/maxent) is frequently used for presence-only species distribution modeling. Maxent requires, however, that input ASCII raster files be aligned with one another and have the same spatial extent. This tool pre-processes raster data in preparation for Maxent modeling to ensure that all rasters have the same extent, same cell size, and aren't missing data. There are two version of this geoprocessing modeling. The advanced version is for the ArcGIS Advanced license. The basic version is the the ArcGIS Advanced license. Both versions require Spatial Analyst. The difference between the two is that the advanced version creates a polygon shapefile that shows the difference between the template raster and the processed raster. Ideally, this should generate a polygon with empty output, but if it doesn't you can use it to diagnose problems. The tool first resamples the raster, then uses a focalmean (3x3 and 5x5) to fill gaps, and mosaics the resampled, 3x3, and 5x5 rasters together, and converts to ASCII.Recommended citation format: Dilts, T.E. (2015) Prepare Rasters for Maxent Tool for ArcGIS 10.1. University of Nevada Reno. Available at: http://www.arcgis.com/home/item.html?id=11bf7e689c92413f8d31933b3e1f56b1

The Western Atlantic population of Red Knot (Calidris canutus rufa) has undergone dramatic declines in recent decades and conservation biologists have sought to improve knowledge about the species' ecology in an effort to understand these declines. One major information gap has been the lack of a detailed understanding of range and habitat use during the breeding season, when the species is distributed sparsely across the Canadian Arctic. Airborne radio-telemetry surveys of Red Knots tagged in Delaware Bay, New Jersey were conducted across the south and central Canadian Arctic, from Victoria Island in the west to Baffin Island in the east. Intensive field surveys were conducted on Southampton Island, Nunavut over successive summer field seasons to locate nesting Red Knots and record characteristics of their nesting habitat. Maximum entropy modeling (Maxent) and geographic information system (GIS) data on environmental characteristics were used to predict Red Knot habitat suitability at two spatial scales: of nesting site location suitability at the local scale across Southampton Island, and of breeding habitat suitability (i.e., both nesting and foraging habitat) at a broader, regional scale across the south and central Canadian Arctic.

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman & Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

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Objective: Exploring the changing process of the geographical distribution pattern of Tetracentron sinense Oliv. and its main influencing factors since the last interglacial period can provide a scientific basis for the effective protection and management of the species. Methods: The MaxEnt model was used to construct the potential distribution areas of T. sinense in different periods such as the last interglacial (LIG), the last glacial maximum (LGM), the Mid-Holocene (MID), the current and future (2050s, 2070s). On the premise of discussing the influence of dominant environmental factors on its distribution model, the suitable area changes of T. sinense under different ecological climate situations were quantitatively analyzed. Results: (1) The AUC and TSS values predicted by the optimized model were 0.959 and 0.835, respectively, indicating a good predictive effect by the MaxEnt model; the potential suitable areas for T. sinense in the current period are mainly located in southwest C..., , , # Evaluating the vulnerability of Tetracentron sinense habitats to climate-induced latitudinal shifts

https://doi.org/10.5061/dryad.vx0k6dk0m

Datasets

Utilizing the MaxEnt model and ArcGIS spatial analysis technology, this study examines potentially suitable areas for T. sinense across historical periods (the last interglacial period, the last glacial maximum, and the Middle Holocene) as well as current and future periods (2050s and 2070s). The objectives of this study are to (1) analyze the dynamic changes in potentially suitable areas, (2) investigate the main environmental factors driving changes in the distribution pattern of Tetracentron sinense, and (3) suggest a scientific basis for the effective protection and management of Tetracentron sinense.

Occurrence points: The distribution records of Tetracentron sinense are sourced from various data platforms such as Flora Reipublicae Popularis Sinicae ([http://iplant.cn/](http:...