Incorrect and incomplete distribution data can mislead species modeling: a case study of the endangered Litsea auriculata (Lauraceae)

Incorrect and incomplete distribution data can mislead species modeling: a case study of the endangered Litsea auriculata (Lauraceae) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Incorrect and incomplete distribution data can mislead species modeling: a case study of the endangered Litsea auriculata (Lauraceae) Chao Tan, David Kay Ferguson, Yong Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3978669/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Global warming has caused many species to become endangered or even extinct. Describing and predicting how species will respond to global warming is one of the hot topics in the field of biodiversity research. Species distribution modeling predicts the potential distribution of species based on species occurrence records. However, it remains ambiguous how the accuracy of the distribution data impacts on the prediction results. To address this question, we used the endangered plant species Litsea auriculata (Lauraceae) as a case study. By collecting and assembling six different datasets of Litsea auriculata , we used MaxEnt model to perform species distribution modeling and then conducted comparative analyses. The results show that the distribution of Litsea auriculata is mainly in the Dabie Mountain region, southwestern Hubei and northern Zhejiang, and that mean diurnal temperature range (bio2) and temperature annual range (bio7) play important roles in the distribution of Litsea auriculata. Compared with the correct data, the dataset including misidentified specimens leads to a larger and expanded range in the predicted distribution area, whereas the species modeling based on the correct but incomplete data predicts a smaller and contracted range. According to the analysis of the local protection status of Litsea auriculata , we found that only about 23.38% of this species is located within nature reserves, so there is a large conservation gap. Our study suggests that the accurate distribution data is important for species modeling, and incomplete and incorrect data normally gives rise to misleading prediction results. In addition, our study also revealed the distribution characteristics and conservation gaps of Litsea auriculata , laying the foundation for the development of rational conservation strategies for this species. Conservation Lauraceae Litsea auriculata MaxEnt Species distribution modeling Specimen identification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction With global warming, populations of many species have been lost and fragmented, leading to an endangered status and even extinction of species (Power et al. 2019; Chase et al. 2020 ; Richards et al. 2020 ). A large number of species have become adapted to new distribution areas by modifying the pre-existing community composition and hence the ecosystem function (Babcock et al. 2019 ; Román-Palacios et al. 2020; Nielsen et al. 2021 ). Understanding the impact of future climate change on species potential habitats is important for the development of species conservation strategies (Austin et al. 2011; Hole et al. 2011 ; Moitz & Agudo 2013). Species Distribution Models (SDMs) attempt to associate the distribution information of species with the corresponding environmental variables, establish models and predict the potential distribution of species in a certain area under specific spatial and temporal conditions in the future, which can quantify the regional and local distribution of species abundance at different scales (Guisan and Thuiller 2005 ; Araujo and Peterson 2012 ). SDMs mainly include a Generalized Linear Model (GLM), Classification and Regression Tree (CART), Random Forest (RF), Maximum Entropy (MaxEnt) etc. (Guo et al. 2020 ). The MaxEnt is one of the SDMs, based on extant species occurrence records and environmental data. It has the advantage of great accuracy, small sample size requirement, and good stability, and thus has become a widely used modeling approach (Wisz et al. 2008 ; Fitzpatrick et al. 2013 ; Merow et al. 2013 ; Morales et al. 2017 ; Wu et al. 2022 ). In recent years, there has been a steady increase in the literature on SDMs using the MaxEnt as a keyword in the Web of Science (Fig. 1). The use of SDMs makes it possible to predict the potential distribution of species in new space or time (Liu et al. 2022 ), and thus provides an important reference for species conservation. SDMs are based on distribution site data and environmental factor data, so uncertainty in the location of species sampling sites will inevitably increase the uncertainty of modeling results (Guo et al. 2020 ). Specimen data have become an important data source for SDM predictions (Meineke et al. 2018 ). More than 3,000 herbaria in the world have collected over 400 million plant specimens (Thiers 2020 ). With the rapid digitization of plant specimens worldwide, specimen data have widely been used for different purposes, e.g. taxonomy, biogeography, phenology, and SDMs (Jaca et al. 2018 ; Jukonienė et al. 2018; Cámara-Leret et al. 2020; Meineke et al. 2018 ). However, it is worth pointing out that the herbarium collections and digitized specimens contain samples of cultivated plants far from their natural range as well as mis-identified material. There is a lack of quantitative description and research on how these misidentified and non-native specimen data have affected the results of SDMs. Litsea auriculata is a deciduous tree species of the family Lauraceae. This species is characterized by scale-like exfoliating bark, large and auriculate leaves, long petioles, black ovoid fruits, and a cup-shaped receptacle. It has important economic and medicinal value, its wood has been used for furniture, while the fruits and roots have been employed as a traditional Chinese medicine (TCM) (Yang and Huang 1982 ). Litsea auriculata is sporadically distributed in a few mountainous areas at 500 − 1500m in Zhejiang, Anhui, Henan etc., and was listed as vulnerably endangered because of habitat loss and fragmentation (Fu and Jin 1992 ; Qin et al. 2017 ). As a result, it is important to investigate the conservation status of Litsea auriculata and identify any conservation gaps. Species distribution modeling (SDM) should be based on complete sampling of accurately identified specimens, which is essential for understanding the suitable distribution area of species and for formulating reasonable conservation strategies (Costa et al. 2015 ; Fei and Yu 2016 ). Geng et al. ( 2017 ) conducted a study on community genetics and ecological niche modeling of Litsea auriculata , and predicted ecological niche shifts under different climate changes based on data from three populations in Tianmu Mountain of Zhejiang, Dabie Mountain of Anhui and Henan, and found that the habitat showed a trend towards contraction and decline in east-central China. However, the sampling range of this study is obviously inadequate, especially for the marginal areas of its distribution range. Based on specimens and literature data, Yang et al. (2018) documented the distribution of the species in Chun'an and Tiantai Counties in Zhejiang, Huoshan and She Counties in Anhui, Yingshan County and Shennongjia forest area in Hubei; Geng et al. ( 2017 ) did not include these localities. In addition, the Chinese Virtual Herbarium (abbreviated as CVH), the largest digitized herbarium data source, contains misidentified and cultivated specimens. However, it remains unclear how the incomplete sampling, misidentified and cultivated specimen data impact on the distribution modeling of this species. In this study, we collected and collated six different datasets of Litsea auriculata and predicted each dataset using the MaxEnt, and compared the differences of the species distribution modeling results based on these different datasets. By doing this, we plan to answer the following three questions: 1) what are the impacts of misidentified and cultivated specimen data on the results of SDMs? 2) what are the differences between SDM results of inadequate sampling and complete and accurate datasets? 3) identify the conservation gap of the species based on our new species modeling results and indicate what action needs to be taken to conserve the species? 2 Materials and Methods 2.1 Data collection and processing 2.1.1 Distribution data of Litsea auriculata The distribution data of Litsea auriculata were obtained from the Chinese Virtual Herbarium (CVH, https://www.cvh.ac.cn/ ), National Specimen Information Infrastructure (NSII, http://www.nsii.org.cn/2017/home.php ), authoritative regional floras, and published papers (Sun 2014 ; Geng et al. 2017 ). We annotated the data source of each distribution record to generate different datasets. The distribution records were cross-checked for spelling errors. All the specimen records were visually identified by the corresponding author (Yong Yang), and the misidentified and cultivated records were labeled. Then, the collected data were further processed and separated into six datasets (see Table S1 ): dataset 1 (correct) including all the correctly identified records from herbarium specimens and literature; dataset 2 (cultivated) containing correctly identified and cultivated specimens; dataset 3 (misidentified) encompassing correctly identified and misidentified specimens but excluding cultivated specimens; dataset 4 (specimen) including only correctly identified specimens; dataset 5 (population) was collected from the literature, contained field population investigations (correctly identified but incomplete); dataset 6 (including all different sources) included all the distribution records of population investigations and herbarium data (correctly identified, misidentified and cultivated). We used Google Maps ( http://maps.google.cn/ ) to obtain the geographic coordinates of the distribution records. We removed duplicate specimens and redundant records within the different datasets, before MaxEnt analysis and imported the distribution data into ArcGIS 10.2 to eliminate duplicate points, i.e., only one of the distribution records within 10 km was retained (Zhou et al. 2021 ). 2.1.2 Environment variable data Altogether 19 environmental variable data at 2.5′ resolution were downloaded from WorldClim ( https://www.worldclim.org/ ) (see Table S2), including current climatic data (1970–2000) and future climate predictions. The future climatic data were based on the climate model of the Beijing Climate Center Climate System Model Version 1.1 (BCC-CSM 1.1), which was constructed under RCP 2.6, RCP 4.5, and RCP 8.5 for 2050 (average value over the period 2041–2060) and 2070 (average value over the period 2061–2080) for the three representative concentration pathways (RCPs) (Luo et al. 2009). The climate layers were extracted using the software ArcGIS 10.2, and the extracted layers were converted to the ASCII format. In order to avoid influencing the final assessment of the model of high correlations between environmental variables (Luo et al. 2017 ), we conducted Pearson correlation analyses of 19 climatic variables for each period using the cor function of R software, and the climatic factors with r<|0.85| that were more closely related to species distribution were retained (Yan et al. 2017 ; Zhu et al. 2019). Finally, we performed principal component analyses (PCA) on the variables under current climatic conditions to identify the key drivers influencing the distribution of Litsea auriculata. 2.2 Potential distribution prediction using MaxEnt Firstly, we imported the six distribution datasets (.CSV format) and climatic data (.ASCII format) for each period into MaxEnt 3.4.1 software for species ecological niche simulation. Secondly, different procedures for simulating the potential distribution were performed for datasets with different sample sizes. For data sets with fewer than 25 coordinate points, the Jackknife method was used for simulation evaluation. For species modeling, one of the coordinates was removed and the model was built based on the remaining n-1 coordinates, so that n models could be built and the optimal model selected for the MaxEnt ecological niche simulation. For data sets with more than 25 available coordinate points, 75% of the species distribution data was set as the training set and 25% as the test set, the number of operational iterations was set to 10, and the rest was used as default values (Pearson et al. 2007 ; Zhou et al. 2021 ). The area under curves (AUC) with receiver operator characteristic (ROC) was used to evaluate the reliability of the simulation results (Guo et al. 2019 ). The range of AUC values was 0 to 1, the closer to 1 indicating the higher reliability of the simulation. The simulation result was considered to be very accurate when the AUC value was between 0.9 and 1, accurate when the AUC was 0.8 − 0.9, average when the AUC was between 0.7 and 0.8, and unreliable when the AUC result was less than 0.7 (Elith et al. 2016 ; Jiang et al. 2016 ). Finally, the simulation results of MaxEnt were entered into ArcGIS 10.2 software and transformed into raster layers for visualization, and the natural breaks method was selected to calculate the fitness index P. Based on previous studies, P>0.75 was used as a hotspot for species survival (Shi et al. 2022 ), and the proportion of the area in different distribution data types was calculated. 2.3 Calculating hotspots in protected areas To describe and evaluate the local conservation status of Litsea auriculata , we assembled 2569 nature reserves (including 440 national nature reserves and 2,129 provincial and county nature reserves) established during 1956 − 2021 (Zhang et al. 2015 ). In ArcGIS 10.2, the base map data of China's nature reserves superimposed on the samples were used to calculate the area of the contemporary hotspot area located within the reserve, and to evaluate the protection efficiency of Litsea auriculata . 3 Results 3.1 Current distribution pattern of Litsea auriculata Six distribution datasets were assembled in this study. Dataset 4 contains 16 records, and was based on herbarium specimen data from CVH and NSII. Dataset 5 was collected from the literature, and contained 9 records. Dataset 1 was an integration of dataset 4 and dataset 5, and consisted of a total of 18 records after removing duplicate records. Both dataset 2 and dataset 3 were assembled using specimen data from CVH and NSII, each containing 22 records. Dataset 6 was an integration of dataset 2, dataset 3, dataset 4, and dataset 5, and contained a total of 26 records after deleting duplicate records. According to the correct and complete dataset (dataset 1), Litsea auriculata was distributed in Dabie Shan at the border of Henan and Anhui, Qingliang Mountain at the border of Anhui and Zhejiang, Daming Mountain in Zhejiang, and Nanzhao County of Henan and Shennongjia forestry district in Hubei (Fig. 2). This species was introduced to botanical gardens outside its native range for the purpose of ex situ conservation, e.g. Ming Xiaoling Mausoleum in Jiangsu, Hangzhou Botanical Garden in Zhejiang, Lushan Botanical Garden in Jiangxi, and Kunming Botanical Garden in Yunnan (Fig. 2). Wrong identification records expanded the distribution range of the species, e.g. Chongyi County in Jiangxi, Fengkai County in Guangdong, Jiangshan County in Zhejiang, and Sandu Shui Autonomous County of Guizhou (Fig. 2). The six datasets were screened for environmental variables based on Pearson correlation analyses. The results show that dataset 1 and dataset 3 each retained six climate factors in the final MaxEnt model analyses. The remaining datasets retained five climate factors in the final model analysis, respectively (Table 1 ). Table 1 Screened environmental variables of different datasets for the final MaxEnt model analysis. Details of climate variables see Table S2. Type bio1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 datase 1 * * * * * * datase 2 * * * * * datase 3 * * * * * * datase 4 * * * * * datase 5 * * * * * datase 6 * * * * * 3.2 Spatial pattern and driving factors of potential distribution areas of various data Based on the assembled distribution datasets and environmental data, the potential geographical distribution area of this species was simulated using the optimal MaxEnt model. The results show that the AUC value of the simulated curves of all six datasets was greater than 0.994, indicating that the prediction results of the model are very reliable (see Table S3). The potential distribution patterns based upon different datasets were significantly different under current climatic condition. The suitable areas predicted for Litsea auriculata based on the correct dataset (dataset 1) were mainly distributed in Dabie Mountain, Huangshan Mountain and southwestern Hubei, and a small area in Zhejiang (Fig. 3a). Under-sampled datasets predicted distribution areas showing minor differences from the correct dataset (dataset 1). Compared with the predicted result of dataset 1, the extent of the fitness zone based upon the specimen dataset (dataset 4) extended in easterly and westerly directions and shrunk in the middle part (Fig. 3d), while the suitable area based upon the population dataset (dataset 5) shrunk gradually from the periphery to the middle (Fig. 3e). The difference between the suitable areas based upon the inaccurate dataset (datasets 2, 3 & 6) and the correct dataset (dataset 1) was rather obvious, and the potential distribution areas based upon these three datasets were widely distributed and extended in all directions, throughout the middle and lower reaches of the Yangtze River (Fig. 3b,c,f). Besides, under the 2050s and 2070s RCP 2.6/4.5/8.5 climate scenarios, the suitable areas based upon these different datasets were basically consistent with those under contemporary conditions (see Figure S1 ). The predicted hotspot areas based upon different datasets showed distinct trends under various climatic conditions (Fig. 4). The correct but incomplete datasets (datasets 4 & 5) displayed minor differences from the correct dataset (dataset 1), ranging from 0.01–0.54%. The largest hotspot area anomaly was under the 2050s RCP 2.6 condition, where the population dataset (dataset 5) differs from the correct dataset (dataset 1) by 0.54% with an area of 51,900 km². The smallest hotspot area occurred under multiple climate scenarios, the suitable area based on the specimen dataset (dataset 4) differed from that based on the correct dataset (dataset 1) by 0.01% with only 1,000 km² under the 2050s RCP 4.5/8.5 climatic conditions. The same result appears in the 2070s RCP 8.5, with the specimen dataset (dataset 4) and population dataset (dataset 5) differing by 1,000 km² from the correct dataset (dataset 1). The incorrect dataset (datasets 2, 3 & 6) and the correct dataset (dataset 1), on the other hand, exhibited a large difference of 0.03%−0.88%. The largest hot spot area discrepancy value occurred in the misidentified dataset (dataset 3) for the 2070s RCP 8.5 with 0.88% and an area of 82,600 km². The smallest area gap of 2,900 km² occurred in all datasets (dataset 6) under 2050s RCP 2.6. In addition, the maximum hotspot area difference in all climatic environments occurred in the predicted fitness zones of the misidentified dataset (dataset 3), except for 2070s RCP 2.6 which materialized in the cultivated dataset (dataset 2) (Table 2 ). Table 2 The proportion of hotspot areas in different datasets (hotspots/selected regions). Type Present 2050s 2070s RCP2.6 RCP4.5 RCP8.5 RCP2.6 RCP4.5 RCP8.5 dataset 1 0.16% 0.70% 0.14% 0.07% 0.39% 0.15% 0.08% dataset 2 0.30% 0.29% 0.30% 0.31% 0.92% 0.25% 0.18% dataset 3 0.42% 0.92% 0.50% 0.57% 0.91% 0.46% 0.96% dataset 4 0.09% 0.48% 0.13% 0.08% 0.12% 0.12% 0.07% dataset 5 0.11% 0.16% 0.07% 0.10% 0.11% 0.12% 0.07% dataset 6 0.38% 0.67% 0.47% 0.30% 0.53% 0.43% 0.19% 3.3 PCA of Litsea auriculata different datasets under current climatic condition The contribution of environmental variables varied when conducting PCA studies based on different datasets under current climatic condition (see Table S4). Mean diurnal temperature range (bio2) and temperature annual range (bio7) played a decisive role in the correct dataset (dataset 1) of Litsea auriculata (Fig. 5a). bio7 and Isothermality (bio3) had the largest impact on the specimen dataset (dataset 4) prediction (Fig. 5d), while bio2 and precipitation seasonality (bio15) determined the distribution of the population dataset (dataset 5) (Fig. 5e). In the incorrect datasets (datasets 2, 3 & 6), the two most important determinants for the distribution of cultivated (dataset 2) and all recorded datasets (dataset 6) were bio7 and temperature seasonality (bio4) (Fig. 5b,d), while the distribution of the misidentified dataset (dataset 3) was limited by mean temperature of the driest quarter (bio9) and bio7 (Fig. 5c). 3.4 Distribution and conversation status of Litsea auriculata using different datasets under current climatic condition Under contemporary climatic conditions, the hotspots and protection status predicted based on the different datasets displayed great discrepancies. The hotspots based on the correct dataset (dataset 1) were mainly distributed in Dabie and Huangshan Mountains, with small stands in southwestern Hubei and Zhejiang. The total area was 15,400 km², of which 3,600 km² (23.38%) was located in nature reserves (Fig. 6a). The range of hotspots predicted by the inaccurate dataset (datasets 2, 3 & 6) displayed a certain degree of expansion compared with the correct dataset (dataset 1). The hotspots of the cultivated dataset (dataset 2) were concentrated in the Dabie and Huangshan Mountains, with a small area in Hunan, a total area of 28,800 km², of which 2,600 km² (9.03%) was in a protected area (Fig. 6b). The range of hotspots predicted by the inclusive dataset (dataset 6) was similar to that of the cultivated dataset, with additional distribution areas in southwestern Zhejiang; the total area of the hotspot range was 36,500 km², only 3,300 km² (9.04%) was located in a protected area (Fig. 6c). The misidentified dataset (dataset 3) predicted the largest hotspot area of 40,400 km², which formed a dense area in southwestern Hubei and northwestern Hunan compared with the cultivated dataset, and extended outwards from the Dabie and Huangshan Mountains, with only 5,300 km² (13.18%) located in a protected area (Fig. 6f). The distribution range of hotspot regions predicted by the correct but incomplete dataset (datasets 4 & 5) was similar to the correct dataset (dataset 1), and showed an overall contraction. The hotspot areas of the population dataset (dataset 4) contracted towards the central area of the correct dataset (dataset 1), possessed a total area of 11,500 km²with only 1,800 km²(15.65%) in nature reserves (Fig. 6e). Species modeling based on the specimen dataset (dataset 5) showed a shrinking trend in the hotspots and a scattered occurrence in southwestern Hubei, the total area covering ca. 8,600 km² with only 1,700 km² (19.77%) hotspot area in nature reserves (Fig. 6d). Table 3 The hotspot areas, area and proportion of the hotspots in nature reserves according to species modeling using different datasets under contemporary climatic conditions. (Unit: km²) Type Hotspot areas Predicted areas in nature reserves Proportion dataset 1 15,400 3,600 23.38% dataset 2 28,800 2,600 9.03% dataset 3 40,400 5,300 13.18% dataset 4 11,500 1,800 15.65% dataset 5 8,600 1,700 19.77% dataset 6 36,500 3,300 9.04% 4 Discussion 4.1 Importance of accurate identification and complete species distribution records for species modeling Distribution data is the basis for species modeling predictions. Kadmon et al. ( 2004 ) conducted a comparative study on the distribution modeling of 149 woody plant species in Israel, which revealed that data biases can reduce the accuracy of species modeling, the same conclusion was found by Kramer-Schadt et al. ( 2013 ). Raes & ter Steege ( 2007 ) performed a null model test on species modeling and found that modeling with incorrect distribution data showed significantly different results from the correct data set, demonstrating the impact of data bias on species modeling, which was further corroborated by Wolmarans et al. ( 2010 ) and Chen et al. ( 2015 ). In this study, we compared predictions based on distribution records containing cultivated/misidentified records (datasets 2, 3 & 6) with those based on correctly identified and complete natural distribution records (dataset 1). Our results indicate that the dataset containing misidentified specimens can result in expansion of the fitness areas, thus significantly reducing the accuracy of the model. We compared the prediction results based on the distribution dataset containing cultivated records with those of the correctly identified complete natural distribution records, and found that the suitable distribution area expands greatly from the center to the surrounding area. This indicates that the modeling accuracy decreases with increasingly biased data. Our comparative study of species modeling results based on incomplete natural distribution records (datasets 4 & 5) and correctly identified, complete natural distribution records (dataset 1) suggests that the suitable area showed a conspicuous contraction trend with a very narrow distribution. Species modeling predictions based on such misidentified and inaccurate specimen data can arrive at misleading conclusions. With the rapid development of digital cameras, computers, and internet information technology, a large number of herbarium specimens throughout the world have been digitized and are available for biodiversity studies (Meineke et al. 2018 ; Davis 2023 ). By June 2023, 0.24 billion specimens had been included in the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/ ) and 11.59 millions of specimen data deposited in the Australian Biological Atlas / Atlas of Living Australia (ALA, https://www.ala.org.au/ ). The National Plant Specimen Resource Center (NPSRC, http://www.cvh.ac.cn/ ), the largest digital plant specimen integration platform in China, has collected 8.27 million digitized plant specimens. National Specimen Information Infrastructure (NSII) contains about 16.45 million digital plant specimens. These digitized specimens have become important sources for research in ecology, biogeography, phenology, and conservation biology (Merow et al. 2016 ; Nualart et al. 2017 ; Jones and Daehler 2018 ; Herbling 2022 ; Yang et al. 2022 ; Lee et al. 2022 ; Davis 2023 ). However, over 50% of the herbarium specimens were not correctly identified (Goodwin et al. 2015 ). Digitized specimens thus contain lots of identification errors and cultivated records, and are the main source of erroneous data in species modeling. Incorrect distribution information often leads to severe range deviations and obscures the true species model (Orr et al. 2021 ). As a result, it is necessary to remove and correct the misidentified records and cultivated records before conducting species model predictions. Because published floras record older data and often contain incomplete information, the integration of floras cannot resolve the problem of data completeness. In this study, we found that the Flora of China records the distribution of Litsea auriculata in Tianmu Mountain and Tiantai Mountain in Zhejiang and She County in Anhui (Yang and Huang 1982 ), and misses many other distribution localities. Our new inventory in this study has added the records of Litsea auriculata in Hubei and Henan, and Chun'an County in Zhejiang. The Flora of Anhui is comprehensive at the county level, but remains ambiguous regarding the distribution below the county level. The Jiangxi Seed Plant List contains an incorrect record of Litsea auriculata , which originates from misidentified digitized specimens (Liu et al. 2010 ). The distribution information in these botanical catalogs is fragmentary and cannot be used directly for species modeling, and needs to be verified and integrated. Only when complete and accurate data are available we can obtain valuable research results, which can help understand the distribution characteristics of species and provide important references for biodiversity conservation. Specimens comprise the primary source of species distribution data, and should be correctly identified by taxonomists before utilization. Correct identification is fundamental not only for species distribution modeling, but also for biodiversity conservation. However, taxonomy as a traditional discipline is handicapped in the assessment and evaluation system of many different research institutions (Ma 2014 ). Most research funding has been deployed in more fashionable and advanced research areas, e.g. genome sequencing, making it difficult to train traditional taxonomists. As a result, no taxonomists work in the herbaria to correct the misidentified specimens. To overcome this drawback, it is necessary to promote traditional taxonomy and maintain a permanent taxonomic research team. 4.2 Potential distribution and conservation assessment based on accurate identification and complete dataset of Litsea auriculata In this study, we established a reliable potential distribution area for Litsea auriculata based on an accurately identified and complete dataset (dataset 1). The modeling results show that, compared with other plants of the Lauraceae family (Zheng et al. 2018 ), the distribution range of this species is generally northerly and is currently located mainly on montane forest slopes in the mid-latitudes of central-eastern China. The predicted distribution is similar to the distribution characteristics of gymnosperm species (Tang et al. 2006 ; Xie et al. 2021 ). With global warming in the future, the suitable distribution area of Litsea auriculata will tend to contract, and eventually decrease in the central-eastern part of China, which corroborates a previous study (Geng et al. 2017 ). The predicted hotspot areas using accurately identified and complete datasets (dataset 1) under the contemporary climate shifted southwards compared to Geng et al. ( 2017 ). This difference may be caused by the bias of distribution data, as Geng et al. ( 2017 ) did not fully record the distribution of the species in southern regions such as Anhui and Zhejiang. The potential distribution trend of Litsea auriculata shows a clear mismatch with subtropical broadleaved evergreen forest plants. Previous studies have suggested that subtropical broadleaved evergreen forest species will expand northwards and eastwards under future climatic conditions (Hu et al. 2017 ; Lim et al. 2018 ; Wu et al. 2016 ). The potential distribution ranges of Litsea auriculata do not vary significantly across time, with an overall range of only 0.09%−0.54%, the only local expansion and contraction occurring in some mountains and plains at the edges of the subtropical broadleaved evergreen forests. Coincidentally, a similar pattern was also found in a study of the genus Cinnamomum (Zhou et al. 2021 ). In addition, as in many gymnosperms, Litsea auriculata may have survived by elevational shifts during the late Quaternary glacial oscillations (Cun and Wang 2015 ). The survival of Litsea auriculata is at least partially attributable to its habitat dilemma. Previous studies have shown that the genetic structure of Litsea auriculata continues to diverge and expand, forming small-scale populations (Sun 2014 ). Increased random genetic variation, high levels of inbreeding and reduced gene numbers, combined with a progressively warmer climate, have led to a dramatic decline in the distribution area of this species (Geng et al. 2017 ). In our study, the predicted results based on an accurately identified and complete dataset (dataset 1) for hotspot areas of Litsea auriculata under contemporary climatic conditions show that the species continues to spread in all directions in the future, with increased fragmentation, a gradual reduction in living space, and a further decrease in area, which is consistent with the results of previous studies. Besides, the narrow and concentrated distribution area has increased the threat level of Litsea auriculata (Qin et al. 2017 ), and irreversible damage will occur if these small areas are disturbed. Species distribution models can suggest the chances of survival of endangered plants and facilitate the development of targeted in situ conservation measures (Aguilar-Soto et al. 2015 ). In this paper, habitat prediction in combination with an analysis of Chinese nature reserves, indicates that only 23.38% of Litsea auriculata is currently located in nature reserves, so a large conservation gap remains. The areas outside the nature reserves are mainly located in southern Anhui, west-central and east-central Zhejiang. These areas have suffered from severe deforestation, habitat loss and habitat fragmentation (Wei and Jiang 2012 ), which may have lead to a significant decrease in the number and population size of Litsea auriculata. The area of the species within the nature reserve will gradually shrink under future warming scenarios, and may even deviate excessively from the reserve in the 2070s RCP 2.6 scenario (Table 4 ), thus greatly increasing the threat level. Therefore, in the face of such a situation, a protected area should be established for Litsea auriculata , and special staff should be assigned to protect the forest land, prohibit indiscriminate logging practices, and reduce human interference. According to previous studies, we found that a large number of threatened gymnosperms also survive in the distribution area of Litsea auriculata (Lü et al. 2018 ; Xie et al. 2021 ), so it is crucial to strengthen the protection of these areas for other threatened plants as well. In addition, because the genetic differentiation among populations of Litsea auriculata is large and gene flow is low (Sun 2014 ), it would be beneficial to increase the level of genetic diversity of Litsea auriculata if a sufficient number of individuals within all populations could be selected for intensive translocation and conservation. Table 4 Predicted hotspot area of Litsea auriculata based on correct dataset (dataset 1) and area located within the protected area. (Unit: km²) Period Hotspot areas Nature reserves areas Proportion Current 15,400 1,700 23.38% 2050s RCP2.6 67,300 6,400 9.06% 2050s RCP4.5 13,500 2,200 16.30% 2050s RCP8.5 6,700 1,300 19.40% 2070s RCP2.6 37,500 2,900 7.73% 2070s RCP4.5 14,400 2,300 15.97% 2070s RCP8.5 7,700 1,200 15.58% 5 Conclusion It remains ambiguous how the identification errors, cultivated collections and data incompleteness impact on species distribution modeling. We assembled six datasets and made a comparative study here. We show that misidentification, cultivated specimen data, and data incompleteness all have significant impacts on species modeling prediction results. We identified new areas of potential distribution of Litsea auriculata based on correctly identified and more complete datasets, revealed that the current main distribution range of Litsea auriculata is located in the mountainous areas of the middle and lower reaches of the Yangtze River, with a tendency to contraction in future climate change scenarios. In addition, our assessment of the conservation status of Litsea auriculata , reveals that currently about 23.38% of the suitable areas for the species have been protected in nature reserves, so there are still relatively large conservation gaps. The resulting information can be used to support management, conservation, and recovery plans for Litsea auriculata . Declarations Funding This work was supported by the National Natural Science Foundation of China [32270217 and 31970205] and the Metasequoia funding of the Nanjing Forestry University. The authors declare no competing interests. Author Contribution Chao Tan and Yong Yang wrote the main manuscript text and Chao Tan prepared figures 1-6. All authors reviewed the manuscript Acknowledgements We are grateful to the contributors to the Chinese Virtual Herbarium. Data Availability Statement All data used in the study are included in this paper are available in the supporting datasets. References Aguilar-Soto V, Melgoza-Castillo A, Villarreal-Guerrero F, Wehenkel C, Pinedo-Alvarez C (2015) Modeling the potential distribution of Picea chihuahuana Martínez, an endangered species at the sierra madre occidental. Mexico Forests 6:692–707. https://doi.org/10.3390/f6030692 Araujo MB, Peterson AT (2012) Uses and misuses of bioclimatic envelope modeling. Ecology 93:1527–1539. https://doi.org/10.1890/11-1930.1 Austin MP, Van Niel KP (2011) Improving species distribution models for climate change studies: variable selection and scale. 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Biodivers Sci 29:697–711. https://doi.org/10.17520/biods.2020482 Zhu YY, Xu XT (2019) Effects of climate change on the distribution of wild population of Metasequoia glyptostroboides , an endangered and endemic species in China. Chin J Ecol 38:1629–1636. https://doi.org/10.13292/j.1000-4890.201906.018 Additional Declarations No competing interests reported. Supplementary Files Supportinginformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3978669","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274446069,"identity":"3141724c-0608-4617-a704-fb1e773135bb","order_by":0,"name":"Chao Tan","email":"","orcid":"","institution":"Nanjing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Tan","suffix":""},{"id":274446070,"identity":"46a39c32-8405-4f36-93d2-f36667bcaae5","order_by":1,"name":"David Kay Ferguson","email":"","orcid":"","institution":"University of Vienna","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"Kay","lastName":"Ferguson","suffix":""},{"id":274446071,"identity":"b8e7afbe-12a9-4072-a4da-230cc3352a0d","order_by":2,"name":"Yong Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDADfgaGhA9AmrGBaC2SDQyJM0jTYnCAgZE4LQbHzx5+zVNxx27z7YaHzTwMNrIbDjA/e4BXy5m8NGueM8+St905kAjUkma84QCbuQF+9+SYGfO2HU42u5GQ/piH4XDihgM8bBJ4tZx/A9FiPCMBZMt/IrTcyDF+DNRiZyAB1nKAsBbJG2/MGOecOZwgcSMhsXGOQbLxzMNsZni18J3PMf7wpuKwPf+MnMSGNxV2sn3Hm5/h1aJwgIFNioeBIbGBgScB6E6gEDM+9UAg38DA/PEHA4M9AwP7AQJqR8EoGAWjYKQCACx1UfDsmM5QAAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Yong","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-02-22 13:14:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3978669/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3978669/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51721121,"identity":"5571c31c-e91b-46f7-b3b4-5420fc363de0","added_by":"auto","created_at":"2024-02-27 21:49:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38105,"visible":true,"origin":"","legend":"\u003cp\u003eWeb of Science changes in the literature of SDMs studies using MaxEnt in the database\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3978669/v1/5d598c47562d28d44501d91b.png"},{"id":51721122,"identity":"48ea45b9-809e-4a64-864e-714df5c15ffe","added_by":"auto","created_at":"2024-02-27 21:49:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":705286,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of \u003cem\u003eLitsea auriculata\u003c/em\u003e according to different datasets (dataset 1 consisting of dataset 4 and dataset 5; dataset 6 including dataset 1, dataset 2, and dataset 3)\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3978669/v1/48b53b6fbc12ce9696b9e6c3.png"},{"id":51721128,"identity":"88ed0a1e-8608-4909-a992-a92375c13022","added_by":"auto","created_at":"2024-02-27 21:49:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2770128,"visible":true,"origin":"","legend":"\u003cp\u003ePotential distribution patterns of \u003cem\u003eLitsea auriculata\u003c/em\u003e under current climatic conditions. (a) dataset 1; (b) dataset 2; (c) dataset 3; (d) dataset 4; (e) dataset 5; (f) dataset 6.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3978669/v1/a561d56245b965b98bb18d44.png"},{"id":51721124,"identity":"1fd42f76-2db6-4aa3-8e31-43942b454ffc","added_by":"auto","created_at":"2024-02-27 21:49:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6708368,"visible":true,"origin":"","legend":"\u003cp\u003eProjected hotspot regions of \u003cem\u003eLitsea auriculata\u003c/em\u003e based upon different datasets under the 2050s and 2070s RCP 2.6/4.5/8.5 climate scenarios. (a) dataset 1; (b) dataset 2; (c) dataset 3; (d) dataset 4; (e) dataset 5; (f) dataset 6.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3978669/v1/0f4eefb925ae8c5c74abca3d.png"},{"id":51721125,"identity":"42bc905c-7b75-45bb-9685-dee8ced45d29","added_by":"auto","created_at":"2024-02-27 21:49:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":795020,"visible":true,"origin":"","legend":"\u003cp\u003ePCA of\u003cem\u003e Litsea auriculata \u003c/em\u003eunder current climatic condition\u003cem\u003e. \u003c/em\u003e(a) dataset 1; (b) dataset 2; (c) dataset 3; (d) dataset 4; (e) dataset 5; (f) dataset 6. The bar chart represents the contribution of variables.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3978669/v1/6e6a9bec362c4e0a185b3d5e.png"},{"id":51721123,"identity":"667caae7-3dee-4eab-a053-2713cc21fc19","added_by":"auto","created_at":"2024-02-27 21:49:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3358297,"visible":true,"origin":"","legend":"\u003cp\u003eHotspots located in all protected areas under current climate condition. (a) dataset 1; (b) dataset 2; (c) dataset 3; (d) dataset 4; (e) dataset 5; (f) dataset 6.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3978669/v1/7e8ea11905ec598f688925db.png"},{"id":52389311,"identity":"b1155a2b-efbc-406d-950b-b1db827d0561","added_by":"auto","created_at":"2024-03-11 01:39:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3328502,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3978669/v1/91545074-264b-4e7a-9392-7012524a2943.pdf"},{"id":51721127,"identity":"aa04ba13-b908-47d0-aea5-4dc66834675c","added_by":"auto","created_at":"2024-02-27 21:49:23","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":1149369,"visible":true,"origin":"","legend":"","description":"","filename":"Supportinginformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3978669/v1/80df2ecdb95fe7f1d93a58b9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Incorrect and incomplete distribution data can mislead species modeling: a case study of the endangered Litsea auriculata (Lauraceae)","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWith global warming, populations of many species have been lost and fragmented, leading to an endangered status and even extinction of species (Power et al. 2019; Chase et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Richards et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). A large number of species have become adapted to new distribution areas by modifying the pre-existing community composition and hence the ecosystem function (Babcock et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rom\u0026aacute;n-Palacios et al. 2020; Nielsen et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Understanding the impact of future climate change on species potential habitats is important for the development of species conservation strategies (Austin et al. 2011; Hole et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Moitz \u0026amp; Agudo 2013).\u003c/p\u003e\n\u003cp\u003eSpecies Distribution Models (SDMs) attempt to associate the distribution information of species with the corresponding environmental variables, establish models and predict the potential distribution of species in a certain area under specific spatial and temporal conditions in the future, which can quantify the regional and local distribution of species abundance at different scales (Guisan and Thuiller \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Araujo and Peterson \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). SDMs mainly include a Generalized Linear Model (GLM), Classification and Regression Tree (CART), Random Forest (RF), Maximum Entropy (MaxEnt) etc. (Guo et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The MaxEnt is one of the SDMs, based on extant species occurrence records and environmental data. It has the advantage of great accuracy, small sample size requirement, and good stability, and thus has become a widely used modeling approach (Wisz et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Fitzpatrick et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Merow et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Morales et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wu et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). In recent years, there has been a steady increase in the literature on SDMs using the MaxEnt as a keyword in the Web of Science (Fig.\u0026nbsp;1). The use of SDMs makes it possible to predict the potential distribution of species in new space or time (Liu et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), and thus provides an important reference for species conservation.\u003c/p\u003e\n\u003cp\u003eSDMs are based on distribution site data and environmental factor data, so uncertainty in the location of species sampling sites will inevitably increase the uncertainty of modeling results (Guo et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Specimen data have become an important data source for SDM predictions (Meineke et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). More than 3,000 herbaria in the world have collected over 400\u0026nbsp;million plant specimens (Thiers \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). With the rapid digitization of plant specimens worldwide, specimen data have widely been used for different purposes, e.g. taxonomy, biogeography, phenology, and SDMs (Jaca et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jukonienė et al. 2018; C\u0026aacute;mara-Leret et al. 2020; Meineke et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, it is worth pointing out that the herbarium collections and digitized specimens contain samples of cultivated plants far from their natural range as well as mis-identified material. There is a lack of quantitative description and research on how these misidentified and non-native specimen data have affected the results of SDMs.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLitsea auriculata\u003c/em\u003e is a deciduous tree species of the family Lauraceae. This species is characterized by scale-like exfoliating bark, large and auriculate leaves, long petioles, black ovoid fruits, and a cup-shaped receptacle. It has important economic and medicinal value, its wood has been used for furniture, while the fruits and roots have been employed as a traditional Chinese medicine (TCM) (Yang and Huang \u003cspan class=\"CitationRef\"\u003e1982\u003c/span\u003e). \u003cem\u003eLitsea auriculata\u003c/em\u003e is sporadically distributed in a few mountainous areas at 500\u0026thinsp;\u0026minus;\u0026thinsp;1500m in Zhejiang, Anhui, Henan etc., and was listed as vulnerably endangered because of habitat loss and fragmentation (Fu and Jin \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e; Qin et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). As a result, it is important to investigate the conservation status of \u003cem\u003eLitsea auriculata\u003c/em\u003e and identify any conservation gaps.\u003c/p\u003e\n\u003cp\u003eSpecies distribution modeling (SDM) should be based on complete sampling of accurately identified specimens, which is essential for understanding the suitable distribution area of species and for formulating reasonable conservation strategies (Costa et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fei and Yu \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Geng et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) conducted a study on community genetics and ecological niche modeling of \u003cem\u003eLitsea auriculata\u003c/em\u003e, and predicted ecological niche shifts under different climate changes based on data from three populations in Tianmu Mountain of Zhejiang, Dabie Mountain of Anhui and Henan, and found that the habitat showed a trend towards contraction and decline in east-central China. However, the sampling range of this study is obviously inadequate, especially for the marginal areas of its distribution range. Based on specimens and literature data, Yang et al. (2018) documented the distribution of the species in Chun'an and Tiantai Counties in Zhejiang, Huoshan and She Counties in Anhui, Yingshan County and Shennongjia forest area in Hubei; Geng et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) did not include these localities. In addition, the Chinese Virtual Herbarium (abbreviated as CVH), the largest digitized herbarium data source, contains misidentified and cultivated specimens. However, it remains unclear how the incomplete sampling, misidentified and cultivated specimen data impact on the distribution modeling of this species.\u003c/p\u003e\n\u003cp\u003eIn this study, we collected and collated six different datasets of \u003cem\u003eLitsea auriculata\u003c/em\u003e and predicted each dataset using the MaxEnt, and compared the differences of the species distribution modeling results based on these different datasets. By doing this, we plan to answer the following three questions: 1) what are the impacts of misidentified and cultivated specimen data on the results of SDMs? 2) what are the differences between SDM results of inadequate sampling and complete and accurate datasets? 3) identify the conservation gap of the species based on our new species modeling results and indicate what action needs to be taken to conserve the species?\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data collection and processing\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Distribution data of \u003cem\u003eLitsea auriculata\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe distribution data of \u003cem\u003eLitsea auriculata\u003c/em\u003e were obtained from the Chinese Virtual Herbarium (CVH, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cvh.ac.cn/\u003c/span\u003e\u003cspan address=\"https://www.cvh.ac.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), National Specimen Information Infrastructure (NSII, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nsii.org.cn/2017/home.php\u003c/span\u003e\u003cspan address=\"http://www.nsii.org.cn/2017/home.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), authoritative regional floras, and published papers (Sun \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Geng et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We annotated the data source of each distribution record to generate different datasets. The distribution records were cross-checked for spelling errors. All the specimen records were visually identified by the corresponding author (Yong Yang), and the misidentified and cultivated records were labeled. Then, the collected data were further processed and separated into six datasets (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e): dataset 1 (correct) including all the correctly identified records from herbarium specimens and literature; dataset 2 (cultivated) containing correctly identified and cultivated specimens; dataset 3 (misidentified) encompassing correctly identified and misidentified specimens but excluding cultivated specimens; dataset 4 (specimen) including only correctly identified specimens; dataset 5 (population) was collected from the literature, contained field population investigations (correctly identified but incomplete); dataset 6 (including all different sources) included all the distribution records of population investigations and herbarium data (correctly identified, misidentified and cultivated). We used Google Maps (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://maps.google.cn/\u003c/span\u003e\u003cspan address=\"http://maps.google.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to obtain the geographic coordinates of the distribution records. We removed duplicate specimens and redundant records within the different datasets, before MaxEnt analysis and imported the distribution data into ArcGIS 10.2 to eliminate duplicate points, i.e., only one of the distribution records within 10 km was retained (Zhou et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Environment variable data\u003c/h2\u003e \u003cp\u003eAltogether 19 environmental variable data at 2.5\u0026prime; resolution were downloaded from WorldClim (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldclim.org/\u003c/span\u003e\u003cspan address=\"https://www.worldclim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (see Table S2), including current climatic data (1970\u0026ndash;2000) and future climate predictions. The future climatic data were based on the climate model of the Beijing Climate Center Climate System Model Version 1.1 (BCC-CSM 1.1), which was constructed under RCP 2.6, RCP 4.5, and RCP 8.5 for 2050 (average value over the period 2041\u0026ndash;2060) and 2070 (average value over the period 2061\u0026ndash;2080) for the three representative concentration pathways (RCPs) (Luo et al. 2009).\u003c/p\u003e \u003cp\u003eThe climate layers were extracted using the software ArcGIS 10.2, and the extracted layers were converted to the ASCII format. In order to avoid influencing the final assessment of the model of high correlations between environmental variables (Luo et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), we conducted Pearson correlation analyses of 19 climatic variables for each period using the \u003cem\u003ecor\u003c/em\u003e function of R software, and the climatic factors with r\u0026lt;|0.85| that were more closely related to species distribution were retained (Yan et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhu et al. 2019). Finally, we performed principal component analyses (PCA) on the variables under current climatic conditions to identify the key drivers influencing the distribution of \u003cem\u003eLitsea auriculata.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Potential distribution prediction using MaxEnt\u003c/h2\u003e \u003cp\u003eFirstly, we imported the six distribution datasets (.CSV format) and climatic data (.ASCII format) for each period into MaxEnt 3.4.1 software for species ecological niche simulation. Secondly, different procedures for simulating the potential distribution were performed for datasets with different sample sizes. For data sets with fewer than 25 coordinate points, the Jackknife method was used for simulation evaluation. For species modeling, one of the coordinates was removed and the model was built based on the remaining n-1 coordinates, so that n models could be built and the optimal model selected for the MaxEnt ecological niche simulation. For data sets with more than 25 available coordinate points, 75% of the species distribution data was set as the training set and 25% as the test set, the number of operational iterations was set to 10, and the rest was used as default values (Pearson et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The area under curves (AUC) with receiver operator characteristic (ROC) was used to evaluate the reliability of the simulation results (Guo et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The range of AUC values was 0 to 1, the closer to 1 indicating the higher reliability of the simulation. The simulation result was considered to be very accurate when the AUC value was between 0.9 and 1, accurate when the AUC was 0.8\u0026thinsp;\u0026minus;\u0026thinsp;0.9, average when the AUC was between 0.7 and 0.8, and unreliable when the AUC result was less than 0.7 (Elith et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Finally, the simulation results of MaxEnt were entered into ArcGIS 10.2 software and transformed into raster layers for visualization, and the natural breaks method was selected to calculate the fitness index P. Based on previous studies, P\u0026gt;0.75 was used as a hotspot for species survival (Shi et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and the proportion of the area in different distribution data types was calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Calculating hotspots in protected areas\u003c/h2\u003e \u003cp\u003eTo describe and evaluate the local conservation status of \u003cem\u003eLitsea auriculata\u003c/em\u003e, we assembled 2569 nature reserves (including 440 national nature reserves and 2,129 provincial and county nature reserves) established during 1956\u0026thinsp;\u0026minus;\u0026thinsp;2021 (Zhang et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In ArcGIS 10.2, the base map data of China's nature reserves superimposed on the samples were used to calculate the area of the contemporary hotspot area located within the reserve, and to evaluate the protection efficiency of \u003cem\u003eLitsea auriculata\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Current distribution pattern of \u003cem\u003eLitsea auriculata\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eSix distribution datasets were assembled in this study. Dataset 4 contains 16 records, and was based on herbarium specimen data from CVH and NSII. Dataset 5 was collected from the literature, and contained 9 records. Dataset 1 was an integration of dataset 4 and dataset 5, and consisted of a total of 18 records after removing duplicate records. Both dataset 2 and dataset 3 were assembled using specimen data from CVH and NSII, each containing 22 records. Dataset 6 was an integration of dataset 2, dataset 3, dataset 4, and dataset 5, and contained a total of 26 records after deleting duplicate records.\u003c/p\u003e\n \u003cp\u003eAccording to the correct and complete dataset (dataset 1), \u003cem\u003eLitsea auriculata\u003c/em\u003e was distributed in Dabie Shan at the border of Henan and Anhui, Qingliang Mountain at the border of Anhui and Zhejiang, Daming Mountain in Zhejiang, and Nanzhao County of Henan and Shennongjia forestry district in Hubei (Fig.\u0026nbsp;2). This species was introduced to botanical gardens outside its native range for the purpose of ex situ conservation, e.g. Ming Xiaoling Mausoleum in Jiangsu, Hangzhou Botanical Garden in Zhejiang, Lushan Botanical Garden in Jiangxi, and Kunming Botanical Garden in Yunnan (Fig.\u0026nbsp;2). Wrong identification records expanded the distribution range of the species, e.g. Chongyi County in Jiangxi, Fengkai County in Guangdong, Jiangshan County in Zhejiang, and Sandu Shui Autonomous County of Guizhou (Fig.\u0026nbsp;2).\u003c/p\u003e\n \u003cp\u003eThe six datasets were screened for environmental variables based on Pearson correlation analyses. The results show that dataset 1 and dataset 3 each retained six climate factors in the final MaxEnt model analyses. The remaining datasets retained five climate factors in the final model analysis, respectively (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eScreened environmental variables of different datasets for the final MaxEnt model analysis. Details of climate variables see Table S2.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ebio1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edatase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edatase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edatase 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edatase 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edatase 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edatase 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Spatial pattern and driving factors of potential distribution areas of various data\u003c/h2\u003e\n \u003cp\u003eBased on the assembled distribution datasets and environmental data, the potential geographical distribution area of this species was simulated using the optimal MaxEnt model. The results show that the AUC value of the simulated curves of all six datasets was greater than 0.994, indicating that the prediction results of the model are very reliable (see Table S3).\u003c/p\u003e\n \u003cp\u003eThe potential distribution patterns based upon different datasets were significantly different under current climatic condition. The suitable areas predicted for \u003cem\u003eLitsea auriculata\u003c/em\u003e based on the correct dataset (dataset 1) were mainly distributed in Dabie Mountain, Huangshan Mountain and southwestern Hubei, and a small area in Zhejiang (Fig.\u0026nbsp;3a). Under-sampled datasets predicted distribution areas showing minor differences from the correct dataset (dataset 1). Compared with the predicted result of dataset 1, the extent of the fitness zone based upon the specimen dataset (dataset 4) extended in easterly and westerly directions and shrunk in the middle part (Fig.\u0026nbsp;3d), while the suitable area based upon the population dataset (dataset 5) shrunk gradually from the periphery to the middle (Fig.\u0026nbsp;3e). The difference between the suitable areas based upon the inaccurate dataset (datasets 2, 3 \u0026amp; 6) and the correct dataset (dataset 1) was rather obvious, and the potential distribution areas based upon these three datasets were widely distributed and extended in all directions, throughout the middle and lower reaches of the Yangtze River (Fig.\u0026nbsp;3b,c,f). Besides, under the 2050s and 2070s RCP 2.6/4.5/8.5 climate scenarios, the suitable areas based upon these different datasets were basically consistent with those under contemporary conditions (see Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe predicted hotspot areas based upon different datasets showed distinct trends under various climatic conditions (Fig.\u0026nbsp;4). The correct but incomplete datasets (datasets 4 \u0026amp; 5) displayed minor differences from the correct dataset (dataset 1), ranging from 0.01\u0026ndash;0.54%. The largest hotspot area anomaly was under the 2050s RCP 2.6 condition, where the population dataset (dataset 5) differs from the correct dataset (dataset 1) by 0.54% with an area of 51,900 km\u0026sup2;. The smallest hotspot area occurred under multiple climate scenarios, the suitable area based on the specimen dataset (dataset 4) differed from that based on the correct dataset (dataset 1) by 0.01% with only 1,000 km\u0026sup2; under the 2050s RCP 4.5/8.5 climatic conditions. The same result appears in the 2070s RCP 8.5, with the specimen dataset (dataset 4) and population dataset (dataset 5) differing by 1,000 km\u0026sup2; from the correct dataset (dataset 1).\u003c/p\u003e\n \u003cp\u003eThe incorrect dataset (datasets 2, 3 \u0026amp; 6) and the correct dataset (dataset 1), on the other hand, exhibited a large difference of 0.03%\u0026minus;0.88%. The largest hot spot area discrepancy value occurred in the misidentified dataset (dataset 3) for the 2070s RCP 8.5 with 0.88% and an area of 82,600 km\u0026sup2;. The smallest area gap of 2,900 km\u0026sup2; occurred in all datasets (dataset 6) under 2050s RCP 2.6. In addition, the maximum hotspot area difference in all climatic environments occurred in the predicted fitness zones of the misidentified dataset (dataset 3), except for 2070s RCP 2.6 which materialized in the cultivated dataset (dataset 2) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe proportion of hotspot areas in different datasets (hotspots/selected regions).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e2050s\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e2070s\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRCP2.6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRCP4.5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRCP8.5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRCP2.6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRCP4.5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRCP8.5\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edataset 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edataset 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edataset 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edataset 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edataset 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edataset 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 PCA of \u003cem\u003eLitsea auriculata\u003c/em\u003e different datasets under current climatic condition\u003c/h2\u003e\n \u003cp\u003eThe contribution of environmental variables varied when conducting PCA studies based on different datasets under current climatic condition (see Table S4). Mean diurnal temperature range (bio2) and temperature annual range (bio7) played a decisive role in the correct dataset (dataset 1) of \u003cem\u003eLitsea auriculata\u003c/em\u003e (Fig.\u0026nbsp;5a). bio7 and Isothermality (bio3) had the largest impact on the specimen dataset (dataset 4) prediction (Fig.\u0026nbsp;5d), while bio2 and precipitation seasonality (bio15) determined the distribution of the population dataset (dataset 5) (Fig.\u0026nbsp;5e). In the incorrect datasets (datasets 2, 3 \u0026amp; 6), the two most important determinants for the distribution of cultivated (dataset 2) and all recorded datasets (dataset 6) were bio7 and temperature seasonality (bio4) (Fig.\u0026nbsp;5b,d), while the distribution of the misidentified dataset (dataset 3) was limited by mean temperature of the driest quarter (bio9) and bio7 (Fig.\u0026nbsp;5c).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Distribution and conversation status of \u003cem\u003eLitsea auriculata\u003c/em\u003e using different datasets under current climatic condition\u003c/h2\u003e\n \u003cp\u003eUnder contemporary climatic conditions, the hotspots and protection status predicted based on the different datasets displayed great discrepancies. The hotspots based on the correct dataset (dataset 1) were mainly distributed in Dabie and Huangshan Mountains, with small stands in southwestern Hubei and Zhejiang. The total area was 15,400 km\u0026sup2;, of which 3,600 km\u0026sup2; (23.38%) was located in nature reserves (Fig.\u0026nbsp;6a).\u003c/p\u003e\n \u003cp\u003eThe range of hotspots predicted by the inaccurate dataset (datasets 2, 3 \u0026amp; 6) displayed a certain degree of expansion compared with the correct dataset (dataset 1). The hotspots of the cultivated dataset (dataset 2) were concentrated in the Dabie and Huangshan Mountains, with a small area in Hunan, a total area of 28,800 km\u0026sup2;, of which 2,600 km\u0026sup2; (9.03%) was in a protected area (Fig.\u0026nbsp;6b). The range of hotspots predicted by the inclusive dataset (dataset 6) was similar to that of the cultivated dataset, with additional distribution areas in southwestern Zhejiang; the total area of the hotspot range was 36,500 km\u0026sup2;, only 3,300 km\u0026sup2; (9.04%) was located in a protected area (Fig.\u0026nbsp;6c). The misidentified dataset (dataset 3) predicted the largest hotspot area of 40,400 km\u0026sup2;, which formed a dense area in southwestern Hubei and northwestern Hunan compared with the cultivated dataset, and extended outwards from the Dabie and Huangshan Mountains, with only 5,300 km\u0026sup2; (13.18%) located in a protected area (Fig.\u0026nbsp;6f).\u003c/p\u003e\n \u003cp\u003eThe distribution range of hotspot regions predicted by the correct but incomplete dataset (datasets 4 \u0026amp; 5) was similar to the correct dataset (dataset 1), and showed an overall contraction. The hotspot areas of the population dataset (dataset 4) contracted towards the central area of the correct dataset (dataset 1), possessed a total area of 11,500 km\u0026sup2;with only 1,800 km\u0026sup2;(15.65%) in nature reserves (Fig.\u0026nbsp;6e). Species modeling based on the specimen dataset (dataset 5) showed a shrinking trend in the hotspots and a scattered occurrence in southwestern Hubei, the total area covering ca. 8,600 km\u0026sup2; with only 1,700 km\u0026sup2; (19.77%) hotspot area in nature reserves (Fig.\u0026nbsp;6d).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe hotspot areas, area and proportion of the hotspots in nature reserves according to species modeling using different datasets under contemporary climatic conditions. (Unit: km\u0026sup2;)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHotspot areas\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredicted areas in nature reserves\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edataset 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edataset 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28,800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.03%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edataset 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edataset 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edataset 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.77%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edataset 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36,500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Importance of accurate identification and complete species distribution records for species modeling\u003c/h2\u003e \u003cp\u003eDistribution data is the basis for species modeling predictions. Kadmon et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) conducted a comparative study on the distribution modeling of 149 woody plant species in Israel, which revealed that data biases can reduce the accuracy of species modeling, the same conclusion was found by Kramer-Schadt et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Raes \u0026amp; ter Steege (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) performed a null model test on species modeling and found that modeling with incorrect distribution data showed significantly different results from the correct data set, demonstrating the impact of data bias on species modeling, which was further corroborated by Wolmarans et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Chen et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In this study, we compared predictions based on distribution records containing cultivated/misidentified records (datasets 2, 3 \u0026amp; 6) with those based on correctly identified and complete natural distribution records (dataset 1). Our results indicate that the dataset containing misidentified specimens can result in expansion of the fitness areas, thus significantly reducing the accuracy of the model. We compared the prediction results based on the distribution dataset containing cultivated records with those of the correctly identified complete natural distribution records, and found that the suitable distribution area expands greatly from the center to the surrounding area. This indicates that the modeling accuracy decreases with increasingly biased data. Our comparative study of species modeling results based on incomplete natural distribution records (datasets 4 \u0026amp; 5) and correctly identified, complete natural distribution records (dataset 1) suggests that the suitable area showed a conspicuous contraction trend with a very narrow distribution. Species modeling predictions based on such misidentified and inaccurate specimen data can arrive at misleading conclusions.\u003c/p\u003e \u003cp\u003eWith the rapid development of digital cameras, computers, and internet information technology, a large number of herbarium specimens throughout the world have been digitized and are available for biodiversity studies (Meineke et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Davis \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By June 2023, 0.24\u0026nbsp;billion specimens had been included in the Global Biodiversity Information Facility (GBIF, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gbif.org/\u003c/span\u003e\u003cspan address=\"https://www.gbif.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and 11.59 millions of specimen data deposited in the Australian Biological Atlas / Atlas of Living Australia (ALA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ala.org.au/\u003c/span\u003e\u003cspan address=\"https://www.ala.org.au/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The National Plant Specimen Resource Center (NPSRC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cvh.ac.cn/\u003c/span\u003e\u003cspan address=\"http://www.cvh.ac.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the largest digital plant specimen integration platform in China, has collected 8.27\u0026nbsp;million digitized plant specimens. National Specimen Information Infrastructure (NSII) contains about 16.45\u0026nbsp;million digital plant specimens. These digitized specimens have become important sources for research in ecology, biogeography, phenology, and conservation biology (Merow et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nualart et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jones and Daehler \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Herbling \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lee et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Davis \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, over 50% of the herbarium specimens were not correctly identified (Goodwin et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Digitized specimens thus contain lots of identification errors and cultivated records, and are the main source of erroneous data in species modeling. Incorrect distribution information often leads to severe range deviations and obscures the true species model (Orr et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a result, it is necessary to remove and correct the misidentified records and cultivated records before conducting species model predictions.\u003c/p\u003e \u003cp\u003eBecause published floras record older data and often contain incomplete information, the integration of floras cannot resolve the problem of data completeness. In this study, we found that the Flora of China records the distribution of \u003cem\u003eLitsea auriculata\u003c/em\u003e in Tianmu Mountain and Tiantai Mountain in Zhejiang and She County in Anhui (Yang and Huang \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), and misses many other distribution localities. Our new inventory in this study has added the records of \u003cem\u003eLitsea auriculata\u003c/em\u003e in Hubei and Henan, and Chun'an County in Zhejiang. The Flora of Anhui is comprehensive at the county level, but remains ambiguous regarding the distribution below the county level. The Jiangxi Seed Plant List contains an incorrect record of \u003cem\u003eLitsea auriculata\u003c/em\u003e, which originates from misidentified digitized specimens (Liu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The distribution information in these botanical catalogs is fragmentary and cannot be used directly for species modeling, and needs to be verified and integrated. Only when complete and accurate data are available we can obtain valuable research results, which can help understand the distribution characteristics of species and provide important references for biodiversity conservation.\u003c/p\u003e \u003cp\u003eSpecimens comprise the primary source of species distribution data, and should be correctly identified by taxonomists before utilization. Correct identification is fundamental not only for species distribution modeling, but also for biodiversity conservation. However, taxonomy as a traditional discipline is handicapped in the assessment and evaluation system of many different research institutions (Ma \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Most research funding has been deployed in more fashionable and advanced research areas, e.g. genome sequencing, making it difficult to train traditional taxonomists. As a result, no taxonomists work in the herbaria to correct the misidentified specimens. To overcome this drawback, it is necessary to promote traditional taxonomy and maintain a permanent taxonomic research team.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Potential distribution and conservation assessment based on accurate identification and complete dataset of \u003cem\u003eLitsea auriculata\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eIn this study, we established a reliable potential distribution area for \u003cem\u003eLitsea auriculata\u003c/em\u003e based on an accurately identified and complete dataset (dataset 1). The modeling results show that, compared with other plants of the Lauraceae family (Zheng et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the distribution range of this species is generally northerly and is currently located mainly on montane forest slopes in the mid-latitudes of central-eastern China. The predicted distribution is similar to the distribution characteristics of gymnosperm species (Tang et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Xie et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With global warming in the future, the suitable distribution area of \u003cem\u003eLitsea auriculata\u003c/em\u003e will tend to contract, and eventually decrease in the central-eastern part of China, which corroborates a previous study (Geng et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The predicted hotspot areas using accurately identified and complete datasets (dataset 1) under the contemporary climate shifted southwards compared to Geng et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This difference may be caused by the bias of distribution data, as Geng et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) did not fully record the distribution of the species in southern regions such as Anhui and Zhejiang.\u003c/p\u003e \u003cp\u003eThe potential distribution trend of \u003cem\u003eLitsea auriculata\u003c/em\u003e shows a clear mismatch with subtropical broadleaved evergreen forest plants. Previous studies have suggested that subtropical broadleaved evergreen forest species will expand northwards and eastwards under future climatic conditions (Hu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lim et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The potential distribution ranges of \u003cem\u003eLitsea auriculata\u003c/em\u003e do not vary significantly across time, with an overall range of only 0.09%\u0026minus;0.54%, the only local expansion and contraction occurring in some mountains and plains at the edges of the subtropical broadleaved evergreen forests. Coincidentally, a similar pattern was also found in a study of the genus \u003cem\u003eCinnamomum\u003c/em\u003e (Zhou et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, as in many gymnosperms, \u003cem\u003eLitsea auriculata\u003c/em\u003e may have survived by elevational shifts during the late Quaternary glacial oscillations (Cun and Wang \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe survival of \u003cem\u003eLitsea auriculata\u003c/em\u003e is at least partially attributable to its habitat dilemma. Previous studies have shown that the genetic structure of \u003cem\u003eLitsea auriculata\u003c/em\u003e continues to diverge and expand, forming small-scale populations (Sun \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Increased random genetic variation, high levels of inbreeding and reduced gene numbers, combined with a progressively warmer climate, have led to a dramatic decline in the distribution area of this species (Geng et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In our study, the predicted results based on an accurately identified and complete dataset (dataset 1) for hotspot areas of \u003cem\u003eLitsea auriculata\u003c/em\u003e under contemporary climatic conditions show that the species continues to spread in all directions in the future, with increased fragmentation, a gradual reduction in living space, and a further decrease in area, which is consistent with the results of previous studies. Besides, the narrow and concentrated distribution area has increased the threat level of \u003cem\u003eLitsea auriculata\u003c/em\u003e (Qin et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and irreversible damage will occur if these small areas are disturbed.\u003c/p\u003e \u003cp\u003eSpecies distribution models can suggest the chances of survival of endangered plants and facilitate the development of targeted \u003cem\u003ein situ\u003c/em\u003e conservation measures (Aguilar-Soto et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In this paper, habitat prediction in combination with an analysis of Chinese nature reserves, indicates that only 23.38% of \u003cem\u003eLitsea auriculata\u003c/em\u003e is currently located in nature reserves, so a large conservation gap remains. The areas outside the nature reserves are mainly located in southern Anhui, west-central and east-central Zhejiang. These areas have suffered from severe deforestation, habitat loss and habitat fragmentation (Wei and Jiang \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which may have lead to a significant decrease in the number and population size of \u003cem\u003eLitsea auriculata.\u003c/em\u003e The area of the species within the nature reserve will gradually shrink under future warming scenarios, and may even deviate excessively from the reserve in the 2070s RCP 2.6 scenario (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e), thus greatly increasing the threat level. Therefore, in the face of such a situation, a protected area should be established for \u003cem\u003eLitsea auriculata\u003c/em\u003e, and special staff should be assigned to protect the forest land, prohibit indiscriminate logging practices, and reduce human interference. According to previous studies, we found that a large number of threatened gymnosperms also survive in the distribution area of \u003cem\u003eLitsea auriculata\u003c/em\u003e (L\u0026uuml; et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xie et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), so it is crucial to strengthen the protection of these areas for other threatened plants as well. In addition, because the genetic differentiation among populations of \u003cem\u003eLitsea auriculata\u003c/em\u003e is large and gene flow is low (Sun \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), it would be beneficial to increase the level of genetic diversity of \u003cem\u003eLitsea auriculata\u003c/em\u003e if a sufficient number of individuals within all populations could be selected for intensive translocation and conservation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredicted hotspot area of \u003cem\u003eLitsea auriculata\u003c/em\u003e based on correct dataset (dataset 1) and area located within the protected area. (Unit: km\u0026sup2;)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHotspot areas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNature reserves areas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2050s RCP2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.06%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2050s RCP4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2050s RCP8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2070s RCP2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.73%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2070s RCP4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.97%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2070s RCP8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.58%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIt remains ambiguous how the identification errors, cultivated collections and data incompleteness impact on species distribution modeling. We assembled six datasets and made a comparative study here. We show that misidentification, cultivated specimen data, and data incompleteness all have significant impacts on species modeling prediction results. We identified new areas of potential distribution of \u003cem\u003eLitsea auriculata\u003c/em\u003e based on correctly identified and more complete datasets, revealed that the current main distribution range of \u003cem\u003eLitsea auriculata\u003c/em\u003e is located in the mountainous areas of the middle and lower reaches of the Yangtze River, with a tendency to contraction in future climate change scenarios. In addition, our assessment of the conservation status of \u003cem\u003eLitsea auriculata\u003c/em\u003e, reveals that currently about 23.38% of the suitable areas for the species have been protected in nature reserves, so there are still relatively large conservation gaps. The resulting information can be used to support management, conservation, and recovery plans for \u003cem\u003eLitsea auriculata\u003c/em\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China [32270217 and 31970205] and the \u003cem\u003eMetasequoia\u003c/em\u003e funding of the Nanjing Forestry University. The authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eChao Tan and Yong Yang wrote the main manuscript text and Chao Tan prepared figures 1-6. All authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe are grateful to the contributors to the Chinese Virtual Herbarium.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eAll data used in the study are included in this paper are available in the supporting datasets.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAguilar-Soto V, Melgoza-Castillo A, Villarreal-Guerrero F, Wehenkel C, Pinedo-Alvarez C (2015) Modeling the potential distribution of \u003cem\u003ePicea chihuahuana\u003c/em\u003e Mart\u0026iacute;nez, an endangered species at the sierra madre occidental. 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Chin J Ecol 38:1629\u0026ndash;1636. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.13292/j.1000-4890.201906.018\u003c/span\u003e\u003cspan address=\"10.13292/j.1000-4890.201906.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Conservation, Lauraceae, Litsea auriculata, MaxEnt, Species distribution modeling, Specimen identification","lastPublishedDoi":"10.21203/rs.3.rs-3978669/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3978669/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobal warming has caused many species to become endangered or even extinct. Describing and predicting how species will respond to global warming is one of the hot topics in the field of biodiversity research. Species distribution modeling predicts the potential distribution of species based on species occurrence records. However, it remains ambiguous how the accuracy of the distribution data impacts on the prediction results. To address this question, we used the endangered plant species \u003cem\u003eLitsea auriculata\u003c/em\u003e (Lauraceae) as a case study. By collecting and assembling six different datasets of \u003cem\u003eLitsea auriculata\u003c/em\u003e, we used MaxEnt model to perform species distribution modeling and then conducted comparative analyses. The results show that the distribution of \u003cem\u003eLitsea auriculata\u003c/em\u003e is mainly in the Dabie Mountain region, southwestern Hubei and northern Zhejiang, and that mean diurnal temperature range (bio2) and temperature annual range (bio7) play important roles in the distribution of \u003cem\u003eLitsea auriculata.\u003c/em\u003e Compared with the correct data, the dataset including misidentified specimens leads to a larger and expanded range in the predicted distribution area, whereas the species modeling based on the correct but incomplete data predicts a smaller and contracted range. According to the analysis of the local protection status of \u003cem\u003eLitsea auriculata\u003c/em\u003e, we found that only about 23.38% of this species is located within nature reserves, so there is a large conservation gap. Our study suggests that the accurate distribution data is important for species modeling, and incomplete and incorrect data normally gives rise to misleading prediction results. In addition, our study also revealed the distribution characteristics and conservation gaps of \u003cem\u003eLitsea auriculata\u003c/em\u003e, laying the foundation for the development of rational conservation strategies for this species.\u003c/p\u003e","manuscriptTitle":"Incorrect and incomplete distribution data can mislead species modeling: a case study of the endangered Litsea auriculata (Lauraceae)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-27 21:49:18","doi":"10.21203/rs.3.rs-3978669/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b46d4b4-7e69-44bb-a08a-0b9566f65d46","owner":[],"postedDate":"February 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-11T01:31:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-27 21:49:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3978669","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3978669","identity":"rs-3978669","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}