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Heterogeneity-diversity Relationships in Natural Areas of Yunnan, China

Feng LIU Jinming HU Feiling YANG Xinwang LI

LIU Feng, HU Jinming, YANG Feiling, LI Xinwang, 2021. Heterogeneity-diversity Relationships in Natural Areas of Yunnan, China. Chinese Geographical Science, 31(3): 506−521 doi:  10.1007/s11769-021-1207-7
Citation: LIU Feng, HU Jinming, YANG Feiling, LI Xinwang, 2021. Heterogeneity-diversity Relationships in Natural Areas of Yunnan, China. Chinese Geographical Science, 31(3): 506−521 doi:  10.1007/s11769-021-1207-7

doi: 10.1007/s11769-021-1207-7

Heterogeneity-diversity Relationships in Natural Areas of Yunnan, China

Funds: Under the auspices of National Key R&D Program of China (No. 2017YFC0505200), the Second Tibetan Plateau Scientific Expedition and Research Program (No. 2019QZKK0502), the National Natural Science Foundation of China (No. 41461018), Youth Program of National Natural Science Foundation of China (No. 41701110), the Applied Basic Research Foundation of Yunnan Province (No. 2015FA011), Yunnan University’s Research Innovation Fund for Graduate Students (No. 2019z058)
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  • Figure  1.  Location of the study area, showing 32 nature reserves, variation in terrain elevation within Yunnan, China

    Figure  2.  The correlation coefficient matrix among the 17 environmental heterogeneity parameters and area of selected 32 nature reserves in Yunnan, China. Abbreviation of environmental heterogeneity parameters can be seen in Table 1; A, area (hm²)

    Figure  3.  The relationship between biodiversity indices and latitude, longitude, altitude within selected nature reserves of Yunnan, China. a, b and c refer to the ecosystem diversity (EI); d, e and f refer to the plant diversity (PI); g, h and i refer to the animal diversity (AI)

    Table  1.   Summary of the variables in the measurement of environmental heterogeneity (EH) of 32 nature reserve in Yunnan

    SubjectVariableAbbreviationUnitsSource
    Soil Soil type SL National Soil Survey Office of Yunnan Province, 1996
    Topography Altitude ALD m WorldClim v1.0; Hijmans et al., 2005
    Slope SPE ° WorldClim v1.0; Hijmans et al., 2005
    Aspect APT WorldClim v1.0; Hijmans et al., 2005
    Climate Annual mean temperature AMT WorldClim v1.0; Hijmans et al., 2005
    Temperature annual range TAR WorldClim v1.0; Hijmans et al., 2005
    Meantemperature of wettest quarter WMT WorldClim v1.0; Hijmans et al., 2005
    Mean Temperature during the driest quarter DMT WorldClim v1.0; Hijmans et al., 2005
    Mean temperature of warmest quarter WAMT WorldClim v1.0; Hijmans et al., 2005
    Mean temperature of coldest quarter CMT WorldClim v1.0; Hijmans et al., 2005
    Mean diurnal range MDR WorldClim v1.0; Hijmans et al., 2005
    Annual precipitation AP mm WorldClim v1.0; Hijmans et al., 2005
    Precipitation seasonality coefficient of variation PS mm WorldClim v1.0; Hijmans et al., 2005
    Precipitation of wettest quarter WQP mm WorldClim v1.0; Hijmans et al., 2005
    Precipitation of driest quarter DQP mm WorldClim v1.0; Hijmans et al., 2005
    Precipitation of warmest quarter WAQP mm WorldClim v1.0; Hijmans et al., 2005
    Precipitation of coldest quarter CQP mm WorldClim v1.0; Hijmans et al., 2005
    下载: 导出CSV

    S1.   The 17 environmental heterogeneity parameters and area of the 32 selected nature reserves in Yunnan, China

    Nature reservesSLALDSPEAPTAMTWMTDMTWAMTCMT
    Ailao Mountain 1.667862 2.335548 1.352042 2.091193 1.797252 1.812973 1.752694 1.812973 1.720881
    Baima Snow Mountain 2.461842 3.165355 1.467865 2.085038 2.632611 2.578325 2.642719 2.578325 2.653061
    Cangshan Mountain/ Erhai Lake 2.128009 2.634032 1.217715 2.122486 2.154536 2.162459 2.126759 2.162459 2.126759
    Dashanbao Reserve 0.832455 2.041262 1.198090 2.072562 1.469970 1.497213 1.338321 1.497213 1.338321
    Dawei Mountain 2.349897 2.935836 1.397559 2.077047 2.286734 2.279908 2.207283 2.279908 2.275763
    Daxue Mountain 1.457631 2.714037 1.449478 2.076356 2.075203 2.050750 2.024928 2.050750 2.024928
    Fenshuiling Reserve 1.461247 2.601040 1.356440 2.017458 2.020648 1.991883 1.898647 1.991883 2.036144
    Gaoligong Mountains 1.940080 3.246562 1.495871 2.082780 2.666805 2.560746 2.633706 2.560746 2.698653
    Huanglian Mountain 1.907912 2.941956 1.364074 2.077093 2.322463 2.278164 2.334580 2.278164 2.334580
    Huizi Grus nigricollis Reserve 1.595847 1.808427 1.169565 2.088142 1.158752 1.201556 1.164409 1.201556 1.164409
    Jiaozi Snow Mountain 1.618842 2.797049 1.495434 2.082007 2.250169 2.230187 2.209583 2.230187 2.209583
    Naban River 1.749447 2.749075 1.181675 2.016277 2.166828 2.134186 2.179690 2.165846 2.142779
    Nangun River 2.341318 3.036810 1.408510 2.085745 2.399916 2.349086 2.286036 2.349335 2.343474
    Wenshan Reserve 1.456593 2.670050 1.360571 2.079148 2.010561 2.028400 1.782807 2.028400 1.865968
    Wumeng Mountain 2.015245 2.263778 1.443708 2.090530 1.491402 1.574689 1.355031 1.574689 1.355031
    Wuliang Mountain 1.526558 2.513346 1.466559 2.085630 1.891191 1.906574 1.849813 1.906574 1.849813
    Xishuangbanna Reserve 1.456008 2.114732 1.257078 2.095004 1.572665 1.525867 1.541507 1.545654 1.601770
    Yaoshan Reserve 2.318834 3.143578 1.596806 2.082194 2.566902 2.570284 2.485561 2.570284 2.485561
    Yuanjiang River 1.939129 2.673780 1.403520 2.087419 2.090511 2.105219 2.136997 2.111532 1.993252
    Tianchi Lake of Yunlong 1.200632 2.389496 1.457324 2.063217 1.769356 1.742098 1.737892 1.742098 1.737892
    Bita Lake 1.169299 1.854602 0.086352 2.088859 1.298096 1.236511 1.389794 1.236511 1.389794
    Gulinjing Reserve 1.401731 2.715584 0.105101 2.041939 2.124039 2.085871 1.981799 2.085871 2.126836
    Haba Snow Mountain 2.037909 3.404666 0.439853 2.025915 2.767578 2.743083 2.801875 2.743083 2.786546
    Lancang River 2.345915 2.559271 0.100917 2.079797 1.951577 1.927319 2.056936 1.927319 1.923441
    Yunling Mountain 1.357957 2.526327 0.142829 2.090381 1.965187 1.942896 1.961642 1.942896 1.947718
    Nuozadu Reserve 2.111375 2.287367 0.098667 2.088661 1.691638 1.683805 1.744197 1.696726 1.682008
    Tongbiguan Reserve 1.852157 3.110715 0.034912 2.060968 2.446807 2.361396 2.409774 2.369305 2.409774
    Tuoniang River 1.132712 2.463056 0.069694 2.088686 1.856626 1.878482 1.833658 1.878482 1.833658
    Taiyang River 1.164993 1.646345 0.000559 2.087777 0.977347 1.066386 0.965697 1.066386 1.023557
    Xiaohei Mountain 1.376716 2.271980 0.058391 2.068666 1.531951 1.499893 1.447812 1.499893 1.447812
    Zhanyiheifeng Reserve 1.483134 1.287928 0.013568 2.107391 0.864467 0.916407 0.846894 0.916407 0.846894
    The Source of Pearl River 1.948837 1.689251 0.033888 2.105754 1.270357 1.275333 1.322737 1.275333 1.322737
    Nature reserves MDR TAR AP WQP DQP WAQP CQP PS A
    Ailao Mountain 1.184633 1.602546 1.770795 2.032265 1.131527 1.881272 1.131527 1.50506 67700
    Baima Snow Mountain 1.810130 2.136391 1.954175 2.130668 1.626413 1.955891 1.706344 2.442835 281640
    Cangshan Mountain/ Erhai Lake 0.637137 1.08927 0.909191 1.592575 2.086372 1.471186 2.086372 2.400125 79700
    Dashanbao Reserve 0.785689 1.157445 1.384774 1.627382 0.851959 1.467471 0.851959 0.971359 19200
    Dawei Mountain 2.470396 1.533675 2.418824 2.507612 2.305446 2.345748 2.283601 2.354396 43993
    Daxue Mountain 1.121692 1.518228 1.048472 1.25281 0.654897 1.110697 0.726952 0.888739 17541
    Fenshuiling Reserve 2.410179 1.713387 2.078918 1.941096 1.71669 1.806274 1.959666 1.788162 42027
    Gaoligong Mountains 1.852918 2.124821 2.66815 2.799496 2.762524 2.612127 2.833445 2.369806 405549
    Huanglian Mountain 1.734375 1.07726 1.605083 1.897854 1.322814 1.776712 1.322814 1.851751 61860
    Huizi Grus nigricollis Reserve 1.370728 0.87554 1.235913 1.572836 0.805782 1.442577 0.805782 1.698066 12911
    Jiaozi Snow Mountain 1.555337 1.272771 1.751373 1.841011 1.035048 1.695769 1.035048 0.740283 16456
    Naban River 0.893393 1.123775 2.060593 2.306215 0.547874 1.876542 1.107055 2.159268 26600
    Nangun River 1.89583 2.164163 0.946678 0.890203 1.616965 1.053455 1.760282 0.882664 50887
    Wenshan Reserve 0.792043 1.226446 1.918278 2.127651 1.512261 1.950419 1.633035 1.629935 26867
    Wumeng Mountain 1.284468 1.410487 1.214558 1.437847 1.217901 1.226783 1.217901 1.640569 26187
    Wuliang Mountain 0.716559 1.376285 1.487089 1.819138 0.881812 1.616874 0.881812 1.687463 30938
    Xishuangbanna Reserve 1.64714 2.135046 2.020561 2.341812 2.227309 2.058889 2.338579 2.598459 242510
    Yaoshan Reserve 1.733043 1.960675 2.208913 2.463151 1.52954 2.145219 1.52954 1.719756 20141
    Yuanjiang River 1.480922 1.747734 1.930567 2.213096 1.243855 2.159576 1.054985 2.315064 22379
    Tianchi Lake of Yunlong 0.68395 0.683308 0.504704 0.636514 1.900656 0.636514 1.900656 2.070081 14475
    Bita Lake 0.624541 0.936723 0.688826 0.454932 0.256316 0.36736 0.256316 1.229852 14133
    Gulinjing Reserve 1.858616 1.029368 1.902313 2.289283 2.170462 2.074081 1.888074 2.254487 6832
    Haba Snow Mountain 1.003415 1.153407 0.711863 0.750503 1.534212 0.748954 1.55095 1.678158 21908
    Lancang River 1.389286 1.512822 2.083762 2.261202 1.04074 2.06998 0.909995 1.546292 89504
    Yunling Mountain 0.518868 0.757315 1.152291 0.850422 1.901191 0.762523 1.906196 2.109826 75894
    Nuozadu Reserve 0.767974 0.979563 1.609072 1.854898 0.466754 1.912214 0.916908 1.739181 18997
    Tongbiguan Reserve 1.881027 2.132452 1.709361 2.17328 2.495465 1.930715 2.495465 2.444569 51651
    Tuoniang River 1.258264 1.501105 1.347572 1.687498 1.701757 1.587358 1.701757 2.018823 19128
    Taiyang River 0.509816 0.509816 1.128208 1.538332 0.687787 1.122312 0.987467 1.378391 7035
    Xiaohei Mountain 0.865576 1.378474 0.662852 0.94474 1.138792 0.690942 1.138792 1.531609 5805
    Zhanyiheifeng Reserve 0.711198 0.714714 0.696851 0.777775 0.856418 0.554416 0.856418 1.002708 26610
    The Source of Pearl River 1.657259 1.413215 1.249075 1.228121 1.256107 1.087871 1.256107 1.138963 117934
    Note: Abbreviation of environmental heterogeneity parameters can be seen in Table 1; A, area (hm²)
    下载: 导出CSV

    Table  2.   Biodiversity indices of the selected nature reserves in Yunnan, China

    Nature ReservesEIPIAINature ReservesEIPIAI
    Ailao Mountain 20 2486 3820 Xishuangbanna Reserve 49 4879 7323
    Baima Snow Mountain 37 2685 4182 Yaoshan Reserve 32 2589 2209
    Cangshan Mountain/Erhai Lake 18 2782 3883 Yuanjiang River 33 2733 4076
    Dashanbao Reserve 16 408 2476 Tianchi Lake of Yunlong 17 1768 2160
    Dawei Mountain 25 5964 4099 Bita Lake 34 2808 2758
    Daxue Mountain 15 2501 4912 Gulinjing Reserve 12 3762 2429
    Fenshuiling Reserve 24 4838 4410 Haba Snow Mountain 28 2134 2779
    Gaoligong Mountains 44 5897 4177 Lancang River 25 1542 5567
    Huanglian Mountain 11 4102 3838 Yunling Mountain 21 2065 2749
    Huize Grus nigricollis Reserve 19 736 1417 Nuozadu Reserve 27 3016 4198
    Jiaozi Snow Mountain 17 1271 1186 Tongbiguan Reserve 38 4995 5628
    Naban River 28 3195 4164 Tuoniang River 11 2859 3404
    Nangun River 44 3696 5968 Taiyang River 14 2954 4122
    Wenshan Reserve 18 5397 3473 Xiaohei Mountain 29 3392 4742
    Wumeng Mountain 35 2694 3861 Zhanyi Heifeng Reserve 14 874 1747
    Wuliang Mountain 17 3455 4585 The Source of Pearl River 35 2075 844
    Notes: EI, ecosystem diversity index; PI, plant diversity index; AI, animal diversity index
    下载: 导出CSV

    Table  3.   The determination coefficients (R2adj) of the single- and multi-predictor ordinary least squares (OLS) between biodiversity indices and environmental heterogeneity (EH) parameters of the selected natural reserves in Yunnan, China

    EHSingle-predictor OLS modelsMulti-predictor OLS models
    SubjectParametersEI (R2adj)PI (R2adj)AI (R2adj)EI (R2adj)PI (R2adj)AI (R2adj)
    Soil SL 0.193** 0.023 0.021 0.193** 0.023 0.021
    Topography −0.014 0.011 −0.010
    ALD 0.054
    SPE −0.019 0.001 −0.019
    APT −0.036 0.012 −0.0552
    Climate 0.463*** 0.423*** 0.263*
    AMT
    WMT
    DMT 0.041
    WAMT
    CMT 0.159*
    MDR
    TAR 0.469*** 0.199** 0.281**
    AP 0.033 0.252**
    WQP 0.066
    DQP
    WAQP
    CQP 0.095 0.405*** 0.06
    PS 0.036 0.245** 0.081
    Area A 0.301*** 0.103* 0.057 0.301*** 0.103* 0.057
    Notes: The primary EH parameters selected for biodiversity index by OLS models are marked with a tick, blanks in the table mean that variables are not ecologically significant in the single- and multi-predictor ordinary least squares (OLS); the17environmental heterogeneity parameters are defined in Table 1; A, area (hm²). Significance levels: ***P < 0.001; ** P < 0.01; *P < 0.05
    下载: 导出CSV

    Table  4.   The optimal models for biodiversity indices based on the Akaike Information Criterion of nature reserves natural areas of Yunnan

    Response variablesPredictorsR 2adjP
    EISL (0.284), DMT (−0.246), TAR (0.647), AP (−0.242), A (0.319)0.5690.001
    PIAP (0.317), CQP (0.525)0.5450.001
    AITAR (0.498), PS (0.201)0.2960.002
    Notes: Standardized regression coefficients are bracketed, and the statistically significant parameters are shown in the bold. EI: Ecosystem diversity index; PI: Plant diversity index; AI: Animal diversity index; Abbreviated predictors include soil types (SL), mean temperature of driest quarter (DMT), temperature annual range (TAR), annual precipitation (AP), precipitation of coldest quarter (CQP), precipitation seasonality (coefficient of variation) (PS); A, area (hm²)
    下载: 导出CSV
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Heterogeneity-diversity Relationships in Natural Areas of Yunnan, China

doi: 10.1007/s11769-021-1207-7
    基金项目:  Under the auspices of National Key R&D Program of China (No. 2017YFC0505200), the Second Tibetan Plateau Scientific Expedition and Research Program (No. 2019QZKK0502), the National Natural Science Foundation of China (No. 41461018), Youth Program of National Natural Science Foundation of China (No. 41701110), the Applied Basic Research Foundation of Yunnan Province (No. 2015FA011), Yunnan University’s Research Innovation Fund for Graduate Students (No. 2019z058)
    通讯作者: HU Jinming. E-mail: hujm@ynu.edu.cn

English Abstract

LIU Feng, HU Jinming, YANG Feiling, LI Xinwang, 2021. Heterogeneity-diversity Relationships in Natural Areas of Yunnan, China. Chinese Geographical Science, 31(3): 506−521 doi:  10.1007/s11769-021-1207-7
Citation: LIU Feng, HU Jinming, YANG Feiling, LI Xinwang, 2021. Heterogeneity-diversity Relationships in Natural Areas of Yunnan, China. Chinese Geographical Science, 31(3): 506−521 doi:  10.1007/s11769-021-1207-7
    • Regional systematic conservation planning needs to consider current and future biodiversity distribution patterns under a changing environment (Schloss et al., 2011; Heller et al., 2015; Jones et al., 2016; Tukiainen et al., 2017; Reside and Adams, 2018). Many studies have revealed that ecosystems and species’ ranges will shift with environmental changes (Hoffmann and Sgrò 2011; Aguilée et al., 2016; Levine et al., 2016), which may prevent existing reserve networks from effectively conserving biodiversity in the long-term (Schloss et al., 2011; Scriven et al., 2015; Regos et al., 2016); this is of particular concern in high mountains and plateaus (Ackerly et al., 2010; Zhang et al., 2012; Zomer et al., 2015; Lehikoinen et al., 2019). More critically, it is difficult to accurately predict future distribution patterns of biodiversity, considering the uncertainty around how environments will change and what impacts those changes will have on ecosystems and species distributions (Schloss et al., 2011; Aguilée et al., 2016; Jones et al., 2016).

      To deal with this dilemma, some researchers have proposed dealing with systematic conservation planning by coupling environmental variables (e.g., topography, climate, soil) with biodiversity surrogates (Hufford et al., 2014; Heller et al., 2015; Tukiainen et al., 2017). The essence of idea is that conserving environmentally heterogeneous landscapes supports diverse species and communities in a region, which is consistent with niche theory (MacArthur 1970; Ricklefs 1977; Stein et al., 2014). Coupling environmentally heterogeneous landscapes into existing reserve networks on a regional scale would help conserve current and future biodiversity in a changing environment (Tingley et al., 2014; Van Schalkwyk et al., 2017).

      The above studies have been largely based on positive regional heterogeneity-diversity relationships (HDRs) (Veech and Crist 2007; Stein et al., 2014; 2015). However, there is no universal method for understanding regional HDRs because of environmental variables and biodiversity and therefore the methods used to measure them vary among regions (Bar-Massada and Wood, 2014; Chocron et al., 2015; Stein et al., 2015). Regional HDRs could be positive, negative, unimodal, or without significant correlation (Lundholm 2009; Gazol et al., 2013; Laanisto et al., 2013; Bar-Massada and Wood, 2014). Hence, it is necessary to study HDRs in specific regions before selecting suitable regional environmental variables for systematic conservation planning.

      Additionally, with the rapid development of human society and economies, inevitably, the biodiversity would be disturbed by human disturbance almost all over the world (Lehikoinen et al., 2019). While regions with a low degree of human disturbance and a high degree of naturalness (natural areas) have low conservation costs but high potential conservation value (Theobald et al., 2012; Triviño et al., 2018). And ecosystem structure in natural areas is primarily intact and largely unaffected by human influence; thus, it can effectively protect biodiversity and ecological processes (Kormos et al., 2016), thereby providing the ideal option for regional biodiversity conservation. Furthermore, only natural areas can present original HDRs meaningful for regional systematic conservation planning. Besides, Seiferling et al., (2014) performed a comprehensive analysis and found that the HDRs in natural areas showed complex and equivocal relationships. Therefore, the HDRs of natural areas would provide important guidance for the optimization of regional protected areas system.

      Hence, this study selected all 20 national and 12 of the 38 provincial nature reserves in Yunnan, China to represent the relatively natural areas and to examine the relationship between environmental heterogeneity (EH) and biodiversity. Although only 32 nature reserves were selected for this study, these selected nature reserves represent over 80% of Yunnan’s geographic elements (soil types, topographic units, climatic units), 90% of its ecosystem types, and 90% of its national key protected wild animal and plant species. More importantly, nature reserves with biodiversity information have been collected as far as possible. For each selected nature reserve, we calculated three types of biodiversity indices and 17 EH parameters, and explored three questions: 1) how well does EH parameter correlate with the biodiversity index? 2) What are the primary and important EH parameters for the biodiversity index? and 3) to what extent can biodiversity index be explained by relevant primary EH parameter(s)? We hope to find explicit HDRs that can be used to optimize the Yunnan reserve network and a methodology that can be used in other regional studies.

    • Yunnan is located in the southwest border of China (97°3′E–106°12′E, 21°08′N–29°15′N) (Fig. 1), the southeast edge of the Qinghai-Tibet plateau, where there are strongly topographic and climatic gradient changes and significant environmental heterogeneity. As a well-known global biodiversity hotspot (Myers et al., 2000; Yang et al., 2004; Zhang et al., 2014; Zomer et al., 2015), although Yunnan has established 20 national, 38 provincial, 56 municipal or prefectural, and 46 county-level (160 in total) nature reserves, covering 7.4% of Yunnan Province (Forestry Department of Yunnan Province, 2017), the impact of environmental change has reduced the conservation effectiveness of nature reserves and increased the likelihood of species extinction (Zhang et al., 2014; Zomer et al., 2015). Therefore, optimizing nature reserve network of Yunnan for better conserving its biodiversity is facing environmental changes (Zhang et al., 2014; Zomer et al., 2015; Wang et al., 2018). However, knowledge is limited to the relationship between Yunnan’s biodiversity distribution and environmental heterogeneity.

      Figure 1.  Location of the study area, showing 32 nature reserves, variation in terrain elevation within Yunnan, China

    • Each nature reserve had detailed scientific survey reports and other study materials (Appendix S1) for its biodiversity status (ecosystem types, plant and animal species). These reports or study materials were collected to gather data on vegetation formations and plant and animal species in the reserves.

      The quantitative method (Li et al., 2011) was used to calculate three types of biodiversity indices: ecosystem diversity index (EI), plant diversity index (PI), and animal diversity index (AI). EIi (i represents each nature reserve from 1–32, hereafter the same) was measured directly by the number of vegetation formations derived from the vegetation map of each nature reserve. PIi and AIi were measured by the numbers of plant and animal species recorded in nature reserve i using equations (1) and (2), respectively. Like a previous study (Li et al., 2011; Song et al., 2016), we assigned weights of 100 and 50 to national I- and II-level protected wild species, respectively, which we used to evaluate regional biodiversity conservation values (Song et al., 2016).

      $$ P{I_i} = V{P_i} + {\rm{ }}100 \times NP{P_{Ii}} + {\rm{ }}50 \times NP{P_{Iii}} $$ (1)

      where VPi refers to the number of vascular plant species recorded in nature reserve i, NPPIi and NPPIIi are the numbers of national I- and II-level protected wild plant species, respectively, in nature reserve i (Yang et al., 2016).

      $$ A{I_i} = V{S_i} + I{S_i} + {\rm{ }}100 \times NP{A_{Ii}} + {\rm{ }}50 \times NP{A_{IIi}} $$ (2)

      where VSi and ISi refer to the numbers of vertebrate species and insects, respectively, recorded in nature reserve i, and NPAIi and NPAIIi are the numbers of national I- and II-level protected wild animal species, respectively, in nature reserve i (MFPRC and MAPRC, 1988; Yang et al., 2016).

    • Previous studies have demonstrated that topographic, climatic, and soil heterogeneity show strong correlations with plant and animal diversity (Irl et al., 2015; Stein et al., 2014; 2015). Topographic heterogeneity plays a more important role in shaping the species distribution and the pattern of species diversity than elevation itself (Tukiainen et al., 2017). Meanwhile, several researches have shown that climatic heterogeneity determinate species diversity pattern of terrestrial vertebrates and vascular plants, especially variables associate with water and energy availability (Veech and Crist, 2007; Stein et al., 2014; 2015; Tukiainen et al., 2017). Additionally, Zhang et al (2012) have found that several climate factors, such as annual mean temperature, temperature annual range, annual precipitation, precipitation of driest month, and precipitation seasonality, are crucial to predict the distribution of plant diversity in Yunnan. Thus, it is very meaningful to explore the relationship between climate heterogeneity and biodiversity in Yunnan, China. Furthermore, edaphic heterogeneity is critical to driving the diversity pattern (Hufford et al., 2014; Hulshof and Spasojevic, 2020).

      Collectively, combined with the accessibility of environmental data we derived three subject areas: topography, climate and soil, and 17 environmental variables in each nature reserve (Table 1). All variables were produced at the same resolutions: 30 arc-seconds (except soil data). To better measure the environmental heterogeneity of 32 nature reserves, the study divided the soil into 144 types following the Second National Soil Survey (National Soil Survey Office of Yunnan Province, 1996). With regard to topographic variables: altitude, slope, and aspect, we reclassified the altitude into 55 classes by 100m intervals. The slope was reclassified into six categories: flat (0–5°), gentle (5°–15°), pitched (15°–25°), steep (25°–35°), hard (35°–45°), and extreme (≥ 45°). The aspect was reclassified into north, northeast, east, southeast, south, southwest, west, northwest, and no slope, a total of 9 categories. According to Zomer et al., (2015), 13 climate variables selected for nature reserve in Yunnan can be reclassified into 33 classes. All those 17 reclassified environmental variables were used to calculate 17 EH measures at each selected nature reserve.

      Table 1.  Summary of the variables in the measurement of environmental heterogeneity (EH) of 32 nature reserve in Yunnan

      SubjectVariableAbbreviationUnitsSource
      Soil Soil type SL National Soil Survey Office of Yunnan Province, 1996
      Topography Altitude ALD m WorldClim v1.0; Hijmans et al., 2005
      Slope SPE ° WorldClim v1.0; Hijmans et al., 2005
      Aspect APT WorldClim v1.0; Hijmans et al., 2005
      Climate Annual mean temperature AMT WorldClim v1.0; Hijmans et al., 2005
      Temperature annual range TAR WorldClim v1.0; Hijmans et al., 2005
      Meantemperature of wettest quarter WMT WorldClim v1.0; Hijmans et al., 2005
      Mean Temperature during the driest quarter DMT WorldClim v1.0; Hijmans et al., 2005
      Mean temperature of warmest quarter WAMT WorldClim v1.0; Hijmans et al., 2005
      Mean temperature of coldest quarter CMT WorldClim v1.0; Hijmans et al., 2005
      Mean diurnal range MDR WorldClim v1.0; Hijmans et al., 2005
      Annual precipitation AP mm WorldClim v1.0; Hijmans et al., 2005
      Precipitation seasonality coefficient of variation PS mm WorldClim v1.0; Hijmans et al., 2005
      Precipitation of wettest quarter WQP mm WorldClim v1.0; Hijmans et al., 2005
      Precipitation of driest quarter DQP mm WorldClim v1.0; Hijmans et al., 2005
      Precipitation of warmest quarter WAQP mm WorldClim v1.0; Hijmans et al., 2005
      Precipitation of coldest quarter CQP mm WorldClim v1.0; Hijmans et al., 2005
    • Studies have shown that the Shannon-Wiener index of environmental variables is an effective measure of EH (Stein et al., 2015). The 17 EH parameters (Table 1, Appendix S2) were calculated:

      Table S1.  The 17 environmental heterogeneity parameters and area of the 32 selected nature reserves in Yunnan, China

      Nature reservesSLALDSPEAPTAMTWMTDMTWAMTCMT
      Ailao Mountain 1.667862 2.335548 1.352042 2.091193 1.797252 1.812973 1.752694 1.812973 1.720881
      Baima Snow Mountain 2.461842 3.165355 1.467865 2.085038 2.632611 2.578325 2.642719 2.578325 2.653061
      Cangshan Mountain/ Erhai Lake 2.128009 2.634032 1.217715 2.122486 2.154536 2.162459 2.126759 2.162459 2.126759
      Dashanbao Reserve 0.832455 2.041262 1.198090 2.072562 1.469970 1.497213 1.338321 1.497213 1.338321
      Dawei Mountain 2.349897 2.935836 1.397559 2.077047 2.286734 2.279908 2.207283 2.279908 2.275763
      Daxue Mountain 1.457631 2.714037 1.449478 2.076356 2.075203 2.050750 2.024928 2.050750 2.024928
      Fenshuiling Reserve 1.461247 2.601040 1.356440 2.017458 2.020648 1.991883 1.898647 1.991883 2.036144
      Gaoligong Mountains 1.940080 3.246562 1.495871 2.082780 2.666805 2.560746 2.633706 2.560746 2.698653
      Huanglian Mountain 1.907912 2.941956 1.364074 2.077093 2.322463 2.278164 2.334580 2.278164 2.334580
      Huizi Grus nigricollis Reserve 1.595847 1.808427 1.169565 2.088142 1.158752 1.201556 1.164409 1.201556 1.164409
      Jiaozi Snow Mountain 1.618842 2.797049 1.495434 2.082007 2.250169 2.230187 2.209583 2.230187 2.209583
      Naban River 1.749447 2.749075 1.181675 2.016277 2.166828 2.134186 2.179690 2.165846 2.142779
      Nangun River 2.341318 3.036810 1.408510 2.085745 2.399916 2.349086 2.286036 2.349335 2.343474
      Wenshan Reserve 1.456593 2.670050 1.360571 2.079148 2.010561 2.028400 1.782807 2.028400 1.865968
      Wumeng Mountain 2.015245 2.263778 1.443708 2.090530 1.491402 1.574689 1.355031 1.574689 1.355031
      Wuliang Mountain 1.526558 2.513346 1.466559 2.085630 1.891191 1.906574 1.849813 1.906574 1.849813
      Xishuangbanna Reserve 1.456008 2.114732 1.257078 2.095004 1.572665 1.525867 1.541507 1.545654 1.601770
      Yaoshan Reserve 2.318834 3.143578 1.596806 2.082194 2.566902 2.570284 2.485561 2.570284 2.485561
      Yuanjiang River 1.939129 2.673780 1.403520 2.087419 2.090511 2.105219 2.136997 2.111532 1.993252
      Tianchi Lake of Yunlong 1.200632 2.389496 1.457324 2.063217 1.769356 1.742098 1.737892 1.742098 1.737892
      Bita Lake 1.169299 1.854602 0.086352 2.088859 1.298096 1.236511 1.389794 1.236511 1.389794
      Gulinjing Reserve 1.401731 2.715584 0.105101 2.041939 2.124039 2.085871 1.981799 2.085871 2.126836
      Haba Snow Mountain 2.037909 3.404666 0.439853 2.025915 2.767578 2.743083 2.801875 2.743083 2.786546
      Lancang River 2.345915 2.559271 0.100917 2.079797 1.951577 1.927319 2.056936 1.927319 1.923441
      Yunling Mountain 1.357957 2.526327 0.142829 2.090381 1.965187 1.942896 1.961642 1.942896 1.947718
      Nuozadu Reserve 2.111375 2.287367 0.098667 2.088661 1.691638 1.683805 1.744197 1.696726 1.682008
      Tongbiguan Reserve 1.852157 3.110715 0.034912 2.060968 2.446807 2.361396 2.409774 2.369305 2.409774
      Tuoniang River 1.132712 2.463056 0.069694 2.088686 1.856626 1.878482 1.833658 1.878482 1.833658
      Taiyang River 1.164993 1.646345 0.000559 2.087777 0.977347 1.066386 0.965697 1.066386 1.023557
      Xiaohei Mountain 1.376716 2.271980 0.058391 2.068666 1.531951 1.499893 1.447812 1.499893 1.447812
      Zhanyiheifeng Reserve 1.483134 1.287928 0.013568 2.107391 0.864467 0.916407 0.846894 0.916407 0.846894
      The Source of Pearl River 1.948837 1.689251 0.033888 2.105754 1.270357 1.275333 1.322737 1.275333 1.322737
      Nature reserves MDR TAR AP WQP DQP WAQP CQP PS A
      Ailao Mountain 1.184633 1.602546 1.770795 2.032265 1.131527 1.881272 1.131527 1.50506 67700
      Baima Snow Mountain 1.810130 2.136391 1.954175 2.130668 1.626413 1.955891 1.706344 2.442835 281640
      Cangshan Mountain/ Erhai Lake 0.637137 1.08927 0.909191 1.592575 2.086372 1.471186 2.086372 2.400125 79700
      Dashanbao Reserve 0.785689 1.157445 1.384774 1.627382 0.851959 1.467471 0.851959 0.971359 19200
      Dawei Mountain 2.470396 1.533675 2.418824 2.507612 2.305446 2.345748 2.283601 2.354396 43993
      Daxue Mountain 1.121692 1.518228 1.048472 1.25281 0.654897 1.110697 0.726952 0.888739 17541
      Fenshuiling Reserve 2.410179 1.713387 2.078918 1.941096 1.71669 1.806274 1.959666 1.788162 42027
      Gaoligong Mountains 1.852918 2.124821 2.66815 2.799496 2.762524 2.612127 2.833445 2.369806 405549
      Huanglian Mountain 1.734375 1.07726 1.605083 1.897854 1.322814 1.776712 1.322814 1.851751 61860
      Huizi Grus nigricollis Reserve 1.370728 0.87554 1.235913 1.572836 0.805782 1.442577 0.805782 1.698066 12911
      Jiaozi Snow Mountain 1.555337 1.272771 1.751373 1.841011 1.035048 1.695769 1.035048 0.740283 16456
      Naban River 0.893393 1.123775 2.060593 2.306215 0.547874 1.876542 1.107055 2.159268 26600
      Nangun River 1.89583 2.164163 0.946678 0.890203 1.616965 1.053455 1.760282 0.882664 50887
      Wenshan Reserve 0.792043 1.226446 1.918278 2.127651 1.512261 1.950419 1.633035 1.629935 26867
      Wumeng Mountain 1.284468 1.410487 1.214558 1.437847 1.217901 1.226783 1.217901 1.640569 26187
      Wuliang Mountain 0.716559 1.376285 1.487089 1.819138 0.881812 1.616874 0.881812 1.687463 30938
      Xishuangbanna Reserve 1.64714 2.135046 2.020561 2.341812 2.227309 2.058889 2.338579 2.598459 242510
      Yaoshan Reserve 1.733043 1.960675 2.208913 2.463151 1.52954 2.145219 1.52954 1.719756 20141
      Yuanjiang River 1.480922 1.747734 1.930567 2.213096 1.243855 2.159576 1.054985 2.315064 22379
      Tianchi Lake of Yunlong 0.68395 0.683308 0.504704 0.636514 1.900656 0.636514 1.900656 2.070081 14475
      Bita Lake 0.624541 0.936723 0.688826 0.454932 0.256316 0.36736 0.256316 1.229852 14133
      Gulinjing Reserve 1.858616 1.029368 1.902313 2.289283 2.170462 2.074081 1.888074 2.254487 6832
      Haba Snow Mountain 1.003415 1.153407 0.711863 0.750503 1.534212 0.748954 1.55095 1.678158 21908
      Lancang River 1.389286 1.512822 2.083762 2.261202 1.04074 2.06998 0.909995 1.546292 89504
      Yunling Mountain 0.518868 0.757315 1.152291 0.850422 1.901191 0.762523 1.906196 2.109826 75894
      Nuozadu Reserve 0.767974 0.979563 1.609072 1.854898 0.466754 1.912214 0.916908 1.739181 18997
      Tongbiguan Reserve 1.881027 2.132452 1.709361 2.17328 2.495465 1.930715 2.495465 2.444569 51651
      Tuoniang River 1.258264 1.501105 1.347572 1.687498 1.701757 1.587358 1.701757 2.018823 19128
      Taiyang River 0.509816 0.509816 1.128208 1.538332 0.687787 1.122312 0.987467 1.378391 7035
      Xiaohei Mountain 0.865576 1.378474 0.662852 0.94474 1.138792 0.690942 1.138792 1.531609 5805
      Zhanyiheifeng Reserve 0.711198 0.714714 0.696851 0.777775 0.856418 0.554416 0.856418 1.002708 26610
      The Source of Pearl River 1.657259 1.413215 1.249075 1.228121 1.256107 1.087871 1.256107 1.138963 117934
      Note: Abbreviation of environmental heterogeneity parameters can be seen in Table 1; A, area (hm²)
      $$ {H}_{ij}^{{'}}=-\sum _{k=1}^{n}{(S}_{ijk}/{S}_{i})\times {\ln}\left({S}_{ijk}/{S}_{i}\right) $$ (3)

      where $ {H}_{ij}^{{'}} $ the heterogeneity of environmental variable j (1–17) of the selected nature reserve i (1–32), k represents the reclassified type or class of each environmental variable, among which soil variable has 144 types, topographic variables (altitude, slope, and aspect) have 55, 6 and 9 classes, respectively, and each climatic variable has 33 classes. Sijk is the area occupied by the type or class k of environmental variable j in nature reserve i. Si is the area of nature reserve i.

    • We used Spearman rank correlation function in R package Corrplot (Dormann et al., 2013; Li and Wang 2013; Wei and Simko, 2016) to analyze the collinearity of EH parameters (Fig. 2) and performed a single predictor ordinary least squares (OLS) regression to examine how well EH parameters explain biodiversity index (by determination coefficient R2adj). If the Spearman correlation coefficient was higher than 0.7, the EH parameter with higher explanation power was identified as the primary EH parameter. We used multi-predictor OLS regression to examine how well the subject explained the biodiversity index. Because the previous study found that correlations were observed among the areas, EH parameters, and biodiversity indices, hence, the area factor was also considered a key subject area in this study (Li and Wang 2013).

      Figure 2.  The correlation coefficient matrix among the 17 environmental heterogeneity parameters and area of selected 32 nature reserves in Yunnan, China. Abbreviation of environmental heterogeneity parameters can be seen in Table 1; A, area (hm²)

      Whereafter, we identified the primary environmental variables of EI were area and the EH measures of soil, slope, aspect, mean temperature of driest quarter, temperature annual range, annual precipitation, precipitation of coldest quarter and precipitation seasonality; the primary environmental variables of PI were area and the EH measures soil, slope, aspect, mean temperature of coldest quarter, temperature annual range, annual precipitation, precipitation of coldest quarter and precipitation seasonality; and the primary environmental variables of AI were area and the EH measures of soil, altitude, slope, aspect, temperature annual range, precipitation of wettest quarter, precipitation of coldest quarter and precipitation seasonality. Considering these environmental variables, the optimal EH measure interpretation model for each biodiversity index was constructed using the Akaike information criterion (AIC) (Quinn and Keough, 2002). The standard regression coefficient of each EH parameter in the optimal model reflected the degree of importance for the corresponding biodiversity index. All statistical analyses were performed in R version 3.4.4 (R Core Team, 2016).

    • The spatial distribution of biodiversity in the natural areas of Yunnan varied greatly. The three nature reserves with the highest ecosystem diversity were Xishuangbanna, Nangun River, and Gaoligong Mountains reserves. Gaoligong Mountain, Dawei Mountain, and Wenshan reserves showed the highest plant diversity, while the richest diversity of animal species was found in Xishuangbanna, Nangun River , and Tongbiguan reserves. Overall, Xishuangbanna, Gaoligong Mountains, and Tongbiguan reserves were the most diverse nature reserves (Table 2).

      Table 2.  Biodiversity indices of the selected nature reserves in Yunnan, China

      Nature ReservesEIPIAINature ReservesEIPIAI
      Ailao Mountain 20 2486 3820 Xishuangbanna Reserve 49 4879 7323
      Baima Snow Mountain 37 2685 4182 Yaoshan Reserve 32 2589 2209
      Cangshan Mountain/Erhai Lake 18 2782 3883 Yuanjiang River 33 2733 4076
      Dashanbao Reserve 16 408 2476 Tianchi Lake of Yunlong 17 1768 2160
      Dawei Mountain 25 5964 4099 Bita Lake 34 2808 2758
      Daxue Mountain 15 2501 4912 Gulinjing Reserve 12 3762 2429
      Fenshuiling Reserve 24 4838 4410 Haba Snow Mountain 28 2134 2779
      Gaoligong Mountains 44 5897 4177 Lancang River 25 1542 5567
      Huanglian Mountain 11 4102 3838 Yunling Mountain 21 2065 2749
      Huize Grus nigricollis Reserve 19 736 1417 Nuozadu Reserve 27 3016 4198
      Jiaozi Snow Mountain 17 1271 1186 Tongbiguan Reserve 38 4995 5628
      Naban River 28 3195 4164 Tuoniang River 11 2859 3404
      Nangun River 44 3696 5968 Taiyang River 14 2954 4122
      Wenshan Reserve 18 5397 3473 Xiaohei Mountain 29 3392 4742
      Wumeng Mountain 35 2694 3861 Zhanyi Heifeng Reserve 14 874 1747
      Wuliang Mountain 17 3455 4585 The Source of Pearl River 35 2075 844
      Notes: EI, ecosystem diversity index; PI, plant diversity index; AI, animal diversity index

      Based on the spatial distribution of biodiversity (Fig. 3), the distributions of plant and animal diversity were negatively correlated with latitude (R2 = 0.251, P = 0.002; R2 = 0.252, P = 0.002, respectively, (Figs. 3d, 3g). The PI and AI showed a decreasing trend from south to north, whereas no significant correlation was detected between ecosystem diversity and latitude (R2 = −0.017, P = 0.491) (Fig. 3a). From west to east, ecosystem and animal diversities were significantly negatively correlated with longitude (R2 = 0.123, P = 0.028; R2 = 0.232, P = 0.003, respectively) (Figs. 3b, 3h), whereas this was not observed for plant diversity. Concerning elevation, ecosystem diversity was irregularly distributed, and PI and AI decreased with elevation (R2 = 0.167, P = 0.011; R2 = 0.113, P = 0.033, respectively) (Figs. 3c, 3f, 3i).

      Figure 3.  The relationship between biodiversity indices and latitude, longitude, altitude within selected nature reserves of Yunnan, China. a, b and c refer to the ecosystem diversity (EI); d, e and f refer to the plant diversity (PI); g, h and i refer to the animal diversity (AI)

    • The results of single predictor OLS models indicated that temperature annual range heterogeneity was the strongest predictor of EI (R2 = 0.469, P = 0.001) (Table 3), and precipitation of coldest quarter heterogeneity was the best predictor of the diversity pattern in PI (R2 = 0.405, P = 0.001), followed by annual precipitation, precipitation seasonality, temperature annual range, and mean temperature of coldest quarter, which all had good explanatory power for the PI (R2 = 0.252, P = 0.002; R2 = 0.245, P = 0.003; R2 = 0.199, P = 0.004; and R2 = 0.159, P = 0.023, respectively). For the AI, only the temperature annual range heterogeneity was able to sufficiently explain the variation (R2 = 0.281, P = 0.01).

      Table 3.  The determination coefficients (R2adj) of the single- and multi-predictor ordinary least squares (OLS) between biodiversity indices and environmental heterogeneity (EH) parameters of the selected natural reserves in Yunnan, China

      EHSingle-predictor OLS modelsMulti-predictor OLS models
      SubjectParametersEI (R2adj)PI (R2adj)AI (R2adj)EI (R2adj)PI (R2adj)AI (R2adj)
      Soil SL 0.193** 0.023 0.021 0.193** 0.023 0.021
      Topography −0.014 0.011 −0.010
      ALD 0.054
      SPE −0.019 0.001 −0.019
      APT −0.036 0.012 −0.0552
      Climate 0.463*** 0.423*** 0.263*
      AMT
      WMT
      DMT 0.041
      WAMT
      CMT 0.159*
      MDR
      TAR 0.469*** 0.199** 0.281**
      AP 0.033 0.252**
      WQP 0.066
      DQP
      WAQP
      CQP 0.095 0.405*** 0.06
      PS 0.036 0.245** 0.081
      Area A 0.301*** 0.103* 0.057 0.301*** 0.103* 0.057
      Notes: The primary EH parameters selected for biodiversity index by OLS models are marked with a tick, blanks in the table mean that variables are not ecologically significant in the single- and multi-predictor ordinary least squares (OLS); the17environmental heterogeneity parameters are defined in Table 1; A, area (hm²). Significance levels: ***P < 0.001; ** P < 0.01; *P < 0.05

      The multi-predictor OLS models (Table 3) showed that for EI, PI, and AI, climatic heterogeneity had stronger explanatory power for biodiversity indices (R 2= 0.463, P = 0.001; R2 = 0.423, P = 0.001; R2 = 0.263, P = 0.014, respectively) than soil or topographic heterogeneity. Topographic heterogeneity almost had no explanatory power for three biodiversity indices. Soil heterogeneity could only influence EI (R2 = 0.193, P = 0.007), and had very weak explanatory power for the other biodiversity indices. The area had a limited ability to interpret EI and PI (R2 = 0.301, P = 0.001; R2= 0.103, P = 0.034, respectively).

      The AIC optimal model (Table 4) constructed using the soil, mean temperature of driest quarter, temperature annual range, annual precipitation heterogeneity, and area effectively explained the spatial variation in EI (R2 = 0.569, P = 0.001). The optimal model formed by the annual precipitation and precipitation of coldest quarter heterogeneity effectively explained the spatial variation in PI (R2 = 0.545, P = 0.001). The model consisting of the temperature annual range and precipitation seasonality heterogeneity explained the spatial variation in AI (R2 = 0.296, P = 0.002). According to the standard regression coefficients, the temperature annual range heterogeneity was the most important environmental factor affecting EI and AI (0.647, P = 0.001; 0.498, P = 0.01, respectively), and precipitation of coldest quarter heterogeneity was the most important environmental variable for the PI (0.525, P = 0.001). Hence, we concluded that the temperature annual range and precipitation of coldest quarter heterogeneity were not only positively correlated with biodiversity, but also the primary driving forces of the biodiversity patterns in the natural areas of Yunnan.

      Table 4.  The optimal models for biodiversity indices based on the Akaike Information Criterion of nature reserves natural areas of Yunnan

      Response variablesPredictorsR 2adjP
      EISL (0.284), DMT (−0.246), TAR (0.647), AP (−0.242), A (0.319)0.5690.001
      PIAP (0.317), CQP (0.525)0.5450.001
      AITAR (0.498), PS (0.201)0.2960.002
      Notes: Standardized regression coefficients are bracketed, and the statistically significant parameters are shown in the bold. EI: Ecosystem diversity index; PI: Plant diversity index; AI: Animal diversity index; Abbreviated predictors include soil types (SL), mean temperature of driest quarter (DMT), temperature annual range (TAR), annual precipitation (AP), precipitation of coldest quarter (CQP), precipitation seasonality (coefficient of variation) (PS); A, area (hm²)
    • Our analysis revealed that soil heterogeneity best explained the EI (R2 = 0.193, P = 0.007, Table 3), which is consistent with the thesis that different combinations of soil types could provide a variety of nutritional levels (habitat selection) for biological communities (Hufford et al., 2014). Given the AIC optimal model for EI, which couples soil, climatic (temperature, precipitation) heterogeneity, and area factors; this further confirms the basic conditions (complex physical environment) required to form a variety of ecosystems (Lapin and Barnes, 1995). Soil heterogeneity cannot effectively explain species diversity (AI, PI), perhaps because the heterogeneity of soil structure and organic matter composition may relate more to species diversity at the finer scale (Cramer and Verboom, 2017).

      Himalayan orogeny is generally believed to drive environmental factors to rapidly compress in space, and extreme changes in topography may be one of the reasons for the formation of high species diversity in Yunnan (Xing and Ree, 2017). However, only the altitude heterogeneity presented explanatory power for the PI ( P = 0.018) in this study. Topographic heterogeneity most likely had weak explanatory power because its direct effects on biodiversity were not significant at the non-uniform scale of ‘nature reserves’. More explicitly measured environmental heterogeneity explained biodiversity patterns better than did crude topographic measures such as mean slope, altitude range, and mean aspect (Bailey et al., 2017). Additionally, topographic heterogeneity may have explained biodiversity so well because it is an excellent proxy for several sources of climatic and soil heterogeneity (Bailey et al., 2017). For example, the highest plant diversity in this study was in the Gaoligong Mountains (Table 2), which are distributed among the Hengduan Mountains, one of the world's biodiversity hotspots (Xing and Ree, 2017). Complex regional topographic conditions in these areas can increase the climatic heterogeneity (Wang et al., 2015), which may have a greater effect on the distribution of species diversity in topographic heterogeneous regions (Irl et al., 2015). Perhaps we can assume that topographic heterogeneity might indirectly affect the distribution of biodiversity by affecting climatic heterogeneity in Yunnan. Hence, the synergistic effect of topographic and climatic heterogeneity on biodiversity should be explored when HDRs are studied in topographically complex areas.

      We found that climatic heterogeneity sufficiently explained biodiversity (EI, PI, AI), and its variables influenced biodiversity the most, particularly temperature annual range and precipitation of coldest quarter heterogeneity (Table 3). Yunnan is located at low latitudes, where the environmental tolerances of species are weaker because where the annual temperature range is lower than in high latitude regions. According to the climate stability hypothesis, Stevens (1989) suggested that regions with a stable climate are more likely to promote the formation of narrow niches for species. Additionally, Klopfer and MacArthur (1960, 1961) proposed that a smaller annual range of climatic conditions reduces niche overlap and supports species with narrower niches. This is consistent with a large number of species with narrow ranges in Yunnan and the fact that Yunnan is an important global centre for endemic species (Li, 1994; Wang and Zhang, 1994; Huang et al., 2012; 2016). In this study, the water-related variable of precipitation of coldest quarter heterogeneity had the greatest influence on the PI. The result that precipitation of coldest/driest quarter is collinear (Fig. 2) indicates that precipitation heterogeneity is the main driving factor for the PI when species or communities face limited resources. Moreover, these results indicate that the water-energy dynamics hypothesis (Veech and Crist, 2007; Stein et al., 2014) could explain the plant diversity in the natural areas of Yunnan.

      The spatial scale (spatial extent or cell size) represents an unavoidable problem in regional HDR research. In this study, the area size affected the HDRs, particularly on the ecosystem diversity (R2 = 0.301, P = 0.001). Through climate-vegetation models, it is generally known that climate type is strongly correlated with vegetation distribution (Kaplan and New, 2006). Regarding species diversity, the results were consistent with a previous study concluding that the spatial scale does not affect the overall trend in HDRs (Seiferling et al., 2014). Additionally, the negative correlation in HDRs is primarily due to an increased degree of heterogeneity, reduces the effective habitat area of each species and increase the probability of random extinction (Laanisto et al., 2013; Chocron et al., 2015). Finer-scale environmental heterogeneity is likely to intensify habitat fragmentation and therefore threaten regional biodiversity (Stein et al., 2015). According to species-area curves, however, only areas of a certain size (such as a nature reserve) can effectively protect biodiversity. More importantly, larger areas can more effectively regulate the introduction of exotic species and the rate of species renewal (Stein et al., 2014; Bailey et al., 2017). Therefore, these discussions confirm that regional HDR research is more suitable on a macro scale and that HDRs could help generate more rational and effective conservation planning for biodiversity.

      Despite data limitation, this study did not explore thoroughly the discrepancies of environmental heterogeneity on the drivers of biodiversity in different nature reserves, and could not elucidate the mechanisms of the formation of biodiversity patterns in different spatially distributed nature reserves. However, we provide a paradigm for studying regional HDRs. With sufficiently accurate geographic distribution information of species collected, conservationists can study micro-scale natural area HDRs in-depth and devising appropriate conservation strategies for a local area in future. More importantly, micro-scale HDRs can include critical thermal maximum and minimum physiological limitation factors, particularly, the role of environmental heterogeneity on the driving forces of animal diversity in natural areas of Yunnan, China.

    • Climatic heterogeneity had the best explanatory power for the biodiversity distribution patterns in relatively natural areas of Yunnan. The region’s diverse hydrothermal conditions produce selection pressures for species and promote interspecific diversification or even the formation of new species (Hua and Wiens 2013, Irl et al., 2015). Further studies have shown that more species could coexist within a climatic heterogeneous region, through improving the fitness of a species increases the probability that it exhibits phenotypic plasticity (Gianoli and Valladares, 2012, Lázaro-Nogal et al., 2015). Therefore, we argue that climatic heterogeneity is the primary driving force for the species diversification and biodiversity patterns in the natural areas of Yunnan. Additionally, climate conditions are more stable in climatic heterogeneous regions (Ackerly et al., 2010), which could allow species to migrate over less distance to locate suitable habitats and reduce species extinction rates under future climate change. Climatic heterogeneity could help mitigate the effects of climate change on biodiversity, and therefore we emphasize that climatically heterogeneous regions have large conservation significance for biodiversity in Yunnan under climate change scenarios.

    • The effective conservation of ecosystem diversity involves maintaining a region’s important ecological processes and ecological stability, particularly in the context of climate change (Levine et al., 2016). However, conservation in Yunnan currently only takes into account the conservation value of specific vegetation types (Zhang et al., 2013). Although studies showing that vegetation community heterogeneity is most likely a direct driver of species diversity (Stein et al., 2014; Levine et al., 2016), the significance of regional ecosystem diversity is still ignored. In the face of conservation gaps in ecosystem diversity in the province, the region’s complex soil composition and climatic heterogeneity must be prioritized.

      Heterogeneity-based priority conservation areas represent a novel approach that could assist in effectively protecting species diversity under climate change, based on understanding regional HDRs (Heller et al., 2015; Paudel and Heinen, 2015). According to the AIC optimal models (Table 4), regions with heterogeneity of annual precipitation or preccipitation of coldest quater or temperature annual range or precipitation seasonality heterogeneous indicate areas with rich plant and animal diversity. Moreover, the results also demonstrate that water-related variables more effectively explained the spatial distribution of the PI, whereas temperature-related factors better explained the AI. In other words, the PI and the AI have different environmental drivers, indicating that one of these indices cannot replace the other. Consistently no single biodiversity surrogate can fully reflect regional biodiversity (Di Minin and Moilanen, 2014, Yang et al., 2016), and this is universal, even in areas with environmental heterogeneity. Therefore, coupling the environmentally heterogeneous regions, which have multiple dimensions of biodiversity, will help increase the effectiveness of biodiversity conservation within priority conservation areas based on environmental heterogeneity.

    • The study of heterogeneity-diversity relationships in natural areas will help conservationists and decision-makers to have a more explicit recognition of the process of shaping regional diversity patterns and provide scientific support for coupling environmentally heterogeneous areas in future systematic conservation planning. Our research explored the relationships between biodiversity and soil, topographic, and climatic heterogeneity in natural areas of Yunnan. We demonstrated that water-related and temperature-related factors are the most important environmental driver for plant and animal diversity, respectively. In general, climatic heterogeneity holds the most important role in the AIC optimal models and also have appreciable explanatory power to ecosystem (56.9%), plant (54.5%), and animal (29.6%) diversity.

      Although this study has some limitation with its biodiversity data and spatial scales, and nor are there further studies of the synergies effects of environmental heterogeneity on biodiversity in different subject areas. Nevertheless, our study of natural area heterogeneity-diversity relationships indicates that climatically heterogeneous areas are maybe pivotal for coupling environmental heterogeneity in systematic conservation planning and optimizing existing protected areas of Yunnan Province in future. To achieve effectively protect the biodiversity of Yunnan under environmental changes, we have some suggestions that we should investigate: 1) the effect of spatial scales on regional HDRs and 2) the conservation effectiveness of coupled climatically heterogeneous regions into systematic conservation planning for biodiversity conservation in Yunnan, China under climate change.

    • We are grateful to HUA Chaolang at the Yunnan Institute of Forest Inventory and Planning for providing the Yunnan Nature Reserve information.

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