Impacts of climatic and edaphic factors on the diversity, structure and biomass of species-poor and structurally-complex forests

Journal Pre-proof Impacts of climatic and edaphic factors on the diversity, structure and biomass of species-poor and structurally-complex forests

Arshad Ali, Anvar Sanaei, Mingshi Li, Omid Asadi, Khaled Ahmadaali, Mohsen Javanmiri Pour, Ahmad Valipour, Jalil Karami, Mohammad Aminpour, Hasan Kaboli, Yousef Askari PII:

S0048-9697(19)35714-6

DOI:

https://doi.org/10.1016/j.scitotenv.2019.135719

Reference:

STOTEN 135719

To appear in:

Science of the Total Environment

Received date:

14 September 2019

Revised date:

10 November 2019

Accepted date:

22 November 2019

Please cite this article as: A. Ali, A. Sanaei, M. Li, et al., Impacts of climatic and edaphic factors on the diversity, structure and biomass of species-poor and structurallycomplex forests, Science of the Total Environment (2019), https://doi.org/10.1016/ j.scitotenv.2019.135719

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© 2019 Published by Elsevier.

Journal Pre-proof Research Article, for submission to: Science of the Total Environment

Impacts of climatic and edaphic factors on the diversity, structure and biomass of species-poor and structurally-complex forests

Arshad Ali1* , Anvar Sanaei2 , Mingshi Li1 , Omid Asadi3 , Khaled Ahmadaali4 , Mohsen Javanmiri Pour5 , Ahmad Valipour6 , Jalil Karami3 , Mohammad Aminpour7 , Hasan Kaboli8 , Yousef

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Department of Forest Resources Management, College of Forestry, Nanjing Forestry

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Askari9

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University, Nanjing 210037, Jiangsu, China

CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology,

Natural Resources Faculty, Gorgan University of Agricultural Sciences and Natural

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Resources, Iran

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Chinese Academy of Sciences, Shenyang 110164, China

Department of Reclamation of Arid and Mountainous Regions, Natural Resources Faculty,

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University of Tehran, Karaj, Iran 5

Natural Resources Faculty, University of Tehran, Karaj, Iran

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Department of Forestry and The Center for Research and Development of Northern Zagros

Forestry, University of Kurdistan, Iran 7

Natural Resources and Watershed Management office, West Azerbaijan Province, Urmia,

Iran 8

Faculty of Desert Studies, Semnan University, Semnan, Iran

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Research Division of Natural Resources, Kohgiluyeh and Boyerahmad Agriculture and

Natural Resources Research and Education Center, AREEO, Yasouj, Iran 1

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* Corresponding author. Email: [email protected] ORCID: 0000-0001-9966-2917 (Arshad Ali)

Running title: Abiotic drivers of species-poor forests Contribution of the co-authors: AA designed idea; AS compiled data provided by OA, MJP, AV, JK, MA, HK and YA; AA and AS analyzed the data; AA wrote the first draft of

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the manuscript through great support from AS whereas ML, OA, KA, MJP, AV, JK, MA, HK and YA contributed greatly to the draft. AS, OA, MJP, AV, JK, MA, HK and YA provided

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general information about the study area, sites and forests. All authors approved the

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ACKNOWLEDGEMENTS

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manuscript for submission. Authors declared that they do not have any conflict of interest.

The authors would like to thank the Department of Forestry and Forest Economics at

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University of Tehran; Department of Forestry and the Center for Research and Development

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of Northern Zagros Forestry at University of Kurdistan; Department of Forestry at Natural

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Resources Faculty, Gorgan University of Agricultural Sciences and Natural Resources; Faculty of Desert Studies at Semnan University; and Natural Resources and Research Division of Natural Resources, Kohgiluyeh and Boyerahmad Agriculture and Natural Resources Research and Education Center, AREEO, Yasouj for providing data. AA is supported by the Metasequoia Faculty Research Startup Funding at Nanjing Forestry University (Grant No. 163010230). ML is supported by the National Natural Science Foundation of China (Grant No. 31670552), and the 2017 Qinglan Project sponsored by Jiangsu Province.

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Journal Pre-proof Research Article, for submission to: Science of the Total Environment

Impacts of climatic and edaphic factors on the diversity, structure and biomass of species-poor and structurally-complex forests

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Running title: Abiotic drivers of species-poor forests

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Abstract Understanding the impacts of multiple climatic and edaphic factors on forest diversity, structure and biomass is crucial to predicting how forests will react to global environmental change. Here, we addressed how do forest structural attributes (i.e. top 1% big, top 25% big medium and small trees; in terms of tree height, diameter, and crown), species richness, and aboveground biomass respond to temperature-related and water-related climatic factors as

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well as to edaphic factors. By assuming disturbance as a constant factor in the study forests,

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we hypothesize that water-related and temperature-related climatic factors play contrasting

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roles whereas edaphic factors play an additional role in shaping forest diversity, structure and aboveground biomass in species-poor and structurally-complex forests. We used forest

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inventory and environmental factors data from 248 forest plots (moist temperate, semi-

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humid, and semi-arid) across 12 sites in Iran. We developed multiple linear mixed-effect models for each response variable by using multiple climatic and edaphic factors as fixed

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effects whereas sites as a random effect. Top 1% big, top 25% big, medium, and small trees

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enhanced with mean annual temperature but declined with water-related climatic (i.e. mean

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annual precipitation, cloud cover, potential evapotranspiration, and wet day frequency) factors, whereas soil texture (i.e. sand content) and pH were of additional importance. Species richness increased with precipitation and cloud cover but decreased with temperature, potential evapotranspiration, soil fertility and sand content. Aboveground biomass increased along temperature gradient but decreased with potential evapotranspiration, clay and sand contents. Temperature seemed to be the main driver underlying the increase in forest structure (i.e. diameter-related attributes) and biomass whereas precipitation did so for species richness. We argue that the impacts of multiple climatic factors on forest structural

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Journal Pre-proof attributes, diversity and biomass should be properly evaluated in order to better understand the responses of species-poor forests to climate change. KEYWORDS: big trees, climatic factors, edaphic factors, forest functioning, medium-small

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trees, species richness

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1. INTRODUCTION Moist temperate, semi-humid and semi-arid forests are the species-poor and structurallycomplex forests having great potential for forest productivity and carbon sequestration (Bohn et al., 2018; Bonan, 2008; Pan et al., 2013). Understanding the impacts of multiple climatic and edaphic factors on forest diversity, stand structure and biomass is crucial to predicting how forests will react to global environmental change, biological conservation and forest

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management strategies that maximize forest functions and services (Bohn et al., 2018;

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Coomes et al., 2014b; Keeling and Phillips, 2007; Rodrigues et al., 2016). Changes in forest

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diversity, structure and aboveground biomass attributed to climatic and edaphic factors have already been explored across different forest type across the globe (Ali et al., 2018; Bohn et

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al., 2018; Coomes et al., 2014a; Jucker et al., 2018; Michaletz et al., 2018; Yuan et al., 2018).

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However, compared to species-rich and structurally-complex tropical forests (Ali et al., 2018; Corlett, 2016), there is remarkably little evidence of regional impacts of climatic and edaphic

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forests.

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factors on the diversity, structure and biomass of species-poor and structurally-complex

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The forest structure, diversity and biomass are not only due to the natural processes and human disturbances but are also largely due to the changes in environmental (e.g. climatic and edaphic) conditions (Ali, 2019; Corlett, 2016; Yuan et al., 2019). For example, at local or small scale, variations in forest structure, diversity and biomass might not be attributable to climatic factors (e.g. temperature and precipitation) but might be attributable to edaphic and topographic factors (Clark et al., 1998; Rodrigues et al., 2019). Moreover, topographic factors, such as elevation and slope, have been considered as the key spatial factors driving changes in forest diversity, structure and biomass through variations in climatic and edaphic factors (Jucker et al., 2018). For example, topographic complexity 6

Journal Pre-proof drives environmental filtering and habitat heterogeneity, thereby eventually controlling the diversity, structure and biomass of forests (Coomes et al., 2014b; Rodrigues et al., 2016). Local edaphic and micro-climatic factors can vary greatly over small-scale whereas macroclimatic factors can vary greatly over regional or large-scale, and hence, the topographic complexity incorporates multiple resources and non-resources gradients (Bohn et al., 2018; Rodrigues et al., 2019; Toledo et al., 2012; Wang et al., 2009). At the large regional scale (e.g. across forest types), patterns of forest structure,

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diversity and biomass might be related to the ranges of species tolerance which are in turn greatly related to climatic factors and a less extent to edaphic factors (Ali et al., 2018; Phillips

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et al., 2010). For example, temperature usually decreases along increasing elevational

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gradient, which controls tree growth at higher elevations (Ali et al., 2019b; Körner, 2007). As

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such, climatic water is a key resource for trees, which could dramatically influence tree species distribution, structure, and functioning (Ali et al., 2018; Poorter et al., 2017; Toledo et

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al., 2012). In sum, forest community located on sites having lower temperature and climatic

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water might vary in individual tree size variation from a community that receives more

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precipitation and temperature, or less precipitation and more temperature, or vice versa (Ali et al., 2019b; Bohn et al., 2018; Coomes et al., 2014a; Phillips et al., 2010). Hence, different temperature-related and water-related climatic factors may differently affect species richness, structure and biomass, because different species having different individual tree sizes have generally different resource-use requirements in species-poor and structurally-complex forests (Bohn et al., 2018; Coomes et al., 2009; Weiser et al., 2018). Tree species will compete more intensely when the available temperature-related and water-related resources are limited, and hence, variation in forest structural attributes (e.g. big, medium and small trees) will enhance the efficiency of resource-use (Gillman and Wright, 2014; Paquette and

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Journal Pre-proof Messier, 2011). Alternatively, big trees may limit the performance of medium and small trees in terms of resource-use and competitive dominance (Ali et al., 2019a; Corlett, 2016). Beside temperature-related and water-related climatic factors, soil chemical and physical properties are also very important influencing factors of forest diversity, structure and functioning because soil chemical factors determine nutrients availability (Paoli et al., 2005; Rodrigues et al., 2016) whereas the soil textural properties determine the soil water availability for plant growth and survival (Sala et al., 1988; Sanaei et al., 2018; Toledo et al.,

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2012). In this case, the soil nutrients hypothesis posits that plants can develop faster when soil nutrients are abundant, thereby leading to greater tree growth and recruitment rates but it

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may also increase species competition resulting in higher mortality and turnover rates

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(Quesada et al., 2012). Nutrient-rich soils can increase forest diversity, structural attributes

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and aboveground biomass due to the maintenance of higher degree of niche differentiation and facilitation, but it can also decrease these biotic factors due to the interspecific

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competition for available resources in natural forests (Ali, 2019; Coomes et al., 2009;

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Rodrigues et al., 2016). Likewise, the inverse-texture hypothesis predicts that high

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productivity is located on wet soils having high clay content in humid regions while located on dry soils having high sand or gravel content in arid or dry regions (Noy-Meir, 1973; Sala et al., 1988). Yet, most of the previous studies have overlooked the influences of soil textural properties on species diversity, stand structural complexity and aboveground biomass in natural forests (but see, Ali et al., 2018), as compared to herbaceous vegetation (Lane et al., 1998; Sanaei et al., 2018). In this study, we use tree height, diameter at breast height (DBH) and crown diameter to quantify forest structural attributes (i.e. top 1% big, top 25% big, medium and small trees) within each plot (in total 248 plots) across 12 sites of moist temperate, semi-humid and semiarid forests in Iran. Tree height, DBH and crown dimension could determine changes in the 8

Journal Pre-proof species' functional strategies in different environments, especially when comparing resource acquisition and conservation strategies. For example, tree maximum (e.g. 99%ile and 75%ile) height, DBH and crown diameter could relate to competitive dominance, and hence, could be controlled by temperature-related and water-related climatic factors as well as by soil nutrients and textural properties (Ali et al., 2019a; Jucker et al., 2018; Westoby et al., 2002). To this end, we aim to better understand how forest structural attributes (i.e. top 1% big, top 25% big, medium and small trees; but in terms of tree height, stem diameter, and crown

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diameter), species richness, and aboveground biomass of species-poor and structurally-

well as to soil nutrients and textural properties.

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complex forests respond to multiple temperature-related and water-related climatic factors as

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In this study, we use multiple linear mixed-effect models (in addition to generalized

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additive mixed-effect models) in order to explore how multiple climatic and edaphic factors drive changes in each of forest structural attributes, species richness, and aboveground

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biomass while considering for the random effect (i.e. sites variations). By assuming

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disturbance as a constant factor in the study forests, we hypothesize that water-related and

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temperature-related climatic factors play contrasting roles whereas edaphic factors play an additional role in shaping forest diversity, structure and aboveground biomass in species-poor and structurally-complex forests. We expect that: 1) temperature-related factors promote forest structural attributes (i.e. top 1% big, top 25% big, medium and small trees) and aboveground biomass rather than species richness, probably due to the fact that available energy may support individual tree size variation and hence productivity in species-poor forests; 2) water-related factors (e.g. climatic water availability) promote species richness and aboveground biomass compared to forest structural attributes, probably due to the longer length of growing season and water – energy dynamics effects; and 3) soil nutrients and

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Journal Pre-proof textural properties restrict species richness, forest structural attributes and aboveground biomass, probably due to the species competition in resource-limited environment.

2. MATERIALS AND METHODS 2.1. Study area and forests This study was conducted in the species-poor and structurally-complex forests of Iran

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covering 32°00ˊ-39°05ˊ N in latitude, 43°02ˊ -57°01ˊ E in longitude, and 114 to 2512 m

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above sea level (Figure S1 in Appendix A). Of the total land area of Iran, about 52.4% are

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rangelands; 8.6% are forests and 19.5% are deserts including bare salty lands. There are three main climatic zones in Iran, including arid and semi-arid regions of the interior and far south,

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Mediterranean climate (mainly in the western Zagros mountains, the high plateau of

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Azerbaijan, and the Alborz mountains) and humid and semi-humid regions (mainly in the Caspian, but also in west Azerbaijan and the southwest Zagros). Of the total area of the

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Iranian forest cover only some parts of the Caspian Hyrcanian forests in the north are used for

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commercial purposes. Other forest areas are important for biological diversity conservation

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and protection. Almost all forest areas are adversely under threat by different factors such as drought, fire, grazing, flood, diseases and uncontrolled deforestation (CBD Report, 2010; Talebi et al., 2013).

The study forests included moist temperate (110 plots), semi-humid (127 plots), and semi-arid (11 plots) forests across 12 forest sites, and hence, we used data from 248 forest plots. The standard forest inventory datasets across several sites and plots were collected from different researchers in the regions where they have conducted fieldwork from 2005 to 2017. Although the human disturbance is common in the study forests, we randomly selected those plots which represent good plant diversity, stand density and diverse tree sizes, in 10

Journal Pre-proof relation to reference forests, in order to meet the assumptions of forest diversity – structure – functioning studies in temperate, semi-humid and semi-arid regions (CBD Report, 2010; Talebi et al., 2013). Hence, study forests contained both secondary and old-growth forests as well as mixed forests in order to cover the existing quality of the forests and to cover large environmental gradient. However, we considered the natural and human disturbances as a constant factor in this study because most of the study plots are subjected to different levels of disturbances in history or in the current situations. According to the standardized protocol

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for diversity-functioning studies, most of the datasets had covered more than 75% live woody stems within each plot. Within each plot, tree diameter at breast height (DBH), height and

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crown dimension for all recorded individual trees (25240 stems) were measured. Tree DBH

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and crown diameter were measured using the measuring tape whereas the tree height was

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measured using Clinometer in most cases, but the observational records were also used in some difficult situations. As such, the crown diameter was measured from either north –

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south or east – west direction, based on the situation and existed difficulties for spreading the

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measuring tape on the ground. The size of plots included 0.02 ha (20 plots), 0.1 (137 plots),

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0.2 ha (74 plots) and 1 ha (17 plots), and the number of plots ranged from 6 to 53 across 12 forest sites (see Figure S1).

We considered the study forests as species-poor and structurally-complex forests because the species richness ranged between 1 and 11 but showed higher variations in both forest structure and biomass across the plots (see Table S1 in Appendix A). In sum, our study forests contained 47 tree species belonged from 28 genera and 16 families, and hence covered small, medium and big trees. The top ten most dominant tree species (based on the average relative basal area across observed plots) in the whole-dataset were: Quercus macranthera (moist temperate), Fagus orientalis (moist temperate), Crataegus aronia (semi-arid), Pterocarya fraxinifolia (moist temperate), Pyrus communis (semi-arid), Carpinus betulus 11

Journal Pre-proof (moist temperate), Quercus infectoria (semi-humid), Tilia platyphyllos (moist temperate), Quercus brantii (semi-humid), and Quercus libani (semi-humid). In the context of climate change, it is very important to evaluate the environmental drivers of forest diversity, structure and biomass of the species-poor and structurally-complex forests of Iran because these forests are already under several stresses such as climatic drought and uncontrolled deforestation. For example, the average annual precipitation in Iran (i.e. about 240 mm) is less than a third of world average precipitation, due to Iran’s location

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and geographic features. Hence, most rivers are seasonal and their flows depend heavily upon the amount of precipitation (CBD Report, 2010; Talebi et al., 2013). Yet, the study forests

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and other forests in Iran are overlooked in the diversity – functioning studies at both global

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and regional scales. Hence, understanding the major environmental divers of the study forests

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will enhance our understanding regarding their role in global carbon cycle and climate

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change mitigation strategies.

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2.2. Predictor variables: climatic and edaphic factors

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In this study, we compiled data for 6 climatic and 5 edaphic factors which were extracted from the global databases using the longitude, latitude and elevation of each plot. The climatic factors were extracted from CRU TS4.01 (https://catalogue.ceda.ac.uk/uuid/58a8802721c94c66ae45c3baa4d814d0) and WorldClim (http://worldclim.org/version2) databases with the spatial resolution 0.5° or 30s. These climatic factors included 2 temperature-related factors, i.e., mean annual temperature (°C), and growing degree days (number of days above 5 °C), and 4 water-related factors, i.e., cloud cover (%), mean annual precipitation (mm year−1 ), wet day frequency (days) and potential evapotranspiration (mm year−1 ). For the calculation of mean annual temperature, we first used the monthly data for each year to calculate the mean annual values, which were then 12

Journal Pre-proof averaged over available years to calculate final value within each plot (Harris and Jones, 2017). For the calculation of mean annual precipitation and wet day frequency, we first used the monthly data for each year to calculate the total values for each factor, which were then averaged over available years to calculate a final value for each factor within each plot (Harris and Jones, 2017). Potential evapotranspiration data were extracted from the Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2 (https://figshare.com/articles/Global_Aridity_Index_and_Potential_Evapotranspiration_ET0_

was extracted from Climate Research Unit Database

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Climate_Database_v2/7504448/3) (Trabucco and Zomer, 2019). Growing degree days data

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(http://www.cru.uea.ac.uk/~markn/cru05/cru05_intro.html). Edaphic factors (0-100 cm

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depth), included soil cation exchange capacity (cmol kg-1 ), pH, soil bulk density (g cm-3 ), and

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soil textural properties (clay and sand, in %), were extracted from the SoilGrids database (https://soilgrids.org).

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In addition, differences in the regional-scale precipitation, temperature, elevation, and

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soil conditions have shaped three main types of the natural forest in the study area. However,

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it may be true that climatic and edaphic factors, extracted from global databases, may not strongly vary within each forest type due to small spatial scale but may moderately vary at regional-scale when using data from large number of plots across several sites and forest types (e.g. semi-arid, semi-humid and moist temperate in our study). Hence, climatic and edaphic datasets, extracted from global bases, are increasingly used in ecological modelling, probably due to good variation in the data at regional-scale (Figueiredo et al., 2018; Peterson and Nakazawa, 2008). As such, we also found a good variation in climatic and edaphic factors certainly due to using the regional-scale data across several sites and three main forest types. For example, across the studied plots, the mean annual precipitation ranged between 263 and 1040 (at average of 625.67 mm year−1 ), mean annual temperature ranged between 13

Journal Pre-proof 7.95 and 17.07 (at average of 11.68 °C), and as such, soil cation exchange capacity ranged between 16 and 36 (at average of 25.70 cmol kg-1 ). A summary of the climatic and edaphic factors used in the analysis is provided in Table S1 (Appendix A).

2.3. Response variables: forest structure, diversity, and aboveground biomass In this study, we used 14 response variables, and amongst them, 12 variables represented forest structure in terms of top 1% big, top 25% big (i.e. large-diameter, tall-stature, and big-

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crown), medium (i.e. medium-diameter, medium-stature, and medium-crown), and small (i.e. small-diameter, short-stature, and small-crown) trees. Within each plot, we used the 99th , 75th,

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50th , and 25th percentile scores for trees' height (in m), diameter (in cm), and crown diameter

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(in m) to quantify top 1%, top 25%, medium, and small trees, respectively (Ali et al., 2019a;

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Lutz et al., 2018). Here, we defined top 1% large-diameter, tall-stature and big-crown trees are those trees which secured highest percentile scores, in terms of tree DBH, height and

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crown diameter, compared to other trees in the forest plot, and hence covering the canopy of

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the forests in most cases. As such, top 25% large-diameter, tall-stature and big-crown trees

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refer to co-dominant trees, whereas medium and small trees refer to understorey trees, i.e., growing under the canopy trees.

We used top 1% (i.e. 99th percentile) approach to quantify big trees because many tree species and individuals cannot attain the maximum DBH threshold (e.g. DBH ≥ 60 cm) in most of the forest stands, particularly in secondary forests (Ali et al., 2019a; Lutz et al., 2018). However, based on the subset of data (i.e. DBH ≥ 60 cm), we showed that top 1% big trees were positively related with the density of large-diameter trees, and hence both approaches were equally important for stand-level aboveground biomass (see Figure S2 in Appendix A). In addition, some constraints existed to define top 25% big, medium and small trees based on the threshold size, and hence, we decided to use the percentile score for a 14

Journal Pre-proof consistent approach across tree size classes. Using the percentile score allowed us to use extended data from many plots and sites in order to cover large-scale climatic and edaphic gradients. The recorded woody species within each plot was used to represent the species richness per plot, which was calculated using the vegan package (Oksanen et al., 2015). We estimated aboveground biomass (AGB) for individual trees using the Iranian multispecies allometric equation based on tree DBH, height (H) and species' wood density (ρ); AGB =

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1.04 × exp(−4.67 + 𝑙𝑛(1.13 × DBH2 × H × ρ)) (Vahedi, 2016). Species' wood density

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values were obtained from the global database (Zanne et al., 2009), and in case of missing species, we used genus, family, or plot level average values as suggested by previous studies

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(Ali et al., 2019a; Jucker et al., 2018). Within each plot, the aboveground biomass of all

2.4. Statistical analyses

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individuals was summed and converted to Mg ha-1 .

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To answer the main research question, how top 1% big (i.e. large-diameter, tall-stature, and

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big-crown), top 25% big (i.e. large-diameter, tall-stature, and big-crown), medium (i.e. medium-diameter, medium-stature, and medium-crown) and small trees (i.e. small-diameter, short-stature, and small-crown), species richness, and aboveground biomass of species-poor and structurally-complex forests respond to multiple temperature-related and water-related climatic factors as well as to soil nutrients and textural properties, we developed multiple linear mixed-effect models (LMM) for each response variable (i.e. 14 models in total) (Prado-Junior et al., 2016; Sanaei et al., 2018; Zirbel et al., 2019). More specifically, 6 climatic (i.e. mean annual temperature, mean annual precipitation, cloud cover, potential evapotranspiration, wet day frequency, and growing degree days) and 5 edaphic (pH, sand, clay, bulk density, and cation exchange capacity) factors were included as fixed effects, 15

Journal Pre-proof whereas sites and forest types were considered as the random effects in each model to account for the nestedness of the plots within each site and forest type. Hence, we tested three types of LMM models, for each response variable, based on the random effects, i.e., (1|Sites), (1|Sites/Forests) and (1|Forests). We then selected the optimal model type, i.e. included the random effect of sites only (1|Sites) based on the lowest AIC. Although the LMM model having both sites and forest types (1|Sites/Forests) as the random effect had the same AIC with LMM model having only sites as the random effect (1|Sites), the model did not converge

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in some cases and we got the similar results from both models. We, therefore, selected the LMM model having sites only as the random effect, because sites and forest types were

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almost the best proxies for each other but sites rather than forest types covered a wide range

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of reasonable grouping of unbalanced plots within each forest type. Hence, we showed that

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the LMM model having forest types only as the random effect had highest AIC than model having sites only as a random effect (Table S8).

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For the selection of the main 11 predictors in the full model, we first conducted

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several tests to avoid all those highly correlated predictors having VIF > 5. By doing so, 11

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retained predictors across 14 models had a variance inflation factor (VIF) < 4, demonstrating that multicollinearity amongst predictors did not strongly influence the results from multiple linear mixed-effect models (see Table S2). We then evaluated the subsets of models for each response variable, and selected the best model having lowest AICc. However, we used a 2 ∆AICc cutoff to evaluate whether the selected model had a good fit to the data (Zirbel et al., 2019). If more than one models had ∆AICc less than 2 units, we then selected the most parsimonious model (i.e. having few predictors) (see model selection approach in Tables S3S7). For each response variable, we calculated the R2 conditional (R2 c) and marginal (R2 m) where R2 c indicated the variance explained by both fixed and random effects, whereas R2 m indicated the variance explained by fixed effects only. Specifically, if R2 m value was close to 16

Journal Pre-proof R2 c, then most of the variation explained in the response variable was due to the fixed effects (i.e. predictors) compared to the random effect (i.e. sites variations) (Nakagawa and Schielzeth, 2013). Multiple linear mixed-effect models were constructed and tested using the lme4 package (Bates et al., 2019) and all subsets regression analyses were conducted using the MuMIn package (Barton´, 2019) in R. 3.6.0 (R Development Core Team, 2019). In addition, we evaluated whether or not the relationships amongst the best predictors and corresponding response variable were nonlinear. For this purpose, we developed

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generalized additive mixed-effect models (GAMM) with a smooth term to evaluate nonlinear relationships, using mgcv package (Wood, 2019). The AICc values for LMM and GAMM

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models showed that LMM was the best model for showing the responses of top 1% big, top

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25% big, medium and small trees, and aboveground biomass to multiple climatic and edaphic

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factors, whereas GAMM was the best model for species richness (Table S8). However, we also found that both LMM and GAMM models had suggested almost similar conclusion but

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LMM model was the best to show the standardized impacts of predictors on response

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variables whereas GAMM model was the best to show the clear trends of predictors with

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response variables. We, therefore, considered both LMM and GAMM models to be equally important for demonstrating the responses of forest diversity, structure and biomass to multiple climatic and edaphic factors. All predictor and response variables were natural-log transformed and standardized in order to meet the assumptions of linearity and normality, and to compare the effects of multiple variables in models (Ali et al., 2019a; Zuur et al., 2009). Pearson’s correlation coefficients amongst tested predictor variables in multiple linear mixed-effect models are provided in Figure S3. In addition, in order to support the results from LMM and GAMM models, we conducted bivariate relationships amongst best predictors and corresponding response variable using pooled data, and across site and forest types. We also showed that 17

Journal Pre-proof plot size (ranged between 0.02 – 1 ha) had weak or non-significant correlations with both response and predictor variables (Fig. S3), and hence we considered that plot size may not strongly affect the results from LMM and GAMM models.

3. RESULTS The best LMM models showed that top 1% large-diameter trees increased with mean annual

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temperature (β = 0.23, P = 0.013) but declined with clay content in the soils (β = –0.15, P =

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0.032) (Figure 1a). Top 1% tall-stature trees decreased with cloud cover (β = –0.41, P =

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0.030) (Figure 1b) whereas big-crown trees increased (but marginally) with mean annual temperature (β = 0.16, P = 0.089) (Figure 1c) (also see Table S9 in Appendix A). Top 25%

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large-diameter trees increased with mean annual temperature (β = 0.21, P = 0.010) (Figure

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1d), whereas top 25% large-stature trees decreased with potential evapotranspiration (β = – 0.30, P = 0.016), cloud cover (β = –0.33, P = 0.027) and wet day frequency (β = –0.22, P =

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0.019) (Figure 1e). As such, big-crown trees decreased with potential evapotranspiration (β =

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–0.41, P = 0.008) and sand content (β = –0.08, P = 0.061) (Figure 1f) (also see Table S10 in

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Appendix A). The GAMM models showed that top 1% large-diameter trees had a slight nonlinear trend with clay content (Figure 2b) whereas top 1% big-crown trees had a slight nonlinear tend with mean annual temperature (Figure 2d). As such, top 2% large-stature trees showed a slight non-linear trend with wet day frequency (Figure 2h) whereas top 25% bigcrown showed a slight non-linear trend with potential evapotranspiration (Figure 2j). The best LMM models showed that medium-diameter (β = 0.21, P = 0.009) and medium-crown (β = 0.18, P = 0.025) trees increased with mean annual temperature but declined with cloud cover (β = –0.34, P = 0.035; β = –0.41, P = 0.014, respectively) (Figure 3a and 3c). Medium-stature trees decreased with potential evapotranspiration (β = –0.34, P = 18

Journal Pre-proof 0.006), cloud cover (β = –0.34, P = 0.013) and wet days frequency (β = –0.29, P = 0.002) (Figure 3b) (also see Table S11 in Appendix A). Small-diameter trees decreased with wet days frequency (β = –0.22, P = 0.089) (Figure 3d), whereas short-stature trees decreased with potential evapotranspiration (β = –0.30, P = 0.012), cloud cover (β = –0.41, P = 0.003) and wet days frequency (β = –0.25, P = 0.004) (Figure 3e). Small-crown trees enhanced with mean annual temperature (β = 0.21, P = 0.009) but decreased with cloud cover (β = –0.26, P = 0.038), sand content (β = –0.17, P = 0.004) and pH (β = –0.23, P = 0.012) (Figure 2f) (also

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see Table S12 in Appendix A). The GAMM models showed that most of the best predictors had slight to moderate non-linear trends with medium and small trees (Figure 4).

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The best LMM model showed that species richness increased with mean annual

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precipitation (β = 0.55, P < 0.001) and cloud cover (β = 0.75, P < 0.001) but declined with

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mean annual temperature (β = –0.42, P < 0.001), potential evapotranspiration (β = –0.54, P = 0.003), soil fertility (β = –0.31, P = 0.004) and sand content (β = –0.16, P = 0.023) (Figure

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5a). This result indicated that high species richness was positively controlled by water-related

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but negatively controlled by temperature-related factors on nutrient-poor soils (see Table S13

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in Appendix A). Aboveground biomass increased with mean annual temperature (β = 0.25, P = 0.012) but decreased with potential evapotranspiration (β = –0.53, P = 0.001), clay (β = – 0.31, P = 0.009) and sand (β = –0.18, P = 0.025) contents, indicated almost similar patterns as observed for top 1% and 25% big trees (Figure 5b) (also see Table S13 in Appendix A). The GAMM model showed a slight non-linear but decreasing trend of species richness with potential evapotranspiration (Figure 6a) whereas increasing non-linear trend with cloud cover (Figure 6d). As such, aboveground biomass showed slight non-linear trends with potential evapotranspiration and mean annual temperature (Figure 6a and 6h). These results from best LMM and GAMM models showed that top 1% big, top 25% big, medium, and small trees enhanced with mean annual temperature but declined with 19

Journal Pre-proof water-related climatic (i.e. mean annual precipitation, cloud cover, potential evapotranspiration, and wet day frequency) factors, whereas soil texture (i.e. sand content) and pH were of additional importance. Note (for top 1% tall-stature trees, top 25% tall-stature trees, 25% large-crown trees, and medium-stature trees), although mean annual temperature was not retained as the best predictor in the optimal LMM models, it was the best alternative predictor in the other best subset of models (i.e. ΔAICc less than 2 units). The same observations were noted for mean annual precipitation and edaphic factors as noted for mean

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annual temperature, but we reported the main results from the most parsimonious LMM models (see Tables S3-S6 in Appendix A). In sum, best LMM and GAMM models showed

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that mean annual temperature seemed to be the key driver underlying the increase in top 1%

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big, 25% big, medium and small trees, and aboveground biomass whereas precipitation did so

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for species richness. These results indicated that temperature, precipitation and cloud cover shaped forest diversity and stand structure (including biomass) through opposing mechanisms.

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Interestingly, potential evapotranspiration, soil texture (i.e. sand and clay) and soil nutrients

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restricted forest diversity and stand structure (including biomass) through a similar

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mechanism (Figures 1, 2, 3, 4, 5, and 6). However, the impacts of climate and edaphic factors on forest structure, diversity and biomass were greatly dependent on the sites variations as indicated by best LMM models (i.e. R2 c – R2 m; Figures 1, 3 and 5), and bivariate relationships (Figures S4 and S5).

4. DISCUSSION We assessed the relative effects of temperature-related and water-related climatic factors as well as edaphic factors on top 1% big, top 25% big, medium and small trees, species richness, and aboveground biomass in species-poor forests. Overall, our results (from optimal models and following best models having AICc less than 2 units) showed that top 1% big, top 25% 20

Journal Pre-proof big, medium and small trees as well as aboveground biomass increased along temperature gradient but decreased with water-related climatic factors, whereas species richness showed an opposite trend. In addition, soil textural properties and soil nutrients were of additional importance, but likewise restricted top 1% big, top 25% big, medium and small trees as well as species richness and aboveground biomass. This study provides support to the general notion that available energy controls stand structure and function whereas climatic water controls species richness in natural forests, if the disturbance in the forests remains constant.

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The results, in this study, enhance our understanding regarding the main limiting abiotic factor of forest diversity, structure and functioning. Our results also showed that the random

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effect of site on top 1% big, top 25% big, medium and small trees, species richness, and

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abiotic and biotic factors in natural forests.

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aboveground biomass is crucial to understand the context-dependent relationships amongst

Mean annual temperature, mean annual precipitation, cloud cover, potential

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evapotranspiration, wet day frequency, soil pH, clay content, sand content and soil fertility

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either alone or in joint, accounted for most of the variation in top 1% big, top 25% big,

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medium and small trees, species richness, and aboveground biomass across moist temperate, semi-humid and semi-arid forests. More specifically, we found that top 1% big, top 25% big, medium and small trees increased with increasing temperature but the strength and magnitude of the relationship increased with increasing tree size, indicating that available energy strongly promotes a few big trees in species-poor and structurally-complex forests. In addition, we also found that top 1% big, top 25% big, medium and small trees, particularly tree height-related attributes, decreased with increasing water-related climatic factors such as cloud cover, potential evapotranspiration and wet day frequency. It is generally well understood that favorable warming temperatures generally increase the length of the growing season (Bohn et al., 2018), and as such, we also found that mean annual temperature was 21

Journal Pre-proof positively correlated with mean annual precipitation and cloud cover. If other resources, such as climatic water are not limiting, then increasing temperature will lead to longer vegetation period (Luo, 2007; Poorter et al., 2017), and hence, increase in forest structure and aboveground biomass (Ali et al., 2019b). However, temperature alters photosynthesis, respiration and growth rates of trees (Heskel et al., 2016; Piao et al., 2010), and hence, the magnitude and strength for the effect of temperature might be dependent on tree sizes (Ali et al., 2019b; Michaletz et al., 2018). In species-poor forests, both positive and negative effects

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of temperature on aboveground biomass productivity (Bohn et al., 2018; Jump et al., 2006; McMahon et al., 2010; Pan et al., 2013) have been reported. Our results suggest that

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temperature sensitivity of aboveground biomass in species-poor forests is influenced by tree

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influenced by tree height-related attributes.

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diameter-related and crown-related attributes, but water-related sensitivity of biomass is

In contrast, we found that species richness increased with increasing mean annual

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precipitation and cloud cover but decreased with mean annual temperature and potential

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evapotranspiration, indicating that climatic water availability rather than available energy is

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the main determining abiotic factor for species distribution in species-poor and structurallycomplex forests. This result might be happened due to the fact that forest structure modifies species richness or vice versa (Ali et al., 2019a; Yachi and Loreau, 2007), but net primary productivity controls forest structural attributes, and hence, both temperature-related and water-related climatic factors control productivity or aboveground biomass (Brown, 2014; Currie et al., 2004; O'Brien, 2006; Storch et al., 2018). Moreover, if the forest structural attributes promote aboveground biomass, then species richness and metabolic kinetics should be influenced by climatic factors because both temperature-related and water-related climatic factors control large-scale variation in primary productivity (Currie et al., 2004; Hawkins et al., 2003; Michaletz et al., 2018; O'Brien, 2006). For example, the positive effect of mean 22

Journal Pre-proof annual precipitation whereas the negative effect of potential evapotranspiration on species richness indicating that the lack of climatic water availability is clearly responsible (Ali et al., 2018; Evans et al., 2005; Li et al., 2013). Our results suggest that the inadequate quantity of climatic water leads to the poor utilization of the available energy by all plant species, and hence, providing support to the water-energy dynamics theory (Ali et al., 2019b; Evans et al., 2005; O'Brien, 2006). Hence, the negative effect of temperature on species richness is possibly due to the interactions between resource availability and physiological capabilities

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(e.g. variation in metabolic rates) of the species (Michaletz et al., 2018; O'Brien et al., 2000). Forest canopies promote an effective exchange of heat and moisture between the earth

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surface and the atmosphere, and hence increasing the net available energy through

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moderating the surface temperature (Teuling et al., 2017). The resulting extra energy is

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needed for evapotranspiration, and thus, higher evapotranspiration generally promotes cloud formation and then precipitation (Teuling et al., 2017; van Heerwaarden and Teuling, 2014).

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In this study, the negative impact of cloud cover on forest structure (particularly tree height-

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related attributes) might be due to the cooler temperatures because less of the sun's energy is

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able to reach the forest canopy surface if skies are cloudy which in turn causes the forests to heat up more slowly. Through this understanding, we further argue that higher potential evapotranspiration decreased forest structure (particularly tall-stature trees) and biomass probably due to the fact that evapotranspiration was positively related to cloud cover but negatively related to temperature in the studied forests (see Figure S3). Hence, higher cloud cover can locally offset the warming effect due to the lower proportion of solar radiation reflected by a surface (i.e. albedo) which can further lead to both energy and water use efficiency in forests (Corlett, 2016; Freedman et al., 2001; Teuling et al., 2017). For example, in our studied forests, the energy use efficiency might promote forest structure and biomass, whereas the water use efficiency might promote species richness. Here, we anticipate that our 23

Journal Pre-proof study will encourage further studies on integrative modeling for exploring the multivariate relationships amongst climatic factors, species richness, forest structural attributes (i.e. top 1% big, top 25% big, medium and small trees) and aboveground biomass along ecogeographical gradients in species-poor and structurally-complex forests. Soil textural properties and fertility were of additional importance for top 1% big, top 25% big, medium and small trees, species richness, and aboveground biomass in studied species-poor forests. It is generally well known that vegetation diversity, structure and

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productivity characterized by sandy soils in an arid region is generally low because of irregular precipitation pattern and soil textural properties which result in poor nutrient

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availability and unstable structure (Li et al., 2013; Sanaei et al., 2018). Soil textural properties

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of moist temperate, semi-humid and semi-arid regions have diverse physical and chemical

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constraints, e.g., poor soil structural stability, poor nutrient holding capacity and low cation exchange capacity (i.e. soil fertility), and hence, soil organic matter is the main driver of

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fertility, nutrient storage, aggregate stability, and microbial activities (Lal, 2005). In this case,

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it is understandable that high diversity, stand structure and biomass were located on nutrient-

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poor and coarse-textured soils (i.e. having equivalent low sandy and clayey contents; < 30% at average) in the studied forests. However, we found that effects of soil nutrients and texture were generally lower than the effects of temperature-related and water-related climatic factors on top 1% big, top 25% big, medium and small trees, species richness, and aboveground biomass. This result supports the general notion that climate rather than soils greatly determined forest diversity, structure and biomass in large-scale species-rich forests (Ali et al., 2018; Poorter et al., 2017), and hence could be extended to species-poor forests.

5. CONCLUSIONS 24

Journal Pre-proof This study shows that forest diversity, structure and aboveground biomass are regulated by temperature-related and water-related climatic factors as well as by edaphic factors. Tree diameter-related attributes (e.g., top 1% and top 25% large-diameter trees) and biomass increase with temperature-related climatic factors but tree height-related attributes (e.g. top 1% and top 25% tall-stature trees) decreased with water-related climatic factors, indicating that available energy promotes forest structural attributes in species-poor forests. In contrast, species richness increases with water-related climatic factors but decreases with temperature-

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related factors and potential evapotranspiration, indicating that available climatic water increases species diversity in structurally-complex forests. In broad understanding, higher

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cloud cover can locally offset the warming effect due to the lower proportion of solar

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radiation reflected by a surface which can further lead to energy use efficiency for enhancing

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forest structure and biomass but water use efficiency for promoting species diversity in forests. In addition, we argue that climatic factors rather than edaphic factors greatly

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determine forest diversity, structure and biomass in large-scale species-poor and structurally-

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complex forests. This study suggests that potential water-related and temperature-related

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factors, as well as edaphic factors, should be evaluated for each of forest structure (i.e. top 1% big, top 25% big, medium and small trees), diversity and biomass in order to better understand the responses of species-poor and structurally-complex forests to climate change.

Data availability statement Variables used in the analyses are provided in Appendix B; whereas R codes are provided in Appendix C for reproducing the figures or main results.

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Figures captions FIGURE 1. Standardized regression coefficients (β) obtained from multiple linear mixedeffect models (LMM) for each of top 1% big and top 25% big trees. a) top 1% largediameter, b) top 1% tall-stature, c) top 1% big-crown, d) top 25% large-diameter, e) top25% tall-stature, and f) top 25% big-crown trees. The marginal and conditional coefficient of determinations (R2 m and R2 c) of the model are also given. Error bars are 95% confidence intervals. A summary of the models is presented in Tables S9 and S10 (Appendix A).

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Abbreviations: MAT, Mean annual temperature; GDD, Growing degree days; MAP, Mean annual precipitation; PET, Potential evapotranspiration; CC, Cloud cover; WDF, Wet day

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frequency; CEC, Cation exchange capacity; BD, Bulk Density; pH; Clay, clay content; Sand,

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sand content.

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FIGURE 2. Estimated responses of top 1% big and top 25% big trees to best predictors, obtained from the generalized additive mixed-effect model (GAMM). a-b) top 1% large-

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diameter, c) top 1% tall-stature, d) top 1% big-crown, e) top 25% large-diameter, f-h) top25%

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tall-stature, and i-j) top 25% big-crown trees. Gray shaded area represents 95% confidence

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intervals. All abbreviations for variables are explained in Figure 1. FIGURE 3. Standardized regression coefficients (β) obtained from multiple linear mixedeffect models (LMM) for each of medium and small trees. a) medium-diameter, b) mediumstature, c) medium-crown, d) small-diameter, e) small-stature, and f) small-crown trees. The marginal and conditional coefficient of determinations (R2 m and R2 c) of the model are also given. Error bars are 95% confidence intervals. A summary of the models is presented in Tables S11 and S12. All abbreviations for variables are explained in Figure 1. FIGURE 4. Estimated responses of medium and small trees to best predictors, obtained from the generalized additive mixed-effect model (GAMM). a-b) medium-diameter, c-e) mediumstature, f-g) medium-crown, h) small-diameter, i-k) small-stature, and l-o) small-crown trees. 34

Journal Pre-proof Gray shaded area represents 95% confidence intervals. All abbreviations for variables are explained in Figure 1. FIGURE 5. Standardized regression coefficients (β) obtained from multiple linear mixedeffect models for each of (a) species richness and (b) aboveground biomass. The marginal and conditional coefficient of determinations (R2 m and R2 c) of the model are also given. Error bars are 95% confidence intervals. A summary of the models is presented in Table S13. FIGURE 6. Estimated responses of (a-f) species richness and (g-j) aboveground biomass to

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best predictors, obtained from the generalized additive mixed-effect model (GAMM). Gray shaded area represents 95% confidence intervals. All abbreviations for variables are

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“Impacts of climatic and edaphic factors on the diversity, structure and biomass of species-poor and structurally-complex forests”

Highlights: Temperature promoted large-diameter trees as well as biomass

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Precipitation increased but temperature decreased species richness

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Water-related climatic factors decreased forest structure and biomass

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Soil nutrients and texture were of additional importance

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Forest structure, diversity and biomass were dependent on sites variation

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