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Long-term empirical monitoring indicates the tolerance of the giant panda habitat to climate change under contemporary conservation policies
Ecological Indicators 110 (2020) 105886
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Long-term empirical monitoring indicates the tolerance of the giant panda habitat to climate change under contemporary conservation policies
T
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Ting Lia,b, Peng Luoa,b, , Chuan Luoa,b, Hao Yanga, Yuejiao Lia, Dandan Zuoa,b, Qinli Xionga, Li Moc, Chengxiang Mua,b, Xiaodong Gud, Shiqiang Zhoue, Jinyan Huange, Honglin Lia,b, Sujuan Wua,b, Weiqing Caof, Yubo Zhangg, Mengjun Wangh, Jiali Lii, Yin Liuj, Peijun Gouj, Zhongfu Zhuk, Dayong Wangl, Yin Liangm, Song Baim, Yi Zoun a
CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China University of Chinese Academy of Sciences, Beijing 100049, China c Sichuan Key Laboratory of Conservation Biology on Endangered Wildlife, Chengdu Research Base of Giant Panda Breeding, Chengdu 610041, China d The Wildlife Protection Division of the Forestry Department of Sichuan Province, Chengdu 610041, China e Key Lab. Of State Forestry and Grassland Administration on Conservation Biology of Rare Animals in the Giant Pandas National Park, China Conservation and Research Center for the Giant Panda, Dujiangyan 611830, China f The State Forestry Farm, Luding County, Sichuan 626102, China g College of Forestry, Beijing Forestry University, Beijing 100083, China h Kunming Survey & Design Institute of State Forestry Administration, Kunming 650216, China i Shikefeng Chemical Industry Co. Ltd., Shangdong 276000, China j Xiaojin County Forestry Bureau, Sichuan 624200, China k Administration of Jiuzhaigou National Nature Reserve Sichuan, 623400, China l Sichuan Yele National Nature Reserve, Sichuan 615699, China m Sichuan Meigu Dafengding National Nature Reserve Administration, Sichuan 616450, China n Department of Health and Environmental Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Adaptive capacity and strategies Bamboo Biotic interaction Plant community Plant functional groups Plant functional traits
Climate change has been predicted as a major threat to giant panda habitat. While modelling prediction of the impact of climate change on habitat quality may overlook or underestimate biological interactions and adaptations, long-term monitoring is therefore essential approach to see the real situation. We analyzed the changes in plant composition and structure of 107 long-term monitoring plots in the giant panda habitat over four decades, and found that 1) the climate has become warmer and drier in the overall giant panda habitat; 2) plant species richness, different functional groups and dominant trees species abundance have kept relatively stable without human interference, and plant community canopy has not changed significantly; 3) the abundance of the giant panda’s main food, bamboo, has increased; 4) specific leaf area had a significant relationship with dominant plant species abundance over time, which implies that plant functional traits would be potential indicators of assessing the impacts of climate change on habitat quality. Our study suggests that threats of climate change to giant panda habitat might be mitigated by contemporary conservation, highlighting the importance long-term protection of the natural processes and the control of human disturbances in the conservation of giant panda and other endangered animal species.
1. Introduction Habitat degradation driven by climate change has been identified as a major reason for the decline or extinction of many endangered species (Butchart et al., 2010; Urban, 2015). The giant panda (Ailuropoda melanoleuca) is a flagship species in nature conservation, for the effect ⁎
of climate change on giant panda and its habitat, different researches have different opinions. For example, many studies based on climate models and ecological niche modelling have shown that climate change would cause significant degradation to various habitats, as characterized by decreases in the habitat quality and/or habitat loss (Li et al., 2015a; Shen et al., 2015; Tuanmu et al., 2013; Yan et al., 2017).
Corresponding author. E-mail address: [email protected] (P. Luo).
https://doi.org/10.1016/j.ecolind.2019.105886 Received 4 May 2019; Received in revised form 30 September 2019; Accepted 29 October 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.
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Fig. 1. Distribution of the long-term monitoring plots used in this study (habitat boundaries are provided by the Sichuan Province Department of Forestry, which showed the habitat area in third giant panda national survey scope).
over the past four decades in protected areas, allowing us to evaluate the habitat quality change of the giant panda under climate change. Plants, as primary producers, provide food and canopy environment for animals, and play an important role in maintaining habitat stability. The diversity and structure of the plant communities in a habitat are important indicators of the habitat’s quality (Bascompte and Jordano, 2007). For example, the target species in a habitat with low plant diversity are less adaptive to or more vulnerable to climate change (Matthias et al., 2016). Temperate coniferous and broad-leaved mixed forests are considered suitable habitats for the giant panda (State Forestry Administration, 2006); the key vegetation structural parameters of these forests, such as plant species diversity, bamboo (panda’s main food source) abundance, and the sensitivity and stability of the plant communities under climate change can be used as indicators for evaluating the habitat quality of the giant panda. Plant functional traits (PFTs) and plant functional groups (PFGs) are
However, four national investigations found that both wild giant panda populations and the vegetation coverage in their habitats have steadily increased (State Forestry Administration, 2006, Forestry Department of Sichuan Province, 2015: WWF, 1989). A recent study by Wang et al. (2018) reported that climate-only modelling approaches might have overestimated the impact of climate change, while giant panda had a potential tolerance to climate change when biotic interactions were taken into consideration. In order to provide a clear guidance for policy makers, it is therefore necessary to carry out further studies about the impact of climate change on giant panda and its habitat. Recently, Kang et al. (2019) proposed that in-depth studies are required to analyze the relationship between different components of the habitat (for example, plant-plant and plant-animal interactions) that may contribute to habitat conservation and restoration. Long-term monitoring studies is particular essential. In the meantime, Chinese governments have established hundreds of long-term monitoring plots 2
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national nature reserves in China – the Wolong National Nature Reserve (102°52′ –103°24′ E, 30°45′ –31°25′ N). This nature reserve covers an area of approximately 200,000 ha and has more than 10% of the entire wild panda population (State Forestry Administration, 2006; Forestry Department of Sichuan Province, 2015; WWF and State Forestry Administration, 1989). The initial survey was carried out by the Chengdu Institute of Biology, Chinese Academy of Sciences (CAS) and the Institute of Botany, CAS (1974–1976). Follow-up surveys were conducted by Sichuan Agricultural University (2009), Beijing Forestry University (2008, 2011), China Conservation and Research Center for the Giant Panda (1985, 1996, 2001, 2006, 2012), Sichuan Forestry Investigation and Design Institute (1979–2012), and the Chengdu Institute of Biology at CAS (2017). The meteorological data consisted of in-situ monitoring data of each nature reserve and was provided by the Maoxian Ecological Experiment Station of the Chengdu Institute of Biology at CAS, the Sichuan Meteorological Bureau, various Nature Reserves, and various County Meteorological Bureaus. The vegetation surveys were all conducted between July and September (the peak period of plant growth). All the plots were located at least 150 m from a road to avoid edge effects. In each plot, trees were investigated from a 20 m × 30 m plot and shrubs from three 5 m × 5 m sub-plots and herbs from three 1 m × 1 m sub-plots. The data from the subplots were then pooled. All plant species, the number of individuals (abundance), and the cover of each layer (tree, shrub, and herb) were recorded. Plant species identification and classification are based on Flora of China (www.eFloras.org). We distinguished woody plant as shrubs and trees, with trees are ≥10 cm in diameter at breast height (Crowther et al., 2015). On average, each study plot was measured 4 (SD = 2.16) times at an interval of 15.87 (SD = 1.02) years (Table S1) over 40 years in the Sichuan giant panda habitat. The study plots in the Wolong National Nature Reserve were monitored more frequently, i.e., monitoring occurred 7 times with a 7-year interval.
key elements to understand the ecological adaptation strategies of plants in response to abiotic environmental (climate) changes (Green et al., 2008; Violle et al., 2014). At the population level, changes in PFTs provide an understanding of the sensitivity and stability of a plant population to climate change (Green et al., 2008), whereas at the plant community level, changes in PFGs can explain or predict the responses of the plant community composition to climate change (Violle et al., 2014). Previous studies have reported that climate change would significantly alter the vegetation distribution and cover of the giant panda habitat (Tuanmu et al., 2013); however, the details of this process are largely unknown. An investigation of the impact of climate change on PFTs and PFGs will fill this knowledge gap, allowing us to understand and predict habitat changes. This type of knowledge is also crucial for developing adaptive management strategies of the giant panda habitat. The goal of this study is to quantify the changes in the giant panda habitat under climate change and determine how these changes are related to the PFTs using long-term vegetation monitoring data. In particular, we would like to answer the following questions: i) What are characteristics and trends of climate change in the giant panda habitat over past four decades in local monitoring? ii) what are the changes in plant diversity, community structure, and bamboo abundance that occurred under climate change? iii) How does the change of plant abundance response climate change correlated to the PFTs? The answers to these questions are vital to predict habitat change under realistic future climate scenarios. This study provides a reference for management agencies to determine whether or how to adopt suitability management measures for climate change. 2. Methods 2.1. Study area and vegetation survey This study was conducted in the Sichuan giant panda habitat, which is located in an alpine valley in the transition region from the QinghaiTibet plateau to the Sichuan basin; the area is part of the subtropical evergreen broadleaf forest region and warm temperate deciduous broadleaf forest region (Sichuan Vegetation Cooperation Group, 1980). Historical data from the Sichuan Forest Ecology Survey were obtained, including Sichuan province vegetation survey data (1974–1976), Sichuan forestry survey data (1979–2012), and vegetation data collected by research institutions and universities in the giant panda habitat. We chose forest plots in areas of no human disturbances and logging in the initial year; these plots were continuously protected by developing new reserves and logging bans were in effect with regular patrolling in the later decades. In mature forest areas, we screened the plots (107) with a consistent survey method. Mature forest would minimize the effects of vegetation succession (Robert, 1981). We used 107 sampling plots (20 m × 30 m) located in the Minshan, Qionglai, Xiaoxiangling, and Liangshan mountains (Fig. 1, Table S1). The number of sampling plots in the initial survey year (1974–1976, 2000–2001) and 2017 are the most, which in 1985 and other middle survey years are less (Table S1). These sampling plots are mainly in Wolong Nature Reserve and Wanglang Nature Reserve, where are protected from 1960s – the core area of the giant panda habitat. We acknowledge that the total habitat area varied between each of the four giant panda national surveys (due to the change of nature reserve area), the selected 107 sampling plots have always been located in the preserved giant panda habitat. The plant communities of the 107 plots were mainly classified as temperate coniferous and broad-leaved mixed forests, particularly in the areas where bamboo (Bashania fangiana, and Fargesia robusta) was observed (Fig. S1). The altitude of the plots ranged from 2000 to 3600 m. There were no effects of the two earthquakes (2008, 2013) in the sampling plots and no extreme climate events, pest outbreaks, and fires during the study period. Among the 107 sampling plots, there were 14 permanent plots that were located in one of the earliest and foremost
2.2. PFT measurements Based on the frequency and abundance of each plant species in each plot, we selected the dominant species, including Bashania fangiana (BfK), Fargesia robusta (FrY), Rhododendron calophytum. Franch (RcF), Abies faxoniana (AfR), Corylus ferox (CfW), Acer laxiflorum (AlP), Betula albosinensis (BaB), and Tsuga chinensis (Franch.) Pritz (TcP). These eight dominant species are consistent with the characteristics of the dominant species of coniferous and broad-leaved mixed forests in Sichuan vegetation (Sichuan Vegetation Cooperation Group, 1980). We measured four functional traits of the eight dominant plant species in 2017. The measured functional traits included specific leaf area (SLA, cm2g−1), leaf area (LA, cm2), leaf thickness (LT, mm), and leaf dry matter content (LDMC, g g−1). These traits reflect the substance exchange balance between plant resource acquisition and protection under environmental change (Bernard-Verdier et al., 2012). The functional trait data were obtained from five individuals for each species from 14 plots in the Wolong Nature Reserves; there were 25 replicates per individual of each species and international standards were used for the measurements (Pérez-Harguindeguy et al., 2013). 2.3. Data analysis We used ordinary linear regression to analyze the trend of the mean annual temperature (MAT) and annual precipitation over the past 40 years. The results showed that the MAT and annual precipitation were highly correlated with time (Fig. 2) since this finding explicitly answer time as climate change reliable proxies based on long-term field observational. Linear mixed effect models (LMEs) are used widely in ecological 3
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Fig. 2. Inter-annual dynamics of the mean annual temperature (a) and annual precipitation (b) during 1975–2016 based on data from the Wolong meteorological station (1925 m above sea level, Wolong Nature Reserve administration, Sichuan, China). Lines and shaded areas depict the model fit and standard error of the linear regression respectively.
based on the minimum Akaike information criterion (AIC). All analyses were conducted in R 3.4.3 (R Core Team, 2015) and we used the nlme package (Pinheiro et al., 2014) for the LMEs and the MuMIn package (Bartoń, 2015) for the dredge function. All models were validated according to the suggestions provided by Zuur et al. (2009).
research (Zuur et al., 2009) and were used in this study to investigate the long-term changes in plant diversity. The plant (trees and shrubs) richness and the tree abundance were the response variables (family = Gaussian), the sampling year was the explanatory variable, and the sampling plots (site) represented the random variable. Here, we pooled data from all study plots.
Yi = a1 ∗ year + a2 ∗ sitei + ei
(1) 3. Results
where i = 1, 2, …,64 in 1974–1976, i = 1, 2, …,14 in 1985, i = 1, 2, …,14 in 1996, i = 1, 2, …, 57 in 2000–2001, i = 1, 2, …,14 in 2006, i = 1, 2, …, 20 in 2009, i = 1, 2, …,42 in 2011–2012, i = 1, 2, …,107 in 2017; ei is the residual vector; a1, a2 are the regression coefficients. The response variable Yi represents either tree richness or abundance in the sitei plots in 1974–1976, 1985, 1996, 2000–2001, 2006, 2009, 2011–2012, and 2017 in overall habitat. Since the vegetation measurements were conducted more frequently in the Wolong National Nature Reserve than in the other study plots, the 14 permanent plots in Wolong were used as a case study to model the change in the species composition and functional groups over time. To confirm whether climate change affected the plant functional groups (PFGs), we classified the trees into evergreen broad-leaved trees, deciduous broad-leaved trees, and evergreen coniferous trees and the shrubs into evergreen and deciduous shrubs according to the phenological characteristics of the forest plants. LMEs were also used. The response variable included the plant species richness and the canopy cover of the trees, shrubs, and herbs and the abundance of the functional groups (Gaussian error distribution), with the sampling year as the explanatory variable and the sampling plot as the random variable (Zuur et al., 2009). Eq. (1) was used, where i = 1, 2, …,14 in the Wolong Nature Reserves in 1975, 1985, 1996, 2001, 2006, 2012, and 2017; ei is the residual vector; and a1, a2 are the regression coefficients. The response variable Yi represents either the richness or the cover of the trees, shrubs, and grasses and the PFGs abundance in the sitei plots. To determine the stability of the plant community under climate change, we first used the LMEs to evaluate the change in the abundance of the eight most dominant species over time independently (study site as the random factor). The correlation coefficient between the time and the dominant species abundance was denoted as the abundance-time slope (ATS). The dominant species abundance data were normalized (zero-mean normalization). All the functional trait data were log10 transformed (Pérez-Harguindeguy et al., 2013). The regression coefficients were then obtained for the following functional traits of the different plant species: SLA, LA, LT, and LDMC. We applied an ordinary linear regression using the dredge function and selected the best models
3.1. Climate change trends From 1975 to 2016, the MAT showed a warming trend from 7.73 °C to 9.52 °C with an annual warming rate of 0.043 °C (95% CI: 0.033–0.053 °C, linear regression R2 = 0.634, P < 0.001, Fig. 2a) and a cumulative increase of 1.76 °C. In contrast, the annual precipitation declined over time from 1021.25 mm to 895.53 mm with an annual decline of 2.64 mm (95% CI: 4.71–0.57 mm, R2 = 0.121, P = 0.014, Fig. 2b) and a cumulative decline of 108 mm in Wolong National Nature Reserve. Table S2 shows that the temperature in all nature reserves increased significantly and the annual warming rate was 0.034 °C. Although the annual precipitation changes exhibited fluctuations, Table S1 shows that the precipitation decreased in all habitats, except for the Jiuzhaigou Nature Reserve.
3.2. Plant community change The results of the 107 long-term monitoring plots in the giant panda habitat showed that there was no significant change in the total plant species richness (Fig. 3a), while there was a significant increase in trees abundance over past decades (95% CI: 0.0038–0.13, P = 0.038, Fig. 3b). In the Wolong Nature Reserve, trees richness increased significantly over time (95% CI: 0.044–0.13, P < 0.001), whereas shrubs richness and herbs richness exhibited no significant change over the past decades (Fig. 3c-e). The canopy cover of trees and herbs showed no significant change over time. The cover of shrubs layer increased significantly (95% CI: 0.30–0.88, P < 0.001, Fig. 3f-h). There was no significant change in the abundance of different PFGs (evergreen broad-leaved trees, deciduous broad-leaved trees, and evergreen coniferous trees) or the abundance of evergreen and deciduous shrubs over time (Fig. 3i-m). For the dominant plant species, the abundances of BfK (95% CI: 0.13–0.38, P = 0.0004, Table 1) and FrY (95% CI: 0.025–0.099, P = 0.021, Table 1) increased significantly over time, whereas no significant trend was observed for the other species (P > 005). 4
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Fig. 3. Plant diversity in the giant panda habitat (a-b) and the plant communities in the Wolong Nature Reserve (c-m) over time. The lines and shaded areas represent the estimates and standard errors of the coefficients of the linear mixed effect models respectively. The stars refer to the significance levels (***P < 0.001, **P < 0.01, *P < 0.05). 5
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4. Discussion
Table 1 Regression results showing the responses of the dominant species abundance over time. B is the regression coefficient; SE B is the SE of the regression coefficient. For the linear mixed effect models, the P-values indicate the significance of the fixed effects (***P < 0.001, **P < 0.01, *P < 0.05). Bashania fangiana (BfK), Fargesia robusta (FrY), Rhododendron calophytum. Franch (RcF), Abies faxoniana (AfR), Corylus ferox (CfW), Acer laxiflorum (AlP), Betula albosinensis (BaB), and Tsuga chinensis (Franch.) Pritz (TcP).
ATS
Species
B
SE B
T value
df
P-value
BfK FrY RcF AfR CfW AlP BaB TcP
0.083 0.12 0.11 0.034 −0.097 −0.026 0.069 −0.30
0.020 0.034 0.18 0.14 0.17 0.18 0.20 0.18
4.16 3.47 0.63 0.25 −0.57 −0.15 0.35 −1.64
23
0.0004*** 0.0021** 0.53 0.80 0.57 0.88 0.73 0.11
4.1. Climate change characteristics and trends Since 1975, the climate in the giant panda habitat has become warmer and drier in our study region. The trend is similar to Jian et al. (2014) based on IPCC climate data from 1989 to 2002, who predicted a 1–2 °C warming trend and stable precipitation from 2002 to 2050, but differ from Liu et al. (2016) who predicated an increase of 8.2 °C by 2050 based on World Climate data under the IPCC A2 greenhouse-gas emissions scenario. Different results furthermore suggest large spatial scale models cannot reliably determine the impact of altered microclimates on vegetation (Suggitt et al., 2018), suggesting the necessity of reginal-specific management policies related to climate change impact. 4.2. Changes in the plant community diversity Over the last four decades, the tree abundance increased whereas the total species richness remained unchanged in the study area, indicating relatively stable vegetation diversity in the giant panda habitat. Compared with the sample size of the survey in the initial year and 2017, the sample size in 1985–2012 was small but this does not affect the evaluation of the changes in plant diversity over the past 40 years for the entire habitat, when no caring about the plant diversity change process. Vellend et al. (2013) stated that mean temporal change in species diversity over periods of 5–261 years was not different from exhibiting no changes in local vegetation plots because increases were as likely as decreases over time, whereas sites that experienced postdisturbance succession showed increases in richness over time. Our results indicated that long-term protected mature natural forests in overall habitat were resistant to climate change if there were no human disturbance, fire, or insect outbreaks. Here, the possible reasons include the adaptation and tolerance of the plant community and subsequent strict protection (Mickael et al., 2019).
Table 2 Linear regression analysis results showing the relationship between PFTs and the response parameter of abundance of the dominant species over time (ATS). B is the regression coefficient; SE B is the SE of the regression coefficient. For the regression model, the P-values indicate the significance of the whole model and that of each parameter (**P < 0.01, *P < 0.05). Models ATS
B
SE B
df
Adjusted R2
P-value
SLA + LDMC 0.66 0.028* SLA 0.82 0.26 5 0.025* LDMC 0.97 0.26 0.13 Individual plant traits that were significant predictors of the ATS SLA 0.30 0.39 6 −0.062 0.47 LA 0.066 0.41 −0.16 0.88 LDMC 0.53 0.35 0.16 0.18 LT −0.54 0.34 0.17 0.17
3.3. Contribution of PFTs to the ATS 4.3. The stability of the plant community structure in different layers The individual PFTs did not explain the changes in the dominant species abundance as a result of climate change (Table 2). The SLA of the eight main dominant species increased over time. An analysis of the response relationship between the PFTs and ATS showed that the LDMC and SLA models explained 66% of the change in the ATS (Table 2 and Fig. 4). As shown in Fig. 4, BfK and FrY exhibited an adequate fit of the model, suggesting that the SLA + LDMC models satisfactorily reflected the response relationship between BfK/FrY abundance and time.
Long-term permanent monitoring of the Wolong Nature Reserve indicated that over the past 40 years the species richness of trees increased significantly but not for shrubs and herbs. The different response to climate change of different plant layer composition may exhibit a synergistic response, which is consistent with the results reported by Hoeppner and Dukes (2012). The presence of an upper canopy layer (shrubs/trees), for example, commonly modifies the abiotic conditions in dryland ecosystems and leads to an altered performance of the undergrowth, such as herbs (Soliveres et al., 2015). The might be a shift in the structure of plant species to mitigate the pressure on the forest resulting from climate change (Zhang et al., 2018a). Moreover, plant communities with higher biodiversity are more resistant and resilient to extensive climate change (Isbell et al., 2015). As a World Natural Heritage site, the giant panda habitat contains a high diversity of plant species (UNESCO, 2006); therefore, the biotic interactions between the species may be the reason that the plant communities are able to adapt to climate change. For example, environmental severity effects have been shown to affect increasing net facilitative interactions among species, which would mitigate the negative effect of environmental changes (Michalet et al., 2014), which may include not only climate change, but other natural influences. Here, coordinated changes in plant species diversity in different layers may alleviate pressures from climate change. Our results showed that there was a significant positive effect on shrub cover but not on tree or herb cover. The total cover of the plant community was higher than 100% over the past 40 years. Here, the total cover of the plant community did not show a decline under climate change. In addition, there was no significant change in the abundances of the different PFGs; this result is in contrast to the
Fig. 4. Relationship between the abundance-time slope (ATS) and leaf dry matter content and specific leaf area (n = 8 species) Note: Corylus ferox (CfW), Bashania fangiana (BfK), Tsuga sp. (TcP), Acer laxiflorum (AlP), Betula albosinensis (BaB), Abies faxoniana (AfR), Rhododendron sp. (RrF), Fargesia robusta (FrY). 6
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findings of Sophie et al. (2012), who determined that deciduous canopy species increased and evergreen sub-canopy and shade-tolerant species decreased under drought. Such differences may stem from the differences in the species’ tolerance and adaptability. This result indicates that although the tree species richness changed significantly, it did not significantly impact the overall cover and abundance of the different PFGs. The effects of climate change are considered the most likely explanation for the changes in the tree richness and shrub cover; however, there are also other possible reasons (e.g. nitrogen deposition, conservation). Our results suggest that the plant community composition structure remained stable over time. The results showed that the abundances of the two bamboo species increased over time, which is different from the results of previous studies that used climate-only and non-continuous time change approaches and predicted a loss between 45.6% and 86.9% in the bamboo’s cover (Tuanmu et al., 2013; Zhang et al., 2018b). The cover percentage would not alter our results regarding habitat suitability unless the cover was lower than 50%; however, pandas are consistently found in areas with bamboo cover higher than 50% regardless of the bamboo availability in the panda range (Hull et al., 2014). Therefore, results suggest that the bamboo abundance increased; and the ecosystem services of the habitat for pandas did not decline over the past four decades under contemporary conservation policies.
common (Wei et al., 2018). In addition, this study predicted that the BfK/FrY abundance would increase in the future under the continuous influence of climate change. However, it remains unclear how the functional traits affect the BfK/FrY abundance under climate change (e.g., physiological reasons) and further analyses and studies are needed.
4.5. Stability of giant panda habitat over time The indicators of the quality of the giant panda habitat include 1) the amount of continuous habitat distribution (degree of habitat fragmentation) (Shen et al., 2015), 2) the forest composition and species richness in the habitat (Kang et al., 2017; Kang, et al., 2018), 3) the distribution and abundance of bamboo (Liu et al., 2001), 4) anthropogenic interferences, and 5) the topography, slope, aspect, and density of the river system (Zhou, 2008). Habitat fragmentation is commonly caused by human disturbances such as road construction (Shen et al., 2015) and these problems will be mitigated by stronger protection practices. The topography and slope indicators are not influenced by climate change; therefore, in this study, the plant diversity of the typical plant community preferred by the giant panda and the bamboo abundance were used as indicators of the change in the quality of the giant panda habitat under climate change. The key findings obtained from our long-term monitoring study were that the habitat of the giant panda has remained relatively stable over the past four decades, indicating tolerance of the habitat to climate change under contemporary conservation policies. These results confirmed the modelling results of Wang et al. (2018), who concluded that the giant panda habitat may be tolerant to climate change due to biotic interactions (State Forestry Administration, 2006; Forestry Department of Sichuan Province, 2015; WWF and State Forestry Administration, 1989). However, our results differ from those of previous modelling predictions that climate change would result in significant degradation of the giant panda habitat (Tuanmu et al., 2013; Zhang et al., 2018b). The reasons for the differences in the results may be the survey and analysis methods and the underlying theory and hypotheses (climate and a few abiotic factors are the main determinants of the bamboo’s geographical range). For example, we used local long-term meteorological and vegetation data, and took plant-plant interactions, vegetation tolerance and resilience into consideration, and considered bamboo as panda’s main food source. Such empirical approach differs from studies based on different climate change scenarios models and generally did not include biological interactions (Tuanmu et al., 2013; Zhang et al., 2018b). Difference might also come from the lower increases trend of temperature in meteorological recoding than model predication. Our study suggested that the biotic interactions (plant-plant, plant-animal) and biotic adaptive behavior and ability should be considered when forecasting plant community responses to climate change and changes in the species distribution.
4.4. Variation of functional traits to adapt to climate change To evaluate the adaptability of the habitat of the giant panda to climate change, we considered the adaptation behavior and changes in the functional traits of the bamboo to climate change. With regard to different dominant species, the abundance of BfK and FrY changed significantly over time. There are two possible explanations for this result. First, the communities might adapt to climate change by adjusting the species compositions, for example, by reducing the abundance of species that are less adaptable to climate change and increasing the abundance of species with better adaptive capacity (Fauset et al., 2012; Kleynhans et al., 2016). Second, morphological trait compositions (e.g., leaf morphology) at the individual and population levels might change in response to resource and environmental variations in order to adapt to climate change (Bateman and Johnson, 2012). In this study, we found that the SLA (Table 2) of the eight dominant species increased, which indicated that the plants increased their SLA to increase their energy resources and gain a competitive advantage as a result of increased competition, which is supported by the findings of Li et al. (2015b). LDMC increases with drought (Ackerly, 2004). Here, the LDMC was not significant in response to ATS, which may suggest a decrease in precipitation is not a major threat for plant communities in the wet and cold giant panda habitat. Some studies suggested that a general tolerance to drought exists in species growing in this environment and that this plant community will resist future drought conditions (Giménez-Benavides et al., 2007; Losapio and Schöb, 2017). Our study area is located in the rainy zone of West China (Sichuan Vegetation Cooperation Group, 1980); thus, the drought risk caused by declines in precipitation is low. The variation in the SLA of plants obtained indicated that successful resource utilization and self-protection strategies were used by the plants in response to climate change. These strategies provided a solid foundation for the stability of the plant communities. Environmental change has a selective function for plants with specific PFTs changes, and the abundance of a plant population with such PFTs will exhibit variations in response to environmental changes (Violle, 2009). In this study, the SLA and LDMC satisfactorily reflected the response relationship between the BfK/FrY abundance and time, suggesting that changes in the BfK/FrY leaf functional traits allowed these species to adapt to climate change. Consequently, the BfK/FrY abundance increased, which might be the reason for the steady bamboo food source at a certain altitude range, at which livestock is not
5. Conclusion Here we used the long-term empirical monitoring data to study the response of plant communities in giant panda habitat to climate change. Our results revealed that the quality of habitat was relatively stable over decades, while model prediction needs to consider both biotic interaction and adaptation. Although vegetation characters does not necessarily associate with the occurrence of giant panda, proper habitat evaluation is the first step to understand the population’s future trend. Our study suggests that threats of climate change to giant panda habitat might be mitigated by contemporary conservation, highlighting the importance long-term protection of the natural processes and the control of human disturbances in the conservation of giant panda and other endangered animal species. 7
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Acknowledgments
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Evolutionary response to coexistence with close relatives: increased resistance against specialist herbivores without cost for climatic-stress resistance. Ecol. Lett Online. Pérez-Harguindeguy, N.D.S., Garnier, E., Lavorel, S., Poorter, H., Jaureguiberry, P., BretHarte, M.S., Cornwell, W.K., Craine, J.M., Gurvich, D.E., Urcelay, C., 2013. New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 61, 167–234. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., 2014. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-118. Core Team, R., 2015. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Robert, P.M., 1981. Succession and ecological theory. In: West, D.C., Shugart, H.H., Botkin, D.F. (Eds.), Forest succession: concepts and application. Springer, Berlin, Germany. Shen, G., Pimm, S.L., Feng, C., 2015. 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Extinction risk from climate change is reduced by microclimatic buffering. Nat. Clim. Change 8, 713–717. Tuanmu, M.N., Viña, A., Winkler, J.A., Li, Y., Xu, W., Ouyang, Z., Liu, J., 2013. Climatechange impacts on understorey bamboo species and giant pandas in China's Qinling Mountains. Nat. Clim. Change 3, 249–253. UNESCO, 2006. Sichuan Giant Panda Sanctuaries-Wolong, Mt Siguniang and Jiajin Mountains. Urban, M.C., 2015. Accelerating extinction risk from climate change. Science 348, 571–573. Vellend, M., Baeten, L., Myerssmith, I.H., 2013. Global meta-analysis reveals no net change in local-scale plant biodiversity over time. PNAS 110, 19456–19459. Violle, C., 2009. Plant traits related to competition: how do they shape the functional diversity of communities? Commun. Ecol. 10, 131–137. Violle, C., Reich, P.B., Pacala, S.W., Enquist, B.J., Kattge, J., 2014. The emergence and promise of functional biogeography. PNAS 111, 13690–13696. Wang, F., Zhao, Q., McShea, W.J., Songer, M., Huang, Q.Y., Zhang, X.F., Zhou, L.G., 2018. Incorporating biotic interactions reveals potential climate tolerance of giant pandas. Conserv. Lett. 11, e12592. Wei, W., Swaisgood, R., Dai, Q., Yang, Z., Yuan, S., Owen, M., Pilfod, N., Yang, X., Gu, X., Zhou, H., 2018. Giant panda distributional and habitat-use shifts in a changing landscape. Conserv. Lett. 11, e12575. WWF, State Forestry Administration, 1989. In: The 2nd National Survey Report on Giant Panda in China. Sichuan Science and Technology Publishers, Beijing, China, pp. 89–96 in chinese. Yan, T.T., Zhao, C.H., Zhong, X., Liang, M.Y., 2017. Climate-change impacts on bamboo distribution and giant panda habitat in Qionglai mountains. Acta Ecol. Sin. 37, 2360–2367. Zhang, T., Niinemets, Ü., Sheffield, J., Lichstein, J.W., 2018a. Shifts in tree functional composition amplify the response of forest biomass to climate. Nature 556, 99–102. 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This study was supported by the Demonstration of Monitoring and Protection of Important Species Habitat (2016YFC0503305) and the Management Framework and Capability Building for Development of Ya'an Giant Panda National Park (NOR/15/301/16/002). We thank the China Conservation and Research Center for the Giant Panda, the Sichuan Forestry Investigation and Design Institute, and the Forestry Department of Sichuan Province for providing the climate data and field plant data. Pu Tao provided the photo of the forest in 1986. We thank Yin Kaipu for helping with the search for historical data. We also thank Zhang Jinlong, Lai Jiangshan, and Yang Jiang from the Institute of Botany, Chinese Academy of Sciences for their valuable discussions on the statistical analyses. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2019.105886. References Ackerly, D., 2004. Functional strategies of chaparral shrubs in relation to seasonal water deficit and disturbance. Ecol. Monogr. 74, 25–44. Bartoń, K., 2015. MuMIn: multi-model inference. R package version 1 (13), 4. Bascompte, J., Jordano, P., 2007. Plant-animal mutualistic networks: the architecture of biodiversity. Annu. Rev. Ecol. Evol. Syst. 38, 567–593. Bateman, B.L., Johnson, C.N., 2012. Biotic interactions influence the projected distribution of a specialist mammal under climate change. Divers. Distrib. 18, 861–872. Bernard-Verdier, M., Navas, M.L., Vellend, M., Violle, C., Fayolle, A., Garnier, E., 2012. Community assembly along a soil depth gradient: contrasting patterns of plant trait convergence and divergence in a Mediterranean rangeland. J. Ecol. 100, 1422–1433. Butchart, S.H., Walpole, M., Collen, B., 2010. Global biodiversity: indicators of recent declines. Science 328, 1164–1168. Crowther, T.W., Glick, H.B., Covey, K.R., Bettigole, D.S., Thomas, S.M., Smith, J.R., 2015. Mapping tree density at a global scale. Nature 525, 201–205. Fauset, S., Baker, T.R., Lewis, S.L., 2012. Drought-induced shifts in the floristic and functional composition of tropical forests in Ghana. Ecol. Lett. 15, 1120–1129. Forestry Department of Sichuan Province, 2015. In: Sichuan Forestry Department Giant Pandas in Sichuan: Report of the Fourth Giant Panda Survey in Sichuan. Sichuan Science and Techology Publishers, Beijing, China, pp. 123–130 (in Chinese). Giménez-Benavides, L., Escudero, A., Iriondo, J.M., 2007. Local adaptation enhances seedling recruitment along an altitudinal gradient in a high mountain Mediterranean plant. Ann. Bot. 99, 723–734. Green, J.L., Bohannan, B.J.M., Whitaker, R.J., 2008. Microbial Biogeography: From Taxonomy to Traits. Science 320, 1039–1043. Hull, V., Roloff, G., Zhang, J., Liu, W., Zhou, S., Huang, J., Liu, J., 2014. A synthesis of giant panda habitat selection. Ursus 25 (2), 148–162. Hoeppner, S.S., Dukes, J.S., 2012. Interactive responses of old-field plant growth and composition to warming and precipitation. Glob. Change Biol. 18, 1754–1768. Isbell, F., Craven, D., Connolly, J., 2015. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574. Jian, J., Jiang, H., Jiang, Z.S., Zhou, G.M., Yu, S.Q., Peng, S.L., Wang, J.X., 2014. Predicting giant panda habitat with climate data and calculated habitat suitability index (HSI) map. Meteorol. Appl. 21, 210–217. Kang, D., Wang, X., Li, S., Li, J., 2017. Comparing the plant diversity between artificial forest and nature growth forest in a giant panda habitat. Sci. Rep. 7, 3561. Kang, D., Lv, J., Li, S., Chen, X., Wang, X., Li, J., 2018. Integrating indices to evaluate the effect of artificial restoration based on different comparisons in the Wanglang Nature Reserve. Ecol. Ind. 91, 423–428. Kang, D., Lv, J., Li, S., Chen, X., Wang, X., Li, J., 2019. Relationship between bamboo growth status and woody plants in a giant panda habitat. Ecol. Ind. 98, 840–843. Kleynhans, E.J., Otto, S.P., Reich, P.B., Vellend, M., 2016. Adaptation to elevated CO2 in different biodiversity contexts. Nat. Commun. 7, 12358. Li, R., Xu, M., Wong, M.H.G., 2015a. Climate change threatens giant panda protection in the 21st century. Biol. Conserv. 182, 93–101. Li, R., Zhu, S., Chen, H.Y., 2015b. Are functional traits a good predictor of global change impacts on tree species abundance dynamics in a subtropical forest? Ecol. Lett. 18, 1181–1189. Liu, J., Linderman, M., Ouyang, Z., An, L., Yang, J., Zhang, H., 2001. Ecological
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Long-term empirical monitoring indicates the tolerance of the giant panda habitat to climate change under contemporary conservation policies
T
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Ting Lia,b, Peng Luoa,b, , Chuan Luoa,b, Hao Yanga, Yuejiao Lia, Dandan Zuoa,b, Qinli Xionga, Li Moc, Chengxiang Mua,b, Xiaodong Gud, Shiqiang Zhoue, Jinyan Huange, Honglin Lia,b, Sujuan Wua,b, Weiqing Caof, Yubo Zhangg, Mengjun Wangh, Jiali Lii, Yin Liuj, Peijun Gouj, Zhongfu Zhuk, Dayong Wangl, Yin Liangm, Song Baim, Yi Zoun a
CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China University of Chinese Academy of Sciences, Beijing 100049, China c Sichuan Key Laboratory of Conservation Biology on Endangered Wildlife, Chengdu Research Base of Giant Panda Breeding, Chengdu 610041, China d The Wildlife Protection Division of the Forestry Department of Sichuan Province, Chengdu 610041, China e Key Lab. Of State Forestry and Grassland Administration on Conservation Biology of Rare Animals in the Giant Pandas National Park, China Conservation and Research Center for the Giant Panda, Dujiangyan 611830, China f The State Forestry Farm, Luding County, Sichuan 626102, China g College of Forestry, Beijing Forestry University, Beijing 100083, China h Kunming Survey & Design Institute of State Forestry Administration, Kunming 650216, China i Shikefeng Chemical Industry Co. Ltd., Shangdong 276000, China j Xiaojin County Forestry Bureau, Sichuan 624200, China k Administration of Jiuzhaigou National Nature Reserve Sichuan, 623400, China l Sichuan Yele National Nature Reserve, Sichuan 615699, China m Sichuan Meigu Dafengding National Nature Reserve Administration, Sichuan 616450, China n Department of Health and Environmental Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Adaptive capacity and strategies Bamboo Biotic interaction Plant community Plant functional groups Plant functional traits
Climate change has been predicted as a major threat to giant panda habitat. While modelling prediction of the impact of climate change on habitat quality may overlook or underestimate biological interactions and adaptations, long-term monitoring is therefore essential approach to see the real situation. We analyzed the changes in plant composition and structure of 107 long-term monitoring plots in the giant panda habitat over four decades, and found that 1) the climate has become warmer and drier in the overall giant panda habitat; 2) plant species richness, different functional groups and dominant trees species abundance have kept relatively stable without human interference, and plant community canopy has not changed significantly; 3) the abundance of the giant panda’s main food, bamboo, has increased; 4) specific leaf area had a significant relationship with dominant plant species abundance over time, which implies that plant functional traits would be potential indicators of assessing the impacts of climate change on habitat quality. Our study suggests that threats of climate change to giant panda habitat might be mitigated by contemporary conservation, highlighting the importance long-term protection of the natural processes and the control of human disturbances in the conservation of giant panda and other endangered animal species.
1. Introduction Habitat degradation driven by climate change has been identified as a major reason for the decline or extinction of many endangered species (Butchart et al., 2010; Urban, 2015). The giant panda (Ailuropoda melanoleuca) is a flagship species in nature conservation, for the effect ⁎
of climate change on giant panda and its habitat, different researches have different opinions. For example, many studies based on climate models and ecological niche modelling have shown that climate change would cause significant degradation to various habitats, as characterized by decreases in the habitat quality and/or habitat loss (Li et al., 2015a; Shen et al., 2015; Tuanmu et al., 2013; Yan et al., 2017).
Corresponding author. E-mail address: [email protected] (P. Luo).
https://doi.org/10.1016/j.ecolind.2019.105886 Received 4 May 2019; Received in revised form 30 September 2019; Accepted 29 October 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.
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Fig. 1. Distribution of the long-term monitoring plots used in this study (habitat boundaries are provided by the Sichuan Province Department of Forestry, which showed the habitat area in third giant panda national survey scope).
over the past four decades in protected areas, allowing us to evaluate the habitat quality change of the giant panda under climate change. Plants, as primary producers, provide food and canopy environment for animals, and play an important role in maintaining habitat stability. The diversity and structure of the plant communities in a habitat are important indicators of the habitat’s quality (Bascompte and Jordano, 2007). For example, the target species in a habitat with low plant diversity are less adaptive to or more vulnerable to climate change (Matthias et al., 2016). Temperate coniferous and broad-leaved mixed forests are considered suitable habitats for the giant panda (State Forestry Administration, 2006); the key vegetation structural parameters of these forests, such as plant species diversity, bamboo (panda’s main food source) abundance, and the sensitivity and stability of the plant communities under climate change can be used as indicators for evaluating the habitat quality of the giant panda. Plant functional traits (PFTs) and plant functional groups (PFGs) are
However, four national investigations found that both wild giant panda populations and the vegetation coverage in their habitats have steadily increased (State Forestry Administration, 2006, Forestry Department of Sichuan Province, 2015: WWF, 1989). A recent study by Wang et al. (2018) reported that climate-only modelling approaches might have overestimated the impact of climate change, while giant panda had a potential tolerance to climate change when biotic interactions were taken into consideration. In order to provide a clear guidance for policy makers, it is therefore necessary to carry out further studies about the impact of climate change on giant panda and its habitat. Recently, Kang et al. (2019) proposed that in-depth studies are required to analyze the relationship between different components of the habitat (for example, plant-plant and plant-animal interactions) that may contribute to habitat conservation and restoration. Long-term monitoring studies is particular essential. In the meantime, Chinese governments have established hundreds of long-term monitoring plots 2
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national nature reserves in China – the Wolong National Nature Reserve (102°52′ –103°24′ E, 30°45′ –31°25′ N). This nature reserve covers an area of approximately 200,000 ha and has more than 10% of the entire wild panda population (State Forestry Administration, 2006; Forestry Department of Sichuan Province, 2015; WWF and State Forestry Administration, 1989). The initial survey was carried out by the Chengdu Institute of Biology, Chinese Academy of Sciences (CAS) and the Institute of Botany, CAS (1974–1976). Follow-up surveys were conducted by Sichuan Agricultural University (2009), Beijing Forestry University (2008, 2011), China Conservation and Research Center for the Giant Panda (1985, 1996, 2001, 2006, 2012), Sichuan Forestry Investigation and Design Institute (1979–2012), and the Chengdu Institute of Biology at CAS (2017). The meteorological data consisted of in-situ monitoring data of each nature reserve and was provided by the Maoxian Ecological Experiment Station of the Chengdu Institute of Biology at CAS, the Sichuan Meteorological Bureau, various Nature Reserves, and various County Meteorological Bureaus. The vegetation surveys were all conducted between July and September (the peak period of plant growth). All the plots were located at least 150 m from a road to avoid edge effects. In each plot, trees were investigated from a 20 m × 30 m plot and shrubs from three 5 m × 5 m sub-plots and herbs from three 1 m × 1 m sub-plots. The data from the subplots were then pooled. All plant species, the number of individuals (abundance), and the cover of each layer (tree, shrub, and herb) were recorded. Plant species identification and classification are based on Flora of China (www.eFloras.org). We distinguished woody plant as shrubs and trees, with trees are ≥10 cm in diameter at breast height (Crowther et al., 2015). On average, each study plot was measured 4 (SD = 2.16) times at an interval of 15.87 (SD = 1.02) years (Table S1) over 40 years in the Sichuan giant panda habitat. The study plots in the Wolong National Nature Reserve were monitored more frequently, i.e., monitoring occurred 7 times with a 7-year interval.
key elements to understand the ecological adaptation strategies of plants in response to abiotic environmental (climate) changes (Green et al., 2008; Violle et al., 2014). At the population level, changes in PFTs provide an understanding of the sensitivity and stability of a plant population to climate change (Green et al., 2008), whereas at the plant community level, changes in PFGs can explain or predict the responses of the plant community composition to climate change (Violle et al., 2014). Previous studies have reported that climate change would significantly alter the vegetation distribution and cover of the giant panda habitat (Tuanmu et al., 2013); however, the details of this process are largely unknown. An investigation of the impact of climate change on PFTs and PFGs will fill this knowledge gap, allowing us to understand and predict habitat changes. This type of knowledge is also crucial for developing adaptive management strategies of the giant panda habitat. The goal of this study is to quantify the changes in the giant panda habitat under climate change and determine how these changes are related to the PFTs using long-term vegetation monitoring data. In particular, we would like to answer the following questions: i) What are characteristics and trends of climate change in the giant panda habitat over past four decades in local monitoring? ii) what are the changes in plant diversity, community structure, and bamboo abundance that occurred under climate change? iii) How does the change of plant abundance response climate change correlated to the PFTs? The answers to these questions are vital to predict habitat change under realistic future climate scenarios. This study provides a reference for management agencies to determine whether or how to adopt suitability management measures for climate change. 2. Methods 2.1. Study area and vegetation survey This study was conducted in the Sichuan giant panda habitat, which is located in an alpine valley in the transition region from the QinghaiTibet plateau to the Sichuan basin; the area is part of the subtropical evergreen broadleaf forest region and warm temperate deciduous broadleaf forest region (Sichuan Vegetation Cooperation Group, 1980). Historical data from the Sichuan Forest Ecology Survey were obtained, including Sichuan province vegetation survey data (1974–1976), Sichuan forestry survey data (1979–2012), and vegetation data collected by research institutions and universities in the giant panda habitat. We chose forest plots in areas of no human disturbances and logging in the initial year; these plots were continuously protected by developing new reserves and logging bans were in effect with regular patrolling in the later decades. In mature forest areas, we screened the plots (107) with a consistent survey method. Mature forest would minimize the effects of vegetation succession (Robert, 1981). We used 107 sampling plots (20 m × 30 m) located in the Minshan, Qionglai, Xiaoxiangling, and Liangshan mountains (Fig. 1, Table S1). The number of sampling plots in the initial survey year (1974–1976, 2000–2001) and 2017 are the most, which in 1985 and other middle survey years are less (Table S1). These sampling plots are mainly in Wolong Nature Reserve and Wanglang Nature Reserve, where are protected from 1960s – the core area of the giant panda habitat. We acknowledge that the total habitat area varied between each of the four giant panda national surveys (due to the change of nature reserve area), the selected 107 sampling plots have always been located in the preserved giant panda habitat. The plant communities of the 107 plots were mainly classified as temperate coniferous and broad-leaved mixed forests, particularly in the areas where bamboo (Bashania fangiana, and Fargesia robusta) was observed (Fig. S1). The altitude of the plots ranged from 2000 to 3600 m. There were no effects of the two earthquakes (2008, 2013) in the sampling plots and no extreme climate events, pest outbreaks, and fires during the study period. Among the 107 sampling plots, there were 14 permanent plots that were located in one of the earliest and foremost
2.2. PFT measurements Based on the frequency and abundance of each plant species in each plot, we selected the dominant species, including Bashania fangiana (BfK), Fargesia robusta (FrY), Rhododendron calophytum. Franch (RcF), Abies faxoniana (AfR), Corylus ferox (CfW), Acer laxiflorum (AlP), Betula albosinensis (BaB), and Tsuga chinensis (Franch.) Pritz (TcP). These eight dominant species are consistent with the characteristics of the dominant species of coniferous and broad-leaved mixed forests in Sichuan vegetation (Sichuan Vegetation Cooperation Group, 1980). We measured four functional traits of the eight dominant plant species in 2017. The measured functional traits included specific leaf area (SLA, cm2g−1), leaf area (LA, cm2), leaf thickness (LT, mm), and leaf dry matter content (LDMC, g g−1). These traits reflect the substance exchange balance between plant resource acquisition and protection under environmental change (Bernard-Verdier et al., 2012). The functional trait data were obtained from five individuals for each species from 14 plots in the Wolong Nature Reserves; there were 25 replicates per individual of each species and international standards were used for the measurements (Pérez-Harguindeguy et al., 2013). 2.3. Data analysis We used ordinary linear regression to analyze the trend of the mean annual temperature (MAT) and annual precipitation over the past 40 years. The results showed that the MAT and annual precipitation were highly correlated with time (Fig. 2) since this finding explicitly answer time as climate change reliable proxies based on long-term field observational. Linear mixed effect models (LMEs) are used widely in ecological 3
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Fig. 2. Inter-annual dynamics of the mean annual temperature (a) and annual precipitation (b) during 1975–2016 based on data from the Wolong meteorological station (1925 m above sea level, Wolong Nature Reserve administration, Sichuan, China). Lines and shaded areas depict the model fit and standard error of the linear regression respectively.
based on the minimum Akaike information criterion (AIC). All analyses were conducted in R 3.4.3 (R Core Team, 2015) and we used the nlme package (Pinheiro et al., 2014) for the LMEs and the MuMIn package (Bartoń, 2015) for the dredge function. All models were validated according to the suggestions provided by Zuur et al. (2009).
research (Zuur et al., 2009) and were used in this study to investigate the long-term changes in plant diversity. The plant (trees and shrubs) richness and the tree abundance were the response variables (family = Gaussian), the sampling year was the explanatory variable, and the sampling plots (site) represented the random variable. Here, we pooled data from all study plots.
Yi = a1 ∗ year + a2 ∗ sitei + ei
(1) 3. Results
where i = 1, 2, …,64 in 1974–1976, i = 1, 2, …,14 in 1985, i = 1, 2, …,14 in 1996, i = 1, 2, …, 57 in 2000–2001, i = 1, 2, …,14 in 2006, i = 1, 2, …, 20 in 2009, i = 1, 2, …,42 in 2011–2012, i = 1, 2, …,107 in 2017; ei is the residual vector; a1, a2 are the regression coefficients. The response variable Yi represents either tree richness or abundance in the sitei plots in 1974–1976, 1985, 1996, 2000–2001, 2006, 2009, 2011–2012, and 2017 in overall habitat. Since the vegetation measurements were conducted more frequently in the Wolong National Nature Reserve than in the other study plots, the 14 permanent plots in Wolong were used as a case study to model the change in the species composition and functional groups over time. To confirm whether climate change affected the plant functional groups (PFGs), we classified the trees into evergreen broad-leaved trees, deciduous broad-leaved trees, and evergreen coniferous trees and the shrubs into evergreen and deciduous shrubs according to the phenological characteristics of the forest plants. LMEs were also used. The response variable included the plant species richness and the canopy cover of the trees, shrubs, and herbs and the abundance of the functional groups (Gaussian error distribution), with the sampling year as the explanatory variable and the sampling plot as the random variable (Zuur et al., 2009). Eq. (1) was used, where i = 1, 2, …,14 in the Wolong Nature Reserves in 1975, 1985, 1996, 2001, 2006, 2012, and 2017; ei is the residual vector; and a1, a2 are the regression coefficients. The response variable Yi represents either the richness or the cover of the trees, shrubs, and grasses and the PFGs abundance in the sitei plots. To determine the stability of the plant community under climate change, we first used the LMEs to evaluate the change in the abundance of the eight most dominant species over time independently (study site as the random factor). The correlation coefficient between the time and the dominant species abundance was denoted as the abundance-time slope (ATS). The dominant species abundance data were normalized (zero-mean normalization). All the functional trait data were log10 transformed (Pérez-Harguindeguy et al., 2013). The regression coefficients were then obtained for the following functional traits of the different plant species: SLA, LA, LT, and LDMC. We applied an ordinary linear regression using the dredge function and selected the best models
3.1. Climate change trends From 1975 to 2016, the MAT showed a warming trend from 7.73 °C to 9.52 °C with an annual warming rate of 0.043 °C (95% CI: 0.033–0.053 °C, linear regression R2 = 0.634, P < 0.001, Fig. 2a) and a cumulative increase of 1.76 °C. In contrast, the annual precipitation declined over time from 1021.25 mm to 895.53 mm with an annual decline of 2.64 mm (95% CI: 4.71–0.57 mm, R2 = 0.121, P = 0.014, Fig. 2b) and a cumulative decline of 108 mm in Wolong National Nature Reserve. Table S2 shows that the temperature in all nature reserves increased significantly and the annual warming rate was 0.034 °C. Although the annual precipitation changes exhibited fluctuations, Table S1 shows that the precipitation decreased in all habitats, except for the Jiuzhaigou Nature Reserve.
3.2. Plant community change The results of the 107 long-term monitoring plots in the giant panda habitat showed that there was no significant change in the total plant species richness (Fig. 3a), while there was a significant increase in trees abundance over past decades (95% CI: 0.0038–0.13, P = 0.038, Fig. 3b). In the Wolong Nature Reserve, trees richness increased significantly over time (95% CI: 0.044–0.13, P < 0.001), whereas shrubs richness and herbs richness exhibited no significant change over the past decades (Fig. 3c-e). The canopy cover of trees and herbs showed no significant change over time. The cover of shrubs layer increased significantly (95% CI: 0.30–0.88, P < 0.001, Fig. 3f-h). There was no significant change in the abundance of different PFGs (evergreen broad-leaved trees, deciduous broad-leaved trees, and evergreen coniferous trees) or the abundance of evergreen and deciduous shrubs over time (Fig. 3i-m). For the dominant plant species, the abundances of BfK (95% CI: 0.13–0.38, P = 0.0004, Table 1) and FrY (95% CI: 0.025–0.099, P = 0.021, Table 1) increased significantly over time, whereas no significant trend was observed for the other species (P > 005). 4
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Fig. 3. Plant diversity in the giant panda habitat (a-b) and the plant communities in the Wolong Nature Reserve (c-m) over time. The lines and shaded areas represent the estimates and standard errors of the coefficients of the linear mixed effect models respectively. The stars refer to the significance levels (***P < 0.001, **P < 0.01, *P < 0.05). 5
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4. Discussion
Table 1 Regression results showing the responses of the dominant species abundance over time. B is the regression coefficient; SE B is the SE of the regression coefficient. For the linear mixed effect models, the P-values indicate the significance of the fixed effects (***P < 0.001, **P < 0.01, *P < 0.05). Bashania fangiana (BfK), Fargesia robusta (FrY), Rhododendron calophytum. Franch (RcF), Abies faxoniana (AfR), Corylus ferox (CfW), Acer laxiflorum (AlP), Betula albosinensis (BaB), and Tsuga chinensis (Franch.) Pritz (TcP).
ATS
Species
B
SE B
T value
df
P-value
BfK FrY RcF AfR CfW AlP BaB TcP
0.083 0.12 0.11 0.034 −0.097 −0.026 0.069 −0.30
0.020 0.034 0.18 0.14 0.17 0.18 0.20 0.18
4.16 3.47 0.63 0.25 −0.57 −0.15 0.35 −1.64
23
0.0004*** 0.0021** 0.53 0.80 0.57 0.88 0.73 0.11
4.1. Climate change characteristics and trends Since 1975, the climate in the giant panda habitat has become warmer and drier in our study region. The trend is similar to Jian et al. (2014) based on IPCC climate data from 1989 to 2002, who predicted a 1–2 °C warming trend and stable precipitation from 2002 to 2050, but differ from Liu et al. (2016) who predicated an increase of 8.2 °C by 2050 based on World Climate data under the IPCC A2 greenhouse-gas emissions scenario. Different results furthermore suggest large spatial scale models cannot reliably determine the impact of altered microclimates on vegetation (Suggitt et al., 2018), suggesting the necessity of reginal-specific management policies related to climate change impact. 4.2. Changes in the plant community diversity Over the last four decades, the tree abundance increased whereas the total species richness remained unchanged in the study area, indicating relatively stable vegetation diversity in the giant panda habitat. Compared with the sample size of the survey in the initial year and 2017, the sample size in 1985–2012 was small but this does not affect the evaluation of the changes in plant diversity over the past 40 years for the entire habitat, when no caring about the plant diversity change process. Vellend et al. (2013) stated that mean temporal change in species diversity over periods of 5–261 years was not different from exhibiting no changes in local vegetation plots because increases were as likely as decreases over time, whereas sites that experienced postdisturbance succession showed increases in richness over time. Our results indicated that long-term protected mature natural forests in overall habitat were resistant to climate change if there were no human disturbance, fire, or insect outbreaks. Here, the possible reasons include the adaptation and tolerance of the plant community and subsequent strict protection (Mickael et al., 2019).
Table 2 Linear regression analysis results showing the relationship between PFTs and the response parameter of abundance of the dominant species over time (ATS). B is the regression coefficient; SE B is the SE of the regression coefficient. For the regression model, the P-values indicate the significance of the whole model and that of each parameter (**P < 0.01, *P < 0.05). Models ATS
B
SE B
df
Adjusted R2
P-value
SLA + LDMC 0.66 0.028* SLA 0.82 0.26 5 0.025* LDMC 0.97 0.26 0.13 Individual plant traits that were significant predictors of the ATS SLA 0.30 0.39 6 −0.062 0.47 LA 0.066 0.41 −0.16 0.88 LDMC 0.53 0.35 0.16 0.18 LT −0.54 0.34 0.17 0.17
3.3. Contribution of PFTs to the ATS 4.3. The stability of the plant community structure in different layers The individual PFTs did not explain the changes in the dominant species abundance as a result of climate change (Table 2). The SLA of the eight main dominant species increased over time. An analysis of the response relationship between the PFTs and ATS showed that the LDMC and SLA models explained 66% of the change in the ATS (Table 2 and Fig. 4). As shown in Fig. 4, BfK and FrY exhibited an adequate fit of the model, suggesting that the SLA + LDMC models satisfactorily reflected the response relationship between BfK/FrY abundance and time.
Long-term permanent monitoring of the Wolong Nature Reserve indicated that over the past 40 years the species richness of trees increased significantly but not for shrubs and herbs. The different response to climate change of different plant layer composition may exhibit a synergistic response, which is consistent with the results reported by Hoeppner and Dukes (2012). The presence of an upper canopy layer (shrubs/trees), for example, commonly modifies the abiotic conditions in dryland ecosystems and leads to an altered performance of the undergrowth, such as herbs (Soliveres et al., 2015). The might be a shift in the structure of plant species to mitigate the pressure on the forest resulting from climate change (Zhang et al., 2018a). Moreover, plant communities with higher biodiversity are more resistant and resilient to extensive climate change (Isbell et al., 2015). As a World Natural Heritage site, the giant panda habitat contains a high diversity of plant species (UNESCO, 2006); therefore, the biotic interactions between the species may be the reason that the plant communities are able to adapt to climate change. For example, environmental severity effects have been shown to affect increasing net facilitative interactions among species, which would mitigate the negative effect of environmental changes (Michalet et al., 2014), which may include not only climate change, but other natural influences. Here, coordinated changes in plant species diversity in different layers may alleviate pressures from climate change. Our results showed that there was a significant positive effect on shrub cover but not on tree or herb cover. The total cover of the plant community was higher than 100% over the past 40 years. Here, the total cover of the plant community did not show a decline under climate change. In addition, there was no significant change in the abundances of the different PFGs; this result is in contrast to the
Fig. 4. Relationship between the abundance-time slope (ATS) and leaf dry matter content and specific leaf area (n = 8 species) Note: Corylus ferox (CfW), Bashania fangiana (BfK), Tsuga sp. (TcP), Acer laxiflorum (AlP), Betula albosinensis (BaB), Abies faxoniana (AfR), Rhododendron sp. (RrF), Fargesia robusta (FrY). 6
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findings of Sophie et al. (2012), who determined that deciduous canopy species increased and evergreen sub-canopy and shade-tolerant species decreased under drought. Such differences may stem from the differences in the species’ tolerance and adaptability. This result indicates that although the tree species richness changed significantly, it did not significantly impact the overall cover and abundance of the different PFGs. The effects of climate change are considered the most likely explanation for the changes in the tree richness and shrub cover; however, there are also other possible reasons (e.g. nitrogen deposition, conservation). Our results suggest that the plant community composition structure remained stable over time. The results showed that the abundances of the two bamboo species increased over time, which is different from the results of previous studies that used climate-only and non-continuous time change approaches and predicted a loss between 45.6% and 86.9% in the bamboo’s cover (Tuanmu et al., 2013; Zhang et al., 2018b). The cover percentage would not alter our results regarding habitat suitability unless the cover was lower than 50%; however, pandas are consistently found in areas with bamboo cover higher than 50% regardless of the bamboo availability in the panda range (Hull et al., 2014). Therefore, results suggest that the bamboo abundance increased; and the ecosystem services of the habitat for pandas did not decline over the past four decades under contemporary conservation policies.
common (Wei et al., 2018). In addition, this study predicted that the BfK/FrY abundance would increase in the future under the continuous influence of climate change. However, it remains unclear how the functional traits affect the BfK/FrY abundance under climate change (e.g., physiological reasons) and further analyses and studies are needed.
4.5. Stability of giant panda habitat over time The indicators of the quality of the giant panda habitat include 1) the amount of continuous habitat distribution (degree of habitat fragmentation) (Shen et al., 2015), 2) the forest composition and species richness in the habitat (Kang et al., 2017; Kang, et al., 2018), 3) the distribution and abundance of bamboo (Liu et al., 2001), 4) anthropogenic interferences, and 5) the topography, slope, aspect, and density of the river system (Zhou, 2008). Habitat fragmentation is commonly caused by human disturbances such as road construction (Shen et al., 2015) and these problems will be mitigated by stronger protection practices. The topography and slope indicators are not influenced by climate change; therefore, in this study, the plant diversity of the typical plant community preferred by the giant panda and the bamboo abundance were used as indicators of the change in the quality of the giant panda habitat under climate change. The key findings obtained from our long-term monitoring study were that the habitat of the giant panda has remained relatively stable over the past four decades, indicating tolerance of the habitat to climate change under contemporary conservation policies. These results confirmed the modelling results of Wang et al. (2018), who concluded that the giant panda habitat may be tolerant to climate change due to biotic interactions (State Forestry Administration, 2006; Forestry Department of Sichuan Province, 2015; WWF and State Forestry Administration, 1989). However, our results differ from those of previous modelling predictions that climate change would result in significant degradation of the giant panda habitat (Tuanmu et al., 2013; Zhang et al., 2018b). The reasons for the differences in the results may be the survey and analysis methods and the underlying theory and hypotheses (climate and a few abiotic factors are the main determinants of the bamboo’s geographical range). For example, we used local long-term meteorological and vegetation data, and took plant-plant interactions, vegetation tolerance and resilience into consideration, and considered bamboo as panda’s main food source. Such empirical approach differs from studies based on different climate change scenarios models and generally did not include biological interactions (Tuanmu et al., 2013; Zhang et al., 2018b). Difference might also come from the lower increases trend of temperature in meteorological recoding than model predication. Our study suggested that the biotic interactions (plant-plant, plant-animal) and biotic adaptive behavior and ability should be considered when forecasting plant community responses to climate change and changes in the species distribution.
4.4. Variation of functional traits to adapt to climate change To evaluate the adaptability of the habitat of the giant panda to climate change, we considered the adaptation behavior and changes in the functional traits of the bamboo to climate change. With regard to different dominant species, the abundance of BfK and FrY changed significantly over time. There are two possible explanations for this result. First, the communities might adapt to climate change by adjusting the species compositions, for example, by reducing the abundance of species that are less adaptable to climate change and increasing the abundance of species with better adaptive capacity (Fauset et al., 2012; Kleynhans et al., 2016). Second, morphological trait compositions (e.g., leaf morphology) at the individual and population levels might change in response to resource and environmental variations in order to adapt to climate change (Bateman and Johnson, 2012). In this study, we found that the SLA (Table 2) of the eight dominant species increased, which indicated that the plants increased their SLA to increase their energy resources and gain a competitive advantage as a result of increased competition, which is supported by the findings of Li et al. (2015b). LDMC increases with drought (Ackerly, 2004). Here, the LDMC was not significant in response to ATS, which may suggest a decrease in precipitation is not a major threat for plant communities in the wet and cold giant panda habitat. Some studies suggested that a general tolerance to drought exists in species growing in this environment and that this plant community will resist future drought conditions (Giménez-Benavides et al., 2007; Losapio and Schöb, 2017). Our study area is located in the rainy zone of West China (Sichuan Vegetation Cooperation Group, 1980); thus, the drought risk caused by declines in precipitation is low. The variation in the SLA of plants obtained indicated that successful resource utilization and self-protection strategies were used by the plants in response to climate change. These strategies provided a solid foundation for the stability of the plant communities. Environmental change has a selective function for plants with specific PFTs changes, and the abundance of a plant population with such PFTs will exhibit variations in response to environmental changes (Violle, 2009). In this study, the SLA and LDMC satisfactorily reflected the response relationship between the BfK/FrY abundance and time, suggesting that changes in the BfK/FrY leaf functional traits allowed these species to adapt to climate change. Consequently, the BfK/FrY abundance increased, which might be the reason for the steady bamboo food source at a certain altitude range, at which livestock is not
5. Conclusion Here we used the long-term empirical monitoring data to study the response of plant communities in giant panda habitat to climate change. Our results revealed that the quality of habitat was relatively stable over decades, while model prediction needs to consider both biotic interaction and adaptation. Although vegetation characters does not necessarily associate with the occurrence of giant panda, proper habitat evaluation is the first step to understand the population’s future trend. Our study suggests that threats of climate change to giant panda habitat might be mitigated by contemporary conservation, highlighting the importance long-term protection of the natural processes and the control of human disturbances in the conservation of giant panda and other endangered animal species. 7
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Acknowledgments
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This study was supported by the Demonstration of Monitoring and Protection of Important Species Habitat (2016YFC0503305) and the Management Framework and Capability Building for Development of Ya'an Giant Panda National Park (NOR/15/301/16/002). We thank the China Conservation and Research Center for the Giant Panda, the Sichuan Forestry Investigation and Design Institute, and the Forestry Department of Sichuan Province for providing the climate data and field plant data. Pu Tao provided the photo of the forest in 1986. We thank Yin Kaipu for helping with the search for historical data. We also thank Zhang Jinlong, Lai Jiangshan, and Yang Jiang from the Institute of Botany, Chinese Academy of Sciences for their valuable discussions on the statistical analyses. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2019.105886. References Ackerly, D., 2004. Functional strategies of chaparral shrubs in relation to seasonal water deficit and disturbance. Ecol. Monogr. 74, 25–44. Bartoń, K., 2015. MuMIn: multi-model inference. R package version 1 (13), 4. Bascompte, J., Jordano, P., 2007. Plant-animal mutualistic networks: the architecture of biodiversity. Annu. Rev. Ecol. Evol. Syst. 38, 567–593. Bateman, B.L., Johnson, C.N., 2012. Biotic interactions influence the projected distribution of a specialist mammal under climate change. Divers. Distrib. 18, 861–872. Bernard-Verdier, M., Navas, M.L., Vellend, M., Violle, C., Fayolle, A., Garnier, E., 2012. Community assembly along a soil depth gradient: contrasting patterns of plant trait convergence and divergence in a Mediterranean rangeland. J. Ecol. 100, 1422–1433. Butchart, S.H., Walpole, M., Collen, B., 2010. Global biodiversity: indicators of recent declines. Science 328, 1164–1168. Crowther, T.W., Glick, H.B., Covey, K.R., Bettigole, D.S., Thomas, S.M., Smith, J.R., 2015. Mapping tree density at a global scale. Nature 525, 201–205. Fauset, S., Baker, T.R., Lewis, S.L., 2012. Drought-induced shifts in the floristic and functional composition of tropical forests in Ghana. Ecol. Lett. 15, 1120–1129. Forestry Department of Sichuan Province, 2015. In: Sichuan Forestry Department Giant Pandas in Sichuan: Report of the Fourth Giant Panda Survey in Sichuan. Sichuan Science and Techology Publishers, Beijing, China, pp. 123–130 (in Chinese). Giménez-Benavides, L., Escudero, A., Iriondo, J.M., 2007. Local adaptation enhances seedling recruitment along an altitudinal gradient in a high mountain Mediterranean plant. Ann. Bot. 99, 723–734. Green, J.L., Bohannan, B.J.M., Whitaker, R.J., 2008. Microbial Biogeography: From Taxonomy to Traits. Science 320, 1039–1043. Hull, V., Roloff, G., Zhang, J., Liu, W., Zhou, S., Huang, J., Liu, J., 2014. A synthesis of giant panda habitat selection. Ursus 25 (2), 148–162. Hoeppner, S.S., Dukes, J.S., 2012. Interactive responses of old-field plant growth and composition to warming and precipitation. Glob. Change Biol. 18, 1754–1768. Isbell, F., Craven, D., Connolly, J., 2015. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574. Jian, J., Jiang, H., Jiang, Z.S., Zhou, G.M., Yu, S.Q., Peng, S.L., Wang, J.X., 2014. Predicting giant panda habitat with climate data and calculated habitat suitability index (HSI) map. Meteorol. Appl. 21, 210–217. Kang, D., Wang, X., Li, S., Li, J., 2017. Comparing the plant diversity between artificial forest and nature growth forest in a giant panda habitat. Sci. Rep. 7, 3561. Kang, D., Lv, J., Li, S., Chen, X., Wang, X., Li, J., 2018. Integrating indices to evaluate the effect of artificial restoration based on different comparisons in the Wanglang Nature Reserve. Ecol. Ind. 91, 423–428. Kang, D., Lv, J., Li, S., Chen, X., Wang, X., Li, J., 2019. Relationship between bamboo growth status and woody plants in a giant panda habitat. Ecol. Ind. 98, 840–843. Kleynhans, E.J., Otto, S.P., Reich, P.B., Vellend, M., 2016. Adaptation to elevated CO2 in different biodiversity contexts. Nat. Commun. 7, 12358. Li, R., Xu, M., Wong, M.H.G., 2015a. Climate change threatens giant panda protection in the 21st century. Biol. Conserv. 182, 93–101. Li, R., Zhu, S., Chen, H.Y., 2015b. Are functional traits a good predictor of global change impacts on tree species abundance dynamics in a subtropical forest? Ecol. Lett. 18, 1181–1189. Liu, J., Linderman, M., Ouyang, Z., An, L., Yang, J., Zhang, H., 2001. Ecological
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