Next Article in Journal
Utilizing SIFT-MS and GC-MS for Phytoncide Assessment in Phytotron: Implications for Indoor Forest Healing Programs
Previous Article in Journal
Direct Experience of Nature as a Predictor of Environmentally Responsible Behaviors
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 14
Issue 11
10.3390/f14112234
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Towards Forest Conservation Planning: How Temperature Fluctuations Determine the Potential Distribution and Extinction Risk of Cupressus funebris Conifer Trees in China

by
Jingtian Yang
1,
Qinggui Wu
1,*,
Mohammed A. Dakhil
2,3,
Marwa Waseem A. Halmy
4,*,
Heba Bedair
5 and
Mai Sayed Fouad
6
1
Ecological Security and Protection Key Laboratory of Sichuan Province, Mianyang Normal University, Mianyang 621000, China
2
Botany and Microbiology Department, Faculty of Science, Helwan University, Cairo 11795, Egypt
3
Centre for Applied Ecology “Professor Baeta Neves”, Research Network in Biodiversity and Evolutionary Biology (CEABN, InBIO), School of Agriculture, University of Lisbon (ISA, UL), Tapada da Ajuda, 1349-017 Lisbon, Portugal
4
Department of Environmental Sciences, Faculty of Science, Alexandria University, Alexandria 21511, Egypt
5
Botany Department, Faculty of Science, Tanta University, Tanta 31527, Egypt
6
Botany Department, Faculty of Science, Fayoum University, Fayoum 63514, Egypt
*
Authors to whom correspondence should be addressed.
Forests 2023, 14(11), 2234; https://doi.org/10.3390/f14112234
Submission received: 11 October 2023 / Revised: 2 November 2023 / Accepted: 10 November 2023 / Published: 13 November 2023
(This article belongs to the Section Forest Meteorology and Climate Change)
Browse Figures
Versions Notes

Abstract

:
This study focused on assessing the impact of climate change on Cupressus funebris, one of the important endemic conifer species in China. The impact of fluctuations in temperature on the potential distribution and extinction risk of the C. funebris was evaluated using species distribution modelling. The outcomes of this current study revealed that the species was mainly distributed in mountainous forest areas, and climate variables played a major role in the distribution of this conifer tree. Under climate change, the threatened conifer will be mainly concentrated in mountainous forests and is projected to undergo contraction in distribution and shift northward. The conservation status of C. funebris is projected to be uplisted to “Near Threatened” status under the SSP5_8.5 scenario by 2040 and to the “Least Concern” status under all the other investigated climate and dispersal scenarios. Despite the high capacity of the species for adaptation to environmental changes, part of its AOO might be lost under severe climate change conditions. Key conservation areas were identified, and suggestions for redesigning some of the current natural reserves in the forested area where the species is found were proposed for the conservation of this key coniferous species. The stable area in the northwestern part of Yunnan in the Hengduan Mountain Forest can serve as a promising area for C. funebris reintroduction through afforestation programs.

1. Introduction

Temperate conifer forests are experiencing threats due to climatic and other environmental changes, which might put some key coniferous species that are especially sensitive to climate change at a high risk of extinction [1]. Nearly 24% of the world’s coniferous trees are native to China [2], of which 53 species are recognized as threatened [1]. Therefore, evaluating the potential effect of the changes in climate on threatened coniferous species endemic to China is highly important, particularly for proper conservation planning. One of the main conifers native to China is Cupressus funebris Endl., also known as C. funebris and the Chinese Weeping Cypress. The conifer species mostly inhabits the temperate biome with a native range limited to Central China, South China, and Northern Vietnam. However, the species is widely planted in China and other regions for ornamental purposes, particularly in monasteries and Buddhist temples. It is frequently referred to as Chamaecyparis funebris in gardening for its flattened leaves and tiny cones [3]. It is valued for its durable wood, which is traditionally utilized in coffin making, hence the name funeral cypress. In Europe, it is not commonly cultivated, and the majority of the older trees in existence are grown from the seeds collected in 1907 by Ernest Wilson from Hubei Province in China. C. funebris is among the coniferous species utilized in cedarwood essential oil production [4]. The commercial Chinese cedarwood oil produced from C. funebris [5] is a vital fragrance material that is well known and referenced in the French Standard, with applications in diverse types of perfumes [6]. Chinese cedarwood oil is characterized for its cedrol, α-cedrene, β-cedrene, and thujopsene terpene content, which are used in cedarwood oil production [4,7]. Traditionally, Chinese cedarwood oil has been used for treating headaches, carbuncles, and gangrene, as well as healing wounds [7]. Studies indicated that cedarwood oil possesses anti-inflammatory, radical scavenging, and antioxidant activities [5,8].
Existing naturalized populations can be found in mixed mountain forest or degraded woodland in calcareous or sandy loam soils. It is also commonly grown and can possibly invade and spread in disturbed vegetation. C. funebris can be found in areas that extend between 300 and 2260 m in altitude. The extensive distribution of C. funebris mentioned in the Chinese literature, particularly in the Flora of China (http://www.iplant.cn/foc/, accessed on 13 February 2023) is mostly based on planted or introduced trees. Its existence in natural forests is sporadic due to extensive deforestation and the alteration of natural vegetation, mainly in Central China. The extent of wild populations is currently unknown [9]. The species is believed to occur naturally only in Guizhou, West Hunan, and Chongqing, east of Sichuan [7].
The likelihood of secondary recruitment from planted trees beyond the original range complicates the evaluation of risks to C. funebris wild populations. Although the species is not deemed endangered, its natural habitat is under threat, particularly in the mixed conifer forests. The threats are mainly due to construction activities. To ascertain the real extent of the species’ wild populations and their conservation status, intensive studies and field work are needed. The IUCN Red List of Threatened Species assessment for C. funebris in 2010 assigned it a Data Deficient status. As a result of widespread planting and the subsequent naturalization of the species throughout vast areas of Central and Southern China, the definition of its “natural habitat” is not clearly known; consequently, there is no justification for the previous global assessments ‘Near Threatened’ and ‘Least Concern’ by [9]. Certainly, the actual status of the wild populations is not clearly known. Hence, it was considered Data Deficient, pending the availability of information on the species’ status in natural habitats [9]. However, it is regionally categorized as “Endangered” in the wild according to the Chinese National Red List [10]. The current wild population is severely fragmented [9]. Although it was recorded in several protected areas; it is uncertain if the detected populations resulted from introductions or naturalization. The existing conservation plans that do not consider the probable changes in climate suitability for C. funebris might not be successful in providing effective conservation under climate change uncertainty [11].
Species distribution is a complex reflection of the evolutionary history and ecology of a species, and it is influenced by the temporal and spatial trends of a multitude of biotic and abiotic variables [12]. Most recently, climate modelling assisted in improving knowledge of the effects of climate changes on species distribution [13]. Species distribution models (SDMs) can help in evaluating the potential influence of environmental changes on species distribution [1,14,15,16,17] and in ascertaining conservation priority areas [18]. Some species may undergo a range shift as a compensatory mechanism in response to changes in climate by adjusting their geographic distribution to more climatically suitable regions [19]. The response may vary according to the ecological tolerance of species and how they can physiologically adjust to the changes in climate and associated consequences [20].
C. funebris might face a high extinction risk in the future because of the fluctuations in the global climate. Therefore, this study sought to (1) predict the distributions of the key forest species C. funebris under current and future climates and different dispersal scenarios; (2) identify the crucial environmental factors best explaining its potential distribution; (3) evaluate the extinction risk as indicated by the loss in the area of occupancy (AOO); and (4) detect priority conservation areas for the species that can guide future conservation actions and afforestation plans.

2. Materials and Methods

2.1. Species Distribution Data

The occurrence points of C. funebris were obtained from (a) the Global Biodiversity Information Facility (https://www.gbif.org/, accessed on 8 September 2023); (b) the Chinese Virtual Herbarium (CVH, http://www.cvh.ac.cn/, accessed on 15 January 2023); (c) the National Specimen Information Infrastructure (http://www.nsii.org.cn/, accessed on 12 February 2023); and (d) Field investigations during 2019–2023, piloted by the Ecological Research Group at Mianyang Normal University. Afterwards, collected data comprising 1657 occurrence records were verified and cleaned by deleting duplicates, points outside China, or outlier locations (such as urban or lakes), using a global land cover map with a spatial resolution of 1 km2 in ArcGIS 10.8 (ESRI, Redlands, CA, USA). This cleaning resulted in 1195 occurrence records (Figure 1).

2.2. Bioclimatic Predictor Variables and Multicollinearity

Nineteen current and future bioclimatic indicators were acquired from WorldClim [21]. To gauge the impact of the changing climate on the geographical allocation of species, two global climate models dubbed BCC-CSM2-MR (Beijing Climate Centre-Climate System Modelling) and the IPSL-CM6A-LR (The Institute Pierre-Simon Laplace- Climate Modelling Centre) were collectively designated to lessen the uncertainty caused by a single GCM [22].
To simplify the estimation of the AOO, Ref. [23] recommended all predictor layers be retested via ArcGIS 10.8 (ESRI) to a resolution of almost 2 × 2 km2.
For multicollinearity analysis, a variance inflation factor (VIF), which tells the extent of clarification of any predictor by the other predictors, was used to minimize overfitting, with highly correlated variables being removed. VIF analysis was applied using the VIFcor and VIFstep functions of the “usdm” package [24] in R 4.2.0 [25]. Variables with a VIF > 5 and a correlation threshold of 0.75 were excluded [26].

2.3. Ensemble Model Building

Three algorithm SDM models, christened the generalized linear model (GLM), boosted regression trees (BRT), and random forests (RF), were selected due to their high stability and the transferability of their predictions compared to other models. We used 70% of the data for training data and 30% as testing data [27]. An ensemble of these three models was constructed to model the distribution of C. funebris and weighed with True Skill Statistic (TSS), using the ‘ensemble’ functions of the ‘sdm’ package in R 4.2.0 [28]. We used maximum training sensitivity plus specificity (MTSS) [29]. We applied the TSS and the area under the curve (AUC) to evaluate models’ accuracy [26]. To visualize habitat shifts, quantitative maps (generated from ensemble modelling) of the current and anticipated habitat suitability were converted to binary maps, relying on the MTSS threshold. Afterwards, a global land cover was employed to adjust the suitability maps, keeping inappropriate regions such as watercourses and urban areas away. Consequently, for the near future (2021–2040) and distant future (2081–2100), two conjoint socioeconomic scenario pathways (low scenario: SSP126 and high scenario: SSP585) were combined to create the mean ensemble of the two GCM outcomes. The preceding lines describe an excellent method that offers superior results over those attained from a single model [17,22].
We reclassified the output maps of suitability under the present climate into three classes of suitability: low (<0.3), moderate (0.3–0.5), and high (>0.5). Moreover, to visualize the changes in habitat (loss, gain, and stable areas), we first generated binary maps from continuous maps (with suitability 0.4 to 6.0) for the present and future (0: absence/1: presence) and then multiplied the future binary maps by 2, which resulted in grid cells with values of (0/2). Then, we subtracted the present binary maps (0/1) from the future (0/2), and this resulted in new grid cells representing four classes: a gain class of grid cells with a value of 2, a stable class of grid cells with a value of 1, a loss class of grid cells with a value of −1, and finally, an unsuitable class of grid cells with a value of 0.

2.4. Extinction Risk under Climate and Dispersal Scenarios

The IUCN guidelines on land use change and dispersal scenarios have been suggested for conservation studies under climate change scenarios [17,23]. These recommended guidelines seek a comprehensive framework for conservation assessment and its sensible scheduling. Applying the area of occupancy (AOO) in extinction risk assessments is well suited to problems with grid orientation or the avoidance of probable origin mistakes as well as geometric uncertainty [23,30].
The binary maps that were developed for both the present and future were cropped to obtain the extent of occurrence (EOO) scenarios using an extent of occurrence (EOO) shapefile generated by the ‘ConR’ package in R 4.2.0 [31].
Owing to being crucial features for conservation planning, both full and limited dispersal scenarios should be recruited together with regard to dispersal hypotheses [27]. The AOOs were computed under full and limited dispersal assumptions. The pixels were preserved as part of the future distribution even if they were not ideal for the projected present range since, under full dispersal, no constraint to the species’ dispersal capabilities was assumed [32]. In accordance with the IUCN Red List Criterion A3 (c), the receding values of the predicted AOOs was used as a measure to assess the extinction risk of species and was calculated as follows: loss < 15% = least concern (LC), loss > 15% = near threatened (NT), loss > 30% = vulnerable (VU) [17,32]. All of the steps are illustrated in a flowchart (Figure 2).

3. Results

3.1. Model Performance and Potential Response to Bioclimatic Variables

Most of the distribution of C. funebris is located in the Central Chinese provinces such as Sichuan and Chongqing (Figure 2). A multicollinearity analysis of all nineteen bioclimatic variables resulted in five uncorrelated variables with VIF < 5 and a correlation threshold of 0.75, which was used in the ensemble modelling. The ensemble model of the generalized linear model (GLM) boosted regression tree (BRT) and random forest (RF) models and showed high accuracy and excellent performance with an AUC of 0.94 and TSS of 0.84 (Table 1). The mean diurnal range (Bio2), temperature seasonality (Bio4), and precipitation of the warmest quarter (Bio18) were the most important variables explaining the potential distribution of the Chinese Weeping Cypress, with relative importance higher than 5% up to 60% (Table 1). The response curves revealed that, with the increase in the monthly temperature fluctuations, the probability of presence decreases (Figure 3).

3.2. Potential Suitability and Projected Area of Occupancy (AOO) under Climate Change Scenarios and Dispersal Scenarios

The potential suitable habitat under the present climate was reduced after adjustment to the global land cover map (Figure 4) to exclude inaccessible areas such as urban areas, wetlands, and croplands. The potential changes in habitat suitability and the percentage of loss in the AOO were slightly similar under both dispersal scenarios and all climate change scenarios (Table 2). The limited dispersal scenario showed a higher AOO loss percentage than the full dispersal scenario under all the climate change scenarios (Table 2).
Under the low climate change scenario (SSP126) for both near and far futures, the southeast part of China was the most likely suitable habitat (green colours), especially in Guizhou, Guangxi, Guangdong, Fujian, Zhejiang, Jiangxi, Hunan, Chonqing, and Hubei (Figure 5). In the northern direction it will expand its stable area, including the northwestern part of Yunnan in the Hengduan Mountains. With the increase in elevation, the northern parts of Xizang and Qinghai Provinces were not suitable for the Chinese Weeping Cypress.
On the other hand, under the high scenarios of climate change (SSP585), the stable area was higher under the far future scenario than the near future scenario (Figure 5). Compared to the low scenario, the loss area was higher, and the stable area decreased. The loss area was observed to be larger in Shaanxi, followed by Sichuan, Hainan, and Taiwan. The gain area was observed mostly in Central Yunnan, and in some regions of Xizang and Sichuan Provinces under all the scenarios.

3.3. Potential Changes in Conservation Status under Climate Change Scenarios

Based on the percentage of loss in the AOO according to the IUCN Red List criterion A3(C) under both climate and dispersal scenarios (Table 2), the projected status of extinction risk was predicted to be uplisted as “Near Threatened” under the climate change scenario SSP585 of the near future ranging from 2021–2040. This loss percentage in the AOO was the highest, with a value of 15% under the limited dispersal scenario under the SSP585_2021-2040 scenario. Under the other scenarios of climate change, the conservation status was downlisted to LC, “Least Concern” (Table 2).

4. Discussion

4.1. Distribution Modelling and Conservation Assessment

The Southeastern provinces with low elevation were the most suitable regions for Funeral Cypress distribution in China, and this is in accordance with [33], who elucidated that the Southeast was a suitable area for old conifer trees in China. Moreover, Ref. [34] revealed that there is a negative correlation between the spatial pattern of Funeral Cypress and micro-topography. Furthermore, the probability of the presence of Funeral Cypress is mostly influenced by mean diurnal range (Bio2), temperature seasonality (Bio4), and precipitation of the warmest quarter (Bio18). A recent study by [16] elucidated that the seasonal variation coefficients of temperature and annual precipitation are the key factors that determine the potential distribution of Funeral Cypress.
The more the temperatures become extreme, the more minimum temperatures decline. Consequently, the mean annual diurnal temperature range increases [35]. C. funebris, like many other conifers, has adapted to a range of diurnal temperature ranges in its native range in China [36]. Several physiological and morphological traits are involved in C. funebris adaptation to diurnal temperature ranges. For example, the species possesses tiny, needle-like leaves that aid in controlling temperature and reducing water loss. Additionally, the leaves have a waxy layer that shields the tree from temperature extremes and helps to reduce water loss [16]. Furthermore, the deep root system in C. funebris also aids in accessing water and nutrients from deeper soil layers, which can assist this conifer in resisting drought and temperature changes. The species is also adaptable to a variety of soil conditions, which may affect how well it resists changes in temperature. A recent study by [37] proved that the root system of C. funebris would utilize a variety of strategies, including increasing the number of internal links, extending root links, increasing the rate at which low-order fine roots branch, minimizing the quantity of external links, and switching the ‘root system architecture’ from a herringbone to a dichotomous branching pattern to enhance water uptake, as an adaptation to drought and climatic changes. Overall, the species is adaptive to diurnal range through a combination of morphological, genetic, or physiological characteristics. Nevertheless, high temperature ranges may potentially negatively affect the existence of the species.
The moisture and nutrient availability in the soil are impacted by precipitation, which is a significant environmental factor that affects C. funebris development, distribution, and physiology [16]. A recent study by [34] clarified that C. funebris grows best under long-duration moderate rainfall. It prefers moderately moist soils and can tolerate drought once established. However, water stress and excessive rainfall can restrict both its growth and survival.

4.2. Impact of Climate Change on Habitat Suitability and AOO Loss

Indeed, because of the prevalence of severe climatic events, it is evident that populations of old coniferous trees are declining rapidly in many places of the world [38]. A recent study by [15,17] investigated the impact of climate change on conifer forest vegetation and endemic dominant conifers in Southwestern China. They found that many dominant conifer species, particularly species with narrow temperature tolerances, are at a high risk of extinction under future climate scenarios. It was noted that the loss of these conifers could have significant ecological and economic consequences, revealing the necessity for conservation strategies that consider the complex interactions between climate, land use, and biodiversity. Overall, such studies provide important insights into the ongoing decline of conifer forests under climate change scenarios and underscore the urgent need for conservation action to protect these valuable ecosystems.
Species dispersal is essential for the prediction of how future climate change may influence species distribution [39]. In this current study, the limited dispersal scenario revealed a larger AOO loss percentage than the full dispersal scenario under all the climate change scenarios. In a limited dispersal scenario, reduced gene flow between populations can result from individuals within a species being unable to move widely and spread throughout the environment [40]. Due to the accumulation of harmful mutations, decreased genetic diversity, and increased inbreeding that can follow from this, the probability of habitat loss, continuing decline, and local extinction of the species may ultimately increase, thus decreasing the resilience of the species to environmental changes [40].
In this current study, climate change is projected to increase the potential distribution of C. funebris. This expectation agrees to some extent with that of [16], who predicted an increase in the range of 12 widely distributed coniferous tree species, including C. funebris, under future climate change scenarios. Areas with potential climatically suitable habitats, such as Guangxi, Fujian, and Hubei, might expand to some extent. Vast mountainous regions will have more coniferous forests in the future relative to the current state. The stable area will expand in a northern direction, including to the northwestern part of Yunnan in the Hengduan Mountains. Current annual precipitation in the vast mountainous areas at the junction of Central Yunnan and Sichuan is 700–1200 mm, but under future climate scenarios, the average annual precipitation will increase by 200 mm [16], which means that the Southwest might become a promising gain area for C. funebris reintroduction through afforestation programs over time. The area is projected to harbour potential climatically suitable habitats for other coniferous trees such as Yunnan pine, dry cedar, and Picea oleifera as a result of the changes in the isothermality and seasonal changes in temperature [16].

4.3. Status under Future Scenarios and Conservation Implications

Habitat loss and overexploitation of C. funebris for its timber and for ornamental and medicinal purposes are major threats to this coniferous tree. For this reason, the status of C. funebris is projected to be downlisted to “Near Threatened” under the SSP585_2021–2040 scenario and to “Least Concern” under all other investigated climate and dispersal scenarios. Despite the species’ high capacity for adaptation to environmental changes, part of its AOO might be lost under severe climate change conditions.
Indeed, effective conservation management should be advocated to contain the consequences of climatic changes and restrain the effects of its associated threats. Projected expansion in potential climatically suitable habitats might help in mitigating the future pressures brought about by global warming. Furthermore, conservation specialists may give priority to the ‘stable’ habitat area predicted by this study for conservation efforts in the near future, which should be informed by in-depth assessments in the field. Finding and protecting climate change refugia—which is a region comparatively protected from human-induced changes that cause habitat loss, degradation, and fragmentation—is one of the conservation strategies in response to climatic change. This approach can contribute to the efforts aiming to maintain the sustainability of natural resources [41]. Building corridors between protected areas to enhance C. funebris habitat connectivity is highly recommended [16]. In addition, the ‘gain’ areas can provide promising spots for the reintroduction of this coniferous tree through afforestation and plantation activities in the near future. In this current study, bioclimatic variables are only one element considered; however, future studies should also evaluate additional factors that affect C. funebris distribution, such as competition with other species, human disturbance, socioeconomic status, soil conditions, and landform.
Overall, this study provides important insights into the potential gain/loss of habitat suitability of C. funebris under climate change scenarios in China and highlights the urgent need for conservation action to protect this valuable species and the supporting ecosystems. The findings can inform decision-making related to management plans for the mitigation of the impacts of climate change on forest ecosystems in China and can also serve as a basis for further research into the complex ecological and economic interactions in the region. Finally, to comprehensively evaluate how climate change and changes in land use affect suitable habitats for J. phoenicea, it is important to consider the limitations related to the scale of space and future land use scenarios. Integrative modelling approaches should consider these limitations to assess the combined impact of climate and land use changes on the species’ habitats. By doing so, this information becomes valuable for guiding general conservation planning efforts. It helps address concerns such as habitat fragmentation, connectivity, and the species’ ability to disperse across different areas, thus aiding in the development of effective conservation strategies.

5. Conclusions

To decipher the effect of climate change on C. funebris distribution patterns, an ensemble modelling approach was applied to predict changes under future climatic conditions and dispersal scenarios in climatic suitability for the conifer species. The results showed that the threatened conifer tree will be mainly concentrated in mountainous forest areas and that the species will contract in distribution and move northward under climate change. Forest management efforts need to be directed towards reducing the overexploitation of C. funebris for its timber and for ornamental and medicinal purposes to reduce the risk of the future downlisting of the species conservation status under climate scenarios. Finally, the expanding stable area in the northwestern part of Yunnan in the Hengduan Mountain forests can serve as a promising area for C. funebris reintroduction through afforestation programs.

Author Contributions

Conceptualization, methodology, software, validation, and formal analysis, J.Y., Q.W. and M.A.D.; resources, J.Y. and Q.W.; data curation, M.A.D., M.S.F., J.Y. and Q.W.; writing—original draft, M.W.A.H., H.B., M.A.D. and M.S.F. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Natural Science Foundation of China (32071747), the National Natural Science Foundation of Sichuan Province (2022NSFSC0087), the Innovation Team Project of Mianyang Normal University (CXTD2023LX01), the Scientific research initiation project of Mianyang Normal University (QD2019A13, QD2021A37, QD2023A01) and from the Open Project from the Ecological Security and Protection Key Laboratory of Sichuan Province (ESP201302, ESP2008).

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

Mai S. Fouad would like to dedicate this work to the soul of her father Sayed Fouad (Late professor in Zoology Department, Faculty of Science, Fayoum University), who was her first instructor and who set her on the right path.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Xie, D.; Du, H.; Xu, W.H.; Ran, J.H.; Wang, X.Q. Effects of climate change on richness distribution patterns of threatened conifers endemic to China. Ecol. Indic. 2022, 136, 108594. [Google Scholar] [CrossRef]
  2. Farjon, A. A Handbook of the World’s Conifers; Brill: Boston, MA, USA, 2017. [Google Scholar]
  3. Shi, G.; Zhou, Z.; Xie, Z. Cupressus foliage shoots and associated seed cones from the Oligocene Ningming Formation of Guangxi, South China. Rev. Palaeobot. Palynol. 2011, 166, 325–334. [Google Scholar] [CrossRef]
  4. Duquesnoy, E.; Dinh, N.H.; Castola, V.; Casanova, J. Composition of a pyrolytic oil from Cupressus funebris Endl. of Vietnamese origin. Flavour Fragr. J. 2006, 21, 453–457. [Google Scholar] [CrossRef]
  5. Zhong, S.Y.; Jiang, H.Y.; Wu, L.P.; Liu, M.Y.; Yu, Y.R.; Li, C.R.; Huang, J. A new neolignan and a new biphenylpropanoid from the leaves of Cupressus funebris Endl. S. Afr. J. Bot. 2021, 139, 140–147. [Google Scholar] [CrossRef]
  6. Anonis, D.P. Woody notes in perfumery: Cedarwood and cedarwood derivatives, Part I. In Fragrance for Personal Care; Allured Publishing: Carol Stream, IL, USA, 2008; pp. 91–98. ISBN 9781932633337. [Google Scholar]
  7. Adams, R.P.; Li, S. The botanical source of Chinese cedarwood oil: Cupressus funebris or Cupressaceae species? J. Essent. Oil Res. 2008, 20, 235–242. [Google Scholar] [CrossRef]
  8. Kuiate, J.R.; Bessière, J.M.; Zollo, P.H.A.; Kuate, S.P. Chemical composition and antidermatophytic properties of volatile fractions of hexanic extract from leaves of Cupressus lusitanica Mill. from Cameroon. J. Ethnopharmacol. 2006, 103, 160–165. [Google Scholar] [CrossRef]
  9. Xiang, Q.; Christian, T.; Zhang, D. Cupressus funebris. In The IUCN Red List of Threatened Species; e.T42218A2962455; International Union for Conservation of Nature (IUCN): Gland, Switzerland, 2013. [Google Scholar] [CrossRef]
  10. Sung, W.; Yan, X. China Species Red List; Red List Higher Education Press: Beijing, China, 2004; Volume 1. [Google Scholar]
  11. Oliver, T.H.; Smithers, R.J.; Beale, C.M.; Watts, K. Are existing biodiversity conservation strategies appropriate in a changing climate? Biol. Conserv 2016, 193, 17–26. [Google Scholar] [CrossRef]
  12. Yi, Y.j.; Cheng, X.; Yang, Z.F.; Zhang, S.H. Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China. Ecol. Eng. 2016, 92, 260–269. [Google Scholar] [CrossRef]
  13. Harris, R.M.B.; Grose, M.R.; Lee, G.; Bindoff, N.L.; Porfirio, L.L.; Fox-Hughes, P. Climate projections for ecologists. Wiley Interdiscip. Rev. Clim. Chang. 2014, 5, 621–637. [Google Scholar] [CrossRef]
  14. Dyderski, M.K.; Pa´z, S.; Frelich, L.E.; Jagodzi´nski, A.M. How much does climate change threaten European forest tree species distributions? Global Chang. Biol. 2018, 24, 1150–1163. [Google Scholar] [CrossRef]
  15. Dakhil, M.A.; Xiong, Q.; Farahat, E.A.; Zhang, L.; Pan, K.; Pandey, B.; Huang, D. Past and future climatic indicators for distribution patterns and conservation planning of temperate coniferous forests in southwestern China. Ecol. Indic. 2019, 107, 105559. [Google Scholar] [CrossRef]
  16. Chen, Y.; LÜ, Y.; Yin, X. Predicting habitat suitability of 12 coniferous forest tree species in southwest China based on climate change. J. Nanjing For. Univ. 2019, 43, 113–120. [Google Scholar]
  17. Dakhil, M.A.; Halmy, M.W.A.; Liao, Z.; Pandey, B.; Zhang, L.; Pan, K.; El-Barougy, R.F. Potential risks to endemic conifer montane forests under climate change: Integrative approach for conservation prioritization in southwestern China. Landsc. Ecol. 2021, 36, 3137–3151. [Google Scholar] [CrossRef]
  18. Li, G.; Xiao, N.; Luo, Z.; Liu, D.; Zhao, Z.; Guan, X.; Zang, C.; Li, J.; Shen, Z. Identifying conservation priority areas for gymnosperm species under climate changes in China. Biol. Conserv. 2021, 253, 108914. [Google Scholar] [CrossRef]
  19. Bellard, C.; Bertelsmeier, C.; Leadley, P.; Thuiller, W.; Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 2012, 15, 365–377. [Google Scholar] [CrossRef]
  20. Guo, F.; Lenoir, J.; Bonebrake, T.C. Land-use change interacts with climate to determine elevational species redistribution. Nat. Commun. 2018, 9, 1315. [Google Scholar] [CrossRef]
  21. Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017, 37, 4302–4315. [Google Scholar] [CrossRef]
  22. Wang, L.; Chen, W. A CMIP5 multimodel projection of future temperature, precipitation, and climatological drought in China. Int. J. Climatol. 2014, 34, 2059–2078. [Google Scholar] [CrossRef]
  23. IUCN Guidelines for Using the IUCN Red List Categories and Criteria.2022, Ver. 15.1. Available online: https://www.iucnredlist.org/resources/redlistguidelines (accessed on 1 January 2023).
  24. Naimi, B. USDM: Uncertainty Analysis for Species Distribution Models; R Foundation for Statistical Computing: Vienna, Austria, 2015; 12p. [Google Scholar]
  25. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2012. [Google Scholar]
  26. Guisan, A.; Thuiller, W.; Zimmermann, N.E. Habitat Suitability and Distribution Models: With Applications in R; Cambridge University Press: Cambridge, UK, 2017. [Google Scholar]
  27. Thuiller, W.; Guéguen, M.; Renaud, J.; Karger, D.N.; Zimmermann, N.E. Uncertainty in ensembles of global biodiversity scenarios. Nat. Commun. 2019, 10, 1446. [Google Scholar] [CrossRef]
  28. Naimi, B.; Araújo, M.B. sdm: A reproducible and extensible R platform for species distribution modelling. Ecography 2016, 39, 368–375. [Google Scholar] [CrossRef]
  29. Liu, C.; Newell, G.; White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 2016, 6, 337–348. [Google Scholar] [CrossRef] [PubMed]
  30. Keith, D.A.; Akçakaya, H.R.; Murray, N.J. Scaling range sizes to threats for robust predictions of risks to biodiversity. Conserv. Biol. 2018, 32, 322–332. [Google Scholar] [CrossRef] [PubMed]
  31. Dauby, G.; Stévart, T.; Droissart, V.; Cosiaux, A.; Deblauwe, V.; Simo-Droissart, M.; Sosef, M.S.M.; Lowry, I.I.P.P.; Schatz, G.E.; Gereau, R.E.; et al. ConR: An R package to assist large-scale multispecies preliminary conservation assessments using distribution data. Ecol. Evol. 2017, 7, 11292–11303. [Google Scholar] [CrossRef]
  32. Kaky, E.; Gilbert, F. Assessment of the extinction risks of medicinal plants in Egypt under climate change by integrating species distribution models and IUCN Red List criteria. J. Arid Environ. 2018, 170, 103988. [Google Scholar]
  33. Xie, C.; Yu, X.; Liu, D.; Fang, Y. Modelling suitable habitat and ecological characteristics of old trees using DIVA-GIS in Anhui Province, China. Pol. J. Environ. Stud. 2020, 29, 1931–1943. [Google Scholar] [CrossRef]
  34. Wu, B.; Qi, S. Effects of underlay on hill-slope surface runoff process of Cupressus funebris Endl. plantations in southwestern China. Forests 2021, 12, 644. [Google Scholar] [CrossRef]
  35. Evans, B.J.; Lyons, T. Bioclimatic extremes drive forest mortality in southwest, Western Australia. Climate 2013, 1, 28–52. [Google Scholar] [CrossRef]
  36. Bannister, P.; Neuner, G. Frost resistance and the distribution of conifers. In Conifer Cold Hardiness; Springer: Berlin/Heidelberg, Germany, 2001; pp. 3–21. [Google Scholar]
  37. He, W.; Luo, C.; Wang, Y.; Wen, X.; Wang, Y.; Li, T.; Chen, G.; Zhao, K.; Li, X.; Fan, C. Response strategies of root system architecture to soil environment: A case study of single-species Cupressus funebris plantations. Front. Plant Sci. 2022, 13, 965. [Google Scholar] [CrossRef]
  38. Lindenmayer, D.B.; Laurance, W.F.; Franklin, J.F. Global decline in large old trees. Science 2012, 338, 1305–1306. [Google Scholar] [CrossRef]
  39. Årevall, J.; Early, R.; Estrada, A.; Wennergren, U.; Eklöf, A.C. Conditions for successful range shifts under climate change: The role of species dispersal and landscape configuration. Divers. Distrib. 2018, 24, 1598–1611. [Google Scholar] [CrossRef]
  40. Bateman, B.L.; Murphy, H.T.; Reside, A.E.; Mokany, K.; VanDerWal, J. Appropriateness of full-, partial-and no-dispersal scenarios in climate change impact modelling. Divers. Distrib. 2013, 19, 1224–1234. [Google Scholar] [CrossRef]
  41. Rojas, I.M.; Jennings, M.K.; Conlisk, E.; Syphard, A.D.; Mikesell, J.; Kinoshita, A.M.; West, K.; Stow, D.; Storey, E.; De Guzman, M.E. A landscape-scale framework to identify refugia from multiple stressors. Conserv. Biol. 2022, 36, e13834. [Google Scholar] [CrossRef] [PubMed]
Forests 14 02234 g001
Figure 1. Elevation map with the distribution of the occurrence records of C. funebris in China.
Figure 1. Elevation map with the distribution of the occurrence records of C. funebris in China.
Forests 14 02234 g001
Forests 14 02234 g002
Figure 2. Flowchart showing step-by-step spatial and ensemble modelling analysis (Modified from [17]).
Figure 2. Flowchart showing step-by-step spatial and ensemble modelling analysis (Modified from [17]).
Forests 14 02234 g002
Forests 14 02234 g003
Figure 3. Response curves of the predictor variables used in the distribution modelling of C. funebris. Abbreviations of the bioclimatic variables are described in Table 1.
Figure 3. Response curves of the predictor variables used in the distribution modelling of C. funebris. Abbreviations of the bioclimatic variables are described in Table 1.
Forests 14 02234 g003
Forests 14 02234 g004
Figure 4. Potential habitat suitability for C. funebris under the present climate: (a) Not adjusted to the global land cover map; (b) Adjusted to the global land cover map.
Figure 4. Potential habitat suitability for C. funebris under the present climate: (a) Not adjusted to the global land cover map; (b) Adjusted to the global land cover map.
Forests 14 02234 g004
Forests 14 02234 g005aForests 14 02234 g005bForests 14 02234 g005c
Figure 5. Potential changes in habitat under the four different climate change scenarios: (a) SSP126 in 2030; (b) SPP126 in 2090; (c) SSP585 in 2030; (d) SSP585 in 2090.
Figure 5. Potential changes in habitat under the four different climate change scenarios: (a) SSP126 in 2030; (b) SPP126 in 2090; (c) SSP585 in 2030; (d) SSP585 in 2090.
Forests 14 02234 g005aForests 14 02234 g005bForests 14 02234 g005c
Table 1. Model summary and relative importance of the selected predictor variables explaining the potential distribution of C. funebris. Correlated variables with variance inflation factor (VIF) values > 5 and a correlation threshold of 0.75 were removed to avoid multicollinearity problems. Averages of the true skill statistic (TSS) and area under the curve (AUC) indicate the accuracy of the ensemble models. MTSS threshold represents the maximum training sensitivity plus specificity.
Table 1. Model summary and relative importance of the selected predictor variables explaining the potential distribution of C. funebris. Correlated variables with variance inflation factor (VIF) values > 5 and a correlation threshold of 0.75 were removed to avoid multicollinearity problems. Averages of the true skill statistic (TSS) and area under the curve (AUC) indicate the accuracy of the ensemble models. MTSS threshold represents the maximum training sensitivity plus specificity.
CodeDescriptionRelative Importance (%)VIFRange
Min.Max.
Bio2mean diurnal range (°C)59.71.416.0215.13
Bio4temperature seasonality (SD × 100)10.61.94413.71141
Bio8mean temperatures of the wettest quarter (°C)3.91.368.728.81
Bio14precipitation of the driest month (mm)5.01.271.04151.3
Bio18precipitation of the warmest quarter (mm)5.51.94184.21021.7
Ensemble Model Summary
AUC0.94 ± 0.05
TSS0.84 ± 0.14
MTSS Threshold0.47 ± 0.10
Table 2. Loss percentage in the area of occupancy (AOO) of C. funebris under the four climate change scenarios and the two dispersal scenarios. Proposed conservation status according to IUCN Red List criterion (AOO). LC is the least concern status, while NT means near threatened.
Table 2. Loss percentage in the area of occupancy (AOO) of C. funebris under the four climate change scenarios and the two dispersal scenarios. Proposed conservation status according to IUCN Red List criterion (AOO). LC is the least concern status, while NT means near threatened.
Climate Change ScenarioAOO Loss %Proposed IUCN
Status
Full Dispersal Limited Dispersal
SSP126 (2021–2040)11.3313.50LC
(2081–2100)10.2312.29LC
SSP585 (2021–2040)12.1815.11NT
(2081–2100)11.4813.61LC
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yang, J.; Wu, Q.; Dakhil, M.A.; Halmy, M.W.A.; Bedair, H.; Fouad, M.S. Towards Forest Conservation Planning: How Temperature Fluctuations Determine the Potential Distribution and Extinction Risk of Cupressus funebris Conifer Trees in China. Forests 2023, 14, 2234. https://doi.org/10.3390/f14112234

AMA Style

Yang J, Wu Q, Dakhil MA, Halmy MWA, Bedair H, Fouad MS. Towards Forest Conservation Planning: How Temperature Fluctuations Determine the Potential Distribution and Extinction Risk of Cupressus funebris Conifer Trees in China. Forests. 2023; 14(11):2234. https://doi.org/10.3390/f14112234

Chicago/Turabian Style

Yang, Jingtian, Qinggui Wu, Mohammed A. Dakhil, Marwa Waseem A. Halmy, Heba Bedair, and Mai Sayed Fouad. 2023. "Towards Forest Conservation Planning: How Temperature Fluctuations Determine the Potential Distribution and Extinction Risk of Cupressus funebris Conifer Trees in China" Forests 14, no. 11: 2234. https://doi.org/10.3390/f14112234

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop