impacts of climate change on medicinal plants in vietnam an assessment

1 THAI NGUYEN UNIVERSITY UNIVERSITY OF AGRICULTURAL AND FORESTRY VU THU TRA IMPACTS OF CLIMATE CHANGE ON MEDICINAL PLANTS IN VIETNAM: AN ASSESSMENT Study Mode: Full-time Major: Environme

1 THAI NGUYEN UNIVERSITY

UNIVERSITY OF AGRICULTURAL AND FORESTRY

VU THU TRA

IMPACTS OF CLIMATE CHANGE ON MEDICINAL PLANTS IN VIETNAM: AN ASSESSMENT

Study Mode: Full-time

Major: Environmental Science and Management Faculty: Advanced Education Program Office Batch: K50

Thai Nguyen, 2023

RESEARCH REPORT

DOCUMENTATION PAGE WITH ABSTRACT

Thai Nguyen University of Agriculture and Forestry

Degree Program Bachelor of Environmental Science and Management Student name Vu Thu Tra

The study results show that climate change will likely significantly influence thedistribution and abundance of medicinal plants in Vietnam The models predict thatmany species are likely to experience range shifts and reductions in suitable habitatunder future climate scenarios The study also highlights the potential for certainspecies to benefit from climate change, particularly those with ranges that expandinto newly suitable habitats

Overall, the research offers valuable insights into the potential effects of climatechange on medicinal plants in Vietnam and identifies the species most at risk Theresults can inform the development of conservation strategies to protect theseimportant medicinal plant species in the face of climate change

Keywords: Climate change, Maxent, Occurrence, Prediction, Coscinium

fenestratum, Morinda officinalis, Anoectochilus setaceus, Stephania japonica, Panax bipinnatifidus, Panax

vietnamensis, Cupressus torulosa, Taxus wallichiana, Berberis julianae.

Number of pages: Date of

Submission:

ACKNOWLEDGEMENT

I wish to start by expressing my sincere appreciation to everyone who helped this

research endeavor come to fruition., Impacts of Climate Change on Medicinal Plants in Vietnam: An Assessment This work would not have been possible without the generous support, guidance, and encouragement from various individuals and organizations I acknowledge and appreciate the collective effort that has gone into making this study a reality, and I would like to thank you in the most sincere way possible every individual who has played a role in shaping this thesis

Firstly, I would like to express my sincere gratitude to Thai Nguyen University of Agriculture and Forestry for their support and resources throughout the course of this research This study was completed successfully thanks in large part to the university's devotion to providing a supportive research environment and its commitment to academic achievement I am thankful for the opportunities provided by the university to engage in meaningful scientific inquiry and for the valuable guidance received from the faculty members and research advisors

I also want to convey my admiration and sincere thanks to my instructor, Truong Thi Anh Tuyet Ph.D., for her invaluable guidance and support throughout this research journey Her expertise, knowledge, and dedication have been instrumental in shaping my understanding of the subject matter and refining my research skills I am genuinely grateful for her unwavering commitment to my academic and personal growth and for the opportunities provided to me under her mentorship Her insightful feedback, encouragement, and willingness to share her expertise have been invaluable assets in the successful completion of the study

I want to sincerely thank the Advanced Education Program for all of their help and assistance with this thesis, especially their crucial advice From the initial stages of providing instructions and guidelines to the final stages of assisting with corrections and ensuring academic integrity, they have played a crucial role in shaping this research project

The program's commitment to excellence, prompt feedback, and encouragement have been instrumental in keeping me on track and motivated to complete this thesis within the designated timeframe I am very appreciative of the possibilities and materials the Advanced Education Program has made available to me, which have contributed immensely to my academic and personal growth

Last but not least, I want to express my sincere appreciation to my caring family and understanding friends for their unfailing support and understanding during this study project Their unwavering encouragement, confidence in my skills, and tolerance of the ups and downs of this journey have been a source of strength and inspiration for me Their unwavering presence, words of encouragement, and willingness to lend an ear or offer a helping hand have made this experience all the more meaningful and rewarding I am extremely fortunate to have such an amazing support system, and I will always be appreciative of their love, compassion, and unshakable faith in my abilities

Sincerely,

Thai Nguyen, July 2023

Vu Thu Tra

2.3 Research regarding Climate change impacts on species Occurrences 24

PART III METHODS 26

3.5 Species Distribution Modelling 30

3.6 Model validation 31

PART IV RESULTS 33

4.1 Model performance 33

4.2 Importance of environment variables 35

4.3 Current habitat suitability 39

4.4 Future potential distribution 48

PART V DISCUSSION AND CONCLUSION 67

5.1 Discussion 67

5.2 Conclusion 76

REFERENCES 77

Figure 3.4.1.a The forecasted suitable area for Coscinium fenestratum in Vietnam under

different climate scenarios 49

Figure 3.4.1.b Habitat map with appropriate levels - Coscinium fenestratum in Vietnam

under current and future climate conditions 49

Figure 3.4.2.a The forecasted suitable area for Morinda officinalis in Vietnam under

different climate scenarios 50

Figure 3.4.2.b Habitat map with appropriate levels - Morinda officinalis in Vietnam under

current and future climate conditions 51

Figure 3.4.3.a The forecasted suitable area for Anoectochilus setaceus in Vietnam under

different climate scenarios 52

Figure 3.4.3.b Habitat map with appropriate levels - Anoectochilus setaceus in Vietnam

under current and future climate conditions 53

Figure 3.4.4.a The forecasted suitable area for Stephania japonica in Vietnam under

different climate scenarios 54

Figure 3.4.4.b Habitat map with appropriate levels - Stephania japonica in Vietnam under

current and future climate conditions 55

Figure 3.4.5.a The forecasted suitable area for Berberis julianae in Vietnam under

different climate scenarios 56

Figure 3.4.5.b Habitat map with appropriate levels - Berberis julianae in Vietnam under

current and future climate conditions 57

Figure 3.4.6.a The forecasted suitable area for Panax vietnamensis in Vietnam under

different climate scenarios 59

Figure 3.4.6.b Habitat map with appropriate levels - Panax vietnamensis in Vietnam under

current and future climate conditions 59

Figure 3.4.7.a The forecasted suitable area for Cupressus torulosa in Vietnam under

different climate scenarios 60

Figure 3.4.7.b Habitat map with appropriate levels - Cupressus torulosa in Vietnam under

current and future climate conditions 61

Figure 3.4.8.a The forecasted suitable area for Taxus wallichiana in Vietnam under

different climate scenarios 62

Figure 3.4.8.b Habitat map with appropriate levels - Taxus wallichiana in Vietnam under

current and future climate conditions 63

Figure 3.4.9.a The forecasted suitable area for Panax bipinnatifidus in Vietnam under

different climate scenarios 64

Figure 3.4.9.b Habitat map with appropriate levels - Panax bipinnatifidus in Vietnam

under current and future climate conditions 65

LIST OF TABLES

Table 3.1 List of medicinal plant species used in the model 27

Table 3.2 The list of climatic environmental variables used in the model 29

Table 4.1 Model performance 33

Table 4.2 Contribution rates of environmental variables in current climate condition 38

Table 4.4.1 Contribution rates of environmental variables - Coscinium fenestratum 49

Table 4.4.2 Contribution rate of environmental variables - Morinda officinalis 51

Table 4.4.3 Contribution rate of environmental variables - Anoectochilus setaceus 53

Table 4.4.4 Contribution rates of environmental variables - Stephania japonica 55

Table 4.4.5 Contribution rates of environmental variables - Berberis julianae 57

Table 4.4.6 Contribution rates of environmental variables - Panax vietnamensis 59

Table 4.4.7 Contribution rates of environmental variables - Cupressus torulosa 61

Table 4.4.8 Contribution rates of environmental variables - Taxus wallichiana 63

Table 4.4.9 Contribution rates of environmental variables - Panax bipinnatifidus 65

LIST OF ABBREVIATIONS

RCP ……… Representative Concentration Pathway ROC ……… Receiver Operating Characteristic

PART I INTRODUCTION 1.1 Research rationale

The variety and distribution of therapeutic plants in Vietnam are significantly impacted by climate change (Applequist et al., 2020) Many plants species' habitat appropriateness is changing due to changes in patterns of rainfall and temperature, which is changing their range and distribution As a result, many medicinal plants are becoming increasingly rare, threatened, or endangered Additionally, changes in the timing and duration of seasons can affect the flowering, fruiting, and other phenological events, leading to a mismatch between pollinators and plants, and reducing the reproductive success of plant populations Climate change can also exacerbate the stress of medicinal plants, making them more susceptible to pests, diseases, and other biotic and abiotic factors These impacts can have serious consequences on the availability and quality of medicinal plant resources, as well as the traditional knowledge and practices associated with their use Hence, it is critical to determine how vulnerable medicinal plants are to climate change, come up with practical conservation plans, and encourage sustainable usage and management techniques In this study, a total of 9 species of precious medicinal

plants with many therapeutic effects were mentioned, including: (1) Coscinium

fenestratum (Gaertn.) Colebr., (2) Morinda officinalis FC How, (3) Anoectochilus setaceus Blume, (4) Stephania japonica (Thunb.) Miers, (5) Berberis julianae CKSchneid, (6)

Panax vietnamensis Ha & Grushv, (7) Cupressus torulosa D Don, (8) Taxus wallichiana Zucc and (9) Panax bipinnatifidus Seem The selection of these 9 medicinal plants for the

research was based on several important factors They are known to have significant medicinal value and are commonly used in traditional medicine practices in Vietnam They represent a diverse range of plant families and exhibit a variety of medicinal properties, making them of great interest for further study On the other hand, the plants selection criteria are based on their ecological and conservation significance Since many of them are unique to Vietnam or have small geographic ranges, they are especially sensitive to the

effects of climate change We can learn more about the possible impacts of climate change on botanical medicines at the species and ecosystem levels by researching these species Furthermore, the selected plants also have relevance in terms of cultural importance and economic value They are widely used in traditional medicine practices and play a vital role in local communities For the purpose of maintaining traditional knowledge and guaranteeing the ongoing availability of medicinal resources for future generations, it is crucial to comprehend the possible effects of climate change on these plants Lastly, the selected plants may have limited existing research on their response to climate change By concentrating on particular species, the study seeks to close information gaps and explain how climate change may affect their distribution

Because of such great medicinal uses, the above medicinal plants are always actively hunted, leading to a situation where demand exceeds supply, thereby pushing up their prices, and at the same time pushing them into the endangered group threatened in the IUCN Red List Besides, they also face the risk of changing land use area Land is exploited and used by people for many purposes such as: land for agricultural, forestry and fishery production, residential land, land for construction of infrastructure, land for production, business and services (Nguyen Van Hieu, 2018) Climate change has also greatly affected tree species (Applequist et al., 2020), specifically: increased temperature affects the ability of plants to grow, leading to changes in yield and output; at the same time, increasing temperature causes water resources to decrease, leading to a decrease in cultivated area; melting ice causes many lands to be encroached on and flooded; changes in climatic conditions will cause biodiversity loss and ecological imbalance; In addition, there will be an increase in extreme, irregular weather events (IPCC, 2007; Stern, 2009) The aforementioned factors have had a significant impact on the species' distribution area

For the purpose of creating efficient conservation and management plans to guarantee the long-term survival of these significant species, assessing the effects of climate change on medicinal plants is essential Thus, this thesis uses the MaxEnt program, a species distribution model (SDM), to evaluate the effects of climate change on a variety of

medicinal plants in Vietnam (Truong et al., 2017) This is an effective method for forecasting the distribution of medicinal plants under various climatic conditions The distribution of these medicinal plants under both present and future climatic scenarios will be modeled using a variety of environmental factors, including temperature and precipitation The findings of this study will aid in identifying the species at risk to climate change and guide the creation of conservation plans to save these significant species of medicinal plants

This study is crucial to clarifying the possible effects of climate change on the number and distribution of medicinal plants in Vietnam as well as for creating methods that mitigate these effects

1.2 Research’s objectives

The research project intends to assess the present and future effects of climate change on a variety of important medicinal plants in Vietnam To estimate the potential distribution of medicinal plants under the existing and predicted climatic conditions, the Species Distribution Model (SDM) will be utilized

1.3 Research questions and hypotheses

b The effects of climate change on the suitable habitat of the 9 medicinal plant species in Vietnam from 2050 to 2080 will result in significant shifts and

changes in their distribution patterns These changes may include range expansions, contractions, or shifts to higher elevations or latitudes

1.4 Limitations

MaxEnt (Maximun Entropy) modelling is a tool specialized for modeling species distribution, including medicinal plants While this software can be useful for assessing the probable impact of climate change on therapeutic plants, it is crucial to be aware of its limitations

MaxEnt software is reliant on the availability and quality of input data This means that the accuracy of the predictions generated by the software relies on the accuracy of the data that is fed into it If the data is incomplete or inaccurate, the predictions generated by the software may be unreliable or misleading

MaxEnt software assumes that species distribution is driven by environmental variables While this assumption may be true in some cases, it does not take into account other important factors that can influence the distribution of medicinal plants For instance, human activities such as logging and deforestation can impact the distribution of medicinal plants, and the software may not be able to model the effects of these activities

Additionally, the program also makes the assumption that species and their environments are in balance Yet, this is often not the case in reality, particularly in the case of invasive species or changing environments due to climate change This means that the predictions generated by the software may not reflect the current or future distribution of medicinal plants

Finally, it is important to note that the outputs generated by MaxEnt software should be interpreted with caution, as they represent potential distributions rather than actual distributions This means that the software can be a useful tool for identifying areas where proposing threats to medicinal plants due to climate change, but it should not be the only tool used in evaluating the effects of climate change on therapeutical plants

In conclusion, while MaxEnt software can be a valuable tool for assessing the potential effects of climate change on medicinal plants, it is vital to acknowledge its limitations and use it in conjunction with other methods to ensure that the outcomes are reliable and accurate

(2) Species Distribution Models (SDMs): Based on known occurrences or presence data, these models are used to forecast the possible distribution of a species in a specific habitat or geographical area SDMs connect environmental factors, such as temperature and precipitation, to the presence of a species using statistical and

mathematical methodologies MaxEnt software is an example of an SDM (Phillips et al., 2006)

(3) Ecological Niche Model (ENM): This kind of SDM simulates the environmental conditions necessary for a species to thrive and procreate The ENM is based on the idea that each species has a specific set of environmental conditions in which it can thrive, and by modeling these conditions, we can forecast the distribution of the species MaxEnt is an example of an ENM (Phillips et al., 2006)

(4) Area Under the Curve (AUC): This is a measurement of the accuracy of a model that predicts the occurrence or absence of a species in a specific environment AUC varies between 0 and 1, with higher numbers suggesting more accuracy A measurement of 0.5 indicates a model that is no better than random chance (Phillips, 2005)

(5) Representative Concentration Pathway (RCP): This is a set of scenarios used by climate scientists to project future greenhouse gas concentrations and resulting climate change The RCPs are named based on their predicted radiative force (the difference between incoming and outgoing radiation in the atmosphere) in the year 2100 (Van et al., 2011) Specifically, RCP2 represents a lower emission scenario, indicating relatively moderate greenhouse gas emissions and a more gradual increase in global temperatures It is based on the idea that substantial efforts will be undertaken to cut greenhouse gas emissions and slow down climate change On the other hand, RCP8 represents a higher emission scenario, implying a continuation of high greenhouse gas emissions throughout the 21st century It projects more significant increases in global temperatures and more severe climate impacts This scenario assumes limited efforts to reduce emissions and a higher dependency on fossil fuels

(6) Receiver Operating Characteristic (ROC): This is a curve that plots the true positive rate (i.e., the proportion of actual species occurrences correctly predicted by the model) against the false positive rate (i.e., the proportion of non-occurrences mistakenly predicted as occurrences) at various threshold values AUC is a measure of the accuracy of the model in predicting species occurrences or absences (Phillips, 2005)

(7) Jackknife of regularized training gain: This statistical technique is employed by MaxEnt to assess the effectiveness and prognostication of species distribution models With this approach, each data point is methodically eliminated one at a time, and the model is then re-run to see how much each item contributes to the model's overall performance The jackknife process is used to choose potentially important data points and to quantify the variability of the model's performance The regularized training gain measures how well the model matches the training data The jackknife of regularized training gain, which combines these two methods, offers a thorough evaluation of the model's predictive ability and can aid in locating regions of ambiguity or potential sources of bias (Phillips et al., 2006)

(8) Marginal response curves: This is used to show how the spread of a species and environmental factors are related Marginal response curves demonstrate how, while maintaining all other factors constant, a species' likelihood of occurrence changes when the value of a single environmental variable changes The functional link between the species and the environment may be visually explored using marginal response curves, which plot the expected likelihood of occurrence versus the value of an environmental variable This can be helpful for comprehending a species' ecological needs, figuring out the environmental constraints on its range, and forecasting how future environmental changes would influence its habitat (Phillips et al., 2006)

1.5.2 Medicinal plants

(1) Taxonomy: Taxonomy is a scientific discipline focused on the classification, nomenclature, and identification of organisms It involves organizing species into a hierarchical system that reflects their evolutionary relationships and helps in understanding the diversity of life

(2) Phenological Events: Phenological events are recurring life cycle stages in plants and animals that are influenced by seasonal changes These events encompass activities such as flowering, fruiting, migration, and hibernation, providing insights into the biological responses to environmental cues

(3) Climatic Environmental Variables: Climatic environmental variables are quantifiable aspects of the climate system, encompassing factors like temperature, precipitation, humidity, and solar radiation They play a pivotal role in shaping the ecological dynamics and distributions of species

(4) Species Occurrences: Species occurrences refer to the documented presence of particular species within specific geographic areas This information aids in mapping the distribution and understanding the geographic range of various organisms

(5) Habitat Suitability: Habitat suitability signifies the degree to which a specific environment provides the requisite conditions for a particular species to thrive and reproduce It considers various ecological factors, including food availability, shelter, and climate suitability

(6) Future Potential Distribution: Future potential distribution denotes the anticipated geographic range where a species could inhabit based on projected changes in environmental conditions It involves modeling species responses to future climatic scenarios, assisting in predicting potential shifts in their habitats

PART II LITERATURE REVIEW

2.1 Climate change

Globally acknowledged issue, climate change has recently attracted more attention It deals with long-term changes in temperature patterns, amounts of precipitation, and general weather patterns that are predominantly attributable to human activity According to the scientific community, greenhouse gas emissions from human activity are the main cause of climate change (Change, IPOC 2007)

The quantities of greenhouse gases in the atmosphere have dramatically grown as a result of human activity, particularly the combustion of fossil fuels like natural gas, petroleum, and coal (Pimm et al., 1995) The greenhouse effect is a phenomena wherein certain gases, such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), retain heat inside the Earth's atmosphere and cause an increase in global temperatures According to the Intergovernmental Panel on Climate Change, the concentrations of these greenhouse gases have risen to previously unheard-of heights since the beginning of the industrial era (Assessment ME, 2005) In addition, industrial operations, land use changes, and deforestation all contribute to greenhouse gas emissions (Foley et al., 2005)

Climate change has far-reaching consequences for natural systems and ecosystems It affects various aspects of the environment, including temperature patterns, precipitation regimes, sea levels, and extreme weather events (Change, IPOC 2007) Sea levels are rising as a result of melting glaciers and polar ice caps brought on by an increase in global temperatures Due to increasing hazards of floods, erosion, and salinization of freshwater supplies, coastal locations are particularly susceptible to this issue Changes in weather patterns have also been observed, leading to alterations in rainfall distribution and intensification of droughts in some regions These changes can have significant implications for agriculture, water resources, and food security (Stern et al., 2007)

Moreover, altered climatic conditions can impact biodiversity, leading to species range shifts, changes in phenology, and increased extinction risks (Thomas et al., 2004)

Combating climate change necessitates the use of both mitigation and adaptation strategies By switching to renewable energy sources, improving the conservation of energy, and enacting sustainable land-use techniques, mitigation aims to lower greenhouse gas emissions Global warming is intended to be kept far below 2 degrees Celsius above pre-industrial levels, and efforts are being made to keep the temperature rise to 1.5 degrees Celsius (UNFCCC, 2015) Building resilience and readiness to deal with the consequences of climate change that are already being felt are the main goals of adaptation methods This includes measures such as improving disaster management systems, implementing sustainable agricultural practices, and enhancing infrastructure resilience

There are several issues associated with the complicated and diverse problem of climate change to our planet's ecosystems and human societies The scientific literature emphasizes how urgent it is to combat climate change using both adaptation and mitigation measures To reduce greenhouse gas emissions, safeguard vulnerable people, and conserve the planet's ecosystems for future generations, governments, scientists, and society as a whole must cooperate

2.2 Species Occurrences

Understanding the factors influencing species occurrence is fundamental to ecology and conservation biology Numerous elements, including abiotic and biotic variables, have an impact on the existence of species Abiotic factors, such as climate, topography, and soil characteristics, play a crucial role in determining species distributions (Phillips, 2008; Pearson, 2010; Figueiredo et al., 2018) Climatic factors, including temperature, precipitation, and seasonality, have been widely recognized as key drivers of species occurrence patterns These variables shape the availability of resources, influence physiological constraints, and impact species' adaptations to specific environments (Phillips, 2008) Biotic factors, including interactions with other species, competition,

predation, and mutualism, also influence species occurrence (Anderson, 2017) The presence or absence of specific species in an environment is significantly influenced by species interactions and ecological linkages For instance, competition for resources can limit the occurrence of certain species, while mutualistic interactions may facilitate coexistence (Palmer et al., 2003)

Researchers employ various methods and approaches to assess species occurrence patterns Field surveys, including presence-absence sampling and occupancy modelling, are commonly used to determine species presence or absence in specific locations (MacKenzie et al., 2004) Remote sensing and satellite imagery are valuable tools for mapping species distributions over larger spatial scales, providing valuable insights into habitat suitability and land cover changes SDMs have gained prominence in recent years for predicting species occurrence and potential habitat suitability In order to forecast species distributions based on observed occurrences and environmental factors, SDMs use environmental variables such climatic data, land cover, and terrain (Phillips et al., 2006) These models enable the assessment of prospective effects of climate change and habitat loss on species occurrence and can offer insightful information about present and future species distributions

Effective biodiversity conservation and management depend on an understanding of the variables that affect species occurrence The evaluation of species occurrence patterns is made easier by a variety of techniques and strategies, including field surveys, remote sensing, and species distribution modeling Further research is needed to delve into the complex interactions and mechanisms that drive species occurrence, especially in the context of changing environmental conditions and human-induced disturbances By integrating multidisciplinary approaches and considering the cumulative effects of multiple factors, we can improve our understanding of species occurrence patterns and implement more targeted conservation strategies to protect and sustainably manage biodiversity

2.3 Research regarding Climate change impacts on Species occurrences

Several studies have demonstrated how species ranges and occurrences are affected by climate change Warming temperatures and altered precipitation patterns have been linked to fluctuations in species distribution and phenology For instance, the observation of increasing global temperatures indicates a notable influence on plant phenology, particularly in terms of altering the temporal patterns of flowering events (Wolf et al., 2017) Revamping in temperature and rainfall can also influence the availability of suitable habitats for different species Research has shown that certain plant species are experiencing range contractions or expansions in response to changing climate conditions (Truong et al., 2017) These changes have the potential to reshape community structures and impact overall ecosystem functioning

Researchers have utilized various methods and approaches to assess climate change impacts on species occurrences SDM is commonly employed to predict possible shifts in species distributions based on climate projections (Forster et al., 2013) These models incorporate climate data, species occurrence records, and environmental variables to estimate changes in species ranges and habitat suitability Understanding the effects of global warming on species occurrences has benefited significantly by research studies using SDMs, particularly those using the Maxent program In order to create prediction models that evaluate prospective changes in species distributions and habitat suitability, this research have used climatic data, species occurrence records, and environmental factors Through the application of Maxent, researchers have gained insights into the changing ranges of various taxa, including plants, animals, and marine organisms, as well as their responses to altered climatic conditions These investigations have improved our knowledge of how shifting climatic patterns affect species distributions, which, in the context of climate change, can help direct conservation and management measures

In conclusion, changing climatic conditions have the potential to influence species distributions, community dynamics, and ecological interactions Species distribution

modelling has provided valuable insights into these impacts and the mechanisms underlying species responses To fully comprehend the intricacies of species reactions to climate change, including interactions with other environmental change agents and potential thresholds or tipping points, further study is required Such information is essential for efficient conservation and management methods that work to protect biodiversity worldwide and lessen the negative effects of global warming on species occurrences

PART III METHODS 3.1 Study Species

Plant species are selected based on 3 criteria: 1 It is a rare and valuable medicinal plant species

2 Being endangered or about to be threatened in Vietnam, according to (1) Government decree No 06/2019/NĐ-CP on Management of endangered, precious and rare species of forest fauna and flora and Observation of convention on international trade in endangered species of wild fauna and flora and (2) Vietnam Red List

3 To ensure the reliability of the model, the assessed species must have a record of the occurrence of at least 10 sites in the world

Table 2.1 provides a comprehensive compilation of important information about the selected plant species that were included in the modeling process This serves as a valuable reference for understanding the characteristics and attributes of each species within the context of this study The table includes several columns to facilitate a comprehensive overview of each species

• Scientific Name: the scientific or botanical name of the plant species, ensuring accuracy and precision in identification

• Familia: the taxonomic family to which each species belongs, providing insights into its classification and relationships with other plants

• Life Form: the overall pattern of development or existence of a species of plant, such as a tree, shrub, herb, or vine This knowledge aids comprehension of the ecological and physical aspects of the species

• Medical Uses: the various medicinal applications and traditional uses associated with each plant species This information underscores the

importance of these plants in traditional medicine practices and their potential significance in healthcare and well-being

• Distribution: insights into the native or natural distribution range of each species, indicating the geographic areas where they are commonly found This information is crucial for understanding the baseline distribution patterns and potential shifts in suitable habitats under changing climatic conditions

• Conservation Status in Vietnam Red List: the evaluation made by the Vietnam Red List regarding the conservation status of each plants This classification reflects the degree of vulnerability or threat faced by the species within the context of the country's conservation efforts

Table 3.1 List of medicinal plant species used in the model

No Science

name Familia Life form Medical uses Distribution

Conservation status in Vietnam

Gia Lai, Kon Tum, Lam Dong and southern provinces

Cao Bang, Bac Ninh, Son La, Lam Dong

Endangered

3 Anoectochilus

setaceus Ochidaceae Shrub

Blood circulation, antibacterial Cure bronchitis, hepatitis, Improve health, Reduce nervous breakdown

Cao Bang, Yen Bai, Hoa Binh, Lao Cai, Tuyen Quang, Lai Chau

Endangered

4 Stephania

japonica Menispermaceae Vines

Vertigo, headache, and sleep disturbances

Ninh Binh, Hoa Binh, Mekong Delta

5 Berberis

julianae Berberidaceae Shrubs

Antipyretic, liver disease and other digestive

ailments, e.g

digestive

disorders or lack of appetite 6 Panax

vietnamensis Araliaceae Perennial Anti-fatigue and life saving Kon Tum, Quang Nam, Lam Dong Endangered

7 Cupressus

torulosa Cupressaceae Evergreen conifer

Stomach pain, diabetes, inflammation, toothache, laryngitis and as contraceptive

Cao Bang, Tuyen Quang, Lang Son

Near Threatened

8 Taxus

wallichiana Taxaceae Evergreen conifer

Common cold, cough, fever, and pain

9 Panax

bipinnatifidus Araliaceae Perennial

Anaemia, general debility,

aphrodisiac, sterility treatment

Lao Cai, Ha Giang

Critically Endangered

3.2 Species occurrences

One of the crucial inputs to the model for predicting species distribution is data on species occurrence that has been observed The website for the Global Biodiversity Information Base (http://www.gbif.org/) is where these emerging data (Table 3.1) were found The observatory for CSP is called the Global Biodiversity Information Facility (GBIF) Governments support this worldwide platform for open access to data It enables everyone to have access to knowledge provided over the world wide web regarding the majority of living forms on Earth The duplicate data is sorted out in Excel once the data has been downloaded

3.3 Environmental variables

Modeling regions suited for nine medicinal plant species using a mix of climate and climate forecasting technologies Eleven temperature measures and eight metrics for precipitation were obtained from the WorldClim database (Table 2.2) (Hijmans et al., 2005) for the time period 1950–2000 (http://www.worldclim.org) Additionally, certain WorldClim data on climate are strongly connected and may result in precise forecasts

(Hijmans et al., 2005; Mbatudde et al., 2012) To regulate the connections between all of the WorldClim climate data for the Southeast Asia area, the Pearson correlation was performed To increase the effectiveness and precision of the prediction model, a variable with a significant correlation was eliminated

Table 3.2 The list of climatic environmental variables used in the model

Bio 2 Mean Diurnal Range (Mean of monthly (max temp - min temp))

°C * 10 Bio 3 Isothermality (BIO2/BIO7) (×100) °C * 10 Bio 4 Temperature Seasonality (standard

deviation ×100)

°C * 100 Bio 5 Max Temperature of Warmest Month °C * 10 Bio 6 Min Temperature of Coldest Month °C * 10 Bio 7 Temperature Annual Range (BIO5-

BIO6)

°C * 10 Bio 8 Mean Temperature of Wettest Quarter °C * 10 Bio 9 Mean Temperature of Driest Quarter °C * 10 Bio 10 Mean Temperature of Warmest Quarter °C * 10 Bio 11 Mean Temperature of Coldest Quarter °C * 10

Bio 13 Precipitation of Wettest Month mm (millimeter) Bio 14 Precipitation of Driest Month mm (millimeter) Bio 15 Precipitation Seasonality (Coefficient of

Variation)

mm (millimeter) Bio 16 Precipitation of Wettest Quarter mm (millimeter) Bio 17 Precipitation of Driest Quarter mm (millimeter) Bio 18 Precipitation of Warmest Quarter mm (millimeter) Bio 19 Precipitation of Coldest Quarter mm (millimeter)

3.4 Future climate scenarios

For this investigation, Hadley Model Matching Center, version 3 (HADCM3) climate forecasts were employed According to Jaramillo et al (2011), when compared to other

models, this model is said to yield good average results, and it is frequently employed in ecological research (IPCC, 2007) The study's future projections were created for the years 2050 to 2080 The CGIAR Research Program on Climate Change, Agriculture, and Food Security provides future statistics (http://www.ccafs-climate.org/downcaling_delta/)

3.5 Species distribution modelling

MaxEnt software (version 3.4.1) was utilized to predict the accuracy to the habitat (Phillips et al., 2006) MaxEnt is one of the most widely used species distribution modeling tools due to its accuracy in prediction and simplicity of usage (Elith et al., 2006; Phillips and Dudk, 2008) Low input requirements for species data, flexibility in handling environmental data, including continuous variables and categorical variables, and the capacity to fit complex responses to environmental variables are just a few of the characteristics that make MaxEnt well suited for modeling the distribution of species (Phillips et al., 2006) MaxEnt is a recommended prediction model since it is notable that it is less susceptible to small sample sizes (Wisz et al., 2008)

Running the model multiple times, often referred to as replicate runs, is a common practice in species distribution modeling to account for the inherent uncertainty and variability in the modeling process Each replicate run involves slightly different randomization or partitioning of the data, leading to variations in model outputs

There are several reasons why replicate run is beneficial:

• Robustness assessment: By running the model multiple times, you can assess the stability and robustness of the results If the model consistently produces similar outputs across the replicate runs, it suggests that the findings are reliable and not heavily influenced by random variations

• Uncertainty estimation: The replicate runs allow for the estimation of uncertainty associated with model outputs The variations among the replicate

runs provide an indication of the range of potential outcomes, helping to quantify the uncertainty surrounding the model predictions

The standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a dataset In the context of the AUC result from replicate runs, the standard deviation of the AUC values provides an estimate of the variability in model performance across the different runs

A low standard deviation suggests that the model outputs are relatively consistent, indicating high stability and confidence in the results On the other hand, a high standard deviation indicates more variability among the replicate runs, suggesting greater uncertainty in the model predictions

By considering both the mean AUC value and the standard deviation, you can assess the average model performance as well as the variability around that performance It helps in understanding the reliability and robustness of the model and provides insights into the uncertainty associated with the predicted habitat suitability for the studied species

3.6 Model validation

AUC is a widely used statistic for evaluating the effectiveness and prognostication precision of SDMs The ROC curve, a visual depiction of the correlation between true positive rate and false positive rate, is the source from which AUC is produced

AUC has a value range of 0 to 1, with higher numbers signifying greater model performance An AUC value of 1 denotes a perfect model with 100% discrimination, whereas an AUC value of 0.5 denotes a model that performs no better than random chance To assess models based on AUC, certain criteria are often applied These criteria can vary depending on the specific study or context, but some common thresholds are:

• AUC < 0.1: This suggests a model with poor performance, worse than random chance It indicates that the model's predictions are in the opposite direction of the actual occurrences

• 0.1 ≤ AUC < 0.4: This range suggests a model with low discrimination and relatively weak predictive power The model's performance is considered to be marginally better than random chance

• 0.4 ≤ AUC < 0.6: This range indicates a model with moderate to high discrimination The model's performance is considered acceptable, with a reasonable ability to distinguish between presence and absence points

• AUC ≥ 0.6: This suggests a model with excellent discrimination and high predictive power The model's performance is considered very good, indicating a strong ability to accurately predict species distribution

By using AUC, researchers can rigorously evaluate the performance of SDMs, assess their predictive accuracy, and determine the reliability of the models in capturing the distribution patterns of medicinal flora species under current and future climatic conditions

3.7 Map making

MaxEnt's logistic output is a map that indexes environmental appropriateness using values from 0 (inappropriate) to 1 (optimal) In order to conduct additional analysis, the MaxEnt findings were imported into ArcGIS 10.4.1 and divided into four categories: unsuitable (0.10), low (0.11-0.40), medium (0.41-0.60), and high potential (0.61) environments Finally, MaxEnt's predicted maps for the present and the future were connected to the various elevation classes

PART IV RESULTS 4.1 Model performance

Table 4.1 and figure 4.1 show the exact AUC for each species’ distribution model Overall, all the AUCs are higher than 0.9 The values indicate that the models are in “excellent” conditions (Phillips et al., 2006) and indicate that the models are very reliable and can reflect their circulation under current and future climate scenarios The standard deviations of the AUCs are less than 0.06, suggest that there are relatively low variability or dispersion in the model performance across the replicate runs This indicates a high level of consistency and stability in the model outputs

Table 4.1 Model performance

Species Occurrences in the world

Average test AUC for the

replicate runs

Standard deviation

Figure 4.1 Area under the ROC curve (AUC) of 9 species

A - Coscinium fenestratum; B - Morinda officinalis; C - Anoectochilus setaceus; D - Stephania japonica; E - Berberis

4.2 Importance of environment variables

After running each models, the results of the top 3 highest contributing environment factors for each species are shown in Table 4.2 and Figure 4.2

1 Coscinium fenestratum: The distribution of Coscinium fenestratum is significantly

influenced by Bio 4 (40.2%) and Bio 12 (25.4%), indicating the importance of temperature and precipitation-related factors

2 Morinda officinalis: The occurrence of Morinda officinalis is notably influenced

by Bio 2 (26.2%) and Bio 18 (18.6%), which highlight the significance of temperature and precipitation patterns in shaping its distribution

3 Anoectochilus setaceus: Although there is no specific contribution rate mentioned

in the table, other environmental variables may have an impact on the distribution of

Anoectochilus setaceus

4 Stephania japonica: The distribution of Stephania japonica is predominantly

influenced by Bio 16 (32.4%) and Bio 18 (22.2%), indicating the significance of temperature and precipitation-related factors

5 Berberis julianae: Berberis julianae exhibits a strong dependence on Bio 14

(35.1%), emphasizing the importance of temperature-related factors in its distribution

6 Panax vietnamensis: Panax vietnamensis shows a significant dependence on Bio

18 (58.3%), highlighting the crucial role of precipitation-related factors in shaping its distribution

7 Cupressus torulosa: The occurrence of Cupressus torulosa is strongly influenced

by Bio 7 (12.5%), indicating the importance of temperature-related variables

8 Taxus wallichiana: The distribution of Taxus wallichiana is notably affected by

Bio 18 (56.6%) and Bio 11 (16%), indicating the critical role of precipitation and temperature variables in its occurrence

9 Panax bipinnatifidus: Panax bipinnatifidus exhibits a substantial reliance on Bio

18 (39%), emphasizing the significance of precipitation-related factors in its distribution.

Figure 4.2 Jackknife of regularized training gain of 9 species

A - Coscinium fenestratum; B - Morinda officinalis; C - Anoectochilus setaceus; D - Stephania japonica; E - Berberis

Table 4.2 Contribution rates of environmental variables in current climate condition Variables C

M officinalis

A setaceus

S japonica

B julianae

P vietnamensis

C torulosa

T wallichiana

P bipinnatifidus

4.3 Current habitat suitability

4.3.1 Coscinium fenestratum

The response curves (Figure 4.3.1.a) demonstrate that: The most suitable areas (0.9–

1) for the distribution of C fenestratum are places with Seasonal Temperatures (Bio 4)

below 0 o C, Precipitation of Wettest Month (Bio 13) is over 600 mm and the coldest quarter (Bio 19) is over 1500 mm The ENM predicts that under current environmental conditions,

the highly suitable habitat area for C fenestratum is 431.24 km2, including the Southern Mekong Delta provinces of Vietnam (Figure 4.3.1.b)

Figure 4.3.1.a Marginal response curves of Coscinium fenestratum under current

climate conditions

Figure 4.3.1.b Predicted current distribution map of Coscinium fenestratum

4.3.2 Morinda officinalis

The response curves (Figure 4.3.2.a) demonstrate that M officinalis is most suitable

in habitats with a Mean Day Range (Bio 2) of about 2-9 o C, and Mean Temperature of Warmest Quarter (Bio 10) is 25-40 o C and Annual Precipitation (Bio 12) is over 1000 mm The ENM predicts that under current environmental conditions, the highly suitable habitat

for M officinalis is 46471.12 km2, including the Northeast and Red River Delta regions of the North Central provinces of Vietnam (Figure 4.3.2.b)

Figure 4.3.2.a Marginal response curve of Morinda officinalis under current climate

conditions