Introduction
Invasive species (IS) pose a significant threat to global biodiversity, having infiltrated nearly all ecosystems and negatively impacting native species, leading to numerous local and global extinctions The environmental damage caused by IS can sometimes be irreversible As the prevalence and effects of IS grow, management resources remain limited, necessitating prioritization in control efforts The Aichi target 9 from the 2011–2020 Convention on Biological Diversity Strategic Plan highlights the need for countries to identify invasive species and implement prioritized management strategies.
Developed countries have established effective programs to prioritize the prevention and control of invasive species (IS), while less developed countries struggle with slow responses Southeast Asia is particularly vulnerable to biological invasions and faces significant challenges in addressing both current and potential threats.
IS (Early et al., 2016) Lack of awareness by the public and managers (Pallewatta et al.,
Institutional constraints on information systems (IS) management are significantly hindering the prevention and control of IS in the region Key issues include unclear responsibilities, a lack of political commitment and collaboration, and insufficient law enforcement.
The lack of research on invasive species (IS) in Southeast Asia significantly hampers the ability to provide effective scientific guidance for garnering political and public support, as well as prioritizing IS management efforts (Nghiem et al., 2013; Peh, 2010) Due to the limited studies in the region, the consequences of current invasions and potential future ecological or economic damages remain largely unacknowledged (Lowry et al., 2013) Additionally, the intricate nature of IS management requires a multifaceted approach to address these challenges effectively.
Conflicting perspectives on facts and values between two parties highlight the importance of understanding human dimensions and political viewpoints in invasion studies However, such comprehensive studies are scarce in Southeast Asia.
In recent decades, Vietnam has faced significant challenges from invasive species (IS), particularly invasive plant species (IPS), which pose a serious threat to biodiversity in protected areas like national parks A notable example is the invasion of the exotic Mimosa pigra in Tram Chim National Park, which has rapidly replaced native vegetation and led to a substantial decline in the population of the Eastern Sarus Crane (Grus antigone sharpii).
In recent years, certain native plant species, particularly Merremia boisiana and M eberhardtii, have emerged as invasive threats to forests in central Vietnam (Hoe, 2011; Le et al., 2012) However, research on invasive plant species (IPS) in the country remains geographically dispersed and incomplete, with most studies limited to short-term field surveys These studies primarily concentrate on the effects of M pigra in the Mekong Delta (Thi et al., 2001; Triet & Balakrishna, 1999; Triet et al., 2004) or involve inventories of IPS within select national parks (Le et al., 2016; Tan et al., 2012).
Aims and objectives of the thesis
This thesis aims to enhance decision-making in invasive species (IS) management in Southeast Asia and Vietnam, addressing the significant risks and impacts of invasive plant species (IPS) in the region Given the limited resources available to manage these species, the research focuses on expanding knowledge to better inform strategies for effective management.
• Identify biases in IS research in SE Asia (Chapter 2);
This article focuses on mapping regions in Southeast Asia and Vietnam that are susceptible to invasion by identifying potential distributions of the most invasive plant species It also explores methodological strategies that can enhance the accuracy of these predictions, as detailed in Chapter 3.
• Assess impacts of IPS on biodiversity in national parks through the case of
Microstegium ciliatum, an aggressive grass invading secondary forests in Vietnam, and its effects on the regeneration of woody species (Chapter 4);
• Review and analyse challenges which constrain the Vietnamese government in offering effective prevention and control strategies against biological invasion in national parks under the institutional arrangement context (Chapter 5); and
• Assess contributions of the thesis research and propose priorities for future research to prevent and mitigate invasive plants and their impacts to biodiversity conservation (Chapter 6)
Structure and significance of the thesis
Invasive species transcend national borders, making it essential to analyze their impact within the Southeast Asian context, particularly in Vietnam Chapter 2 reviews the trends in invasion studies across Southeast Asia to identify existing research gaps, which inform the subsequent chapters' methodologies Chapter 3 investigates which invasive plant species present significant risks and identifies vulnerable regions in Southeast Asia and Vietnam using species distribution modeling and contemporary remote sensing data Finally, Chapter 4 presents a removal experiment aimed at evaluating the specific effects of an invasive plant species on the native plant community and the regeneration of woody species within a Vietnamese national park.
Microstegium ciliatum was selected for its potential widespread distribution in forests, as indicated by modeling results and preliminary surveys Chapter 5 analyzes current institutional constraints affecting decision-making in the management of invasive plants within national parks, drawing on interviews with key managers in Vietnam This chapter evaluates the government's and national parks' responses to invasive species and identifies obstacles to effective management Finally, Chapter 6 synthesizes the main findings, highlighting their contributions and implications for managing invasive plant species in Vietnam's national parks and the broader region.
A systematic review of research efforts on invasive species in Southeast Asia
efforts on invasive species in Southeast Asia
Invasive species (IS) pose significant risks globally, yet invasion studies show a notable geographical bias, particularly in tropical regions like Southeast Asia, which remains under-researched despite its vulnerability This chapter reviews invasion ecology and management, highlighting trends in Southeast Asian studies to pinpoint further research opportunities A systematic review assessed IS research by year, species group, and focus areas, revealing a predominance of studies on animals, primarily documenting their presence and traits However, there is a lack of exploration into invasibility, impacts, and effective management practices, with a notable absence of studies addressing policies and regulations The current emphasis on field observations indicates that the field in Southeast Asia is still in an exploratory phase, lacking a solid scientific foundation for understanding invasion mechanisms Additionally, while the number of studies correlates with educational and research capacity, it does not align with economic development, suggesting that geographic biases may hinder effective IS management in less-studied countries.
Recommendations for future studies to reduce bias and improve invasion science in the region are discussed
In "The Ecology of Invasions by Animals and Plants," Elton (1958) emphasized the urgency of understanding biological invasions, which are increasingly prevalent across continents and oceans Since then, the scientific community has made significant strides in this field (Henderson et al., 2006; Richardson, 2015) However, research shows a pronounced geographical bias, with a focus primarily on developed countries and temperate ecosystems (Genovesi et al., 2013; Lowry et al., 2013) This has resulted in a lack of studies on biological invasions in tropical regions of Africa and Asia, hindering our comprehension of invasion mechanisms in these unique habitats (Pyšek et al.).
Differences in economic status and educational systems across countries contribute to biases in understanding invasive species (IS) management (Pyšek et al., 2008) As a result, regions with insufficient data face significant challenges in preventing and managing IS effectively (Leadley et al., 2014).
Southeast Asia is a region with high risk related to invasive species (Early et al., 2016)
It has been estimated that the total annual economic loss caused by IS in SE Asia is about
The estimated cost of environmental damage is around US $33.2 billion, according to Nghiem et al (2013) However, the true costs may be even greater due to the displacement of native biodiversity and the decline in ecosystem services, which carry intangible or non-market value.
Although damage caused by IS has been recorded in SE Asia, invasion science in the region is still under studied (MacIsaac et al., 2011; Peh, 2010; Sheil & Padmanaba, 2011)
This limits awareness about the impacts of IS and hinders the provision of sound scientific information to support effective decision making for IS management (Peh,
2010) Furthermore, the large gaps in economic development among countries in SE Asia (Thanh, 2008) may lead to imbalances in research on invasion studies within the region
A review by Giam and Wilcove (2012) highlights a geographical bias in conservation ecology research in Southeast Asia, showing that Malaysia, Singapore, and Thailand dominate study numbers, while Cambodia, Laos, Myanmar, and Vietnam lag behind The authors found that areas with higher conservation needs and more threatened species, such as Indonesia and Malaysia, attracted more research, suggesting that national wealth influences study prevalence Additionally, many invasion studies in the region rely on anecdotal evidence (Peh, 2010), indicating potential biases in research types Recognizing these biases is essential for realigning scientific efforts, ultimately leading to better policy outcomes (Darwall et al., 2011; Donaldson et al., 2016).
To improve invasion science in Southeast Asia and address the risks and impacts of invasive species, it is essential to review existing invasion studies in the region This quantitative literature review aims to identify trends and highlight gaps in current research, providing insights into opportunities for further investigation in this critical field.
This chapter explores the landscape of invasion science in Southeast Asia by systematically quantifying studies based on publication years, species groups, research focuses, and the geographical origins of the research and researchers Utilizing a systematic quantitative review approach, as advocated by Pickering & Byrne (2014), allows for the identification of general patterns in the literature while enhancing accuracy and minimizing bias compared to traditional narrative reviews (Lowry et al., 2013; Uman, 2011) The chapter begins with an overview of global invasion science, outlines the methodology for the quantitative literature review, and subsequently presents and analyzes the findings related to trends in invasion science literature specific to Southeast Asia.
Background on invasion science and management
In the 1980s, the Scientific Committee on Problems of the Environment (SCOPE) program marked a significant advancement in invasive species research It highlighted crucial inquiries about the traits of invasive species and the susceptibility of ecosystems to invasion, paving the way for effective management strategies.
Invasion science has evolved internationally, driven by key questions that established a foundational framework for studying invasions (Richardson & Pyšek, 2006; Foxcroft et al., 2011) Building on earlier research, subsequent studies emphasized the importance of species-community interactions in assessing the success and impacts of invasive species, which is crucial for effective management (Drake et al., 1989; Lodge, 1993; Rejmanek et al., 2005) The three primary themes in invasion ecology—invasiveness, invasibility, and impacts—have significantly enhanced our understanding of invasion mechanisms and informed practical invasion control strategies (Alpert et al., 2000) This section reviews these core topics of invasion science through the lenses of species, ecosystem, and management, highlighting how research in each area has contributed to our comprehension and management of biological invasions.
Research into the traits that contribute to the invasiveness of introduced species has been extensive, aiming to uncover why some species thrive as invaders while others do not This involves identifying the inherent characteristics of potential invaders and understanding their progression through the invasion process, which includes introduction, colonization, establishment, and spread The "tens rule" suggests that only 10% of introduced species successfully navigate these stages of invasion.
Successful invaders transition from introduced species to invasive species (IS) by overcoming critical steps in the invasion process Introduced species that produce large numbers of reproductive offspring can spread extensively, as highlighted by Richardson et al (2000).
Different traits are crucial for the success of species invasions at each transition stage When introduced to a new environment, species can only establish themselves if they have characteristics that align with the recipient ecosystem.
Invasion science, as defined by Richardson et al (2000), examines the processes through which introduced species (IS) become invasive in new ecosystems These invasive species exhibit unique traits, such as high reproductive rates, enabling them to spread widely The ecosystems that these species invade, termed invasible ecosystems, are significantly influenced by both the invasiveness of the species and the inherent characteristics of the ecosystem When invasive species alter ecosystem attributes, they are classified as transformers Effective management of invasive species requires a thorough understanding of these dynamics, linking specific strategies—such as prevention, control, and long-term impact reduction—to the various stages of invasion The relationship between the number of invasive species and management effectiveness is illustrated by the "tens rule" hypothesis, highlighting the diminishing returns of management efforts as invasions progress.
In vas iv en e ss
Kleunen et al., 2015) Characteristics of wide environmental tolerance, which can be achieved through genetic diversity and high level of adaptive phenotypic plasticity, allow
Invasive species (IS) thrive in diverse growing conditions due to advantageous traits such as foraging efficiency, photosynthetic capacity, and water-use efficiency, enabling them to exploit resources for growth and reproduction in new environments (Davidson et al., 2011; Molina-Montenegro et al., 2012; Stepien et al., 2005; Kakareko et al., 2013; Rehage et al., 2005; Mcalpine et al., 2008; McDowell, 2002) Dispersal-related traits, including the dispersal vector and propagule characteristics like seed size, are crucial for helping plant species reach suitable habitats and influence their spatial distribution (van Kleunen et al., 2015; Coutts et al., 2011; Huang et al., 2015) Additionally, traits that enhance propagule pressure, such as a high number of propagules, a high germination rate, and prolific reproductive capacity, facilitate the establishment and spread of species, allowing them to overcome Allee and stochastic effects (Colautti et al., 2006; Lockwood et al., 2005; Rejmánek & Richardson, 1996; Tabak et al., 2018; Hierro et al., 2009; Wainwright & Cleland, 2013; Dong et al., 2006; Dorken & Eckert, 2001; Forman & Kesseli, 2003).
Invasibility refers to the characteristics of an ecosystem that influence its vulnerability to invasion, with these characteristics varying across different scales At broader scales, such as global and regional levels, abiotic factors like climate, topography, and soil play a crucial role in a species' ability to establish and thrive Conversely, at finer scales, biotic factors, including competition, predation, and parasitism, significantly impact the likelihood of invasion.
Contemporary remotely sensed data products refine invasive plants risk
products refine invasive plants risk mapping in data poor regions
Invasive plant species (IPS) pose significant threats to global biodiversity and economies, making effective monitoring and control essential Utilizing downscaled climate surfaces and digitized species occurrence records allows for regional to continental forecasts of invasion risk, enhancing management efficiency However, finer-scale predictions in varied landscapes necessitate the inclusion of biotic processes and local abiotic conditions Contemporary remote sensing (RS) data can improve these predictions by offering spatial environmental data beyond just climatic variables This study employed the Global Biodiversity Information Facility (GBIF) and maximum entropy (MaxEnt) models to assess the potential distributions of 14 priority IPS in Southeast Asia, using bioclimatic layers and recent global land cover, vegetation productivity, and soil property data The findings indicated that integrating climate and RS data resulted in reduced suitable habitat predictions without compromising model accuracy, although the impact of RS variables was limited due to uncertainties in land cover data The study advocates for the increased use of quantitative remotely sensed estimates in habitat suitability modeling, revealing significant insights into the predicted area and diversity of invasive species across different life forms.
In Southeast Asia, 35 species pose a significant invasion risk, highlighting the need to address native invasive species, which are frequently underestimated in risk assessments The awareness of invasive plant species (IPS) and their environmental effects remains limited in the region, with a lack of accessible information Utilizing freely available global spatial datasets, particularly from Earth observation programs, can provide essential insights for effective management of environmental threats like invasive species.
Invasive plants pose a significant threat to global biodiversity, leading to the extinction and endangerment of native species while disrupting ecosystem processes While non-native species often receive the most attention, native species can also become invasive as they expand their range, particularly under climate change and human disturbances Due to the profound impacts of invasive species and the challenges of eradication, prioritizing early detection and prevention in conservation policies is essential Identifying areas at risk of invasion from both non-native and native invasive species can enhance management strategies and mitigate further incursions.
Species distribution models (SDMs) are widely used for predicting the geographic distribution of species by analyzing known occurrences and environmental characteristics These models help identify suitable habitats and forecast species distribution in unexplored areas, making them particularly valuable for poorly studied taxa.
Species Distribution Models (SDMs) have emerged as crucial tools for assessing invasibility and predicting the potential distributions of invasive species Since the foundational research by Peterson et al in 2003, which focused on four invasive plants in North America, the application of SDMs has expanded globally, becoming increasingly prevalent in studies of biological invasions.
& Thuiller, 2005; Underwood et al., 2013), especially IPS (Andrew & Ustin, 2009; Barik
In species distribution models (SDMs), the environmental variables utilized can differ significantly across various scales While regional to continental forecasts of invasion risk are predominantly influenced by climatic factors, predictions at finer scales, particularly in landscapes with minimal topographic variation, necessitate the inclusion of biotic processes and local abiotic conditions However, obtaining continuous spatial measurements of these detailed environmental variables over large areas poses significant challenges.
Contemporary remote sensing (RS) offers accessible data products at various spatial and temporal resolutions, enabling the characterization of important ecological patterns and processes (Andrew et al., 2014) This technology allows for the measurement of habitat properties over extensive areas, surpassing the limitations of traditional field surveys (Estes et al., 2008) Additionally, RS enhances the range of spatial environmental variables available for species distribution models (SDMs), allowing for the analysis of both abiotic and biotic niche dimensions beyond just climatic factors An overview of the remotely sensed information used in SDMs is provided in Table 3.1, highlighting the uneven distribution of research efforts and the underutilization of RS capabilities Notably, topography and elevation are the most frequently utilized variables extracted from RS data, accounting for 42% of applications.
A review of 39 studies highlights the development of species distribution models (SDMs) for various plant species utilizing remote sensing (RS) predictors Additionally, researchers have created abiotic predictors, incorporating remotely sensed climate and weather data, such as surface temperature from MODIS sensors and rainfall estimates from TRMM, along with recent contributions from the Global Precipitation Measurement.
37 mission, although studies applying these predictors are limited (Table 3.1) Soil properties, one of the most
Table 3.1: Applications of remote sensing data as environmental variables in plant distribution models
Predictor variables RS data source References
ASTER, Quickbird-2 and WorldView-2, LiDAR, SRTM
Numerous studies have contributed to our understanding of climate observations, including significant research by Andrew & Ustin (2009), Bradley & Mustard (2006), and Buermann et al (2008) Other notable contributions come from Campos et al (2016), Hoffman et al (2008), and Pouteau et al (2015), alongside Parviainen et al (2008, 2013) and Pottier et al (2014) The works of Pradervand et al (2014), Prates-Clark et al (2008), and Questad et al (2014) further enrich this field Additionally, Rew (2005), Saatchi et al (2008), van Ewijk et al (2014), and Zellweger et al (2013) have made significant contributions These studies utilize advanced climate observation tools such as MODIS and TRMM to enhance our knowledge of climate dynamics.
Deblauwe et al., 2016; Saatchi et al., 2008; Waltari et al., 2014
Soil properties Landsat, MODIS Parviainen et al., 2013; Wang et al., 2016
MODIS, NASA Cord & Rửdder, 2011; Cord et al., 2014; Pau et al.,
2013; Stohlgren et al., 2010 Land cover/land use MODIS, Landsat Cord et al., 2014b; Gonỗalves et al., 2016; Morỏn-
Ordúủez et al., 2012; Sousa-Silva et al., 2014; Stohlgren et al., 2010; Pearson et al., 2004; Tuanmu
& Jetz, 2014; Thuiler et al., 2014; Wilson et al., 2013
Normalized difference vegetation index (NDVI)
A comprehensive review of various studies highlights significant findings in the field, including contributions from Engler et al (2013), Evangelista et al (2009), Feilhauer et al (2012), Morisette et al (2006), Parviainen et al (2013), Prates-Clark et al (2008), Schmidt et al (2013), van Ewijk et al (2014), Zellweger et al (2013), and Zimmermann et al (2007) These research efforts collectively advance our understanding of the subject matter, underscoring the importance of collaborative research in yielding impactful insights.
(LAI) MODIS Buermann et al., 2008; Cord & Rửdder, 2011; Engler et al., 2013; Prates-Clark et al., 2008; Saatchi et al.,
Index (EVI) MODIS Cord et al., 2014; Cord & Rửdder, 2011; Morisette et al., 2006; Schmidt et al., 2013; Stohlgren et al., 2010; Schmidt et al., 2013
Phenology MODIS, Landsat Bradley & Mustard, 2016; Gonỗalves et al., 2016;
Morisette et al., 2006; Tuanmu et al., 2010; Wilfong et al., 2009
Tree height LiDAR Alonzo et al., 2014; van Ewijk et al., 2014
Canopy roughness QSCAT Saatchi et al., 2008
Canopy moisture Hyperspectral sensor, QSCAT
Buermann et al., 2008; Prates-Clark et al., 2008
Henderson et al., 2014; Morỏn-Ordúủez et al., 2012; Pottier et al., 2014; Schmidt et al., 2013
Research on the 38 critical factors influencing plant distributions and species invasions (Radosevich et al., 2007) is infrequent (He et al., 2015) However, several recent studies have investigated the application of remotely sensed indicators of soil characteristics in species distribution models (SDMs) (Table 3.1).
Biotic characteristics significantly influence species' spatial patterns alongside abiotic factors (Wisz et al., 2013) Remote sensing (RS) technology is increasingly utilized to estimate various vegetative properties, with applications of land-cover data and vegetation proxies in species distribution models (SDMs) on the rise (Table 3.1) At finer spatial resolutions, land cover is recognized as the primary factor determining species occurrences, surpassing climate considerations (Pearson et al., 2004) Notably, 20% of the 39 studies reviewed support this finding (Table).
Recent studies have utilized land cover products from various sensors, particularly MODIS and Landsat, in species distribution models (SDMs) However, the predominant categorical format of current land cover data can lead to classification errors and may not accurately reflect the relevant classes for specific species In contrast, continuously varying ecosystem properties obtained through remote sensing, along with innovative continuous land cover products, offer a more effective alternative for improving SDMs and mitigating these limitations.
Recent research indicates that continuous estimates of vegetation properties and land cover outperform categorical representations (Cord et al., 2014; Tuanmu & Jetz, 2014; Wilson et al., 2013) Various remotely sensed vegetation measures, including the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index, have been utilized in Species Distribution Models (SDMs) to assess habitat quality variations at fine scales in climatically uniform regions (Table 3.1) Among these metrics, NDVI is particularly valuable as a predictor in SDMs, accounting for 25.6% of usage (Table 3.1) It effectively reflects photosynthetic activity and biomass in plants, serving as an indirect indicator of net primary production (Bradley & Fleishman, 2008) Nonetheless, Phillips et al (2008) highlighted that while NDVI shows a strong correlation with MODIS data, further analysis is warranted.
Gross primary production (GPP) and net primary production (NPP) are less effective indicators of productivity in areas with sparse or dense vegetation Research indicates that GPP is more reliable in predicting biogeographic patterns of species richness; however, there is a lack of studies utilizing GPP in species distribution models (SDMs) for plants Value-added science products like MODIS primary productivity products may offer enhanced insights into vegetation processes and serve as improved environmental predictors for biodiversity spatial models.