INTRODUCTION
Research Rationale
Altering a single element of an ecological system can lead to significant changes throughout the entire ecosystem, as each component is interconnected This fundamental principle of ecology highlights the potential for widespread impact resulting from even minor modifications.
Biodiversity, encompassing the variety and variability of life on Earth, plays a crucial role in ecosystem processes and is essential for conservation efforts Conversely, biological invasion occurs when certain species gain a competitive edge due to the removal of limiting factors, allowing them to spread rapidly and dominate new environments (Valéry et al., 2008).
Biological invasions represent a significant global challenge to biodiversity conservation and ecosystem integrity Invasive species negatively affect ecosystems by reducing native plant species richness, disrupting nutrient cycling, and altering native vegetation dynamics.
Driving global change involves critical factors such as climate change and biological invasions, which are increasingly recognized as both environmental and socio-economic challenges Climate change is expected to intensify the impacts of invasive alien species by providing them with greater opportunities to overcome geographic barriers and establish themselves in new areas Conversely, invasive alien species can influence the dynamics of climate change by altering ecosystem structures and functions This interaction may create a positive feedback loop, where climate change facilitates the spread of invasive species, which in turn makes ecosystems more susceptible to the effects of climate change.
The invasive potential of a species is significantly influenced by its characteristics, as highlighted by Baker (1965), who identified various traits associated with weedy plants; species exhibiting numerous weedy traits are more likely to thrive invasively than those with few Additionally, broad-scale analyses of floras indicate that factors such as plant height, life form, and competitiveness play crucial roles in determining a plant's growth form and habitat suitability (Pyšek et al., 1995).
Environmental factors significantly influence the prevalence of invasive species, with key elements including dispersal distance, groundwater depth, fire, and bio-competition The dispersal mechanism is crucial for invasive species, enhancing their colonization potential and increasing the likelihood of offspring establishing in favorable microsites Introduced species often thrive in new environments due to the absence of limiting factors from their native habitats Groundwater depth is vital for plant diversity, as shifts in hydrological conditions can lead to species replacement, while declining groundwater levels may facilitate the spread of invasive species Fire impacts plant density variably, with some species experiencing reduced numbers, while others may proliferate through mass seed dispersal post-fire Lastly, bio-competition, which involves competition for resources and space, can be assessed through understorey species cover, serving as an indicator of site quality and aiding in forest land classification.
Many tree species utilized in forestry practices, including arboreta, horticulture, afforestation, and gardening, can become invasive when they spread beyond their planted locations Biological invasions frequently lead to issues that arise long after the establishment of forestry operations Acacias, or wattles, are among the most widely planted trees worldwide for afforestation, particularly in Australia.
Acacias, one thing is clear – all the wattles used extensively in forestry have
This study focuses on Acacia longifolia subsp longifolia and Acacia iteaphylla, two species known for their distinct plant characteristics and varying responses to climate and environmental factors These species are also recognized as significant invasive species that negatively impact ecosystems Acacia is a biologically diverse group, boasting around 590 species suitable for cultivation, each exhibiting a wide range of ecological traits, canopy structures, leaf shapes, soil and shading preferences, and ornamental qualities This diversity has led to a global trade in Australian Acacia species.
Acacias gained popularity initially due to horticultural interest, but their cultivation was later propelled by agricultural incentives such as the production of tannin, timber, and pulp Additionally, they play a role in food production and revegetation efforts.
Acacia iteaphylla and Acacia longifolia subsp longifolia are popular garden plants known for their ornamental flowers and windbreaking properties These species are cultivated for various uses, including medical applications, sand dune stabilization, hygiene products, fuel production, and furniture manufacturing Despite being recognized for their significant ecological impact in Australia, as noted by Adair (2008), the propagation of these Acacia species continues to thrive (Australian Native Plants, 2017a; Australian Native Plants, 2017b).
Australian Acacias pose a significant risk of becoming invasive, leading to serious ecological, agricultural, and water resource impacts (Wilson et al., 2011; Holm et al., 1979) Once established, these species are challenging to control and eradicate (Rejmánek et al., 2005) They have naturalised in various locations worldwide, often due to intentional planting or horticultural escapes As a result, many are now classified as serious weeds (van Wilgen et al., 2006; Lorenzo et al., 2010) It is crucial to enhance scientific predictions regarding the invasion risks of these species to alert authorities and protect vulnerable areas from further invasions (Parmesan et al., 2010).
2011) Species Distribution Models (SDM) using algorithms such as MaxEnt, are tools that can be used to detect such invasion potential of species (Thuiller et al.,
In 2005, Representative Concentration Pathways (RCP) were established to help scientists illustrate potential climate scenarios based on human actions to mitigate carbon emissions By combining Species Distribution Models (SDMs) with RCPs, researchers can predict how climate change may impact species distribution in the future.
Research on the ecological effects of Australian Acacias predominantly centers on South Africa, where thirteen species have become naturalized, with eight recognized as ecosystem modifiers that alter biological composition and ecological processes in invaded areas (Henderson, 2001; Richardson & van Wilgen, 2004) Conversely, there is limited research on the extent of invasion and the impacts of these species within Australia, particularly regarding the spread and naturalization of native Australian Acacias (et al., 2014; Birnbaum et al., 2016) This issue poses not only an environmental challenge but also a national concern due to the rapid expansion of invasive populations.
Acacias in Western, Southern and in Eastern Australia indicate broad-scale impacts which are inevitable without the implementation of appropriate control measures
Most studies on A longifolia's invasion in regions such as the Americas, southern Africa, New Zealand, and parts of Asia fail to distinguish between its two subspecies: Acacia longifolia subsp longifolia and Acacia longifolia subsp sophorae Instead, they often refer to both subspecies collectively as Acacia longifolia (Marchante et al., 2008; Dennill et al., 1993; Birnbaum et al., 2014), which is insufficient given that these subspecies exhibit distinct morphology, habitat preferences, and distribution patterns (Maslin, 2001).
Despite extensive research on the invasion of Australian Acacias globally, there is a lack of studies focusing on their local invasion patterns in Western Australia, particularly in Perth This city is significant due to its remaining patches of original vegetation, such as Kings Park, and its sprawling low-density layout, which includes a rapidly expanding peri-urban zone.
Research’s Objectives
This study aims to assess the potential invasion patterns of Acacia longifolia subsp longifolia and Acacia iteaphylla in Australia, utilizing Species Distribution Modelling (SDM) at both national and regional levels Additionally, it seeks to evaluate how climate change may influence the invasion dynamics of these two Acacia species Finally, the research will investigate the impact of various environmental factors—including distance, groundwater depth, understorey cover or disturbance, fire, and the presence of larger trees—on the local-scale invasion patterns of these species in Perth, Western Australia.
Research Questions and Hypotheses
This research study was guided by the following research questions in accordance to the objectives of the study:
A To determine the potential invasion pattern of A longifolia subsp longifolia and A iteaphylla based on climatic and environmental factors in Australia using SDMs at national to regional scales by a What is the effect of the climate factors on the invasion pattern of the two (2) Acacia species in Australia? b What is the effect of the environmental factors on the invasion pattern of the two (2) Acacia species in Australia?
B To determine the potential invasion pattern of the two (2) Acacia species with climate change by investigating the following research question: a What is the effect of climate change on the potential future invasion pattern of the two (2) Acacia species in Australia?
C To determine the impact of environmental factors on the local-scale patterns of invasion of the two (2) Acacia species in Perth, Western Australia by investigating the following research questions: a Is there an edge effect for both Acacia species’ regarding their plant density, basal area and biovolume measurements? b Does the depth to groundwater in bushland affect the plant density of the two (2) Acacia species? c Is there a preference for certain understorey cover class for both
Acacia species? d Does fire affect the plant density, basal area and biovolume of
A longifolia subsp longifolia? e Is there a preference for the occurrence of larger trees for both
These research hypotheses were designed to answer the study’s research questions
A To determine the potential invasion pattern of A longifolia subsp longifolia and A iteaphylla based on climatic and environmental factors in Australia using SDMs at national to regional scales by testing the following hypotheses: a H0 = Climate factors have no effect on the invasion pattern of both Acacia species in Australia
H1 = Climate factors have effect on the invasion pattern of both
Acacia species in Australia b H0 = Environmental factors have no effect on the invasion pattern of both Acacia species in Australia
H1 = Environmental factors have effect on the invasion pattern of both Acacia species in Australia
B To determine the potential invasion pattern of the two (2) Acacia species with climate change by testing the following hypotheses: a H0= Climate change have no effect on the potential future invasion pattern of both Acacia species in Australia
H1= Climate change will have an effect on the potential future invasion pattern of both Acacia species in Australia
These hypotheses were tested using a significance level of 0.05
C To determine the effect of environmental factors on the local-scale invasion patterns of invasion of the two (2) Acacia species in Perth, Western Australia a H0 = Distance from the edge of bushland does not have a significant effect to both Acacia species’ plant density, basal area and biovolume measurements
The distance from the edge of bushland significantly influences the plant density, basal area, and biovolume of Acacia species Conversely, the depth to the groundwater level in bushland does not affect the plant density of these Acacia species.
H1 = Depth to groundwater in bushland have an effect to both
Acacia species’ plant density c H0 = There is no significant evidence for the preference on certain understorey cover class for both Acacia species
Research indicates a strong preference for specific understorey cover by both Acacia species The hypothesis (H0) suggests that fire does not significantly affect the growth performance of A longifolia subsp longifolia, including its plant density, basal area, and biovolume.
H1 = Fire does have a significant impact on A longifolia subsp longifolia’s growth performance (plant density, basal area and biovolume) e H0 = The two (2) Acacia species do not significantly prefer growing under trees
H1 = The two (2) Acacia species significantly prefer growing under trees.
Limitations
Despite achieving its research objectives, the study faced several limitations that impacted the process and findings The three-month timeframe for conducting the study in Perth, Western Australia, restricted the number of sampled sites, compounded by adverse rainy weather Additionally, inaccuracies in fire history data revealed discrepancies, with some observed sites showing no evidence of fire, contradicting the provided maps The weed cover maps also lacked precision Furthermore, the study was constrained by the availability of datasets and variables within the BCCVL and ALA databases As BCCVL primarily focuses on climate change modeling, it offers limited biodiversity databases and options for generating species distribution models (SDMs), which did not account for biotic interactions due to insufficient variable availability.
Definitions
Basal Area - used to gauge the approximate age of the plant
- is the measure of the overall stem area of plants in squared meters per hectare
Biodiversity - is the number of different species occurring in some location
Biovolume - is the measure of the overall plant volume in cubic meters per hectare
Climate Change - is a change in the typical or average weather of a region or city
Introduced species - is an organism that is not native to the place where it is considered introduce and instead has been accidentally or deliberately transported to the new location
Invasive Alien Species are non-native organisms that can negatively impact ecosystems MaxEnt, or Maximum Entropy modelling, is a predictive tool used to determine species occurrences by identifying the most widespread distribution while accounting for environmental variables at known locations.
Native range(s) - refers to the origin of species
Naturalised range(s) - refers to areas outside the origin of species
Plant Density - is the number of plants per hectare
Predicted - calculated values from the models and algorithm used
- refers to the process of using computer algorithms to predict the distribution of species in geographic space based on a mathematical representation of their known distribution in environmental space
Species distribution - refers to the geographical distribution of occurrence of species Understorey cover - refers to the percentage of plants growing between the forest canopy and the ground cover
- indicator of level of disturbance in sites
LITERATURE REVIEW
Species invasion
Biological invasion occurs when non-native species, including plants and animals, gain a competitive edge due to the removal of limiting biotic and abiotic factors, allowing them to spread rapidly and dominate new environments (Valéry et al., 2008) The introduction of these species often results from human activities, both direct and indirect, with socio-economic factors playing a crucial role alongside biological ones Non-native plants have been introduced for various purposes, including afforestation, erosion control, timber production, medicinal uses, and aesthetic appeal (Baker, 1974; Richardson et al., 2015; New, 1984) Additionally, invasive species can unintentionally be introduced through mechanisms such as ship ballast, contaminated crop seeds, attachment to domestic animals, and garden escapes (Ruiz et al., 2000; Baker).
Invasive species can proliferate in new environments through both long-distance dispersal from external sources and short-distance lateral expansion from established populations Key factors that regulate their distribution include the number of propagules, modes of dispersal, and vital rates.
Invasive species have a profound economic impact, with costs estimated between millions to billions of dollars each year (Pimentel et al., 2000) These biological invasions also threaten global biodiversity on multiple levels (Hejda et al., 2009) Biodiversity plays a crucial role in ecosystem processes, many of which remain not fully understood, highlighting the importance of its protection (Schrửter et al., 2005).
Biological invasions represent a significant global change that threatens biodiversity and natural resource conservation (Simberloff et al., 2013) Invasive species rank as the second leading cause of species endangerment and extinction, following habitat destruction (Wilcove et al., 1998) These species disrupt essential ecosystem processes, adversely affecting human well-being by compromising access to secure livelihoods, health, and security, while also altering the availability of vital ecosystem services (Schrửter et al., 2005; Mooney, 2005).
Climate change is significantly altering the geographic distribution of plant and animal species worldwide, driven by factors such as reduced precipitation, rising temperatures, and an increased risk of forest fires This phenomenon, alongside biological invasions, poses severe threats to ecosystems, leading to pressing environmental and socio-economic challenges The interplay between climate change and invasive species creates a positive feedback loop, where climate change facilitates new habitats for invaders, thereby increasing ecosystem vulnerability As these factors continue to reshape terrestrial and aquatic communities, biodiversity loss and species extinction have become critical global crises Even minor changes within an ecological system can trigger widespread impacts, highlighting the interconnectedness of these environmental issues.
Baker (1965) identified key traits that characterize weedy plant species, suggesting that those exhibiting traits such as discontinuous germination, long seed longevity, rapid seedling growth, adaptability to diverse environments, regeneration from severed plant parts, and the ability to reproduce both sexually and asexually are more likely to thrive as weedy species Additionally, the capacity for competition through phenotypic plasticity further enhances their weedy potential.
Broad analyses of plant floras indicate that certain growth forms and habitat traits can effectively predict invasion success Research in the Czech Republic revealed that species invasion success correlates with factors such as plant height, life form, and competitiveness Additionally, a retrospective study on introduced woody plants found a high invasiveness risk linked to traits like vegetative reproduction, minimal pre-germination seed treatment requirements, hermaphroditic flowers, and prolonged fruit attachment Successful invasive species often exhibit r-selected strategies, characterized by pioneer habits, short regeneration times, high fecundity, and rapid growth rates, along with the adaptability to switch between r- and K-selected strategies.
Understanding the specific characteristics of invading species in relation to the invaded habitat is crucial for assessing invasion success Factors such as inability to adapt to new climates, disturbances, competition from native species, and diseases often contribute to invasion failures (Lodge, 1993; Newsome & Noble, 1986) According to Crawley (1986), successful species introduction and invasion involves three essential stages: (i) the introduction of species into a new habitat, (ii) initial colonization and establishment, and (iii) subsequent dispersal and spread into additional habitats Throughout these stages, there is significant potential for genetic changes to occur due to drift or selection.
Environmental factors play a crucial role in limiting the abundance of invasive species, alongside climate, species characteristics, disturbances, and diseases A study by Marchetti (1999) demonstrated that the Sacramento perch (Archoplites interruptus) is threatened by the introduced bluegill (Lepomis macrochirus); however, competitive exclusion of the perch is unlikely due to variable environmental conditions Additionally, Davis and Thompson (2000) introduced a classification scheme for colonization terminology based on ecological and geographical concepts, categorizing species into three distinct aspects: dispersal distance (short or long), novelty, and impact in the new environment.
Dispersal mechanisms refer to the unidirectional movement of offspring away from the parent plant, a common phenomenon across various organisms (Nathan, 2006; Augspurger, 1984) Sessile organisms, like plants, utilize passive dispersal modes, where seeds or diaspores are carried away by wind, water, or animals (Ridley, 1930) Dispersal can be categorized into short-distance (diffusion dispersal) and long-distance (saltation dispersal), with the former being more prevalent unless human intervention occurs (Davis & Thompson, 2000) Augspurger's study (1984) indicated that dispersal benefits nine tropical tree species, supporting the colonization hypothesis that suggests dispersal increases the likelihood of offspring settling in disturbed areas, thereby enhancing seedling establishment and survival.
Changes in hydrological regimes can lead to species replacement, while declines in groundwater levels may facilitate the spread of invasive species (Stromberg et al., 2007; Elmore et al., 2003) Research in China indicates that groundwater levels significantly influence plant species diversity (Chen et al., 2006) Conversely, a study in California suggests that community-intrinsic factors like competition and diversity are more critical in the proliferation of invasive plants than variations in groundwater levels (Mata-González et al.).
The response of plants to fire is influenced by various fire properties, including fire behavior attributes (such as fire-line intensity, rate of spread, and flame length), immediate fire effects (like fuel consumption and soil heating patterns), and fire regime attributes (including fire type, frequency, severity, and seasonality) Additionally, plant characteristics, such as seed bank establishment and post-fire seed dispersal, play a crucial role in survival and establishment While fire behavior characteristics are typically measured during wildfires, their connection to invasiveness is less clear compared to immediate fire effects and fire regime attributes Spatial factors, such as fire size and the distribution of burned and unburned patches, can significantly impact the potential for establishment from unburned areas Moreover, the type, severity, and frequency of fire are critical in determining the persistence and spread of invasive plant populations within burned landscapes.
To enhance our understanding of disturbed environments, it is essential to deepen our ecological knowledge of understory species, including herbaceous plants and shrubs These understory species serve as valuable indicators for estimating site quality and for classifying and mapping forest land, as they can effectively integrate various significant environmental factors that are often challenging to measure Consequently, linking environmental factors to ground-cover species is crucial for accurate ecological assessments.
2.1.3 Invasion patterns of weedy plants in Australia
Australian Acacias are occurring as weeds in other countries and within
Adair (2008) summarised the three (3) types of invasion patterns of Australian weedy native plants namely: (i) disturbance responders, (ii) range extenders and (iii) new bioregion invaders
Disturbance responders are native plants that remain in their original distribution while increasing in density, often as a result of altered management practices such as increased fire frequency or human-induced vegetation disturbances.
Range extenders, as the term suggestsá move out of the boundary of their native range and increase their geographic distribution
New bioregion invaders are similar with range extenders, only that they increase their range by the transgression of large-scale geographical barriers for instance seas, mountains and deserts
Certain species, such as Acacia iteaphylla, exemplify trans-continental invaders by exhibiting multiple invasion patterns simultaneously This species not only spreads within its native range in Southern Australia but has also crossed significant geographic and climatic barriers, including the Australian desert, to invade Western Australia.
Acacia species
The genus Acacia, normally known as wattles, belongs to the family
The Fabaceae family has maintained a relatively stable classification since Bentham's work between 1840 and 1875, yet Acacia has experienced a complex nomenclatural evolution Initially described by Miller in 1754, Acacia has been assigned around thirty generic names, and the number of recognized species has surged to several hundred However, evidence suggests that the genus is not monophyletic, prompting major authors to attempt to organize these species into natural groupings Bentham was the first to clearly define the genus as it is commonly understood today In 1986, Pedley proposed splitting Acacia into three genera: Acacia, Senegalla, and Racosperma, though few have adopted this classification.
Australia diverged from the Pedley classification, as botanists concluded that further research was necessary (Maslin et al., 2003) Consequently, Australian botanists Maslin and Orchard advocated for re-typifying the genus with an Australian species instead of the original African type species, challenging conventional priority guidelines that required validation by the International Botanical Congress (IBC) (Smith & Figueiredo, 2011) This decision faced criticism, leading to a debate among taxonomists and biologists, many of whom chose to continue using the traditional Acacia sensu lato classification, despite the new ruling.
The International Botanical Congress has reaffirmed the decision to apply the name Acacia primarily to Australian species, which represent the vast majority of Acacia sensu lato.
The updated classification of the Acacia genus now includes three major subgenera: subg Acacia, which is pantropical; subg Aculeiferum Vassal, also pantropical; and subg Phyllodineae Seringe (formerly subg Heterophyllum Vassal), primarily found in Australia.
Acacia subg Phyllodineae, also known as the Australian group, has over
The genus Acacia comprises 950 species, with only 18 found outside Australia Recent molecular studies indicate that subgenus Phyllodineae is monophyletic, making it the largest monophyletic group within Acacia These species are extensively cultivated for various purposes, including forestry, afforestation, and trial plots They are also valuable for the production of tannin, timber, and pulp, as well as for food and revegetation efforts.
Acacia species are valued for their ecological traits and diverse applications, including sand dune stabilization, fuel production, medical uses, and as ingredients in hygiene products Their unique canopy architecture, leaf shape, soil preferences, and shading capabilities enhance their ornamental appeal In South Africa, Acacia plantations have been extensively studied, highlighting their significance in various environmental and industrial contexts.
The study of invasions is significantly enhanced by examining various factors that influence both biological and human aspects, as demonstrated in key research (Henderson, 2001; Richardson & van Wilgen, 2004) This case study serves as a valuable illustration of how these elements interact and contribute to the complexities of invasion dynamics (Richardson et al., 2011).
The cultivation of Acacia species in Vietnam represents a relatively recent development in land use, particularly valued in the furniture industry Acacia, alongside Eucalyptus, is favored for its ability to be grown on shorter rotations compared to Pinus species, making it an efficient choice for timber production.
2.2.1 Acacia longifolia and its subspecies
Acacia longifolia has two subspecies namely Acacia longifolia (Andrews)
Willd subsp longifolia and Acacia longifolia (Andrews) Willd subsp sophorae (Labill.) Court which is both native in south-eastern Australia (Maslin, 2001)
Acacia longifolia has been intentionally introduced to various regions, including South Africa, Europe, North America, the Middle East, New Zealand, and parts of its native Australia, mainly for the purposes of enhancing soil quality and stabilizing dunes.
A longifolia is recognized as one of the most invasive woody weeds in Western Australia, alongside A dealbata, A decurrens, and A pycnantha, significantly impacting the local ecology (Adair, 2008; Rejmánek, 2011; Stellatelli et al., 2014) A study examining the invasion of Invasive Alien Species (IAS) in Australia, which included five Acacia species, revealed no significant differences in soil microbial communities between Western and Eastern Australia where A longifolia is present, indicating that soil fungal communities do not hinder the invasion success of this species (Birnbaum et al., 2014).
A longifolia has been identified as a more generalist host than other Acacia species, exhibiting differences in nitrogen-fixing bacteria within its root nodules (Birnbaum et al., 2016) Additionally, it demonstrates faster growth in its naturalized range in Western Australia compared to its native distribution in eastern Australia (Birnbaum et al., 2012).
Acacia longifolia subsp longifolia, known as the Sydney golden wattle, is a shrub native to southeastern Australia, specifically in New South Wales, Queensland, and Victoria This species thrives in diverse environments, including inland forests, woodlands, riparian zones, grasslands, and heathlands It has fully naturalized in southwestern Western Australia and southeastern South Australia, commonly found along roadsides, in swamps, and in native bushland Additionally, A longifolia subsp longifolia has spread globally, including naturalization in New Zealand.
Indonesia, Colombia, Uruguay, Argentina, Israel, Spain, Portugal, Mauritius, southern Africa, and in the USA specifically in California (Maslin, 2001; Queensland Government, n.d.b)
Acacia longifolia subsp sophorae, commonly known as Coastal wattle, is native to the coastal regions of East and South Australia This subspecies, like A longifolia subsp longifolia, has naturalized in southwestern Western Australia and has expanded its range into Victoria, South Australia, and New South Wales In addition to its spread within the coastal districts of these states, Coastal wattle has also infiltrated local environments and plant communities.
In regions where both subspecies of A longifolia coexist, they often blend seamlessly and are frequently misidentified However, A longifolia subsp longifolia can be recognized as an erect shrub reaching heights of up to 10 meters, while A longifolia subsp sophorae typically grows to about 5 meters and is rarely erect The phyllodes of A longifolia subsp longifolia are characterized by their thinner, more pliable structure, measuring 5-20 cm in length and 5-15 mm in width, and are predominantly dark green In contrast, A longifolia subsp sophorae features thicker, elliptical phyllodes that are 5-12 cm long and 10-30 mm wide, often appearing yellowish green and leathery Due to their distinct morphological traits, habitat preferences, and distribution patterns, it is crucial to specify their subspecies in research However, many studies conducted in the Americas, South Africa, New Zealand, and parts of Asia fail to differentiate between the two, often referring to them collectively as A longifolia.
A longifolia in their native regions prefers habitats that have full sun This species does well in humid temperate climate, extending into the Mediterranean climate They are frost resistant down to -6 °C and are also drought-resistant, however, needs no less than 550 mm of rainfall (Werner et al., 2010) Since they can fix nitrogen, they grow in nutritionally poor soils (Grubben & Denton, 2004) Seeds can tolerate high salinity, which then contributes to its invasive ability in sand dunes (Morais et al., 2012)
Although there is considerable documented information about its spread and harm to biodiversity and ecosystems (Marchante et al., 2008; Werner et al.,
2010) and being included in invasive lists, A longifolia is still being sold on the market as an ornamental to be used on slopes, for screening, and as a windbreak
Background on methods used in biodiversity study
2.3.1 Species Distribution Models and invasive species:
Once an introduced species becomes established, eradication becomes challenging (Rejmánek et al., 2005) Thus, preventing the introduction of potential invaders is the most cost-effective management strategy Accurate predictions are crucial for alerting scientists and decision-makers to future risks, helping to prevent further biological changes due to climate change, and supporting the development of proactive strategies to mitigate climate change impacts on biodiversity (Parmesan et al.).
Since 2011, various screening procedures and systems have been developed globally to assess invasion potential before it occurs, exemplified by invasive species monitoring in Australia Species Distribution Models (SDM), also referred to as ecological niche models, bioclimatic envelopes, habitat models, and resource selection functions, serve as scientifically validated tools for evaluating and predicting the effects of climate change by integrating observed species occurrence patterns with environmental and geographic data.
Species Distribution Models (SDMs) are increasingly utilized across various disciplines, including terrestrial, freshwater, marine, evolutionary biology, and epidemiology These models not only identify whether a species is invasive in other regions but also predict its potential distribution patterns Additionally, SDMs offer valuable insights into a species' ability to adapt to environmental features This information enables researchers and policymakers to respond proactively to invasive species threats.
The Biodiversity Climate Change Virtual Laboratory (BCCVL) is an Australian modeling portal designed for biodiversity and climate change research, offering six types of experiments categorized into primary and secondary models The results from a primary Species Distribution Model (SDM) serve as inputs for a secondary Climate Change experiment, which assesses species distribution under various future climate scenarios influenced by different Representative Concentration Pathways (RCPs) These RCPs, including RCP 2.6, RCP 4.5, RCP 6, and RCP 8.5, represent potential future emissions and their impact on global temperature and greenhouse gas concentrations by 2100, with RCP 8.5 indicating the highest emissions and RCP 2.6 the most favorable decline The BCCVL requires essential biological data, such as species occurrence coordinates, and environmental data detailing the conditions of those locations to effectively model these scenarios.
Field surveys and datasets serve as vital sources of biological data, but due to their labor-intensive nature, field surveys are typically conducted on a small scale Consequently, many researchers rely on datasets compiled by other institutions, which are particularly useful for larger-scale studies at global and continental levels The Atlas of Living Australia (ALA) is a highly recommended online resource, offering 194 dataset collections with occurrence records for over 100,000 species (Hujibers et al., 2016a) Additionally, Florabase provides a comprehensive dataset for the Western Australian Flora, including information on current invasive species and their local distributions.
There are four primary environmental regimes that influence physical conditions: moisture (rainfall and evaporation), thermal, radiation (photosynthetically active radiation), and mineral nutrients (soil type) For species distribution models (SDMs), it is crucial to focus on environmental variables that directly impact survival rather than indirect factors like altitude The Australia Climate Collection offers national climate layers, including 19 bioclimatic variables for both current and future climates A study of climate models in Australia revealed that MIROC-m, CSIRO Mk3, and ECHO-G performed the best Raw data alone is inefficient for SDMs due to the high variability of daily measurements; therefore, processing is necessary to derive meaningful variables Key resources for average climate and environmental data in Australia include the Bureau of Meteorology, NationalMap, and the Terrestrial Ecosystem Research Network Spatial scale in SDMs consists of grain (data resolution) and extent (study area size), where grain refers to the size of grid cells that capture the extremes of environmental conditions, influencing species occurrence probabilities.
Scale is a crucial factor in analyzing species and environmental data, encompassing both grain and extent Grain refers to the resolution of individual observations, while extent pertains to the overall geographic area under study, such as fine resolution at 1 km² and coarse resolution at 10 km² (Hujibers et al., 2016a).
MaxEnt (Maximum Entropy) (Philips, et al., 2006) is a program built to model species distribution using presence-only species records (Elith et al.,
The MaxEnt algorithm, established in 2011 and recognized as the most widely used species distribution modeling (SDM) method (Hijmans & Elith, 2018), has been extensively applied to model current species distributions by identifying correlates of species occurrences This algorithm not only generates current map predictions but also forecasts species distributions in new times and locations Its adoption by both government and non-government organizations for large-scale biodiversity mapping applications, such as the Atlas of Living Australia (ALA) and the Biodiversity and Climate Change Virtual Lab (BCCVL), underscores its significance in real-world ecological studies.
2.4.2 Invasion patterns at local-scale
Field surveys are crucial for understanding local invasion patterns, as they provide data from actual environments Invasive species typically undergo three stages: an initial establishment phase with limited spread, an expansion phase characterized by rapid growth, and a saturation phase where spread rates stabilize Successful invaders exhibit unchecked exponential growth until resources become limited However, several biological constraints can hinder their advancement, such as the need for individuals to mature and produce propagules, Allee effects that restrict growth at low densities, and lag phases that delay further spread Factors contributing to lag phases may include low detection of sparse populations, fluctuating environmental conditions during early invasion, and adaptations to local habitats.
METHODS
Materials
a) Atlas of Living Australia (ALA)
This website was used to provide the species occurrence data of both A longifolia subsp longifolia and A iteaphylla and the environmental variables used
This was also utilised to produce a current SDM map for both Acacia species (Appendix 4a) b) Biodiversity Climate Change Virtual Laboratory (BCCVL)
This website was used to provide the climate variables and future climate dataset of the study
This was also used to produce the current and future SDM map of both
Acacia species (Appendix 4b) c) MaxEnt (Maximum Entropy)
The statistical regression model was employed to analyze the Species Distribution Model (SDM), yielding insights into the influence of specific variables on distribution patterns Key outputs included the significance of these variables, represented by their percentage contributions and permutation values, as well as mean response curves, detailed in Appendix 4c.
This website was used to provide the list of current invasive Acacia species in Western Australia (Appendix 4d) e) Climate data and soil data maps
The average climate and soil data maps of Australia were utilized to analyze the generated maps and mean response curves from the Species Distribution Model (SDM) (see Appendix 4e) Additionally, tools such as a rangefinder and measuring tape were employed in the analysis.
These were used to measure all the distance-related data such as the 50 m by 100 m site range, the distance between each Acacia specimens and the edge of the woodlands
These were also used to measure the height and the width of the Acacia specimen (Appendix 4f & 4g) g) Diameter Tape and Tree Calliper
This was used to measure the diameter of each stem of the Acacia specimen to indicate the age of the plant (Appendix 4h & 4i) h) Flagging Tape
Colored tape was utilized to mark each recorded Acacia plant within the site range and to delineate the boundaries of each observed plot (see Appendix 4j) Additionally, a field reference guide for Acacia species was provided to aid in identification.
These were used to guide the researchers in identifying the species on the field (Appendix 4k) j) Printed distribution maps of Acacia plants and pictures of selected sites
These were photos of each species used to guide the researchers to locate where Acacia plants will be sighted (Appendix 4l) k) Microsoft Excel 2016
This software was used to calculate all the statistical analyses in the study (Appendix 4m)
To assess the influence of climate and environmental factors on the invasion patterns of A longifolia subsp longifolia and A iteaphylla in Australia, as well as their potential future distribution due to climate change, Species Distribution Modelling (SDM) was employed, following the methodology established by Sutomo and van Etten (2017).
From April to July 2018, the SDM method was implemented in the computer laboratories of Edith Cowan University, utilizing the Atlas of Living Australia (ALA) and the Biodiversity Climate Change Virtual Laboratory (BCCVL) The resulting habitat suitability maps depict the potential distribution of species based on their preferred environmental conditions.
The FloraBase website (http://florabase.dpaw.wa.gov.au) serves as the authoritative source for information on Western Australia's flora, particularly for identifying invasive Acacia species in the Perth Region It provides essential records, including the origin, naturalised ranges, and fire response of these species.
Modelling current species distribution using climate variables
The Atlas of Living Australia (ALA) website (http://ala.org.au) serves as a comprehensive source for geo-referenced herbarium records across Australia, providing essential species occurrence data for A longifolia subsp longifolia and A iteaphylla To ensure accuracy, the data was filtered to exclude hybrid species and those with incomplete scientific names, focusing solely on preserved specimens For Native Distribution modelling, only records classified as "native and not cultivated" were included, while "non-native records" were added for Naturalised Distribution modelling Additionally, records with identical coordinates were removed to avoid duplicates, and the locations were verified against known native and naturalised ranges of the species, ensuring that specimen location quality met spatial validity standards.
The Biodiversity Climate Change Virtual Laboratory (BCCVL) is an online portal utilized for biodiversity and climate change modeling, allowing the import of filtered occurrence records from the Atlas of Living Australia (ALA) This platform facilitates the creation of species distribution models (SDMs) for A longifolia subsp longifolia and A iteaphylla, using current and future climate data It provides access to a comprehensive climate dataset, which includes nineteen bioclimatic variables, such as precipitation and temperature, sourced from the Australia Climate Current Collection (1976-2005) at a resolution of 30 arcseconds (1km) These variables were essential for modeling the current distribution of both species.
MaxEnt, a statistical regression model, was employed to develop the Species Distribution Model (SDM) by estimating the impact of various variables To mitigate potential overfitting, only those variables that contributed at least 5% to the overall model and individual permutations were included in the final modeling distribution Additionally, highly correlated variables were eliminated, reducing the number of climate variables from 19 to between 5 and 8 The modeling process was conducted twice: first, to identify variables with a minimum 5% contribution to the overall model, and second, to remove highly correlated variables.
• A primary output of an SDM was a map that showed the predicted current distribution of the species
• A response curve, which is the second output of an SDM, showed how these species respond to certain levels of the applied environmental and climatic variables
The Area Under the Curve (AUC) of the Receiver-Operating Characteristics (ROC) plot indicates the accuracy of Species Distribution Model (SDM) predictions The ROC plot features the True Positive Rate (Sensitivity) on the y-axis and the False Positive Rate (1-Specificity) on the x-axis, with a value of 0.5 signifying random predictions AUC values above 0.5 reflect better-than-random predictions, with higher AUC values indicating greater model accuracy Specifically, AUC values between 0.5 and 0.7 indicate poor model performance, values from 0.7 to 0.9 suggest moderate performance, and values exceeding 0.9 denote excellent performance (Hujibers et al., 2016b).
In order to analyse the maps and response curves produced, the researcher used the following:
• climate data maps presented by the Bureau of Meteorology of the Australian Government (http:// www bom gov au/ climate/ data/ index shtml) (Appendix 9a, 9b, 9c, 9d, 9e, 9f & 9g);
The clay percentage data map from the NationalMap of the Department of Prime Minister and Cabinet (https://nationalmap.gov.au) and the soil erosion grade data map from the Australia: State of the Environment report (https://soe.environment.gov.au/theme/land/topic/2016/soil-formation-and-erosion) provide essential insights into soil characteristics and erosion patterns across Australia.
The Terrestrial Ecosystem Research Network (TERN) offers a soil depth data map, accessible via the National Research Infrastructure Strategy (NCRIS) at the CSIRO website This resource provides valuable insights into soil and landscape characteristics, contributing to ecological research and land management For more information, visit the data portal at CSIRO.
Modelling current species distribution using environmental variables
To assess the influence of environmental factors on the invasion patterns of A longifolia subsp longifolia and A iteaphylla, a separate Species Distribution Model (SDM) was developed in ALA, incorporating fifteen environmental variables Similar to the modeling process using only climate data, variables were selected based on a minimum contribution of 5% and permutation results from the MaxEnt analysis The ALA predictive tool automatically identified highly correlated variables, which were then combined with climatic variables from BCCVL to create an SDM that reflects both climatic and environmental conditions.
To assess the future invasion patterns of A longifolia subsp longifolia and A iteaphylla in Australia due to climate change, researchers utilized the Climate Change experiment from BCCVL This experiment projects future species distributions based on current species distribution models (SDMs) and selected climate datasets along with greenhouse gas emissions scenarios By applying the Climate Change Experiment, predictions were made for the future distribution of both species, specifically for the year 2045, using MaxEnt SDM under two different greenhouse gas emission scenarios.
• RCP 4.5 – shows a short period of increase in emissions followed by a gradual decline
– illustrates intermediate pathways that are based on scenarios in which humans apply a range of technologies and strategies to reduce emissions (i.e reduced use of grassland and fossil fuels)
• RCP 8.5 – predicts emissions three (3) times as much as the current emissions by the year 2100
– illustrates the pathway that humans are likely to follow if they continue what they do today, the “business as usual pathway”
The RCP levels illustrate realistic yet differing climate change scenarios Predictions were made utilizing the CSIRO Mark 3.0 climate model, which operates at a 30” (1km) resolution, making it highly suitable for generating Species Distribution Models (SDMs) in Australia.
Site selection
In order to determine the effect of environmental factors on the local-scale patterns of invasion of A longifolia subsp longifolia and A iteaphylla in Perth,
WA, a total of fifteen (15) sites were surveyed from the Swan Coastal Plain- Perth, Western Australia.; eight (8) for A longifolia subsp longifolia and seven
The Swan Coastal Plain in Perth, WA, was selected as a key area for A iteaphylla due to its representation of the population and the presence of numerous reserve sites featuring Acacia species sightings.
A field survey was conducted on A longifolia subsp longifolia across eight sites on the Swan Coastal Plain in Perth, Western Australia, characterized by sandy soils The sites were categorized into five unburnt locations, including two plots in Thomson’s Lake Nature Reserve and others at Anketell Road, De Haer Road, and an additional site, as well as three recently burnt sites in Shirley Balla Swamp Reserve and Forrestdale These locations were specifically chosen due to the known presence of the invasive A longifolia subsp longifolia.
The study of A iteaphylla revealed that this species predominantly inhabits unburnt areas, with no records found in recently burnt sites A total of seven survey locations were examined on the Swan Coastal Plain, including two plots in Neerabup National Park, two in De Haer Road, one along the road of Canning Vale, one in Wandi Nature Reserve, and one on Battersby Road These sites were specifically chosen due to the presence of the invasive A iteaphylla, as documented in Appendix 10.
Using GIS and fire history data from the Western Australian Land Information Authority (Landgate) Firewatch, burnt and unburnt sites were identified A site is classified as burnt if it has experienced a fire incident within the last 10 years, while a site is deemed unburnt if it has not been affected by fire for 20 years or more.
Field survey and data collection
The field surveys were conducted on five (5) separate dates; May 10, May
17, May 30, June 14 and June 25, 2018 The field sampling method was based on Fowler et al (1998) randomised sampling technique
A measured transect was always employed for every site A narrower grid
In the study, a grid system was employed to assess plant density, utilizing a 30 by 100 m area for sites with high plant density and a 50 by 100 m area for those with low density The grid length was adjusted as the density of A longifolia subsp longifolia increased over distance Researchers selected a random point at the edge of the site, near the road, to identify areas with adult invasive A longifolia subsp longifolia and A iteaphylla The survey commenced from this random point, with observations conducted in a zigzag pattern across the plot to ensure comprehensive coverage of all Acacia wildlings, including saplings and seedlings.
This specific technique was utilised since it allowed a high level of detection of
In this study, all Acacia species within each plot and along the boundary line were meticulously counted and measured to assess plant density The distance of each Acacia plant from the edge of the bushland was recorded using a Rangefinder or tape measure, which were also employed to determine plant height Stem diameters were measured approximately 0.5 meters above ground level using a diameter tape and tree caliper The radius was calculated by dividing the stem diameter by two, and the Basal Area for each stem was computed using the formula for the area of a circle: πr² For multi-stemmed individuals, the total Basal Area was obtained by summing the calculated areas of each stem Additionally, a tape measure was used to assess the plants' width, and the Biovolume of each plant was calculated using the formula: π [ (Width].
2 ) 2 (ℎ𝑒𝑖𝑔ℎ𝑡)] It is expected that the stem diameter and height will be related to the age of the plant
Understorey cover was assessed using visual estimates in designated plots, employing the Braun-Blanquet (1965) method to categorize the estimated understorey cover percentage of Acacia plants The classification includes five cover classes: open (1-5%) with no accompanying plants, sparse (6-25%) featuring a small layer of plants with noticeable gaps, mid-dense (26-50%) indicating a significant number of plants with visible spacing, dense (51-75%) showing a thick concentration of plants with minimal gaps, and very dense (> 75%) representing an extremely thick plant presence with no space This classification aims to identify the preferred disturbance levels of the two species (Appendix 12).
In the study, each Acacia plant was assessed for its growth location, specifically whether it was situated under a tree, to understand its habitat preferences and potential bird dispersal A tree was defined as any plant growing taller than the Acacia, regardless of its vitality The overall tree cover percentage at each site was estimated using the Braun-Blanquet method from 1965.
The Perth Groundwater Map, created by the Department of Water and Environment Regulation of Western Australia, provides essential data on the depth to the water table at various survey sites This information is crucial for analyzing each 50-meter section along the designated transects.
Statistical analyses
Statistical analyses were performed using Microsoft Excel (2016), employing regression analysis to determine the r and p-values at a significance level of 0.05 (α = 0.05) This analysis examined the relationship between distance along the transect and the plant density, basal area, and biovolume of A longifolia subsp longifolia and A iteaphylla.
A two-sample Student t-test, assuming unequal variances and a significance level of 0.05 (α = 0.05), was conducted to compare the plant density, basal area, and biovolume of A longifolia subsp longifolia between unburnt and burnt sites.
Chi-squared tests were used to determine whether A longifolia subsp longifolia and A iteaphylla found preferred being under a tree or not with a significance level of 0.05 (α = 0.05) where:
Observed value = Total number of plants under a tree
Expected Value = Tree cover (%) * Total number of plants in sites
Environmental factors, including dispersal distance from the edge and groundwater depth, were analyzed for A longifolia subsp longifolia in unburnt sites, maintaining a consistent distance range of 0-99 m Additionally, understorey cover and tree presence were considered, providing a comprehensive assessment of the overall site effects on this subspecies.
RESULTS AND DISCUSSIONS
Invasion patterns of A longifolia subsp longifolia and A iteaphylla in
This section presents the analyzed data from the study aimed at identifying the invasion patterns of Acacia longifolia subsp longifolia and Acacia iteaphylla, considering climatic and environmental factors Additionally, it explores the potential future invasion patterns of these two Acacia species in Australia through Species Distribution Modelling (SDM) at both national and regional scales The SDM results are represented as habitat suitability maps, which depict the range distribution of the species in their optimal environments.
4.1.1 Invasion patterns in Australia: Climate change factors
4.1.2 Current distribution of A longifolia subsp longifolia and A iteaphylla in Australia
The BCCVL results indicate that the potential distribution of A longifolia subsp longifolia is predominantly concentrated in the southeastern corners of Eastern Australia, exhibiting high probability and density Additionally, it is predicted to spread to Tasmania, although at a low density, while expansion into Western Australia is not anticipated.
Predictions indicate significant potential expansions of A longifolia subsp longifolia in its naturalized ranges, particularly in the southern corners of Western Australia and South Australia, as well as in Tasmania Additionally, an increase in density was observed in Eastern Australia.
The predicted current distribution of A longifolia subsp longifolia, based on its native ranges and current climate conditions, is illustrated in Figure 1 The darker regions indicate a higher likelihood of occurrence for this species.
The predicted current distribution of A longifolia subsp longifolia, based on its naturalised ranges under present climate conditions, is illustrated in Figure 2 The darker areas on the map indicate regions with a higher likelihood of the species' occurrence.
Predictions for A iteaphylla indicate a broader distribution across Australia compared to A longifolia subsp longifolia, which is primarily found in the country's corners The analysis suggests that A iteaphylla has significant potential for expansion in its native range in South Australia, with the possibility of spreading widely in southern Western Australia and the southwest of Eastern Australia, exhibiting medium to high density.
Figure 3: Predicted current distribution of A iteaphylla based on its native ranges under current climate condition produced in BCCVL Darker areas representing a higher likelihood that the species can occur
Modeling based on the naturalized distribution of A iteaphylla under current climate conditions predicts a significant potential expansion across a quarter of Western Australia, particularly in the high-probability southwest region Additionally, a considerable increase in its distribution is anticipated in southern Eastern Australia, along with a high likelihood of suitability for growth in Tasmania, where it is currently absent Furthermore, projections indicate an expansion in range and density in South Australia.
Figure 4: Predicted current distribution of A iteaphylla based on its naturalised ranges under current climate condition produced in BCCVL Darker areas representing a higher likelihood that the species can occur
Based on the current predicted maps of A longifolia subsp longifolia and
A iteaphylla in their native ranges, A longifolia subsp longifolia stayed on its native range in EA and extended in Tasmania with very low probability and density while A iteaphylla had wider distribution ranges which are extending from WA, SA, and EA The same as through with the results from the naturalised distribution prediction, A longifolia subsp longifolia showed narrower expansion as compares to the wide expansion of A iteaphylla (Figure 1, Figure
A longifolia subsp longifolia exhibits a limited distribution, primarily found in Eastern Australia (EA), while A iteaphylla has a broader range across Western Australia (WA), South Australia (SA), and EA Climate variables indicate that A longifolia subsp longifolia is unlikely to expand into WA.
4.1.3 Invasion patterns in Australia: Environmental factors
The SDM models discussed were generated using the ALA prediction tool, incorporating 19 climate variables, such as precipitation and temperature, alongside 15 environmental variables, including moisture index, soil texture, and distance (see Appendix 13 for details).
4.1.4 Current distribution of A longifolia subsp longifolia and A iteaphylla due to environmental variables in Australia
The Species Distribution Model (SDM) for A longifolia subsp longifolia, which incorporates environmental variables from the Atlas of Living Australia (ALA), predicts a potential expansion into the inner southern regions of eastern Australia and Tasmania, surpassing predictions made using only climate variables Notably, the model indicates that suitable environments for this species exist in the southernmost corners of South Australia, an area not identified in the climate-only model Additionally, the inclusion of environmental variables suggests a low probability of occurrence in the southwestern part of Western Australia.
Modeling based on the naturalized range of A longifolia subsp longifolia indicates a potential expansion of its habitat, particularly towards inner Western Australia, South Australia, and Eastern Australia The predictions, which incorporate additional environmental variables, suggest a medium to high probability of occurrence in these regions (see Figures 2 and 6).
The predicted current distribution of A longifolia subsp longifolia is illustrated in Figure 5, based on its native ranges and current climate and environmental variables from ALA The white dots indicate presence locations utilized for training the model, while violet dots represent the test locations.
The predicted distribution of A longifolia subsp longifolia has been modeled based on its naturalized ranges, considering current climate and environmental factors, as illustrated in Figure 6 from the Atlas of Living Australia (ALA) The white dots represent the locations used for training the model, while the violet dots indicate the test locations for validation.
The analysis of A iteaphylla's native ranges, when considering additional environmental variables, revealed a reduced probability of distribution in Eastern Australia (EA) compared to models based solely on climate factors Similarly, in Southern Australia (SA), its distribution decreased but maintained a high likelihood of occurrence Notably, a significant potential for expansion was observed in the westernmost regions of Western Australia (WA), where predictions indicate a strong likelihood of the species moving northward.
Local-scale patterns of invasion of A longifolia subsp longifolia and A
iteaphylla in Perth, WA: Environmental Factors
This section outlines the analyzed data from the study, focusing on its secondary objective: to examine how environmental factors influence the local-scale invasion patterns of A longifolia subsp longifolia and A iteaphylla in Perth, Western Australia.
This study evaluated various environmental factors influencing the growth performance of invasive Acacia species, including distance from the edge, groundwater depth, understorey cover, fire impact, and the presence of larger trees.
4.2.1 Edge effect on plant density, basal area and biovolume of A longifolia subsp longifolia and A iteaphylla
To assess the edge effect on plant density, basal area, and biovolume of A longifolia subsp longifolia and A iteaphylla, a field survey was conducted within the first 100 meters from the edge, which was segmented into ten distinct distance ranges.
The recruitment curve for A longifolia subsp longifolia illustrates the density variations in unburnt and burnt sites, highlighting the differences in population density per hectare at varying distances from the edge of natural vegetation Notably, the Y scale differs between the figures, emphasizing the impact of fire on recruitment patterns.
Standard errors are represented by errors bars
Edge effect: A longifolia subsp longifolia in unburnt sites
A longifolia subsp longifolia surveyed on the unburnt sites showed an overall decrease in density with distance from the edge of the bushland but with a low r-value value of 0.42 and a p-value of 0.04 (Figure 25a) (Appendix 14) but this still suggests that there is an edge effect Out of the five (5) different unburnt sites that were surveyed, three (3) showed a significant evidence of edge effect or decreased plant density with regards to dispersal distance which were Anketell (r
= 0.68; p = 0.02), De Haer (r = 0.46; p = 0.03) and Piara (r = 0.44; p = 0.04) (Figure 25a, 25b & 25c)
Only De Haer (r = 0.47; p = 0.03) and Piara (r = 0.45; p = 0.03) showed significant evidence of decreasing basal area as the distance went farther from the edge of the bushland (Figure 26a & 26b) (Appendix 15)
In the study of A longifolia subsp longifolia, significant decreases in biovolume measurements were noted only at the sites of De Haer (r = 0.46; p = 0.03) and Piara (r = 0.49; p = 0.02) with increasing distance, while unburnt sites showed no significant patterns in basal area or biovolume The remaining sites at Thomsons Lake Nature Reserve exhibited random peaks and absences in relation to distance The three sites affected by edge effects were located in intermediate wetland areas, where dry species like Banksia dominated over wet species such as Melaleuca, Jarrah, and Kunzea Conversely, the first 30 meters of the other two sites were dominated by dry species, transitioning to wet species throughout the wetland Additionally, a gradient effect was observed, with larger plants over 20 cm in diameter surrounded by smaller plants under 20 cm.
Figure 26: Recruitment curve for A longifolia subsp longifolia in a) Anketell and b)
De Haer and, c) Piara for unburnt sites showing their decreasing trendline Density of
A longifolia subsp longifolia per ha at different distances from the edge of natural vegetation (note the difference in the Y scale among figures)
The recruitment curve for A longifolia subsp longifolia demonstrates a declining trend in both De Haer and Piara unburnt sites The basal area of A longifolia subsp longifolia per hectare varies significantly with distance from the edge of natural vegetation, highlighting the impact of proximity on growth patterns.
The recruitment curve for A longifolia subsp longifolia illustrates a decreasing trend in biovolume per hectare at unburnt sites in both De Haer and Piara The data highlights variations in biovolume relative to the distance from the edge of natural vegetation, emphasizing the significance of spatial factors on the species' recruitment dynamics.
In Thomsons Lake Nature Reserve, the total number of A longifolia subsp longifolia plants was assessed in an unburnt site, revealing varying densities of plants categorized by diameter size groups per hectare This analysis was conducted at different distances from the edge of the natural vegetation, providing insights into the distribution patterns of these plants within the reserve.
Density of Plants (#of plants/ha)
0-4.9 cm 5-9.9 cm 10-14.9 cm 15-19.9 cm >20 cm
Edge effect: A longifolia subsp longifolia in burnt sites
As for the surveyed A longifolia subsp longifolia in burnt sites, it showed no overall edge effect for plant density (r = 0.09; p = 0.41), basal area (r = 0.13; p
= 0.31) and biovolume (r = 0.05; p = 0.54) (Figure 24b) (Appendix 17, Appendix
A survey of three sites revealed that one site, located near Shirley Balla Swamp Reserve, exhibited a significant increase in plant density with increasing distance (r = 0.63; p = 0.01) In contrast, the other two sites did not provide sufficient evidence of any discernible patterns in plant density related to dispersal distance All three surveyed sites were burned and predominantly consisted of wetland areas, featuring species such as Banksia littoralis, Xanthorrhoea preissii, Kunzea, and Melaleuca.
The recruitment curve for A longifolia subsp longifolia in Shirley Balla Swamp Reserve indicates a positive trend in population density, particularly in areas affected by fire This species shows varying densities per hectare at different distances from the edge of natural vegetation, highlighting the impact of proximity to undisturbed habitats on its growth.
In a study examining edge effects on plant density, basal area, and biovolume, only A iteaphylla demonstrated a significant edge effect across all metrics, with correlations of r = 0.39 (p = 0.05) for plant density, r = 0.48 (p = 0.03) for basal area, and r = 0.42 (p = 0.04) for biovolume (see Figures 30a, 30b, and 30c) Among the seven surveyed sites, only De Haer Road 1 and De Haer Road 2 showed significant declines in density as the distance from the bushland edge increased, both with a correlation of r = 0.48 (p = 0.03) (refer to Appendix 20 and Figure 31a).
The surveys conducted at De Haer Road 1 and De Haer Road 2 revealed a significant decline in basal area from the edges of the bushlands, with correlation coefficients of r = 0.41 (p = 0.05) and r = 0.47 (p = 0.03), respectively Recruitment curves indicated that A iteaphylla was primarily concentrated within the first 20 meters, showing a decrease in density and absence beyond that range However, notable observations included two A iteaphylla plants located 38 and 73 meters from the edge, found beneath large Jarrah trees Additionally, an A iteaphylla seedling, along with four A longifolia subsp longifolia, was discovered outside the 30 by 100 m grid, also growing under a large tree The seven surveyed sites predominantly featured Banksia coverage, with some wet species like Jarrah and X preissii present.
The overall recruitment curve for A iteaphylla across surveyed sites illustrates key metrics such as plant density, basal area, and biovolume per hectare at varying distances from the edge of natural vegetation Notably, the Y-axis scales differ among the figures, emphasizing the variations in these ecological parameters.
Standard errors are represented by error graphs
The recruitment curve for A iteaphylla demonstrates a declining trend in both De Haer Road 1 and De Haer Road 2 This trend highlights the density of A iteaphylla per hectare at varying distances from the edge of natural vegetation, indicating a significant correlation between recruitment rates and proximity to natural habitats.
Discussion
This study investigates the influence of climate and environmental factors on the invasion patterns of Acacia longifolia subsp longifolia and Acacia iteaphylla in Australia through Species Distribution Modelling (SDM) It also aims to predict the potential future invasion patterns of these Acacia species in the context of climate change in Australia Additionally, the research focuses on understanding the impact of local environmental factors on the invasion patterns of both species in Perth, Western Australia.
4.3.1 Invasion patterns in Australia: SDM using climate and environmental variables
The study's findings indicate that both A longifolia subsp longifolia and A iteaphylla are trans-continental invaders, having expanded beyond their native ranges to establish new populations outside their original distribution areas This aligns with Adair's (2008) description of invasion patterns among weedy plants in Australia, highlighting the significant ecological impact of these species.
Despite numerous observation records of A longifolia subsp longifolia in southern Western Australia, initial predictions of its distribution using only climate variables indicated a low probability of occurrence along the south coast This inaccuracy was likely due to the limited variables considered in the modeling process, highlighting the need for additional environmental factors Once environmental variables were incorporated, the predictions showed a medium probability of occurrence with low density in southern WA, emphasizing the significance of water availability, particularly precipitation and soil moisture Field surveys further confirmed that wetter sites, such as those near groundwater or receiving runoff, are crucial for the species Relying solely on climate models failed to yield accurate predictions, as the average annual rainfall in the outskirts of the country ranges from 500-3000 mm, aligning with the species' growth in areas with rainfall between 500-3472 mm.
In 2010, moisture index variables mirrored the precipitation trends, indicating that suitable habitats for A longifolia subsp longifolia are primarily located in southern Western Australia, South Australia, and Eastern Australia Additionally, the average summer temperature, ideally between 22-25 °C, significantly influences the predicted distribution of this species, highlighting its preference for warmer climates in these regions.
WA, SA, and EA were their suitable habitats for the species (Appendix 8c)
Recent studies indicate a significant shift in the distribution of A longifolia subsp longifolia, highlighting its adaptability to various environments (Correia et al., 2016; Harris et al., 2017; Harris et al., 2012) This invasive species has shown a marked decrease in water requirements, demonstrating its resilience, even in extremely cold winters, making the entire country a viable habitat (Appendix 8d) Predictions suggest it can thrive in regions with low summer rainfall (0-140 mm) and higher winter rainfall (160-880 mm) Notably, the Perth region and southern South Australia experience the lowest summer rainfall compared to other states (Appendix 8e), while southern WA, SA, southeastern EA, and Tasmania provide suitable conditions for its winter water needs (Appendix 8f).
A longifolia subsp longifolia was predicted to be expanding in these naturalised areas The results produced in SDM for both native and naturalised ranges of the species together with the overall climate data demonstrated that south WA specifically Perth was indeed a suitable habitat for A longifolia subsp longifolia and further support the study’s field survey data
A iteaphylla, on the other hand, already showed predicted distributions in
A iteaphylla exhibits a broader distribution in Western Australia (WA) compared to A longifolia subsp longifolia, favoring low isothermality and varying temperatures across different states This species thrives in semi-arid regions, requiring minimal rainfall, which aligns with findings by Wriggley & Fagg (1996) and Mullins (1989) It is predicted to grow in the south-west of WA, eastern Australia (EA), and southern South Australia (SA), where summer rainfall meets its needs However, when environmental variables were considered, density ranges in EA were expected to contract due to poor soil erosion grades, indicating that WA, EA, and SA are suitable habitats for A iteaphylla, particularly in eroded areas A iteaphylla has been shown to help control soil erosion and runoff on steep slopes, but only WA and SA provide the necessary clay percentage of around 31%, while EA's high clay content correlates with decreased density and probability of growth Additionally, SA's lower soil moisture content supports higher plant density compared to WA.
A iteaphylla in its prediction for naturalised ranges, only just expanded in the areas where it was predicted in its native range: WA, SA, and EA It had also formed new distributions in Tasmania, where it was not predicted before The whole country was considered suitable environments for the species due to the need for very low temperatures during winter (0-20 °C) The same as through with the results from the native range prediction, it showed that its isothermality was still low hence explains as to why it was able to expand more in the predicted areas (Appendix 8g) The need for precipitation had also decreased (0-
The expansion of A iteaphylla in Tasmania and the northern regions with higher rainfall has led to a contraction in its probability range Research indicates that this species thrives in highly eroded environments, aligning with findings by Agassi & Ben-Hur (1992) that highlight its preference for disturbed areas Areas in EA, WA, and SA exhibit poor soil erosion grades, making them suitable habitats for A iteaphylla However, the species favors extremely low soil depths, resulting in decreased occurrence probability in EA, which has moderate to high soil depths In contrast, WA, SA, and Tasmania present very low soil depths, correlating with higher densities and occurrence probabilities in these states These predicted outcomes, however, are inconsistent with field survey results from Perth, WA.
A iteaphylla was recorded to be only growing on areas with very high soil depth level (Figure 36 & Appendix 25)
SDM predictions based on the naturalised ranges of species tend to indicate a broader and more realistic distribution compared to those based on native distributions This phenomenon may be attributed to the selection and active cultivation of certain cultivars adapted to drier climates outside their native ranges, aligning with Correia et al (2016), which found that Australian Acacia species exhibited greater fitness in naturalised areas due to the enemy release hypothesis Additionally, genetic drift or phenotypic plasticity among Acacia species established beyond their native range may also contribute to this trend, as supported by the research of Harris et al (2017) and Harris et al (2012).
Acacia had different life-cycle stage, greater biomass and specific leaf area in their introduced ranges as compared to their native range because of trait difference
Climate and environmental factors, such as precipitation, temperature, soil moisture, clay percentage, and soil depth, exhibited higher measurements at the country's edges Notably, elevated soil moisture levels were observed in the western and eastern areas, correlating with the prevalence of A longifolia subsp longifolia in these outskirts This preference for coastal proximity by both A longifolia subsp longifolia and A iteaphylla explains the concentration of predictions along the coastlines However, this observation requires further investigation, as it contradicts existing knowledge that A longifolia subsp sophorae is recognized as the coastal wattle, while A longifolia subsp longifolia predominantly thrives in wetlands.
Future climate predictions indicate that A longifolia subsp longifolia will expand its distribution and increase plant densities in new areas, as supported by multiple studies (Augspurger, 1984; Harris et al., 2012; Correia et al., 2016; Harris et al., 2017) In contrast, A iteaphylla is expected to decrease in density and contract its distribution due to climate change These findings reinforce the notion that A longifolia subsp longifolia is an invasive species capable of establishing new populations, while A iteaphylla, a naturalised plant, is likely to struggle and fail to adapt to changing climatic conditions.
4.3.2 Invasion patterns in Perth, WA: Environmental factors in field survey Edge effect on plant distribution and growth
Field surveys revealed that A iteaphylla and A longifolia subsp longifolia exhibited a notable invasion pattern originating from the bushland edge However, no similar invasion pattern was observed for A longifolia subsp longifolia in recently burnt areas regarding the edge effect.
A longifolia subsp longifolia overall showed no significant trend with distance for plant density, basal area, and biovolume This finding suggests that the introduction of this species has occurred throughout the bushland and it is not spreading from the edge
A iteaphylla on the other hand, had a different response to the edge effect on sites since it was shown that it had a decreasing growth performance with distance from the edge (Figure 22a, Figure 22b & Figure 22c), which could suggest that it highly preferred growing on the edges of bushlands or highly disturbed areas This also suggests that invasion is occurring from the edge due to seed dispersal along roads or from urban areas and then slowly spreading into the bushland This finding further supported the results from MaxEnt algorithm which showed that it preferred highly eroded or disturbed areas (Queensland Government, n.d.a) (Figure 15 & Figure 16)
CONCLUSIONS AND RECOMMENDATIONS
Conclusions
This study investigates the effects of climate and environmental factors on the invasion patterns of A longifolia subsp longifolia and A iteaphylla across their native and naturalized ranges in Australia The findings reveal that A longifolia subsp longifolia has a limited distribution in Eastern Australia and is not expected to occur in Western Australia However, when environmental variables are considered, its density and distribution significantly increase, even extending to Tasmania In contrast, A iteaphylla displays a broader distribution across Western Australia and South Australia.
The inclusion of environmental variables in species distribution models (SDMs) enhanced the prediction accuracy for A longifolia subsp longifolia's distribution in Australia, while also indicating a contraction in the range of A iteaphylla compared to predictions based solely on current climate conditions.
The researcher examined the impact of climate change on the future invasion patterns of Acacia species Under the moderate climate change scenario RCP 4.5, A longifolia subsp longifolia is expected to expand its range in southern Eastern Australia, Western Australia, and Tasmania, with even greater expansion anticipated under the RCP 8.5 scenario, which involves minimal action to reduce greenhouse gas emissions Conversely, the RCP 4.5 scenario is projected to reduce the range of A iteaphylla in South Australia, Western Australia, and Eastern Australia, with further contraction expected under RCP 8.5.
The researcher found that A longifolia subsp longifolia thrives in wet, cooler environments, while A iteaphylla favors dry, warmer areas, highlighting their distinct responses to climate and environmental factors.
Research indicates that the dispersal distance from the edge of bushland significantly impacts the growth of A longifolia subsp longifolia and A iteaphylla A longifolia subsp longifolia exhibited a high plant density near the wetland edge, although further studies are needed to clarify its dispersal distance effects on basal area and biovolume In contrast, A iteaphylla demonstrated improved growth performance when located closer to the edge of the bushland.
Increased groundwater depth significantly affected the plant density of both species studied A longifolia subsp longifolia exhibited higher plant density in areas with low groundwater depth, while A iteaphylla thrived in regions with high groundwater depth.
The researcher was able to identify that A longifolia subsp longifolia and
A iteaphylla had a different preference for understorey cover or disturbance level
A iteaphylla is predominantly found in regions with minimal understorey cover or in heavily disturbed environments, as noted by Richardson et al (1990) In contrast, A longifolia subsp longifolia thrives in areas characterized by dense vegetation and undisturbed conditions.
The study found that fire significantly influenced the plant density of Acacia species, but there was no substantial evidence to indicate that fire affected the basal area and biovolume of Acacia longifolia subsp longifolia and Acacia iteaphylla.
There was a significant evidence that both Acacia species showed a strong preference for growing under larger trees
It was clear on the results from SDM and field survey that, A longifolia subsp longifolia and A iteaphylla had different responses to climate and environmental factors.
Recommendations
Future research should broaden the study's geographical scope to investigate invasion patterns across various vegetation types Additionally, incorporating biotic factors and other environmental elements, such as soil nutrients, soil types, and land use, into the model is essential, as the current study primarily focused on bioclimatic variables and lacked comprehensive datasets Including these factors could significantly improve the accuracy and specificity of species distribution models (SDMs).
Given that it was not clearly supported in this study as to how these
Future studies should investigate the spread of Acacia species in Perth, WA, focusing on whether birds are responsible for their dispersion This can be achieved by collecting mature seeds and placing them on the ground, followed by the use of wildlife motion-sensor cameras to capture images of birds consuming the seeds.
The findings can serve as a foundation for managing introduced species and controlling invasive ones to prevent further invasions Since these species respond differently to climate and environmental factors, it is advisable to adopt tailored management methods based on the specific characteristics of each species.
To manage the invasion of A longifolia subsp longifolia, it is essential to implement strategies such as reducing fire activities in bushlands and proactively protecting potential habitats to prevent future invasions In contrast, A iteaphylla, with its extensive current distribution, requires strict control measures to prevent density increases in novel areas while ensuring its survival Enhancing biodiversity by planting additional species can help decrease site disturbance, and pre-emptive actions should be taken in suitable habitats of A iteaphylla to mitigate further invasions.
The findings may enhance our understanding of the invasion potential of A longifolia subsp longifolia and A iteaphylla in both native and novel environments in Australia, an area where limited research has been conducted.
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APPENDICES Appendix 1: Distribution of Acacia native forest (2013)
Appendix 2: Ownership of Acacia native forest, by state and territory (2013)
Tenure ACT NSW NT Qld SA Tas Vic WA Australia
Note: Totals may not tally due to rounding The six forest tenure categories are defined in Australia’s State of the Forests Report 2013
The six tenure categories include leasehold, which refers to Crown land managed privately; multiple-use public forest, encompassing state-owned forests and timber reserves; nature conservation reserves, designated for environmental and recreational purposes like national parks; other Crown land, allocated for various uses such as utilities, scientific research, and mining; private land, held under freehold title; and unresolved tenure, representing land with insufficient data to ascertain ownership status.
All material in this publication is licensed under a Creative Commons Attribution 3.0 Australia Licence, except for content supplied by third parties, logos and the Commonwealth Coat of Arms
This publication should be attributed as ABARES 2016, Australian forest profiles, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra
The Australian Government, acting through the Department of Agriculture and Water Resources, has exercised due care and skill in preparing and compiling the information and data in this publication
The department, along with its employees and advisers, disclaims all liability for any loss, damage, injury, expense, or cost incurred by individuals accessing, using, or relying on the information or data provided in this publication, to the fullest extent allowed by law, including negligence.
Appendix 3: Table of the current invasive Acacia species in Western Australia (WA)
Total # of Herbarium records from Native Vegetation
Distribution Local Government Areas (LGAS)
Scientific name: Acacia baileyana F Muell
25 10 Mundaring, Manjimup, Harvey S New South
New South Wales, Tasmania, Victoria Scientific name: Acacia decurrens
Mundaring, Murray, Manjimup, Chittering, Donnybrook – Balingup, Murray, Busselton, Manjimup, Kojonup, Manjimup, Harvey, Dardanup Albany
15 9 Armadale, Boyup Brook, Collie, Manjimup,
Scientific Name: Acacia floribunda (Vent.) Willd
Donnybrook – Balingup, Subiaco, Kojonup, Melville, Kondinin, Dardanup, Denmark, Nedlands, Mandurah, Wanneroo, Swan, Albany, Serpentine – Jarrahdale, Chapman Valley
Cockburn, Armadale, Manjimup, Gosnells, Joondalup, Albany, Swan, Jandakot, Wanneroo, Darling, Murray, Dardanup, Bridgetown – Greenbushes, Melville, Canning, Denmark, West Arthur
Scientific Name: Acacia mearnsii De Wild
Manjimup, Esperance, Donnybrook – Balingup, Bridgetown – Greenbushes, Nannup, Augusta – Margaret River, Forrestfield
New South Wales, Tasmania, Victoria Scientific Name: Acacia melanoxylon R.Br
Queensland, NSW, ACT, Victoria, Tasmania Scientific Name: Acacia paradoxa
Wiluna, Gnowangerup, Albany, Capel, Harvey, Waroona, Northampton, Minninup, Darling, Boyup Brook, West Arthur
30 12 Armadale, Chittering, Murray, Nedlands, Gosnells,
Wandering, Serpentine – Jarrahdale, Carnamah S NSW and
Scientific Name: Acacia prominens G Don
Broomehill, Kalamunda, Tambellup, Balingup, Cranbrook, Busselton, Kulin, Mundaring, Bridgetown – Greenbushes, Katanning, Albany, Plantagenet, Manjimup, Narrogin, Nannup, West Arthur, Capel, Wongan – Ballidu, Waroona, Gnowangerup
Appendix 4: Materials used in field survey a Atlas of Living Australia (ALA) b Biodiversity Climate Change Virtual Laboratory (BCCVL) c MaxEnt (Maximum Entropy) d Florabase e Climate data and soil data maps f Rangefinder g Measuring tapes h Diameter tape i Tree calliper j Flagging tape k Printed pictures of Acacia plants l Printed maps m Microsoft Excel 2016
Appendix 5: Filters for species occurrence record from ALA
Acacia longifolia subsp longifolia - Native Distribution
Scientific name Acacia longifolia, Acacia longifolia subsp longifolia
Basis of record Preserved Specimen
Establishment means Not cultivated, native
EXCLUDE: not native Duplicate Record Type EXCLUDE: Identical coordinates
Location New South Wales, Queensland, Victoria
Acacia longifolia subsp longifolia - Naturalised Distribution
Scientific name Acacia longifolia, Acacia longifolia subsp longifolia
Basis of record Preserved Specimen
Establishment means Not cultivated, native, not native
Duplicate Record Type EXCLUDE: Identical coordinates
Basis of record Preserved Specimen
Establishment means Not cultivated, native
EXCLUDE: not native Duplicate Record Type EXCLUDE: Identical coordinates
Basis of record Preserved Specimen
Establishment means Not cultivated, native, not native
Duplicate Record Type EXCLUDE: Identical coordinates
Appendix 6: Percent contribution and permutation importance of climate and environmental variables used in SDM a) A lonifolia subsp longifolia in native ranges – Climate variables only b) A longifolia subsp longifolia in naturalised ranges – Climate variables only c) A iteaphylla in native ranges – Climate variables only d) A iteaphylla in naturalised ranges – Climate variables only e) A longifolia subsp longifolia in native ranges – Climate and Environmental variables f) A longifolia subsp longifolia in naturalised ranges – Climate and
Environmental variables g) A iteaphylla in native ranges – Climate and Environmental variables h) A iteaphylla in naturalised ranges – Climate and Environmental variables
Appendix 7: Correlation of variables produced from MaxEnt – Current climate variables only a) Acacia longifolia subsp longifolia – Native
Distribution b) Acacia longifolia subsp longifolia - Naturalised Distribution c) Acacia iteaphylla – Native Distribution d) Acacia iteaphylla – Naturalised Distribution
Appendix 8: ROC plots of all the model produced
A longifolia subsp longifolia – Native (Climate)
A longifolia subsp longifolia – Naturalised (Climate)
A longifolia subsp longifolia – Native (Climate &
Appendix 9: Climate data maps in Australia a) Average Rainfall - Annual b) Average Root Zone Soil Moisture - Annual c) Average Daily Mean Temperature - Summer d) Average Daily Mean Temperature - Winter e) Average Rainfall - Summer f) Average Rainfall - Winter g) Average Daily Sunshine Hours - Annual h) Average Clay Percentage i) Average Soil Erosion grade j) Average Soil Depth
Appendix 10: Documentations of the Acacia sites a) Thomsons Lake Nature Reserve 1 (A longifolia subsp longifolia-Unburnt-67) b) Thomsons Lake Nature Reserve 2 (A longifolia subsp longifolia-Unbunt-15) c) Anketell Road (A longifolia subsp longifolia-Unburnt-12) d) De Haer Road 1 (A longifolia subsp longifolia-Unburnt-6) (A iteaphylla-2) e) Piara (A longifolia subsp longifolia-Unburnt-4) f) Shirley Balla Swamp 1 (A longifolia subsp longifolia-Burnt-74) g) Shirley Balla Swamp 2 (A longifolia subsp longifolia-Burnt-190) h) Forrestdale (A longifolia subsp longifolia-Burnt-208) i) Canning Vale Road (A iteaphylla-3) j) De Haer Road 2 (A iteaphylla-2) k) Wandi Nature Reserve (A iteaphylla-11) l) Battersby Road (A iteaphylla-2) m) Neerabup 1 (A iteaphylla-23) n) Neerabup 2 (A iteaphylla-3)
Appendix 11: Fire history map of southern wetlands in Perth
Appendix 12: Illustration on the difference between understorey plants and tree species
In this study, any tree species that overshadowed the Acacia plant was classified as a tree, while all plants located beneath and surrounding the Acacia were included in the assessment of understorey cover.
Appendix 13: Table of variables used to influence the models
Bio01 el874 Annual mean temperature (°C)
Bio02 el888 Mean diurnal temperature range (mean (period max-min))
(°C) Bio03 el883 Isothermality (Bio02 ÷ Bio07)
Bio04 el892 Temperature seasonality (C of V)
Bio05 el879 Max temperature of warmest week (°C)
Bio06 el867 Min temperature of coldest week (°C)
Bio07 el862 Temperature annual range (Bio05-Bio06) (°C)
Bio08 el870 Mean temperature of wettest quarter (°C)
Bio09 el875 Mean temperature of driest quarter (°C)
Bio10 el890 Mean temperature of warmest quarter (°C)
Bio11 el876 Mean temperature of coldest quarter (°C)
Bio12 el893 Annual precipitation (mm)
Bio13 el866 Precipitation of wettest week (mm)
Bio14 el872 Precipitation of driest week (mm)
Bio15 el882 Precipitation seasonality (C of V)
Bio16 el886 Precipitation of wettest quarter (mm)
Bio17 el889 Precipitation of driest quarter (mm)
Bio18 el878 Precipitation of warmest quarter (mm)
Bio19 el863 Precipitation of coldest quarter (mm)
Bio28 el891 Annual mean moisture index
Bio29 el884 Highest weekly moisture index
Bio30 el895 Lowest weekly moisture index
Bio31 el885 Moisture index seasonality (C of V)
Bio32 el865 Mean moisture index of wettest quarter
Bio33 el864 Mean moisture index of driest quarter
Bio34 el868 Mean moisture index of warmest quarter
showing a decreasing trendline Density of A iteaphylla per ha at different
The coefficient of determination (r) from regression analysis indicates the relationship between the plant density of A iteaphylla and the distance from bushland edges, with a significance level set at α=0.05 Sites marked with an asterisk (*) demonstrate a significant edge effect, characterized by a high r-value and a p-value of ≤ 0.05 Additionally, the standard error values represent the standard deviation of the population.
Appendix 21: Edge effect on the basal area of A iteaphylla in surveyed sites
Overall Basal Area (all sites)
showing a decreasing trendline Basal Area of A iteaphylla per ha at different
The coefficient of determination (r) from the regression analysis indicates the relationship between the basal area of A iteaphylla and the distance from bushland edges across all surveyed sites, with a significance level of α=0.05 Sites marked with an asterisk (*) demonstrate a significant edge effect, characterized by a high r-value and a p-value of less than or equal to 0.05 Additionally, the standard error values represent the population's standard deviation.
Appendix 22: Edge effect on the biovolume of A iteaphylla in surveyed sites
Overall Biovolume (all sites) (m 3 /ha)
The coefficient of determination (r) from regression analysis indicates the biovolume of A iteaphylla across all surveyed sites in relation to the distance from bushland edges (α=0.05) Sites marked with an asterisk (*) demonstrated significant edge effects, evidenced by high r-values and p-values ≤ 0.05 Standard error values represent the standard deviation of the population.
Appendix 23: The plant density of A longifolia subsp longifolia in unburnt sites at different distance ranges at different depth to groundwater
Appendix 24: The plant density of A longifolia subsp longifolia in burnt sites at different distance ranges at different depth to groundwater
Appendix 25: The plant density of A iteaphylla at different distance ranges at different depth to groundwater
Appendix 26: Occurrence number and percentage of Acacia species on different understorey cover
No of plants % No of plants % No of plants %
Appendix 27: t-Test results determining differences in plant density, basal area and biovolume of A longifolia subsp longifolia in burnt and unburnt sites: Two-sample Assuming Variances (α=0.05)
Plant Density (mean plants/ha)
Unburnt Burnt Unburnt Burnt Unburnt Burnt
Appendix 28: The number of A longifolia subsp longifolia plants under a tree and the overall tree cover percentage in unburnt sites
Unburnt Sites – A longifolia subsp longifolia
Location Site No of Plants under tree
No of plants not under a tree