Mapping and Modelling the Invasion Dynamics of Senna obtusifolia at
Different Levels of Scale in Australia
by
Elizabeth A. Dunlop
B. App. Sci (Hons)
Submitted in fulfilment of the requirements
for the degree of
Doctor of Philosophy
Queensland University of Technology
Abstract i
ABSTRACT
The invasion of natural environments by alien species is a significant threat to the ecological integrity of these systems. Senna obtusifolia is an aggressive invasive weed recently introduced to Australia that is having significant impacts on grassland ecosystems on the Cape York Peninsula. Currently the species is inadequately managed and so range expansion continues. The invasion potential of S. obtusifolia
in Australia remains unknown, as does much about its behaviour and management
in natural systems. This project undertakes extensive mapping and modelling of the
current and future distributions and the invasion dynamics of S. obtusifolia in
Australia to facilitate early detection of outbreak populations and the development of
appropriate management strategies.
The mapping and modelling of S. obtusifolia was conducted at three different scales: continental, landscape and local (population). To address these spatial scales, eco- climatic modelling, remote sensing analysis, field experimentation and creation of a model of seed fate was undertaken.
Using the climatic preferences of S. obtusifolia displayed internationally, an eco- climatic model (using CLIMEX software) ascertained that S. obtusifolia has a very large invasive potential in Australia. The predicted geographic distribution comprised the entire eastern and northern Australian coastlines, with spread further inland being largely restricted by a lack of moisture. The regional distribution of S. obtusifolia was not successfully delineated using remote sensing technology.
Despite possessing favourable traits for detection by remote sensors, poor data quality and inappropriate image scales prevented the weed from being distinguished from other vegetation by multi-spectral satellite imagery and aerial photography. Abstract ii
However, the results indicated that refining the data and the techniques used, single
S. obtusifolia populations may be detectable in the future.
Investigation of the invasion dynamics of S. obtusifolia at the local scale involved
multiple field surveys and manipulative experiments during 2002-2005. Field work
indicated that little variation in population characteristics (e.g. stem density, soil seed
reserve, seed production) existed within populations, but there was variability across
populations and between years: the variation between years was very significant.
The vegetation type adjacent to the weed population did not affect population
attributes; however less competitive, more open and disturbed environments may
better facilitate the invasion. The compartment model of seed fate reflecting S.
obtusifolia population dynamics demonstrated that change in annual rainfall was
unlikely to explain the variation evident between populations and years. Instead, the
rate at which dormancy is broken in seeds and the intensity and regularity of fire
provided a better explanation of the weed’s population dynamics.
Early detection of invaders and the prediction of likely sites of invasion provide the most effective means of preventing future invasions. How best to achieve these goals still remains largely unknown. The process undertaken in this study was a relatively quick and reliable method for assessing the seriousness of S. obtusifolia, predicting future outbreaks and for providing clues to long term management. The appropriate use of fire, maintaining high interspecific competition and shade, as well reducing the rate at which dormancy is broken in seeds are all possible methods of managing S. obtusifolia.
KEYWORDS: competition, CLIMEX, early detection, eco-climatic modelling, invasive species, population dynamics, remote sensing, scale, seed fate model, weed management. Table of Contents iii
TABLE OF CONTENTS
Page
Statement of Original Authorship ...... xv
Acknowledgments...... xvi
Chapter 1 General Introduction...... 1
1.1 Alien invasive species...... 2
1.2 Invasive species management...... 3
1.3 A basis for early detection...... 4
1.4 Spatial scale...... 5
1.5 Assessing the potential broad-scale distribution of a target species...... 7
1.6 Assessing the current distribution of the target species...... 9
1.7 Assessing the local dynamics and future invasion potential at a local
scale...... 13
1.8 Senna obtusifolia...... 16
1.9 Scope and structure of thesis...... 18
Chapter 2 General Methodology...... 21
2.1 Species...... 22
2.2 Study site...... 24
Chapter 3 The Potential Geographic Distribution of Senna obtusifolia
in Australia...... 34
3.1 Introduction...... 35
3.2 Method...... 38
3.2.1 CLIMEX...... 38
3.2.2 The current distribution of Senna obtusifolia...... 39 Table of Contents iv
3.2.3 Reference distribution and Model 1...... 40
3.2.4 Model 2 – Cold intolerant ecotype...... 42
3.2.5 Parameter Fitting...... 43
3.2.5.1 Growth Indices...... 43
3.2.5.1.1 Temperature index...... 44
3.2.5.1.2 Thermal accumulation...... 45
3.2.5.1.3 Moisture index...... 46
3.2.5.2 Stress Indices...... 47
3.2.5.2.1 Cold stress...... 47
3.2.5.2.2 Heat stress...... 48
3.2.5.2.3 Dry stress...... 48
3.2.5.2.4 Wet stress...... 48
3.2.5.2.5 Cold-dry stress...... 48
3.3 Results...... 49
3.4 Discussion...... 54
3.5 Progress towards aims of thesis...... 57
Chapter 4 Evaluation of the Use of Remote Sensing to Map Senna
obtusifolia...... 58
4.1 Introduction...... 59
4.2 Method...... 61
4.2.1 Landsat 7 ETM+ multispectral satellite imagery...... 61
4.2.1.1 Software...... 61
4.2.1.2 Imagery...... 61
4.2.1.3 Image classification...... 63
4.2.1.3.1 Unsupervised classification...... 64
4.2.1.3.2 Supervised classification...... 65
4.2.2 Aerial Photographs...... 68 Table of Contents v
4.2.3 Vector Data...... 68
4.3 Results...... 69
4.3.1 Landsat 7 ETM+ multispectral satellite imagery...... 69
4.2.1.1 Imagery...... 69
4.2.1.2 Unsupervised classification...... 70
4.2.1.3 Supervised classification...... 70
4.3.2 Aerial Photographs...... 76
4.4 Discussion...... 77
4.4.1 Landsat ETM+ Multispectral Satellite Imagery...... 77
4.4.2 Aerial Photography...... 80
4.4.3 Other forms of remote sensing...... 81
4.4.4 Vector data and other spatial technologies...... 83
4.4.5 Recommendations...... 84
4.5 Progress towards aims of thesis...... 85
Chapter 5 The Population Dynamics of Senna obtusifolia in Natural
Ecosystems of Northern Australia...... 86
5.1 Introduction...... 87
5.2 Method...... 89
5.2.1 Population Attributes...... 89
5.2.2 Seed Number...... 90
5.2.3 Soil Seed Reserve...... 91
5.2.4 Seed Germinability...... 92
5.2.4.1 Soil seed...... 92
5.2.4.2 Seed off mature plants...... 94
5.2.4.3 Seed after fire...... 95
5.2.5 Dispersal...... 96
5.2.6 Population Movement...... 99 Table of Contents vi
5.3 Results...... 101
5.3.1 Population Attributes...... 101
5.3.1.1 Within a population...... 101
5.3.1.2 Between populations and populations occurring adjacent to
different habitat types...... 103
5.3.1.4 Between years...... 103
5.3.2 Soil Seed Reserve...... 105
5.3.2.1 Within a population...... 106
5.3.2.2 Between populations and populations occurring adjacent to
different habitat types...... 106
5.3.3 Seed germinability...... 107
5.3.3.1 Soil seed...... 107
5.3.3.2 Seed off mature plants...... 108
5.3.3.2.1 Within a population...... 109
5.3.3.2.2 Between populations and populations occurring
adjacent to different habitat types...... 109
5.3.3.3 Seed after fire...... 110
5.3.4 Dispersal...... 110
5.3.4.1 Between populations...... 111
5.3.4.2 Between adjacent habitats...... 113
5.3.5 Population Movement...... 113
5.4 Discussion...... 119
5.5 Progress towards aims of thesis...... 124
Chapter 6 The Impact of Habitat Type on the Distribution of Senna
obtusifolia...... 125
6.1 Introduction...... 126
6.2 Method...... 128 Table of Contents vii
6.2.1 Study site...... 128
6.2.2 Quantity of Senna obtusifolia in adjacent habitats...... 129
6.2.3 Seed introduction experiments...... 130
6.2.4 Presence and size of soil seed reserve...... 133
6.2.5 Seed survival and germinability...... 134
6.2.6 Effect of habitat components on establishment...... 136
6.3 Results...... 141
6.3.1 Presence of Senna obtusifolia in adjacent habitats...... 141
6.3.2 Seed introduction experiments...... 141
6.3.3 Presence and germinability of soil seed reserve...... 145
6.3.4 Seed survival and germinability...... 146
6.3.5 Effect of habitat components on establishment...... 148
6.4 Discussion...... 150
6.5 Progress towards aims of thesis...... 155
Chapter 7 Population Dynamics of Senna obtusifolia in Iron
Range National Park: a model of seed fate...... 157
7.1 Introduction...... 158
7.2 Model Overview...... 161
7.3 Model Limitations...... 164
7.3.1 Lack of spatial capacity...... 164
7.3.2 Lack of dynamics reflecting early stages of the life cycle...... 164
7.3.3 Lack of inter-specific competition...... 164
7.4 Model Description...... 165
7.4.1 State variable equations...... 165
7.4.2 Seed production...... 172
7.4.3 Seed mortality...... 174
7.4.4 Plant mortality...... 176 Table of Contents viii
7.4.5 Seed burial...... 181
7.4.6 Breaking dormancy...... 182
7.4.7 Germination...... 182
7.4.8 Growth...... 184
7.5 Model Evaluation...... 185
7.5.1 Overview of model evaluation simulations...... 185
7.5.2 Observed data from 2002, 2003 and 2004 Senna obtusifolia
populations...... 187
7.5.3 Independent validation – Senna obtusifolia density vs. rainfall...... 195
7.5.4 Sensitivity Analyses...... 198
7.6 Discussion...... 202
7.7 Progress towards aims of thesis...... 205
Chapter 8 Simulated Responses of Senna obtusifolia Populations
to Different Invasion and Environmental Scenarios...... 206
8.1 Introduction...... 207
8.2 Method...... 208
8.3 Results...... 211
8.3.1 Invasion and Control Scenarios 1-3...... 211
8.3.2 Invasion Scenario 4 – Rainfall Effects...... 214
8.3.2.1 Simulation 1 – ten years of the same rainfall...... 215
8.3.2.2 Simulation 2 and 3 – three and five consecutive years of
low rainfall...... 216
8.3.2.3 Simulation 4 and 5 – three and five consecutive years of
high rainfall...... 218
8.3.2.4 Response to rainfall extremes in a marginal location...... 220
8.3.3 Invasion Scenario 5 – Fire Effects...... 224
8.3.3.1 Simulation 1 – No fire...... 224 Table of Contents ix
8.3.3.2 Simulation 2 – Two fires in ten years...... 224
8.3.3.3 Simulation 3 – Fire every second year...... 226
8.3.3.4 Simulation 4 – Increased fire effectiveness...... 226
8.3.3.5 Simulation 5 – Early annual fire...... 227
8.4 Discussion...... 229
8.5 Progress towards aims of thesis...... 231
Chapter 9 General Discussion...... 233
9.1 Senna obtusifolia – a summary...... 234
9.2 Applications for the management of Senna obtusifolia...... 237
9.2.1 Fire...... 238
9.2.2 Competition...... 239
9.2.3 Seed dormancy...... 242
9.2.4 Shade/Light...... 243
9.2.5 Monitoring and basic maintenance...... 244
9.3 The benefits of an integrated approach...... 246
References...... 251
Appendix 1...... 285
List of Figures x
LIST OF FIGURES
Page
Figure 2.1 - Images of Senna obtusifolia...... 23
Figure 2.2 - Senna obtusifolia life cycle...... 25
Figure 2.3 - Map of study site localities...... 26
Figure 2.4 - The different adjacent habitat types in Lockhart River...... 30
Figure 3.1 - The current global distribution of Senna obtusifolia...... 40
Figure 3.2 - The current distribution of Senna obtusifolia in Australia...... 41
Figure 3.3 - The distribution of Senna obtusifolia in the Americas...... 42
Figure 3.4 - The proposed distribution of the cold susceptible ecotype...... 43
Figure 3.5 - The global CLIMEX prediction of Senna obtusifolia...... 50
Figure 3.6a - The American CLIMEX prediction of all ecotypes...... 51
Figure 3.6b - The North American CLIMEX prediction of the
cold susceptible ecotype ...... 51
Figure 3.7a - The Australian CLIMEX prediction of all ecotypes...... 53
Figure 3.7b - The Australian CLIMEX prediction of the cold susceptible
ecotype...... 53
Figure 4.1 - Iron Range ETM+ multispectral satellite image...... 63
Figure 4.2 - Topographic map of the Lockhart River study area...... 66
Figure 4.3 - Landsat ETM+ imagery over the Lockhart River area...... 67
Figure 4.4 - Unsupervised classification results of ETM+ imagery...... 71
Figure 4.5 - Supervised classification results of ETM+ imagery...... 72
Figure 4.6 - Detailed map of the supervised classification results...... 75
Figure 4.7 - Aerial photograph of the Lockhart River region...... 76
Figure 4.8 - The spectral signature of Senna obtusifolia...... 83
Figure 5.1 - Field design to measure population attributes...... 89
Figure 5.2 - The relationship between pod length and seed number...... 91 List of Figures xi
Figure 5.3a - The structure of dispersal seed traps in 2003...... 98
Figure 5.3b - The structure of dispersal seed traps in 2004...... 98
Figure 5.4 - Field design to monitor the movement of populations over time.... 100
Figure 5.5 - Mean differences in population attributes between the
edge and middle of an infestation ...... 102
Figure 5.6 - Mean differences in population attributes between years...... 105
Figure 5.7 - Mean differences in the soil seed reserve within a population...... 106
Figure 5.8 - Mean differences in seed germinability within a population...... 109
Figure 5.9 - Mean differences in germinability of seed exposed to fire...... 111
Figure 5.10 - Mean emigration and immigration of Senna obtusifolia seed...... 112
Figure 5.11 - Movement of Senna obtusifolia adjacent to rainforest...... 115
Figure 5.12 - Movement of Senna obtusifolia adjacent to B. decumbens...... 116
Figure 5.13 - Movement of Senna obtusifolia adjacent to I. cylindrica...... 117
Figure 5.14 - Movement of Senna obtusifolia adjacent to woodland...... 118
Figure 5.15 - Regression results of population movement and seed production. 119
Figure 6.1 - Field design to measure Senna obtusifolia in adjacent habitats.... 130
Figure 6.2 - Trap design used in the seed introduction experiments...... 131
Figure 6.3 - Mean Senna obtusifolia density located in different habitat types.. 142
Figure 6.4 - Mean differences in establishment in different habitat types...... 144
Figure 6.5 - Mean differences in plant height between different habitat types .. 144
Figure 6.6 - Mean differences in the soil seed reserve between different
habitat types...... 145
Figure 6.7 - Mean differences in seed recovered from different habitat types... 146
Figure 6.8 - Mean differences in germinability of recovered seed from different
habitat types...... 147
Figure 6.9 - Mean Senna obtusifolia establishment when exposed to different
habitat treatments...... 149
Figure 6.10 - Changes in plant number over time when exposed to different List of Figures xii
habitat treatments...... 150
Figure 7.1 - Conceptual STELLA diagram of population dynamics...... 163
Figure 7.2 - The relationship between plant density and seed production...... 173
Figure 7.3 - Graphical representations of model functional relationships...... 177
Figure 7.4 - The mass germination of Senna obtusifolia after fire and rainfall .. 183
Figure 7.5a - Comparison of observed and simulated results in 2003...... 189
Figure 7.5b - Comparison of observed and simulated results in 2004...... 189
Figure 7.6 - Comparison of observed and simulated results of 2003
and 2004...... 190
Figure 7.7 - Simulation of population dynamics over 520 weeks...... 192
Figure 7.8 - The simulated rates of mortality of Senna obtusifolia...... 194
Figure 7.9 - The relationship between simulated plant density and EI...... 197
Figure 7.10 - Sensitivity analyses conducted on model parameters...... 201
Figure 8.1 - ‘Normal’ simulation of plant and soil seed density...... 211
Figure 8.2 - Simulated dynamics when model starts with 180 seeds only...... 212
Figure 8.3 - Simulated dynamics when model starts with no seed production.. 213
Figure 8.4 - Simulated dynamics when model begins with one seed
production event...... 214
Figure 8.5 - Simulated dynamics when exposed to the same annual rainfall.... 215
Figure 8.6a - Simulated dynamics when exposed to three years of low
rainfall...... 217
Figure 8.6b - Simulated dynamics when exposed to five years of low rainfall.. .. 219
Figure 8.7a - Simulated dynamics when exposed to three years of high
rainfall...... 220
Figure 8.7b - Simulated dynamics when exposed to five years of high rainfall... 222
Figure 8.8 - Simulated dynamics when exposed to Charters Towers rainfall. .. 223
Figure 8.9a - Simulated dynamics when exposed to three years of low rainfall
in a marginal location...... 225 List of Figures xiii
Figure 8.9b - Simulated dynamics when exposed to five years of low rainfall
in a marginal location...... 225
Figure 8.10a - Simulated dynamics when exposed to three years of high rainfall
in a marginal location...... 226
Figure 8.10b - Simulated dynamics when exposed to five years of high rainfall
in a marginal location...... 227
Figure 8.11 - Simulated dynamics when exposed to no fire...... 236
Figure 8.12 - Simulated dynamics when exposed to fire twice in ten years...... 225
Figure 8.13 - Simulated dynamics when exposed to fire every second year...... 226
Figure 8.14 - Simulated dynamics when exposed to annual fire with
increased effectiveness...... 227
Figure 8.15 - Simulated dynamics when exposed to annual fire with
decreased effectiveness...... 228
List of Tables xiv
LIST OF TABLES
Page
Table 2.1 - Description of the five habitat study sites...... 28
Table 2.2 - The location and description of study sites...... 32
Table 3.1 - CLIMEX parameter values...... 45
Table 4.1 - Statistics of pixel values from the supervised classification...... 73
Table 4.2 - Standard deviation of pixel values from supervised
classification training regions...... 74
Table 5.1 - Fire history of study sites...... 101
Table 5.2 - Results of nested ANOVA assessing variability in population
attributes between populations and populations occurring adjacent
to different habitat types...... 104
Table 5.3 - Results of nested ANOVA assessing variability in soil seed density
between populations and populations occurring adjacent to different
habitat types...... 107
Table 5.4 - Statistics of soil seed germinability between populations and
corrected soil seed reserve sizes...... 108
Table 5.5 - Area occupied by S. obtusifolia populations over three years...... 114
Table 5.6 - Population attributes in natural and agricultural systems...... 121
Table 6.1 - Distribution of different habitat type treatments...... 150
Table 7.1 - Summary of values of STELLA model parameters ...... 168
Table 7.2 - Average rainfall and EI of locations used in model evaluation...... 196
Table 8.1 - Simulated values when exposed to different rainfall in a
marginal location...... 221 Statement of Original Authorship xv
STATEMENT OF ORIGINAL AUTHORSHIP
With the exception of some material presented in Chapter 3 (see next paragraph), this thesis does not contain any material that has been accepted for the award of any other degree or diploma in any tertiary institution. To the best of my knowledge and belief, it contains no material previously published or written by another person, except where due reference is made in the text.
Chapter 3 of this thesis presents a CLIMEX model predicting the distribution of
Senna obtusifolia in Australia, an activity which I also undertook and submitted in
2001 as part of my B. Appl.Sc. (Hons). While Chapter 3 contains some elements of that earlier work, the methods and results were repeated and improved using both a new data set and a substantially different version of CLIMEX that require model re- parameterisation. The chapter was written for publication, which included responding to referees’ reports, entirely during the course of the PhD. For these reasons the chapter has been included in the thesis for consideration for assessment as part of the thesis entire.
Elizabeth A. Dunlop
School of Natural Resource Sciences
Queensland University of Technology
July, 2007 Acknowledgments xvi
ACKNOWLEDGMENTS
Many people have been involved in the production of this thesis to who I must thank.
Firstly I must acknowledge the sources of project funding – National Heritage Trust and the Growing the Smart State PhD research funding program. Without the resources provided by these funds, the large field component of this project would not have been possible.
Secondly I must acknowledge the involvement of Queensland Parks and Wildlife
Service and the extremely accommodating rangers of Iron Range National Park for the use of their resources, their guidance and the use of their homes! In particular thank you to Damian and Linda Miley, Karl and Andrea Goetze, Michael Harry Evans
(for extra curricular activities!), Dick Henning, Barry Lyon, Chris McMonagle and
Shannon Vasyli, Sean Walsh and Nerida Holznagel. Your hospitality during our stays was both unexpected and great and kept me sane during my time in Iron
Range!
Many field assistants were also involved over the course of time and without them I’d still be in Lockhart River counting seeds! Special thanks to Nathan Jensen who endured more trips than anyone, and to the others who came and went – Eve White,
Peter Spencer, Lindsay Chandler, Noel Meyers, David Wilson, David Elmouttie,
Craig Streatfeild, Daniel Ward, Richard Winstanley and Michael Forbes. Also thank you to Angela Murray for undertaking and providing much needed help with the remote sensing task and Nate Peterson for assisting with diagrams.
Next I must thank those heavily involved in the project who have supported me and given me guidance with my work at various times throughout the course of the Acknowledgments xvii project. Dr Tony Clarke, Dr Joe Scanlan, Dr Peter Mackey, Dr Ian Williamson and
Dr Noel Meyers, thank you for your respective input and patience. Special thanks to my primary supervisor for three years, Dr John Wilson. Your ideas and hard work created this project and although I never said it, I respected you and your work and I thank you for your time and patience over the years.
Finally, I must thank those outside the university circle who have endured this ride with me. My family and partner Scott, you have never doubted my abilities and your belief and trust in me helped me to keep my head above water and complete this thesis. Although I never say or show it much, I listen to all of your words of experience and love and thank you all. Same to my friends, who have been the wearers of tears and tantrums! Thanks for listening.
Chapter 1
Introduction Chapter 1 Introduction - 2 -
1.1 Alien Invasive Species
The official definition given to an invasive species is one whose introduction, establishment and (often rapid) spread threatens ecosystems, habitats or species
(CBD 2002). Invasive species are alien (non-native, non-indigenous, foreign and exotic), having been deliberately or accidentally introduced to an area from their native range, or from another site of introduction (Richardson et al. 2000; Kairo et al.
2005). Whilst new species establishing in a new environment is a naturally occurring process that is critical in structuring natural communities, as seen in succession
(Hengeveld 1989; Lodge 1993), the frequency of anthropomorphic introduction events to new regions has risen considerably in recent times, causing invasive species to be an increasing problem worldwide (Holt and Boose 2000).
The adverse effects associated with invaders in agronomy have long been recognised due to the considerable economic threat they pose to agricultural industries (Dunbar and Facelli 1999). More recently, however, the intrusion of exotics into natural environments has become an important environmental issue
(Rejmanek 1995), with alien invasive species now recognised internationally as one of the most significant drivers of environmental change (Center et al. 1995; Dunbar and Facelli 1999; McNeely et al. 2001; Wittenberg and Cock 2001). Invasions of
introduced species are capable of altering the structure, function, species
composition, abundance and consequently the long-term ecological integrity of
native communities (Humphries et al. 1991; Franklin et al. 1999; Cronk and Fuller
2001). Invasive species now rank as second to habitat destruction and conversion
as a cause of species endangerment and extinction and consequently the global
homogenisation of biological diversity (Wilcove et al. 1998; Cronk and Fuller 2001;
Reaser and Howard 2003). International examples of the long term consequences
of invasive species are many, with no region being immune to their impacts (Reaser
Chapter 1 Introduction - 3 - and Howard 2003). Accordingly, there is an increasing demand for the prevention, eradication and effective management of biological invasions (Hobbs and Humphries
1995).
1.2 Invasive species management
As the impacts of biotic invasions continue to increase, governments and environmental managers are under escalating pressure to address and resolve invasive species related issues (Hulme 2006). Extensive research on biotic invasions dates back only a few decades (Mack et al. 2000). Despite significant advances, much of the invasion research to date has limited practical interest and applicability to species management (Hulme 2006). The reasons behind the success or failure of invasions are still poorly understood, despite the numerous studies attempting to compile a list of intrinsic characteristics and system properties that make species invasive and certain communities’ invasible (e.g. Crawley 1987;
Rejmanek 1995; Lonsdale 1999; Davis et al. 2000). Much of the data remains anecdotal, retrospective and is not based on sound experimentation. The data synthesis lacks definition, and useful generalisations and predictions to assist in management are yet to emerge (Mack et al. 2000).
Identifying future invaders and predicting likely sites of invasion continues to be of scientific and practical interest, due simply to their potential predictive power (Sakai et al. 2001) and by providing the most effective means to prevent future invasions
(Reichard and Hamilton 1997; Mack et al. 2000). However, in the meantime, land managers must manage their invasive species problem through the preferably integrated use of traditional means of control including chemical, biological and mechanical means, as well as restoration techniques.
Chapter 1 Introduction - 4 -
Reaser and Howard (2003) and Wittenberg and Cock (2001) propose four successive steps for the effective use of such management techniques and to best address invasive species management: prevention, early detection, eradication and control. These management responses mirror the sequential stages in the invasion process: introduction, establishment, spread and impact. This highlights the connection between understanding the invasion process and applying effective control (Hulme 2006). The first stages in both the invasion process and the management of invasive species are of the greatest importance, as management success will generally decrease with time post invasion/establishment (Simberloff
2002). Quickly assessing invasion risk, detecting known populations and identifying probable methods of control while populations remain small will ensure the greatest return. If control has become the only option, a deeper understanding of the species biology and its role in the invaded community will be of greater importance
(Simberloff 2002), although early detection undoubtedly also has an important role to play in containment.
1.3 A basis for early detection
Weed populations often display a spatial pattern throughout a landscape whereby an original or main source population exists with multiple new and smaller satellite populations (nascent foci) occurring long distances from the parent population
(Shigesada and Kawasaki 1997). Each population functions independently, increasing in size from the periphery of the infestation (diffusion) and dispersing propagules to create new isolated populations (Hengeveld 1989; Shigesada and
Kawasaki 1997). Assuming that the initial area of the single large population and the collective area of the small foci are equal, and that all populations increase at the same constant rate, the small foci will together occupy more space faster than the original large population (Auld et al. 1978/79; Moody and Mack 1988; Cousens and
Chapter 1 Introduction - 5 -
Mortimer 1995). Therefore, radiation from multiple, disjunct populations provides invaders with a much faster form of range expansion and has been attributed to the success of some of the worlds worst terrestrial weed species (Moody and Mack
1988; Weber 1998 and references therein).
Given the role of small, expanding foci in invasions, weed control efforts are now best targeted at these small populations instead of being focussed on the largest and most conspicuous populations as has been done in the past (Moody and Mack
1988). Early detection of invasive plants when their spatial extent is small reduces the cost of control and increases the possibility of successful management and/or eradication (Simberloff 1997; Rejmanek and Pitcairn 2002; Lass et al. 2005).
Therefore, whether it is the initial species introduction or is localised control, the capacity to quickly identify small population outbreaks is fundamental for the successful long-term control of aggressive invaders.
1.4 Spatial scale
Assessing invasion potential of a plant species for risk analysis and for the direction of search and early control efforts will be heavily influenced by spatial scale. The spatial distribution of a plant can be viewed at a number of scales ranging from local and regional scales, through to national, continental and global. Each level of scale raises different considerations in relation to weed management (Cousens and
Mortimer 1995; Scott 2000). For example, at the level of a paddock, management may be concerned with patterns of emergence and spot eradication, whereas at a broader scale, the concern is most likely of quarantine, economics and prioritisation for management (Cousens and Mortimer 1995). Depending on what the desired management issue is, investigating the invasion in question at all scales may not be necessary. However, if little information is known about the target species, mapping
Chapter 1 Introduction - 6 - and modelling the distribution and dynamics of the species across a variety of spatial scales should adequately address most arising issues, as well as providing necessary insight into the severity and functioning of the invasion process.
Mapping and modelling the extent and behaviour of current and potential infestations at any scale can have many benefits including assistance in creating an assessment of invasion risk (Panetta and Mitchell 1991; Holt and Boose 2000) and in developing sound strategies concerning eradication, containment and control (Baker et al.
2000). Control measures can be prioritised as a result of the modelling and can then be commensurate with the present and potential economic and environmental impact the weed may impose (Sindel 2000). Additionally, this form of analysis provides a sound platform to expose the mechanism(s), dynamics and interactions regulating the spread of an invasive species (Byers et al. 2002), whilst indicating to land managers their district’s susceptibility to future invasion.
To adequately assess both the current and the future distributional limits of an invasive species the following mapping/modelling can be conducted:
1. A prediction of the potential broad-scale species distribution
2. An assessment of the extent of the current distribution
3. An assessment of population dynamics at the local level of scale.
By following these three steps a relatively quick, accurate and adaptable method of assessing invasion risk can be developed, the distribution of current infestations can be assessed and the projection of potential distributions of a target species can be formulated which is relevant across a variety of scales. The following sections outline three preferred approaches to achieve the three goals listed above.
Chapter 1 Introduction - 7 -
1.5 Assessing the potential broad-scale distribution of a target species
Whilst no method to date within weed science has been able to consistently and accurately identify invasive species and their potential distributional limits, recipient habitat compatibility is commonly regarded as a major determinant of invasion success (Rejmanek 2000). Worldwide studies have discovered significant positive correlations between primary and secondary geographic ranges of invasive species where the environmental conditions of the two ranges possess similar characteristics
(Rejmanek 2000). Although the match between the two regions is not always perfect, the correlation is often close (Rejmanek et al. 2005), providing an efficient first-step screening process for managers assessing invasion risk (Thuiller et al.
2005)
Climatic differences have long been recognised as a major component of habitats that determine the broad scale geographical limits of naturalised plant species
(Lindsay 1953). Physiological and functional responses to the abiotic factors of climate, in particular temperature and moisture, ultimately limit the maximum potential distribution of plant populations (Rejmanek et al. 2005). For example, the northern limit of spread of the tropical species Pennisetum clandestinum (kikuyu grass) and Cynodon dactylon (bermuda grass) in California is restricted by their
susceptibility to freezing damage in the regenerative organs – the rhizomes and
stolons (Baker 1974; 1991). Given this relationship, the worldwide examination of
regions possessing similar climates can be a useful tool to predict the potential risk
of invasion by flora into uncolonised areas (Panetta and Mitchell 1991; Holt and
Boose 2000).
Climatic-mapping or eco-climatic models attempt to predict the geographic distribution of a species by simulating its growth potential in foreign environments on
Chapter 1 Introduction - 8 - the basis of its climatic preferences (Sutherst et al. 2004). Baker et al. (2000)
identified three commonly adopted approaches in the literature to creating climatic
models with the process typically varying according to the amount of information
available concerning a species current distribution and its responses to climatic
variables. These approaches are: 1) comparisons of climate based on knowledge of
a pest’s current distribution, but where climatic factors relevant to the pest are
unknown, 2) comparisons where relevant factors are known or have been inferred,
and 3) models which predict environmental suitability based on phenology with or
without consideration of responses to climatic extremes. By combining direct
observations of a species’ climatic responses with estimates based on analyses of
climatic conditions in an organism’s current distribution, the third option is currently
the most rigorous and reliable solution available. This method recognises that a
variety of interrelated factors will be frequently significant in determining the
suitability of conditions for the development and survival of a species (Baker et al.
2000).
Predictions generated from climate analysis using the third option are based on physiological growth models. The overall suitability of a location for permanent occupation by a species is derived from the integration of the species’ response to temperature, moisture and light and to prolonged periods of stressful conditions that constrain plant growth (e.g. drought, flood) (Sutherst and Maywald 1985). Growth and stress parameters are fitted iteratively until the model distribution accurately reflects the chosen observed distribution of the target species. The climatic conditions obtained from this process are then compared and matched to other regions unoccupied by the species, resulting in a predicted distribution based on the probability of specific geographic locations being able to support persistent populations (Sutherst 2003; Sutherst et al. 2004). The validity of these models is dependent on the assumption that (a) climatic conditions directly limit both the
Chapter 1 Introduction - 9 - present and potential distribution of a species and (b) the species currently occupies the full extent of appropriate climates within the reference region (Sutherst and
Maywald 1985).
Whether an exotic species remains insignificant or realizes its biological potential as a significant weed species is obviously not entirely dependent on the climatic suitability of a location. Habitat compatibility will also involve abiotic factors such as micro-climate, edaphic characteristics, topography, disturbance regimes, land use and management practices, and biotic elements including the presence of predators, pathogens and competitors (Panetta and Mitchell 1991; Sindel and Michael 1992;
Mack 1996; Sutherst et al. 2003). Factors intrinsic to a species can also affect the model outcome. For example, genetic variability will determine species adaptability to the new environment (Mack 1996; Baker et al. 2000). Such factors should be considered when interpreting the results of climatic models.
Despite these limitations, climatic modelling, when performed appropriately, is still a useful, and perhaps the best predictor available to determine broad scale potential distributions (Kriticos and Randall 2001; Williamson 2001). The principal uses of climate models to date have been in the prediction of the spread of weeds and insect pests, following either initial introduction or under different climatic scenarios, and of the spread of a species for biological control (e.g. Worner 1988; Spradbery and
Maywald 1992; Pheloung and Scott 1996; Bennett et al. 1998; Holt and Boose 2000;
Kriticos et al. 2003a, 2003b, 2005; Sutherst and Maywald 2005).
1.6 Assessing the current distribution of the target species
Before management decisions can be finalised it is first important to examine the extent of the current distribution by accurately mapping the presence and level of
Chapter 1 Introduction - 10 - population infestations. This procedure enables ecological patterns and trends of weed infestation over time to be monitored, incipient invasion to be detected and allows land managers to make informed decisions regarding resource allocation towards control efforts. Land surveys offer one traditional method to monitor population outbreaks; however, remote sensing technologies provide a more favourable alternative, offering significant opportunities for providing detailed information on invasions (Underwood et al. 2003). In contrast to land surveys, digital imagery enables a quick and accurate assessment of plant invader distribution across large expanses of land (Underwood et al. 2003).
Remote sensing can be defined as ‘the science of acquiring, processing and
interpreting images that record the interaction between electromagnetic energy and
matter’ (Sabins 1996). More simply, it consists of the measurements of
electromagnetic (EM) energy reflected from or emitted by a phenomenon from a
point that is distant from the target (Mather 1999). All matter with a temperature
above absolute zero radiates electromagnetic energy due to thermal molecular
agitation (Janssen and Huurneman 2001). A basic assumption made in remote
sensing is that specific targets or cover types, such as varying soil types and
vegetation species, each uniquely interact with incident radiation. This interaction is
described by the spectral response of the object and is referred to as a spectral
reflection curve or spectral signature (Mather 1999; Gibson 2000; Janssen and
Huurneman 2001). Sensors mounted aboard aircraft (airborne sensors) or satellite
(spaceborne) platforms measure the spectral features of the surface of the earth,
revealing spatial variations in the composition of the materials comprising the earth’s
surface, such as vegetation, water surfaces and exposed rock and soil (Mather
1999).
Chapter 1 Introduction - 11 -
A number of different remote sensors and platforms exist. The most commonly used forms include aerial photography and multispectral and hyperspectral sensors
(Harrison and Jupp 1989; Vande Castle 1998; Lass et al. 2005). These forms all differ in spatial, temporal and spectral resolution and therefore the sensor used should be reflective of what will best capture the nominated surface or object (Vande
Castle 1998). Fortunately, vegetation is a very good absorber of visible light, due to the presence of chlorophyll in plants, and displays distinctly different reflective values in the infra-red portion of the spectrum, where reflectivity increases sharply (Gibson
2000; Janssen and Huurneman 2001). All forms of remote sensing technology have been used successfully to detect a variety of invasive plant species found in a diversity of habitats including forests, rangelands and pasture environments (Lass et al. 2005).
Aerial photography is a useful remote sensor in detecting invasive species when the target species possess visually unique growth patterns or characteristics that differ from surrounding vegetation at particular times in the species’ lifecycle (Everitt et al.
1996; Underwood et al. 2003). For example, leafy spurge (Euphorbia esula) can be
identified by distinctive yellow bracts appearing during flowering in May (Anderson et
al. 1996). Other successful examples include the detection of salt cedar (Tamarix ramosissima) (Everitt et al. 1996), broom snakeweed (Gutierrezia sarothrae) (Everitt et al. 1987) and chinese tamarisk (Tamarix chinensis) (Everitt et al. 1996). Image
acquisition in aerial photography has the benefit of being relatively inexpensive,
however the time and expertise necessary to interpret acquired images can present
a key disadvantage (Arnold et al. 1985; Underwood et al. 2003; Lass et al. 2005).
In contrast, digital multispectral imagery greatly improves on aerial photography by recording reflected light in several different spectral wavelengths in the EM-spectrum
(Lass et al. 2005), as well as offering the opportunity for automated image
Chapter 1 Introduction - 12 - processing, time series analysis and large spatial coverage (Underwood et al. 2003).
Again successes have been recorded with this technology, with moderate to heavy infestations of the target species being identified by differences in phenological activity and correlations with brightness, greenness and wetness values, to name just a few (Dewey et al. 1991; Peters et al. 1992; Carson et al. 1995 but see
Underwood et al. 2003).
Hyperspectral imagery offers an even greater opportunity to enhance the mapping of
invasive species by possessing increased spatial and spectral resolution
(Underwood et al. 2003). These sensors provide data from many continuous narrow
bands allowing small spectral reflectance differences between plant species to be
detected (Lass et al. 2002; 2005), by capitalising on both the biochemical and
structural properties of the target species (Underwood et al. 2003). Hyperspectral
sensors have been used to separate the target species from other vegetation and
examples include the detection of brazilian pepper (Schinus terebinthifolius) (Lass
and Prather 2004), spotted knapweed (Lass et al. 2002), yellow starthistle
(Centaurea solstitialis) (Lass and Thill 2000) and iceplant (Carpobrotus edulis)
(Underwood et al. 2003).
Difficulties with all sensors exist, mostly in the form of prohibitive costs and
unsuitable spatial resolution and inaccuracies in mixed stands. However, the use of
the technology continues to grow and the long term benefits of this refinement will be
large. Early detection of invasive species maximises the potential for long-term
control and by providing improved information on the spatial extent and density of
invasive species, the impact to the environment can be minimised.
Chapter 1 Introduction - 13 -
1.7 Assessing the dynamics and future invasion potential at a local scale
A plant species occurring in an area deemed as ‘eco-climatically suitable’ for its establishment should theoretically be capable of completely inhabiting the entire area. This will rarely be the case as, at this finer level of scale, local environmental heterogeneity creates a variety of biotic and abiotic conditions and interactions capable of interfering in establishment success (Sheppard 2000). Consequently, a mosaic of weed establishment will usually occur across a localised area.
Understanding which conditions impact on weed abundance and how they can affect the movement of the invasion through time (Scott 2000) is essential information for both predicting the position of future infestations and for devising a suitable protocol for control. Consideration should be given to not only the dynamics occurring within the weed populations, but also to the processes and dynamics of the plant communities in which the weed occurs (Sindel 2000).
Parker (2000) suggests that invasion biology must ultimately address distribution patterns at the level of population dynamics, since it is at this level that an invasion either fails or succeeds. Questions such as: What makes a population increase?
What makes them decrease? What will be their ultimate level? and How will they reach it? are all fundamental to the understanding of the functioning of weed populations (Cousens and Mortimer 1995). Such demographic information, combined with knowledge of the interactions between the invading population and its host community, will expose both spatial and temporal patterns of variation within weed populations (Parker 2000). Knowledge of such variation can be used to estimate the potential invasion trajectory of the weed, the invasibility of certain plant communities (Koop 2004) and to formulate effective management programs capable of incorporating the whole ecosystem (Cousens and Mortimer 1995; Mortensen et al.
2000).
Chapter 1 Introduction - 14 -
Although demography and invasion dynamics will vary greatly with different species community types, an invasive species’ long term persistence and range expansion in all cases appears to be heavily dependent on the production of the seed/reproductive propagule and its movement, both spatially and temporally, through a heterogeneous environment. Interactions of seeds with their environment play a critical and often overlooked role in determining population structure.
Typically, it is in the seed phase of the life cycle that mortality has its greatest effect and therefore it is the seed stage where the greatest potential for environmental sorting exists (Niklas 1995). Seed bank survival, the size of the non-dormant proportion of the seed bank and seed germination requirements all affect seedling establishment and are crucial to understanding weed establishment and invasion
(Sheppard 2000).
The processes responsible for regulating population growth and invasion potential are many and often inter-related. Consequently, it is often difficult to accurately assess the individual impacts of each element on the advancement of an invasion
(Sheppard 2000). In an effort to quantify the dynamic processes occurring within a weed population, one method of simplifying the process is to examine the fate of the seed throughout the entire life cycle (Chambers and MacMahon 1994). By discerning the movement of the seed from its parent to the seed bank, through seedling recruitment, growth to maturity and single or multiple reproductive events to death, the probability of survival between each of the possible stages of the life cycle can be estimated to best reflect demographic data recorded in the field (Sheppard
2000). It is these demographic estimates, together with final reproduction, that determine final weed abundance (Sheppard 2000). Internal rates of population seed production, viability and increase, as well as discrete causes of mortality, can be quantified, enabling estimates of the abundance of propagules available for dispersal
Chapter 1 Introduction - 15 - and, accordingly, rates of immigration to be derived (Cousens and Mortimer 1995).
The mechanisms responsible for driving the invasion may also be discovered, as will the favourable traits of the invading population that enable invasion success (Heger
2001; Hyatt and Araki 2006).
Examination of models that outline the invasion process (e.g. Groves 1986; Heger
2001) highlight the integral role of the environment in shaping the course of an invasion (Niklas 1995). Following seed dispersal, the heterogeneous nature of the environment will impose pressures on the seed, leading to patch dependent differences in seed survival, germination and establishment (Schupp 1995). By altering the external conditions set for the seed fate model and the probabilities associated with various life stages, the response of the weed population in different plant communities and/or even regions can be assessed.
The impact of a variety of control and management techniques can be similarly investigated with seed fate models before being applied to the field situation (Parker
2000). In practice, ecological advice to weed managers will often arise from the results of such model simulations (Hulme 2006) and are therefore a tool of great worth to those endeavouring to control a weed. The sensitivity of population growth in the different phases of the life cycle can also be evaluated, with those identified as being highly responsive targeted for control.
In summary, through the use of mapping techniques such as remote sensing and eco-climatic modelling, as well as the localised modelling of invasion dynamics, an accurate assessment of an invasive species current and potential distribution and potential impacts can be made. Ranging from very broad to local scales, mapping and predicting distributions provides a useful basis upon which to make control and
Chapter 1 Introduction - 16 - eradication decisions and is now recognised as a fundamental component of any successful long term weed management strategy.
Given that Australia’s agricultural and natural systems continue to be increasingly threatened by the impacts of invasive species, the need for early detection and containment through the use of such techniques is of growing importance. Senna obtusifolia is one potentially very damaging weed species recently introduced to
Australia that requires rapid management whilst still in the early stages of its invasion. Little is known regarding the true extent of the S. obtusifolia problem in
Australia, making the mapping and modelling of this weed an essential task to both further understand its invasion potential and to provide a sound basis for the development of effective management practices.
1.8 Senna obtusifolia
Senna obtusifolia (L.) Irwin and Barneby (Leguminosae) (sicklepod, java bean) is an annual or short lived perennial with a woody base (Parsons and Cuthbertson 2000).
Believed to have originated from the Caribbean region and tropical South America,
S. obtusifolia has dispersed widely and now exhibits a global pantropical distribution
(Randell 1995; Holm et al. 1997). Senna obtusifolia displays rapid and vigorous growth and is a renowned pest species of agricultural and pastoral districts, disturbed (open) ecosystems and ruderal environments (Holm et al. 1997; Mackey et
al. 1997; Parsons and Cuthbertson 2000).
Senna obtusifolia is regarded as an important weed species of 26 different crops in
up to 67 countries, being most prevalent in soybean, pasture, peanut, cotton and
sugarcane (Holm et al. 1997). Competition and interference from the weed typically
Chapter 1 Introduction - 17 - results in significant reductions in crop productivity, where losses ranging from 20% to 50% are possible (Thurlow and Buchanan 1972; Teem et al. 1980). There is also
evidence of S. obtusifolia plants being toxic, causing serious illness or death to cattle
if consumed (Nicholson et al. 1985/86). It has also been argued that S. obtusifolia is
allelopathic (Waterhouse and Norris, 1987; Mackey et al. 1997), releasing a non-
persistent phototoxic compound that can severely inhibit the germination and growth
of other species (Creel et al. 1968).
Within Australia, S. obtusifolia is yet to establish itself as a major crop weed as it has in the USA (Mackey et al. 1997). However, it has established itself as an important pest of sugarcane and grazing land (James and Fossett 1982/1983; Mackey et al.
1997) and of the vulnerable and culturally valued Imperata cylindrica (L.) Beauv.
grasslands of Cape York Peninsula (Neldner et al. 1997). When left unmanaged, S.
obtusifolia threatens the sustainability of these environments by competing with, and
often completely displacing, resident species through the formation of dense
monospecific stands (Anning et al. 1989; Mackey et al. 1997; Neldner et al. 1997).
Since the relatively recent accidental introduction of S. obtusifolia to Darwin,
Australia during the Second World War (Parsons and Cuthbertson 2000) and again to the Cape York region in the 1970’s as a contaminant of pasture (Stanton 1998), expansion of the plant has been rapid. By 1996 it occurred across 600 000 hectares of land in dense pockets of high rainfall regions in northern Queensland alone
(Mackey et al. 1997; Parson and Cuthbertson 2000). Land uses resulting in nutrient input and high disturbance, including overgrazed and poorly maintained pasture, regularly support infestations of the weed (Anning et al. 1989).
Management to halt the range expansion of S. obtusifolia has been relatively
unsuccessful, with traditional mechanical, chemical and biological approaches to
Chapter 1 Introduction - 18 - control being either inapplicable or only locally and/or temporarily effective. A species specific biological control agent has not been identified and mechanical control methods have proven labour intensive and unfeasible in large areas (James and Fossett 1982/1983; Anning et al. 1989; Mackey et al. 1997). Control by chemicals can be effective, but generally requires specialized timing for the application to be successful and is an ongoing process and can be costly and impractical in some situations (Anning et al. 1989; Mackey et al. 1997). Such a poor response to control places considerable doubt on the efficacy of the estimated AU $1
000 000 spent per annum on S. obtusifolia control procedures in Queensland alone
(Mackey et al. 1997). Given such management difficulties, combined with the early stage of S. obtusifolia invasion, many more natural and modified ecosystems in
Australia are under threat of invasion.
Given the current difficulties associated with effective control of S. obtusifolia, early detection and containment of invading populations must be an essential component of its management in Australia. However, an efficient detection and containment strategy is predicated on the ability to predict areas susceptible to invasion. With this ability strategic control efforts can be maximised, as land managers can be alerted to detect new infestations sufficiently early to implement effective small scale eradication strategies (Moody and Mack 1988; Mack 1996; Kriticos et al. 2003a).
1.9 Scope and structure of thesis
To map the current distribution of S. obtusifolia and predict its invasion potential in
Australia, eco-climatic analysis, remote sensing technology and a model of seed fate will be undertaken. The combined used of these methods will provide assessments of S. obtusifolia establishment on a range of spatial scales that can be used for management purposes. Together, these mapping and modelling techniques, in
Chapter 1 Introduction - 19 - particular the construction of a local model of seed fate, have yet to be widely used in weed management. This research therefore aims to demonstrate the capability, effectiveness and adaptability of this methodology to assess current and potential distributions/impacts and formulate scientifically based hypotheses to be investigated for S. obtusifolia management.
Prior studies on S. obtusifolia in Australia are highly descriptive and are not based on rigorous scientific testing. An understanding of the processes responsible for invasion at a local scale, coupled with a validated eco-climatic model, will allow critical environmental attributes that impact on S. obtusifolia invasiveness to be identified and in the future manipulated in a scientifically based management strategy. Therefore, this study will play a fundamental role in maintaining and restoring the ecological integrity of those environments at risk of invasion by S. obtusifolia. Accordingly, this study aims to:
• Present a general description of the S. obtusifolia life cycle and the study
sites used within the present study (Chapter 2);
• Predict the invasion potential of S. obtusifolia in Australia using eco-climatic
analysis (Chapter 3);
• Evaluate the use of remote sensing technology to identify S. obtusifolia from
digital images and to map the current regional distribution of new and well
established infestations of S. obtusifolia (Chapter 4);
• Investigate the population dynamics within and between S. obtusifolia
infestations across a landscape, so to identify demographic traits that enable
Chapter 1 Introduction - 20 -
rapid population expansion and encroachment into neighbouring vegetation
communities (Chapter 5);
• Determine the critical organism-environment interactions that make some
communities susceptible to S. obtusifolia invasion and others resilient
(Chapter 6);
• Develop a conceptual seed fate model that outlines the key population
processes within S. obtusifolia infestations and factors external to infestations
that are sensitive to change resulting in population decline or increase
(Chapter 7);
• Apply the seed fate model to investigate the response of S. obtusifolia
populations to different environmental scenarios to assist in a greater
understanding of the weed dynamics and provide direction for possible
management regimes (Chapter 8).
• Results are discussed in relation to the specific management of S. obtusifolia
and the overall validity of the approach used in managing biological invasions
(Chapter 9).
Chapter 2
Materials and Methods Chapter 2 General Methodology - 22 -
2.1 Species
Senna obtusifolia is a woody annual or short lived perennial shrub (Singh 1968,
Randell 1995; Holm et al. 1997) (Figure 2.1). Varying in height from approximately 1
– 3 metres, S. obtusifolia plants are erect and robust shrubs that possess a broad tolerance to climate, soil type and pH (Creel et al. 1968; Teem et al. 1980; Anning et al. 1989; Patterson 1993; Mackey et al. 1997). Plants possess single stems and can
be highly branched, depending on habitat suitability and crowding (Mackey et al.
1997).
The life history strategy of S. obtusifolia includes attributes typically associated with successful weed species including the ability to rapidly colonise both natural and modified environments and to compete aggressively for light, moisture, essential nutrients and open space (Creel et al. 1968; Baker 1965; Rejmanek 1995). Such
traits include high germination, growth and maturation rates (Creel et al. 1968), self- fertilisation, copious dimorphic seed production (up to 8000 seeds/plant with approximately 90% dormant and 10% non-dormant) (Retzinger 1984; Baskin et al.
1998), long distance dispersal via water flow and human activity (Parsons and
Cuthbertson 2000), a long-lived soil seed reserve (Baskin et al. 1998) and the ability to germinate in any season given correct conditions (Mackey et al. 1997).
A diagram of the basic life-cycle of S. obtusifolia is illustrated in Figure 2.2. In far northern Australia, the main germination period of S. obtusifolia occurs in mid summer (January-February) after the wet season has begun. The ensuing growth and vegetative phase will vary with climate from 43-84 days before the start of flowering in late summer (Retzinger 1984). Many leaves are shed during this growth period, causing the main stem to be mostly bare. The small yellow flowers continue to be developed until autumn (April – June), when the sickle shaped pods begin to Chapter 2 General Methodology - 23 - appear. These remain on the plant and ripen until midwinter (July – September).
When dry, pods split open releasing seed, mostly to the soil surface at the base of the parent plant. Following pod production, the plants begin to die back in late winter and spring (August – October) (Mackey et al. 1997)
a)
b)
c)
Figure 2.1 Images of Senna obtusifolia. a) flowers and pods, b) stem density and c) as an infestation.
Chapter 2 General Methodology - 24 -
2.2 Study Site
This research was conducted in Iron Range National Park (12° 34’ 0” S, 143° 18’ 55”
E) and adjacent areas in the Lockhart River region on Cape York Peninsula, far northern Queensland, Australia (Figure 2.3). Thought to be one of the most biologically significant regions in Australia (Roberts 1977), the landscape is characterised by a striking variation of distinct soil and vegetation types. The complex and diverse geological substrate supports a mosaic of habitats across the region, with the Iron Range mountain block being the major physiographic unit of the region (Hynes and Tracy 1980). The vegetation mosaic includes dense rainforests, eucalypt woodlands, dwarf heath, shrublands, mangroves, dune complexes and extensive grasslands (Hynes and Tracy 1980; Stanton 1998). The Claudie River dominates the drainage pattern with many small streams contributing to the seasonally sensitive riverine network (Hynes and Tracy 1980).
The climate of Iron Range is not strictly monsoonal when compared to other parts of the peninsula, as wet periods can still occur in the normally dry months of June-
September (Hynes and Tracy 1980). An average of approximately 2000 mm of rain falls annually, however, vegetation growth is still limited annually by a lack of soil moisture for a brief but significant period (approximately August – November)
(Stanton 1998). During this dry period, vegetation is regularly subjected to fire. Fire is a crucial element in both creating and maintaining the community diversity present today (Stanton 1998). In particular, the origin of the grassland environments is a result of pre-European aboriginal burning activities (Stanton 1998).
Chapter 2 General Methodology - 25 -
Seed pods Seed pods ripe, seed fall
appear and ripen of viable, dormant seeds Senna obtusifolia Start of Flowering Life History (main reproductive effort) End of Flowering
Active growth phase Growth slows down Leaves senesce (high temperature and rainfall) (effort on reproduction) and fall off
Early germination begins Main germination
(seed dormancy broken or non period – mid summer
dormant seeds) dependant on after 1st rains have
early rains Die/ fallen
Survive?
Fires on Cape York (break dormancy, lower competition
January February March April May June July August September October November December
Seasonality of Cape WET SEASON WINTER DRY SEASON Pre- Wet Storms York (Wet/Dry)
Figure 2.2 Diagram outlining the phases of the annual life cycle of Senna obtusifolia in relation to the prevailing season in the Lockhart River region. Chapter 2 General Methodology - 26 -
Study sites were established in twelve independent S. obtusifolia populations located on the alluvial plains (12°45’24”S 143°17’09”E) of Iron Range National Park and on the plains occurring south of the Lockhart River township (12°49’02”S 143°18’06”E)
(Figure 2.3).
Brachiaria decumbens
Elevated Woodland
Rainforest
Lockhart River
Woodland & Rainforest Imperata cylindrica
To more I. cylindrica
Figure 2.3 The location of Lockhart River in far north Queensland, and the generalised position of different vegetation types and field sites. See Table 2.1 for more detailed information regarding site localities. Chapter 2 General Methodology - 27 -
Sites were chosen based on low trampling and disturbance by wildlife and the presence of an identifiable ‘invasion front’ between the S. obtusifolia populations and the adjoining vegetation. This boundary between the two vegetation types is very discrete as beyond the invasion front, very few, if any S. obtusifolia plants will occur.
This allowed for the easy delineation of discrete local populations (Figure 2.4a).
Four different habitat/vegetation types were found to be commonly associated with
S. obtusifolia populations throughout the region. These habitats were lowland rainforest, lowland open woodland, Brachiaria decumbens Staph grassland
(abandoned pasture) and Imperata cylindrica (L.) Beauv. grassland. A description of
each habitat type and their relative association with S. obtusifolia is presented in
Table 2.1 and illustrated in Figure 2.4. Three replicate sites of S. obtusifolia
populations occurring adjacent to each habitat type were established. These were
the main S. obtusifolia populations used throughout the study.
Additional sites were selected to satisfactorily fulfil the requirements of some experiments conducted. This generally applies to the dispersal experiments described in Chapter 5 and the seed introduction experiments described in Chapter
6, whereby sites either needed to be free of S. obtusifolia or original sites became unsuitable for other reasons over time. In this instance sites were again selected on the basis of low disturbance. A fifth habitat type, elevated woodland, was added for use in the seed introduction experiments (Figure 2.4f). All site locations and site references used throughout the study are presented in Table 2.2.
Chapter 2 General Methodology - 28 -
Table 2.1 Description of each of the five habitat types that were used in the study, and their relative association with Senna obtusifolia infestations.
Habitat Description Association with References Senna obtusifolia
Rainforest Very tall semi-deciduous mesophyll/notophyll vine forests, occurring as a Dense populations of S. Stanton 1998 narrow belt along major streams and rivers of the area. This forest type obtusifolia frequently exist up is amongst the most biologically rich of the Cape York Peninsula. to, but not inside the rainforest Canopy is comparatively open to other forest types, with the presence of large prominently buttressed trees. Soils are alluvial/colluvial.
Elevated Occurring in more elevated areas, the canopy is dominated by S. obtusifolia did not occur woodland Eucalyptus and Melaleuca species, with an understorey of saplings, within or adjacent to this small tussock grasses and small shrubs, and areas of open soil. Soils habitat. Populations did occur were comparatively drier, rockier and sandier than the lowland at low points surrounding the woodlands habitat
Brachiaria These derived grasslands are dominated by the aggressive exotic Large infestations exist within Stanton 1998 decumbens pasture grass species B. decumbens. Brachiaria decumbens rapidly these grasslands and are the Grassland produces dense swards of grass which can smother resident species. likely point of introduction for Miles et al. 1996 (signal grass) These grasslands occur on alluvial plains and are regularly burnt. S. obtusifolia in the region Chapter 2 General Methodology - 29 -
Habitat Description Association with References S. obtusifolia
Imperata Imperata cylindrica is an aggressive grass capable of creating pure Dense infestations exist in this Neldner et al. cylindrica swards standing to approximately one metre in height. The grass arises habitat type, resulting in large 1997 Grassland from a dense underground mat of rhizomes and occurs on a complex of areas of I.cylindrica being (blady grass) soils including grey vertosols. Grasslands were created and are displaced MacDonald 2004 maintained by fire and are heavily disturbed by feral animals. Stanton 1998
Lowland Located in seasonally wet lowland areas typically adjacent to grasslands, Scattered populations occur woodland these woodlands comprise small areas that are possible transitional throughout this habitat type, environments. Scattered Melaleuca trees dominate, producing a generally becoming less dense relatively open canopy. Tree height, diversity and density increased with in area with higher shade and distance from the grasslands. The understorey was characterised by excessive soil moisture grass and few small shrubs and are subject to regular firing.
Chapter 2 General Methodology - 30 -
a) The Senna obtusifolia invasion front
b) Brachiaria decumbens grassland
c) Rainforest
Chapter 2 General Methodology - 31 -
d) Imperata cylindrica grassland
e) Lowland Woodland
f) Elevated woodland
Figure 2.4 Examples of the different habitat types within the Lockhart River region used throughout the study. Chapter 2 General Methodology - 32 -
Table 2.2 The longitude, latitude and site references for field sites used in Lockhart River.
Habitat Site Ref. Location Latitude (E) Longitude (S)
Demography Studies (Ch 5) Rainforest 1 143°16’36” 12°44’44” Rainforest 2 143°16’41” 12°44’52” Rainforest 3 143°17’54” 12°48’28” Brachiaria decumbens 4 143°16’38” 12°44’37” Brachiaria decumbens 5 143°16’43” 12°44’31” Brachiaria decumbens 6 143°16’34” 12°44’28” Imperata cylindrica 7 143°17’46” 12°48’42” Imperata cylindrica 8 143°23’50” 13°02’14” Imperata cylindrica 9 143°18’08 12°49’03” Lowland Woodland 10 143°15’59” 12°44’18” Lowland Woodland 11 143°16’30” 12°44’27” Lowland Woodland 12 143°17’01” 12°44’54”
Seed Introduction Study (Ch 6) Rainforest R1 143°17’10” 12°46’06” Rainforest R2 143°17’04” 12°44’20” Rainforest R3 143°17’53” 12°48’24” Brachiaria decumbens B1 143°16’55” 12°45’28” Brachiaria decumbens B2 143°17’02” 12°45’24” Brachiaria decumbens B3 143°17’09” 12°45’24” Imperata cylindrica I1 143°18’06” 12°49’02” Imperata cylindrica I2 143°18’00” 12°48’41” Imperata cylindrica I3 143°17’47” 12°48’40” Lowland Woodland W1 143°17’31” 12°47’58” Lowland Woodland W2 143°16’30” 12°44’27” Lowland Woodland W3 143°18’06” 12°49’02” Chapter 2 General Methodology - 33 -
Habitat Site Ref. Location Latitude (E) Longitude (S)
Elevated Woodland H1 143°15’31” 12°44’24” Elevated Woodland H2 143°15’39” 12°44’16” Elevated Woodland H3 143°16’07” 12°44’22”
Dispersal Studies (Ch 5) Rainforest RF1 143°17’53” 12°48’25” Rainforest RF2 143°17’45” 12°48’24” Rainforest RF3 143°17’45” 12°48’24” Brachiaria decumbens BA 143°16’35” 12°44’34” Brachiaria decumbens BB 143°16’33” 12°44’36”
Chapter 3
The potential geographic distribution of Senna obtusifolia in Australia
The work from this chapter can be found in: Dunlop EA, Wilson JC and Mackey AP (2006)
The potential geographic distribution of the invasive weed Senna obtusifolia in Australia.
Weed Research 46, 404-413.
It has been modified here to aid thesis continuity.
Statement of Co-authorship
Elizabeth Dunlop - 75% contribution. Undertook experimental design, conducted all field work, undertook the statistical analysis and produced the manuscript.
John Wilson – 15% contribution. Supervised experimental design and assisted in manuscript production.
Peter Mackey – 10% contribution. Co-supervised experimental design and assisted in editorial process. Chapter 3 Potential Geographic Distribution - 35 -
3.1 Introduction
The ability to predict the location, course and impact of future plant invasions can be advantageous for many purposes including, quarantine and the prevention of entry of certain species types (Cousens and Mortimer 1995), the maximisation of control efforts for troublesome species already present (Mack 1996) and for improving success rates of introduced biological control agents into new environments
(Sutherst 2003). Climate matching is one well established method of achieving such favourable outcomes, as physiological and functional responses to the abiotic factors of climate, in particular temperature and moisture, will ultimately limit the maximum potential of plant populations within their native habitat (Rejmanek et al. 2004).
Given this relationship, the worldwide examination of regions possessing similar climates can be a useful tool to predict the potential risk of invasion by flora into uncolonised areas (Panetta and Mitchell 1991; Holt and Boose 2000).
The invasion of Senna obtusifolia throughout tropical Australia has been rapid
(Mackey et al. 1997; Parsons and Cuthbertson 2000), with its distribution currently largely restricted to the warm and wet areas of north Queensland and the Northern
Territory (Mackey et al. 1997; Parsons and Cuthbertson, 2000). In view of this current distribution and the plants tropical origins in the Caribbean, it would be fair to assume that the weed has already, or is close to reaching, the limits of its distribution in Australia. However, the extensive international distribution of S. obtusifolia described in Chapter 1 comprises a diversity of climates, particularly in the USA, where it reaches into the very cold climates of the north-eastern states. Therefore it is probable that S. obtusifolia has the potential to further its invasion south in
Australia. How great this invasion potential is and what the relative impacts will be remains unknown.
Chapter 3 Potential Geographic Distribution - 36 -
Many distinct biotypes of S. obtusifolia exist in the USA (Retzinger 1984) and populations occurring in different regions tend to vary greatly in their morphology, phenology and response to temperature (Irwin and Barneby 1982; Patterson 1993).
Although S. obtusifolia generally possesses a high optimum temperature for growth,
Patterson (1993) thought that evolution of a cold tolerant ecotype of S. obtusifolia may have occurred at the northern bounds of the distribution, enabling a slow northward expansion of the species in the USA. Accordingly, S. obtusifolia possibly has enormous invasion potential in Australia, as this country does not experience the low temperatures that restrict the weed’s range in the northern USA.
Eco-climatic models attempt to predict the geographic distribution of an organism by simulating its potential to persist in foreign environments, on the basis of its response to climatic variables from within its native range or other distributions (Sutherst et al.
2004; Baker et al. 2000). Climatic models are informative tools for managers to use
at an early stage of an invasion, when little empirical data about the organism exist
(Sutherst and Maywald 2005) and its invasion potential within the new environment
is unknown (Kriticos et al. 2003a). These models are simple to use and can
synthesise indirect scientific information (i.e. distribution and relative abundance
data) and create a rapid, cheap and often robust prediction of climatic suitability in
regions of interest (Kriticos et al. 2005).
Some criticism has been levelled at this form of modelling for failing to address other abiotic and biotic interactions, such as dispersal, competition, predators and land uses that can prevent a species to expand into climatically suitable environments
(e.g. Davis et al. 1998; Samways et al. 1999). Climate models, however, can be used to investigate the concept of the fundamental niche (Kriticos et al. 2005) by
defining the role of climate in determining potential establishment when all other
factors are not considered (Sutherst 2003). By including distributions other than the
Chapter 3 Potential Geographic Distribution - 37 - native range when inferring species’ climatic requirements, major discrepancies between the realised and fundamental niches can be overcome (Rejmanek et al.
2004; Wharton and Kriticos 2004; Kriticos et al. 2005). Factors limiting the native
distribution are unlikely to occur in exotic ranges and, therefore, organisms most
likely will display a wider tolerance to climate in their absence (Mack 1996).
Of the numerous existing computer based systems that estimate the suitability of a new environment for pest species (e.g. CLIMATE, BIOCLIM, DOMAIN) (Kriticos and
Randall 2001), CLIMEX (Sutherst and Maywald 1985) is one of the most successful, rational and demanding eco-climatic models developed (Baker et al. 2000;
Williamson 2001). Using global climate data sets, CLIMEX produces a species- response model that is readily interpretable through the use of indices that change in size relative to their magnitude (Kriticos and Randall 2001; Sutherst et al. 2004).
The potential distribution of S. obtusifolia in Australia remains largely unknown, as does the severity of its impacts. Using CLIMEX V2 (Sutherst et al. 2004), this study aimed to develop a predictive eco-climatic model of the potential geographic distribution of S. obtusifolia in Australia under two scenarios: 1) when all ecotypes within the reference distribution were included; and 2) in the absence of an evolved cold tolerant ecotype. If the predicted distribution of the different eco-types proves grossly different, depending on which eco-type is present in Australia, the outcome would have an important impact on the subsequent prioritisation of the weed and the efforts expended toward its control.
Chapter 3 Potential Geographic Distribution - 38 -
3.2 Method
3.2.1 CLIMEX
CLIMEX predictions are based on an eco-physiological growth model that assumes populations experience a favourable season with positive growth and an unfavourable season that causes population decline. This growth model ultimately defines the critical bounds of a species’ potential range (Sutherst 2003). The threat posed by prolonged or extreme values of moisture and temperature are described by four annual stress indices: hot, cold, wet and dry. Interactions of stresses also can be incorporated into the model if required (Sutherst 2003; Sutherst et al. 2004). The resultant combined growth and stress index is referred to as the ‘eco-climatic index’
(EI) and describes the overall climatic suitability (scaled from 0 (no growth) to 100
(optimal growing conditions)) of a location for permanent occupation by a species, based on long term climatic data (Sutherst and Maywald 1985). Locations attaining
EI values of 20 and above are considered favourable for population persistence, whilst values below 10 indicate locations of marginal suitability, where large annual population fluctuations may occur (Sutherst 2003).
Parameter values on which the EI is computed are set by the user and are inferred from an iterative fitting process to mimic the geographic distribution in the native and/or other ranges (Holt and Boose 2000). The geographic distributions of a species are the preferred sources of data to parameterise models. Difficulty may arise when translating laboratory based data into CLIMEX parameters, due to the averaging process of meteorological data in the CLIMEX database (Wharton and
Kriticos 2004).
Chapter 3 Potential Geographic Distribution - 39 -
3.2.2 The current distribution of Senna obtusifolia
The international distribution of S. obtusifolia was obtained by contacting herbaria located throughout the Americas, Africa, Europe and Asia for records of its presence.
Additional records were obtained through literature and database searches (e.g.
W3TROPICOS – Missouri Botanical Garden, GRIN – USDA-Agricultural Research
Service, ILDIS – International Legume Database and Information Service, PLANTS –
USDA national plants database, CALFLORA – University of California).
Senna obtusifolia currently possesses an extensive tropical and sub-tropical
distribution extending from Africa (South Africa, Namibia, tropical west, central and
east Africa) to India, Sri Lanka, Pakistan, Central America, Malaysia, the Philippines,
Indonesia, Papua New Guinea, South America, the Caribbean, the USA and
Australia (Randell 1995; Holm et al. 1997; Mackey et al. 1997; Parsons and
Cuthbertson 2000) (Figure 3.1). Senna obtusifolia also can persist in higher altitude
environments, being recorded at elevations up to 1600 m in Mexico and Tanzania.
Within Australia, herbarium records (HERBRECS database) indicate S. obtusifolia is
distributed predominantly in the coastal areas of north Queensland and the Northern
Territory (Figure 3.2). Within Queensland, populations extend from Sarina to the top
of Cape York Peninsula, tending to be more dense and abundant in the northern part
of this range (Mackey et al. 1997). Within the Northern Territory, S. obtusifolia is
reported through Arnhem Land (Mackey et al. 1997) and within a number of river
catchments including the Wilton, Roper, Daly, Reynolds, Finiss and Victoria (S.
Wingrave, pers. comm.), with populations most common in land just above flood
level on coastal plains (Mackey et al. 1997).
Chapter 3 Potential Geographic Distribution - 40 -
Figure 3.1 The current global distribution of Senna obtusifolia.
3.2.3 Reference distribution and Model 1
The native range of S. obtusifolia within the Caribbean and the extended distribution throughout North and Central America, ceasing at Costa Rica, were selected to derive the climatic requirements of the plant when fitting parameters to the CLIMEX model (reference region) (Figure 3.3). Records of S. obtusifolia are abundant throughout this region and, within the south-eastern USA S. obtusifolia is reported to
occur in environmental equilibrium. Although it has increased in abundance within
its limits, its range has not altered significantly for approximately 150 years (Teem et al. 1980). Encompassing a diversity of climatic zones, this distribution adequately fulfils the principal assumptions of CLIMEX, enabling a reliable model to be produced. The South American and worldwide distribution were used for model validation purposes only, due to the scarcity of records and meteorological data to reflect the distribution.
Chapter 3 Potential Geographic Distribution - 41 -
Figure 3.2 The current distribution of Senna obtusifolia in Australia.
The precision of the predictive model in part depends on the scale at which the locations of known populations are associated with specific climatic data. Therefore, the CLIMEX database was supplemented with an additional 56 meteorological stations in North America to attain a more even distribution of known climate locations.
Chapter 3 Potential Geographic Distribution - 42 -
3.2.4 Model 2 – cold resistant ecotype
To account for the probability of a cold resistant ecotype and its absence from
Australia, a second model was produced to reflect the reference distribution illustrated in Figure 3.4. The observed distribution reveals a gradation of populations from approximately 36° latitude beyond which the sparser occurrences of populations were assumed to represent the cold resistant ecotype. The effect of altitude on a cold susceptible ecotype was also considered by refitting the distribution around the Appalachian Mountains as seen in Figure 3.4.
Figure 3.3 The distribution of Senna obtusifolia throughout the continental Americas used to construct the CLIMEX model.
Chapter 3 Potential Geographic Distribution - 43 -
Figure 3.4 The proposed distribution boundary (solid line) used as the basis to construct the CLIMEX model of a cold susceptible ecotype of Senna obtusifolia.
3.2.5 Parameter fitting
The parameters used in the CLIMEX models for S. obtusifolia are summarised in
Table 3.1. Growth parameters, followed by the stress indices were adjusted in an iterative fashion until the model closely fitted the reference distribution. Parameters were then further adjusted to fit the South American and worldwide distributions.
3.2.5.1 Growth indices
CLIMEX calculates weekly and annual indices to indicate how favourable each location is for population growth (Sutherst and Maywald 1985).
Chapter 3 Potential Geographic Distribution - 44 -
3.2.5.1.1 Temperature index
Model 1 - The minimum (DV0) and maximum (DV3) threshold temperatures for S. obtusifolia germination were interpreted initially from germination growth experiments detailed in Creel et al. (1968), Teem et al. 1980, Patterson (1993) and Holm et al.
(1997). Germination did not occur below 11-15°C or above approximately 40°C, dependent on the reference and the population from which the plants were drawn.
For the model, DV3 was set at 38°C, whilst DV0 was set at 14°C, as these figures should account for days with temperatures above and below these values within a week. DV0 set at 14°C was also the minimum temperature that allowed growth in northern sections of Illinois, but without causing widespread growth in other northern
U.S.A. states. The minimum and upper temperature thresholds for maximum growth rates (DV1 and DV2) were set at 24° and 34°C, respectively. This enabled maximum growth in tropical areas, whilst maintaining suitability in the cooler northern regions.
Model 2 – DV0 was increased to 17.5°C to reduce the northern boundaries of the distribution in the USA. DV1 also was increased to 26°C to ensure that the optimum conditions required for growth were warm. Other temperature parameters were unchanged, as they are concerned with the performance of the plant at the warm end of the distribution.
Chapter 3 Potential Geographic Distribution - 45 -
Table 3.1 CLIMEX parameter values used for Senna obtusifolia derived from its North and Central American distribution. The parameters for all ecotypes and the cold susceptible ecotype are presented.
All Cold Ecotypes Resistant Ecotype Absent
Temperature Indices (°C) DV0 Temperature below which no growth occurs 14 17.5 DV1 Temperature below which growth is sub- 24 26 optimal DV2 Temperature above which growth is sub- 34 34 optimal DV3 Temperature above which no growth occurs 38 38 PDD Minimum day degrees above DV0 750 1005
Moisture Indices* SM0 Moisture level below which no growth occurs 0.12 0.12 SM1 Moisture level below which growth is sub- 0.4 0.4 optimal SM2 Moisture level above which growth is sub- 1.2 1.2 optimal SM3 Moisture level above which no growth occurs 2 2
Stress Indices TTCS Cold stress temperature threshold (°C) 1 2.5 (below which cold stress accumulates) THCS Cold stress accumulation rate 0.0008 0.001 (x degrees below threshold = stress)(week - 1) TTHS Heat stress temperature threshold (°C) 38 38 THHS Heat stress accumulation rate (week -1) 0.07 0.07 SMDS Dry stress threshold* 0.12 0.12 HDS Dry stress accumulation rate (week -1) 0.032 0.032 SMWS Wet stress threshold* 2 2 HWS Wet stress accumulation rate (week -1) 0.02 0.02 DTCD Cold-dry day degree threshold 15 18 MTCD Cold-dry moisture threshold* 0.24 0.24 PCD Cold-dry stress accumulation rate (week -1) 0.2 0.3 * Denotes dimensionless units
3.2.5.1.2 Thermal accumulation
Model 1 - The minimum annual thermal accumulation (PDD) for populations of S. obtusifolia was calculated as 750 day degrees above DV0. The northern limits of the
Chapter 3 Potential Geographic Distribution - 46 -
North American boundary were used to establish this parameter, being manipulated to remove positive growth from the eastern regions of Pennsylvania and in areas of high altitude through Mexico. A PDD parameter of this value also helped to remove simulated populations occurring in central France where the plant is not reported to occur (G. Alziar, pers.comm; F Medail, pers. comm).
Model 2 – PDD was increased to 1005 day degrees per generation. This caused both the northern limits of the previous distribution as well as higher altitude areas to become climatically unsuitable. The most northern boundary of the modified distribution now occurred in Tennessee.
3.2.5.1.3 Moisture index
As S. obtusifolia is primarily a tropical species, a lack of moisture was assumed to restrict growth. Hoveland and Buchanan (1973), however, also found that S. obtusifolia can tolerate dry conditions, experiencing significant germination under simulated drought conditions. Accordingly, the lower threshold for soil moisture
(SM0) was set at 0.12 (i.e. 12% of plant available soil moisture). This value approaches the point where plants can no longer extract moisture from the soil
(Daubenmire 1974).
Germination of S. obtusifolia is reportedly best when soil moisture is 75% of field capacity; however, growth is slow if soils remain waterlogged after emergence (Holm et al. 1997). The lower and upper thresholds for optimum moisture conditions (SM1 and SM2), therefore, were set at 0.4 and 1.2, respectively, to best describe this relationship. SM3 was set to 2, a level that accounts for the presence of the weed in
Chapter 3 Potential Geographic Distribution - 47 - high rainfall areas, such as Central America, Brazil, northern Australia and southern
USA.
3.2.5.2 Stress indices
The stress indices in CLIMEX are used to represent the limiting abilities of extreme values of temperature and moisture on a species geographical range (Sutherst and
Maywald 1985). Weekly values of the stress indices accumulate to produce an annual value scaled between 0 (no stress) and 100 (lethal conditions) (Sutherst and
Maywald 1985).
3.2.5.2.1 Cold stress
Model 1 - The temperature threshold at which cold stress begins (TTCS) had to be set at a low value of 1°C. This was to enable suitability at the northern boundary of the weed, which is reported to extend as far north as Chicago, Illinois. The cold stress accumulation rate (THCS) was also low at 0.0008, as small increases in the rate of accumulation forced the northern boundaries of the distribution to decrease too far south.
Model 2 – TTCS was increased to 2.5°C, making the plant susceptible to frost damage (see Kriticos et al. 2003a) and, therefore, impacting more heavily on northern populations of the plant. THCS was increased to 0.001, reducing the suitability of areas above the proposed cut-off in Figure 3.4.
Chapter 3 Potential Geographic Distribution - 48 -
3.2.5.2.2 Heat stress
The heat stress threshold (TTHS) was set at 38°C to coincide with the temperature at which population growth ceases. The rate of heat stress accumulation (THHS) was set at 0.07 in accordance with the African distribution of S. obtusifolia (Figure
3.1), where heat stress impacted the north-western limits.
3.2.5.2.3 Dry stress
Moisture related stress cannot begin to occur until moisture levels fall to where positive population growth ceases (SM0). Accordingly, the dry stress threshold
(SMDS) was set at 0.12. The heat stress accumulation rate (HDS) was set at 0.032 to reduce the suitability of the western and central states of the USA.
3.2.5.2.4 Wet stress
The wet stress threshold parameter (SMWS) was set at 2 and the accumulation rate
(HWS) at 0.02. This rate reflected S. obtusifolia’s dislike for waterlogged conditions, but still allowed persistence in the wet areas of southern USA, Brazil, tropical Asia and Africa.
3.2.5.2.5 Cold-dry stress
Model 1 - The cold/dry interaction stress index was adopted to prevent climatic suitability being projected into western portions of Texas and Kansas, where no records of S. obtusifolia exist. The cold-dry day degree threshold (DTCD) was set at
Chapter 3 Potential Geographic Distribution - 49 -
15, with the soil moisture threshold (MTCD) at 0.24 and an accumulation rate (PCD) of 0.2.
Model 2 – The DTCD threshold was increased to 18 and the PCD increased to 0.4 to simulate the greater impact of cooler conditions. This greatly reduced the suitability for persistence on the east coast of Mexico.
The fully parameterised models subsequently were applied to Australia to predict the potential distribution of S. obtusifolia. This prediction was validated against known locations of S. obtusifolia populations in Australia.
3.3 Results
The potential distributions of S. obtusifolia worldwide and in North, Central and South
America for Model 1 are illustrated in Figures 3.5 and 3.6a. The predicted
distribution of Model 1 corresponds closely with the observed distribution of S.
obtusifolia, as well as projecting a good fit within the main reference region in North
America. It also conforms well to the South American distribution where the model
predicts optimal environments in the tropical northern countries of Panama,
Venezuela, Guyana, Surinam and Brazil and no climatic suitability in southern
Argentina and Chile, predominantly due to dry and cold/dry stresses.
The projection of the distribution of a cold susceptible ecotype of S. obtusifolia in
North America (Model 2) also produced a close fit with the demarcated northern boundary (Figure 3.6b). This decreased tolerance to cold conditions also impacted the central Mexican distribution, resulting in several locations becoming marginal or completely unsuitable as a result of cold/dry stress, dry stress and a lack of thermal accumulation in the case of the higher altitude regions.
Chapter 3 Potential Geographic Distribution - 50 -
Figure 3.5 The global distribution of all Senna obtusifolia ecotypes as predicted by CLIMEX. The degree of suitability of a location for permanent occupation is proportional to the indicated ‘eco-climatic’ index (EI). Chapter 3 Potential Geographic Distribution - 51 -
a)
b)
Figure 3.6 The American distribution of Senna obtusifolia as predicted by CLIMEX: a) all ecotypes and b) the North American distribution in the absence of a proposed cold tolerant ecotype. The degree of suitability of a location for permanent occupation is proportional to the indicated ‘eco-climatic’ index (EI). Chapter 3 Potential Geographic Distribution - 52 -
The potential distributions of S. obtusifolia in Australia projected by Models 1 and 2 are shown in Figures 3.7a and b. Both models indicate that an extensive proportion of Australia’s eastern and northern coastal regions are climatically suitable environments for persistent S. obtusifolia populations. The current Australian distribution of S. obtusifolia (Figure 3.2) constitutes only a small proportion of the predicted area. Optimal habitats, as indicated by the highest EI’s, are restricted to islands and a strip of coastal Queensland and northern New South Wales.
Model 2 indicates that in the absence of the cold ecotype, the southern extent of the distribution is much reduced (Figure 3.7b). The suitability of a number of coastal habitats in northern New South Wales and south-eastern Queensland also has been reduced in some cases.
Temperature extremes do not greatly restrict the distribution of S. obtusifolia in
Australia, as would be expected considering the wide climatic tolerances of the species in North America. Instead, inadequate moisture and dry stress were the two principal climatic conditions restricting suitability to Cape York and the northern and eastern coastal fringes. Cold/dry stress and a small degree of cold stress are restrictive at the southern end of the distribution, as is a lack of thermal accumulation in the form of day degrees (PDD). These forms of stress are far more apparent in
Model 2.
Chapter 3 Potential Geographic Distribution - 53 -
a)
b)
Figure 3.7 The Australian distribution of Senna obtusifolia as predicted by CLIMEX: a) all ecotypes and b) in the absence of the cold tolerant ecotype. The degree of suitability of a location for permanent occupation is proportional to the indicated ‘eco- climatic’ index (EI).
Chapter 3 Potential Geographic Distribution - 54 -
3.4 Discussion
The CLIMEX models clearly demonstrate the potential of S. obtusifolia to become an important pest plant species globally. Although a large proportion of the tropical regions of the projected range appear already inhabited by the weed, considerable potential still exists for it to expand its distribution, particularly into regions possessing milder, cooler climates. For example, Europe currently remains free of
S. obtusifolia (Figure 3.1); however, coastal regions of countries on the
Mediterranean are highlighted as climatically suitable for supporting persistent populations (Figure 3.5). Similarly, regions of south-eastern China, where S. obtusifolia does not occur, are also susceptible to invasion. The extent of this possible global distribution under current climatic conditions, in addition to the threat of further expansion under future climatic warming, warrants the development of strategic management, quarantine and education campaigns to prevent the serious economic and environmental repercussions of S. obtusifolia invasion.
Within Australia, the invasion potential of S. obtusifolia is also very extensive, with further expansion possible within the tropical regions through to the cooler climates of the south. Even when its invasion potential is reduced due to the absence of a cold resistant ecotype, the threat remains large, with the suitability of the environments contained within the smaller distribution remaining very high. In addition to the considerable area that S. obtusifolia may inhabit, its possible impacts are amplified given that its potential distribution is largely restricted to coastal regions containing Australia’s prime productive land.
The coastal fringe of Australia has proven to be an ideal environment for the proliferation of many introduced species (Swincer 1986), such as Senecio madagascariensis Poir (Sindel and Michael 1992), Cryptostegia grandiflora R. Br.
Chapter 3 Potential Geographic Distribution - 55 -
(Kriticos et al. 2003a), and Acacia nilotica Willd. Ex Del. (Kriticos et al. 2003b). The moist, warm climate and modified and disturbed environments characteristic of the region (Swincer 1986; BOM 1989) provide opportunities for the establishment of S. obtusifolia in low competition conditions. With development continuing to expand, disturbed conditions in previously intact locations will increase, as will the weeds’ dispersal capabilities when humans inadvertently spread propagules into new areas.
The mild climate associated with the optimal habitat of coastal southern Queensland and north-eastern New South Wales also may result in S. obtusifolia germinating year round. Mackey et al. (1997) report that in Queensland, S. obtusifolia has the capacity to germinate at any time of the year if conditions are warm with sufficient rainfall. Senna obtusifolia populations occurring in the far north of Australia are less likely to support continual germination, due to the distinct dry season that they experience (BOM 1989). Such a lack of moisture restricts germination and/or prevents maturation (authors’ pers. observation). The more southern locations, however, do not experience this same degree of seasonality (BOM 1989) and, theoretically, conditions could support germination and growth at any time of the year.
Potential also exists for S. obtusifolia to establish in locations outside of the predicted distribution. Although variable land use practices often will preclude a species from an otherwise favourable habitat (Mack 1996), they also can result in its expansion into a normally unsuitable environment. Both Models 1 and 2 indicate that insufficient moisture and dry stress largely are limiting the distribution of S. obtusifolia to coastal regions of Australia. Given that a large proportion of land in the western regions of Queensland, New South Wales and Victoria is used for agricultural or pastoral purposes, water stores and irrigation points may provide sufficient moisture to enable localised population persistence. Similar range
Chapter 3 Potential Geographic Distribution - 56 - expansion as a result of additional water has been recorded with other invasive plants; e.g., Senecio madagascarensis (Sindel and Michael 1992), Abutilon theophrasti Medik. (Holt and Boose 2000), Cryptosegia grandiflora (Kriticos et al.
2003a) and Acacia nilotica (Kriticos et al. 2003b).
Whether or not S. obtusifolia can fulfil its entire potential distribution remains unknown. To understand the performance of S. obtusifolia under sub-optimal conditions would be extremely beneficial when considering management priorities.
This is particularly relevant to how a predominantly tropical weed will perform in the cooler climates of southern Australia. Patterson (1993) commented that the major impact of S. obtusifolia as a troublesome species was confined mostly to the southern USA, as the more temperate regions have unfavourable day/night temperature ratios to enable maximum growth. Perhaps this also could apply to any
S. obtusifolia that could persist in southern NSW and Victoria. Winter temperatures in this region can be low (BOM 1989), which may force the plant to be an irregularly occurring ephemeral, rather than a serious weed (Sutherst 2003). However, in view of the possible ongoing evolution of cold tolerant ecotypes in the USA, the impact of the weed in cool regions should not be underestimated and the movement south be observed closely. Devoting resources towards determining the genetic diversity of
S. obtusifolia already in Australia in comparison with that present in the USA would be beneficial to understanding the threat of the weed and to assist in the development of prioritisation and management decisions commensurate with its ecological and economical threats.
The need for containment as a management strategy is reinforced by the models to prevent the uninterrupted spread of S. obtusifolia. Until more reliable and cost efficient containment strategies are devised, land managers of areas at immediate risk of invasion need to be vigilant in detecting the appearance of new S. obtusifolia
Chapter 3 Potential Geographic Distribution - 57 - populations (Moody and Mack 1988). Regular surveillance of properties, particularly along watercourses, will facilitate identification of newly established populations and simple quarantine procedures involving stock, vehicles, etc. will restrict dispersal to new areas. Land managers should manage their land immediately to ensure that overgrazing does not disturb the susceptible land systems excessively, as this would provide optimal conditions for establishment (Anning et al. 1998; Parsons and
Cuthbertson 2000). Upon detection, small-scale eradication protocols need to be implemented before the plants set seed. Once a soil seed bank has formed, long term control becomes increasingly difficult (Teem et al. 1980; Mackey et al. 1997).
To assess fully the potential impact of S. obtusifolia in Australia, quantitative studies are required to investigate population growth and reproductive rates under a range of environmental conditions and disturbance regimes.
3.5 Progress Towards Aims of Thesis
The eco-climatic model developed within this chapter has enabled a rapid risk
assessment of S. obtusifolia at the Australia-wide level of scale to be completed.
Such modelling determined the maximum geographical distributional limits of S.
obtusifolia as determined by climate, highlighting that within Australia moderate
levels of moisture are necessary for the long-term persistence of the weed. Given
that much of the predicted region is yet to be inhabited by the weed, this model is
clearly beneficial for increasing awareness amongst both land and monetary
managers of the potential impact this weed may have in the future. However, the
level of detail attained by this model, using as it does such a broad geographic scale,
does not enable much further information about the invasion dynamics or the on the
ground management of S. obtusifolia to be derived. For this reason it is necessary to conduct investigations into S. obtusifolia at finer scales, which is completed in the following chapters.
Chapter 4
Evaluation of the Use of Remote Sensing to Map Senna obtusifolia
in the Lockhart River Study Area
Statement of Co-authorship
This chapter is based on a technical report prepared under contract for this project.
Elizabeth Dunlop - 30% contribution. Assisted in some analyses and produced the final manuscript.
Angela Murray – 70% contribution. Undertook all remote sensing analyses used and assisted and supervised manuscript production. Chapter 4 Evaluation of Remote Sensing - 59 -
4.1 Introduction
A critical component in controlling plant invasions is identifying and protecting areas that are at high risk of future invasion (Hobbs and Humphries 1995). The ability to accurately delineate the spatial extent, severity and intensity of the invasion provides important baseline information for monitoring future expansion, the effect of management and can identify targets for control (Byers et al. 2002; Moor 2003;
Underwood 2003).
Remote sensing technologies offer a number of prospects to assess weed distributions in new habitats and across extensive land areas. Whilst ground based surveys can provide an accurate estimate of a species abundance and distribution, it is time consuming and labour intensive when compared to the short periods of time needed by remote sensors to attain images of large areas (Shaokui et al. 2006).
The most common forms of remote sensing are aerial photography and multispectral and hyperspectral sensing, with sensors carried by either aircraft or satellites (Vande
Castle 1998). These approaches differ in their resolutions (Vande Castle 1998) and, with respect to weeds, to date there have been two main approaches to detecting the target species from their surrounds (Underwood et al. 2003). These are: (i) imagery with high spatial resolutions, but with low spectral resolution such as black and white photographs (aerial photography); or (ii) using digital images with increased spectral resolution but less refined spatial resolution (multispectral)
(Underwood et al. 2003).
Mapping weed populations by remote sensing has so far produced mixed results, with some studies achieving successful delineation of their target species and the locations of specific cover types (e.g. Lass et al. 1996; Lamb et al. 1999; Lass et al.
2002; Pena-Barragán et al. 2006). Failures also exist, where although the species is
Chapter 4 Evaluation of Remote Sensing - 60 - identifiable by remote sensors, the level of scale or the size of the infestation at which the target species can be accurately identified is inappropriate and/or costly
(e.g. Menges et al. 1985; Richardson et al. 1985; Carson et al. 1995; Medlin et al.
2000). Despite difficulties such as these, the overall attractiveness and potential benefits of this form of mapping is large which lead researchers to continue developing new approaches and applications.
Identifying all populations in S. obtusifolia in far north Queensland is a logistically difficult task. Even in the immediate environments of Lockhart River the lack of roads, or quality of existing tracks, makes access difficult. In more remote areas, it is nearly impossible to identify country infested by S. obtusifolia using traditional ground surveys. To address this problem, the use of remote sensing options was explored.
Considering the morphological traits of S. obtusifolia, and the large infestations present in the Lockhart River area, remote sensing technologies were considered a priori as having good potential for distinguishing S. obtusifolia from other habitat types in the region. Because S. obtusifolia grows in large monospecific stands, and most often forms distinct invasion boundaries with the vegetation surrounding infestations it was hoped that S. obtusifolia will display a clear and consistent spectral reflectance different from that of other cover types. However, due to the annual nature of its life cycle, S. obtusifolia changes form throughout the year and this in turn will alter its spectral characteristics over time (Everitt et al. 1987; Gibson
2000). This is an important feature of the plant that needs to be carefully considered when examining the data.
The primary aim of this component of the project was to determine if remote sensing
techniques could be successfully employed to accurately map the spatial distribution
Chapter 4 Evaluation of Remote Sensing - 61 - of S. obtusifolia in the Lockhart River region. The techniques developed needed to be objective, repeatable and adaptable, so that they could be confidently used to extrapolate the mapping over much broader regions. This evaluation includes preliminary feasibility assessments of Landsat 7 ETM+ multispectral satellite imagery and aerial photographs for the identification of S. obtusifolia infestations. Both data
sources were assessed to determine the detail required, in terms of both spatial and
spectral resolution, to adequately detect S. obtusifolia against surrounding
vegetation types.
4.2 Method
4.2.1 Landsat 7 ETM+ multispectral satellite imagery
4.2.1.1 Software
The satellite image processing software package, ER Mapper (Version 6.3) was selected to support the image processing requirements. ER Mapper is a robust and well documented software which is capable of image enhancement (e.g. contrast stretch, digital filtering), image classification (e.g. identification of spectral signature, unsupervised and supervised classifications), information extraction and image restoration in the case of data not being georectified (Civco 1996; US Army Corps of
Engineers 2002).
4.2.1.2 Imagery
Imagery was acquired from the Queensland Department of Natural Resources,
Mines and Water (NRM&W) (Figure 4.1). NRM&W distribute imagery collected for the State-wide Land Cover and Trees Study (SLATS) project at a significantly
Chapter 4 Evaluation of Remote Sensing - 62 - reduced cost to other available satellite imagery. The multispectral bands (bands 1-
5, 7, pixel size 30m) and the panchromatic band (band 8, pixel size 15m) were purchased. Both data sets used the GDA94 datum, with the multispectral dataset being orthorectified and the panchromatic dataset only being georeferenced. Data was imported into ER Mapper to be processed.
Landsat-TM is the most common type of image-based remote sensing data used by ecologists as it represents an effective compromise between spatial, temporal and spectral scale and is also cost effective (Vande Castle 1998). Providing data in seven spectral regions (green, blue, red near infra-red, middle infra-red as well as thermal channels), each image is resolved with 30 by 30m ground resolution
(Harrison and Jupp 1989; Vande Castle 1998). At this resolution, sufficient detail can be detected to provide meaningful inferences at the stand level and sufficient spectral information to separate coarse classes of data (Carson et al. 1995).
Temporal variation in S. obtusifolia has implications for remote identification, as its spectral signature will alter throughout the year (Everitt et al. 1987; Gibson 2000). It is likely that the plant will be most easily delineated at peak maturity in mid to late autumn (April-May) and virtually undetectable around late spring to early summer
(October–December) when the plant is in the late stages of senescence. The principal limitation of using the SLATS imagery is that only a limited number of datasets are available for the Lockhart River study area. Of the three datasets indicated as being available for the study area (27 October 2001, 21 August 2000, 7
October 1991), the dataset collected in August of 2000 was selected for use.
Chapter 4 Evaluation of Remote Sensing - 63 -
Figure 4.1 Iron Range Landsat ETM+ image, displayed using the bands 3, 2 and 1 (i.e. red, green and blue). The image shows the eastern part of Cape York Peninsula from the southern end of the Jardine River National Park to the southern end of the Hemming Range, south of Lockhart River. The Coral Sea is to the east (right). The scene covers 185km2
4.2.1.3 Image classification
Image classification is the process whereby values associated with each pixel in an image are averaged and mathematically assigned to discrete categories representative of specific features or conditions possessing similar spectral
Chapter 4 Evaluation of Remote Sensing - 64 - reflectances. Classification is achieved by two techniques - unsupervised and supervised classification (Anderson et al. 1993; Verbyla 1995; Gibson 2000).
Unsupervised classification is where pixels in an image are assigned to spectral
classes without the user having any foreknowledge of the existence of those classes
(Richards and Jia 2006). This form of classification is performed commonly using
clustering methods which are used to determine the number and location of the
spectral classes into which the data falls and to determine the spectral class of each
pixel (Richards and Jia 2006). Supervised classification acts to achieve the same
endpoint as the unsupervised method; however it can prove more accurate by
removing some of the subjectivity associated in selecting classes (Verbyla 1995).
Under this technique, the co-ordinates of locations of known populations or cover
types (training fields) are recorded and entered into the classification process,
thereby determining representative spectral values for each cover type. Each pixel
in the image is then classified on the basis of its similarity to those spectral values
derived from the training fields (Carson et al. 1995; Verbyla 1995; Richards and Jia
2006).
For the purposes of identifying S. obtusifolia in the Lockhart River region, both unsupervised and supervised classification techniques were employed, using the
Landsat 7 ETM+ multispectral imagery. The image processing was focussed on the
Lockhart River study area illustrated in Figures 4.1, 4.2 and 4.3).
4.2.1.3.1 Unsupervised classification
Due to the large size of the Landsat scene (434 Mb) (Figure 4.1), and unsuccessful initial attempts at the unsupervised classification, the scene was clipped to 72 Mb to represent only the Lockhart River region (Figures 4.2 and 4.3). Using the values produced in a correlation matrix for S. obtusifolia, the spectral bands 2, 3 and 4 were
Chapter 4 Evaluation of Remote Sensing - 65 - selected for use in the classification procedure. These bands represented the smallest correlations in the data set which, when used, maximised the diversity of the information available. The Self-Organising (ISO) clustering technique was used to perform the unsupervised classification, which was completed after 40 iterations.
4.2.1.3.2 Supervised classification
The supervised classification commenced by defining training regions which essentially control or ‘supervise’ the classification process. The results of the classification process are heavily dependent on the quality of the training regions used, so it was important that they were good representatives of the features being mapped. Lillesand and Keifer (1979) suggest that at lest n + 1 pixel observations must be collected for each training pattern, where n in the number of spectral bands.
Therefore, because all six mulitspectral bands were used for the supervised classification of S. obtusifolia, a minimum of 7 pixel observations were required.
Three training areas were identified for S. obtusifolia which covered 96 pixels (6 hectares). Using these regions the classification was conducted using the statistically based classifier, the maximum likelihood standard neighbour technique.
Chapter 4 Evaluation of Remote Sensing - 66 -
Figure 4.2 Lockhart River study area (143O 15’E, 12 O 50’S, 143 O 22’E, 12 O 44’S). (Source: Royal Australian Survey Corps 1:50,000 topographic maps: Cape Weymouth (7572-1) and Iron Range (7572-2). Currency approximately 1991)
Chapter 4 Evaluation of Remote Sensing - 67 -
Figure 4.3 Landsat ETM+ multispectral imagery over the Lockhart River area. Bands 3, 2 and 1 are displayed as red, green and blue to give a natural colour display.
Accuracy specifications for Landsat 7 ETM+ imagery were determined by collecting control data in the field within the Lockhart River region. Given that the pixel size of
Landsat imagery is 30m, and that S. obtusifolia patches would rarely align exactly within the boundaries of a single pixel, populations used for ground control data had minimum dimensions of 2 pixels (i.e. 60 x 60 m). Points recorded were always further apart than 65 metres to ensure that unique pixels could always be identified.
This minimum distance guarantees that if a ground control point is recorded at the
Chapter 4 Evaluation of Remote Sensing - 68 - edge of a pixel, that the next nearest ground control point will be no closer than the centre of the neighbouring pixel. Points were collected in areas with clear overhead visibility to reduce the level of error associated with handheld GPS receivers.
Other local vegetation types and land covers were also assigned training regions in as part of the delineation of training regions to identify S. obtusifolia. This information was then used to eliminate whole areas of the image from being S. obtusifolia infestations by determining that it is clearly not the weed.
4.2.2 Aerial Photographs
Thirteen aerial photographs of the Cape Weymouth and the Lockhart River area were sourced from the Queensland Parks and Wildlife Services (Environmental
Protection Agency, Queensland Government). The black and white photographs were captured on the 11th of September, 1991 to a scale of 1: 86 600. The photographs were scanned into a digital format and examined systematically for the presence of S. obtusifolia populations.
4.2.3 Vector Data
In addition to the remote sensing data (Landsat ETM, aerial photographs), the availability and quality of relevant vector data was also investigated. Vector data provides useful information about location, context and the local environment, and can significantly assist the interpretation and analysis of remote sensing products. In the case of S. obtusifolia mapping using remote sensing data, localities, roads and rivers (i.e. vector data) will be needed for map reading and orientation, while environmental vector data such as vegetation and soils maps will be useful to help
Chapter 4 Evaluation of Remote Sensing - 69 - analysis of spatial patterns S. obtusifolia populations. Vector data of reasonable quality is available for the Lockhart River study area. These include:
• Topographic Data (1: 250 000) (Available from Geoscience Australia)
• Topographic Data (1: 100 000) (Available from Geoscience Australia)
• Topographic Data (1: 50 000) (Available from Geoscience Australia) (Figure
5.2)
• Digital Vector Data (1: 50 000) (Available from Defence Imagery and
Geospatial Organisation)
• Vegetation Vector Data (1:250 000) (Available from the Queensland
Herbarium)
• Soils Vector Data (1:900 000) Available from the Queensland Department of
Natural Resources and Mines)
Vector data is not specifically referred to again in the results section.
4.3 Results
4.3.1 Landsat 7 ETM+ multispectral satellite imagery
4.3.1.1 Imagery
The August 2000 Landsat scene used for the analysis possessed a significant data quality issue as it contained a lot of cloud cover. The multispectral sensor cannot penetrate cloud cover and therefore many features of the image are blocked.
Although the entire scene possesses only around 10% cloud cover (Figure 4.1), a
Chapter 4 Evaluation of Remote Sensing - 70 - large proportion of it tends to occur over the main area of interest; the area along the eastern coastal strip of Cape York Peninsula.
4.3.1.2 Unsupervised classification
The ISO technique produced 67 different classes (Figure 4.4). Approximately 37 of these generated classes described cloud or cloud shadow. Senna obtusifolia was clearly identified in a single class (class 19) which was shared only with rainforest.
The rainforest identified with S. obtusifolia populations appeared to be located on hilly eastern-aspect locations that were brightly illuminated by the late morning sun
(Landsat imagery is always captured at 9:30 am in eastern Australia).
4.3.1.3 Supervised Classification
The statistics produced from the training regions are shown in Table 5.1. The range of reflectance values determined for S. obtusifolia indicate that the plant has very high reflectance values in the near infrared band (4). The value range extended from 108 to 138 micrometres (on the nominal intensity range of 0 to 255), which means S. obtusifolia would only have partial overlap with Brachiaria decumbens, rainforest, cloud and sand. Due to the highly reflective nature of cloud and sand, these elements possess distinguishing features in other spectral bands (i.e. very high values). Therefore, it was expected that only B. decumbens and rainforest would be an issue when separating out the S. obtusifolia class. Using the red spectral band (3), B. decumbens can be separated from S. obtusifolia. This leaves
only some of the areas of rainforest being mixed with the S. obtusifolia spectral
signature, which was observed on the supervised classification images.
Chapter 4 Evaluation of Remote Sensing - 71 -
Lockhart Airstrip River
Figure 4.4 The unsupervised classification of Landsat 7 ETM+ imagery using bands 2, 3 and 4. The mixed Senna obtusifolia/rainforest class is shown in light blue. Other vegetation is represented in green. The ocean and cloud shadows are mauve and clouds are pink.
There is a large amount of variation in the near infrared band values (band 4) for S. obtusifolia and rainforest as seen by the standard deviation values in Table 4.2.
Combining this information with that ascertained from Table 4.1, it can be seen that
S. obtusifolia and rainforest spectral values actually only possess a small amount of overlap, although it does occur across all bands. For example, in band 4, 95% (two standard deviations) of pixel values occur between 112.6 and 138 for S. obtusifolia and between 42 and 106 for mountainous rainforest. Based on the assumption that the pixel value ranges are normally distributed, this leaves less than 5% of pixel
Chapter 4 Evaluation of Remote Sensing - 72 - values to overlap between the two classes. Nevertheless, rainforest makes up a large proportion of the Lockhart River study area, so even less than 5% of the total remains a sizeable area. The rainforest areas identified as S. obtusifolia can be identified along the Tozer Range to the west of Lockhart River (Figures 4.5 and 4.6).
Airstrip Lockhart River
Figure 4.5 Map of the supervised classification results over the Lockhart River study area. Areas classified as Senna obtusifolia are circled in red.
Chapter 4 Evaluation of Remote Sensing 73
Table 4.1 Statistics of the pixel values obtained from the supervised classification training regions for a Landsat 7 ETM+ band image of Lockhart River.
Supervised Classification Training Data ~ Lockhart River Study Area Landsat 7 ETM+ band Band 1 Band 2 Band 3 Band 4 Band 5 Band 7 Min Max Min Max Min Max Min Max Min Max Min Max Wavelength (um) range 0.45 D 0.52 0.52 D 0.60 0.63 D 0.69 0.76 D 0.90 1.55 D 1.75 2.08 D 2.35 Typical Applications Water body penetration, soil- Veg discrimination, veg Chlorophyll absorption Veg type, veg vigour, biomass Veg and soil moisture Mineral and rock type veg discrimination, forest type vigour & cultural feature id. region for plant spc id. Also content. Water body content. discrimination. Also veg mapping & cultural feature id. cultural feat id. delineation and soil moisture moisture content. discrimination.
Band Description Visible Blue Visible Green Visible Red Near Infrared Mid Infrared Mid Infrared Pixel Value: Intensity of Radiant Energy Band 1 Band 2 Band 3 Band 4 Band 5 Band 7 Vegetation Cover Min Mean Max Min Mean Max Min Mean Max Min Mean Max Min Mean Max Min Mean Max Sicklepod 33 36 39 24 27 30 16 19 20 108 126 138 68 75 90 19 21 27 Brachiaria (weed) grassland 36 40 47 29 33 36 23 28 31 101 107 111 85 93 99 25 28 32 Imperata (native) grassland 37 39 43 27 29 30 22 23 25 84 89 97 89 94 99 29 31 32 Mangrove - trees 26 29 33 18 20 21 11 12 14 61 83 96 19 24 36 6 8 12 Mangrove - wetland 31 35 43 21 24 28 15 24 28 48 55 63 27 42 51 11 15 19 Rainforest 24 31 58 18 22 36 11 14 32 62 86 128 27 50 78 9 15 30 Rainforest - mountainous 22 28 32 17 20 24 10 14 19 45 74 119 26 44 76 9 14 32 Woodland - closed 31 36 43 20 23 26 13 18 22 54 63 78 39 49 64 12 16 21 Woodland - medium 32 38 48 23 26 30 17 22 28 56 67 77 54 68 81 17 23 31 Woodland - scattered 35 40 48 24 27 32 20 25 33 56 65 91 49 80 99 17 28 38 Woodland - scarse 35 40 45 25 26 30 21 25 30 51 57 68 70 82 104 25 30 42
Other Land Covers Airport tarmac 41 45 52 26 29 32 26 30 34 43 50 57 62 86 110 33 37 43 Burnt areas 32 41 56 20 25 27 16 23 28 36 44 53 39 52 72 18 25 34 Cloud 64 205 255 39 112 150 33 114 147 29 131 248 40 173 249 24 95 141 Cloud shadow 23 35 78 15 21 43 9 15 41 20 37 77 13 25 76 6 10 40 Cloud over water 90 174 253 43 87 141 35 89 146 32 85 150 30 125 212 22 74 120 Rocky exposed or barren 35 52 103 24 35 62 22 38 75 37 64 96 64 110 180 25 46 90 Sand 97 229 240 53 137 144 59 137 141 80 175 218 83 234 243 31 144 154 Settlement 41 54 92 30 39 62 31 45 76 61 77 93 95 121 165 38 53 98 Water - deep 37 49 66 18 24 32 11 13 17 11 12 15 7 9 14 4 7 10 Water - shallow or reef 38 68 102 25 42 66 17 38 69 13 31 75 1 11 80 2 7 27 W ater - inland 28 34 44 16 19 26 9 13 21 10 16 41 0 11 36 0 7 14
Chapter 4 Evaluation of Remote Sensing - 74 -
Table 4.2 Standard deviation of the pixel values obtained from the supervised classification training regions for a Landsat 7 ETM+ band image of Lockhart River.
Supervised Classification Training Data ~ Lockhart River Study Area Landsat 7 ETM+ band Band 1 Band 2 Band 3Band 4Band 5 Band 7 Band Description Visible Blue Visible Green Visible Red Near Infrared Mid Infrared Mid Infrared
Vegetation Cover Standard Deviation of Reflectance Values Sicklepod 1.9 1.3 2.0 6.7 5.3 2.3 Brachiaria (weed) grassland 2.1 1.3 1.9 2.5 3.8 1.7 Imperata (native) grassland 2.1 1.2 1.4 3.7 2.8 1.0 Mangrove - trees 1.1 0.8 0.8 5.6 3.2 1.1 Mangrove - wetland 2.3 1.5 2.0 3.5 5.0 1.9 Rainforest 2.2 1.3 1.3 9.2 5.4 1.6 Rainforest - mountainous 1.8 1.4 1.4 16.0 8.7 2.4 Woodland - closed 2.8 1.2 1.6 3.2 4.2 1.7 Woodland - medium 3.0 1.4 2.1 3.2 5.4 2.5 Woodland - scattered 2.6 1.3 2.0 5.6 7.1 2.9 Woodland - scarse 1.7 0.8 1.4 3.2 5.6 2.7
Other Land Covers Airport tarmac 2.4 1.4 2.2 3.8 12.5 3.0 Burnt areas 4.4 1.9 2.1 2.8 6.3 2.8 Cloud 42.7 29.0 29.8 46.9 49.4 23.9 Cloud shadow 5.7 2.6 3.0 8.4 7.6 2.9 Cloud over water 37.1 20.3 24.4 23.8 33.9 18.7 Rocky exposed or barren 12.1 8.0 10.7 9.4 19.3 10.8 Sand 28.9 17.1 12.3 26.6 22.6 20.0 Settlement 9.0 5.8 8.6 6.4 13.2 8.6 Water - deep 7.4 3.7 1.2 0.6 0.9 0.7 Water - shallow or reef 10.9 7.0 9.0 14.5 8.8 2.3 Water - inland 2.7 1.8 2.2 5.7 4.3 1.6
Chapter 4 Evaluation of Remote Sensing - 75 -
Figure 4.6 Detailed map of the supervised classification results over the Lockhart River study area.
Chapter 4 Evaluation of Remote Sensing - 76 -
4.3.2 Aerial Photographs
The aerial photographs were found to be of bad quality. Images possessed numerous marks across them and had bad glare and shadowing. These in combination reduced the effective contrast of the images in some areas (Figure 5.7).
This poor quality is a significant issue for the identification of S. obtusifolia populations using the aerial images, as the area covered by the infestations will be at most only a few millimetres on the photographs. Detail on aerial photographs therefore needs to be clear and consistent to support the accurate identification of the weed. Extensive ground truthing would be required to support the accurate mapping of S. obtusifolia using such image sets. Due to these difficulties, the images were not used to identify S. obtusifolia infestations.
Airstrip
Lockhart River
Figure 4.7 An example of the aerial photographs taken of the Lockhart River area.
Chapter 4 Evaluation of Remote Sensing - 77 -
4.4 Discussion
The findings of this preliminary work demonstrate some support for the use of remote sensing techniques to undertake landscape-scale mapping of S. obtusifolia
infestations. The conditions of this statement are that the training areas used are fair
representations of S. obtusifolia infestations and that sufficiently cloud-free imagery
can be obtained over the areas of interest. Although such work promises a useful
outcome, it will require extensive modifications to be of any real value in mapping the
current distribution of S. obtusifolia or for use in detecting new and/or localised
populations. The data acquired would need to be of far greater quality and of a scale
more amenable to delineating populations smaller than well established (i.e. large)
infestations. Other potential data sources and methods to overcome these
difficulties are described in detail later in this discussion.
4.4.1 Landsat ETM+ Multispectral Satellite Imagery
The preliminary classification using the Landsat ETM+ imagery appears promising in being able to physically detect S. obtusifolia. Both the supervised and unsupervised classification techniques identified S. obtusifolia populations with limited spectral overlap with other vegetation types. With a greater quantity of training regions, the range of reflectance values of S. obtusifolia may decrease and, with some extensive ground truthing, it may become possible to identify S. obtusifolia as a separate entity.
Conversely, the addition of training regions may increase the range of reflectance values making the separation from rainforest areas more difficult. One solution to separating the two vegetation types may be to use vegetation mapping to mask out areas of rainforest. The Queensland Herbarium has completed vegetation community mapping at the scale of 1:250,000 across the Cape York Peninsula
(EPA, 2006). Working on the assumption that S. obtusifolia does not encroach on
Chapter 4 Evaluation of Remote Sensing - 78 - rainforest (Chapters 5 and 6), the rainforest boundaries delineated by such mapping should reduce the area of overlap considerably. Additionally, other land covers such as B. decumbens and I. cylindrica grasslands and burnt areas could realistically be identified without too much further investment in time and resources. This data may provide useful information about the distribution of vegetation communities and, given that S. obtusifolia will be limited by some habitat types, it may contribute to the identification of future potential infestation sites.
Similarly, the remotely sensed data could be easily integrated with other spatial technologies, namely GIS, enabling a more in-depth assessment of population distribution on regional scales as GIS programs allow the incorporation of vector data into the analysis (Child and de Waal 1997). As more details concerning the biology and invasion characteristics of S. obtusifolia come to light, a detailed population distribution map created by remote sensing can be overlaid with regional geographic characteristics. These include topography, streams, watersheds, land usage, soil types and other vegetation types, which will limit the area in which the weed will be present. It further allows for analyses concerning the position and impact of S. obtusifolia infestations in relation to certain environmental features
(Dewey et al. 1991; Child and de Waal 1997).
The primary limitation involved in the classifications of the Landsat images was the degree of cloud cover. This has implications for the analysis as the multi-spectral sensors cannot penetrate cloud cover, blocking sections of the image features
(Harrison and Jupp 1989). Obtaining cloud free images is difficult in the Lockhart
River region as only approximately 13% of the year is cloud free (BOM 2002). This difficulty is particularly problematic during the wet summer months where S. obtusifolia vigour is high (Mackey et al. 1997) and therefore most favourable for analysis. The cloud cover most likely occurs in this area due to the local ranges, the
Chapter 4 Evaluation of Remote Sensing - 79 - northern tip of the Great Dividing Range, and the orientation and proximity of the ranges to the moist maritime influence of the Coral Sea. Using the SLATS Landsat imagery was a compromise between cost and choice of data. The data available through the SLATS archive is limited to only the scenes selected and processed for the SLATS project. With further funding, data could be sourced from the full Landsat imagery archive (ACRES) which would increase the probability of obtaining less cloud affected data.
The other major limitation for the supervised classifications was the number of pixel observations used and the availability of adequate training regions. The usefulness of the supervised classification can be substantially influenced by the number and size of training fields and the extent of temporal and structural variation of the cover types within them (Verbyla 1995). In the current assessment, the number of pixels was satisfactory, although more pixels and training regions from across the entire study area would ensure that the results are representative of the entire range of plant conditions (Verbyla 1995). The spectral signature that is identified as a result can then be more confidently used to extend S. obtusifolia mapping across the entire region. In addition, the use of only one dataset is also a limitation in comparison to the use of multi-temporal datasets, particularly given that the plant is an annual and changes its form throughout the year. Ideally multi-temporal image datasets should be used as these could represent the different plant stages of the life cycle (Gibson
2000).
Even with these recommended changes, the results obtained from Landsat imagery may have limited practical value. This is due mostly to the spatial resolution at which
Landsat operates. A pixel size of 30m is very coarse and therefore the infestations being detected are extremely large (minimum of 60m2) and are obviously very well established, dense and widespread. This has limited use when attempting to identify
Chapter 4 Evaluation of Remote Sensing - 80 - incipient infestations to target for control and containment measures. Presumably when first entering an area, S. obtusifolia populations will remain interspersed with other species, until its numbers grow sufficient to dominate the reflectance spectrum
(Carson et al. 1995) through the creation of monospecific stands. Unfortunately, for managers concerned with containment and spot eradication, it is the small, mixed stands of S. obtusifolia early in the invasion process that would need to be identified giving any maps produced only a general applicability (Underwood et al. 2003). This issue of spatial resolution as well as the limited number of spectral bands have been predominant factors preventing the successful mapping of fine scale weed populations using multispectral satellite imagery (Underwood et al. 2003). However, international examples do exist where weed distributions have been successfully mapped and predicted (e.g. Isatis tinctoria, Dewey et al. 1991; Bromus tectorum,
Bradley and Mustard 2005) and population densities recorded (e.g. Hieracium pratense, Carson et al. 1995) which suggests that technological issues can be overcome with perseverance and/or adequate funding.
4.4.2 Aerial photography
The aerial photographs did not produce any useful results due to their poor quality
and, as with the satellite imagery, they were at an unsuitable scale for identifying S.
obtusifolia infestations. Underwood et al. (2003) suggest that aerial photography can
be a useful form of remote sensing, with a number of examples existing whereby
aerial photographs have mapped weed distributions by capitalising on unique visual
characteristics at certain times during the plants life cycle (e.g. Euphoriba esula,
Everitt et al. 1995 and Tamarix chinensis, Everitt et al. 1996). Underwood et al.
(2003) believe that aerial photographs are useful as typically they are a relatively
inexpensive method of remote sensing, they have high spatial resolution (0.1-2m)
and there are often extensive amounts of archival data available (i.e. multi-temporal
Chapter 4 Evaluation of Remote Sensing - 81 - data sets). However, they also outline the disadvantages of the approach which includes relying on the weed to possess unique visual characteristics and manual labour for processing is extensive and requires experience. The combination of these two points limits the feasible spatial area from which data can be collected
(Carson et al. 1995; Medlin et al. 2000).
Recommendations for any future mapping of S. obtusifolia using aerial photography
would be to collect higher quality colour photographs at a finer scale. A consistent
high quality image will enable the application of systematic image classification
techniques to be consistent and reliable. Colour photographs would also provide
more information about the spectral characteristics of S. obtusifolia and thus greatly
assist with detecting populations. Collection of finer scale photographs would
additionally provide more useful information to support the mapping of much smaller
S. obtusifolia patches. The photos obtained for this study are at a scale of 1: 86 600.
Ideally the scale should be approximately 1:25 000, which gives a minimum area for
interpretation of 0.1 hectares.
4.4.3 Other forms of remote sensing
Other forms of data exist that may assist in the delineation of S. obtusifolia
populations. The most obvious and most sought after method is airborne
hyperspectral imagery. This form of imaging has increased spatial resolution and
much finer spectral resolution, offering up to 224 wave bands. These attributes offer
an enhanced potential for mapping invasive plant species (Underwood et al. 2003).
Hyperspectral imagery is different to multi-spectral imagery because information is
gathered using a high-resolution spectrometer rather than a radiometer. Using the
detailed wavelength-by-wavelength data provided by the spectrometer, spectral data
can be assessed to determine their constituent components and read the spectral
Chapter 4 Evaluation of Remote Sensing - 82 - signature of the materials in the image (Gomez 2001). With the additional depth of spectral (and spatial) data provided by the hyperspectral imagery, it is anticipated that greater detail would be obtained with regards to S. obtusifolia and its distribution. This level of detail would be particularly useful when discriminating between image features that share similar spectral characteristics to S. obtusifolia
(i.e. the rainforest).
The methods currently used for target detection in hyperspectral imagery require that the spectral signature of the target species has already been developed (Bruce
2002). Therefore, before such technology could be used successfully, spectral signatures that represent S. obtusifolia throughout the entirety of its life cycle and under different conditions (e.g. dry, wet) would need to be developed. A spectral signature for S. obtusifolia has actually been developed in the USA (Bruce 2002)
(Figure 4.8), however, the conditions under which it was determined are not presented (e.g. was the plant flowering on wet soils?). Uncertainty therefore exists as to how well the signature could be expected to match hyperspectral datasets collected under different conditions (e.g. Australian conditions).
The commercial availability of low cost hyperspectral imagery remains the critical limiting factor for adoption of this technology (Vande Castle 1998). In 2001, the cost of accessing airborne sensors was upwards of $US50 000 per flight (Gomez 2001).
However, with the increasing growth of this technology, it may be prudent to at least be aware of the capabilities of the hyperspectral tools to ensure that when viable options become available that they are capitalized on. Techniques being developed internationally will certainly contribute to the accelerated development of approaches for mapping natural populations of S. obtusifolia under Australian conditions.
Chapter 4 Evaluation of Remote Sensing - 83 -
Figure 4.8 The spectral signature of Senna obtusifolia (sicklepod) and the similar broadleaf weeds of kudzu (Pueraria Montana var lobata), tropapple (Solanum viarum), horseweed (Conyza canadensis) and dogfennel (Eupatorium capillifolium) (Taken from Bruce 2002).
4.4.4 Vector data and other spatial technologies
An additional benefit of mapping using remote sensors is that it can be easily integrated with other spatial technologies, namely GIS, which can produce a more in- depth assessment of population distribution. Vector data compliments remote sensing (raster) data by providing information about a multitude of local cultural and natural resource features. Such features include topography, streams, watersheds, land usage, soil types, roads and other vegetation types, which can be easily incorporated into the remotely sensed population distribution. Infestation position and impact can then be analysed in relation to the presence of certain environmental features (Child and de Waal 1997). Any known correlation can then be used for
Chapter 4 Evaluation of Remote Sensing - 84 - predictive purposes by highlighting proximate propagule sources and identifying other areas possessing similar environmental characteristics as being at risk of invasion (Dewey et al. 1991).
4.4.5 Recommendations
Given the pros and cons of each remotely sensed data type assessed above to
accurately detect the current distribution of S. obtusifolia in the Lockhart River
region, the following is a list of recommended specifications to improve the quality,
accuracy and usefulness of further remote sensing mapping of S. obtusifolia.
1. Use the pixel resolution of 30m to produce 1: 120, 000 scale mapping to support
broad-scale identification of large (>60m2) S. obtusifolia patches.
2. Collect imagery as close as possible to the April-May period to ensure that S.
obtusifolia plants are at full vigour. Collection of imagery from approximately
October through to December is not recommended as the plant would most likely
be undetectable.
3. Select images that are cloud free over the infestation areas, or if unavailable,
choose those with minimal cloud cover.
4. If accurate area, distance or directions measurements are required, ensure that
orthorectified imagery is used.
5. To support the accurate identification of infestations, use colour photographs or
multi-spectral imagery where possible.
6. Ensure that adequate ground control data is available to support any supervised
classification of imagery. It is recommended that data be representative of the
range of conditions that may be found across the image be collected. Generally,
the more ground control data the more accurate the result will be.
Chapter 4 Evaluation of Remote Sensing - 85 -
7. As a prerequisite for use of hyperspectral imagery mapping, establish a spectral
signature for S. obtusifolia, including the variations in signature due to natural
plant growth phases and climatic cycles.
8. As a prerequisite for use of hyperspectral imagery mapping, ensure that access
to adequate atmospheric correction data is available.
4.5 Progress towards aims of thesis
The remote sensing techniques undertaken in this chapter attempted to create a landscape scale image of the distribution of S. obtusifolia populations throughout the
Lockhart River region. Unfortunately, as discussed, the delineation of all S. obtusifolia populations in the region was not successful. Success at this level of scale would have been beneficial for a number of reasons including monitoring spread, identifying targets for control and allowing analysis to determine features of the landscape capable of influencing the invasion of S. obtusifolia. However, even if mapping at this scale had been successful, the level of detail is still insufficient to address fine scale environmental heterogeneity and consequently factors controlling local level individual and population success of S. obtusifolia in the Lockhart River region.
Chapter 5
The population dynamics of Senna obtusifolia in natural ecosystems of
northern Australia
The associated publication with this work is: Dunlop EA, Clarke AR and Wilson JC (2006)
The population dynamics of Senna obtusifolia in natural ecosystems of northern Australia.
Plant Ecology (in review).
The manuscript has been modified here to aid thesis continuity
Statement of Co-authorship
Elizabeth Dunlop - 80% contribution. Undertook experimental design, conducted all field work, undertook the statistical analysis and produced the manuscript.
Anthony Clarke – 10% contribution. Supervised statistical analysis and manuscript production.
John Wilson – 10% contribution. Supervised experimental design. Chapter 5 Population Dynamics - 87 -
5.1 Introduction
Achieving weed management on a long term, sustainable basis has become a desirable objective amongst land managers and a focus of weed scientists (Hobbs and Humphries 1995; Cousens and Mortimer 1997). Integrating traditional chemical, biological and mechanical means of control with the manipulation of ecosystem processes and restoration techniques (Groves 1989) can provide a foundation to steadily reduce weed densities, weed fitness and, accordingly, weed impacts
(Mortensen et al. 2000). Reducing weeds by manipulating ecosystem processes, for
example by increasing inter-specific competition and reducing recruitment
opportunities, means that the changing system can be left relatively undisturbed
(Swanton and Weise 1991), as opposed to one that has been grossly and rapidly
altered through mass weed mortality and/or removal (Cousens and Mortimer 1997).
Mass removal of weeds often creates ideal conditions for subsequent reinvasion by
the same or other weed species (Groves 1989; Hobbs and Humphries 1995).
Central to a successful long-term approach to weed management is an
understanding of both the internal and extrinsic population dynamics of the weed and
the surrounding plant community (Cousens and Mortimer 1997): it is at this
population level where an invasion can either fail or succeed (Parker 2000).
Weed populations possess a set of attributes fundamental to their functioning and
success and can be used to describe the state of the population and its response to
change (Cousens and Mortimer 1997). Exposing spatial and temporal patterns of
variation in characteristics such as weed density, reproductive output, dispersal
capabilities, rates of increase, population size and spatial limits, provides a
systematic view of the interactions of the invading species and an understanding of
the weed population necessary for making management programmes effective
(Parker 2000; Bin Bakar 2001). Further assessment and projection of demographic
Chapter 5 Population Dynamics - 88 - data enables weed populations to be explored in detail (Golubov et al. 1999), can
clarify important issues in control by identifying life history traits that most strongly
affect population growth (Hyatt and Araki 2006) and provides a tool to increase our
ability to understand and predict changes in weed populations over time (Gonzalez-
Andujar et al. 2005).
Considerable information regarding the biology and management of S. obtusifolia is available from international work; however, much of this is focussed on the weed in agricultural systems (eg Buchanan et al. 1978; Teem et al. 1980; Currey et al. 1981;
Hauser et al. 1982; Senseman and Oliver 1993; Brecke and Shilling 1996; Webster and Coble 1997). Few references are made to the temporal and spatial dynamics of the weed, nor can they explain the magnitude and behaviour of S. obtusifolia
invasion in natural ecosystems of Australia. Detailed research and surveys of
attributes of S. obtusifolia populations in Australia presents one method to overcome
this knowledge gap and assess its invasion dynamics (Bin Bakar 2001).
The principal aim of this study was to obtain basic information concerning the dynamics of S. obtusifolia populations occurring in natural floodplain ecosystems of far northern Australia. The characteristics of twelve S. obtusifolia populations were surveyed across an invasion period spanning three years. Each population occurred adjacent to one of four different habitat types (rainforest, Brachiaria decumbens grassland, Imperata cylindrica grassland and lowland woodland). Using this information it was aimed to characterise the spatial and temporal population dynamics of S. obtusifolia. Secondary objectives were to measure the advancement of S. obtusifolia populations into each neighbouring vegetation type and compare the results of this study with population attributes recorded in international agricultural ecosystems.
Chapter 5 Population Dynamics - 89 -
5.2 Method
5.2.1 Population Attributes
Senna obtusifolia forms identifiable “invasion fronts” with adjoining vegetation allowing easy delineation of discrete local populations (Figure 2.4a). During 2002, three transects at random intervals were run ten metres from the invasion front
(edge) back into each S. obtusifolia population. During 2003 and 2004, the transect number was increased to five to increase the accuracy of the estimates calculated in the data. Each transect was divided into two sections based on distance into the population (0-2 metres and 2-10 metres) (Figure 5.1). Within each division, one 500 x 500 millimetre quadrat was randomly placed and the following data recorded: mature S. obtusifolia stem diameter (basal area), height, stem density and seed pod number. Data were not collected in populations adjacent to lowland woodland during
2002 as S. obtusifolia infestations were not associated with this habitat type until
2003.
S. obtusifolia population
2-10 m
0 - 2 m Invasion Front ↓
Transect 4 Transect 1 Transect 3 Transect 5 Transect 2
Adjacent Habitat
Figure 5.1 Field design to measure the population dynamics of Senna obtusifolia. Five transects were laid from the invasion front ten metres into each population. Quadrats were laid randomly within 0-2m and 2-10m distances.
Chapter 5 Population Dynamics - 90 -
All attribute data except the height category was square root transformed to ensure the assumptions of the ANOVA were correct and to minimise the risk of heteroscedasticity. Data were firstly used to assess differences in attributes within a population - between the edge (0-2m) and interior (2-10m). Quadrat data within each distance band was averaged within each site across all years. A paired t-test was conducted to assess for variation in each population attribute between the edge and interior of S. obtusifolia populations.
Following the method outlined in Dunn and Clark (1987), a hierarchical or nested designed ANOVA was used to determine if within 2002, 2003 and 2004, attribute data differed between individual populations and between individual populations occurring adjacent to the four different habitat types. A two-way ANOVA was further conducted to evaluate any differences between years, given the effect of site on each of the population attributes. The analysis was conducted using sites 1-9 only due to the woodland sites (10 – 12) not appearing until 2003.
5.2.2 Seed Number
Senna obtusifolia plants produce copious amounts of seed (Retzinger 1984), making the assessment of reproductive output by counting seed unfeasible. Therefore the length of S. obtusifolia pods was examined to determine if it could be used as an alternative indicator of seed number. During 2002 and 2003, 752 seed pods were collected from multiple S. obtusifolia populations throughout the Lockhart River region. Pods were allocated into size classes based on 10mm increments of pod length and the corresponding numbers of seeds were recorded for each pod. A linear regression was used to determine the strength of the relationship between pod length and seed number. The relationship between pod length and seed number proved highly significant (F1, 750 = 6633, p = <0.001, R² = 0.898, y = 0.183x + 6.5164)
Chapter 5 Population Dynamics - 91 -
(Figure 5.2). Average seed number for each pod size class was derived using the regression output, enabling the total number of seeds for the pods collected from quadrats to be estimated.
50
45
40
35
30
25
20 Seed number Seed
15
10
5
0 0 50 100 150 200 Pod length (mm) (size class midpoint)
Figure 5.2 The relationship evident between Senna obtusifolia pod length and S. obtusifolia seed number. Pod length is represented by the midpoint of pod size categories occurring in 10mm increments.
5.2.3 Soil Seed Reserve
Using the same field design as that described in section 5.2.1 to measure S. obtusifolia population attributes in 2002, a soil sample of approximately 1900 cm3
(195 x 195 x 50 mm) was collected from within each quadrat. This was to assess the presence and extent of a S. obtusifolia soil seed bank. This method was altered during 2003 and 2004 in response to the low variability detected within populations.
In these two years, five soil samples of the same size were randomly collected from
0 metres (the invasion front) to 10 metres inside each S. obtusifolia population (i.e.
Chapter 5 Population Dynamics - 92 - no distinction in distance). All samples were collected prior to the current season’s seed fall to ensure that any seedlings emerging were derived from the soil seed reserve. Soil samples were not taken from S. obtusifolia populations occurring next to lowland woodlands until 2003.
Presence and relative abundance of the soil seed reserves of each population was determined by recording seed germination. Each year, soil samples were emptied into shallow seedling trays and seeds were left to germinate inside a greenhouse (to exclude wildlife). Samples were watered regularly. Emerging S. obtusifolia germinants were recorded weekly for up to 26 weeks, with each germinant being removed after counting. Seeds were considered germinated once the radicle emerged from the seed coat (if seeds above ground) or if a seedling was present
(seeds below ground).
Data were log transformed and as in the population attribute data analysis, replicate data per site was averaged and a paired t-test conducted to analyse differences in the abundance of soil seed stores within each population (2002 only). A nested
ANOVA design was employed to assess differences between populations and between populations adjacent to each different habitat type within each year.
Between years comparisons could not be accurately made in this instance due to an unplanned disparity between the data collection periods of germinating seed.
5.2.4 Seed germinability
5.2.4.1 Soil seed
Because the total number of seed present in the soil samples was not quantified (i.e. germinated and ungerminated), it was attempted to determine the germinability of
Chapter 5 Population Dynamics - 93 - soil seed using a known number of seeds. This would allow for more accurate comparisons of soil seed abundance between populations, as it would be known if soil seed germinability is affected by site. During the same period that soil was sampled for the above experiment in 2004, additional soil was taken from each of the twelve S. obtusifolia populations and sieved until 600 seeds/site (3 x 200 seed replicates) were obtained.
Seeds retrieved were then planted into shallow seedling trays containing soil
collected and combined from multiple S. obtusifolia populations. This was necessary
to remove the effect of site on germination. Each tray was divided into two, with one
half being used to plant the seeds, and the other half acting as a control to quantify
any S. obtusifolia seedlings arising from a soil seed bank. Trays were regularly
watered and seeds left to germinate. Numbers of germinants were counted and
removed weekly for ten weeks during 2004. Seeds were considered germinated
once the radicle emerged from the seed coat (if seeds above ground) or if a seedling
was present (seeds below ground).
The impact of site on the germinability of soil seed was analysed using a univariate
analysis of co-variance (ANCOVA). This statistical method was selected as it
adjusted the recorded means of seed germinability to account for seedlings arising
from the control portion of each tray.
Based on the adjusted means obtained from the ANCOVA analysis, the mean per
cent germinability of soil seed for each site was determined. These values were
then used to determine the size of the ungerminated portion of the soil samples used
in section 5.2.3 which established the extent of the soil seed reserve in 2004. The
size estimates for each site determined using the method in section 5.2.3, were then
altered to reflect a more accurate size estimate of the soil seed reserves at each site.
Chapter 5 Population Dynamics - 94 -
Any differences in the size of the recalculated soil seed reserves between each site in 2004 were determined using a one-way ANOVA. Due to a non-normal distribution, data were log transformed before the analysis was conducted.
5.2.4.2 Seed off mature plants
Germinability of S. obtusifolia seed was assessed by collecting 600 S. obtusifolia seeds/site (3 x 200 seed replicates) directly from mature plants in each population during 2002, 2003 and 2004. In 2002 and 2003, seeds were planted into shallow trays containing soil known to support S. obtusifolia germination. In 2004 however, the method was altered and seeds were planted into shallow trays as described in the soil seed germinability experiments in section 5.2.4.1. In all years, germinant numbers were counted and removed weekly for approximately 26 weeks.
Differences in germinability between the edge and the interior of S. obtusifolia
populations were examined using the 2002 data only. Replicate data within each
site was averaged and used in a paired t-test. A nested ANOVA was used to assess
differences in germinability between different populations and between populations
occurring adjacent to differing neighbouring vegetation in 2002 and 2003. The
germinability of seed from populations growing adjacent to lowland woodland was
not assessed in 2002.
The nested ANOVA analysis was inappropriate for the 2004 data due to the
presence of controls. Therefore to account for the controls in 2004, two ANCOVA’s
were used to assess for differences in germinability between sites and between
populations occurring adjacent to different habitat types.
Chapter 5 Population Dynamics - 95 -
5.2.4.3 Seed after fire
It has been suggested that fire encourages the spread of S. obtusifolia by promoting seed germination (Anning et al. 1989). However, as yet, there is no empirical evidence to support this theory. If fire does trigger germination it is likely that increased soil temperatures are partially responsible for this effect, in which case, direct sunlight that bakes seeds which occurs once seeds fall from the plants might be equally effective at breaking dormancy. This experiment was therefore designed to assess the germinability of S. obtusifolia seeds that have been exposed to fire and of those which have been baking in the sun.
In October 2002, a very hot wildfire burnt large areas of vegetation in Iron Range
National Park. Three sites which were dominated by S. obtusifolia prior to the fire were reduced to bare, burnt soil with very few stems left standing. Two weeks after the fire, soil (< 1cm deep) was collected from these sites, and sieved to remove S. obtusifolia seeds. Seeds were divided into two groups based on colour: (1) Black – seeds were classified as black if at least 80% of the seed coat was burnt black. The majority of these seeds had a crumbly, charcoal-like texture and were expected to be unlikely to germinate, and (2) Brown - all other seeds were classified as brown.
Seeds were also collected from soil beneath three S. obtusifolia stands in nearby
areas which had not been burnt. Although these seeds had not been exposed to
fire, they had been exposed to high temperatures in the sun beneath senescing S.
obtusifolia plants. No burnt black seeds were present.
Thirty six shallow seedling trays were filled with soil collected from an unburnt, S. obtusifolia free pasture area within the National Park. One hundred seeds of each
Chapter 5 Population Dynamics - 96 - seed type were sprinkled onto the soil surface, well watered and left to germinate.
Six replicate trays of each treatment were created. Trays were kept inside a shade house and numbers of germinants were recorded weekly for 15 weeks.
Data were non-normal and was therefore log-transformed before a one way ANOVA was conducted to establish the effect of fire and baking in the sun on the germinability of S. obtusifolia seed.
5.2.5 Dispersal
The object of this experiment was to quantify the amount of S. obtusifolia seed that is being dispersed by water out of a patch directly into the adjacent environment and S. obtusifolia seed that is being dispersed by water into a patch produced by neighbouring S. obtusifolia populations.
To assess emigration into environments adjacent to S. obtusifolia populations, seed traps constructed from buckets and shade cloth were placed in natural depressions approximately 2-5 metres into the adjacent community from the invasion front. As seen in Figure 5.3a the traps possess two one metre long shadecloth arms designed to catch seed and direct it into the bucket to be caught. The bucket itself was bottomless with layers of shade cloth secured in the bottom to catch seed whilst still enabling water to move through the trap.
The traps were deployed at the end of the dry season, once mature S. obtusifolia
plants had finished dropping seed. Five traps were placed approximately ten metres
apart along a S. obtusifolia invasion front. Traps were constructed in 12 populations
with three replicates of each of the adjacent vegetation types. Traps were inspected
Chapter 5 Population Dynamics - 97 - for seed the following June after the wet season (floods) had dispersed the S. obtusifolia seed.
Due to the trap design and the lack of gradation in the land where the traps were placed, it was probable that seed did not get funnelled into the bucket as hoped, but instead hit the seed fence and was incorporated into the soil/leaf matter at its base.
Therefore to account for the probability of seed in the soil at the base of the traps, it was decided to take a standard scraping of soil from each to search for seeds. In addition to any material caught in the bucket itself, one spade width (18 cm) of soil was scraped once off the surface along the length of each trap arm as well as the area in front of the bucket. All soil was then sieved with water and the number of S. obtusifolia seeds recorded for each trap.
The trap design was modified in 2004 to minimise the subjectivity associated with the seed collection, to reduce the area travelled by the seed from the population to the trap and also to measure rates of immigration of S. obtusifolia seed into a patch.
This was achieved by constructing seed traps made from a fine gauge metal mesh immediately adjacent to the S. obtusifolia invasion front. As demonstrated in Figure
5.3b, two 40 x 20 x 20 rectangular traps were placed facing opposite directions
(open towards and away from the S. obtusifolia population) enabling seed dispersed by water in either direction to be caught. Each trap possessed a bottom, where all seed and other material settled whilst enabling water to still move through. The very edge of the trap was buried into a shallow trench to ensure seed washed into the trap and not underneath it.
The traps were deployed at the end of the dry season, once mature S. obtusifolia
plants had finished dropping seed. Five pairs of traps were placed approximately ten
metres apart along the ‘invasion front’. Sites where water courses/drainage
Chapter 5 Population Dynamics - 98 - channels were apparent (i.e. obvious point for dispersal) on the back of the S. obtusifolia populations had an additional five trap pairs erected along the patch edge.
Traps were constructed in nine S. obtusifolia populations, with each habitat type being replicated three times. Woodland habitats were not represented due to there being no suitable S. obtusifolia populations existing in these areas. Most traps were
not constructed at the same patches as in 2003 due to S. obtusifolia failing to
reoccur in a large proportion of the sites used. New patches were therefore used
(Chapter 2). Traps were inspected for seed the following June before seed fall. All
material in the base of each trap was collected and sieved with water to remove S.
obtusifolia seed.
a) b) Senna Senna obtusifolia obtusifolia Adjacent Vegetation
Shade cloth arms
Shade Cloth Bottomless Adjacent Bottom Bucket Vegetation
Figure 5.3 - Illustration of the structure of seed traps in a) 2003 and b) 2004 designed to measure Senna obtusifolia seed dispersal.
An estimate of seed production following the method described in section 5.2.2 was conducted in each of the new patches to enable comparisons of seed produced and seed dispersed.
Chapter 5 Population Dynamics - 99 -
All data were converted to seed/m2 and log transformed. One way ANOVA’s were conducted to assess differences in dispersal between sites and between the different vegetation types. The proportion of total seed produced that was immigrating and emigrating was also determined.
5.2.6 Population movement
This experiment was designed to quantify the expansion of S. obtusifolia populations
by monitoring the movement of the “invasion front” into the adjacent vegetation type.
Two permanent points were marked 50 metres apart along the invasion front of each
S. obtusifolia population in 2003 and a tape tied between them. At 50 centimetre
intervals, measurements were made at right angles to the tape, to the stem of the
first S. obtusifolia plant encountered and to the first stem where the dominant S.
obtusifolia population began (i.e. no or very little understorey of other species).
These same measurements were also made on the opposite side of the tape to
account for the presence of any satellite populations. In 2004 and 2005, the method
was altered to compensate for patchiness in populations which emerged during the
2004 season. In these years, additional measurements to the start and end of each
fragment within each S. obtusifolia population were also made (Figure 5.4). Senna
obtusifolia populations can be up to 100 hectares in size and therefore, from the
ground, impractical to measure the entire area they occupy. Thus measurements
were most commonly made in a 50 x 50 m block, with the block being imbedded
within a larger, continuous S. obtusifolia population. Where populations displayed
patchiness, the measurement area was generally increased to the point where
patchiness ceased, or when applicable, until the entire population was quantified.
Patch movement was monitored across twelve populations, with three replicates of
populations being adjacent to each of rainforest, woodland, Brachiaria and Imperata
grassland habitats.
Chapter 5 Population Dynamics - 100 -
For the purposes of analysis, distance measurements made to S. obtusifolia plants
were rounded to the closest 0.5 metre. The total area of each population was
quantified by creating a 0.5 x 0.5 m grid and determining how many squares were
occupied by S. obtusifolia plants each year. The movement was calculated by
adding or subtracting the previous year’s data from the current year. To determine if
habitat type influenced the invasion success of S. obtusifolia, the area of each
population in 2005 was calculated as a proportion of the first measurement of each
population made in 2003. Proportions were arcsine transformed and used in a one-
way ANOVA.
Satellite Population
First Plant 50 m
Start Patch
Dominant Invasion Front S. obtusifolia End Patch
Direction of invasion
Last Plant
Figure 5.4 Field design to monitor the invasion of Senna obtusifolia populations over time. Measurements were made in half metre increments and at right angles to a 50 metre tape, running between two permanent markers along the S. obtusifolia invasion front.
A multiple stepwise regression analysis was used to determine if any detected population retreat or expansion between the two years could be explained by the population characteristics of stem density/m2 (square root transformation), plant
height, basal area/m2 (square root transformation) seed production/m2 (square root transformation) presence/absence of fire, seed germinability (arcsine transformation) and soil seed reserve/m2 (log transformation) from the previous year (i.e. can the
Chapter 5 Population Dynamics - 101 -
2003 transect data explain movement detected in 2004, etc). A summary of fire history for each site is presented in Table 5.1.
Table 5.1 A summary of the fire history of each site used in the stepwise regression
Site Adjacent Habitat 2003 2004 1 Rainforest No fire No fire 2 Rainforest Fire Fire 3 Rainforest Fire No fire 4 B. decumbens Fire Fire 5 B. decumbens Fire No fire 6 B. decumbens Fire Fire 7 I. cylindrica Fire Fire 8 I. cylindrica Fire Fire 9 I. cylindrica Fire Fire 10 Woodland Fire Fire 11 Woodland Fire No fire 12 Woodland Fire Fire
5.3 Results
5.3.1 Population Attributes
Using data from all sites and all years the mean values (+SE) for S. obtusifolia
population density, reproductive effort, stem basal area and average height were:
density, 82 + 8 stems/m2; reproductive effort, 14 517 + 990 seeds/m2; stem basal area, 1808 + 190 cm3/m2; and height, 1671 + 80 mm.
5.3.1.1 Within a population
Senna obtusifolia populations showed differences in structure between the edge and
2 2 interior in the seed production/m (t27 = -2.467, p = 0.020) and plant height/m (t27 = -
Chapter 5 Population Dynamics - 102 -
2 4.073, p = < 0.001) attributes, but exhibited no variability in the stem density/m (t27 =
2 -0.506, p = 0.617) and stem basal area/m (t27 = -1.563, p = 0.130) attributes (Figure
5.5). Mean height and mean seed production were slightly higher within interior
sections of the stand (Figure 5.5).
a) b)
2500 180
160 2000 140
120 (+/- SE) (+/- SE) (+/- 1500 2 2 100
80 1000
60
40 Plant height/m 500 Stem density/m
20
0 0 123456789101112 123456789101112 Site Site c) d)
4000 25000
20000 3000 (+/- SE) (+/- 2 15000 (+/- SE)
2000 2
10000 Seeds/m 1000 5000 Stem basal area/m
0 0 123456789101112 123456789101112 Site Site
Figure 5.5 Mean (+ SE) (a) stem density/m2, (b) plant height/m2, (c) stem basal area/m2 and (d) seeds/m2 between the edge (0-2 m) and interior (2-10 m) of Senna obtusifolia populations between 2002 and 2004 in the Lockhart River region. Sites 1-3 represent S. obtusifolia populations occurring adjacent to rainforest habitats, sites 4-6 were adjacent to Brachiaria decumbens grasslands, sites 7-9 were adjacent to Imperata cylindrica grassland habitats and sites 10-12 were adjacent to lowland woodland habitats.
Chapter 5 Population Dynamics - 103 -
5.3.1.2 Between populations and populations occurring adjacent to different
habitat types
The characteristics of stem density/m2, seed production/m2, stem basal area/m2 and
plant height/m2 exhibited significant variability between individual S. obtusifolia
populations in 2002, 2003 and 2004 (Table 5.2). Once this variation between sites
within habitats was taken into account, there was no variation evident in population
attributes between the different habitat types occurring adjacent to the S. obtusifolia
population (Table 5.2).
5.3.1.3 Between years
The attributes of stem density/m2, seed production/m2, stem basal area/m2 and plant height/m2 at each site also demonstrated variability between 2002, 2003 and 2004
(for sites 1-9). Significant main effects were detected for the site and year variables and a significant interaction was also detected between year and site in all population attributes, demonstrating that differences between years was inconsistent
2 and dependent on site (site*year interaction - stem density/m – F16, 207 = 14.512, p =
2 2 < 0.001; seed production/m – F16, 207 = 20.252, p = < 0.001; stem basal area/m –
2 F16, 207 = 10.778, p = < 0.001; and plant height/m – F16, 207 – 41.089, p = <
0.001)(Figure 5.6). A similar pattern was detected between years for sites 10-12.
Although no analysis was conducted, Figure 5.6 clearly illustrates the variation of populations across years, with any interpretation of differences still being dependent on site.
Chapter 5 Population Dynamics - 104 -
Table 5.2 The results of a nested ANOVA assessing variability in the population attributes of stem density/m2, plant height/m2, stem basal area/m2 and seeds/m2 between Senna obtusifolia populations and between S. obtusifolia populations occurring adjacent to different habitat types across the region of Lockhart River during 2002 (3 habitat types), 2003 and 2004 (4 habitat types). Results in bold indicate significant differences.
Habitat Type Site Within Habitat Type ANOVA ANOVA F p df F p df
Stem Density/m2 2002 0.741 0.515 2, 6 4.04 0.003 6, 45 2003 0.843 0.508 3, 8 12.14 < 0.001 8, 108 2004 1.553 0.275 3, 8 16.66 < 0.001 8, 108
Plant Height/m2 2002 0.280 0.765 2, 6 7.05 < 0.001 6, 45 2003 0.662 0.598 3, 8 29.89 < 0.001 8, 108 2004 1.100 0.404 3, 8 55.40 < 0.001 8, 108
Stem Basal Area/m2 2002 1.422 0.312 2, 6 4.08 0.002 6, 45 2003 0.616 0.624 3, 8 9.07 < 0.001 8, 108 2004 1.131 0.393 3, 8 16.04 < 0.001 8, 108
Seeds/m2 2002 0.803 0.491 2, 6 4.80 0.001 6, 45 2003 0.599 0.633 3, 8 21.78 < 0.001 8, 108 2004 0.973 0.451 3, 8 46.27 < 0.001 8, 108
Chapter 5 Population Dynamics - 105 - a) b)
2500 250
2000 200
(+/- SE) (+/- 1500 (+/- SE) (+/- 2
2 150
1000 100
Plant height/m 500
Stem density/m Stem 50
0 0 123456789101112 123456789101112 Site Site
c) d)
30000 5000
25000 4000
(+/- SE) (+/- 20000 2 3000 (+/- SE) (+/-
2 15000
2000 10000 Seeds/m
1000 5000 Stem basal Stem area/m
0 0 123456789101112 123456789101112 Site Site
Figure 5.6 Mean (+SE) (a) stem density/m2 (b) plant height (c) stem basal area/m2 and (d) seeds/m2 of twelve Senna obtusifolia populations between 2002, 2003 and 2004. Sites 10, 11 and 12 were not measured in 2002. Sites 1-3 represent S. obtusifolia populations occurring adjacent to rainforest habitats, sites 4-6 were adjacent to Brachiaria decumbens grasslands, sites 7-9 were adjacent to Imperata cylindrica grassland habitats and sites 10-12 were adjacent to lowland woodland habitats.
5.3.2 Soil Seed Reserve
A viable S. obtusifolia soil seed reserve was located at each site. Using data from all years, the mean (+SE) soil seed reserve size was estimated at 1020 + 220 seeds/m2.
Chapter 5 Population Dynamics - 106 -
5.3.2.1 Within a population
The number of S. obtusifolia germinants arising from the S. obtusifolia soil seed bank, did not differ between soil samples taken at the edge (0-2m) and interior (2-
10m) of the populations in 2002 (t8 = 0.686, p = 0.512)(Figure 5.7).
4000
3000 (+/- SE) 2 2000
1000 Soil seeds/m
0 123456789 Site
Figure 5.7 The mean number (+ SE) of germinable Senna obtusifolia seeds/m2 present in the soil seed bank between the edge (0-2 m) and interior (2-10 m) of S. obtusifolia populations in 2002. Sites 1-3 represent S. obtusifolia populations occurring adjacent to rainforest habitats, sites 4-6 were adjacent to Brachiaria decumbens grasslands and sites 7-9 occurred adjacent to Imperata cylindrica grassland habitats.
5.3.2.2 Between populations and populations occurring adjacent to different
habitat types
The size of the S. obtusifolia soil seed bank varied significantly between different
populations within 2002, 2003 and 2004 but did not vary between populations
occurring adjacent to the different neighbouring vegetation types (Table 5.3).
Chapter 5 Population Dynamics - 107 -
Table 5.3 The results of a nested ANOVA assessing variability in the density of Senna obtusifolia seeds/m2 present in the soil seed bank between S. obtusifolia populations and between S. obtusifolia populations occurring adjacent to different habitat types across the region of Lockhart River during 2002 (3 habitat types), 2003 and 2004 (4 habitat types). Results in bold indicate significant differences.
Habitat Type Site Within Habitat Type ANOVA ANOVA F p df F p df Soil Seeds/m2 2002 0.25 0.786 2, 6 7.62 < 0.001 6, 45 2003 0.903 0.481 3, 8 3.71 0.002 8, 48 2004 1.613 0.261 3, 8 6.11 < 0.001 8, 48
5.3.3 Seed germinability
5.3.3.1 Soil seed
The germination of soil seed in 2004 was quite high, ranging from 57% to as high as
92% (Table 5.4). The preliminary analysis evaluating the homogeneity of slopes assumption indicated that the relationship between the control and the treatment did not differ significantly as a function of site (F11, 12 = 2.383, p = 0.076). The ANCOVA
once again demonstrated that site had a strong influence of germination, with
germinability varying significantly between them (F11, 23 = 3.969, p = 0.003).
The rate of germination was less than 100% in all soil seed samples. Therefore, it is
likely that the soil seed reserve size estimates for 2004 obtained in the previous
section, have been underestimated. Using the germinability percentages seen in
Table 5.4, a corrected size estimate of each soil seed reserve in 2004 was
determined (Table 5.4). The corrected quantities of soil seed/m2 present at each site
Chapter 5 Population Dynamics - 108 -
increased substantially, with the overall mean rising 1.39 times from 1712 + 274 to
2387 + 348 seeds/m2. Although the size of each soil seed reserve was altered, they
remained significantly different between sites (F11, 48 = 12.092, p = < 0.001) (Table
5.4).
Table 5.4 The mean (+ SE) percent germinability of Senna obtusifolia soil seed and the size of soil seed banks (+ SE) in 2004 for 12 populations from Iron Range National Park. “Original” soil seed bank is based on direct counts of germinants from a soil seed bank of unknown size, while “corrected” soil seed bank modifies this value based on germinability of a known number of soil seeds from the same site. Percentages and means with different letters indicate differences between sites.
Soil seed Standard Original Standard Corrected soil Standard Site germinability error (+/-) soil seed error (+/-) seed bank (m2) error (+/-) (%) bank (m2) 1 57.75 11.726 1930 a 453.56 3327.59 ad 781.99 2 55.85 12.257 715 ac 235.66 1276.79 abd 420.83 3 92.36 11.741 5400 a 1419.68 5869.57 ad 1543.13 4 61.88 11.304 3100 a 665.07 5000 ad 1072.69 5 78.28 11.571 4265 a 893.92 5467.95 ad 1146.06 6 63.98 11.501 1570 a 366.18 2453.13 ad 572.16 7 62.27 11.606 30 bcd 5 48.39 cd 8.06 8 67.16 11.302 10 bc 6.12 37.31 c 11.80 9 76.95 11.388 205 c 138.38 266.23 bcd 179.72 10 57.28 11.399 50 bc 23.72 87.72 c 41.61 11 71.44 11.369 1795 a 540.97 2528.17 ad 761.93 12 64.05 11.959 1465 acd 914.52 2289.06 d 1428.94
5.3.3.2 Seed off mature plants
Senna obtusifolia seed germinability was low with an average germination rate of 8 +
1% across all years surveyed.
Chapter 5 Population Dynamics - 109 -
5.3.3.2.1 Within a population
The paired t-test showed that germinability of S. obtusifolia seed was not influenced by its location within a population (t8 = 0.623, p = 0.550) (Figure 5.8).
50
40
30
20
% Germination (+/- SE) % 10
0 123456789 Site
Figure 5.8 The mean (+ SE) % germination of Senna obtusifolia seeds between the edge (0-2 m) and interior (2-10 m) of S. obtusifolia populations in 2002. Sites 1- 3 represent S. obtusifolia populations occurring adjacent to rainforest habitats, sites 4-6 were adjacent to Brachiaria decumbens grasslands and sites 7-9 occurred adjacent to Imperata cylindrica grassland habitats.
5.3.3.2.2 Between populations and populations occurring adjacent to different
habitat types
Seed germinability was significantly different between sites in 2002 (F6, 45 = 6.01, p =
< 0.001) but not between habitat types (F2, 6 = 0.351, p = 0.718). In 2003
germinability did not differ between sites (F8, 24 = 1.08, p = 0.410), but did show
Chapter 5 Population Dynamics - 110 -
variability between the different habitat types (F3, 8 = 7.5, p = 0.010). The ANCOVA analysis indicated that germinability did not differ between sites (F11, 23 = 1.269, p =
0.301) after the homogeneity of slopes analysis found no interaction present
between the germination rate of the control plots and site (F11, 12 = 1.475, p = 0.257).
Similarly, germinability in 2004 did not differ between the different habitat types
(Homogeneity of slopes – F3, 4 = 0.695, p = 0.602; ANCOVA – F3, 7 = 2.019, p =
0.200).
5.3.3.3 Seed after fire
Germination of S. obtusifolia seed exposed to a hot fire was significantly reduced in
comparison to seed that was not burnt (F2, 35 = 57.818, p = < 0.001). Burnt seed in the black condition possessed the lowest germinability, however the germinability of both classes of burnt seed were significantly lower than the unburnt seed (Figure
5.9). Seed that had baked in the sun possessed an average germination rate of 33
+ 4%. This is 4.13 times the germination rate recorded for S. obtusifolia seed that
was collected off mature plants shortly after seed production.
5.3.4 Dispersal
Senna obtusifolia seed was recorded as present in the bucket seed traps developed in 2003 and 2004 with an average of 58 + 11 seeds/m2 being dispersed out of a population. 38 + 6 seeds/m2 were recorded as entering a S. obtusifolia population.
As a proportion of total seeds produced/m2, only 0.37 + 0.096 % of S. obtusifolia seeds produced in 2003 emigrated, whilst in 2005 this figure rose only slightly to
0.54%. Immigration was also extremely low with 0.26% of total seed produced entering populations.
Chapter 5 Population Dynamics - 111 -
100 b
10 a a
1 Number of seed germinated (+/- SE) (+/- seed germinated Number of
0.1 Black Burnt Brown Burnt Unburnt Seed Condition
Figure 5.9 The mean number (+ SE) of Senna obtusifolia seeds that germinated after being exposed to fire. Seeds from fired sites were divided into categories based on their condition being either black or brown. Unburnt seeds were collected from a population not exposed to fire. Different letters above columns indicate significant differences. Data is presented on a log y scale.
5.3.4.1 Between populations
The amount of S. obtusifolia seed being dispersed out of a population over the
2003/2004 wet season varied with site (F10, 52 = 13.465, p = < 0.001). Post hoc analysis revealed sites 3, 6 and 10 to be responsible for much of the variation present (Figure 5.10). The results of the 2004 trap design indicated that site had no impact on the degree of seed being dispersed out of a patch (F7, 38 = 1.758, p =
0.132), nor did site affect the amount of seed immigrating into a population (F7, 37 =
0.494, p = 0.832) (Figure 5.10). Traps that were placed on the back of some populations near drainage pits, showed no difference in the number of seeds collected and therefore were added to the dataset and used as extra replicates.
Chapter 5 Population Dynamics - 112 -
a) 1000 ce ae ae (+/- SE) ae 2 100 ade cd ade abd abd
10 bcd c
1 Number of seeds dispersed/m of seeds Number
0.1 123456789101112 Site
1000 b) (+/- SE) 2
100
10 Number of seed emigrating/m
1 123456789 Site
c) 1000 (+/- SE) 2
100
10 Number of seedNumber immigrating/m 1 123456789 Site
Figure 5.10 The mean (+ SE) number of seeds a) emigrating out of 11 different Senna obtusifolia populations during the 2003/2004 wet season, b) emigrating out of 8 different S. obtusifolia populations during the 2004/2005 wet season, and c) immigrating into the same 8 different S. obtusifolia populations during the 2004/2005 wet season. Data is presented on a log y scale.
Chapter 5 Population Dynamics - 113 -
5.3.4.2 Between adjacent habitats
Adjacent vegetation type had no impact on the amount of seed that was dispersed out of a S. obtusifolia patch in 2003 (F3, 10 = 1.336, p = 0.337), nor did it impact on the rates of immigration and emigration in 2004 (F2, 7 = 3.421, p = 0.116; F2, 7 = 0.401,
p = 0.689).
5.3.5 Population Movement
The total area (m2) occupied by each S. obtusifolia population in 2003, 2004 and
2005 is given in Table 5.5. All populations displayed an unexpectedly high level of
variation over time, with almost all undergoing a large size reduction in 2004, nearly
to the point of disappearing (Figure 5.11 – 5.14) (Table 5.5). Populations 8 and 9
(Figure 5.13) were the only patches to expand in area by the forward movement of
the invasion front into the neighbouring environment. In 2005, most populations
increased in size from 2004; however patches remained significantly smaller than
their measured state in 2003.
Senna obtusifolia patches opposite rainforest sustained the greatest total reduction in size (Table 5.5), however, this reduction was not significantly greater than populations opposite the Brachiaria, Imperata and woodland habitats (F3, 8 = 0.458, p
= 0.719).
The stepwise regression identified seed production/m2 as the characteristic most responsible for driving the size changes in S. obtusifolia populations, accounting for approximately 30% (Adjusted R-square = 0.270) of the variation seen across all 12 populations between 2002 and 2005 (F1, 21 = 9.131, p = 0.006) (Figure 5.15). No
Chapter 5 Population Dynamics - 114 - other variable was identified that could significantly help explain the movement of S. obtusifolia populations.
Table 5.5 The total area (m2) occupied by 12 Senna obtusifolia populations from Iron Range National Park, in 2003, 2004 and 2005. Numbers in brackets with asterisks indicate the percent reduction of population size from the previous year. Numbers in brackets and in bold show the percentage population expansion since the previous year.
Year Site Adjacent 2003 2004 2005 Total reduction Habitat in area (%) 1 Rainforest 2434.5 10 (99.59)* 68.5 (585) 97.19 2 Rainforest 2353.5 1 (99.96)* 157 (15600) 93.33 3 Rainforest 2524.5 198 (92.16)* 80 (59.61)* 96.83 Mean % reduction (+ SE) of patches adjacent to rainforest (arcsine 78.38 + 1.68 transformation)
4 Brachiaria 3391 571 (83.16)* 2232 (290.89) 34.18 5 Brachiaria 1723 1.5 (99.91)* 2 (33.33) 99.88 6 Brachiaria 3289 42 (98.72)* 774 (1742) 76.47 Mean % reduction (+ SE) of patches adjacent to Brachiaria (arcsine 61.59 + 15.08 transformation)
7 Imperata 1917.5 1001.5 (47.77)* 48 (95.21)* 97.5 8 Imperata 3161.5 3399.5 (7.53) Site inaccessible 0 9 Imperata 2691.5 4332.5 (60.97) 246.5 (94.31)* 90.84 Mean % reduction (+ SE) of patches adjacent to Imperata (arcsine 51.10 + 25.67 transformation)
10 Woodland 948 2.5 (99.74)* 190.5 (7520) 79.91 11 Woodland 2323.5 68 (97.07)* 1741.5 (2461) 25.05 12 Woodland 1224 323 (73.61)* 10 (96.90)* 99.18 Mean % reduction (+ SE) of patches adjacent to woodland (arcsine 59.40 + 15.94 transformation)
Chapter 5 Population Dynamics - 115 -
Rainforest – Population 1
-40
-20
0
20
40
0 20 40 0 20 40 0 20 40
Rainforest – Population 2
-40
-20
0
20
40
0 20 40 0 20 40 0 20 40
Rainforest – Population 3
-40
-20
0
20
40
0 20 40 0 20 40 0 20 40
Figure 5.11 The invasion of Senna obtusifolia into rainforest over three years at three different sites. Each set of three graphs represents the state of one population in each year measured. Grey areas indicate S. obtusifolia.
Chapter 5 Population Dynamics - 116 -
Brachiaria decumbens grassland – Population 4
-60
-40
-20
0
20
40
0 20 40 0 20 40 0 20 40
Brachiaria decumbens grassland – Population 5
-40
-20
0
20
40
60
0 20 40 0 20 40 0 20 40
Brachiaria decumbens grassland – Population 6
-60
-40
-20
0
20
40
0 20 40 0 20 40 0 20 40
Figure 5.12 The invasion of Senna obtusifolia into Brachiaria decumbens over three years at three different sites. Each set of three graphs represents the state of one population as recorded in 2003, 2004 and 2005. Grey areas indicate S. obtusifolia.
Chapter 5 Population Dynamics - 117 -
Imperata cylindrica grasslands – Population 7
-40
-20
0
20
40
60
0 20 40 0 20 40 0 20 40
Imperata cylindrica grasslands – Population 8
-60
-40
-20
0
20
40
0 20 40 0 20 40
Imperata cylindrica grasslands – Population 9
-60
-40
-20
0
20
40
60
0 20 40 0 20 40 0 20 40
Figure 5.13 The invasion of Senna obtusifolia into Imperata cylindrica over three years at three different sites. Each set of three graphs represents the state of one population as recorded in 2003, 2004 and 2005. Grey areas indicate S. obtusifolia.
Chapter 5 Population Dynamics - 118 -
Lowland Woodland – Population 10
-20
0
20
40
0 20 40 0 20 40 0 20 40 Lowland Woodland – Population 11
-40
-20
0
20
40
0 20 40 0 20 40 0 20 40
Lowland Woodland – Population 12
-40
-20
0
20
40
0 20 40 0 20 40 0 20 40
Figure 5.14 The invasion of Senna obtusifolia into lowland woodland habitats over three years at three different sites. Each set of three graphs represents the state of one population in each year measured. Grey areas indicate S. obtusifolia.
Chapter 5 Population Dynamics - 119 -
20
0 0 20 40 60 80 100 120 140 160 -20
-40
-60
-80 Adjusted R-squared = 0.270
-100
% population change -120
-140
-160
-180 Seeds/m2 (square root transformation)
Figure 5.15 The linear relationship between the percentage of retreat or expansion of Senna obtusifolia populations and seed production/m2.
5.4 Discussion
The results of this study demonstrate the highly variable and dynamic nature of S.
obtusifolia populations occurring within natural ecosystems of Iron Range National
Park. Senna obtusifolia populations with high plant densities, large reproductive
effort and long-lived soil seed reserves, clearly have the capacity to dominate large
areas of natural vegetation. However, it is apparent from the small to very large
scale variations in population attributes detected between sites and years that S.
obtusifolia does not dominate, nor expand in a consistent manner on a spatial or
temporal basis. The sudden decline of populations seen in 2004 also alludes to an
organism-environment interaction that may be critical to the success of S. obtusifolia
in unmodified environments. Given the large site-to-site and year-to-year variations,
Chapter 5 Population Dynamics - 120 - it is most likely that conditions for germination and successful establishment of S. obtusifolia are very site and time specific.
Senna obtusifolia possesses a number of life history traits that are typically associated with successful annual weed species. These include high growth and maturation rates (Creel et al. 1968) allelopathy (Creel et al. 1968; Waterhouse and
Norris 1987), long distance dispersal by water (Parsons and Cuthbertson 2000) and a prolific reproductive output of dimorphic seeds which create extensive soil seed banks (Anning et al. 1989; Baskin et al. 1998). How these individual traits contribute
to population growth and maintenance is dependent on the environment into which
the plant encounters. The site to site and year to year variation evident in this study
is of this a basic example. Not only did the neighbouring vegetation differ with sites,
but also factors such as light and moisture availability, soil type, soil disturbance,
grazing intensity, etc. Which factor was responsible for variation in S. obtusifolia was
not examined. However, although the environmental signal is not always evident
(Huenneke 1987), factors such as these have repeatedly been demonstrated to
cause demographic variation in shrub species (Ehrenfeld 1999 and references
therein).
A comparison of S. obtusifolia population traits recorded in a natural system in this study with agricultural systems highlights further differences in the response of S. obtusifolia to its environment (Table 5.6). Whilst many traits demonstrated similarity between the two systems, stem density and seed production proved grossly different. Natural populations possessed eight times the number of stems per unit area than agricultural systems, whilst seed production per plant was eight times greater in the modified system. It is hard to assess what densities S. obtusifolia populations are capable of achieving in cropping environments, as the low densities recorded are most likely a product of management within the system. However,
Chapter 5 Population Dynamics - 121 - what is evident is a pattern of increased seed rain with a reduction in density. On closer examination of densities and seed production recorded across all populations in this study, a similar relationship between the two traits is revealed. Smith and
Jordan (1994) and Irwin and Barneby (1982) reported similar behaviour in this species, finding that lateral branching and height can be impacted by crowding and starving, reducing the opportunity for seed production. The disparity in seed production between natural and agro-ecosystems is likely to be overcome by the sheer density of plants present in the wild populations, and in both cases annual seed production in both systems remains vast. Therefore it was not surprising that seed number/m2 was identified as the population attribute most responsible for the
rapid and dense invasion within this study (Figure 5.15). This infers that a gradual
reduction in the quantity of seed produced is a crucial element of control.
Table 5.6 The mean value of Senna obtusifolia traits measured in a natural (Cape York) environment and within agricultural ecosystems.
Trait Natural Agricultural References for Ecosystem Ecosystem agricultural ecosystems Density/m2 82 ~10 Mackey et al. 1997 and references within Height 1700 mm 1500-1800 Patterson 1993 Seeds/plant 180 1500-1600 Retzinger 1984 Seeds/pod 23 24-28 Retzinger 1984 Soil Seed Bank *1020 seeds/m2 †2272 seeds/m2 Anning et al. 1989 (1/14 of total seeds) (1/10 of total seeds) % Germination 8 1.6-23 Creel et al. 1968; Teem et al. 1980 * The germinable portion of the total soil seed bank only † Assuming S. obtusifolia seeds in the agricultural system are the same weight as in the wild populations (0.0132g)
Chapter 5 Population Dynamics - 122 -
The soil seed reserve is another population attribute that deserves attention in both natural and agricultural systems. Up to 90% of S. obtusifolia seed is produced with physical (hard seed coat) dormancy (Baskin et al. 1998), which unless broken through scarification, will be incorporated into the soil seed reserve, where it reportedly can remain viable for up to ten years (Anning et al. 1989; Anon 1989).
Although this seed reserve was not detected in this study as statistically important in
S. obtusifolia invasion, other observations from this study suggest it may be critical to population success in northern Australia. These observations include the appearance of extensive populations adjacent to lowland woodlands in 2003 when absent in 2002, the re-occurrence of populations after hot fire and the very low number of populations present in 2003 in comparison to those in 2002 and 2004.
The S. obtusifolia populations arising adjacent to woodland in 2003 that were assumed as new, were comparatively no different in area occupied or in any population characteristic to populations that were present in 2002. It seems unlikely that a new population could occupy such large areas in such high densities without there being some prior seed source. This was confirmed when an extensive soil seed reserve was identified for each woodland population. Soil seed may have arrived by dispersal, but this is considered unlikely due to the density of soil seeds recorded, relative to amount of surface seed dispersing from individual populations.
Much more likely is that S. obtusifolia had previously inhabited and reproduced in the region, but had disappeared from these areas during 2002. The number of germinable seeds in the soil seed bank is only a fraction of the total seeds in the soil.
However, this provides the potential for a large proportion of plants/m2 to arise and contribute to each generation (approximately 100 – 380/m2 with 10 – 38% germinability). In view of such numbers, a whole generation can arise from the seed bank at the same densities recorded with new surface seed.
Chapter 5 Population Dynamics - 123 -
As demonstrated in Figure 5.9, hot fires can destroy S. obtusifolia surface seed and with fire being an annual event in most parts of Iron Range National Park, large numbers of seeds produced are destroyed every year. However, populations arise the following year (e.g. 2002 seed was destroyed, but 2003 populations present
(Figure 5.13)), with no obvious differences in their dynamics. The re-emergence of
S. obtusifolia under such conditions must be at least partially, if not totally, a result of the soil seed reserve. Plants arising from a seed bank after such a disturbance have an additional competitive advantage by being able to establish immediately if conditions are suitable (Witkowski and Wilson 2001). Dense re-invasion emanating from soil seed reserves alone has been recorded with other weed species, including the invasive Chromolaena odorata (siam weed) (Witkowski and Wilson 2001).
Fire undoubtedly plays a large role in the success of S. obtusifolia populations
throughout the Lockhart River region. Whilst this role is not yet fully understood,
areas excluded from fire for several seasons have been successfully re-colonised by
grassland vegetation (pers. obs). Like many weedy species, S. obtusifolia is
opportunistic and quickly colonises bare soil, furthering its invasion. Fire creates
such opportunities by providing a large scale annual disturbance within the
ecosystem, as well as promoting mass germination from the S. obtusifolia seed bank
(Anning et al. 1989). However, fire events alone cannot explain the invasion
success of S. obtusifolia or the vast temporal variation illustrated in Figures 5.11-
5.14. The majority of populations measured experienced fire annually although they
did not always return the following year. Therefore some other organism-
environment interaction is capable of suppressing S. obtusifolia invasion. One
possibility is that without disturbance, competition from resident vegetation is so
rigorous that S. obtusifolia cannot establish in the ensuing season. This does not
explain though why sometimes after disturbance S. obtusifolia does not re-appear.
Perhaps site-specific timing is a greater issue involving fire, competition and other
Chapter 5 Population Dynamics - 124 - environmental factors such as rainfall. The interaction of all factors may have a greater effect than each one individually. Competition studies and trials with fire would provide a strong basis to begin to understand some critical interactions of S. obtusifolia in natural ecosystems.
5.5 Progress towards aims of thesis
The field surveys conducted in this chapter have described the internal structure and dynamics of S. obtusifolia populations throughout the Lockhart River region.
Populations differed in characteristics both spatially and temporally. This result will make predictions of the future behaviour of S. obtusifolia populations in the area and the development of ecologically sound management strategies difficult, as every population is likely to respond differently to treatments applied. However, undertaking such surveys has provided a positive first step in understanding the local invasion dynamics of S. obtusifolia in natural environments of Australia. Further investigation at the local scale is necessary in order to understand what features of the local environment may be causing variability and driving change in the region.
Chapter 6
The impact of habitat type on the distribution of Senna obtusifolia
The associated publication with this work is: Dunlop EA, Clarke AR and Wilson JC (2006)
The impact of habitat type on the invasion of Senna obtusifolia in Iron Range National Park, far northern Queensland. Plant Ecology (in review).
The manuscript has been modified here for thesis continuity
Statement of Co-authorship
Elizabeth Dunlop - 75% contribution. Undertook experimental design, conducted all field work, undertook the statistical analysis and produced the manuscript.
Anthony Clarke – 15% contribution. Supervised statistical analysis and manuscript production.
John Wilson – 10% contribution. Supervised experimental design. Chapter 6 Impact of Habitat Type - 126 -
6.1 Introduction
The ability to understand the spatial patterns of an invasive species can provide important insight into the invasion dynamics of the species which in turn can have important implications for management (Grice et al. 2000). What governs and limits the spatial distribution and abundance of an invading plant species is one of the most basic questions in ecology (Cid-Benevento and Werner 1986), yet is one of the most difficult to determine. Fundamental physiological intolerances to unsuitable conditions will ultimately restrict a species success and generally provide the final determinants of its distributional limits (Panetta and Mitchell 1991). At a broad scale, abiotic factors such as climate have been used to successfully delineate species’ boundaries and can provide a relatively accurate prediction of an invader’s potential range in a foreign environment (Mack 1996; Baker et al. 2000; Kriticos and Randall
2001). However, at finer scales, such broad environmental correlates have limited use as they cannot describe the responses of the invader to the matrix of variable habitat conditions caused by localised environmental heterogeneity (Mack 1996;
Sheppard 2000; Rejmanek et al. 2004).
Very rarely will a species have the ability to survive throughout all habitats which
occur within its geographic range (Wiser et al. 1998), rather, distributions remain
patchy. A patchy distribution of a new invader may initially reflect limited dispersal
from points of introduction and dispersal ability (Cid-Benevento and Werner 1986;
Cousens and Mortimer 1995; Wiser et al. 1998). As the invasion progresses, the
distribution of the invader will reflect patch dependent differences in seed survival,
germination and establishment (Schupp 1995). This exposes the spatial variability of
suitable habitat (Cousens and Mortimer 1995; Koop 2004).
Chapter 6 Impact of Habitat Type - 127 -
Theoretical models outlining the invasion process highlight the role of the seed, both its movement and its interaction with the environment, as critical in shaping the course of a plant invasion (Groves 1986; Chambers and McMahon 1994; Heger
2001). Due to the complexity of abiotic and biotic interactions occurring within a habitat, it can be difficult to tease apart these elements to accurately assess their individual impacts on the advancement of the invasion (Sheppard 2000). However, understanding how an invasive species responds to contrasting environments as a whole can be an important component of effective management (Sheppard et al.
2002). Knowledge of the interactions between the invading population and host communities are fundamental to the understanding of the functioning of populations.
Furthermore, such knowledge can assist in making realistic predictions of invasion potential and the invasion path (Cousens and Mortimer 1995; Parker 2000) and can assist in the development of efficient control (Grice et al. 2000).
The results presented in Chapter 5 demonstrated that the vegetation type adjacent to a S. obtusifolia infestation had no impact on the attributes within the population.
However, despite the apparent dominance of S. obtusifolia populations within some areas, it remains patchily distributed throughout the Lockhart River region, dominating some plant communities, but being sparse or completely absent in others. This pattern of distribution provides some support for the hypothesis discussed in Chapter 5 that reasoned that some undisturbed, highly competitive vegetation types may be capable of inhibiting the establishment of S. obtusifolia populations, thereby explaining some of the observed large year to year variation in population sizes. Therefore, the main goal of the following chapter was to determine whether variable habitat types differentially affected S. obtusifolia recruitment, seed mortality and germinability. Using seed introduction experiments, it was hoped that by investigating the response of S. obtusifolia to each of the five habitats described in Table 2.1, that the vulnerability of each habitat type to invasion could be
Chapter 6 Impact of Habitat Type - 128 - ascertained, determining if habitat type is responsible for the spatial and temporal patchiness of the S. obtusifolia distribution.
Given that S. obtusifolia does respond differently to each habitat, this chapter further
explores the effect of the habitat components of soil type, shade and leaf litter of
each habitat on S. obtusifolia establishment. Understanding if one of these elements can singly inhibit growth would greatly assist in understanding the spatial distribution of the species and provide important clues to long-term management.
Finally, it was wished to determine if the observed patchy distribution throughout the
Lockhart River region is a product of limited dispersal. Senna obtusifolia seed is
dispersed primarily by water and by ‘hitchhiking’ and it is conceivable that the
distribution is restricted by a poor dispersal capacity. This was achieved by
investigating the presence and absence of viable soil seed reserves in each of the
different habitat types studied.
6.2 Method
6.2.1 Study Site
In addition to the four habitats used in Chapter 5 (i.e. rainforest, B. decumbens and I. cylindrica grasslands and lowland woodland) a fifth habitat type, elevated woodland, was also used for a restricted range of experiments in this Chapter. See Table 2.1 for the characteristics of the elevated woodland habitat.
Within each of the five different habitat types, three replicate study sites were established (i.e. 3 sites/habitat, 15 sites in total). Each of these sites were situated either adjacent to established S. obtusifolia populations or were located at a distance
Chapter 6 Impact of Habitat Type - 129 - from S. obtusifolia to reduce the likelihood of soil seed being present. These sites were still located within the general vicinity where S. obtusifolia grew to eliminate gross changes in habitat components that may inhibit growth. Where sites were located was dependent on the experiment being conducted. All sites chosen had to be relatively undisturbed.
6.2.2 Abundance of Senna obtusifolia in different habitat types
During August/September 2004, the quantity of S. obtusifolia in rainforest, B.
decumbens, I. cylindrica and lowland woodland habitats was quantified (i.e. 12 sites
in total) to see if certain habitats naturally support higher densities of S. obtusifolia
than others. Sites were chosen that occurred directly adjacent to a S. obtusifolia
population. Within each site, as illustrated in Figure 6.1, five ten metre transects
were randomly laid in the adjacent habitat. Each transect extended from the edge of
the S. obtusifolia ‘invasion front’ (0 metres) into the adjacent habitat type (10
metres). Transects were divided into two sections based on distance into the
habitat. These were 0-2 metres and 2-10 metres. In each distance section, along
each transect, one 500 x 500 mm quadrat was randomly laid. Within each quadrat
S. obtusifolia stem density and plant height was recorded. Using the method
described in Chapter 5 (section 5.2.2), reproductive effort (i.e. seed number) in each
quadrat was also assessed.
Densities of S. obtusifolia within sites were recorded and all sites pooled to determine the average density of the weed in each habitat type. One way ANOVA’s were also conducted to assess if variation in density of S. obtusifolia was influenced by distance away from the S. obtusifolia invasion front.
Chapter 6 Impact of Habitat Type - 130 -
Adjacent Habitat
2-10 m
0 - 2 m Invasion Front ↓
Transect 4 Transect 1 Transect 3 Transect 5 Transect 2
S. obtusifolia population
Figure 6.1 Field design to assess the abundance of Senna obtusifolia in the different habitats occurring adjacent to S. obtusifolia infestations. Five transects were laid into the adjacent habitat from the invasion front of a S. obtusifolia infestation. Quadrats were laid randomly within 0-2 m and 2-10 m distances.
6.2.3 Seed Introduction Experiments
To test if habitat type can impact on the establishment of S. obtusifolia populations,
seed introduction experiments in the field were conducted. This is where seed
belonging to the species of interest is deliberately introduced to unoccupied
environments to assess if recruitment is possible (Turnbull et al. 2000).
Three replicate sites of each habitat were selected for seed introduction in the field,
based on the prior absence of S. obtusifolia. Each site was still located within the
general vicinity of an S. obtusifolia to ensure no gross geographical changes in
habitat/landscape occurred that may prevent S. obtusifolia growth. Three 1m² x
0.5m (high) seed enclosures made from 70% shade cloth (2003/04) or fireproof fine
metal mesh (2004/05) were erected within each replicate site (Figure 6.2). Traps
were tall and fencelike to prevent seed added to each trap from being dispersed
during seasonal heavy rainfall and contaminating clean sites with S. obtusifolia, and
Chapter 6 Impact of Habitat Type - 131 - to deter wildlife from stepping in the plots and displacing seed. Shadecloth and metal mesh were used to still allow light to enter the trap but prevent seed from washing in or out. To account for growth from any potentially existing S. obtusifolia soil seed reserve, a 1m² control plot was pegged out approximately 40 cm from each trap.
One-thousand five-hundred seeds collected and mixed from multiple S. obtusifolia populations were sprinkled into each trap during November 2003 and 2004. Senna obtusifolia seed-fall normally occurs between the months of August-October, however in this instance seed could not be planted any earlier than late November due to the risk of fire destroying seeds (see Chapter 5, section 5.3.3.3). Seed sprinkled in the trap was not buried in order to mimic the action of seed falling off plants. To ensure seed used was germinable, additional seeds were taken from the seed pool and used as controls for initial seed quality. Seed was planted into tubs filled with soil known to support wild S. obtusifolia populations and were left in a shade house exposed to prevailing environmental conditions.
Figure 6.2 The fence-like trap design used to hold Senna obtusifolia seed in the seed introduction experiments.
Chapter 6 Impact of Habitat Type - 132 -
All traps were left over the wet season and were checked during June/July of the following dry season. The number of reproductive individuals (plants with flowers or pods), non-reproductive or juvenile plants and the average height of each category of S. obtusifolia plants was recorded. However, it had been observed previously that germination, the survival of seedlings and of newly emerged plants was heavily dependent on the immediate climatic conditions. Therefore, counts of these young plants provided an instantaneous estimate only of the suitability of the habitat for growth. On the basis of this observation it was decided that reproductive individuals only would be included in the analysis. Furthermore, this measure is also appropriate because conditions for establishment can often be far more stringent than for germination (Turnbull et al. 2000). By counting reproductive individuals only, a more accurate reflection of habitat suitability for S. obtusifolia establishment would be obtained.
Due to the self-fertile nature of S. obtusifolia (Retzinger 1984), it was decided that flowers would be used as an indicator of reproductive maturity. Reproductive effort could not be determined due to permit regulations which denied the presence of mature (i.e. seeding) plants in non-infested areas preventing this measurement being recorded.
For the purposes of analysis, mean values of establishment in each habitat were ignored. This was due primarily to control plots producing mature plants, the number of which in many cases, being larger than the treatment. Attempts to correct the treatment data in an appropriate manner failed thereby preventing meaningful analysis and interpretation of the data. Instead, the frequency of established plants within each treatment plot (i.e. seed added) versus no established plants within each treatment plot within each habitat was recorded. Data from across the two years was pooled and analysed using a chi-squared test for association. This analysis was
Chapter 6 Impact of Habitat Type - 133 - conducted to demonstrate whether S. obtusifolia establishment was influenced by the habitat type that the S. obtusifolia seed was exposed to. In using this form of analysis it was assumed that each trap containing the added seeds within a site were independent of each other.
To further ensure that any S. obtusifolia soil seed reserve present in the area of the
traps was not influencing the results of the treatment, another chi-squared test of
association was conducted. In this analysis, treatment plot values were used in the
analysis for all sites where no established plants arose from the control plots. This
was to demonstrate that in the absence of a soil seed reserve, treatment plots could
still produce mature plants, which must therefore be a result of the S. obtusifolia
seed that was deliberately added to the plot.
Based on the assumption that fecundity increases with S. obtusifolia plant size
(could not measure reproduction because of permit restrictions), mean differences in
the height of the reproductive plants between the different habitat types was also
analysed using a one way ANOVA. Because the height data were found to be non-
normal, the data were log transformed before the analysis was conducted. This was
also necessary to minimise the risk of heteroscedasticity.
6.2.4 Presence and size of soil seed reserve
The patchy distribution of S. obtusifolia throughout a region may be a result of the
dispersal process, with seed simply not reaching all of the habitat types. Therefore
to determine if dispersal has occurred into each habitat, the presence and
abundance of soil seed reserves were examined. Senna obtusifolia seed has the
ability to remain dormant in soil for many years until suitable conditions for
germination arise. Because of this ability, it is possible that dormant seed remains
Chapter 6 Impact of Habitat Type - 134 - within the habitat if dispersal has been successful, even if mature plants are absent or rare.
Presence and relative abundance of the soil seed stores of the different environments was determined by assessing germination. Three replicate sites of each of the four primary habitat types were selected where the habitat formed a distinct invasion front with a S. obtusifolia population. Five soil samples of
approximately 1900 cm3 (195 x 195 x 50 mm) were collected at each site early in the
dry seasons of 2002, 2003 and 2004, before S. obtusifolia seed fall had begun.
Given the abrupt nature of the S. obtusifolia invasion front, samples were taken
randomly within 10 metres of the invasion front only. Each sample was placed into
shallow seedling trays inside a greenhouse, watered regularly, and left to germinate.
Germinant numbers were recorded weekly for 26 weeks, with each germinant being
removed after counting. After this period of time, seeds generally stopped
germinating. The viability of all seeds present in the soil was not determined and
therefore the results give a relative measure of soil seed abundance only.
Data were identified as non-normal and was therefore log transformed before a one- way ANOVA was used to analyse differences in the relative abundance of the soil seed stores between the four habitat types.
6.2.5 Seed survival and germinability
This experiment was designed to assess the effect of habitat type on seed survival in the seed reserve and its subsequent germinability. To assess seed survival, 200 mixed seeds collected from multiple S. obtusifolia populations were placed into sealed, screen mesh bags (approximately 100 x 100 mm). Bags were tied securely with fishing line to a metal peg and partially buried below the soil surface and
Chapter 6 Impact of Habitat Type - 135 - covered with the existing leaf litter. One bag was buried in each site of each habitat type during November 2003 (after fire).
Mesh bags were left in the environment over one wet season and were retrieved at the beginning of the dry season in June 2004, six months after their burial. The number of seeds remaining in each bag was counted and recorded. Differences in the survivability of the buried S. obtusifolia seeds between the different habitat types
was analysed using a one-way ANOVA.
To assess the germinability of recovered seeds, after counting, seeds were placed in shallow seedling trays and onto the surface of a uniform soil type known to naturally support S. obtusifolia populations in the field. Seedling trays were filled with soil
collected and combined from multiple S. obtusifolia populations: this was to remove
the effect of site on germination. Each tray was divided into two, with one half being
used to plant the recovered seeds, whilst the other half acted as a control to quantify
any S. obtusifolia seedlings being expressed from the seed bank. Trays were
regularly watered and seeds left to germinate in a shade house. Numbers of
germinants were counted and removed weekly for 10 weeks during 2004.
An additional 1200 seeds (6 replicates of 200 seeds) from the same seed pool as those buried, were left untreated at the time of seed burial, stored in paper bags and kept in containers inside an outdoor garden shed. These seeds were primarily designed as controls to demonstrate that the S. obtusifolia seed buried was in fact germinable, proving that any germination failure from recovered seeds was not a result of seed being of inferior quality. These controls also acted to provide an indication of the level of germinability of S. obtusifolia seed that had not been exposed to the conditions of any habitat, thereby providing a basis for comparison of the effect of the burial.
Chapter 6 Impact of Habitat Type - 136 -
Statistical analysis could not be conducted on the seed data to determine any differences in the germinability of the S. obtusifolia seeds retrieved from the different habitat types. This was due to difficulties in ascertaining if seedlings arising from the treatment were a result of those that were retrieved from the burial experiment.
However, the means of the uncorrected data are presented in the results.
6.2.6 Effect of habitat components on establishment
Senna obtusifolia is seen to possess a distinct invasion front where its growth appears often impeded by the adjacent vegetation type. This infers that S. obtusifolia success may be restricted by certain environmental factors within a habitat either singly or in combination. Such environmental thresholds existing across a landscape will on a local scale of invasion, have significant effects on the invasion trajectory that S. obtusifolia will be forced to follow. To determine what
conditions restrict S. obtusifolia invasion would greatly contribute to assessing the
true invasion potential of the weed across a local area and also at a larger regional
level of scale. Additionally it would provide valuable insight into possible strategies
for control and long term management.
As this study is predominantly focused on mapping and modelling the invasion of S.
obtusifolia, determining physical threshold levels of environmental factors that can
impinge on the growth of the weed is not a primary aim. Instead it was aimed to
determine the effect of predominant, generalised habitat factors such as degree of
shade, leaf litter densities and soil types that can easily be applied to all vegetation
classifications throughout a region. Although these abiotic factors are generalised,
they are known to influence the germination, establishment and consequently
invasion success of many species. Therefore to help explain any variable patterns
Chapter 6 Impact of Habitat Type - 137 - of S. obtusifolia invasion across the rainforest, woodland and grassland habitats, the impact of the leaf litter, soil type and level of shade of each environment on S. obtusifolia establishment was investigated.
This experiment was conducted in a common garden area so that each of the habitat characteristics tested could be isolated from their surroundings to more easily determine their impact on S. obtusifolia. Similarly, all treatments were exposed to the same prevailing environmental conditions by all being contained within the same common area.
Table 6.1 outlines which soil, shade and leaf litter treatments were applied to each habitat type. Each treatment was replicated six times per habitat type. Consistent with field estimations of S. obtusifolia seed production/m2, the soil surface of each
replicate (185 x 300 mm) was sprinkled with 1450 seeds. Individual treatments
applied to replicates are as follows:
Soil
Treatment (A) Approximately 8000 cm3 of soil was collected from the
rainforest and put into potting containers. 1450 S. obtusifolia
seeds were then sprinkled onto the surface.
Treatment (B) As per (A) except soil was collected from B. decumbens
grasslands.
Treatment (C) As per (A) except soil was collected from I. cylindrica
grasslands.
Treatment (D) As per (A) except soil was collected from lowland woodlands.
Chapter 6 Impact of Habitat Type - 138 -
Soil + Leaf Litter A (seed on the soil surface under the leaf litter) Treatment (E) An area of leaf litter proportional to the area of the
experimental containers (555 cm2) was collected from the
rainforest. This leaf litter was then placed on top of rainforest
soil which had previously been sprinkled with 1450 S.
obtusifolia seeds.
Treatment (F) As per (E) except leaf litter was collected from B. decumbens
grasslands.
Treatment (G) As per (E) except leaf litter was collected from I. cylindrica
grasslands.
Treatment (H) As per (E) except leaf litter was collected from lowland
woodlands.
Soil + Shade
Treatment (I) Light readings for rainforest were averaged from a number of
rainforest sites. Light levels of approximately 85% for
rainforest were replicated using different shade cloth densities.
A layer of 70% shade cloth was overlayed with 30% shade
cloth to recreate the rainforest shade. 1450 S. obtusifolia
seeds were sprinkled directly onto the surface of rainforest
soil.
Treatment (J) Light readings for lowland woodland were averaged from a
number of woodland sites. Light levels of approximately 60%
were replicated using a single layer of 70% shade cloth. 1450
seeds were sprinkled directly onto the surface of lowland
woodland soil.
Chapter 6 Impact of Habitat Type - 139 -
Soil + Leaf Litter (B) (Seeds on the surface of the leaf litter) Treatment (K) Due to the distinctive mat associated with swards of B.
decumbens, a second soil + leaf litter treatment was carried
out for the B. decumbens habitat in order to determine if this
grass mat prevents seed from making contact with the soil.
The treatment was carried out as per treatment (f) except seed
was sprinkled directly onto the surface of the leaf litter instead
of being placed onto the B. decumbens soil surface.
Soil + Leaf Litter + Shade
Treatment (L) 1450 seeds were exposed to a combination of factors in the
rainforest environment. Rainforest leaf litter was spread over
the seeds on the surface of rainforest soil and placed into 85%
shade.
Treatment (K) As per treatment (L) except lowland woodland leaf litter was
spread over woodland soil and placed into 60% shade.
The number of plants in each treatment was counted weekly for 21 weeks during
December to April over the 2003/2004 wet season. This was sufficient time to allow the species to germinate, undergo any self-thinning and establish.
A series of one-way ANOVA’s were used to determine whether habitat type influenced the rate of establishment of S. obtusifolia within each category of treatments (e.g. Soil (treatments – a, b, c, d)). Not all habitats were used in each category of treatments (see Table 6.1) and were therefore, where appropriate, left out of the analysis.
Chapter 6 Impact of Habitat Type - 140 -
The impact of the individual treatments that were applied within each habitat type on the establishment of S. obtusifolia was also examined using one-way ANOVA’s. For example, referring to Table 6.1, within the I. cylindrica habitat the rate of
establishment between treatments C and G would be tested. How the number of S.
obtusifolia plants changed with treatment within each habitat over time was also
examined. This was to determine if any one treatment type could delay S. obtusifolia
germination/establishment.
Table 6.1 The treatments that were applied in a common garden experiment to determine the effect of varying habitat components on Senna obtusifolia establishment. The percentages in the shade column indicate the level of shade that was created to mimic light levels in the field. Different letters represent a group of six replicate pots.
Treatment Applied Habitat Soil Soil + Leaf Shade Soil + Leaf Soil + Leaf Litter (A) + Soil Litter (B) Litter + (seeds under (seeds on litter Shade litter) surface)
Rainforest A E I L (85%)
B.decumben B F K s grassland
I.cylindrica C G grassland
Woodland D H J M (60%)
This experiment was also conducted on a much larger scale where S. obtusifolia was growing naturally in 2 x 2 metre plots. This was in order to determine if the application of any of the above treatments could have large scale effects on S.
Chapter 6 Impact of Habitat Type - 141 - obtusifolia establishment. Manipulation of successful treatments would then have provided essential clues for management. However, despite the best efforts to place the experiment in a low risk area, all S. obtusifolia plots and erected shade houses
were destroyed by a hot grass fire before plant numbers could be assessed.
6.3 Results
6.3.1 Presence of Senna obtusifolia in adjacent habitats
During the 2004 season, S. obtusifolia plants were located in the adjacent I. cylindrica grasslands only. Two of the three I. cylindrica sites possessed plants at a mean (+ SE) density of 4 + 2.9 stems/m2 and 19.2 + 5.1 stems/m2 respectively. The
I. cylindrica habitat therefore possessed an overall expected average of
approximately 7.73 + 3.4 stems/m2, at a height of 130.6 + 9.3 cm and a reproductive effort of 6554 + 1979 seeds/m2. This low density of plants was in complete contrast
to the very high densities of plants recorded within the S. obtusifolia population,
clearly demonstrating the distinctive nature of the invasion front (Figure 6.3).
Distance into the adjacent vegetation did not influence the number of plants recorded
at either I. cylindrica site (Site 1, t8 = 0.525, p = 0.614; Site 2, t8 = -0.296, p = 0.775).
6.3.2 Seed Introduction Experiments
The establishment of reproductively mature S. obtusifolia in the treatment plots
across all habitat types was unexpectedly very low, with less than 1% of seed
establishing. Such low rates of survival were not the result of inferior seed, as seed
drawn from the same seed pools and grown under shade house conditions reached
approximately 5% maturity (See Appendix 1 for raw data).
Chapter 6 Impact of Habitat Type - 142 -
140
120
100
80 (+/- SE) 2 60
40 Stems/m 20
0
2-10 0-2 0-2 2-10
Distance into vegetation
Figure 6.3 The mean (+ SE) of Senna obtusifolia stems found in S. obtusifolia infestations (black) and in the different habitat types occurring immediately adjacent to the infestation at two different distance intervals (0-2 m and 2-10 m into the patch/vegetation). The four habitat types illustrated are rainforest (circle), Brachiaria decumbens grassland (square), Imperata cylindrica grassland (star) and lowland woodland (triangle).
If only one adult plant survives in a new environment where seed has been added, the seed introduction experiment can be deemed successful, inferring that the environment is capable of supporting the plant (see review Turnbull et al. 2000). In both years the experiment was conducted, mature individuals of S. obtusifolia were recorded in each grassland habitat and both woodland environments, but were never found within the rainforest habitat. Chi-squared analysis confirmed this observation indicating that the habitat type into which seed was introduced, influenced the
2 establishment rate of reproductively mature S. obtusifolia individuals (χ 4 = 18.501, p
Chapter 6 Impact of Habitat Type - 143 -
= 0.001). Although the rates of establishment in each habitat were unexpectedly low, as demonstrated in Figure 6.4, the rainforest environment clearly prevented growth to maturity when compared to the success of the other communities.
Figure 6.4, also illustrates the numbers of plants recorded in the control plots of each habitat. Again although establishment rates within the treatment traps was low, the simple fact that established plants were present in the controls of three of the five environments is a clear indication that S. obtusifolia naturally has the capacity to establish within B. decumbens, I. cylindrica and lowland woodland environments.
The elevated woodland and rainforest habitats had no recorded individuals within the controls. This result supports general field observations. The chi-squared analysis conducted to ensure that the soil seed reserve was not responsible for the significant result of the test above, showed that treatment traps could still produce reproductive
S. obtusifolia in all habitats except rainforest, even when (presumably) no soil seed
2 reserve was present (χ 1 = 8.42, p = 0.001). Approximately 40% of all treatment plots contained established plants when their paired control plots possessed none.
The height (and presumably fecundity) of reproductive S. obtusifolia plants found in the treatment plots of each habitat (except rainforest) was significantly different (F3, 13
= 5.220, p = 0.014). Plants in the B. decumbens habitat were approximately twelve times smaller than those plants present in the I. cylindrica habitat and nine times smaller than those in the lowland woodland (Figure 6.5).
Chapter 6 Impact of Habitat Type - 144 -
100
10
1 Reproductive plants (+/- SE) (+/- plants Reproductive
0.1 Rainf B. decum I. cylin Low. wood Elev. wood Habitat
Figure 6.4 The mean proportion (+ SE) of reproductively mature Senna obtusifolia plants establishing from seed deliberately introduced into the treatment (black) and from the naturally occurring soil seed reserve in the control (white) in rainforest, B.decumbens, I. cylindrica, lowland woodland and elevated woodland environments (data is presented on a log y scale).
1000
100
10 Height (cm) (+/- SE)
1 B. decum I. cylin Low. wood Elev. wood Habitat
Figure 6.5 The mean height (+ SE) of reproductive Senna obtusifolia plants found in the treatment plots of the seed introduction experiments in B. decumbens, I. cylindrica, lowland woodland and elevated woodland environments (data is presented on a log y scale).
Chapter 6 Impact of Habitat Type - 145 -
6.3.3 Presence and germinability of soil seed reserve
A germinable soil seed reserve was present in all four habitats, but varied in abundance between them (F3, 25 = 8.968, p = < 0.001). Post hoc analysis (Tukey
test) identified the B. decumbens environment to be harbouring 2 times as much
seed than the next closest habitat, rainforest, and up to 2.24 and 2.45 times the
amount of seed present in the I. cylindrica grasslands and lowland woodland respectively (Figure 6.6). The mean size of the germinable soil seed reserve present in B. decumbens is comparable to that present within a S. obtusifolia infestation
(Figure 6.6).
10000
b
1000 (+/- SE) 2
a a 100 a Soil seed/m
10 Rainf. B. decu I. cyli Low. wood S. obtusifolia
Habitat
Figure 6.6 The mean (+ SE) abundance of a germinable Senna obtusifolia soil seed reserve recorded in rainforest, B. decumbens, I. cylindrica and lowland woodland environments occurring directly adjacent to S. obtusifolia populations. Habitats with different letters possess significant differences in seed abundance. The average density of soil seeds located within S. obtusifolia infestations is also presented (data is presented on a log y scale).
Chapter 6 Impact of Habitat Type - 146 -
6.3.4 Seed survival and germinability
Only 38% of the 2400 seeds buried were recovered after being exposed to the conditions of each habitat type for one wet season. Seed survival between the different habitats was variable (F3, 8 = 7.511, p = 0.010), with seed exposed to
rainforest conditions experiencing significantly lower survival (4.6%) than the B. decumbens (43.3%), I. cylindrica (45%) and lowland woodland (44.5%) environments (Figure 6.7).
140
b 120 b b 100
80
60
40 Seeds recovered (+/- SE) recovered Seeds
20 a
0 Rainf. B. decu I. cyli Low. wood Habitat
Figure 6.7 The mean (+ SE) number of buried Senna obtusifolia seed recovered from rainforest, B. decumbens, I. cylindrica and lowland woodland environments after being exposed to conditions over one wet season. Habitats with different letters are significantly different from one another.
Figure 6.8 presents the data for the germinability of the seeds recovered from the
burial experiment. Illustrated are the mean number of seeds planted in each habitat,
the average number of seedlings arising from the treatment and the average number
Chapter 6 Impact of Habitat Type - 147 - of seedlings arising from the paired control within each habitat. With the exception of the rainforest habitat, it can be seen that the treatments possess more seedlings than the controls, which infers that germination from the added seeds was successful on some unknown level. Just what proportion of the seeds added germinated could not be determined. Based on the data presented in Figure 6.8, it also appears that the germinability of the buried seed has not been reduced or increased relative to the control seed that was not buried.
200
150
100
50 Number of plants (+/- SE) (+/- plants of Number
0 Rainf. B. decum I. cylin Low. wood. Not Buried
Habitat
Figure 6.8 The mean (+ SE) number of Senna obtusifolia plants arising from germinability trials involving the seeds recovered from the seed survival experiment. Graph bars illustrate the number of seeds planted in the treatment (black), the number of plants arising from the treatment (light grey) and the number of plants arising from the control (dark grey) of each of the rainforest, B. decumbens, I. cylindrica and lowland woodland environments. The same is also shown for seed that was not buried.
Chapter 6 Impact of Habitat Type - 148 -
6.3.5 Effect of different habitat components on establishment
Habitat type significantly influenced S. obtusifolia establishment when examined within the soil (F3, 20 = 4.053, p = 0.021) and the soil + shade treatments (F1, 10 =
6.233, p = 0.032), but not within the soil + leaf litter (F3, 19 = 1.656, p = 0.210) and the soil + shade + leaf litter treatments (F1, 10 = 3.377, p = 0.096). Post hoc analysis
indicated that rainforest supported significantly less establishment of S. obtusifolia
than I. cylindrica in the soil treatment, whilst in the soil + shade treatment, the
woodland habitat possessed 1.9 times as many S. obtusifolia plants than the
rainforest.
When the impact of treatment type within each habitat type was assessed, significant
differences in the establishment of S. obtusifolia were found in the rainforest (F3, 20 =
9.994, p = < 0.001) and woodland habitats (F3, 20 = 6.268, p = 0.004). Post hoc analysis (Tukey test) showed that the soil + leaf litter treatment in the rainforest and woodland environments allowed for significantly higher establishment than the soil + shade and soil + shade + leaf litter treatments (Figure 6.9). No differences existed between the soil and soil + leaf litter treatments within I. cylindrica (F1, 10 = 0.512, p =
0.491) and B. decumbens habitats (F2, 14 = 1.014, p = 0.388).
Germination/establishment was not seen to be delayed by any treatment in any habitat as demonstrated in Figure 6.10. Germination occurred en masse across all treatments types in week three which coincided with the first major rainfall of the season. The maximum numbers of plants in each treatment were reached during week four or week five, with a secondary peak occurring in week nine, after which numbers appeared to have quickly stabilised.
Chapter 6 Impact of Habitat Type - 149 -
200
150 b b
100 ab ab a
Plant Number (+/- SE) 50 a a a
0 Rainfor. B. decum. I. cylin. Low. wood.
Habitat
Figure 6.9 The mean (+ SE) establishment of Senna obtusifolia when exposed to different treatments in a common garden experiment. Each of the different habitats (rainforest, B. decumbens, I. cylindrica and lowland woodland) were treated with some or all of the following treatments: Soil, Soil + Leaf Litter, Soil + Shade, Soil + Leaf Litter + Shade, Soil + Leaf litter (B. decumbens only). Letters above columns represent differences in treatments within each habitat type. See text for a more detailed description of treatments.
Chapter 6 Impact of Habitat Type - 150 -
Rainforest B. decumbens 1200 1200 1000 1000 800 800
600 600
400 400 Plant number Plant 200 200
0 0 1 3 5 7 9 11 13 15 17 19 21 1 3 5 7 9 111315171921
I. cylindrica Woodland
1200 1200
1000 1000 800 800
600 600 400 400 Plant number Plant 200 200 0 0 1 3 5 7 9 111315171921 1 3 5 7 9 111315171921 Week Week
Figure 6.10 The number of Senna obtusifolia plants recorded in each treatment of each habitat over time. Treatments are: soil soil + leaf litter soil + shade soil + leaf litter + shade and soil + leaf litter (B) (B. decumbens only).
6.4 Discussion
The results of this study demonstrate the integral role of the environment, which a seed encounters following dispersal, in influencing the success and course of an invasion (Niklas 1995). Small scale variations in suites of environmental factors between different habitat types imposed variable pressures on S. obtusifolia seeds.
This resulted in the differential seed survival and weed establishment success
(sensu Schupp 1995) between the rainforest and the B. decumbens, I. cylindrica and
Chapter 6 Impact of Habitat Type - 151 - woodland habitats. Limited dispersal could not explain the patchy distribution of the weed, due to the presence of a viable S. obtusifolia soil seed reserve in each habitat investigated. Given these patch dependent differences in invasion success, the relative abundance, distribution and contiguity of favourable and unfavourable environments throughout the Lockhart River region will contribute substantially to the heterogeneous distribution of S. obtusifolia. Large tracts of unsuitable habitat will act as natural barriers to successful spatial dispersal, thereby governing the maximum spread of the weed. These habitats will, however, also have the ability to harbour dormant viable seed and in the event of a release of limiting conditions (i.e. a major disturbance) germination and establishment of seed may occur, furthering the distribution of the weed.
The susceptibility of each of the tested habitat types to S. obtusifolia invasion was shown to be both low and relatively equal, with the rainforest habitat preventing establishment to maturity. This pattern of equality between habitat types was not reflected in the field, where there were some obvious distinctions in distribution and abundance. Given common theories on factors that increase the invasibility of a habitat such as frequent disturbances, low species diversity/richness, and high propagule pressure (e.g. Elton 1958; Fox and Fox 1986; Williamson 1996; Lonsdale
1999; Rejmanek 1999; Davis et al. 2000), this equal result amongst habitats was
largely unexpected. It was thought that the grassland habitats would show much
higher establishment than the other environments tested due predominantly to their
extremely low diversity, their high exposure to light and their much higher rate of
disturbance from wildlife (e.g. pigs, cattle and horses) and human activity (e.g. fire).
Brachiaria decumbens grasslands had the additional factor of having a
comparatively increased level of propagule pressure in the form of its much enlarged
soil seed reserve (Figure 6.6); however this also failed to promote invasion by S.
obtusifolia.
Chapter 6 Impact of Habitat Type - 152 -
Some small differences, though generally not statistically significant, did appear between habitats, favouring at least one grassland community. The S. obtusifolia seed exposed to the conditions of I. cylindrica in both the field and the greenhouse were generally observed to possess higher germinability, record higher establishment and possess taller plants than the other habitats tested (see Figures
6.3 - 6.5 and 6.8 – 6.10). Whilst the majority of these differences were quite small, their overall trend may be indicative of larger scale variability that can be observed in the field. Due to the high degree of variability experienced with the results in this data set, to confirm such trends would require a larger degree of rigorous testing.
Reasons for the very low rates of recruitment recorded across all of the habitats in both the field and greenhouse experiments could be many and may include incorrect conditions to break innate dormancy in the seeds, an unsuitable climate (e.g. rainfall) and a lack of disturbance to reduce competition (Anning et al., 1989; Mackey et al.,
1997; Baskin et al. 1998). Such conditions are likely to vary annually, as was demonstrated by the differences in recruitment evident between the two years in which the seed addition experiments were conducted. The 2003/2004 wet season as a whole enabled more S. obtusifolia plants to establish than that seen in the
2004/2005 wet season. What caused this difference was has not been determined.
The relationships between the weed and its surroundings are thus highly variable and complex and even finer scale studies over longer time periods may be needed to gain more accurate assessments of the response of S. obtusifolia to the habitat conditions. In addition to the characteristics we manipulated, other habitat characteristics which may influence establishment success include habitat structure, species composition, moisture and nutrient availability, fire frequency and animal disturbance, herbivory, as well as the presence or absence of fungal and pathogenic organisms (e.g. Facelli and Picket 1991; Harrington 1991; Chambers and MacMahon
Chapter 6 Impact of Habitat Type - 153 -
1994; Cousens and Mortimer 1995; Nash Suding and Goldberg 1999; Emery and
Gross 2005; Van Mourik et al. 2005). Accordingly, it is perhaps not unexpected that we could not determine differences in habitat invasibility nor identify a unique factor which inhibited S. obtusifolia establishment across the entire landscape.
Nevertheless, the results of the seed introduction experiments and the common garden experiment in section 6.3.5, do imply that sufficient light may be crucial for S. obtusifolia seed survival, germination and establishment. The heavy shade caused by an overhead canopy clearly has the capacity to decrease rates of establishment
and germination, although as seen in Figures 6.9 and 6.10 it does not prevent
germination. This was consistent with patterns in the field, as germinated seed was
recorded in the treatment plots of the rainforest in both years of the seed addition
experiments (authors’ unpublished data), but established plants were never seen.
Light must therefore have a more important role in establishment than germination,
inferring that conditions for seedling growth are more stringent than those required
for germination (Turnbull et al. 2000). Seed survivability was also very low in the
rainforest habitat, which possibly provides another explanation for the lack of S.
obtusifolia establishment within this environment. It is possible that the increased
levels of moisture in rainforest provide better conditions for germination than the
other habitats, but because establishment is not possible, seedlings die and
consequently the seed is destroyed. Another possibility lies in the rainforest soil
type/soil fauna, which as seen in section 6.3.5, results in the lowest germination and
establishment of plants, possibly due to the decreased seed survivability.
Understanding what conditions can limit S. obtusifolia growth provides possible avenues to employ in an integrated management strategy. If conditions for germination are less stringent than they are for growth, inducing conditions that prompt germination but will not allow establishment is one method to deplete seed
Chapter 6 Impact of Habitat Type - 154 - reserves. Large losses from soil seed banks can be an important factor in disrupting the population dynamics of invasive species and limiting rates of expansion
(Westerman et al. 2003). From the results of this study, shade may provide one option as a management tool; however, it may not be useful in all circumstances, particularly on a large scale.
The S. obtusifolia soil seed reserve was found to be most extensive in the B. decumbens grassland, and even though the survivability of seed in this habitat was
high, the pure extent of the reserve clearly suggests that a factor other than canopy
shade is preventing the establishment of the weed. Competition for light may be
important in determining outcomes of plant to plant interactions in multi-layer
communities, but is an unlikely limiting factor in grassland environments (Brown et al.
1998). Dense S. obtusifolia infestations are often associated with grassland habitats, however, it has also been observed that in the absence of a major disturbance (e.g. fire); B. decumbens can encroach dramatically into populations of the weed in just one season (authors’ unpublished data). A very dense layer of trash/dead (approximately 20-40 cm deep (authors’ unpublished data)) grass is typically associated with the B. decumbens habitat, occurring at the base of the green leafy layer of grass. Brown et al. (1998) and Brown and Archer (1989) suggest that when light is not a limiting factor, the most effective means of restricting light to emerging shrub seedlings is via the build-up of a continuous litter layer.
Therefore, it is possible that this dense layer of B. decumbens grass is having a negative impact on the emergence of S. obtusifolia seedlings and may provide one explanation for the high density of seeds in the soil. Caution need be applied in presuming this outcome however, because, as demonstrated in section 6.3.5, the leaf litter treatment caused the highest levels of recruitment across all habitats. This could be explained by the observation made by Facelli and Pickett (1991) whereby, moderate levels of herbaceous litter can actually improve chances of woody seedling
Chapter 6 Impact of Habitat Type - 155 - establishment as it moderates harsh environmental conditions. Therefore, by maintaining a very dense ground cover of B. decumbens in restricted areas may perhaps provide another possible avenue of control of S. obtusifolia (Anning et al.
1989; King 1993).
King (1993) demonstrated that grass covered soils can be up to 15°C cooler than
bare soils. This reduced level of heat can prevent soils reaching a temperature
sufficient to break the physical (hard seed coat) dormancy of S. obtusifolia seed.
Therefore, by maintaining a dense cover of grass (i.e. minimise disturbances), S.
obtusifolia may exist at a far more manageable level (Anning et al. 1989; King 1993).
This supports the observations of Anning et al. (1989), whereby S. obtusifolia
invasion was greatly reduced in pastures which were not overgrazed. It also
conforms to the use of Brachiaria sp. in China to manage the highly invasive
Chromolaena odorata (Siam weed) (Wu Renrun and Xu Xuejun 2000). The
aggressive, decumbent and smothering habit of swards of Brachiaira spp. reportedly
have poor compatibility with many broadleaf species (Miles et al. 1996; Wu Renrun
and Xu Xuejun 2000), and therefore the proper maintenance of this grassland and
potentially others in the Lockhart River region may significantly reduce the
prominence of S. obtusifolia.
6.5 Progress Towards Aims of Thesis
The experimentation undertaken at the local scale in this chapter found that S.
obtusifolia seed mortality and establishment was differentially affected by different
habitat types of Iron Range National Park and the surrounding Lockhart River region.
The conditions created by the localised environments and their differential affects on
S. obtusifolia seed may provide some explanation for the spatial and temporal
variation evident in S. obtusifolia populations across the landscape. These
Chapter 6 Impact of Habitat Type - 156 - environments, although capable of supporting monocultures of S. obtusifolia (except rainforest), also have the ability to inhibit establishment. To determine what causes inhibition and where this may occur in the life cycle of S. obtusifolia would be of great
benefit for both knowledge and for management. Most certainly, the distribution of
heavily shaded and competitive environments such as rainforest, which prevented
the establishment of mature S. obtusifolia, will provide barriers to its successful
dispersal and significantly contribute to the observed patchy distribution of the weed.
Maintaining a dense cover of grass in grasslands by reducing disturbances could
also potentially limit S. obtusifolia invasion and deserves further attention as a
strategy for management.
Chapter 7
Population dynamics of Senna obtusifolia in Iron Range National Park: a
model of seed fate
Chapter 7 Population Model Development - 158 -
7.1 Introduction
Weed populations are subject to intrinsic sources of variability such as alterations to demographic parameters (e.g. births and deaths), intra-specific interference and plant to plant variability in performance (Freckleton and Watkinson 1994), as well as variation in extrinsic factors including management (land use), weather and interactions with other biotic and abiotic agents (Freckleton and Watkinson 1994;
Cousens and Mortimer 1995). Given that a reduction in density of an invasive species is the central aim of weed management (Cousens and Mortimer 1995), an obvious step in population analysis is to investigate how these demographic and extrinsic parameters can interact to impinge on, or promote population growth. Such knowledge greatly assists in predicting the population trajectory of the species over time and space, a device of great benefit to weed managers.
Ecosystems of any description are biologically complex units. This makes it difficult to ascertain the impact of each system component on invasion success, in isolation and in combination, without lengthy experimentation. Whilst ecosystem mechanisms and processes can be elucidated from experimentation, often more questions are raised which require answering before the system can be understood (Cousens and
Mortimer 1995). Ecological and mathematical modelling presents an additional tool, enabling the synthesis of the numerous components and relationships present within ecosystems and communities (Gertseva et al. 2004).
Ecological modelling enables the user to formulate testable hypotheses regarding the ecological outcomes of different plant histories and help discover logical consequences of assumptions about the biology of a system (Venable 1984).
Attributes of the invader itself, and the community it survives in, will both contribute to a successful invasion (Crawley 1987; Buckley et al. 2004). Models enable the
Chapter 7 Population Model Development - 159 - consideration and estimation of the influence of many simultaneous, biological, chemical and physical factors and the interrelationships among them (Jørgensen and Koryavov 1990; Angelini and Petrere 2000). Moreover, they provide further opportunity to explore the system beyond the realm of the data set used to create the model’ via hypothetical simulations that are not possible in practice (Venable
1984; Angelini and Petrere 2000). For example, sensitivity analyses can be used to identify life-history stages that have the greatest impact on population growth rates, which can then be appropriately targeted for control (Buckley et al. 2003; Shea and
Kelly 1998). Similarly, models can be used to allow the direct evaluation of the impact of a variety of population management techniques and the timing of their application (Angelini and Petrere 2000; Buckley et al. 2004). For example, Buckley et al. (2001) discovered through modelling the population dynamics of
Tripleurospermum perforatum (scentless chamomile), that management regimes would have to incorporate severe and sustained reductions in fecundity and in survival late in the growing season. If management was to be targeted early in the growing season, the level of infestation can actually be exacerbated. Numerous other recent examples also exist where a range of models have been used to assess the impact of management regimes for invasive species (e.g. Rees and Paynter
1997; Rees and Hill 2001; Buckley et al. 2005).
Computer technology has allowed the use of mathematical expressions of population and ecosystem dynamics to be more feasible, realistic and practical relative to approaches used in the past (Gertseva et al. 2004). STELLA software (Hulbert et al.
2000) is one example of programs designed to explore the complex dynamics of a variety of systems. Possessing a user friendly interface and array functions,
STELLA has achieved broad recognition and has been successfully applied to a number of studies (e.g. Krivtsov et al. 2000; Blackwell et al. 2001; Gertseva et al.
2004; Weclaw and Hudson 2004; Scanlan et al. 2006).
Chapter 7 Population Model Development - 160 -
Based on the data obtained from the field experiments outlined in the previous chapters, this chapter describes the development of a STELLA model designed to mimic the population dynamics of a S. obtusifolia infestation in the Lockhart River region of northern Australia. By following the fate of S. obtusifolia seed produced in one generation through the annual life cycle of S. obtusifolia, the gains and losses from the system can be assessed and the behaviour of S. obtusifolia across many generations can be predicted. The model was specifically designed to investigate aspects of the species’ life history which are sensitive to change and are vital in maintaining persistent populations. The discovery of such traits may provide a useful basis for management - the effectiveness of which can be predicted by assessing the long term dynamics of the population from the model output.
Secondarily, the response of S. obtusifolia to environmental conditions and management regimes alternate to those ‘normally’ experienced can be assessed and again provide a useful tool for managers. The model is to be used an exploratory tool only.
Grant et al. (1997) propose four phases in the creation and use of a deterministic model. These are: the formulation of a conceptual model, quantifying the model specifications, evaluation of the model and using the model. This chapter aims to address the first three phases – those involved in developing the model. To adhere to this system of thinking, this chapter does not conform to traditional chapter format and is instead broken into a series of sections that are designed to logically describe and discuss elements of the model as they occur. These sections are: section 7.2 a brief overview of the system of interest and how the different components of the model will be related to one another (i.e. the conceptual model), section 7.3 describes the major limitations and assumptions of the model, section 7.4 describes the quantitative aspect of the model by translating the relationships in the conceptual
Chapter 7 Population Model Development - 161 - model into a series of mathematical equations, and, section 7.5 which evaluates the usefulness of the model in meeting the model objectives/purpose. The fourth phase of the modelling process is described in Chapter 8.
7.2 Model Overview
The model simulates the effect of rainfall on germination and seedling development, the density-dependent effects of overcrowding and the effects of fire on mortality and the annual dynamics of S. obtusifolia populations. The population dynamics are modelled on a weekly time step for 52 weeks of the year. The first time step or
Week 1 starts on the 1st of November. This is to represent the beginning of the life
cycle, where all mature plants have dropped their seed and died, and the first
germination rains are likely. Senna obtusifolia seed now remains on the soil surface
awaiting an environmental cue for germination.
The model represents the annual dynamics of a population of S. obtusifolia by following the fate of S. obtusifolia seed and the plants that germinate, establish and mature (Figure 7.1). Newly produced seeds are released by mature S. obtusifolia plants in a dormant (dsrain) or non-dormant (ndsrain) state. Dormant seed remains on the soil surface (SS) in a dormant state where: it is susceptible to fire (fire) which can result in its death (ssmf), it can be buried (ssb) and incorporated into the soil seed reserve (SSR), destroyed by causes other than fire (ssm), or its dormant state can be broken (bd) by natural forces and join the non-dormant portion of seed on the soil surface (ND). Similarly non-dormant seed can be destroyed by fire (ndmf), die due to natural causes other than fire (ndm) or alternatively it will germinate (germ) in response to weekly rainfall (wrain). Seeds stored in the soil seed reserve (SSR) that remain viable or have not died as a result of other natural causes (ssrm) will also respond to weekly rainfall and germinate (ssr germ). Germinating seeds grow into
Chapter 7 Population Model Development - 162 -
seedlings (SO0) and further develop (gri for i = 1-4) through all stages of vegetative
growth (SOi for i = 1-4) to reproductive maturity (SO5). At each growth stage except maturity, the developing plants are susceptible to death from density-dependent effects with plant numbers in more advanced stages of growth causing mortality in the younger stages (somi for i = 0-4). Plants in early growth stages (SO1, SO2) are
also susceptible to rainfall effects, where low soil moisture caused by a lack of
rainfall in the previous four weeks (mrain) will not sustain growth of young plants and
cause mortality (somr, som2). Plants that successfully survive to the last stage of
growth become reproductively mature (SO5). A small proportion of these may die
from natural causes prior to seed set (som5), whilst the majority will produce and
release seed onto the soil surface and then die (am), signalling the end of the annual
life cycle.
The dynamics represented in this model are set at the level of the population, with all population estimates being at a scale of 1m2. All field experimentation and observations were made at this level of scale (see field methodology Chapters 5 and
6). Immigration and emigration are not explicitly considered, as they are assumed to be equal with no net effect on seed numbers. For some field sites and for some areas within a population (i.e. the edge or invasion front) this may be an invalid assumption.
Chapter 7 Population Model Development 163
Figure 7.1 The conceptual STELLA model diagram, illustrating the key variables and relationships in the population dynamics of Senna obtusifolia. See text for definitions of model parameters.
Chapter 7 Population Model Development - 164 -
7.3 Model Limitations
7.3.1 Lack of spatial capacity
The model currently represents the landscape in which S. obtusifolia occurs as
homogeneous. Different simulations are modelled by altering the external drivers of
the system (e.g. rainfall and fire). Mortality and reproduction parameters could be
altered to reflect different locations if sufficient data existed to indicate variability.
The principal issue with homogeneous location models is that they fail to account for dynamic immigration and emigration (Scanlan et al. 2006). This model assumes that the population is large enough to regard immigration and emigration as small in comparison to the total population and that net migration is zero. Because no spatial component is incorporated into the model, spread or invasion progress can not be simulated.
7.3.2 Lack of dynamics reflecting early stages of the life cycle
The dynamics involved in germination and early establishment can be complex and highly detailed. Whilst it is recognised that the dynamics occurring at this stage of the life cycle are very important in the overall dynamics of the S. obtusifolia system, it was impossible to collect data on a daily basis as would be required to develop a mechanistic model.
7.3.3 Lack of inter-specific competition
This model incorporates the impact of intra-specific competition only. More specifically it represents the effects of self regulation from overcrowding. Model
Chapter 7 Population Model Development - 165 - simulations can apply only to locations that have been recently disturbed, with no other species of plant being present, or in monocultures. Inter-specific competition was omitted from the model due to insufficient reliable data reflecting the competitive ability of S. obtusifolia with other plant species/communities at any stage of the life cycle.
7.4 Model Description
The model was formulated as a deterministic compartment model based on difference equations (∆ t = 1 week), programmed in STELLA (Hulbert et al. 2000).
Deterministic models are based on parameters that are single point estimates and are not subject to random fluctuations. The modelled system will therefore be entirely defined by the initial conditions input into the model (Renshaw 1991; Grant et al. 1997) A summary of the mean values, definitions and information sources for model parameters are provided in Table 7.1 and a graphical representation of functional relationships used in the model are presented in Figure 7.3.
7.4.1 State Variable Equations
The equation for S. obtusifolia seed that has been released by mature plants onto the soil surface is:
SS t+1 = SS t + (dsrain t – ssm t – ssb t – ssmf t – bd t) ∆ t, (7.1)
where SS is the number of S. obtusifolia seeds on the soil surface that are not capable of germination at time t; dsrain is the dormant portion of seed production by mature plants at time t; ssm is the natural mortality of seeds on the soil surface at
Chapter 7 Population Model Development - 166 - time t; ssb is the burial and incorporation of surface seeds into the soil seed reserve at time t; ssmf is the mortality of surface seeds caused by a fire at time t; and bd is
the number of surface seeds that have had their hard seed coat dormancy broken at
time t.
The equation for S. obtusifolia seed on the soil surface that is non dormant seed is:
ND t+1 = ND t + (ndsraint + bd t – ndmft - ndm t – germ t) ∆ t, (7.2)
Where ND is the number of seeds on the soil surface that are in a non-dormant state at time t; ndsrain is the number of non-dormant seeds produced at time t; ndm is the natural mortality of non-dormant seeds at time t; and germ is the number of non- dormant seeds that germinate at time t.
The equation for S. obtusifolia seed stored in the soil seed reserve is:
SSR t + 1 = SSR t + (ssb t – ssr germ t – ssrm t) ∆ t, (7.3)
Where SSR is the number of S. obtusifolia seeds present in the soil seed reserve at time t; ssr germ is the germination of seed buried in the soil seed reserve at time t;
and ssrm is the natural mortality of seed stored in the soil seed reserve at time t.
For S. obtusifolia seed surviving to seedlings, the equation is:
SO0 t + 1 = SO0 t + (ssr germ t + germ t – som0 t – gr0 t) ∆ t, (7.4)
Chapter 7 Population Model Development - 167 - where, SO0 is the number of S. obtusifolia seedlings at time t; som0 is the mortality of seedlings caused by density-dependent effects and other natural causes at time t; and gr0 is the number of S. obtusifolia seedlings that move to the next growth phase at time t.
For S. obtusifolia seedlings that survive a further week of growth to a second growth
phase, the equation is:
SO1 t +1 = SO1 t + (gr0 t – gr1 t – somr t – som1 t) ∆ t, (7.5)
Where, SO1 is the number of S. obtusifolia plants at the second growth phase at
time t; gr1 is the number of S. obtusifolia plants that move to the next growth phase
at time t; somr is the mortality of S. obtusifolia plants caused by insufficient moisture
(rainfall) to sustain growth at time t; som1 is the mortality of plants caused by
density-dependent effects and other natural causes at time t;
The equation for S. obtusifolia plants surviving to the next growth phase is:
4 4 SO2 t(i) +1 = ∑∑ t(i) t −−+ tt )gr2som2gr1(SO2 Δ t (7.6) i=1 i=1
(This state variable differs from those described above because it does not act solely as a reservoir, but instead is known as a conveyor, which provides a pause in the model system. Senna obtusifolia plants will start in the conveyor at time step 5 and remain in the conveyor for the number of weeks (i.e. transit time) designated in the model development. This conveyor has a designated transit time of 4 weeks).
Where, SO2 is the number of S. obtusifolia plants at the third growth phase at time t;
som2 is the mortality of S. obtusifolia plants caused by density-dependent effects, a
Chapter 7 Population Model Development - 168 - lack of moisture to maintain growth and other natural causes at time t; gr2 is the number of plants that move to the next growth phase at time t.
Table 7.1 Summary of mean values, definitions and sources of model parameters
Parameter Value Description Source
k1 See Fig Seed production as a function Based on field 7.3a of plant density observations and (Eq’s 7.10 – experimental data in 7.14) Chapter 5; Smith & Jordan (1994)
k2 0.005 Mortality rate of surface seed Estimate based on field observations
k3 t See Fig Relative decrease in surface Based on field 7.3b seed mortality by fire as a observations and (Eq 7.16 & function of time (i.e. certain experimental data in 7.17) weeks of the year will support Chapter 5 hotter fires)
k4 0.7 Maximum rate of surface seed Based on field mortality resulting from fire observations and experimental data in Chapter 5
K5 0.15 Mortality rate of non-dormant Estimate based on seed field observations
K6 0.006 Mortality rate of buried seed Egley & Chandler (1978;83)
Chapter 7 Population Model Development - 169 -
Anon (1989) Creel et al. (1968)
k7 1 Maximum rate plant mortality Estimate based on in SO0 field observations
k8 t See Fig 7.3c Relative increase in mortality Estimate based on (Eq 7.20) of plants in SO0 as a function field observations of plant density in SO2
k9 1 Maximum rate of mortality of Estimate based on plants in SO1 field observations
k10 t See Fig Relative increase in mortality Estimate based on 7.3d of plants SO1 of plant density field observations (Eq 7.21) in SO2
k11 t See Fig Relative increase in mortality Estimate based on 7.3e of plants in SO1 as a function field observations (Eq 7.22) of rainfall that has fallen over the previous four weeks
k12 0.2 Rate of plant mortality in SO2 Estimate based on caused by factors other than field observations rainfall and density
k13 See Eq 7.25 Rate of plant mortality in SO2 Estimate based on caused by rainfall and density field observations combined and Chapter 3
k14 t See Fig 7.3f Relative increase in plant Based on field (Eq 7.25) mortality in SO2 as a function observations, spot of rainfall that has fallen over counts and Chapter the previous four weeks 3
k15 t See Fig Relative increase in plant Estimate based on
Chapter 7 Population Model Development - 170 -
7.3g mortality in SO2 as a function field observations (Eq 7.25) of plant density in SO4
k16 0.4 Maximum rate of plant Estimate based on mortality in SO3 field observations
k17 t See Fig Relative increase in plant Estimate based on 7.3h mortality in SO3 as a function field observations (Eq 7.26) of plant density in SO4
k18 t See Fig 7.3i Relative increase in plant Estimate based on (Eq 7.27) mortality in SO4 as a function field observations of plant density in SO5
k19 0.01 Mortality rate of plants in SO5 Estimate based on field observations
k20 0.008 Rate of burial of surface seed Estimate based on field observations
k21 0.01 Rate at which dormancy is Estimate based on broken in surface seeds field observations
k22 0.4 Maximum rate of germination Estimate based on of ND seed field observations
k23 t See Fig 7.3j Relative increase in Estimate based on (Eq 7.33) germination of ND seed as a field observations, function of weekly rainfall Chapter 3; Mackey et al. 1997
k24 0.02 Maximum rate of germination Based on field of SSR (buried seed) observations and experimental data in Chapter 5
Chapter 7 Population Model Development - 171 -
k25 t See Fig 7.3k Relative increase in Based on field (Eq 7.34) germination of buried seed as observations and a function of weekly rainfall experimental data in Chapters 3 and 5
The equation for S. obtusifolia plants that survive to the fourth growth phase is:
SO3 t+1 = SO3 t + (gr2 t – gr3 t – som3 t) ∆ t, (7.7)
Where, SO3 is the number of S. obtusifolia plants at the fourth growth phase at time t; gr3 is the number of plants that move to the next growth phase at time t; som3 is
the mortality of plants caused by density-dependent effects and other natural causes
at time t.
For the number of S. obtusifolia plants present at the fifth growth phase the equation
is:
12 12 SO4 t(i) +1 = ∑∑ t(i) tt −−+ t )som4gr4gr3(SO4 Δ t (7.8) i=1 i=1
(This variable is another conveyor. The designated transit time is 12 weeks)
Where, SO4 is the number of S. obtusifolia plants at the fifth growth phase at time t; gr4 is the number of plants that move to the next growth phase at time t; som4 is the mortality of plants caused by density-dependent effects and other natural causes at time t.
The equation for the number of S. obtusifolia plants that reach reproductive maturity is:
Chapter 7 Population Model Development - 172 -
SO5 t + 1 = SO5 t + (gr4 t – som5 t – am t) ∆ t, (7.9)
Where, SO5 is the number of reproductively mature S. obtusifolia plants at time t;
som5 is the mortality of mature plants by natural causes at time t; am is the annual
mortality of all mature plants in the population after seed production and release has
occurred at time t = 36.
7.4.2 Seed Production
The time of seed release by mature plants is set at 36 weeks (approximately
August). Although seed production and release would normally occur before and
after this week, it was decided to confine the seed rain to one week to keep the
representation simple. It is assumed that the pattern of seed fall prior to and after
week 36 would not greatly affect the overall dynamics, except in the event of
abnormal rainfall events. Senna obtusifolia produces dimorphic seeds with
approximately 90% being green and dormant and 10% being brown and non-
dormant (Baskin et al. 1998) and it is likely that only a portion of this 10% would
germinate. The two types of seed therefore had to be differentiated in the seed rain.
Seed predominantly falls to the soil surface at the base of the parent plant.
The seed rain was determined by calculating the number of seeds being produced
per plant within a square metre. As illustrated in Figure 7.2, the relationship between
the number of seeds per plant and the density of plants is quite significant, with the
number of seeds produced declining after stems reach a density of approximately
75/m2. Log transformation of the numbers (seeds/m2) below indicates an even
clearer relationship as seen in Figure 7.3a. The slope of the line attained is used in
Equation 7.12 and is the basis for determining seed rain.
Chapter 7 Population Model Development - 173 -
30000
25000
20000 2
Seeds/m 15000
10000
5000 0 50 100 150 200 250
2 Stem Density/m
Figure 7.2 The relationship between the production of Senna obtusifolia seeds/m2 and the density of S. obtusifolia stems/m2 (see Chapter 5 for more detail).
The number of green, dormant S. obtusifolia seeds produced and dropped by mature plants annually is calculated as:
dsrain k1 ××= 9.05SO when t = 36 (7.10)
36or when 0dsrain when t ><= 36or (7.11)
)SO5 0.0139 - 6.441( - 0.0139 × )SO5 where 1 = ek (7.12)
The number of brown, non-dormant S. obtusifolia seeds produced and dropped by
mature plants annually is calculated as:
ndsrain = k1 × × 1.0SO5 when t = 36 (7.13)
36or when 0ndsrain when t ><= 36or (7.14)
Chapter 7 Population Model Development - 174 -
Where SO5 is the number of reproductively mature S. obtusifolia plants at time t; and
k1 is the seed production as a function of density of mature plants (Figure 7.3a).
7.4.3 Seed Mortality
The causes of mortality of S. obtusifolia seed in the Iron Range area are not well understood. A major external driver in the entire system is fire, which can destroy seed (see Chapter 5), as well as cause mass germination from the soil seed bank
(Anning et al. 1989). Other possible causes of mortality could include age (loss of viability) and pathogenic and fungal infection. No known seed predators exist, however, no investigation was conducted to clarify this.
The proportion of green, dormant S. obtusifolia seed on the soil surface dying per week due to causes other than fire is calculated as:
(7.15) SS ssm SS ×= k 2
Where SS is the number of seeds present on the soil surface in week t and k2 is the rate of surface seed mortality (Table 7.1).
The proportion of green, dormant seeds on the soil surface dying due to a fire event is calculated as:
ssmf SS t ××= kk 43 (7.16)
Where SS is the number of dormant seeds present on the soil surface in week t; k3t is the relative decrease in seed mortality caused by fire as a function of time in
Chapter 7 Population Model Development - 175 -
weeks (Figure 7.3b); and k4 is the maximum rate of surface seed mortality that can
result from a fire (Table 7.1)
The proportion of brown, non-dormant seeds on the soil surface dying due to a fire
event is calculated as:
NDndmf t ××= kk 43 (7.17)
where ND is the numbe r of no n -dorm a nt see d s pres e nt on the sol surface in week t.
The proportion of non-dormant seeds on the soil surface dying as a result of natural causes is calculated as:
NDndm ×= k5 (7.18)
Where k5 is the mortality rate of non-dormant seed (Table 7.1).
The proportion of S. obtusifolia seeds that experience mortality in the soil seed reserve is calculated as:
SSRssrm ×= k6 (7.19)
Where SSR is the nu m ber of S. obtusifolia se eds in the soil seed reserve in week t;
and k6 is the mortality rate of the buried seed (Table 7.1).
Chapter 7 Population Model Development - 176 -
7.4.4 Plant mortality
Due to the ma ss germination of S. obtusifolia seed at the beginning of a season, plant mortality early in the developmental stages is most likely to be a result of self thinning and density-dependent competition. Insufficient rainfall can also be detrimental to the young plants, causing mass mortality if conditions are unsuitable.
Since germination generally occurs during the wet season, this is not usually of concern. However, abnormal rainfall during the dry season may be sufficient to cause germination from newly fallen seeds and/or the soil seed bank. If rainfall is limited to one event only, any remaining moisture is unlikely to be able to support further growth of the germinated seed. Due to the annual life cycle of S. obtusifolia, all mature plants must die by the end of the year. Therefore all mature plants are killed in week 44 (end of September/beginning of October) indicating the end of the annual lifecycle. Again plants will die before and after this date, but for simplicity it was set for one week, as death occurring other than in this week will have no impact on the production of seed.
The proportion of S. obtusifolia seedlings dying from density-dependent effects is
calculated as:
= SO0som0 × k × k87 t (7.20)
Where SO0 is the numb er o f S. obtusifolia seedlings in week t; k7 is the maximum
rate of mortality of seedlings in SO0 occurring as a result of density dependence
(Table 7.1); and k8t is the relative increase in mortality of plants in SO0 as a function of plant density in SO2 (Figure 7.3c).
Chapter 7 Population Model Development - 177 - a) b)
7 1.2
6 1.0
5 0.8 4
k1 0.6 k3 3
0.4 2
1 0.2
0 0.0 0 50 100 150 200 250 0 1020304050 Plant Density/m2 Week
c) d)
1.2 1.2
1.0 1.0
0.8 0.8
0.6 0 k8 0.6 k1
0.4 0.4
0.2 0.2
0.0 0.0 0 100 200 300 400 500 0 100 200 300 400 500 2 Plant Density/m Plant Density/m2
e) f)
1.2 1.2
1.0 1.0
0.8 0.8
0.6 0.6 k11 k14
0.4 0.4
0.2 0.2
0.0 0.0 0 20406080100 0 100 200 300 400 500 Rainfall (mm) Rainfall (mm)
Chapter 7 Population Model Development - 178 - g) h)
1.2 1.2
1.0 1.0
0.8 0.8
0.6 0.6 k15 k17
0.4 0.4
0.2 0.2
0.0 0.0 0 100 200 300 400 0 100 200 300 400 500 2 2 Plant Density/m Plant Density/m i) j)
1.2 1.2
1.0 1.0
0.8 0.8
0.6 0.6 k18 k23
0.4 0.4
0.2 0.2
0.0 0.0 0 50 100 150 200 250 0 1020304050 Plant Density/m2 Rainfall (mm) k) 1.2
1.0
0.8
0.6 k25
0.4
0.2
0.0 0 1020304050 Rainfall (mm)
Figure 7.3 Graphical representation of the functional relationships within the model: a) seed production and plant density; b) surface seed mortality by fire and time that fire occurs; c) seedling mortality and plant density; d) mortality of young plants and plant density, e) mortality of young plants and rainfall; f) mortality of established plants and rainfall; g) mortality of established plants and plant density; h) mortality of maturing plants and plant density; i) mortality of pre-reproductive plants and plant density; j) surface seed germination and rainfall, and; k) soil seed germination and rainfall.
Chapter 7 Population Model Development - 179 -
The proportion of plants at the second growth stage dying from density-dependent effects is calculated as:
S O1 som1 SO1 ××= kk 109 t (7.21)
Where SO1 is the number of S. obtusifolia plants at the second growth stage in week
t; k9 is the maximum rate of mortality of plants in SO1 caused by density dependence (Table 7.1); and k10t is the relative increase in mortality of plants in SO1 as a function of plant density in SO2 (Figure 7.3d).
The proportion of plants at the second growth stage dying from insufficient moisture is calculated as:
SO1somr ×= k11 t (7.22)
Where SO1 is the number of S. obtusifolia plants at the second growth stage in week t; and k11t is the relative increase in mortality of plants in SO1 as a function of rain that has fallen over the previous four weeks (measure of soil moisture) (Figure 7.3e).
The proportion of S. obtusifolia plants in the third growth stage dying from both
density-dependent effects and insufficient moisture is calculated as:
= SO2som2 × + kk 1312 )( + kk 1312 < 1)(for (7.23)
= SO2som2 + kk 1312 > 1)(for (7.24)
when t1413 )1( ×−+= kkkk 15t14 t (7.25)
Chapter 7 Population Model Development - 180 -
Where SO2 is the number of S. obtusifolia plants at the third growth stage in week t;
k12 is the mortality rate of plants in SO2 caused by factors other than rainfall and density (Table 7.1); k13 is the rate of mortality of plants in SO2 caused by rainfall and density combined; k14t is the relative increase in mortality of plants in SO2 as a function of rainfall that has fallen over the previous four weeks (Figure 7.3f); and k15t is the relative increase in mortality of plants in SO2 as a function of plant density in
SO4 (Figure 7.3g).
The proportion of S. obtusifolia plants in the fourth growth stage dying from density- dependent effects is calculated as:
= SO33som × × kk 1716 t (7.26)
Where SO3 is the number of S. obtusifolia plants at the fourth growth stage in week t; k16 is the maximum rate of mortality of plants in SO3 caused by density
dependence (Table 7.1), and; k17t is the relative increase in mortality of plants in SO3
as a function of plant density in SO5 (Figure 7.3h).
The proportion of S. obtusifolia plants dying at the fifth stage of growth from density- dependent effects is calculated as:
= SO44som × k t18 (7.27)
Where SO4 is the number of S. obtusifolia plants at the fifth growth stage in week t,
and; k18t is the relative increase in plant mortality in SO4 as a function of plant density in SO5 (Figure 7.3i).
Chapter 7 Population Model Development - 181 -
The proportion of S. obtusifolia plants in the final stage of growth dying from natural causes before seed set is calculated as:
= SO5som5 × k19 (7.28)
Where SO5 is the number of reproductively mature S. obtusifolia plants in week t,
and; k19 is the mortality rate of mature plants (Table 7.1).
The mortality of all mature S. obtusifolia plants once they have released their seed as part of the annual S. obtusifolia life cycle is calculated as:
= SO5am t = 44for (7.29)
= 0am for t >< 44or (7.30)
Where SO5 is the number of mature S. obtusifolia plants in week t = 44.
7.4.5 Seed burial
Senna obtusifolia has a long lived soil seed reserve that is extremely beneficial in the
invasion process. A proportion of seed on the soil surface must at some stage be
incorporated into the soil seed reserve to maintain it. Therefore, the proportion of
surface seed buried is calculated as:
SSssb ×= k20 (7.31)
Where SS is the number of S. obtusifolia seeds on the soil surface and k20 is the rate at which seed is buried (Table 7.1).
Chapter 7 Population Model Development - 182 -
7.4.6 Breaking Dormancy
As mentioned earlier, 90% of S. obtusifolia seed is produced in a dormant state.
Scarification of this seed must occur in order for it to germinate (Creel et al. 1968).
Scarification is thought to occur by chemical (soil acids and micro-organisms)
(Parsons and Cuthbertson 2000) and mechanical means (ploughing, pounded by
heavy rain) (James and Fossett 1982/83; Mackey et al. 1997) as well as by high
field/soil temperatures (Baskin and Baskin 1977) and potentially fire (Anning et al.
1989).
The proportion of seeds on the surface that have their non-dormant state broken is calculated as:
SSbd ×= k21 (7.32)
Where SS is the number of seeds on the soil surface in week t, and; k21 is the rate at which dormancy is broken in hard surface seed (Table 7.1).
7.4.7 Germination
The germination of S. obtusifolia seed is onset by the arrival of the summer rains.
Germination can occur from both the surface seed and from the soil seed once dormancy has been broken. Senna obtusifolia can emerge en masse (Figure 7.4).
The extent of this flush may confer S. obtusifolia a competitive advantage at a very
early stage of development.
Chapter 7 Population Model Development - 183 -
Figure 7.4 Illustration of the large scale germination of Senna obtusifolia after fire. All green seen in these photographs are S. obtusifolia seedlings. This germination was in response to light rainfall in early November, and all plants died after approximately two weeks due to insufficient moisture to sustain them.
The proportion of seed germinating from the soil surface is calculated as:
NDgerm ××= kk 2322 t (7.33)
Where ND is the number of surface seeds that are non-dormant in week t, k22 is the maximum rate of germination of non-dormant seed (Table 7.1), and k23t is the relative increase in germination as a function of weekly rainfall (Figure 7.3j).
The proportion of seed germinating from the soil seed reserve is calculated as:
SSRgermssr SSRgermssr ××= kk t2524 (7.34)
Chapter 7 Population Model Development - 184 -
Where SSR is the number of seeds in the soil seed reserve in week t, k24 is the maximum rate of germination of buried seed (Table 7.1); and k25t is the relative
increase in germination of buried seed as a function of weekly rainfall (Figure 7.3k).
7.4.8 Growth
The proportion of S. obtusifolia seedlings developing to the next growth stage is calculated as:
−= som0SO0gr0 (7.35)
Where SO0 is the number of seedlings in week t, and; som0 is the number of
seedlings dying (see equation 20).
The proportion of S. obtusifolia plants in SO1 maturing to SO2 is calculated as:
som1SO1gr1 −−= somr (7.36)
Where SO1 is the number plants in the second growth stage in week t, and; som1
and somr are the number of SO1 plants dying from density-dependent and rainfall
effects (see equations 21 and 22).
The proportion of S. obtusifolia plants in SO2 maturing to SO3 is calculated as:
= SO2gr2 (4) (7.37)
Chapter 7 Population Model Development - 185 -
Where SO2 is the number of plants in the third growth stage in week t (This is the output from the four week conveyor. gr2 is the final number of plants to emerge after conveyor leakage (i.e. plant mortality) has been calculated).
The proportion of S. obtusifolia plants in SO3 maturing to SO4 is calculated as:
−= som3SO3gr3 (7.38)
Where SO3 is the number of plants in the fourth growth stage in week t, and; som3
is the number of plants dying due to density-dependent effects.
The proportion of S. obtusifolia plants in SO4 maturing to SO5 is calculated as:
= SO4gr4 (12) (7.39)
Where SO4 is the number of plants in the fifth growth stage in week t. (This is the
output from the twelve week conveyor. gr4 is the final number of plants to emerge
after conveyor leakage (i.e. plant mortality) has been calculated).
7.5 Model Evaluation
7.5.1 Overview of model evaluation simulations
The ability of the model to represent S. obtusifolia population dynamics in far
northern Queensland had to be evaluated before the model could be used as a tool
Chapter 7 Population Model Development - 186 - to assess future population behaviour and test the response of the population to various hypotheses (Chapter 8). This evaluation was achieved primarily by parameterising the model to represent rainfall and fire conditions of S. obtusifolia
populations throughout the Lockhart River region for which appropriate field data
were available. Nine of the sites possessed data sets across three years, whilst the
remaining three possessed data for two years. All populations experienced
fluctuations in size and density temporally and were generally subjected to fire on an
annual basis (Table 5.1).
Reliable data were not recorded for all of the individual stages of growth described in the model, due to a large number of failed experiments. Numerous experiments were designed to quantify the majority of model components; however for reasons beyond control, satisfactory results could not be obtained. Table 7.1 indicates where sources of quantitative data were used for model parameterisation. In many cases, a general knowledge of the system acquired by spot counts and observation throughout the four years of field work was used to develop the model and ensure that parameters were realistic. Appropriate published data were also used to guide model development and evaluation (Table 7.1).
It was initially anticipated that the demographic data derived from one population could be used to predict the behaviour of remaining populations, thereby evaluating the accuracy of the model. However, as a result of the moderately low level of complexity built into the model, the main drivers of system change included were found to be insufficient to account for the extreme variation that was observed between S. obtusifolia populations within and between years. This made the use of one population largely invalid, as almost all future predictions made would be very inaccurate. Instead, average values for seed production, stem density and soil seed reserves for all areas with S. obtusifolia plants present were calculated for each year
Chapter 7 Population Model Development - 187 - measurements were recorded (i.e. 2002, 2003 and 2004). The averaged data values from the first year of measurements, 2002, were used to initialise all future simulations unless stated otherwise.
Model evaluation comprises a number of steps which include assessing the reasonableness of the model structure and functional relationships, to evaluate the correspondence between model behaviour and expected patterns of behaviour, examining the correspondence between model predictions and the real system data and finally determining the sensitivity of model predictions to changes in the values of important parameters (Grant et al. 1997). Within the following section, each of these issues is not singly addressed. The first two points were largely considered during the quantifying component of the model to ensure the relationships and output was realistic. Therefore the following section focuses on the final two methods of evaluation.
Three major evaluations were conducted. These were: (1) an assessment of the model’s ability to predict the annual population averages of S. obtusifolia obtained in
2003 and 2004, (2) an independent validation of the model by comparing model output with the results of the CLIMEX model, and (3) a sensitivity analysis of model parameters to ensure that each section of the model was behaving appropriately.
7.5.2 Observed data from 2002, 2003 and 2004 S. obtusifolia populations
This step in model evaluation focuses on the correspondence between model predictions and real system data (Grant et al. 1997). Three model simulations were specifically run to verify that the model was adequately representing the dynamics of
S. obtusifolia as represented by the observational data obtained during the dry
seasons of 2002, 2003 and 2004 (Chapter 5). Using the corresponding rainfall for
Chapter 7 Population Model Development - 188 - each year, these three simulations were: (1) to predict the dynamics of the 2003 populations based on average data recorded in 2002, (2) to predict the dynamics of
2004 populations based on average data recorded in 2003, and (3) to predict the population dynamics in 2003 and 2004 consecutively (i.e. in one model run only (104 weeks)) using average 2002 data to initiate the model.
The observed values and the results of each simulation are illustrated in Figures 7.5 and 7.6. All three simulations only produced a satisfactory fit to the field observations in both 2003 and 2004, with predictions only falling within two standard errors of the mean observed data 40% of the time.
The poor fit of the model predictions with the observed soil seed reserve data were not entirely unexpected as the 2003 soil seed estimate was comparatively and inexplicably much lower than the 2002 and 2004 observations (e.g. 919seeds/m2 vs.
2634seeds/m2 (2002)). The accuracy of the results of all three simulations was
therefore impacted upon.
Processes associated with weed soil seed reserves such as rates of survival, size of
the non-dormant portion of seed, incorporation rates and germination requirements,
are thought to be amongst the most difficult to characterise in population dynamics
(Sheppard 2002). Due to the failed field experimentation, few data were available on
which to base representation of these processes in S. obtusifolia populations.
Clearly what caused the decline of soil seed has not been incorporated into the
model. Unless ‘normal’ internal dynamics or one of the external drivers of the model
system is capable of significantly depleting the soil seed reserve in one season, it is
unlikely that this low soil seed reserve can be predicted by the model. Some other
factor or an extreme value of fire (i.e. the second external driver) must have caused
the response. It is also probable that there is some error associated with the
Chapter 7 Population Model Development - 189 - estimates of the soil seed reserves due to the extrapolation of the results of the 2004 soil seed germinability data (Chapter 5) across all years to assess their relative size.
a)
160
6000 140
120 5000 2
100 2 4000
80
3000 60 Soil Seeds/m Mature Plants/m
40 2000
20 1000
0 0 1020304050 Week b)
160 6000 140
120 5000 2
100 2 4000 80 3000 60 Soil Seeds/m Soil Mature Plants/m 40 2000
20 1000 0 0 1020304050 Week
Figure 7.5 The simulated (lines) and observed (mean +/- SE) measures of mature plants/m2 (black) and soil seeds/m2 (red) of Senna obtusifolia populations used in the model evaluation. Two standard errors of observed results are shown at the approximate time observations were made. Predictions are (a) 2003 based on 2002 data and (b) 2004 based on 2003 data.
Chapter 7 Population Model Development - 190 -
160
6000 140
120 5000 2 100 2 4000
80
3000 60 Soil Seeds/m Mature Plants/m 40 2000
20 1000
0 2003 2004 Week
Figure 7.6 The simulated (lines) and observed (mean +/- SE) measures of mature plants/m2 (black) and soil seeds/m2 (red) of Senna obtusifolia populations. Two standard errors of observed results are shown at the approximate time observations were made. 2002 population data were used to make consecutive predictions (104 weeks) of populations in 2003 and 2004.
Although quantitative inaccuracies are evident between the predicted and the
observed results, Figure 7.6 clearly indicates a realistic stable-like pattern of both soil
seeds and stem density, which correctly reflects both the overall timing and
behaviour of S. obtusifolia populations in the region. Germination occurs in
response to the summer rains, soil seed numbers increase after the first reproductive
episode in 2003 and decrease again during the time of germination in the next
generation. Soil seed and stem density estimates never reached unreasonable
numbers that S. obtusifolia stands were incapable of achieving. Although higher or
lower than ‘average’, most, if not all of the results fell within the overall range of data
collected (i.e. not the averaged yearly values used for the comparison). Therefore,
Chapter 7 Population Model Development - 191 - qualitative analysis reveals very encouraging outcomes, because although the specific numbers may not be entirely accurate, the dynamics of the simulation represent a situation that is realistic.
Using the 2002 data to initialise the model, a ten year simulation was run to ensure that model predictions did not become obscured over a longer time period. Figure
7.7 illustrates the result of the simulation, which again appears reasonable and acceptable for further use of the model. Numbers of mature plants and the size of the soil seed reserve demonstrate additional variability across the longer time period, but never exhibit values in complete excess of observed numbers. The soil seed reserve reaches a pattern of stability after the first three years. It does not continue to gradually increase in size after each seed rain, which suggests that mortality and germination parameters in the model are working sufficiently to prevent excess numbers from occurring. Such stability also highlights the contribution of the seed bank to the mature cohort annually, and most likely reflects the response of the buried seed to the external environmental drivers included in the system.
The density of mature plants also responded well to the external environmental drivers; with plants always appearing early in the year in response to the summer rains and mortality occurring in times of low or excess rain (Figure 7.8) as well as in response to overcrowding. Annual variability demonstrated by the number of mature plants also expectedly appears to be in response to rainfall, with lower plant densities corresponding to lower rainfall years (e.g. weeks 312-364).
Chapter 7 Population Model Development - 192 -
160 5000
140
4000 120 2
100 2 3000 80
60 2000 Seeds/m Soil Mature Plants/m
40
20 1000
0 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 7.7 The simulated measures of mature plants/m2 (black) and soil seeds/m2 (red) of Senna obtusifolia populations over 520 weeks.
Mortality imposed by rainfall effects was more predominant than death caused by density-dependence during the wet season (Figure 7.8). How dominant this form of mortality was appears to depend on the sum of rainfall. Most rainfall induced death seems to be a result of excess moisture, except for the peaks occurring in weeks 38-
39 which would be a result of insufficient rain. In a drier year (Figure 7.8b) the proportion of mortality resulting from excess moisture decreases and as a result death caused by intra-specific competition increases. The large peak occurring in weeks 38-39, clearly illustrates the function of equations 7.22-25 in the model. Large scale mortality of young S. obtusifolia occurred due to rainfall being insufficient to support growth after an intermediate amount fell late in the season, prompting late germination.
Chapter 7 Population Model Development - 193 -
The ten year simulation failed to predict the complete collapse of a S. obtusifolia population which does not reflect observations made in the field (Figure 5.18). This result is not overly concerning as it was not expected that the model could predict such behaviour. The model fails to incorporate means of mortality other than those caused by density-dependent intra-specific competition, rainfall and fire. Whilst these environmental and competition parameters undoubtedly assist in shaping the resultant population trajectory, it is evident from the simulations that they are not solely responsible for the success and/or failure of S. obtusifolia invasion. Other features external to the system such as inter-specific competition must play an even greater role in determining invasion success. (A lack of inter-specific competition is an assumption of the model stated in section 7.3.)
In consideration of the model limitations, the model objectives (i.e. is an exploratory tool) and the model projections of S. obtusifolia populations thus far, the evidence supports the use of the model as an exploratory tool to assess the dynamics of the weed in northern Australia. The observational data used to evaluate the model is not completely independent of the data used to develop the model. Therefore to further assess the accuracy of the model, a way of conducting an independent validation was sought.
Chapter 7 Population Model Development - 194 -
a)
700
600
500 2
400
300 Plant Number/m 200
100
0 0 1020304050 Week
b)
700
600
500 2
400
300 Plant Number/m 200
100
0 0 1020304050 Week
Figure 7.8 The simulated mean number of Senna obtusifolia plants in SO1 dying each week in one year in a) an average year of rainfall and b) in a dry year, from rainfall effects (black) and from density-dependent effects (grey).
Chapter 7 Population Model Development - 195 -
7.5.3 Independent validation - Senna obtusifolia density vs. rainfall
Rainfall is one of the two external, environmental drivers of change in S. obtusifolia populations built into the current model. Rainfall is an important trigger for mass germination of seed after the dry season and is necessary for the growth and development of seedlings to maturity. Therefore rainfall is in many ways responsible for the success and failure of S. obtusifolia populations. The CLIMEX model developed in Chapter 3 has already identified a relationship between S. obtusifolia
persistence and rainfall, demonstrating that a lack of moisture and dry stress were
the predominant factors causing locations to be unsuitable for S. obtusifolia.
Given this relationship with moisture, nine locations in Queensland that vary in rainfall were selected to be entered into the STELLA model to conduct an independent evaluation of the accuracy of the model, as well as testing the strength of rainfall effects on S. obtusifolia. By generalising the results of the CLIMEX model, it would be expected that higher densities of reproductive plants would be predicted by the STELLA model in towns with high rainfall than those possessing low rainfall.
The results of the CLIMEX model provide a completely independent data source to test the model results. None of the data used to develop the STELLA model was input for the creation of the CLIMEX model and vice versa. Furthermore, the source of the data to create both models was very different. The CLIMEX model was developed primarily from international distribution records, whilst, population data from S. obtusifolia infestations in the Lockhart River region was used to build the
STELLA model. Therefore the data sources themselves and the feedback functions
that control suitability/mortality in both models are completely independent and
therefore worthy to be used in the evaluation process.
Chapter 7 Population Model Development - 196 -
Weekly rainfall figures for ten years for nine towns with low, medium and high rainfall were entered into the STELLA model (Table 7.2) to reflect unsuitable, marginal and favourable rainfall conditions for S. obtusifolia growth. The eco-climatic index (EI) as
predicted by the CLIMEX model for each town was also obtained from Chapter 2
(Table 7.2). The model was used to simulate the response of S. obtusifolia
populations in each town as if it was the first introduction of the weed to each
location (i.e. no soil seed reserve). Simulations were initialised with 1000 seeds
dropped onto the soil surface and the model then run for 520 weeks. Fire was
excluded from the model, as the likelihood of the locations chosen being exposed to
annual fire would be very low (with the exception of Lockhart River). The average
density of reproductive plants was assessed for each location, using the last 260
weeks only. The first 260 weeks were run to allow the system to stabilise.
Table 7.2 The average annual rainfall (mm) and the eco-climatic index developed by CLIMEX for nine different locations selected for the model evaluation (Dunlop et al. 2006).
Suitability Location Average annual Eco-climatic rainfall (mm) index (EI) Unsuitable Cloncurry 471 0
Unsuitable Longreach 447 0
Unsuitable Hughenden 491 0
Marginal Croydon 729 0
Marginal Charters Towers 580 20
Marginal Emerald 556 20
Favourable Cairns 2003 47
Favourable Mackay 2032 50
Favourable Lockhart River 2140 50
Chapter 7 Population Model Development - 197 -
Figure 7.9 illustrates the results of the population model relative to the results of the
CLIMEX modelling data for each location. Projected population densities were seen to generally increase with both rainfall, and suitability as determined by the EI.
Anomalies did also occur, with Croydon displaying much higher than expected densities of mature plants, whilst Emerald and Mackay possessed lower than expected densities (as determined by their allocated suitability). Locations possessing EI’s of 0 (i.e. location is unsuitable) also produced viable populations of
S. obtusifolia.
160
140 Lockhart River Croydon Cairns
120 2 Mackay 100 Charters Towers
80
Cloncurry 60 Hughenden Mature Plants/m Longreach 40 Emerald
20
0 0 102030405060 EI
Figure 7.9 The average number of mature Senna obtusifolia plants/m2 predicted by the model relative to the eco-climatic index (EI) of each respective location (See Chapter 3 for further information on EI’s).
Climate comprises a number of components in addition to rainfall such as temperature, humidity and evaporation rates. These factors in combination will dictate the suitability of a location for persistence of a weed population. This level of
Chapter 7 Population Model Development - 198 - climatic detail was not incorporated into the model and again demonstrates a limitation of its use. The inclusion of such climatic parameters would most likely greatly modify the way in which rainfall affects the suitability of a location, causing the plant and the resultant density to respond differently. For example, rainfall in locations such as Croydon may become far less effective. The distribution of rain across the year may also impact on the final result. For example, Emerald whilst possessing a higher annual rainfall than Croydon, receives rainfall over a longer period of the year in comparison to Croydon which receives almost all of its rain during the summer months when S. obtusifolia is germinating. This must clearly be of benefit to the establishment of the weed.
Given the overwhelming positive trend between suitability and density exhibited in
Figure 7.9, the discrepancies evident are not of great concern. If the trend was less obvious, greater attention may need to be paid to the result, but similar to section
7.5.2, the overall dynamics displayed are sufficient to satisfy the model objectives.
7.5.4 Sensitivity Analyses
The final stage of model evaluation was to conduct a series of sensitivity analyses on a variety of the parameters developed to describe the population dynamics of S. obtusifolia. The objective of sensitivity analysis is to determine the degree of response, or sensitivity, of model behaviour to changes in various model components (Grant et al. 1997). This is achieved by systematically altering the value of one parameter at a time by a known amount throughout the entire simulation and observing the subsequent effect on model behaviour (Grant et al. 1997). This form of analysis is necessary as it increases confidence in the model by ensuring that the model is behaving appropriately. It can be made certain that parameters that should
Chapter 7 Population Model Development - 199 - bear little impact on the overall system behaviour are not highly sensitive to change, causing large alterations to the final outcome.
Using the S. obtusifolia population averages derived from the 2002 data to initiate the model, sensitivity analyses were conducted on parameters k4, k5, k6, k7, k9, k12,
k16, k19, k20, k21, k22 and k24 (see Table 7.1 for parameter definitions). Each
parameter was tested individually using eleven equally spaced values to measure
the sensitivity of reproductive plants (SO5) to the parameter change.
Figure 7.10 demonstrates the main pattern of output obtained from the sensitivity analyses. k21 (rate of breaking dormancy in seed) demonstrated the greatest degree
of sensitivity, with the number of mature plants increasing markedly with an
increment step as small as 0.005 (Figure 7.10a). Above this first value, the change
in population tends to decrease. Such a significant change indicates that the
breaking of dormancy in seeds at a rate above 0.01 (the base parameter) per week,
will most likely lead to much larger densities of S. obtusifolia. This appears intuitively
correct, as the proportion of seed with the hard seed coat broken will be larger, and
therefore more seed is available for immediate germination.
Mature plant density also demonstrated some sensitivity in response to altered rates
of germination (k22) (Figure 7.10b). Although the response was lower than that of
k21, the change in population size that occurred between the values 0 and 0.2, (0.1 increments) is much larger than any values above 0.2. Again the result is logical, as it would be expected that the response of SO5 would increase significantly with a change from 0% germination. In addition to this though, the analysis tends to demonstrate that changes in medium rates of germination have little impact on the final densities of reproductive S. obtusifolia. It also reveals in part the role and/or contribution of germination of seed arising from the soil seed reserve and the level at
Chapter 7 Population Model Development - 200 - which it and germination from surface seed interact. Prior to there being any germination from surface seed, a stand of mature plants can still develop solely from the soil seed reserve. Once simultaneous germination begins from both the surface and soil seed pools, any changes in germination rates in either parameter will mostly stop impacting on final plant numbers. It will be at this stage that density-dependent regulation will instead begin to control numbers reaching maturity.
The remaining parameters tested had minimal impact on final densities of mature plants. Generally, only small, even gradations of change occurred with each increase in parameter value, as demonstrated by k9 (SO1 mortality) in Figure 7.10c.
None of the parameters tested in the sensitivity analysis displayed an unexpected or illogical response to altered values. Therefore confidence is the model was reinforced, and in combination with the overall results from the model evaluation process, the STELLA model was found suitable to use to test developed hypotheses.
Chapter 7 Population Model Development - 201 -
a) k21
180
160
140
2 120
100 0.015
80
Mature Plants/m 60 0.01 40
20
0 0 5 10 15 20 25 30 35 40 45 50 Week
b) k22
100
80 2
60 0.2 40 Mature Plants/m
20 0
0 0 5 10 15 20 25 30 35 40 45 50 Week
c) k9
80
60 2
40 Mature Plants/m
20
0 0 5 10 15 20 25 30 35 40 45 50 Week
Figure 7.10 The results of sensitivity analyses for parameters a) k21-rate of dormancy being broken in seeds (values of 0.01-0.5), b) k22-the maximum rate of
germination (values of 0-1) and c) k9 -maximum rate of SO1 mortality (values of 0-1).
Chapter 7 Population Model Development - 202 -
7.6 Discussion
Analytical modelling is recognised for its ability to highlight ecosystem parameters of interest (Buckley et al. 2004). The modelling approach developed in this chapter produced a simple compartment model designed to simulate the dynamics of a S. obtusifolia population. Given the assumptions and limitations of the current model, the results of the model evaluation indicate that under such circumstances the model performs correctly and is capable of satisfactorily imitating the behaviour of S.
obtusifolia populations occurring in the Lockhart River region in the absence of
competition (see model limitations section 7.3). Output from the sensitivity analyses
identified the importance of the innate dormancy of S. obtusifolia seed and rates of
germination in driving observed population dynamics, whilst an independent
validation technique demonstrated the predictive power of the model based on the
rainfall conditions of a location.
The model identified the importance of two population characteristics in determining the internal dynamics and final densities of S. obtusifolia. Small alterations to the rate at which innate dormancy in seeds is broken (k21) and to rates of germination
(k22) (Table 7.1), resulted in significant changes in the final densities of S. obtusifolia infestations (Figure 7.10). The ability to reduce the rate of either of these parameters would therefore be a central aim for successful management of the weed for long term. Unfortunately, both of these factors deal directly with the S. obtusifolia seed bank (i.e. soil seed and surface seed), which is probably the most difficult portion of the weed life cycle to understand and therefore manage appropriately
(Sheppard 2002).
Within the current model, the values used to reflect the roles of k21 and k22 in the overall dynamics of S. obtusifolia were not based on experimental data, but were
Chapter 7 Population Model Development - 203 - instead based on a general knowledge and logic of the system. Due to the significance of the values in determining final densities of reproductive plants and their potential in effective control, it is recommended that further investigation be conducted to determine experimentally the parameter values. Presently, the maximum value of k22 in the model is fixed and therefore the proportion of seed
germinating is highly dependent on two things. One is that sufficient rain has fallen
to prompt germination (Figure 7.3j). The second is how much soft, or non-dormant,
seed is available for germination, which will mostly depend on the rate at which
dormancy is broken (k21). The k21 value is also fixed and is not influenced by any factor. This relationship and the role of dormancy in the system are most likely over simplified and not a true reflection of the situation, reinforcing the need for further investigation into each parameter to improve the accuracy of the model output.
Seed dormancy, in its simplest form, is an innate seed property that defines the
environmental conditions required for germination (Fenner and Thompson 2005;
Finch-Savage and Leubner-Metzger 2006). Germination is delayed until micro-site
and climatic conditions are favourable (Harper 1977), increasing the chance of
seedling establishment (Williams et al 2003). The rate of parameter k21 would therefore undoubtedly be influenced by the environment and therefore vary annually in response to the current conditions. Similarly, the proportion of seeds becoming non-dormant in the soil seed bank would be influenced by the environmental conditions on the surface, resulting in an inconsistent rate of germination occurring from the reserve. Including a factor such as this would alter the long term dynamics of the soil seed reserve, presenting a pattern of instability relative to that demonstrated in Figure 7.7. These relationships, as were many others, were excluded from the model. This was not because they weren’t considered important, but simply because there were no experimental data and/or insufficient knowledge to incorporate them into the model.
Chapter 7 Population Model Development - 204 -
The relationships excluded from the model, in addition to stated limitations, reduce the applicability of the model to simulate/predict S. obtusifolia behaviour in situations
external to far north Queensland. Any simulations run outside of these areas should
be interpreted with great caution. The independent validation in section 7.5.3 which
used rainfall and EI’s to predict location suitability provides an example of this. As
illustrated in Figure 7.9, the model satisfactorily predicted a general trend of
increased location suitability with rainfall. This sensitivity of S. obtusifolia with
variable rainfall in locations outside of Lockhart River is an encouraging result in
terms of model development, as it demonstrates the ability of the model to predict
population success in response to rainfall. However, locations known or presumed
to be unsuitable were still capable of supporting a population. The simple
relationship of rainfall with germination means that if sufficient rain has fallen to
prompt germination and sustain growth then the weed will occur, regardless of other
factors present. This 1:1 relationship of density and rainfall is obviously highly
simplified and for the model to correctly simulate dynamics in more marginal
locations, other factors need to be incorporated into the model. Therefore model
users would need to exercise caution when extrapolating the model to locations far
from the Lockhart River region, without first ascertaining the response of the weed to
other climatic factors such as temperature.
The most significant omission in the current model was the impact of competition from other vegetation. Competition is a major factor in controlling the success of any plant species (e.g. Watkinson 1981; Freckleton and Watkinson 1998; Morrison and
Molofsky 1998; Davis et al. 2000) and has already been hypothesised in previous chapters to be responsible in part for the localised variation present between S. obtusifolia populations. Observations reported by Anning et al. (1989), whereby S.
obtusifolia growth is suppressed in areas of good grass cover, also provides good
Chapter 7 Population Model Development - 205 - support for the important role different vegetation types will have on invasion success. Attributes of the invader and invaded community both contribute to successful invasions, so sustainable management of invaders may involve manipulating both the weed as well as creating favourable establishment opportunities for native competitors (Buckley et al. 2004). To create a model system reflecting the dynamics between S. obtusifolia and the principal vegetation types it encounters in the Lockhart River region (i.e. B. decumbens, I. cylindrica, rainforest
and woodlands) would greatly improve the worth of the current model.
7.7 Progress Towards Aims of Thesis
Overall the model of S. obtusifolia population dynamics provides an easily interpretable overview of the life-cycle of S. obtusifolia and the internal and external parameters that can cause change. This model is an exploratory tool and will not produce robust and highly accurate predictions. For this reason it is not recommended that this version of the model be used for predictive purposes that need accurate results for money expenditure etc. The number of ecosystem components excluded and relationships not experimentally derived would most likely greatly alter the overall dynamics. However, the model does provide a sound and realistic framework for investigating the relative importance of a number of system components in driving the observed population dynamics and offers a basis for further study and understanding.
Chapter 8
Simulated responses of Senna obtusifolia populations to different
invasion and environmental scenarios
Chapter 8 Population Model Simulations - 207 -
8.1 Introduction
Chapter 7 described the development, quantification and evaluation of a conceptual model designed in STELLA to simulate the population dynamics of S. obtusifolia populations in the Lockhart River region. This systems approach broke down the complexity of the S. obtusifolia-environment system by focussing solely on the processes that drive and comprise a S. obtusifolia infestation. Satisfied with system structure and behaviour exhibited during the model evaluation, the model is now used to further predict population behaviour and resolve a set of testable hypotheses concerning the population responses of S. obtusifolia infestations to a variety of environmental conditions and invasion scenarios. These include prolonged periods of low and high rainfall, different fire regimes and fire intensities and the outcome of initiating annual control early in the life cycle.
Rainfall and fire are the only two external environmental factors driving change in S. obtusifolia populations within the current STELLA model. Annual seasonal rainfall and annual firing are two very obvious factors in the environment of the Lockhart
River region that may directly influence the success of S. obtusifolia populations.
With the exception of inter-specific competition, it is assumed that they may together, or singly, be responsible in part for variation evident in S. obtusifolia infestations within and between years. Moisture has already proven itself to be strongly involved in the success of an S. obtusifolia population, as demonstrated by the CLIMEX model in Chapter 2 and the results of the model evaluation illustrated in Figure 7.9.
In contrast, fire has not been proven to be influential on S. obtusifolia, however the results of previous chapters provide some evidence to suggest that it could. Fire regime and intensity has also been documented in the literature on numerous occasions as being capable of affecting the dynamics of legume species (e.g. Floyd
1976; Bradstock and Auld 1995; Smith et al. 2000). Although it is believed that both fire and rainfall will impact on the dynamics of S. obtusifolia, how they will cause
Chapter 8 Population Model Simulations - 208 - change and how severe the population response will be is not known. To determine the population response of S. obtusifolia is therefore a central aim of this chapter and accordingly several different scenarios involving both factors were devised to establish this.
The other main goal of this chapter is to determine the long term response of S. obtusifolia following first introduction to an environment and when it is controlled early in the life cycle on an annual basis. The long lived soil seed reserve is a key attribute of S. obtusifolia that makes the weed so difficult to control. Both simulations should highlight the role of the soil seed bank, as well as indicating the minimum time in which control will be achieved with maximum effort. Control and management are obviously a central theme in the study of any invasive species and, whilst this study does not focus on this, the use of this model will allude to the effectiveness of certain management techniques and manipulations. In addition, it will help to identify major knowledge gaps and assist in defining important future research on the population dynamics of S. obtusifolia.
8.2 Method
Using the STELLA model developed in Chapter 7, to assess the response of S. obtusifolia populations to the variable rainfall conditions, fire regimes, and timing of management, five classes of different model simulations were devised. These were:
1. To represent a new invasion scenario (without competition), whereby one plant
survives in a new environment and introduces seed to the environment for the
first time. As determined from Chapter 5, seed production begins with 180 seeds
(seeds/plant) and with no seed in the soil seed reserve;
Chapter 8 Population Model Simulations - 209 -
2. To represent the effect of a control technique that annually destroys the
population before seed set. The simulation begins with no seed present on the
surface, but with an established soil seed reserve. No seed production occurs
throughout the simulation;
3. To represent the effect of a control technique that destroys the population after
seed set for the first year and then conducted before seed set every year after.
The simulation begins with seed present on the soil surface, an established soil
seed reserve but no seed production occurs throughout the simulation;
4. To determine the response of S. obtusifolia populations to variable rainfall
conditions. The simulation begins with seed on the soil surface, an established
soil seed reserve and with the ability to produce seed annually. Specifically, the
rainfall scenarios tested were:
• ten years of the same (average (1980)) rainfall,
• three consecutive years of low rainfall,
• five consecutive years of low rainfall,
• three consecutive years of high rainfall,
• five consecutive years of high rainfall;
5. To determine the response of S. obtusifolia populations to a range of fire
regimes. The simulation begins with both seed on the soil surface and in the soil
seed reserve with the ability to produce seed annually. Specifically, the fire
scenarios tested were:
• No fire,
• Two fires in ten years (third and eighth year),
Chapter 8 Population Model Simulations - 210 -
• Fire every second year,
• Increased annual fire effectiveness (maximum mortality of
0.9),
• Reduced fire effectiveness/ (earlier annual fire (week 44))
For each scenario, unless otherwise stated, the model was initialised with average surface seed and soil seed numbers derived from all populations monitored in
Lockhart River across 2002-2004 (see Chapter 5). The number of mature plants, the seed rain, the soil seed reserve and causes of mortality were all monitored in each simulation, with the model running over a 10 year period (1996 – 2006).
A ten year simulation of S. obtusifolia dynamics using the default model settings developed in Chapter 7 is presented in Figure 8.1 (i.e. real variable rainfall across 10 years, annual fire in week 50 and the values of all model parameters listed in Table
7.1). This simulation was initialised with the same data as that described for the above scenarios, and the resultant output was used to comparatively assess the impact/response of the population to each of the above scenarios. All references made to the ‘normal’ or ‘default’ population behaviour throughout the results section of this chapter refers to the results of this simulation.
Chapter 8 Population Model Simulations - 211 -
160 4000
140 3500 120 2
100 2 3000
80
2500 60 Soil Seeds/m Soil Mature Plants/m
40 2000 20
0 1500 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 8.1 Simulated mature plant density (black) and soil seed reserve size (red) of Senna obtusifolia over 520 weeks. Model initiated with data averaged over 2002, 2003 and 2004 initialised the model run (as per Figure 7.7)
8.3 Results
8.3.1 Invasion and Control Scenarios 1 – 3
Figures 8.2 – 8.4 illustrate the results of the first three invasion scenarios which
simulated how S. obtusifolia populations would increase from the introduction of one
plant into a favourable area and how populations would respond over time to the
cessation of seed production. The first simulation (Figure 8.2) whereby the
population begins with 180 seeds produced by a single plant, demonstrates that in a
disturbed area with no competition, it takes only a short time for S. obtusifolia to
reach population characteristics like those attained at Lockhart River. By the sixth
generation (weeks 264-312), mature plant density had risen from 1 to 140 plants,
Chapter 8 Population Model Simulations - 212 - equating to the density of the same cohort in the ‘normal’ ten year simulation.
Densities in the prior two cohorts, although not aligned with the ‘normal’ figures, were high (in the 100’s) and would undoubtedly be sufficient for the weed to already be a problem even after such a short period of time. The soil seed reserve took slightly longer to build up to normal levels, with numbers not aligning with the ten year simulation until the end of the seventh year (approximately week 337). Again, like the stem density, the number of seeds stored in the reserve reaches the thousands during the fourth cohort, which is sufficient for the plant to be already be a problem.
160 3500
140 3000
120 2500 2
100 2 2000 80 1500 60 Soil Seeds/m Soil Mature Plants/m Mature 1000 40
20 500
0 0 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 8.2 Simulated mature plant density and soil seed reserve size of Senna obtusifolia over 520 weeks 180 seeds were introduced to a disturbed environment free of a S. obtusifolia soil seed reserve.
The second and third simulations both illustrate the important role of the soil seed
bank in the invasion process and in sustaining S. obtusifolia populations. Starting with a value of 2800 seeds in the soil seed bank and no seed production occurring across time, the second scenario shows how S. obtusifolia populations of significant
Chapter 8 Population Model Simulations - 213 - density can still occur as a result of the soil seed reserve only (Figure 8.3). Large populations are sustained for 3 years, decreasing quite dramatically in density for the ensuing three generations. As expected with their being no annual seed addition to the soil reserve, the size of the soil seed bank steadily declines at an almost exponential rate, and by the end of the fifth year (week 260) the soil seed reserve has decreased to approximately 2% of its original size.
100 3000
2500 80
2 2000 2 60
1500
40
1000 Soil Seeds/m Mature Plants/m
20 500
0 0 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 8.3 Simulated mature plant density (black) and soil seed reserve size (red) of Senna obtusifolia over 520 weeks when a seed bank was present but no seed production occurred.
Scenario 3 (Figure 8.4) produced a very similar result to scenario 2. By allowing the
S. obtusifolia population to yield seed before destroying the plants, the depletion rate of the soil seed bank was slightly reduced, with approximately 4% of the original seed remaining after 260 weeks. The seed yield however did not result in plant populations occurring for a greater number of years. Instead, the populations simply possessed greater densities than those seen in Figure 8.3.
Chapter 8 Population Model Simulations - 214 -
120 3500
3000 100
2500 2 80 2 2000 60 1500
40 Soil Seeds/m Mature Plants/m 1000
20 500
0 0 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 8.4 Simulated mature plant density (black) and soil seed reserve size (red) of Senna obtusifolia over 520 weeks when a seed bank and was present and the seed production from one year lay on the soil surface.
8.3.2 Invasion Scenario 4 – Rainfall Effects
The response of S. obtusifolia to rainfall was assessed by simulating the response of populations to different rainfall scenarios over a 520 week period. The rainfall treatments were in all cases (except the first) applied after 260 weeks (five years) to enable the populations to firstly stabilise. The rainfall data used as the treatments were obtained from long term Lockhart River rainfall data (70s to the present). The five driest and five wettest years (not consecutive) were extracted from the rainfall data set to be used as the treatments and were input into the model to replace the default rainfall values in the appropriate years. Senna obtusifolia population data
used to initialise the model were population averages of all three years data
combined.
Chapter 8 Population Model Simulations - 215 -
8.3.2.1 Simulation 1 – Ten years of the same rainfall
Exposing S. obtusifolia to the same average annual rainfall for ten years produced very stable population dynamics (Figure 8.5). Populations tended to respond in the exact same manner after 140 weeks, where stem densities stabilised at 136 plants/m2. The soil seed reserve settled at a mean of approximately 2050 seeds/m2
(fluctuating between 2000 and 3000 annually) after 244 weeks. This average plant density is comparatively higher than the 112 plants/m2 experienced under the default
conditions. The soil seed reserve exhibited no difference to that in Figure 8.1.
160 4000
140 3500 120 2
100 2 3000
80
2500 60 Soil Seeds/m Mature Plants/m 40 2000 20
0 1500 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 8.5 Simulated mature plant density (black) and soil seed reserve size (red) of Senna obtusifolia over 520 weeks when exposed to the same annual rainfall.
Chapter 8 Population Model Simulations - 216 -
8.3.2.2 Simulation 2 and 3 – Three and five consecutive years of low rainfall
Three and five of the lowest years of rainfall recorded in Lockhart River were incorporated into the model to fall between weeks 157-312 (Figure 8.6a) and weeks
157-416 (Figure 8.6b). The three consecutive years of low rainfall had only little impact on the stem density of mature plants recording a density of 126 plants/m2 after 296 weeks in comparison to the 142 plants/m2 produced under the normal conditions. The overall mean stem density of all ten years was slightly higher than that produced under normal conditions (115 plants/m2 vs 111 plants/m2). Whilst
population sizes were smaller in some years, the populations in the fourth and fifth
years actually increased in size explaining why the overall density was unexpectedly
higher. These increases in density most likely reflect the reduced mortality of
seedlings in response to elevated rainfall such as the pattern demonstrated in Figure
7.8. The soil seed reserve size also displayed a small decrease after 296 weeks,
possessing 1684 seeds/m2 in comparison to 1880 seeds/m2 under normal conditions. The overall average was largely unaffected.
The extended five year period of low rainfall produced similar outcomes to the three years of dry conditions. After 400 weeks, stem density had dropped to 114 plants/m2 whilst the soil seed reserve showed a small increase to 1894 seeds/m2 (Figure 8.6b)
Under normal conditions, this week produced densities of 143 plants/m2 and 2022
seeds/m2. The overall averages remained the same as in the three years of low
rainfall scenario, and again is most likely a reflection of the reduced mortality caused
by excess water under ‘average’ conditions.
Chapter 8 Population Model Simulations - 217 -
a) Low rainfall years
160 4000
140 3500 120 2
100 2 3000
80
2500 60 Soil Seeds/m Soil Mature Plants/m Mature 40 2000 20
0 1500 0 52 104 156 208 260 312 364 416 468 520 Week
b) Low rainfall years 160 4000
140 3500 120 2
100 2 3000
80
2500 60 Soil Seeds/m Mature Plants/m 40 2000 20
0 1500 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 8.6 Simulated mature plant density (black) and soil seed reserve size (red) of Senna obtusifolia over 520 weeks when a) exposed to three and b) five years of low rainfall conditions.
Chapter 8 Population Model Simulations - 218 -
8.3.2.3 Simulation 4 and 5 – Three and five consecutive years of high rainfall
Following the method described for low rainfall, three and five of the highest years of rainfall recorded in Lockhart River were incorporated into the model to fall between weeks 157-312 (Figure 8.7a) and weeks 157-416 (Figure 8.7b). After 296 weeks
(three years of high rainfall), plant and soil seed densities/m2 were at 111 and 1621
respectively, and, after 400 weeks (five years of high rainfall), plant and soil seed
densities/m2 were 104 and 1638 respectively. These densities are comparatively smaller than the simulations run under ‘normal’ conditions and the low rainfall conditions. The overall, ten year averages of plants and soil seeds did not differ from the ‘normal’ conditions when exposed to three wet years, however the five year scenario did produce a reduced stem and soil seed density (107 plants/m2 and 1787
soil seeds/m2). This result again provides evidence for the changes in mortality in
response to high rainfall being responsible for increased population densities in the
low rainfall scenario.
Although the results of the low and high rainfall scenarios demonstrated that rainfall can impact upon S. obtusifolia dynamics to some degree, the trend was not overwhelming. This may indicate that the change in rainfall may have to be far more severe than used in this study to cause a significant negative population response.
This limited population response to poor rainfall conditions was largely unexpected.
Therefore to further investigate if S. obtusifolia is sensitive to variable rainfall, an extra scenario involving prolonged periods of wet and dry conditions was run using a location that is marginal for S. obtusifolia growth. Given that conditions are not always suitable for growth in a marginal location, it is anticipated that the population response of S. obtusifolia to high and low rainfall conditions will be far more evident.
Chapter 8 Population Model Simulations - 219 -
a) High rainfall years 160 4000
140 3500 120 2
100 2 3000
80
2500 60 Soil Seeds/m Soil Mature Plants/m Mature 40 2000 20
0 1500 0 52 104 156 208 260 312 364 416 468 520 Week
b) High rainfall years 160 4000
140 3500 120 2
100 2 3000
80
2500 60 Soil Seeds/m Mature Plants/m 40 2000 20
0 1500 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 8.7 Simulated mature plant density (black) and soil seed reserve size (red) of Senna obtusifolia over 520 weeks when a) exposed to three and b) five years of high rainfall conditions.
Chapter 8 Population Model Simulations - 220 -
8.3.2.4 Response to rainfall extremes in a marginal location
Charters Towers was chosen as the marginal location to test if S. obtusifolia really is sensitive to variable rainfall. Charters Towers was chosen based on the results from section 7.5.4 which showed that this location supported S. obtusifolia populations of medium density. To test the effect of rainfall the same procedure was used as in sections 8.3.2.2 and 8.3.2.3. Three and five year periods of high and low rainfall were selected from long term rainfall data of Charters Towers (1970-2006) and inserted into the ‘normal’ rainfall pattern used in the model at weeks 157-312 and
157-416. 10000 surface seeds (9300 SS and 700 ND) and 1900 soil seeds (SSR) were used to initialise the model. The population response of S. obtusifolia over 520
weeks under the default Charters Towers rainfall used in the model is illustrated in
Figure 8.8.
140 5000
120
4000 100 2 2
80 3000 60 Soil Seeds/m Mature Plants/m Mature 40 2000
20
0 1000 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 8.8 Simulated mature plant density (black) and soil seed reserve size (red) of Senna obtusifolia over 520 weeks in Charters Towers under the default rainfall conditions in the model (1996 – 2006).
Chapter 8 Population Model Simulations - 221 -
Both the three and five year period of dry and wet conditions caused a much greater population response than that evident in the Lockhart River simulations. This is clearly evident in Figures 8.9 and 8.10, where both the soil seed and stems react to the varying conditions. The dry condition in particular caused a dramatic population decline, with numbers plummeting to as low as 4 plants. The five year wet condition also caused a large response, this time numbers increasing by 2.2 times the expected number. Soil seed reserve densities in all four scenarios showed varying degrees of decline. All stem and soil seed densities for each scenario are shown in
Table 8.1.
Table 8.1 Simulated densities of mature plants/m2 and soil seeds/m2 of Senna obtusifolia in Charters Towers when exposed to five differing rainfall conditions.
‘Normal’ Low Low High High rainfall rainfall rainfall rainfall (3 years) (5 years) (3 years) (5 years) 10 year average density
Mature Plants/m2 72 30 29 69 74
Soil Seeds/m2 2816 2036 1968 2599 2441
Density at week 296
Mature Plants/m2 81 4 - 55 -
Soil Seeds/m2 2888 1554 - 2414 -
Density at week 400
Mature Plants/m2 45 - 28 n/a 102
Soil Seeds/m2 3841 - 1236 n/a 2585
- denotes that these numbers were not assessed.
Chapter 8 Population Model Simulations - 222 -
The results given in Table 8.1 and Figures 8.9 and 8.10 confirm the original expectations of S. obtusifolia behaviour in response to variable rainfall. Populations tended to decline rapidly in times of very dry conditions and increase substantially when high rainfall was experienced.
a)
140 5000
120 Low rainfall years 4000 100 2 2
80 3000 60 Soil Seeds/m Mature Plants/m Mature 40 2000
20
0 1000 0 52 104 156 208 260 312 364 416 468 520 Week b)
140 5000
120 Low rainfall years 4000 100 2 2
80 3000 60 Soil Seeds/m Mature Plants/m 40 2000
20
0 1000 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 8.9 Simulated mature plant density (black) and soil seed reserve size (red) of Senna obtusifolia over 520 weeks in Charters Towers when a) exposed to three and b) five years of low rainfall conditions.
Chapter 8 Population Model Simulations - 223 -
High rainfall years a)
140 5000
120
4000 100 2 2
80 3000 60 Soil Seeds/m Mature Plants/m 40 2000
20
0 1000 0 52 104 156 208 260 312 364 416 468 520 Week
High rainfall years b)
140 5000
120
4000 100 2 2
80 3000 60 Soil Seeds/m Soil Mature Plants/m Mature 40 2000
20
0 1000 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 8.10 Simulated mature plant density (black) and soil seed reserve size (red) of Senna obtusifolia over 520 weeks in Charters Towers when a) exposed to three and b) five years of high rainfall conditions.
Chapter 8 Population Model Simulations - 224 -
8.3.3 Invasion Scenario 5 – Fire Effects
Given that fire appears to be a very important external influence on the dynamics of
S. obtusifolia in the Lockhart River region, it was decided to simulate the response of
S. obtusifolia to a range of fire regimes across a 520 week period. Simulation results were compared to Figure 8.1 which demonstrates the population behaviour under the default model settings (annual fire, maximum mortality of 70%).
8.3.3.1 Simulation 1 – No fire
The first simulation removed fire from the system entirely to gauge exactly how fire affects population behaviour. Figure 8.11 displays the simulation results. The most obvious impact of there being no fire is the steadily increasing soil seed reserve.
This continued to increase until approximately the eighth year (weeks 364-416), where numbers begin to stabilise. This increase is clearly in response to the reduced mortality of viable seed on the soil surface. A smaller proportion of seed is used for germination and therefore a much greater amount of surface seed is available to be incorporated into the soil seed reserve. The larger viable seed pool available on the soil surface also resulted in an increase in plant densities (127 vs.
111 plants/m2).
8.3.3.2 Simulation 2 – Two fires in ten years
The slow re-introduction of fire back into the system (now occurring twice in ten
years (year three and eight)) resulted in a small change of S. obtusifolia dynamics.
Whilst, plant densities did not change from the previous simulation, an obvious
reduction in the soil seed is evident after the first fire event (Figure 8.12). The
second fire event did not have the same impact. This may be because the soil seed
Chapter 8 Population Model Simulations - 225 - numbers had already reached a level where one fire is insufficient to cause a response. The average soil seed numbers declined from the no fire simulation by approximately 300 seeds.
180 6500
6000 160 5500 140 5000
2 120 4500 2
100 4000
80 3500
3000 60 Soil Seeds/m Mature Plants/m Mature 2500 40 2000 20 1500
0 1000 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 8.11 Simulated mature plant density (black) and soil seed reserve size (red) of Senna obtusifolia over 520 weeks when fire is completely removed from the environment.
Fire Event
180 6500
6000 160 5500 140 5000
2 120 4500 2
100 4000
80 3500
3000 60 Seeds/m Soil Mature Plants/m Mature 2500 40 2000 20 1500
0 1000 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 8.12 Simulated mature plant density (black) and soil seed reserve size (red) of Senna obtusifolia over 520 weeks when fire occurs in the third and eighth years.
Chapter 8 Population Model Simulations - 226 -
8.3.3.3 Simulation 3 – Fire every second year
Fire that occurred at more regular, steady intervals of every second year, caused a substantial change in both stem and soil seed densities (Figure 8.13). Stem densities were approximately 120/m2, whilst the soil seed reserve had decreased by approximately 1000 seeds/m2 from the previous simulation. The soil seed reserve displayed a more stable pattern, whereby plant numbers tended to oscillate more consistently between two numbers. Although S. obtusifolia dynamics are far more regular, both densities remain higher than those of the default or ‘normal’ simulation where fire occurs annually.
Fire Event
180 6500
6000 160 5500 140 5000
2 120 4500 2
100 4000
80 3500
3000 60 Soil Seeds/m Mature Plants/m 2500 40 2000 20 1500
0 1000 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 8.13 Simulated mature plant density (black) and soil seed reserve size (red) of Senna obtusifolia over 520 weeks when fire occurs in every second year.
8.3.3.4 Simulation 4 – Increased fire effectiveness
Fire effectiveness was increased by altering parameter k4 (Table 7.1). The maximum mortality rate of surface seed was increased from 0.7 to 0.9 annually. As
Chapter 8 Population Model Simulations - 227 - expected, this caused a large change in the dynamics of the soil seed reserve
(Figure 8.14). The overall density of soil seeds/m2 was reduced from 2048 in the default simulation to 1538, with numbers steadily oscillating between 1500 and 2500 seeds after an initial decline. Plant density was also reduced, with an average of 97 plants/m2 in comparison to 111 plants/m2 in the default simulation.
160 6500
6000 140 5500 120 5000 2
100 4500 2
4000 80 3500
60 3000 Soil Seeds/m Mature Plants/m Mature 2500 40 2000 20 1500
0 1000 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 8.14 Simulated mature plant density (black) and soil seed reserve size (red) of Senna obtusifolia over 520 weeks when fire the maximum surface seed mortality
(k4) is increased from 0.7 to 0.9.
8.3.3.5 Simulation 5 – Early annual fire
Instead of altering k4 to reduce the effectiveness of fire, the fire event was
programmed to occur earlier in the year, when conditions are less suitable for hot
fires. The fire was programmed to occur in week 43, seven weeks earlier in the year
than in the default simulation. In contrast to the strong change in dynamics caused
Chapter 8 Population Model Simulations - 228 - by the increased fire effectiveness, this simulation had the opposite effect, having exactly the same impact on dynamics as the no fire simulation (Figure 8.15).
Although fire still occurred annually, fire set at an effectiveness of 0.396 obviously has no impact on seed fate. Conditions in Lockhart River around week 43 are more moist and consequently the vegetation greener than during week 50 when the default model is set for a fire event to occur. Programming the fire to occur in week
43 therefore mimics a much cooler fire, which apparently greatly reduces the fire’s effectiveness in causing seed mortality and influencing annual population dynamics.
180 6500
6000 160 5500 140 5000
2 120 4500 2
100 4000
80 3500 3000 60 Soil Seeds/m Mature Plants/m Mature 2500 40 2000
20 1500
0 1000 0 52 104 156 208 260 312 364 416 468 520 Week
Figure 8.15 Simulated mature plant density (black) and soil seed reserve size (red) of Senna obtusifolia over 520 weeks when fire occurs annually, but with reduced effectiveness.
Chapter 8 Population Model Simulations - 229 -
8.4 Discussion
The STELLA model simulation output indicated that the intensity and frequency of fire events had a significant impact on dynamics, generally suppressing both the density of stems in a population and the longevity and extent of the soil seed reserve. The implementation of annual control techniques before seed set similarly resulted in a gradual decline in S. obtusifolia stem and soil seed density. Rainfall, in comparison, which acted as the principal external driver in the model system, appeared to have a fairly minimal impact on the success of a population in any given year within the Lockhart River environment, but S. obtusifolia demonstrated sensitivity to moisture outside of the region. Overall, the dynamics of S. obtusifolia populations were found to be very robust, requiring a severe system perturbation to cause a major and prolonged negative population response. This provides a possible explanation as to why S. obtusifolia has been able to thrive and dominate vegetation in the Lockhart River region so well.
Fire proved to be a very influential environmental factor in the model, causing variable responses of S. obtusifolia populations to different fire intensities and
frequencies. The negative response that S. obtusifolia displayed in Figures 8.13 and
8.14 suggests that fire has attractive qualities for the potential use as an S.
obtusifolia management tool in the natural environment. Annual and hot fires
occurring late in the life cycle caused a general decline in the extent of the soil seed
reserve, thereby limiting the period of time that the seed bank can act as a
mechanism for population persistence by suppling propagules for re-establishment
after disturbances (Lunt 1997; Auld et al. 2000; Williams 2005). Regular controlled
burns, in conjunction with another management tool such as herbicide to prevent
new seed production, may quickly deplete the seed reserve. Figure 8.3
demonstrates that with the removal of the seed crop annually for five years, the seed
Chapter 8 Population Model Simulations - 230 - reserve fell to 2% of its original size. Assuming no dispersal, the population could be gone after 10 years. An infestation from a single plant could also arise though after only three years (Figure 8.2) and thus monitoring would have to be an ongoing process.
In contrast to the use of regular and hot prescribed fires in management, the prevention of fire from the ecosystem for a prolonged period may also offer a viable option for control. The rate at which dormancy is broken in seeds was identified in
Chapter 7 as being one of the most significant population attributes responsible for change in S. obtusifolia densities. The large scale release of dormancy is typically initiated by an environmental cue. Preventing or manipulating such a cue could perhaps provide a method of effective management, though mostly likely difficult to achieve. What factor is responsible for breaking dormancy in S. obtusifolia seeds is still uncertain (Mackey et al. 1997), however, within the Lockhart River region, one cannot look past the effect of fire, given its high rate of occurrence in the region. Fire itself may cause scarification of the seed, or alternatively the elevated soil temperatures resulting from fire may break the dormancy, as has been demonstrated in many other legume species (Williams et al. 2004 and references therein). Anning et al. (1989) reported mass germination of S. obtusifolia after fire, which fits observations made in Lockhart River. Therefore fire prevention may be critical in control as has been discussed in previous chapters.
The positive association between S. obtusifolia and fire is not reflected in the model as demonstrated in Figures 8.11 – 8.15, where the impact of fire usually resulted in mortality and lower plant densities. This relationship was excluded from the model due to the lack of experimental data and insufficient knowledge to incorporate it into the model. Whether the best use of fire be exclusion or regular prescribed burning cannot be concluded from the current model output. However, the impact of fire
Chapter 8 Population Model Simulations - 231 - demonstrated by the model certainly warrants further urgent, experimental investigation into the role of fire, to both improve the model and accordingly increase its ability to assist in early management decisions.
The model output also helped to clarify some aspects of the role of rainfall in shaping the annual dynamics of S. obtusifolia in the Lockhart River region. Overall, the weed
showed great resilience when exposed to the prolonged dry and wet conditions of
Lockhart River. Since temporal variability in weather can profoundly affect
population numbers (Firbank et al. 1984; Reader 1985), this result was surprising as
it was expected that rainfall would play a much larger role in driving variability in
population dynamics, especially since successful S. obtusifolia germination is reliant on it (Mackey et al. 1997). However, outside of Lockhart River, the weed did show
sensitivity to rainfall, with plant and seed bank densities responding strongly to the
wet and dry conditions (Figures 8.9 – 8.10). It is possible that the conditions within
Lockhart River may be highly suitable for S. obtusifolia, providing a somewhat ‘ideal’
environment with the dry and wet extremes of the rainfall conditions in the area not
severe enough to impart large negative responses in S. obtusifolia populations.
Therefore, rainfall cannot provide an explanation for the extreme variation in S.
obtusifolia occurrence throughout the study area across the four years it was studied
(Chapter 5).
8.5 Progress Towards Aims of Thesis
The conceptual model designed in Chapter 7 enabled simulations of S. obtusifolia populations under alternative management and environmental situations. This provided a further understanding of the role of fire and rainfall in driving change in S. obtusifolia, and showed that even under ideal conditions where no further seed is allowed to set, that the management of S. obtusifolia will be difficult and a long term
Chapter 8 Population Model Simulations - 232 - process. Manipulation of the environmental cue responsible for the large scale release of dormancy in S. obtusifolia seed and/or the use of fire are two possible management techniques arising from the simulations. These strategies should of course be extensively tested in the field in order to ensure they are robust and biologically meaningful control strategies (Buckley et al. 2004). Knowing such outcomes will also refine the current model, expanding its accuracy and usefulness in understanding the population dynamics of S. obtusifolia.
Chapter 9
General Discussion Chapter 9 Discussion - 234 -
9.1 Senna obtusifolia – a summary
The current study investigated the invasion dynamics of the alien invasive plant species S. obtusifolia across multiple scales to obtain ecological data concerning the invasion of the weed in natural environments in Australia. The results may assist managers in the rapid, early detection of S. obtusifolia, its prioritisation as a risk to
Australia and the natural environment, and in developing an effective strategy for long
term control.
The principal result to come from this thesis is that individual populations of S. obtusifolia are highly dynamic, both spatially and temporally. Population traits including density, size and reproductive output were consistent within each population, but were highly variable between infestations and between years, tending to move from one extreme to the other in just one season (Chapter 5). Despite such variability, in all cases densities of S. obtusifolia plants were found to be relatively high, with seed production and the amount of seed in the soil being very high. With thousands of seeds being produced per m2 annually, as well as being present in the
soil ready for germination when the correct conditions arise, it is easy to understand
why S. obtusifolia is capable of dominating a landscape.
The discovery of a germinable soil seed reserve inside S. obtusifolia populations
(Chapter 5) and the vegetation community adjacent to the S. obtusifolia infestation
(Chapter 6) was important for a number of reasons. Within S. obtusifolia infestations, possessing a viable soil seed reserve is highly beneficial for enabling population persistence to continue when the most recent cohort of seeds are destroyed (e.g. by fire). It also provides S. obtusifolia with an “escape mechanism”, enabling re- establishment when favourable conditions once again arise. Discovering a viable S. obtusifolia soil seed reserve within the adjacent vegetation communities Chapter 9 Discussion - 235 - demonstrated that the patchy distribution of S. obtusifolia throughout the floodplains of the Lockhart River region is not a product of limited spatial dispersal. Rather, it caused me to reason that some other factor must prevent S. obtusifolia establishment in some environments (Chapter 6).
Given the high levels of variability displayed by S. obtusifolia in just about all experiments conducted, it remains difficult to determine what internal or external factor/s are responsible for the differences between populations and years. Similarly, it is difficult to determine what factors may prevent S. obtusifolia establishment. One logical explanation is that the conditions of dissimilar vegetation communities result in the differential success of the weed. Observations would certainly suggest that some environments (i.e. the grasslands) are more amenable to invasion than others (i.e. the rainforest and woodlands). Yet, once again, when tested in Chapters 5 and 6, the results of this hypothesis showed high levels of variability, preventing a conclusive result from being drawn. However, it is believed that low levels of disturbance, high interspecific competition and dense shade (Chapter 6) can decrease the rate of S. obtusifolia establishment and the vigour of plants.
The distribution of fire and the distribution of environmental cues that break dormancy in S. obtusifolia seeds and elevate rates of germination can also impact upon the success of the weed. This was demonstrated by the 10 year future simulation of the behaviour of S. obtusifolia populations used in Chapter 7 and 8. Even though this model represented a simplified view of the life cycle of S. obtusifolia and its interaction with the environment, it enabled an analysis of the sensitivity of the different components of the life cycle to change and the response of the population over time to variable environmental conditions.
Chapter 9 Discussion - 236 -
Where patterns of distribution are not well understood, the ability to successfully map populations on a regional basis is beneficial. This way spatial change over time could be monitored and long term patterns of distribution analysed to provide more information regarding the target species. Unfortunately, success in mapping infestations of S. obtusifolia using remote sensing techniques (Landsat multispectral satellite imagery and aerial photography) in the current study was limited as a result of poor quality data and a scale too coarse to detect small S. obtusifolia populations.
However, it appeared that the attributes of S. obtusifolia populations are favourable for detection by remote sensors and therefore, with some refinement, the techniques employed in this study may be successfully used in the future.
Eco-climatic mapping at the broad, Australia wide level of scale indicated that S. obtusifolia has great invasion potential within this country. Based on the climatic preferences of S. obtusifolia, the weed is capable of expanding its currently restricted northern distribution, to include the entire eastern coast of Australia, as well as regions of the Great Australian Bight and the western and northern coastlines. The primarily coastal distribution is limited predominantly by the dry conditions of inland
Australia, where S. obtusifolia experiences significant degrees of stress. Moisture at any level of scale may therefore produce difficult conditions for the weed to continue to survive.
Overall, the results of this study have clarified/identified some important characteristics of the invasiveness of S. obtusifolia. However, due to the high level of variability recorded in the three year time frame of the study, it is clear that significantly more research is necessary before a true understanding of factors influencing distribution become clear. The logistical difficulty of conducting fieldwork in such a remote area has also impacted on the clarity of results in some instances.
Experimental success was undoubtedly influenced by our inability to establish tight Chapter 9 Discussion - 237 - experimental controls as a result of the highly (and unexpected) dynamic nature of the system and being absent from the region during the wet season and main periods of germination.
Nevertheless, despite such logistical difficulties, the work conducted in this study has undertaken significant first steps in identifying differences between how S. obtusifolia behaves in natural environments of Australia in comparison to the documented agricultural systems of international studies. Understanding distinctions between the two systems will greatly aid in modifying control techniques currently used in agricultural situations for application in non-agricultural conditions.
9.2 Applications for the management of Senna obtusifolia
The early identification and detection of an invasive species and the prediction of probable locations for invasion by the target species currently provides one of the most effective means of mediating the impacts that these species can have (Sakai et al. 2001). Early detection increases the chances of successful eradication or containment, minimising spread and negative effects of the species at hand
(Simberloff 2002). Although such swift action is highly beneficial, often the techniques employed can only provide short term relief if the eradication attempt is not 100% successful. In this instance, it is important that in conjunction with these initial reactions to an invasive species, that more in-depth assessments into the current and potential extent of the problem are conducted. Research should also be conducted into the dynamics of the weed and its invasion/interaction with the surrounding environment. The work undertaken in this study has demonstrated that such detail allows for increased understanding of the system, which in turn allows for Chapter 9 Discussion - 238 - the opportunity to discover critical attributes and interactions that may assist in the development of more appropriate longer term control strategies.
For both practical and economic reasons, the distribution of S. obtusifolia in Australia
is now far too broad to consider eradication as a primary focus. However, early
detection remains paramount in the fight to contain spread and minimise the current
impacts of the weed. How detection and management are decided upon and
implemented are obviously very dependent upon the scale at which the weed is
considered and what is the desired management outcome. Although this study was
not developed to specifically determine techniques to manage S. obtusifolia in natural
environments, it was hoped that some positive clues would surface. Through the
investigation of aspects of S. obtusifolia invasion dynamics, some plant/system traits
emerged as potential avenues for long term control. These include fire, shade, soil
temperatures, germination rates and the rate of release of seed from dormancy.
Whilst the worth, exploitation and application of each of these factors as a viable
method of control requires extensive further research, this thesis has provided a
positive first step into realising a more sophisticated, appropriate and large scale
management scenario. The following discusses five major areas/population traits
identified for potential inclusion in a regime to manage S. obtusifolia in the Lockhart
River region:
9.2.1 Fire
The negative impact of fire on the viability of S. obtusifolia seed (Chapter 5) and on
the density of S. obtusifolia populations over time (Chapter 8) provides great support
for the use of fire as a mechanism for control. If hot fire can result in the large scale
destruction of newly produced S. obtusifolia seed then, as described in Chapter 8,
regular (e.g. annual) and hot fires programmed to burn late in the S. obtusifolia life Chapter 9 Discussion - 239 - cycle will cause a decline in the extent of the soil seed reserve. This is beneficial because as long as germination is being promoted from the soil seed reserve, the period of time that the seed bank can support re-establishment and population persistence will be greatly reduced.
Weed and ecosystem management through the use of an appropriate fire regime as part of an integrated strategy has had some success in tropical woody weeds
(Downey 1999) such as rubber vine (Cryptostegia grandiflora) (Grice 1997; Bebawi and Campbell 2002) and mimosa (Mimosa pigra) (Lonsdale and Miller 1993).
Undesirable increases in recruitment have, however, also resulted (e.g. scotch broom
(Cytisus scoparius) (Bossard 1990). In the instance of S. obtusifolia, whilst the use of fire as a tool for control appears promising due to rates of mortality, it is firstly essential to clearly understand the role fire plays in the ecology of S. obtusifolia and
the surrounding ecosystem (Grice 1997). With the results of the seed germinability
tests aside, the negative effects of fire on S. obtusifolia in this study have largely
been inferred from observation after very hot fires only and not from sound scientific
experimentation. Therefore, it is critical that further research be conducted before fire
is used as a tool for S. obtusifolia control in the Lockhart River region. Questions
such as how does S. obtusifolia respond to fire, what is the post-fire interaction of S.
obtusifolia with other species and how does S. obtusifolia modify pre-invasion fire
regimes should be addressed (Downey 1999). Other factors to consider when using
fire include the period of prolonged bare earth, erosion, impacts on other species in
the ecosystem and nutrient loss (Moor 2003).
9.2.2 Competition
Given that S. obtusifolia is predominantly an opportunistic, early colonising species, locations that are exposed to high rates of disturbance are more likely to support Chapter 9 Discussion - 240 - persistent populations of the weed (Colinaux 1986; Brown and Southwood 1987).
Disturbances can be characterised by their type, intensity, extent, frequency and duration (Shea et al. 2004). Alteration to just one of these characteristics may have a significant scale dependent impact on S. obtusifolia success. Given the observations made in this study and the life history of S. obtusifolia, minimising disturbance intensity, frequency and extent and maintaining high levels of inter-specific competition may greatly diminish the quality of conditions encouraging S. obtusifolia establishment.
The results of this study again can only provide circumstantial evidence to support this statement, with most of the inference arising from observation. The greatest observation once again surrounds fire. Annual firing on the floodplains of the
Lockhart River region presents the largest form of disturbance that the plant communities undergo. Where fire has been consistently excluded from the landscape, S. obtusifolia success appears to have diminished significantly (Chapters
5, 6 and 8). Why this occurs is not understood as it has not been tested, however, the presence of strong inter-specific competition must intuitively, have some important impact. Competition can affect community and landscape vegetation patterns (Bengtsson et al. 1994; Brown et al. 1998) and it has been previously hypothesised that herbaceous (grass) competition can limit shrub establishment and growth (Gordon et al. 1989; McPherson 1993), although this is yet to be conclusively proven (Brown et al. 1998). This theory of the negative impact of competition is supported in some respects by the results of Chapter 6, whereby the establishment rate of S. obtusifolia was kept extremely low when seed was placed into the different types of intact vegetation communities. Anning et al. (1989) further provides observational evidence in the agricultural context, where poorly kept pasture tends to be heavily invaded by the weed and well maintained pasture cover prevents S. obtusifolia establishment. Chapter 9 Discussion - 241 -
A large proportion of weed species are r-strategists/pioneer or opportunistic species that require disturbances to create open, resource rich and uncontested environments into which they can establish (Newsome and Noble 1986; Rejmanek
1995; Sax and Brown 2000). This is possibly one reason why disturbance is so well regarded as a precursor to successful invasion in any environment (Hobbs 1989;
Rejmanek 1989; Sakai et al. 2001). Senna obtusifolia has proven that it can be competitive with other species (i.e. establishment in Chapter 6, establishment in crop situations (e.g. Bozsa 1989; Jones 1993), but across long periods of time if good vegetative cover can be maintained, the opportunities to establish and the vigour of the plants should decrease.
Senna obtusifolia infestations are most commonly associated with B. decumbens and
I. cylindrica grasslands throughout the Lockhart River region. These grasslands can
be highly disturbed due to firing and trampling and rooting by feral animals. Both
species of grass are renowned for their competitive ability, in particular I. cylindrica.
Although both species are weeds in themselves, promoting their growth in confined
areas (typically enclosed by rainforest) to manage the S. obtusifolia problem is
preferable to the monoculture of S. obtusifolia infestations. Therefore, particularly in
the case of these grasslands, if disturbances can be minimised it is quite possible
that these species may be able to again dominate the landscape. Such a gradual
technique also leaves behind a stable ecosystem, as opposed to the large areas of
bare soil that fire will leave behind. This should reduce the likelihood of new weedy
species moving into the space that S. obtusifolia once inhabited. Clearly the
feasibility in achieving such a goal in a natural ecosystem would have to be
considered after further research; in particular it is necessary to determine the
importance of competition relative to other causative factors (Brown et al. 1998).
Chapter 9 Discussion - 242 -
9.2.3 Seed Dormancy
Another benefit of maintaining high grass/vegetation cover across areas where S. obtusifolia has previously grown is the prevention of mass germination events of S.
obtusifolia from the soil seed reserve. As demonstrated in Chapter 6, B. decumbens
is evidently inhibiting large-scale germination and establishment of S. obtusifolia from
the soil seed reserve, as this environment is harbouring the same density of soil
seeds/m2 as inside a S. obtusifolia infestation. Why this is so cannot again be
answered completely and therefore would require further research. However, as
discussed in Chapter 5, it is possible that the grass cover cools ground temperatures
sufficiently to prevent a large scale breaking of dormancy in the soil seeds (King
1993).
Further understanding about what factor breaks dormancy in S. obtusifolia seeds in natural conditions will be important in attempting to prevent mass germination, as the percentage of seeds in the seed bank that germinate in a given year is influenced by the species and the environment encountered by them (Buhler et al. 1997). If the effects of important environmental cues can be mediated, germination and establishment may be greatly inhibited. High ground temperatures are a common cue of the environment that will break dormancy in hard seeds (Baskin et al. 1998) and, therefore, cooling ground temperatures by maintaining a good cover of vegetation may greatly suppress the seed bank activity until viability in the soil seeds no longer exists. Of course, for this method to be effective, seed rain/addition to the soil seed reserve must discontinue.
Chapter 9 Discussion - 243 -
9.2.4 Shade/Light
In a similar context to that suggested above, producing shade/reducing light over S. obtusifolia infestations is another potential avenue for management. The distinct lack of mature S. obtusifolia plants in rainforests and patchy distribution in woodlands,
despite being exposed to similar propagule pressure, has to be testament to the
negative effect of shade. This was experimentally supported to some degree by the results of the Chapter 6 garden experiment, which indicated that seed exposed to shaded conditions produced lower establishment. Whether or not the reduced establishment is a result of incorrect conditions to break dormancy or merely prevent establishment was not determined. However, due to the low survival of seed in the rainforest and the low soil seed reserve, it is more likely that establishment cannot occur. This assumption is in agreement with the life strategy of S. obtusifolia in which maximum resources are allocated to the liberal production of small seeds that can survive under open conditions (Salisbury 1975). Therefore, because of their depleted provisions for germination and vigour they will exhibit low survival in modified conditions such as shade (Venable and Brown 1988).
Given this trend, integrating higher levels of shade into the natural environment could be highly beneficial in inhibiting S. obtusifolia establishment. Revegetation of highly disturbed areas with trees that will create a canopy will increase competition as well as provide shade. This however, will be difficult with respect to the grassland areas that are to be conserved as the distribution of shrubs will cause them to retract over time. One possible method is to sacrifice at least portions of the grasslands to produce an interim environment comprised of species that will out compete and shade S. obtusifolia. This would have to be maintained for several years until the soil seed reserve had depleted significantly and propagule pressure greatly reduced. At Chapter 9 Discussion - 244 - this time fire and revegetation could be re-introduced to the environment to restore the original grassland habitat.
9.2.5 Monitoring and basic maintenance
Whilst the long term control of S. obtusifolia using novel approaches remains uncertain, basic steps can still be undertaken to reduce the impacts of S. obtusifolia.
These include:
Firstly, stopping the spread of S. obtusifolia into non infested areas is of paramount importance as prevention is the first and most cost effective line of defence against alien species (Wittenberg and Cock 2001). The most comprehensive approach to achieve this is to identify the pathways/vectors that lead to the creation of new invasions and then manage the risk associated with these (Wittenberg and Cock
2001). Anthropogenic disturbances, in particular rights of way (e.g. roads and trails) are positively associated with the distribution of alien invasive species (Gelbard and
Bellnap 2003; Rew et al. 2006). Therefore one of the most important pathways of S. obtusifolia introduction is by vehicles, which from observation of S. obtusifolia in the
Lockhart River region, is highly apparent. The risk associated with major access routes can be greatly reduced by simply creating an S. obtusifolia free buffer zone between the roadsides and other traffic and the main infestation. Waterways are another likely vector of S. obtusifolia dispersal and perhaps it is necessary to enforce similar management on their banks to prevent seed flow into rivers/creeks during annual flood conditions.
Secondly, where spot outbreaks do occur along roadsides or are isolated from major infestations, spot eradication should be performed prior to seed set. This will prevent the creation of a viable soil seed reserve. For this to be achieved effectively, a Chapter 9 Discussion - 245 - regular routine of monitoring will have to be developed. When considering the detrimental impacts of the species, failure to take proactive steps to prevent new invasions, and inaction or slow response to the discovery of a newly established population, is an implicit management decision in its own right (Byers et al. 2002).
Monitoring is an essential phase of invasive species management, as not only does it
assist in containing spread, but is also provides critical information on how patches
are changing with time, what impacts are being incurred in the ecosystem and how
effective ongoing management is being (Rew et al. 2006).
Finally, the most obvious and common sense approach to manage S. obtusifolia is to
control the plant early in the life cycle while it is small and reproductively immature.
This will eliminate the production of a new seed crop, which will over time cause a
depletion of the soil seed reserve. Eliminating the plant whilst it is young can be
achieved successfully using herbicide (Mackey et al. 1997), however due to the
extreme dominance of S. obtusifolia it would be necessary to have some form of
restoration once the plant was removed. Through the gradual elimination of young
plants, the size of large infestations should decrease over time.
Due to the robust nature of the population dynamics of S. obtusifolia in the Lockhart
River region, it is probably necessary to integrate a number of control methods to
ensure success. This may involve the use of some of the methods listed above as
well as the use of herbicides and mechanical efforts used in the past. Additionally,
due to the extreme variability witnessed in the population characteristics of each
infestation both spatially and temporally, it is likely that no single method of
integrating the techniques will exist. Instead, programs will have to be customised to
the location, size, age and intensity of each infestation (Moor 2003). Understanding
what it is about the invaded ecosystems that initially made them susceptible to weed
invasion should also be made a research priority. By restoring one or two critical Chapter 9 Discussion - 246 - components of the system, the trajectory of the system can be altered from tending towards an equilibrium dominated by a pest to one more reminiscent of the natural system.
9.3 The benefits of an integrated approach
Scale is an issue entwined into every ecological discipline as ultimately all attempts to define patterns and processes characterising the physical environment are in some manner sensitive to scale (Ludwig et al. 2000). Ecological patterns at any given scale will in most cases determine or be influenced by processes occurring at larger or finer scales (Wiens 1989). Consequently, to understand the ecological dynamics of a given species or system in a complete manner, data must be derived from multiple spatial and temporal scales.
Biological invasions of plants are thought to be the consequences of features that are described at multiple spatial scales. Multiple sequential stages occur during a plant invasion and at any of these from seed dispersal, to establishment and seed production, the plants experience scale dependent ecological constraints (Pauchard and Shea 2006). Integrated multi-scale studies reduce the uncertainty of confounding spatial and temporal gradients that can be associated with each of these steps, thereby contributing significantly to the overall understanding of the causes and effects of a plant invasion. Controlling invasive species requires an understanding of the mechanisms that underlie the invasion and thus such a multi- scale approach will greatly assist in the identification of, or improve the effectiveness and efficiency of, management and control strategies (Pauchard and Shea 2006).
The current study addressed three scales: broad (i.e. continental), regional/landscape and local (population). Because there is no single obvious scale at which the Chapter 9 Discussion - 247 - phenomenon of invasions should be addressed (Levin 1992; Rouget and Richardson
2003), it was decided that these three scales would encompass sufficient spatial differences to produce meaningful data regarding the ecology/behaviour of S. obtusifolia invasions and its management and control. The top-down approach adopted recognised that each level of scale would have different implications for the invasion of S. obtusifolia. It continually sought a higher level of detail (by reducing
the scale) to explain some of the observations made at the broader scale. Although
this study was targeted to enable early detection, in undergoing this process it has
also provided baseline data to begin to understand the mechanisms of S. obtusifolia invasion and ensure the most appropriate control can be applied at specific scales.
For example, knowing that S. obtusifolia seed has low survivability in the rainforests of Iron Range National Park has no applicability to those responsible for creating policies or legislation regarding the nationwide treatment of the weed. This information will, however, contribute to the overall understanding of the driving processes of the weed and its invasion and therefore be of use to those managers responsible for manipulating individual plants and populations of S. obtusifolia
(Rouget and Richardson 2003)
Many past studies investigating mechanisms of invasions typically focussed at defining fine scale, site specific features that influenced the invasion (Johnson et al.
2006). Whilst these studies indisputably have their role to play in invasive species management, because of their specific nature it can be very difficult to ascertain useful generalisations from observed patterns (Costanza and Maxwell 1994). At very fine scales, chance events can make systems unpredictable (Levin 1992) and thus statistical predictability is thought to increase by moving up the hierarchy of spatial scale (Ward 2006). Rouget and Richards (2003) found a similar pattern when developing predictive models. Predictive power of the models was greater at larger Chapter 9 Discussion - 248 - than at finer scales, as the species’ response to the environment could be better detected.
All of this evidence supports the use of the eco-climatic model developed in Chapter
3. Analysing such models has the advantage of detecting influencing features that can be blind to the observer at finer scales (Ward 2006). For example, in this case, it was the role of climate and the effects of insufficient or too much moisture which was not obvious when focussing only on the Iron Range sites. Furthermore, as mentioned in the example used previously, information borne from such broad scale mapping has important implications for managers responsible for weed issues in large regional areas, state, national or even global regions. Eco-climatic modelling is a method which can rapidly assess invasion risk, which in turn can affect issues of prioritisation, control efforts and appropriate monetary delegation. This is particularly useful in the event of a new species arrival. In such cases, global scale efforts are as important as local control initiatives in the process of early detection and confinement of new populations (White and Schwarz 1998 but see Pauchard and Shea 2006).
Examining landscape (regional) dynamics has similar implications to studying the broader scale, however, the level of information that can be obtained increases with the higher observed level of detail. Patterns of population distribution at this scale become far more evident, providing greater clues to the direction of a species invasion. Examining S. obtusifolia at the landscape scale was largely unsuccessful, due to the difficulties experienced with the remote sensing. This was unfortunate as information pertaining to propagule flow, invasion extent, preferred habitat types and habitat usage could not be obtained. Landscape elements are commonly perceived to influence the initial colonisation of uninhabited sites. For example, factors such as habitat fragmentation and the amount of human activity and development may influence dispersal and colonisation, with human disturbed areas supporting Chapter 9 Discussion - 249 - established population of invasives and roads serving as conduits (Johnson et al.
2006). This could most certainly apply to the invasion of S. obtusifolia, given the
dominance of infestations along roadsides and waterways.
Examining S. obtusifolia at the landscape scale also gave rise to the idea of fire affecting the variability present in S. obtusifolia both spatially and temporally
(Chapters 5, 6, 7 and 8). At the level of the individual or population, fire is not evident as a major issue, however, at the landscape scale the distribution of fire across the landscape can be monitored. Fire is certainly an environmental driver that can occur haphazardly across a local landscape, thereby subjecting individual populations to varying fire regimes.
Whilst, the landscape scale can influence colonisation, it is thought to be acting in synergy with local conditions that will promote individual plant success (Hobbs 2001;
Sakai et al. 2001). Features of the local scale, such as soil characteristics, competition, nutrient distribution and local management practices, will determine the success of arriving propagules and therefore the overall vulnerability of a site to invasion (Johnson et al. 2006). It is most likely at this level, where the ‘Achilles Heel’ in the life cycle of the plant or in the population structure can be best understood, that research will eventually lead to understanding patterns of abundance and local eradication and management (Pauchard and Shea 2006). However, it is unlikely that much of what is discovered can be made sense of without the broad scale information. For example, landscape scale modelling, if effective, will identify common landscape elements associated with populations that may assist in invasion success that may otherwise go unnoticed. Similarly, it is probably useless in managing one population if it is in the immediate direction of main propagule flow.
This cannot be understood without larger scale observation. Nevertheless, the local scale experimentation conducted in this thesis has led to a great amount of Chapter 9 Discussion - 250 - information being now known about the weed in natural environments. Likely distributions, population dynamics, performance in competitive environments and possible performance over time have all been revealed during this study, as have some clues towards integrated/long term management and further research.
However, because we are aware of factors occurring at other scales, simply acting only to control individual infestations at S. obtusifolia at this localised level of scale will not have a sustainable effect in diminishing the threat posed by the weed
(Pauchard and Shea 2006).
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APPENDIX 1 Appendix 1 - 286 -
Table A1 Raw data set used in the seed introduction experiments (Chapter 6, section 6.3.2) showing the number of reproductively mature Senna obtusifolia plants recorded in each treatment and control trap in each of the different habitat types in 2004 and 2005.
Habitat Site Trap # 2003/2004 2004/2005 Treatment Control Treatment Control
Rainforest 1 1 0 0 0 0 2 0 0 0 0 3 0 0 0 0 2 1 0 0 0 0 2 0 0 0 0 3 0 0 0 0 3 1 0 0 0 0 2 0 0 0 0 3 0 0 0 0 Brachiaria decumbens grassland 4 1 Site destroyed 10 4 2 Site destroyed 2 4 3 Site destroyed 6 10 5 1 Site destroyed 0 0 2 Site destroyed 0 0 3 Site destroyed 0 0 6 1 Site destroyed 0 0 2 Site destroyed 1 0 3 Site destroyed 0 0 Imperata cylindrica grasslands 7 1 15 0 0 0 2 7 4 0 0 3 5 7 0 0 8 1 3 2 0 0 2 12 54 0 0 3 0 0 0 0
Appendix 1 - 287 -
Habitat Site Trap # 2003/2004 2004/2005 Treatment Control Treatment Control
9 1 24 1 1 0 2 22 0 0 0 3 2 0 0 0 Lowland Woodland 10 1 0 0 1 24 2 0 4 3 5 3 10 2 6 12 11 1 172 49 0 0 2 65 256 0 0 3 31 148 3 0 12 1 1 0 1 0 2 5 0 1 0 3 0 0 0 0 Elevated Woodland 13 1 1 0 0 0 2 35 0 0 0 3 4 0 0 0 14 1 12 0 0 0 2 13 0 1 0 3 23 0 0 0 15 1 0 0 0 0 2 8 0 5 0 3 0 0 0 0