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Biological Sciences    作業代寫

Biological Sciences 作業代寫


 
 
 
 
 Biological Sciences    

CONTENTS
SUMMARY. 3
1.INTRODUCTION. 3
1.1 Identification. 3
1.2 Background. 4
2.DESCRIPTION OF MODEL. 8
2.1Methodology. 8
2.2 Description of the Algorithm.. 8
2.3 Metrics. 9
3. MODELLING RESULTS. 10
4. CONCLUSION. 12
5. DISCUSSION. 13
REFERENCES. 16
APPENDIX. 17
 
 

SUMMARY
In this paper, we proposed CA algorithm to predict the growth and spread of C. taxifolia, which is a noxious species in NWS. The model fits well regardless of the factor of season and indicates the major direction of further experimental research which will enable improvement in the predictive power of the model. The simulation results describe the observed data on the C.taxifolia invasion and spread reasonably well but still can not be used for prediction because of the lack of information on death rate or seasonal biomass fluctuations.
1.INTRODUCTION
1.1 Identification

Key features
l  Frond height 3-25cm
l  Flattened fronds, bright green colour
l  Known to turn pale & white during winter in colder waters
l  Leaflets on fronds attach directly opposite each other, curve upwards
l  Leaflets constricted at base
 
1.2 Background
1.2.1.Description of C.taxifolia
Caulerpa taxifolia (Caulerpa) is a fast growing marine alga native to tropical Australia and the South Pacific that has colonised various areas outside its natural range, including several NSW waterways. Caulerpa was first found in NSW in April 2000, and it has now been detected in 14 NSW estuaries and lakes and one small oceanic population.
In the past, Caulerpa has been widely used as a decorative plant in the NSW marine aquarium trade. This alga can invade cool temperate waters, and Caulerpa has become established in several countries and areas outside its natural range.
Caulerpa is a Class 1 noxious species in all NSW waters under the Fisheries Management Act 1994. It is illegal to possess or sell the alga; fines of up to $11,000 apply. The noxious listing also provides NSW DPI with the power to seize and destroy Caulerpa, or require its destruction.
The invasive nature of Caulerpa has raised concerns as it has the potential to grow rapidly, alter marine habitats and affect biodiversity.
Aquatic pests, including Caulerpa are usually extremely difficult to eliminate once they have become established in the wild. It is therefore important to prevent noxious species such as Caulerpa from entering new waterways.[4]
C. taxifolia is capable of growing extremely quickly and vegetative growth seems to be the primary mode by which the alga has invaded large areas of seafloor in NSW and in other countries (Meinesz et al. 1993; Smith and Walters 1999). Evidence to date indicates that invasive C. Taxifolia in the Mediterranean rarely, if ever, reproduces sexually (?uljevi? and Antoli? 2000). Species of Caulerpa are capable of regenerating from small pieces of stolon or frond (Jacobs 1994), so fragments have the potential to be an effective means of dispersal (Belsher and Meinesz 1995;Ceccherelli and Cinelli 1999a).[4]
 
1.2.2.Control AND Treatment
Several methods for controlling Caulerpa have been trialled overseas and within Australia, ranging from physical removal (by hand or mechanically) to smothering and treatment with various chemicals. From 2001, I&I NSW conducted experimental trials of some of the most promising of these methods (particularly smothering with various materials, suction dredging and salt treatment). Primary Industries and Resources South Australia have also trialled various methods, including large-scale treatment with fresh water
All of these methods have advantages and disadvantages relating to their specificity, risk of increased fragmentation, ecological impacts, ease of implementation, and the need for repeat treatments and cost. Most of these factors depend to some extent on the size and nature of the affected area. No single method has yet been demonstrated as achieving permanent eradication of large, established areas of Caulerpa.
I&I NSW has found salt treatment to be the most useful control option to date in the short?term control of Caulerpa. Covering beds with around 3 to 6 centimetres of coarse salt (equivalent to 50 kg per m2) rapidly kills almost all of the treated Caulerpa. Although other species such as seagrasses and benthic invertebrates are also affected in the short term, they appear to recover relatively rapidly, apart from Posidonia seagrass, which is susceptible to salting.
Since 2001, I&I NSW has conducted an extensive Caulerpa control program utilising salt in the majority of affected estuaries. This has involved the application of over 1500 tonnes of salt to more than 6 hectares of Caulerpa beds. During this time, significant reductions in Caulerpa were achieved at Lake Macquarie and Narrawallee Inlet. However, although salt treatment is able to dramatically reduce the amount of Caulerpa at a site, to date the permanent eradication of Caulerpa by using this method has not been demonstrated in NSW except at Lake Macquarie. In addition, the area covered by existing Caulerpa beds is far too large to allow comprehensive treatment of all areas. Current knowledge of the effectiveness of control work utilising salt has resulted in control work being restricted to small outbreaks in newly affected estuaries such as Wallagoot Lake.
Consequently, new estuaries infested with Caulerpa need to be individually assessed and a priority list for treatment developed to allow the most effective use of available resources.
 
1.2.3.Study Region
The study uses data from Lake Conjola, located in New South Wales, Australia, Figure 1. Our study focussed on Lake Conjola (35°16'S 150°30'E), a medium sized coastal lake (surface area of 5.9 km2) approximately 210 km south of Sydney that is predominantly open to the ocean. Caulerpa taxifolia was discovered within the Lake in April 2000 and by early 2004, 165 ha of the lake bottom were infested (), frequently in dense and extensive mats (  and ). In this lake, C. taxifolia occurs in the same habitat as other seagrass on fine mud to clean sand over a depth range of at least 1–10 m.[5]
The primary aim of this project is to investigate and analyse the seaweed Caulerpa taxifolia and create a suitable (CA) model for NSW DPI. Our aim is to develop an algorithm and results for a stylised model of Caulerpa taxifolia growth and spread in Lake Conjola. The model algorithm used information on prevailing weather conditions, seagrass distribution and some depth assumptions.
 
 
Figure 1:Map showing the location of the study sities.Lake Conjola located in New South Wales,Australia. CW — Conjola West, RB — Robert's Bay, RP — Robert's Point, PB — Picnic Bay, MMB — Mella Mella Bay. The Lake opens in the east into the Tasman Sea.
 
1.2.4. Data
(3 Where and how will the data be collected?)
Data was collected by the NSW fisheries in 2003 by field mapping known infestations of C. taxifolia when it was highly infectious. This was done in the summer of March 2003, March 2004 and again after winter decay in August 2003 and September 2004. Lynne McArthur et. al. [2]
Once this data was collected and complied, it was sent to RMIT University where it was modelled into a Cellular Automata Model using Matlab, by Lynne McArthur, and subsequently emailed to us as a part of our project.
2.DESCRIPTION OF MODEL
2.1Methodology
In this paper, we used Cellular Automata (CA), a widely used model to predict the growth and spread of C. taxifolia. Based on the study undertaken by McArthur et al (2006), the model uses a square gridcell representation of the region where the biomass of each cell is contained in an  array. . The biomass data are stored in a matrix of cells  where each cell , ,contains the recorded biomass at time t, . Here the indices x and y correspond to the longitude and latitude coordinate pair. And a discrete CA algorithm is designed to determine biomass  of the current cell using the primary rule of the discrete Laplacian system. Biomass of −1 refers to land, and the other values represent the relative quantity of the weed in the cell; none, sparse or dense respectively. The Equation of  can be expressed as follow ( more details can be found in McArthur et al. 2013) :
.
Let the sum of the biomass in the neighbouring cells be given by:

then the growth and spread equation can be formulated as

2.2 Description of the Algorithm
Fluctuations correspond to the growth rate  and the spread coefficient. The biomass in a given cell is a function of the biomass in that cell, the biomass in the adjacent cells, the growth rate which is influenced by the presence or absence of seagrass, and the spread from adjacent cells, which is driven by the wind.
2.2.1. Threshold Parameters
Two parameters, p and d are introduced as threshold values for the equation of . The contribution to growth in the central cell is determined by the biomass of the surrounding cells, S, and if the sum of the biomass in the surrounding cells is not greater than or equal to the threshold p then the biomass in the central cell does not increase. Similarly, if the biomass of the surrounding cells is below d, then degradation occurs. Again, this reflects the rules of the Game of Life (Gardner, 1983).
2.2.2. Growth and Spread
Growth occurs subject to  and decay occurs subject to ,Spread occurs when growth is positive, given that the surrounding cells have sufficient biomass, specified by the threshold p. Given that plant growth is seasonally dependant we should describe  by a cyclic function. However, because our model aims at predict the biomass one year later, so we can just set  as 1. Plant spread within estuaries is not uniform, but in order to make our model easier, we let .
2.3 Metrics
Some metrics, devised by McArthur et al. (2006), are introduced to aid in the estimation of the parameters used in the algorithm:



where  and  are the number of cells containing biomass in the simulated data and empirical data respectively, is the number of cells that make up the lake, a constant,  and  are the total biomass present in the simulated and empirical data respectively and and  are the largest eigenvalues of the squared symmetric matrix where is the biomass matrix. We can also see that  and that values close to zero indicate the best fit, , where values close to one are desirable and  where values close to one indicate ‘sameness’.
As we are trying to analyse the attitudes and behaviours of such a temperamental biological sample, it will be beneficial to analyse indicators required for model improvement. This will include analysing the ways in which the model might be affected. These factors include the growth and spread of seagrass in regards to C. taxifolia, the effect of seasonal events such as wind, rain, hail, etc; the effect of season biomass fluctuations, Fragment recruitment from neighbouring cells, depth limitations on growth, anthropogenic influences on distribution.
This model is designed to inform resource managers and government bodies of the most effective methods of eradication. [2]
3. MODELLING RESULTS
The spread of C. taxifolia is illustrated in Figures 1-6, which is selected from each steps in one year. The growth is based upon initialisation with four cells of dense biomass, starting at the four point indicated in McArthur et al. (2013) . We have devised a number of simple metrics mentioned in section 2.3 that aid in the estimation of the parameters. To begin, we set , the threshold biomass in the surrounding cells,  the intrinsic growth rate, and the carrying capacity.
We calculate  for March 2003 at each time step and when it is very close to zero, we note the timestep. This is repeated for March 2004, August 2003 and September 2004. Table 1 shows the results. The number of timesteps that give the ‘best fit’ in terms of the metric  for March 2003 is t = 530. The number for March 2004 is t = 545. And the manifested in the Aug and Sep data sets is the ‘natural’ seasonal dieback, as can be noted in Table 1 indicating the total number of cells with biomass in each of the data sets. This table indicates how many timesteps t were required in each case to obtain .
Table 1. The number of time steps required to minimize  for the four data sets when model is initialised by four biomass cells.
 
Data set t
Mar 2003 15549 15582 530
Aug 2003 11175 11178 421
Mar 2004 16143 16176 545
Sep 2004 14547 14566 504
 
(6 Given level of simplicity does simulation results could be used for practical prediction of the C.taxifolia biomass?)
To illustrated how well the model fits the observed data, Table 2 shows the results with an abundant of criterion. In Table 2, different values of the parameters are also considered.
Table 2. The values of the three metrics for varying values of the parameters q, p and K
 
q p K t
0.05 2 4 15 0.16 3.04 0.90
0.01 2 4 15 0.05 1.83 0.93
0.001 2 4 15 0.02 0.92 1.02
0.1 2 4 15 0.27 4.1 0.95
0.05 1 4 15 0.19 3.04 0.90
0.05 0 4 15 0.70 3.31 0.90
0.05 2 2 15 0.13 1.50 0.95
0.05 2 6 15 0.16 4.54 1.17
0.05 1 2 15 0.17 1.57 0.95
 
Table 2 indicates that as q is reduced, all metrics improve, except  It is difficult to understand the physical interpretation of the comparison of eigenvalues, but in simple terms the closer they are the more similar the matrices are, which is essentially what we are trying to achieve. As p decreases from 2 to 0, both and  increase, indicating increasing difference in the two data sets. This is to be expected since it allows C. taxifolia to spread with less consideration of the biomass in the surrounding cells. As K changes, we note that  and  change very little, but  improves as K is reduced which again is intuitive. Based upon these results, we choose , and  for the simulation of the March 2003 data for validation with the March 2004 empirical data.
 Biological Sciences    作業代寫
4. CONCLUSION
This paper presents the algorithm and results for a CA model of Caulerpa taxifolia growth. The main question is how can we trust the modelling results and how reliable could the resource management actions be based on these results?
(4 How reliable could the resource management actions be based on the results?)
Through the results of the simulation on the growth of C. taxifolia, we can get the conclusion that 15 steps approximately equals to one year. But if we use such strong conclusion to affirm that the C. taxifolia invasion begun 35 years ago (), it may seems unreliable. However, several important and practically valuable results of this modeling work should be mentioned.
The modelling experiment implemented in the present work has two major outcomes. Firstly, it demonstrates that the stylised diffusion model delivers the simulation results which describe reasonably well the observed data on the C. taxifolia invasion. This good match of simulated
C. taxifolia spread to the observed figures can be illustrated graphically and by using the analytical metrics (Table 2). The graphical representation demonstrates that the modelling results obtained reproduces quite well the geometry of the of the C. taxifolia population in Lake Conjola.
The second main outcome is that the given level of simplicity does not provide simulation results which could be used for practical prediction of the C. taxifolia biomass. The major problem associated with this stylised approach is that no information on death rate or seasonal biomass
fluctuations (most importantly, the winter decay of the biomass) is available and these effects couldn’t be included in the model. As a result the time slices of August 2003 and September 2004 recorded during the winter periods are assigned by the model to the periods preceding March 2003 and March 2004, respectively (Table 2).Biological Sciences    作業代寫
5. DISCUSSION
This model was not used to accurately model the growth and spread of C. taxifolia but to get an indication of the drivers of growth and spread of the patterns of this weed. Figure 1-6 shows the spread of C. taxifolia from Mar. 2003 to Mar. 2004. However, there are varies factors can be introduced to the model for improment:
(1 What external factors will affect the growth and spread of Caulerpa taxifolia?)
l  Seasonal biomass fluctuations, in particular the depletion of biomass during winter: The greatest growth occurs during summer with zero growth in winter Komatsu and Meinesz (1997). The winter months also produce a greater loss with low reattachment probability of fragments to the soil surface resulting in a decay in the total biomass of C. taxifolia in a given waterway Meinesz et al. (1995)
l  Fragment recruitment from neighbouring cells as spread occurs also from fragmentation colonization Meinesz (1996); Depth limitations on growth: Minimal growth of C. taxifolia has been observed at depths greater than 10 metres in the Mediterranean Sea. At Lake Conjola, no growth has been observed at depths exceeding 10 metres.
The incorporation of seagrass data to indicate growth areas: Existing native seagrass beds provide abundant nutrients and shelter for C. taxifolia and also provide substrate structure where fragments of the species can be trapped by the complex physical structure of seagrass species in the waterway Johansson and Nilsson (1993).
(2How will the modified CA model assist in helping the NSW DPI?)
The model created can be help to predict the growth and spread of C. taxifolia, regardless of the factors unconsidered (especially the seasonal biomass fluctuations), this model works well for predicting the state of C. taxifolia one year later. This information can be used by NSW DPI in the future to focus the searches for it in other geographic areas. This is not so important in Lake Conjola because C. taxifolia covers very large areas, but in many other estuaries, it occupies small pockets, finding which requires huge resources.
There are also many fields remained to be discussed: Asexual Reproduction: fragmentation and stolon extension as well as the Interactions with seagrasses and behaviors:
(7 Asexual Reproduction: fragmentation and stolon extension)
In the absence of evidence for sexual reproduction, it appears that asexual reproduction via fragments and stolon growth contribute importantly to the establishment and spread of Caulerpa taxifolia. Large numbers of unattached fragments are always present in or near infestations of C. taxifolia and have the potential to disseminate and produce new infestations. It has been suggested that detached fragments are capable of wide natural dispersal; for example, drifting fragments were observed from a submarine at depths of 45-100m in the Mediterranean (Belsher and Meinesz1995). Work in the Mediterranean also confirms that drifting fragments can attach and successfully establish, although their subsequent success (i.e. continued growth and expansion) shows considerable spatial and temporal variability (Ceccherelli and Cinelli 1999a). These authors attributed the observed differences to a number of factors including temperature, type of substratum and water flow. The means by which fragments are generated by anthropogenic activities and subsequently transported to new locations are considered in chapter 4.
Field observations strongly suggest that water movement associated with storms has enormous potential to create fragments of C. taxifolia and this may account for the spatial and temporal variation observed in fragment abundance. The extent to which natural fragmentation contributes to the overall fragment abundance remains unclear, but these experiments showed that fragments were at least twice and up to six times as abundant after a storm than at other times of sampling. Although creating fragments, storms did not appear to alter the size range of fragments observed. Numbers of all types of fragments increased following storms, but their relative sizes remained the same.
This study has not examined the fate of drifting fragments and so the relative importance of fragment type (frond, stolon and thallus) and size in establishing new plants is unknown in the field setting in NSW. Laboratory experiments confirm that all fragment types were capable of regrowing from very small fragments and thereby have the potential to establish new infestations. Once an infestation is established stolon growth leads to the rapid cover of the substratum by the alga. Stolon growth was strongly seasonal, peaking during summer at rates of up to 13 mm per day. The growth rates of stolons were faster over bare substrata than in the middle of dense patches of C.taxifolia. Experiments removing stolons and adding fragments provide evidence that stolon growth rather than the presence of fragments contributes most significantly to increases in biomass as infestations spread. As this work was done under the auspices of an ARC Postdoctoral Fellowship it will be reported elsewhere.
(5 What the interaction with seagrasses and herbivores?)
Experiments and monitoring reveal that fragments of C. taxifolia are often positively correlated with structural heterogeneity. Given these findings, the biogenic structure that seagrasses impart onto these habitats would be expected to trap fragments and thereby heighten interactions amongthese organisms. Preliminary data from seagrass beds indicate that fragments of C. taxifolia are frequently spread throughout beds of Zostera capricorni, but are not present in dense beds of Posidonia australis. Instead fragments accumulate at the edges of the beds of this slow growing seagrass. Stolons of C. taxifolia can extend beneath the canopy of Posidonia australis and, over a 14 month period, we documented some seagrass loss, although the mechanism by which this occurs is not known and is deserving of much closer attention.
The responses of native herbivores to C. taxifolia indicate that they are unlikely to intercede in the spread or control of this invader. Laboratory and field feeding trials show avoidance of fronds of C.taxifolia or solvent extracts of this alga; in field trials, palatable agar feeding discs were used to assess the responses of native herbivores to extracts of C. taxifolia, ensuring no chance of disseminating the alga. In “no choice” feeding trials herbivores often consumed C. taxifolia or its extracts, but when offered a choice of algae, C. taxifolia was ranked low in preference. Small saccoglossan molluscs will consume C. taxifolia, but their distribution is extremely patchy and hence their utility in ‘augmentative biocontrol’ remains to be established.
 
REFERENCES
[1]2008, Pergent et al, ‘Competition between the invasive macrophyte Caulerpa taxifolia and the seagrass Posidonia oceanica: contrasting strategies’,
 
[2]2013, Lynne C. McArthur, ‘Modelling the spread and growth of Caulerpa taxifolia in closed waterways in southern Australia using cellular automata’, School of Mathematical and Geospatial Sciences RMIT University
 
[3]1992, Guerriero, A., Meinesz, A., D'Ambrosio, M., & Pietra, F, ‘Isolation of Toxic and Potentially Toxic Sesqui-and Monoterpenes from the Tropical Green Seaweed Caulerpa taxifolia Which Has Invaded the Region of Cap Martin and Monaco’, Helvetica Chimica Acta, 75(3), 689-695.
 
[4]2011, NSW Department of Primary Industries, ‘Caulerpa Taxifolia’, viewed 10/04/13
<http://www.dpi.nsw.gov.au/fisheries/pests-diseases/marine-pests/nsw/caulerpa-taxifolia>
 
[5] , 15 April 2009, Pages 163–169 Structural complexity facilitates accumulation and retention of fragments of the invasive alga, Caulerpa taxifolia
 
APPENDIXBiological Sciences    作業代寫
Figure 1-6:  the spread of C. taxifolia  6 color are used and yellow for -1, blue for 0, green for biomass
   
Figure 1     1st  step Figure 2  2nd step
   
Figure 3     3rd  step Figure 4  5th step
   
Figure 5     10th  step Figure 6  12th step

Figure 7-8: comparison between simulation results and empirical results on Mar 2004
   
simulation results Empirical results
Figure 9  growth of C. taxifolia with 600 steps
 

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