using dispersion statistic scales as an indicator for ... · standard deviation 2 1 ¦ n x x n i i,...
TRANSCRIPT
Using Dispersion Statistic Scales as an Indicator for assessing the Biological
Diversity
ABSTRACT
Using statistical analysis is important for ecology
ministry, municipality organization, agriculture and
industrial sectors. Means, standard deviations, variance,
CV% and correlations were used as an indicator to
assess the biological diversity. SPSS and PAST
programs were used to analysis the data collected from
natural habitats. Based on the dispersion statistics
variance and CV% showed the most informative
measures. High values of CV% recorded the highest
biological diversity as well variance showed similar
findings. CV% affirmed that diversity increased with
increasing the elevation of regions above sea level. The
coefficient of variation is valuable since the standard
deviation of facts must always be known in the context
of the mean of the data. Correlation coefficient (R)
demonstrated the negative relationship between the
species grown at the wild habitats. Detrended
correspondence analysis (DCA) was used and
emphasized that there is wide diversity among the wild
type biological species. Statistical tools and its
applications are very important in evaluation the
biological diversity which helping in developing
conservation action plan for national, regional and
international level.
Keyword: ANOVA, Correlation, CV%, Jordan, PAST,
Standard deviation.
Advance Research Journal of Multi-Disciplinary Discoveries I Vol. 30.0 I Issue – I ISSN NO : 2456-1045
ISSN : 2456-1045 (Online)
(ICV-MCS/Impact Value): 63.78
(GIF) Impact Factor: 4.126
Publishing Copyright @ International Journal Foundation
Journal Code: ARJMD/MCS/V-30.0/I-1/C-5/OCT-2018
Category : MATHEMETICAL SCIENCE
Volume : 30.0 / Chapter- V / Issue -1 (OCTOBER-2018)
Journal Website: www.journalresearchijf.com
Paper Received: 29.10.2018
Paper Accepted: 07.11.2018
Date of Publication: 15-11-2018
Page: 26-31
Name of the Author (s):
Remal Shaher Al-Gounmeein1, Ibrahim M. Alrawashdeh2
1Mathematic Department, Science Faculty, AlHussein Bin Talal
University, Ma'an, Jordan.
2Biology Department, Science Faculty, AlHussein Bin Talal
University, Ma'an, Jordan.
Citation of the Article
Original Research Article
AL-Gounmeein RS; ALrawashdeh IM (2018) Using
Dispersion Statistic Scales as an Indicator for assessing the Biological Diversity ; Advance Research Journal of
Multidisciplinary Discoveries.30(5)pp. 26-31
Peer Reviewed , Open Access and Indexed Academic Journal ( www.journalresearchijf.com) Page I 26
Advance Research Journal of Multi-Disciplinary Discoveries I Vol. 30.0 I Issue – I ISSN NO : 2456-1045
1
)(
deviation Standard
2
1
n
xxn
i
i,
The positive square root of the variance is called standard
deviation.
%100* variationoft CoefficienX
S , Where )(S is standard
deviation & )(X is the mean.
2
1
2
1
1
)()(
))((
t coefficienn Correlatio
n
i
i
n
i
i
i
n
i
i
yyxx
yyxx
III. RESULTS
In many ecological studies, a key metric for assessing stability has been based on the coefficient of variation (CV) of the
functional response, defined as the ratio of the standard deviation
to the mean response (Carnus et al., 2014). ANOVA for
biological diversity is illustrated in Table 1. Mean, standard deviation, variance, CV% and skewness for biological diversity
at four sites were shown in the Tables (2, 3, 4, 5). Analysis of
data showed that the range of variance was (0.17-110.41), (0.07-
0.46),(0.07-26.67) and (0.07- 64.29), for alshoubak/ Doshaq- fujij with elevation 1273m,Tafilah/ Qadesiah with elevation
1275m, alshoubak/ Abu-Eid with elevation 1420m, alshoubak/
Aljhair with elevation1554m, respectively Table (2, 3, 4 and 5)
with highest value (110.41) recorded to Hordeum sp. at alshoubak district (1273 m), Artemisia herba alba 0.46 at
Tafilah/ Qadesiah (1275m), Achillea santolinea 26.67 in
alshoubak/ Abu-Eid with 1420m, and 64.29 was recorded for
Lasiopogon muscoides at 1554m alshoubak/ Aljhair. The range of coefficient of variation registered for alshoubak/Doshaq-fujij
with elevation 1273m was 105-280, Tafilah/Qadesiah with
elevation 1275m was 85-388, alshoubak/Abu-Eid with elevation
1420m was 86-388, alshoubak/ Aljhair with elevation1554m was 100-385, respectively Table (2, 3, 4 and 5), where the highest
values recorded for Hordeum vulgare., Artemisia herba alba,
Achillea santolina and ( Lasiopogon muscoides, Avena sterilis ;
Sinapis arvensis), respectively Tables ( 2,3,4 and 5). On the hand, the skewness for alshoubak/ Doshaq- fujij with elevation
1273m was ranged 0.44-2.62,Tafilah/Qadesiah with elevation
1275m was0.21-3.13, alshoubak/Abu-Eid with elevation 1420m
was 0.89-3.13, alshoubak/ Aljhair with elevation1554m was 0.85-2.98, respectively Table (2, 3, 4 and 5). The association
among biological species illustrated at Table 6. In the Table 6,
Negative association was set up between plant individuals. For
example Scorzonera judaica species showed negative relation with Centaurea sp., Hordeum vulgare, Anabasis syriaca,
Artemisia herba-alba Asso., Hordeum sp., Noaea mucronata,
Anthemis tinctoria, and Sinapis arvensis. Positive correlation
was found between Scorzonera judaica and Achillea santolina, Lasiopogon muscoides and Avena sterilis (Table 6). Correlation
(R) among studied biological samples was found in the Table (6
and 7) and (Figure 1 and 2). Figure 1 showed that diversity
increased with increase the biological samples. Figure 2 explain that the distribution of biological diversity patterns was scattered
with grouping some of associated biological species based on the
Detrended correspondence analysis. Highly significant
Peer Reviewed , Open Access and Indexed Academic Journal ( www.journalresearchijf.com) Page I 27
I. INTRODUCTION
Statistic is a science that assist in summarizing data, familiar terms such as data Mean, variance. These terms as well
detrended correspondence used to relate species communities at the
level of plants and animals to known variation in the environment.
The one-way ANOVA problem with unequal variances is a simplification of the well-known Behrens-Fisher problem, which is
one of the oldest and most exciting problems in statistics (Sadooghi-
Alvandi et al., 2102). Analysis of variance is mainly valuable
implement for analyzing highly planned experimental data in agriculture, animals and humans located at different habitats. Also,
help in understanding of ecosystem changes which is needed for
ecologists that supplies society with the knowledge necessary for
judicious management or action plan of the ground and its biological resources. Correlations measure the strength of linear
association between two continuous variables. Sari et al., (2017)
used the Pearson correlation coefficients to estimate the correlation
between cherry tomato variables. The coefficient of variation (CV) is a unit less measure typically used to evaluate the variability of a
population relative to its standard deviation and is normally
presented as a percentage (Canchola et al., 2017). Carnus et al.,
(2014) stated that to date, ecological studies that aim to measure stability in ecosystem function across a range in diversity have
almost universally used the coefficient of variation (CV, the ratio of
standard deviation (SD) of functional response to its mean), or its
inverse 1/CV, in reaching conclusions. Coefficient of variation was intensively used in many previous studies such as diversity of
physic nut (Shabanimofrad et al., 2011). Lepš, (2004) measured the
variability by the coefficient of variation between years, from the
fifth to the eighth year of the experiment. Detrended correspondence along with other correspondence analysis were effectively used to
examine society constitution and its fundamental environmental
source, as well as several marine studies. Ayoub-Hannaa et al.,
(2013) stated that detrended correspondence analysis (DCA) is a simple multivariate technique for arranging for species and samples
along environmental gradients was used for reconstructing
palaeoecology patterns and biostratigraphy. Detrended
correspondence analysis (DCA), an ordination technique used to explain patterns in complex data sets, arranged the sample sites
along an ordination axis that explained 76% of the variation in the
phytoplankton abundance data matrix (Garono et al., 1996). Gomaa,
(2012) said that by using ten quadrats (1x1) m per stand a total of 71 species belonging to 22 families and 61 genera were observed. On
the other hand, he used detrended correspondence analysis (DCA)
and showed that these groups are clearly distinguished by the first
two DCA axes. ANOVA as a technique of analyzing highly planned data by decomposing variance into different sources, and comparing
the explained variance at each level to what would be expected by
chance alone. Because in adequate information was published about
the biological diversity of studied areas under this work and mainly Scorzonera judaica species. The aim of this study was focusing on
application of statistical tools and techniques in analysis of
biological diversity at the wild habitats.
II. MATERIALS AND METHODS
Data were obtained based on the Quadrate - transects technique for assessing the biological diversity at four locations in
alshoubak and one at Altafilah areas. These areas were located in
the southern part of Jordan. The study area is characterized by dry
climate with hot summer and cool winter. Sixty quadrates (1x1) meter were used along three randomly transects overall studied
areas. The data were inserted on excel sheet then transferred into
programs of analysis.
III. DATA ANALYSIS
Means, standard deviations, variance and correlations
calculated according to formulas outlined by (Steel and Torrie, 1980 and Hammer et al., 2001). Detrended correspondence was analyzed
using the PAST software program ver. 2.18c (Hammer et al., 2001).
Correlation was obtained through using SPSS, V. (11.0), software.
n
x
Mean
n
i
i 1
2
1
1
)(
Variance
n
xxn
i
i
AD
VA
NC
E R
ESEA
RC
H J
OU
RN
AL
OF
MU
LTID
ISC
IPLI
NA
RY
DIS
CO
VER
IES
Advance Research Journal of Multi-Disciplinary Discoveries I Vol. 30.0 I Issue – I ISSN NO : 2456-1045
relationship was found between certain biological species such as Lasiopogon muscoides and Avena sterilis (1.00) it seems more
associated and those favorite the same niches and Anthemis tinctoria and Hordeum vulgare (0.79); Hordeum vulgare and Anabasis syriaca
(0.76) Table 7.
Table 1: One-way ANOVA
Sum of square df Mean square F P
Between groups 88.149 11 8.0135 1.447 0.147
Within groups 3921.12 708 5.5383
Total 4009.12 719
Table 2: Mean, standard deviation, variance and skewness for biological diversity at (1273) meter elevation at
al shoubak sub district.
Scorzonera judaica Centaurea sp. Anabasis syriaca Hordeum sp. Hordeum vulgare
Mean± standard deviation
0.6±0.63 0.4±1.06 0.2±0.414 4.13±10.51 1.00±2.8
Variance
0.40 1.11 0.17 110.41 7.86
Skewness
0.44 2.62 1.35 1.94 2.36
CV%
105 265 207 254 280
Table 3: Mean, standard deviation, variance and skewness for biological diversity at (1275 ) meter elevation at
al Tafilah sub district.
Scorzonera judaica Artemisia herba-alba Asso. Noaea mucronata Hordeum sp.
Mean± standard deviation
0.267±0.46 0.80±0.68 0.13±0.35 0.067±0.26
Variance
0.21 0.46 0.12 0.07
Skewness
0.95 0.21 1.94 3.13
CV%
172 85 269 388
Table 4: Mean, standard deviation , variance and skewness for biological diversity at (1420) meter elevation at
al alshoubak sub district.
Scorzonera judaica Noaea mucronata Artemisia herba-alba Asso. Achillea santolina Centaurea sp.
Mean± standard deviation
0.20±0.414 0.27±0.59 0.93±0.80 1.33±5.16 0.07±0.26
Variance
0.17 0.35 0.64 26.67 0.07
Skewness
1.35 1.84 0.89 3.13 3.13
CV%
207 218 86 388 371
Table 5: Mean, standard deviation , variance and skewness for biological diversity at (1554) meter elevation at
al alshoubak sub district.
Scorzone
ra
judaica
Artemisia
herba-
alba
Asso.
Hordeum
sp.
Centaurea
sp.
Anthemis
tinctoria
Achillea
santolina
Lasiopog
on muscoides
Avena
sterilis
Sinapis
arvensis
Mean± standard deviation
0.29±0.47 0.86±0.86 0.64±2.41 1.00±1.62 0.21±0.58 1.79±6.68 2.14±8.02 0.07±0.27 0.07±0.27
Variance
0.22 0.75 5.79 2.62 0.34 44.64 64.29 0.07 0.07
Skewness
0.85 0.91 2.98 1.42 2.23 2.98 2.98 2.98 2.98
CV%
162 100 377 162 276 373 375 385 385
Analysis of data showed that the range of variance was (0.17-110.41), (0.07-0.46),(0.07-26.67) and (0.07- 64.29), for alshoubak/ Doshaq- fujij with elevation 1273m,Tafilah/ Qadesiah with elevation 1275m, alshoubak/ Abu-Eid with elevation 1420m, alshoubak/ Aljhair with
elevation1554m, respectively Table (2, 3, 4 and 5) with highest value (110.41) recorded to Hordeum sp. at alshoubak district (1273 m),
Artemisia herba alba 0.46 at Tafilah/ Qadesiah (1275m), Achillea santolinea 26.67 in alshoubak/ Abu-Eid with 1420m, and 64.29 was
Peer Reviewed , Open Access and Indexed Academic Journal ( www.journalresearchijf.com) Page I 28
Advance Research Journal of Multi-Disciplinary Discoveries I Vol. 30.0 I Issue – I ISSN NO : 2456-1045
Table 6: Measure of association between Scorzonera judaica and plant species grown within natural habitats of
Alshoubak and Altafilah areas.
Species association R R square
Scorzonera judaica* Centaurea sp -.175 .031
Scorzonera judaica* Hordeum vulgare -.157 .025
Scorzonera judaica* Anabasis syriaca -.138 .019
Scorzonera judaica* Artemisia herba-alba Asso. -.121 .015
Scorzonera judaica* Hordeum sp. -.115 .013
Scorzonera judaica* Noaea mucronata -.102 .010
Scorzonera judaica*Achillea santolina .074 .006
Scorzonera judaica* Anthemis tinctoria -.121 .015
Scorzonera judaica*Lasiopogon muscoides .166 .027
Scorzonera judaica* Avena sterilis .166 .027
Scorzonera judaica* Sinapis -.089 .008
Table7: Correlation coefficient among biological diversity of plants using SPSS program.
Species
A B C D E F G H I J K L
Scorzoner
a judaica
Centaure
a sp.
Anabasi
s syriaca
Hordeu
m sp.
Hordeu
m
vulgare
Artemisi
a herba-
alba
Asso.
Noaea
mucronat
a
Achillea
santolin
a
Anthemi
s
tinctoria
Lasiopo
gon
muscoi
des
Avena
sterilis
Sinapis
arvensis
A Scorzonera
judaica 1
B
Centaurea
sp.
- 0.175 1
C Anabasis
syriaca 0.175 -0.081 1
D Hordeum
sp. - 0.138 0.36 0.369* 1
E Hordeum
vulgare - 0.121 0.062
0.767*
* 0.604** 1
F
Artemisia
herba-alba
Asso.
- 0.115 0.015 0.107 0.064 0.004 1
G Noaea
mucronata - 0.102 0.052 - 0.065 0.045 -0.050 -0.057 1
H Achillea
santolina 0.074 0.264* - 0.042 0.040 -0.032 -0.023 -0.053 1
I Anthemis
tinctoria -0.121 0.173
0.498*
* 0.604** 0.794** -0.153 -0.050 0.265* 1
J
Lasiopogo
n
muscoides
0.166 0.085 - 0.030 0.005 -0.023 0.061 -0.037 -0.024 -0.023 1
K Avena
sterilis 0.166 0.085 - 0.030 0.005 -0.023 0.061 -0.037 -0.024 -0.023 1.00** 1
L Sinapis
arvensis - 0.890 0.477** - 0.030 0.029 -0.023 -0.113 -0.037
0.617*
* 0.436** -0.017
-
0.017 1
Peer Reviewed , Open Access and Indexed Academic Journal ( www.journalresearchijf.com) Page I 29
Advance Research Journal of Multi-Disciplinary Discoveries I Vol. 30.0 I Issue – I ISSN NO : 2456-1045
Figure 1: Correlation between studied of herbaceous plant
samples using PAST program.
Figure 2: Detrended correspondence with (95% ellipses and convex
hulls) among different plant samples using PAST program.
IV. DISCUSSION
In our study high values of variance were recorded for
biological species with high number of individuals compared with low number of other species. Our result were in agreement with
finding of Katsanevakis, (2006) who was reported that the large
standard deviation resulted mainly from the variance in densities of
small individuals and especially those of the 3 to 5 m zone. The variance in densities of large individuals was much lower and their
number was estimated to be 4146 ± 1405 individuals. CV has been
effectively used in the recent and past in determining the genetic
diversity and in identifying regions of high diversity. CV% showed highest value of species abundance and the presence of biological
species is more related with region elevation. The CVspecies is
often not constant and decreases with species abundance, depending
on how the variance is scaled with the mean (Lepš, 2004). Reed-Dustin et al., (2012) ) reported that the relationship between native
species richness and the coefficient of variation was not statistically
significant (R2=0.43; p-value=0.16) and therefore did not support
our hypothesis that native plants diversity in wetland prairies increases with topographic variation. To estimate the Pearson
coefficients of correlation between cherry tomato variables with a
95% confidence interval amplitude equal to 0.4, it is necessary to
sample 275 plants in the 250m² greenhouse and 200 plants in the 200m2 greenhouse (Sari et al., 2017). Detrended correspondence
was used to show the distribution of biological patterns within
restricted areas. In this research, DCA assessed that the Scorzonera
judaica species was completely differ from the rest biological species which indicate it has diverge genetic makeup. The DCA
ordination analysis showed differences between the studied areas.
Peer Reviewed , Open Access and Indexed Academic Journal ( www.journalresearchijf.com) Page I 30
Gomaa, (2012) found that the studied biological species are
clearly distinguished by the first two DCA axes. DCA is a highly efficient technique for studying the environmental, taxonomic
and biomass relationships.
V. CONCLUSION
1. Variance measurement assessed that diversity was
increased with increased the sit elevation.
2. CV% was highest affirmed the variability
associated with elevation. 3. Correlation (R) demonstrated that negative
relationship appeared between biological species .
4. Using statistical tool help in know the factors
effect on the distribution of biological diversity such as climate change, human interfering and
unexpected disasters.
5. Detrended correspondence analysis is an effective
technique that can be used in demographic and
environmental studies.
REFERENCES
[1]. Ayoub-Hannaa W, Huntley JW, Fürsich FT.
Significance of Detrended Correspondence Analysis (DCA) in palaeoecology and iostratigraphy: A case
study from the Upper Cretaceous of Egypt. Journal of
African Earth Sciences. 2013: 80: 48-59
[2]. Canchola, JA., Tang S, Hemyari P, Paxinos E, Marins ED. Correct use of percent coefficient of
variation (%CV) formula for log-transformed data.
MOJ Proteomics Bioinform. 2017: 6(4):316‒317.
[3]. Garono RJ, Heath RT, Hwang S-J. Detrended Correspondence Analysis of Phytoplankton Abundance
and Distribution in Sandusky Bay and Lake Erie.
Journal of Great Lakes Research.1996: 22(4):818-829.
[4]. Carnus T, Finn JA, Kirwan L, ConnollyJ. Assessing the relationship between biodiversity and stability of
ecosystem function—is the coefficient of variation
always the best metric? Ideas in Ecology and
Evolution.2014: 7: 89–96. doi:10.4033/iee.2014.7.20.c
[5]. Gomaa NH. Composition and diversity of weed
communities in Al-Jouf province, northern Saudi
Arabia. Saudi Journal of Biological Sciences. 2012: 19:
369–376
[6]. Hammer Ø, Harper DAT, Ryan PD. PAST:
Paleontological statistics software Package for
education and data analysis. Palaeontologia
Electronica. 2001: 4(1).art.4:9pp. 178kb. http://palaeo-electronica.org/2001_1/past/issue1_01.htm.
[7]. Katsanevakis S. Population ecology of the endangered
fan mussel Pinna nobilis in a marine lake. 2006: Vol. 1:
51–59, 2006, Previously ESR 1: 1–9.
[8]. Lepsˇ J. Variability in population and community
biomass in a grassland community affected by
environmental productivity and diversity. 2004:
OIKOS. 107: 64-71
[9]. Reed-Dustin C, Logsdon W, Lytton R, Maloney S,
Paulus A, Piazza T, StuparitzT. Assessing the
relationship between topography and plant diversity in
restored and remnant Wet Prairies. Oregon Undergraduate Research Journal. 2012: 3(1):25-37
[12]. Shabanimofrad M, Yusop MR, Saad MS, Wahab
PEM, Biabanikhanehkahdani A, Latif MA. Diversity of physic
nut (Jatropha curcas) in Malaysia: application of DIVA-
AD
VA
NC
E R
ESEA
RC
H J
OU
RN
AL
OF
MU
LTID
ISC
IPLI
NA
RY
DIS
CO
VER
IES
Advance Research Journal of Multi-Disciplinary Discoveries I Vol. 30.0 I Issue – I ISSN NO : 2456-1045
[10]. Sadooghi-Alvandi SM, Jafari AA, H. A. Mardani-
Fard HA. One-way ANOVA with Unequal Variances,
Communications in Statistics - Theory and Methods.2012: 41:22, 4200-4221, DOI:
10.1080/03610926.2011.573160
[11]. Sari BG, Lúcio AD, Santana CS, Krysczun DK,
Tischler AL, Drebes L. Sample size for estimation of the Pearson correlation coefficient in cherry tomato
tests. Ciência Rural, Santa Maria. 2017:7(10):1-6
[12]. Shabanimofrad M, Yusop MR, Saad MS, Wahab
PEM, Biabanikhanehkahdani A, Latif MA. Diversity of physic nut (Jatropha curcas) in Malaysia:
application of DIVA-geographic information system
and cluster analysis. Australian Journal of Crops
Science. 2011: 5(4):361-368
[13]. Steel RGD, Torrie JH. Principles and Procedures
Statistics Abiometrical Approach, Second Edition.
McGraw-Hill Co. INC. 1980: 633 pp.
*****
Peer Reviewed , Open Access and Indexed Academic Journal ( www.journalresearchijf.com) Page I 31
AD
VA
NC
E R
ESEA
RC
H J
OU
RN
AL
OF
MU
LTID
ISC
IPLI
NA
RY
DIS
CO
VER
IES