4.1 Species Selection
We chose nine candidate species for species prioritization, of which seven mammal and two bird species. Only mammal and bird species were selected since our research team has more knowledge of these taxonomic classes. IUCN statuses and records were considered when selecting these species. As an exception, Acomy cilicicus and Panthera pardus tulliana have no records on IUCN. However, Acomys cilicicus has a restricted population in the area between Silifke and Erdemli. According to Çetintaş et al. (2017)’s data, the population trend showed a drastic decline and the species should have a status of Critically Endangered (CR) on IUCN. As for Panthera pardus tulliana, the species was recorded in 2022 in Turkey for the first time since 1974. Therefore, we took this species into the species prioritization process due to the low number of individuals. Species names, IUCN statuses, population trends, and distribution ranges of these species are presented in Table 1.
Table 1
Species names, IUCN status, population trends, and distribution ranges of candidate species. The table was filled according to information on IUCN. * CR - Critically Endangered, EN - Endangered, VU - Vulnerable, NT - Near Threatened, DD - Data Deficient, LC - Least Concern.
Species
|
IUCN Status (Global)*
|
Population Trend
|
Distribution Range
|
Acomys cilicicus
(Asia Minor Spiny Mouse)
|
DD
|
Unknown
|
Endemic to Turkey
|
Panthera pardus tulliana
(Anatolian Leopard)
|
-
|
Unknown
|
Turkey and parts of Asia
|
Miniopterus schreibersii
(Schreiber's Bent-winged Bat)
|
VU
|
Decreasing
|
Asia, Europe and Africa
|
Lynx lynx
(Eurasian Lynx)
|
LC
|
Decreasing
|
Western Europe and Asia
|
Rhinolophus ferrumequinum
(Greater Horseshoe Bat)
|
LC
|
Decreasing
|
Asia and Europe
|
Spermophilus citellus
(European Souslik)
|
EN
|
Decreasing
|
Turkey and Balkans
|
Hyaena hyaena
(Striped Hyaena)
|
NT
|
Decreasing
|
Asia and Africa
|
Neophron percnopterus
(Egyptian Vulture)
|
EN
|
Decreasing
|
Asia, Europe and Africa
|
Vanellus gregarius
(Sociable Lapwing)
|
CR
|
Decreasing
|
Asia, Europe and Africa
|
4.2 Criteria Selection
We selected 39 criteria used in actual conservation studies in addition to IUCN criteria to maximize the accuracy of species prioritization. With the high number of criteria, it becomes easier to avoid individual bias. Therefore, this broad set of criteria provided us with lean species prioritization on a scientific basis. We split the selected criteria into eight titles; i) ecologic ii) biogeographic iii) biological iv) scientific knowledge v) social value vi) threat factors vii) population state viii) conservation level. These titles were used in AHP as well, making total criteria number 47. The criteria and sources are given in Table 2.
Table 2
Selected criteria/sub-criteria and their sources.
1st level criteria
|
2nd level criteria
|
3rd level criteria
|
Source
|
Ecologic
|
(C1)
|
Habitat Specificity
|
(C1.1)
|
|
|
Bertolino et al., 2014.
|
Niche Breadth
|
(C1.2)
|
|
|
Carscaden et al., 2020; Clavel et al., 2011; Hernando et al., 2022.
|
Umbrella Species
|
(C1.3)
|
|
|
Roberge and Angelstam, 2004; Branton and Richardson, 2011.
|
Keystone Species
|
(C1.4)
|
|
|
Delibes-Mateos et al., 2007.
|
Predator-Prey Interactions
|
(C1.5)
|
|
|
Schneider, 2001.
|
Competitors
|
(C1.6)
|
|
|
Caut et al., 2007.
|
Biodiversity
|
(C1.7)
|
|
|
Bacchetta et al., 2012.
|
Biogeographic
|
(C2)
|
Species Range
|
(C2.1)
|
|
|
Eeley and Foley, 1999.
|
Dispersal Characteristics
|
(C2.2)
|
|
|
Sweanor et al., 2000.
|
Endemism
|
(C2.3)
|
|
|
Burlakova et al., 2011.
|
Biological
|
(C3)
|
Reproduction Biology
|
(C3.1)
|
Rate of Reproduction
|
(C3.1.1)
|
McNab, 2006.
|
Fecundity
|
(C3.1.2)
|
van de Kerk et al., 2013.
|
Sexual Maturity Age
|
(C3.1.3)
|
Congdon et al., 1993.
|
Juvenile Survival Rate
|
(C3.1.4)
|
Sarno et al., 1999
|
Genetic Diversity
|
(C3.2)
|
|
|
Doyle et al., 2015; Evans and Sheldon, 2008.
|
Scientific Knowledge
|
(C4)
|
Accessibility
|
(C4.1)
|
|
|
Karamanlidis et al., 2016.
|
Phylogenetic Information
|
(C4.2)
|
|
|
Purvis et al., 2005.
|
Taxonomic Information
|
(C4.3)
|
|
|
Ely et al., 2017.
|
Social Value
|
(C5)
|
Economic Value
|
(C5.1)
|
|
|
Rani et al., 2020.
|
Flagship Species
|
(C5.2)
|
|
|
Home et al., 2009; Smith and Sutton, 2008.
|
Threat Factors
|
(C6)
|
Environmental Pollution
|
(C6.1)
|
|
|
Eeva et al., 2006; Berckmoes et al., 2005.
|
Climate Change
|
(C6.2)
|
|
|
Hetem et al., 2014; Moritz et al., 2008; Crick, 2004.
|
Habitat Loss and Fragmentation
|
(C6.3)
|
|
|
Zanin et al., 2015.
|
Disease
|
(C6.4)
|
|
|
Thorne and Williams, 1988.
|
Being Affected by Invasive Species
|
(C6.5)
|
|
|
Risch et al., 2021; Mollot et al., 2017.
|
Anthropogenic Factors
|
(C6.6)
|
Tourism and Recreation
|
(C6.6.1)
|
Anderson et al., 2015; Yorio et al., 2001.
|
Agriculture
|
(C6.6.2)
|
Shamoon et al., 2018.
|
Human Population Density
|
(C6.6.3)
|
McKee et al., 2014; Cardillo et al., 2004.
|
Wildlife trade
|
(C6.6.4)
|
Zhang et al., 2008.
|
Transportation
|
(C6.6.5)
|
Blake et al., 2017.
|
Residential and Industrial Development
|
(C6.6.6)
|
Sushinsky et al., 2013.
|
Overexploitation
|
(C6.6.7)
|
Rosser and Mainka, 2002.
|
Population State
|
(C7)
|
Population Reduction
|
(C7.1)
|
|
|
Dadi et al., 2012.
|
Very Small or Restricted Population
|
(C7.2)
|
|
|
IUCN.
|
Population Size
|
(C7.3)
|
|
|
Reed, 2005; Charlesworth, 2009.
|
Population Density
|
(C7.4)
|
|
|
Sainsbury et al., 2019
|
Conservation Level
|
(C8)
|
Individual Conservation
|
(C8.1)
|
|
|
Musto et al., 2020.
|
Habitat Conservation
|
(C8.2)
|
|
|
Ficetola, 2015.
|
Population Conservation
|
(C8.3)
|
|
|
Ottewell et al., 2016.
|
4.3 The Analytic Hierarchy Process (AHP)
The Analytic Hierarchy Process (Saaty, 1977) is a frequently used Multi-Criteria Decision Making (MCDM) method. This method is used to select the "best" or "some of the best" alternatives or to determine the importance of criteria. As the name of the method implies, in such problems with related elements in a hierarchical structure, the calculations are based on pairwise comparison of criteria/sub-criteria and/or alternatives. For these comparisons, the 1–9 scale proposed by Saaty (1977) is used. This scale is given in Table 3. The basic steps of the method and explanations of the steps are summarized below (Vaidya & Kumar 2006).
Table 3
The 1–9 scale and its description.
Intensity of importance
|
Definition
|
Explanation
|
1
|
Equal importance
|
Two activities contribute equally to the objective
|
3
|
Weak importance of one over another
|
Experience and judgment slightly favor one activity over another
|
5
|
Essential or strong importance
|
Experience and judgment slightly favor one activity over another
|
7
|
Demonstrated importance
|
An activity is strongly favored and its dominance is demonstrated in practice
|
9
|
Absolute importance
|
The evidence favoring one activity over another is of the highest possible order of affirmation
|
2,4,6,8
|
Intermediate values between the two adjacent judgements
|
When compromise is needed
|
Reciprocals of the above nonzero
|
If activity has one of the above nonzero numbers assigned to it when compared with activity, then has the reciprocal value compared with
|
|
Rationals
|
Ratios arising from the scale
|
If consistency were to be forced bu obtaining n numerical values to span the matrix
|
Step 1. The evaluation criteria/sub-criteria and/or alternatives of the problem for a specific purpose are determined and the hierarchical structure of the problem is created.
Step 2. For the calculations, firstly, pairwise comparison matrices should be created. For this purpose, the elements at the relevant level in the hierarchical structure are compared with each other using the 1–9 scale in Table 3. In this comparison process, only \(n(n-1)/2\) comparisons are sufficient for \(n\) elements. These values are the values in the upper triangle of the pairwise comparison matrix. The inverses of these values according to the multiplication operator will be the lower triangle values. Finally, the diagonal elements will be equal to 1. (The comparison of each element with itself is equally important). Therefore, in the following equation, the value \({a}_{ij}\) indicating the \(i\)th row \(j\)th column elements in the pairwise comparison matrix for \(i, j \in \left\{\text{1,2},\dots ,n\right\}\) takes the values \({a}_{ii}=1\) and \({a}_{ij}=\frac{1}{{a}_{ji}}\).
$$A={a}_{ij}=\left[1 {a}_{12} \dots {a}_{1n} {a}_{21} 1 \dots {a}_{2n} ⋮ ⋮ \ddots ⋮ {a}_{n1} {a}_{n2} \dots 1 \right]$$
Step 3. In this step, the relative weights are calculated for each of the pairwise comparison matrices. In order to calculate the relative weights, firstly the sum of each column of the relevant matrix is obtained. Then, normalize the pairwise comparison matrix by dividing each element in the columns by the corresponding column sum. Finally, calculate the mean of each row in the normalized matrix. The values obtained are the relative weight values of the items compared in the respective matrix.
Step 4. In order to obtain the correct solution of the problem, the accuracy of these relative weights should be checked. Therefore, in the third step, the consistency of each pairwise comparison matrix is examined by using the calculated relative weight values. This value is referred to as the Consistency Ratio (CR), and it should be below 10%. (Saaty, 1977)
Step 5. If there is an inconsistency in the pairwise comparison matrices, the inconsistency is eliminated by rearranging the relevant matrices and the process is repeated from the third step. Finally, the weights are brought together by considering the hierarchy of the problem once consistencies are achieved in all matrices.
4.4 Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS)
One of the most frequently used multi-criteria decision making methods is the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method proposed by Hwang and Yoon (1981). In the TOPSIS method, alternatives are evaluated according to their distance to two points called positive ideal solution (the most desirable situation) and negative ideal solution (the most undesirable situation). The alternative with the smallest distance to the positive ideal solution and the largest distance to the negative ideal solution will be the best alternative. Alternatives are evaluated according to these distances and ranked from best to worst.
The basic steps of the TOPSIS method and explanations of the steps are summarized below.
Step 1. A decision matrix \(D\) is created for the evaluation of \(m\) alternatives according to \(n\) criteria. In addition, a weight vector \(W\) containing the weight values of the criteria is created. The sum of the weight values of this criteria in this vector should be equal to 1.
$${C}_{1} {C}_{2} \dots {C}_{n}$$
$$D={A}_{1} {A}_{2} ⋮ {A}_{m} \left[{x}_{11} {x}_{12} \dots {x}_{1n} {x}_{21} {x}_{22} \dots {x}_{2n} ⋮ ⋮ \ddots ⋮ {x}_{m1} {x}_{m2} \dots {x}_{mn} \right]$$
$$W=\left[{w}_{1}, {w}_{2}, \dots ,{w}_{n}\right]$$
Step 2. Since there will be criteria with different units in the \(\left(m\times n\right)\) dimensional decision matrix created in the previous step, this matrix must first be normalized. In other words, the comparison results according to the criteria are made unitless. In this study, vector normalization method, one of these methods mentioned in (Shih et al, 2007).
$${r}_{ij}=\frac{{x}_{ij}}{\sqrt{{\sum }_{i=1}^{m}{x}_{ij}^{2}}} ,i=\text{1,2},\dots ,m j=1,...,n.$$
Step 3. The weighted normalized decision matrix \(V\) is obtained by multiplying the obtained normalized decision matrix with the criteria weight values.
$$V={v}_{ij}=\left[{{w}_{1}r}_{11} {{w}_{2}r}_{12} \dots {{w}_{n}r}_{1n} {{w}_{1}r}_{21} {{w}_{2}r}_{22} \dots {{w}_{n}r}_{2n} ⋮ ⋮ \ddots ⋮ {{w}_{1}r}_{m1} {{w}_{2}r}_{m2} \dots {{w}_{n}r}_{mn} \right]$$
Step 4. In this step, positive (\({A}^{+}\)) and negative (\({A}^{-}\)) ideal solutions are determined. These values are determined for each criterion depending on whether the criteria are benefit or cost criteria. The positive ideal solution will consist of the maximum value among the alternatives for the benefit criterion and the minimum value among the alternatives for the cost criterion. It can be expressed mathematically as follows.
$${A}^{+}=\left\{{v}_{1}^{*}, \dots ,{v}_{n}^{*}\right\} = \left\{i=\text{1,2},\dots ,m\right\}$$
$${A}^{-}=\left\{{v}_{1}^{-}, \dots ,{v}_{n}^{-}\right\}=\left\{i=\text{1,2},\dots ,m\right\}$$
Step 5. The distances of each alternative from the positive and negative ideal solutions determined in the previous step are obtained as follows.
$${d}_{i}^{+}=\sqrt{{\sum }_{j=1}^{n}{\left({v}_{ij}-{v}_{j}^{+}\right)}^{2}} i=\text{1,2},\dots ,m$$
$${d}_{i}^{-}=\sqrt{{\sum }_{j=1}^{n}{\left({v}_{ij}-{v}_{j}^{-}\right)}^{2}} i=\text{1,2},\dots ,m$$
Step 6. The closeness coefficient of each alternative to the positive ideal solution is obtained as follows.
$${CC}_{i}=\frac{{d}_{i}^{+}}{{d}_{i}^{+}+{d}_{i}^{-}} ,i=\text{1,2},\dots ,m$$
Step 7. \({A}_{i}\) alternatives are ranked from largest to smallest according to the closeness coefficient values obtained in the previous step. The larger the closeness coefficient value, the better the performance of the alternative.
AHP and TOPSIS Modeling
The implementation of this study was mainly performed in 2 phases. The first is to obtain the weights of the criteria using the AHP method. In the second phase, these weights are determined using the TOPSIS method.
The hierarchical structure for the first phase, the AHP method, is as shown in Fig. 1. As can be seen from the hierarchical structure, there are 8 main criteria at the 1st level, 30 sub-criteria at the 2nd level and 11 sub-sub-criteria at the 3rd level.
After the hierarchical structure was finalized, four experts were consulted to determine the weight values of all criteria/sub-criteria in this study. Therefore, separate pairwise comparison matrices were obtained from four decision makers and their consistency was examined. Inconsistencies in the matrices were resolved by re-evaluation with the decision makers, and the inconsistencies obtained from the pairwise comparison matrices of each decision maker were brought to an acceptable level.
The information in Table 4 is as follows. In the columns DM1-DM4, there are the relative weight values obtained as a result of the pairwise comparison matrices made by each of the four decision makers. The next column, the Equal Weight column, is the arithmetic mean of the relative weight values of these four decision makers. In the Unequal Weight column, the weighted average results are presented with the weight of DM1, the decision maker with more experience, as 0.40 and the weights of the other three decision makers as 0.20.
Table 4
Criteria weight values calculated according to equal and unequal weights of decision makers.
1st level
|
DM1
|
DM2
|
DM3
|
DM4
|
Equal Weight
|
Unequal Weight
|
2nd level
|
DM1
|
DM2
|
DM3
|
DM4
|
Equal Weight
|
Unequal Weight
|
|
|
|
|
|
|
|
C1
|
0.32
|
0.17
|
0.23
|
0.31
|
0.25
|
0.27
|
C1.1
|
0.15
|
0.26
|
0.23
|
0.21
|
0.21
|
0.20
|
|
|
|
|
|
|
|
C1.2
|
0.23
|
0.38
|
0.34
|
0.21
|
0.29
|
0.28
|
|
|
|
|
|
|
|
C1.3
|
0.02
|
0.02
|
0.03
|
0.06
|
0.03
|
0.03
|
|
|
|
|
|
|
|
C1.4
|
0.39
|
0.04
|
0.18
|
0.06
|
0.16
|
0.21
|
|
|
|
|
|
|
|
C1.5
|
0.06
|
0.08
|
0.04
|
0.03
|
0.05
|
0.06
|
|
|
|
|
|
|
|
C1.6
|
0.10
|
0.08
|
0.06
|
0.11
|
0.09
|
0.09
|
|
|
|
|
|
|
|
C1.7
|
0.04
|
0.14
|
0.11
|
0.32
|
0.16
|
0.13
|
|
|
|
|
|
|
|
C2
|
0.16
|
0.12
|
0.08
|
0.20
|
0.14
|
0.14
|
C2.1
|
0.56
|
0.32
|
0.24
|
0.74
|
0.46
|
0.48
|
|
|
|
|
|
|
|
C2.2
|
0.32
|
0.12
|
0.09
|
0.09
|
0.15
|
0.19
|
3rd level
|
DM1
|
DM2
|
DM3
|
DM4
|
Equal Weight
|
Unequal Weight
|
C2.3
|
0.12
|
0.56
|
0.67
|
0.18
|
0.38
|
0.33
|
C3
|
0.19
|
0.06
|
0.11
|
0.06
|
0.11
|
0.12
|
C3.1
|
0.33
|
0.33
|
0.20
|
0.83
|
0.43
|
0.41
|
C3.1.1
|
0.13
|
0.13
|
0.13
|
0.48
|
0.22
|
0.20
|
C3.1.2
|
0.06
|
0.06
|
0.06
|
0.27
|
0.11
|
0.10
|
C3.1.3
|
0.30
|
0.30
|
0.26
|
0.16
|
0.25
|
0.26
|
C3.1.4
|
0.51
|
0.51
|
0.55
|
0.09
|
0.41
|
0.43
|
C3.2
|
0.67
|
0.67
|
0.80
|
0.17
|
0.58
|
0.59
|
|
|
C4
|
0.03
|
0.03
|
0.03
|
0.06
|
0.04
|
0.04
|
C4.1
|
0.68
|
0.68
|
0.70
|
0.12
|
0.55
|
0.57
|
|
C4.2
|
0.20
|
0.20
|
0.11
|
0.23
|
0.18
|
0.19
|
|
C4.3
|
0.12
|
0.12
|
0.19
|
0.65
|
0.27
|
0.24
|
|
C5
|
0.02
|
0.02
|
0.02
|
0.02
|
0.02
|
0.02
|
C5.1
|
0.67
|
0.75
|
0.67
|
0.25
|
0.58
|
0.60
|
|
C5.2
|
0.33
|
0.25
|
0.33
|
0.75
|
0.42
|
0.40
|
|
C6
|
0.11
|
0.25
|
0.32
|
0.11
|
0.20
|
0.18
|
C6.1
|
0.07
|
0.36
|
0.15
|
0.10
|
0.17
|
0.15
|
|
C6.2
|
0.11
|
0.23
|
0.05
|
0.07
|
0.11
|
0.11
|
|
C6.3
|
0.27
|
0.13
|
0.47
|
0.39
|
0.31
|
0.31
|
|
C6.4
|
0.38
|
0.03
|
0.21
|
0.04
|
0.17
|
0.21
|
|
C6.5
|
0.03
|
0.05
|
0.03
|
0.24
|
0.09
|
0.08
|
|
C6.6
|
0.15
|
0.20
|
0.09
|
0.16
|
0.15
|
0.15
|
C6.6.1
|
0.11
|
0.04
|
0.23
|
0.36
|
0.19
|
0.17
|
C6.6.2
|
0.24
|
0.09
|
0.02
|
0.24
|
0.15
|
0.17
|
C6.6.3
|
0.07
|
0.24
|
0.11
|
0.16
|
0.14
|
0.13
|
C6.6.4
|
0.03
|
0.07
|
0.06
|
0.10
|
0.06
|
0.06
|
C6.6.5
|
0.05
|
0.17
|
0.03
|
0.07
|
0.08
|
0.07
|
C6.6.6
|
0.35
|
0.37
|
0.39
|
0.04
|
0.29
|
0.30
|
C6.6.7
|
0.15
|
0.03
|
0.16
|
0.03
|
0.09
|
0.10
|
C7
|
0.07
|
0.29
|
0.16
|
0.20
|
0.18
|
0.16
|
C7.1
|
0.47
|
0.55
|
0.30
|
0.48
|
0.45
|
0.45
|
|
|
C7.2
|
0.28
|
0.27
|
0.53
|
0.27
|
0.34
|
0.32
|
C7.3
|
0.16
|
0.12
|
0.11
|
0.16
|
0.14
|
0.14
|
C7.4
|
0.10
|
0.06
|
0.06
|
0.09
|
0.08
|
0.08
|
C8
|
0.09
|
0.06
|
0.05
|
0.03
|
0.06
|
0.07
|
C8.1
|
0.54
|
0.54
|
0.32
|
0.65
|
0.51
|
0.52
|
C8.2
|
0.16
|
0.16
|
0.12
|
0.25
|
0.18
|
0.17
|
C8.3
|
0.30
|
0.30
|
0.56
|
0.10
|
0.31
|
0.31
|
Table 5 shows the final priority values of all criteria/sub-criteria as a result of combining the relative weights obtained in both ways according to the hierarchical structure. It is observed that there are slight differences in the priority values of the decision makers depending on two different weight scenarios (equal and unequal weights).
Table 5
Final priority values of the criteria for both scenarios.
Criteria
|
Equal Priority
|
Unequal Priority
|
|
Criteria
|
Equal Priority
|
Unequal Priority
|
C1.1
|
0.0544
|
0.0538
|
|
C6.1
|
0.0334
|
0.0267
|
C1.2
|
0.0742
|
0.0746
|
|
C6.2
|
0.0225
|
0.0204
|
C1.3
|
0.0084
|
0.0083
|
|
C6.3
|
0.0622
|
0.0550
|
C1.4
|
0.0419
|
0.0557
|
|
C6.4
|
0.0326
|
0.0372
|
C1.5
|
0.0138
|
0.0150
|
|
C6.5
|
0.0172
|
0.0136
|
C1.6
|
0.0224
|
0.0241
|
|
C6.6.1
|
0.0055
|
0.0045
|
C1.7
|
0.0396
|
0.0355
|
|
C6.6.2
|
0.0044
|
0.0044
|
C2.1
|
0.0649
|
0.0692
|
|
C6.6.3
|
0.0043
|
0.0035
|
C2.2
|
0.0214
|
0.0268
|
|
C6.6.4
|
0.0019
|
0.0015
|
C2.3
|
0.0533
|
0.0473
|
|
C6.6.5
|
0.0023
|
0.0019
|
C3.1.1
|
0.0098
|
0.0100
|
|
C6.6.6
|
0.0085
|
0.0080
|
C3.1.2
|
0.0052
|
0.0052
|
|
C6.6.7
|
0.0027
|
0.0028
|
C3.1.3
|
0.0115
|
0.0132
|
|
C7.1
|
0.0817
|
0.0726
|
C3.1.4
|
0.0187
|
0.0217
|
|
C7.2
|
0.0611
|
0.0520
|
C3.2
|
0.0610
|
0.0731
|
|
C7.3
|
0.0252
|
0.0229
|
C4.1
|
0.0207
|
0.0212
|
|
C7.4
|
0.0140
|
0.0130
|
C4.2
|
0.0070
|
0.0070
|
|
C8.1
|
0.0310
|
0.0347
|
C4.3
|
0.0102
|
0.0088
|
|
C8.2
|
0.0106
|
0.0116
|
C5.1
|
0.0128
|
0.0135
|
|
C8.3
|
0.0189
|
0.0207
|
C5.2
|
0.0092
|
0.0090
|
|
TOTAL
|
1.0000
|
1.0000
|
The resulting priority values of the 39 evaluation criteria were used in the TOPSIS method to evaluate the prioritization for species conservation in Table 1, which forms another important part of this study. For this purpose, 4 separate TOPSIS tables were created in line with the opinions of the same four experts. Then these tables were brought together according to the weights of the experts as in the AHP method. The first scenario is that the experts have equal weights with the results given in Table 6. In the second scenario, the weight of the first decision maker is 0.40 and the others are 0.20 and the results are as shown in Table 7.
Using the values in both tables, the results for the conservation prioritization of the alternatives were obtained as in Table 8. When analyzing these results, it is observed that the ranking remains unchanged, except for the 6th and 7th positions. This variation occurs when decision makers are assigned equal weights compared to a scenario where the weight of the first decision maker is 0.40. This difference in weighting is due to the higher experience level of the first decision maker, while the weights assigned to the other decision makers are 0.20 each.
Table 8
Ranking results of species when decision makers have equal and unequal weights.
|
Equal Weight
|
Unequal Weight
|
Species
|
CCi
|
Rank
|
CCi
|
Rank
|
Acomys cilicicus
|
0.7878
|
1
|
0.7877
|
1
|
Panthera pardus tulliana
|
0.6707
|
2
|
0.6688
|
2
|
Miniopterus schreibersii
|
0.3533
|
7
|
0.3586
|
6
|
Lynx lynx
|
0.5411
|
3
|
0.5462
|
3
|
Rhinolophus ferrumequinum
|
0.2949
|
8
|
0.3124
|
8
|
Spermophilus citellus
|
0.5337
|
4
|
0.4985
|
4
|
Hyaena hyaena
|
0.3905
|
5
|
0.4133
|
5
|
Neophron percnopterus
|
0.3544
|
6
|
0.3430
|
7
|
Vanellus gregarius
|
0.2423
|
9
|
0.2453
|
9
|