1- University of Kurdistan
Abstract: (71 Views)
Extended Abstract
Introduction and Objective: Honeybees have an important role in pollination of plants. Apis florea Fabricius, 1787 have been described over the last two centuries. A. florea is distributed in Vietnam, south-eastern China, India, Nepal, southern Thailand, Sri Lanka, Sudan, southern Iran, Pakistan, Saudi Arabia and Oman. The climatic zones of A. florea change from tropical rainforest in the east to savannah in the west. Furthermore, westwards, the climatic zones change from tropical to subtropical steppe and semi-desert. Morphometric data was used in addition to analyses of molecular data to study the honeybees. Morphometric analyses are flexible tools employed by users in different areas of the world. The geometric morphometric method uses landmarks that can be located precisely on vein junctions of wings. The geometric morphometric method has been used to compare the populations of A. florea. In addition, traditional or standard morphometric methods have been used to study the populations of A. florea in various areas in the world. The landmark configurations eliminate effects of position, orientation and size of shapes. The data on shapes is converted into Procrustes coordinates using the geometric morphometric method. Regression analysis between centroid size and Procrustes coordinates creates new data on the residuals which can be applied in other analyses. No published research has utilized residual coordinates (residual data) to compare the populations of A. florea. Therefore, the objective of the current research was a comparison of performance efficacy of residual and Procrustes coordinates in differentiation and relationships among the populations of A. florea in various areas of the world.
Material and Methods: The samples of honeybee species of A. florea were prepared from their distributed areas. The right forewings of honeybees were used to study the relationships among the various populations. Eighty samples were selected from each area. The tps file was built by importing the right forewing pictures into TpsUtil V. 1.64 software. Then, the tps file was loaded into tpsDig V. 2.18. Twenty landmarks were digitized in the vein junctions of the forewings. This was followed by the raw data being obtained from landmarks and loaded into the MorphoJ software V. 1.06d and converted into Procrustes coordinates for use in future analyses. The Procrustes coordinates were then analyzed. Using permutation tests, Mahalanobis distances and canonical variates were obtained. The regression between Procrustes data and centroid sizes was calculated and an allometry test performed. The residual coordinates (residual data) were obtained after the removal of size effect (size correction) from shape variables. The residual and Procrustes data were imported into PAST software v.3.19 and the populations of A. florea compared using Canonical Variate Analysis (CVA). Moreover, clusters were drawn with the residual and Procrustes data using SAS v.8 software.
Results: Multivariate (MANOVA) and pair-wise analyses of residual and Procrustes data of the populations of A. florea were tested. The Procrustes and residual data of the populations showed significant differences statistically using MANOVA (P<0.001). Moreover, pairwise comparisons were tested and all populations indicated significant differences in Procrustes and residual data (P<0.001). The landmark configurations of forewings were superimposed and variations between the populations obtained. The highest variation was found in the vein junction of R and Rs, landmark 19 (S2= 0.0000622). The lowest variation was observed in the vein junction of Cu and 1m-cu, landmark 8 (S2= 0.0000109). The populations of various areas were compared using Canonical Variate Analysis (CVA). In the CVA of Procrustes data (Procrustes coordinates), the first and second components included 75.94% of all variation (CV1= 28.74% and CV2= 47.20%). In addition, in the CVA of residual data, the first and second components included 83.06 % of all variation (CV1= 31.46 % and CV2=51.60 %). The CVA results of Procrustes data showed that Pakistan samples overlapped with Iranian samples, except the Kerman population. Sudan samples overlapped with Iranian samples of Bushehr, Shiraz and Sistan and Balochestan. Additionally, Oman samples showed partial overlapping with South India and Kerman (Iran). The CVA results of residual data showed that Pakistan samples overlapped with Iranian samples, except the Kerman population. Sudan samples differentiated from Iranian samples. In addition, Oman samples showed partial overlapping with South India. Both Procrustes and residual data differentiated populations of Thailand and Vietnam from other populations. Cluster analysis was used to compare populations of A. florea in various areas. The cluster derived from Procrustes data indicated that Sri Lanka was closer to the populations of Iran except the Kerman samples. Furthermore, the Sudan and Pakistan populations were categorized under one group. In addition, the cluster derived from the residual data indicated that the Pakistan population was closer to the populations of Iran except the Kerman samples. Sri Lanka population indicated a closer relationship with India and the Sudan population was differentiated from other populations.
Conclusions: The recent findings showed that residual data revealed greater efficacy than Procrustes data in differentiation and relationships between the populations of Apis florea. The results of derived cluster from residual data indicated closer relationships of A. florea populations of Pakistan and Sri Lanka with Iran and South India, respectively.
Introduction and Objective: Honeybees have an important role in pollination of plants. Apis florea Fabricius, 1787 have been described over the last two centuries. A. florea is distributed in Vietnam, south-eastern China, India, Nepal, southern Thailand, Sri Lanka, Sudan, southern Iran, Pakistan, Saudi Arabia and Oman. The climatic zones of A. florea change from tropical rainforest in the east to savannah in the west. Furthermore, westwards, the climatic zones change from tropical to subtropical steppe and semi-desert. Morphometric data was used in addition to analyses of molecular data to study the honeybees. Morphometric analyses are flexible tools employed by users in different areas of the world. The geometric morphometric method uses landmarks that can be located precisely on vein junctions of wings. The geometric morphometric method has been used to compare the populations of A. florea. In addition, traditional or standard morphometric methods have been used to study the populations of A. florea in various areas in the world. The landmark configurations eliminate effects of position, orientation and size of shapes. The data on shapes is converted into Procrustes coordinates using the geometric morphometric method. Regression analysis between centroid size and Procrustes coordinates creates new data on the residuals which can be applied in other analyses. No published research has utilized residual coordinates (residual data) to compare the populations of A. florea. Therefore, the objective of the current research was a comparison of performance efficacy of residual and Procrustes coordinates in differentiation and relationships among the populations of A. florea in various areas of the world.
Material and Methods: The samples of honeybee species of A. florea were prepared from their distributed areas. The right forewings of honeybees were used to study the relationships among the various populations. Eighty samples were selected from each area. The tps file was built by importing the right forewing pictures into TpsUtil V. 1.64 software. Then, the tps file was loaded into tpsDig V. 2.18. Twenty landmarks were digitized in the vein junctions of the forewings. This was followed by the raw data being obtained from landmarks and loaded into the MorphoJ software V. 1.06d and converted into Procrustes coordinates for use in future analyses. The Procrustes coordinates were then analyzed. Using permutation tests, Mahalanobis distances and canonical variates were obtained. The regression between Procrustes data and centroid sizes was calculated and an allometry test performed. The residual coordinates (residual data) were obtained after the removal of size effect (size correction) from shape variables. The residual and Procrustes data were imported into PAST software v.3.19 and the populations of A. florea compared using Canonical Variate Analysis (CVA). Moreover, clusters were drawn with the residual and Procrustes data using SAS v.8 software.
Results: Multivariate (MANOVA) and pair-wise analyses of residual and Procrustes data of the populations of A. florea were tested. The Procrustes and residual data of the populations showed significant differences statistically using MANOVA (P<0.001). Moreover, pairwise comparisons were tested and all populations indicated significant differences in Procrustes and residual data (P<0.001). The landmark configurations of forewings were superimposed and variations between the populations obtained. The highest variation was found in the vein junction of R and Rs, landmark 19 (S2= 0.0000622). The lowest variation was observed in the vein junction of Cu and 1m-cu, landmark 8 (S2= 0.0000109). The populations of various areas were compared using Canonical Variate Analysis (CVA). In the CVA of Procrustes data (Procrustes coordinates), the first and second components included 75.94% of all variation (CV1= 28.74% and CV2= 47.20%). In addition, in the CVA of residual data, the first and second components included 83.06 % of all variation (CV1= 31.46 % and CV2=51.60 %). The CVA results of Procrustes data showed that Pakistan samples overlapped with Iranian samples, except the Kerman population. Sudan samples overlapped with Iranian samples of Bushehr, Shiraz and Sistan and Balochestan. Additionally, Oman samples showed partial overlapping with South India and Kerman (Iran). The CVA results of residual data showed that Pakistan samples overlapped with Iranian samples, except the Kerman population. Sudan samples differentiated from Iranian samples. In addition, Oman samples showed partial overlapping with South India. Both Procrustes and residual data differentiated populations of Thailand and Vietnam from other populations. Cluster analysis was used to compare populations of A. florea in various areas. The cluster derived from Procrustes data indicated that Sri Lanka was closer to the populations of Iran except the Kerman samples. Furthermore, the Sudan and Pakistan populations were categorized under one group. In addition, the cluster derived from the residual data indicated that the Pakistan population was closer to the populations of Iran except the Kerman samples. Sri Lanka population indicated a closer relationship with India and the Sudan population was differentiated from other populations.
Conclusions: The recent findings showed that residual data revealed greater efficacy than Procrustes data in differentiation and relationships between the populations of Apis florea. The results of derived cluster from residual data indicated closer relationships of A. florea populations of Pakistan and Sri Lanka with Iran and South India, respectively.
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