Volume 16, Issue 2 (5-2025)
Res Anim Prod 2025, 16(2): 126-137 |
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Nazemi-Rafie J, Modaber M, Ghasemi T. (2025). Residual Data Reveals Greater Efficacy than Procrustes Data in Differentiation and Relationships Between the Populations of Apis florea. Res Anim Prod. 16(2), 126-137. doi:10.61882/rap.2024.1480
URL: http://rap.sanru.ac.ir/article-1-1480-en.html
URL: http://rap.sanru.ac.ir/article-1-1480-en.html
1- Department of Plant Protection, Faculty of Agriculture, University of Kurdistan, Sanandaj, Kurdistan, Iran
2- Department of Plant Protection, Faculty of Agriculture, University of Kurdistan
2- Department of Plant Protection, Faculty of Agriculture, University of Kurdistan
Abstract: (671 Views)
Extended Abstract
Background: Honeybees have an important role in the pollination of plants. Apis florea Fabricius, 1787, has 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 were 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 the 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 of the world. The landmark configurations eliminate the effects of position, orientation, and size of shapes. Data on shapes are 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 current research aimed to compare the performance efficacy of residual and Procrustes coordinates in differentiation and relationships among the populations of A. florea in various areas of the world.
Methods: Samples of the A. florea honeybee species 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. A 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 obtained from landmarks, loaded into the MorphoJ software V. 1.06d, and converted into Procrustes coordinates for use in future analyses, followed by analyzing the Procrustes coordinates. Mahalanobis distances and canonical variates were obtained using permutation tests. The regression between Procrustes data and centroid sizes was calculated, and an allometry test was performed afterward. The residual coordinates (residual data) were obtained after the removal of the 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 were 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 were tested for the populations of A. florea. The Procrustes and residual data of the populations showed statistically significant differences using MANOVA (p < 0.001). Moreover, the tested pairwise comparisons indicated that all populations were significantly different in Procrustes and residual data (p < 0.001). The landmark configurations of forewings were superimposed, and variations were obtained between the populations. 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 the Pakistan samples overlapped with the Iranian samples, except for the Kerman population. Sudan samples overlapped with the 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 the Pakistan samples overlapped with the Iranian samples, except for the Kerman population. Sudan samples were differentiated from Iranian samples. In addition, the Oman samples showed partial overlapping with South India. Both Procrustes and residual data differentiated the populations of Thailand and Vietnam from the other populations. Cluster analysis was used to compare the 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 for 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 for the Kerman samples. The Sri Lankan population indicated a closer relationship with India, and the Sudanese population was differentiated from the other populations.
Conclusions: The recent findings showed that residual data revealed greater efficacy than Procrustes data in differentiation and relationships between the populations of A. florea. The results of the derived cluster from residual data indicated closer relationships of A. florea populations from Pakistan and Sri Lanka with Iran and South India, respectively.
Background: Honeybees have an important role in the pollination of plants. Apis florea Fabricius, 1787, has 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 were 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 the 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 of the world. The landmark configurations eliminate the effects of position, orientation, and size of shapes. Data on shapes are 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 current research aimed to compare the performance efficacy of residual and Procrustes coordinates in differentiation and relationships among the populations of A. florea in various areas of the world.
Methods: Samples of the A. florea honeybee species 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. A 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 obtained from landmarks, loaded into the MorphoJ software V. 1.06d, and converted into Procrustes coordinates for use in future analyses, followed by analyzing the Procrustes coordinates. Mahalanobis distances and canonical variates were obtained using permutation tests. The regression between Procrustes data and centroid sizes was calculated, and an allometry test was performed afterward. The residual coordinates (residual data) were obtained after the removal of the 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 were 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 were tested for the populations of A. florea. The Procrustes and residual data of the populations showed statistically significant differences using MANOVA (p < 0.001). Moreover, the tested pairwise comparisons indicated that all populations were significantly different in Procrustes and residual data (p < 0.001). The landmark configurations of forewings were superimposed, and variations were obtained between the populations. 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 the Pakistan samples overlapped with the Iranian samples, except for the Kerman population. Sudan samples overlapped with the 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 the Pakistan samples overlapped with the Iranian samples, except for the Kerman population. Sudan samples were differentiated from Iranian samples. In addition, the Oman samples showed partial overlapping with South India. Both Procrustes and residual data differentiated the populations of Thailand and Vietnam from the other populations. Cluster analysis was used to compare the 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 for 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 for the Kerman samples. The Sri Lankan population indicated a closer relationship with India, and the Sudanese population was differentiated from the other populations.
Conclusions: The recent findings showed that residual data revealed greater efficacy than Procrustes data in differentiation and relationships between the populations of A. florea. The results of the derived cluster from residual data indicated closer relationships of A. florea populations from Pakistan and Sri Lanka with Iran and South India, respectively.
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