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Biology subjects

Pratiwi, A.

Publications and source records attributed to Pratiwi, A..

2 recordsLinked to original sources

Diffuse phenotypic structure in native chickens across Java and Wallacea is consistent with human-mediated connectivity and decentralised selection

Objective: This study aimed to investigate morphometric and plumage-colour variation among native chickens from Wallacea and Java, Indonesia, and to evaluate the contributions of geographic and production-system factors to phenotypic diversity. Methods: Native chickens were sampled from 259 households across eight provinces in Indonesia. Household-level information on production systems and farmer selection practices was collected through structured surveys. Morphometric traits and HSV-derived plumage-colour variables were analysed using principal component analysis (PCA), permutational multivariate analysis of variance (PERMANOVA), and hierarchical variance partitioning to assess phenotypic variation across locations, provinces, and islands. Results: Substantial phenotypic variation was observed among sampled populations, with significant province- and island-level differences detected for several morphometric traits. However, extensive overlap among locations was evident in both morphometric and plumage-colour space, indicating diffuse rather than discrete structuring. Within-location variation accounted for the largest proportion of total phenotypic variance across most traits, consistent with decentralised smallholder management and continued human-mediated exchange. Conclusion: Geographic fragmentation and agroecological heterogeneity alone appear insufficient to generate strongly differentiated phenotypic groups when connectivity among populations is maintained. Whether this phenotypic connectivity corresponds to contemporary gene flow, retained ancestral variation, or convergent environmental responses remains to be tested using genomic data.

ecology↗

Classification of down-core foraminifera image sets using convolutional neural networks.

Manual identification of foraminifera species or morphotypes under stereoscopic microscopes is time-consuming for the taxonomist, and a long-time goal has been automating this process to improve efficiency and repeatability. Recent advances in computation hardware have seen deep convolutional neural networks emerge as the state-of-the-art technique for image-based automated classification. Here, we describe a method for classifying large down-core foraminifera image set using convolutional neural networks. Construction of the classifier is demonstrated on the publically available endless forams image set with an best accuracy of approximately 90%. A complete down-core analysis is performed for benthic species in the Holocene period for core MD02-2518 from the North Eastern Pacific, and the relative abundances compare favourably with manual counting, showing the same signal dynamics. Using our workflow opens the way to automated paleo-reconstruction based on computer image analysis, and can be employed using our labelling and classification software ParticleTrieur.View Full Text

paleontology↗