International Journal of Molecular Evolution and Biodiversity 2024, Vol.14, No.4, 186-196 http://ecoevopublisher.com/index.php/ijmeb 192 5.3 Case studies of integrative taxonomy The study on Limnadopsis species employed both mitochondrial (COI) and nuclear (EF1α) markers along with morphological characters to delineate species. This integrative approach allowed for the identification of 11 species, including three previously undescribed ones. The congruence between molecular and morphological data under various species concepts underscored the robustness of this method (Schwentner et al., 2011). In the Brachionus calyciflorus complex, widespread mitonuclear discordance posed a challenge for species delimitation. By integrating molecular data (ITS1 and 28S nuclear DNA markers) with ecological information, researchers were able to resolve phylogenetic conflicts and infer species boundaries. This approach demonstrated the potential of integrative taxonomy to address complex introgression scenarios and provided insights into the competitive abilities of different species under various growth conditions (Papakostas et al., 2016). The integrative taxonomic approach applied to Homoscleromorph sponges combined multiple datasets, including external morphology, anatomy, cytology, spicule shapes, geography, reproduction, genetic sequences, and metabolomics. This comprehensive approach not only facilitated the description of new species but also elucidated their phylogenetic relationships within the phylum Porifera. The study highlighted the importance of integrating diverse data types for both species delimitation and classification (Boury-Esnault et al., 2013). 6 Challenges and Limitations 6.1 Data integration challenges Integrative taxonomy aims to combine multiple data sources, such as morphological, molecular, and ecological data, to achieve more accurate species delimitation. However, integrating these diverse data types presents significant challenges. One major issue is the lack of standardized methods for combining different data types, which can lead to inconsistencies and difficulties in interpretation (Schwentner et al., 2015). For instance, while molecular data can provide high-resolution insights into genetic differences, morphological data may be subject to phenotypic plasticity, complicating the integration process (Darienko et al., 2015). Additionally, the computational tools required for such integration, like convolutional neural networks or machine learning algorithms, are still in developmental stages and may not be universally applicable across all taxa (Yang et al., 2021; Pyron, 2023). 6.2 Inconsistencies in data sets Inconsistencies between different data sets are another significant limitation in integrative taxonomy. Molecular data, for example, can sometimes show discordance with morphological or ecological data due to phenomena like introgression, incomplete lineage sorting, or horizontal gene transfer (Papakostas et al., 2016; Yang et al., 2021). This discordance can lead to conflicting species boundaries, making it difficult to draw definitive conclusions (Gebiola et al., 2012; Papakostas et al., 2016). Moreover, the choice of molecular markers can also influence the results, as different markers may provide varying levels of resolution and may not always correlate with morphological or ecological traits (Darienko et al., 2015; Heethoff et al., 2011). These inconsistencies necessitate a cautious and iterative approach to species delimitation, often requiring multiple rounds of data collection and analysis to resolve conflicts (Yeates et al., 2011; Gebiola et al., 2012). 6.3 Technical and practical limitations Technical and practical limitations also pose significant challenges to integrative taxonomy. High-quality data collection is resource-intensive, requiring specialized equipment and expertise in multiple disciplines, including molecular biology, ecology, and computational science (Pante et al., 2015; Papakostas et al., 2016). Additionally, the need for large datasets to train machine learning models or to perform robust statistical analyses can be a limiting factor, especially for rare or poorly studied taxa (Yang et al., 2021). Practical issues such as the availability of specimens, the preservation of samples, and the accessibility of remote or understudied habitats further complicate the process (Heethoff et al., 2011; Pante et al., 2015). These limitations highlight the need for collaborative efforts and the development of more efficient and accessible methodologies to advance the field of integrative taxonomy (Dayrat, 2005; Heethoff et al., 2011).
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