The host microbiome is integral to metabolism, immune function, and resilience against pathogens. However, reliance on relative abundance (RA) to estimate host-associated microbiomes introduces compositional biases, while limited tools for absolute abundance (AA) quantification hinder broader applications. To address these challenges, we developed DspikeIn (https://github.com/mghotbi/DspikeIn), an R package paired with a versatile wet-lab methodology for AA quantification. Using RA and AA to compare core microbiome distributions across herpetofauna orders and their natural histories revealed starkly distinct results, driven by aggregate effects, including inherited compositional biases in RA and additional multifactorial influences. Focusing on two closely relatedDesmognathusspecies demonstrated that AA quantification enhanced resolution in differential abundance analyses and minimized false discovery rates (FDR) when identifying enriched taxa in their gut microbiomes. Keystone taxa identified through network associations also differed between RA and AA data. For example,LactococcusandCetobacteriumwere core members in Anura and Caudata, whileBasidiobolusandMortierellawere core to Chelonia and Squamata, facilitating host adaptation to diverse environments, insights undetectable with RA data. AA-based network analysis further revealed that removing theBasidiobolussubnetwork increased negative interactions, highlighting its role in promoting gut homeostasis through cross-domain connectivity. Despite low redundancy, theBasidiobolusnode exhibited high betweenness, efficiency, and degree, serving as a critical bridge linking disconnected nodes or modules and indirectly supporting microbiome stability, consistent with Burt’s structural hole theory. DspikeIn represents a transformative tool for microbiome research, enabling the transition from RA to AA quantification and delivering more accurate, consistent, and comparable results across studies.Graphical abstract DspikeIn cheatsheet