Dietary changes can alter the human microbiome with potential detrimental consequences for health. Given that environment, health, and evolution are interconnected, we ask: Could diet‐driven microbiome perturbations have consequences that extend beyond their immediate impact on human health? We address this question in the context of the urgent health challenges posed by global climate change. Drawing on recent studies, we propose that not only can diet‐driven microbiome changes lead to dysbiosis, they can also shape life‐history traits and fuel human evolution. (...) We posit that dietary shifts prompt mismatched microbiome‐host genetics configurations that modulate human longevity and reproductive success. These mismatches can also induce a heritable intra‐holobiont stress response, which encourages the holobiont to re‐establish equilibrium within the changed nutritional environment. Thus, while mismatches between climate change‐related genetic and epigenetic configurations within the holobiont increase the risk and severity of diseases, they may also affect life‐history traits and facilitate adaptive responses. These propositions form a framework that can help systematize and address climate‐related dietary challenges for policy and health interventions. (shrink)

Despite the importance of network analysis in biological practice, dominant models of scientific explanation do not account satisfactorily for how this family of explanations gain their explanatory power in every specific application. This insufficiency is particularly salient in the study of the ecology of the microbiome. Drawing on Coyte et al. (2015) study of the ecology of the microbiome, Deulofeu et al. (2021) argue that these explanations are neither mechanistic, nor purely mathematical, yet they are substantially empirical. Building on their (...) criticisms, in the present work we make a step further elucidating this kind of explanations with a general analytical framework according to which scientific explanations are ampliative, specialized embeddings (ASE), which has recently been successfully applied to other biological subfields. We use ASE to reconstruct in detail the Coyte et al.’s case study and on its basis, we claim that network explanations of the ecology of the microbiome, and other similar explanations in ecology, gain their epistemological force in virtue of their capacity to embed biological phenomena in non-accidental generalizations that are simultaneously ampliative and specialized. (shrink)