Back

Examining the Macroecological Context of Life-History, Niche Regime, and Phylogeny in Serpentine Reptiles and Amphibians

Richards, J.

2025-12-16 ecology
10.64898/2025.12.13.694135 bioRxiv
Show abstract

Life history is key to understanding macroecological patterns. Measurable characteristics are particularly critical to the study of ecology. These quantifiable traits can be compared between numerous taxonomic clades to examine how evolutionary forces have shaped biological systems. Although numerous in-depth studies have focused on assessing the life histories of mammalian and avian taxa, few scientific endeavors have addressed the diversity of measurable reproductive characteristics in other vertebrate clades. Here we present the first work examine the macroecology of life history traits in serpentine reptiles and amphibians. Both chosen clades comprise taxa that are limbless, predatory, and primarily restricted to tropical and subtropical latitudes. To test the hypothesis that phylogeny limits the extent to which life-history may overlap, the body mass, lifespan, hatchling size, and clutch size of serpentine amphibians and reptiles will be compared using statistical inference methods (t-tests). The distributions of body mass, lifespan, hatchling size, and clutch size differ among serpentine reptiles and amphibians. Linear models suggest that how hatchling and clutch size vary with body mass also differ between the two. Only the linear model examining how lifespan varies with body mass indicates that broad similarities in life histories exist between serpentine amphibians and reptiles. Our results--that little overlap exists in life-history--suggest that the same macroevolutionary processes that allow serpentine amphibian and reptile ecological niche regimes to converge are not acting on their quantifiable life-history traits.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.