Towards an animal economics spectrum for ecosystem research (Q6686496)

The framework of the plant economics spectrum advanced our understanding of plant ecology and proved as a unifying concept across plant taxonomy, growth forms and biomes. Similar approaches for animals mostly focus on linking life-history and metabolic theory, but not on their application in ecosystem research. To fill this gap, we propose the animal economics spectrum (AES) based on broadly available traits that describe organismal size, biological times and rates. To exemplify the feasibility and general usefulness of constructing the AES, we compiled data on adult and offspring body mass, life span, age at first reproduction, reproductive and metabolic rate of 98 terrestrial taxa from seven selected animal classes and mapped these taxa into an exemplary quantitative trait space. The AES consists of two principal axes related to reproductive strategies and the pace of life; both axes are linked by animal metabolism. The AES thus closely mirrors seminal ideas on fundamental life-history strategies and more recent discoveries and developments in the fields of life-history and metabolic theory. Furthermore, we find associations between the positions of animals within the AES and taxonomy, thermoregulation and body plan. The AES shows that key dimensions describing different ecological strategies of animals can be depicted with functional traits that are relatively easily accessible for a broad spectrum of animal taxa. We suggest future steps towards an application of the AES in ecosystem research aiming at the understanding of ecological processes and ecosystem functions. Additionally, we urge for databases that compile comparable functional traits for a large proportion of animals but also for further groups of organisms with the ultimate goal to map the economics spectrum of life. The framework of the AES will be relevant for understanding ecological processes across animal taxa at species, community and ecosystem level. We further discuss how it can facilitate predictions on how the functional composition and diversity of animal communities can be affected by global change.