COMADRE: a global database of animal demography

Abstract

Summary1. The open-access scientific philosophy has been widely adopted and proven to promote considerable progress in the fields of ecology and evolution. Openaccess global databases now exist on animal migration, the distribution of species, and conservation status, to mention a few. However, a gap exists for databases on population dynamics spanning the rich diversity of the animal kingdom. This information is fundamental to our understanding of the conditions that have shaped variation in animal life histories and their relationships with the environment. Furthermore, an animal population's schedules of reproduction and mortality determine its invasive potential, and its risk of local extinction, which are at the core of conservation biology. worldwide, from 272 studies, with a total of 1,575 population projection matrices. COMADRE also contains ancillary information (e.g. ecoregion, taxonomy, biogeography, etc.) that facilitates interpretation of the numerous demographic metrics that can be derived from its MPMs.. CC-BY-NC-ND4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint .

Senescence: Still an Unsolved Problem of Biology

“…We used the COMADRE Animal Matrix Database 2 (v. 3.0.0) and COMPADRE Plant Matrix Database 3 (v. 5.0.0) to obtain age trajectories of survival and reproduction. These open-access data repositories consist of matrix population models 13 (MPMs) incorporating high-resolution demographic information on the survival and reproduction patterns of over 1,000 animal and plant species worldwide 2, 3 . Both databases include information on species for which the data have been digitised and thoroughly error-checked.…”

“…Here, we utilise high-resolution demographic information for 48 animal 2 and 260 plant 3 species worldwide to (i) provide a quantitative evaluation of the rates of actuarial senescence – the progressive age-dependent increase in mortality risk with age after maturation – across multicellular organisms, (ii) test whether the classical evolutionary framework explains the examined diversity of senescence rates, with special attention to predictions from germ-soma separation, and (iii) propose how to widen the classical evolutionary framework of ageing to better encompass the tree of life.…”

“…seven decades of theoretical elaboration aiming to explain the problem since Medawar first outlined it, we argue that this fundamental problem of biology remains unsolved. Here, we utilise demographic information 2, 3 for 308 multicellular species to derive age-based trajectories of mortality and reproduction that provide evidence against the predictions of the classical, still prevailing, theories of ageing 1, 4, 5, 6 . These theories predict the inescapability of senescence 1, 4 , or its universality at least among species with a clear germ-soma barrier 5, 6 .…”

An Ecological Framework for Modeling the Geography of Disease Transmission

“…Current initiatives such as the Global Mammal Database [81], the Malaria Atlas Project [82], and COMADRE [83] could lay the groundwork to implement the host-space framework and serve as a platform to integrate data from different sources similar to large-scale biodiversity repositories (e.g., the Global Biodiversity Information Facility [84]). Coupling ecophylogenetics with ENM could increase our understanding of how external environmental conditions (e.g., climate) and host traits explain disease distributions, although these approaches have rarely been applied until recently [78].…”

Demography beyond the population

“…Yet, most demographic studies are highly variable in nature and implementation and not designed with the foresight to connect with other data sets and extend beyond the original study goals. With emergent open-access demographic data repositories (Lebreton et al 2010;Salguero-G omez et al 2015bSalguero-G omez et al , 2016) and a greater capacity to quantitatively integrate data sources, demographers should consider how to maximize the broader potential of their data when designing studies. For example, while collecting demographic data, researchers may want to consider certain covariates that are broadly informative (e.g.…”