Biodiversity loss is a major challenge. Over the past century, the average rate of vertebrate extinction has been about 100-fold higher than the estimated background rate and population declines continue to increase globally. Birth and death rates determine the pace of population increase or decline, thus driving the expansion or extinction of a species. Design of species conservation policies hence depends on demographic data (e.g., for extinction risk assessments or estimation of harvesting quotas). However, an overview of the accessible data, even for better known taxa, is lacking. Here, we present the Demographic Species Knowledge Index, which classifies the available information for 32,144 (97%) of extant described mammals, birds, reptiles, and amphibians. We show that only 1.3% of the tetrapod species have comprehensive information on birth and death rates. We found no demographic measures, not even crude ones such as maximum life span or typical litter/clutch size, for 65% of threatened tetrapods. More field studies are needed; however, some progress can be made by digitalizing existing knowledge, by imputing data from related species with similar life histories, and by using information from captive populations. We show that data from zoos and aquariums in the Species360 network can significantly improve knowledge for an almost eightfold gain. Assessing the landscape of limited demographic knowledge is essential to prioritize ways to fill data gaps. Such information is urgently needed to implement management strategies to conserve at-risk taxa and to discover new unifying concepts and evolutionary relationships across thousands of tetrapod species.

Identifying what drives individual heterogeneity has been of long interest to ecologists, evolutionary biologists and biodemographers, because only such identification provides deeper understanding of ecological and evolutionary population dynamics. In natural populations one is challenged to accurately decompose the drivers of heterogeneity among individuals as genetically fixed or selectively neutral. Rather than working on wild populations we present here data from a simple bacterial system in the lab, Escherichia coli. Our system, based on cutting‐edge microfluidic techniques, provides high control over the genotype and the environment. It therefore allows to unambiguously decompose and quantify fixed genetic variability and dynamic stochastic variability among individuals. We show that within clonal individual variability (dynamic heterogeneity) in lifespan and lifetime reproduction is dominating at about 90–92%, over the 8–10% genetically (adaptive fixed) driven differences. The genetic differences among the clonal strains still lead to substantial variability in population growth rates (fitness), but, as well understood based on foundational work in population genetics, the within strain neutral variability slows adaptive change, by enhancing genetic drift, and lowering overall population growth. We also revealed a surprising diversity in senescence patterns among the clonal strains, which indicates diverse underlying cell‐intrinsic processes that shape these demographic patterns. Such diversity is surprising since all cells belong to the same bacteria species, E. coli, and still exhibit patterns such as classical senescence, non‐senescence, or negative senescence. We end by discussing whether similar levels of non‐genetic variability might be detected in other systems and close by stating the open questions how such heterogeneity is maintained, how it has evolved, and whether it is adaptive.

21Identifying what drives individual heterogeneity has been of long interest to ecologists, evolutionary 22 biologists and biodemographers, because only such identification provides deeper understanding of 23 ecological and evolutionary population dynamics. In natural populations one is challenged to 24 accurately decompose the drivers of heterogeneity among individuals as genetically fixed or selectively 25 neutral. Rather than working on wild populations we present here data from a simple bacterial system 26 in the lab, Escherichia coli. Our system, based on cutting-edge microfluidic techniques, provides high 27 control over the genotype and the environment. It therefore allows to unambiguously decompose and 28 quantify fixed genetic variability and dynamic stochastic variability among individuals. We show that 29 within clonal individual variability (dynamic heterogeneity) in lifespan and lifetime reproduction is 30 dominating at about 82-88%, over the 12-18% genetically (adaptive fixed) driven differences. The 31 genetic differences among the clonal strains still lead to substantial variability in population growth 32 rates (fitness), but, as well understood based on foundational work in population genetics, the within 33 strain neutral variability slows adaptive change, by enhancing genetic drift, and lowering overall 34 population growth. We also revealed a surprising diversity in senescence patterns among the clonal 35 strains, which indicates diverse underlying cell-intrinsic processes that shape these demographic 36 patterns. Such diversity is surprising since all cells belong to the same bacteria species, E. coli, and still 37 exhibit patterns such as classical senescence, non-senescence, or negative senescence. We end by 38 discussing whether similar levels of non-genetic variability might be detected in other systems and 39 close by stating the open questions how such heterogeneity is maintained, how it has evolved, and 40 whether it is adaptive.41 Heterogeneity among individuals has important ecological and evolutionary implications because it 42 determines the pace of ecological and evolutionary adaptation and shapes eco-evolutionary feedbacks 43 (Hartl and Clark 2007, Steiner and Tuljapurkar 2012, Vindenes and Langangen 2015). Despite 44 substantial methodological and empirical efforts, it remains challenging to unambiguously differentiate 45 the causes that drive the observed heterogeneity among individuals in their life courses, their traits, and 46 their fitness components (Steiner and Tuljapurkar 2012, Bonnet and Postma 2016, Cam et al. 2016). 47 There is consensus that heterogeneity among individuals is caused by changes in the environment, by 48 variation in the genotype, by the genotype-by-environment interaction, and by noise or intrinsic 49 processes many of which show stochastic properties (Endler 1986, Finch and Kirkwood 2000, 50 Kirkwood et al. 2005). The latter cause has either been deemed as noise associated with non-biological 51 processes, e.g. measurement error, and with unkn...

This paper contains data on intentionally deployed wrecks to serve as artificial reefs from 1942 to 2016. The deployment of decommissioned vessels and other available wrecks is a common practice in many coastal countries, such as the USA, Australia, Malta, and New Zealand. We obtained data of georeferenced sites of wrecks from the scientific literature, local databases, and diving web sites published in the English language. Furthermore, we included information regarding the type of structure, location, depth, country, year of deployment and estimated life span. Moreover, we provide information on whether the wreck is located inside one of the World׳s Protected Areas, key biophysical Standard Level Data from the World Ocean Database, distance to reefs from the Coral Trait Database, and distances to 597 aquariums that are members of the Species360 global network of Aquariums and Zoological institutions, in the Zoological Information Management System (ZIMS). We provide data for wrecks with monitoring surveys in the peer-review literature, although these only comprise 2% of the records (36 of 1907 wrecks). The data we provide here can be used for research and evaluation of already deployed reefs, especially if combined with additional spatial information on biodiversity and threats.