The use of eDNA to detect the presence/absence of rare or invasive species is well documented and its use in biodiversity monitoring is expanding. Preliminary laboratory research has also shown a positive correlation between the concentration of species‐specific eDNA particles and the density/biomass of a species in a given environment. However, the extent to which these results can be extended to natural environments has yet to be formally quantified. We collated data from experiments that examined the correlation between eDNA and two metrics of abundance (biomass and density) and, using mixed‐effects meta‐analysis, quantified the strength of that correlation across laboratory and natural environments. We found that eDNA particle concentration was more strongly correlated with abundance in laboratory environments compared to natural environments, accounting for approximately 82% and 57% of the observed variation in abundance, respectively. We found some evidence of potential publication bias that may have impacted the estimation of the correlation in natural environments; after smaller studies were removed from the dataset, eDNA particle concentration accounted for approximately 50% of the observed variation in abundance in natural environments. No effect of abundance metric was found on the strength of correlation between eDNA particle concentration and abundance. Despite a weaker general correlation in natural environments, eDNA concentration often still explained substantial variation in abundance. eDNA research is still an emergent field of study; with only moderate improvements in technology or technique, it could represent a powerful new tool for quantifying abundance.

Organism abundance is a critical parameter in ecology, but its estimation is often challenging.Approaches utilizing eDNA to indirectly estimate abundance have recently generated substantial interest. However, preliminary correlations observed between eDNA concentration and abundance in nature are typically moderate in strength with significant unexplained variation.Here we apply a novel approach to integrate allometric scaling coefficients into models of eDNA concentration and organism abundance. We hypothesize that eDNA particle production scales non-linearly with mass, with scaling coefficients < 1. Wild populations often exhibit substantial variation in individual body size distributions; we therefore predict that the distribution of mass across individuals within a population will influence population-level eDNA production rates. To test our hypothesis, we collected standardized body size distribution and mark-recapture abundance data using whole-lake experiments involving nine populations of brook trout. We correlated eDNA concentration with three metrics of abundance: density (individuals/ha), biomass (kg/ha), and allometrically scaled mass (ASM) (∑(individual mass 0.73 )/ha). Density and biomass were both significantly positively correlated with eDNA concentration (adj. R 2 = 0.59 and 0.63, respectively), but ASM exhibited improved model fit (adj. R 2 = 0.78). We also demonstrate how estimates of ASM derived from eDNA samples in 'unknown' systems can be converted to biomass or density estimates with additional size structure data. Future experiments should empirically validate allometric scaling coefficients for eDNA production, particularly where substantial intraspecific size distribution variation exists. Incorporating allometric scaling may improve predictive models to the extent that eDNA concentration may become a reliable

It is widely thought that small populations should have less additive genetic variance and respond less efficiently to natural selection than large populations. Across taxa, we meta‐analytically quantified the relationship between adult census population size (N) and additive genetic variance (proxy: h 2) and found no reduction in h 2 with decreasing N; surveyed populations ranged from four to one million individuals (1735 h 2 estimates, 146 populations, 83 species). In terms of adaptation, ecological conditions may systematically differ between populations of varying N; the magnitude of selection these populations experience may therefore also differ. We thus also meta‐analytically tested whether selection changes with N and found little evidence for systematic differences in the strength, direction or form of selection with N across different trait types and taxa (7344 selection estimates, 172 populations, 80 species). Collectively, our results (i) indirectly suggest that genetic drift neither overwhelms selection more in small than in large natural populations, nor weakens adaptive potential/h 2 in small populations, and (ii) imply that natural populations of varying sizes experience a variety of environmental conditions, without consistently differing habitat quality at small N. However, we caution that the data are currently insufficient to determine whether some small populations may retain adaptive potential definitively. Further study is required into (i) selection and genetic variation in completely isolated populations of known N, under‐represented taxonomic groups, and nongeneralist species, (ii) adaptive potential using multidimensional approaches and (iii) the nature of selective pressures for specific traits.

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Research has demonstrated consistent positive correlations between organism abundance and absolute environmental DNA (eDNA) concentrations. Robust correlations in laboratory experiments indicate strong functional links, suggesting the potential for eDNA to monitor organism abundance in nature. However, correlations between absolute eDNA concentrations and organism abundance in nature tend to be weaker because myriad biotic and abiotic factors influence steady-state eDNA concentrations, decoupling its direct functional link with abundance. Additional technical challenges can also weaken correlations between relative organism abundance and relative eDNA data derived from metabarcoding. Future research must account for these factors to improve the inference of organism abundance from eDNA, including integrating the effects of organism physiology on eDNA production, eDNA dynamics in lentic/lotic systems, and key environmental parameters that impact estimated steady-state concentrations. Additionally, it is critical to manage expectations surrounding the accuracy and precision that eDNA can provide -eDNA, for example, cannot provide abundance estimates comparable to intensively managed freshwater fisheries that enumerate every individual fish. Recent developments, however, are encouraging. Current methods could provide meaningful information regarding qualitative conservation thresholds and emergent research has demonstrated that eDNA concentrations in natural ecosystems can provide rough quantitative estimates of abundance, particularly when models integrate physiology and/or eDNA dynamics.Operationalizing eDNA to infer abundance will probably require more than simple correlations with organism biomass/density. Nevertheless, the future is promisingmodels that integrate eDNA dynamics in nature could represent an effective means to infer abundance, particularly when traditional methods are considered too "costly" or difficult to obtain.