A critical assessment of estimating census population size from genetic population size (or vice versa) in three fishes

Integrating physiology and environmental dynamics to operationalize environmental DNA (eDNA) as a means to monitor freshwater macro-organism abundance

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 promising - models 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.

The relationship between eDNA particle concentration and organism abundance in nature is strengthened by allometric scaling

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 nonlinearly 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. r²  = 0.59 and 0.63, respectively), but ASM exhibited improved model fit (adj. r²  = 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 indicator of abundance in nature.

Next-generation metrics for monitoring genetic erosion within populations of conservation concern

Genetic erosion is a major threat to biodiversity because it can reduce fitness and ultimately contribute to the extinction of populations. Here, we explore the use of quantitative metrics to detect and monitor genetic erosion. Monitoring systems should not only characterize the mechanisms and drivers of genetic erosion (inbreeding, genetic drift, demographic instability, population fragmentation, introgressive hybridization, selection) but also its consequences (inbreeding and outbreeding depression, emergence of large-effect detrimental alleles, maladaptation and loss of adaptability). Technological advances in genomics now allow the production of data the can be measured by new metrics with improved precision, increased efficiency and the potential to discriminate between neutral diversity (shaped mainly by population size and gene flow) and functional/adaptive diversity (shaped mainly by selection), allowing the assessment of management-relevant genetic markers. The requirements of such studies in terms of sample size and marker density largely depend on the kind of population monitored, the questions to be answered and the metrics employed. We discuss prospects for the integration of this new information and metrics into conservation monitoring programmes.

Estimating the effective number of breeders from single parr samples for conservation monitoring of wild populations of Atlantic salmon Salmo salar

This study assesses whether the effective number of breeders (N_b ) can be estimated using a time and cost-effective protocol using genetic sibship reconstruction from a single sample of young-of-the-year (YOY) for the purposes of Atlantic salmon Salmo salar population monitoring. N_b was estimated for 10 consecutive reproductive seasons for S. salar in the River Nivelle, a small population located at the rear-edge of the species distribution area in France, chronically under its conservation limit and subjected to anthropogenic and environmental changes. Subsampling of real and simulated data showed that accurate estimates of N_b can be obtained from YOY genotypes, collected at moderate random sampling intensity, achievable using routine juvenile electrofishing protocols. Spatial bias and time elapsed since spawning were found to affect estimates, which must be accounted for in sampling designs. N_b estimated in autumn for S. salar in the River Nivelle was low and variable across years from 23 (95% C.I. 14-41) to 75 (53-101) and was not statistically correlated with the estimated number of returning adults, but it was positively correlated with the estimated number of YOY at age 9 months. N_b was found to be lower for intermediate levels of redd aggregation, suggesting that the strength of the competition between males to access females affects reproductive success variance depending on redd spatial configuration. Thus, environmental factors such as habitat availability and quality for spawning and YOY development predominate over demographic ones (number of returning adults) in driving long-term population viability for S. salar in the River Nivelle. This study showcases N_b as an integrated parameter, encompassing demographic and ecological information about a reproductive event, relevant to the assessment of both short-term effects of management practices and long-term population conservation status.

Genetic substructure and admixture as important factors in linkage disequilibrium-based estimation of effective number of breeders in recovering wildlife populations

The number of effective breeders (Nb ) and effective population size (Ne ) are population parameters reflective of evolutionary potential, susceptibility to stochasticity, and viability. We have estimated these parameters using the linkage disequilibrium-based approach with LDNE through the latest phase of population recovery of the brown bears (Ursus arctos) in Finland (1993-2010; N = 621). This phase of the recovery was recently documented to be associated with major changes in genetic composition. In particular, differentiation between the northern and the southern genetic cluster declined rapidly within 1.5 generations. Based on this, we have studied effects of the changing genetic structure on Nb and Ne , by comparing estimates for whole Finland with the estimates for the two genetic clusters. We expected a potentially strong relationship between estimate sizes and genetic differentiation, which should disappear as the population recovers and clusters merge. Consistent with this, our estimates for whole Finland were lower than the sum of the estimates of the two genetic clusters and both approaches produced similar estimates in the end. Notably, we also found that admixed genotypes strongly increased the estimates. In all analyses, our estimates for Ne were larger than Nb and likely reflective for brown bears of the larger region of Finland and northwestern Russia. Conclusively, we find that neglecting genetic substructure may lead to a massive underestimation of Nb and Ne . Our results also suggest the need for further empirical analysis focusing on individuals with admixed genotypes and their potential high influence on Nb and Ne .

A critical assessment of estimating census population size from genetic population size (or vice versa) in three fishes

Technological and methodological advances have facilitated the use of genetic data to infer census population size (Nc) in natural populations, particularly where traditional mark-and-recapture is challenging. The effective number of breeders (Nb) describes how many adults effectively contribute to a cohort and is often correlated with Nc. Predicting Nc from Nb or vice versa in species with overlapping generations has important implications for conservation by permitting (i) estimation of the more difficult to quantify variable and (ii) inferences of Nb/Nc relationships in related species lacking data. We quantitatively synthesized Nb/Nc relationships in three salmonid fishes where sufficient data have recently accumulated. Mixed-effects models were analysed in which each variable was included as a dependent variable or predictor term (Nb from Nc and vice versa). Species-dependent Nb/Nc slope estimates were significantly positive in two of three species. Variation in species slopes was likely due to varying life histories and reinforce caution when inferring Nb/Nc from taxonomically related species. Models provided maximum probable estimates for Nb and Nc for two species. However, study, population and year effects explained substantial amounts of variation (39%-57%). Consequently, prediction intervals were wide and included or were close to zero for all population sizes and species; model predictive utility was limited. Cost-benefit trade-offs when estimating Nb and/or Nc were also discussed using a real-world system example. Our findings based on salmonids suggest that no short cuts currently exist when estimating population size and researchers should focus on quantifying the variable of interest or be aware of caveats when inferring the desired variable because of cost or logistics. We caution that the salmonid species examined share life-history traits that may obscure relationships between Nb and Nc. Sufficient data on other taxa were unavailable; additional research examining Nb/Nc relationships in species with potentially relevant life-history trait differences (e.g., differing survival curves) is needed.