Genetic diversity is necessary for evolutionary response to changing environmental conditions such as those facing many threatened and endangered species. To investigate the relationship between genetic diversity and conservation status, we conducted a systematic, quantitative review of vertebrate microsatellite data published since 1990: we screened 5165 previously published articles and identified 1941 microsatellite datasets spanning 17,988 loci that characterized wild populations distributed among five vertebrate classes. We analyzed these data in the context of conservation by comparing empirical estimates of heterozygosity and allelic richness between threatened and non-threatened species. We found that both heterozygosity and allelic richness are reduced in threatened species, suggesting that inbreeding and drift are both effective at removing genetic diversity in endangered populations. We then considered the criteria typically used to rank species of conservation concern (including declining population size, species range extent, and the number of mature individuals) to determine which of these criteria are most effective at identifying genetically depauperate species. However, we found that the existing criteria failed to systematically identify populations with low genetic diversity. To rectify this, we suggest a novel approach for identifying species of conservation need by estimating the expected loss of genetic diversity. We then evaluated the efficacy of our new approach and found that it performs significantly better than the existing methods for identifying species that merit conservation concern in part because of reduced genetic diversity.
Environmental DNA (eDNA) is DNA that has been isolated from field samples, and it is increasingly used to infer the presence or absence of particular species in an ecosystem. However, the combination of sampling procedures and subsequent molecular amplification of eDNA can lead to spurious results. As such, it is imperative that eDNA studies include a statistical framework for interpreting eDNA presence/absence data. We reviewed published literature for studies that utilized eDNA where the species density was known and compared the probability of detecting the focal species to the sampling and analysis protocols. Although biomass of the target species and the volume per sample did not impact detectability, the number of field replicates and number of samples from each replicate were positively related to detection. Additionally, increased number of PCR replicates and increased primer specificity significantly increased detectability. Accordingly, we advocate for increased use of occupancy modelling as a method to incorporate effects of sampling effort and PCR sensitivity in eDNA study design. Based on simulation results and the hierarchical nature of occupancy models, we suggest that field replicates, as opposed to molecular replicates, result in better detection probabilities of target species.
Biologists routinely use molecular markers to identify conservation units, to quantify genetic connectivity, to estimate population sizes, and to identify targets of selection. Many imperiled eagle populations require such efforts and would benefit from enhanced genomic resources. We sequenced, assembled, and annotated the first eagle genome using DNA from a male golden eagle (Aquila chrysaetos) captured in western North America. We constructed genomic libraries that were sequenced using Illumina technology and assembled the high-quality data to a depth of ∼40x coverage. The genome assembly includes 2,552 scaffolds >10 Kb and 415 scaffolds >1.2 Mb. We annotated 16,571 genes that are involved in myriad biological processes, including such disparate traits as beak formation and color vision. We also identified repetitive regions spanning 92 Mb (∼6% of the assembly), including LINES, SINES, LTR-RTs and DNA transposons. The mitochondrial genome encompasses 17,332 bp and is ∼91% identical to the Mountain Hawk-Eagle (Nisaetus nipalensis). Finally, the data reveal that several anonymous microsatellites commonly used for population studies are embedded within protein-coding genes and thus may not have evolved in a neutral fashion. Because the genome sequence includes ∼800,000 novel polymorphisms, markers can now be chosen based on their proximity to functional genes involved in migration, carnivory, and other biological processes.
Sample preparation for mass spectrometry analysis in proteomics requires enzymatic cleavage of proteins into a peptide mixture. This process involves numerous incubation and liquid transfer steps in order to achieve denaturation, reduction, alkylation, and cleavage. Adapting this workflow onto an automated workstation can increase efficiency and reduce coefficients of variance, thereby providing more reliable data for statistical comparisons between sample types. We previously described an automated proteomic sample preparation workflow 1 . Here, we report the development of a more efficient and better controlled workflow with the following advantages: 1) The number of liquid transfer steps is reduced from nine to six by combining reagents; 2) Pipetting time is reduced by selective tip pipetting using a 96-position pipetting head with multiple channels; 3) Potential throughput is increased by the availability of up to 45 deck positions; 4) Complete enclosure of the system provides improved temperature and environmental control and reduces the potential for contamination of samples or reagents; and 5) The addition of stable isotope labeled peptides, as well as β-galactosidase protein, to each sample makes monitoring and quality control possible throughout the entire process. These hardware and process improvements provide good reproducibility and improve intra-assay and inter-assay precision (CV of less than 20%) for LC-MS based protein and peptide quantification. The entire workflow for digesting 96 samples in a 96-well plate can be completed in approximately 5 hours.
Molecular ecologists have good reasons to be excited about the newest DNA/RNA sequencing technologies. However, this exuberance should be tempered with a hefty dose of reality: new sequencing technologies come with significant new challenges. Herein, we offer a brief overview of some practical problems encountered during transcriptomics studies conducted in our laboratory, and of nontrivial issues that prospective practitioners should consider. These include template contamination (e.g. from xenobiotics) and the cutting-room floor problem, whereby most of the data are often unassembled, unannotated and unused. We also highlight computational requirements, including hardware, personnel time and associated skill sets. We are very optimistic about the future of molecular ecology, but we hope this cautionary overview will help neophytes better recognize some key challenges associated with new technologies.
Meta-analysis, the statistical synthesis of pertinent literature to develop evidence-based conclusions, is relatively new to the field of molecular ecology, with the first metaanalysis published in the journal Molecular Ecology in 2003 (Slate & Phua 2003). The goal of this article is to formalize the definition of meta-analysis for the authors, editors, reviewers and readers of Molecular Ecology by completing a review of the meta-analyses previously published in this journal. We also provide a brief overview of the many components required for meta-analysis with a more specific discussion of the issues related to the field of molecular ecology, including the use and statistical considerations of Wright's F ST and its related analogues as effect sizes in meta-analysis. We performed a literature review to identify articles published as 'metaanalyses' in Molecular Ecology, which were then evaluated by at least two reviewers. We specifically targeted Molecular Ecology publications because as a flagship journal in this field, meta-analyses published in Molecular Ecology have the potential to set the standard for meta-analyses in other journals. We found that while many of these reviewed articles were strong meta-analyses, others failed to follow standard meta-analytical techniques. One of these unsatisfactory meta-analyses was in fact a secondary analysis. Other studies attempted metaanalyses but lacked the fundamental statistics that are considered necessary for an effective and powerful metaanalysis. By drawing attention to the inconsistency of studies labelled as meta-analyses, we emphasize the importance of understanding the components of traditional meta-analyses to fully embrace the strengths of quantitative data synthesis in the field of molecular ecology.
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