Quantitative evaluation of intraspecific genetic diversity in a natural fish population using environmental DNA analysis

Recent advances in environmental DNA (eDNA) analysis using high‐throughput sequencing (HTS) enable evaluation of intraspecific genetic diversity in a population. As the intraspecific genetic diversity provides invaluable information for wildlife conservation and management, there is an increasing demand to apply eDNA analysis to population genetics and the phylogeography by quantitative evaluation of intraspecific diversity. However, quantitative evaluations of intraspecific genetic diversity using eDNA is not straightforward because the number of eDNA sequence reads obtained by HTS may not be an index of the quantity of eDNA. In this study, to quantitatively evaluate genetic diversity using eDNA analysis, we applied a quantitative eDNA metabarcoding method using the internal standard DNAs. We targeted Ayu (Plecoglossus altivelis altivelis) and added internal standard DNAs with known copy numbers to each eDNA sample obtained from three rivers during the library preparation process. The sequence reads of each Ayu haplotype were successfully converted to DNA copy numbers based on the relationship between the copy numbers and sequence reads of the internal standard DNAs. In all rivers, the calculated copy number of each haplotype showed a significant positive correlation with the haplotype frequency estimated by a capture‐based survey. Furthermore, estimates of genetic indicators such as nucleotide diversity based on the eDNA copy numbers were comparable with those estimated based on a capture‐based study. Our results demonstrate that eDNA analysis with internal standard DNAs enables reasonable quantification of intraspecific genetic diversity, and this method could thus be a promising tool in the field of population genetics and phylogeography.     

Using density surface models to estimate spatio-temporal changes in population densities and trend

Precise measures of population abundance and trend are needed for species conservation; these are most difficult to obtain for rare and rapidly changing populations. We compare uncertainty in densities estimated from spatio–temporal models with that from standard design-based methods. Spatio–temporal models allow us to target priority areas where, and at times when, a population may most benefit. Generalised additive models were fitted to a 31-year time series of point-transect surveys of an endangered Hawaiian forest bird, the Hawai‘i ‘ākepa Loxops coccineus. This allowed us to estimate bird densities over space and time. We used two methods to quantify uncertainty in density estimates from the spatio–temporal model: the delta method (which assumes independence between detection and distribution parameters) and a variance propagation method. With the delta method we observed a 52% decrease in the width of the design-based 95% confidence interval (CI), while we observed a 37% decrease in CI width when propagating the variance. We mapped bird densities as they changed across space and time, allowing managers to evaluate management actions. Integrating detection function modelling with spatio–temporal modelling exploits survey data more efficiently by producing finer-grained abundance estimates than are possible with design-based methods as well as producing more precise abundance estimates. Model-based approaches require switching from making assumptions about the survey design to assumptions about bird distribution. Such a switch warrants consideration. In this case the model-based approach benefits conservation planning through improved management efficiency and reduced costs by taking into account both spatial shifts and temporal changes in population abundance and distribution.     

岷江上游4个栽培树种细根功能性状垂直分布的差异性

细根(直径≤2 mm)功能性状及垂直分布格局是反映植物对土壤资源吸收策略和影响森林地下生态过程的关键。本研究以岷江上游4个人工林树种连香树(Cercidiphyllum japonicum)、白桦(Betula platyphylla)、华山松(Pinus armandii)和油松(P.tabuliformis)为对象,调查不同海拔树木细根功能性状及其在不同土层间的垂直分布格局,并分析细根功能性状分布与构型之间的相关关系。结果表明:阔叶树种比针叶树种有更大的根长密度、生物量、比根长和比表面积,而直径反之;4个树种细根集中在0~20 cm土层,根长密度和生物量在较高海拔地段均显著大于较低海拔,且均随土壤深度增加而减少,但比根长、比表面积和直径无显著的海拔差异,随土层加深也无明显的垂直变化规律;针阔树种间的细根构型差异显著,但不受海拔差异的影响,阔叶树的细根分支强度与一级根数量显著大于针叶树种;一级根数和根尖密度与比根长以及分根比与根长密度和生物量均呈显著正相关,而分叉与几个细根功能参数均呈负相关;随着土层深度增加,细根总生长量明显减少,但细根资源利用效率和策略不变;5个细根功能性状的垂直分布格局差异明显,且受树种特性影响显著,细根分支构型对其垂直分布及资源获取策略具有显著影响。     

What would it take to describe the global diversity of parasites?

How many parasites are there on Earth? Here, we use helminth parasites to highlight how little is known about parasite diversity, and how insufficient our current approach will be to describe the full scope of life on Earth. Using the largest database of host–parasite associations and one of the world’s largest parasite collections, we estimate a global total of roughly 100 000–350 000 species of helminth endoparasites of vertebrates, of which 85–95% are unknown to science. The parasites of amphibians and reptiles remain the most poorly described, but the majority of undescribed species are probably parasites of birds and bony fish. Missing species are disproportionately likely to be smaller parasites of smaller hosts in undersampled countries. At current rates, it would take centuries to comprehensively sample, collect and name vertebrate helminths. While some have suggested that macroecology can work around existing data limitations, we argue that patterns described from a small, biased sample of diversity aren’t necessarily reliable, especially as host–parasite networks are increasingly altered by global change. In the spirit of moonshots like the Human Genome Project and the Global Virome Project, we consider the idea of a Global Parasite Project: a global effort to transform parasitology and inventory parasite diversity at an unprecedented pace.     

Three critical factors affecting automated image species recognition performance for camera traps

Ecological camera traps are increasingly used by wildlife biologists to unobtrusively monitor an ecosystems animal population. However, manual inspection of the images produced is expensive, laborious, and time‐consuming. The success of deep learning systems using camera trap images has been previously explored in preliminary stages. These studies, however, are lacking in their practicality. They are primarily focused on extremely large datasets, often millions of images, and there is little to no focus on performance when tasked with species identification in new locations not seen during training. Our goal was to test the capabilities of deep learning systems trained on camera trap images using modestly sized training data, compare performance when considering unseen background locations, and quantify the gradient of lower bound performance to provide a guideline of data requirements in correspondence to performance expectations. We use a dataset provided by Parks Canada containing 47,279 images collected from 36 unique geographic locations across multiple environments. Images represent 55 animal species and human activity with high‐class imbalance. We trained, tested, and compared the capabilities of six deep learning computer vision networks using transfer learning and image augmentation: DenseNet201, Inception‐ResNet‐V3, InceptionV3, NASNetMobile, MobileNetV2, and Xception. We compare overall performance on “trained” locations where DenseNet201 performed best with 95.6% top‐1 accuracy showing promise for deep learning methods for smaller scale research efforts. Using trained locations, classifications with <500 images had low and highly variable recall of 0.750 ± 0.329, while classifications with over 1,000 images had a high and stable recall of 0.971 ± 0.0137. Models tasked with classifying species from untrained locations were less accurate, with DenseNet201 performing best with 68.7% top‐1 accuracy. Finally, we provide an open repository where ecologists can insert their image data to train and test custom species detection models for their desired ecological domain.     

Straightforward method for calibration of mechanistic cation exchange chromatography models for industrial applications

Mechanistic modeling of chromatography processes is one of the most promising techniques for the digitalization of biopharmaceutical process development. Possible applications of chromatography models range from in silico process optimization in early phase development to in silico root cause investigation during manufacturing. Nonetheless, the cumbersome and complex model calibration still decelerates the implementation of mechanistic modeling in industry. Therefore, the industry demands model calibration strategies that ensure adequate model certainty in a limited amount of time. This study introduces a directed and straightforward approach for the calibration of pH-dependent, multicomponent steric mass action (SMA) isotherm models for industrial applications. In the case investigated, the method was applied to a monoclonal antibody (mAb) polishing step including four protein species. The developed strategy combined well-established theories of preparative chromatography (e.g. Yamamoto method) and allowed a systematic reduction of unknown model parameters to 7 from initially 32. Model uncertainty was reduced by designing two representative calibration experiments for the inverse estimation of remaining model parameters. Dedicated experiments with aggregate-enriched load material led to a significant reduction of model uncertainty for the estimates of this low-concentrated product-related impurity. The model was validated beyond the operating ranges of the final unit operation, enabling its application to late-stage downstream process development. With the proposed model calibration strategy, a systematic experimental design is provided, calibration effort is strongly reduced, and local minima are avoided.     

Uncloaking a Lost Cause: Decolonizing ancestry estimation in the United States

Since the professionalization of US-based forensic anthropology in the 1970s, ancestry estimation has been included as a standard part of the biological profile, because practitioners have assumed it necessary to achieve identifications in medicolegal contexts. Simultaneously, forensic anthropologists have not fully considered the racist context of the criminal justice system in the United States related to the treatment of Black, Indigenous, and People of Color; nor have we considered that ancestry estimation might actually hinder identification efforts because of entrenched racial biases. Despite ongoing criticisms from mainstream biological anthropology that ancestry estimation perpetuates race science, forensic anthropologists have continued the practice. Recent years have seen the prolific development of retooled typological approaches with 21st century statistical prowess to include methods for estimating ancestry from cranial morphoscopic traits, despite no evidence that these traits reflect microevolutionary processes or are suitable genetic proxies for population structure; and such approaches have failed to critically evaluate the societal consequences for perpetuating the biological race concept. Around the country, these methods are enculturated in every aspect of the discipline ranging from university classrooms, to the board-certification examination marking the culmination of training, to standard operating procedures adopted by forensic anthropology laboratories. Here, we use critical race theory to interrogate the approaches utilized to estimate ancestry to include a critique of the continued use of morphoscopic traits, and we assert that the practice of ancestry estimation contributes to white supremacy. Based on the lack of scientific support that these traits reflect evolutionary history, and the inability to disentangle skeletal-based ancestry estimates from supporting the biological validity of race, we urge all forensic anthropologists to abolish the practice of ancestry estimation.     

Upper bound estimators of the population size based on ordinal models for capture‐recapture experiments

Capture‐recapture studies have attracted a lot of attention over the past few decades, especially in applied disciplines where a direct estimate for the size of a population of interest is not available. Epidemiology, ecology, public health, and biodiversity are just a few examples. The estimation of the number of unseen units has been a challenge for theoretical statisticians, and considerable progress has been made in providing lower bound estimators for the population size. In fact, it is well known that consistent estimators for this cannot be provided in the very general case. Considering a case where capture‐recapture studies are summarized by a frequency of frequencies distribution, we derive a simple upper bound of the population size based on the cumulative distribution function. We introduce two estimators of this bound, without any specific parametric assumption on the distribution of the observed frequency counts. The behavior of the proposed estimators is investigated using several benchmark datasets and a large‐scale simulation experiment based on the scheme discussed by Pledger.     

A penalized structural equation modeling method accounting for secondary phenotypes for variable selection on genetically regulated expression from PrediXcan for Alzheimer's disease

As the global burden of mental illness is estimated to become a severe issue in the near future, it demands the development of more effective treatments. Most psychiatric diseases are moderately to highly heritable and believed to involve many genes. Development of new treatment options demands more knowledge on the molecular basis of psychiatric diseases. Toward this end, we propose to develop new statistical methods with improved sensitivity and accuracy to identify disease‐related genes specialized for psychiatric diseases. The qualitative psychiatric diagnoses such as case control often suffer from high rates of misdiagnosis and oversimplify the disease phenotypes. Our proposed method utilizes endophenotypes, the quantitative traits hypothesized to underlie disease syndromes, to better characterize the heterogeneous phenotypes of psychiatric diseases. We employ the structural equation modeling using the liability‐index model to link multiple genetically regulated expressions from PrediXcan and the manifest variables including endophenotypes and case‐control status. The proposed method can be considered as a general method for multivariate regression, which is particularly helpful for psychiatric diseases. We derive penalized retrospective likelihood estimators to deal with the typical small sample size issue. Simulation results demonstrate the advantages of the proposed method and the real data analysis of Alzheimer's disease illustrates the practical utility of the techniques. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative database.