True Abundance Research Articles

Methodology comparison of environmental sediment fungal community analysis

Fungi play important roles in ecosystems. Analyzing fungal communities in environments has long been a challenge due to the large difference in compositions retrieved using different methods or sequencing regions, obscuring the true abundance and species information. Our study aimed to compare and determine more accurate approach for evaluating fungal populations in river sediment. To achieve this, different primer sets in the internal transcribed spacer (ITS) (ITS5/ITS1R, ITS1F/ITS2), 18S rRNA gene (0817F/1196R) for High-throughput sequencing (HTS), metagenomic shotgun sequencing (MS) directly from environmental samples, and HTS using ITS primers for the fungal samples collected from plate cultivation were used to characterize the fungal communities. We calculated diversity index and used FungalTraits to analyze methods preferences for fungal species. The study revealed that when analyzing the fungal species directly from environmental samples, amplification and sequencing of ITS region demonstrated more accuracy than MS and 18S rRNA gene sequencing methods, but displayed significant primer preference. Over 30 % fungal species from HTS after plate cultivation were not present in HTS from the environmental samples. NMDS analysis demonstrated significant disparities in species diversity among different methods, suggesting potential complementarity between them. Over 85% species identified by HTS using ITS primers belonged to filamentous fungi, while the MS mostly identified yeast (62%). Therefore, to get more accurate fungal community information in sediment, multiple methods were recommended by using cultivation, molecular biological methods dependent on PCR techniques like ITS1F/ITS2 primer for HTS and PCR independent method such as metagenomic shotgun sequencing techniques.

Absence of measurable quantities of Candida auris and Cryptococcus spp. in the gut microbiota of Ghanaian individuals with and without HIV infection as confirmed by applying multiple real-time PCR assays.

Introduction. Fungal infections are relevant health risks for individuals with acquired immunodeficiency in the resource-limited tropics, but available surveillance data are scarce. For Candida auris and Cryptococcus spp., the evolution from environmental reservoirs to human pathogens causing life-threatening diseases is currently discussed as a public health concern in the context of climate change and limited treatment options.Gap statement. Uncovering the gastrointestinal tract as an epidemiological niche of fungi emerging from the environment into individuals for whom fungal infections are not diagnosed.Aim. To contribute to data on the local epidemiology of C. auris and Cryptococcus spp. in Western African Ghana by analysing gastrointestinal samples of Ghanaian individuals.Methodology. Four real-time PCR assays targeting C. auris and five real-time PCR assays targeting Cryptococcus spp. were applied with stool samples of 875 non-age-stratified Ghanaian HIV patients and 30 Ghanaian control individuals without known HIV infection. Also, 664 samples from Ghanaian children under 2 years of age were investigated. The true abundance of the target micro-organism was considered as unlikely in the case of one or fewer positive signals, likely in the case of two to three positive signals and highly likely in the case of four or more positive signals per sample in the real-time PCR assays.Results. The combined application of sensitive, target-specific real-time PCR assays indicates that neither C. auris, Cryptococcus neoformans complex nor Cryptococcus gattii complex were part of the gut microbiota of Ghanaian individuals with or without HIV infection.Conclusion. Despite the significant disease burden from these pathogens in immunosuppressed Ghanaian individuals, detection from gastrointestinal samples was unlikely, which should be taken into account when discussing screening strategies for these fungi of public health concern. In contrast, the detection of these fungi from such samples should not routinely be considered as commensal colonization flora.

Geese as keystone species in the Low Arctic of central Canada: the Karrak Lake Research Station

True abundance of lesser snow ( Anser caerulescens caerulescens, Linnaeus 1758) and Ross's geese ( Anser rossii, Cassin 1861, collectively referred to as “light geese”, in North America had been unknown in the 1980s. However, different indices of abundance suggested steady increases at the time. The Karrak Lake Research Station (KLRS) was established partly because it was not known if the breeding biology from more southern latitudes (the only information available at the time) was representative of more northern colonies where 90% of the midcontinent population of lesser snow geese nested. Visits in 1990 confirmed the continued importance of Karrak Lake and surrounding areas in Canada’s central Arctic to a large nesting concentration of both snow and Ross's geese documented previously. Key activities at KLRS were (1) colony-wide annual monitoring of nesting goose abundance and nesting performance and (2) large-scale marking efforts to monitor survival, range-wide abundance, distribution, and exploitation rate from hunter harvest and natural mortality rate. The scope of research expanded to include their interaction with local vegetation and other sympatric wildlife, including Arctic foxes ( Vulpes lagopus, Linnaeus 1758), king eiders ( Somateria spectabilis, Linnaeus 1758), long-tailed ducks ( Clangula hyemalis, Linnaeus 1758), and cackling geese ( Branta hutchinsii). Following exponential growth in the 1990s and 2000s, nesting populations of light geese at Karrak lake collapsed since 2012, presenting a unique opportunity to examine how local ecosystems recover and revert from heavy grazing by hyperabundant geese.

MetaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score=0.67-0.90) in genus diversity and showed a high correlation (rSpearman=0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA

AbstractObjectiveLong‐term standardized monitoring programs are fundamental to assessing how fish populations respond to anthropogenic stressors. Standardized monitoring programs may need to adopt new methods to adapt to rapid environmental changes that are associated with a changing climate. In the upper Yellowstone River, Montana, biologists have used a standardized, mark–recapture monitoring protocol to annually estimate the abundance of trout since 1978 to assess population status and trends. However, within the past two decades, climate change has caused changes in discharge timing that have prevented standardized monitoring from occurring annually.MethodsWe investigated the feasibility of using two analytical methods, N‐mixture models and mean capture probability, for estimating the abundance of three trout species in the upper Yellowstone River using the historical long‐term data set; these methods allow abundance to be estimated when a mark–recapture estimate cannot be obtained due to hydrologic conditions.ResultWhen compared with abundance estimates from mark–recapture methods, N‐mixture models most often resulted in negatively biased abundance estimates, whereas mean capture probability analyses resulted in positively biased abundance estimates. Additionally, N‐mixture models produced negatively biased estimates when tested against true abundance values from simulated data sets. The bias in the N‐mixture model estimates was caused by poor model fit and variation in capture probability. The bias in the mean capture probability estimates was caused by heterogeneity in capture probability, likely caused by variable environmental conditions, which were not accounted for in the models.ConclusionN‐mixture models and mean capture probability are not viable alternatives for estimating abundance in the upper Yellowstone River. Thus, exploring additional adaptations to sampling methodologies and analytical approaches, including models that require individually marked fish, will be valuable for this system. Climate change will undoubtedly necessitate changes to standardized sampling methods throughout the world; thus, developing alternative sampling and analytical methods will be important for maintaining the utility of long‐term data sets.

Correlations among complex organic molecules around protostars: Effects of physical structure

Context. Complex organic molecules have been observed toward many protostars. Their column density ratios are generally constant across protostellar systems, with some low-level scatter. However, the scatter in the column density ratio of formamide (NH2CHO) to methanol (CH3OH), NNH2CHO/NCH3OH, is one of the highest compared to other ratios. The larger scatter for NNH2CHO/NCH3OH (or weak correlation of these two molecules) is sometimes interpreted as evidence of gas-phase formation of NH2CHO. Aims. In this work, we propose an alternative interpretation in which this scatter is produced by differences in the snowline locations related to differences in binding energies of these species (formamide typically has a ≳2000 K larger binding energy than methanol) and the small-scale structure of the envelope and the disk system. Therefore, we do not include chemistry in our models in order to isolate the effect of physical factors. We also include CH3CN in our work as a control molecule, as it has a similar binding energy to CH3OH. Methods. We used radiative transfer models to calculate the emission from NH2CHO, CH3OH, and CH3CN in protostellar systems with and without disks. The abundances of these species were parameterized in our models, and we fit the calculated emission lines to find the column densities and excitation temperatures of these species, as done in real observations. Results. Given the difference in binding energies of NH2CHO and CH3OH, we find the gas-phase NNH2CHO/NCH3OH needs to be multiplied by a correction factor of approximately ten in order to give the true abundance ratio of these two species in the ices. This factor is much smaller (i.e., ~2) for NCH3CN/NCH3OH (the control molecule). We find that models with different disk sizes, luminosities, and envelope masses produce a scatter in this correction factor, and hence in NNH2CHO/NCH3OH comparable with that of observations. The scatter in NNH2CHO/NCH3OH is larger than that of NCH3CN/NCH3OH in models consistent with the observations. However, the scatter in the models for NCH3CN/NCH3OH is smaller than observations by a factor of around two, as expected from the similar binding energies of CH3OH and CH3CN pointing to the need for some chemical effects in the gas or ice to explain the observed ratios. We show that the scatter in NNH2CHO/NCH3OH will be lower than previously measured if we correct for the difference in sublimation temperatures of these two species in observations of ~40 protostellar systems with ALMA. Conclusions. The scatter in NNH2CHO/NCH3OH (or the ratio of any two molecules with a large binding energy difference) can be partially explained by the difference in their binding energies. Correction for this bias makes the scatter in this ratio similar to that in ratios of other complex organics in the observations, making NH2CHO a “normal” molecule. Therefore, we conclude that gas-phase chemistry routes for NH2CHO are not necessary to explain the larger scatter of NNH2CHO/NCH3OH compared with other ratios.

Plasminogen missense variants and their involvement in cardiovascular and inflammatory disease.

Human plasminogen (PLG), the zymogen of the fibrinolytic protease, plasmin, is a polymorphic protein with two widely distributed codominant alleles, PLG/Asp453 and PLG/Asn453. About 15 other missense or non-synonymous single nucleotide polymorphisms (nsSNPs) of PLG show major, yet different, relative abundances in world populations. Although the existence of these relatively abundant allelic variants is generally acknowledged, they are often overlooked or assumed to be non-pathogenic. In fact, at least half of those major variants are classified as having conflicting pathogenicity, and it is unclear if they contribute to different molecular phenotypes. From those, PLG/K19E and PLG/A601T are examples of two relatively abundant PLG variants that have been associated with PLG deficiencies (PD), but their pathogenic mechanisms are unclear. On the other hand, approximately 50 rare and ultra-rare PLG missense variants have been reported to cause PD as homozygous or compound heterozygous variants, often leading to a debilitating disease known as ligneous conjunctivitis. The true abundance of PD-associated nsSNPs is unknown since they can remain undetected in heterozygous carriers. However, PD variants may also contribute to other diseases. Recently, the ultra-rare autosomal dominant PLG/K311E has been found to be causative of hereditary angioedema (HAE) with normal C1 inhibitor. Two other rare pathogenic PLG missense variants, PLG/R153G and PLG/V709E, appear to affect platelet function and lead to HAE, respectively. Herein, PLG missense variants that are abundant and/or clinically relevant due to association with disease are examined along with their world distribution. Proposed molecular mechanisms are discussed when known or can be reasonably assumed.

Trends and Environmental Drivers of Marine Fish Landings in Cuba’s Most Productive Shelf Area

Marine finfish landings in Cuba have decreased during the last 30 years. However, in Cuba’s most productive fishing region, certain species, including rays, herrings, and snappers, have had increased landings over the past decade. Despite these anomalies, no comprehensive analysis of the interactions among multispecies landing dynamics, environmental factors, and fishing efforts has been carried out. This study estimates the dynamics of multispecies finfish landings between 1981 and 2017 on the southeastern coast of Cuba. A log-normal generalized additive model (GAM) was fit to evaluate the effects of various environmental and effort-related variables on the total landings. During the period analyzed, the finfish landings and fishing effort decreased by 46% and over 80%, respectively. Despite concerns about overfishing, landings per unit of effort (LPUE) increased by 2.8 times. The total fish landings were significantly related to changes in the fishing effort, coastal vegetation, rainfall, chlorophyll-a, and the Southern Oscillation Index (SOI). This study highlights the changing relationship between the landings and fishing effort, suggesting that LPUE may not accurately reflect true stock abundance. The findings of this study will assist in integrating the dynamics of finfish species, ecosystem status, and management actions for Cuba’s most productive fishing zone.

CLASSY. IX. The Chemical Evolution of the Ne, S, Cl, and Ar Elements

To study the chemical evolution across cosmic epochs, we investigate Ne, S, Cl, and Ar abundance patterns in the Cosmic Origins Spectrograph Legacy Archive Spectroscopic SurveY (CLASSY). CLASSY comprises local star-forming galaxies (SFGs; 0.02 < z < 0.18) with enhanced star formation rates, making them strong analogues to high-z SFGs. With direct measurements of electron temperature, we derive accurate ionic abundances for all elements and assess ionization correction factors (ICFs) to account for unseen ions and derive total abundances. We find Ne/O, S/O, Cl/O, and Ar/O exhibit constant trends with gas-phase metallicity for 12+log(O/H) < 8.5 but significant correlation for Ne/O and Ar/O with metallicity for 12+log(O/H) > 8.5, likely due to ICFs. Thus, the applicability of the ICFs to integrated spectra of galaxies could bias results, underestimating true abundance ratios. Using CLASSY as a local reference, we assess the evolution of Ne/O, S/O, and Ar/O in galaxies at z > 3, finding no cosmic evolution of Ne/O, while the lack of direct abundance determinations for S/O and Ar/O can bias the interpretation of the evolution of these elements. We determine the fundamental metallicity relationship (FMR) for CLASSY and compare to the high-redshift FMR, finding no evolution. Finally, we perform the first mass–neon relationship analysis across cosmic epochs, finding a slight evolution to high Ne at later epochs. The robust abundance patterns of CLASSY galaxies and their broad range of physical properties provide essential benchmarks for interpreting the chemical enrichment of the early galaxies observed with the JWST.

Discretion rather than rules in multiple-species fisheries

This paper evaluates the bioeconomic performance of individual fishing quota (IFQ) regulations in multiple-species fisheries. In our model, a manager chooses the aggregate quotas under uncertainty over the true stock abundances of two jointly-harvested fish species. Fishers conduct harvest operations under full knowledge of the species-specific productivities of fishing gear. We derive the profit maximizing fishing mortality and economic rent outcomes implemented by fishers under various regulatory designs. We compare bioeconomic performance of an IFQ regulation with discretion over the mix of harvested species against an IFQ regulation that bans at-sea discarding. Both regulations eliminate discards. Discretion allows closer alignment between fisher implemented outcomes and those that maximize long term expected fishery value. Incorporating discretion into regulatory designs provides new prospects for improving fisheries management.

A multi-taxa approach reveals contrasting responses of arthropod communities and related ecosystem services to field margin proximity and crop type

Agricultural intensification and landscape simplification are among the major drivers of biodiversity loss in agricultural landscapes. Increasing field edges might be a key solution for enhancing biodiversity and related ecosystem services within arable fields. In this study, we investigated the spatial distribution of nine arthropod groups and weed seed and pest predation rates across different distances from field edges and crop types. Furthermore, we tested the relationship between local carabid abundance and species richness and directly measured weed seed and pest predation rates. Most of the investigated taxa were not affected by field margin proximity, except for carabid species richness and the abundance of bees and wasps, hoverflies, and myriapods, which were high near the edge. We found a higher abundance of carabids and herbivores in oilseed rape compared to other crops, while hoverflies, bees and wasps were more abundant in cereal. True bug abundance was significantly higher in oilseed rape interiors compared to edges. Weed seed predation and pest predation by small mammals were the highest at 36 m from the field margin, probably due to small mammal distribution, while pest predation by arthropods did not show any significant pattern. Both weed seed predation rate and arthropod pest predation were positively related to carabid abundance and negatively to species richness. Contrasting responses across nine investigated arthropod taxa indicate that re-designing agricultural landscapes to support biodiversity across taxa will be challenging, and further studies are needed to fully understand the spatial distribution of arthropods and related ecosystem services in agricultural landscapes.

Environmental, behavioral, and design-related factors affect accuracy and precision of beluga abundance estimates from aerial surveys

Abundance estimation of wildlife populations is frequently derived from systematic survey data. Accuracy and precision of estimates, however, depend on the number of replicate surveys, and on adjustments made for animals unavailable to (availability bias), or available but undetected (perception bias) by observers. This study offers a comprehensive analysis of the relative influence of methodological, environmental and behavioral factors on availability bias estimates from photographic and visual aerial surveys of a small cetacean with a highly clumped distribution, the beluga (Delphinapterus leucas). It also estimates the effect of the number of surveys on accuracy and precision of abundance estimates, using 28 replicate visual surveys flown within a 16—29 day window depending on survey year. Availability bias was estimated using detailed dive data from 27 beluga from the St. Lawrence Estuary, Canada, and applied to systematic visual and photographic aerial surveys of this population, flown using various survey platforms. Dive and surface interval durations varied among individuals, and averaged (weighted) 176.6 s (weighted s.e. = 12.6 s) and 51.6 s (weighted s.e. = 4.5 s), respectively. Dive time and instantaneous availability, but not surface time, were affected by local turbidity, seafloor depth, whale behavior (i.e., whether beluga were likely in transit or not), and latent processes that were habitat-specific. Overall, adjustments of availability for these effects remained minor compared to effects from survey design (photographic or visual) and type of platform, and observer search patterns. For instance, mean availability varied from 0.33—0.38 among photographic surveys depending on sightings distribution across the study area, but exceeded 0.40 for all visual surveys. Availability also varied considerably depending on whether observers searched within 0-90° (0.42—0.60) or 170° (0.70—0.80). Simulation-based power analysis indicates a large benefit associated with conducting more than 1 or 2 survey reps, but a declining benefit of conducting &amp;gt; 5—10 survey reps. An increase in sample size from 2, to 5, and 10 reps decreased the CV from 30, to 19 and 13%, respectively, and increased the probability of the abundance estimate being within 15% of true abundance from 0.42, to 0.59 and 0.69 in species like beluga.

Fewer pests and more ecosystem service‐providing arthropods in shady African cocoa farms: Insights from a data integration study

Abstract Agricultural intensification is leading to conversion of cocoa agroforestry towards monocultures across the tropics. In the context of cocoa agriculture, arthropods provide a range of ecosystem services and dis‐services. Arthropod pests (e.g., mirids and mealybugs) can cause major damage to crops, whilst pollinators and natural enemies (e.g., predatory insects and parasitoids) have the potential to enhance agricultural yields. Understanding how intensification of cocoa farming affects different arthropod groups is therefore important in maximising the abundance of beneficial arthropod taxa and reducing pest burdens. However, little is known about the influences of agricultural intensification on tropical arthropod communities, especially in Africa, where ~70% of the world's cocoa is produced. Most research on arthropod communities considers data from different sampling methods separately, as proxies of abundance; whilst these proxies can be informative, estimating true abundance enables direct comparison between arthropod taxa, and therefore the study of community dynamics. Here, we develop a Bayesian hierarchical model that integrates data from three common arthropod survey techniques to estimate population size of arthropod orders and to investigate how arthropod community composition responds to farm shade cover (an indicator of management intensity). Our results show that eight of 11 arthropod taxa responded to farm shade cover; importantly, brown capsids (the primary pest of cocoa in Africa), Coleoptera pests and Hemiptera pests decreased with increasing farm shade cover, whilst Araneae (natural enemies) and Diptera (potential pollinators) were more abundant in shady farms. Synthesis and applications. To achieve lower pest burdens and higher abundances of potential pollinators and natural enemies, African cocoa farms should maintain a dense canopy of shade trees. The current shift towards high‐intensity cocoa farming in Africa could result in long‐term losses due to pest infestations and loss of arthropod‐mediated ecosystem services.

Misleading overestimation bias in methods to estimate wolf abundance that use spatial models.

Abstract 1. Population abundance is the main criterion used by agencies to manage and conserve species and it allows adaptive decision-making in response to impacts. Its estimation is particularly important for large mammals, especially carnivores that are notoriously difficult to monitor yet have high ecological and economic importance to humans. Reversal of their historic population decline is also vital to restoring ecosystem health through the ecosystem service of trophic interactions. 2. Because bias and precision (variance) are the independent yardsticks of the quality and reliability of population estimates, we preliminarily assessed new abundance estimators for wolves that non-traditionally use spatial models to estimate the area that an observation represents. Among many biases and problems identified by recent assessments, we identified suspected biases due to obvious violation of the closure assumption in occupancy modeling used to determine the area occupied by territorial pack members. Thus, we chose to simulate the effect of spatial resolution (grid size) on the direction and magnitude of that inherent bias in a recent method called iPOM used in Montana. We also examined the potential bias in their estimate of variance and confidence intervals. 3. We found that even the use of small grid cells (relative to wolf territory size), biased the total area occupied and the number of packs used to calculate abundance. The bias rapidly increases with increasing grid cell size. At the grid cell size used in iPOM for Montana (600 km 2 ) there was a severe overestimation bias of 150% that proliferated through the iPOM submodel structure and resulted in estimated wolf abundance 2.5 times larger than true abundance. 4. This bias alone when combined with a misapplication and underreporting of iPOM's estimate of variance (biased low) results in a precariously misleading situation for decision-makers that threatens wolf populations. Other identified biases that inflate abundance likely make this situation worse but they should be further examined and tested. 5. Due to these biases and the high sensitivity of iPOM's spatial models to estimate area, we suggest that such spatial models should not be used in population estimation methods or such methods, iPOM for example, should be improved and/or restructured with submodels robust to assumption violation thereby reducing bias. Given iPOM's current design, there is no ability to detect change let alone determine wolf population size. 6. We provide a comparative framework for testing and improvement and strongly suggest proper model-assisted sampling designs and hierarchical modeling such as is used in capture-recapture models, especially those that use non-invasive procedures that avoid costly capture and marking. We also recommend collaborative activities that lead to using the best available scientific methods to determine carnivore abundance.

Space-time multi-level modeling for zooplankton abundance employing double data fusion and calibration

An important objective for marine biologists is to forecast the distribution and abundance of planktivorous marine predators. To do so, it is critically important to understand the spatiotemporal dynamics of their prey. Here, the prey we study are zooplankton and we build a novel space-time hierarchical fusion model to describe the distribution and abundance of zooplankton species in Cape Cod Bay (CCB), MA, USA. The data were collected irregularly in space and time at sites within the first half of the year over a 17 year period, using two different sampling methods. We focus on sea surface zooplankton abundance and incorporate sea surface temperature as a primary driver, also collected with two different sampling methods. So, with two sources for each, we observe true abundance or true sea surface temperature with measurement error. To account for such error, we apply calibrations to align the data sources and use the fusion model to develop a prediction of daily spatial zooplankton abundance surfaces throughout CCB. To infer average abundance on a given day within a given year in CCB, we present a marginalization of the zooplankton abundance surface. We extend the inference to consider abundance averaged to a bi-weekly or annual scale as well as to make an annual comparison of abundance.

Monitoring wildlife population trends with sample counts: a case study on the Alpine ibex (Capra ibex)

Monitoring population dynamics is of fundamental importance in conservation but assessing trends in abundance can be costly, especially in large and rough areas. Obtaining trend estimations from counts performed in only a portion of the total area (sample counts) can be a cost‐effective method to improve the monitoring and conservation of species difficult to count.We tested the effectiveness of sample counts in monitoring population trends of wild animals, using as a model population the Alpine ibex Capra ibex in the Gran Paradiso National Park (Italy), both with computer simulations and using historical count data collected over the last 65 years. Despite sample counts failed to correctly estimate the true population abundance, sampling half of the target area could reliably monitor the trend of the target population. In case of strong changes in abundance, an even lower proportion of the total area could be sufficient to identify the direction of the population trend. However, when there is a high yearly trend variability, the required number of samples increases and even counting in the entire area can be ineffective to detect population trends. The effect of other parameters, such as which portion of the area is sampled and detectability, was lower, but these should be tested case by case.Sample counts could therefore constitute a viable alternative to assess population trends, allowing for important, cost‐effective improvements in the monitoring of wild animals of conservation interest.

Cross-study analyses of microbial abundance using generalized common factor methods

BackgroundBy creating networks of biochemical pathways, communities of micro-organisms are able to modulate the properties of their environment and even the metabolic processes within their hosts. Next-generation high-throughput sequencing has led to a new frontier in microbial ecology, promising the ability to leverage the microbiome to make crucial advancements in the environmental and biomedical sciences. However, this is challenging, as genomic data are high-dimensional, sparse, and noisy. Much of this noise reflects the exact conditions under which sequencing took place, and is so significant that it limits consensus-based validation of study results.ResultsWe propose an ensemble approach for cross-study exploratory analyses of microbial abundance data in which we first estimate the variance-covariance matrix of the underlying abundances from each dataset on the log scale assuming Poisson sampling, and subsequently model these covariances jointly so as to find a shared low-dimensional subspace of the feature space.ConclusionsBy viewing the projection of the latent true abundances onto this common structure, the variation is pared down to that which is shared among all datasets, and is likely to reflect more generalizable biological signal than can be inferred from individual datasets. We investigate several ways of achieving this, demonstrate that they work well on simulated and real metagenomic data in terms of signal retention and interpretability, and recommend a particular implementation.

Implications of porpoise echolocation and dive behaviour on passive acoustic monitoring.

Harbour porpoises are visually inconspicuous but highly soniferous echolocating marine predators that are regularly studied using passive acoustic monitoring (PAM). PAM can provide quality data on animal abundance, human impact, habitat use, and behaviour. The probability of detecting porpoise clicks within a given area (P̂) is a key metric when interpreting PAM data. Estimates of P̂ can be used to determine the number of clicks per porpoise encounter that may have been missed on a PAM device, which, in turn, allows for the calculation of abundance and ideally non-biased comparison of acoustic data between habitats and time periods. However, P̂ is influenced by several factors, including the behaviour of the vocalising animal. Here, the common implicit assumption that changes in animal behaviour have a negligible effect on P̂ between different monitoring stations or across time is tested. Using a simulation-based approach informed by acoustic biologging data from 22 tagged harbour porpoises, it is demonstrated that porpoise behavioural states can have significant (up to 3× difference) effects on P̂. Consequently, the behavioural state of the animals must be considered in analysis of animal abundance to avoid substantial over- or underestimation of the true abundance, habitat use, or effects of human disturbance.

Quantitative Amplicon Sequencing Is Necessary to Identify Differential Taxa and Correlated Taxa Where Population Sizes Differ.

High-throughput, multiplexed-amplicon sequencing has become a core tool for understanding environmental microbiomes. As researchers have widely adopted sequencing, many open-source analysis pipelines have been developed to compare microbiomes using compositional analysis frameworks. However, there is increasing evidence that compositional analyses do not provide the information necessary to accurately interpret many community assembly processes. This is especially true when there are large gradients that drive distinct community assembly processes. Recently, sequencing has been combined with Q-PCR (among other sources of total quantitation) to generate "Quantitative Sequencing" (QSeq) data. QSeq more accurately estimates the true abundance of taxa, is a more reliable basis for inferring correlation, and, ultimately, can be more reliably related to environmental data to infer community assembly processes. In this paper, we use a combination of published data sets, synthesis, and empirical modeling to offer guidance for which contexts QSeq is advantageous. As little as 5% variation in total abundance among experimental groups resulted in more accurate inference by QSeq than compositional methods. Compositional methods for differential abundance and correlation unreliably detected patterns in abundance and covariance when there was greater than 20% variation in total abundance among experimental groups. Whether QSeq performs better for beta diversity analysis depends on the question being asked, and the analytic strategy (e.g., what distance metric is being used); for many questions and methods, QSeq and compositional analysis are equivalent for beta diversity analysis. QSeq is especially useful for taxon-specific analysis; QSeq transformation and analysis should be the default for answering taxon-specific questions of amplicon sequence data. Publicly available bioinformatics pipelines should incorporate support for QSeq transformation and analysis.

A review of N‐mixture models

AbstractN‐mixture models were born in 2004 of the necessity to model animal population size from point counts with imperfect detection of individuals, where capture‐recapture methods are infeasible. Initially developed for applications where population size was assumed constant, N‐mixture models were extended in 2011 to include population dynamics, allowing application to populations whose size fluctuates during the study. A further extension in 2014 accommodates populations with multiple “states” such as age class or sex. More recent extensions model spatial movement of animals among habitat patches or the spatial spread of infectious disease in a human population. The core idea underlying this class of models is a hierarchical structure, where the observation model is defined conditional on the model for true abundance. This hierarchy allows researchers to incorporate information about observation and abundance processes, while permitting distinct inferences about elements affecting detection and those affecting abundance. Another benefit of the hierarchical approach is the ability to accommodate many existing sampling protocols such as removal sampling and distance sampling. One drawback to N‐mixture models is that since they estimate both abundance and detection from replicated but unmarked counts, model parameters may not be clearly identifiable. A second drawback is that when observed counts are large, calculating the N‐mixture likelihood is computationally infeasible. This difficulty motivated an approximate likelihood based on the normal approximation to the binomial. The normal approximation provides a diagnostic of parameter estimability based on the closed‐form expression of the Fisher information matrix for a multivariate normal likelihood.This article is categorized under:Data: Types and Structure &gt; Image and Spatial DataStatistical Learning and Exploratory Methods of the Data Sciences &gt; Modeling Methods