Amplidiff: an optimized amplicon sequencing approach to estimating lineage abundances in viral metagenomes.

BACKGROUND: Metagenomic profiling algorithms commonly rely on genomic differences between lineages, strains, or species to infer the relative abundances of sequences present in a sample. This observation plays an important role in the analysis of diverse microbial communities, where targeted sequencing of 16S and 18S rRNA, both well-known hypervariable genomic regions, have led to insights into microbial diversity and the discovery of novel organisms. However, the variable nature of discriminatory regions can also act as a double-edged sword, as the sought-after variability can make it difficult to design primers for their amplification through PCR. Moreover, the most variable regions are not necessarily the most informative regions for the purpose of differentiation; one should focus on regions that maximize the number of lineages that can be distinguished. RESULTS: Here we present AmpliDiff, a computational tool that simultaneously finds highly discriminatory genomic regions in viral genomes of a single species, as well as primers allowing for the amplification of these regions. We show that regions and primers found by AmpliDiff can be used to accurately estimate relative abundances of SARS-CoV-2 lineages, for example in wastewater sequencing data. We obtain errors that are comparable with using whole genome information to estimate relative abundances. Furthermore, our results show that AmpliDiff is robust against incomplete input data and that primers designed by AmpliDiff also bind to genomes sampled months after the primers were selected. CONCLUSIONS: With AmpliDiff we provide an effective, cost-efficient alternative to whole genome sequencing for estimating lineage abundances in viral metagenomes.

Finite mixtures in capture-recapture surveys for modeling residency patterns in marine wildlife populations.

This work aims to show how prior knowledge about the structure of a heterogeneous animal population can be leveraged to improve the abundance estimation from capture-recapture survey data. We combine the Open Jolly-Seber model with finite mixtures and propose a parsimonious specification tailored to the residency patterns of the common bottlenose dolphin. We employ a Bayesian framework for our inference, discussing the appropriate choice of priors to mitigate label-switching and nonidentifiability issues, commonly associated with finite mixture models. We conduct a series of simulation experiments to illustrate the competitive advantage of our proposal over less specific alternatives. The proposed approach is applied to data collected on the common bottlenose dolphin population inhabiting the Tiber River estuary (Mediterranean Sea). Our results provide novel insights into this population's size and structure, shedding light on some of the ecological processes governing its dynamics.

IS-Seq: a bioinformatics pipeline for integration sites analysis with comprehensive abundance quantification methods.

BACKGROUND: Integration site (IS) analysis is a fundamental analytical platform for evaluating the safety and efficacy of viral vector based preclinical and clinical Gene Therapy (GT). A handful of groups have developed standardized bioinformatics pipelines to process IS sequencing data, to generate reports, and/or to perform comparative studies across different GT trials. Keeping up with the technological advances in the field of IS analysis, different computational pipelines have been published over the past decade. These pipelines focus on identifying IS from single-read sequencing or paired-end sequencing data either using read-based or using sonication fragment-based methods, but there is a lack of a bioinformatics tool that automatically includes unique molecular identifiers (UMI) for IS abundance estimations and allows comparing multiple quantification methods in one integrated pipeline. RESULTS: Here we present IS-Seq a bioinformatics pipeline that can process data from paired-end sequencing of both old restriction sites-based IS collection methods and new sonication-based IS retrieval systems while allowing the selection of different abundance estimation methods, including read-based, Fragment-based and UMI-based systems. CONCLUSIONS: We validated the performance of IS-Seq by testing it against the most popular  analytical workflow available in the literature (INSPIIRED) and using different scenarios. Lastly, by performing extensive simulation studies and a comprehensive wet-lab assessment of our IS-Seq pipeline we could show that in clinically relevant scenarios, UMI quantification provides better accuracy than the currently most widely used sonication fragment counts as a method for IS abundance estimation.

Analysis of Hyperspectral Data to Develop an Approach for Document Images.

Hyperspectral data analysis is being utilized as an effective and compelling tool for image processing, providing unprecedented levels of information and insights for various applications. In this manuscript, we have compiled and presented a comprehensive overview of recent advances in hyperspectral data analysis that can provide assistance for the development of customized techniques for hyperspectral document images. We review the fundamental concepts of hyperspectral imaging, discuss various techniques for data acquisition, and examine state-of-the-art approaches to the preprocessing, feature extraction, and classification of hyperspectral data by taking into consideration the complexities of document images. We also explore the possibility of utilizing hyperspectral imaging for addressing critical challenges in document analysis, including document forgery, ink age estimation, and text extraction from degraded or damaged documents. Finally, we discuss the current limitations of hyperspectral imaging and identify future research directions in this rapidly evolving field. Our review provides a valuable resource for researchers and practitioners working on document image processing and highlights the potential of hyperspectral imaging for addressing complex challenges in this domain.

Pooling of intra-site measurements inflates variability of the correlation between environmental DNA concentration and organism abundance.

Environmental DNA (eDNA) analysis can promote efficient ecosystem monitoring and resource management. However, limited knowledge of the factors affecting the relationship between eDNA concentration and organism abundance causes uncertainty in relative abundance estimates based on eDNA concentration. Pooling of data points obtained from multiple locations within a site has been used to mitigate intra-site variation in eDNA and abundance estimates, but decreases the sample size used for estimating the relationship. I here assessed how the pooling of intra-site measurements of eDNA concentration and organism abundance impacted the reliability of the correlative relationship between eDNA concentration and organism abundance. Mathematical models were developed to simulate measurements of eDNA concentrations and organism abundances from multiple locations in a given survey site, and the CVs (coefficient of variability) of the correlations were compared depending on whether data points from different locations were individually treated or pooled. Although the mean and median values of the correlation coefficients were similar between the scenarios, the CVs of the simulated correlations were substantially higher under the pooled scenario than the individual scenario. Additionally, I re-analyzed two empirical studies conducted in lakes, both showing higher CVs of the correlations by pooling intra-site measurements. This study suggests that it would make eDNA-based abundance estimation more reliable and reproducible to individually analyze target eDNA concentrations and organism abundance estimates.

Can nuclear aquatic environmental DNA be a genetic marker for the accurate estimation of species abundance?

Environmental DNA (eDNA) analysis is a promising tool for the sensitive and effective monitoring of species distribution and abundance. Traditional eDNA analysis has targeted mitochondrial DNA (mtDNA) fragments due to their abundance in cells; however, the quantification may vary depending on cell type and physiology. Conversely, some recent eDNA studies have targeted multi-copy nuclear DNA (nuDNA) fragments, such as ribosomal RNA genes, in water, and reported a higher detectability and more rapid degradation than mitochondrial eDNA (mt-eDNA). These properties suggest that nuclear eDNA (nu-eDNA) may be useful for the accurate estimation of species abundance relative to mt-eDNA, but which remains unclear. In this study, we compiled previous studies and re-analyzed the relationships between mt- and nu-eDNA concentration and species abundance by comparing the R2 values of the linear regression. We then performed an aquarium experiment using zebrafish (Danio rerio) to compare the relationships across genetic regions, including single-copy nuDNA. We found more accurate relationships between multi-copy nu-eDNA and species abundance than mt-eDNA in these datasets, although the difference was not significant upon weighted-averaging the R2 values. Moreover, we compared the decay rate constants of zebrafish eDNA across genetic regions and found that multi-copy nu-eDNA degraded faster than mt-eDNA under pH 7, implying a quick turnover of multi-copy nu-eDNA in the field. Although further empirical studies of nu-eDNA applications are necessary to support our findings, this study provides the groundwork for improving the estimation accuracy of species abundance via eDNA analysis.

Estimating fish population abundance by integrating quantitative data on environmental DNA and hydrodynamic modelling.

Molecular analysis of DNA left in the environment, known as environmental DNA (eDNA), has proven to be a powerful and cost-effective approach to infer occurrence of species. Nonetheless, relating measurements of eDNA concentration to population abundance remains difficult because detailed knowledge on the processes that govern spatial and temporal distribution of eDNA should be integrated to reconstruct the underlying distribution and abundance of a target species. In this study, we propose a general framework of abundance estimation for aquatic systems on the basis of spatially replicated measurements of eDNA. The proposed method explicitly accounts for production, transport and degradation of eDNA by utilizing numerical hydrodynamic models that can simulate the distribution of eDNA concentrations within an aquatic area. It turns out that, under certain assumptions, population abundance can be estimated via a Bayesian inference of a generalized linear model. Application to a Japanese jack mackerel (Trachurus japonicus) population in Maizuru Bay revealed that the proposed method gives an estimate of population abundance comparable to that of a quantitative echo sounder method. Furthermore, the method successfully identified a source of exogenous input of eDNA (a fish market), which may render a quantitative application of eDNA difficult to interpret unless its effect is taken into account. These findings indicate the ability of eDNA to reliably reflect population abundance of aquatic macroorganisms; when the "ecology of eDNA" is adequately accounted for, population abundance can be quantified on the basis of measurements of eDNA concentration.

Comparison of Aerial Thermal Infrared Imagery and Helicopter Surveys of Bison (Bison bison) in Grand Canyon National Park, USA.

Aerial thermal infrared (TIR) surveys are an attractive option for estimating abundances of large mammals inhabiting extensive and heterogeneous terrain. Compared to standard helicopter or fixed-wing aerial surveys, TIR flights can be conducted at higher altitudes translating into greater spatial coverage and increased observer safety; however, monetary costs are much greater. Further, there is no consensus on whether TIR surveys offer improved detection. Consequently, we performed a study to compare results of a TIR and helicopter survey of bison (Bison bison) on the Powell Plateau in Grand Canyon National Park, USA. We also compared results of both surveys to estimates obtained using a larger dataset of bison helicopter detections along the entire North Rim of the Grand Canyon. Observers in the TIR survey counted fewer individual bison than helicopter observers (101 to 127) and the TIR survey cost was 367% higher. Additionally, the TIR estimate was 18.8% lower than the estimate obtained using a larger dataset, while the comparative helicopter survey was 9.3% lower. Despite our small sample size, we found that helicopter surveys are currently the best method for estimating bison abundances in dense canopy cover sites due to ostensibly more accurate estimates and lower cost compared to TIR surveys. Additional research will be needed to evaluate the efficacy of these methods, as well as very high resolution satellite imagery, for bison populations in more open landscapes.

An estimate of the number of white sharks Carcharodon carcharias interacting with ecotourism in Guadalupe Island.

The aim of the present study is to provide an estimate of the number of white sharks Carcharodon carcharias that seasonally interact with ecotourism boats in Guadalupe Island using Schnabel's mark-recapture method and 6316 records of white sharks during 2012-2014. The results of the estimation highlight an abundance of 78 white sharks 95% C.I. (62.1, 105.6) interacting with ecotourism. The regulations regarding the number of tourists, boats and the monitoring of white sharks should be assessed to improve management decisions regarding the conservation and sustainable use of this threatened species.

Differential analysis of combinatorial protein complexes with CompleXChange.

BACKGROUND: Although a considerable number of proteins operate as multiprotein complexes and not on their own, organism-wide studies so far are only able to quantify individual proteins or protein-coding genes in a condition-specific manner for a sizeable number of samples, but not their assemblies. Consequently, there exist large amounts of transcriptomic data and an increasing amount of data on proteome abundance, but quantitative knowledge on complexomes is missing. This deficiency impedes the applicability of the powerful tool of differential analysis in the realm of macromolecular complexes. Here, we present a pipeline for differential analysis of protein complexes based on predicted or manually assigned complexes and inferred complex abundances, which can be easily applied on a whole-genome scale. RESULTS: We observed for simulated data that results obtained by our complex abundance estimation algorithm were in better agreement with the ground truth and physicochemically more reasonable compared to previous efforts that used linear programming while running in a fraction of the time. The practical usability of the method was assessed in the context of transcription factor complexes in human monocyte and lymphoblastoid samples. We demonstrated that our new method is robust against false-positive detection and reports deregulated complexomes that can only be partially explained by differential analysis of individual protein-coding genes. Furthermore we showed that deregulated complexes identified by the tool potentially harbor significant yet unused information content. CONCLUSIONS: CompleXChange allows to analyze deregulation of the protein complexome on a whole-genome scale by integrating a plethora of input data that is already available. A platform-independent Java binary, a user guide with example data and the source code are freely available at https://sourceforge.net/projects/complexchange/ .

MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples.

BACKGROUND: High throughput sequencing has spurred the development of metagenomics, which involves the direct analysis of microbial communities in various environments such as soil, ocean water, and the human body. Many existing methods based on marker genes or k-mers have limited sensitivity or are too computationally demanding for many users. Additionally, most work in metagenomics has focused on bacteria and archaea, neglecting to study other key microbes such as viruses and eukaryotes. RESULTS: Here we present a method, MiCoP (Microbiome Community Profiling), that uses fast-mapping of reads to build a comprehensive reference database of full genomes from viruses and eukaryotes to achieve maximum read usage and enable the analysis of the virome and eukaryome in each sample. We demonstrate that mapping of metagenomic reads is feasible for the smaller viral and eukaryotic reference databases. We show that our method is accurate on simulated and mock community data and identifies many more viral and fungal species than previously-reported results on real data from the Human Microbiome Project. CONCLUSIONS: MiCoP is a mapping-based method that proves more effective than existing methods at abundance profiling of viruses and eukaryotes in metagenomic samples. MiCoP can be used to detect the full diversity of these communities. The code, data, and documentation are publicly available on GitHub at: https://github.com/smangul1/MiCoP .

On the robustness of N-mixture models.

N-mixture models provide an appealing alternative to mark-recapture models, in that they allow for estimation of detection probability and population size from count data, without requiring that individual animals be identified. There is, however, a cost to using the N-mixture models: inference is very sensitive to the model's assumptions. We consider the effects of three violations of assumptions that might reasonably be expected in practice: double counting, unmodeled variation in population size over time, and unmodeled variation in detection probability over time. These three examples show that small violations of assumptions can lead to large biases in estimation. The violations of assumptions we consider are not only small qualitatively, but are also small in the sense that they are unlikely to be detected using goodness-of-fit tests. In cases where reliable estimates of population size are needed, we encourage investigators to allocate resources to acquiring additional data, such as recaptures of marked individuals, for estimation of detection probabilities.

Google Haul Out: Earth Observation Imagery and Digital Aerial Surveys in Coastal Wildlife Management and Abundance Estimation.

As the sampling frequency and resolution of Earth observation imagery increase, there are growing opportunities for novel applications in population monitoring. New methods are required to apply established analytical approaches to data collected from new observation platforms (e.g., satellites and unmanned aerial vehicles). Here, we present a method that estimates regional seasonal abundances for an understudied and growing population of gray seals (Halichoerus grypus) in southeastern Massachusetts, using opportunistic observations in Google Earth imagery. Abundance estimates are derived from digital aerial survey counts by adapting established correction-based analyses with telemetry behavioral observation to quantify survey biases. The result is a first regional understanding of gray seal abundance in the northeast US through opportunistic Earth observation imagery and repurposed animal telemetry data. As species observation data from Earth observation imagery become more ubiquitous, such methods provide a robust, adaptable, and cost-effective solution to monitoring animal colonies and understanding species abundances.

Dynamics of a low-density tiger population in Southeast Asia in the context of improved law enforcement.

Recovering small populations of threatened species is an important global conservation strategy. Monitoring the anticipated recovery, however, often relies on uncertain abundance indices rather than on rigorous demographic estimates. To counter the severe threat from poaching of wild tigers (Panthera tigris), the Government of Thailand established an intensive patrolling system in 2005 to protect and recover its largest source population in Huai Kha Khaeng Wildlife Sanctuary. Concurrently, we assessed the dynamics of this tiger population over the next 8 years with rigorous photographic capture-recapture methods. From 2006 to 2012, we sampled across 624-1026 km(2) with 137-200 camera traps. Cameras deployed for 21,359 trap days yielded photographic records of 90 distinct individuals. We used closed model Bayesian spatial capture-recapture methods to estimate tiger abundances annually. Abundance estimates were integrated with likelihood-based open model analyses to estimate rates of annual and overall rates of survival, recruitment, and changes in abundance. Estimates of demographic parameters fluctuated widely: annual density ranged from 1.25 to 2.01 tigers/100 km(2) , abundance from 35 to 58 tigers, survival from 79.6% to 95.5%, and annual recruitment from 0 to 25 tigers. The number of distinct individuals photographed demonstrates the value of photographic capture-recapture methods for assessments of population dynamics in rare and elusive species that are identifiable from natural markings. Possibly because of poaching pressure, overall tiger densities at Huai Kha Khaeng were 82-90% lower than in ecologically comparable sites in India. However, intensified patrolling after 2006 appeared to reduce poaching and was correlated with marginal improvement in tiger survival and recruitment. Our results suggest that population recovery of low-density tiger populations may be slower than anticipated by current global strategies aimed at doubling the number of wild tigers in a decade.

Double-observer line transect surveys with Markov-modulated Poisson process models for animal availability.

We develop maximum likelihood methods for line transect surveys in which animals go undetected at distance zero, either because they are stochastically unavailable while within view or because they are missed when they are available. These incorporate a Markov-modulated Poisson process model for animal availability, allowing more clustered availability events than is possible with Poisson availability models. They include a mark-recapture component arising from the independent-observer survey, leading to more accurate estimation of detection probability given availability. We develop models for situations in which (a) multiple detections of the same individual are possible and (b) some or all of the availability process parameters are estimated from the line transect survey itself, rather than from independent data. We investigate estimator performance by simulation, and compare the multiple-detection estimators with estimators that use only initial detections of individuals, and with a single-observer estimator. Simultaneous estimation of detection function parameters and availability model parameters is shown to be feasible from the line transect survey alone with multiple detections and double-observer data but not with single-observer data. Recording multiple detections of individuals improves estimator precision substantially when estimating the availability model parameters from survey data, and we recommend that these data be gathered. We apply the methods to estimate detection probability from a double-observer survey of North Atlantic minke whales, and find that double-observer data greatly improve estimator precision here too.

Computational aspects of N-mixture models.

The N-mixture model is widely used to estimate the abundance of a population in the presence of unknown detection probability from only a set of counts subject to spatial and temporal replication (Royle, 2004, Biometrics 60, 105-115). We explain and exploit the equivalence of N-mixture and multivariate Poisson and negative-binomial models, which provides powerful new approaches for fitting these models. We show that particularly when detection probability and the number of sampling occasions are small, infinite estimates of abundance can arise. We propose a sample covariance as a diagnostic for this event, and demonstrate its good performance in the Poisson case. Infinite estimates may be missed in practice, due to numerical optimization procedures terminating at arbitrarily large values. It is shown that the use of a bound, K, for an infinite summation in the N-mixture likelihood can result in underestimation of abundance, so that default values of K in computer packages should be avoided. Instead we propose a simple automatic way to choose K. The methods are illustrated by analysis of data on Hermann's tortoise Testudo hermanni.

Impact of reference design on estimating SARS-CoV-2 lineage abundances from wastewater sequencing data.

BACKGROUND: Sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA from wastewater samples has emerged as a valuable tool for detecting the presence and relative abundances of SARS-CoV-2 variants in a community. By analyzing the viral genetic material present in wastewater, researchers and public health authorities can gain early insights into the spread of virus lineages and emerging mutations. Constructing reference datasets from known SARS-CoV-2 lineages and their mutation profiles has become state-of-the-art for assigning viral lineages and their relative abundances from wastewater sequencing data. However, selecting reference sequences or mutations directly affects the predictive power. RESULTS: Here, we show the impact of a mutation- and sequence-based reference reconstruction for SARS-CoV-2 abundance estimation. We benchmark 3 datasets: (i) synthetic "spike-in"' mixtures; (ii) German wastewater samples from early 2021, mainly comprising Alpha; and (iii) samples obtained from wastewater at an international airport in Germany from the end of 2021, including first signals of Omicron. The 2 approaches differ in sublineage detection, with the marker mutation-based method, in particular, being challenged by the increasing number of mutations and lineages. However, the estimations of both approaches depend on selecting representative references and optimized parameter settings. By performing parameter escalation experiments, we demonstrate the effects of reference size and alternative allele frequency cutoffs for abundance estimation. We show how different parameter settings can lead to different results for our test datasets and illustrate the effects of virus lineage composition of wastewater samples and references. CONCLUSIONS: Our study highlights current computational challenges, focusing on the general reference design, which directly impacts abundance allocations. We illustrate advantages and disadvantages that may be relevant for further developments in the wastewater community and in the context of defining robust quality metrics.

Ecology and abundance of a relict population of the bush cricket Saga pedo in the Northern Apennines, Italy.

The expansion of forest cover and intensification of agriculture represent the main threats to the bush cricket Saga pedo, currently listed as Vulnerable globally by the IUCN and included in Annex IV of the European Union Habitats Directive. Gathering information on its ecology and population size is challenging due to its low abundance and localized distribution. Additionally, the elusive and cryptic behavior of this species reduces the likelihood of its detection, potentially resulting in population underestimations. Thus, in this study, we aimed to (1) estimate S. pedo population size in relation to environmental variables and prey availability and (2) predict abundance of S. pedo in our study area for future monitoring in nearby territories. We found that the population of S. pedo in our study area consists of 197 (±115) individuals with a detection probability of 21.01% (±11.09). Detection probability of S. pedo further decreases on windy days. Moreover, we found that the investigated population of S. pedo occupies suboptimal areas, as highlighted not only by the predicted abundances but also by the association between S. pedo and other subfamilies of orthoptera that are ecologically very distant from our target species and mostly linked to mesophilic biotopes. Most of the individuals we observed are concentrated in small clearings completely within wooded matrices and therefore isolated from each other. Based on our results, it is possible that forest expansion toward open meadows represents the main threat to this population, transforming the clearings and xeric meadows (to which S. pedo is linked) into small and fragmented patches that are suboptimal and insufficient to host viable populations.

Endmember extraction and abundance estimation algorithm based on double-compressed sampling.

Based on double-compressed sampling, a hyperspectral spectral unmixing algorithm (SU_DCS) is proposed, which could directly complete the endmember extraction and abundance estimation. On the basis of the linear mixed model (LMM), we designed spatial and spectral sampling matrices, obtained spatial and spectral measurement data, and constructed a joint unmixing model containing endmember and abundance information. By using operator separation and Lagrangian multiplier algorithm, the endmember matrix, abundance matrix and remixing image can be quickly obtained by matrix operation. The parameters of the unmixing algorithm, including regularization parameter, convergence threshold and spatial sampling rate, are determined using synthetic simulated hyperspectral data. The proposed algorithm is applied to two kinds of real hyperspectral data, with or without ground truth, in order to verify the effectiveness and reliability of the algorithm. Firstly, we provide the performance of the algorithm on real datasets without ground truth. Compared with algorithm VCA_FCLS and algorithm CPPCA_VCA_FCLS, the endmember spectral curve extracted by the proposed SU_DCS is almost consistent with that obtained by VCA_FCLS, and is more smooth than that of obtained by CPPCA_VCA_FCLS. Additionally, the abundance estimation map estimated by the SU_DCS has consistency with the results obtained by VCA_FCLS. Moreover, the proposed SU_DCS has higher peak signal-to-noise ratio (PSNR) for remixing images with higher computational efficiency. Secondly, we provide the performance of the proposed algorithm on four real datasets with ground truth, including dataset Cuprite, dataset Samson, dataset Jasper and dataset Urban. We provide the results of endmember extraction and abundance estimation from the compressed data under different sampling rate conditions. The extracted endmember maintains good consistency with the true spectral curves, and the estimated abundance map can also maintain good spatial consistency with the ground truth. The comparison results with other four comparative algorithms also indicate that the proposed algorithm can obtain relatively accurate endmembers and abundance information from compressed data, the reliability and validity of the proposed algorithm have been proved. In summary, the main innovation of the proposed algorithm is that it can extract endmembers and estimate abundance with high accuracy from a small amount of measurement data.

Age is not just a number: How incorrect ageing impacts close-kin mark-recapture estimates of population size.

Population size is a key parameter for the conservation of animal species. Close-kin mark-recapture (CKMR) relies on the observed frequency and type of kinship among individuals sampled from the population to estimate population size. Knowledge of the age of the individuals, or a surrogate thereof, is essential for inference with acceptable precision. One common approach, particularly in fish studies, is to measure animal length and infer age using an assumed age-length relationship (a 'growth curve'). We used simulation to test the effect of misspecifying the length measurement error and the growth curve on population size estimation. Simulated populations represented two fictional shark species, one with a relatively simple life history and the other with a more complex life history based on the grey reef shark (Carcharhinus amblyrhynchos). We estimated sex-specific adult abundance, which we assumed to be constant in time. We observed small median biases in these estimates ranging from 1.35% to 2.79% when specifying the correct measurement error standard deviation and growth curve. CI coverage was adequate whenever the growth curve was correctly specified. Introducing error via misspecified growth curves resulted in changes in the magnitude of the estimated adult population, where underestimating age negatively biased the abundance estimates. Over- and underestimating the standard deviation of length measurement error did not introduce a bias and had negligible effect on the variance in the estimates. Our findings show that assuming an incorrect standard deviation of length measurement error has little effect on estimation, but having an accurate growth curve is crucial for CKMR whenever ageing is based on length measurements. If ageing could be biased, researchers should be cautious when interpreting CKMR results and consider the potential biases arising from inaccurate age inference.