Imperfect Detection Research Articles

How do ecologists estimate occupancy in practice?

Over 20 years ago, ecologists were introduced to the site occupancy model (SOM) for estimating occupancy rates from detection‐nondetection data. In the ensuing decades, the SOM and its hierarchical modeling extensions have become mainstays of quantitative ecology, and estimating occupancy rates has become one of the most common applications of ecological field data. Here, we review 364 peer‐reviewed articles published between 2019–2021 that estimated occupancy. We first document broad patterns in study design and statistical methods to provide educators, developers of methodology and software, and ecologists with a clear picture of the landscape of methodologies used to estimate animal occupancy. Second, we conduct a focused review of a subset of 98 papers that applied the hierarchical SOM, drawing from methodological literature to identify discrepancies between SOM applications and methodological best practices. We discuss limits to statistical power, issues with model checking and model selection procedures, potential problems arising from unmodeled non‐independence, and reproducibility. We highlight areas of rapid advancement in interpreting animal occupancy related to animal movement, imperfect detection, and the occupancy–density relationship. We aim to help readers understand the landscape of methods available, motivate shifts toward robust and reproducible science, and inspire new software and methodological research.

Mitigating unbalanced loss in entanglement assisted absorptive measurements with a phase-sensitive amplifier

Correlated interferometry offers various advantages in precision measurements, such as sub-shot noise sensitivity, high resolution, and prevention of detector saturation benefiting from dark output. In certain practical scenarios, e.g. absorptive measurement, there is usually unbalanced loss between the probe and reference beams, which destroys the correlation and remains a significant obstacle in the efficient application of entanglements. Here, we study the performance of a nonlinear interferometer whose arms experience unbalanced loss and demonstrate the use of a phase-sensitive amplifier in the sensing or reference arms to amplify or squeeze the light field, which can both reduce the degradation of quantum enhancement from unbalanced absorption in a lossy environment. We find that the loss after amplification, i.e. from imperfect interferometry and detection, can be mitigated when the phase-sensitive amplifier is placed at the sensing arm and functions as a noiseless amplifier. Our approach may be helpful to the practical application of quantum-enhanced absorptive measurement.

A novel connectivity index for microstructures imperfection detection and rectification in multiscale structure design

A novel connectivity index for microstructures imperfection detection and rectification in multiscale structure design

Factors Affecting Mammalian Occupancy and Species Richness in Annapurna Conservation Area, Nepal.

Species richness is an important metric used for undertaking conservation management decisions. However, species richness estimates are influenced by species detection probabilities, with potential to entirely overlook species during surveys. Occupancy models which account for imperfect detection provide unbiased estimates, ensuring accurate estimates of richness. We carried out a camera trap survey in the mountains of north-central Nepal during 2017 and documented a total of 21 mammal species. Here, we used multi-species occupancy models within a Bayesian hierarchical framework to reassess our initial species richness estimate and to understand the influence of environmental covariates on occupancy and species richness of mammals in the area. Our model estimated the mean species richness was ~26 species (95% CRI: 21-36 species), suggesting we might have missed ~5 species during the survey. The mean probability of occupancy and detection of mammal species were estimated to be and 0.02 (95% CRI:0.01-0.03) respectively. Mammalian species richness of the area had an anticipated positive relationship with tree canopy cover though its positive relationship with anthropogenic disturbance was surprising . Mammalian species richness had a quadratic relationship with elevation as is expected. This research contributes to baseline information of the mammal community ecology in north-central Nepal and supports the need for future multi-season surveys to understand the influence of temporal factors on mammalian community and species richness in the area.

What Determines the Probability of Discovering a Species? A Study of the Completeness of Bryophyte Inventories in Tianmushan National Nature Reserve (Zhejiang, China).

Knowing the total number of species in a region has been a question of great interest motivated by the need to provide a reference point for current and future losses of biodiversity. Unfortunately, obtaining an accurate number is constrained by the fact that most species remain to be discovered, due to the imperfect detection of species in the field collection or because of temporal turnover in species composition. Here, to understand the inventory completeness at the local scale, we studied the temporal dynamics in the species richness and composition of bryophytes in Tianmushan National Nature Reserve. We used β-diversity to measure the temporal variation and analyzed the species attributes of newly discovered species collected this time. Furthermore, we evaluated our sampling strategy to measure sampling effect and estimated the species that remain to be discovered. We found that total β-diversity was largely driven by turnover. The analysis of species attributes showed that epiphytic species dominate the newly recorded species in both the narrow and wide elevational range species. Further, if only one of the methods was adopted, 26%-29% of the newly discovered species would be missed, and we inferred that there are likely are 185 bryophytes yet to be discovered. Our results indicate that when the same effort was made, an appropriate sampling methodology is crucial to accelerate newly recorded species discovery. Further, the results of our study highlight that species temporal turnover should be considered when assessing the completeness of species inventories at the local scale.

Thinking beyond the closure assumption: Designing surveys for estimating biological truth with occupancy models

Abstract Occupancy models estimate distributions of imperfectly detected species, but violations of the closure assumption can bias results. However, researchers working with mobile animals may find it impossible to eliminate such violations. Here, we tested the hypothesis that occupancy models fit to realistic sampling data can generate unbiased occupancy estimates for an itinerant Wood Thrush (Hylocichla mustelina) population. In 2013 and 2014, we tracked movements of 41 breeding Wood Thrush males. We modelled territory shift probabilities using logistic exposure models and within‐territory movements using continuous‐time stochastic process models. We then constructed an individual‐based model, simulated (1000 iterations) spatiotemporal locations for individuals and simulated sampling these populations using 162 different point count protocols with variable spatial (sampling radius and point placement method), and temporal (survey length, between‐survey intervals and number of surveys) characteristics. We compared occupancy estimates with true values of instantaneous, daily and seasonal occupancy from the simulations. We parameterized continuous time stochastic process models based on movements within 34 unique territories and estimated a daily territory shift probability of 0.0099 (95% CI: 0.0060, 0.0152). Simulated data indicated that estimates of occupancy ranged from 0.18 (0.06, 1.00) to 0.80 (0.71, 0.89) depending on protocol characteristics. Occupancy estimates increased with increasing survey radius, survey length and between‐survey interval. Protocols using shorter surveys and between‐survey intervals were good estimators for instantaneous occupancy (low bias and mean‐squared error) but poor estimators for daily and seasonal occupancy; longer surveys and intervals generated unbiased estimators of daily occupancy but underestimated seasonal occupancy. Logistic regression models that ignored imperfect detection outperformed occupancy models for estimating instantaneous occupancy but not daily or seasonal occupancy. For mobile animals, occupancy of sampling sites changes in space and time. Consequently, the spatial and temporal aspects of a sampling protocol have strong, but predictable, effects on occupancy model parameter estimates. Our results demonstrate that how these factors interact is critical for designing surveys that produce occupancy estimates representative of the biological process of interest to a researcher.

Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery

Abstract Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High‐resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status. We present a model that accounts for false negative (missed plants) and false positive (misclassified or double‐counted plants) error in counts from high‐resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post‐wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating >16 ha of UAS imagery with >700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance. Raw counts of plant abundance in high‐resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting (p) adult plants (0.25 m tall) varied between sites within 0.52 < < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < < 0.3. On average, we estimate that 19% of all detected plants were false positive errors, which varied spatially in relation to topographic predictors. Abundance declined toward the interior of previous wildfires and was positively associated with terrain roughness. Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. We believe such models will further improve inference on plant population dynamics—relevant to restoration, wildlife habitat and related objectives—and echo previous calls for remote sensing applications to better differentiate between ecological and observational processes.

Improving Inference Within Freshwater Community Studies: Accounting for Variable Detection Rates of Amphibians and Fish.

Research into freshwater communities often aims to link patterns of species distribution in ponds with underlying biotic factors. However, errors with species detection (e.g. false negatives) may underestimate distribution and bias assessments of community structure. Occupancy models that account for imperfect detection offer a solution to this problem. Here, we used three methods (call/visual encounter surveys, dip-netting and newt trapping) to survey amphibians and fish (potential amphibian predators) at 100 ponds in an urbanised landscape in Hungary over one breeding season. We estimated species detection probabilities for amphibians (all life stages combined) and fish using occupancy models to gain insight into amphibian-fish relationships and other survey-specific variables. We detected nine amphibian and 20 fish species. There were relatively low but variable estimated probabilities of detection for amphibians (mean: 0.320, 95% Bayesian credible interval: 0.142-0.598), with three species having detection rates < 0.1. Probabilities of detection peaked in the middle of the breeding season and increased with survey effort. Detection probabilities of five species were negatively associated with the detection of fish at a pond, while there were positive relationships between detection and emergent vegetation cover. We found no substantial differences in detection rates among the three survey methods. The probability of detecting fish was much higher than for amphibians (0.588, 0.503-0.717) but was lower at ponds with high emergent vegetation where amphibian detection was higher. Our results underscore the importance of accounting for the imperfect detection of both response organisms and potentially interacting species in aquatic community studies. We recommend applying multi-species occupancy models to enable inference for both common and rare species at ponds in landscapes subjected to human disturbances.

Spatial modelling and estimation of mammals' mortalities by Pantanal 2020 megafires

Abstract Extreme wildfire events, such as the Pantanal 2020 megafires, are expected to become more common. Assessing the impacts of such extreme events on wildlife is imperative for conservation planning. Direct observation of carcasses can provide valuable information on how these impacts relate to spatial heterogeneity. Here, we use double‐observer carcass surveys to assess the numbers and spatial patterns of direct mortalities of medium‐ to large‐sized mammals resulting from the Pantanal 2020 megafires in a large reserve (1080 km2) in the northern Pantanal wetland. Accounting for imperfect detection, we model the spatial variation in mortality occurrence and abundance, testing the effects of habitat‐related variables and wildfire severity using multi‐species N‐mixture models. We found that 26 out of 27 species of medium and large‐sized mammals died from the fires with a mean estimate of around 49 thousand individuals. The most affected species included capuchin monkeys, agoutis, peccaries, tapirs, brocket deer, tamanduas, coatis and capybaras. Direct mortality of mammals by wildfires was affected by landscape factors related to species habitats, species traits (probably related to escape or refuge strategies), and the intensity of the wildfires. Mortalities presented a general positive relationship with non‐flooded forests (a fire‐sensitive habitat in Pantanal) and with the severity of wildfires. Artificial water bodies, a common landscape structure in Pantanal, had 7.5 times more deaths than other areas. Synthesis and applications: With the approach used, we were able to: (i) identify mortality hotspots associated with landscape features; (ii) simultaneously identify the most affected species and assess the average relationships for all the assemblage of medium‐ and large‐sized mammals. We discuss conservation and management actions in two contexts: species prioritization for rescuing and monitoring; and territory prioritization for fire prevention and fighting.

Bringing circuit theory into spatial occupancy models to assess landscape connectivity

Abstract Occupancy models were originally developed to better understand species distribution while accounting for imperfect detection. Because species distribution is not only shaped by habitat quality but also by the ability of individuals to reach suitable habitats, spatial dynamic occupancy models have been proposed to extend the original framework by defining that site colonisation was a function of the Euclidean distance to occupied sites. However, not all sites in the landscape are equally accessible due to the presence of barriers, that of corridors, etc. To account for connectivity between sites, the Euclidean distance has recently been replaced by a least‐cost path distance, which explicitly accounts for landscape resistance, but assumes that individuals will follow the optimal route. To relax this assumption, we first developed a new spatial occupancy model that incorporates commute‐time distance derived from circuit theory to model accessibility across sites. This distance has the advantage of modelling movement as a random walk and accounting for the fact that colonisation could be achieved from multiple paths. Our approach allows for the explicit estimation of landscape connectivity from detection/non‐detection data and a direct measure of connectivity uncertainty. We implemented the model in the Bayesian framework using the nimble R package, which allows useful R connectivity functions to be called from within the model. Second, we carried out a simulation study to assess the performance of our model by considering four scenarios depicting an increasing level of landscape resistance. Third, to illustrate our new approach, we studied the recolonisation of two carnivores in France. We quantified the degree to which rivers facilitate Eurasian otter (Lutra lutra) colonisation and highways impede Eurasian lynx (Lynx lynx) colonisation. Overall, spatial occupancy models provide a flexible framework to accommodate any distance metric designed to align with species dispersal ecology.

Importance of accounting for imperfect detection of plants in the estimation of population growth rates

Detection of plant individuals is imperfect. Not accounting for this issue can result in biased estimates of demographic parameters as important as population growth rates. In mobile organisms, a common practice is to explicitly account for detection probability during the estimation of most demographic parameters, but no study in plant populations has examined the consequences of ignoring imperfect detectability on the estimation of population growth rates. The lack of accounting for detection probability occurs because plant demographers have frequently assumed that detection is perfect, and because there is a scarcity of studies that formally compare the performance of estimation methods that incorporate detection probabilities with respect to methods that ignore detectabilities. Based on field data of five plant species and data simulations, we compared the performance of three methods that estimate population growth rates, two that do not estimate detection probabilities (direct counts of individuals and the minimum‐number‐alive method) and the other that explicitly accounts for detection probabilities (temporal symmetry models). Our aims were 1) to estimate detection probabilities, and 2) to evaluate the performance of these three methods by calculating bias, accuracy, and precision in their estimates of population growth rates. Our five plant species had imperfect detection. Estimates of population growth rates that explicitly incorporate detectabilities had better performance (less biased estimates, with higher accuracy and precision) than those obtained with the two methods that do not calculate detection probabilities. In these latter methods, bias increases as detection probability decreases. Our findings highlight the importance of using robust analytical methods that account for detection probability of plants during the estimation of critical demographic parameters such as population growth rates. In this way, estimates of plant population parameters will reliably indicate their actual status and quantitative trends.

Spatio‐temporal occupancy models with INLA

Abstract Modern methods for quantifying, predicting and mapping species distributions have played a crucial part in biodiversity conservation. Occupancy models have become a popular choice for analysing species occurrence data due to their ability to separate out the observational error induced by imperfect detection of a species, and the sources of bias affecting the occupancy process. However, the spatial and temporal variation in occupancy that is not accounted for by environmental covariates is often ignored or modelled through simple spatial structures as the computational costs of fitting explicit spatio‐temporal models is too high. In this work, we demonstrate how Integrated Nested Laplace Approximation (INLA) may be used to fit complex spatio‐temporal occupancy models and how the R‐INLA package can provide a user‐friendly interface to make such complex models available to users. We show how occupancy models, provided some simplification on the detection process is assumed, can be framed as latent Gaussian models and, as such, benefit from the powerful INLA framework. A large selection of complex modelling features, and random effect models, including spatio‐temporal models, have already been implemented in R‐INLA. These also become available for occupancy models, providing the user with an efficient, reliable and flexible toolbox. We illustrate how INLA provides a flexible and computationally efficient framework for developing and fitting complex occupancy models using two complex case studies. Through these, we show how different spatio‐temporal models that include spatial‐varying trends, smooth terms and spatio‐temporal random effects can be fitted to aggregated detection/non‐detection data. At the cost of limiting the complexity of the detection model structure, INLA can incorporate a range of rather complex structures in the ecological process of interest and hence, extend the functionality of occupancy models. The limitations of occupancy models in terms of scalability for large spatio‐temporal data sets remain a challenge and an active area of research. INLA‐based occupancy models provide an alternative inferential and computational framework to fit complex spatio‐temporal occupancy models. The need for new and more flexible computationally efficient approaches to fit such models makes INLA an attractive option for addressing complex ecological problems, and a promising area of research.

Imperfect Measurement Devices Impact the Security of Tomography‐Based Source‐Independent Quantum Random Number Generator

AbstractSource‐independent quantum random number generators (SI‐QRNGs) can generate secure random numbers with untrusted and uncharacterized sources. Recently, a tomography‐based SI‐QRNG protocol has garnered significant attention for its higher randomness generation rate[Phys. Rev. A 99, 022328 (2019)], achieved through measurements utilizing three mutually unbiased bases. However, imperfect and inadequately characterized measurement devices would impact the security and performance of this protocol. In this work, considering the imperfect basis modulation, afterpulse effect and detection efficiency mismatch, it is demonstrated that the imperfect measurement devices would reduce the extractable randomness and lead to the incorrect estimation of the conditional min‐entropy. Additionally, the influences of the finite‐size effect and the performances of the protocol based on different parameter estimation methods are investigated and compared. To guarantee the security of generated random numbers, accurate conditional min‐entropy estimation methods that are compatible with imperfect factors are also developed. The work emphasizes the significance of considering the imperfections in measurement devices and establishing tighter bounds for parameter estimation, especially in high‐speed systems, thereby enhancing the robustness and performance of the protocol.

A Multi-Strategy Hybrid Sparse Reconstruction Method Based on Spatial-Temporal Sparse Wave Number Analysis for Enhancing Pipe Ultrasonic-Guided Wave Anomaly Imaging.

Ultrasonic-guided waves (UGWs) in defective pipes are subject to severe coherent noise caused by imperfect detection conditions, mode conversion, and intrinsic characteristics (dispersion and multiple modes), inducing the limited performance of anomaly imaging. To achieve the high resolution and accuracy of anomaly imaging, a multi-strategy hybrid sparse reconstruction (MHSR) method based on spatial-temporal sparse wavenumber analysis (ST-SWA) is proposed. MHSR leverages the capability of ST-SWA to extract the wavenumber dispersion curves, thereby providing a more refined and precise search space for MHSR. Furthermore, it mitigates the impact of coherent noise by conducting dispersion compensation on the reconstructed signal. The sparse compensated signals through MHSR are employed for sparse reconstruction imaging. To validate the efficacy of the proposed method, UGW testing is performed on the defective steel pipe, and the results demonstrate the significant enhancement of anomaly imaging in defect resolution and positioning accuracy. The lowest estimated errors for axial and circumferential defect positions are 10 mm and 4 mm, respectively.

Integrating niche and occupancy models to infer the distribution of an endemic fossorial snake (Atractus lasallei).

Understanding species distribution and habitat preferences is crucial for effective conservation strategies. However, the lack of information about population responses to environmental change at different scales hinders effective conservation measures. In this study, we estimate the potential and realized distribution of Atractus lasallei, a semi-fossorial snake endemic to the northwestern region of Colombia. We modelled the potential distribution of A. lasallei based on ecological niche theory (using maxent), and habitat use was characterized while accounting for imperfect detection using a single-season occupancy model. Our results suggest that A. lasallei selects areas characterized by slopes below 10°, with high average annual precipitation (>2500mm/year) and herbaceous and shrubby vegetation. Its potential distribution encompasses the northern Central Cordillera and two smaller centers along the Western Cordillera, but its habitat is heavily fragmented within this potential distribution. When the two models are combined, the species' realized distribution sums up to 935 km2, highlighting its vulnerability. We recommend approaches that focus on variability at different spatio-temporal scales to better comprehend the variables that affect species' ranges and identify threats to vulnerable species. Prompt actions are needed to protect herbaceous and shrub vegetation in this region, highly demanded for agriculture and cattle grazing.

The Detection of ChatGPT’s Textual Crumb Trails is an Unsustainable Solution to Imperfect Detection Methods

Abstract A recent disruptive innovation to scientific publishing is OpenAI’s ChatGPT, a large language model. The International Committee of Medical Journal Editors and COPE, and COPE member journals or publishers, set limitations to ChatGPT’s involvement in academic writing, requesting authors to declare its use. Those guidelines are practically useless because they ignore two fundamentals: first, academics who cheat to achieve success will not declare the use of ChatGPT; second, they fail to explicitly assign the responsibility of detection to editors, journals, and publishers. Using two primers, i.e., residual text that may reflect traces of ChatGPT’s output but that authors may have forgotten to remove from their articles, this commentary draws readers’ attention to 46 open-access examples sourced from PubPeer. Even though editors should be obliged to investigate such cases, a primer-based detection of ChatGPT’s textual crumb trails is only a temporary measure and not a sustainable solution because it relies on the detection of carelessness.

Modeling the 2014-2015 Vesicular Stomatitis Outbreak in the United States Using an SEIR-SEI Approach.

Vesicular stomatitis (VS) is a vector-borne livestock disease caused by the vesicular stomatitis New Jersey virus (VSNJV). This study presents the first application of an SEIR-SEI compartmental model to analyze VSNJV transmission dynamics. Focusing on the 2014-2015 outbreak in the United States, the model integrates vertebrate hosts and insect vector demographics while accounting for heterogeneous competency within the populations and observation bias in documented disease cases. Key epidemiological parameters were estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) methods, including the force of infection, effective reproduction number (Rt), and incubation periods. The model revealed significant underreporting, with only 10-24% of infections documented, 23% of which presented with clinical symptoms. These findings underscore the importance of including competence and imperfect detection in disease models to depict outbreak dynamics and inform effective control strategies accurately. As a baseline model, this SEIR-SEI implementation is intended to serve as a foundation for future refinements and expansions to improve our understanding of VS dynamics. Enhanced surveillance and targeted interventions are recommended to manage future VS outbreaks.

Correcting for measurement errors in a long‐term aerial survey with auxiliary photographic data

AbstractLong‐term, large‐scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large‐scale, long‐term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species‐specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large‐scale, long‐term datasets.

Range-wide salamander densities reveal a key component of terrestrial vertebrate biomass in eastern North American forests.

Characterizing the population density of species is a central interest in ecology. Eastern North America is the global hotspot for biodiversity of plethodontid salamanders, an inconspicuous component of terrestrial vertebrate communities, and among the most widespread is the eastern red-backed salamander, Plethodon cinereus. Previous work suggests population densities are high with significant geographic variation, but comparisons among locations are challenged by lack of standardization of methods and failure to accommodate imperfect detection. We present results from a large-scale research network that accounts for detection uncertainty using systematic survey protocols and robust statistical models. We analysed mark-recapture data from 18 study areas across much of the species range. Estimated salamander densities ranged from 1950 to 34 300 salamanders ha-1, with a median of 9965 salamanders ha-1. We compared these results to previous estimates for P. cinereus and other abundant terrestrial vertebrates. We demonstrate that overall the biomass of P. cinereus, a secondary consumer, is of similar or greater magnitude to widespread primary consumers such as white-tailed deer (Odocoileus virginianus) and Peromyscus mice, and two to three orders of magnitude greater than common secondary consumer species. Our results add empirical evidence that P. cinereus, and amphibians in general, are an outsized component of terrestrial vertebrate communities in temperate ecosystems.

Crater identification by perspective cone alignment

Crater identification aims to match the craters of a planetary body observed in an image to the corresponding entries in a catalogue of known craters of the same body. It is a key step in crater-based navigation algorithms for planetary missions. Many crater identification methods extract descriptors from the observed craters and compare them to an index of descriptors constructed a priori from the crater catalogue. The need for viewpoint invariance motivates descriptors defined over crater tuples (e.g., crater triads for projective invariance). However, this implies a descriptor index that grows rapidly (e.g., cubically) with the size of the catalogue, which practically limits the spatial resolution and/or coverage of the catalogue. Another serious drawback is the sensitivity of the descriptors and the matching accuracy to noise and outliers in the crater detection result, which are inevitable due to imperfect crater detection algorithms. In this paper, we propose a novel descriptorless crater identification technique. At its core, our method solves the perspective cone alignment problem with geometric verification iteratively over the crater catalogue. We show that our simple algorithm, with time and space complexities that are linear in the catalogue size, is substantially more accurate than state-of-the-art descriptor-based methods in the presence of noise. Moreover, the availability of multi-core onboard processors raises the prospect of speeding up our method through parallelisation.