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Coastal whaling targeting Baird’s beaked whales has a long history, and more than a quarter of a century has passed since the last abundance estimation of this species was conducted for management purposes. Here, we estimated the latest and time series abundances of Baird’s beaked whales in the waters off the Pacific coast of Japan since 2008 using standard line transect analyses. Sighting surveys dedicated to estimating the abundance of Baird’s beaked whales were conducted four times. Additionally, we used the Baird’s beaked whale sighting dataset from a sighting survey targeting baleen whale in 2016. Two types of detection functions with multiple covariates were fitted to sighting data from these surveys. Abundances were estimated using the half-normal model to be 1524 (coefficient of variation, CV = 0.72) in 2008, 1546 (CV = 0.81) in 2009, 1093 (CV = 0.54) in 2015, 1034 (CV = 0.51) in 2016, and 3596 (CV = 0.82) in 2017. Some of these estimates had imperfect coverage, but all estimates sufficiently represented abundances in the main habitat in the study region. Overall, our abundance estimates were smaller than past estimates from the early 1990s, implying that further monitoring of the abundance are needed to manage and conserve populations of Baird’s beaked whales in this region.

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.

Densities of elusive terrestrial mammals are commonly estimated from camera-trap data. Typically, this is a 2-step process involving 1) fitting conventional closed population capture-recapture models to estimate abundance, and 2) using ad hoc methods to determine the effective trapping area. The methodology needs to be accurate, robust, and reliable when results are used to guide wildlife management. We critically review 47 published studies and discuss the problems associated with contemporary population estimates of elusive species from camera-trap data. In particular we discuss 1) individual identification, 2) sample size and capture probability, 3) camera location and spacing, 4) the size of the study area, and 5) ad hoc density estimation from the calculation of an effective trapping area. We also discuss the recently developed spatially explicit capture-recapture (SECR) models as an alternative approach that does not require the intermediate step of estimating an effective trapping area. We recommend 1) greater transparency in study design and quality of the data, 2) greater rigor when reviewing manuscripts, and 3) that more attention is given to the survey design to ensure data are of sufficient quality for analysis.

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.

Metagenomic profiling, predicting the presence and relative abundances of microbes in a sample, is a critical first step in microbiome analysis. Alignment-based approaches are often considered accurate yet computationally infeasible. Here, we present a novel method, Metalign, that performs efficient and accurate alignment-based metagenomic profiling. We use a novel containment min hash approach to pre-filter the reference database prior to alignment and then process both uniquely aligned and multi-aligned reads to produce accurate abundance estimates. In performance evaluations on both real and simulated datasets, Metalign is the only method evaluated that maintained high performance and competitive running time across all datasets.

Reliable estimates of the distribution of species abundance are a key element in wildlife studies, but such information is usually difficult to obtain for large spatial or long temporal scales. Wildlife–vehicle collision (WVC) data is systematically registered in many countries and could be used as a proxy of population abundance if the number of WVC in each territory increase with the population abundance. However, factors such as road density or human population should be controlled to obtain accurate abundance estimations from WVC data. Here, we propose a hierarchical modeling approach using the Royle–Nichols model for detection–non‐detection data to obtain population abundance indices from WVC. Relative abundance and individual detectability were modeled for two species, wild boar Sus scrofa and roe deer Capreolus capreolus at 10 × 10 km cells in mainland Spain from WVC data using environmental, anthropological and temporal covariates. For each cell, a detection was annotated if at least one WVC was recorded at each month (used as survey occasion). The predicted abundance indices were compared with raw hunting statistics at region level to assess the performance of the modeling approach. Site specific covariates such as road density or administrative region and the month of the year, affected individual detectability, with higher WVC probability between October and December for wild boar and between April and July for roe deer. Wild boar and roe deer abundance can be explained by both, bioclimatic and land cover covariates. Abundance indices obtained from WVC data were significantly positively correlated with regional raw hunting yields for both species. We presented empirical evidence supporting that accurate wildlife abundance indices at fine spatial resolution can be generated from WVC data when individual detectability is considered in the modeling process.

For the 40 years after the end of commercial whaling in 1976, humpback whale populations in the North Pacific Ocean exhibited a prolonged period of recovery. Using mark–recapture methods on the largest individual photo-identification dataset ever assembled for a cetacean, we estimated annual ocean-basin-wide abundance for the species from 2002 through 2021. Trends in annual estimates describe strong post-whaling era population recovery from 16 875 (± 5955) in 2002 to a peak abundance estimate of 33 488 (± 4455) in 2012. An apparent 20% decline from 2012 to 2021, 33 488 (± 4455) to 26 662 (± 4192), suggests the population abruptly reached carrying capacity due to loss of prey resources. This was particularly evident for humpback whales wintering in Hawai‘i, where, by 2021, estimated abundance had declined by 34% from a peak in 2013, down to abundance levels previously seen in 2006, and contrasted to an absence of decline in Mainland Mexico breeding humpbacks. The strongest marine heatwave recorded globally to date during the 2014–2016 period appeared to have altered the course of species recovery, with enduring effects. Extending this time series will allow humpback whales to serve as an indicator species for the ecosystem in the face of a changing climate.

Precise abundance estimation via environmental DNA (eDNA) analysis is challenging because of myriad effects of biotic/abiotic factors on eDNA persistence in water. However, it remains unclarified how the relationship between eDNA concentration and species abundance is influenced by environmental conditions. Warm temperature can accelerate eDNA degradation and its turnover in water, which may make eDNA signals fresher and strengthen the relationship. Here, mathematical models were present to simulate the effect of temperature-dependent eDNA degradation, modeled based on the previous meta-analysis, on the relationship between eDNA concentration and species abundance under different temperature and eDNA production scenarios. The R2 values tended to be higher at warmer temperatures regardless of eDNA production scenarios, although differences in R2 between temperatures were relatively small. Additionally, the R2 values were higher for a smaller variance of eDNA production rate. Furthermore, previous studies regarding the relationship between fish eDNA concentration and their abundance were meta-analyzed and correlation coefficients of the relationship were significantly higher for warmer temperatures. The findings indicated the importance of taking water temperatures into account for better abundance estimation via eDNA, although further technical consideration in the modelling framework was needed.

The use of drones to survey and monitor wildlife populations has increased exponentially. A common protocol used for data collection is planning flights with substantial overlap between successive photographs and lateral lines and then creating orthomosaics by merging the collected images. Because available methods for orthomosaic building assume that landscapes are static, unintended errors arise when counting moving animals. Here, we describe these sources of error and discuss potential solutions and future developments needed. Individuals can appear multiple times, be omitted or appear as faint ghosts or cut in half in the final mosaic. These errors can significantly impact abundance estimates but are rarely acknowledged. Researchers should carefully consider if using orthomosaics is really needed for surveying wildlife. Currently, there is a lack of methods to prevent these errors from arising and to explicitly accommodate them in modelling approaches. Future developments should focus on (a) creating methods to build orthomosaics that minimize these errors in the context of counting moving animals; (b) developing modelling approaches to estimate abundance while accounting for these errors; and (c) exploring alternative flight settings (e.g. amount of lateral overlap, sensor type, flight height and speed). Using an example on Giant Amazon Turtles, we illustrate potential solutions with a method for orthomosaic building that prioritizes moving animals and a modelling approach to estimate the detection errors and correct abundance estimates. The developed prototype approach for creating orthomosaics revealed many more turtle individuals than the conventional approach, although it presented more double counts as well. In the modelling approach, we found that a turtle available for detection during the survey can have a probability of 31% of being omitted or ghosted during the conventional orthomosaic building process. We also found that 12% of the turtles appearing in a conventional orthomosaic correspond to double counts. Resumo O uso de drones para amostrar e monitorar animais silvestres tem crescido exponencialmente. Um protocolo comumente usado para a coleta de dados é planejar os voos com uma considerável sobreposição entre fotos sequenciais e linhas laterais e então criar um ortomosaico através da junção de todas as imagens coletadas. Como os métodos disponíveis para a construção de ortomosaicos consideram que as paisagens são estáticas, erros inesperados surgem durante a contagem de animais em movimento. Aqui, nós descrevemos essas fontes de erro e discutimos potenciais soluções e desenvolvimentos futuros que são necessários. Indivíduos podem aparecer múltiplas vezes, serem omitidos ou aparecerem transparentes ou cortados no mosaico final. Esses erros podem afetar significativamente as estimativas de abundância, mas são raramente reconhecidos na literatura. Os pesquisadores devem considerar cuidadosamente se usar ortomosaicos é realmente necessário para amostrar animais silvestres. Atualmente, há uma lacuna de métodos para prevenir que esses erros ocorram e para explicitamente acomodar eles em abordagens de modelagem. Desenvolvimentos futuros devem focar em: (1) explorar configurações alternativas de voo (e.g., grau de sobreposição lateral, tipo de sensor, altura e velocidade de voo, etc.); (2) criar métodos para construção desses mosaicos que minimizem os erros no contexto de contagem de animais em movimento; e (3) desenvolver abordagens de modelagem para estimar abundância que levem em conta esses erros. Usando um exemplo com tartarugas‐da‐amazônia, nós ilustramos as soluções potenciais com um método para construção de ortomosaicos que prioriza animais em movimento e uma abordagem de modelagem para estimar os erros de detecção e corrigir as estimativas de abundância. O protótipo da abordagem desenvolvida para criar ortomosaicos revelou muito mais tartarugas do que a abordagem convencional, embora também tenha apresentado mais contagens duplas. Na abordagem de modelagem, nós encontramos que uma tartaruga disponível para detecção durante a amostragem tem uma probabilidade de 31% de ser omitida ou aparecer transparente durante o processo convencional de montagem do mosaico. Também encontramos que 12% das tartarugas que aparecem em um ortomosaico convencional correspondem a contagens duplas.

We investigate spatially explicit models and ensemble modeling techniques for estimating animal abundance from line-transect survey data. Spatially explicit models are expected to be statistically more efficient, resulting in more precise abundance estimates, than design-based abundance estimators that rely heavily on assumptions about survey design and realization. Ensemble modeling reduces error by averaging among models, and allows for model selection uncertainty to propagate to the abundance estimator. We develop density surface models using Matérn covariance functions and spline-based smooths for a case study, belugas ( Delphinapterus leucas ) from the Eastern Bering Sea (EBS) stock. EBS belugas are upper trophic level predators in a rapidly changing ecosystem and are a vital nutritional and cultural resource for Alaska Natives. Effective management of this stock requires regular monitoring to derive accurate and unbiased estimates of abundance. Since 1992, aerial line-transect surveys have been the primary means of surveying and estimating abundance of EBS belugas in the region. We compare EBS beluga abundance estimates for 2017 and 2022 that were derived using post-stratified, design-based abundance estimators with analogous estimates the we derive using spatially explicit and ensemble modeling methods. The estimated precision in the abundance estimates from the individual density surface models (DSMs) and the ensemble average of DSMs is higher than for the design-based estimator in both survey years. The design-based models estimated that there were 12,269 belugas in 2017 (coefficient of variation (CV) = 0.118) and 19,811 belugas within a larger study area in 2022 (CV = 0.343). The ensemble spatial models estimate that there were 11,654 belugas in 2017 (CV = 0.118) and 13,313 belugas in 2022 (CV = 0.216). Among the individual spatially explicit models, abundance estimates range from 11,242 to 11,963 (CV = 0.111 to 0.114) in 2017 and 12,023 to 15,593 (CV = 0.172 to 0.198) in 2022. Because spatial models identify spatial patterns in beluga density at finer resolutions than design-based models, we argue that ensembles of spatially explicit density models provide a reasonable path forward for estimating EBS beluga abundance and distribution in a way that is useful to management and conservation efforts.