Abundance Estimates Research Articles

Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue

We define and identify a new class of control genes for next-generation sequencing called total RNA expression genes (TREGs), which correlate with total RNA abundance in cell types of different sizes and transcriptional activity. We provide a data-driven method to identify TREGs from single-cell RNA sequencing data, allowing the estimation of total amount of RNA when restricted to quantifying a limited number of genes. We demonstrate our method in postmortem human brain using multiplex single-molecule fluorescent in situ hybridization and compare candidate TREGs against classic housekeeping genes. We identify AKT3 as a top TREG across five brain regions.

Evaluation of breeding distribution and chronology of North American scoters

North America's scoter species are poorly monitored relative to other waterfowl. Black Melanitta americana, surf M. perspicillata, and white‐winged M. deglandi scoter abundance and trend estimates are thus uncertain in many parts of these species' ranges. The most extensive source of waterfowl abundance and distribution data in North America is the Waterfowl breeding population and habitat survey (WBPHS). Although the WBPHS effectively monitors most species, both its timing and geographic coverage may preclude accurate scoter monitoring. Therefore, our goal was to better define when and where scoters breed to help interpret survey results and optimize potential supplemental survey efforts for scoters. We integrated satellite telemetry tracking data from scoters marked at multiple molting, staging, breeding, and wintering areas along the Atlantic and Pacific coasts to quantify continent‐wide breeding chronology and distribution. We also examined possible drivers of variation in timing of arrival, length of stay, and departure at nesting locations. We documented a northwest to southeast distribution of estimated breeding sites across Alaska and Canada. On average, scoters arrived at nest sites on 1 June. Surf scoters and Pacific black scoters arrived earliest and departed earliest. Pacific‐wintering black and white‐winged scoters began breeding earlier than Atlantic‐wintering birds. Additionally, birds arrived at nesting locations earlier in years with earlier snowmelt, and later snowmelt reduced lengths of stay for males. Breeding chronology also varied by age group, with adults arriving earlier than subadults. Our study is the first to comprehensively describe spatial variation in timing of breeding of both Atlantic and Pacific populations of all three scoter species across North America. Our results increase our understanding of how current surveys enumerate scoters and will inform possible supplemental efforts to improve continental monitoring of scoter populations.

Understanding implications of detection heterogeneity in wildlife abundance estimation

AbstractNegative bias in mark‐recapture abundance estimators due to heterogeneity in detection (capture) probability is a well‐known problem, but we believe most biologists do not understand why heterogeneity causes bias and how bias can be reduced. We demonstrate how heterogeneity creates dependence and bias in mark‐recapture approaches to abundance estimation. In comparison, heterogeneity, and hence estimator bias, is not as problematic for distance sampling and mark‐resight methods because both techniques estimate detection probabilities based on a known quantity. We show how the introduction of a known number of individuals planted into a study population prior to a mark‐recapture survey can reduce bias from heterogeneity in detection probability. We provide examples with simulation and an analysis of motion‐sensitive camera data from a study population of introduced eastern wild turkeys (Meleagris gallopavo silvestris) of known size with a subset of telemetered birds. In choosing a method for abundance estimation, careful consideration should be given to assumptions and how heterogeneity in detection probability can be accommodated for each application.

A blind convolutional deep autoencoder for spectral unmixing of hyperspectral images over waterbodies

Harmful algal blooms have dangerous repercussions for biodiversity, the ecosystem, and public health. Automatic identification based on remote sensing hyperspectral image analysis provides a valuable mechanism for extracting the spectral signatures of harmful algal blooms and their respective percentage in a region of interest. This paper proposes a new model called a non-symmetrical autoencoder for spectral unmixing to perform endmember extraction and fractional abundance estimation. The model is assessed in benchmark datasets, such as Jasper Ridge and Samson. Additionally, a case study of the HSI2 image acquired by NASA over Lake Erie in 2017 is conducted for extracting optical water types. The results using the proposed model for the benchmark datasets improve unmixing performance, as indicated by the spectral angle distance compared to five baseline algorithms. Improved results were obtained for various metrics. In the Samson dataset, the proposed model outperformed other methods for water (0.060) and soil (0.025) endmember extraction. Moreover, the proposed method exhibited superior performance in terms of mean spectral angle distance compared to the other five baseline algorithms. The non-symmetrical autoencoder for the spectral unmixing approach achieved better results for abundance map estimation, with a root mean square error of 0.091 for water and 0.187 for soil, compared to the ground truth. For the Jasper Ridge dataset, the non-symmetrical autoencoder for the spectral unmixing model excelled in the tree (0.039) and road (0.068) endmember extraction and also demonstrated improved results for water abundance maps (0.1121). The proposed model can identify the presence of chlorophyll-a in waterbodies. Chlorophyll-a is an essential indicator of the presence of the different concentrations of macrophytes and cyanobacteria. The non-symmetrical autoencoder for spectral unmixing achieves a value of 0.307 for the spectral angle distance metric compared to a reference ground truth spectral signature of chlorophyll-a. The source code for the proposed model, as implemented in this manuscript, can be found at https://github.com/EstefaniaAlfaro/autoencoder_owt_spectral.git.

Leveraging the strengths of citizen science and structured surveys to achieve scalable inference on population size

Abstract Population size is a key metric for management and policy decisions, yet wildlife monitoring programmes are often limited by the spatial and temporal scope of surveys. In these cases, citizen science data may provide complementary information at higher resolution and greater extent. We present a case study demonstrating how data from the eBird citizen science programme can be combined with regional monitoring efforts by the US Fish and Wildlife Service to produce high‐resolution estimates of golden eagle abundance. We developed a model that uses aerial survey data from the western United States to calibrate high‐resolution annual estimates of relative abundance from eBird. Using this model, we compared regional population size estimates based on the calibrated eBird information with those based on aerial survey data alone. Population size estimates based on the calibrated eBird information had strong correspondence to estimates from aerial survey data in two out of four regions, and population trajectories based on the two approaches showed high correlations. We demonstrate how the combination of citizen science data and targeted surveys can be used to (a) increase the spatial resolution of population size estimates, (b) extend the spatial extent of inference and (c) predict population size beyond the temporal period of surveys. Findings based on this case study can be used to refine policy metrics used by the US Fish and Wildlife Service and inform permitting regulations (e.g. mortality/harm associated with wind energy development). Policy implications: Our results demonstrate the ability of citizen science data to complement targeted monitoring programmes and improve the efficacy of decision frameworks that require information on population size or trajectory. After validating citizen science data against survey‐based benchmarks, agencies can harness strengths of citizen science data to supplement information needs and increase the resolution and extent of population size predictions.

High-redshift metallicity calibrations for JWST spectra: insights from line emission in cosmological simulations

ABSTRACT Optical emission-line ratios are traditionally used to estimate gas metallicities from observed galaxy spectra. While such estimators have been calibrated primarily at low redshift, they are commonly used to interpret observations of high-redshift galaxies, where their applicability may be questioned. We use comprehensive emission-line catalogues of galaxies from the IllustrisTNG simulation including ionization by stars, active galactic nuclei, and shocks to reassess the calibrations of both optical and ultraviolet metallicity estimators at redshifts $0\lesssim z \lesssim 8$. For present-day galaxies, the predicted optical-line calibrations are consistent with previously published ones, while we find different ultraviolet-line ratios, such as He ii λ1640/C iii] λ1908, can provide powerful metallicity diagnostics. At fixed metallicity, most emission-line ratios are predicted to strongly increase or decrease with redshift (with the notable exception of N2O2 = [N ii]λ6584/[O ii] λ3727), primarily because of a change in ionization parameter. The predicted dependence of R3 = [O iii]λ5007/H β and R23 = ([O ii] λ3727 + [O iii]λ5007)/H β, and to a slightly lesser extent R2 = [O ii] λ3727/H β and O32 = [O iii]λ5007/[O ii] λ3727, on O abundance for galaxies at z = 4–8 agrees remarkably well with Te-based measurements in 14 galaxies observed with JWST. This success motivates us to provide new calibrations of optical and ultraviolet metallicity estimators specifically designed for galaxies at z > 4, to guide interpretations of future, high-redshift spectroscopic surveys. We further demonstrate that applying classical z = 0 calibrations to high-redshift galaxies can bias oxygen abundance estimates downward by up to 1 dex, leading to the inference of stronger evolution of the mass–metallicity relation than is actually occurring.

Validation of satellite imagery for quantitative population assessment and monitoring of Atlantic walrus

Monitoring animal population abundance and distribution is essential for conservation and management. Recent advances in very high-resolution (VHR) satellite imagery allow for unprecedented detection and identification of animals, offering a potential alternative to conventional methods of population assessment such as aerial surveys. Here, we compare satellite and aerial survey images of a terrestrial walrus haul-out site in the Canadian High Arctic taken 1.5 h apart. While the 30-cm resolution of the commercially available satellite image was insufficient to reliably count individual walruses visually, a manual digitization method successfully identified and delineated image areas confirmed visually to have walrus. Estimates of numbers of hauled out walrus obtained by multiplying the detected area by a range of densities in the aerial image (231) agreed well with actual counts in 35 and 55 mm aerial images (217–240). Potential applications of this approach include quantitative abundance estimation or more simple monitoring of presence/absence over large expanses of remote areas that are logistically difficult to cover using survey aircraft.

Biomass burning in the Neotropics is exposing migrating birds to elevated fine particulate matter concentrations

AbstractAimA unique risk faced by nocturnally migrating birds is the disorienting influence of artificial light at night (ALAN). ALAN originates from anthropogenic activities that can generate other forms of environmental pollution, including the emission of fine particulate matter (PM2.5). PM2.5 concentrations can display strong seasonal variation whose origin can be natural or anthropogenic. How this variation affects seasonal associations with ALAN and PM2.5 for nocturnally migrating bird populations has not been explored.LocationWestern Hemisphere.Time Period2021Major Taxa StudiedNocturnally migrating passerine (NMP) bird species.MethodsWe combined monthly estimates of PM2.5 and ALAN with weekly estimates of relative abundance for 164 NMP species derived using observations from eBird. We identified groups of species with similar associations with monthly PM2.5. We summarized their shared environmental, geographical, and ecological attributes.ResultsPM2.5 was lowest in North America, especially at higher latitudes during the boreal winter. PM2.5 was highest in the Amazon Basin, especially during the dry season (August–October). ALAN was highest within eastern North America, especially during the boreal winter. For NMP species, PM2.5 associations reached their lowest levels during the breeding season (<10 μg/m3) and highest levels during the nonbreeding season, especially for long‐distance migrants that winter in Central and South America (~20 μg/m3). Species that migrate through Central America in the spring encountered similarly high PM2.5 concentrations. ALAN associations reached their highest levels for species that migrate (~12 nW/cm2/sr) or spend the nonbreeding season (~15 nW/cm2/sr) in eastern North America.Main ConclusionsWe did not find evidence that the disorienting influence of ALAN enhances PM2.5 exposure during stopover in the spring and autumn for NMP species. Rather, our findings suggest biomass burning in the Neotropics is exposing NMP species to consistently elevated PM2.5 concentrations for an extended period of their annual life cycles.

Hierarchical model to predict common carp and bigmouth buffalo abundance from electrofishing data

Catch per unit effort (CPUE) is used as an index of fish abundance under the premise that changes in CPUE result from changes in true density. However, catchability may also vary based on environmental conditions that affect observed CPUE. We developed a hierarchical model for estimating common carp ( Cyprinus carpio) and bigmouth buffalo ( Ictiobus cyprinellus) relative abundance with electrofishing survey data from six shallow lakes in northwest Iowa, USA, between 2018 and 2020. Common carp catchability was negatively associated with lake perimeter but unrelated to lake surface area, water depth, Secchi depth, temperature, and month of sampling. Bigmouth buffalo catchability was negatively associated with Secchi depth and water temperature and unrelated to other environmental variables. Hierarchical model posterior distributions of bigmouth buffalo density were less precise than Schnabel estimates, whereas common carp posterior distribution abundance estimates were more precise than Schnabel estimates. Our results indicate that hierarchical models can be a viable substitute for labor-intensive capture–mark–recapture methods to estimate unknown latent variables like relative abundance, and could be applied to other species, sampling gears, and management frameworks.

Mechanistic modeling of climate effects on redistribution and population growth in a community of fish species.

Understanding community responses to climate is critical for anticipating the future impacts of global change. However, despite increased research efforts in this field, models that explicitly include important biological mechanisms are lacking. Quantifying the potential impacts of climate change on species is complicated by the fact that the effects of climate variation may manifest at several points in the biological process. To this end, we extend a dynamic mechanistic model that combines population dynamics, such as species interactions, with species redistribution by allowing climate to affect both processes. We examine their relative contributions in an application to the changing biomass of a community of eight species in the Gulf of Maine using over 30 years of fisheries data from the Northeast Fishery Science Center. Our model suggests that the mechanisms driving biomass trends vary across space, time, and species. Phase space plots demonstrate that failing to account for the dynamic nature of the environmental and biologic system can yield theoretical estimates of population abundances that are not observed in empirical data. The stock assessments used by fisheries managers to set fishing targets and allocate quotas often ignore environmental effects. At the same time, research examining the effects of climate change on fish has largely focused on redistribution. Frameworks that combine multiple biological reactions to climate change are particularly necessary for marine researchers. This work is just one approach to modeling the complexity of natural systems and highlights the need to incorporate multiple and possibly interacting biological processes in future models.

Impacts of sample storage time on estimates of phytoplankton abundance: how long is too long?

Abstract Phytoplankton play a key role in marine ecosystems, making the accurate quantification of functional groups/species, using standardized microscopy techniques, essential in many research efforts. In this study we aimed to evaluate the effects of (a) storage time on the abundance of microphytoplankton in samples fixed with acid Lugol’s solution; (b) storage time on the abundance of pico- and nanophytoplankton in frozen microscopy slides; (c) storage time on the abundance of pico- and nanophytoplankton in fixed refrigerated samples and (d) storage protocol (refrigerated versus frozen) on the abundance of pico- and nanophytoplankton. Microphytoplankton were analyzed using inverted microscopy, and pico- and nanophytoplankton using epifluorescence microscopy. Results indicate storage time negatively impacted the abundances of all phytoplankton size classes; however, effects were group-specific. For accurate abundance estimates, we suggest that samples fixed with acid Lugol’s should be analyzed within 30 and 180 days, for dinoflagellates/total microphytoplankton and diatoms, respectively. For picoeukaryotes, glutaraldehyde fixed samples should be kept refrigerated for up to 4 days, and slides should be prepared immediately before observation. It is recommended that authors specify the exact lag times between sample collection, fixation and analysis, to allow the comparability of phytoplankton datasets across different studies and/or monitoring programs.

Hybrid mark–resight approaches account for heterogeneous detection bias in aerial surveys

Abstract Population abundance estimation is a critical yet challenging task for wildlife management decision‐making. For aerial surveys of terrestrial mammals in highly heterogenous landscapes, estimation models that address multiple sources of detection bias are needed. A typical hybrid mark–resight model includes an additive parameter that accounts for detection probabilities derived from marked animals. While this additive hybrid method improves abundance estimates, there remains a need to account for heterogeneity due to the conditional dependence in detection probability inherent in aerial surveys with multiple observers. We propose a modified approach, referred to as a conditional hybrid model, to address this heterogeneity directly. The conditional hybrid model applies a mark–resight approach typically used to account for tag loss in mark–recapture studies to aerial surveys with a subset of the population marked with GPS collars. We conduct a structured comparison between the additive and conditional hybrid estimation models and fit these two hybrid models to data from a 2020 aerial survey of mule deer in southern California. Both the additive and conditional hybrid models had general agreement in overall model structure and variable importance, however, the conditional model outperformed the additive model in capturing sources of detection heterogeneity and overall abundance estimates differed by 9%. Our top‐ranked models suggest that deer movement, larger group sizes, and rougher topography increased detection probability, whereas high cloud cover, obscuring vegetation and high observer fatigue and helicopter height decreased detection probability. Our findings illustrate the need to assess multiple correction methods when estimating abundance from aerial survey data to ensure accurate abundance estimates are being used to inform wildlife management strategies, with the caveat that specific survey conditions may dictate which method is the most appropriate for achieving robust estimates. These approaches represent the cutting‐edge science on wildlife abundance estimation from aerial survey data, a critical ingredient for effective wildlife management. By testing innovative hybrid methods, we present the first formal comparison of the strengths and weaknesses of each method within a highly heterogeneous landscape, providing practitioners with necessary information for future application.

Advances in wildlife abundance estimation using pedigree reconstruction.

The conservation and management of wildlife populations, particularly for threatened and endangered species are greatly aided with abundance, growth rate, and density measures. Traditional methods of estimating abundance and related metrics represent trade-offs in effort and precision of estimates. Pedigree reconstruction is an emerging, attractive alternate approach because its use of one-time, noninvasive sampling of individuals to infer the existence of unsampled individuals. However, advances in pedigree reconstruction could improve its utility, including forming a measure of precision for the method, establishing required spatial sampling effort for accurate estimates, ascertaining the spatial extent of abundance estimates derived from pedigree reconstruction, and assessing how population density affects the estimator's performance. Using established relationships for a stochastic, spatially explicit simulated moose (Alces americanus) population, pedigree reconstruction provided accurate estimates of the adult moose population size and trend. Novel bootstrapped confidence intervals performed as expected with intensive sampling but underperformed with moderate sampling efforts that could produce abundance estimates with low bias. Adult population estimates more closely reflected the total number of adults in the extant population, rather than number of adults inhabiting the area where sampling occurred. Increasing sampling effort, measured as the proportion of individuals sampled and as the proportion of a hypothetical study area, yielded similar asymptotic patterns over time. Simulations indicated a positive relationship between animal density and sampling effort required for unbiased estimates. These results indicate that pedigree reconstruction can produce accurate abundance estimates and may be particularly valuable for surveying smaller areas and low-density populations.

Investigating the Primordial Universe through nuclear physics

Big Bang Nucleosynthesis (BBN) requires several nuclear physics inputs and nuclear reaction rates. An up-to-date compilation of direct cross sections of is given, being these ones among the most uncertain bare-nucleus cross sections. A particular attention is devoted to recently indirectly measured cross-section which give important hints for the nuclear astrophysics community.In reality, a significant experimental effort has been made over the past 10 years to explore reactions important to the BBN and determine their astrophysical S(E)-factor using the Trojan Horse Method (THM). Then, numerical calculations are made in the relevant temperature ranges for BBN (0.01<T9 <10) to determine the reaction rates and the relative error for the four reactions of interest. The effects of these values on the calculated primordial abundances and isotopical composition for H, He, and Li were then assessed by using them as input physics for computations of primordial nucleosynthesis. Additionally, recent findings regarding the 7Be(n,alpha)4He reaction rate were taken into consideration. These were put up against estimates of primordial abundance derived from observation at various astrophysical places. Additionally, perspectives on reactions will be examined.

Predatory impacts of invasive Blue Catfish in an Atlantic coast estuary

AbstractObjectivePredatory invasive fishes may consume species of management interest and alter food webs. Blue CatfishIctalurus furcatusis a large‐bodied, salinity‐tolerant species that exhibits broad diet breadth and preys on species of both conservation concern and fisheries management interest. To better understand the ecological consequences of the establishment of Blue Catfish fisheries, estimates of predatory impacts are needed.MethodsUsing a Monte Carlo simulation, we integrated abundance estimates, diet information, and consumption‐to‐biomass ratios to estimate population‐level Blue Catfish predation for a large Chesapeake Bay tributary along the mid‐Atlantic coast of the United States, the James River.ResultPopulation‐level annual predation estimates by Blue Catfish exceeded 100 metric tons for several species or taxa of interest, including an estimated 400.7 metric tons (95% CI = 272.6–613.2) of blue crabCallinectes sapidus. Prey species abundances were unknown and thus limited opportunities to evaluate prey population responses. For instance, effects of Blue Catfish on blue crab populations remain unknown without tributary‐specific estimates of blue crab abundance, but comparisons to landings data suggests that Blue Catfish predation on blue crab in the James River may be low compared with harvest.ConclusionEstimation of Blue Catfish predatory effects may inform development of management goals and objectives that balance diverse stakeholder interests. This work provides beneficial information to assess trade‐offs of Blue Catfish fisheries and their effects on coastal aquatic resources.

Examining environmental conditions that influence long-term Antarctic blue whale migration patterns in the Southern Hemisphere using an 18-year passive acoustic dataset

Antarctic blue whales (Balaenoptera musculus intermedia) are difficult to study due to the low numbers and cryptic behaviour. The largest subspecies of blue whale migrates from Antarctic feeding areas to temperate and tropical breeding grounds every year. Their use of these remote areas makes it difficult to detect them using visual survey methods. However, blue whales are vocal animals and sing throughout the year especially during their annual migration. By using passive acoustic monitoring, we can study the long-term migration patterns of Antarctic blue whales across the Southern Hemisphere. Using an 18-year passive acoustic dataset from the CTBTO, we examined the migration patterns of Antarctic blue whales across four different locations spread across all three major ocean basins. We modelled blue whale acoustic detections with environmental variables including El Niño Southern Oscillation, the Southern Annular Mode, and yearly Antarctic sea-ice extent. We also examined krill abundance estimates from Krill Base in our models. We found that the whale call detections at the four sites were correlated with different variables. The variables were also correlated with various degrees of lag. Our study provides evidence that these whales vary their migration patterns from year-to-year with respect to changing oceanic, and climatic conditions.

Assessing trends in population size of three unmarked species: A comparison of a multi-species N-mixture model and random encounter models.

Estimation of changes in abundances and densities is essential for the research, management, and conservation of animal populations. Recently, technological advances have facilitated the surveillance of animal populations through the adoption of passive sensors, such as camera traps (CT). Several methods, including the random encounter model (REM), have been developed for estimating densities of unmarked populations but require additional information. Hierarchical abundance models, such as the N-mixture model (NMM), can estimate abundances without performing additional fieldwork but do not explicitly estimate the area effectively sampled. This obscures the interpretation of its densities and requires its users to focus on relative measures of abundance instead. Hence, the main objective of our study is to evaluate if REM and NMM yield consistent results qualitatively. Therefore, we compare relative trends: (i) between species, (ii) between years and (iii) across years obtained from annual density/abundance estimates of three species (fox, wild boar and red deer) in central Spain monitored by a camera trapping network for five consecutive winter periods. We reveal that NMM and REM provided density estimates in the same order of magnitude for wild boar, but not for foxes and red deer. Assuming a Poisson detection process in the NMM was important to control for inflation of abundance estimates for frequently detected species. Both methods consistently ranked density/abundance across species (between species trend), but did not always agree on relative ranks of yearly estimates within a single population (between years trend), nor on its linear population trends across years (across years trend). Our results suggest that relative trends are generally consistent when the range of variability is large, but can become inconsistent when the range of variability is smaller.

Estimating effective survey duration in camera trap distance sampling surveys.

Among other approaches, camera trap distance sampling (CTDS) is used to estimate animal abundance from unmarked populations. It was formulated for videos and observation distances are measured at predetermined 'snapshot moments'. Surveys recording still images with passive infrared motion sensors suffer from frequent periods where animals are not photographed, either because of technical delays before the camera can be triggered again (i.e. 'camera recovery time') or because they remain stationary and do not immediately retrigger the camera following camera recovery time (i.e. 'retrigger delays'). These effects need to be considered when calculating temporal survey effort to avoid downwardly biased abundance estimates. Here, we extend the CTDS model for passive infrared motion sensor recording of single images or short photo series. We propose estimating 'mean time intervals between triggers' as combined mean camera recovery time and mean retrigger delays from the time interval distribution of pairs of consecutive pictures, using a Gamma and Exponential function, respectively. We apply the approach to survey data on red deer, roe deer and wild boar. Mean time intervals between triggers were very similar when estimated empirically and when derived from the model-based approach. Depending on truncation times (i.e. the time interval between consecutive pictures beyond which data are discarded) and species, we estimated mean time intervals between retriggers between 8.28 and 15.05 s. Using a predefined snapshot interval, not accounting for these intervals, would lead to underestimated density by up to 96% due to overestimated temporal survey effort. The proposed approach is applicable to any taxa surveyed with camera traps. As programming of cameras to record still images is often preferred over video recording due to reduced consumption of energy and memory, we expect this approach to find broad application, also for other camera trap methods than CTDS.

Forecasting wildlife movement with spatialcapture–recapture

AbstractWildlife movement is an important process affecting species population biology and community interactions in myriad ways. Studies of wildlife movement have focused on retrospectively estimating movements of small numbers of individuals by outfitting them with GPS and telemetry tags. Recent developments in spatial capture–recapture modelling permit the integration of movement models that can estimate the movement of untagged and undetected individuals. Additionally, hidden Markov movement models provide a framework for forecasting individuals' movements, which may be valuable in the conservation of threatened species facing risks that vary across space and time.We describe maximum likelihood estimators for spatial capture–recapture models integrated with simple, biased and correlated random walk movement models formulated as hidden Markov models. Additionally, we demonstrate how to forecast wildlife movement based on these models and hidden Markov model algorithms. We conducted a simulation study to test the performance of the models' abundance estimators and movement forecasts when fit to data simulated under different movement models. We also fit the models to spatial capture–recapture data collected on North Atlantic right whales off the Atlantic Coast of the southeastern United States.Random walk movement models improved abundance estimation and movement forecasts in our simulation study and received greater support from the data in the right whale case study than did activity centre movement models.Forecasts of wildlife movement made under integrated spatial capture–recapture movement models will be most valuable when individuals have been observed recently, when sampling for individuals is extensive and efficient, and when the scale of individuals' movements is small relative to the scale of the study area and sampling process.

Stable microbial community in compacted bentonite after 5 years of exposure to natural granitic groundwater.

The Materials Corrosion Test (MaCoTe) at the Underground Research Laboratory in Grimsel, Switzerland, assesses the microbiology and corrosion behavior of engineered barrier components of a deep geological repository (DGR) for long-term disposal of high-level nuclear waste. Diversity and temporal changes of bentonite-associated microbial community profiles were assessed under DGR-like conditions for compacted Wyoming MX-80 bentonite (1.25 g/cm3 and 1.50 g/cm3 targeted dry densities) exposed to natural groundwater. Using culture-dependent and molecular techniques, samples taken from the outside layer of 5-year borehole modules revealed up to 66% and 23% of 16S rRNA gene sequences affiliated with Desulfosporosinus and Desulfovibrio, respectively. Putatively involved in sulfate reduction, these taxa were almost undetectable within the bentonite core. Instead, microbial profiles of the inner bentonite core were similar to uncompacted bentonite used to pack modules years earlier, and were consistent with a previously published 1-year time point, revealing no detectable microbial growth. Abundances of culturable aerobic and anaerobic heterotrophic bacteria in the uncompacted bentonite were relatively low, with less than 1,000 and 100 colony-forming units (CFUs) per gram dry weight, respectively. Nearly 5 years after emplacement, culturable heterotrophic bacterial CFUs and sulfate-reducing bacteria did not change significantly inside the bentonite core. Phospholipid fatty acid data indicated similar lipid abundance, and corresponding cell abundance estimates, for inner 5-year MaCoTe bentonite samples compared to those previously obtained for 1-year incubations. Collectively, our results provide complementary evidence for microbial stability inside highly compacted bentonite exposed to conditions that mimic engineered barrier components of a deep geological repository. IMPORTANCE The long-term safety of a deep geological repository for used nuclear fuel is dependent on the performance of the engineered and natural barriers. Microbial activity can produce chemical species that can influence the corrosion of the disposal containers for used nuclear fuel. Although previous studies have evaluated the microbiology of compacted bentonite clay within subsurface environments, these have been limited to relatively short incubations (i.e., 1 year). The current study provides a unique 5-year perspective that reinforces previous findings of growth inhibition for bentonite clay exposed to in situ subsurface conditions.