Geology topics

Ben C. Augustine

Publications and source records attributed to Ben C. Augustine.

16 recordsLinked to original sources

USGS2026

Temporal replication and sampling efficiency in high-frequency autonomous and manual eDNA sampling

Environmental DNA (eDNA) sampling is a valuable tool for ecological research, but its reliability as a quantitative approach can be improved by accounting for unexplained spatiotemporal variability and autocorrelation in eDNA concentrations. Sampling designs with temporal replication can separate sampling variation from temporal variation, but obtaining true temporal replicates is difficult when samples are collected sequentially. Autosamplers may facilitate high-frequency eDNA sampling, but their sequential sampling structure raises questions about when closely spaced samples can reasonably be treated as temporal replicates. This 19.5-day study evaluated temporal variation in invasive Round Goby ( Neogobius melanostomus ) eDNA concentration at a single large-river site with short-term hydrologic variability. We used two synchronized autosamplers and a backpack sampler to collect samples as frequently as every 15 min using polyethersulfone (PES) or mixed cellulose ester (MCE) filters. We fit hierarchical temporal models that decomposed sampling and temporal variation, evaluated evidence for residual temporal correlation under alternative priors for the effective range, and used simulation to assess bias when closely spaced sequential samples were treated as simultaneous temporal replicates. We found evidence of residual temporal correlation at short lag scales, but the effective range of temporal correlation was weakly identified and sensitive to prior assumptions. Hydrologic covariates had limited explanatory support once temporal structure was accounted for. Simulations showed that treating sequential samples collected over 15–60 min windows as simultaneous temporal replicates can bias estimates of sampling and temporal variation, especially when temporal correlation is relatively weak. MCE filters produced higher expected recovered copy numbers than PES filters, while evidence for systematic differences among sampler types was weaker. These results emphasize that eDNA temporal replication should be collected as close together in time as possible, and that uncertainty in temporal correlation should be explicitly considered when designing and analyzing high-frequency eDNA surveys.

New York↗

USGS2026

Accounting for temporal variation and correlation in environmental DNA sampling can improve ecological inferences

Environmental DNA (eDNA) concentration varies through space and time, and measurements collected close together are often correlated. Ignoring this dependence can inflate the rate of incorrect ecological inferences (Type I error rate). Although spatial correlation in eDNA has received considerable attention, temporal correlation has been less well studied. Statistical models and study designs that account for temporal correlation are increasingly important to understand time-dependent effects in complex systems. We developed a hierarchical model that separates temporal ecological variation from variability stemming from sampling and laboratory processes and applied it to four single-site eDNA time series collected over 17–24 days, three of which provided sufficient information for parameter estimation. We then used the empirically estimated parameter magnitudes in a simulation study to evaluate alternative temporal sampling designs that considered (1) how a fixed number of samples are allocated across different numbers of sampling times with different levels of temporal replication and (2) equally spaced versus cluster-spaced sampling (short bursts separated by longer gaps). Across the three time series sufficient for analysis, we observed substantial sampling variability, temporal variability, and temporal correlation, although correlation was estimated imprecisely (large coefficient of variation). Simulations showed that when sampling intervals were shorter than the effective temporal correlation range, models that ignored temporal dependence produced inflated Type I error rates and frequently detected spurious temporal trends. Accounting for temporal correlation substantially reduced this inflated Type I error rate. Optimal sampling strategies depended on study objectives. Clustered sampling most effectively estimated temporal correlation. When temporal dependence was negligible, evenly spaced sampling maximized power to detect trends. Estimating sampling variability required concentrating effort into fewer sampling times with more replicates per time, whereas estimating temporal variance was most precise with intermediate levels of replication. Together, these results indicate that temporal dependence can strongly affect inference from quantitative eDNA time series when sampling intervals approach the correlation timescale. Designs that ignore this dependence risk inferring ecological change or difference where none exists. Our framework provides practical guidance for allocating sampling effort in temporally intensive eDNA monitoring and for interpreting trends from short time series.

Idaho, Florida, Montana, Wisconsin↗

USGS2025

Spatially explicit capture-mark-recapture to evaluate demographic status of the Louisiana black bear

Louisiana black bears ( Ursus americanus luteolus ) occur in semi-isolated fragments of bottomland hardwood forest in the lower Mississippi Alluvial Valley and were listed as threatened under the United States Endangered Species Act in 1992. A population viability analysis based on radio-telemetry and capture-mark-recapture (CMR) data collected from 2002 to 2012 revealed that the probability of bears persisting in at least 1 subpopulation was >0.999, which prompted the United States Fish and Wildlife Service to remove the Louisiana black bear from the threatened species list in 2016. A post-delisting monitoring plan was developed, which included continued noninvasive CMR surveys to monitor subpopulation trends. We used genetic CMR data based on non-invasively collected hair samples for a post-delisting assessment of the demographic status of the black bear subpopulations in Louisiana, USA. The assessment included primary range in the Tensas River Basin (TRB; 2006–2020), the Upper Atchafalaya River Basin (UARB; 2007–2020), and the Three Rivers Complex (TRC; 2014 and 2021), where bears had been reintroduced beginning in 2001, and adjacent areas of possible range expansion (i.e., secondary range). We used spatially explicit closed-population capture-recapture models to estimate abundance ( N ), density ( D ), and the realized population growth rate ( λ ) for female bears at TRB, UARB, and TRC. Model-averaged estimates of N for primary range at TRB ranged from 135 (95% CI = 121–149) female bears in 2006 to 140 (95% CI = 123–157) in 2020 with mean λ of 1.003 (95% CI = 0.991–1.015), which suggested a stable primary population. An estimated 56 (95% CI = 19–92) additional female bears occurred in secondary range at TRB in 2020. Model-averaged estimates of N for primary range at UARB ranged from 35 (95% CI = 28–43) females in 2007 to 33 (95% CI = 28–43) in 2020, with mean λ = 0.996 (95% CI = 0.972–1.020), again suggesting a stable population. We estimated 3 (95% CI = 0–6) additional female bears occurred in secondary range at UARB during 2020. The estimate of N at TRC was 28 (95% CI = 18–44) females in 2014 and 42 (95% CI = 19–94) in 2021. The number of bears in the secondary ranges suggests some bears may have emigrated from the primary areas and colonized secondary areas, likely facilitated by increases in bottomland hardwood forests adjacent to the core populations. The stable primary populations, the reintroduced population at TRC, and the number of bears in secondary range adjacent to TRB indicate the overall number of Louisiana black bears has increased since monitoring began.

Louisiana↗

USGS2024

Effectiveness of canine-assisted surveillance and human searches for early detection of invasive spotted lanternfly

Prevention and early detection of invasive species are championed as the most cost-effective and efficient strategies for reducing or preventing negative impacts on ecosystems. Spotted lanternfly (SLF), Lycorma delicatula , is a recently introduced invasive insect whose range in the United States has been expanding rapidly since it was first discovered in Pennsylvania in 2014. Feeding by this planthopper can cause severe impacts on agricultural production, particularly grapes ( Vitis spp.). Human visual surveys are the most common search method employed for detection but can be ineffective due to the insect's cryptic egg masses and low density during early stages of infestation. Therefore, finding alternative early detection methods has become a priority for agencies tasked with addressing SLF management. This study experimentally tested whether trained detector dogs could improve the probability of detecting SLF in both agricultural and forest settings. We surveyed transects in 20 vineyards and their adjacent wooded areas in Pennsylvania and New Jersey, USA, and used a multiscale occupancy model to estimate detection probability achieved by human observers and detection dogs as a function of SLF infestation level, weather, and habitat covariates. We modeled transect-level occupancy of SLF as a function of infestation level, habitat type, topographic position index, and distance to forests. Occupancy probability of SLF was higher on vines within vineyards than in forests, and occupancy declined with increasing distance from forests, which is informative for future search efforts. Detection probability of SLF was lower at forested sites but was higher at high infestation sites. Detection dogs had a lower detection probability than humans in the vineyards, but the detection probability of dogs was >3× greater than that of humans in forested sites. Our study suggests that detection dogs are more effective than human visual searches as an early detection method for SLF in forested areas, and utilizing detector dogs could strengthen SLF early detection efforts. This study demonstrates the potential applicability of using canine-assisted search strategies combined with occupancy models to enhance the surveillance and prevention of other difficult-to-detect invasive species.

New Jersey, Pennsylvania↗

USGS2024

Abundance-mediated species interactions

Species interactions shape biodiversity patterns, community assemblage, and the dynamics of wildlife populations. Ecological theory posits that the strength of interspecific interactions is fundamentally underpinned by the population sizes of the involved species. Nonetheless, prevalent approaches for modeling species interactions predominantly center around occupancy states. Here, we use simulations to illuminate the inadequacies of modeling species interactions solely as a function of occupancy, as is common practice in ecology. We demonstrate erroneous inference into species interactions due to error in parameter estimates when considering species occupancy alone. To address this critical issue, we propose, develop, and demonstrate an abundance-mediated interaction framework designed explicitly for modeling species interactions involving two or more species from detection/non-detection data. We present Markov chain Monte Carlo (MCMC) samplers tailored for diverse ecological scenarios, including intraguild predation, disease- or predator-mediated competition, and trophic cascades. Illustrating the practical implications of our approach, we compare inference from modeling the interactions in a three-species network involving coyotes ( Canis latrans ), fishers ( Pekania pennanti ), and American marten ( Martes americana ) in North America as a function of occupancy states and as a function of abundance. When modeling interactions as a function of abundance rather than occupancy, we uncover previously unidentified interactions. Our study emphasizes that accounting for abundance-mediated interactions rather than simple co-occurrence patterns can fundamentally alter our comprehension of system dynamics. Through an empirical case study and comprehensive simulations, we demonstrate the importance of accounting for abundance when modeling species interactions, and we present a statistical framework equipped with MCMC samplers to achieve this paradigm shift in ecological research.

Ecology↗

USGS2024

A hierarchical model for eDNA fate and transport dynamics accommodating low concentration samples

Environmental DNA (eDNA) sampling is an increasingly important tool for answering ecological questions and informing aquatic species management; however, several factors currently limit the reliability of ecological inference from eDNA sampling. Two particular challenges are (1) determining species source location(s) and (2) accurately and precisely measuring low concentration eDNA samples in the presence of multiple sources of ecological and measurement variability. The recently introduced eDNA Integrating Transport and Hydrology (eDITH) model provides a framework for relating eDNA measurements to source locations in riverine networks, but little empirical work has been done to test and refine model assumptions or accommodate low concentration samples, that can be systematically undermeasured. To better understand eDNA fate and transport dynamics and our ability to reliably quantify low concentration samples, we developed a hierarchical model and used it to evaluate a fate and transport experiment. Our model addresses several low concentration challenges by modeling the number of copies in each PCR replicate as a latent variable with a count distribution and conditioning detection and quantification on replicate copy number. We provide evidence that the eDNA removal rate declined through time, estimating that over 80% of eDNA was removed over the first 10 m, traversed in 41 s. After this initial period of rapid decay, eDNA decayed slowly with consistent detection through our farthest site 1 km from the release location, traversed in 67.8 min. Our model further allowed us to detect extra-Poisson variation in the allocation of copies to replicates. We extended our hierarchical model to accommodate a continuous effect of inhibitors and used our model to provide evidence for the inhibitor hypothesis and explore the potential implications. While our model is not a panacea for all challenges faced when quantifying low-concentration eDNA samples, it provides a framework for a more complete accounting of uncertainty.

Environmental and Ecological Statistics↗

USGS2024

Field trials of an autonomous eDNA sampler in lotic waters

Environmental DNA (eDNA) analysis has become a transformative technology, but sample collection methods lack standardization and sampling at effective frequencies requires considerable field effort. Autonomous eDNA samplers that can sample water at high frequencies offer potential solutions to these problems. We present results from four case studies using a prototype autonomous eDNA sampler as part of the U.S. Geological Survey’s Rapid Environmental eDNA Assessment and Deployment Initiative & Network (READI-Net) project. These case studies involved short-term deployments of an eDNA autosampler (Smith-Root) across a range of riverine habitats with the objectives of (a) identifying what insights could be gained from high-frequency autosampling and (b) benchmarking these autosamples against manually collected samples. The high frequency autosampling revealed high temporal variability of eDNA concentrations and provided valuable insights about eDNA associations with environmental covariates, such as discharge and turbidity. Benchmarking assessments indicated autosamples had similar detection rates to manual samples and obtained similar or greater eDNA quantities. We did find minimal carryover contamination in autosampler field controls. We conclude that eDNA autosamplers have potential to improve freshwater biosurveillance by reducing logistical sampling barriers, standardizing collection methods, and clarifying the influence of environmental covariates on eDNA results.

Idaho, Missouri, Montana, New York↗

USGS2024

Autonomous samplers and environmental DNA metabarcoding: Sampling day and primer choice have greatest impact on fish detection probabilities

Unprecedented rates of biodiversity loss and ecosystem function necessitate the use of rapid, efficacious, and cost-effective biomonitoring tools. The combination of autonomous samplers and high throughput sequencing (i.e., “metabarcoding”) of environmental DNA ( eDNA ) samples enables characterization of entire communities at high frequency and can be an important tool for conservation and management, allowing researchers to track fluctuations in biodiversity. We deployed two autonomous samplers at two U.S. Geological Survey streamgage sites in the upper Snake River (Wyoming and Idaho, USA) to collect eDNA samples from July-September 2021 and 2022 to characterize fish diversity. We used a probabilistic approach to evaluate the effects of water temperature, water discharge, filter pore size, water volume filtered, number of samples collected, timing, and primers on the probability of detecting eDNA from fish species known to be present. We detected eDNA from 13/15 species present in these areas of the Snake River. Overall, we did not find evidence that filter pore size, water volume filtered, water discharge, and water temperature affected the probability of detecting fish species’ eDNA . By contrast, primers and sampling day affected fish detection probabilities, indicating that primer choice and sampling day can either over- or under- estimate species diversity. These results indicate that users would ideally consider sampling on non-consecutive days and which primer set will maximize species detections.

Metabarcoding & Metagenomics↗

USGS2024

A hierarchical model for eDNA fate and transport dynamics accommodating low concentration samples

Environmental DNA (eDNA) sampling is an increasingly important tool for answering ecological questions and informing aquatic species management; however, several factors currently limit the reliability of ecological inference from eDNA sampling. Two particular challenges are 1) determining species source location(s) and 2) accurately and precisely measuring low concentration eDNA samples in the presence of multiple sources of ecological and measurement variability. The recently introduced eDNA Integrating Transport and Hydrology (eDITH) model provides a framework for relating eDNA measurements to source locations in riverine networks, but little empirical work has been done to test and refine model assumptions or accommodate low concentration samples, that can be systematically undermeasured. To better understand eDNA fate and transport dynamics and our ability to reliably quantify low concentration samples, we developed a hierarchical model and used it to evaluate a fate and transport experiment. Our model addresses several low concentration challenges by modeling the number of copies in each PCR replicate as a latent variable with a count distribution and conditioning detection and quantification on replicate copy number. We provide evidence that the eDNA removal rate declined through time, estimating that over 80% of eDNA was removed over the first 10 meters, traversed in 41 seconds. After this initial period of rapid decay, eDNA decayed slowly with consistent detection through our farthest site 1km from the release location, traversed in 250 seconds. Our model further allowed us to detect extra-Poisson variation in the allocation of copies to replicates. We extended our hierarchical model to accommodate a continuous effect of inhibitors and used our model to provide evidence for the inhibitor hypothesis and explore the potential implications. While our model is not a panacea for all challenges faced when quantifying low-concentration eDNA samples, it provides a framework for a more complete accounting of uncertainty.

BioRxiv↗

USGS2024

Estimating species-specific U.S. waterfowl harvest

The U.S. Fish and Wildlife Service monitors species-specific waterfowl (ducks, seaducks, geese, and brant) harvest through two hunter surveys, one that estimates the total harvest for each waterfowl group, and a second that estimates the species composition of each waterfowl group. Point estimates for species-specific harvest can be computed by multiplying the estimated total harvest by the estimated proportion of the total harvest of each species. However, to date, no uncertainty estimates have been available. Here, we combine these two data sources to provide species-specific harvest estimates at the state and flyway level while characterizing the uncertainty via Bayesian estimation. We take a similar approach to Smith et al . (2022) , providing both estimates that treat yearly data as independent and estimates that share information across years via a random walk process. We then discuss the advantages and disadvantages of each approach.

BioRxiv↗

USGS2022

The occupancy-abundance relationship and sampling designs using occupancy to monitor populations of Asian bears

Designing a population monitoring program for Asian bears presents challenges associated with their low densities and detectability, generally large home ranges, and logistical or resource constraints. The use of an occupancy-based method to monitor bear populations can be appropriate under certain conditions given the mechanistic relationship between occupancy and abundance. The form of the occupancy–abundance relationship is dependent on species-specific characteristics such as home range size and population density, as well as study area size. To assess the statistical power of tests to detect population change of Asian bears, we conducted a study using a range of scenarios by simulating spatially explicit individual-based capture-recapture data from a demographically open model. Simulations assessed the power to detect changes in population density via changes in site-level occupancy or abundance through time, estimated using a standard occupancy model or a Royle-Nichols model, both with point detectors (representing camera traps). We used IUCN Red List criteria as a guide in selection of two population decline scenarios (20% and 50%), but we chose a shorter time horizon (10 years = 1 bear generation), meaning that declines were steeper than used for IUCN criteria (3 generations). Our simulations detected population declines of 50% with high power (>0.80) and low false positive rates (FPR: incorrectly detecting a decline) (<0.10) when detectors were spaced at > 0.67 times the home range diameter (home-range spacing ratio: HRSR, a measure of spatial correlation), such that bears would tend to overlap no more than two detectors. There was high (0.85) correlation between realized occupancy and N in these scenarios. The FPR increased as the HRSR decreased because of spatial correlation in the occupancy process induced when individual home ranges overlap multiple detectors. The mean statistical power to detect more gradual population declines (20% in 10 years) with HRSR > 0.67 was low for occupancy models 0.22 (maximum power 0.67) and Royle-Nichols models (0.24; maximum power 0.67), suggesting that declines of this magnitude may not be described reliably with 10 years of monitoring. Our results demonstrated that under many realistic scenarios that we explored, false positive rates were unacceptably high. We highlight that when designing occupancy studies, the spacing between point detectors be at least 0.67 times the diameter of the home range size of the larger sex (e.g., males) when the assumptions of the spatial capture-recapture model used for simulation are met.

Global Ecology and Conservation↗

USGS2020

Spatial capture–recapture with random thinning for unidentified encounters

Spatial capture–recapture (SCR) models have increasingly been used as a basis for combining capture–recapture data types with variable levels of individual identity information to estimate population density and other demographic parameters. Recent examples are the unmarked SCR (or spatial count model), where no individual identities are available and spatial mark–resight (SMR) where individual identities are available for only a marked subset of the population. Currently lacking, though, is a model that allows unidentified samples to be combined with identified samples when there are no separate classes of “marked” and “unmarked” individuals and when the two sample types cannot be considered as arising from two independent observation models. This is a common scenario when using noninvasive sampling methods, for example, when analyzing data on identified and unidentified photographs or scats from the same sites. Here we describe a “random thinning” SCR model that utilizes encounters of both known and unknown identity samples using a natural mechanistic dependence between samples arising from a single observation model. Our model was fitted in a Bayesian framework using NIMBLE. We investigate the improvement in parameter estimates by including the unknown identity samples, which was notable (up to 79% more precise) in low‐density populations with a low rate of identified encounters. We then applied the random thinning SCR model to a noninvasive genetic sampling study of brown bear ( Ursus arctos ) density in Oriental Cantabrian Mountains (North Spain). Our model can improve density estimation for noninvasive sampling studies for low‐density populations with low rates of individual identification, by making use of available data that might otherwise be discarded.

Ecology & Evolution↗

USGS2020

Spatial proximity moderates genotype uncertainty in genetic tagging studies

Accelerating declines of an increasing number of animal populations worldwide necessitate methods to reliably and efficiently estimate demographic parameters such as population density and trajectory. Standard methods for estimating demographic parameters from noninvasive genetic samples are inefficient because lower-quality samples cannot be used, and they assume individuals are identified without error. We introduce the genotype spatial partial identity model (gSPIM), which integrates a genetic classification model with a spatial population model to combine both spatial and genetic information, thus reducing genotype uncertainty and increasing the precision of demographic parameter estimates. We apply this model to data from a study of fishers ( Pekania pennanti ) in which 37% of hair samples were originally discarded because of uncertainty in individual identity. The gSPIM density estimate using all collected samples was 25% more precise than the original density estimate, and the model identified and corrected three errors in the original individual identity assignments. A simulation study demonstrated that our model increased the accuracy and precision of density estimates 63 and 42%, respectively, using three replicated assignments (e.g., PCRs for microsatellites) per genetic sample. Further, the simulations showed that the gSPIM model parameters are identifiable with only one replicated assignment per sample and that accuracy and precision are relatively insensitive to the number of replicated assignments for high-quality samples. Current genotyping protocols devote the majority of resources to replicating and confirming high-quality samples, but when using the gSPIM, genotyping protocols could be more efficient by devoting more resources to low-quality samples.

Proceedings of the National Academy of Sciences↗

USGS2019

Spatial capture–recapture for categorically marked populations with an application to genetic capture–recapture

Recently introduced unmarked spatial capture–recapture (SCR), spatial mark–resight (SMR), and 2‐flank spatial partial identity models (SPIMs) extend the domain of SCR to populations or observation systems that do not always allow for individual identity to be determined with certainty. For example, some species do not have natural marks that can reliably produce individual identities from photographs, and some methods of observation produce partial identity samples as is the case with remote cameras that sometimes produce single‐flank photographs. Unmarked SCR, SMR, and SPIM share the feature that they probabilistically resolve the uncertainty in individual identity using the spatial location where samples were collected. Spatial location is informative of individual identity in spatially structured populations because a sample is more likely to have been produced by an individual living near the trap where it was recorded than an individual living further away from the trap. Further, the level of information about individual identity that a spatial location contains is related to two key ecological concepts, population density and home range size, which we quantify using a proposed Identity Diversity Index (IDI). We show that latent and partial identity SCR models produce imprecise and biased density estimates in many high IDI scenarios when data are sparse. We then extend the unmarked SCR model to incorporate categorical, partially identifying covariates, which reduce the level of uncertainty in individual identity, increasing the reliability and precision of density estimates, and allowing reliable density estimation in scenarios with higher IDI values and with more sparse data. We illustrate the performance of this “categorical SPIM” via simulations and by applying it to a black bear data set using microsatellite loci as categorical covariates, where we reproduce the full data set estimates with only slightly less precision using fewer loci than necessary for confident individual identification. We then discuss how the categorical SPIM can be applied to other wildlife sampling scenarios such as remote camera surveys, where natural or researcher‐applied partial marks can be observed in photographs. Finally, we discuss how the categorical SPIM can be added to SMR, 2‐flank SPIM, or other latent identity SCR models.

Ecosphere↗

USGS2019

Early genetic outcomes of American black bear reintroductions in the Central Appalachians, USA

Habitat loss and overexploitation extirpated American black bears (Ursus americanus) from most of the Central Appalachians, USA, by the early twentieth century. To attempt to restore bears to the southwestern portion of this region, 2 reintroductions that used small founder groups (n = 27 and 55 bears), but different release methods (hard versus soft), were conducted during the 1990s. We collected hair samples from black bears in the reintroduced Big South Fork (BSF) and Kentucky–Virginia populations (KVP), their respective Great Smoky Mountains (GSM) and Shenandoah National Park (SNP) source populations, and a neighboring population in southern West Virginia (SWV) to investigate the early genetic outcomes of bear reintroduction. Despite having undergone genetic bottlenecks, genetic diversity remained similar between reintroduced populations and their sources ~15 years after the founder events (ranges: AR = 4.86–5.61; HO = 0.67–0.75; HE = 0.65–0.71). Effective population sizes of the reintroduced KVP and BSF (NE = 31 and 36, respectively) were substantially smaller than their respective SNP and GSM sources (NE = 119 and 156, respectively), supporting founder effects. Genetic structure analysis indicated that the hard released (i.e., no acclimation period) KVP founder group likely declined considerably, whereas the soft released BSF founder group remained mostly intact, suggesting superior effectiveness of soft releases. Asymmetrical gene flow via immigration from the SWV has resulted in the KVP recovering from the initial founder group reduction. Sustained isolation, small NE, and small population size of the BSF may warrant continued genetic monitoring to determine if gene flow from neighboring populations is established or NE declines. For future bear reintroductions, we suggest managers consider sourcing founders from populations with high genetic diversity and soft-releasing bears to locales that are, if possible, within the dispersal capability of extant populations to mitigate the potential consequences of founder effects and isolation.

Central Appalachians↗

USGS2018

Spatial capture–recapture with partial identity: An application to camera traps

Camera trapping surveys frequently capture individuals whose identity is only known from a single flank. The most widely used methods for incorporating these partial identity individuals into density analyses discard some of the partial identity capture histories, reducing precision, and, while not previously recognized, introducing bias. Here, we present the spatial partial identity model (SPIM), which uses the spatial location where partial identity samples are captured to probabilistically resolve their complete identities, allowing all partial identity samples to be used in the analysis. We show that the SPIM outperforms other analytical alternatives. We then apply the SPIM to an ocelot data set collected on a trapping array with double-camera stations and a bobcat data set collected on a trapping array with single-camera stations. The SPIM improves inference in both cases and, in the ocelot example, individual sex is determined from photographs used to further resolve partial identities—one of which is resolved to near certainty. The SPIM opens the door for the investigation of trapping designs that deviate from the standard two camera design, the combination of other data types between which identities cannot be deterministically linked, and can be extended to the problem of partial genotypes.

Annals of Applied Statistics↗