That's not the Mona Lisa! How to interpret spatial capture-recapture density surface estimates.

Spatial capture-recapture methods are often used to produce density surfaces, and these surfaces are often misinterpreted. In particular, spatial change in density is confused with spatial change in uncertainty about density. We illustrate correct and incorrect inference visually by treating a grayscale image of the Mona Lisa as an activity center intensity or density surface and simulating spatial capture-recapture survey data from it. Inferences can be drawn about the intensity of the point process generating activity centers, and about the likely locations of activity centers associated with the capture histories obtained from a single survey of a single realization of this process. We show that treating probabilistic predictions of activity center locations as estimates of the intensity of the process results in invalid and misleading ecological inferences, and that predictions are highly dependent on where the detectors are placed and how much survey effort is used. Estimates of the activity center density surface should be obtained by estimating the intensity of a point process model for activity centers. Practitioners should state explicitly whether they are estimating the intensity or making predictions of activity center location, and predictions of activity center locations should not be confused with estimates of the intensity.

A flexible framework for spatial capture-recapture with unknown identities.

Camera traps or acoustic recorders are often used to sample wildlife populations. When animals can be individually identified, these data can be used with spatial capture-recapture (SCR) methods to assess populations. However, obtaining animal identities is often labor-intensive and not always possible for all detected animals. To address this problem, we formulate SCR, including acoustic SCR, as a marked Poisson process, comprising a single counting process for the detections of all animals and a mark distribution for what is observed (eg, animal identity, detector location). The counting process applies equally when it is animals appearing in front of camera traps and when vocalizations are captured by microphones, although the definition of a mark changes. When animals cannot be uniquely identified, the observed marks arise from a mixture of mark distributions defined by the animal activity centers and additional characteristics. Our method generalizes existing latent identity SCR models and provides an integrated framework that includes acoustic SCR. We apply our method to estimate density from a camera trap study of fisher (Pekania pennanti) and an acoustic survey of Cape Peninsula moss frog (Arthroleptella lightfooti). We also test it through simulation. We find latent identity SCR with additional marks such as sex or time of arrival to be a reliable method for estimating animal density.

Spatial correlation structures for detections of individuals in spatial capture-recapture models.

Spatial capture-recapture (SCR) models are commonly used to estimate animal density from surveys on which detectors passively detect animals without physical capture, for example, using camera traps, hair snares, or microphones. An individual is more likely to be recorded by detectors close to its activity center, the centroid of its movement throughout the survey. Existing models to account for this spatial heterogeneity in detection probabilities rely on an assumption of independence between detection records at different detectors conditional on the animals' activity centers, which are treated as latent variables. In this paper, we show that this conditional independence assumption may be violated due to the way animals move around the survey region and encounter detectors, such that additional spatial correlation is almost inevitable. We highlight the links between the well-studied issue of unmodeled temporal heterogeneity in nonspatial capture-recapture and this variety of unmodeled spatial heterogeneity in SCR, showing that the latter causes predictable bias in the same way as the former. We address this by introducing a latent detection field into the model, and illustrate the resulting approach with a simulation study and an application to a camera-trap survey of snow leopards Panthera uncia. Our method is a unifying model for several existing SCR approaches, with special cases including standard SCR, models that account for nonspatial individual heterogeneity, and models with overdispersed detection counts.

A latent capture history model for digital aerial surveys.

We anticipate that unmanned aerial vehicles will become popular wildlife survey platforms. Because detecting animals from the air is imperfect, we develop a mark-recapture line transect method using two digital cameras, possibly mounted on one aircraft, which cover the same area with a short time delay between them. Animal movement between the passage of the cameras introduces uncertainty in individual identity, so individual capture histories are unobservable and are treated as latent variables. We obtain the likelihood for mark-recapture line transects without capture histories by automatically enumerating all possibilities within segments of the transect that contain ambiguous identities, instead of attempting to decide identities in a prior step. We call this method "Latent Capture-history Enumeration" (LCE). We include an availability model for species that are periodically unavailable for detection, such as cetaceans that are undetectable while diving. External data are needed to estimate the availability cycle length, but not the mean availability rate, if the full availability model is employed. We compare the LCE method with the recently developed cluster capture-recapture method (CCR), which uses a Palm likelihood approximation, providing the first comparison of CCR with maximum likelihood. The LCE estimator has slightly lower variance, more so as sample size increases, and close to nominal coverage probabilities. Both methods are approximately unbiased. We illustrate with semisynthetic data from a harbor porpoise survey.

Comparison of In-Clinic Diagnostic Testing Methods for Macrorhabdus ornithogaster.

Macrorhabdus ornithogaster is an ascomycete yeast often found at the isthmus of the ventriculus and proventriculus of infected birds. Antemortem diagnosis has traditionally involved direct visualization of organisms on wet-mount or gram-stained fecal preparations, cloacal and crop swabs, or by both methods; however, different in-clinic diagnostic techniques have never been compared to establish an optimum test for the identification of M ornithogaster in an avian patient. We compared 5 microscopically evaluated diagnostic testing methods: fecal Gram's stain, direct fecal wet preparation, macro suspension technique, macro suspension with Gram's stain, and macro suspension stained with new methylene blue. Each technique was performed on 96 fecal samples collected during the treatment of M ornithogaster-infected budgerigars with water-soluble amphotericin B. The macro suspension technique produced statistically higher organism counts than the other 4 techniques and was always estimated to have the largest detection probability. We recommend that the macro suspension technique be implemented as the most efficacious diagnostic test for in-clinic assessment of avian patients possibly infected with M ornithogaster.

Cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations.

Capture-recapture methods for estimating wildlife population sizes almost always require their users to identify every detected animal. Many modern-day wildlife surveys detect animals without physical capture-visual detection by cameras is one such example. However, for every pair of detections, the surveyor faces a decision that is often fraught with uncertainty: are they linked to the same individual? An inability to resolve every such decision to a high degree of certainty prevents the use of standard capture-recapture methods, impeding the estimation of animal density. Here, we develop an estimator for aerial surveys, on which two planes or unmanned vehicles (drones) fly a transect over the survey region, detecting individuals via high-definition cameras. Identities remain unknown, so one cannot discern if two detections match to the same animal; however, detections in close proximity are more likely to match. By modeling detection locations as a clustered point process, we extend recently developed methodology and propose a precise and computationally efficient estimator of animal density that does not require individual identification. We illustrate the method with an aerial survey of porpoise, on which cameras detect individuals at the surface of the sea, and we need to take account of the fact that they are not always at the surface.

Identifying prognostic structural features in tissue sections of colon cancer patients using point pattern analysis.

Diagnosis and prognosis of cancer are informed by the architecture inherent in cancer patient tissue sections. This architecture is typically identified by pathologists, yet advances in computational image analysis facilitate quantitative assessment of this structure. In this article, we develop a spatial point process approach to describe patterns in cell distribution within tissue samples taken from colorectal cancer (CRC) patients. In particular, our approach is centered on the Palm intensity function. This leads to taking an approximate-likelihood technique in fitting point processes models. We consider two Neyman-Scott point processes and a void process, fitting these point process models to the CRC patient data. We find that the parameter estimates of these models may be used to quantify the spatial arrangement of cells. Importantly, we observe characteristic differences in the spatial arrangement of cells between patients who died from CRC and those alive at follow up.

Evidence of amphotericin B resistance in Macrorhabdus ornithogaster in Australian cage-birds.

Amphotericin B is widely used for the treatment of Macrorhabdus ornithogaster infections. To date, however, there have been no randomized controlled trials confirming its efficacy where cure was confirmed by postmortem examination. To determine the efficacy of amphotericin B against M. ornithogaster, a three-part study was undertaken. Treatment outcomes of M. ornithogaster infected birds treated amphotericin B were reviewed. A pilot treatment trial with two naturally infected birds (Melopsittacus undulatus and Agapornis roseicollis) was undertaken, administering amphotericin B at 100 mg/kg twice daily for 30 days. Finally, a randomized controlled trial using experimentally infected chickens treated with amphotericin B at 25 mg/kg and 100 mg/kg twice daily for 10 days was performed. Retrospective analysis indicated treatment failure in 80.4% of 36 cases that met the inclusion criteria. The pilot study showed that amphotericin B did not clear, but significantly decreased Macrorhabdus ornithogaster burden, followed by profound rebound effect of the number of organisms shed in the feces. Finally, the randomized controlled trial found that amphotericin B given at 100 mg/kg did not clear, but significantly decreased the burden of M. ornithogaster compared with both the 25 mg/kg group (P = .037) and the no treatment control group (P = .001). A strong curvilinear correlation between body weight and M. ornithogaster infection burden was present in the infected chickens. These findings represent treatment failure in three scenarios and indicate that treatment with amphotericin B has poor efficacy against Macrorhabdus ornithogaster.

Spatial connectivity of reef manta rays across the Raja Ampat archipelago, Indonesia.

The reef manta ray Mobula alfredi is present throughout most island groups that form the Raja Ampat archipelago, Indonesia. The species is protected regionally and nationally and is currently managed as a single homogeneous population within the 6.7 million ha archipelago. However, scientific evidence is currently lacking regarding the spatial connectivity and population structure of M. alfredi within this archipelago. Using network analysis and an array of 34 acoustic receivers deployed throughout Raja Ampat between February 2016 and September 2021, we examined the movements of 72 subadult and adult M. alfredi tagged in seven regions of Raja Ampat. A total of 1094 M. alfredi movements were recorded and were primarily concentrated between nearby receiver stations, highlighting frequent local movements within, and limited long-distance movements between regional acoustic receiver arrays. Network analysis revealed highly connected nodes acting as hubs important for M. alfredi movements. A community detection algorithm further indicated clusters within the network. Our results suggest the existence of a metapopulation comprising three demographically and geographically distinct subpopulations within the archipelago. They also reveal the importance of Eagle Rock as a critical node in the M. alfredi movement network, justifying the urgent inclusion of this site within the Raja Ampat marine protected area network.

Density and population size estimates of the endangered northern yellow-cheeked crested gibbon Nomascus annamensis in selectively logged Veun Sai-Siem Pang National Park in Cambodia using acoustic spatial capture-recapture methods.

Many gibbon species are threatened with extinction, including the endangered northern yellow-cheeked crested gibbon, Nomascus annamensis. Assessing gibbon populations and understanding how human disturbances and environmental factors impact these populations is vital for effective conservation planning. In 2010, auditory surveys revealed that Veun Sai-Siem Pang National Park (VSSP) in Cambodia contains one of the largest known N. annamensis populations in the world, with an estimated 456 (95% CI 421-490) gibbon groups. Illegal selective logging is common in the park, but the impact of continued logging on the gibbon population has not been investigated. To determine any change in the N. annamensis population since 2010, between January and April 2019 we conducted auditory surveys at 13 sites that were at least 4 km apart. We surveyed each site for three days, each day recording the gibbon calls heard over 3.25 hours from three listening posts located 500 m apart. At the same sites, we assessed the logging intensity using transects and ecological plots. Gibbon densities can be influenced by various environmental factors such as canopy height and forest type. Therefore, in addition to investigating the relationship between the density of N. annamensis groups and logging, we included five additional environmental variables in our acoustic spatial capture-recapture models. Our best fit model with the lowest AIC value included canopy height, forest type, distance to villages, and logging. We estimate that there are 389 (95% CI 284-542) N. annamensis groups currently in VSSP. Selective logging is widespread in the park, primarily targeting four tree species. The estimated felling time of these logged trees, together with previous reports, indicate that the species most targeted in VSSP varies over time. To conserve the N. annamensis population in VSSP, it is crucial that action is taken to reduce illegal logging.

An Efficient Acoustic Density Estimation Method with Human Detectors Applied to Gibbons in Cambodia.

Some animal species are hard to see but easy to hear. Standard visual methods for estimating population density for such species are often ineffective or inefficient, but methods based on passive acoustics show more promise. We develop spatially explicit capture-recapture (SECR) methods for territorial vocalising species, in which humans act as an acoustic detector array. We use SECR and estimated bearing data from a single-occasion acoustic survey of a gibbon population in northeastern Cambodia to estimate the density of calling groups. The properties of the estimator are assessed using a simulation study, in which a variety of survey designs are also investigated. We then present a new form of the SECR likelihood for multi-occasion data which accounts for the stochastic availability of animals. In the context of gibbon surveys this allows model-based estimation of the proportion of groups that produce territorial vocalisations on a given day, thereby enabling the density of groups, instead of the density of calling groups, to be estimated. We illustrate the performance of this new estimator by simulation. We show that it is possible to estimate density reliably from human acoustic detections of visually cryptic species using SECR methods. For gibbon surveys we also show that incorporating observers' estimates of bearings to detected groups substantially improves estimator performance. Using the new form of the SECR likelihood we demonstrate that estimates of availability, in addition to population density and detection function parameters, can be obtained from multi-occasion data, and that the detection function parameters are not confounded with the availability parameter. This acoustic SECR method provides a means of obtaining reliable density estimates for territorial vocalising species. It is also efficient in terms of data requirements since since it only requires routine survey data. We anticipate that the low-tech field requirements will make this method an attractive option in many situations where populations can be surveyed acoustically by humans.