A latent capture history model for digital aerial surveys

Funding: This work was part-funded by the Royal Society of New Zealand Marsden grant UOA-1418, Leverhulme grant RF-2018-213\9 and EPSRC IAA grant ‘High Definition digital aerial survey software’.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.PostprintPeer reviewe

A flexible framework for spatial capture-recapture with unknown identities

Funding: We acknowledge the funding support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and the University of St Andrews. The fisher study was funded by InnoTech Alberta grants, Government of Alberta (Environment and Parks), The Beaver Hills Initiative, Alberta Conservation Association, NSERC, Royal Canadian Geographic Society, TD Friends of the Environment Foundation, and the Fur Institute of Canada scholarships.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.Peer reviewe

Long-term monitoring of wildlife populations for protected area management in Southeast Asia

Long-term monitoring of biodiversity in protected areas (PAs) is critical to assess threats, link conservation action to species outcomes, and facilitate improved management. Yet, rigorous longitudinal monitoring within PAs is rare. In Southeast Asia (SEA), there is a paucity of long-term wildlife monitoring within PAs, and many threatened species lack population estimates from anywhere in their range, making global assessments difficult. Here, we present new abundance estimates and population trends for 11 species between 2010 and 2020, and spatial distributions for 7 species, based on long-term line transect distance sampling surveys in Keo Seima Wildlife Sanctuary in Cambodia. These represent the first robust population estimates for four threatened species from anywhere in their range and are among the first long-term wildlife population trend analyses from the entire SEA region. Our study revealed that arboreal primates and green peafowl (Pavo muticus) generally had either stable or increasing population trends, whereas ungulates and semiarboreal primates generally had declining trends. These results suggest that ground-based threats, such as snares and domestic dogs, are having serious negative effects on terrestrial species. These findings have important conservation implications for PAs across SEA that face similar threats yet lack reliable monitoring data

Statistical ecology comes of age

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.Peer reviewe

Genetic diversity and connectivity of southern right whales (Eubalaena australis) found in the Brazil and Chile-Peru wintering grounds and the South Georgia (Islas Georgias del Sur) feeding ground

As species recover from exploitation, continued assessments of connectivity and population structure are warranted to provide information for conservation and management. This is particularly true in species with high dispersal capacity, such as migratory whales, where patterns of connectivity could change rapidly. Here we build on a previous long-term, large-scale collaboration on southern right whales (Eubalaena australis) to combine new (nnew) and published (npub) mitochondrial (mtDNA) and microsatellite genetic data from all major wintering grounds and, uniquely, the South Georgia (Islas Georgias del Sur: SG) feeding grounds. Specifically, we include data from Argentina (npub mtDNA/microsatellite=208/46), Brazil (nnew mtDNA/microsatellite=50/50), South Africa (nnew mtDNA/microsatellite=66/77, npub mtDNA/microsatellite=350/47), Chile-Peru (nnew mtDNA/microsatellite=1/1), the Indo-Pacific (npub mtDNA/microsatellite=769/126), and SG (npub mtDNA/microsatellite=8/0, nnew mtDNA/microsatellite=3/11) to investigate the position of previously unstudied habitats in the migratory network: Brazil, SG and Chile-Peru. These new genetic data show connectivity between Brazil and Argentina, exemplified by weak genetic differentiation and the movement of one genetically identified individual between the South American grounds. The single sample from Chile-Peru had a mtDNA haplotype previously only observed in the Indo-Pacific and had a nuclear genotype that appeared admixed between the Indo-Pacific and South Atlantic, based on genetic clustering and assignment algorithms. The SG samples were clearly South Atlantic, and were more similar to the South American than the South African wintering grounds. This study highlights how international collaborations are critical to provide context for emerging or recovering regions, like the SG feeding ground, as well as those that remain critically endangered, such as Chile-Peru

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

This work was funded by a joint EPSRC/NERC PhD grant (No. EP/1000917/1), by the EPSRC through a Doctoral Prize Fellowship, and by the Royal Society of New Zealand through Marsden grant 14-UOA-155.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.PostprintPeer reviewe