Integrating data from different survey types for population monitoring of an endangered species: the case of the Eld's deer

A model for the noninvasive, habitat-inclusive estimation of upper limit abundance for synanthropes, exemplified by M. fascicularis

Accurately estimating population sizes for free-ranging animals through noninvasive methods, such as camera trap images, remains particularly limited by small datasets. To overcome this, we developed a flexible model for estimating upper limit populations and exemplified it by studying a group-living synanthrope, the long-tailed macaque (Macaca fascicularis). Habitat preference maps, based on environmental and GPS data, were generated with a maximum entropy model and combined with data obtained from camera traps, line transect distance sampling, and direct sightings to produce an expected number of individuals. The mapping between habitat preference and number of individuals was optimized through a tunable parameter ρ (inquisitiveness) that accounts for repeated observations of individuals. Benchmarking against published data highlights the high accuracy of the model. Overall, this approach combines citizen science with scientific observations and reveals the long-tailed macaque populations to be (up to 80%) smaller than expected. The model's flexibility makes it suitable for many species, providing a scalable, noninvasive tool for wildlife conservation.

Population abundance estimates in conservation and biodiversity research

Measuring and tracking biodiversity from local to global scales is challenging due to its multifaceted nature and the range of metrics used to describe spatial and temporal patterns. Abundance can be used to describe how a population changes across space and time, but it can be measured in different ways, with consequences for the interpretation and communication of spatiotemporal patterns. We differentiate between relative and absolute abundance, and discuss the advantages and disadvantages of each for biodiversity monitoring, conservation, and ecological research. We highlight when absolute abundance can be advantageous and should be prioritized in biodiversity monitoring and research, and conclude by providing avenues for future research directions to better assess the necessity of absolute abundance in biodiversity monitoring.

A comparison of manual and automated detection of rusa deer (

Abstract Context Monitoring is an essential part of managing invasive species; however, accurate, cost-effective detection techniques are necessary for it to be routinely undertaken. Current detection techniques for invasive deer are time consuming, expensive and have associated biases, which may be overcome by exploiting new technologies. Aims We assessed the accuracy and cost effectiveness of automated detection methods in comparison to manual detection of thermal footage of deer captured by remotely piloted aircraft systems. Methods Thermal footage captured by RPAS was assessed using an algorithm combining two object-detection techniques, namely, YOLO and Faster-RCNN. The number of deer found using manual review on each sampling day was compared with the number of deer found on each day using machine learning. Detection rates were compared across survey areas and sampling occasions. Key results Overall, there was no difference in the mean number of deer detected using manual and that detected by automated review (P = 0.057). The automated-detection algorithm identified between 66.7% and 100% of deer detected using manual review of thermal imagery on all but one of the sampling days. There was no difference in the mean proportion of deer detected using either manual or automated review at three repeated sampling events (P = 0.174). However, identifying deer using the automated review algorithm was 84% cheaper than the cost of manual review. Low cloud cover appeared to affect detectability using the automated review algorithm. Conclusions Automated methods provide a fast and effective way to detect deer. For maximum effectiveness, imagery that encompasses a range of environments should be used as part of the training dataset, as well as large groups for herding species. Adequate sensing conditions are essential to gain accurate counts of deer by automated detection. Implications Machine learning in combination with RPAS may decrease the cost and improve the detection and monitoring of invasive species.

Generalizability and comparability of prevalence estimates in the wild bird literature: methodological and epidemiological considerations

Wild birds have been the focus of a great deal of research investigating the epidemiology of zoonotic bacteria and antimicrobial resistance in the environment. While enteric pathogens (e.g. Campylobacter, Salmonella, and E. coli O157:H7) and antimicrobial resistant bacteria of public health importance have been isolated from a wide variety of wild bird species, there is a considerable variation in the measured prevalence of a given microorganism from different studies. This variation may often reflect differences in certain ecological and biological factors such as feeding habits and immune status. Variation in prevalence estimates may also reflect differences in sample collection and processing methods, along with a host of epidemiological inputs related to overall study design. Because the generalizability and comparability of prevalence estimates in the wild bird literature are constrained by their methodological and epidemiological underpinnings, understanding them is crucial to the accurate interpretation of prevalence estimates. The main purpose of this review is to examine methodological and epidemiological inputs to prevalence estimates in the wild bird literature that have a major bearing on their generalizability and comparability. The inputs examined here include sample type, microbiological methods, study design, bias, sample size, definitions of prevalence outcomes and parameters, and control of clustering. The issues raised in this review suggest, among other things, that future prevalence studies of wild birds should avoid opportunistic sampling when possible, as this places significant limitations on the generalizability of prevalence data.