Can owls be used to monitor the impacts of urbanisation? A cautionary tale of variable detection

2017 ◽  
Vol 44 (7) ◽  
pp. 573 ◽  
Author(s):  
Raylene Cooke ◽  
Hannah Grant ◽  
Isabel Ebsworth ◽  
Anthony R. Rendall ◽  
Bronwyn Isaac ◽  
...  

Context Due to their important ecological roles, predators are increasingly being suggested as targets for biodiversity studies investigating how they respond to landscape change and transformation. But there is limited literature investigating our capacity to accurately monitor changes in their occupancy. Aims To test the efficacy of playback surveys for monitoring owls as a basis for investigating change in owl occupancy over time. We ask whether playback is an effective tool, and whether it can be optimised to improve its utility. Methods Using the urban–forest interface of Melbourne, Australia, as a case study, we used playback techniques to survey for the presence of three owl species: the powerful owl (Ninox strenua); southern boobook (Ninox boobook); and eastern barn owl (Tyto javanica). Sites were repeat surveyed at least 16 times throughout the year and occupancy models were developed to establish how season and temperature influence nightly detection probabilities of owls. Key results All three species of owl were detected through playback survey approaches, but the detection probabilities varied greatly between species and across seasons and temperature conditions. Eastern barn owls are poor candidates for playback surveys due to their low detection probabilities. The southern boobook and powerful owl are responsive to playback, but detection probabilities are influenced by season and/or temperature conditions. To optimise survey approaches, southern boobooks should be surveyed during spring and summer and the powerful owl should be surveyed on nights where the minimum temperature is near 20°C. Conclusions Although there is considerable interest in using predators such as owls to monitor biodiversity impacts associated with landscape change, poor detection rates can limit their utility. However, optimising survey approaches that consider shifting detection probabilities under different conditions such as time of year or temperature may improve the utility of predators as surrogates in biodiversity monitoring. Implications Optimising survey approaches for owls considerably reduces the window of opportunity in which to conduct surveys. To counter this, the intensity of survey effort needs to be increased during key periods. The use of highly trained citizen science teams may be one effective way of delivering such an approach.

Oryx ◽  
2021 ◽  
pp. 1-9
Author(s):  
Nick van Doormaal ◽  
A. M. Lemieux ◽  
Stijn Ruiter ◽  
Paul M. R. R. Allin ◽  
Craig R. Spencer

Abstract Many studies of wildlife poaching acknowledge the challenges of detecting poaching activities, but few address the issue. Data on poaching may be an inaccurate reflection of the true spatial distribution of events because of low detection rates. The deployment of conservation and law enforcement resources based on biased data could be ineffective or lead to unintended outcomes. Here, we present a rigorous method for estimating the probabilities of detecting poaching and for evaluating different patrol strategies. We illustrate the method with a case study in which imitation snares were set in a private nature reserve in South Africa. By using an experimental design with a known spatial distribution of imitation snares, we estimated the detection probability of the current patrol strategy used in the reserve and compared it to three alternative patrol strategies: spatially focused patrols, patrols with independent observers, and systematic search patterns. Although detection probabilities were generally low, the highest proportion of imitation snares was detected with systematic search strategies. Our study provides baseline data on the probability of detecting snares used for poaching, and presents a method that can be modified for use in other regions and for other types of wildlife poaching.


2016 ◽  
Author(s):  
Jean M. Daniels ◽  
Weston Brinkley ◽  
Michael D. Paruszkiewicz

2016 ◽  
Vol 3 (10) ◽  
pp. 160368 ◽  
Author(s):  
Campbell Murn ◽  
Graham J. Holloway

Species occurring at low density can be difficult to detect and if not properly accounted for, imperfect detection will lead to inaccurate estimates of occupancy. Understanding sources of variation in detection probability and how they can be managed is a key part of monitoring. We used sightings data of a low-density and elusive raptor (white-headed vulture Trigonoceps occipitalis ) in areas of known occupancy (breeding territories) in a likelihood-based modelling approach to calculate detection probability and the factors affecting it. Because occupancy was known a priori to be 100%, we fixed the model occupancy parameter to 1.0 and focused on identifying sources of variation in detection probability. Using detection histories from 359 territory visits, we assessed nine covariates in 29 candidate models. The model with the highest support indicated that observer speed during a survey, combined with temporal covariates such as time of year and length of time within a territory, had the highest influence on the detection probability. Averaged detection probability was 0.207 (s.e. 0.033) and based on this the mean number of visits required to determine within 95% confidence that white-headed vultures are absent from a breeding area is 13 (95% CI: 9–20). Topographical and habitat covariates contributed little to the best models and had little effect on detection probability. We highlight that low detection probabilities of some species means that emphasizing habitat covariates could lead to spurious results in occupancy models that do not also incorporate temporal components. While variation in detection probability is complex and influenced by effects at both temporal and spatial scales, temporal covariates can and should be controlled as part of robust survey methods. Our results emphasize the importance of accounting for detection probability in occupancy studies, particularly during presence/absence studies for species such as raptors that are widespread and occur at low densities.


2016 ◽  
Vol 32 ◽  
pp. 200-210 ◽  
Author(s):  
Bogdan Mihai ◽  
Constantin Nistor ◽  
Liviu Toma ◽  
Ionuţ Săvulescu

Author(s):  
Herman Njoroge Chege

Point 1: Deep learning algorithms are revolutionizing how hypothesis generation, pattern recognition, and prediction occurs in the sciences. In the life sciences, particularly biology and its subfields,  the use of deep learning is slowly but steadily increasing. However, prototyping or development of tools for practical applications remains in the domain of experienced coders. Furthermore, many tools can be quite costly and difficult to put together without expertise in Artificial intelligence (AI) computing. Point 2: We built a biological species classifier that leverages existing open-source tools and libraries. We designed the corresponding tutorial for users with basic skills in python and a small, but well-curated image dataset. We included annotated code in form of a Jupyter Notebook that can be adapted to any image dataset, ranging from satellite images, animals to bacteria. The prototype developer is publicly available and can be adapted for citizen science as well as other applications not envisioned in this paper. Point 3: We illustrate our approach with a case study of 219 images of 3 three seastar species. We show that with minimal parameter tuning of the AI pipeline we can create a classifier with superior accuracy. We include additional approaches to understand the misclassified images and to curate the dataset to increase accuracy. Point 4: The power of AI approaches is becoming increasingly accessible. We can now readily build and prototype species classifiers that can have a great impact on research that requires species identification and other types of image analysis. Such tools have implications for citizen science, biodiversity monitoring, and a wide range of ecological applications.


2012 ◽  
Vol 32 (2) ◽  
pp. 409-419 ◽  
Author(s):  
Minerva Campos ◽  
Alejandro Velázquez ◽  
Gerardo Bocco Verdinelli ◽  
Margaret Skutsch ◽  
Martí Boada Juncà ◽  
...  

2011 ◽  
Vol 2 (1) ◽  
pp. 117-121 ◽  
Author(s):  
Roger D. Applegate ◽  
Robert E. Kissell ◽  
E. Daniel Moss ◽  
Edward L. Warr ◽  
Michael L. Kennedy

Abstract Point count data are used increasingly to provide density estimates of bird species. A favored approach to analyze point count data uses distance sampling theory where model selection and model fit are important considerations. We used uniform and half normal models and assessed model fit using χ2 analysis. We were unsuccessful in fitting models to 635 northern bobwhite Colinus virginianus observations from 85 avian point locations spanning 6 y (P ≤ 0.05). Most observations (74%) occurred in the outermost (>100-m) distance radius. Our results violated the assumptions that all observations at the point are detected. The assumption that birds were assigned to the correct distance interval also was probably violated. We caution managers in implementing avian point counts with distance sampling when estimating northern bobwhite population density. We recommend exploring other approaches such as occupancy-estimation and modeling for estimating detection probabilities.


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