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Time-to-detection occupancy methods: performance and utility for improving efficiency of surveys.

ABSTRACT: Occupancy methods propelled the quantitative study of species distributions forward by separating the observation process, or the imperfect detectability of species, from the ecological processes of interest governing species distributions. Occupancy studies come at a cost, however: the collection of additional data to account for nondetections at sites where the species is present. The most common occupancy designs (repeated-measures designs) require repeat visits to sites or the use of multiple observers or detection methods. Time-to-detection methods have been identified as a potentially efficient alternative, requiring only one visit to each site by a single observer. A comparison of time-to-detection methods to repeated-measures designs for visual encounter surveys would allow researchers to evaluate whether time-to-detection methods might be appropriate for their study system and can inform optimal survey design. We collected time-to-detection data during two different repeated-measures design occupancy surveys for four amphibians and compared the performance of time-to-detection methods to the other designs using the location (potential bias) and precision of posterior distributions for occurrence parameters. We further used results of time-to-detection surveys to optimize survey design. Time-to-detection methods performed best for species that are widespread and have high detection probabilities and rates, but performed less well for cryptic species with lower probability of occurrence or whose detection was strongly affected by survey conditions. In all cases, single surveys were most efficient in terms of person-hours expended, but under some conditions the survey duration required to achieve high detection probabilities would be prohibitively long for a single survey. Regardless of occupancy survey design, time-to-detection methods provide important information that can be used to optimize surveys, allowing researchers and resource managers to efficiently achieve monitoring and conservation goals. Collecting time-to-detection data while conducting repeated-measures occupancy surveys requires only small modifications to field methods but could have large benefits in terms of time spent surveying in the long term.

SUBMITTER: Halstead BJ

PROVIDER: S-EPMC8047884 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.

Project description:Dogs play an important role in our society as companions and work partners, and proper training of these dogs is pivotal. For companion dogs, training helps preventing or managing dog behavioral problems-the most frequently cited reason for relinquishing and euthanasia, and it promotes successful dog-human relationships and thus maximizes benefits humans derive from bonding with dogs. For working dogs, training is crucial for them to successfully accomplish their jobs. Dog training methods range widely from those using predominantly aversive stimuli (aversive methods), to those combining aversive and rewarding stimuli (mixed methods) and those focusing on the use of rewards (reward methods). The use of aversive stimuli in training is highly controversial and several veterinary and animal protection organizations have recommended a ban on pinch collars, e-collars and other techniques that induce fear or pain in dogs, on the grounds that such methods compromise dog welfare. At the same time, training methods based on the use of rewards are claimed to be more humane and equally or more effective than aversive or mixed methods. This important discussion, however, has not always been based in solid scientific evidence. Although there is growing scientific evidence that training with aversive stimuli has a negative impact on dog welfare, the scientific literature on the efficacy and efficiency of the different methodologies is scarce and inconsistent. Hence, the goal of the current study is to investigate the efficacy and efficiency of different dog training methods. To that end, we will apply different dog training methods in a population of working dogs and evaluate the outcome after a period of training. The use of working dogs will allow for a rigorous experimental design and control, with randomization of treatments. Military (n = 10) and police (n = 20) dogs will be pseudo-randomly allocated to two groups. One group will be trained to perform a set of tasks (food refusal, interrupted recall, dumbbell retrieval and placing items in a basket) using reward methods and the other group will be trained for the same tasks using mixed methods. Later, the dogs will perform a standardized test where they will be required to perform the trained behaviors. The reliability of the behaviors and the time taken to learn them will be assessed in order to evaluate the efficacy and efficiency, respectively, of the different training methods. This study will be performed in collaboration with the Portuguese Army and with the Portuguese Public Security Police (PSP) and integrated with their dog training programs.

Project description:BackgroundThe modified Rankin Scale (mRS) is the most common endpoint in acute stroke trials, but its power is limited when analyzed dichotomously and its indication of effect size is challenging to interpret when analyzed ordinally. To address these issues, the utility-weighted-mRS (UW-mRS) has been developed as a patient-centered, linear scale. However, appropriate data visualizations of UW-mRS results are needed, as current stacked bar chart displays do not convey crucial utility-weighting information.Design/methodsTwo UW-mRS display formats were devised: (1) Utility Staircase charts, and (2) choropleth-stacked-bar-charts (CSBCs). In Utility Staircase displays, mRS segment height reflects the utility value of each mRS level. In CSBCs, mRS segment color intensity reflects the utility of each mRS level. Utility Staircase and CSBC figures were generated for 15 randomized comparisons of acute ischemic/hemorrhagic stroke therapies, including fibrinolysis, endovascular reperfusion, blood pressure moderation, and hemicraniectomy. Display accuracy in showing utility outcomes was assessed with the Tufte-lie-factor and ease-of-use assessed by formal ratings completed by a panel of 4 neurologists and emergency physicians and one nurse-coordinator.ResultsThe Utility Staircase and CSBC displays rapidly conveyed patient-centered valuation of trial outcome distributions not available in conventional ordinal stacked bar charts. Tufte-lie-factor (LF) scores indicated "substantial distortion" of utility-valued outcomes for 93% (14/15) of conventional stacked bar charts, vs. "no distortion" for all Utility Staircase and CSBC displays. Clinician ratings on the Figural Display Questionnaire indicated that utility information encoded in row height (Utility Staircase display) was more readily assimilated than that conveyed in segment hue intensity (CSBC), both superior to conventional stacked bar charts.ConclusionsUtility Staircase displays are an efficient graphical format for conveying utility weighted-modified Rankin Scale primary endpoint results of acute stroke trials, and choropleth-stacked-bar-charts a good alternative. Both are more accurate in depicting quantitative, health-related quality of life results and preferred by clinician users for utility results visualization, compared with conventional stacked bar charts.

Project description:International investment in the response to HIV and AIDS has plateaued and its future level is uncertain. With many countries committed to ending the epidemic, it is essential to allocate available resources efficiently over different response periods to maximize impact. The objective of this study is to propose a technique to determine the optimal allocation of funds over time across a set of HIV programmes to achieve desirable health outcomes.We developed a technique to determine the optimal time-varying allocation of funds (1) when the future annual HIV budget is pre-defined and (2) when the total budget over a period is pre-defined, but the year-on-year budget is to be optimally determined. We use this methodology with Optima, an HIV transmission model that uses non-linear relationships between programme spending and associated programmatic outcomes to quantify the expected epidemiological impact of spending. We apply these methods to data collected from Zambia to determine the optimal distribution of resources to fund the right programmes, for the right people, at the right time.Considering realistic implementation and ethical constraints, we estimate that the optimal time-varying redistribution of the 2014 Zambian HIV budget between 2015 and 2025 will lead to a 7.6% (7.3% to 7.8%) decrease in cumulative new HIV infections compared with a baseline scenario where programme allocations remain at 2014 levels. This compares to a 5.1% (4.6% to 5.6%) reduction in new infections using an optimal allocation with constant programme spending that recommends unrealistic programmatic changes. Contrasting priorities for programme funding arise when assessing outcomes for a five-year funding period over 5-, 10- and 20-year time horizons.Countries increasingly face the need to do more with the resources available. The methodology presented here can aid decision-makers in planning as to when to expand or contract programmes and to which coverage levels to maximize impact.

Project description:Simple and low-cost recombinant enzyme-free seamless DNA cloning methods have recently become available. In vivo Escherichia coli cloning (iVEC) can directly transform a mixture of insert and vector DNA fragments into E. coli, which are ligated by endogenous homologous recombination activity in the cells. Seamless ligation cloning extract (SLiCE) cloning uses the endogenous recombination activity of E. coli cellular extracts in vitro to ligate insert and vector DNA fragments. An evaluation of the efficiency and utility of these methods is important in deciding the adoption of a seamless cloning method as a useful tool. In this study, both seamless cloning methods incorporated inserting DNA fragments into linearized DNA vectors through short (15-39 bp) end homology regions. However, colony formation was 30-60-fold higher with SLiCE cloning in end homology regions between 15 and 29 bp than with the iVEC method using DH5? competent cells. E. coli AQ3625 strains, which harbor a sbcA gene mutation that activates the RecE homologous recombination pathway, can be used to efficiently ligate insert and vector DNA fragments with short-end homology regions in vivo. Using AQ3625 competent cells in the iVEC method improved the rate of colony formation, but the efficiency and accuracy of SLiCE cloning were still higher. In addition, the efficiency of seamless cloning methods depends on the intrinsic competency of E. coli cells. The competency of chemically competent AQ3625 cells was lower than that of competent DH5? cells, in all cases of chemically competent cell preparations using the three different methods. Moreover, SLiCE cloning permits the use of both homemade and commercially available competent cells because it can use general E. coli recA- strains such as DH5? as host cells for transformation. Therefore, between the two methods, SLiCE cloning provides both higher efficiency and better utility than the iVEC method for seamless DNA plasmid engineering.

Project description:BACKGROUND:Accurate estimation of operative case-time duration is critical for optimizing operating room use. Current estimates are inaccurate and earlier models include data not available at the time of scheduling. Our objective was to develop statistical models in a large retrospective data set to improve estimation of case-time duration relative to current standards. STUDY DESIGN:We developed models to predict case-time duration using linear regression and supervised machine learning. For each of these models, we generated an all-inclusive model, service-specific models, and surgeon-specific models. In the latter 2 approaches, individual models were created for each surgical service and surgeon, respectively. Our data set included 46,986 scheduled operations performed at a large academic medical center from January 2014 to December 2017, with 80% used for training and 20% for model testing/validation. Predictions derived from each model were compared with our institutional standard of using average historic procedure times and surgeon estimates. Models were evaluated based on accuracy, overage (case duration > predicted + 10%), underage (case duration < predicted - 10%), and the predictive capability of being within a 10% tolerance threshold. RESULTS:The machine learning algorithm resulted in the highest predictive capability. The surgeon-specific model was superior to the service-specific model, with higher accuracy, lower percentage of overage and underage, and higher percentage of cases within the 10% threshold. The ability to predict cases within 10% improved from 32% using our institutional standard to 39% with the machine learning surgeon-specific model. CONCLUSIONS:Our study is a notable advancement toward statistical modeling of case-time duration across all surgical departments in a large tertiary medical center. Machine learning approaches can improve case duration estimations, enabling improved operating room scheduling, efficiency, and reduced costs.

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Time-to-detection occupancy methods: performance and utility for improving efficiency of surveys.

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