scholarly journals Using interview surveys and multispecies occupancy models to inform vertebrate conservation

2021 ◽  
Author(s):  
Rajeev Pillay ◽  
David A.W. Miller ◽  
R. Raghunath ◽  
Atul A. Joshi ◽  
Charudutt Mishra ◽  
...  
Keyword(s):  
Occupancy Models

An automated model for forecasting non-elective hospital bed demand in the entire English National Health System: retrospective process assessment study (Preprint)

2020 ◽  
Author(s):  
Kanan Shah ◽  
Akarsh Sharma ◽  
Chris Moulton ◽  
Simon Swift ◽  
Clifford Mann ◽  
...  
Keyword(s):  
National Health ◽  
Mean Squared Error ◽  
Winter Season ◽  
National Health System ◽  
Forecast Accuracy ◽  
Occupancy Models ◽  
Bed Occupancy ◽  
Median Error ◽  
Hospital Bed ◽  
Forecasting Techniques

BACKGROUND From 2006/2007 to 2017/2018, there was a 26% increase in emergency department (ED) attendances and 32% increase in total admissions in the National Health Service in England (NHS). Growing demand puts severe strain on hospitals, resulting in bed, nursing, clinical and equipment shortages. Nevertheless, scheduling issues can still result in significant under-utilization of beds. It is imperative to optimize the allocation of existing healthcare resources, including hospital beds. More accurate and reliable long-term hospital bed occupancy rate prediction would help managers plan ahead for their population’s hospital requirements, ultimately resulting in greater efficiencies and better patient care. OBJECTIVE This study aimed to compare widely used automated time series forecasting techniques to predict short-term daily non-elective bed occupancy at all trusts in the NHS. METHODS Bed occupancy models that accounted for patterns in occupancy were created for each trust in the NHS. Daily non-elective midnight trust occupancy data from April 2011 to March 2017 for 121 NHS trusts were utilized to generate these models. Forecasts were generated using the three most widely used automated forecasting techniques: Exponential Smoothing (ES); Seasonal Autoregressive Integrated Moving Average (SARIMA); Trigonometric, Box-Cox transform, ARMA errors, Trend and Seasonal components (TBATS). The NHS Modernization Agency’s recommended forecasting method prior to 2020, was also replicated. A comparative analysis of forecast accuracy was conducted by comparing forecasted daily non-elective occupancy with actual non-elective occupancy in the out-of-sample dataset for each week forecasted. Percentage root mean squared error (RMSE) was reported. RESULTS The accuracy of the models varied based on the season during which occupancy was forecasted. For the summer season, percent RMSE values for each model remained relatively stable across six forecasted weeks. However, only the TBATS model (median error 2.45% for six weeks) outperformed the NHS Modernization Agency’s recommended method (median error 2.63% for six weeks). In contrast, during the winter season, percent RMSE values increased as we forecasted further into the future. ES generated the most accurate forecasts (median error 4.91% over four weeks), but all models outperformed the NHS Modernization Agency’s recommended method prior to 2020 (median 8.5% error over four weeks). CONCLUSIONS It is possible to create automated models, similar to those recently published by the NHS, that can be used at a hospital level for a large, national healthcare system in order to predict non-elective bed admissions and thus schedule elective procedures. CLINICALTRIAL N/A

scholarly journals Camera Trapping to Assess Status and Composition of Mammal Communities in a Biodiversity Hotspot in Myanmar

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 880
Author(s):  
Giacomo Cremonesi ◽  
Francesco Bisi ◽  
Lorenzo Gaffi ◽  
Thet Zaw ◽  
Hla Naing ◽  
...  
Keyword(s):  
Human Disturbance ◽  
Habitat Degradation ◽  
Single Species ◽  
Wildlife Trade ◽  
Ecosystem Functions ◽  
Occupancy Models ◽  
Apex Predators ◽  
Disturbed Area ◽  
The Status ◽  
Mammal Communities

Tropical forests comprise a critically impacted habitat, and it is known that altered forests host a lower diversity of mammal communities. In this study, we investigated the mammal communities of two areas in Myanmar with similar environmental conditions but with great differences in habitat degradation and human disturbance. The main goal was to understand the status and composition of these communities in an understudied area like Myanmar at a broad scale. Using camera trap data from a three-year-long campaign and hierarchical occupancy models with a Bayesian formulation, we evaluated the biodiversity level (species richness) and different ecosystem functions (diet and body mass), as well as the occupancy values of single species as a proxy for population density. We found a lower mammal diversity in the disturbed area, with a significantly lower number of carnivores and herbivores species. Interestingly, the area did not show alteration in its functional composition. Almost all the specific roles in the community were present except for apex predators, thus suggesting that the effects of human disturbance are mainly effecting the communities highest levels. Furthermore, two species showed significantly lower occupancies in the disturbed area during all the monitoring campaigns: one with a strong pressure for bushmeat consumption and a vulnerable carnivore threatened by illegal wildlife trade.

Using occupancy models to assess the effectiveness of underwater cameras to detect rare stream fishes

2020 ◽  
Vol 30 (3) ◽  
pp. 565-576 ◽  
Author(s):  
Rowshyra A. Castañeda ◽  
Olaf L.F. Weyl ◽  
Nicholas E. Mandrak
Keyword(s):  
Stream Fishes ◽  
Occupancy Models ◽  
Underwater Cameras

scholarly journals Using areas of known occupancy to identify sources of variation in detection probability of raptors: taking time lowers replication effort for surveys

2016 ◽  
Vol 3 (10) ◽  
pp. 160368 ◽  
Author(s):  
Campbell Murn ◽  
Graham J. Holloway
Keyword(s):  
Detection Probability ◽  
Spatial Scales ◽  
Survey Methods ◽  
A Priori ◽  
Low Density ◽  
Occupancy Models ◽  
Imperfect Detection ◽  
Factors Affecting ◽  
Sources Of Variation ◽  
Time Of Year

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.

scholarly journals Fitting and Interpreting Occupancy Models

PLoS ONE ◽  
2013 ◽  
Vol 8 (1) ◽  
pp. e52015 ◽  
Author(s):  
Alan H. Welsh ◽  
David B. Lindenmayer ◽  
Christine F. Donnelly
Keyword(s):  
Occupancy Models

scholarly journals Use of Occupancy Models to Evaluate Expert Knowledge-based Species-Habitat Relationships

2012 ◽  
Vol 7 (2) ◽  
Author(s):  
Monica N. Iglecia ◽  
Jaime A. Collazo ◽  
Alexa J. McKerrow
Keyword(s):  
Expert Knowledge ◽  
Occupancy Models ◽  
Knowledge Based
Sign in / Sign up

Export Citation Format

Share Document