scholarly journals Dark states of quantum search cause imperfect detection

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Felix Thiel ◽  
Itay Mualem ◽  
Dror Meidan ◽  
Eli Barkai ◽  
David A. Kessler
Keyword(s):  
Quantum Search ◽  
Imperfect Detection

scholarly journals Population estimates of Antillean manatees in Puerto Rico: an analytical framework for aerial surveys using multi-pass removal sampling

2019 ◽  
Vol 100 (4) ◽  
pp. 1340-1349
Author(s):  
Jaime A Collazo ◽  
Matthew J Krachey ◽  
Kenneth H Pollock ◽  
Francisco J Pérez-Aguilo ◽  
Jan P Zegarra ◽  
...  
Keyword(s):  
Puerto Rico ◽  
Detection Probability ◽  
Analytical Framework ◽  
Population Estimates ◽  
High Concentration ◽  
Imperfect Detection ◽  
Factors Affecting ◽  
Management Actions ◽  
Aerial Surveys ◽  
Population Size Estimates

AbstractEffective management of the threatened Antillean manatee (Trichechus manatus manatus) in Puerto Rico requires reliable estimates of population size. Estimates are needed to assess population responses to management actions, and whether recovery objectives have been met. Aerial surveys have been conducted since 1976, but none adjusted for imperfect detection. We summarize surveys since 1976, report on current distribution, and provide population estimates after accounting for apparent detection probability for surveys between June 2010 and March 2014. Estimates in areas of high concentration (hotspots) averaged 317 ± 101, three times higher than unadjusted counts (104 ± 0.56). Adjusted estimates in three areas outside hotspots also differed markedly from counts (75 ± 9.89 versus 19.5 ± 3.5). Average minimum island-wide estimate was 386 ± 89, similar to the maximum estimate of 360 suggested in 2005, but fewer than the 700 recently suggested by the Puerto Rico Manatee Conservation Center. Manatees were more widespread than previously understood. Improving estimates, locally or island-wide, will require stratifying the island differently and greater knowledge about factors affecting detection probability. Sharing our protocol with partners in nearby islands (e.g., Cuba, Jamaica, Hispaniola), whose populations share genetic make-up, would contribute to enhanced regional conservation through better population estimates and tracking range expansion.El manejo efectivo del manatí antillano amenazado en Puerto Rico requiere estimados de tamaños de poblaciónes confiables. Dichas estimaciones poblacionales son necesarias para evaluar las respuestas a las acciones de manejo, y para determinar si los objetivos de recuperación han sido alcanzados. Se han realizado censos aéreos desde 1976, pero ninguno de ellos han sido ajustados para detecciones imperfectas. Aquí resumimos los censos desde 1976, actualizamos la distribución, y reportamos los primeros estimados poblacionales ajustados para la probabilidad de detección aparente en los censos de Junio 2010 a Marzo 2014. Las estimaciones poblacionales en áreas de mayor concentración del manatí promedió 317 ± 103, tres veces más abundante que los conteos sin ajuste (104 ± 0.56). Las estimaciones poblacionales en tres áreas fuera de las áreas de mayor concentración del manatí también fueron marcadamente diferentes (75 ± 9.89 vs 19.5 ± 3.5). El estimado mínimo poblacional en la isla entera fue de 386 ± 89, similar al estimado máximo de 360 sugerido en el año 2005, pero menor a los 700 sugeridos recientemente por el Centro de Conservación de Manatíes de Puerto Rico. Documentamos que el manatí tiene una distribución más amplia de lo que se sabía con anterioridad. El mejoramiento de los estimados poblacionales locales o a nivel de isla requerirá que se estratifique a la isla en forma diferente y que se investiguen los factores que influencian a la probabilidad de detección. Compartir protocolos como este con colaboradores de islas vecinas (por. ej., Cuba, Jamaica, Española), cuyas poblaciones de manatíes comparten material genético, contribuiría a la conservación regional mediante mejores estimaciones poblacionales y monitoreo de la expansión de su ámbito doméstico.

scholarly journals Implementation of efficient quantum search algorithms on NISQ computers

2021 ◽  
Vol 20 (7) ◽  
Author(s):  
Kun Zhang ◽  
Pooja Rao ◽  
Kwangmin Yu ◽  
Hyunkyung Lim ◽  
Vladimir Korepin
Keyword(s):  
Search Algorithms ◽  
Quantum Search ◽  
Quantum Search Algorithms

scholarly journals Imperfect detection biases extinction‐debt assessments

2021 ◽  
Author(s):  
Fielding A. Montgomery ◽  
Scott M. Reid ◽  
Nicholas E. Mandrak
Keyword(s):  
Extinction Debt ◽  
Imperfect Detection

scholarly journals Modelling the Spatial Distribution of ASF-Positive Wild Boar Carcasses in South Korea Using 2019–2020 National Surveillance Data

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1208
Author(s):  
Jun-Sik Lim ◽  
Timothée Vergne ◽  
Son-Il Pak ◽  
Eutteum Kim
Keyword(s):  
Spatial Distribution ◽  
Risk Factor ◽  
South Korea ◽  
Wild Boar ◽  
African Swine Fever Virus ◽  
Significant Risk ◽  
African Swine Fever ◽  
Credible Interval ◽  
Imperfect Detection ◽  
Wild Boars

In September 2019, African swine fever (ASF) was reported in South Korea for the first time. Since then, more than 651 ASF cases in wild boars and 14 farm outbreaks have been notified in the country. Despite the efforts to eradicate ASF among wild boar populations, the number of reported ASF-positive wild boar carcasses have increased recently. The purpose of this study was to characterize the spatial distribution of ASF-positive wild boar carcasses to identify the risk factors associated with the presence and number of ASF-positive wild boar carcasses in the affected areas. Because surveillance efforts have substantially increased in early 2020, we divided the study into two periods (2 October 2019 to 19 January 2020, and 19 January to 28 April 2020) based on the number of reported cases and aggregated the number of reported ASF-positive carcasses into a regular grid of hexagons of 3-km diameter. To account for imperfect detection of positive carcasses, we adjusted spatial zero-inflated Poisson regression models to the number of ASF-positive wild boar carcasses per hexagon. During the first study period, proximity to North Korea was identified as the major risk factor for the presence of African swine fever virus. In addition, there were more positive carcasses reported in affected hexagons with high habitat suitability for wild boars, low heat load index (HLI), and high human density. During the second study period, proximity to an ASF-positive carcass reported during the first period was the only significant risk factor for the presence of ASF-positive carcasses. Additionally, low HLI and elevation were associated with an increased number of ASF-positive carcasses reported in the affected hexagons. Although the proportion of ASF-affected hexagons increased from 0.06 (95% credible interval (CrI): 0.05–0.07) to 0.09 (95% CrI: 0.08–0.10), the probability of reporting at least one positive carcass in ASF-affected hexagons increased from 0.49 (95% CrI: 0.41–0.57) to 0.73 (95% CrI: 0.66–0.81) between the two study periods. These results can be used to further advance risk-based surveillance strategies in the Republic of Korea.

scholarly journals Simple Algorithm for Partial Quantum Search

2006 ◽  
Vol 5 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Vladimir E. Korepin ◽  
Lov K. Grover
Keyword(s):  
Simple Algorithm ◽  
Quantum Search

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 Using a capture-recapture approach for modelling the detectability and distribution of Houbara Bustard in southern Tunisia

2011 ◽  
Vol 22 (3) ◽  
pp. 288-298 ◽  
Author(s):  
M. CHAMMEM ◽  
S. SELMI ◽  
T. KHORCHANI ◽  
S. NOUIRA
Keyword(s):  
Regression Analyses ◽  
Species Occurrence ◽  
Imperfect Detection ◽  
Southern Tunisia ◽  
Species Of Conservation Concern ◽  
Habitat Factors ◽  
Houbara Bustard ◽  
Capture Recapture ◽  
Conservation Concern ◽  
Free Ranging

SummaryModelling the distribution of species of conservation concern is an important issue in population ecology. Classically, logistic regression analyses are conducted to estimate species’ distributions from detection/non-detection data in a sample of sites and to test for the significance of several environmental variables in predicting the probability of occurrence. These modelling approaches assume that species detection probability is constant and equals one in all sampled sites, which is critical, notably in the case of rare, shy and cryptic species. The capture-recapture-like approach developed by Mackenzie et al. (2002, 2003) provides a reliable tool that accounts for imperfect detection when estimating species occurrence, as well as for assessing the relevance of site features as predictors of species occurrence probability. The aim of this study was to explore the possibility of using this approach in the context of Houbara Bustard Chlamydotis undulata in southern Tunisia. Our results show once more the low detectability of this emblematic species and stress the need to take this factor into account when estimating Houbara spatial distribution. The distribution of Houbara in southern Tunisia is more likely to be shaped by human-related than by habitat factors. In particular, Houbara occurrence was positively associated with site remoteness and camel numbers. Houbara seemed to avoid areas with high human presence and shared the most remote and agriculture-free zones with free-ranging camels.

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