Factors affecting detection probability of burrowing owls in southwest agroecosystem environments

2011 ◽  
Vol 75 (7) ◽  
pp. 1558-1567 ◽  
Author(s):  
Jeffrey A. Manning
Keyword(s):  
Detection Probability ◽  
Factors Affecting ◽  
Burrowing Owls

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 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.

FACTORS AFFECTING DETECTION PROBABILITY OF CALIFORNIA BLACK RAILS

2004 ◽  
Vol 68 (2) ◽  
pp. 360-370 ◽  
Author(s):  
COURTNEY J. CONWAY ◽  
CHRISTINA SULZMAN ◽  
BARBARA E. RAULSTON
Keyword(s):  
Detection Probability ◽  
Factors Affecting

scholarly journals Factors affecting detection probability in plant distribution studies

2009 ◽  
Vol 97 (6) ◽  
pp. 1383-1389 ◽  
Author(s):  
Guoke Chen ◽  
Marc Kéry ◽  
Jinlong Zhang ◽  
Keping Ma
Keyword(s):  
Detection Probability ◽  
Plant Distribution ◽  
Factors Affecting ◽  
Distribution Studies

scholarly journals An Evaluation of the Factors Affecting ‘Poacher’ Detection with Drones and the Efficacy of Machine-Learning for Detection

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4074
Author(s):  
Katie E. Doull ◽  
Carl Chalmers ◽  
Paul Fergus ◽  
Steve Longmore ◽  
Alex K. Piel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection Probability ◽  
Visible Spectrum ◽  
Time Of Day ◽  
Automated Detection ◽  
Probability Of Detection ◽  
Factors Affecting ◽  
Camera Angle ◽  
Manual Analysis ◽  
Stationary Test

Drones are being increasingly used in conservation to tackle the illegal poaching of animals. An important aspect of using drones for this purpose is establishing the technological and the environmental factors that increase the chances of success when detecting poachers. Recent studies focused on investigating these factors, and this research builds upon this as well as exploring the efficacy of machine-learning for automated detection. In an experimental setting with voluntary test subjects, various factors were tested for their effect on detection probability: camera type (visible spectrum, RGB, and thermal infrared, TIR), time of day, camera angle, canopy density, and walking/stationary test subjects. The drone footage was analysed both manually by volunteers and through automated detection software. A generalised linear model with a logit link function was used to statistically analyse the data for both types of analysis. The findings concluded that using a TIR camera improved detection probability, particularly at dawn and with a 90° camera angle. An oblique angle was more effective during RGB flights, and walking/stationary test subjects did not influence detection with both cameras. Probability of detection decreased with increasing vegetation cover. Machine-learning software had a successful detection probability of 0.558, however, it produced nearly five times more false positives than manual analysis. Manual analysis, however, produced 2.5 times more false negatives than automated detection. Despite manual analysis producing more true positive detections than automated detection in this study, the automated software gives promising, successful results, and the advantages of automated methods over manual analysis make it a promising tool with the potential to be successfully incorporated into anti-poaching strategies.

Factors affecting detection probability, effective area surveyed, and species misidentification in grassland bird point counts

The Condor ◽  
2019 ◽  
Vol 121 (3) ◽  
Author(s):  
Elizabeth A Rigby ◽  
Douglas H Johnson
Keyword(s):  
Wind Speed ◽  
Detection Probability ◽  
Effective Area ◽  
Probability Of Detection ◽  
Bird Songs ◽  
Grassland Bird ◽  
Point Count ◽  
Factors Affecting ◽  
Logistic Regression Models ◽  
Point Counts

ABSTRACT We simulated bird surveys using recorded bird songs to assess factors affecting detection probability in grassland bird point counts. We used mixed effects logistic regression models to estimate effects of those factors and to estimate and visualize the variation in the area around the observer where birds can be perceived (the perception area). We simulated surveys with 8,926 binary opportunities for detection in Minnesota grasslands in 2011 and 2012. Species, distance to the observer, wind speed and direction, observer, and density of vegetation all affected detection of recorded bird songs. Species had a strong effect; the size of the predicted perception area around the observer differed by an order of magnitude among species. Wind also had a strong effect on detection. As wind speed increased, probability of detection downwind of the observer was reduced and the perception area around the observer became smaller and more asymmetrical. The effective distance at which an observer is more likely to detect a bird than not detect it may differ among species and angles to the wind, even within the same survey. Eight of 10 species had low probability of misidentification (≤0.03), but Grasshopper Sparrow (Ammodramus savannarum) and LeConte’s Sparrow (Ammospiza leconteii) were frequently misidentified (probability = 0.09–0.24 among observers), contributing to a low rate of correct detection for those species. We recommend collecting point-count data within distance bands so that data can be analyzed based on the effective radius for each species and standardizing surveys across wind conditions to reduce variation in detection probability.

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