Variability in Grassland Bird Counts Related to Observer Differences and Species Detection Rates

Diefenbach, Duane R.; Brauning, Daniel W.; Mattice, Jennifer A.

doi:10.1642/0004-8038(2003)120[1168:vigbcr]2.0.co;2

Cited by 180 publications

(68 citation statements)

References 12 publications

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“…The transect route was created so that no part of the survey polygon would be N25 m from the surveyor's path. We chose this pattern because 25 m is a range where detection of available grassland birds is virtually 100% (Diefenbach et al, 2003). Throughout the study, we looked for evidence of detection bias caused by noise from wells or roads by visually examining histograms of bird detections binned by distance from transect (Buckland et al, 2005).…”

Section: Methodsmentioning

confidence: 99%

Avoidance of unconventional oil wells and roads exacerbates habitat loss for grassland birds in the North American great plains

Thompson

Johnson

Niemuth

et al. 2015

Biological Conservation

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Section: Methodsmentioning

confidence: 99%

Avoidance of unconventional oil wells and roads exacerbates habitat loss for grassland birds in the North American great plains

Thompson

Johnson

Niemuth

et al. 2015

Biological Conservation

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“…Counts based on point-count surveys are considered an index of the actual population present at a given site because some portion of the population is not present on the breeding grounds during the time of the survey (i.e., probability of presence), is present but does not signal its presence during a survey (i.e., availability bias), or signals but is not detected by the surveyor (i.e., perceptibility bias) (Rosenstock et al 2002;Elphick 2008;Nichols et al 2009). This ''imperfect detection'' may not affect the interpretation of results as long as the ratio of detection and counts are unbiased or relatively constant (Johnson 2008), but several studies have shown that detection probability often varies by observers (Diefenbach et al 2003;Alldredge et al 2007;Simons et al 2007;Reidy et al 2014), by habitat (Pacifici et al 2008;Amundson et al 2014), with weather conditions Amundson et al 2014), and through time (Selmi and Boulinier 2003;Diefenbach et al 2007). Further, if detection probability differs between habitats but is not accounted for, differences in detection may be misattributed to species habitat associations (Amundson et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Landscape and local effects on occupancy and densities of an endangered wood-warbler in an urbanizing landscape

et al. 2015

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Context Golden-cheeked warblers (Setophaga chrysoparia), an endangered wood-warbler, breed exclusively in woodlands co-dominated by Ashe juniper (Juniperus ashei) in central Texas. Their breeding range is becoming increasingly urbanized and habitat loss and fragmentation are a main threat to the species' viability.Objectives We investigated the effects of remotely sensed local habitat and landscape attributes on point occupancy and density of warblers in an urban preserve and produced a spatially explicit density map for the preserve using model-supported relationships. Methods We conducted 1507 point-count surveys during spring 2011-2014 across Balcones Canyonlands Preserve (BCP) to evaluate warbler habitat associations and predict density of males. We used hierarchical Bayesian models to estimate multiple components of detection probability and evaluate covariate effects on detection probability, point occupancy, and density. Results Point occupancy was positively related to landscape forest cover and local canopy cover; mean occupancy was 0.83. Density was influenced more by local than landscape factors. Density increased with greater amounts of juniper and mixed forest and decreased with more open edge. There was a weak negative relationship between density and landscape urban land cover. Conclusions Landscape composition and habitat structure were important determinants of warbler occupancy and density, and the large intact patches of juniper and mixed forest on BCP ([2100 ha) supported a high density of warblers. Increasing urbanization and fragmentation in the surrounding landscape will likely result in lower breeding density due to loss of juniper and mixed forest and increasing urban land cover and edge.

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“…The approach shown in our study can improve monitoring efficiency, not only for amphibians but also for other taxa with imperfect detection patterns, such as birds or mammals (Bibby and Buckland 1987;Kéry 2002;Diefenbach et al 2003;Tracey et al 2005;Wintle et al 2005;Evans et al 2010). It allows decision-makers to explicitly evaluate options concerning realistic objectives and investments.…”

Section: Discussionmentioning

confidence: 84%

Integrating variability in detection probabilities when designing wildlife surveys: a case study of amphibians from south-eastern Australia

et al. 2011

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Occupancy-based monitoring programs rely on survey data to infer presence or absence of the target species. However, species may occupy a site and go undetected, leading to erroneous inference of absence ('false absence'). If detectability is influenced by the time of year or weather conditions, survey protocols can be adjusted to minimize the chance of false absences. In this study, detection probabilities for three amphibian species from south-eastern Australia were modelled using a Bayesian approach. For aural surveys, we compared basic models, which only included effects of survey date, duration and time of day on detection, to models including additional effects of weather. Model selection using deviance information criterion (DIC) suggested that the basic model was the most parsimonious for Crinia signifera, while models including relative humidity and water temperature were most supported for Limnodynastes dumerilii and L. tasmaniensis respectively. When predictive performance was assessed by cross validation, DIC results were largely matched for C. signifera and L. dumerilii, while models of detection for L. tasmaniensis were indistinguishable, AUC scores suggesting inadequate performance. We show how results such as these can be used to design surveys, developing protocols for individual surveys and estimating the number of surveys required under those protocols to achieve a threshold cumulative probability of detection. Conservation managers can use these models to maximize the efficiency of surveys. This will improve the accuracy of occupancy data, and reduce the risk of misdirected conservation actions resulting from false absences.

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Variability in Grassland Bird Counts Related to Observer Differences and Species Detection Rates

Cited by 180 publications

References 12 publications

Avoidance of unconventional oil wells and roads exacerbates habitat loss for grassland birds in the North American great plains

Avoidance of unconventional oil wells and roads exacerbates habitat loss for grassland birds in the North American great plains

Landscape and local effects on occupancy and densities of an endangered wood-warbler in an urbanizing landscape

Integrating variability in detection probabilities when designing wildlife surveys: a case study of amphibians from south-eastern Australia

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