Landbird Counting Techniques: Current Practices and an Alternative

Rosenstock, Steven S.; Anderson, David R.; Giesen, Kenneth M.; Carter, Michael F.

doi:10.2307/4090011

Cited by 110 publications

(140 citation statements)

References 19 publications

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“…Comparing avian abundance across studies can be misleading due to differences in habitat structure, methods, and detectability of birds (Remsen and Good 1996, Rosenstock et al 2002, Thompson 2002. However, such comparisons may at least suggest differences in avian use of different sites.…”

Section: Discussion Habitat Associations and Relative Abundancementioning

confidence: 99%

“…For 1997For , 1998, and 2000 survey data (for which we had recorded distance to birds), we used the program DISTANCE to calculate approximate densities (birds km Ϫ2 ) based on best-fit models chosen by the lowest Akaike's Information Criterion (AIC) values (Buckland et al 2001, Thomas et al 2003. The DISTANCE program accounts for detectability of birds in calculating density estimates in each habitat and this should help overcome the potential for varying detectabilities of birds in different habitats (Rosenstock et al 2002). Even though the DIS-TANCE program calculates densities, we feel it most appropriate to consider these as relative abundances because of the difficulty of detecting actual densities at a time of year when birds are so mobile.…”

Section: Count Surveysmentioning

confidence: 99%

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Habitat Associations, Relative Abundance, and Species Richness of Autumn Landbird Migrants in Southwestern Idaho

Carlisle

Stock

Kaltenecker

et al. 2004

Condor

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Abstract. We used count surveys and mist-net captures to compare habitat associations, relative abundance, species richness, and community similarity of migrant landbirds among four major habitats in the Boise Foothills of southwestern Idaho. Count surveys were conducted from August through October 1997-2000 in conifer forest, mountain shrubland, shrubsteppe, and riparian shrubland. We compared bird detections among habitats for all birds pooled, individual species, and three migration strategies: Neotropical, temperate, and resident (including irruptive migrants). Mountain shrubland supported the highest numbers of temperate migrants; both mountain shrubland and riparian shrubland had the highest numbers of Neotropical migrants; and conifer forest had the highest numbers of residents. Species richness was highest in riparian shrubland and lowest in shrubsteppe, whereas diversity and evenness were highest in conifer forest and mountain shrubland. Mist netting was conducted from mid-July to mid-October in two habitats: mountain shrubland (1997)(1998)(1999)(2000)(2001)(2002) and riparian shrubland (1998)(1999). Captures (adjusted for effort) were compared among habitats in 1998-1999 and were similar for temperate migrants, whereas mountain shrubland had higher abundance of Neotropical migrants and riparian shrubland had higher abundance of irruptive migrants. Richness, diversity, and evenness were similar and there was high community similarity between mountain shrub and riparian shrubland habitats. These results emphasize the importance of montane habitats, especially deciduous shrub communities, to migrants in the Intermountain West.

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Section: Discussion Habitat Associations and Relative Abundancementioning

confidence: 99%

Section: Count Surveysmentioning

confidence: 99%

Habitat Associations, Relative Abundance, and Species Richness of Autumn Landbird Migrants in Southwestern Idaho

Carlisle

Stock

Kaltenecker

et al. 2004

Condor

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“…Typically, only small portions of the landscape can be surveyed (Stohlgren et al 1997). A common approach therefore is to rely on a stratified random sampling design and then extrapolate data across the landscape (Stohlgren et al 1997;Rosenstock et al 2002).…”

Section: Discussionmentioning

confidence: 99%

Developing robust field survey protocols in landscape ecology: a case study on birds, plants and butterflies

et al. 2014

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Sustainable land management requires scientists to provide reliable data on diversity distribution patterns. Resource restrictions limit the affordable sampling effort, both with respect to number of survey sites and amount of effort per site. We compared different levels of survey effort in a case study in Central Romania, varying the number of repeats per site and number of survey sites. Target taxa were plants, birds and butterflies. For plants, we surveyed three 10 m 2 plots and ten plots of 1 m 2 at each site. For birds, we used point counts and for butterflies Pollard walks, in both cases with four repeats. We fitted hierarchical community models to estimate true species richness per site. Estimates of true species richness per site strongly correlated with observed species richness. However, hierarchical community models yielded unrealistically high estimates of true species richness per site, hence we used observed richness for further analyses. For each species group, we compared diversity indices from subsets of the dataset with the full dataset. Findings obtained with a reduced survey effort reflected well those obtained with full effort. Moreover, we conducted a power analysis to assess how the number of survey sites affected the minimum detectable effect of landscape heterogeneity on species richness, and found there was an exponential decrease in the minimum detectable effect with increasing number of sites. In combination, our findings suggest that assessing broad 123Biodivers Conserv (2015) 24:33-46 DOI 10.1007 diversity patterns in abundant and readily detectable organisms may be possible with relatively low survey effort per site. Our study demonstrates the utility of conducting pilot studies prior to designing large-scale studies on diversity distribution patterns.

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“…Nevertheless, there is still considerable disagreement over the importance of estimating detection probabilities associated with individual counts (Rosenstock et al 2002). Although common survey methods are largely unvalidated, most practitioners assume that current methods for estimating detection probability are accurate, and that observer training obviates the need to account for measurement and misclassification errors in point count data.…”

Section: Introductionmentioning

confidence: 99%

“…Avian abundance estimates can vary over space and time due to actual differences in abundance, differences in detection probabilities among counts, and differences associated with measurement and misclassification errors (Nichols et al 2008;Nichols et al 2000;Farnsworth et al 2002;Pollock et al 2002;Rosenstock et al 2002;Thompson 2002).…”

Section: Introductionmentioning

confidence: 99%

Sources of Measurement Error, Misclassification Error, and Bias in Auditory Avian Point Count Data

Simons

Pollock²,

Wettroth³

et al. 2009

Modeling Demographic Processes in Marked Populations

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Avian point counts vary over space and time due to actual differences in abundance, differences in detection probabilities among counts, and differences associated with measurement and misclassification errors. However, despite the substantial time, effort, and money expended counting birds in ecological research and monitoring, the validity of common survey methods remains largely untested, and there is still considerable disagreement over the importance of estimating detection probabilities associated with individual counts. Most practitioners assume that current methods for estimating detection probability are accurate, and that observer training obviates the need to account for measurement and misclassification errors in point count data. Our approach combines empirical data from field studies with field experiments using a system for simulating avian census conditions when most birds are identified by sound. Our objectives are to: identify the factors that influence detection probability on auditory point counts, quantify the bias and precision of current sampling methods, and find new applications of sampling theory and methodologies that produce practical improvements in the quality of bird census data.We have found that factors affecting detection probabilities on auditory counts, such as ambient noise, can cause substantial biases in count data. Distance sampling data are subject to substantial measurement error due to the difficulty of estimating the distance to a sound source when visual cues are lacking. Misclassification errors are also inherent in time of detection methods due to the difficulty of accurately identifying and localizing sounds during a count. Factors affecting detection probability, measurement errors, and misclassification errors are important but often ignored components of the uncertainty associated with point-count-based abundance estimates.

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Landbird Counting Techniques: Current Practices and an Alternative

Cited by 110 publications

References 19 publications

Habitat Associations, Relative Abundance, and Species Richness of Autumn Landbird Migrants in Southwestern Idaho

Habitat Associations, Relative Abundance, and Species Richness of Autumn Landbird Migrants in Southwestern Idaho

Developing robust field survey protocols in landscape ecology: a case study on birds, plants and butterflies

Sources of Measurement Error, Misclassification Error, and Bias in Auditory Avian Point Count Data

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