Northern Parula (Parula americana)

Moldenhauer, Ralph R.; Regelski, Daniel J.

doi:10.2173/tbna.215.p

Cited by 2 publications

(6 citation statements)

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“…We applied our model to point count data collected on the northern parula (Parula americana), a migratory passerine. This species maintains well-defined home ranges during the breeding season [Moldenhauer and Regelski (1996)], and thus our modeling effort was focused on estimating the number and location of home range centers. Points were located on a 50 m grid, which ensured spatial correlation since home ranges typically have >50 m radii and because their song can be heard from distance >50 m [Moldenhauer and Regelski (1996)].…”

Section: Applicationsmentioning

confidence: 99%

“…This species maintains well-defined home ranges during the breeding season [Moldenhauer and Regelski (1996)], and thus our modeling effort was focused on estimating the number and location of home range centers. Points were located on a 50 m grid, which ensured spatial correlation since home ranges typically have >50 m radii and because their song can be heard from distance >50 m [Moldenhauer and Regelski (1996)]. This small grid spacing contrasts with the conventional practice of spacing points by >200 m to obtain i.i.d.…”

Section: Applicationsmentioning

confidence: 99%

“…Uniform priors were also used in the second set, with the exception of an informative prior for the scale parameter σ ∼ Gamma(13, 10). We arrived at this prior using the methods described by Royle, Kéry and Guélat (2011) and published information on home range size and detection probability [Moldenhauer and Regelski (1996), Simons et al (2009)]. More details on this derivation are found in the supplementary material [Chandler and Royle (2013)].…”

Section: Applicationsmentioning

confidence: 99%

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Spatially explicit models for inference about density in unmarked or partially marked populations

Chandler¹,

Royle²

2013

Ann. Appl. Stat.

277

424

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Recently developed spatial capture-recapture (SCR) models represent a major advance over traditional capture-recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Furthermore, unlike nonspatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers and sample locations. Although the utility of SCR methods is gaining recognition, the requirement that all individuals can be uniquely identified excludes their use in many contexts. In this paper, we develop models for situations in which individual recognition is not possible, thereby allowing SCR concepts to be applied in studies of unmarked or partially marked populations. The data required for our model are spatially referenced counts made on one or more sample occasions at a collection of closely spaced sample units such that individuals can be encountered at multiple locations. Our approach includes a spatial point process for the animal activity centers and uses the spatial correlation in counts as information about the number and location of the activity centers. Camera-traps, hair snares, track plates, sound recordings, and even point counts can yield spatially correlated count data, and thus our model is widely applicable. A simulation study demonstrated that while the posterior mean exhibits frequentist bias on the order of 5-10% in small samples, the posterior mode is an accurate point estimator as long as adequate spatial correlation is present. Marking a subset of the population substantially increases posterior precision and is recommended whenever possible. We applied our model to avian point count data collected on an unmarked population of the northern parula (Parula americana) and obtained a density estimate (posterior mode) of 0.38 (95% CI: 0.19-1.64) birds/ha. Our paper challenges sampling and analytical

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Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

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Spatially explicit models for inference about density in unmarked or partially marked populations

Chandler¹,

Royle²

2013

Ann. Appl. Stat.

277

424

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“…Th e northern parula is common in the bottomland hardwood and riverine forests of the Southeastern United States (Moldenhauer and Regelski 1996). It also occupies mixed pinehardwoods, though at lower densities (Moldenhauer and Regelski 1996). Th e northern parula has two competing habitat requirements: a preference for canopy gaps and large forest blocks.…”

Section: Natural Historymentioning

confidence: 99%

“…Th e northern parula forages in the mid-to upper canopy layers (Moldenhauer and Regelski 1996), so it is not surprising that it prefers microsites with high basal area (Robbins and others 1989), high canopy cover, and tall canopies (James 1971), and avoids areas with dense understories (often associated with open canopies) (Torres and Leberg 1996). In the Southeast, this species nests almost exclusively in Spanish moss (Moldenhauer and Regelski 1996). However, no studies have identifi ed Spanish moss as limiting.…”

Section: Natural Historymentioning

confidence: 99%

Multiscale habitat suitability index models for priority landbirds in the Central Hardwoods and West Gulf Coastal Plain/Ouachitas Bird Conservation Regions

Tirpak¹,

Jones-Farrand²,

Thompson³

et al. 2009

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Ecoregional conservation planning for priority landbirds requires methods that explicitly link populations to habitat conditions at multiple scales. We developed Habitat Suitability Index (HSI) models to assess habitat quality for 40 priority bird species in the Central Hardwoods and West Gulf Coastal Plain/Ouachitas Bird Conservation Regions. The models incorporated both site and landscape environmental variables derived from one of six nationally consistent datasets: ecological subsections from the National Ecological Unit Hierarchy, National Land Cover Dataset, National Elevation Dataset, National Hydrography Dataset, State Soil Geographic Database, and Forest Inventory and Analysis data. We initially defi ned potential habitat for each species from unique landform, landcover, and successional age class combinations. Species-specifi c environmental variables identifi ed from the literature were used to refi ne initial habitat estimates. We verifi ed models by comparing subsection-level HSI scores and Breeding Bird Survey (BBS) abundance via Spearman rank correlation. To validate models, we developed generalized linear models that predicted BBS abundance as a function of HSI score and Bird Conservation Region. We considered models that included a signifi cant (P ≤ 0.100) positive coefficient on the BBS predictor to be valid and useful for conservation planning.

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Northern Parula (Parula americana)

Cited by 2 publications

References 0 publications

Spatially explicit models for inference about density in unmarked or partially marked populations

Spatially explicit models for inference about density in unmarked or partially marked populations

Multiscale habitat suitability index models for priority landbirds in the Central Hardwoods and West Gulf Coastal Plain/Ouachitas Bird Conservation Regions

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