Variation in Peccary Populations: Landscape Composition or Competition by an Invader?

Gabor, Timothy M.; Hellgren, Eric C.

doi:10.1890/0012-9658(2000)081[2509:vipplc]2.0.co;2

Cited by 33 publications

(13 citation statements)

References 79 publications

(101 reference statements)

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“…They went on to introduce compositional analysis (CA) as a tool for analyzing resource selection data. Since its introduction, CA has become one of the most widely used habitat analysis procedures (Pendleton et al 1998, Conner et al 1999, Miller et al 1999, Gabor and Hellgren 2000.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Distance-Based and Classification-Based Analyses of Habitat Use

Conner

Smith

Burger

2003

Ecology

166

170

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Quantifying habitat use is vital to understanding animal ecology. Herein, we contrast classification‐based (i.e., animal locations are placed into habitat categories for subsequent analyses) and distance‐based (i.e., distance between animal locations and habitat features are used in subsequent analyses) approaches for analyzing habitat use data. Compositional analysis (CA) and a distance‐based analysis (DA) were used to quantify habitat selection of Northern Bobwhite (Colinus virginianus). We qualitatively compared the outcome of these different procedures to illustrate advantages of the DA approach. The DA approach identified edges as an important habitat feature, and location error did not alter conclusions from DA. In contrast, CA did not detect the importance of edge, and presence of location errors altered conclusions. Moreover, modeling the distribution of location error did not effectively reduce sensitivity to error within CA. Distance‐based approaches to habitat analyses are not restricted to linear or point habitat features, require no explicit error handling, and permit extraction of more information from the data than classification‐based analyses alone. Corresponding Editor: J. M. Fryxell

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Section: Introductionmentioning

confidence: 99%

A Comparison of Distance-Based and Classification-Based Analyses of Habitat Use

Conner

Smith

Burger

2003

Ecology

166

170

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“…The difference was probably because of higher abundance of peccaries on the east side. Although we did not measure peccary abundance directly, our results suggest that the markedly higher densities that Gabor and Hellgren (2000) detected from 1993 to 1995 on the east side have persisted. Our conclusion that density may influence detection of peccary sign agrees with Royle and Nichols' (2003) notion that density can influence detection.…”

Section: Discussionmentioning

confidence: 56%

“…We modeled detection probabilities as similar among surveys (constant), specific to surveys, specific to each side of the CWMA, and influenced by rainfall. We considered detection probabilities specific to each side of the CWMA to assess if probable differences in peccary density on each side influenced detection probabilities (Gabor and Hellgren 2000). PRESENCE also estimates an occupancy parameter, the proportion of sampling units occupied by peccary.…”

Section: Methodsmentioning

confidence: 99%

“…Fences 2.5 m high surrounded CWMA and bisected it into east and west halves (Gabor and Hellgren 2000). Although peccaries moved through the fences, Gabor and Hellgren (2000) detected peccary densities that were 5-8 times higher on the east half of CWMA from 1993 to 1995. From our observations during 50 days of field work at the CWMA, it was likely that higher peccary density on the east half persisted during our study.…”

Section: Study Areamentioning

confidence: 97%

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Estimating Detection Probabilities from Sign of Collared Peccary

Longoria

Weckerly

2007

J Wildl Manag

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Determining presence or absence of collared peccaries (Pecari tajacu) from surveys of sign (tracks and feces) requires information on whether animals in sample units are detected. We estimated detection probabilities of collared peccary from sign surveys using occupancy models. Because it was unlikely that residence status of collared peccary in sampling units remained the same over a survey season, which is a primary assumption of occupancy models, we first determined the time interval for which to pool data. We then examined the influence of rainfall and peccary abundance on detection probabilities. We placed 90 sign stations (25‐m‐diam circular plots) throughout Chaparral Wildlife Management Area, south Texas, USA. We surveyed plots weekly for the presence or non‐presence of collared peccary during 2 11‐week sampling seasons in spring and fall 2003. We examined sign data weekly and we pooled the data in intervals from 2 weeks to 5 weeks. Estimates of detection probabilities increased from 1 week to 3 weeks of pooled data and leveled off thereafter. We needed a 3‐week time interval to meet the assumption of unchanging residence status. Using sign data pooled in 3‐week increments, detection probabilities were influenced by areas that differed in peccary abundance, but they were not influenced by rainfall. Estimates of detection probabilities ranged from 0.43 to 0.77 for 3‐week time intervals. Sign surveys and occupancy modeling of data can be used to measure spatial patterns of collared peccary in south Texas as long as multiple 3‐week time intervals are sampled.

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“…Compositional analysis has been recommended as a preferred method of quantifying habitat use (Aebischer et al 1993). This method has been used to analyze home range and habitat use of a wide variety of animals, including game birds (Carroll et al 1995, Manley et al 2000, raptors (Beier and Drennan 1997), large mammals (Mladenoff et al 1999, Gabor andHellgren 2000), and passerines (Gates and Evans 1998). Compositional analysis has become a popular method for the analysis of wildlife-habitat relationships because it uses the radiomarked animal, rather than individual radio locations, as the experimental unit.…”

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confidence: 99%

Comparison of Type I Error Rates for Statistical Analyses of Resource Selection

Bingham

Brennan

2004

Journal of Wildlife Management

111

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During the past decade, compositional analysis (CA) has been used widely in wildlife habitat and resource selection studies. However, critical aspects of CA have not been tested for potential systematic biases such as an inflated Type I error rate. We used computer‐simulated data based on known habitat use and availability parameters and found that compositional analysis could result in large Type I error rates. These inflated Type I errors occurred when available habitat types that were not used by animals were included in the resource selection analysis. These error rates arise because of the recommended substitution of an arbitrarily small value, such as 0.01, for each 0% utilization value for any animal. We observed, based on a series of computer‐simulation analyses, that progressively larger Type I error rates in CA resulted from substituting progressively smaller positive values for each 0% utilization of a habitat category. The Type I error rate in CA also increased when the number of experimental animals was increased for a fixed number of observations per animal. Two other resource selection analysis methods (Neu et al. [1974] and the Euclidean distance‐based analysis [DA] method of Conner and Plowman [2001]) did not exhibit inflated Type I error rates for the same simulated data. Our computer simulations cause us to question the veracity of CA habitat selection analyses that include habitat patches or categories with relatively small areas of availabilities and 0% use.

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Variation in Peccary Populations: Landscape Composition or Competition by an Invader?

Cited by 33 publications

References 79 publications

A Comparison of Distance-Based and Classification-Based Analyses of Habitat Use

A Comparison of Distance-Based and Classification-Based Analyses of Habitat Use

Estimating Detection Probabilities from Sign of Collared Peccary

Comparison of Type I Error Rates for Statistical Analyses of Resource Selection

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