Population Estimation from Mark-Recapture Experiments Using a Sequential Bayes Algorithm

Gazey, William J.; Staley, Michael J.

doi:10.2307/1939816

Cited by 85 publications

(77 citation statements)

References 14 publications

(22 reference statements)

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“…The Gazey and Staley Bayesian algorithm (Gazey and Staley 1986) was used in R (Ihaka and Gentleman 1996) to estimate how many OR genes could possibly be amplified using the GPC1 and GPC2 primers (described above) for each species. This algorithm is typically used to estimate the size of animal populations by analyzing ''capture-mark-re-capture'' data from field studies.…”

Section: Gazey and Staley Algorithmmentioning

confidence: 99%

Ecological adaptation determines functional mammalian olfactory subgenomes

Hayden¹,

Bekaert²,

Crider³

et al. 2009

Genome Res.

215

307

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The ability to smell is governed by the largest gene family in mammalian genomes, the olfactory receptor (OR) genes. Although these genes are well annotated in the finished human and mouse genomes, we still do not understand which receptors bind specific odorants or how they fully function. Previous comparative studies have been taxonomically limited and mostly focused on the percentage of OR pseudogenes within species. No study has investigated the adaptive changes of functional OR gene families across phylogenetically and ecologically diverse mammals. To determine the extent to which OR gene repertoires have been influenced by habitat, sensory specialization, and other ecological traits, to better understand the functional importance of specific OR gene families and thus the odorants they bind, we compared the functional OR gene repertoires from 50 mammalian genomes. We amplified more than 2000 OR genes in aquatic, semi-aquatic, and flying mammals and coupled these data with 48,000 OR genes from mostly terrestrial mammals, extracted from genomic projects. Phylogenomic, Bayesian assignment, and principle component analyses partitioned species by ecotype (aquatic, semi-aquatic, terrestrial, flying) rather than phylogenetic relatedness, and identified OR families important for each habitat. Functional OR gene repertoires were reduced independently in the multiple origins of aquatic mammals and were significantly divergent in bats. We reject recent neutralist views of olfactory subgenome evolution and correlate specific OR gene families with physiological requirements, a preliminary step toward unraveling the relationship between specific odors and respective OR gene families.

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Section: Gazey and Staley Algorithmmentioning

confidence: 99%

Ecological adaptation determines functional mammalian olfactory subgenomes

Hayden¹,

Bekaert²,

Crider³

et al. 2009

Genome Res.

215

307

View full text Add to dashboard Cite

show abstract

“…While Bayesian methods are gaining use in ecology and conservation biology (e.g., Gazey and Staley 1986, McCarthy et al 2001, O'Hara et al 2002, Dorazio and Johnson 2003, elicitation and models that incorporate expert information through priors have been underutilized (Carpenter 2002). One exception is the work by Crome et al (1996), which demonstrates how expert beliefs on the effects of logging could be incorporated as informative priors in a Bayesian model to assess the impacts on birds and mammals.…”

Section: Introductionmentioning

confidence: 99%

The Power of Expert Opinion in Ecological Models Using Bayesian Methods: Impact of Grazing on Birds

Martin¹,

Kuhnert²,

Mengersen³

et al. 2005

Ecological Applications

193

176

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Abstract. One of our greatest challenges as researchers is predicting impacts of land use on biota, and predicting the impact of livestock grazing on birds is no exception. Insufficient data and poor survey design often yield results that are not statistically significant or that are difficult to interpret because researchers cannot disentangle the effects of grazing from other disturbances. This has resulted in few publications on the impact of grazing on birds alone.Ecologists with extensive experience in bird ecology in grazed landscapes could inform an analysis when time and monetary constraints limit the amount of data that can be collected. Using responses from 20 well-recognized ecologists throughout Australia, we captured this expert knowledge and incorporated it into a statistical model using Bayesian methods. Although relatively new to ecology, Bayesian methods allow straightforward probability statements to be made about specific models or scenarios and the integration of different types of information, including scientific judgment, while formally accommodating and incorporating the uncertainty in the information provided.Data on bird density were collected across three broad levels of grazing (no/low, moderate, and high) typical of subtropical Australia. These field data were used in conjunction with expert data to produce estimates of species persistence under grazing. The addition of expert data through priors in our model strengthened results under at least one grazing level for all but one bird species examined. When experts were in agreement credible intervals were tightened substantially, whereas, when experts were in disagreement, results were similar to those evaluated in the absence of expert information. In fields where there is extensive expert knowledge, yet little published data, the use of expert information as priors for ecological models is a cost-effective way of making more confident predictions about the effect of management on biodiversity.

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“…Like the MND approach and in contrast to the traditional CMR (DDO) approach, the Bayesian model developed by Gazey & Staley (1986) allows using every single observation of each individual for population estimation. Another single session approach which has been developed for non-invasive population estimation is program CAPWIRE (Miller et al 2005), which, however, is best suited for populations of , 100 individuals and tends to produce overestimates for large populations and when sample size is low.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the closed capture mixture model estimates, we calculated population estimates using a single sampling session Bayesian model (Gazey & Staley 1986, Petit & Valie`re 2006, which is especially suitable for non-invasive data because it can make full use of all sampling information in the data set. To examine the assumption of capture homogeneity, we carried out a test in which we simulated the sampling process under the assumption of homogeneity and that the expected number of captures is compared with the observed number of captures/individual (Puechmaille & Petit 2007).…”

Section: Population Size Estimationmentioning

confidence: 99%

Edge Effect on Density Estimates of a Radiotracked Population of Yellow‐Necked Mice

et al. 2009

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Density estimates for small‐mammal populations from capture‐mark‐recapture (CMR) data have played an important role in many studies of theoretical and applied ecology. Defining effective trapping area (ETA) is one of the main issues affecting accuracy of density estimates. Our objective was to assess sensitivity of CMR density estimates to correctors based on movement parameters calculated from trapping and radiotelemetry data. From May to November 2005, we conducted monthly CMR trapping in a beech (Fagus sylvaticus) forest of the province of Trento, northern Italy. In conjunction with CMR, we radio‐marked 32 yellow‐necked mice (Apodemus flavicollis) captured from July to October and located them daily using radiotelemetry. We estimated population size (N) by model averaging with Program MARK. We calculated ETA using several definitions of the boundary strip, including full and half mean maximum distance moved (MMDM) from capture‐recapture and telemetry data and mean radius of mean monthly home ranges. The boundary strip (W) increased with the amount of behavioral information embodied in the estimates. The largest W and lowest density values were based on radius of mean home ranges followed by MMDM calculated from telemetry data. The ETA based on movement distances increased more than proportionally when N decreased, suggesting that low population density combined with scarce resources results in rodents moving more in search of food, thus leading to overestimated ETA and underestimated density values. Although robust behavioral information would certainly improve density estimates, we suggest caution in relating ranging movements to capture probability and hence in using correctors based on movement distances to infer density values.

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Population Estimation from Mark-Recapture Experiments Using a Sequential Bayes Algorithm

Cited by 85 publications

References 14 publications

Ecological adaptation determines functional mammalian olfactory subgenomes

Ecological adaptation determines functional mammalian olfactory subgenomes

The Power of Expert Opinion in Ecological Models Using Bayesian Methods: Impact of Grazing on Birds

Edge Effect on Density Estimates of a Radiotracked Population of Yellow‐Necked Mice

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