Inference about density and temporary emigration in unmarked populations

Chandler, Richard B.; Royle, J. Andrew; King, David I.

doi:10.1890/10-2433.1

Cited by 203 publications

(275 citation statements)

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“…We used generalized binomial N-mixture models for repeated count data with negative binomial distributions to Avian Conservation and Ecology 11(2): 3 http://www.ace-eco.org/vol11/iss2/art3/ simultaneously evaluate covariates potentially related to abundance, availability, and detectability (Royle 2004, Chandler et al 2011). The models consisted of three hierarchical levels: one described the total number of individuals that potentially use habitats around each station, referred to here as the abundance process; another described the proportion of total individuals present within plots during surveys, referred to here as the availability process; and another described the proportion of available individuals observed during surveys, referred to here as the detectability process (Chandler et al 2011). We chose a negative binomial distribution because models with this distribution had lower Akaike's Information Criterion (AIC) values for each species compared with models using a Poisson distribution.…”

Section: Analysesmentioning

confidence: 99%

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Modeling detection probability to improve marsh bird surveys in southern Canada and the Great Lakes states

Tozer¹,

Drake²,

Falconer³

2016

ACE

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ABSTRACT. Marsh birds are notoriously elusive, with variation in detection probability across species, regions, seasons, and different times of day and weather. Therefore, it is important to develop regional field survey protocols that maximize detections, but that also produce data for estimating and analytically adjusting for remaining differences in detections. We aimed to improve regional field survey protocols by estimating detection probability of eight elusive marsh bird species throughout two regions that have ongoing marsh bird monitoring programs: the southern Canadian Prairies (Prairie region) and the southern portion of the Great Lakes basin and parts of southern Québec (Great Lakes-St. Lawrence region). We accomplished our goal using generalized binomial N-mixture models and data from ~22,300 marsh bird surveys conducted between 2008 and 2014 by Bird Studies Canada's Prairie, Great Lakes, and Québec Marsh Monitoring Programs. Across all species, on average, detection probability was highest in the Great Lakes-St. Lawrence region from the beginning of May until mid-June, and then fell throughout the remainder of the season until the end of June; was lowest in the Prairie region in mid-May and then increased throughout the remainder of the season until the end of June; was highest during darkness compared with light; and did not vary significantly according to temperature (range: 0-30°C), cloud cover (0%-100%), or wind (0-20 kph), or during morning versus evening. We used our results to formulate improved marsh bird survey protocols for each region. Our analysis and recommendations are useful and contribute to conservation of wetland birds at various scales from local single-species studies to the continental North American Marsh Bird Monitoring Program. Modélisation de la probabilité de détection visant l'amélioration des suivis d'oiseaux de marais dans le sud du Canada et les États des Grands LacsRÉSUMÉ. Les oiseaux de marais sont célèbres pour leur moeurs discrètes, entraînant du coup des probabilités de détection variables selon les espèces, les régions, les saisons, le moment de la journée et les conditions météorologiques. Il est donc important d'élaborer des protocoles régionaux de relevé sur le terrain qui maximisent les détections, mais produisent aussi des données permettant d'estimer et d'ajuster analytiquement les différences de détectabilité restantes. Notre objectif était d'améliorer les protocoles régionaux de relevé sur le terrain en estimant la probabilité de détection de huit espèces de marais discrètes dans deux régions hôtes de programmes réguliers de suivis d'oiseaux de marais : le sud des Prairies canadiennes (région des Prairies), et la partie sud du bassin des Grands Lacs et des zones du Québec méridional (région des Grands-Lacs-Saint-Laurent). Pour ce faire, nous avons utilisé des modèles N-mélange binomiaux généralisés et les données de 22 300 relevés d'oiseaux de marais réalisés entre 2008 et 2014 dans le cadre des Programmes de surveillance des marais des Prairies, des Gran...

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Section: Analysesmentioning

confidence: 99%

“…To facilitate model convergence and comparison of estimates across variables and species, we z-transformed all of the covariates prior to analysis (Kéry and Chandler 2012). Models were fitted using the gpcount function in the package unmarked (Chandler et al 2011.…”

Section: Analysesmentioning

confidence: 99%

Modeling detection probability to improve marsh bird surveys in southern Canada and the Great Lakes states

Tozer¹,

Drake²,

Falconer³

2016

ACE

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“…However, conventional approaches to abundance estimation based on line transect distance sampling do not permit modeling abundance as a function of environmental covariates that can affect both spatial distribution pattern and population density. To this end, it is encouraging to note that modeling approaches such as the hierarchical modeling framework of Royle et al (2004), and its extension to allow temporary emigration (Chandler et al, 2011), will prove useful to improve accuracy and precision of density estimates and statistical inference regarding covariate effects on density. Finally, the availability of software packages such as unmarked (Fiske et al, 2012) will facilitate the implementation of recently developed density estimation and modeling approaches using data collected from distance sampling.…”

Section: Discussionmentioning

confidence: 99%

Line Transects by Design: The Influence of Study Design, Spatial Distribution and Density of Objects on Estimates of Abundance

Nomani¹

2012

TOECOLJ

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Abstract:The line transect distance sampling method provides unbiased estimates of abundance when organisms are distributed randomly or line transects are laid out randomly, sample sizes are large and other assumptions of the method are met; such, however, is rarely the case in real life. We conducted a simulation study to investigate how spatial distribution and density of objects, and total length, layout and number of transects influence bias, precision, and accuracy of estimates of abundance obtained by distance sampling along line transects. Overall, density estimated using the distance sampling method was within 4.9% of the true density, but it varied substantially depending upon spatial distribution of objects. Of the three spatial distribution patterns considered, estimates of density were least biased, and most precise and accurate when objects were distributed randomly; they were most biased, and least precise and accurate when objects followed a clumped distribution. The estimated bias (% difference between true density and estimated density) for clumped, random and uniform distribution was 13.1%, -0.4%, and 2.1%, respectively; precision (% coefficient of variation, CV( D )) was 13.7%, 9.1%, and 9.2%; and accuracy (root mean-squared error, RMSE) was 27.9%, 7.4%, and 11.7% for clumped, random, and uniform distribution, respectively. Increasing total transect length and using several short transects (as opposed to few long transects) generally reduced bias, and increased accuracy and precision of estimates of abundance. A systematic layout of transects worked as well as, or better than, random layout, except when objects were distributed uniformly in space. This study advances the utility of the line transect method by providing information both on how study design affects accuracy and precision of abundance estimates, and how it can be improved when assumptions of the method are not strictly met based on a priori knowledge of the spatial distribution and presumed density of the target organism through appropriate changes in the study design.

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“…Distance sampling data were analyzed using the gdistsamp function based on the method proposed by Royle et al (2004) and extended by Chandler et al (2011). This model is similar to the N-mixture model, although it adds a component to the likelihood to address variability in detection probability as a function of range from the survey transect.…”

Section: Methodsmentioning

confidence: 99%

Estimating Sturgeon Abundance in the Carolinas Using Side‐Scan Sonar

Flowers

Hightower

2015

Mar Coast Fish

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Sturgeons (Acipenseridae) are one of the most threatened taxa worldwide, including species in North Carolina and South Carolina. Populations of Atlantic Sturgeon Acipenser oxyrinchus in the Carolinas have been significantly reduced from historical levels by a combination of intense fishing and habitat loss. There is a need for estimates of current abundance, to describe status, and for estimates of historical abundance in order to provide realistic recovery goals. In this study we used N‐mixture and distance models with data acquired from side‐scan sonar surveys to estimate abundance of sturgeon in six major sturgeon rivers in North Carolina and South Carolina. Estimated abundances of sturgeon greater than 1 m TL in the Carolina distinct population segment (DPS) were 2,031 using the count model and 1,912 via the distance model. The Pee Dee River had the highest overall abundance of any river at 1,944 (count model) or 1,823 (distance model). These estimates do not account for sturgeon less than 1 m TL or occurring in riverine reaches not surveyed or in marine waters. Comparing the two models, the N‐mixture model produced similar estimates using less data than the distance model with only a slight reduction of estimated precision. Received May 3, 2014; accepted October 14, 2014

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Inference about density and temporary emigration in unmarked populations

Cited by 203 publications

References 19 publications

Modeling detection probability to improve marsh bird surveys in southern Canada and the Great Lakes states

Modeling detection probability to improve marsh bird surveys in southern Canada and the Great Lakes states

Line Transects by Design: The Influence of Study Design, Spatial Distribution and Density of Objects on Estimates of Abundance

Estimating Sturgeon Abundance in the Carolinas Using Side‐Scan Sonar

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