Plausible combinations: An improved method to evaluate the covariate structure of Cormack-Jolly-Seber mark-recapture models

Bromaghin, Jeffrey F.; McDonald, Trent L.; Amstrup, Steven C.

doi:10.4236/oje.2013.31002

Cited by 34 publications

(52 citation statements)

References 36 publications

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“…Secondary candidate set strategies fit submodels independently and combine the top set of models from each sub-model for selection in a final stage (Bromaghin et al 2013). Sequential-by-sub-model strategies focus on one sub-model at a time (parameter such as detection, p; survival, /; or occupancy, Ψ) with modeling of subsequent sub-models dependent on the selected model structures from the previous stages.…”

Section: Multi-stage Modeling Strategiesmentioning

confidence: 99%

“…However, using a general sub-model structure for non-targets (allowing for inclusion of covariates or temporal and spatial structure) to avoid constraining the fit of target sub-models has often been advocated (Lebreton et al 1992, Doherty et al 2012, Bromaghin et al 2013. Using the null structure (i.e., not varying through time and space, also referred to as (.)…”

Section: Multi-stage Modeling Strategiesmentioning

confidence: 99%

“…To address these possible omissions in early stages, the plausible combinations strategy (Bromaghin et al 2013) carries forward not only sub-model structures within a DAIC threshold, but also any submodel structures with a greater likelihood than the models within the DAIC threshold, allowing consideration of models with more estimated parameters in subsequent steps. To address these possible omissions in early stages, the plausible combinations strategy (Bromaghin et al 2013) carries forward not only sub-model structures within a DAIC threshold, but also any submodel structures with a greater likelihood than the models within the DAIC threshold, allowing consideration of models with more estimated parameters in subsequent steps.…”

Section: Multi-stage Modeling Strategiesmentioning

confidence: 99%

“…Thus, the structure of one sub-model (i.e., capture probability or survival) can influence the structure of another (defined as leak in Catchpole et al 2004 andBromaghin et al 2013). Thus, the structure of one sub-model (i.e., capture probability or survival) can influence the structure of another (defined as leak in Catchpole et al 2004 andBromaghin et al 2013).…”

Section: Introductionmentioning

confidence: 99%

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Is your ad hoc model selection strategy affecting your multimodel inference?

et al. 2020

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Ecologists routinely fit complex models with multiple parameters of interest, where hundreds or more competing models are plausible. To limit the number of fitted models, ecologists often define a model selection strategy composed of a series of stages in which certain features of a model are compared while other features are held constant. Defining these multi-stage strategies requires making a series of decisions, which may potentially impact inferences, but have not been critically evaluated. We begin by identifying key features of strategies, introducing descriptive terms when they did not already exist in the literature. Strategies differ in how they define and order model building stages. Sequential-by-sub-model strategies focus on one sub-model (parameter) at a time with modeling of subsequent sub-models dependent on the selected sub-model structures from the previous stages. Secondary candidate set strategies model sub-models independently and combine the top set of models from each sub-model for selection in a final stage. Build-up approaches define stages across sub-models and increase in complexity at each stage. Strategies also differ in how the top set of models is selected in each stage and whether they use null or more complex sub-model structures for non-target sub-models. We tested the performance of different model selection strategies using four data sets and three model types. For each data set, we determined the "true" distribution of AIC weights by fitting all plausible models. Then, we calculated the number of models that would have been fitted and the portion of "true" AIC weight we recovered under different model selection strategies. Sequential-by-sub-model strategies often performed poorly. Based on our results, we recommend using a build-up or secondary candidate sets, which were more reliable and carrying all models within 5-10 AIC of the top model forward to subsequent stages. The structure of non-target sub-models was less important. Multi-stage approaches cannot compensate for a lack of critical thought in selecting covariates and building models to represent competing a priori hypotheses. However, even when competing hypotheses for different sub-models are limited, thousands or more models may be possible so strategies to explore candidate model space reliably and efficiently will be necessary.

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Section: Multi-stage Modeling Strategiesmentioning

confidence: 99%

Section: Multi-stage Modeling Strategiesmentioning

confidence: 99%

Section: Multi-stage Modeling Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Is your ad hoc model selection strategy affecting your multimodel inference?

et al. 2020

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“…We utilized the plausible combinations (PC) strategy (Bromaghin et al 2013), with an Akaike's information criterion (AIC c ) model weight (Burnham and Anderson 2002) of 2.5% as an inclusion threshold, to objectively base inference on a reduced model space. We utilized the plausible combinations (PC) strategy (Bromaghin et al 2013), with an Akaike's information criterion (AIC c ) model weight (Burnham and Anderson 2002) of 2.5% as an inclusion threshold, to objectively base inference on a reduced model space.…”

Section: Modeling Strategiesmentioning

confidence: 99%

Polar bear population dynamics in the southern Beaufort Sea during a period of sea ice decline

Bromaghin

McDonald

Stirling

et al. 2015

Ecological Applications

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191

172

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In the southern Beaufort Sea of the United States and Canada, prior investigations have linked declines in summer sea ice to reduced physical condition, growth, and survival of polar bears (Ursus maritimus). Combined with projections of population decline due to continued climate warming and the ensuing loss of sea ice habitat, those findings contributed to the 2008 decision to list the species as threatened under the U.S. Endangered Species Act. Here, we used mark-recapture models to investigate the population dynamics of polar bears in the southern Beaufort Sea from 2001 to 2010, years during which the spatial and temporal extent of summer sea ice generally declined. Low survival from 2004 through 2006 led to a 25-50% decline in abundance. We hypothesize that low survival during this period resulted from (1) unfavorable ice conditions that limited access to prey during multiple seasons; and possibly, (2) low prey abundance. For reasons that are not clear, survival of adults and cubs began to improve in 2007 and abundance was comparatively stable from 2008 to 2010, with ~900 bears in 2010 (90% CI 606-1212). However, survival of subadult bears declined throughout the entire period. Reduced spatial and temporal availability of sea ice is expected to increasingly force population dynamics of polar bears as the climate continues to warm. However, in the short term, our findings suggest that factors other than sea ice can influence survival. A refined understanding of the ecological mechanisms underlying polar bear population dynamics is necessary to improve projections of their future status and facilitate development of management strategies.

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Detectability and impact of repetitive surveys on threatened West African crocodylians

Ahizi

Kouman

Ouattara

et al. 2021

Ecology and Evolution

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West African crocodylians are among the most threatened and least studied crocodylian species globally. Assessing population status and establishing a basis for population monitoring is the highest priority action for this region. Monitoring of crocodiles is influenced by many factors that affect detectability, including environmental variables and individual‐ or population‐level wariness. We investigated how these factors affect detectability and counts of the critically endangered Mecistops cataphractus and the newly recognized Crocodylus suchus. We implemented 195 repetitive surveys at 38 sites across Côte d’Ivoire between 2014 and 2019. We used an occupancy‐based approach and a count‐based GLMM analysis to determine the effect of environmental and anthropogenic variables on detection and modeled crocodile wariness over repetitive surveys. Despite their rarity and level of threat, detection probability of both species was relatively high (0.75 for M. cataphractus and 0.81 for C. suchus), but a minimum of two surveys were required to infer absence of either species with 90% confidence. We found that detection of M. cataphractus was significantly negatively influenced by fishing net encounter rate, while high temperature for the previous 48 h of the day of the survey increased C. suchus detection. Precipitation and aquatic vegetation had significant negative and positive influence, respectively, on M. cataphractus counts and showed the opposite effect for C. suchus counts. We also found that fishing encounter rate had a significant negative effect on C. suchus counts. Interestingly, survey repetition did not generally affect wariness for either species, though there was some indication that at least M. cataphractus was more wary by the fourth replicate. These results are informative for designing future survey and monitoring protocols for these threatened crocodylians in West Africa and for other endangered crocodylians globally.

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Plausible combinations: An improved method to evaluate the covariate structure of Cormack-Jolly-Seber mark-recapture models

Cited by 34 publications

References 36 publications

Is your ad hoc model selection strategy affecting your multimodel inference?

Is your ad hoc model selection strategy affecting your multimodel inference?

Polar bear population dynamics in the southern Beaufort Sea during a period of sea ice decline

Detectability and impact of repetitive surveys on threatened West African crocodylians

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