Exploring the limits of spatiotemporal and design-based index standardization under reduced survey coverage
出版年份 2023 全文链接
标题
Exploring the limits of spatiotemporal and design-based index standardization under reduced survey coverage
作者
关键词
-
出版物
ICES JOURNAL OF MARINE SCIENCE
Volume -, Issue -, Pages -
出版商
Oxford University Press (OUP)
发表日期
2023-10-13
DOI
10.1093/icesjms/fsad155
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- (2022) Christian J. C. Commander et al. PeerJ
- On comparing design-based estimation versus model-based prediction to assess the abundance of biological populations
- (2022) Philippe Aubry et al. ECOLOGICAL INDICATORS
- Cross validation for model selection: A review with examples from ecology
- (2022) Luke A. Yates et al. ECOLOGICAL MONOGRAPHS
- Incorporating vertical distribution in index standardization accounts for spatiotemporal availability to acoustic and bottom trawl gear for semi-pelagic species
- (2021) Cole C Monnahan et al. ICES JOURNAL OF MARINE SCIENCE
- Bridging the gap between commercial fisheries and survey data to model the spatiotemporal dynamics of marine species
- (2021) Marie‐Christine Rufener et al. ECOLOGICAL APPLICATIONS
- Estimating fine‐scale movement rates and habitat preferences using multiple data sources
- (2021) James T. Thorson et al. FISH AND FISHERIES
- Predicting abundance indices in areas without coverage with a latent spatio-temporal Gaussian model
- (2021) Olav Nikolai Breivik et al. ICES JOURNAL OF MARINE SCIENCE
- Monitoring change in a dynamic environment: spatio-temporal modelling of calibrated data from different types of fisheries surveys of Pacific halibut
- (2020) Raymond A. Webster et al. CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES
- Highly resolved spatiotemporal simulations for exploring mixed fishery dynamics
- (2020) Paul J. Dolder et al. ECOLOGICAL MODELLING
- SimSurvey: An R package for comparing the design and analysis of surveys by simulating spatially-correlated populations
- (2020) Paul M. Regular et al. PLoS One
- Assessing spillover from marine protected areas and its drivers: A meta‐analytical approach
- (2020) Manfredi Di Lorenzo et al. FISH AND FISHERIES
- The surprising sensitivity of index scale to delta-model assumptions: Recommendations for model-based index standardization
- (2020) James T. Thorson et al. FISHERIES RESEARCH
- The effect of random and density‐dependent variation in sampling efficiency on variance of abundance estimates from fishery surveys
- (2019) Stan Kotwicki et al. FISH AND FISHERIES
- Evaluation of the impacts of different treatments of spatio-temporal variation in catch-per-unit-effort standardization models
- (2019) Arnaud Grüss et al. FISHERIES RESEARCH
- Developing spatio-temporal models using multiple data types for evaluating population trends and habitat usage
- (2019) Arnaud Grüss et al. ICES JOURNAL OF MARINE SCIENCE
- Investigating the value of including depth during spatiotemporal index standardization
- (2019) Kelli F. Johnson et al. FISHERIES RESEARCH
- Trade‐offs in covariate selection for species distribution models: a methodological comparison
- (2019) Stephanie Brodie et al. ECOGRAPHY
- A unified framework for analysis of individual-based models in ecology and beyond
- (2019) Stephen J. Cornell et al. Nature Communications
- Constructing Priors that Penalize the Complexity of Gaussian Random Fields
- (2018) Geir-Arne Fuglstad et al. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
- Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments
- (2018) James T. Thorson FISHERIES RESEARCH
- Comparing estimates of abundance trends and distribution shifts using single- and multispecies models of fishes and biogenic habitat
- (2017) James T. Thorson et al. ICES JOURNAL OF MARINE SCIENCE
- Implementing a generic method for bias correction in statistical models using random effects, with spatial and population dynamics examples
- (2016) James T. Thorson et al. FISHERIES RESEARCH
- TMB: Automatic Differentiation and Laplace Approximation
- (2016) Kasper Kristensen et al. Journal of Statistical Software
- virtualspecies, an R package to generate virtual species distributions
- (2015) Boris Leroy et al. ECOGRAPHY
- Geostatistical delta-generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes
- (2015) James T. Thorson et al. ICES JOURNAL OF MARINE SCIENCE
- Spatial semiparametric models improve estimates of species abundance and distribution
- (2014) Andrew Olaf Shelton et al. CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES
- Evaluation of alternative age-based methods for estimating relative abundance from survey data in relation to assessment models
- (2013) Casper W. Berg et al. FISHERIES RESEARCH
- Likelihood-based and Bayesian methods for Tweedie compound Poisson linear mixed models
- (2012) Yanwei Zhang STATISTICS AND COMPUTING
- Data weighting in statistical fisheries stock assessment models
- (2011) R.I.C. Chris Francis CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES
- Annual egg production estimates of cod (Gadus morhua), plaice (Pleuronectes platessa) and haddock (Melanogrammus aeglefinus) in the Irish Sea: The effects of modelling choices and assumptions
- (2011) David L. Maxwell et al. FISHERIES RESEARCH
- An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach
- (2011) Finn Lindgren et al. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
- Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love
- (2010) James S. Hodges et al. AMERICAN STATISTICIAN
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