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  • 1
    Online Resource
    Online Resource
    Canadian Science Publishing ; 2017
    In:  Canadian Journal of Fisheries and Aquatic Sciences Vol. 74, No. 11 ( 2017-11), p. 1794-1807
    In: Canadian Journal of Fisheries and Aquatic Sciences, Canadian Science Publishing, Vol. 74, No. 11 ( 2017-11), p. 1794-1807
    Abstract: Estimating trends in abundance from fishery catch rates is one of the oldest endeavors in fisheries science. However, many jurisdictions do not analyze fishery catch rates due to concerns that these data confound changes in fishing behavior (adjustments in fishing location or gear operation) with trends in abundance. In response, we developed a spatial dynamic factor analysis (SDFA) model that decomposes covariation in multispecies catch rates into components representing spatial variation and fishing behavior. SDFA estimates spatiotemporal variation in fish density for multiple species and accounts for fisher behavior at large spatial scales (i.e., choice of fishing location) while controlling for fisher behavior at fine spatial scales (e.g., daily timing of fishing activity). We first use a multispecies simulation experiment to show that SDFA decreases bias in abundance indices relative to ignoring spatial adjustments and fishing tactics. We then present results for a case study involving petrale sole (Eopsetta jordani) in the California Current, for which SDFA estimates initially stable and then increasing abundance for the period 1986–2003, in accordance with fishery-independent survey and stock assessment estimates.
    Type of Medium: Online Resource
    ISSN: 0706-652X , 1205-7533
    Language: English
    Publisher: Canadian Science Publishing
    Publication Date: 2017
    detail.hit.zdb_id: 7966-2
    detail.hit.zdb_id: 1473089-3
    SSG: 21,3
    SSG: 12
    Location Call Number Limitation Availability
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  • 2
    Online Resource
    Online Resource
    Canadian Science Publishing ; 2019
    In:  Canadian Journal of Fisheries and Aquatic Sciences Vol. 76, No. 3 ( 2019-03), p. 401-414
    In: Canadian Journal of Fisheries and Aquatic Sciences, Canadian Science Publishing, Vol. 76, No. 3 ( 2019-03), p. 401-414
    Abstract: Stock assessment models are fitted to abundance-index, fishery catch, and age–length–sex composition data that are estimated from survey and fishery records. Research has developed spatiotemporal methods to estimate abundance indices, but there is little research regarding model-based methods to generate age–length–sex composition data. We demonstrate a spatiotemporal approach to generate composition data and a multinomial sample size that approximates the estimated imprecision. A simulation experiment comparing spatiotemporal and design-based methods demonstrates a 32% increase in input sample size for the spatiotemporal estimator. A Stock Synthesis assessment used to manage lingcod (Ophiodon elongatus) in the California Current also shows a 17% increase in sample size and better model fit using the spatiotemporal estimator, resulting in smaller standard errors when estimating spawning biomass. We conclude that spatiotemporal approaches are feasible for estimating both abundance-index and compositional data, thereby providing a unified approach for generating inputs for stock assessments. We hypothesize that spatiotemporal methods will improve statistical efficiency for composition data in many stock assessments and recommend that future research explore the impact of including additional habitat or sampling covariates.
    Type of Medium: Online Resource
    ISSN: 0706-652X , 1205-7533
    Language: English
    Publisher: Canadian Science Publishing
    Publication Date: 2019
    detail.hit.zdb_id: 7966-2
    detail.hit.zdb_id: 1473089-3
    SSG: 21,3
    SSG: 12
    Location Call Number Limitation Availability
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  • 3
    In: Ecography, Wiley, Vol. 43, No. 1 ( 2020-01), p. 11-24
    Abstract: Species distribution models (SDMs) are a common approach to describing species’ space‐use and spatially‐explicit abundance. With a myriad of model types, methods and parameterization options available, it is challenging to make informed decisions about how to build robust SDMs appropriate for a given purpose. One key component of SDM development is the appropriate parameterization of covariates, such as the inclusion of covariates that reflect underlying processes (e.g. abiotic and biotic covariates) and covariates that act as proxies for unobserved processes (e.g. space and time covariates). It is unclear how different SDMs apportion variance among a suite of covariates, and how parameterization decisions influence model accuracy and performance. To examine trade‐offs in covariation parameterization in SDMs, we explore the attribution of spatiotemporal and environmental variation across a suite of SDMs. We first used simulated species distributions with known environmental preferences to compare three types of SDM: a machine learning model (boosted regression tree), a semi‐parametric model (generalized additive model) and a spatiotemporal mixed‐effects model (vector autoregressive spatiotemporal model, VAST). We then applied the same comparative framework to a case study with three fish species (arrowtooth flounder, pacific cod and walleye pollock) in the eastern Bering Sea, USA. Model type and covariate parameterization both had significant effects on model accuracy and performance. We found that including either spatiotemporal or environmental covariates typically reproduced patterns of species distribution and abundance across the three models tested, but model accuracy and performance was maximized when including both spatiotemporal and environmental covariates in the same model framework. Our results reveal trade‐offs in the current generation of SDM tools between accurately estimating species abundance, accurately estimating spatial patterns, and accurately quantifying underlying species–environment relationships. These comparisons between model types and parameterization options can help SDM users better understand sources of model bias and estimate error.
    Type of Medium: Online Resource
    ISSN: 0906-7590 , 1600-0587
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2024917-2
    detail.hit.zdb_id: 1112659-0
    SSG: 12
    Location Call Number Limitation Availability
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  • 4
    In: Global Ecology and Biogeography, Wiley, Vol. 28, No. 11 ( 2019-11), p. 1561-1577
    Abstract: Studies that attempt to measure shifts in species distributions often consider a single species in isolation. However, understanding changes in spatial overlap between predators and their prey might provide deeper insight into how species redistribution affects food web dynamics. Predator–prey overlap metrics Here, we review a suite of 10 metrics [range overlap, area overlap, the local index of collocation (Pianka's O ), Hurlbert's index, biomass‐weighted overlap, asymmetrical alpha, Schoener's D , Bhattacharyya's coefficient, the global index of collocation and the AB ratio] that describe how two species overlap in space, using concepts such as binary co‐occurrence, encounter rates, spatial niche similarity, spatial independence, geographical similarity and trophic transfer. We describe the specific ecological insights that can be gained using each overlap metric, in order to determine which is most appropriate for describing spatial predator–prey interactions for different applications. Simulation and case study We use simulated predator and prey distributions to demonstrate how the 10 metrics respond to variation in three types of predator–prey interactions: changing spatial overlap between predator and prey, changing predator population size and changing patterns of predator aggregation in response to prey density. We also apply these overlap metrics to a case study of a predatory fish (arrowtooth flounder,  Atheresthes stomias ) and its prey (juvenile walleye pollock,  Gadus chalcogrammus ) in the Eastern Bering Sea, AK, USA. We show how the metrics can be applied to understand spatial and temporal variation in the overlap of species distributions in this rapidly changing Arctic ecosystem. Conclusions Using both simulated and empirical data, we provide a roadmap for ecologists and other practitioners to select overlap metrics to describe particular aspects of spatial predator–prey interactions. We outline a range of research and management applications for which each metric may be suited.
    Type of Medium: Online Resource
    ISSN: 1466-822X , 1466-8238
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 1479787-2
    detail.hit.zdb_id: 2021283-5
    SSG: 12
    Location Call Number Limitation Availability
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