Search

A sample of 365 individuals completed three scales designed to assess various elements of the personal construct of sense of humor. Data were factor-analyzed to estimate what elements the scales measure. Inadequacies are identified, and additional elements of sense of humor are recommended for a new, multidimensional humor scale.

A subsample of 145 people high in intrinsic religious motivation was extracted from a larger group (N = 346) that had completed a death anxiety (DA) scale and Hoge's Scale of Intrinsic Religious Motivation (IRM). Those people high in IRM scored significantly lower in DA, and their DA and IRM means displayed a significant negative correlation. Factor analysis and item analysis of their DA data indicate that people high in IRM have, with the exception of afterlife belief, constructions of death and dying that are similar to samples from the adult population in general.

Residents of a county nursing home underwent a forced, mass relocation. Gerontologists advised on preparation of staff and residents. No increase in residents' mortality or morbidity was found after the relocation. The highest death rate was in the anticipatory period, the year prior to the move.

The objective of this study was to simplify understandings of relationships between multidimensional elements of the fear of death and dying with a unidimensional construct of intrinsic religiosity. A total of 346 individuals who ranged in age from 18 to 88 completed a death anxiety scale and a measure of intrinsic religious motivation. Respondents who were older and who were higher in intrinsic religiosity were significantly lower in death anxiety. Differing constructions of death and dying are examined.

The American eel (Anguilla rostrata) occupies a vast range in the West Atlantic Ocean and inflowing waters. Despite its presumed panmictic status, management of this species is geographically fragmented. There have been widespread calls for internationally coordinated efforts towards a range-wide stock assessment, but such an objective faces obstacles of a high degree of heterogeneity in major life history characteristics and the near-absence of such data in the northern, western, and southern parts of the species' range. This paper reviews novel and underutilized methods and data sources that may aid progress to an eventual range-wide assessment. Methods for obtaining information on distribution and abundance include mining of extant data and field surveys by glass bottom boat, electrofishing boat, net enclosures, environmental DNA (eDNA) and ocean larval tows. Analytic resources and tools include environmental databases, fetch as a covariate of abundance, accounting for the net effects of small ponds, GIS-oriented habitat modeling, glass eel-oriented population modeling, estimation of age structure from length structure, use of life history parameter clines to fill gaps in assessment input values, and life cycle modeling. eDNA is a cost-effective technique that has the potential to clarify American eel distribution, and possibly relative abundance, over the very large areas where data for this species are sparse. Most other techniques reviewed in this paper have substantial costs, which may constrain their use in the southern part of the American eel range where resources of aquatic science are often limited. [ABSTRACT FROM AUTHOR]

Detecting and analyzing patterns of distribution shifts and range expansion/contraction is important to understand population dynamics and changes in stock status. Here, we develop a spatio‐temporal model for yellow croaker (Larimichthys polyactis), which was fitted to bottom trawl survey biomass data collected in the Yellow Sea in the winter of 2001–2017. The model accounts for both spatial and spatio‐temporal structure and can potentially include the effects of surface temperature and of an annual index, the Pacific Decadal Oscillation, represented using a recently developed spatially varying coefficient model. We selected a spatio‐temporal model with no covariates based on Akaike's information criterion. The center of gravity for yellow croaker was found to move northwest between 2001 and 2010, and then southwest over the period 2010–2017. These results reflected the predicted progressive disappearance of yellow croaker density hotspots (i.e., highest density areas) in the north and southeast areas of the Yellow Sea, which resulted in the central area of the Yellow Sea becoming the only yellow croaker density hotspot in 2017. This finding has important implications for fisheries management in the context of the China–South Korea fisheries agreement, as it indicates a measurable displacement of yellow croaker biomass toward China. The exclusion of covariates from the spatio‐temporal model was not expected a priori and may be due to the facts that environmental variations are not pronounced in winter in the Yellow Sea and that the representation of spatial and spatio‐temporal structure in spatio‐temporal models accounts for a large proportion of the variability in the data. [ABSTRACT FROM AUTHOR]

The northern Bering Sea is transitioning from an Arctic to subarctic fish community as climate warms. Scientists and managers aim to understand how these changing conditions are influencing fish biomass and spatial distribution in this region, as both are used to inform stock assessments and fisheries management advice. Here, we use a spatio‐temporal model for walleye pollock (Gadus chalcogrammus) to provide two inputs to its stock assessment model: (a) an alternative model‐based biomass index and (b) alternative model‐based age compositions. Both inputs were derived from multiple fishery‐independent data that span different regions of space and time. We developed an assessment model that utilizes both the standard and model‐based inputs from multiple surveys despite inconsistencies in spatial and temporal coverage, and we found that using these data provide an improved spatial and temporal scope of total pollock biomass. Age composition information indicated that pollock density is increasing and moving farther north, particularly for older pollock. We found that including an index of cold pool extent could be used to extrapolate pollock densities in the northern Bering Sea in unsampled years. Stock assessment parameter estimates were similar for standard and model‐based input. This study demonstrates that spatio‐temporal model‐based estimates of a biomass index and age composition can facilitate rapid changes in stock assessment structure in response to climate‐driven shifts in spatial distribution. We conclude that assimilating data from regions neighboring standard survey areas, such as the Chukchi Sea and western Bering Sea, would improve understanding and management efforts as fish distributions change under a warming climate. [ABSTRACT FROM AUTHOR]

Fisheries managers use biological reference points (BRPs) as targets or limits on fishing and biomass to maintain productive levels of fish stock biomass. There are multiple ways to calculate BRPs when biological parameters are time varying. Using summer flounder (Paralichthys dentatus) as a case study, we investigated time-varying approaches in concert with climate-linked population models to understand the impact of environmentally driven variability in natural mortality, recruitment, and size-at-age on two commonly used BRPs [ B 0 (t) and F 35%(⁠ t ⁠)]. We used the following two approaches to calculate time-varying BRPs: dynamic-BRP and moving-average-BRP. We quantified the variability and uncertainty of different climate dependencies and estimation approaches, attributed BRP variation to variation in life-history processes, and evaluated how using different approaches impacts estimates of stock status. Results indicate that the dynamic-BRP approach using the climate-linked natural mortality model produced the least variable reference points compared to others calculated. Summer flounder stock status depended on the estimation approach and climate model used. These results emphasize that understanding climate dependencies is important for summer flounder reference points and perhaps other species, and careful consideration is warranted when considering what time-varying approach to use, ideally based upon simulation studies within a proposed set of management procedures. [ABSTRACT FROM AUTHOR]

Characterizing population distribution and abundance over space and time is central to population ecology and conservation of natural populations. However, species distribution models and population dynamic models have rarely been integrated into a single modelling framework. Consequently, fine‐scale spatial heterogeneity is often ignored in resource assessments. We develop and test a novel spatiotemporal assessment framework to better address fine‐scale spatial heterogeneities based on theories of fish population dynamic and spatiotemporal statistics. The spatiotemporal model links species distribution and population dynamic models within a single statistical framework that is flexible enough to permit inference for each state variable through space and time. We illustrate the model with a simulation–estimation experiment tailored to two exploited marine species: snow crab (Chionoecetes opilio, Oregoniidae) in the Eastern Bering Sea and northern shrimp (Pandalus borealis, Pandalidae) in the Gulf of Maine. These two species have different types of life history. We compare the spatiotemporal model with a spatially aggregated model and systematically evaluate the spatiotemporal model based on simulation experiments. We show that the spatiotemporal model can recover spatial patterns in population and exploitation pressure as well as provide unbiased estimates of spatially aggregated population quantities. The spatiotemporal model also implicitly accounts for individual movement rates and can outperform spatially aggregated models by accounting for time‐and‐size varying selectivity caused by spatial heterogeneity. We conclude that spatiotemporal modelling framework is a feasible and promising approach to address the spatial structure of natural resource populations, which is a major challenge in understanding population dynamics and conducting resource assessments and management. [ABSTRACT FROM AUTHOR]

Fisheries scientists use biological models to determine sustainable fishing rates and forecast future dynamics. These models require both life‐history parameters (mortality, maturity, growth) and stock‐recruit parameters (juvenile production). However, there has been little research to simultaneously predict life‐history and stock‐recruit parameters. I develop the first data‐integrated life‐history model, which extends a simple model of evolutionary dynamics to field measurements of life‐history parameters as well as historical records of spawning output and subsequent recruitment. This evolutionary model predicts recruitment productivity (steepness) and variability (variance and autocorrelation in recruitment deviations) as well as mortality, maturity, growth, and size, and uses these to predict intrinsic growth rate (r) for all described fishes. The model confirms previous analysis showing little correlation between steepness and either natural mortality or asymptotic maximum size (W∞). However, it does reveal taxonomic patterns, where family Sebastidae has lower steepness (mean=0.72) and Salmonidae has elevated steepness (mean=0.79) relative to the prediction for bony fishes (class Actinopterygii, mean=0.74). Similarly, genus Sebastes has growth rate r (0.09) approaching that of several shark families (Lamniformes: 0.02; Carcharhiniformes: 0.02). A cross‐validation experiment confirms that the model is accurate, explains a substantial portion of variance (32%–67%), but generates standard errors that are somewhat too small. Predictive intervals are tighter for species than for higher‐level organizations (e.g. families), and predictions (including intervals) are available for all fishes worldwide in R package FishLife. I conclude by outlining how multivariate predictions of life‐history and stock‐recruit parameters could be useful for stock assessment, decision theory, ensemble modelling and strategic management. [ABSTRACT FROM AUTHOR]

Fishing communities are increasingly required to adapt to environmentally driven changes in the availability of fish stocks. Here, we examined trends in the distribution and biomass of five commercial target species (dover sole, thornyheads, sablefish, lingcod, and petrale sole) on the US west coast to determine how their availability to fishing ports changed over 40 years. We show that the timing and magnitude of stock declines and recoveries are not experienced uniformly along the coast when they coincide with shifts in species distributions. For example, overall stock availability of sablefish was more stable in southern latitudes where a 40% regional decline in biomass was counterbalanced by a southward shift in distribution of >200 km since 2003. Greater vessel mobility and larger areal extent of fish habitat along the continental shelf buffered northerly ports from latitudinal changes in stock availability. Landings were not consistently related to stock availability, suggesting that social, economic, and regulatory factors likely constrain or facilitate the capacity for fishers to adapt to changes in fish availability. Coupled social–ecological analyses such as the one presented here are important for defining community vulnerability to current and future changes in the availability of important marine species. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Fisheries & Aquatic Sciences is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)