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In the face of biodiversity loss worldwide, it is paramount to quantify species' extinction risk to guide conservation efforts. The International Union for the Conservation of Nature (IUCN)'s Red List is considered the global standard for evaluating extinction risks. IUCN criteria also inform national extinction risk assessments. Bayesian models, including the state‐of‐the‐art JARA ('Just Another Red List Assessment') tool, deliver probabilistic statements about species falling into extinction risk categories, thereby enabling characterisation and communication of uncertainty in extinction risk assessments.We coupled the state‐of‐the‐art VAST ('Vector Autoregressive Spatio‐Temporal') modelling tool and JARA, for better informed Red List assessments of marine fishes. In this framework, VAST is fitted to scientific survey catch rate data to provide indices to JARA whose uncertainty is propagated to JARA outcomes suggesting extinction risk categories (under the population reduction criterion). In addition, VAST delivers a valuable habitat assessment to better understand what may be driving extinction risk in the study region. Here, we demonstrate the coupled VAST‐JARA modelling framework by applying it to five contrasting North Sea species, with or without a quantitative stock assessment and with different conservation statuses according to the latest global Red List assessments.The North Sea application coupled with previous assessments and studies suggest that, among the three elasmobranchs, starry ray is in most need of urgent research (and conservation actions where appropriate), followed by spurdog, while lesser‐spotted dogfish is increasing in biomass. Moreover, both the VAST‐JARA modelling framework and previous research indicate that, while European plaice is not of conservation concern, cod has likely met the IUCN criteria for being listed as Endangered recently.Synthesis and applications. The predictions of the VAST‐JARA modelling framework for North Sea species, including JARA output and VAST habitat assessment, constitute valuable supporting information to make interpretations based on Red List guidelines, which will help decision‐makers in their next North Sea Red List assessment. We foresee applications of the modelling framework to assist Red List assessments of numerous marine fishes worldwide. Our modelling framework has many potential advantageous uses, including informing resource management about climate change impacts on species' extinction risks. [ABSTRACT FROM AUTHOR]

Attention Deficit-Hyperactivity Disorder (ADHD) was first identified in 1902 by a British pediatrician, Sir George Still, as an abnormal defect in children’s moral control that caused poor control of their behavior despite their average and/or above intellectual capacity. It was not until the late 20th century when the American Psychiatric Association formally recognized ADHD as a mental disorder, whose hallmarks are excessive amounts of inattention, hyperactivity, and impulsivity. Also known as the triad of impairments in ADHD, these symptoms are pervasive, impairing in multiple contexts, and otherwise age-inappropriate. Only recently, Hallowell and Ratey (2021) introduced the concept of Variable Attention Stimulus Trait (VAST) in their book ADHD 2.0 to describe the condition. They argued that VAST/ADHD is unrelated to intelligence and has nothing to do with the deficit of attention. VAST/ADHD describes about how each brain/mind is uniquely wired affecting the way one deals with stimulation and attention. In this paper, the authors have chosen to explore two of the traits on the VAST/ADHD spectrum - (i) attentional control/sustained attention, and (ii) emotional regulation/impulse control - to illustrate the condition of ADHD as a variability of attention rather than a deficit of attention. [ABSTRACT FROM AUTHOR]

Aim: There has been a wide interest in the effect of biotic interactions on species' occurrences and abundances at large spatial scales, coupled with a vast development of the statistical methods to study them. Still, evidence for whether the effects of within‐trophic‐level biotic interactions (e.g. competition and heterospecific attraction) are discernible beyond local scales remains inconsistent. Here, we present a novel hypothesis‐testing framework based on joint dynamic species distribution models and functional trait similarity to dissect between environmental filtering and biotic interactions. Location: France and Finland. Taxon: Birds. Methods: We estimated species‐to‐species associations within a trophic level, independent of the main environmental variables (mean temperature and total precipitation) for common species at large spatial scale with joint dynamic species distribution (a multivariate spatiotemporal delta model) models. We created hypotheses based on species' functionality (morphological and/or diet dissimilarity) and habitat preferences about the sign and strength of the pairwise spatiotemporal associations to estimate the extent to which they result from biotic interactions (competition, heterospecific attraction) and/or environmental filtering. Results: Spatiotemporal associations were mostly positive (80%), followed by random (15%), and only 5% were negative. Where detected, negative spatiotemporal associations in different communities were due to a few species. The relationship between spatiotemporal association and functional dissimilarity among species was negative, which fulfils the predictions of both environmental filtering and heterospecific attraction. Main conclusions We showed that processes leading to species aggregation (mixture between environmental filtering and heterospecific attraction) seem to dominate assembly rules, and we did not find evidence for competition. Altogether, our hypothesis‐testing framework based on joint dynamic species distribution models and functional trait similarity is beneficial in ecological interpretation of species‐to‐species associations from data covering several decades and biogeographical regions. [ABSTRACT FROM AUTHOR]

Modeling spatiotemporal variability of fish stocks is important for interpreting changes in magnitude and distribution over time. Biomass indices for four stocks with differences in life history, spatial extent, proportion of zero observations, biomass trajectory, and stock status were modeled using the Vector Autoregressive Spatio-Temporal (VAST) model. We iteratively tested model settings following a workflow developed to find the best model for each stock in the context of developing an absolute index of abundance from fisheries independent data. The best model was determined based on AIC, RMSE, cross-validation, and several metrics of deviation from design-based estimates if one is available for comparison. Biomass indices from the best VAST model had good agreement with design-based indices in three of the stocks. Improvements in model fit tended to asymptote at around 1000 knots for all stocks. The generalized gamma error distribution with a logit link was the best model for two of the stocks, and the gamma error distribution with Poisson link was the best model for the other two. Occasionally a model was selected with much larger estimated biomass compared to the design-based estimates, and metrics based on comparisons with design-based estimates helped resolve model selection in these instances. Simulation self-tests were performed from the best model and fitted with several estimation models, and the model selection tools comparing fitted models to design-based indices calculated from the simulated data were found to be robust in recovering the specifications of the original model. Additional visualizations for spatial residuals and clustering were used to aid interpretation of model fit and to highlight latent spatial patterns. These metrics may be useful in identifying important covariates. We added a depth covariate to the best model for each stock to demonstrate how future model building might proceed. [ABSTRACT FROM AUTHOR]

The fishing grounds of the Pacific saury began shifting eastward after 2013 and deviated further east in 2019 and 2020. Consequently, the migration pattern from the pre‐fishing distribution to fishing grounds may be changing. This study investigated inter‐annual variations in westward spawning migration parameters using a migration model. Case studies were conducted to estimate optimal values for the initiation date of spawning migration and migration speed during spawning migration. The estimated optimal initiation date of spawning migration from 2003 to 2018 was July 13 to August 8, with an average of July 31. However, in 2019 and 2020, the initiation date of spawning migration was August 22 and September 5, respectively, significantly later than that in 2003–2018 (p <.01). The estimated optimal swimming speed during spawning migration in 2019 and 2020 was not significantly different from that in 2003–2018 (p >.7). These results indicate that delays in the initiation of spawning migration occurred in 2019 and 2020, leading to an eastward shift of fishing grounds. The mean body weight of the age‐1 Pacific saury in the pre‐fishing season had a significant negative correlation (p <.01) with the initiation date of spawning migration. This result suggests that Pacific saury with low body weight continues to feed for growth instead of initiating spawning migration. However, body weight at initiation of spawning migration had a significant decreasing trend (p <.01), indicating that the Pacific saury had to start spawning migration despite low body weight in recent years. [ABSTRACT FROM AUTHOR]

Population sizes of many birds are declining alarmingly and methods for estimating fluctuations in species' abundances at a large spatial scale are needed. The possibility to derive indicators from the tendency of specific species to co‐occur with others has been overlooked. Here, we tested whether the abundance of resident titmice can act as a general ecological indicator of forest bird density in European forests. Titmice species are easily identifiable and have a wide distribution, which makes them potentially useful ecological indicators. Migratory birds often use information on the density of resident birds, such as titmice, as a cue for habitat selection. Thus, the density of residents may potentially affect community dynamics. We examined spatio‐temporal variation in titmouse abundance and total bird abundance, each measured as biomass, by using long‐term citizen science data on breeding forest birds in Finland and France. We analyzed the variation in observed forest bird density (excluding titmice) in relation to titmouse abundance. In Finland, forest bird density linearly increased with titmouse abundance. In France, forest bird density nonlinearly increased with titmouse abundance, the association weakening toward high titmouse abundance. We then analyzed whether the abundance (measured as biomass) of random species sets could predict forest bird density better than titmouse abundance. Random species sets outperformed titmice as an indicator of forest bird density only in 4.4% and 24.2% of the random draws, in Finland and France, respectively. Overall, the results suggest that titmice could act as an indicator of bird density in Northern European forest bird communities, encouraging the use of titmice observations by even less‐experienced observers in citizen science monitoring of general forest bird density. [ABSTRACT FROM AUTHOR]

Allocating quotas among stakeholders requires an agreed‐upon formula. If the stock unit spans multiple management jurisdictions, the formula may require updated biomass estimates of the stock's spatial distribution with respect to those jurisdictions. Data for calculating stock biomass often come from fishery‐independent surveys. While stratified random sampling is a common design, strata boundaries may not always align with state or national jurisdictions, requiring post hoc stratification and imputation to calculate area‐specific biomass. The vector autoregressive spatiotemporal (VAST) model was explored as a tool for calculating fish biomass within subareas of a defined stock unit for three different stocks jointly managed by the United States and Canada on Georges Bank in the Northwest Atlantic Ocean. VAST estimated proportions of stock biomass in each nation's waters were compared with an existing allocation algorithm that utilises a loess smooth through the average design‐based swept area biomass from three fishery‐independent surveys. The ability of VAST to impute biomass when no tows occur in a subarea of a survey stratum was demonstrated, as well as the relatively smoother biomass trend compared with design‐based estimates, which may be desirable if the intent is to avoid large inter‐annual swings in allocated quota. [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]

Multispecies models are widely used to evaluate management trade‐offs arising from species interactions. However, identifying climate impacts and sensitive habitats requires integrating spatial heterogeneity and environmental impacts into multispecies models at fine spatial scales. We therefore develop a spatio‐temporal model of intermediate complexity for ecosystem assessments (a "MICE‐in‐space"), which is fitted to survey sampling data and time series of fishing mortality using maximum‐likelihood techniques. The model is implemented in the VAST R package, and it can be configured to range from purely descriptive to including ratio‐dependent interactions among species. We demonstrate this model using data for four groundfishes in the Gulf of Alaska using data from 1982 to 2015. Model selection for this case‐study shows that models with species interactions are parsimonious, although a model specifying separate density dependence without interactions also has substantial support. The AIC‐selected model estimates a significant, negative impact of Alaska pollock (Gadus chalcogrammus, Gadidae) on productivity of other species and suggests that recent fishing mortality for Pacific cod (G. microcephalus, Gadidae) is above the biological reference point (BRP) resulting in 40% of unfished biomass; other models show similar trends but different scales due to different BRP estimates. A simulation experiment shows that fitting a model with fewer species at a coarse spatial resolution degrades estimation performance, but that interactions and biological reference points can still be estimated accurately. We conclude that MICE‐in‐space models can simultaneously estimate fishing impacts, species trade‐offs, biological reference points and habitat quality. They are therefore suitable to forecast short‐term climate impacts, optimize survey designs and designate protected habitats. [ABSTRACT FROM AUTHOR]

Highlights • Stock, ecosystem, habitat, and climate assessments use different methods and vocabulary. • Population density across space, time, and species is a "common currency". • I provide guidance for 15 major decisions when using Vector Autoregressive Spatio-Temporal (VAST) models. • Three case-studies illustrate decisions in different assessment types. • Economic and survey-optimization assessments are important future research. Abstract Fisheries scientists provide stock, ecosystem, habitat, and climate assessments to support interdisplinary fisheries management in the US and worldwide. These assessment activities have evolved different models, using different review standards, and are communicated using different vocabulary. Recent research shows that spatio-temporal models can estimate population density for multiple locations, times, and species, and that this is a "common currency" for addressing core goals in stock, ecosystem, habitat, and climate assessments. I therefore review the history and "design principles" for one spatio-temporal modelling package, the Vector Autoregressive Spatio-Temporal (VAST) package. I then provide guidance on fifteen major decisions that must be made by users of VAST, including: whether to use a univariate or multivariate model; when to include spatial and/or spatio-temporal variation; how many factors to use within a multivariate model; whether to include density or catchability covariates; and when to include a temporal correlation on model components. I finally demonstrate these decisions using three case studies. The first develops indices of abundance, distribution shift, and range expansion for arrowtooth flounder (Atheresthes stomias) in the Eastern Bering Sea, showing the range expansion for this species. The second involves "species ordination" of eight groundfishes in the Gulf of Alaska bottom trawl survey, which highlights the different spatial distribution of flathead sole (Hippoglossoides elassodon) relative to sablefish (Anoplopoma fimbria) and dover sole (Microstomus pacificus). The third involves a short-term forecast of the proportion of coastwide abundance for five groundfishes within three spatial strata in the US West Coast groundfish bottom trawl survey, and predicts large interannual variability (and high uncertainty) in the distribution of lingcod (Ophiodon elongatus). I conclude by recommending further research exploring the benefits and limitations of a "common currency" approach to stock, ecosystem, habitat, and climate assessments, and discuss extending this approach to optimal survey design and economic assessments. [ABSTRACT FROM AUTHOR]

Aim: Lean methods are widely used in engineering for improving processes. These methods have also been begun to be used in the service sector. Value and waste are the most common parameters in the lean methodology. In this study, we focused on waste in laboratory with spaghetti diagrams to improve our processes before drawing the process flow map in order to create more ergonomic laboratory. Methods: This process improvement study was practiced between February and September in Izmir Aliaga State Hospital. We used kaizen chart, timer and brainstorming for Microsoft office in measurements. 13 main processes were measured and redesigned. 37 steps were investigated and discussed. This procedure was repeated three times. We compared the first and last results. Results: Our total walks were 283 meters at the beginning of the study. After the improvement this figures decreased to165 meters. We observed totally 41.5% saving in processes. Conclusion: These findings suggested that using principles of lean methodologies in hospital laboratory are useful tools to improve process. The spaghetti diagram seems to be the basic tool for beginning to draw the process map and become more ergonomic for the laboratory environment. [ABSTRACT FROM AUTHOR]

'Vast' is a word often applied to environmental terrain that is perceived to have large spatial extent. This judgment is made even at viewing distances where traditional metric depth cues are not useful. This paper explores the perceptual basis of vast experience, including reliability and visual precursors. Experiment 1 demonstrated strong agreement in ratings of the spatial extent of two-dimensional (2D) scene images by participants in two countries under very different viewing conditions. Image categories labeled 'vast' often exemplified scene attributes of ruggedness and openness (Oliva & Torralba, 2001). Experiment 2 quantitatively assessed whether these properties predict vastness. High vastness ratings were associated with highly open, or moderately open but rugged, scenes. Experiment 3 provided evidence, consistent with theory, that metric distance perception does not directly mediate the observed vastness ratings. The question remains as to how people perceive vast space when information about environmental scale is unavailable from metric depth cues or associated scene properties. We consider possible answers, including contribution from strong cues to relative depth. [ABSTRACT FROM AUTHOR]

Background Several regulators of programmed cell death (PCD) in plants encode proteins with putative lipid-binding domains. Among them, VAD1 is a regulator of PCD propagation harboring a GRAM putative lipid-binding domain. However the function of VAD1 at the subcellular level is unknown and the domain architecture of VAD1 has not been analyzed in details. Results We analyzed sequence conservation across the plant kingdom in the VAD1 protein and identified an uncharacterized VASt (VAD1 Analog of StART) domain. Using profile hidden Markov models (profile HMMs) and phylogenetic analysis we found that this domain is conserved among eukaryotes and generally associates with various lipid-binding domains. Proteins containing both a GRAM and a VASt domain include notably the yeast Ysp2 cell death regulator and numerous uncharacterized proteins. Using structure-based phylogeny, we found that the VASt domain is structurally related to Bet v1-like domains. Conclusion We identified a novel protein domain ubiquitous in Eukaryotic genomes and belonging to the Bet v1-like superfamily. Our findings open perspectives for the functional analysis of VASt-containing proteins and the characterization of novel mechanisms regulating PCD. [ABSTRACT FROM AUTHOR]

Aims: To assess the responsive fetal extremity movement to vibro-acoustic stimulaton test (VAST). Methods: The moving velocity of fetal femur was assessed after VAST by pulsed Doppler device. The ultrasonic beam was insonated at a right angle to the fetal femur. The following parameters were determined: limb retreat velocity in accelerative slope (Pk1); limb replenishment velocity in decelerative slope (Pk2); mean flexion to extension velocity; and the response time to VAST. Among 80 normal singleton pregnancies in 33–41 weeks, 68 were weekly evaluated and the others were assessed for two or more times during the study period, for a total of 680 studies of fetal kinetics. Results: The Pk1 declined from 9.6 to 6.26 cm/s; Pk2 decreased from 2.6 to 1.3 cm/s; mean velocity was reduced from 6.0 to 4.25 cm/s; whereas the response time increased from 0.1 to 0.3 s throughout the study period, i.e., fetal response reduces and the response time increases as maturation progresses. Conclusion: The pulsed Doppler may assess fetal activity in any body structure. Reflex responses become slow and complex on both the velocity and response time as maturation increases with gestational age. Our observations have resulted in a novel and easy method for the quantitative assessment of fetal reflex reactivity to external stimuli. [ABSTRACT FROM AUTHOR]

• Scale of estimated abundance indices varies for different distribution functions using delta models. • Comparison with design-based indices shows similar scale for delta-gamma and Tweedie models. • Epsilon bias-correction mitigates sensitivity to spatial resolution during index standardization. • Simulation experiment confirms that delta-gamma and Tweedie work well even when mis-specified. Delta-models (a.k.a. hurdle models) are widely used to fit biomass samples that include zeros and a skewed response for positive catches, and spatio-temporal extensions of these models are increasingly used to quantify trends in abundance (i.e., estimate abundance indices). Previous research has shown estimated indices are proportional to changes in abundance. However, little research has tested the performance of delta-models for estimating "scale"; that is, whether abundance indices are not just proportional to population changes but also have the correct absolute value. We use data for twenty species in the eastern Bering Sea and Gulf of Alaska as well as a factorial experiment conditioned on data for Gulf of Alaska Pacific cod to support five conclusions related to scale in spatio-temporal delta-models. First, we show that conventional (nonspatial) delta-models are surprisingly sensitive to the a priori choice of probability distribution for positive catches, where gamma and Tweedie models give similar scale estimates but other distributions generally differ. Second, these same distributions also estimate widely different scales when using spatio-temporal delta-models, and the delta-gamma and Tweedie models provide similar scale to design-based indices. Third, model selection using marginal AIC often identifies the lognormal distribution as most parsimonious, despite it resulting in systematically higher abundance than design-based indices for many species. Fourth, scale is sensitive to the spatial resolution (i.e., number of knots) used in fitting the spatio-temporal model when using a naïve "empirical Bayes" estimator, but less sensitive when applying an epsilon bias-correction estimator. Fifth, the factorial simulation experiment suggests that the Tweedie and delta-gamma distributions perform well even when applied to data simulated from an inverse-Gaussian or lognormal distribution, whereas the opposite is not true. We conclude that index scale is sensitive to delta-model specification, and we make five recommendations when using spatio-temporal delta-models for index standardization: (1) apply the epsilon or other bias-correction methods to reduce sensitivity of index scale on spatio-temporal model resolution; either (2) compare the scale of delta-model indices with that of design-based indices when design-based indices are available or (3) use the delta-gamma or Tweedie distribution by default when design-based indices are not available; (4) do not assume that AIC will identify the model specification that results in the most appropriate scale; and (5) consider apparent mismatches in index scale depending upon whether an assessment model specifies or estimates the associated catchability coefficient and whether the design-based index is believed to measure total abundance for a fully-selected age or length-class. [ABSTRACT FROM AUTHOR]

• Calanus , Metridia and Neocalanus copepods were sampled during pollock age-0 year. • Strong correlation between lipid-rich copepods and pollock abundance 3 years later. • Higher correlation to age-3 pollock with model than sample-based copepod indices. • Age-3 pollock indices significantly related to cold pool area (age-0 year). In the southeastern Bering Sea, years with above-average sea ice extent have been associated with elevated abundances of large lipid-rich copepods in both net tows and age-0 walleye pollock (Gaduschalcogrammus) diets, followed by high overwinter pollock survival and stronger recruitment into the fishery three years later. In this study, we directly compare large copepod abundances of taxa important in age-0 pollock diets (Calanus glacialis/marshallae , Metridia pacifica and Neocalanus spp.) to age-3 pollock abundance from stock assessments for year classes 2002–2015 (excluding 2013). Copepod samples from bongo nets were collected on fishery oceanography surveys in late summer during warm (low ice) and cold (high ice) climate stanzas. In addition to mean estimates of large copepods among stations (sample-based), we implemented a spatial delta-generalized linear mixed model using the Vector Autoregressive Spatio-Temporal (VAST) package to account for variations in survey coverage among years. Positive significant linear relationships were found for both of these estimates of large copepods during the age-0 year and the abundance of these pollock at age-3. Correlations were stronger using VAST model-based indices compared with sample-based indices (R 2 = 0.74 versus 0.43, respectively, all copepod taxa combined). A significant relationship was also observed between the bottom cold pool (< 2 °C) area (indicative of sea ice coverage in the prior winter) during the age-0 year and subsequent age-3 pollock abundance (R 2 = 0.56) and recruits per spawner, (ln(age-3 abundance /spawning stock biomass), R 2 = 0.77). Consequently, the large copepod index or cold pool index may be used to predict future recruitment success of pollock three years in advance. Results provide support for the revised oscillating control hypothesis that suggests as the climate warms, reductions in sea ice and consequent reduced availability of ice-associated algae, an early spring food source, could be detrimental to large copepods and recruitment of the pollock stock in the region. [ABSTRACT FROM AUTHOR]

• A CAPAM workshop highlighted five conclusions about spatio-temporal models for index standardization. • These models improve density predictions in areas with little to no data. • They can weight density predictions by area to account for spatial changes in sampling intensity. • They can calculate age/length composition and weight these by fishery catch to inform fishery removals. • They can estimate distribution shifts, and combine data from multiple sources to improve spatial scale for indices. Abstract Spatio-temporal models are increasingly used to develop indices of population abundance from fishery-dependent and –independent data. Developments in spatio-temporal index standardization were discussed at a workshop hosted by The Center for the Advancement of Population Assessment Methodology (CAPAM) titled " Development and application of spatio-temporal models to derive indices of relative abundance " (La Jolla, CA, USA; Feb. 26-March 2, 2018). This special issue includes ten submissions that highlight potential benefits arising from spatio-temporal index standardization including: (1) improved prediction for areas with little-to-no data; (2) capacity to weight densities by area to account for sampling rates that vary spatially; (3) calculation of the composition (e.g. age or length) of the indices and to weight predictions by catch to inform the composition of fishery removals, (4) estimation of distribution shifts and range expansion/contraction; and (5) capacity to combine data sources. The articles in this Special Issue highlight the need for continued development and testing for spatio-temporal index standardization methods, so that the relative strengths and weaknesses of different approaches are fully understood. The articles also identify topics that warrant further research including improved model diagnostics, accounting for biased rates of sampling ("preferential sampling"), and improvements in computational efficiency. Keywords: Index standardization; Spatial analysis; Vector autoregressive spatio-temporal model; VAST; Distribution shift; Stock assessment; Catch-per-unit-effort Spatio-temporal models are increasingly used to develop indices of population abundance from fishery-dependent and –independent data. Developments in spatio-temporal index standardization were discussed at a workshop hosted by The Center for the Advancement of Population Assessment Methodology (CAPAM) titled " Development and application of spatio-temporal models to derive indices of relative abundance " (La Jolla, CA, USA; Feb. 26-March 2, 2018). This special issue includes ten submissions that highlight potential benefits arising from spatio-temporal index standardization including: (1) improved prediction for areas with little-to-no data; (2) capacity to weight densities by area to account for sampling rates that vary spatially; (3) calculation of the composition (e.g. age or length) of the indices and to weight predictions by catch to inform the composition of fishery removals, (4) estimation of distribution shifts and range expansion/contraction; and (5) capacity to combine data sources. The articles in this Special Issue highlight the need for continued development and testing for spatio-temporal index standardization methods, so that the relative strengths and weaknesses of different approaches are fully understood. The articles also identify topics that warrant further research including improved model diagnostics, accounting for biased rates of sampling ("preferential sampling"), and improvements in computational efficiency. [ABSTRACT FROM AUTHOR]

• Distribution modelling and CPUE standardizations showed similar trends. • Trends were inconsistent with biological parameters or integrated population models. • Fisheries catch rates are unlikely to represent biomass. • Distribution modelling standardization could not account for fisher targeting. • Single-area integrated population models outperformed distribution modelling. Most models used for assessing the status of fish stocks and providing quantitative advice for fisheries management are not spatially explicit despite the well-known spatial heterogeneity of fish populations. Statistical spatial distribution modelling, which can derive spatially-resolved biomass from catch rates, is a method increasingly used to infer trends in population biomass, particularly where integrated population models are not available. Although the spatial distribution modelling tool VAST was developed to analyze survey data, such methods have also been used with fisheries-dependent catch and effort data, particularly when survey data are not available. We developed a statistical spatial distribution model for Antarctic toothfish (Dissostichus mawsoni) in the Ross Sea region of the Southern Ocean using the VAST (Vector Autoregressive Spatio-Temporal model) approach. We based the VAST model on catch-rate (catch per unit effort) data from the long-line fishery and compared the biomass time series obtained to that of existing single-area and spatially-explicit integrated population models which have also been developed for this population. The time series obtained from the statistical spatial distribution modelling approach was highly variable between years, inconsistent with constraints imposed on population dynamics by biological parameters, and substantially different from biomass trends obtained from the integrated models. Although it has been used successfully in other analyses, in this instance, the spatial distribution modelling could not overcome fine-scale spatial and vessel-based variability in fishery catch rates to estimate the underlying abundance of toothfish at the scale of the Ross Sea region measurable with the more-informed integrated models. [ABSTRACT FROM AUTHOR]