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Highly structured nursery habitats promote the survival of juvenile stages of many species by providing foraging opportunities and refuge from predators. Through integrated laboratory and field experiments, we demonstrate that nursery habitat structure affects survival and predator-prey interactions of red king crab Paralithodes camtschaticus. Crabs (< 1 yr old [Age 0]; 8 to 10 mm carapace length [CL]) preferred complex biogenic habitats formed by structural invertebrates and macroalgae over structural mimics and sand in the absence of predators in laboratory experiments, yet they associated with any available structural habitat when fish predators were present. Survival was higher in the presence of complex habitat for Age 0 crabs (5 to 7.5 mm CL) with Pacific cod Gadus macrocephalus predators in the laboratory and for Age 0 (4 to 8 mm CL) and Age 1 (16 to 28 mm CL) crabs with fish and invertebrate predators in the field. Crab activity and refuge response behavior varied with crab stage and habitat. Age 0 crabs were cryptic, avoiding predators by associating with habitat structure or remaining motionless in the absence of structure, and were less likely to respond to an attack. In contrast, Age 1 crabs were more likely to respond to an attacking predator and were less likely to remain motionless in the absence of structural refuge, suggesting an ontogenetic shift in behavior. Complex habitats, cryptic behavior, and direct defense improve juvenile red king crab survival against certain predators, including demersal fishes.

Although species distribution models (SDMs) are commonly used to hindcast finescale population metrics, there remains a paucity of information about how well these models predict future responses to climate. Many conventional SDMs rely on spatiallyexplicit but time-invariant conditions to quantify species distributions and densities. We compared these status quo 'static' models with more climate-informed 'dynamic' SDMs to assess whether the addition of time-varying processes would improve hindcast performance and/or forecast skill. Here, we present two groundfish case studies from the Bering Sea -a high latitude system that has recently undergone considerable warming. We relied on conventional statistics (R 2 , % deviance explained, UBRE or GCV) to evaluate hindcast performance for presence-absence, numerical abundance and biomass of arrowtooth flounder Atheresthes stomias and walleye pollock Gadus chalcogrammus. We then used retrospective skill testing to evaluate near-term forecast skill. Retrospective skill testing enables direct comparisons between forecasts and observations through a process of fitting and forecasting nested submodels within a given time series. We found that the inclusion of time-varying covariates improved hindcasts. However, dynamic models either did not improve or decreased forecast skill relative to static SDMs. This is likely a result of rapidly changing temperatures within the ecosystem, which required models to predict species responses to environmental conditions that were outside the range of observed values. Until additional model development allows for fully dynamic predictions, static model forecasts (or persistence forecasts from dynamic models) may serve as reliable placeholders, especially when anomalous conditions are anticipated. Nonetheless, our findings demonstrate support for the use of retrospective skill testing rather than selecting forecast models a priori based on their ability to quantify species-habitat associations in the past.

Processes that structure subarctic marine communities, particularly in glaciated regions, are not well understood. This understanding is needed as a baseline and to manage these communities in the face of future climatedriven changes. This study investigates two coastal regions of Southeast Alaska with the goals to (a) identify and compare patterns of subtidal community structure for macroalgal, fish, macroinvertebrate ([5 cm), and small epibenthic invertebrate (\5 cm) communities between inner coast and outer coast sites and (b) link patterns of community structure to habitat and environmental parameters. Species assemblage and benthic habitat data were used to compare species diversity and community composition at 6 m and 12 m depths at nine inner coast and nine outer coast sites. Multivariate analysis was applied to reduce environmental variables to major gradients, to resolve community structure, and to relate community structure to habitat and environmental variables. Increased salinity and decreased temperature at outer coast sites compared with inner coast sites were associated with community structure, with greater species diversity at outer coast sites at 6 m depth. Invertebrate community composition was associated with benthic habitat, including crust and coralline algae for macroinvertebrates, and algal cover and substrate for small epibenthic invertebrates. This research suggests that marine communities in glaciated regions are strongly influenced by freshwater input and that future climate-driven changes in freshwater input will likely result in marine community composition changes.

For marine fish with ontogenetic shifts in habitat requirements, survival is dependent upon oceanographic transport of pelagic larvae from spawning locations and the arrival of settlement-stage larvae to nursery habitats. Settlement success for fish with nurseries on the continental shelf, such as many flatfish, relies on routes of transport that facilitate the delivery of larvae from offshore to suitable inshore habitats. To address spatial and temporal coupling between spawning, transport, and settlement, we utilized an individual-based biophysical model for the years 2000-2011 in combination with a juvenile habitat suitability model for arrowtooth flounder (Atheresthes stomias), an abundant predatory flatfish in the oceans off Alaska. Settlement success was inversely related to the availability of nursery habitat, but oceanographic variability dictated interannual patterns in larval supply to nurseries. Paradoxically, the majority of larvae were advected offshore and arrived to nurseries on the continental shelf. Shoreward bathymetric steering through glacial troughs that resulted in directed transport to nurseries was minimal despite the high proportion of larvae that accessed trough features. Based on modeling results and empirical observations, mesoscale eddies and retention near suitable settlement habitats enhanced settlement and recruitment magnitude. In advective ecosystems such as the Gulf of Alaska, settlement success and cross-shelf transport are augmented by transient retentive features that influence recruitment by facilitating the delivery of larvae to nursery habitats on the continental shelf. Survival for marine fish with a planktonic larval stage and a demersal juvenile stage is restricted by the successful transition from pelagic to benthic habitats, which can create a bottleneck to recruitment. High mortality during this early life-stage transition can be the result of intrinsic costs associated with morphological changes such as muscle and fin development and sensory improvements (Doherty et al. 2004; Almany and Webster 2006),

Resource managers in the United States and worldwide are tasked with identifying and mitigating trade-offs between human activities in the deep sea (e.g., fishing, energy development, and mining) and their impacts on habitat-forming invertebrates, including deep-sea corals, and sponges (DSCS). Related management decisions require information about where DSCS occur and in what densities. Species distribution modeling (SDM) provides a cost-effective means of identifying potential DSCS habitat over large areas to inform these management decisions and data collection. Here we describe good practices for DSCS SDM, especially in the context of data collection and management applications. Managers typically need information regarding DSCS encounter probabilities, densities, and sizes, defined at sub-regional to basin-wide scales and validated using subsequent, targeted data collections. To realistically achieve these goals, analysts should integrate available data sources in SDMs including fine-scale visual sampling and broad-scale resource surveys (e.g., fisheries trawl surveys), include environmental predictor variables representing multiple spatial scales, model residual spatial autocorrelation, and quantify prediction uncertainty.Winship et al. Deep-Sea Coral Modeling Good PracticesWhen possible, models fitted to presence-absence and density data are preferred over models fitted only to presence data, which are difficult to validate and can confound estimated probability of occurrence or density with sampling effort. Ensembles of models can provide robust predictions, while multi-species models leverage information across taxa, and facilitate community inference. To facilitate the use of models by managers, predictions should be expressed in units that are widely understood and validated at an appropriate spatial scale using a sampling design that provides strong statistical inference. We present three case studies for the Pacific Ocean that illustrate good practices with respect to data collection, modeling, and validation; these case studies demonstrate it is possible to implement our good practices in real-world settings.