This title provides a summary of 57 studies and six poster sessions related to evidence of climate change, and its effects on freshwater and marine aquatic environments, and on the dynamics of fish populations and fisheries. Numerous examples of the relationships between fish abundance trends and the environment are included. Most investigations are from the North Pacific Ocean, but studies done in the North Atlantic Ocean, the Bering Sea, the Yellow Sea, and selected freshwater lakes are also included. The effect of climate change on cod, pollock, salmon, herring, Dungeness crab, Japanese sardine, and Californian anchovy (among others) is discussed.

Climate change and northern fish populations

This paper describes a method for the analysis of groundfish survey data by incorporating zero and non-zero values into a single model. This is done by using a model which modifies the delta-distribution approach to fit into the GLM framework and uses maximum likelihood to estimate parameters. No prior assumptions of homogeneity are used for the structure of the zero or non-zero values. The method is primarily applicable to fixed-station designs, although extensions to other designs are possible. The maximum likelihood estimation reduces to fitting a GLM to 0/1 values and another GLM to the positive abundance values. The new model is tested on Icelandic groundfish survey data. It is seen that the model can be used for evaluating the eVect of diVerent factors on catch rates as well as estimating abundance indices. Results from diVerent models are compared on the basis of tuned VPA runs. ? 1996 International Council for the Exploration of the Sea

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Analysis of groundfish survey abundance data: combining the GLM and delta approaches

Generalized additive models (GAMs) were applied to examine the relative influence of various factors on fishery performance, defined as nominal catch- per-unit-effort (CPUE) of swordfish (Xiphias gladius) and blue shark (Prionace glauca) in the Hawaii-based swordfish fishery. Commercial fisheries data for the analysis consisted of a 5 year (1991–1995) time series of 27 901 longline sets. Mesoscale relationships were analysed for seven physical variables (latitude, longitude, SST, SST frontal energy, temporal changes in SST (ΔSST), SST frontal energy (ΔSST frontal energy) and bathymetry), all of which may affect the availability of swordfish and blue shark to the fishery, and three variables (number of lightsticks per hook, lunar index, and wind velocity) which may relate to the effectiveness of the fishing gear. Longline CPUE data were analysed in relation to SST data on three spatiotemporal scales (18 km weekly, 1°-weekly, 1°-monthly). Depending on the scale of SST data, GAM analysis accounted for 39–42% and 44–45% of the variance in nominal CPUE for swordfish and blue shark, respectively. Stepwise GAM building revealed the relative importance of the variables in explaining the variance in CPUE. For swordfish, by decreasing importance, the variables ranked: (1) latitude, (2) time, (3) longitude, (4) lunar index, (5) lightsticks per hook, (6) SST, (7) ΔSST frontal energy, (8) wind velocity, (9) SST frontal energy, (10) bathymetry, and (11) ΔSST. For blue shark, the variables ranked: (1) latitude, (2) longitude, (3) time, (4) SST, (5) lightsticks per hook, (6) ΔSST, (7) ΔSST frontal energy, (8) SST frontal energy, (9) wind velocity, (10) lunar index, and (11) bathymetry. Swordfish CPUE increased with latitude to peak at 35–40°N and increased in the vicinity of temperature fronts and during the full moon. Shark CPUE also increased with latitude up to 40°N, and increased westward, but declined abruptly at SSTs colder than 16°C.



As a comparison with modelling fishery performance in relation to specific environmental and fishery operational effects, fishery performance was also modelled as a function of categorical time (month) and area (2° squares) variables using a generalized linear model (GLM) approach. The variance accounted for by the GLMs was ≈ 1–3% lower than the variance explained by the GAMs. Time series of swordfish and blue shark CPUE standardized for the environmental and operational variables quantified in the GAM and for the time-area effects in the GLM are presented. For swordfish, both nominal and standardized time series indicate a decline in CPUE, whereas the opposite trend was seen for blue shark.

Environmental effects on swordfish and blue shark catch rates in the US North Pacific longline fishery

We present an objective method for identifying associations between environmental (habitat) conditions and the distributions of marine fishes using survey data. The method tests the null hypothesis...

Identifying Habitat Associations of Marine Fishes Using Survey Data: An Application to the Northwest Atlantic

The emergence of longline fishing around the world has been concomitant with an increase in depredation-interactions by odontocete whales (removal of fish caught on hooks), resulting in substantial socio-economic and ecological impacts. The extent, trends and underlying mechanisms driving these interactions remain poorly known. Using long-term (2003–2017) datasets from seven major Patagonian toothfish (Dissostichus eleginoides) longline fisheries, this study assessed the levels and inter-annual trends of sperm whale (Physeter macrocephalus) and/or killer whale (Orcinus orca) interactions as proportions of fishing time (days) and fishing area (spatial cells). The role of fishing patterns in explaining between-fisheries variations of probabilities of odontocete interactions was investigated. While interaction levels remained globally stable since the early 2000s, they varied greatly between fisheries from 0 to >50% of the fishing days and area. Interaction probabilities were influenced by the seasonal concentration of fishing effort, size of fishing areas, density of vessels, their mobility and the depth at which they operated. The results suggest that between-fisheries variations of interaction probabilities are largely explained by the extent to which vessels provide whales with opportunities for interactions. Determining the natural distribution of whales will, therefore, allow fishers to implement better strategies of spatio-temporal avoidance of depredation.

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Commercial Fishing Patterns Influence Odontocete Whale-Longline Interactions in the Southern Ocean

Generalized additive models (GAM) are herein applied to trawl survey data in the eastern Bering Sea with an eye to (1) detecting trends in groundfish distributions and (2) improving abundance estim

Chisheng Huang

Papers

Spatial Analysis of Bering Sea Groundfish Survey Data Using Generalized Additive Models

Modeling the effect of algal biomass on multispecies aquatic microcosms response to copper toxicity