From fishers to farmers, people across the planet who rely directly upon natural resources for their livelihoods and well-being face extensive impacts from climate change. However, local- and ...regional-scale impacts and associated risks can vary geographically, and the implications for development of adaptation pathways that will be most effective for specific communities are underexplored. To improve this understanding at relevant local scales, we developed a coupled social-ecological approach to assess the risk posed to fishing fleets by climate change, applying it to a case study of groundfish fleets that are a cornerstone of fisheries along the U.S. West Coast. Based on the mean of three high-resolution climate projections, we found that more poleward fleets may experience twice as much local temperature change as equatorward fleets, and 3–4 times as much depth displacement of historical environmental conditions in their fishing grounds. Not only are they more highly exposed to climate change, but some poleward fleets are >10x more economically-dependent on groundfish. While we show clear regional differences in fleets’ flexibility to shift to new fisheries via fisheries diversification (‘adapt in-place’) or shift their fishing grounds in response to future change through greater mobility (‘adapt on-the-move’), these differences do not completely mitigate the greater exposure and economic dependence of more poleward fleets. Therefore, on the U.S. West Coast more poleward fishing fleets may be at greater overall risk due to climate change, in contrast to expectations for greater equatorward risk in other parts of the world. Through integration of climatic, ecological, and socio-economic data, this case study illustrates the potential for widespread implementation of risk assessment at scales relevant to fishers, communities, and decision makers. Such applications will help identify the greatest opportunities to mitigate climate risks through pathways that enhance flexibility and other dimensions of adaptive capacity.
We investigate whether fishery-dependent time series can be used to fill in spatial and temporal data gaps where scientific, fishery-independent data are not available. Limitations in sampling ...coverage combined with a historical focus on continental slope-dwelling groundfish resulted in a gap in understanding Oregon’s nearshore groundfish fishery. Although fisheries-independent surveys have been conducted across most of the fishery’s depth range, the data are limited by years and seasons surveyed, as well as the absence of data for areas shallower than 55 m water depth. Fishery-dependent data are available for those shallow waters and for a broader temporal range. However, these data are self-reported and the coverage was determined by where fishers choose to fish. To investigate the potential for future combined uses for these data sources, we analyzed spatial and temporal changes in catch rates, as well as gaps in fishery (logbook) and scientific (NOAA survey) data, for six flatfishes. We found that more heavily targeted species that live in deeper water, like Dover sole and petrale sole, had more spatial scientific sampling coverage compared to less frequently targeted species that live in shallow water, such as starry flounder and sand sole. Overlap between datasets was variable in space and time but consistently higher near large ports. We identified the winter season, and the pre-2003 time-period as having the highest potential to benefit from complementary use of fishery-dependent data. Prior to 2003, the survey design was variable and there was greater spatial and temporal coverage of logbook data compared to post-2003. Integration of these data sets may be useful for future research given their differences in spatial and temporal range. This work provides a new perspective on the value of using fishery-dependent data to understand the spatial distribution of species in habitats that are under sampled in scientific surveys.
Uncertainty in fish behavior can introduce bias into density calculations from fishery-independent bottom trawl surveys that provide relative abundance estimates and population trends for stock ...assessments. In situ video was used to quantify flatfish behavioral responses to a bottom trawl sweep to improve the understanding of survey and assessment results. The behavior of 632 flatfishes was recorded during four tows. More than 90% of fish were observed in a perpendicular orientation away from the sweeps indicating a herding response. There was no significant effect of fish length on fish orientation or whether it reacted or remained stationary during the observation. Only 1.3% of fish were observed escaping the sweeps. A generalized linear model was used to estimate that at a distance of 73.8cm (±3.4 SE) 50% of observed fish reacted to the sweep. The mean distance that stationary fish were first observed reacting to the sweep was 36.6cm (±2.0 SE). Quantitative analysis indicates that flatfish herding occurs along trawl sweeps and the effective area swept is greater than the wing spread. Thus, the use of wing spread to calculate relative abundance estimates explains bias in stock assessment estimates of survey catchability that are greater than expected.
Fishery management plans for U.S. fisheries are required to specify status determination criteria (e.g. whether the stock is overfished and whether overfishing is occurring) and typically use harvest ...control rules to adjust target and limit fishing mortality and catch levels to prevent overfishing, achieve optimum yield and rebuild overfished stocks. The status determination criteria are based on the concept of the fishing mortality rate (
F
MSY) that maximizes long-term catch as the upper limit on the allowable rate of fishing and the associated
B
MSY, the spawning biomass which produces MSY, is the target for rebuilding of overfished stocks. In practice, proxies for the biological reference points
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MSY and
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MSY are often employed. Although several methods exist for estimating these quantities, it is unclear which performs best. Simulation is therefore used to evaluate alternative estimators for these quantities. These estimators differ in terms of whether a stock–recruitment relationship is estimated, and whether a prior based on Bayesian meta-analysis is used as a penalty on steepness, a critical parameter of the stock–recruitment relationship. The simulations consider three life histories: a long-lived unproductive rockfish, a moderately long-lived and productive flatfish, and a hake, which is also moderately long-lived and productive, but exhibits highly variable recruitment. Results indicate that estimator performance varies among reference points. However, estimators of
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0, the average spawning biomass in the absence of exploitation, and stock depletion based on a fitted stock–recruitment relationship generally perform best.
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0 is estimated either better (the rockfish and flatfish) or similarly (the hake) to stock depletion. Estimating
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MSY from the fit of the stock–recruitment relationship performed best for the rockfish and flatfish life histories; average recruitment estimators proved to be best for the flatfish and hake life histories. Proxy methods of calculating
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MSY generally performed relatively poor in comparison to the non-proxy measures. The performance of estimators of biological reference points was generally better for the rockfish and flatfish life histories, which were similar, than for the hake life history. Estimator performance was generally poorer in the presence of high recruitment variability.
Monte Carlo simulations and equilibrium analyses are used to explore the behavior of threshold management strategies. The analyses explore the medium- to long-term implications of uncertainty about ...the steepness of the stock–recruitment relationship, variation as well as possible temporal autocorrelation in recruitment, and estimation and implementation uncertainty. The results highlight the trade-offs among various management strategies, in particular the trade-offs between average catch, inter-annual variation in catch, and the risk of dropping below the Pacific Fishery Management Council (PFMC) overfished threshold of 25% of the average unfished spawning biomass. Uncertainty regarding steepness is the major source of variation in the final size of the resource and whether it is below the overfished threshold, although the extent of recruitment variability also impacts these quantities. The extent of inter-annual variation in catches is determined primarily by the amount of implementation error. Although the values for the various performance measures depend on all of the factors examined to some extent, the impact of interactions among these factors is generally small.
•Stock assessments can be rejected during peer-review.•There are various reasons why an assessment can be rejected, including use of plots and diagnostics.•Most jurisdictions have ways to provide ...management advice when assessments are rejected.
Model-based stock assessments form a key component of the management advice for fish and invertebrate stocks worldwide. It is important for such assessments to be peer-reviewed and to pass scientific scrutiny before they can be used to inform management decision making. While it is desirable for management decisions to be based on quantitative assessments that use as much of the available data as possible, this is not always the case. A proposed assessment may be found to be unsatisfactory during the peer-review process (even if it utilizes all of the available data), leading to decisions being made using simpler approaches. This paper provides a synthesis across seven jurisdictions of the types of diagnostic statistics and plots that can be used to evaluate whether a proposed assessment is ‘best available science’, summarizes several cases where a proposed assessment was not accepted for use in management, and how jurisdictions are able to provide management advice when a stock assessment is ‘rejected.’ The paper concludes with recommended general practices for reducing subjectivity when deciding whether to accept an assessment and how to provide advice when a proposed assessment is rejected.
Geographic information system (GIS) analysis with bathymetric, substrate, and side scan sonar (SSS) data was used to assess both spatial and temporal expansion of exotic dreissenid mussels onto ...sedimentary habitats in Lake Erie. These data were used for developing multiple regression models with substrate types and SSS data to interpret the expansion of Dreissena assemblages across the central and western basins of Lake Erie from 1994 to 1998. The 1994-1996 GIS model predicted the 1997 SSS measurements of Dreissena coverage correctly in 84% of the cases (n = 50). Similarly, the 1994-1997 GIS model predicted the 1998 SSS measurements of Dreissena coverage correctly in 80% of the cases (n = 20). These models indicated that Dreissena coverage ranged from$<$1% on muds in 1994 to 67% on sands and gravels in 1997. Based on all of the substrates, the 1994-1997 model indicates that Dreissena beds have been expanding since 1994 at 1,000 ± 6 km2yr-1and presently occupy 5,484 ± 32 km2of the 25,734 km2sedimentary bottom of Lake Erie. Our observations indicate that expanding Dreissena beds are altering soft-substrate habitats and influencing the ecosystem dynamics throughout Lake Erie. Furthermore, this study demonstrates that the distribution, abundance, and ecosystem impacts of invasive species in other watersheds can be accurately described and interpreted over diverse spatial and temporal scales using GIS models.
Identifying juvenile habitats is critical for understanding a species' ecology and for focusing spatial fishery management by defining references like essential fish habitat (EFH). Here, we used ...vector autoregressive spatio-temporal models (VAST) to delineate spatial and temporal patterns in juvenile density for 13 commercially important species of groundfishes off the US west coast. In particular, we identified hotspots with high juvenile density. Three qualitative patterns of distribution and abundance emerged. First, Dover sole Microstomus pacificus, Pacific grenadier Coryphaenoides acrolepis, shortspine thornyhead Sebastolobus alascanus, and splitnose rockfish Sebastes diploproa had distinct, spatially-limited hotspots that were spatially consistent through time. Next, Pacific hake Merluccius productus and darkblotched rockfish Sebastes crameri had distinct, spatially limited hotspots, but the location of these hotspots varied through time. Finally, arrowtooth flounder Atheresthes stomias, English sole Parophrys vetulus, sablefish Anoplopoma fimbria, Pacific grenadier Coryphaenoides acrolepis, lingcod Ophiodon elongatus, longspine thornyhead Sebastolobus altivelis, petrale sole Eopsetta jordani, and Pacific sanddab Citharichthys sordidus had large hotspots that spanned a broad latitudinal range. These habitats represent potential, if not likely, nursery areas, the location of which will inform spatial management.
Abstract
We investigated the hypothesis that synchronous recruitment is due to a shared susceptibility to environmental processes using stock–recruitment residuals for 52 marine fish stocks within ...three
N
ortheast
P
acific large marine ecosystems: the
E
astern
B
ering
S
ea and
A
leutian
I
slands,
G
ulf of
A
laska, and
C
alifornia
C
urrent. There was moderate coherence in exceptionally strong and weak year‐classes and correlations across stocks. Based on evidence of synchrony from these analyses, we used
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ayesian hierarchical models to relate recruitment to environmental covariates for groups of stocks that may be similarly influenced by environmental processes based on their life histories. There were consistent relationships among stocks to the covariates, especially within the
G
ulf of
A
laska and
C
alifornia
C
urrent. The best
G
ulf of
A
laska model included Northeast Pacific sea surface height as a predictor of recruitment, and was particularly strong for stocks dependent on cross‐shelf transport during the larval phase for recruitment. In the
C
alifornia
C
urrent the best‐fit model included
S
an
F
rancisco coastal sea level height as a predictor, with higher recruitment for many stocks corresponding to anomalously high sea level the year before spawning and low sea level the year of spawning. The best
E
astern
B
ering
S
ea and
A
leutian Islands model included several environmental variables as covariates and there was some consistent response across stocks to these variables. Future research may be able to utilize these across‐stock environmental influences, in conjunction with an understanding of ecological processes important across early life history stages, to improve identification of environmental drivers of recruitment.
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