Data used to develop these indicators comes from multispecies diet data collected on the Northeast Fisheries Science Center (NEFSC) and NorthEast Area Monitoring and Assessment Program (NEAMAP) bottom trawl surveys. Bottom temperature data is described in Bottom temperature - High Resolution.
Data Processing
The basic workflow is to develop a dataset of stomach contents data where fish predators act as samplers of the prey field, then fit a vector autoregressive spatio-temporal (VAST) model to this dataset to generate an index. Dataset development is described here.
NEFSC survey food habits data were extracted and provided by Brian Smith (NEFSC). NEAMAP survey food habits data were extracted and processed by James Gartland (VIMS). Macrobenthos and Megabenthos categories were those used in Northeast US food web models. The Macrobenthos Rpath category has 833 food habits database species codes. The Megabenthos Rpath category has 105 food habits database species codes. All are listed at this link.
Benthic predator/size combinations were selected using a cluster analysis of a diet similarity matrix provided by Brian Smith. Species categorized as pelagic or piscivorous feeders were eliminated, and all other species were retained as general benthivores. This resulted in 88 predator/size combinations used to "sample" benthic invertebrates. The predator/size list is available at this link.
These input datasets were processed, aggregated, and combined with bottom temperature data to become VAST model input datasets using the script at this link.
Data Analysis
VAST spatio-temporal modeling [@thorson_comparing_2017; @thorson_guidance_2019] is described here.
The approach follows that used for the forage fish index [@gaichas_assessing_2023], which was in turn based on @ng_predator_2021.
Two stages of model selection determined whether to include:
spatial and spatio-temporal random effects, and
vessel effects, and "catchability" covariates affecting the observation process: mean predator size, number of predators, and bottom temperature.
Using REML in stage 1, models including spatial and spatio-temporal random effects as well as anisotropy were best supported by the data. This was true for the spring dataset and the fall dataset for both macrobenthos and megabenthos.
In stage 2, combinations of catchability covariates were better supported by the data than vessel effects. Model comparisons led us to the best model fit using mean predator length, number of predator species, and bottom temperature at a survey station as catchability covariates.
Model selection results are reported at this link.
@article{thorson_comparing_2017,
title = {Comparing estimates of abundance trends and distribution shifts using single- and multispecies models of fishes and biogenic habitat},
volume = {74},
issn = {1054-3139},
url = {https://doi.org/10.1093/icesjms/fsw193},
doi = {10.1093/icesjms/fsw193},
abstract = {Several approaches have been developed over the last decade to simultaneously estimate distribution or density for multiple species (e.g. “joint species distribution” or “multispecies occupancy” models). However, there has been little research comparing estimates of abundance trends or distribution shifts from these multispecies models with similar single-species estimates. We seek to determine whether a model including correlations among species (and particularly species that may affect habitat quality, termed “biogenic habitat”) improves predictive performance or decreases standard errors for estimates of total biomass and distribution shift relative to similar single-species models. To accomplish this objective, we apply a vector-autoregressive spatio-temporal (VAST) model that simultaneously estimates spatio-temporal variation in density for multiple species, and present an application of this model using data for eight US Pacific Coast rockfishes (Sebastes spp.), thornyheads (Sebastolobus spp.), and structure-forming invertebrates (SFIs). We identified three fish groups having similar spatial distribution (northern Sebastes, coastwide Sebastes, and Sebastolobus species), and estimated differences among groups in their association with SFI. The multispecies model was more parsimonious and had better predictive performance than fitting a single-species model to each taxon individually, and estimated fine-scale variation in density even for species with relatively few encounters (which the single-species model was unable to do). However, the single-species models showed similar abundance trends and distribution shifts to those of the multispecies model, with slightly smaller standard errors. Therefore, we conclude that spatial variation in density (and annual variation in these patterns) is correlated among fishes and SFI, with congeneric fishes more correlated than species from different genera. However, explicitly modelling correlations among fishes and biogenic habitat does not seem to improve precision for estimates of abundance trends or distribution shifts for these fishes.},
number = {5},
urldate = {2021-11-04},
journal = {ICES Journal of Marine Science},
author = {Thorson, James T. and Barnett, Lewis A. K.},
month = may,
year = {2017},
pages = {1311--1321},
file = {Full Text PDF:/Users/sarahgaichas/Zotero/storage/BDBIBD5D/Thorson and Barnett - 2017 - Comparing estimates of abundance trends and distri.pdf:application/pdf;Snapshot:/Users/sarahgaichas/Zotero/storage/F62SPRTP/2907795.html:text/html},
}
@article{ng_predator_2021,
title = {Predator stomach contents can provide accurate indices of prey biomass},
volume = {78},
issn = {1054-3139},
url = {https://doi.org/10.1093/icesjms/fsab026},
doi = {10.1093/icesjms/fsab026},
abstract = {Diet-based annual biomass indices can potentially use predator stomach contents to provide information about prey biomass and may be particularly useful for species that are otherwise poorly sampled, including ecologically important forage fishes. However, diet-based biomass indices may be sensitive to underlying ecological dynamics between predators and prey, such as predator functional responses and changes in overlap in space and time. To evaluate these factors, we fit spatio-temporal models to stomach contents of five Atlantic herring (Clupea harengus) predators and survey catch data for predators and Atlantic herring. We identified drivers of variation in stomach contents, evaluated spatial patterns in stomach content data, and produced predator-specific indices of seasonal Atlantic herring biomass. After controlling for spatio-temporal processes and predator length, diet-based indices of biomass shared similar decadal trends but varied substantially between predators and seasons on shorter time scales. Diet-based indices reflected prey biomass more than prey availability, but weak correlations indicated that not all biological processes were controlled for. Results provide potential guidance for developing diet-based biomass indices and contribute to a body of evidence demonstrating the utility of predator diet data to provide information about relative prey biomass.},
number = {3},
urldate = {2021-09-01},
journal = {ICES Journal of Marine Science},
author = {Ng, Elizabeth L and Deroba, Jonathan J and Essington, Timothy E and Grüss, Arnaud and Smith, Brian E and Thorson, James T},
month = jul,
year = {2021},
pages = {1146--1159},
file = {Full Text PDF:/Users/sarahgaichas/Zotero/storage/69FKJSA6/Ng et al. - 2021 - Predator stomach contents can provide accurate ind.pdf:application/pdf;Snapshot:/Users/sarahgaichas/Zotero/storage/2X7SANRP/6145864.html:text/html},
}
@article{thorson_guidance_2019,
title = {Guidance for decisions using the {Vector} {Autoregressive} {Spatio}-{Temporal} ({VAST}) package in stock, ecosystem, habitat and climate assessments},
volume = {210},
issn = {0165-7836},
url = {http://www.sciencedirect.com/science/article/pii/S0165783618302820},
doi = {10.1016/j.fishres.2018.10.013},
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.},
language = {en},
urldate = {2020-02-24},
journal = {Fisheries Research},
author = {Thorson, James T.},
month = feb,
year = {2019},
keywords = {Climate vulnerability analysis, Distribution shift, Habitat assessment, Index standardization, Integrated ecosystem assessment, Spatio-temporal model, Stock assessment, VAST},
pages = {143--161},
file = {ScienceDirect Full Text PDF:/Users/sarahgaichas/Zotero/storage/38KBWBLZ/Thorson - 2019 - Guidance for decisions using the Vector Autoregres.pdf:application/pdf;ScienceDirect Snapshot:/Users/sarahgaichas/Zotero/storage/85BILR75/S0165783618302820.html:text/html},
}
@article{gaichas_assessing_2023,
title = {Assessing small pelagic fish trends in space and time using piscivore stomach contents},
issn = {0706-652X, 1205-7533},
url = {https://cdnsciencepub.com/doi/10.1139/cjfas-2023-0093},
doi = {10.1139/cjfas-2023-0093},
abstract = {Changing distribution and abundance of small pelagic fishes may drive changes in predator distributions, affecting predator availability to fisheries and surveys. However, small pelagics are difficult to survey directly, so we developed a novel method of assessing the aggregate abundance of 21 small pelagic forage taxa via predator stomach contents. We used stomach contents collected from 22 piscivore species captured by multiple bottom trawl surveys within a vector autoregressive spatio-temporal model to assess trends of small pelagics on the Northeast US shelf. The goal was to develop a spatial “forage index” to inform survey and (or) fishery availability in the western North Atlantic bluefish ( Pomatomus saltatrix) stock assessment. This spatially resolved index compared favorably with more traditional design-based survey biomass indices for forage species well sampled by surveys. However, our stomach content-based index better represented smaller unmanaged forage species that surveys are not designed to capture. The stomach-based forage index helped explain bluefish availability to the recreational fishery for stock assessment and provided insight into pelagic forage trends throughout the regional ecosystem.},
language = {en},
urldate = {2024-02-15},
journal = {Canadian Journal of Fisheries and Aquatic Sciences},
author = {Gaichas, Sarah K. and Gartland, James and Smith, Brian E. and Wood, Anthony D. and Ng, Elizabeth L. and Celestino, Michael and Drew, Katie and Tyrell, Abigail S. and Thorson, James T.},
month = oct,
year = {2023},
pages = {cjfas--2023--0093},
file = {Full Text PDF:/Users/sarahgaichas/Zotero/storage/W4CMYQYY/Gaichas et al. - 2023 - Assessing small pelagic fish trends in space and t.pdf:application/pdf},
}
Data Source(s)
Data used to develop these indicators comes from multispecies diet data collected on the Northeast Fisheries Science Center (NEFSC) and NorthEast Area Monitoring and Assessment Program (NEAMAP) bottom trawl surveys. Bottom temperature data is described in Bottom temperature - High Resolution.
Data Processing
The basic workflow is to develop a dataset of stomach contents data where fish predators act as samplers of the prey field, then fit a vector autoregressive spatio-temporal (VAST) model to this dataset to generate an index. Dataset development is described here.
NEFSC survey food habits data were extracted and provided by Brian Smith (NEFSC). NEAMAP survey food habits data were extracted and processed by James Gartland (VIMS). Macrobenthos and Megabenthos categories were those used in Northeast US food web models. The Macrobenthos Rpath category has 833 food habits database species codes. The Megabenthos Rpath category has 105 food habits database species codes. All are listed at this link.
Benthic predator/size combinations were selected using a cluster analysis of a diet similarity matrix provided by Brian Smith. Species categorized as pelagic or piscivorous feeders were eliminated, and all other species were retained as general benthivores. This resulted in 88 predator/size combinations used to "sample" benthic invertebrates. The predator/size list is available at this link.
These input datasets were processed, aggregated, and combined with bottom temperature data to become VAST model input datasets using the script at this link.
Data Analysis
VAST spatio-temporal modeling [@thorson_comparing_2017; @thorson_guidance_2019] is described here.
The approach follows that used for the forage fish index [@gaichas_assessing_2023], which was in turn based on @ng_predator_2021.
Two stages of model selection determined whether to include:
Using REML in stage 1, models including spatial and spatio-temporal random effects as well as anisotropy were best supported by the data. This was true for the spring dataset and the fall dataset for both macrobenthos and megabenthos.
In stage 2, combinations of catchability covariates were better supported by the data than vessel effects. Model comparisons led us to the best model fit using mean predator length, number of predator species, and bottom temperature at a survey station as catchability covariates.
Model selection results are reported at this link.
Scripts used to run the model selection and to produce the final bias corrected models are posted at https://github.com/NOAA-EDAB/benthosindex/tree/main/VASTscripts
Bibiography
@article{thorson_comparing_2017, title = {Comparing estimates of abundance trends and distribution shifts using single- and multispecies models of fishes and biogenic habitat}, volume = {74}, issn = {1054-3139}, url = {https://doi.org/10.1093/icesjms/fsw193}, doi = {10.1093/icesjms/fsw193}, abstract = {Several approaches have been developed over the last decade to simultaneously estimate distribution or density for multiple species (e.g. “joint species distribution” or “multispecies occupancy” models). However, there has been little research comparing estimates of abundance trends or distribution shifts from these multispecies models with similar single-species estimates. We seek to determine whether a model including correlations among species (and particularly species that may affect habitat quality, termed “biogenic habitat”) improves predictive performance or decreases standard errors for estimates of total biomass and distribution shift relative to similar single-species models. To accomplish this objective, we apply a vector-autoregressive spatio-temporal (VAST) model that simultaneously estimates spatio-temporal variation in density for multiple species, and present an application of this model using data for eight US Pacific Coast rockfishes (Sebastes spp.), thornyheads (Sebastolobus spp.), and structure-forming invertebrates (SFIs). We identified three fish groups having similar spatial distribution (northern Sebastes, coastwide Sebastes, and Sebastolobus species), and estimated differences among groups in their association with SFI. The multispecies model was more parsimonious and had better predictive performance than fitting a single-species model to each taxon individually, and estimated fine-scale variation in density even for species with relatively few encounters (which the single-species model was unable to do). However, the single-species models showed similar abundance trends and distribution shifts to those of the multispecies model, with slightly smaller standard errors. Therefore, we conclude that spatial variation in density (and annual variation in these patterns) is correlated among fishes and SFI, with congeneric fishes more correlated than species from different genera. However, explicitly modelling correlations among fishes and biogenic habitat does not seem to improve precision for estimates of abundance trends or distribution shifts for these fishes.}, number = {5}, urldate = {2021-11-04}, journal = {ICES Journal of Marine Science}, author = {Thorson, James T. and Barnett, Lewis A. K.}, month = may, year = {2017}, pages = {1311--1321}, file = {Full Text PDF:/Users/sarahgaichas/Zotero/storage/BDBIBD5D/Thorson and Barnett - 2017 - Comparing estimates of abundance trends and distri.pdf:application/pdf;Snapshot:/Users/sarahgaichas/Zotero/storage/F62SPRTP/2907795.html:text/html}, }
@article{ng_predator_2021, title = {Predator stomach contents can provide accurate indices of prey biomass}, volume = {78}, issn = {1054-3139}, url = {https://doi.org/10.1093/icesjms/fsab026}, doi = {10.1093/icesjms/fsab026}, abstract = {Diet-based annual biomass indices can potentially use predator stomach contents to provide information about prey biomass and may be particularly useful for species that are otherwise poorly sampled, including ecologically important forage fishes. However, diet-based biomass indices may be sensitive to underlying ecological dynamics between predators and prey, such as predator functional responses and changes in overlap in space and time. To evaluate these factors, we fit spatio-temporal models to stomach contents of five Atlantic herring (Clupea harengus) predators and survey catch data for predators and Atlantic herring. We identified drivers of variation in stomach contents, evaluated spatial patterns in stomach content data, and produced predator-specific indices of seasonal Atlantic herring biomass. After controlling for spatio-temporal processes and predator length, diet-based indices of biomass shared similar decadal trends but varied substantially between predators and seasons on shorter time scales. Diet-based indices reflected prey biomass more than prey availability, but weak correlations indicated that not all biological processes were controlled for. Results provide potential guidance for developing diet-based biomass indices and contribute to a body of evidence demonstrating the utility of predator diet data to provide information about relative prey biomass.}, number = {3}, urldate = {2021-09-01}, journal = {ICES Journal of Marine Science}, author = {Ng, Elizabeth L and Deroba, Jonathan J and Essington, Timothy E and Grüss, Arnaud and Smith, Brian E and Thorson, James T}, month = jul, year = {2021}, pages = {1146--1159}, file = {Full Text PDF:/Users/sarahgaichas/Zotero/storage/69FKJSA6/Ng et al. - 2021 - Predator stomach contents can provide accurate ind.pdf:application/pdf;Snapshot:/Users/sarahgaichas/Zotero/storage/2X7SANRP/6145864.html:text/html}, }
@article{thorson_guidance_2019, title = {Guidance for decisions using the {Vector} {Autoregressive} {Spatio}-{Temporal} ({VAST}) package in stock, ecosystem, habitat and climate assessments}, volume = {210}, issn = {0165-7836}, url = {http://www.sciencedirect.com/science/article/pii/S0165783618302820}, doi = {10.1016/j.fishres.2018.10.013}, 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.}, language = {en}, urldate = {2020-02-24}, journal = {Fisheries Research}, author = {Thorson, James T.}, month = feb, year = {2019}, keywords = {Climate vulnerability analysis, Distribution shift, Habitat assessment, Index standardization, Integrated ecosystem assessment, Spatio-temporal model, Stock assessment, VAST}, pages = {143--161}, file = {ScienceDirect Full Text PDF:/Users/sarahgaichas/Zotero/storage/38KBWBLZ/Thorson - 2019 - Guidance for decisions using the Vector Autoregres.pdf:application/pdf;ScienceDirect Snapshot:/Users/sarahgaichas/Zotero/storage/85BILR75/S0165783618302820.html:text/html}, }
@article{gaichas_assessing_2023, title = {Assessing small pelagic fish trends in space and time using piscivore stomach contents}, issn = {0706-652X, 1205-7533}, url = {https://cdnsciencepub.com/doi/10.1139/cjfas-2023-0093}, doi = {10.1139/cjfas-2023-0093}, abstract = {Changing distribution and abundance of small pelagic fishes may drive changes in predator distributions, affecting predator availability to fisheries and surveys. However, small pelagics are difficult to survey directly, so we developed a novel method of assessing the aggregate abundance of 21 small pelagic forage taxa via predator stomach contents. We used stomach contents collected from 22 piscivore species captured by multiple bottom trawl surveys within a vector autoregressive spatio-temporal model to assess trends of small pelagics on the Northeast US shelf. The goal was to develop a spatial “forage index” to inform survey and (or) fishery availability in the western North Atlantic bluefish ( Pomatomus saltatrix) stock assessment. This spatially resolved index compared favorably with more traditional design-based survey biomass indices for forage species well sampled by surveys. However, our stomach content-based index better represented smaller unmanaged forage species that surveys are not designed to capture. The stomach-based forage index helped explain bluefish availability to the recreational fishery for stock assessment and provided insight into pelagic forage trends throughout the regional ecosystem.}, language = {en}, urldate = {2024-02-15}, journal = {Canadian Journal of Fisheries and Aquatic Sciences}, author = {Gaichas, Sarah K. and Gartland, James and Smith, Brian E. and Wood, Anthony D. and Ng, Elizabeth L. and Celestino, Michael and Drew, Katie and Tyrell, Abigail S. and Thorson, James T.}, month = oct, year = {2023}, pages = {cjfas--2023--0093}, file = {Full Text PDF:/Users/sarahgaichas/Zotero/storage/W4CMYQYY/Gaichas et al. - 2023 - Assessing small pelagic fish trends in space and t.pdf:application/pdf}, }