http://lod.bco-dmo.org/id/dataset/753142
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2019-01-17
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Trawl survey data and species distribution model predictions for presence, absence and abundance
2019-03-05
publication
2019-03-05
revision
Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)
2019-04-08
publication
https://doi.org/10.1575/1912/bco-dmo.753142.1
Malin Pinsky
Rutgers University
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Pinsky, M., Selden, R. (2019) Trawl survey data and species distribution model predictions for presence, absence and abundance. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2019-03-05 [if applicable, indicate subset used]. doi:10.1575/1912/bco-dmo.753142.1 [access date]
Dataset Description: <p>NMFS Trawl Survey data used to fit species distribution models and the resulting modeled predictions for presence/absence and abundance.</p>
<p><span style="font-size:13px">These data were published in Selden (2018).<br />
<br />
The "Get Data" button on this page provides a tabular version of this dataset.&nbsp; These data are also available in the following R Datafile containing a DataFrame named “historical.”</span><br />
<a href="https://datadocs.bco-dmo.org/data/305/CC_Fishery_Adaptations/753142/1/data/historical.RData" target="_blank">https://datadocs.bco-dmo.org/data/305/CC_Fishery_Adaptations/753142/1/data/historical.RData</a><br />
<br />
Related dataset:<br />
"Projected species probability of occupancy and abundance under ocean warming":&nbsp;https://www.bco-dmo.org/dataset/753188</p> Methods and Sampling: NMFS Trawl Survey data was used to fit species distribution models and the resulting modeled predictions for presence/absence and abundance.
We analyzed the influence of environmental characteristics on the spatial distribution of our eight focal species using data collected by the Northeast Fisheries Science Center (NEFSC) spring (March-May), and fall (September-November) bottom trawl surveys along the Northeast US Shelf (65-75°W longitude, and 35-45°N latitude) for the period 1968-2014. Sea surface temperature, sea bottom temperature, and depth were sampled concurrently with trawl samples. We used sediment grain size as a measure of substrate type using existing data layers from the Nature Conservancy. Surveys were trimmed to strata that were sampled in at least 43 of the 46 years. Data from the fall and spring surveys 1968-2014 were combined in order to fit species distribution models (SDMs) using a generalized additive model (GAM) fit separately for each species. Presence or absence of species x in haul location i in year y was modeled using a logistic model with a binomial error distribution and a logit link function. The probability of species occurrence in each haul was modeled as an additive function of the five environmental variables and regional species biomass: haul-specific observations of sea surface temperature, sea bottom temperature, depth, sediment grain size, and average region-wide biomass (kg/tow) of species x in year y in the season in which the haul was conducted. Penalized regression splines were fitted using the “gam” function in the mgcv package in R.
In addition to examining changes in overall range size, we also predicted historical and projected biomass using a delta-lognormal GAM that combines the predictions of the presence/absence model with that for biomass when present.
Range size and species overlap wer calculated using the R-file species_overlap_BCO.R available in the "Supplemental Documents" section on this page.
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1426891 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1426891
completed
Malin Pinsky
Rutgers University
848-932-8242
14 College Farm Rd.
New Brunswick
NJ
08901
USA
malin.pinsky@rutgers.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
spp
haulid
lat_25
lon_25
btemp
stemp
depth
sed_grain
mean_wtcpue
wtcpue
preds1
preds1_upr
preds1_lwr
preds
year
pres2
theme
None, User defined
species scientific name (binomial)
haul_id
latitude
longitude
water temperature at measurement depth
depth
sediment grain size
wet_weight_biomass
No BCO-DMO term
year
featureType
BCO-DMO Standard Parameters
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
Adaptations of fish and fishing communities to rapid climate change
https://osprey.bco-dmo.org/project/559948
Adaptations of fish and fishing communities to rapid climate change
<p><em>Description from NSF award abstract:</em><br />
Climate change presents a profound challenge to the sustainability of coastal systems. Most research has overlooked the important coupling between human responses to climate effects and the cumulative impacts of these responses on ecosystems. Fisheries are a prime example of this feedback: climate changes cause shifts in species distributions and abundances, and fisheries adapt to these shifts. However, changes in the location and intensity of fishing also have major ecosystem impacts. This project's goal is to understand how climate and fishing interact to affect the long-term sustainability of marine populations and the ecosystem services they support. In addition, the project will explore how to design fisheries management and other institutions that are robust to climate-driven shifts in species distributions. The project focuses on fisheries for summer flounder and hake on the northeast U.S. continental shelf, which target some of the most rapidly shifting species in North America. By focusing on factors affecting the adaptation of fish, fisheries, fishing communities, and management institutions to the impacts of climate change, this project will have direct application to coastal sustainability. The project involves close collaboration with the National Oceanic and Atmospheric Administration, and researchers will conduct regular presentations for and maintain frequent dialogue with the Mid-Atlantic and New England Fisheries Management Councils in charge of the summer flounder and hake fisheries. To enhance undergraduate education, project participants will design a new online laboratory investigation to explore the impacts of climate change on fisheries, complete with visualization tools that allow students to explore inquiry-driven problems and that highlight the benefits of teaching with authentic data. This project is supported as part of the National Science Foundation's Coastal Science, Engineering, and Education for Sustainability program - Coastal SEES.</p>
<p>The project will address three questions:<br />
1) How do the interacting impacts of fishing and climate change affect the persistence, abundance, and distribution of marine fishes?<br />
2) How do fishers and fishing communities adapt to species range shifts and related changes in abundance? and<br />
3) Which institutions create incentives that sustain or maximize the value of natural capital and comprehensive social wealth in the face of rapid climate change?</p>
<p>An interdisciplinary team of scientists will use dynamic range and statistical models with four decades of geo-referenced data on fisheries catch and fish biogeography to determine how fish populations are affected by the cumulative impacts of fishing, climate, and changing species interactions. The group will then use comprehensive information on changes in fisher behavior to understand how fishers respond to changes in species distribution and abundance. Interviews will explore the social, regulatory, and economic factors that shape these strategies. Finally, a bioeconomic model for summer flounder and hake fisheries will examine how spatial distribution of regulatory authority, social feedbacks within human communities, and uncertainty affect society's ability to maintain natural and social capital.</p>
CC Fishery Adaptations
largerWorkCitation
project
eng; USA
oceans
-75.75
-65.75
26
44.5
1968-01-01
2014-01-01
Northeast US Continental Shelf Large Marine Ecosystem
0
BCO-DMO catalogue of parameters from Trawl survey data and species distribution model predictions for presence, absence and abundance
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
http://lod.bco-dmo.org/id/dataset-parameter/763634.rdf
Name: spp
Units: unitless
Description: <p>species scientific name</p>
http://lod.bco-dmo.org/id/dataset-parameter/763635.rdf
Name: haulid
Units: unitless
Description: <p>trawl haulid created from CRUISE6 (196803), STATION with max of 3 digits (8), and STRATUM (1010) from NEFSC survey data</p>
http://lod.bco-dmo.org/id/dataset-parameter/763636.rdf
Name: lat_25
Units: decimal degrees (DD)
Description: <p>latitude</p>
http://lod.bco-dmo.org/id/dataset-parameter/763637.rdf
Name: lon_25
Units: decimal degrees (DD)
Description: <p>longitude</p>
http://lod.bco-dmo.org/id/dataset-parameter/763638.rdf
Name: btemp
Units: degrees Celsius
Description: <p>in situ bottom temperature from trawl</p>
http://lod.bco-dmo.org/id/dataset-parameter/763639.rdf
Name: stemp
Units: degrees Celsius
Description: <p>in situ surface temperature from trawl</p>
http://lod.bco-dmo.org/id/dataset-parameter/763640.rdf
Name: depth
Units: meters (m)
Description: <p>trawl depth</p>
http://lod.bco-dmo.org/id/dataset-parameter/763641.rdf
Name: sed_grain
Units: unitless
Description: <p>relative sediment grain size, a proxy for habitat type</p>
http://lod.bco-dmo.org/id/dataset-parameter/763642.rdf
Name: mean_wtcpue
Units: kilograms per tow (kg/tow)
Description: <p>annual mean biomass (kg) per tow for the species for all hauls in region</p>
http://lod.bco-dmo.org/id/dataset-parameter/763643.rdf
Name: wtcpue
Units: kilograms per tow (kg/tow)
Description: <p>biomass per tow in given haul</p>
http://lod.bco-dmo.org/id/dataset-parameter/763644.rdf
Name: preds1
Units: dimensionless
Description: <p>predicted probability of occurrence (0-1)</p>
http://lod.bco-dmo.org/id/dataset-parameter/763645.rdf
Name: preds1_upr
Units: dimensionless
Description: <p>upper bound of predicted probability of occurrence (fit + 2SE)</p>
http://lod.bco-dmo.org/id/dataset-parameter/763646.rdf
Name: preds1_lwr
Units: dimensionless
Description: <p>lower bound of predicted probability of occurrence (fit - 2SE)</p>
http://lod.bco-dmo.org/id/dataset-parameter/763647.rdf
Name: preds
Units: kilograms (kg)
Description: <p>predicted biomass</p>
http://lod.bco-dmo.org/id/dataset-parameter/763648.rdf
Name: year
Units: unitless
Description: <p>year in format yyyy</p>
http://lod.bco-dmo.org/id/dataset-parameter/763649.rdf
Name: pres2
Units: unitless
Description: <p>observed presence (TRUE) or absence (FALSE)</p>
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
19020057
https://darchive.mblwhoilibrary.org/bitstream/1912/23985/1/dataset-753142_observed-and-modeled-presence-1968-2014__v1.tsv
download
https://doi.org/10.1575/1912/bco-dmo.753142.1
download
onLine
dataset
NMFS Trawl Survey data was used to fit species distribution models and the resulting modeled predictions for presence/absence and abundance.
We analyzed the influence of environmental characteristics on the spatial distribution of our eight focal species using data collected by the Northeast Fisheries Science Center (NEFSC) spring (March-May), and fall (September-November) bottom trawl surveys along the Northeast US Shelf (65-75°W longitude, and 35-45°N latitude) for the period 1968-2014. Sea surface temperature, sea bottom temperature, and depth were sampled concurrently with trawl samples. We used sediment grain size as a measure of substrate type using existing data layers from the Nature Conservancy. Surveys were trimmed to strata that were sampled in at least 43 of the 46 years. Data from the fall and spring surveys 1968-2014 were combined in order to fit species distribution models (SDMs) using a generalized additive model (GAM) fit separately for each species. Presence or absence of species x in haul location i in year y was modeled using a logistic model with a binomial error distribution and a logit link function. The probability of species occurrence in each haul was modeled as an additive function of the five environmental variables and regional species biomass: haul-specific observations of sea surface temperature, sea bottom temperature, depth, sediment grain size, and average region-wide biomass (kg/tow) of species x in year y in the season in which the haul was conducted. Penalized regression splines were fitted using the “gam” function in the mgcv package in R.
In addition to examining changes in overall range size, we also predicted historical and projected biomass using a delta-lognormal GAM that combines the predictions of the presence/absence model with that for biomass when present.
Range size and species overlap wer calculated using the R-file species_overlap_BCO.R available in the "Supplemental Documents" section on this page.
Specified by the Principal Investigator(s)
BCO-DMO data manager processing notes:
* exported RData as csv and imported into the BCO-DMO data system.
* periods in column names in the RData Frame changed to underscores in exported csv version to support import into the BCO-DMO data system.
* columns rounded to three decimal places during csv export: "sed.grain","mean.wtcpue","wtcpue","preds1","preds1.upr","preds1.lwr","preds"
Specified by the Principal Investigator(s)
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact