http://lod.bco-dmo.org/id/dataset/753188
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
2019-01-18
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Predicted probability of occupancy and abundance under a doubling of carbon dioxide using simulations from GFDL CM2.6
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.753188.1
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) Predicted probability of occupancy and abundance under a doubling of carbon dioxide using simulations from GFDL CM2.6. 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.753188.1 [access date]
Dataset Description: Predicted probability of occupancy and abundance under a doubling of carbon dioxide using simulations from GFDL CM2.6. These data were published in Selden (2018).
The "Get Data" button on this page provides a tabular version of this dataset. These data are also available in the following R Datafile containing a DataFrame named “projected.”
https://datadocs.bco-dmo.org/data/305/CC_Fishery_Adaptations/753188/1/data/projected.RData
Related dataset:
"Observed and modeled presence 1968-2014": https://www.bco-dmo.org/dataset/753142 Methods and Sampling: To evaluate how future warming may affect species overlap, we examined projections of ocean temperature from experimental runs of CM2.6—a high-resolution global climate model developed by the National Oceanographic and Atmospheric Administration’s Geophysical Fluid Dynamics Laboratory. The climate model simulates an annual 1% increase in atmospheric CO2 over the course of 80-years, reaching a doubling of CO2 by year 70. Under the IPCC’s RCP 8.5 emissions scenario, CO2 is predicted to approximately double by 2075 (van Vuuren et al., 2011). The CM2.6 model projects temperature as the change in temperature from the initial year, such that projections are in relative units (ΔºC). We use ΔºC projections for surface and bottom waters for the spring months of March, April and May. To convert projected temperature change (ΔºC) to absolute temperatures (ºC), projected temperature changes were added to the long-term mean climatology in each 0.25°latitude x 0.25°longitude grid cell. The fitted species distribution models were then projected with the CM2.6 sea bottom and sea surface temperatures for each simulated time step (t). Projections were made for each 0.25°latitude x 0.25°longitude grid cell j in the study region while holding species biomass constant at its overall mean and using the mean depth and substrate for each grid cell. Projections were also made with average biomass set equal to 50% and 150% of the historical mean to explore the effect of abundance on projected species occupancy.
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
lon_deg
lat_deg
scen
btemp
stemp
preds1
preds
se_fit
preds1_upr
preds1_lwr
theme
None, User defined
taxon
latitude
longitude
time_elapsed
water temperature at measurement depth
No BCO-DMO term
wet_weight_biomass
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
44.25
2019-03-05
Northeast US Continental Shelf Large Marine Ecosystem
0
BCO-DMO catalogue of parameters from Predicted probability of occupancy and abundance under a doubling of carbon dioxide using simulations from GFDL CM2.6
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/763906.rdf
Name: spp
Units: unitless
Description: <p>species scientific name</p>
http://lod.bco-dmo.org/id/dataset-parameter/763907.rdf
Name: lon_deg
Units: decimal degrees (DD)
Description: <p>longitude of grid cell</p>
http://lod.bco-dmo.org/id/dataset-parameter/763908.rdf
Name: lat_deg
Units: decimal degrees (DD)
Description: <p>latitude of grid cell</p>
http://lod.bco-dmo.org/id/dataset-parameter/763909.rdf
Name: scen
Units: unitless
Description: <p>year from GFDL simulation. CO2 increases 1% per year over 80 years, doubling from initial levels at year 70 (see Saba et al. 2016 for details)</p>
http://lod.bco-dmo.org/id/dataset-parameter/763910.rdf
Name: btemp
Units: degrees Celsius
Description: <p>predicted bottom temperature from GFDL simulation</p>
http://lod.bco-dmo.org/id/dataset-parameter/763911.rdf
Name: stemp
Units: degrees Celsius
Description: <p>predicted surface temperature from GFDL simulation</p>
http://lod.bco-dmo.org/id/dataset-parameter/763912.rdf
Name: preds1
Units: dimensionless
Description: <p>predicted probability of occurrence (0-1)</p>
http://lod.bco-dmo.org/id/dataset-parameter/763913.rdf
Name: preds
Units: kilograms (kg)
Description: <p>predicted biomass</p>
http://lod.bco-dmo.org/id/dataset-parameter/763914.rdf
Name: se_fit
Units: kilograms (kg)
Description: <p>standard error of prediction for probability of occurrence</p>
http://lod.bco-dmo.org/id/dataset-parameter/763915.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/763916.rdf
Name: preds1_lwr
Units: dimensionless
Description: <p>lower bound of predicted probability of occurrence (fit -2SE)</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
38879297
https://darchive.mblwhoilibrary.org/bitstream/1912/23986/1/dataset-753188_projected-species-probability-occupancy-and-abundance-under-ocean-warming__v1.tsv
download
https://doi.org/10.1575/1912/bco-dmo.753188.1
download
onLine
dataset
To evaluate how future warming may affect species overlap, we examined projections of ocean temperature from experimental runs of CM2.6—a high-resolution global climate model developed by the National Oceanographic and Atmospheric Administration’s Geophysical Fluid Dynamics Laboratory. The climate model simulates an annual 1% increase in atmospheric CO2 over the course of 80-years, reaching a doubling of CO2 by year 70. Under the IPCC’s RCP 8.5 emissions scenario, CO2 is predicted to approximately double by 2075 (van Vuuren et al., 2011). The CM2.6 model projects temperature as the change in temperature from the initial year, such that projections are in relative units (ΔºC). We use ΔºC projections for surface and bottom waters for the spring months of March, April and May. To convert projected temperature change (ΔºC) to absolute temperatures (ºC), projected temperature changes were added to the long-term mean climatology in each 0.25°latitude x 0.25°longitude grid cell. The fitted species distribution models were then projected with the CM2.6 sea bottom and sea surface temperatures for each simulated time step (t). Projections were made for each 0.25°latitude x 0.25°longitude grid cell j in the study region while holding species biomass constant at its overall mean and using the mean depth and substrate for each grid cell. Projections were also made with average biomass set equal to 50% and 150% of the historical mean to explore the effect of abundance on projected species occupancy.
Specified by the Principal Investigator(s)
Range size and species overlap were calculated using the R-file species_overlap_BCO.R available in the "Supplemental Documents" section on this page.
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: "btemp","stemp","preds1","preds","se.fit","preds1.upr","preds1.lwr"
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