http://lod.bco-dmo.org/id/dataset/991182
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
2026-01-08
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Results of a species pool analysis identifying species of interest responding to climate changes in Port Fourchon, LA in 2006, 2016, 2022 and 2023
2026-01-08
publication
2026-01-08
revision
Woods Hole Oceanographic Institution (WHOI BCO-DMO)
2026-04-14
publication
https://doi.org/10.26008/1912/bco-dmo.991182.1
James Nelson
University of Georgia
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: Nelson, J. (2026) Results of a species pool analysis identifying species of interest responding to climate changes in Port Fourchon, LA in 2006, 2016, 2022 and 2023. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2026-01-08 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.991182.1 [access date]
Dataset Description: <p>This is one of four datasets that were produced with the "Fourchon Nekton Turnover Workflow"&nbsp; (v1.0.0, doi: https://doi.org/10.5281/ZENODO.18165331). The datasets and supplemental data produced by this workflow have had minor modifications to enhance the interoperability of the data (See more in section "BCO-DMO Processing"). The workflow contains the exact formats of the data files produced and used by the workflow scripts. It also contains scripts, configurations, readme files, and input/output files for four stages listed below. Each workflow stage corresponds to a dataset in the "Related Datasets" section.<br />
<br />
"Fourchon Nekton Turnover Workflow" steps with corresponding dataset IDs:</p>
<ul>
<li>"1_raw_data" = includes raw drop-sampling data corresponding to BCO-DMO dataset 991168&nbsp;(doi: 10.26008/1912/bco-dmo.991168.1)</li>
<li>"2_gbif_workflow" = includes&nbsp;GBIF species observation data&nbsp;corresponding to metadata in BCO-DMO dataset 991175&nbsp;(doi:&nbsp;10.26008/1912/bco-dmo.991175.1)</li>
<li>"3_CTI_calculations" = includes community temperature index (CTI) data corresponding to BCO-DMO dataset 941250&nbsp;(doi: 10.26008/1912/bco-dmo.941250.1)</li>
<li>"4_species_of_interest" = includes&nbsp;the results of a species pool analysis identifying species of interest&nbsp;corresponding to BCO-DMO dataset 991182 (doi:&nbsp;10.26008/1912/bco-dmo.991182.1)</li>
</ul>
<p>The workflow release (v1.0.0) contains data and scripts used to run analyses and produce figures for publication Leavitt, H; Thomas, A; Doerr, J; Johnson, D; Nelson, J. (In press) Resilient Nekton Composition in the Face of Climate-Driven Foundation Species Shifts. Ecology. Accepted 2025-11-14<br />
<br />
The original source data, including licenses and rights holders for the species occurrence records accessed from GBIF,&nbsp;can be found in the related dataset https://www.bco-dmo.org/dataset/991175 table "991175_v1_port-fourchon_cleaned-occurrence.csv." See the data user agreement and citation guidelines at GBIF:<br />
GBIF Data User Agreement: https://www.gbif.org/terms/data-user&nbsp;&nbsp;<br />
GBIF data usage and citation guidelines: https://www.gbif.org/citation-guidelines</p> Methods and Sampling: <p>Species Thermal Indices (STIs) were calculated from cleaned GBIF occurrence records to represent the typical thermal conditions occupied by each taxon across its realized range. For each species, all vetted occurrence records with valid geographic coordinates were compiled and associated with air temperature values extracted from the WorldClim bioclimatic dataset (BIO6: mean temperature of the coldest month), used here as a consistent proxy for thermal regime in coastal and estuarine systems. To ensure all points could be associated with a land-surface temperature, temperature was calculated as the mean value of pixels within 0.05 degrees of the point.&nbsp;</p>
<p>To minimize spatial sampling bias caused by uneven GBIF reporting density, occurrence records were spatially thinned prior to STI calculation. Records were projected into a metric coordinate system and overlaid with a 50-km hexagonal grid. Within each grid cell, a single occurrence per species was retained at random, ensuring that no region disproportionately influenced thermal estimates.</p>
<p>Following thinning, STI for each species was calculated as the mean SST across all retained occurrences. To characterize uncertainty and thermal breadth, we also computed the 2.5th and 97.5th percentiles of SST values, their difference (thermal range), and a robust estimate of the northern thermal limit based on the 99th percentile of latitude. These metrics describe both central tendency and variability in species’ thermal associations while reducing sensitivity to outliers and oversampled regions.</p>
<p>Community Temperature Index (CTI) was calculated by integrating species-level STIs with site-level community composition data. A community matrix containing species abundances (or counts) per sample was first aligned with the STI dataset so that only taxa present in both datasets were included.&nbsp; See related BCO-DMO dataset (941250) for more information about the CTI calculations (linked in "Related Datasets" section of this page).</p>
<p>For each sample, CTI was computed as the abundance-weighted mean of species STIs, such that species contributing more individuals to a community had proportionally greater influence on the index. Formally, CTI for a given sample is the sum of each species’ abundance multiplied by its STI, divided by the total abundance of all species in that sample. This formulation yields a continuous metric reflecting the thermal affinity of the community as a whole.</p>
<p>To estimate the average low-temp limit for the community, parallel CTI calculations were performed using the lower (2.5th percentile) STI estimates and STI thermal ranges, producing complementary community-level metrics that reflect minimum temperature estimates and thermal niche breadth. These CTI values were then appended to the community dataset along with sample metadata (e.g., year and sample ID) for downstream analyses of temporal and spatial trends.</p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-2418012 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=2418012
completed
James Nelson
University of Georgia
jimmy.nelson@uga.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
species
AphiaID
LSID
detected_2016
detected_2023
detected_2006
p_false_absence_2016
p_false_absence_2023
p_false_absence_2006
p_false_absence_2006_2016
theme
None, User defined
taxon
taxon_code
Life Science Identifier (LSID)
sample description
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.
CAREER: Integrating Seascapes and Energy Flow: learning and teaching about energy, biodiversity, and ecosystem function on the frontlines of climate change
http://www.nelsonecolab.net/career
CAREER: Integrating Seascapes and Energy Flow: learning and teaching about energy, biodiversity, and ecosystem function on the frontlines of climate change
<p><em>NSF Award Abstract:</em><br />
Coastal marshes provide a suite of vital functions that support natural and human communities. Humans frequently take for granted and exploit these ecosystem services without fully understanding the ecological feedbacks, linkages, and interdependencies of these processes to the wider ecosystem. As demands on coastal ecosystem services have risen, marshes have experienced substantial loss due to direct and indirect impacts from human activity. The rapidly changing coastal ecosystems of Louisiana provide a natural experiment for understanding how coastal change alters ecosystem function. This project is developing new metrics and tools to assess food web variability and test hypotheses on biodiversity and ecosystem function in coastal Louisiana. The research is determining how changing habitat configuration alters the distribution of energy across the seascape in a multitrophic system. This work is engaging students from the University of Louisiana Lafayette and Dillard University in placed-based learning by immersing them in the research and local restoration efforts to address land loss and preserve critical ecosystem services. Students are developing a deeper understanding of the complex issues facing coastal regions through formal course work, directed field work, and outreach. Students are interacting with stakeholders and managers who are currently battling coastal change. Their directed research projects are documenting changes in coastal habitat and coupling this knowledge with the consequences to ecosystems and the people who depend on them. By participating in the project students are emerging with knowledge and training that is making them into informed citizens and capable stewards of the future of our coastal ecosystems, while also preparing them for careers in STEM. The project is supporting two graduate students and a post-doc.</p>
<p>The transformation and movement of energy through a food web are key links between biodiversity and ecosystem function. A major hurdle to testing biodiversity ecosystem function theory is a limited ability to assess food web variability in space and time. This research is quantifying changing seascape structure, species diversity, and food web structure to better understand the relationship between biodiversity and energy flow through ecosystems. The project uses cutting edge tools and metrics to test hypotheses on how the distribution, abundance, and diversity of key species are altered by ecosystem change and how this affects function. The hypotheses driving the research are: 1) habitat is a more important indirect driver of trophic structure than a direct change to primary trophic pathways; and 2) horizontal and vertical diversity increases with habitat resource index. Stable isotope analysis is characterizing energy flow through the food web. Changes in horizontal and vertical diversity in a multitrophic system are being quantified using aerial surveys and field sampling. To assess the spatial and temporal change in food web resources, the project is combining results from stable isotope analysis and drone-based remote sensing technology to generate consumer specific energetic seascape maps (E-scapes) and trophic niche metrics. In combination these new metrics are providing insight into species’ responses to changing food web function across the seascape and through time.</p>
<p>This project is jointly funded by Biological Oceanography and the Established Program to Stimulate Competitive Research (EPSCoR).</p>
<p>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.</p>
Louisiana E-scapes
largerWorkCitation
project
eng; USA
oceans
-90.244
-90.16
29.095
29.168
2006-01-08
2023-01-08
Saltmarsh ecosystem near Port Fourchon, LA
0
BCO-DMO catalogue of parameters from Results of a species pool analysis identifying species of interest responding to climate changes in Port Fourchon, LA in 2006, 2016, 2022 and 2023
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/996675.rdf
Name: species
Units: unitless
Description: <p>Species (Scientific Name)</p>
http://lod.bco-dmo.org/id/dataset-parameter/996676.rdf
Name: AphiaID
Units: unitless
Description: <p>AphiaID for the species 'x'. The World Register of Marine Species (WoRMS) taxonomic name identifier"</p>
http://lod.bco-dmo.org/id/dataset-parameter/996677.rdf
Name: LSID
Units: unitless
Description: <p>Life Science Identifier (LSID) for species "x"</p>
http://lod.bco-dmo.org/id/dataset-parameter/996678.rdf
Name: detected_2016
Units: unitless
Description: <p>TRUE/FALSE if species was observed in 2016</p>
http://lod.bco-dmo.org/id/dataset-parameter/996679.rdf
Name: detected_2023
Units: unitless
Description: <p>TRUE/FALSE if species was observed in 2023</p>
http://lod.bco-dmo.org/id/dataset-parameter/996680.rdf
Name: detected_2006
Units: unitless
Description: <p>TRUE/FALSE if species was observed in 2006</p>
http://lod.bco-dmo.org/id/dataset-parameter/996681.rdf
Name: p_false_absence_2016
Units: unitless
Description: <p>probability of false absence in 2016</p>
http://lod.bco-dmo.org/id/dataset-parameter/996682.rdf
Name: p_false_absence_2023
Units: unitless
Description: <p>probability of false absence in 2023</p>
http://lod.bco-dmo.org/id/dataset-parameter/996683.rdf
Name: p_false_absence_2006
Units: unitless
Description: <p>probability of false absence in 2006</p>
http://lod.bco-dmo.org/id/dataset-parameter/996684.rdf
Name: p_false_absence_2006_2016
Units: unitless
Description: <p>probability of false absence in both 2006 and 2016</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
https://doi.org/10.26008/1912/bco-dmo.991182.1
download
onLine
dataset
<p>Species Thermal Indices (STIs) were calculated from cleaned GBIF occurrence records to represent the typical thermal conditions occupied by each taxon across its realized range. For each species, all vetted occurrence records with valid geographic coordinates were compiled and associated with air temperature values extracted from the WorldClim bioclimatic dataset (BIO6: mean temperature of the coldest month), used here as a consistent proxy for thermal regime in coastal and estuarine systems. To ensure all points could be associated with a land-surface temperature, temperature was calculated as the mean value of pixels within 0.05 degrees of the point.&nbsp;</p>
<p>To minimize spatial sampling bias caused by uneven GBIF reporting density, occurrence records were spatially thinned prior to STI calculation. Records were projected into a metric coordinate system and overlaid with a 50-km hexagonal grid. Within each grid cell, a single occurrence per species was retained at random, ensuring that no region disproportionately influenced thermal estimates.</p>
<p>Following thinning, STI for each species was calculated as the mean SST across all retained occurrences. To characterize uncertainty and thermal breadth, we also computed the 2.5th and 97.5th percentiles of SST values, their difference (thermal range), and a robust estimate of the northern thermal limit based on the 99th percentile of latitude. These metrics describe both central tendency and variability in species’ thermal associations while reducing sensitivity to outliers and oversampled regions.</p>
<p>Community Temperature Index (CTI) was calculated by integrating species-level STIs with site-level community composition data. A community matrix containing species abundances (or counts) per sample was first aligned with the STI dataset so that only taxa present in both datasets were included.&nbsp; See related BCO-DMO dataset (941250) for more information about the CTI calculations (linked in "Related Datasets" section of this page).</p>
<p>For each sample, CTI was computed as the abundance-weighted mean of species STIs, such that species contributing more individuals to a community had proportionally greater influence on the index. Formally, CTI for a given sample is the sum of each species’ abundance multiplied by its STI, divided by the total abundance of all species in that sample. This formulation yields a continuous metric reflecting the thermal affinity of the community as a whole.</p>
<p>To estimate the average low-temp limit for the community, parallel CTI calculations were performed using the lower (2.5th percentile) STI estimates and STI thermal ranges, producing complementary community-level metrics that reflect minimum temperature estimates and thermal niche breadth. These CTI values were then appended to the community dataset along with sample metadata (e.g., year and sample ID) for downstream analyses of temporal and spatial trends.</p>
Specified by the Principal Investigator(s)
<p>Identification of climate co-migrator candidates</p>
<p>Species potentially acting as climate co-migrators were identified using a sequential, filter-based framework integrating community abundance trends, species thermal limits, range position, and detection probability. All filters were applied conservatively and in series, such that only species passing every step were retained. Data for this analysis comes from three other datasets associated with this project cited in the related datasets section.&nbsp;</p>
<p>Filter 1: Abundance trends and recent appearance</p>
<p>Community abundance data with CTI metrics were first reshaped into long format to quantify temporal trends in species abundance. For each species, mean abundance was calculated per year across all sampled sites. We then evaluated evidence for population increase using two complementary criteria:</p>
<p>Abundance trend: Pearson correlation between year and mean abundance, with species retained if the correlation coefficient exceeded 0.7.</p>
<p>Presence gain: Logical detection criteria identifying species absent in early years (2006) but present in later surveys (2016 and/or 2023).</p>
<p>Species meeting either criterion were retained as preliminary co-migrator candidates. This step ensured inclusion of both steadily increasing species and newly established taxa that may not yet show strong linear trends.</p>
<p>Filter 2: GBIF data sufficiency</p>
<p>To ensure reliable estimation of species thermal indices, candidate species were required to have sufficient spatial representation in the GBIF-derived STI dataset. Species with fewer than five spatially thinned GBIF grid cells were excluded, reducing sensitivity to sparse or geographically biased occurrence records.</p>
<p>Filter 3: Thermal tolerance relative to local climate</p>
<p>Remaining species were filtered by minimum thermal tolerance using the lower bound of their Species Thermal Index (STI). Species were retained only if the 2.5th percentile of their STI exceeded ~3 °C, corresponding to the approximate lower bound of winter temperatures at Port Fourchon. This step excluded cold-associated and temperate species.&nbsp;</p>
<p>Filter 4: Range-edge proximity</p>
<p>To target species likely responding to poleward or range-edge expansion rather than long-established residents, we retained only taxa whose northern range limit (estimated as the 99th percentile of latitude from GBIF occurrences) fell south of 30.7° N. This threshold reflects proximity to the study region and emphasizes species near their contemporary northern distribution limit.</p>
<p>Filter 5: False-absence probability</p>
<p>For species passing the first four filters, we explicitly quantified the likelihood that apparent absences in early years reflected sampling failure rather than true absence. Detection probability was estimated as the average proportion of sampled sites in which a species was detected when present. Using the number of sites sampled per year, we calculated the probability of failing to detect a species despite its presence (false absence) for each year.</p>
<p>Species were retained only if the combined probability of false absence in key early comparisons (2006–2016) was &lt;5%. This step ensured that inferred range expansion was unlikely to be an artifact of limited sampling or low detectability.</p>
<p>Species passing all five filters were designated as final co-migrator candidates&nbsp;<br />
<br />
This dataset corresponds to Step 4 of the study's processing workflow&nbsp;'Fourchon Nekton Turnover Workflow' (doi: 10.5281/zenodo.18165331).&nbsp; See "Description" and "BCO-DMO Processing" sections for context about the relationship between the workflow files and the data as published at BCO-DMO.</p>
<hr />
<h1 dir="auto">Workflow README for Step "4_species_of_interest" :<br />
Step 4: Species of Interest and CTI Plotting</h1>
<h2 dir="auto">Abstract</h2>
<p dir="auto">This step screens the Port Fourchon nekton assemblage to pinpoint potential mangrove co‑migrators. It chains abundance/presence trends across survey years with GBIF sampling sufficiency, thermal niche position, and range-edge proximity, then flags taxa with low false-absence risk. The pipeline outputs species retained at each filter stage and visualizes CTI trajectories against local winter temperatures to highlight warm-adapted taxa advancing into the study area.</p>
<p dir="auto">Purpose: identify co-migrator candidates via sequential filters (abundance trends, GBIF sufficiency, thermal limits, range position, false-absence probability) and plot CTI alongside winter temperatures.</p>
<p dir="auto">Primary script</p>
<ul dir="auto">
<li dir="auto">ID_species_of_interest_BCODMO.R: annotated script applying filters and generating CTI vs winter temperature plot.</li>
</ul>
<p dir="auto">Inputs</p>
<ul dir="auto">
<li dir="auto">../3_CTI_calculations/outputs/pivot_clean.csv: community matrix with CTI metrics.</li>
<li dir="auto">../3_CTI_calculations/outputs/STI_results_by_taxon.csv: species thermal indices.</li>
<li dir="auto">../1_raw_data/inputs/species_presence_2006_2016_2022.csv: coarse presence by year.</li>
<li dir="auto">../1_raw_data/inputs/avg_winter_temp_yearly.csv: Port Fourchon winter temperatures.</li>
</ul>
<p dir="auto">Outputs (outputs/)</p>
<ul dir="auto">
<li dir="auto">filter1_species.csv,&nbsp;filter2_species.csv,&nbsp;filter3_species.csv,&nbsp;filter4_species.csv,&nbsp;filter_final.csv: species retained after each filter stage.</li>
<li dir="auto">plots/species_temp_plot.png: CTI (weighted and unweighted) overlaid with winter temperature.</li>
</ul>
<p dir="auto">Software</p>
<ul dir="auto">
<li dir="auto">R &gt;= 4.3 with tidyverse, ggplot2, ggrepel, janitor, brms, ggspatial, maptiles, sf.</li>
</ul>
<p dir="auto">Run order</p>
<ol dir="auto">
<li dir="auto">Ensure STI/CTI outputs from Step 3 are available.</li>
<li dir="auto">Run&nbsp;ID_species_of_interest_BCODMO.R.</li>
<li dir="auto">Review filtered species lists and plot in&nbsp;outputs/.</li>
</ol>
Specified by the Principal Investigator(s)
Version 1 (2026-01-08):
Data from the processing workflow were prepared and published at BCO-DMO after reorganization into datasets with minor changes performed to meet the required conventions implemented by BCO-DMO designed for interoperability, standardization, and a variety of data access methods.
Submitted data files for this dataset correspond to the study's outputs in workflow (doi: 10.5281/zenodo.18165331) step 4:
"Fourchon Nekton Turnover Workflow/4_species_of_interest/output/"
* filter1_species.csv - (added as a supplemental file after minor changes)
* filter2_species.csv - (added as a supplemental file after minor changes)
* filter3_species.csv - (added as a supplemental file after minor changes)
* filter4_species.csv - (added as a supplemental file after minor changes)
* filter_final.csv - (imported into the BCO-DMO data system as the primary table for this dataset. Added to dataset as "991182_v1_port-fourchon_species-of-interest.csv"(imported into the BCO-DMO data system as the primary table for this dataset.
Changes made to all tables in this dataset:
* The unnamed first column was not imported. The data submitter indicated this is an unnecessary legacy column.
* Taxon identifiers added to all filter#_species.csv tables by joining the name in the dataset to the from the related dataset BCO-DMO dataset (991168) aphia_codex.csv "true_name" column. "Panopeus spp." was not present in the aphia_codex.csv so the AphiaID and WoRMS value were filled in using the WoRMS identifiers for the genus.
* Columnn names adjusted to conform with naming conventions designed for interoperability. This includes changing columns that start with numbers (e.g. 2016_detected -> detected_2016).
Specified by BCO-DMO Data Managers
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