http://lod.bco-dmo.org/id/dataset/948167
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.
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2025-01-08
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Habitat scale model output of Port Fourchon, LA dervied from drone and satellite imagery taken in fall and spring 2023.
2025-01-08
publication
2025-01-08
revision
Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)
2025-04-07
publication
https://doi.org/10.26008/1912/bco-dmo.948167.1
James Nelson
University of Louisiana at Lafayette
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: Leavitt, H., Thomas, A., Nelson, J. (2025) Habitat scale model output of Port Fourchon, LA dervied from drone and satellite imagery taken in fall and spring 2023. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2025-01-08 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.948167.1 [access date]
Habitat scale model outputs Port Fourchon, 2022 Dataset Description: Methods and Sampling: <p>No raw data is included in this dataset. For collection methods of data used in this analysis, please refer to methods outlined in the linked datasets.</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
onGoing
James Nelson
University of Louisiana at Lafayette
jimmy.nelson@uga.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
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
-90.269831
-90.149744
29.092646
29.164671
2022-09-23
2022-09-29
Saltmarsh ecosystem near Port Fourchon, LA
0
BCO-DMO catalogue of parameters from Habitat scale model output of Port Fourchon, LA dervied from drone and satellite imagery taken in fall and spring 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
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
2542
https://darchive.mblwhoilibrary.org/server/api/core/bitstreams/fb6df494-e23f-439d-8d83-d95f609b98b9/content
download
121895
https://darchive.mblwhoilibrary.org/server/api/core/bitstreams/02c3e9e2-e2f8-4502-9099-07054bab86fc/content
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138519
https://darchive.mblwhoilibrary.org/server/api/core/bitstreams/2cc60913-f040-4827-b49a-452fcda3a881/content
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14711
https://darchive.mblwhoilibrary.org/server/api/core/bitstreams/952b6852-0931-4439-9c69-fb0a7bd202b8/content
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18402
https://darchive.mblwhoilibrary.org/server/api/core/bitstreams/61582b8b-33a9-4018-9ea4-fd7e648af121/content
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4998
https://darchive.mblwhoilibrary.org/server/api/core/bitstreams/da4a7230-1082-4369-88a8-b16f88b4ad99/content
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https://doi.org/10.26008/1912/bco-dmo.948167.1
download
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dataset
<p>No raw data is included in this dataset. For collection methods of data used in this analysis, please refer to methods outlined in the linked datasets.</p>
Specified by the Principal Investigator(s)
<p>The data processing pipeline for this analysis begins with the preparation of input datasets, which include habitat data derived from satellite imagery and community composition data. The habitat data files, such as<em> google2022_edge[edge]_buf[buffer].csv</em>, contain spatial metrics including mangrove edge length, land-water ratios, and percent edge cover. The community composition data<em>, PtFouSept2022count_ns.csv</em>, provides site-specific counts for the target species (eg. Litopenaeus setiferus, abbreviated as PENSET) along with environmental variables like salinity, temperature, and geographic coordinates. These datasets are merged using a common identifier (site_date_key) to align spatial and ecological information.</p>
<p>Once merged, all predictor variables, such as <em>% mangrove edge</em> and <em>% land-water ratio</em>, are standardized by subtracting the mean and dividing by the standard deviation to ensure consistent scaling. A correlation matrix is computed to identify and exclude highly correlated predictor pairs (correlation &gt; 0.8) to reduce multicollinearity in subsequent models. The core modeling process is conducted by the <em>model_compile </em>function, which iterates over combinations of buffer radii (100–600 m or satscale, 20-150 for smallscale) and edge distances (1, 3, 5 m) to assess species-habitat relationships. For each combination, spatial metrics are calculated, and generalized additive models (GAMs) are fitted using subsets of predictors. Knots for all GAM models were limited to k=4 to try and limit over fitting. Models with correlated variables are excluded, and the remaining models are ranked by Akaike Information Criterion (AIC). The best models within two AIC points of the top model are recorded, capturing their formula, AIC, buffer, and edge values. This process outputs several files.&nbsp;</p>
<p>The top model is then identified based on its frequency of selection across all scales and is saved for further analysis. Using this top model, the <em>univariate_edge </em>function evaluates individual predictor performance across scales. Residuals are examined for spatial autocorrelation using Moran’s I test, and the significance of predictors, including their interactions (e.g., mangrove edge × land-water ratio), is assessed. These results are consolidated and saved in <em>outputs_over_scales.csv.&nbsp;</em></p>
<p>Visualization and diagnostics follow, with R² values plotted across buffer sizes and edge distances to identify the habitat scale with the strongest explanatory power. Key predictors, such as mangrove edge and percent edge cover, are visualized in relation to species abundance, with significance indicated by custom point shapes (determined using threshold of p&lt;0.05). At the best scale, relationships between key variables and predicted species abundance are visualized, while residual and diagnostic plots (e.g., Cook’s distance) are used to assess model fit. Predictions from the top model are compared to observed counts to validate performance.</p>
<p>The final outputs of this pipeline include several CSV files and visualizations. The&nbsp; <em>species_aic </em>files show the AIC scores for all the models considered for each species at each scale. Then, the <em>model_list </em>files list all models that were within 2 points of the lowest AIC score for each set. Next, the&nbsp; <em>model_compare</em> file shows how many times a particular model occurs within <em>model_list</em> along with it's mean R-squared value. Finally, the <em>topmodel.txt </em>file contains the formula for the GAM model that was ultimately selected for further analysis. The <em>output_over_scales.csv</em> file summarizes model results across scales, including R² values, p-values, and significant predictors. Visualizations include plots of R² across scales and relationships between predictors and species abundance, as well as diagnostic plots for residuals and influential observations. This comprehensive pipeline rigorously evaluates the influence of spatial metrics on species abundance, identifying the optimal habitat scale for common in Port Fourchon, LA.</p>
<p>This analysis was performed in R version 4.3.2. Relevant packages in this analysis are as follows:&nbsp;<br />
mgcv_1.9-0, nlme_3.1-163, gam_1.22-4, foreach_1.5.2, spdep_1.3-5, sf_1.0-16, spData_2.3.1, lubridate_1.9.3, forcats_1.0.0, stringr_1.5.1, dplyr_1.1.4, purrr_1.0.2, readr_2.1.4, tidyr_1.3.0, tibble_3.2.1, ggplot2_3.5.1, tidyverse_2.0.0, scales_1.3.0, statmod_1.5.0, pscl_1.5.9&nbsp;&nbsp;&nbsp; &nbsp;</p>
<p>loaded via a namespace (and not attached): gtable_0.3.4, lattice_0.21-9, tzdb_0.4.0, vctrs_0.6.5, tools_4.3.2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; generics_0.1.3, proxy_0.4-27, fansi_1.0.5, pkgconfig_2.0.3, Matrix_1.6-1.1, KernSmooth_2.23-22, lifecycle_1.0.4&nbsp;&nbsp;&nbsp; compiler_4.3.2, farver_2.1.1, deldir_2.0-4, munsell_0.5.0, codetools_0.2-19, class_7.3-22, pillar_1.9.0&nbsp; crayon_1.5.2, MASS_7.3-60, classInt_0.4-10, wk_0.9.1, iterators_1.0.14, boot_1.3-28.1, tidyselect_1.2.1, stringi_1.8.2&nbsp; labeling_0.4.3 , grid_4.3.2, colorspace_2.1-0, cli_3.6.1, magrittr_2.0.3, utf8_1.2.4, e1071_1.7-14, withr_3.0.2, sp_2.1-3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; timechange_0.2.0, hms_1.1.3, viridisLite_0.4.2, s2_1.1.6, rlang_1.1.2, Rcpp_1.0.11, glue_1.6.2, DBI_1.1.3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; rstudioapi_0.15.0, R6_2.5.1, units_0.8-5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>
Specified by the Principal Investigator(s)
* Created zip files for submitted datasets
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