Geography, not human impact, is the predominant predictor in a 150-year stable isotope fish record from the coastal United States. (April 2020)
- Record Type:
- Journal Article
- Title:
- Geography, not human impact, is the predominant predictor in a 150-year stable isotope fish record from the coastal United States. (April 2020)
- Main Title:
- Geography, not human impact, is the predominant predictor in a 150-year stable isotope fish record from the coastal United States
- Authors:
- Oczkowski, Autumn
Kreakie, Betty
Gutierrez, M. Nicole
Pelletier, Marguerite
Charpentier, Mike
Santos, Emily
Kiddon, John - Abstract:
- Highlights: We explore human impacts on coastal food webs of the continental United States. Stable isotopes measured in modern and archival fish (~1904) were linked with indicator variables. Random forest analyses indicated that geography was the best predictor of fish isotopes. While human-created nitrogen is an important source to coastal food webs, so is location. Abstract: Since the 1940s, anthropogenic nitrogen (N) inputs have grown to dominate global N cycles, particularly in fluvial systems. Negative impacts of this enrichment on downstream estuaries are well documented. Efforts at N reductions are increasingly successful but evaluating ecosystem response trajectories is difficult because of a lack of knowledge of historic conditions. To document continental-scale coastal food web N-dynamics prior to large increases in human N-loads, we sampled 208 fish from an archival collection, taken from coastal waters across the continental U.S., with a median collection year of 1904. The archival fish were compared with 526 samples collected in 2015 from 126 estuaries also along the U.S. coastline. We used stable isotopes of N (δ 15 N) and carbon (δ 13 C) as a proxy for human inputs and organic matter sources. Watershed attributes from 1910 and 2012, census data, fish life histories, and basic estuarine geography were used to develop random forest models that determined which variables were the best predictors of isotope values. State, latitude, and fish trophic level wereHighlights: We explore human impacts on coastal food webs of the continental United States. Stable isotopes measured in modern and archival fish (~1904) were linked with indicator variables. Random forest analyses indicated that geography was the best predictor of fish isotopes. While human-created nitrogen is an important source to coastal food webs, so is location. Abstract: Since the 1940s, anthropogenic nitrogen (N) inputs have grown to dominate global N cycles, particularly in fluvial systems. Negative impacts of this enrichment on downstream estuaries are well documented. Efforts at N reductions are increasingly successful but evaluating ecosystem response trajectories is difficult because of a lack of knowledge of historic conditions. To document continental-scale coastal food web N-dynamics prior to large increases in human N-loads, we sampled 208 fish from an archival collection, taken from coastal waters across the continental U.S., with a median collection year of 1904. The archival fish were compared with 526 samples collected in 2015 from 126 estuaries also along the U.S. coastline. We used stable isotopes of N (δ 15 N) and carbon (δ 13 C) as a proxy for human inputs and organic matter sources. Watershed attributes from 1910 and 2012, census data, fish life histories, and basic estuarine geography were used to develop random forest models that determined which variables were the best predictors of isotope values. State, latitude, and fish trophic level were consistently the most important predictors, while human impacts played a lesser role. When the fish were collected (~1914 vs 2015) was not an important predictor, rather where the fish was collected was the best predictor of N source. The model results illustrate the important role that geography plays in coastal food web dynamics and underscore the importance of offshore N-sources to coastal food webs. … (more)
- Is Part Of:
- Ecological indicators. Volume 111(2020)
- Journal:
- Ecological indicators
- Issue:
- Volume 111(2020)
- Issue Display:
- Volume 111, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 111
- Issue:
- 2020
- Issue Sort Value:
- 2020-0111-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-04
- Subjects:
- Random forest -- Machine learning -- Stable isotope -- Nitrogen -- Carbon -- Anthropogenic -- Fish -- Estuaries -- United States
Environmental monitoring -- Periodicals
Environmental management -- Periodicals
Environmental impact analysis -- Periodicals
Environmental risk assessment -- Periodicals
Sustainable development -- Periodicals
333.71405 - Journal URLs:
- http://www.sciencedirect.com/science/journal/1470160X/ ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.ecolind.2019.106022 ↗
- Languages:
- English
- ISSNs:
- 1470-160X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3648.877200
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