Topography Mediates the Response of Soil CO2 Efflux to Precipitation Over Days, Seasons, and Years
Spatiotemporal heterogeneity in soil CO 2 efflux ( F S ) underlies one of our greatest gaps in understanding global carbon (C) cycles. Though scientists recognize this heterogeneity, F S sampling schemes often average across spatial heterogeneity or fail to capture fine temporal heterogeneity, and m...
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creator | Kopp, Marissa Kaye, Jason Smeglin, Yuting He Adams, Thomas Primka, Edward J. Bradley, Brosi Shi, Yuning Eissenstat, David |
description | Spatiotemporal heterogeneity in soil CO
2
efflux (
F
S
) underlies one of our greatest gaps in understanding global carbon (C) cycles. Though scientists recognize this heterogeneity,
F
S
sampling schemes often average across spatial heterogeneity or fail to capture fine temporal heterogeneity, and many ecosystem models assume flat terrain. Here, we test the idea that simple, remotely sensible terrain variables improve regression models of spatiotemporal variation in
F
S
. We used automatic chambers that, for the first time, capture
F
S
in complex temperate forest terrain at fine temporal resolution with 177,477 hourly
F
S
measurements at 8 locations from ridgetop to valley along planar and swale hillslopes, across three years ranging from dry to record wet precipitation. In two of these years, we measured
F
S
weekly at 50 additional locations distributed across the 8-ha catchment. Growing season
F
s estimates were 1.25 times greater when sampling hourly versus weekly. At ridgetops, growing season
F
S
increased by an average of 463 gC m
−2
180 day
−1
(75.9%) from dry to wet years, while valleys decreased by 208 gC m
−2
180 day
−1
(− 20.1%). This bidirectional response to interannual moisture was identified in distinct Random Forest models of
F
s for convergent (water accumulating) or non-convergent (water shedding) hillslope positions. We hypothesize that different
F
S
constraints drive these opposing responses—water availably to biota limits
F
S
from ridgetops while slow oxygen diffusion limits
F
S
from wet valleys. Accounting for hillslope position and shape reduces variance of
F
S
estimates in complex terrain, which could improve
F
S
sampling, C budgets, and modeling. |
doi_str_mv | 10.1007/s10021-022-00786-1 |
format | Article |
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2
efflux (
F
S
) underlies one of our greatest gaps in understanding global carbon (C) cycles. Though scientists recognize this heterogeneity,
F
S
sampling schemes often average across spatial heterogeneity or fail to capture fine temporal heterogeneity, and many ecosystem models assume flat terrain. Here, we test the idea that simple, remotely sensible terrain variables improve regression models of spatiotemporal variation in
F
S
. We used automatic chambers that, for the first time, capture
F
S
in complex temperate forest terrain at fine temporal resolution with 177,477 hourly
F
S
measurements at 8 locations from ridgetop to valley along planar and swale hillslopes, across three years ranging from dry to record wet precipitation. In two of these years, we measured
F
S
weekly at 50 additional locations distributed across the 8-ha catchment. Growing season
F
s estimates were 1.25 times greater when sampling hourly versus weekly. At ridgetops, growing season
F
S
increased by an average of 463 gC m
−2
180 day
−1
(75.9%) from dry to wet years, while valleys decreased by 208 gC m
−2
180 day
−1
(− 20.1%). This bidirectional response to interannual moisture was identified in distinct Random Forest models of
F
s for convergent (water accumulating) or non-convergent (water shedding) hillslope positions. We hypothesize that different
F
S
constraints drive these opposing responses—water availably to biota limits
F
S
from ridgetops while slow oxygen diffusion limits
F
S
from wet valleys. Accounting for hillslope position and shape reduces variance of
F
S
estimates in complex terrain, which could improve
F
S
sampling, C budgets, and modeling.</description><identifier>ISSN: 1432-9840</identifier><identifier>EISSN: 1435-0629</identifier><identifier>DOI: 10.1007/s10021-022-00786-1</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Biomedical and Life Sciences ; Biota ; Carbon cycle ; Carbon dioxide ; Convergence ; Diffusion rate ; Drought ; Ecology ; Ecosystem models ; Ecosystems ; Efflux ; Environment models ; Environmental Management ; Estimates ; Geoecology/Natural Processes ; Growing season ; Heterogeneity ; Hydrology/Water Resources ; Life Sciences ; Moisture effects ; Plant Sciences ; Precipitation ; Rain ; Regression analysis ; Regression models ; Sampling ; Seasons ; Soils ; Spatial heterogeneity ; Temperate forests ; Temporal resolution ; Terrain ; Topography ; Valleys ; Zoology</subject><ispartof>Ecosystems (New York), 2023-06, Vol.26 (4), p.687-705</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c270t-4d4466dd92a1af5129b7fb6408761f3656ff2a89d33b3773f3801b322d08816c3</cites><orcidid>0000-0002-5995-5624</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10021-022-00786-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10021-022-00786-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Kopp, Marissa</creatorcontrib><creatorcontrib>Kaye, Jason</creatorcontrib><creatorcontrib>Smeglin, Yuting He</creatorcontrib><creatorcontrib>Adams, Thomas</creatorcontrib><creatorcontrib>Primka, Edward J.</creatorcontrib><creatorcontrib>Bradley, Brosi</creatorcontrib><creatorcontrib>Shi, Yuning</creatorcontrib><creatorcontrib>Eissenstat, David</creatorcontrib><title>Topography Mediates the Response of Soil CO2 Efflux to Precipitation Over Days, Seasons, and Years</title><title>Ecosystems (New York)</title><addtitle>Ecosystems</addtitle><description>Spatiotemporal heterogeneity in soil CO
2
efflux (
F
S
) underlies one of our greatest gaps in understanding global carbon (C) cycles. Though scientists recognize this heterogeneity,
F
S
sampling schemes often average across spatial heterogeneity or fail to capture fine temporal heterogeneity, and many ecosystem models assume flat terrain. Here, we test the idea that simple, remotely sensible terrain variables improve regression models of spatiotemporal variation in
F
S
. We used automatic chambers that, for the first time, capture
F
S
in complex temperate forest terrain at fine temporal resolution with 177,477 hourly
F
S
measurements at 8 locations from ridgetop to valley along planar and swale hillslopes, across three years ranging from dry to record wet precipitation. In two of these years, we measured
F
S
weekly at 50 additional locations distributed across the 8-ha catchment. Growing season
F
s estimates were 1.25 times greater when sampling hourly versus weekly. At ridgetops, growing season
F
S
increased by an average of 463 gC m
−2
180 day
−1
(75.9%) from dry to wet years, while valleys decreased by 208 gC m
−2
180 day
−1
(− 20.1%). This bidirectional response to interannual moisture was identified in distinct Random Forest models of
F
s for convergent (water accumulating) or non-convergent (water shedding) hillslope positions. We hypothesize that different
F
S
constraints drive these opposing responses—water availably to biota limits
F
S
from ridgetops while slow oxygen diffusion limits
F
S
from wet valleys. Accounting for hillslope position and shape reduces variance of
F
S
estimates in complex terrain, which could improve
F
S
sampling, C budgets, and modeling.</description><subject>Biomedical and Life Sciences</subject><subject>Biota</subject><subject>Carbon cycle</subject><subject>Carbon dioxide</subject><subject>Convergence</subject><subject>Diffusion rate</subject><subject>Drought</subject><subject>Ecology</subject><subject>Ecosystem models</subject><subject>Ecosystems</subject><subject>Efflux</subject><subject>Environment models</subject><subject>Environmental Management</subject><subject>Estimates</subject><subject>Geoecology/Natural Processes</subject><subject>Growing season</subject><subject>Heterogeneity</subject><subject>Hydrology/Water Resources</subject><subject>Life Sciences</subject><subject>Moisture effects</subject><subject>Plant Sciences</subject><subject>Precipitation</subject><subject>Rain</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Sampling</subject><subject>Seasons</subject><subject>Soils</subject><subject>Spatial heterogeneity</subject><subject>Temperate forests</subject><subject>Temporal resolution</subject><subject>Terrain</subject><subject>Topography</subject><subject>Valleys</subject><subject>Zoology</subject><issn>1432-9840</issn><issn>1435-0629</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kMtKAzEUhoMoWKsv4CrgttHcJskspbYqVCq2LlyFzEzSTqmTMZmKfXtjR3Dn5lzg_86BD4BLgq8JxvImpkoJwpSitCqByBEYEM4yhAXNjw8zRbni-BScxbjBmGSK8wEolr71q2Da9R4-2ao2nY2wW1v4YmPrm2ihd3Dh6y0czymcOLfdfcHOw-dgy7qtO9PVvoHzTxvgndnHEVxYExM3gqap4Js1IZ6DE2e20V789iF4nU6W4wc0m98_jm9nqKQSd4hXnAtRVTk1xLiM0LyQrhAcKymIYyITzlGj8oqxgknJHFOYFIzSCitFRMmG4Kq_2wb_sbOx0xu_C016qaliQmRKSpVStE-VwccYrNNtqN9N2GuC9Y9L3bvUyaU-uNQkQayHYgo3Kxv-Tv9DfQNywHUU</recordid><startdate>20230601</startdate><enddate>20230601</enddate><creator>Kopp, Marissa</creator><creator>Kaye, Jason</creator><creator>Smeglin, Yuting He</creator><creator>Adams, Thomas</creator><creator>Primka, Edward J.</creator><creator>Bradley, Brosi</creator><creator>Shi, Yuning</creator><creator>Eissenstat, David</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SN</scope><scope>7ST</scope><scope>7XB</scope><scope>88I</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>M2O</scope><scope>M2P</scope><scope>MBDVC</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-5995-5624</orcidid></search><sort><creationdate>20230601</creationdate><title>Topography Mediates the Response of Soil CO2 Efflux to Precipitation Over Days, Seasons, and Years</title><author>Kopp, Marissa ; Kaye, Jason ; Smeglin, Yuting He ; Adams, Thomas ; Primka, Edward J. ; Bradley, Brosi ; Shi, Yuning ; Eissenstat, David</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c270t-4d4466dd92a1af5129b7fb6408761f3656ff2a89d33b3773f3801b322d08816c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Biomedical and Life Sciences</topic><topic>Biota</topic><topic>Carbon cycle</topic><topic>Carbon dioxide</topic><topic>Convergence</topic><topic>Diffusion rate</topic><topic>Drought</topic><topic>Ecology</topic><topic>Ecosystem models</topic><topic>Ecosystems</topic><topic>Efflux</topic><topic>Environment models</topic><topic>Environmental Management</topic><topic>Estimates</topic><topic>Geoecology/Natural Processes</topic><topic>Growing season</topic><topic>Heterogeneity</topic><topic>Hydrology/Water Resources</topic><topic>Life Sciences</topic><topic>Moisture effects</topic><topic>Plant Sciences</topic><topic>Precipitation</topic><topic>Rain</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Sampling</topic><topic>Seasons</topic><topic>Soils</topic><topic>Spatial heterogeneity</topic><topic>Temperate forests</topic><topic>Temporal resolution</topic><topic>Terrain</topic><topic>Topography</topic><topic>Valleys</topic><topic>Zoology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kopp, Marissa</creatorcontrib><creatorcontrib>Kaye, Jason</creatorcontrib><creatorcontrib>Smeglin, Yuting He</creatorcontrib><creatorcontrib>Adams, Thomas</creatorcontrib><creatorcontrib>Primka, Edward J.</creatorcontrib><creatorcontrib>Bradley, Brosi</creatorcontrib><creatorcontrib>Shi, Yuning</creatorcontrib><creatorcontrib>Eissenstat, David</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Research Library (Corporate)</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><jtitle>Ecosystems (New York)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kopp, Marissa</au><au>Kaye, Jason</au><au>Smeglin, Yuting He</au><au>Adams, Thomas</au><au>Primka, Edward J.</au><au>Bradley, Brosi</au><au>Shi, Yuning</au><au>Eissenstat, David</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Topography Mediates the Response of Soil CO2 Efflux to Precipitation Over Days, Seasons, and Years</atitle><jtitle>Ecosystems (New York)</jtitle><stitle>Ecosystems</stitle><date>2023-06-01</date><risdate>2023</risdate><volume>26</volume><issue>4</issue><spage>687</spage><epage>705</epage><pages>687-705</pages><issn>1432-9840</issn><eissn>1435-0629</eissn><abstract>Spatiotemporal heterogeneity in soil CO
2
efflux (
F
S
) underlies one of our greatest gaps in understanding global carbon (C) cycles. Though scientists recognize this heterogeneity,
F
S
sampling schemes often average across spatial heterogeneity or fail to capture fine temporal heterogeneity, and many ecosystem models assume flat terrain. Here, we test the idea that simple, remotely sensible terrain variables improve regression models of spatiotemporal variation in
F
S
. We used automatic chambers that, for the first time, capture
F
S
in complex temperate forest terrain at fine temporal resolution with 177,477 hourly
F
S
measurements at 8 locations from ridgetop to valley along planar and swale hillslopes, across three years ranging from dry to record wet precipitation. In two of these years, we measured
F
S
weekly at 50 additional locations distributed across the 8-ha catchment. Growing season
F
s estimates were 1.25 times greater when sampling hourly versus weekly. At ridgetops, growing season
F
S
increased by an average of 463 gC m
−2
180 day
−1
(75.9%) from dry to wet years, while valleys decreased by 208 gC m
−2
180 day
−1
(− 20.1%). This bidirectional response to interannual moisture was identified in distinct Random Forest models of
F
s for convergent (water accumulating) or non-convergent (water shedding) hillslope positions. We hypothesize that different
F
S
constraints drive these opposing responses—water availably to biota limits
F
S
from ridgetops while slow oxygen diffusion limits
F
S
from wet valleys. Accounting for hillslope position and shape reduces variance of
F
S
estimates in complex terrain, which could improve
F
S
sampling, C budgets, and modeling.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10021-022-00786-1</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0002-5995-5624</orcidid></addata></record> |
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subjects | Biomedical and Life Sciences Biota Carbon cycle Carbon dioxide Convergence Diffusion rate Drought Ecology Ecosystem models Ecosystems Efflux Environment models Environmental Management Estimates Geoecology/Natural Processes Growing season Heterogeneity Hydrology/Water Resources Life Sciences Moisture effects Plant Sciences Precipitation Rain Regression analysis Regression models Sampling Seasons Soils Spatial heterogeneity Temperate forests Temporal resolution Terrain Topography Valleys Zoology |
title | Topography Mediates the Response of Soil CO2 Efflux to Precipitation Over Days, Seasons, and Years |
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