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Dyson K, Nicolau AP, Tenneson K, Francesconi W, Daniels A, Andrich G, Caldas B, Castaño S, de Campos N, Dilger J, Guidotti V, Jaques I, McCullough IM, McDevitt AD, Molina L, Nekorchuk DM, Newberry T, Pereira CL, Perez J, Richards-Dimitrie T, Rivera O, Rodriguez B, Sales N, Tello J, Wespestad C, Zutta B, Saah D. Coupling remote sensing and eDNA to monitor environmental impact: A pilot to quantify the environmental benefits of sustainable agriculture in the Brazilian Amazon. PLoS One 2024; 19:e0289437. [PMID: 38354171 PMCID: PMC10866516 DOI: 10.1371/journal.pone.0289437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 12/01/2023] [Indexed: 02/16/2024] Open
Abstract
Monitoring is essential to ensure that environmental goals are being achieved, including those of sustainable agriculture. Growing interest in environmental monitoring provides an opportunity to improve monitoring practices. Approaches that directly monitor land cover change and biodiversity annually by coupling the wall-to-wall coverage from remote sensing and the site-specific community composition from environmental DNA (eDNA) can provide timely, relevant results for parties interested in the success of sustainable agricultural practices. To ensure that the measured impacts are due to the environmental projects and not exogenous factors, sites where projects have been implemented should be benchmarked against counterfactuals (no project) and control (natural habitat) sites. Results can then be used to calculate diverse sets of indicators customized to monitor different projects. Here, we report on our experience developing and applying one such approach to assess the impact of shaded cocoa projects implemented by the Instituto de Manejo e Certificação Florestal e Agrícola (IMAFLORA) near São Félix do Xingu, in Pará, Brazil. We used the Continuous Degradation Detection (CODED) and LandTrendr algorithms to create a remote sensing-based assessment of forest disturbance and regeneration, estimate carbon sequestration, and changes in essential habitats. We coupled these remote sensing methods with eDNA analyses using arthropod-targeted primers by collecting soil samples from intervention and counterfactual pasture field sites and a control secondary forest. We used a custom set of indicators from the pilot application of a coupled monitoring framework called TerraBio. Our results suggest that, due to IMAFLORA's shaded cocoa projects, over 400 acres were restored in the intervention area and the community composition of arthropods in shaded cocoa is closer to second-growth forests than that of pastures. In reviewing the coupled approach, we found multiple aspects worked well, and we conclude by presenting multiple lessons learned.
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Affiliation(s)
- Karen Dyson
- Spatial Informatics Group, LLC, Pleasanton, California, United States of America
| | - Andréa P. Nicolau
- Spatial Informatics Group, LLC, Pleasanton, California, United States of America
| | - Karis Tenneson
- Spatial Informatics Group, LLC, Pleasanton, California, United States of America
| | - Wendy Francesconi
- Alliance of Biodiversity International and International Center for Tropical Agriculture (CIAT), Kasarani, Nairobi
| | - Amy Daniels
- United States Agency for International Development (USAID), Washington, DC, United States of America
| | - Giulia Andrich
- Instituto de Manejo e Certificação Florestal e Agrícola (IMAFLORA), Piracicaba, Brazil
| | - Bernardo Caldas
- Alliance of Biodiversity International and International Center for Tropical Agriculture (CIAT), Kasarani, Nairobi
| | - Silvia Castaño
- Alliance of Biodiversity International and International Center for Tropical Agriculture (CIAT), Kasarani, Nairobi
| | - Nathanael de Campos
- Instituto de Manejo e Certificação Florestal e Agrícola (IMAFLORA), Piracicaba, Brazil
| | - John Dilger
- Spatial Informatics Group, LLC, Pleasanton, California, United States of America
| | - Vinicius Guidotti
- Instituto de Manejo e Certificação Florestal e Agrícola (IMAFLORA), Piracicaba, Brazil
| | - Iara Jaques
- Spatial Informatics Group, LLC, Pleasanton, California, United States of America
| | - Ian M. McCullough
- Spatial Informatics Group, LLC, Pleasanton, California, United States of America
| | | | - Luis Molina
- Alliance of Biodiversity International and International Center for Tropical Agriculture (CIAT), Kasarani, Nairobi
| | - Dawn M. Nekorchuk
- Spatial Informatics Group, LLC, Pleasanton, California, United States of America
| | - Tom Newberry
- University of Salford, Salford, Manchester, United Kingdom
| | | | - Jorge Perez
- Alliance of Biodiversity International and International Center for Tropical Agriculture (CIAT), Kasarani, Nairobi
| | | | - Ovidio Rivera
- Alliance of Biodiversity International and International Center for Tropical Agriculture (CIAT), Kasarani, Nairobi
| | - Beatriz Rodriguez
- Alliance of Biodiversity International and International Center for Tropical Agriculture (CIAT), Kasarani, Nairobi
| | - Naiara Sales
- University of Salford, Salford, Manchester, United Kingdom
| | - Jhon Tello
- Alliance of Biodiversity International and International Center for Tropical Agriculture (CIAT), Kasarani, Nairobi
| | - Crystal Wespestad
- Spatial Informatics Group, LLC, Pleasanton, California, United States of America
| | - Brian Zutta
- Spatial Informatics Group, LLC, Pleasanton, California, United States of America
| | - David Saah
- University of San Francisco, San Francisco, California, United States of America
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2
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De Palma‐Dow A, McCullough IM, Brentrup JA. Turning up the heat: Long‐term water quality responses to wildfires and climate change in a hypereutrophic lake. Ecosphere 2022. [DOI: 10.1002/ecs2.4271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
| | - Ian M. McCullough
- Department of Fisheries and Wildlife Michigan State University East Lansing Michigan USA
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3
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McCullough IM, Hanly PJ, King KBS, Wagner T. Freshwater corridors in the conterminous United States: A coarse‐filter approach based on lake‐stream networks. Ecosphere 2022. [DOI: 10.1002/ecs2.4326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Ian M. McCullough
- Department of Fisheries and Wildlife Michigan State University East Lansing Michigan USA
| | - Patrick J. Hanly
- Department of Fisheries and Wildlife Michigan State University East Lansing Michigan USA
| | - Katelyn B. S. King
- Department of Fisheries and Wildlife Michigan State University East Lansing Michigan USA
| | - Tyler Wagner
- U.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit Pennsylvania State University University Park Pennsylvania USA
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4
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Lofton ME, Brentrup JA, Beck WS, Zwart JA, Bhattacharya R, Brighenti LS, Burnet SH, McCullough IM, Steele BG, Carey CC, Cottingham KL, Dietze MC, Ewing HA, Weathers KC, LaDeau SL. Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density. Ecol Appl 2022; 32:e2590. [PMID: 35343013 PMCID: PMC9287081 DOI: 10.1002/eap.2590] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 08/16/2021] [Accepted: 09/16/2021] [Indexed: 06/01/2023]
Abstract
Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.
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Affiliation(s)
- Mary E. Lofton
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
| | - Jennifer A. Brentrup
- Department of Biological SciencesDartmouth CollegeHanoverNew HampshireUSA
- Present address:
Biology and Environmental Studies DepartmentSt. Olaf CollegeNorthfieldMinnesotaUSA
| | - Whitney S. Beck
- Department of Biology and Graduate Degree Program in EcologyColorado State UniversityFort CollinsColoradoUSA
- Present address:
U.S. Environmental Protection AgencyWashingtonDistrict of ColumbiaUSA
| | - Jacob A. Zwart
- U.S. Geological SurveyIntegrated Information Dissemination DivisionMiddletonWisconsinUSA
| | - Ruchi Bhattacharya
- Legacies of Agricultural Pollutants (LEAP)University of WaterlooWaterlooOntarioCanada
| | | | - Sarah H. Burnet
- Department of Fish and Wildlife ResourcesUniversity of IdahoMoscowIdahoUSA
| | - Ian M. McCullough
- Department of Fisheries and WildlifeMichigan State UniversityEast LansingMichiganUSA
| | | | - Cayelan C. Carey
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
| | | | - Michael C. Dietze
- Department of Earth and EnvironmentBoston UniversityBostonMassachusettsUSA
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5
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Soranno PA, Cheruvelil KS, Liu B, Wang Q, Tan PN, Zhou J, King KBS, McCullough IM, Stachelek J, Bartley M, Filstrup CT, Hanks EM, Lapierre JF, Lottig NR, Schliep EM, Wagner T, Webster KE. Ecological prediction at macroscales using big data: Does sampling design matter? Ecol Appl 2020; 30:e02123. [PMID: 32160362 DOI: 10.1002/eap.2123] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 12/13/2019] [Accepted: 01/06/2020] [Indexed: 06/10/2023]
Abstract
Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially extensive data set to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8-million-km2 area. We estimated model predictive performance for different subsets of the data set to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non-random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in data sets from some forms of targeted sampling may limit predictive performance, compiling existing spatially extensive data sets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.
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Affiliation(s)
- Patricia A Soranno
- Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, East Lansing, Michigan, 48824, USA
| | - Kendra Spence Cheruvelil
- Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, East Lansing, Michigan, 48824, USA
- Lyman Briggs College, Michigan State University, 919 East Shaw Lane, East Lansing, Michigan, 48825, USA
| | - Boyang Liu
- Department of Computer Science and Engineering, Michigan State University, 428 South Shaw Lane, East Lansing, Michigan, 48824, USA
| | - Qi Wang
- Department of Computer Science and Engineering, Michigan State University, 428 South Shaw Lane, East Lansing, Michigan, 48824, USA
| | - Pang-Ning Tan
- Department of Computer Science and Engineering, Michigan State University, 428 South Shaw Lane, East Lansing, Michigan, 48824, USA
| | - Jiayu Zhou
- Department of Computer Science and Engineering, Michigan State University, 428 South Shaw Lane, East Lansing, Michigan, 48824, USA
| | - Katelyn B S King
- Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, East Lansing, Michigan, 48824, USA
| | - Ian M McCullough
- Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, East Lansing, Michigan, 48824, USA
| | - Joseph Stachelek
- Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, East Lansing, Michigan, 48824, USA
| | - Meridith Bartley
- Department of Statistics, The Pennsylvania State University, 324 Thomas Building, University Park, Pennsylvania, 16802, USA
| | - Christopher T Filstrup
- Natural Resources Research Institute, University of Minnesota Duluth, 5013 Miller Trunk Highway, Duluth, Minnesota, 55811, USA
| | - Ephraim M Hanks
- Department of Statistics, The Pennsylvania State University, 324 Thomas Building, University Park, Pennsylvania, 16802, USA
| | - Jean-François Lapierre
- Sciences Biologiques, Universite de Montreal, Pavillon Marie-Victorin, CP 6128, succursale Centre-Ville, Montreal, Quebec, H3C 3J7, Canada
| | - Noah R Lottig
- Center for Limnology Trout Lake Station, University of Wisconsin Madison, Boulder Junction, Wisconsin, 54512, USA
| | - Erin M Schliep
- Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, Missouri, 65211, USA
| | - Tyler Wagner
- U.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit, Pennsylvania State University, Forest Resources Building, University Park, Pennsylvania, 16802, USA
| | - Katherine E Webster
- Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, East Lansing, Michigan, 48824, USA
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6
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Filstrup CT, King KBS, McCullough IM. Evenness effects mask richness effects on ecosystem functioning at macro-scales in lakes. Ecol Lett 2019; 22:2120-2129. [PMID: 31621180 DOI: 10.1111/ele.13407] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 09/05/2019] [Accepted: 09/23/2019] [Indexed: 11/26/2022]
Abstract
Biodiversity-ecosystem functioning (BEF) theory has largely focused on species richness, although studies have demonstrated that evenness may have stronger effects. While theory and numerous small-scale studies support positive BEF relationships, regional studies have documented negative effects of evenness on ecosystem functioning. We analysed a lake dataset spanning the continental US to evaluate whether strong evenness effects are common at broad spatial scales and if BEF relationships are similar across diverse regions and trophic levels. At the continental scale, phytoplankton evenness explained more variance in phytoplankton and zooplankton resource use efficiency (RUE; ratio of biomass to resources) than richness. For individual regions, slopes of phytoplankton evenness-RUE relationships were consistently negative and positive for phytoplankton and zooplankton RUE, respectively, and most slopes did not significantly differ among regions. Findings suggest that negative evenness effects may be more common than previously documented and are not exceptions restricted to highly disturbed systems.
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Affiliation(s)
| | - Katelyn B S King
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA
| | - Ian M McCullough
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA
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7
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McCullough IM, Cheruvelil KS, Lapierre JF, Lottig NR, Moritz MA, Stachelek J, Soranno PA. Do lakes feel the burn? Ecological consequences of increasing exposure of lakes to fire in the continental United States. Glob Chang Biol 2019; 25:2841-2854. [PMID: 31301168 DOI: 10.1111/gcb.14732] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 04/03/2019] [Accepted: 04/27/2019] [Indexed: 05/21/2023]
Abstract
Wildfires are becoming larger and more frequent across much of the United States due to anthropogenic climate change. No studies, however, have assessed fire prevalence in lake watersheds at broad spatial and temporal scales, and thus it is unknown whether wildfires threaten lakes and reservoirs (hereafter, lakes) of the United States. We show that fire activity has increased in lake watersheds across the continental United States from 1984 to 2015, particularly since 2005. Lakes have experienced the greatest fire activity in the western United States, Southern Great Plains, and Florida. Despite over 30 years of increasing fire exposure, fire effects on fresh waters have not been well studied; previous research has generally focused on streams, and most of the limited lake-fire research has been conducted in boreal landscapes. We therefore propose a conceptual model of how fire may influence the physical, chemical, and biological properties of lake ecosystems by synthesizing the best available science from terrestrial, aquatic, fire, and landscape ecology. This model also highlights emerging research priorities and provides a starting point to help land and lake managers anticipate potential effects of fire on ecosystem services provided by fresh waters and their watersheds.
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Affiliation(s)
- Ian M McCullough
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan
| | - Kendra Spence Cheruvelil
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan
- Lyman Briggs College, Michigan State University, East Lansing, Michigan
| | | | - Noah R Lottig
- Trout Lake Station, University of Wisconsin-Madison, Boulder Junction, Wisconsin
| | - Max A Moritz
- Bren School of Environmental Science & Management, University of California Santa Barbara, Santa Barbara, California
- University of California Cooperative Extension, Agriculture and Natural Resources Division, Santa Barbara, California
| | - Joseph Stachelek
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan
| | - Patricia A Soranno
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan
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8
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McCullough IM, Cheruvelil KS, Collins SM, Soranno PA. Geographic patterns of the climate sensitivity of lakes. Ecol Appl 2019; 29:e01836. [PMID: 30644621 DOI: 10.1002/eap.1836] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Revised: 10/16/2018] [Accepted: 11/13/2018] [Indexed: 05/22/2023]
Abstract
Climate change is a well-recognized threat to lake ecosystems and, although there likely exists geographic variation in the sensitivity of lakes to climate, broad-scale, long-term studies are needed to understand this variation. Further, the potential mediating role of local to regional ecological context on these responses is not well documented. In this study, we examined relationships between climate and water clarity in 365 lakes from 1981 to 2010 in two distinct regions in the northeastern and midwestern United States. We asked (1) How do climate-water-clarity relationships vary across watersheds and between two geographic regions? and (2) Do certain characteristics make some lakes more climate sensitive than others? We found strong differences in climate-water-clarity relationships both within and across the two regions. For example, in the northeastern region, water clarity was often negatively correlated with summer precipitation (median correlation = -0.32, n = 160 lakes), but was not correlated with summer average maximum temperature (median correlation = 0.09, n = 205 lakes). In the midwestern region, water clarity was not related to summer precipitation (median correlation = -0.04), but was often negatively correlated with summer average maximum temperature (median correlation = -0.18). There were few strong relationships between local and sub-regional ecological context and a lake's sensitivity to climate. For example, ecological context variables explained just 16-18% of variation in summer precipitation sensitivity, which was most related to total phosphorus, chlorophyll a, lake depth, and hydrology in both regions. Sensitivity to summer maximum temperature was even less predictable in both regions, with 4% or less of variation explained using all ecological context variables. Overall, we identified differences in the climate sensitivity of lakes across regions and found that local and sub-regional ecological context weakly influences the sensitivity of lakes to climate. Our findings suggest that local to regional drivers may combine to influence the sensitivity of lake ecosystems to climate change, and that sensitivities among lakes are highly variable within and across regions. This variability suggests that lakes are sensitive to different aspects of climate change (temperature vs. precipitation) and that responses of lakes to climate are heterogeneous and complex.
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Affiliation(s)
- Ian M McCullough
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, 48824, USA
| | - Kendra Spence Cheruvelil
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, 48824, USA
- Lyman Briggs College, Michigan State University, East Lansing, Michigan, 48824, USA
| | - Sarah M Collins
- Center for Limnology, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Patricia A Soranno
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, 48824, USA
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9
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Engel F, Farrell KJ, McCullough IM, Scordo F, Denfeld BA, Dugan HA, de Eyto E, Hanson PC, McClure RP, Nõges P, Nõges T, Ryder E, Weathers KC, Weyhenmeyer GA. A lake classification concept for a more accurate global estimate of the dissolved inorganic carbon export from terrestrial ecosystems to inland waters. Naturwissenschaften 2018; 105:25. [PMID: 29582138 PMCID: PMC5869952 DOI: 10.1007/s00114-018-1547-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 02/12/2018] [Accepted: 02/23/2018] [Indexed: 12/03/2022]
Abstract
The magnitude of lateral dissolved inorganic carbon (DIC) export from terrestrial ecosystems to inland waters strongly influences the estimate of the global terrestrial carbon dioxide (CO2) sink. At present, no reliable number of this export is available, and the few studies estimating the lateral DIC export assume that all lakes on Earth function similarly. However, lakes can function along a continuum from passive carbon transporters (passive open channels) to highly active carbon transformers with efficient in-lake CO2 production and loss. We developed and applied a conceptual model to demonstrate how the assumed function of lakes in carbon cycling can affect calculations of the global lateral DIC export from terrestrial ecosystems to inland waters. Using global data on in-lake CO2 production by mineralization as well as CO2 loss by emission, primary production, and carbonate precipitation in lakes, we estimated that the global lateral DIC export can lie within the range of \documentclass[12pt]{minimal}
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\begin{document}$$ {0.70}_{-0.31}^{+0.27} $$\end{document}0.70−0.31+0.27 to \documentclass[12pt]{minimal}
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\begin{document}$$ {1.52}_{-0.90}^{+1.09} $$\end{document}1.52−0.90+1.09 Pg C yr−1 depending on the assumed function of lakes. Thus, the considered lake function has a large effect on the calculated lateral DIC export from terrestrial ecosystems to inland waters. We conclude that more robust estimates of CO2 sinks and sources will require the classification of lakes into their predominant function. This functional lake classification concept becomes particularly important for the estimation of future CO2 sinks and sources, since in-lake carbon transformation is predicted to be altered with climate change.
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Affiliation(s)
- Fabian Engel
- Department of Ecology and Genetics/Limnology, Uppsala University, Norbyvägen 18D, 752 36, Uppsala, Sweden.
| | - Kaitlin J Farrell
- Odum School of Ecology, University of Georgia, Athens, GA, 30602, USA
- Department of Biological Sciences, Virginia Tech, Derring Hall, Blacksburg, VA, 24061, USA
| | - Ian M McCullough
- Bren School of Environmental Science and Management, University of California, Santa Barbara, CA, 93106, USA
| | - Facundo Scordo
- Instituto Argentino de Oceanografía (UNS-CONICET), Florida 8000 (Camino La Carrindanga km 7,5), B8000BFW, Bahía Blanca, Buenos Aires, Argentina
| | - Blaize A Denfeld
- Department of Ecology and Environmental Sciences, Umeå University, Linnaeus väg 6, 901 87, Umeå, Sweden
| | - Hilary A Dugan
- Center for Limnology, University of Wisconsin-Madison, 680 N. Park St., Madison, WI, USA
| | | | - Paul C Hanson
- Center for Limnology, University of Wisconsin-Madison, 680 N. Park St., Madison, WI, USA
| | - Ryan P McClure
- Department of Biological Sciences, Virginia Tech, Derring Hall, Blacksburg, VA, 24061, USA
| | - Peeter Nõges
- Centre for Limnology, Estonian University of Life Sciences, Kreutzwaldi 1, 51014, Tartu, Estonia
| | - Tiina Nõges
- Centre for Limnology, Estonian University of Life Sciences, Kreutzwaldi 1, 51014, Tartu, Estonia
| | - Elizabeth Ryder
- Centre for Freshwater and Environmental Studies, Dundalk Institute of Technology, Dundalk, Co Louth, Ireland
| | | | - Gesa A Weyhenmeyer
- Department of Ecology and Genetics/Limnology, Uppsala University, Norbyvägen 18D, 752 36, Uppsala, Sweden
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10
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Hannah L, Flint L, Syphard AD, Moritz MA, Buckley LB, McCullough IM. Place and process in conservation planning for climate change: a reply to Keppel and Wardell-Johnson. Trends Ecol Evol 2015; 30:234-5. [DOI: 10.1016/j.tree.2015.03.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Revised: 03/06/2015] [Accepted: 03/10/2015] [Indexed: 11/15/2022]
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11
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Hannah L, Flint L, Syphard AD, Moritz MA, Buckley LB, McCullough IM. Fine-grain modeling of species’ response to climate change: holdouts, stepping-stones, and microrefugia. Trends Ecol Evol 2014; 29:390-7. [DOI: 10.1016/j.tree.2014.04.006] [Citation(s) in RCA: 223] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2013] [Revised: 04/15/2014] [Accepted: 04/24/2014] [Indexed: 11/26/2022]
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