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153 results on '"Compson, Zacchaeus G."'

6. Decreased P Cycling Rate and Increased P-Use Efficiency after Phyllostachys edulis (Carrière) J. Houz. Expansion into Adjacent Secondary Evergreen Broadleaved Forest.

Author

Song, Shuwang, Wang, Lin, Compson, Zacchaeus G., Xie, Tingting, Liao, Chuyin, Huang, Dongmei, Liu, Jun, Yang, Qingpei, and Song, Qingni

Subjects
FOREST succession, BIOGEOCHEMICAL cycles, PLANT litter, MOUNTAIN forests, PHYLLOSTACHYS
Abstract

(1) Background: Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) expansion has seriously altered the species composition and structure of adjacent forest ecosystems in subtropical regions. However, the shift in phosphorus (P) biogeochemical cycling has yet to be assessed, which is a critical gap considering the great variation in ecophysiological properties between invasive bamboo and the displaced native tree species. (2) Methods: We investigated and compared expansion-induced changes in P pools (plant, litter, and soil) and P fluxes (plant uptake and litterfall return) using paired sampling of the bamboo-dominated forest (BDF) and secondary evergreen broadleaved forest (EBF) at Jiangxi province's Dagang Mountain National Forest Ecological Station. (3) Results: Both the P storage of the plants and litter were significantly greater by 31.8% and 68.2% in the BDF than in the EBF, respectively. The soil total P and available P storage were 28.9% and 40.4% lower, respectively, in the BDF than in the EBF. Plant P uptake was 15.6% higher in the BDF than in the EBF, and the annual litter P return was 26.1% lower in the BDF than in the EBF due to higher P resorption efficiency for moso bamboo compared with evergreen broadleaved tree species. The ecosystem P cycling rate was reduced by 36.1% in the BDF compared with the EBF. (4) Conclusions: Moso bamboo expansion slowed the broadleaved forest ecosystem's P cycle rate, likely because moso bamboo has higher P-use efficiency, reserving more P in its tissues rather than returning it to the soil. The results from this study elucidate an understudied element cycle in the context of forest succession, demonstrating the ecosystem consequences related to bamboo invasion. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Dryland Rivers and Streams

Author

Compson, Zacchaeus G., primary, Monk, Wendy A., additional, Sarremejane, Romain, additional, DelVecchia, Amanda G., additional, Burrows, Ryan M., additional, Gao, Shang, additional, Ruddell, Benjamin L., additional, Hong, Yang, additional, and Allen, Daniel C., additional

Published
2022
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11. Combined benthic and stream edge sampling better represent macroinvertebrate assemblages than benthic sampling alone along an aridity gradient

Author

Gill, Brian A., primary, Allen, Daniel C., additional, Mims, Meryl C., additional, Neeson, Thomas M., additional, Ruhi, Albert, additional, Atkinson, Carla L., additional, Shogren, Arial J., additional, Apgar, Travis M., additional, Compson, Zacchaeus G., additional, Cook, Stephen, additional, Trumbo, Daryl R., additional, Busch, Michelle H., additional, Hollien, Kelsey D., additional, Leathers, Kyle, additional, Malish, Megan C., additional, O'Malley, Grace L., additional, Silknetter, Samuel, additional, Smith, Chelsea R., additional, Dunleavy, Howard, additional, and Bogan, Michael T., additional

Published
2024
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12. Closely Related Tree Species Differentially Influence the Transfer of Carbon and Nitrogen from Leaf Litter Up the Aquatic Food Web

Author

Compson, Zacchaeus G, Hungate, Bruce A, Koch, George W, Hart, Steve C, Maestas, Jesse M, Adams, Kenneth J, Whitham, Thomas G, and Marks, Jane C

Subjects
stable isotope tracers, functional food webs, trophic structure, nutrient cycling, decomposition, cottonwood, aquatic insect community, Environmental Sciences, Biological Sciences, Ecology
Abstract

Decomposing leaf litter in streams provides habitat and nutrition for aquatic insects. Despite large differences in the nutritional qualities of litter among different plant species, their effects on aquatic insects are often difficult to detect. We evaluated how leaf litter of two dominant riparian species (Populus fremontii and P. angustifolia) influenced carbon and nitrogen assimilation by aquatic insect communities, quantifying assimilation rates using stable isotope tracers (13C, 15N). We tested the hypothesis that element fluxes from litter of different plant species better define aquatic insect community structure than insect relative abundances, which often fail. We found that (1) functional communities (defined by fluxes of carbon and nitrogen from leaf litter to insects) were different between leaf litter species, whereas more traditional insect communities (defined by relativized taxa abundances) were not different between leaf litter species, (2) insects assimilated N, but not C, at a higher rate from P. angustifolia litter compared to P. fremontii, even though P. angustifolia decomposes more slowly, and (3) the C:N ratio of material assimilated by aquatic insects was lower for P. angustifolia compared to P. fremontii, indicating higher nutritional quality, despite similar initial litter C:N ratios. These findings provide new evidence for the effects of terrestrial plant species on aquatic ecosystems via their direct influence on the transfer of elements up the food web. We demonstrate how isotopically labeled leaf litter can be used to assess the functioning of insect communities, uncovering patterns undetected by traditional approaches and improving our understanding of the association between food web structure and element cycling.

Published
2015

13. eDNA and Bioassessment of Rivers

Author

McGee, Katie M., primary, Robinson, Chloe, additional, Porter, Teresita M., additional, Compson, Zacchaeus G., additional, Hajibabaei, Mehrdad, additional, and Baird, Donald J., additional

Published
2021
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22. Linking DNA Metabarcoding and Text Mining to Create Network-Based Biomonitoring Tools: A Case Study on Boreal Wetland Macroinvertebrate Communities

Author

Compson, Zacchaeus G., primary, Monk, Wendy A., additional, Curry, Colin J., additional, Gravel, Dominique, additional, Bush, Alex, additional, Baker, Christopher J.O., additional, Al Manir, Mohammad Sadnan, additional, Riazanov, Alexandre, additional, Hajibabaei, Mehrdad, additional, Shokralla, Shadi, additional, Gibson, Joel F., additional, Stefani, Sonja, additional, Wright, Michael T.G., additional, and Baird, Donald J., additional

Published
2018
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23. Bamboo expansion promotes radial growth of surviving trees in a broadleaf forest.

Author

Chao Gong, Xiaoxia Zeng, Xianglong Zhu, Wenhui Huang, Compson, Zacchaeus G., Zewen Ren, Huan Ran, Qingni Song, Qingpei Yang, Dongmei Huang, and Jun Liu

Subjects
BROADLEAF forests, TREE growth, FOREST management, BAMBOO, PLANT anatomy, PHYLLOSTACHYS
Abstract

Introduction: Considerable evidence indicates that some trees are more vulnerable than others during bamboo (Phyllostachys edulis) expansion, which can affect plant community structure and alter the environment, but there has been insufficient research on the growth status of surviving individuals in colonized forests. Methods: In this study, we compared the annual growth increment, growth rate, and onset, cessation, and duration of radial growth of Alniphyllum fortunei, Machilus pauhoi, and Castanopsis eyrei in a bamboo-expended broadleaf forest (BEBF) and a bamboo-absent broadleaf forest (BABF) using high-resolution point dendrometers. Results: We found that the annual radial growth of A. fortunei, M. pauhoi, and C. eyrei was 22.5%, 172.2%, and 59.3% greater in BEBF than in BABF, respectively. The growth rates of M. pauhoi and C. eyrei in BEBF were significantly higher than in BABF by13.9 μm/d and 19.6 μm/d, whereas A. fortunei decreased significantly by 7.9 μm/d from BABF to BEBF. The onset and cessation of broad-leaf tree growth was later, and the growth duration was longer in BEBF compared to BABF. For example, A. fortunei and M. pauhoi in BEBF had more than one month longer growth duration than in BABF. Additionally, the nighttime growth rates of some surviving broad-leaf trees in BEBF was significantly higher than that in BABF. Discussion: These results suggest that the surviving trees have plasticity and can adapt to atmospheric changes and competitive relationships after expansion of bamboo in one of two ways: by increasing their growth rates or by modifying onset and cessation of growth to extend the growth duration of trees or avoid the period of intense competition with bamboo, thereby growing better. Our research reveals for the first time how the growth of surviving broad-leaf trees adjusts to bamboo expansion. These results provide insights into how biological expansions impact primary production and have implications for forest management in the Anthropocene. [ABSTRACT FROM AUTHOR]

Published
2023
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36. Key questions for next-generation of biomonitoring

Author

Makiola, Andreas, Compson, Zacchaeus G., Baird, Donald J, Barnes, Matthew, Boerlijst, Sam P, Bouchez, Agnes, Brennan, Georgina, Bush, Alex, Canard, Elsa, Cordier, Tristan, Creer, Simon, Curry, Allen, Dumbrell, Alex J, Gravel, Dominique, Hajibabaei, Mehrdad, van Der Hoorn, Berry, Jarne, Philippe, Jones, J.Iwan, Karimi, Battle, Keck, François, Kelly, Martyn, Knot, Ineke, Krol, Louie, Massol, François, Monk, Wendy A., Murphy, John, Pawlowski, Jan, Poisot, Timothée, M. Porter, Teresita, C. Randall, Kate, Ransome, Emma, Ravigné, Virginie, Raybould, Alan, Robin, Stephane, Schrama, Maarten, Schatz, Bertrand, Tamaddoni- Nezhad, Alireza, Trimbos, Krijn B., Vacher, Corinne, Vasselon, Valentin, Wood, Susie, Woodward, Guy, Bohan, David, Agroécologie [Dijon], Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Environment and Climate Change Canada @ Canadian Rivers Institute, Department of Biology, University of New Brunswick, NB, Canada, Centre for Environmental Genomics Applications, St. John’s, NL, Canada, Department of Natural Resources Management, Texas Tech University, Lubbock, TX, United States, Naturalis Biodiversity Center [Leiden], Institute of Environmental Sciences, Leiden University, Leiden, Netherlands, Centre Alpin de Recherche sur les Réseaux Trophiques et Ecosystèmes Limniques (CARRTEL), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), School of Natural Sciences, Bangor University, Bangor, United Kingdom, Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université de Genève = University of Geneva (UNIGE), Canadian Rivers Institute, Biology, Forestry and Environmental Management, University of New Brunswick, Fredericton, NB, Canada, School of Biological Sciences, University of Essex, Colchester, United Kingdom, Département de Médecine Nucléaire et Radiobiologie - Université de Sherbrooke - Canada, Université de Sherbrooke (UdeS), Centre for Biodiversity Genomics and Department of Integrative Biology, University of Guelph, Guelph, ON, Canada, Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UPVM)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, School of Biological and Chemical Sciences, Queen Mary University of London, London, United Kingdom, Bowburn Consultancy, Durham, United Kingdom, Institute for Biodiversity and Ecosystem Dynamics, Department of Evolutionary and Population Biology, University of Amsterdam, Amsterdam The Netherlands, Évolution, Écologie et Paléontologie (Evo-Eco-Paleo) - UMR 8198 (Evo-Eco-Paléo (EEP)), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, NB, Canada, Département de sciences biologiques, Université de Montréal, Montreal, QC, Canada, Great Lakes Forestry Centre, Canadian Forest Service, Natural Resources Canada, Sault Ste. Marie, ON, Canada, Imperial College London, Peuplements végétaux et bioagresseurs en milieu tropical (UMR PVBMT), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Université de La Réunion (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Syngenta Crop Protection AG, Basel, Switzerland, School of Social and Political Science, The University of Edinburgh, Edinburgh, United Kingdom, The Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Global Academy of Agriculture and Food Security, Edinburgh, United Kingdom, Mathématiques et Informatique Appliquées (MIA Paris-Saclay), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Surrey (UNIS), Biodiversité, Gènes & Communautés (BioGeCo), Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agence Française pour la Biodiversité, Pôle R&D ECLA, Évian-les-Bains, France, Cawthron Institute, Nelson, New Zealand, Naturalis Biodiversity Center, Leiden, Netherlands, CARRTEL, USMB, INRA, Thonon-les-Bains, France, Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Department of Genetics and Evolution, University of Geneva, Science III, Geneva, Switzerland, Université Paul-Valéry - Montpellier 3 (UPVM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Évolution, Écologie et Paléontologie (Evo-Eco-Paleo) - UMR 8198 (Evo-Eco-Paléo), Department of Life Sciences, Silwood Park Campus, Imperial College London, London, United Kingdom, Mathématiques et Informatique Appliquées (MIA-Paris), and BIOGECO, INRA, Univ. Bordeaux, Pessac, France

Subjects
metabarcoding, ecological networkseDNA, ecological networks, eDNA, artificial intelligence, biodiversity assessment, [SHS]Humanities and Social Sciences
Abstract

International audience; Classical biomonitoring techniques have focused primarily on measures linked tovarious biodiversity metrics and indicator species. Next-generation biomonitoring (NGB)describes a suite of tools and approaches that allow the examination of a broaderspectrum of organizational levels—from genes to entire ecosystems. Here, we frame10 key questions that we envisage will drive the field of NGB over the next decade. Whilenot exhaustive, this list covers most of the key challenges facing NGB, and provides thebasis of the next steps for research and implementation in this field. These questionshave been grouped into current- and outlook-related categories, corresponding to theorganization of this paper.

Published
2020

38. Key questions for next-generation biomonitoring

Author

Makiola, Andreas, Compson, Zacchaeus G., Baird, Donald J., Barnes, Matthew A., Boerlijst, Sam P., Bouchez, Agnès, Brennan, Georgina, Bush, Alex, Canard, Elsa, Cordier, Tristan, Creer, Simon, Curry, Allen R., David, Patrice, Dumbrell, Alex J., Gravel, Dominique, Hajibabaei, Mehrdad, Hayden, Brian, Van der Hoorn, Berry, Jarne, Philippe, Jones, J. Iwan, Karimi, Battle, Keck, François, Kelly, Martyn, Knot, Ineke E., Krol, Louie, Massol, François, Monk, Wendy A., Murphy, John, Pawlowski, Jan, Poisot, Thimotée, Porter, Teresita M., Randall, Kate C., Ransome, Emma, Ravigné, Virginie, et al., Makiola, Andreas, Compson, Zacchaeus G., Baird, Donald J., Barnes, Matthew A., Boerlijst, Sam P., Bouchez, Agnès, Brennan, Georgina, Bush, Alex, Canard, Elsa, Cordier, Tristan, Creer, Simon, Curry, Allen R., David, Patrice, Dumbrell, Alex J., Gravel, Dominique, Hajibabaei, Mehrdad, Hayden, Brian, Van der Hoorn, Berry, Jarne, Philippe, Jones, J. Iwan, Karimi, Battle, Keck, François, Kelly, Martyn, Knot, Ineke E., Krol, Louie, Massol, François, Monk, Wendy A., Murphy, John, Pawlowski, Jan, Poisot, Thimotée, Porter, Teresita M., Randall, Kate C., Ransome, Emma, Ravigné, Virginie, and et al.

Abstract

Classical biomonitoring techniques have focused primarily on measures linked to various biodiversity metrics and indicator species. Next-generation biomonitoring (NGB) describes a suite of tools and approaches that allow the examination of a broader spectrum of organizational levels—from genes to entire ecosystems. Here, we frame 10 key questions that we envisage will drive the field of NGB over the next decade. While not exhaustive, this list covers most of the key challenges facing NGB, and provides the basis of the next steps for research and implementation in this field. These questions have been grouped into current- and outlook-related categories, corresponding to the organization of this paper.

Published
2020

45. Key Questions for Next-Generation Biomonitoring

Author

Makiola, Andreas, primary, Compson, Zacchaeus G., additional, Baird, Donald J., additional, Barnes, Matthew A., additional, Boerlijst, Sam P., additional, Bouchez, Agnès, additional, Brennan, Georgina, additional, Bush, Alex, additional, Canard, Elsa, additional, Cordier, Tristan, additional, Creer, Simon, additional, Curry, R. Allen, additional, David, Patrice, additional, Dumbrell, Alex J., additional, Gravel, Dominique, additional, Hajibabaei, Mehrdad, additional, Hayden, Brian, additional, van der Hoorn, Berry, additional, Jarne, Philippe, additional, Jones, J. Iwan, additional, Karimi, Battle, additional, Keck, Francois, additional, Kelly, Martyn, additional, Knot, Ineke E., additional, Krol, Louie, additional, Massol, Francois, additional, Monk, Wendy A., additional, Murphy, John, additional, Pawlowski, Jan, additional, Poisot, Timothée, additional, Porter, Teresita M., additional, Randall, Kate C., additional, Ransome, Emma, additional, Ravigné, Virginie, additional, Raybould, Alan, additional, Robin, Stephane, additional, Schrama, Maarten, additional, Schatz, Bertrand, additional, Tamaddoni-Nezhad, Alireza, additional, Trimbos, Krijn B., additional, Vacher, Corinne, additional, Vasselon, Valentin, additional, Wood, Susie, additional, Woodward, Guy, additional, and Bohan, David A., additional

Published
2020
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46. Studying Ecosystems With DNA Metabarcoding:Lessons From Biomonitoring of Aquatic Macroinvertebrates

Author

Bush, Alex, Compson, Zacchaeus G., Monk, Wendy A., Porter, Teresita M., Steeves, Royce, Emilson, Erik, Gagne, Nellie, Hajibabaei, Mehrdad, Roy, Mélanie, Baird, Donald J., Bush, Alex, Compson, Zacchaeus G., Monk, Wendy A., Porter, Teresita M., Steeves, Royce, Emilson, Erik, Gagne, Nellie, Hajibabaei, Mehrdad, Roy, Mélanie, and Baird, Donald J.

Abstract

An ongoing challenge for ecological studies has been the collection of data with high precision and accuracy at a suitable scale to detect and manage critical global change processes. A major hurdle has been the time-consuming and challenging process of sorting and identification of organisms, but the rapid development of DNA metabarcoding as a biodiversity observation tool provides a potential solution. As high-throughput sequencing becomes more rapid and cost-effective, a “big data” revolution is anticipated, based on higher and more accurate taxonomic resolution, more efficient detection, and greater sample processing capacity. These advances have the potential to amplify the power of ecological studies to detect change and diagnose its cause, through a methodology termed “Biomonitoring 2.0.” Despite its promise, the unfamiliar terminology and pace of development in high-throughput sequencing technologies has contributed to a growing concern that an unproven technology is supplanting tried and tested approaches, lowering trust among potential users, and reducing uptake by ecologists and environmental management practitioners. While it is reasonable to exercise caution, we argue that any criticism of new methods must also acknowledge the shortcomings and lower capacity of current observation methods. Broader understanding of the statistical properties of metabarcoding data will help ecologists to design, test and review evidence for new hypotheses. We highlight the uncertainties and challenges underlying DNA metabarcoding and traditional methods for compositional analysis, specifically comparing the interpretation of otherwise identical bulk-community samples of freshwater benthic invertebrates. We explore how taxonomic resolution, sample similarity, taxon misidentification, and taxon abundance affect the statistical properties of these samples, but recognize these issues are relevant to applications across all ecosystem types. In conclusion, metabarcoding has the

Published
2019

47. Towards a general framework for the assessment of interactive effects of multiple stressors on aquatic ecosystems:Results from the Making Aquatic Ecosystems Great Again (MAEGA) workshop

Author

Van den Brink, Paul J., Bracewell, Sally A., Bush, Alex, Chariton, Anthony, Choung, Catherine B., Compson, Zacchaeus G., Dafforn, Katherine A., Korbel, Kathryn, Lapen, David A., Mayer-Pinto, Mariana, Monk, Wendy A., O'Brien, Allyson L., Rideout, Natalie K., Schäfer, Ralf B., Sumon, Kizar A., Verdonschot, Ralf C.M., Baird, Donald J., Van den Brink, Paul J., Bracewell, Sally A., Bush, Alex, Chariton, Anthony, Choung, Catherine B., Compson, Zacchaeus G., Dafforn, Katherine A., Korbel, Kathryn, Lapen, David A., Mayer-Pinto, Mariana, Monk, Wendy A., O'Brien, Allyson L., Rideout, Natalie K., Schäfer, Ralf B., Sumon, Kizar A., Verdonschot, Ralf C.M., and Baird, Donald J.

Abstract

A workshop was held in Wageningen, The Netherlands, in September 2017 to collate data and literature on three aquatic ecosystem types (agricultural drainage ditches, urban floodplains, and urban estuaries), and develop a general framework for the assessment of multiple stressors on the structure and functioning of these systems. An assessment framework considering multiple stressors is crucial for our understanding of ecosystem responses within a multiply stressed environment, and to inform appropriate environmental management strategies. The framework consists of two components: (i) problem identification and (ii) impact assessment. Both assessments together proceed through the following steps: 1) ecosystem selection; 2) identification of stressors and quantification of their intensity; 3) identification of receptors or sensitive groups for each stressor; 4) identification of stressor-response relationships and their potential interactions; 5) construction of an ecological model that includes relevant functional groups and endpoints; 6) prediction of impacts of multiple stressors, 7) confirmation of these predictions with experimental and monitoring data, and 8) potential adjustment of the ecological model. Steps 7 and 8 allow the assessment to be adaptive and can be repeated until a satisfactory match between model predictions and experimental and monitoring data has been obtained. This paper is the preface of the MAEGA (Making Aquatic Ecosystems Great Again) special section that includes three associated papers which are also published in this volume, which present applications of the framework for each of the three aquatic systems.

Published
2019

48. Towards a general framework for the assessment of interactive effects of multiple stressors on aquatic ecosystems : Results from the Making Aquatic Ecosystems Great Again (MAEGA) workshop

Author

Van den Brink, Paul J., Bracewell, Sally A., Bush, Alex, Chariton, Anthony, Choung, Catherine B., Compson, Zacchaeus G., Dafforn, Katherine A., Korbel, Kathryn, Lapen, David R., Mayer-Pinto, Mariana, Monk, Wendy A., O'Brien, Allyson L., Rideout, Natalie K., Schäfer, Ralf B., Sumon, Kizar A., Verdonschot, Ralf C.M., Baird, Donald J., Van den Brink, Paul J., Bracewell, Sally A., Bush, Alex, Chariton, Anthony, Choung, Catherine B., Compson, Zacchaeus G., Dafforn, Katherine A., Korbel, Kathryn, Lapen, David R., Mayer-Pinto, Mariana, Monk, Wendy A., O'Brien, Allyson L., Rideout, Natalie K., Schäfer, Ralf B., Sumon, Kizar A., Verdonschot, Ralf C.M., and Baird, Donald J.

Abstract

A workshop was held in Wageningen, The Netherlands, in September 2017 to collate data and literature on three aquatic ecosystem types (agricultural drainage ditches, urban floodplains, and urban estuaries), and develop a general framework for the assessment of multiple stressors on the structure and functioning of these systems. An assessment framework considering multiple stressors is crucial for our understanding of ecosystem responses within a multiply stressed environment, and to inform appropriate environmental management strategies. The framework consists of two components: (i) problem identification and (ii) impact assessment. Both assessments together proceed through the following steps: 1) ecosystem selection; 2) identification of stressors and quantification of their intensity; 3) identification of receptors or sensitive groups for each stressor; 4) identification of stressor-response relationships and their potential interactions; 5) construction of an ecological model that includes relevant functional groups and endpoints; 6) prediction of impacts of multiple stressors, 7) confirmation of these predictions with experimental and monitoring data, and 8) potential adjustment of the ecological model. Steps 7 and 8 allow the assessment to be adaptive and can be repeated until a satisfactory match between model predictions and experimental and monitoring data has been obtained. This paper is the preface of the MAEGA (Making Aquatic Ecosystems Great Again) special section that includes three associated papers which are also published in this volume, which present applications of the framework for each of the three aquatic systems.

Published
2019

49. Network-Based Biomonitoring: Exploring Freshwater Food Webs With Stable Isotope Analysis and DNA Metabarcoding

Author

Compson, Zacchaeus G., primary, Monk, Wendy A., additional, Hayden, Brian, additional, Bush, Alex, additional, O'Malley, Zoë, additional, Hajibabaei, Mehrdad, additional, Porter, Teresita M., additional, Wright, Michael T. G., additional, Baker, Christopher J. O., additional, Al Manir, Mohammad Sadnan, additional, Curry, R. Allen, additional, and Baird, Donald J., additional

Published
2019
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