1
|
Tumolo BB, Collins SM, Guan Y, Krist AC. Resource quantity and quality differentially control stream invertebrate biodiversity across spatial scales. Ecol Lett 2023; 26:2077-2086. [PMID: 37787116 DOI: 10.1111/ele.14317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/03/2023] [Accepted: 09/15/2023] [Indexed: 10/04/2023]
Abstract
Resource quantity controls biodiversity across spatial scales; however, the importance of resource quality to cross-scale patterns in species richness has seldom been explored. We evaluated the relationship between stream basal resource quantity (periphyton chlorophyll a) and invertebrate richness and compared this to the relationship of resource quality (periphyton stoichiometry) and richness at local and regional scales across 27 North American streams. At the local scale, invertebrate richness peaked at intermediate levels of chlorophyll a, but had a shallow negative relationship with periphyton C:P and N:P. However, at the regional scale, richness had a strong negative relationship with chlorophyll a and periphyton C:P and N:P. The divergent relationships of periphyton chlorophyll a and stoichiometry with invertebrate richness suggest that autochthonous resource quantity limits diversity more than quality, consistent with patterns of eutrophication. Collectively, we provide evidence that patterns in resource quantity and quality play important, yet differing roles in shaping freshwater biodiversity across spatial scale.
Collapse
Affiliation(s)
- Benjamin B Tumolo
- Department of Zoology and Physiology, University of Wyoming, Laramie, Wyoming, USA
| | - Sarah M Collins
- Department of Zoology and Physiology, University of Wyoming, Laramie, Wyoming, USA
- Program in Ecology and Evolution, University of Wyoming, Laramie, Wyoming, USA
| | - Yawen Guan
- Department of Statistics, Colorado State University, Fort Collins, Colorado, USA
| | - Amy C Krist
- Department of Zoology and Physiology, University of Wyoming, Laramie, Wyoming, USA
- Program in Ecology and Evolution, University of Wyoming, Laramie, Wyoming, USA
| |
Collapse
|
2
|
SanClements MD, Record S, Rose KC, Donnelly A, Chong SS, Duffy K, Hallmark A, Heffernan JB, Liu J, Mitchell JJ, Moore DJP, Naithani K, O'Reilly CM, Sokol ER, Stack Whitney K, Weintraub‐Leff SR, Yang D. People, infrastructure, and data: A pathway to an inclusive and diverse ecological network of networks. Ecosphere 2022. [DOI: 10.1002/ecs2.4262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
| | - Sydne Record
- Department of Wildlife, Fisheries, and Conservation Biology University of Maine Orono Maine USA
| | - Kevin C. Rose
- Department of Biological Sciences Rensselaer Polytechnic Institute Troy New York USA
| | - Alison Donnelly
- Department of Geography University of Wisconsin‐Milwaukee Milwaukee Wisconsin USA
| | - Steven S. Chong
- University of California Berkeley Library University of California Berkeley California USA
| | - Katharyn Duffy
- School of Informatics, Computing and Cyber Systems Northern Arizona University Flagstaff Arizona USA
| | - Alesia Hallmark
- National Ecological Observatory Network Battelle Boulder Colorado USA
| | - James B. Heffernan
- Nicholas School of the Environment Duke University Durham North Carolina USA
| | - Jianguo Liu
- Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife Michigan State University East Lansing Michigan USA
| | | | - David J. P. Moore
- School of Natural Resources and the Environment University of Arizona Tucson Arizona USA
| | - Kusum Naithani
- Department of Biological Sciences University of Arkansas Fayetteville Arkansas USA
| | - Catherine M. O'Reilly
- Department of Geography, Geology, and the Environment Illinois State University Normal Illinois USA
| | - Eric R. Sokol
- National Ecological Observatory Network Battelle Boulder Colorado USA
| | - Kaitlin Stack Whitney
- Science, Technology & Society Department Rochester Institute of Technology Rochester New York USA
| | | | - Di Yang
- Wyoming Geographic Information Science Center University of Wyoming Laramie Wyoming USA
| |
Collapse
|
3
|
Fu G, Xiao N, Qi Y, Wang W, Li J, Zhao C, Cao M, Xia J. Fusing multidimensional hierarchical information into finer spatial landscape metrics. Ecol Evol 2021; 11:15225-15236. [PMID: 34765173 PMCID: PMC8571621 DOI: 10.1002/ece3.8206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 09/10/2021] [Accepted: 09/21/2021] [Indexed: 12/03/2022] Open
Abstract
One of the core issues of ecology is to understand the effects of landscape patterns on ecological processes. For this, we need to accurately capture changes in the fine landscape structures to avoid losing information about spatial heterogeneity. The landscape pattern indicators (LPIs) can characterize the spatial structures and give some information about landscape patterns. However, researches on LPIs had mainly focused on the horizontal structure of landscape patterns, while few studies addressed vertical relationships between the levels of hierarchical landscape structures. Thus, the ignorance of the vertical hierarchical relationships may cause serious biases and reduce LPIs' representational ability and accuracy. The hierarchy theory about the landscape pattern structures could notably reduce the loss of hierarchical information, and the information entropy could quantitatively describe the vertical status of landscape units. Therefore, we established a new multidimensional fusion method of LPIs based on hierarchy theory and information entropy. Here, we created a general fusion formula for commonly used simple LPIs based on two-grade land use data (whose land use classification system contains two grades/levels) and derived 3 fusion landscape pattern indicators (FLIs) with a case study. The results show that the information about fine spatial structure is captured by the fusion method. The regions with the most differences between the FLIs and the traditional LPIs are those with the largest vertical structure such as the ecological ecotones, where vertical structure was ignored before. The FLIs have a finer spatial representational ability and accuracy, not only retaining the main trend information of first-grade land use data, but also containing the internal detail information of second-grade land use data. Capturing finer spatial information of landscape patterns should encourage the application of fusion method, which should be suitable for more LPIs or more dimensional data. And the increased accuracy of FLIs will improve ecological models that rely on finer spatial information.
Collapse
Affiliation(s)
- Gang Fu
- College of Water SciencesBeijing Normal UniversityBeijingChina
- State Environmental Protection Key Laboratory of Regional Eco‐process and Function AssessmentChinese Research Academy of Environmental SciencesBeijingChina
| | - Nengwen Xiao
- State Environmental Protection Key Laboratory of Regional Eco‐process and Function AssessmentChinese Research Academy of Environmental SciencesBeijingChina
- State Key Laboratory of Environmental Criteria and Risk AssessmentChinese Research Academy of Environmental SciencesBeijingChina
| | - Yue Qi
- State Environmental Protection Key Laboratory of Regional Eco‐process and Function AssessmentChinese Research Academy of Environmental SciencesBeijingChina
- State Key Laboratory of Environmental Criteria and Risk AssessmentChinese Research Academy of Environmental SciencesBeijingChina
| | - Wei Wang
- State Environmental Protection Key Laboratory of Regional Eco‐process and Function AssessmentChinese Research Academy of Environmental SciencesBeijingChina
- State Key Laboratory of Environmental Criteria and Risk AssessmentChinese Research Academy of Environmental SciencesBeijingChina
| | - Junsheng Li
- State Environmental Protection Key Laboratory of Regional Eco‐process and Function AssessmentChinese Research Academy of Environmental SciencesBeijingChina
- State Key Laboratory of Environmental Criteria and Risk AssessmentChinese Research Academy of Environmental SciencesBeijingChina
| | - Caiyun Zhao
- State Environmental Protection Key Laboratory of Regional Eco‐process and Function AssessmentChinese Research Academy of Environmental SciencesBeijingChina
- State Key Laboratory of Environmental Criteria and Risk AssessmentChinese Research Academy of Environmental SciencesBeijingChina
| | - Ming Cao
- State Environmental Protection Key Laboratory of Regional Eco‐process and Function AssessmentChinese Research Academy of Environmental SciencesBeijingChina
- State Key Laboratory of Environmental Criteria and Risk AssessmentChinese Research Academy of Environmental SciencesBeijingChina
| | - Juyi Xia
- State Key Laboratory of Environmental Criteria and Risk AssessmentChinese Research Academy of Environmental SciencesBeijingChina
- School of Environment and Natural ResourcesRenmin University of ChinaBeijingChina
| |
Collapse
|
4
|
St Mary C, Powell THQ, Kominoski JS, Weinert E. Rescaling Biology: Increasing Integration Across Biological Scales and Subdisciplines to Enhance Understanding and Prediction. Integr Comp Biol 2021; 61:2031-2037. [PMID: 34472603 DOI: 10.1093/icb/icab191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The organization of the living world covers a vast range of spatiotemporal scales, from molecules to the biosphere, seconds to centuries. Biologists working within specialized subdisciplines tend to focus on different ranges of scales. Therefore, developing frameworks that enable testing questions and predictions of scaling require sufficient understanding of complex processes across biological subdisciplines and spatiotemporal scales. Frameworks that enable scaling across subdisciplines would ideally allow us to test hypotheses about the degree to which explicit integration across spatiotemporal scales is needed for predicting the outcome of biological processes. For instance, how does genomic variation within populations allow us to explain community structure? How do the dynamics of cellular metabolism translate to our understanding of whole-ecosystem metabolism? Do patterns and processes operate seamlessly across biological scales, or are there fundamental laws of biological scaling that limit our ability to make predictions from one scale to another? Similarly, can sub-organismal structures and processes be sufficiently understood in isolation of potential feedbacks from the population, community, or ecosystem levels? And can we infer the sub-organismal processes from data on the population, community, or ecosystem scale? Concerted efforts to develop more cross-disciplinary frameworks will open doors to a more fully integrated field of biology. In this paper we discuss how we might integrate across scales, specifically by 1. Identifying scales and boundaries, 2. Determining analogous units and processes across scales, 3. Developing frameworks to unite multiple scales, and 4. Extending frameworks to new empirical systems.
Collapse
|
5
|
Sparrow BD, Edwards W, Munroe SE, Wardle GM, Guerin GR, Bastin J, Morris B, Christensen R, Phinn S, Lowe AJ. Effective ecosystem monitoring requires a multi-scaled approach. Biol Rev Camb Philos Soc 2020; 95:1706-1719. [PMID: 32648358 PMCID: PMC7689690 DOI: 10.1111/brv.12636] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 06/18/2020] [Accepted: 06/22/2020] [Indexed: 01/11/2023]
Abstract
Ecosystem monitoring is fundamental to our understanding of how ecosystem change is impacting our natural resources and is vital for developing evidence-based policy and management. However, the different types of ecosystem monitoring, along with their recommended applications, are often poorly understood and contentious. Varying definitions and strict adherence to a specific monitoring type can inhibit effective ecosystem monitoring, leading to poor program development, implementation and outcomes. In an effort to develop a more consistent and clear understanding of ecosystem monitoring programs, we here review the main types of monitoring and recommend the widespread adoption of three classifications of monitoring, namely, targeted, surveillance and landscape monitoring. Landscape monitoring is conducted over large areas, provides spatial data, and enables questions relating to where and when ecosystem change is occurring to be addressed. Surveillance monitoring uses standardised field methods to inform on what is changing in our environments and the direction and magnitude of that change, whilst targeted monitoring is designed around testable hypotheses over defined areas and is the best approach for determining the causes of ecosystem change. The classification system is flexible and can incorporate different interests, objectives, targets and characteristics as well as different spatial scales and temporal frequencies, while also providing valuable structure and consistency across distinct ecosystem monitoring programs. To support our argument, we examine the ability of each monitoring type to inform on six key types of questions that are routinely posed for ecosystem monitoring programs, such as where and when change is occurring, what is the magnitude of change, and how can the change be managed? As we demonstrate, each type of ecosystem monitoring has its own strengths and weaknesses, which should be carefully considered relative to the desired results. Using this scheme, scientists and land managers can design programs best suited to their needs. Finally, we assert that for our most serious environmental challenges, it is essential that we include information from each of these monitoring scales to inform on all facets of ecosystem change, and this is best achieved through close collaboration between the scales. With a renewed understanding of the importance of each monitoring type, along with greater commitment to monitor cooperatively, we will be well placed to address some of our greatest environmental challenges.
Collapse
Affiliation(s)
- Ben D. Sparrow
- Terrestrial Ecosystem Research Network, The School of Biological SciencesThe University of AdelaideAdelaideSouth Australia5005Australia
| | - Will Edwards
- Terrestrial Ecosystem Research Network, College of Science and EngineeringJames Cook UniversityPO Box 6811CairnsQueensland4870Australia
| | - Samantha E.M. Munroe
- Terrestrial Ecosystem Research Network, The School of Biological SciencesThe University of AdelaideAdelaideSouth Australia5005Australia
| | - Glenda M. Wardle
- Terrestrial Ecosystem Research Network, Desert Ecology Research Group, School of Life and Environmental SciencesUniversity of SydneySydneyNew South Wales2006Australia
| | - Greg R. Guerin
- Terrestrial Ecosystem Research Network, The School of Biological SciencesThe University of AdelaideAdelaideSouth Australia5005Australia
| | - Jean‐Francois Bastin
- Computational and Applied Vegetation Ecology Lab, Department of Applied Ecology and Environmental Biology, Faculty of Bioscience EngineeringGhent UniversityGhent9000Belgium
| | - Beryl Morris
- Terrestrial Ecosystem Research NetworkThe University of QueenslandSt LuciaQueensland4072Australia
| | - Rebekah Christensen
- Institute for Future EnvironmentsQueensland University of TechnologyGardens PointBrisbaneQueensland4000Australia
| | - Stuart Phinn
- School of Earth and Environmental SciencesThe University of QueenslandSt LuciaQueensland4072Australia
| | - Andrew J. Lowe
- School of Biological SciencesThe University of AdelaideAdelaideSouth Australia5005Australia
| |
Collapse
|
6
|
Carey CC, Farrell KJ, Hounshell AG, O'Connell K. Macrosystems EDDIE teaching modules significantly increase ecology students' proficiency and confidence working with ecosystem models and use of systems thinking. Ecol Evol 2020; 10:12515-12527. [PMID: 33250990 PMCID: PMC7679539 DOI: 10.1002/ece3.6757] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 07/27/2020] [Accepted: 08/18/2020] [Indexed: 11/30/2022] Open
Abstract
Simulation models are increasingly used by ecologists to study complex, ecosystem-scale phenomena, but integrating ecosystem simulation modeling into ecology undergraduate and graduate curricula remains rare. Engaging ecology students with ecosystem simulation models may enable students to conduct hypothesis-driven scientific inquiry while also promoting their use of systems thinking, but it remains unknown how using hands-on modeling activities in the classroom affects student learning. Here, we developed short (3-hr) teaching modules as part of the Macrosystems EDDIE (Environmental Data-Driven Inquiry & Exploration) program that engage students with hands-on ecosystem modeling in the R statistical environment. We embedded the modules into in-person ecology courses at 17 colleges and universities and assessed student perceptions of their proficiency and confidence before and after working with models. Across all 277 undergraduate and graduate students who participated in our study, completing one Macrosystems EDDIE teaching module significantly increased students' self-reported proficiency, confidence, and likely future use of simulation models, as well as their perceived knowledge of ecosystem simulation models. Further, students were significantly more likely to describe that an important benefit of ecosystem models was their "ease of use" after completing a module. Interestingly, students were significantly more likely to provide evidence of systems thinking in their assessment responses about the benefits of ecosystem models after completing a module, suggesting that these hands-on ecosystem modeling activities may increase students' awareness of how individual components interact to affect system-level dynamics. Overall, Macrosystems EDDIE modules help students gain confidence in their ability to use ecosystem models and provide a useful method for ecology educators to introduce undergraduate and graduate students to ecosystem simulation modeling using in-person, hybrid, or virtual modes of instruction.
Collapse
Affiliation(s)
| | - Kaitlin J. Farrell
- Department of Biological SciencesVirginia TechBlacksburgVAUSA
- Odum School of EcologyUniversity of GeorgiaAthensGAUSA
| | | | | |
Collapse
|
7
|
Bolduc B, Hodgkins SB, Varner RK, Crill PM, McCalley CK, Chanton JP, Tyson GW, Riley WJ, Palace M, Duhaime MB, Hough MA, Saleska SR, Sullivan MB, Rich VI. The IsoGenie database: an interdisciplinary data management solution for ecosystems biology and environmental research. PeerJ 2020. [DOI: 10.7717/peerj.9467] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Modern microbial and ecosystem sciences require diverse interdisciplinary teams that are often challenged in “speaking” to one another due to different languages and data product types. Here we introduce the IsoGenie Database (IsoGenieDB; https://isogenie-db.asc.ohio-state.edu/), a de novo developed data management and exploration platform, as a solution to this challenge of accurately representing and integrating heterogenous environmental and microbial data across ecosystem scales. The IsoGenieDB is a public and private data infrastructure designed to store and query data generated by the IsoGenie Project, a ~10 year DOE-funded project focused on discovering ecosystem climate feedbacks in a thawing permafrost landscape. The IsoGenieDB provides (i) a platform for IsoGenie Project members to explore the project’s interdisciplinary datasets across scales through the inherent relationships among data entities, (ii) a framework to consolidate and harmonize the datasets needed by the team’s modelers, and (iii) a public venue that leverages the same spatially explicit, disciplinarily integrated data structure to share published datasets. The IsoGenieDB is also being expanded to cover the NASA-funded Archaea to Atmosphere (A2A) project, which scales the findings of IsoGenie to a broader suite of Arctic peatlands, via the umbrella A2A Database (A2A-DB). The IsoGenieDB’s expandability and flexible architecture allow it to serve as an example ecosystems database.
Collapse
Affiliation(s)
- Benjamin Bolduc
- Department of Microbiology, The Ohio State University, Columbus, OH, USA
| | | | - Ruth K. Varner
- Earth Systems Research Center, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, Durham, NH, USA
- Department of Earth Sciences, College of Engineering and Physical Sciences, University of New Hampshire, Durham, NH, USA
| | - Patrick M. Crill
- Department of Geological Sciences and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
| | - Carmody K. McCalley
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY, USA
| | - Jeffrey P. Chanton
- Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, USA
| | - Gene W. Tyson
- Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
| | - William J. Riley
- Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Michael Palace
- Earth Systems Research Center, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, Durham, NH, USA
- Department of Earth Sciences, College of Engineering and Physical Sciences, University of New Hampshire, Durham, NH, USA
| | - Melissa B. Duhaime
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
| | - Moira A. Hough
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Scott R. Saleska
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Matthew B. Sullivan
- Department of Microbiology, The Ohio State University, Columbus, OH, USA
- Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH, USA
| | - Virginia I. Rich
- Department of Microbiology, The Ohio State University, Columbus, OH, USA
| | | |
Collapse
|
8
|
Zuckerberg B, Strong C, LaMontagne JM, St. George S, Betancourt JL, Koenig WD. Climate Dipoles as Continental Drivers of Plant and Animal Populations. Trends Ecol Evol 2020; 35:440-453. [DOI: 10.1016/j.tree.2020.01.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 01/20/2020] [Accepted: 01/27/2020] [Indexed: 12/15/2022]
|
9
|
Koontz MJ, North MP, Werner CM, Fick SE, Latimer AM. Local forest structure variability increases resilience to wildfire in dry western U.S. coniferous forests. Ecol Lett 2020; 23:483-494. [PMID: 31922344 DOI: 10.1111/ele.13447] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 09/06/2019] [Accepted: 11/20/2019] [Indexed: 12/24/2022]
Abstract
A 'resilient' forest endures disturbance and is likely to persist. Resilience to wildfire may arise from feedback between fire behaviour and forest structure in dry forest systems. Frequent fire creates fine-scale variability in forest structure, which may then interrupt fuel continuity and prevent future fires from killing overstorey trees. Testing the generality and scale of this phenomenon is challenging for vast, long-lived forest ecosystems. We quantify forest structural variability and fire severity across >30 years and >1000 wildfires in California's Sierra Nevada. We find that greater variability in forest structure increases resilience by reducing rates of fire-induced tree mortality and that the scale of this effect is local, manifesting at the smallest spatial extent of forest structure tested (90 × 90 m). Resilience of these forests is likely compromised by structural homogenisation from a century of fire suppression, but could be restored with management that increases forest structural variability.
Collapse
Affiliation(s)
- Michael J Koontz
- Graduate Group in Ecology, University of California, Davis, CA, USA.,Department of Plant Sciences, University of California, Davis, CA, USA.,Earth Lab, University of Colorado-Boulder, Boulder, CO, USA
| | - Malcolm P North
- Department of Plant Sciences, University of California, Davis, CA, USA.,Pacific Southwest Research Station, USDA Forest Service, Mammoth Lakes, CA, USA
| | - Chhaya M Werner
- Department of Plant Sciences, University of California, Davis, CA, USA.,Center for Population Biology, University of California, Davis, CA, USA.,German Centre for Integrative Biodiversity Research, Halle-Jena-Leipzig, Germany
| | - Stephen E Fick
- US Geological Survey, Southwest Biological Science Center, Moab, UT, USA.,Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, USA
| | - Andrew M Latimer
- Department of Plant Sciences, University of California, Davis, CA, USA
| |
Collapse
|
10
|
Fulton EA, Blanchard JL, Melbourne-Thomas J, Plagányi ÉE, Tulloch VJD. Where the Ecological Gaps Remain, a Modelers' Perspective. Front Ecol Evol 2019. [DOI: 10.3389/fevo.2019.00424] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
|
11
|
King K, Cheruvelil KS, Pollard A. Drivers and spatial structure of abiotic and biotic properties of lakes, wetlands, and streams at the national scale. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2019; 29:e01957. [PMID: 31240779 PMCID: PMC7337605 DOI: 10.1002/eap.1957] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 05/21/2019] [Accepted: 06/11/2019] [Indexed: 05/31/2023]
Abstract
Broad-scale studies have improved our ability to make predictions about how freshwater biotic and abiotic properties will respond to changes in climate and land use intensification. Further, fine-scaled studies of lakes, wetlands, or streams have documented the important role of hydrologic connections for understanding many freshwater biotic and abiotic processes. However, lakes, wetlands, and streams are typically studied in isolation of one another at both fine and broad scales. Therefore, it is not known whether these three freshwater types (lakes, wetlands, and streams) respond similarly to ecosystem and watershed drivers nor how they may respond to future global stresses. In this study, we asked, do lake, wetland, and stream biotic and abiotic properties respond to similar ecosystem and watershed drivers and have similar spatial structure at the national scale? We answered this question with three U.S. conterminous data sets of freshwater ecosystems. We used random forest (RF) analysis to quantify the multi-scaled drivers related to variation in nutrients and biota in lakes, wetlands, and streams simultaneously; we used semivariogram analysis to quantify the spatial structure of biotic and abiotic properties and to infer possible mechanisms controlling the ecosystem properties of these freshwater types. We found that abiotic properties responded to similar drivers, had large ranges of spatial autocorrelation, and exhibited multi-scale spatial structure, regardless of freshwater type. However, the dominant drivers of variation in biotic properties depended on freshwater type and had smaller ranges of spatial autocorrelation. Our study is the first to document that drivers and spatial structure differ more between biotic and abiotic variables than across freshwater types, suggesting that some properties of freshwater ecosystems may respond similarly to future global changes.
Collapse
Affiliation(s)
- Katelyn King
- 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
| | - Amina Pollard
- U.S. Environmental Protection Agency Office of Water, Washington, D.C. 20004 USA
| |
Collapse
|
12
|
Soranno PA, Wagner T, Collins SM, Lapierre JF, Lottig NR, Oliver SK. Spatial and temporal variation of ecosystem properties at macroscales. Ecol Lett 2019; 22:1587-1598. [PMID: 31347258 DOI: 10.1111/ele.13346] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 06/03/2019] [Accepted: 06/26/2019] [Indexed: 01/16/2023]
Abstract
Although spatial and temporal variation in ecological properties has been well-studied, crucial knowledge gaps remain for studies conducted at macroscales and for ecosystem properties related to material and energy. We test four propositions of spatial and temporal variation in ecosystem properties within a macroscale (1000 km's) extent. We fit Bayesian hierarchical models to thousands of observations from over two decades to quantify four components of variation - spatial (local and regional) and temporal (local and coherent); and to model their drivers. We found strong support for three propositions: (1) spatial variation at local and regional scales are large and roughly equal, (2) annual temporal variation is mostly local rather than coherent, and, (3) spatial variation exceeds temporal variation. Our findings imply that predicting ecosystem responses to environmental changes at macroscales requires consideration of the dominant spatial signals at both local and regional scales that may overwhelm temporal signals.
Collapse
Affiliation(s)
- Patricia A Soranno
- Department of Fisheries and Wildlife, Michigan St. University, 480 Wilson Rd, East Lansing, MI, 48824, USA
| | - Tyler Wagner
- U.S. Geological Survey, Pennsylvania Cooperative Fish & Wildlife Research Unit, Pennsylvania State University, 402 Forest Resources Building, University Park, PA, 16802, USA
| | - Sarah M Collins
- Department of Zoology and Physiology, University of Wyoming, Laramie, WY, 82071, USA
| | - Jean-Francois Lapierre
- Department of Biological Science, University of Montreal, Montreal, Quebec, Canada, H3C 3J7
| | - Noah R Lottig
- Trout Lake Research Station, Univ. of Wisconsin, 3110 Trout Lake Station Drive, Boulder Junction, WI, 54512, USA
| | - Samantha K Oliver
- Upper Midwest Water Science Center, U.S. Geological Survey, 8505 Research Way, Middleton, WI, 53562, USA
| |
Collapse
|
13
|
Rammer W, Seidl R. A scalable model of vegetation transitions using deep neural networks. Methods Ecol Evol 2019; 10:879-890. [PMID: 31244986 PMCID: PMC6582592 DOI: 10.1111/2041-210x.13171] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 02/26/2019] [Indexed: 11/29/2022]
Abstract
In times of rapid global change, anticipating vegetation changes and assessing their impacts is of key relevance to managers and policy makers. Yet, predicting vegetation dynamics often suffers from an inherent scale mismatch, with abundant data and process understanding being available at a fine spatial grain, but the relevance for decision-making is increasing with spatial extent.We present a novel approach for scaling vegetation dynamics (SVD), using deep learning to predict vegetation transitions. Vegetation is discretized into a large number (103-106) of potential states based on its structure, composition and functioning. Transition probabilities between states are estimated via a deep neural network (DNN) trained on observed or simulated vegetation transitions in combination with environmental variables. The impact of vegetation transitions on important ecological indicators is quantified by probabilistically linking attributes such as carbon storage and biodiversity to vegetation states.Here, we describe the SVD approach and present results of applying the framework in a meta-modelling context. We trained a DNN using simulations of a process-based forest landscape model for a complex mountain forest landscape under different climate scenarios. Subsequently, we evaluated the ability of SVD to project long-term vegetation dynamics and the resulting changes in forest carbon storage and biodiversity. SVD captured spatial (e.g. elevational gradients) and temporal (e.g. species succession) patterns of vegetation dynamics well, and responded realistically to changing environmental conditions. In addition, we tested the computational efficiency of the approach, highlighting the utility of SVD for country- to continental scale applications. SVD is the-to our knowledge-first vegetation model harnessing deep neural networks. The approach has high predictive accuracy and is able to generalize well beyond training data. SVD was designed to run on widely available input data (e.g. vegetation states defined from remote sensing, gridded global climate datasets) and exceeds the computational performance of currently available highly optimized landscape models by three to four orders of magnitude. We conclude that SVD is a promising approach for combining detailed process knowledge on fine-grained ecosystem processes with the increasingly available big ecological datasets for improved large-scale projections of vegetation dynamics.
Collapse
Affiliation(s)
- Werner Rammer
- Department of Forest‐ and Soil SciencesInstitute of SilvicultureUniversity of Natural Resources and Life Sciences (BOKU) ViennaViennaAustria
| | - Rupert Seidl
- Department of Forest‐ and Soil SciencesInstitute of SilvicultureUniversity of Natural Resources and Life Sciences (BOKU) ViennaViennaAustria
| |
Collapse
|
14
|
Williams GJ, Graham NAJ, Jouffray JB, Norström AV, Nyström M, Gove JM, Heenan A, Wedding LM. Coral reef ecology in the Anthropocene. Funct Ecol 2019. [DOI: 10.1111/1365-2435.13290] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
| | | | - Jean-Baptiste Jouffray
- Stockholm Resilience Centre; Stockholm University; Stockholm Sweden
- Global Economic Dynamics and the Biosphere Academy Programme; Royal Swedish Academy of Sciences; Stockholm Sweden
| | | | - Magnus Nyström
- Stockholm Resilience Centre; Stockholm University; Stockholm Sweden
| | - Jamison M. Gove
- NOAA Pacific Islands Fisheries Science Center; Honolulu Hawaii
| | - Adel Heenan
- School of Ocean Sciences; Bangor University; Anglesey UK
| | - Lisa M. Wedding
- Center for Ocean Solutions; Stanford University; Stanford California
| |
Collapse
|
15
|
Hansen WD, Turner MG. Origins of abrupt change? Postfire subalpine conifer regeneration declines nonlinearly with warming and drying. ECOL MONOGR 2019. [DOI: 10.1002/ecm.1340] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Winslow D. Hansen
- Department of Integrative Biology; University of Wisconsin-Madison; Madison Wisconsin 53706 USA
| | - Monica G. Turner
- Department of Integrative Biology; University of Wisconsin-Madison; Madison Wisconsin 53706 USA
| |
Collapse
|
16
|
Cheruvelil KS, Soranno PA. Data-Intensive Ecological Research Is Catalyzed by Open Science and Team Science. Bioscience 2018. [DOI: 10.1093/biosci/biy097] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Kendra Spence Cheruvelil
- Professor in Lyman Briggs College and the Department of Fisheries and Wildlife
- Conceptualization and writing of this article
| | - Patricia A Soranno
- Professor in the Department of Fisheries and Wildlife, at Michigan State University, in East Lansing
- Conceptualization and writing of this article
| |
Collapse
|
17
|
Mollenhauer H, Kasner M, Haase P, Peterseil J, Wohner C, Frenzel M, Mirtl M, Schima R, Bumberger J, Zacharias S. Long-term environmental monitoring infrastructures in Europe: observations, measurements, scales, and socio-ecological representativeness. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 624:968-978. [PMID: 29275260 DOI: 10.1016/j.scitotenv.2017.12.095] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 12/08/2017] [Accepted: 12/08/2017] [Indexed: 06/07/2023]
Abstract
The challenges posed by climate and land use change are increasingly complex, with ever-increasing and accelerating impacts on the global environmental system. The establishment of an internationally harmonized, integrated, and long-term operated environmental monitoring infrastructure is one of the major challenges of modern environmental research. Increased efforts are currently being made in Europe to establish such a harmonized pan-European observation infrastructure, and the European network of Long-Term Ecological Research sites - LTER-Europe - is of particular importance. By evaluating 477 formally accredited LTER-Europe sites, this study gives an overview of the current distribution of these infrastructures and the present condition of long-term environmental research in Europe. We compiled information on long-term biotic and abiotic observations and measurements and examined the representativeness in terms of continental biogeographical and socio-ecological gradients. The results were used to identify gaps in both measurements and coverage of the aforementioned gradients. Furthermore, an overview of the current state of the LTER-Europe observation strategies is given. The latter forms the basis for investigating the comparability of existing LTER-Europe monitoring concepts both in terms of observational design as well as in terms of the scope of the environmental compartments, variables and properties covered.
Collapse
Affiliation(s)
- Hannes Mollenhauer
- Helmholtz Centre for Environmental Research - UFZ, Department Monitoring and Exploration Technologies, Permoserstr. 15, D-04318 Leipzig, Germany.
| | - Max Kasner
- Helmholtz Centre for Environmental Research - UFZ, Department Monitoring and Exploration Technologies, Permoserstr. 15, D-04318 Leipzig, Germany
| | - Peter Haase
- Senckenberg Research Institute and Natural History Museum Frankfurt, Department of River Ecology and Conservation, Clamecystr. 12, D-63571 Gelnhausen, Germany; Faculty of Biology, University of Duisburg-Essen, Universitätsstr. 5, D-45141 Essen, Germany
| | - Johannes Peterseil
- Environment Agency Austria, Department for Ecosystem Research and Monitoring, Spittelauer Lände 5, A-1090 Vienna, Austria
| | - Christoph Wohner
- Environment Agency Austria, Department for Ecosystem Research and Monitoring, Spittelauer Lände 5, A-1090 Vienna, Austria
| | - Mark Frenzel
- Helmholtz Centre for Environmental Research - UFZ, Department of Community Ecology, Theodor-Lieser-Strasse 4, D-06120 Halle, Germany
| | - Michael Mirtl
- Environment Agency Austria, Department for Ecosystem Research and Monitoring, Spittelauer Lände 5, A-1090 Vienna, Austria
| | - Robert Schima
- University of Rostock, Faculty of Mechanical Engineering and Marine Technology, Chair of Ocean Engineering, Albert-Einstein-Str. 2, D-18059 Rostock, Germany
| | - Jan Bumberger
- Helmholtz Centre for Environmental Research - UFZ, Department Monitoring and Exploration Technologies, Permoserstr. 15, D-04318 Leipzig, Germany
| | - Steffen Zacharias
- Helmholtz Centre for Environmental Research - UFZ, Department Monitoring and Exploration Technologies, Permoserstr. 15, D-04318 Leipzig, Germany
| |
Collapse
|
18
|
Toward a Social-Ecological Theory of Forest Macrosystems for Improved Ecosystem Management. FORESTS 2018. [DOI: 10.3390/f9040200] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
19
|
Collins SL, Avolio ML, Gries C, Hallett LM, Koerner SE, La Pierre KJ, Rypel AL, Sokol ER, Fey SB, Flynn DFB, Jones SK, Ladwig LM, Ripplinger J, Jones MB. Temporal heterogeneity increases with spatial heterogeneity in ecological communities. Ecology 2018; 99:858-865. [PMID: 29352480 DOI: 10.1002/ecy.2154] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Revised: 12/07/2017] [Accepted: 01/02/2018] [Indexed: 11/12/2022]
Abstract
Heterogeneity is increasingly recognized as a foundational characteristic of ecological systems. Under global change, understanding temporal community heterogeneity is necessary for predicting the stability of ecosystem functions and services. Indeed, spatial heterogeneity is commonly used in alternative stable state theory as a predictor of temporal heterogeneity and therefore an early indicator of regime shifts. To evaluate whether spatial heterogeneity in species composition is predictive of temporal heterogeneity in ecological communities, we analyzed 68 community data sets spanning freshwater and terrestrial systems where measures of species abundance were replicated over space and time. Of the 68 data sets, 55 (81%) had a weak to strongly positive relationship between spatial and temporal heterogeneity, while in the remaining communities the relationship was weak to strongly negative (19%). Based on a mixed model analysis, we found a significant but weak overall positive relationship between spatial and temporal heterogeneity across all data sets combined, and within aquatic and terrestrial data sets separately. In addition, lifespan and successional stage were negatively and positively related to temporal heterogeneity, respectively. We conclude that spatial heterogeneity may be a predictor of temporal heterogeneity in ecological communities, and that this relationship may be a general property of many terrestrial and aquatic communities.
Collapse
Affiliation(s)
- Scott L Collins
- Department of Biology, University of New Mexico, Albuquerque, New Mexico, 87131, USA
| | - Meghan L Avolio
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, 21218, USA
| | - Corinna Gries
- Center for Limnology, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Lauren M Hallett
- Environmental Studies Program and Department of Biology, University of Oregon, Eugene, Oregon, 97403, USA
| | - Sally E Koerner
- Department of Biology, University of North Carolina Greensboro, Greensboro, North Carolina, 27402, USA
| | | | - Andrew L Rypel
- Department of Wildlife, Fish and Conservation Biology, University of California, Davis, California, 95616, USA
| | - Eric R Sokol
- National Ecological Observatory Network, Boulder, Colorado, 80301, USA
| | - Samuel B Fey
- Biology Department, Reed College, Portland, Oregon, 97202, USA
| | - Dan F B Flynn
- The Arnold Arboretum of Harvard University, Boston, Massachusetts, 02130, USA
| | - Sydney K Jones
- Department of Biology, University of New Mexico, Albuquerque, New Mexico, 87131, USA
| | - Laura M Ladwig
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Julie Ripplinger
- Department of Botany and Plant Sciences, University of California, Riverside, California, 92521, USA
| | - Matt B Jones
- National Center for Ecological Analysis and Synthesis, 735 State Street, Suite 300, Santa Barbara, California, 93101, USA
| |
Collapse
|
20
|
Costanza JK, Coulston JW, Wear DN. An empirical, hierarchical typology of tree species assemblages for assessing forest dynamics under global change scenarios. PLoS One 2017; 12:e0184062. [PMID: 28877258 PMCID: PMC5587308 DOI: 10.1371/journal.pone.0184062] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Accepted: 08/17/2017] [Indexed: 11/30/2022] Open
Abstract
The composition of tree species occurring in a forest is important and can be affected by global change drivers such as climate change. To inform assessment and projection of global change impacts at broad extents, we used hierarchical cluster analysis and over 120,000 recent forest inventory plots to empirically define forest tree assemblages across the U.S., and identified the indicator and dominant species associated with each. Cluster typologies in two levels of a hierarchy of forest assemblages, with 29 and 147 groups respectively, were supported by diagnostic criteria. Groups in these two levels of the hierarchy were labeled based on the top indicator species in each, and ranged widely in size. For example, in the 29-cluster typology, the sugar maple-red maple assemblage contained the largest number of plots (30,068), while the butternut-sweet birch and sourwood-scarlet oak assemblages were both smallest (6 plots each). We provide a case-study demonstration of the utility of the typology for informing forest climate change impact assessment. For five assemblages in the 29-cluster typology, we used existing projections of changes in importance value (IV) for the dominant species under one low and one high climate change scenario to assess impacts to the assemblages. Results ranged widely for each scenario by the end of the century, with each showing an average decrease in IV for dominant species in some assemblages, including the balsam fir-quaking aspen assemblage, and an average increase for others, like the green ash-American elm assemblage. Future work should assess adaptive capacity of these forest assemblages and investigate local population- and community-level dynamics in places where dominant species may be impacted. This typology will be ideal for monitoring, assessing, and projecting changes to forest communities within the emerging framework of macrosystems ecology, which emphasizes hierarchies and broad extents.
Collapse
Affiliation(s)
- Jennifer K. Costanza
- Department of Forestry and Environmental Resources, North Carolina State University, Research Triangle Park, North Carolina, United States of America
- * E-mail:
| | - John W. Coulston
- Southern Research Station, USDA Forest Service, Blacksburg, Virginia, United States of America
| | - David N. Wear
- Southern Research Station, USDA Forest Service, Raleigh, North Carolina, United States of America
| |
Collapse
|