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Suding KN, Collins CG, Hallett LM, Larios L, Brigham LM, Dudney J, Farrer EC, Larson JE, Shackelford N, Spasojevic MJ. Biodiversity in changing environments: An external-driver internal-topology framework to guide intervention. Ecology 2024:e4322. [PMID: 39014865 DOI: 10.1002/ecy.4322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 01/15/2024] [Accepted: 03/08/2024] [Indexed: 07/18/2024]
Abstract
Accompanying the climate crisis is the more enigmatic biodiversity crisis. Rapid reorganization of biodiversity due to global environmental change has defied prediction and tested the basic tenets of conservation and restoration. Conceptual and practical innovation is needed to support decision making in the face of these unprecedented shifts. Critical questions include: How can we generalize biodiversity change at the community level? When are systems able to reorganize and maintain integrity, and when does abiotic change result in collapse or restructuring? How does this understanding provide a template to guide when and how to intervene in conservation and restoration? To this end, we frame changes in community organization as the modulation of external abiotic drivers on the internal topology of species interactions, using plant-plant interactions in terrestrial communities as a starting point. We then explore how this framing can help translate available data on species abundance and trait distributions to corresponding decisions in management. Given the expectation that community response and reorganization are highly complex, the external-driver internal-topology (EDIT) framework offers a way to capture general patterns of biodiversity that can help guide resilience and adaptation in changing environments.
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Affiliation(s)
- Katharine N Suding
- Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, Colorado, USA
- Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado, USA
| | - Courtney G Collins
- Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado, USA
- Biodiversity Research Centre, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Lauren M Hallett
- Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado, USA
- Department of Biology and Environmental Studies Program, University of Oregon, Eugene, Oregon, USA
| | - Loralee Larios
- Department of Botany & Plant Sciences, University of California Riverside, Riverside, California, USA
| | - Laurel M Brigham
- Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, Colorado, USA
- Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado, USA
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California, USA
| | - Joan Dudney
- Environmental Studies Program, Santa Barbara, California, USA
- Bren School of Environmental Science & Management, UC Santa Barbara, Santa Barbara, California, USA
| | - Emily C Farrer
- Department of Ecology and Evolutionary Biology, Tulane University, New Orleans, Louisiana, USA
| | - Julie E Larson
- Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, Colorado, USA
- Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado, USA
- USDA Agricultural Research Service, Eastern Oregon Agricultural Research Center, Burns, Oregon, USA
| | - Nancy Shackelford
- Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado, USA
- School of Environmental Studies, University of Victoria, Victoria, British Columbia, Canada
| | - Marko J Spasojevic
- Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado, USA
- Department of Evolution, Ecology, and Organismal Biology, University of California Riverside, Riverside, California, USA
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2
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Kass JM, Fukaya K, Thuiller W, Mori AS. Biodiversity modeling advances will improve predictions of nature's contributions to people. Trends Ecol Evol 2024; 39:338-348. [PMID: 37968219 DOI: 10.1016/j.tree.2023.10.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 10/17/2023] [Accepted: 10/17/2023] [Indexed: 11/17/2023]
Abstract
Accurate predictions of ecosystem functions and nature's contributions to people (NCP) are needed to prioritize environmental protection and restoration in the Anthropocene. However, our ability to predict NCP is undermined by approaches that rely on biophysical variables and ignore those describing biodiversity, which have strong links to NCP. To foster predictive mapping of NCP, we should harness the latest methods in biodiversity modeling. This field advances rapidly, and new techniques with promising applications for predicting NCP are still underutilized. Here, we argue that employing recent advances in biodiversity modeling can enhance the accuracy and scope of NCP maps and predictions. This enhancement will contribute significantly to the achievement of global objectives to preserve NCP, for both the present and an unpredictable future.
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Affiliation(s)
- Jamie M Kass
- Macroecology Laboratory, Graduate School of Life Sciences, Tohoku University, Sendai, Miyagi, Japan; Biodiversity and Biocomplexity Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa, Japan.
| | - Keiichi Fukaya
- Biodiversity Division, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan
| | - Wilfried Thuiller
- Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LECA, F-38000 Grenoble, France
| | - Akira S Mori
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
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3
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Argiroff WA, Carrell AA, Klingeman DM, Dove NC, Muchero W, Veach AM, Wahl T, Lebreux SJ, Webb AB, Peyton K, Schadt CW, Cregger MA. Seasonality and longer-term development generate temporal dynamics in the Populus microbiome. mSystems 2024; 9:e0088623. [PMID: 38421171 PMCID: PMC10949431 DOI: 10.1128/msystems.00886-23] [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: 08/22/2023] [Accepted: 02/08/2024] [Indexed: 03/02/2024] Open
Abstract
Temporal variation in community composition is central to our understanding of the assembly and functioning of microbial communities, yet the controls over temporal dynamics for microbiomes of long-lived plants, such as trees, remain unclear. Temporal variation in tree microbiomes could arise primarily from seasonal (i.e., intra-annual) fluctuations in community composition or from longer-term changes across years as host plants age. To test these alternatives, we experimentally isolated temporal variation in plant microbiome composition using a common garden and clonally propagated plants, and we used amplicon sequencing to characterize bacterial/archaeal and fungal communities in the leaf endosphere, root endosphere, and rhizosphere of two Populus spp. over four seasons across two consecutive years. Microbial community composition differed among seasons and years (which accounted for up to 21% of the variation in microbial community composition) and was correlated with seasonal dissimilarity in climatic conditions. However, microbial community dissimilarity was also positively correlated with time, reflecting longer-term compositional shifts as host trees aged. Together, our findings demonstrate that temporal patterns in tree microbiomes arise from both seasonal fluctuations and longer-term changes, which interact to generate unique seasonal patterns each year. In addition to shedding light on two important controls over the assembly of plant microbiomes, our results also suggest future studies of tree microbiomes should account for background temporal dynamics when testing the drivers of spatial patterns in microbial community composition and temporal responses of plant microbiomes to environmental change.IMPORTANCEMicrobiomes are integral to the health of host plants, but we have a limited understanding of the factors that control how the composition of plant microbiomes changes over time. Especially little is known about the microbiome of long-lived trees, relative to annual and non-woody plants. We tested how tree microbiomes changed between seasons and years in poplar (genus Populus), which are widespread and ecologically important tree species that also serve as important biofuel feedstocks. We found the composition of bacterial, archaeal, and fungal communities differed among seasons, but these seasonal differences depended on year. This dependence was driven by longer-term changes in microbial composition as host trees developed across consecutive years. Our findings suggest that temporal variation in tree microbiomes is driven by both seasonal fluctuations and longer-term (i.e., multiyear) development.
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Affiliation(s)
- William A. Argiroff
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Alyssa A. Carrell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Dawn M. Klingeman
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Nicholas C. Dove
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Wellington Muchero
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Allison M. Veach
- Department of Integrative Biology, The University of Texas, San Antonio, Texas, USA
| | - Toni Wahl
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Steven J. Lebreux
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Amber B. Webb
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Kellie Peyton
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Christopher W. Schadt
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
- Department of Microbiology, University of Tennessee, Knoxville, Tennessee, USA
| | - Melissa A. Cregger
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
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4
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Wright DL, Kimmel DG, Roberson N, Strausz D. Joint species distribution modeling reveals a changing prey landscape for North Pacific right whales on the Bering shelf. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2023; 33:e2925. [PMID: 37792562 DOI: 10.1002/eap.2925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/20/2023] [Accepted: 08/18/2023] [Indexed: 10/06/2023]
Abstract
The eastern North Pacific right whale (NPRW) is the most endangered population of whale and has been observed north of its core feeding ground in recent years with low sea ice extent. Sea ice and water temperature are important drivers for zooplankton dynamics within the whale's core feeding ground in the southeastern Bering Sea, seasonally forming stable fronts along the shelf that give rise to distinct zooplankton communities. A northward shift in NPRW distribution driven by changing distribution of prey resources could put this species at increased risk of entanglement and vessel strikes. We modeled the abundance of NPRW prey, Calanus glacialis, Neocalanus, and Thysanoessa species, using a dynamic biophysical food web model of nine zooplankton guilds in the Bering shelf zooplankton community during a period of warming (2006-2016). This model is unique among prior zooplankton studies from the region in that it includes density dependence, thereby allowing us to ask whether species interactions influence zooplankton dynamics. Modeling confirmed the importance of sea ice and ocean temperature to zooplankton dynamics in the region. Density-independent growth drove community dynamics, while dependent factors were comparatively minimal. Overall, Calanus responded to environment terms, with the strength and direction of response driven by copepodite stage. Neocalanus and Thysanoessa responses were weaker, likely due to their primary occurrence on the outer shelf. We also modeled the steady-state (equilibrium) abundance of Calanus in conditions with and without wind gusts to test whether advection of outer shelf species might disrupt the steady-state dynamics of Calanus abundance; the results did not support disruption. Given the annual fall sampling design, we interpret our results as follows: low-ice-extent winters induced stronger spring winds and weakened fronts on the shelf, thereby advecting some outer shelf species into the study region; increased development rates in these warm conditions influenced the proportion of C. glacialis copepodite stages over the season. Residual correlation suggests missing drivers, possibly predators, and phytoplankton bloom composition. Given the continued loss of sea ice in the region and projected continued warming, our findings suggest that C. glacialis will move northward, and thus, whales may move northward to continue targeting them.
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Affiliation(s)
- Dana L Wright
- Duke University Marine Laboratory, Beaufort, North Carolina, USA
- Cooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington, Seattle, Washington, USA
- NOAA, Marine Mammal Laboratory, Seattle, Washington, USA
| | - David G Kimmel
- NOAA, Alaska Fisheries Science Center, Seattle, Washington, USA
| | - Nancy Roberson
- NOAA, Resource Assessment and Conservation Engineering Division, Seattle, Washington, USA
| | - David Strausz
- Cooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington, Seattle, Washington, USA
- NOAA, Pacific Marine Environmental Laboratory, Seattle, Washington, USA
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5
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Tang B, Roberts SM, Clark JS, Gelfand AE. Mechanistic modeling of climate effects on redistribution and population growth in a community of fish species. GLOBAL CHANGE BIOLOGY 2023; 29:6399-6414. [PMID: 37789712 DOI: 10.1111/gcb.16963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 09/01/2023] [Accepted: 09/19/2023] [Indexed: 10/05/2023]
Abstract
Understanding community responses to climate is critical for anticipating the future impacts of global change. However, despite increased research efforts in this field, models that explicitly include important biological mechanisms are lacking. Quantifying the potential impacts of climate change on species is complicated by the fact that the effects of climate variation may manifest at several points in the biological process. To this end, we extend a dynamic mechanistic model that combines population dynamics, such as species interactions, with species redistribution by allowing climate to affect both processes. We examine their relative contributions in an application to the changing biomass of a community of eight species in the Gulf of Maine using over 30 years of fisheries data from the Northeast Fishery Science Center. Our model suggests that the mechanisms driving biomass trends vary across space, time, and species. Phase space plots demonstrate that failing to account for the dynamic nature of the environmental and biologic system can yield theoretical estimates of population abundances that are not observed in empirical data. The stock assessments used by fisheries managers to set fishing targets and allocate quotas often ignore environmental effects. At the same time, research examining the effects of climate change on fish has largely focused on redistribution. Frameworks that combine multiple biological reactions to climate change are particularly necessary for marine researchers. This work is just one approach to modeling the complexity of natural systems and highlights the need to incorporate multiple and possibly interacting biological processes in future models.
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Affiliation(s)
- Becky Tang
- Department of Mathematics and Statistics, Middlebury College, Middlebury, Vermont, USA
- Department of Statistical Science, Duke University, Durham, North Carolina, USA
| | - Sarah M Roberts
- Department of Earth, Marine, and Environmental Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - James S Clark
- Nicholas School of the Environment, Duke University, Durham, North Carolina, USA
| | - Alan E Gelfand
- Department of Statistical Science, Duke University, Durham, North Carolina, USA
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6
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Heiland L, Kunstler G, Šebeň V, Hülsmann L. Which demographic processes control competitive equilibria? Bayesian calibration of a size-structured forest population model. Ecol Evol 2023; 13:e10232. [PMID: 37408631 PMCID: PMC10318622 DOI: 10.1002/ece3.10232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 06/12/2023] [Indexed: 07/07/2023] Open
Abstract
In forest communities, light competition is a key process for community assembly. Species' differences in seedling and sapling tolerance to shade cast by overstory trees is thought to determine species composition at late-successional stages. Most forests are distant from these late-successional equilibria, impeding a formal evaluation of their potential species composition. To extrapolate competitive equilibria from short-term data, we therefore introduce the JAB model, a parsimonious dynamic model with interacting size-structured populations, which focuses on sapling demography including the tolerance to overstory competition. We apply the JAB model to a two-"species" system from temperate European forests, that is, the shade-tolerant species Fagus sylvatica L. and the group of all other competing species. Using Bayesian calibration with prior information from external Slovakian national forest inventory (NFI) data, we fit the JAB model to short time series from the German NFI. We use the posterior estimates of demographic rates to extrapolate that F. sylvatica will be the predominant species in 94% of the competitive equilibria, despite only predominating in 24% of the initial states. We further simulate counterfactual equilibria with parameters switched between species to assess the role of different demographic processes for competitive equilibria. These simulations confirm the hypothesis that the higher shade tolerance of F. sylvatica saplings is key for its long-term predominance. Our results highlight the importance of demographic differences in early life stages for tree species assembly in forest communities.
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Affiliation(s)
- Lukas Heiland
- Bayreuth Center of Ecology and Environmental Research (BayCEER), Ecosystem Analysis and Simulation (EASI) LabUniversity of BayreuthBayreuthGermany
- Theoretical EcologyUniversity of RegensburgRegensburgGermany
| | | | | | - Lisa Hülsmann
- Bayreuth Center of Ecology and Environmental Research (BayCEER), Ecosystem Analysis and Simulation (EASI) LabUniversity of BayreuthBayreuthGermany
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7
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Scher CL, Clark JS. Species traits and observer behaviors that bias data assimilation and how to accommodate them. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2023; 33:e2815. [PMID: 36717358 DOI: 10.1002/eap.2815] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/14/2022] [Accepted: 12/27/2022] [Indexed: 06/18/2023]
Abstract
Datasets that monitor biodiversity capture information differently depending on their design, which influences observer behavior and can lead to biases across observations and species. Combining different datasets can improve our ability to identify and understand threats to biodiversity, but this requires an understanding of the observation bias in each. Two datasets widely used to monitor bird populations exemplify these general concerns: eBird is a citizen science project with high spatiotemporal resolution but variation in distribution, effort, and observers, whereas the Breeding Bird Survey (BBS) is a structured survey of specific locations over time. Analyses using these two datasets can identify contradictory population trends. To understand these discrepancies and facilitate data fusion, we quantify species-level reporting differences across eBird and the BBS in three regions across the United States by jointly modeling bird abundances using data from both datasets. First, we fit a joint Species Distribution Model that accounts for environmental conditions and effort to identify reporting differences across the datasets. We then examine how these differences in reporting are related to species traits. Finally, we analyze species reported to one dataset but not the other and determine whether traits differ between reported and unreported species. We find that most species are reported more in the BBS than eBird. Specifically, we find that compared to eBird, BBS observers tend to report higher counts of common species and species that are usually detected by sound. We also find that species associated with water are reported less in the BBS. Species typically identified by sound are reported more at sunrise than later in the morning. Our results quantify reporting differences in eBird and the BBS to enhance our understanding of how each captures information and how they should be used. The reporting rates we identify can also be incorporated into observation models through detectability or effort to improve analyses across species and datasets. The method demonstrated here can be used to compare reporting rates across any two or more datasets to examine biases.
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Affiliation(s)
- C Lane Scher
- Nicholas School of the Environment, Duke University, Durham, North Carolina, USA
| | - James S Clark
- Nicholas School of the Environment, Duke University, Durham, North Carolina, USA
- Department of Statistical Science, Duke University, Durham, North Carolina, USA
- Mountain Ecosystems and Societies Laboratory, National Research Institute for Agriculture, Food and Environment (INRAE), Saint-Martin-d'Hères Cedex, France
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8
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Paniw M, García-Callejas D, Lloret F, Bassar RD, Travis J, Godoy O. Pathways to global-change effects on biodiversity: new opportunities for dynamically forecasting demography and species interactions. Proc Biol Sci 2023; 290:20221494. [PMID: 36809806 PMCID: PMC9943645 DOI: 10.1098/rspb.2022.1494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023] Open
Abstract
In structured populations, persistence under environmental change may be particularly threatened when abiotic factors simultaneously negatively affect survival and reproduction of several life cycle stages, as opposed to a single stage. Such effects can then be exacerbated when species interactions generate reciprocal feedbacks between the demographic rates of the different species. Despite the importance of such demographic feedbacks, forecasts that account for them are limited as individual-based data on interacting species are perceived to be essential for such mechanistic forecasting-but are rarely available. Here, we first review the current shortcomings in assessing demographic feedbacks in population and community dynamics. We then present an overview of advances in statistical tools that provide an opportunity to leverage population-level data on abundances of multiple species to infer stage-specific demography. Lastly, we showcase a state-of-the-art Bayesian method to infer and project stage-specific survival and reproduction for several interacting species in a Mediterranean shrub community. This case study shows that climate change threatens populations most strongly by changing the interaction effects of conspecific and heterospecific neighbours on both juvenile and adult survival. Thus, the repurposing of multi-species abundance data for mechanistic forecasting can substantially improve our understanding of emerging threats on biodiversity.
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Affiliation(s)
- Maria Paniw
- Department of Conservation Biology and Global Change, Estación Biológica de Doñana (EBD-CSIC), Seville, 41001 Spain.,Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich 8057, Switzerland
| | - David García-Callejas
- Department of Integrative Ecology, Estación Biológica de Doñana (EBD-CSIC), Seville, 41001 Spain.,Instituto Universitario de Investigación Marina (INMAR), Departamento de Biología, Universidad de Cádiz, Campus Río San Pedro, 11510 Puerto Real, Spain
| | - Francisco Lloret
- Center for Ecological Research and Forestry Applications (CREAF), Cerdanyola del Vallès 08193, Spain.,Department Animal Biology, Plant Biology and Ecology, Universitat Autònoma Barcelona, Cerdanyola del Vallès 08193, Spain
| | - Ronald D Bassar
- Department of Biological Sciences, Auburn University, Auburn, AL 36849, USA
| | - Joseph Travis
- Department of Biological Science, Florida State University, Tallahassee, FL 32306, USA
| | - Oscar Godoy
- Instituto Universitario de Investigación Marina (INMAR), Departamento de Biología, Universidad de Cádiz, Campus Río San Pedro, 11510 Puerto Real, Spain
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9
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Guo Q, Chen A, Crockett ETH, Atkins JW, Chen X, Fei S. Integrating gradient with scale in ecological and evolutionary studies. Ecology 2023; 104:e3982. [PMID: 36700858 DOI: 10.1002/ecy.3982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 12/05/2022] [Accepted: 12/28/2022] [Indexed: 01/27/2023]
Abstract
Gradient and scale are two key concepts in ecology and evolution that are closely related but inherently distinct. While scale commonly refers to the dimensional space of a specific ecological/evolutionary (eco-evo) issue, gradient measures the range of a given variable. Gradient and scale can jointly and interactively influence eco-evo patterns. Extensive previous research investigated how changing scales may affect the observation and interpretation of eco-evo patterns; however, relatively little attention has been paid to the role of changing gradients. Here, synthesizing recent research progress, we suggest that the role of scale in the emergence of ecological patterns should be evaluated in conjunction with considering the underlying environmental gradients. This is important because, in most studies, the range of the gradient is often part of its full potential range. The difference between sampled (partial) versus potential (full) environmental gradients may profoundly impact observed eco-evo patterns and alter scale-gradient relationships. Based on observations from both field and experimental studies, we illustrate the underlying features of gradients and how they may affect observed patterns, along with the linkages of these features to scales. Since sampled gradients often do not cover their full potential ranges, we discuss how the breadth and the starting and ending positions of key gradients may affect research design and data interpretation. We then outline potential approaches and related perspectives to better integrate gradient with scale in future studies.
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Affiliation(s)
- Qinfeng Guo
- USDA FS - Southern Research Station, Research Triangle Park, North Carolina, USA
| | - Anping Chen
- Department of Biology & Graduate Degree Program in Ecology, Colorado State University, Fort Collins, Colorado, USA
| | - Erin T H Crockett
- USDA FS - Southern Research Station, Research Triangle Park, North Carolina, USA.,Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA.,Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, New Hampshire, USA
| | - Jeff W Atkins
- USDA Forest Service Southern Research Station, New Ellenton, South Carolina, USA
| | - Xiongwen Chen
- Department of Biological and Environmental Sciences, Alabama A & M University, Normal, Alabama, USA
| | - Songlin Fei
- Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana, USA
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10
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Modeling Community Dynamics Through Environmental Effects, Species Interactions and Movement. JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2022. [DOI: 10.1007/s13253-022-00520-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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11
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Botella C, Bonnet P, Hui C, Joly A, Richardson DM. Dynamic Species Distribution Modeling Reveals the Pivotal Role of Human-Mediated Long-Distance Dispersal in Plant Invasion. BIOLOGY 2022; 11:biology11091293. [PMID: 36138772 PMCID: PMC9495778 DOI: 10.3390/biology11091293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/19/2022] [Accepted: 08/23/2022] [Indexed: 11/16/2022]
Abstract
Plant invasions generate massive ecological and economic costs worldwide. Predicting their spatial dynamics is crucial to the design of effective management strategies and the prevention of invasions. Earlier studies highlighted the crucial role of long-distance dispersal in explaining the speed of many invasions. In addition, invasion speed depends highly on the duration of its lag phase, which may depend on the scaling of fecundity with age, especially for woody plants, even though empirical proof is still rare. Bayesian dynamic species distribution models enable the fitting of process-based models to partial and heterogeneous observations using a state-space modeling approach, thus offering a tool to test such hypotheses on past invasions over large spatial scales. We use such a model to explore the roles of long-distance dispersal and age-structured fecundity in the transient invasion dynamics of Plectranthus barbatus, a woody plant invader in South Africa. Our lattice-based model accounts for both short and human-mediated long-distance dispersal, as well as age-structured fecundity. We fitted our model on opportunistic occurrences, accounting for the spatio-temporal variations of the sampling effort and the variable detection rates across datasets. The Bayesian framework enables us to integrate a priori knowledge on demographic parameters and control identifiability issues. The model revealed a massive wave of spatial spread driven by human-mediated long-distance dispersal during the first decade and a subsequent drastic population growth, leading to a global equilibrium in the mid-1990s. Without long-distance dispersal, the maximum population would have been equivalent to 30% of the current equilibrium population. We further identified the reproductive maturity at three years old, which contributed to the lag phase before the final wave of population growth. Our results highlighted the importance of the early eradication of weedy horticultural alien plants around urban areas to hamper and delay the invasive spread.
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Affiliation(s)
- Christophe Botella
- Centre for Invasion Biology (CIB), Department of Botany & Zoology, Stellenbosch University, Stellenbosch 7602, South Africa
- Correspondence:
| | - Pierre Bonnet
- Botany and Modeling of Plant Architecture and Vegetation (AMAP), CIRAD, CNRS, INRAE, IRD, University of Montpellier, 34398 Montpellier, France
| | - Cang Hui
- Centre for Invasion Biology, Department of Mathematical Sciences, Stellenbosch University, Stellenbosch 7602, South Africa
- Biodiversity Informatics Unit, African Institute for Mathematical Sciences, Cape Town 7945, South Africa
| | - Alexis Joly
- Inria, LIRMM, University of Montpellier, 34095 Montpellier, France
| | - David M. Richardson
- Centre for Invasion Biology (CIB), Department of Botany & Zoology, Stellenbosch University, Stellenbosch 7602, South Africa
- Department of Invasion Ecology, Institute of Botany, The Czech Academy of Sciences, 252 43 Průhonice, Czech Republic
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12
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Collins CG, Elmendorf SC, Smith JG, Shoemaker L, Szojka M, Swift M, Suding KN. Global change re-structures alpine plant communities through interacting abiotic and biotic effects. Ecol Lett 2022; 25:1813-1826. [PMID: 35763598 DOI: 10.1111/ele.14060] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 01/31/2022] [Accepted: 05/17/2022] [Indexed: 11/30/2022]
Abstract
Global change is altering patterns of community assembly, with net outcomes dependent on species' responses to the abiotic environment, both directly and mediated through biotic interactions. Here, we assess alpine plant community responses in a 15-year factorial nitrogen addition, warming and snow manipulation experiment. We used a dynamic competition model to estimate the density-dependent and -independent processes underlying changes in species-group abundances over time. Density-dependent shifts in competitive interactions drove long-term changes in abundance of species-groups under global change while counteracting environmental drivers limited the growth response of the dominant species through density-independent mechanisms. Furthermore, competitive interactions shifted with the environment, primarily with nitrogen and drove non-linear abundance responses across environmental gradients. Our results highlight that global change can either reshuffle species hierarchies or further favour already-dominant species; predicting which outcome will occur requires incorporating both density-dependent and -independent mechanisms and how they interact across multiple global change factors.
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Affiliation(s)
- Courtney G Collins
- Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado, USA.,Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sarah C Elmendorf
- Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado, USA
| | - Jane G Smith
- Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado, USA
| | - Lauren Shoemaker
- Department of Botany, University of Wyoming, Laramie, Wyoming, USA
| | - Megan Szojka
- Department of Botany, University of Wyoming, Laramie, Wyoming, USA
| | - Margaret Swift
- Nicholas School of the Environment, Duke University, Durham, North Carolina, USA
| | - Katharine N Suding
- Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado, USA
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13
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Romero GQ, Gonçalves-Souza T, Roslin T, Marquis RJ, Marino NAC, Novotny V, Cornelissen T, Orivel J, Sui S, Aires G, Antoniazzi R, Dáttilo W, Breviglieri CPB, Busse A, Gibb H, Izzo TJ, Kadlec T, Kemp V, Kersch-Becker M, Knapp M, Kratina P, Luke R, Majnarić S, Maritz R, Mateus Martins P, Mendesil E, Michalko J, Mrazova A, Novais S, Pereira CC, Perić MS, Petermann JS, Ribeiro SP, Sam K, Trzcinski MK, Vieira C, Westwood N, Bernaschini ML, Carvajal V, González E, Jausoro M, Kaensin S, Ospina F, Cristóbal-Pérez EJ, Quesada M, Rogy P, Srivastava DS, Szpryngiel S, Tack AJM, Teder T, Videla M, Viljur ML, Koricheva J. Climate variability and aridity modulate the role of leaf shelters for arthropods: A global experiment. GLOBAL CHANGE BIOLOGY 2022; 28:3694-3710. [PMID: 35243726 DOI: 10.1111/gcb.16150] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 02/20/2022] [Indexed: 06/14/2023]
Abstract
Current climate change is disrupting biotic interactions and eroding biodiversity worldwide. However, species sensitive to aridity, high temperatures, and climate variability might find shelter in microclimatic refuges, such as leaf rolls built by arthropods. To explore how the importance of leaf shelters for terrestrial arthropods changes with latitude, elevation, and climate, we conducted a distributed experiment comparing arthropods in leaf rolls versus control leaves across 52 sites along an 11,790 km latitudinal gradient. We then probed the impact of short- versus long-term climatic impacts on roll use, by comparing the relative impact of conditions during the experiment versus average, baseline conditions at the site. Leaf shelters supported larger organisms and higher arthropod biomass and species diversity than non-rolled control leaves. However, the magnitude of the leaf rolls' effect differed between long- and short-term climate conditions, metrics (species richness, biomass, and body size), and trophic groups (predators vs. herbivores). The effect of leaf rolls on predator richness was influenced only by baseline climate, increasing in magnitude in regions experiencing increased long-term aridity, regardless of latitude, elevation, and weather during the experiment. This suggests that shelter use by predators may be innate, and thus, driven by natural selection. In contrast, the effect of leaf rolls on predator biomass and predator body size decreased with increasing temperature, and increased with increasing precipitation, respectively, during the experiment. The magnitude of shelter usage by herbivores increased with the abundance of predators and decreased with increasing temperature during the experiment. Taken together, these results highlight that leaf roll use may have both proximal and ultimate causes. Projected increases in climate variability and aridity are, therefore, likely to increase the importance of biotic refugia in mitigating the effects of climate change on species persistence.
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Affiliation(s)
- Gustavo Q Romero
- Laboratory of Multitrophic Interactions and Biodiversity, Department of Animal Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Thiago Gonçalves-Souza
- Laboratory of Ecological Synthesis and Biodiversity Conservation, Department of Biology, Federal Rural University of Pernambuco (UFRPE), Recife, Brazil
| | - Tomas Roslin
- Spatial Foodweb Ecology Group, Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden
- Spatial Foodweb Ecology Group, Department of Agricultural Sciences, University of Helsinki, Helsinki, Finland
| | - Robert J Marquis
- Whitney R. Harris World Ecology Center, Department of Biology, University of Missouri-St. Louis, St. Louis, Missouri, USA
| | - Nicholas A C Marino
- Programa de Pós-Graduação em Ecologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Vojtech Novotny
- Biology Centre, Czech Academy of Sciences, Institute of Entomology, Ceske Budejovice, Czech Republic
- Faculty of Science, University of South Bohemia, Ceske Budejovice, Czech Republic
| | - Tatiana Cornelissen
- Centre for Ecological Synthesis and Conservation, Department of Genetics, Ecology and Evolution, UFMG, Belo Horizonte, Brazil
| | - Jerome Orivel
- CNRS, UMR Ecologie des Forêts de Guyane (EcoFoG), AgroParisTech, CIRAD, INRAE, Université de Guyane, Université des Antilles, Campus agronomique, Kourou cedex, France
| | - Shen Sui
- New Guinea Binatang Research Center, Nagada Harbour, Madang, Papua New Guinea
| | - Gustavo Aires
- Laboratory of Ecological Synthesis and Biodiversity Conservation, Department of Biology, Federal Rural University of Pernambuco (UFRPE), Recife, Brazil
| | - Reuber Antoniazzi
- Arthur Temple College of Forestry and Agriculture, Stephen F. Austin State University, Nacogdoches, Texas, USA
| | - Wesley Dáttilo
- Red de Ecoetología, Instituto de Ecología A.C, Xalapa, Mexico
| | - Crasso P B Breviglieri
- Laboratory of Multitrophic Interactions and Biodiversity, Department of Animal Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Annika Busse
- Department of Nature Conservation and Research, Bavarian Forest National Park, Grafenau, Germany
| | - Heloise Gibb
- Department of Ecology, Environment and Evolution, La Trobe University, Melbourne, Victoria, Australia
| | - Thiago J Izzo
- Departamento de Botânica e Ecologia, Universidade Federal de Mato Grosso, Cuiabá, Brasil
| | - Tomas Kadlec
- Department of Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Victoria Kemp
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Monica Kersch-Becker
- Department of Entomology, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Michal Knapp
- Department of Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Pavel Kratina
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Rebecca Luke
- Department of Biological Sciences, Royal Holloway University of London, Egham, Surrey, UK
| | - Stefan Majnarić
- Faculty of Science, Department of biology, University of Zagreb, Zagreb, Croatia
| | - Robin Maritz
- Department of Biodiversity and Conservation Biology, University of the Western Cape, Bellville, South Africa
| | - Paulo Mateus Martins
- Laboratory of Ecological Synthesis and Biodiversity Conservation, Department of Biology, Federal Rural University of Pernambuco (UFRPE), Recife, Brazil
- Programa de Pós-graduação em Etnobiologia e Conservação da Natureza, Universidade Federal Rural de Pernambuco (UFRPE) [Federal Rural University of Pernambuco], Recife, Brazil
- Department of Zoology, University of Otago, Dunedin, New Zealand
| | - Esayas Mendesil
- Department of Horticulture and Plant Sciences, Jimma University, Jimma, Ethiopia
| | - Jaroslav Michalko
- Institute of Biotechnology, Faculty of Biotechnology and Food Sciences, Slovak University of Agriculture, Nitra, Slovakia
- Mlynany Arboretum, Institute of Forest Ecology, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Anna Mrazova
- Biology Centre, Czech Academy of Sciences, Institute of Entomology, Ceske Budejovice, Czech Republic
- Faculty of Science, University of South Bohemia, Ceske Budejovice, Czech Republic
| | - Samuel Novais
- Red de Interacciones Multitróficas, Instituto de Ecología A.C, Xalapa, Mexico
| | - Cássio C Pereira
- Centre for Ecological Synthesis and Conservation, Department of Genetics, Ecology and Evolution, UFMG, Belo Horizonte, Brazil
| | - Mirela S Perić
- Faculty of Science, Department of biology, University of Zagreb, Zagreb, Croatia
| | - Jana S Petermann
- Department of Environment and Biodiversity, University of Salzburg, Salzburg, Austria
| | - Sérvio P Ribeiro
- Laboratory of Ecoehalth, Ecology of Canopy Insects and Natural Succession, NUPEB-Universidade Federal de Ouro Preto, Ouro Preto, Brazil
| | - Katerina Sam
- Biology Centre, Czech Academy of Sciences, Institute of Entomology, Ceske Budejovice, Czech Republic
- Faculty of Science, University of South Bohemia, Ceske Budejovice, Czech Republic
| | - M Kurtis Trzcinski
- Department of Forest & Conservation Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Camila Vieira
- Pós-graduação em Ecologia e Conservação de Recursos Naturais, Universidade Federal de Uberlândia, Uberlândia, MG, Brazil
| | - Natalie Westwood
- Dept. of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Maria L Bernaschini
- Instituto Multidisciplinario de Biología Vegetal (CONICET-Universidad Nacional de Córdoba), Córdoba, Argentina
| | - Valentina Carvajal
- Laboratorio de Ecologia, Grupo de Investigación en Ecosistemas Tropicales, Facultad de Ciencias Exactas y Naturales, Universidad de Caldas, Manizales, Colombia
| | - Ezequiel González
- Department of Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic
- Institute for Environmental Science, University of Koblenz-Landau, Landau, Germany
| | - Mariana Jausoro
- Departamento de Ciencias Basicas, Universidad Nacional de Chilecito, Chilecito, Spain
| | - Stanis Kaensin
- New Guinea Binatang Research Center, Nagada Harbour, Madang, Papua New Guinea
| | - Fabiola Ospina
- Departamento de Ciencias Biológicas, Facultad de Ciencias Exactas y Naturales, Universidad de Caldas, Manizales, Colombia
| | - E Jacob Cristóbal-Pérez
- Laboratorio Nacional de Análisis y Síntesis Ecológica (LANASE), Escuela Nacional de Estudios Superiores Unidad Morelia
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, Michoacán, México
| | - Mauricio Quesada
- Laboratorio Nacional de Análisis y Síntesis Ecológica (LANASE), Escuela Nacional de Estudios Superiores Unidad Morelia
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, Michoacán, México
| | - Pierre Rogy
- Dept. of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Diane S Srivastava
- Dept. of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Scarlett Szpryngiel
- Department of Zoology, The Swedish Museum of Natural History, Stockholm, Sweden
| | - Ayco J M Tack
- Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden
| | - Tiit Teder
- Department of Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic
- Department of Zoology, Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia
| | - Martin Videla
- Instituto Multidisciplinario de Biología Vegetal (CONICET-Universidad Nacional de Córdoba), Córdoba, Argentina
| | - Mari-Liis Viljur
- Department of Zoology, Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia
- Field Station Fabrikschleichach, Department of Animal Ecology and Tropical Biology (Zoology III), Julius Maximilians University Würzburg, Rauhenebrach, Germany
| | - Julia Koricheva
- Department of Biological Sciences, Royal Holloway University of London, Egham, Surrey, UK
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14
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Weiss-Lehman CP, Werner CM, Bowler CH, Hallett LM, Mayfield MM, Godoy O, Aoyama L, Barabás G, Chu C, Ladouceur E, Larios L, Shoemaker LG. Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modelling approach. Ecol Lett 2022; 25:1263-1276. [PMID: 35106910 PMCID: PMC9543015 DOI: 10.1111/ele.13977] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 12/07/2021] [Accepted: 01/02/2022] [Indexed: 11/30/2022]
Abstract
Modelling species interactions in diverse communities traditionally requires a prohibitively large number of species‐interaction coefficients, especially when considering environmental dependence of parameters. We implemented Bayesian variable selection via sparsity‐inducing priors on non‐linear species abundance models to determine which species interactions should be retained and which can be represented as an average heterospecific interaction term, reducing the number of model parameters. We evaluated model performance using simulated communities, computing out‐of‐sample predictive accuracy and parameter recovery across different input sample sizes. We applied our method to a diverse empirical community, allowing us to disentangle the direct role of environmental gradients on species’ intrinsic growth rates from indirect effects via competitive interactions. We also identified a few neighbouring species from the diverse community that had non‐generic interactions with our focal species. This sparse modelling approach facilitates exploration of species interactions in diverse communities while maintaining a manageable number of parameters.
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Affiliation(s)
| | - Chhaya M Werner
- Botany Department, University of Wyoming, Laramie, Wyoming, USA
| | - Catherine H Bowler
- School of Biological Sciences, University of Queensland, Brisbane, Queensland, Australia
| | - Lauren M Hallett
- Biology Department, University of Oregon, Eugene, Oregon, USA.,Environmental Studies Program, University of Oregon, Eugene, Oregon, USA
| | - Margaret M Mayfield
- School of Biological Sciences, University of Queensland, Brisbane, Queensland, Australia
| | - Oscar Godoy
- Departamento de Biología, Instituto Universitario de Investigación Marina (INMAR), Universidad de Cádiz, Puerto Real, Spain
| | - Lina Aoyama
- Biology Department, University of Oregon, Eugene, Oregon, USA.,Environmental Studies Program, University of Oregon, Eugene, Oregon, USA
| | - György Barabás
- Division of Theoretical Biology, Department of IFM, Linköping University, Linköping, Sweden
| | - Chengjin Chu
- Department of Ecology, State Key Laboratory of Biocontrol and School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Emma Ladouceur
- German Centre for Integrative Biodiversity Research (iDiv) Leipzig-Halle-Jena, Leipzig, Germany.,Department of Physiological Diversity, Helmholtz Centre for Environmental Research -UFZ, Leipzig, Germany
| | - Loralee Larios
- Department of Botany and Plant Sciences, University of California Riverside, Riverside, California, USA
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15
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Roberts SM, Halpin PN, Clark JS. Jointly modeling marine species to inform the effects of environmental change on an ecological community in the Northwest Atlantic. Sci Rep 2022; 12:132. [PMID: 34997068 PMCID: PMC8742080 DOI: 10.1038/s41598-021-04110-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 12/15/2021] [Indexed: 11/10/2022] Open
Abstract
Single species distribution models (SSDMs) are typically used to understand and predict the distribution and abundance of marine fish by fitting distribution models for each species independently to a combination of abiotic environmental variables. However, species abundances and distributions are influenced by abiotic environmental preferences as well as biotic dependencies such as interspecific competition and predation. When species interact, a joint species distribution model (JSDM) will allow for valid inference of environmental effects. We built a joint species distribution model of marine fish and invertebrates of the Northeast US Continental Shelf, providing evidence on species relationships with the environment as well as the likelihood of species to covary. Predictive performance is similar to SSDMs but the Bayesian joint modeling approach provides two main advantages over single species modeling: (1) the JSDM directly estimates the significance of environmental effects; and (2) predicted species richness accounts for species dependencies. An additional value of JSDMs is that the conditional prediction of species distributions can use not only the environmental associations of species, but also the presence and abundance of other species when forecasting future climatic associations.
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Affiliation(s)
- Sarah M Roberts
- Nicholas School of the Environment, Duke University, Durham, NC, 27708, USA.
| | - Patrick N Halpin
- Nicholas School of the Environment, Duke University, Durham, NC, 27708, USA
| | - James S Clark
- Nicholas School of the Environment, Duke University, Durham, NC, 27708, USA
- Department of Statistical Science, Duke University, Durham, NC, 27708, USA
- INRAE, 2 rue de la Papeterie, BP 76, 38402, Saint-Martin-d'Heres Cedex, France
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16
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QnAs with James S. Clark. Proc Natl Acad Sci U S A 2021; 118:2116719118. [PMID: 34725169 DOI: 10.1073/pnas.2116719118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/10/2021] [Indexed: 11/18/2022] Open
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17
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Akbar S, Saritha SK. Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change. Sci Rep 2021; 11:14332. [PMID: 34253748 PMCID: PMC8275618 DOI: 10.1038/s41598-021-93122-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 06/21/2021] [Indexed: 02/06/2023] Open
Abstract
Community detection remains little explored in the analysis of biodiversity change. The challenges linked with global biodiversity change have also multiplied manifold in the past few decades. Moreover, most studies concerning biodiversity change lack the quantitative treatment central to species distribution modeling. Empirical analysis of species distribution and abundance is thus integral to the study of biodiversity loss and biodiversity alterations. Community detection is therefore expected to efficiently model the topological aspect of biodiversity change driven by land-use conversion and climate change; given that it has already proven superior for diverse problems in the domain of social network analysis and subgroup discovery in complex systems. Thus, quantum inspired community detection is proposed as a novel technique to predict biodiversity change considering tiger population in eighteen states of India; leading to benchmarking of two novel datasets. Elements of land-use conversion and climate change are explored to design these datasets viz.-Landscape based distribution and Number of tiger reserves based distribution respectively; for predicting regions expected to maximize Tiger population growth. Furthermore, validation of the proposed framework on the said datasets is performed using standard community detection metrics like-Modularity, Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), Degree distribution, Degree centrality and Edge-betweenness centrality. Quantum inspired community detection has also been successful in demonstrating an association between biodiversity change, land-use conversion and climate change; validated statistically by Pearson's correlation coefficient and p value test. Finally, modularity distribution based on parameter tuning establishes the superiority of the second dataset based on the number of Tiger reserves-in predicting regions maximizing Tiger population growth fostering species distribution and abundance; apart from scripting a stronger correlation of biodiversity change with land-use conversion.
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Affiliation(s)
- Sana Akbar
- Department of CSE, MANIT, Bhopal, India.
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18
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Rigge M, Shi H, Postma K. Projected change in rangeland fractional component cover across the sagebrush biome under climate change through 2085. Ecosphere 2021. [DOI: 10.1002/ecs2.3538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Matthew Rigge
- U.S. Geological Survey (USGS) Earth Resources Observation and Science Center Sioux Falls South Dakota57198USA
| | - Hua Shi
- AFDS Sioux Falls South Dakota57198USA
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19
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Abstract
We propose a dedicated research effort on the determinants of settlement persistence in the ancient world, with the potential to significantly advance the scientific understanding of urban sustainability today. Settlements (cities, towns, villages) are locations with two key attributes: They frame human interactions and activities in space, and they are where people dwell or live. Sustainability, in this case, focuses on the capacity of structures and functions of a settlement system (geography, demography, institutions) to provide for continuity of safe habitation. The 7,000-y-old experience of urbanism, as revealed by archaeology and history, includes many instances of settlements and settlement systems enduring, adapting to, or generating environmental, institutional, and technological changes. The field of urban sustainability lacks a firm scientific foundation for understanding the long durée, relying instead on narratives of collapse informed by limited case studies. We argue for the development of a new interdisciplinary research effort to establish scientific understanding of settlement and settlement system persistence. Such an effort would build upon the many fields that study human settlements to develop new theories and databases from the extensive documentation of ancient and premodern urban systems. A scientific foundation will generate novel insights to advance the field of urban sustainability.
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20
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Bystrova D, Poggiato G, Bektaş B, Arbel J, Clark JS, Guglielmi A, Thuiller W. Clustering Species With Residual Covariance Matrix in Joint Species Distribution Models. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.601384] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Modeling species distributions over space and time is one of the major research topics in both ecology and conservation biology. Joint Species Distribution models (JSDMs) have recently been introduced as a tool to better model community data, by inferring a residual covariance matrix between species, after accounting for species' response to the environment. However, these models are computationally demanding, even when latent factors, a common tool for dimension reduction, are used. To address this issue, Taylor-Rodriguez et al. (2017) proposed to use a Dirichlet process, a Bayesian nonparametric prior, to further reduce model dimension by clustering species in the residual covariance matrix. Here, we built on this approach to include a prior knowledge on the potential number of clusters, and instead used a Pitman–Yor process to address some critical limitations of the Dirichlet process. We therefore propose a framework that includes prior knowledge in the residual covariance matrix, providing a tool to analyze clusters of species that share the same residual associations with respect to other species. We applied our methodology to a case study of plant communities in a protected area of the French Alps (the Bauges Regional Park), and demonstrated that our extensions improve dimension reduction and reveal additional information from the residual covariance matrix, notably showing how the estimated clusters are compatible with plant traits, endorsing their importance in shaping communities.
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21
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Clark JS, Andrus R, Aubry-Kientz M, Bergeron Y, Bogdziewicz M, Bragg DC, Brockway D, Cleavitt NL, Cohen S, Courbaud B, Daley R, Das AJ, Dietze M, Fahey TJ, Fer I, Franklin JF, Gehring CA, Gilbert GS, Greenberg CH, Guo Q, HilleRisLambers J, Ibanez I, Johnstone J, Kilner CL, Knops J, Koenig WD, Kunstler G, LaMontagne JM, Legg KL, Luongo J, Lutz JA, Macias D, McIntire EJB, Messaoud Y, Moore CM, Moran E, Myers JA, Myers OB, Nunez C, Parmenter R, Pearse S, Pearson S, Poulton-Kamakura R, Ready E, Redmond MD, Reid CD, Rodman KC, Scher CL, Schlesinger WH, Schwantes AM, Shanahan E, Sharma S, Steele MA, Stephenson NL, Sutton S, Swenson JJ, Swift M, Veblen TT, Whipple AV, Whitham TG, Wion AP, Zhu K, Zlotin R. Continent-wide tree fecundity driven by indirect climate effects. Nat Commun 2021; 12:1242. [PMID: 33623042 PMCID: PMC7902660 DOI: 10.1038/s41467-020-20836-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 12/01/2020] [Indexed: 01/31/2023] Open
Abstract
Indirect climate effects on tree fecundity that come through variation in size and growth (climate-condition interactions) are not currently part of models used to predict future forests. Trends in species abundances predicted from meta-analyses and species distribution models will be misleading if they depend on the conditions of individuals. Here we find from a synthesis of tree species in North America that climate-condition interactions dominate responses through two pathways, i) effects of growth that depend on climate, and ii) effects of climate that depend on tree size. Because tree fecundity first increases and then declines with size, climate change that stimulates growth promotes a shift of small trees to more fecund sizes, but the opposite can be true for large sizes. Change the depresses growth also affects fecundity. We find a biogeographic divide, with these interactions reducing fecundity in the West and increasing it in the East. Continental-scale responses of these forests are thus driven largely by indirect effects, recommending management for climate change that considers multiple demographic rates.
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Affiliation(s)
- James S. Clark
- grid.26009.3d0000 0004 1936 7961Nicholas School of the Environment, Duke University, Durham, NC USA ,grid.450307.5INRAE, LESSEM, University Grenoble Alpes, Saint-Martin-d’Heres, France
| | - Robert Andrus
- grid.266190.a0000000096214564Department of Geography, University of Colorado Boulder, Boulder, CO USA
| | - Melaine Aubry-Kientz
- grid.266096.d0000 0001 0049 1282School of Natural Sciences, University of California, Merced, Merced, CA USA
| | - Yves Bergeron
- grid.265695.bForest Research Institute, University of Quebec in Abitibi-Temiscamingue, Rouyn-Noranda, QC Canada
| | - Michal Bogdziewicz
- grid.5633.30000 0001 2097 3545Department of Systematic Zoology, Faculty of Biology, Adam Mickiewicz University, Poznan, Poland
| | - Don C. Bragg
- grid.497399.90000 0001 2106 5338USDA Forest Service, Southern Research Station, Monticello, AR USA
| | - Dale Brockway
- grid.472551.00000 0004 0404 3120USDA Forest Service Southern Research Station, Auburn, AL USA
| | - Natalie L. Cleavitt
- grid.5386.8000000041936877XNatural Resources, Cornell University, Ithaca, NY USA
| | - Susan Cohen
- grid.10698.360000000122483208Institute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Benoit Courbaud
- grid.450307.5INRAE, LESSEM, University Grenoble Alpes, Saint-Martin-d’Heres, France
| | - Robert Daley
- grid.454846.f0000 0001 2331 3972Greater Yellowstone Network, National Park Service, Bozeman, MT USA
| | - Adrian J. Das
- grid.2865.90000000121546924USGS Western Ecological Research Center, Three Rivers, CA USA
| | - Michael Dietze
- grid.189504.10000 0004 1936 7558Earth and Environment, Boston University, Boston, MA USA
| | - Timothy J. Fahey
- grid.472551.00000 0004 0404 3120USDA Forest Service Southern Research Station, Auburn, AL USA
| | - Istem Fer
- grid.8657.c0000 0001 2253 8678Finnish Meteorological Institute, Helsinki, Finland
| | - Jerry F. Franklin
- grid.34477.330000000122986657Forest Resources, University of Washington, Seattle, WA USA
| | - Catherine A. Gehring
- grid.261120.60000 0004 1936 8040Department of Biological Science, Northern Arizona University, Flagstaff, AZ USA
| | - Gregory S. Gilbert
- grid.205975.c0000 0001 0740 6917University of California, Santa Cruz, Santa Cruz, CA USA
| | - Cathryn H. Greenberg
- grid.472551.00000 0004 0404 3120USDA Forest Service, Bent Creek Experimental Forest, Asheville, NC USA
| | - Qinfeng Guo
- grid.472551.00000 0004 0404 3120USDA Forest Service Southern Research Station, Eastern Forest Environmental Threat Assessment Center, Research Triangle Park, NC USA
| | - Janneke HilleRisLambers
- grid.34477.330000000122986657Department of Biology, University of Washington, Seattle, WA USA
| | - Ines Ibanez
- grid.214458.e0000000086837370School for Environment and Sustainability, University of Michigan, Ann Arbor, MI USA
| | - Jill Johnstone
- grid.25152.310000 0001 2154 235XDepartment of Biology, University of Saskatchewan, Saskatoon, SK Canada
| | - Christopher L. Kilner
- grid.26009.3d0000 0004 1936 7961Nicholas School of the Environment, Duke University, Durham, NC USA
| | - Johannes Knops
- grid.440701.60000 0004 1765 4000Health and Environmental Sciences Department, Xian Jiaotong-Liverpool University, Suzhou, China
| | - Walter D. Koenig
- grid.47840.3f0000 0001 2181 7878Hastings Reservation, University of California Berkeley, Carmel Valley, CA USA
| | - Georges Kunstler
- grid.450307.5INRAE, LESSEM, University Grenoble Alpes, Saint-Martin-d’Heres, France
| | - Jalene M. LaMontagne
- grid.254920.80000 0001 0707 2013Department of Biological Sciences, DePaul University, Chicago, IL USA
| | - Kristin L. Legg
- grid.454846.f0000 0001 2331 3972Greater Yellowstone Network, National Park Service, Bozeman, MT USA
| | - Jordan Luongo
- grid.26009.3d0000 0004 1936 7961Nicholas School of the Environment, Duke University, Durham, NC USA
| | - James A. Lutz
- grid.53857.3c0000 0001 2185 8768Department of Wildland Resources, Utah State University Ecology Center, Logan, UT USA
| | - Diana Macias
- grid.266832.b0000 0001 2188 8502Department of Biology, University of New Mexico, Albuquerque, NM USA
| | | | - Yassine Messaoud
- grid.265704.20000 0001 0665 6279Université du Québec en Abitibi-Témiscamingue, Rouyn-Noranda, Quebec Canada
| | - Christopher M. Moore
- grid.254333.00000 0001 2296 8213Department of Biology, Colby College, Waterville, ME USA
| | - Emily Moran
- grid.266190.a0000000096214564Department of Geography, University of Colorado Boulder, Boulder, CO USA
| | - Jonathan A. Myers
- grid.4367.60000 0001 2355 7002Department of Biology, Washington University in St. Louis, St. Louis, MO USA
| | - Orrin B. Myers
- grid.266832.b0000 0001 2188 8502University of New Mexico, Albuquerque, NM USA
| | - Chase Nunez
- grid.507516.00000 0004 7661 536XDepartment for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, Konstanz, Germany
| | - Robert Parmenter
- grid.454846.f0000 0001 2331 3972Valles Caldera National Preserve, National Park Service, Jemez Springs, NM USA
| | - Sam Pearse
- grid.2865.90000000121546924Fort Collins Science Center, Fort Collins, CO USA
| | - Scott Pearson
- grid.435676.50000 0000 8528 5973Department of Natural Sciences, Mars Hill University, Mars Hill, NC USA
| | - Renata Poulton-Kamakura
- grid.26009.3d0000 0004 1936 7961Nicholas School of the Environment, Duke University, Durham, NC USA
| | - Ethan Ready
- grid.26009.3d0000 0004 1936 7961Nicholas School of the Environment, Duke University, Durham, NC USA
| | - Miranda D. Redmond
- grid.47894.360000 0004 1936 8083Department of Forest and Rangeland Stewardship, Colorado State University, Fort Collins, CO USA
| | - Chantal D. Reid
- grid.26009.3d0000 0004 1936 7961Nicholas School of the Environment, Duke University, Durham, NC USA
| | - Kyle C. Rodman
- grid.450307.5INRAE, LESSEM, University Grenoble Alpes, Saint-Martin-d’Heres, France
| | - C. Lane Scher
- grid.26009.3d0000 0004 1936 7961Nicholas School of the Environment, Duke University, Durham, NC USA
| | - William H. Schlesinger
- grid.26009.3d0000 0004 1936 7961Nicholas School of the Environment, Duke University, Durham, NC USA
| | - Amanda M. Schwantes
- grid.17063.330000 0001 2157 2938Ecology and Evolutionary Biology, University of Toronto, Toronto, ON Canada
| | - Erin Shanahan
- grid.454846.f0000 0001 2331 3972Greater Yellowstone Network, National Park Service, Bozeman, MT USA
| | - Shubhi Sharma
- grid.26009.3d0000 0004 1936 7961Nicholas School of the Environment, Duke University, Durham, NC USA
| | - Michael A. Steele
- grid.268256.d0000 0000 8510 1943Department of Biology, Wilkes University, Wilkes-Barre, PA USA
| | - Nathan L. Stephenson
- grid.2865.90000000121546924USGS Western Ecological Research Center, Three Rivers, CA USA
| | - Samantha Sutton
- grid.26009.3d0000 0004 1936 7961Nicholas School of the Environment, Duke University, Durham, NC USA
| | - Jennifer J. Swenson
- grid.26009.3d0000 0004 1936 7961Nicholas School of the Environment, Duke University, Durham, NC USA
| | - Margaret Swift
- grid.26009.3d0000 0004 1936 7961Nicholas School of the Environment, Duke University, Durham, NC USA
| | - Thomas T. Veblen
- grid.450307.5INRAE, LESSEM, University Grenoble Alpes, Saint-Martin-d’Heres, France
| | - Amy V. Whipple
- grid.261120.60000 0004 1936 8040Department of Biological Science, Northern Arizona University, Flagstaff, AZ USA
| | - Thomas G. Whitham
- grid.261120.60000 0004 1936 8040Department of Biological Science, Northern Arizona University, Flagstaff, AZ USA
| | - Andreas P. Wion
- grid.47894.360000 0004 1936 8083Department of Forest and Rangeland Stewardship, Colorado State University, Fort Collins, CO USA
| | - Kai Zhu
- grid.205975.c0000 0001 0740 6917University of California, Santa Cruz, Santa Cruz, CA USA
| | - Roman Zlotin
- grid.411377.70000 0001 0790 959XGeography Department and Russian and East European Institute, Bloomington, IN USA
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22
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Lasky JR, Hooten MB, Adler PB. What processes must we understand to forecast regional-scale population dynamics? Proc Biol Sci 2020; 287:20202219. [PMID: 33290672 PMCID: PMC7739927 DOI: 10.1098/rspb.2020.2219] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 11/12/2020] [Indexed: 12/14/2022] Open
Abstract
An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.
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Affiliation(s)
- Jesse R. Lasky
- Department of Biology, Pennsylvania State University, University Park, PA, USA
| | - Mevin B. Hooten
- U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University, Fort Collins, CO, USA
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, USA
- Department of Statistics, Colorado State University, Fort Collins, CO, USA
| | - Peter B. Adler
- Department of Wildland Resources and the Ecology Center, Utah State University, Logan, UT, USA
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23
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Stange M, Barrett RDH, Hendry AP. The importance of genomic variation for biodiversity, ecosystems and people. Nat Rev Genet 2020; 22:89-105. [PMID: 33067582 DOI: 10.1038/s41576-020-00288-7] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/07/2020] [Indexed: 11/09/2022]
Abstract
The 2019 United Nations Global assessment report on biodiversity and ecosystem services estimated that approximately 1 million species are at risk of extinction. This primarily human-driven loss of biodiversity has unprecedented negative consequences for ecosystems and people. Classic and emerging approaches in genetics and genomics have the potential to dramatically improve these outcomes. In particular, the study of interactions among genetic loci within and between species will play a critical role in understanding the adaptive potential of species and communities, and hence their direct and indirect effects on biodiversity, ecosystems and people. We explore these population and community genomic contexts in the hope of finding solutions for maintaining and improving ecosystem services and nature's contributions to people.
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Affiliation(s)
- Madlen Stange
- Redpath Museum, McGill University, Montreal, QC, Canada
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