ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    facet.materialart.
    Unknown
    Time lags: insights from the U.S. Long Term Ecological Research Network (2021)
    Ecological Society of America
    Details
    Publication Date: 2022-10-27
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Rastetter, E. B., Ohman, M. D., Elliott, K. J., Rehage, J. S., Rivera-Monroy, V. H., Boucek, R. E., Castaneda-Moya, E., Danielson, T. M., Gough, L., Groffman, P. M., Jackson, C. R., Miniat, C. F., & Shaver, G. R. Time lags: insights from the U.S. Long Term Ecological Research Network. Ecosphere, 12(5), (2021): e03431, https://doi.org/10.1002/ecs2.3431.
    Description: Ecosystems across the United States are changing in complex ways that are difficult to predict. Coordinated long-term research and analysis are required to assess how these changes will affect a diverse array of ecosystem services. This paper is part of a series that is a product of a synthesis effort of the U.S. National Science Foundation’s Long Term Ecological Research (LTER) network. This effort revealed that each LTER site had at least one compelling scientific case study about “what their site would look like” in 50 or 100 yr. As the site results were prepared, themes emerged, and the case studies were grouped into separate papers along five themes: state change, connectivity, resilience, time lags, and cascading effects and compiled into this special issue. This paper addresses the time lags theme with five examples from diverse biomes including tundra (Arctic), coastal upwelling (California Current Ecosystem), montane forests (Coweeta), and Everglades freshwater and coastal wetlands (Florida Coastal Everglades) LTER sites. Its objective is to demonstrate the importance of different types of time lags, in different kinds of ecosystems, as drivers of ecosystem structure and function and how these can effectively be addressed with long-term studies. The concept that slow, interactive, compounded changes can have dramatic effects on ecosystem structure, function, services, and future scenarios is apparent in many systems, but they are difficult to quantify and predict. The case studies presented here illustrate the expanding scope of thinking about time lags within the LTER network and beyond. Specifically, they examine what variables are best indicators of lagged changes in arctic tundra, how progressive ocean warming can have profound effects on zooplankton and phytoplankton in waters off the California coast, how a series of species changes over many decades can affect Eastern deciduous forests, and how infrequent, extreme cold spells and storms can have enduring effects on fish populations and wetland vegetation along the Southeast coast and the Gulf of Mexico. The case studies highlight the need for a diverse set of LTER (and other research networks) sites to sort out the multiple components of time lag effects in ecosystems.
    Description: This research was supported by the National Science Foundation Long Term Ecological Research program grants to the Arctic (Grants DEB-1637459 and 1026843), California Current (Grants OCE-1637632 and 1026607), Coweeta (Grants DEB-1637522, 1440485, 0823293, 9632854, and 0218001), and Florida Coastal Everglades (Grants DEB-9910514 and 1237517 and DBI-0620409) sites. We also acknowledge the sustained efforts of the CalCOFI program, present and previous staff of the SIO Pelagic Invertebrate Collection, and the late Ed Brinton for his pioneering insights in euphausiid ecology. The Coweeta research and synthesis were also supported by the USDA Forest Service, Southern Research Station, Coweeta Hydrologic Laboratory. Partial funding to VHRM was provided by the U.S. Department of the Interior South-Central Climate Science Center through Cooperative Agreement # G12AC00002.
    Keywords: Climate change ; Climate change detection ; Climate signal filtering ; Ecosystem response ; Special Feature: Forecasting Earth's Ecosystems with Long-Term Ecological Research
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER (OA)
  • 2
    facet.materialart.
    Unknown
    Relative exposure to microplastics and prey for a pelagic forage fish (2022)
    IOP Publishing
    Details
    Publication Date: 2022-10-27
    Description: © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chavarry, J. M., Law, K. L., Barton, A. D., Bowlin, N. M., Ohman, M. D., & Choy, C. A. Relative exposure to microplastics and prey for a pelagic forage fish. Environmental Research Letters, 17(6), (2022): 064038, https://doi.org/10.1088/1748-9326/ac7060.
    Description: In the global ocean, more than 380 species are known to ingest microplastics (plastic particles less than 5 mm in size), including mid-trophic forage fishes central to pelagic food webs. Trophic pathways that bioaccumulate microplastics in marine food webs remain unclear. We assess the potential for the trophic transfer of microplastics through forage fishes, which are prey for diverse predators including commercial and protected species. Here, we quantify Northern Anchovy (Engraulis mordax) exposure to microplastics relative to their natural zooplankton prey, across their vertical habitat. Microplastic and zooplankton samples were collected from the California Current Ecosystem in 2006 and 2007. We estimated the abundance of microplastics beyond the sampled size range but within anchovy feeding size ranges using global microplastic size distributions. Depth-integrated microplastics (0–30 m depth) were estimated using a depth decay model, accounting for the effects of wind-driven vertical mixing on buoyant microplastics. In this coastal upwelling biome, the median relative exposure for an anchovy that consumed prey 0.287–5 mm in size was 1 microplastic particle for every 3399 zooplankton individuals. Microplastic exposure varied, peaking within offshore habitats, during the winter, and during the day. Maximum exposure to microplastic particles relative to zooplankton prey was higher for juvenile (1:23) than adult (1:33) anchovy due to growth-associated differences in anchovy feeding. Overall, microplastic particles constituted fewer than 5% of prey-sized items available to anchovy. Microplastic exposure is likely to increase for forage fishes in the global ocean alongside declines in primary productivity, and with increased water column stratification and microplastic pollution.
    Description: This work originated from the Plastic Awareness Global Initiative (PAGI) international workshop, hosted by the Center for Marine Biodiversity and Conservation (CMBC) at Scripps Institution of Oceanography at the University of California San Diego in 2018, with support from Igor Korneitchouk and the Wilsdorf Mettler Future Foundation. We thank the workshop participants for early discussions and a collaborative meeting space. We thank Kelly Lance for her illustration contributions, and the SIO Communications Office for their support. We thank Miriam Doyle and Ryan Rykaczewski for their assistance in data acquisition, and we thank Penny Dockry and Stuart Sandin of CMBC for administrative and logistical support. Julia Chavarry was supported by the San Diego Fellowship. This paper is a contribution from the California Current Ecosystem Long Term Ecological Research site, supported by the National Science Foundation.
    Keywords: Upwelling ecosystems ; Food webs ; Climate change ; Engraulis mordax
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER (OA)
  • 3
    facet.materialart.
    Unknown
    Machine learning techniques to characterize functional traits of plankton from image data (2022)
    Association for the Sciences of Limnology and Oceanography
    Details
    Publication Date: 2022-10-18
    Description: © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Orenstein, E., Ayata, S., Maps, F., Becker, É., Benedetti, F., Biard, T., Garidel‐Thoron, T., Ellen, J., Ferrario, F., Giering, S., Guy‐Haim, T., Hoebeke, L., Iversen, M., Kiørboe, T., Lalonde, J., Lana, A., Laviale, M., Lombard, F., Lorimer, T., Martini, S., Meyer, A., Möller, K.O., Niehoff, B., Ohman, M.D., Pradalier, C., Romagnan, J.-B., Schröder, S.-M., Sonnet, V., Sosik, H.M., Stemmann, L.S., Stock, M., Terbiyik-Kurt, T., Valcárcel-Pérez, N., Vilgrain, L., Wacquet, G., Waite, A.M., & Irisson, J. Machine learning techniques to characterize functional traits of plankton from image data. Limnology and Oceanography, 67(8), (2022): 1647-1669, https://doi.org/10.1002/lno.12101.
    Description: Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.
    Description: SDA acknowledges funding from CNRS for her sabbatical in 2018–2020. Additional support was provided by the Institut des Sciences du Calcul et des Données (ISCD) of Sorbonne Université (SU) through the support of the sponsored junior team FORMAL (From ObseRving to Modeling oceAn Life), especially through the post-doctoral contract of EO. JOI acknowledges funding from the Belmont Forum, grant ANR-18-BELM-0003-01. French co-authors also wish to thank public taxpayers who fund their salaries. This work is a contribution to the scientific program of Québec Océan and the Takuvik Joint International Laboratory (UMI3376; CNRS - Université Laval). FM was supported by an NSERC Discovery Grant (RGPIN-2014-05433). MS is supported by the Research Foundation - Flanders (FWO17/PDO/067). FB received support from ETH Zürich. MDO is supported by the Gordon and Betty Moore Foundation and the U.S. National Science Foundation. ECB is supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) under the grant agreement no. 88882.438735/2019-01. TB is supported by the French National Research Agency (ANR-19-CE01-0006). NVP is supported by the Spanish State Research Agency, Ministry of Science and Innovation (PTA2016-12822-I). FL is supported by the Institut Universitaire de France (IUF). HMS was supported by the Simons Foundation (561126) and the U.S. National Science Foundation (CCF-1539256, OCE-1655686). Emily Peacock is gratefully acknowledged for expert annotation of IFCB images. LS was supported by the Chair VISION from CNRS/Sorbonne Université.
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER (OA)
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...