Biome partitioning of the global ocean based on phytoplankton biogeography

Elizondo, Urs Hofmann; Righetti, Damiano; Benedetti, Fabio; Vogt, Meike

doi:10.1016/j.pocean.2021.102530

Cited by 12 publications

(21 citation statements)

References 158 publications

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“…The so-called "Silhouette Method" measures how closely clusters are related to one another by deriving a value ranging from 1 to -1 (i.e., the average Silhouette width in S2B Fig) : the higher the value the less related the clusters are to one another [38,39]. In our study, the choice of a K value of two was clearly not appropriate as it would lead to the over-generalization of any potential inherent biogeographical structures within the dataset [40]. The next level of a higher average Silhouette width value appears when the K value is set at either 20, 23, 26 or 29 (S2B Fig) . We performed cluster analysis and compared their dendrograms at these different K values (S3-S6 Figs).…”

Section: Discussionmentioning

confidence: 99%

Global biogeography of living brachiopods: Bioregionalization patterns and possible controls

Shi

Bitner

2021

PLoS ONE

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The global distribution patterns of 14918 geo-referenced occurrences from 394 living brachiopod species were mapped in 5° grid cells, which enabled the visualization and delineation of distinct bioregions and biodiversity hotspots. Further investigation using cluster and network analyses allowed us to propose the first systematically and quantitatively recognized global bioregionalization framework for living brachiopods, consisting of five bioregions and thirteen bioprovinces. No single environmental or ecological variable is accountable for the newly proposed global bioregionalization patterns of living brachiopods. Instead, the combined effects of large-scale ocean gyres, climatic zonation as well as some geohistorical factors (e.g., formation of land bridges and geological recent closure of ancient seaways) are considered as the main drivers at the global scale. At the regional scale, however, the faunal composition, diversity and biogeographical differentiation appear to be mainly controlled by seawater temperature variation, regional ocean currents and coastal upwelling systems.

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Section: Discussionmentioning

confidence: 99%

Global biogeography of living brachiopods: Bioregionalization patterns and possible controls

Shi

Bitner

2021

PLoS ONE

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“…We used the zooplankton occurrences dataset (geolocated and dated presences) compiled by Benedetti et al (2021). Occurrences corresponding to benthic and parasitic copepods, occurrences with missing spatial coordinates, sampling dates, sampling depth, or not identified at species-level were removed.…”

Section: Species Occurrence Datamentioning

confidence: 99%

“…Occurrences corresponding to benthic and parasitic copepods, occurrences with missing spatial coordinates, sampling dates, sampling depth, or not identified at species-level were removed. Data from drilling holes, grid cells within 25 km of the nearest shoreline, or with a maximum sampling depth >500 m were also discarded (see Benedetti et al, 2021). To address sampling effort biases in the copepod occurrence data (Appendix S1) that could inflate models performance metrics or over-represent portions of the environmental space and thus hinder model predictability and interpretation (Veloz, 2009;Hijmans, 2012), we thinned occurrences using a randomization algorithm for each month and each species (30 randomizations per species dataset) so that monthly species occurrences were at least 500 km apart (Aiello-Lammens, Boria, Radosavljevic, Vilela, & Anderson, Zooplankton species occurrences dataset (Benedetti et al, 2021)…”

Section: Species Occurrence Datamentioning

confidence: 99%

“…To our knowledge, other qualitative traits such as trophic group or spawning mode have been investigated but not using a global CWM trait approach (McGinty et al, 2018; Benedetti, Vogt et al, 2018). If patterns of CWM values are clustered in space and time, they can be used to define ocean regions sharing common ecological characteristics (Longhurst, 2010; Reygondeau et al, 2017; Hofmann Elizondo et al, 2021). Although new data compilations make it possible, neither copepod FGs nor CMW-based regionalization schemes have been investigated on a global scale, yet this could be highly informative of large-scale marine food web and ecosystem dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Copepod functional traits and groups show divergent biogeographies in the global ocean

Benedetti

Wydler

Vogt

2022

Preprint

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Aim: To define global zooplankton functional groups (FGs) and to estimate their environmental niche and habitat distribution. We model the spatial patterns of copepod FGs habitat and identify regions sharing similar functional trait expression at the community level. Taxon: Marine planktonic Neocopepoda. Location: Global ocean. Methods: Factor analysis on mixed data and hierarchical clustering were used to identify copepod FGs based on five species-level functional traits. An ensemble of species distribution models was used to estimate the environmental niches of the modelled species, project the mean annual habitat suitability of the FGs, and to estimate the community weighted mean values of the traits studied. Ocean regions were defined based on their community-level mean trait expression using a principal component analysis and hierarchical clustering. Results: Eleven global copepod FGs were identified. They displayed contrasting latitudinal patterns in mean annual habitat suitability that could be explained by differences in environmental niche preferences: two FGs were associated with polar conditions, one followed the global temperature gradient, five were associated with tropical oligotrophic gyres, and the remaining three with boundary currents and counter currents. Four main regions of varying community weighted mean trait values emerged: the Southern Ocean, the northern and southern high latitudes, the tropical gyres, and the boundary currents and upwelling systems. Conclusions: We build on an exhaustive species trait dataset to put forward novel FGs that will improve the representation of zooplankton in global marine ecosystem models. Our results contribute to our understanding of the spatial patterns and drivers of marine plankton trait biogeography and will serve as a basis for studying the links between zooplankton biodiversity and ecosystem functioning and how they might evolve in the context of climate change.

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“…Dynamic models such as species distribution models (SDMs; Figure 5c; e.g., Cimino et al, 2020;Gagné et al, 2020) and semi-analytical models (Messié and Chavez, 2017) use environmental variables from satellite remote sensing to predict the occupancy or abundances of species ranging from plankton (e.g., Righetti et al, 2019;Elizondo et al, 2021) to higher trophic levels, including endangered species (Breece et al, 2018). Predictive models can also acquire water column information (e.g., stratification, mixed layer depth) from data-assimilative ocean models, which use satellite data (e.g., SST, altimetry) to improve model realism (Neveu et al, 2016).…”

Section: Methods 4 Species Distribution and Semi-analytical Modelsmentioning

confidence: 99%

Satellite Remote Sensing and the Marine Biodiversity Observation Network: Current Science and Future Steps

Kavanaugh¹,

Bell²,

Catlett³

et al. 2021

Oceanog

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Coastal ecosystems are rapidly changing due to human-caused global warming, rising sea level, changing circulation patterns, sea ice loss, and acidification that in turn alter the productivity and composition of marine biological communities. In addition, regional pressures associated with growing human populations and economies result in changes in infrastructure, land use, and other development; greater extraction of fisheries and other natural resources; alteration of benthic seascapes; increased pollution; and eutrophication. Understanding biodiversity is fundamental to assessing and managing human activities that sustain ecosystem health and services and mitigate humankind’s indiscretions. Remote-sensing observations provide rapid and synoptic data for assessing biophysical interactions at multiple spatial and temporal scales and thus are useful for monitoring biodiversity in critical coastal zones. However, many challenges remain because of complex bio-optical signals, poor signal retrieval, and suboptimal algorithms. Here, we highlight four approaches in remote sensing that complement the Marine Biodiversity Observation Network (MBON). MBON observations help quantify plankton functional types, foundation species, and unique species habitat relationships, as well as inform species distribution models. In concert with in situ observations across multiple platforms, these efforts contribute to monitoring biodiversity changes in complex coastal regions by providing oceanographic context, contributing to algorithm and indicator development, and creating linkages between long-term ecological studies, the next generations of satellite sensors, and marine ecosystem management.

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Biome partitioning of the global ocean based on phytoplankton biogeography

Cited by 12 publications

References 158 publications

Global biogeography of living brachiopods: Bioregionalization patterns and possible controls

Global biogeography of living brachiopods: Bioregionalization patterns and possible controls

Copepod functional traits and groups show divergent biogeographies in the global ocean

Satellite Remote Sensing and the Marine Biodiversity Observation Network: Current Science and Future Steps

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