Aim The distribution of zooplankton functional traits is a key factor for regulating food web dynamics and carbon cycling in the oceans. Yet, we lack a clear understanding of how many functional groups (FGs) exist in the zooplankton and how their traits are distributed on a global scale. Here, we model and map the environmental habitats of copepod (i.e. the main component of marine zooplankton) FGs to 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 species modelled and the community weighted mean (CWM) values of the traits studied. Ocean regions were defined based on their community‐level mean trait expression using principal component analysis and hierarchical clustering. Results Eleven global copepods 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 CWM trait values emerged: the Southern Ocean, the northern and southern high latitudes, the tropical gyres and the boundary currents and upwelling systems. Conclusions The present FGs will improve the representation of copepods in global marine ecosystem models. This study improves the understanding of the patterns and drivers of copepods trait biogeography and will serve as a basis for studying links between zooplankton biodiversity and ecosystem functioning in a context of climate change.
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.
<p>Lakes are highly biodiverse ecosystems and are providing a wide range of ecosystem services to human wellbeing such as drinking water, water for irrigation, access to fisheries and recreational areas. Anthropogenic activities threaten these services both through local impacts on water bodies (e.g. eutrophication) and globally (e.g. climate change). The trophic state and the aquatic carbon cycle are sensitive indicators to evaluate the state and health of lake ecosystems. Monitoring the spatial and temporal dynamics of primary production is therefore a high priority in lake research.<br />Primary production can be assessed in several ways. The most common approach involves the incubation and measurement of labelled carbon isotopes in lake water samples that are exposed to certain light conditions. Alternatively, primary production can be estimated using diel variations in oxygen concentration or fast repetition rate fluorometry. Both approaches are accurate but can hardly be used to cover large spatial heterogeneities. For global assessments, only bio-optical primary production models based on remote sensing data allow a consistent upscaling in a cost-efficient manner.<br />A wide range of bio-optical primary production models exist and have been applied to lakes. Generally, these models describe the availability of light in the water column and the efficiency of the algae particles to absorb photon energy and to use this energy for subsequent carbon assimilation. The main challenges related to such approaches are to accurately the retrieve required information from satellite data and to precisely estimate sensible model parameters. The upcoming hyperspectral mission Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) by NASA will help to improve the accuracy of primary productivity estimates.<br />This contribution aims to improve understanding of sensitivities and validity of available bio-optical primary production models to eventually maximise the benefits of improved information retrievals from PACE. We particularly reviewed state-of-the-art primary production models for remote sensing data of oceans and lakes, provided an overview of relevant model inputs and calculated Sobol sensitivity indices for a range of input parameters and models. Our results facilitate future applications of primary production models to hyperspectral PACE data and will particularly help to identify most sensitive input variables, to improve empirical model parameterizations and to guide the selection of suited models for freshwater systems.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.