Aug 31, The purpose of this work was to explore the effects of phytoplankton diversity on . The relationships among plankton species composition and. Jun 4, Journal of Plankton Research, Volume 35, Issue 5, 1 September , Pages . The goals of this study were (i) to describe the environmental. Aug 5, This highlights the need for conserving diversity in order to maintain We sampled only small lakes and ponds to ensure that plankton.
Ascendancy accounts for the size of the network and for the information stored in such a network, but it also implies a temporal component Ulanowicz, It is our purpose, therefore, to apply these concepts to lake plankton systems, as such an approach has not previously been carried out in field studies. This lack of field studies may explain why it is still largely unclear how emergy can relate to the abiotic environment.
Underwater light climate might be a candidate for controlling emergy, but this remains untested. Nutrient contents might also impinge on emergy through control of phytoplankton and hence bottom-up control might be in operation.
Exergy, however, could be more dependent on trophic status since it is a function of biomass fractions and these partly depend upon nutrients. A composite variable in plankton communities is body size, which certainly relates to a variety of ecosystem functions Platt, Size and size spectra appear to be better descriptors of the pelagic ecosystem structure than taxonomic species alone, and show plankton communities in a more tractable form than the purely descriptive one Gaedke, Many ecosystem features in pelagic environments have been related to size spectra, i.
However, the relationship between size and EGFs has remained elusive up to now. Notwithstanding this, we must acknowledge that body size and its extension, the size spectrum, does not embrace or explain all plankton ecology.
The size spectrum, in particular, only suggests the main route of energy in pelagic systems Platt, Other features, such as life histories, taxonomic traits, etc. For example, the feeding ecology of zooplankton often dictates plankton community structure Gliwicz, It has been suggested that food webs and their connectance are related to trophic status Harris, Carney, using productivity as a surrogate of trophic status, reports some evidence of an intermediate relationship between connectance and trophic status, namely, the highest connectance in the plankton takes place at intermediate levels of eutrophy in lakes and oceans Carney, Also, since it has been suggested that size spectra relate to trophic status Sprules and Munawar,the relationship connectance—trophic status might be linked to size.
Furthermore, connectance could be considered as a weak surrogate of ascendancy because this EGF takes into account the size of the network Ulanowicz,and network size is both species and linkage dependent. Here we have carried out a study to explore the usefulness of EGFs in planktonic environments. We are well aware that, because of its exploratory nature, some conclusions may appear to be anticipatory and must be improved after further attempts with more data sets.
However, we think that this type of study will be rewarding if it provides further steps towards a more comprehensive plankton ecology. The aim of our study is three-fold. First, to relate size spectra and EGFs exergy, emergy and connectance as a proxy of ascendancy to each other on an empirical basis in lake plankton. Secondly, to explore the relationships between connectance, trophic status and plankton size spectra.
Thirdly, to search for relationships among EGFs and limnetic variables. Study sites Our study has been carried out in a Spanish conservation area: It is a quaternary valley heavily influenced by the nearby marl substrates. More than gravel pit lakes are to be found there, covering all degrees of trophic status, according to TP contents.
We have selected 13 lakes to carry out this study Figure 1encompassing a wide range of trophic degree. Picoplankton comprised blue-green cyanophytes as judged by Schubert's Schubert, method. Phytoplankton composition included Peridinium spp. Ciliates belonged to the genera Didinium, Coleps, Strombidium and Vorticella. Rotifers included Keratella, Polyarthra, Hexarthra and Filinia spp.
Further information can be found in Arauzo et al. No attempt was made to classify either bacteria or heterotrophic nanoflagellates HNFalthough most bacteria were cocci and rods and did not grow on particles.
All lakes are surrounded by a littoral reed belt, but planktivorous fish are absent. Method This study is based on two sampling trips carried out in ; they were chosen as highly representative of the average environmental conditions following preliminary surveys in — The first campaign took place over a week, when the lakes were in late mixing state, and could be considered as one when phytoplankton would experience light limitation.
The second field trip was undertaken in midsummer. Clear-water phases are thought to have occurred by late May, and some grazing experiments using the dilution method Landry and Hassett, have revealed low grazing rates in the chosen periods of this study M. All lakes were sampled at a central point by means of a Niskin bottle and one compound sample was taken in a given lake for each plankton fraction.
Shortly after collection, different fractions were fixed in either formalin bacteria, zooplankton or Lugol's phytoplankton.
The Relationship between Phytoplankton Evenness and Copepod Abundance in Lake Nansihu, China
Also, more samples were collected to carry out chemical and size analyses in the laboratory. Concurrently, a temperature profile and a vertical attenuation profile were recorded by means of a YSI instrument and a LI-COR B device, respectively, which were used to calculate a mixing depth and a vertical attenuation coefficient for each lake and sampling period. Plankton were counted using either fluorescence or inverted microscopy.
Bacterial enumeration followed the acridine orange method of Hobbie et al. All crustaceans occurring in each sample were counted. Picoplankton were counted in a Zeiss fluorescence microscope following the Weisse method Weisse,HNF were also counted with the same microscope, but after acridine staining.
Although size was considered our main raw variable, classification of taxonomic species was also carried out following standard texts. Size calculations relied on optical microscopy measurements. Thirty fresh specimens of each taxonomic species were measured for each lake and sampling date, and those data were used for biovolume calculations [phytoplankton, HNF, ciliates and rotifers; Rott, ; McCauley, ] and regression calculations [crustaceans; McCauley, ].
These results provide new insights into the relationship between diversity and ecosystem functioning in aquatic ecosystems. Introduction The Earth ecosystem is experiencing an unprecedented rate of biodiversity loss as a result of global climate change, eutrophication, and overexploitation of natural resources [ 123 ].
Lake ecosystems are relatively vulnerable and the loss of biodiversity may cause catastrophic consequences, such as algae blooms [ 45 ]. Phytoplankton, the most important primary producer in lakes, is particularly sensitive to variations in environmental factors [ 567 ]. Understanding the effects of phytoplankton diversity on ecosystem functioning is essential to developing appropriate conservation strategies in aquatic ecosystems [ 58 ].
Aquatic ecosystems are special because their primary organisms i. Phytoplankton diversity not only impacts the productivity, stability, resource use efficiency measured as the amount of phytoplankton biomass produced per unit of phosphorusand community turnover in its own trophic level [ 8101112 ], but also influences zooplankton through predator-prey interaction [ 1314 ]. It is commonly believed that communities with greater numbers of coexisting species are more resistant to predation [ 13 ].
The increase of producer species richness reduces the predation pressure and leads to smaller predator communities [ 1516 ]. In addition, the relationship between biodiversity and ecosystem functioning varies with different metrics of diversity. For example, Ptacnik et al. Due to their short lifecycles, plankton communities respond rapidly potentially more rapidly than other trophic levels to these processes.
Plankton-based indicators therefore have the potential to detect those changes at an early stage.
The Relationship between Phytoplankton Evenness and Copepod Abundance in Lake Nansihu, China
Plankton is also essential for organisms higher up the food web, such as shellfish, fish and seabirds, and changes in the plankton community can thus impact on the whole marine ecosystem. This indicator, based on phytoplankton biomass and zooplankton abundance, provides a means to identify changes anomalies in key groups within the plankton community.
These changes represent deviations from the assumed natural variability in the plankton time series. The changes are identified as small, important or extreme. This indicator can also help to understand changes in other parts of the marine food web. It has been assessed at two scales: When combined with the two other pelagic indicators that look at changes in plankton lifeform and changes in plankton diversityit will enable a more sensitive detection of change at the plankton community level.
Total phytoplankton as biomass using chlorophyll-a or Phytoplankton Colour Index as a proxy and zooplankton as abundance - using total copepods abundance represent key components of the plankton community. They account for the largest part of the plankton biomass and thus having an important role in the whole plankton production, as well as grazing, lysis auto- and viraladvection and sedimentation processes.
Being at the base of the food web, plankton represent directly or indirectly a food resource for numerous species at higher trophic levels, such as fish of commercial interest. Variability in phytoplankton biomass and zooplankton abundance can have significant impacts on the structure and function of the marine food web as a whole, as well as on ecosystem processes such as nutrient recycling. The intrinsic characteristics of plankton organisms, such as their small size, lack of commercial exploitation, short lifecycles and a global distribution, render them particularly interesting for monitoring programmes aiming to assess the state of marine ecosystems.
Short lifecycles and sensitivity to abiotic factors mean that plankton indicators can provide early warning of change in the marine ecosystem Batt et al. Increased pressure on the marine system can be expected to lead to greater and more frequent changes at the base of the food web.
Early warning of ecosystem change can help to prompt management actions when changes are still manageable see Burthe et al. A current challenge is to separate expected natural variability from the variability induced by human pressures, a particularly difficult objective that is not yet resolved by plankton science.
Robust statistical techniques exist to identify significant components of variation and changes at multiple scales for plankton. These changes may indicate major changes in the marine system involving consequences for other ecosystem components and processes.
Significant changes in this indicator are evaluated through time series analysis. However, since plankton dynamics are not well understood, it is difficult to judge what should be the minimum length of a time series for assessment. As a starting point, it is recommended that minimum time series that should be used is five years, but preferably a minimum ten years.
It is also necessary that the monitoring data are acquired at regular intervals through consistent sampling and analytical procedures.
The methodology can be applied to fixed-monitoring station time series the most frequent situation for monitoring European countries and to large-scale spatio-temporal data sets such as the Continuous Plankton Recorder CPR data or satellite data.
For instance, coastal data from fixed monitoring stations can be used to identify plankton indicators that could be linked to human pressures, while large-scale spatio-temporal data in the open ocean can be used to define plankton indicators that could be linked to large-scale hydro-meteorological changes or to indirect human pressures e. An important advantage of these plankton indicators is that the concepts are relatively easily transferable to other regions Gowen et al.
For the future development of these indicators the definition of reference periods will require knowledge of environmental and human pressure data. There was no comparable regional assessment of phytoplankton biomass and zooplankton abundance.
The methodology has been adapted mainly in the first steps of data preparation due to the type of data used. First, phytoplankton and zooplankton are considered separately. Second, two main data types related to different acquisition systems are considered here: Specificities related to Data Type Phytoplankton Data The same approach as for zooplankton applies for total phytoplankton biomass.
Instead of looking at a particular species or group, the bulk phytoplankton community is considered through the total phytoplankton biomass. Phytoplankton biomass can be measured as biovolume, carbon content or can be assessed through a proxy, using chlorophyll-a, which is present in all phytoplankton organisms. This method estimates the green colour of the plankton community sampled onto a silk net.
It should be noted that all data at the large spatial scale used for this indicator assessment originate from the CPR data collection on major shipping routes. Both chlorophyll-a and PCI are used in this assessment as they represent the two types of data regularly monitored in many areas. Chlorophyll-a concentration is already used as an OSPAR common indicator for the assessment of eutrophication. Zooplankton Data For zooplankton, only copepods total copepod abundance are considered in the calculation.
Reduction from total zooplankton abundance to total copepod abundance is justified given that copepods are the best described zooplankton group, consistently determined in samples and are generally the most abundant and ubiquitous zooplankton taxa, both in space and time.
In practice, the use of groups, such as copepods, is often favoured over single indicator species. Indeed some species such as meroplankton can have a very patchy distribution and highly variable fluctuation in abundance between years.
These fluctuations are often due to natural physical dynamics rather than human pressures De Jonge, An indicator based on only one species is also unlikely to represent the whole trophic level to which it belongs and which is required here for the present indicator assessment. To use a group as large as copepods allows the comparison of most of the zooplankton time series which can bring valuable understanding to the plankton dynamic.
Thus, as a first step, all copepod species abundance per time unit sample must be summed. Non-station Data For the CPR time-series, and large spatio-temporal pelagic data sets in general, the first step is geographic sub-division.
Pelagic habitat boundaries are not fixed and are characterized by high spatio-temporal dynamics. Deciding on the geographic sub-divisions involved preparatory work on the identification of eco-hydrodynamic zones, which in turn required physico-chemical data. The model is however weaker for the Celtic Seas region.
Based on water column structure, there are six predominant EHD types: Work is on-going to define ecohydrodynamic zones in the Bay of Biscay and Iberian Coast. The script can be applied to any data set in the Greater North Sea and Celtic Seas as long as the longitude and latitude coordinates of the samples are known. The R script has been provided as supplementary information associated with this assessment.
Recommendations for precision are written within the script. One of the main considerations is that years missing more than four months of data should be omitted when running the analysis to avoid bias in the analysis. The coordinates for the CPR samples cannot be provided due to data sharing restrictions. Calculation of monthly means After the data have been fitted to the correct geographic scale, and before running the time series analysis, the data were averaged per month over the whole time series.
This needs to be done for each data set independently of the type of data. For the Celtic Seas, some EHDs did not contain enough data to run the analysis and so have been excluded. However, due to limited time and resources available, only some station data have been used in this assessment those from France, Sweden and one for the United Kingdom. Other data are available those for Danish and German stations and will be assessed in the next cycle. The data used for this assessment are as follows.