Ecological And Metabolic Studies On Free-Living Nematodes From An Estuarine Mud-Flat

Nematodes from a mud-flat in the river Lynher estuary, Cornwall, U.K., have a population density ranging between 8 and 9 × 106 m−2 in the winter months, corresponding to a dry weight of 1·4 and 1·6 g m−2. They reach a peak abundance of 22·86 × 106 m−2 (3·4 g) in May. About 40 species are present, and the species composition remained seasonally stable over the period of study. Analysis of age-structure suggests that the major species have continuous asynchronous reproduction. Respiration rates of 16 species have been determined at 20 °C using Cartesian diver respirometry. For five species, respiration/body size regressions were obtained in the form log10R = log10a+b log10V, where R = respiration in nl O2 ind−1 h−1 and V = body volume in nl: Mesotheristus setosus (log10a = −0·04,b = 0·74), Sphaerolaimus hirsutus (log10a = 0·11, b = 0·68), Axonolaimus paraspinosus (log10a = 0·00, b = 0·79), Metachromadora vivipara (log10a = −0·59, b = 1·07), Praeacanthonchus punctatus (log10a = 0·00, b = 0·55). For the remaining 11 species, several animals were used in each diver and, by assuming b = 0·75, log10a′ values were calculated: Viscosia viscosa (log10a′ = 0·188), Innocuonema tentabundum (−0·012), Ptycholaimellus ponticus (−0·081), Odontophora setosa (−0·092), Sphaerolaimus balticus (−0·112), Dichromadora cephalata (−0·133), Atrochromadora microlaima (−0·142), Cylindrotheristus normandicus (−0·150), Terschellingialongicaudata (−0·170), Sabatieria pulchra (−0·197), Terschellingia communis (−0·277). These values are compared with recalculated values for other species from the literature. Annual respiration of the nematode community is 28·01 O2 m−2, equivalent to 11·2 g carbon metabolised. Community respiration is compared with figures from N. American saltmarshes. At 20 °C, a respiration of about 61 O2 m−2 year−1 g−1 wet weight of nematodes appears to be typical. Annual production is estimated to be 6·6 g C m−2. The correlation between feeding-group, body-size, habitat and the repiration rate of individual species is discussed.

Parameterisation of bivalve functional traits for mechanistic eco-physiological dynamic energy budget (DEB) models

Mechanistic models such as those based on dynamic energy budget (DEB) theory are emergent ecomechanics tools to investigate the extent of fitness in organisms through changes in life history traits as explained by bioenergetic principles. The rapid growth in interest around this approach originates from the mechanistic characteristics of DEB, which are based on a number of rules dictating the use of mass and energy flow through organisms. One apparent bottleneck in DEB applications comes from the estimations of DEB parameters which are based on mathematical and statistical methods (covariation method). The parameterisation process begins with the knowledge of some functional traits of a target organism (e. g. embryo, sexual maturity and ultimate body size, feeding and assimilation rates, maintenance costs), identified from the literature or laboratory experiments. However, considering the prominent role of the mechanistic approach in ecology, the reduction of possible uncertainties is an important objective. We propose a revaluation of the laboratory procedures commonly used in ecological studies to estimate DEB parameters in marine bivalves. Our experimental organism was Brachidontes pharaonis. We supported our proposal with a validation exercise which compared life history traits as obtained by DEBs (implemented with parameters obtained using classical laboratory methods) with the actual set of species traits obtained in the field. Correspondence between the 2 approaches was very high (>95%) with respect to estimating both size and fitness. Our results demonstrate a good agreement between field data and model output for the effect of temperature and food density on age-size curve, maximum body size and total gamete production per life span. The mechanistic approach is a promising method of providing accurate predictions in a world that is under in creasing anthropogenic pressure.

Ecological equivalence of species within phytoplankton functional groups

1.There are tens of thousands of species of phytoplankton found throughout the tree of life. Despite this diversity, phytoplankton are often aggregated into a few functional groups according to metabolic traits or biogeochemical role. We investigate the extent to which phytoplankton species dynamics are neutral within functional groups. 2.Seasonal dynamics in many regions of the ocean are known to affect phytoplankton at the functional group level leading to largely predictable patterns of seasonal succession. It is much more difficult to make general statements about the dynamics of individual species. 3.We use a 7 year time-series at station L4 in the Western English Channel with 57 diatom and 17 dinoflagellate species enumerated weekly to test if the abundance of diatom and dinoflagellate species vary randomly within their functional group envelope or if each species is driven uniquely by external factors. 4.We show that the total biomass of the diatom and dinoflagellate functional groups is well predicted by irradiance and temperature and quantify trait values governing the growth rate of both functional groups. The biomass dynamics of the functional groups are not neutral and each has their own distinct responses to environmental forcing. Compared to dinoflagellates, diatoms have faster growth rates, and grow faster under lower irradiance, cooler temperatures, and higher nutrient conditions. 5.The biomass of most species vary randomly within their functional group biomass envelope, most of the time. As a consequence, modelers will find it difficult to predict the biomass of most individual species. Our analysis supports the approach of using a single set of traits for a functional group and suggests that it should be possible to determine these traits from natural communities.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.