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Aquatic Communities: Pesticide Impacts
Published in Brian D. Fath, Sven E. Jørgensen, Megan Cole, Managing Water Resources and Hydrological Systems, 2020
David P. Kreutzweiser, Paul K. Sibley
Traditional measures of community-level impacts have focused on structure and have usually been expressed in terms of single-variable indices such as species richness, diversity, or abundance. These indices are useful descriptors of community structure but suffer from the fact that they reduce complex community data to a single summary metric and may miss subtle or ecologically important changes in species composition across sites or times. Over the last couple of decades, ecotoxicologists have increasingly turned to multivariate statistical techniques for analyzing community response data.[13] A variety of multivariate statistical techniques and software are available and are usually considered superior for the analysis of community data because they retain and incorporate the spatial and temporal multidimensional nature of biological communities.[14] This includes various ordination techniques that can provide graphical representation of spatiotemporal patterns in community structure in which points that lie close together in the ordination plot represent communities of similar composition (richness, abundance), while communities with dissimilar species composition are plotted further apart.
Coastal Environments
Published in Yeqiao Wang, Coastal and Marine Environments, 2020
Coastal zones are an important area for human habitation, industry, location of centers of energy production, military activities, fisheries, bird life, and recreation. The fast development of coastal regions[19] will inevitably lead to the creation of vast built-up areas (such as construction of ports and tourist facilities) at the expense of natural habitats (e.g., dunes, saltmarshes) and as a result, will damage or destroy a substantial part of the natural coastline’s habitats. In France, for example, 15% of natural areas on the coast have disappeared since 1976 and are continuing to do so at the rate of 1% a year. Italy, which had around 700,000 ha of coastal marshes at the end of the last century, had no more than 192,000 ha in 1972 and has less than 100,000 ha today. Some estimates suggest that about one-third of the coastal dunes in northwestern Europe and three quarters in the western Mediterranean have disappeared. Such large-scale habitat destruction will inevitably lead to a decline in species distribution and abundance.
Giant Clams (Bivalvia: Cardiidae: Tridacninae): A Comprehensive Update of Species and their Distribution, Current Threats and Conservation Status
Published in S. J. Hawkins, A. J. Evans, A. C. Dale, L. B. Firth, D. J. Hughes, I. P. Smith, Oceanography and Marine Biology, 2017
Mei Lin Neo, Colette C.C. Wabnitz, Richard D. Braley, Gerald A. Heslinga, Cécile Fauvelot, Simon Van Wynsberge, Ser.G.E. Andréfouët, Charles Waters, Aileen Shau-Hwai Tan, Edgardo D. Gomez, Mark J. Costello, Peter A. Todd
At national and local (archipelago, island, reef) scales, giant clam conservation management has focused on fishing regulations and restocking (see previous sections). Assessing the effectiveness of such conservation efforts for a particular location requires an understanding, and ideally modelling, of processes and factors that influence their distribution and abundance. These include aspects of the species’ biology, population dynamics (e.g. size-structure, density, recruitment, mortality), life-history traits (e.g. growth-fertility, reproduction and spawning occurrences) (Apte & Dutta 2010, Black et al. 2011, Yau et al. 2014, Dolorosa et al. 2014, Neo et al. 2013b, 2015b, Menoud et al. 2016, Van Wynsberge et al. 2017), and larval flux (Neo et al. 2013a). Human uses and impacts are also important factors to consider (Van Wynsberge et al. 2015, 2016). Recently, mass mortality in semi-enclosed atolls due to unusual physical oceanographic conditions has been identified as a key driver of population dynamics (Andréfouët et al. 2013) and climate change is likely to make these events more frequent (Andréfouët et al. 2015). These examples highlight the importance of monitoring physical conditions and their integration into models (Neo et al. 2015b, Van Wynsberge et al. 2017). Finally, but this has never been attempted, an ecosystem-based characterization including spatio-temporal variation in predation, competition, and food availability, is also likely to influence the accuracy of models simulating the effectiveness of conservation measures.
Sampling error correlated among observations: origin, impacts, and solutions
Published in Applied Earth Science, 2020
Victor Miguel Silva, João Felipe Coimbra Costa Leite
Geochemical, environmental, or mining databases commonly contain thousands of samples, each of which may have individual values for more than 50 different chemical elements or species. This abundance of data provides an opportunity to discover a wide range of geological processes within a surveyed area. Multivariate statistics makes it possible to interpret the significance and relationships of tens of variables. Inevitably, all sample measurements are affected by the intrinsic presence of sampling error (Gy 1982). Two or more variables may be affected by the same source of error. The error associated with observations of different variables may be highly correlated. The measured correlation or covariance from observations affected by this type of error can be greater than that of the underlying true process, overestimating the real association between geological processes based on data available.
Characterization of bacterial community structure in a hydrocarbon-contaminated tropical African soil
Published in Environmental Technology, 2018
Lateef B. Salam, Mathew O. Ilori, Olukayode O. Amund, Yee LiiMien, Hideaki Nojiri
Microbial diversity, which constitutes an extraordinary reservoir of life in the biosphere [15], is composed of species richness and evenness. In essence, highest diversity is observed in communities with many different species present (richness) in relatively equal abundance (evenness). The determination of abundances of sequence types in a diversity study is useful in predicting abundances of microorganisms in the studied environment [64]. The Shannon–Weiner Diversity Index (H′) of the bacterial clone library in this study was 5.59 (97%; species delineation). This is higher than 4.41 reported by Nogales et al. [20] for bacterial DNA clone library constructed from polychlorinated biphenyl polluted soil. It is equally higher than 3.93, 3.78 and 3.30 reported by Popp et al. [23] and Zhang et al. [19] from hydrocarbon-contaminated soils. It is however lower than 6.49–9.54 reported by Sutton et al. [65] for diesel-contaminated soil.
Commercial formulation amendment transiently affects the microbial composition but not the biogas production of a full scale methanogenic UASB reactor
Published in Environmental Technology, 2020
A. Cabezas, P. Bovio, C. Etchebehere
The structure and assembling of the microbial communities were compared as suggested by Lucas et al [19] using the following parameters: richness (R), Diversity (1D), Eveness (1E) and Community organization (Co). The richness is a parameter reflecting the number of species present in the community, the diversity takes into account the number of species and also their abundance. The evenness and the Community organization reflect the dominance of the different species in the community. Comparing the values obtained for the samples before and after the amendment, no clear differences were observed (Table 2).