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Articles by Michael L. Zettler
Total Records ( 2 ) for Michael L. Zettler
  Mayya Gogina , Michael Glockzin and Michael L. Zettler
  In this study we relate patterns in the spatial distribution of macrofaunal communities to patterns in near-bottom environmental parameters, analysing the data observed in a limited area in the western Baltic Sea. The data used represents 208 stations, sampled during the years 2000 to 2007 simultaneously for benthic macrofauna, associated sediment and near-bottom environmental characteristics, in a depth range from 7.5 to 30 m. Only one degree of longitude wide, the study area is geographically bounded by the eastern part of the Mecklenburg Bight and the southwestern Darss Sill Area. Spatial distribution of benthic macrofauna is related to near-bottom environmental patterns by means of various statistical methods (e.g. rank correlation, hierarchical clustering, nMDS, BIO-ENV, CCA). Thus, key environmental descriptors were disclosed. Within the area of investigation, these were: water depth, regarded as a proxy for other environmental factors, and total organic content. Distinct benthic assemblages are defined and discriminated by particular species (Hydrobia ulvae–Scoloplos armiger, Lagis koreni–Mysella bidentata and Capitella capitata–Halicryptus spinulosus). Each assemblage is related to different spatial subarea and characterised by a certain variability of environmental factors. This study represents a basis for the predictive modeling of species distribution in the selected study area.
  Mayya Gogina , Michael Glockzin and Michael L. Zettler
  The detailed analysis of patterns of benthic community distribution related to selected environmental parameters provides a basis for predictive modelling of species distribution. Species-specific models predicting the probability of occurrence relative to environmental and sedimentological characteristics were developed in this study for 29 macrofaunal species common for our study area using a logistic regression modelling approach. This way, a good description of the occurrence of species along gradients of single environmental variables was obtained. Subsequently, we used a technique for a predictive modelling of species distributions in response to abiotic parameters based on single-factor logistic regression models, utilizing AIC and Akaike weights for multimodel inference. Thus, probabilities of occurrence for selected exemplary species (Arctica islandica, Hediste diversicolor, Pygospio elegans, Tubificoides benedii and Scoloplos armiger) were modelled and mapped. For all species the use of this newly available combination of methods provided fairly accurate results of a distribution prediction. Water depth that represents a type of integral parameter remained the key factor determining the species distribution among the parameters considered within the study scale. This is particularly relevant for species that find their optima habitat here, but also for those as H. diversicolor that occur only locally and in comparatively low densities. Total organic content, sorting and, for S. armiger, salinity also had noticeable effect in the determination of suitable habitats for benthic macrofauna. The employed technique proved to be appropriate for modelling of the benthic species habitat suitability, at least within comparable spatial scales and variability of environmental factors.
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