Effects of site-selection bias on estimates of biodiversity change

Estimates of biodiversity change are essential for the management and conservation of ecosystems. Accurate estimates rely on selecting representative sites, but monitoring often focuses on sites of special interest. How such site-selection biases influence estimates of biodiversity change is largely unknown. Site-selection bias potentially occurs across four major sources of biodiversity data, decreasing in likelihood from citizen science, museums, national park monitoring, and academic research. We defined site-selection bias as a preference for sites that are either densely populated (i.e., abundance bias) or species rich (i.e., richness bias). We simulated biodiversity change in a virtual landscape and tracked the observed biodiversity at a sampled site. The site was selected either randomly or with a site-selection bias. We used a simple spatially resolved, individual-based model to predict the movement or dispersal of individuals in and out of the chosen sampling site. Site-selection bias exaggerated estimates of biodiversity loss in sites selected with a bias by on average 300–400% compared with randomly selected sites. Based on our simulations, site-selection bias resulted in positive trends being estimated as negative trends: richness increase was estimated as 0.1 in randomly selected sites, whereas sites selected with a bias showed a richness change of −0.1 to −0.2 on average. Thus, site-selection bias may falsely indicate decreases in biodiversity. We varied sampling design and characteristics of the species and found that site-selection biases were strongest in short time series, for small grains, organisms with low dispersal ability, large regional species pools, and strong spatial aggregation. Based on these findings, to minimize site-selection bias, we recommend use of systematic site-selection schemes; maximizing sampling area; calculating biodiversity measures cumulatively across plots; and use of biodiversity measures that are less sensitive to rare species, such as the effective number of species. Awareness of the potential impact of site-selection bias is needed for biodiversity monitoring, the design of new studies on biodiversity change, and the interpretation of existing data.     

Fossil bog soils (‘<Emphasis Type="Italic">dwog</Emphasis> horizons’) and their relation to Holocene coastal changes in the Jade Weser region,southern North Sea,Germany

After the deceleration of the postglacial marine transgression, the German North Sea coast was subject to deposition of a complex pattern of subtidal to terrestrial facies. This study aims at reconstructing these facies changes in the ‘Land Wursten’ region (Lower Saxony) by focussing on so-called dwog horizons (incipient soil horizons of the pre-engineered salt marshes). We explore their implications for relative sea-level reconstructions and their indication for early settlement activities. Archive drilling data (1960, provided by the ‘Landesamt für Bergbau, Energie und Geologie’) were analysed to create five high-resolution stratigraphic cross sections. Three new drilling records were subjected to sedimentological and microfaunal investigations and interpreted to verify and calibrate the archive data. Two dwog horizons were found and ¹⁴C-AMS dated. We found basal salt marsh deposits inundated by a high-energy event and covered by tidal flat sediments. The thick tidal flat unit is again overlain by salt marsh deposits indicating the transition from a shallow marine to a terrestrial environment where dwogs were developed and covered by episodic marine incursions. The ages of the dwogs (1128–969 cal BC; cal AD 1426–1467) do not correlate with known layers of adjacent settlement sites and are critically discussed. However, we show that they correlate with phases of stagnant regional relative sea level (RSL) and can be used as RSL indicators. The combined archive and modern data provide valuable information for the RSL reconstruction and palaeoenvironmental changes. However, further research is recommended to accomplish more detailed information about coastal response during the Holocene sea-level changes and implications for settlement dynamics.     

A general approach to the analysis of habitat selection

The investigation of animal habitat selection aims at the detection of selective usage of habitat types and the identification of covariates influencing their selection. The results not only allow for a better understanding of the habitat selection process but are also intended to help improve the conservation of animals. Usually, habitat selection by larger animals is assessed by radio-tracking or visual observation studies, where the chosen habitat is determined for some animals at a set of specific points in time. Hence the resulting data often have the following structure: a categorical variable indicating the habitat type selected by an animal at a specific point in time is repeatedly observed and will be explained by covariates. These may either describe properties of the habitat types currently available and/or properties of the animal. In this paper, we present a general approach to the analysis of such data in a categorical regression setup. The proposed model generalizes and improves upon several of the approaches previously discussed in the literature. In particular, it accounts for changing habitat availability due to the movement of animals within the observation area. It incorporates both habitat- and animal-specific covariates, and includes individual-specific random effects to account for correlations introduced by the repeated measurements on single animals. Furthermore, the assumption that the effects are linear can be dropped by including the effects in nonparametric manner based on a penalized spline approach. The methodology is implemented in a freely available software package. We demonstrate the general applicability and the potential of the proposed approach in two case studies: The analysis of a songbird community in South-America and a study on brown bears in Central Europe.     

Antiestrogenic activity of anthropogenic and natural chemicals

A number of natural and man-made chemicals possess antiestrogenic activity, i.e. they antagonize a broad spectrum of estrogen-induced responses in vertebrates. Examples of antiestrogens include dioxin, furan and PCB congeners, certain PAHs, pesticides and indol-3-carbinol derivatives. Major mechanisms of antiestrogenicity are antagonistic action of chemicals at the estrogen receptor, or binding of chemicals to the arylhydrocarbon (Ah) receptor and subsequent interaction with estrogen-responsive genes. Toxicological consequences resulting from antiestrogenic activity have not been conclusively demonstrated to date, although antiestrogenic compounds could critically affect sensitive reproductive and developmental processes.