A new context-based approach to assess marine mammal behavioral responses to anthropogenic sounds

Acute effects of anthropogenic sounds on marine mammals, such as from military sonars, energy development, and offshore construction, have received considerable international attention from scientists, regulators, and industry. Moreover, there has been increasing recognition and concern about the potential chronic effects of human activities (e.g., shipping). It has been demonstrated that increases in human activity and background noise can alter habitats of marine animals and potentially mask communications for species that rely on sound to mate, feed, avoid predators, and navigate. Without exception, regulatory agencies required to assess and manage the effects of noise on marine mammals have addressed only the acute effects of noise on hearing and behavior. Furthermore, they have relied on a single exposure metric to assess acute effects: the absolute sound level received by the animal. There is compelling evidence that factors other than received sound level, including the activity state of animals exposed to different sounds, the nature and novelty of a sound, and spatial relations between sound source and receiving animals (i.e., the exposure context) strongly affect the probability of a behavioral response. A more comprehensive assessment method is needed that accounts for the fact that multiple contextual factors can affect how animals respond to both acute and chronic noise. We propose a three-part approach. The first includes measurement and evaluation of context-based behavioral responses of marine mammals exposed to various sounds. The second includes new assessment metrics that emphasize relative sound levels (i.e., ratio of signal to background noise and level above hearing threshold). The third considers the effects of chronic and acute noise exposure. All three aspects of sound exposure (context, relative sound level, and chronic noise) mediate behavioral response, and we suggest they be integrated into ecosystem-level management and the spatial planning of human offshore activities.     

Evaluating the sources of potential migrant species: implications under climate change

As changes in climate become more apparent, ecologists face the challenge of predicting species responses to the new conditions. Most forecasts are based on climate envelopes (CE), correlative approaches that project future distributions on the basis of the current climate often assuming some dispersal lag. One major caveat with this approach is that it ignores the complexity of factors other than climate that contribute to a species' distributional range. To overcome this limitation and to complement predictions based on CE modeling we carried out a transplant experiment of resident and potential-migrant species. Tree seedlings of 18 species were planted side by side from 2001 to 2004 at several locations in the Southern Appalachians and in the North Carolina Piedmont (U.S.A.). Growing seedlings under a large array of environmental conditions, including those forecasted for the next decades, allowed us to model seedling survival as a function of variables characteristic of each site, and from here we were able to make predictions on future seedling recruitment. In general, almost all species showed decreased survival in plots and years with lower soil moisture, including both residents and potential migrants, and in both locations, the Southern Appalachians and the Piedmont. The detrimental effects that anticipated arid conditions could have on seedling recruitment contradict some of the projections made by CE modeling, where many of the species tested are expected to increase in abundance or to expand their ranges. These results point out the importance of evaluating the potential sources of migrant species when modeling vegetation response to climate change, and considering that species adapted to the new climate and the local conditions may not be available in the surrounding regions.     

A novel statistical method for classifying habitat generalists and specialists

We develop a novel statistical approach for classifying generalists and specialists in two distinct habitats. Using a multinomial model based on estimated species relative abundance in two habitats, our method minimizes bias due to differences in sampling intensities between two habitat types as well as bias due to insufficient sampling within each habitat. The method permits a robust statistical classification of habitat specialists and generalists, without excluding rare species a priori. Based on a user-defined specialization threshold, the model classifies species into one of four groups: (1) generalist; (2) habitat A specialist; (3) habitat B specialist; and (4) too rare to classify with confidence. We illustrate our multinomial classification method using two contrasting data sets: (1) bird abundance in woodland and heath habitats in southeastern Australia and (2) tree abundance in second-growth (SG) and old-growth (OG) rain forests in the Caribbean lowlands of northeastern Costa Rica. We evaluate the multinomial model in detail for the tree data set. Our results for birds were highly concordant with a previous nonstatistical classification, but our method classified a higher fraction (57.7%) of bird species with statistical confidence. Based on a conservative specialization threshold and adjustment for multiple comparisons, 64.4% of tree species in the full sample were too rare to classify with confidence. Among the species classified, OG specialists constituted the largest class (40.6%), followed by generalist tree species (36.7%) and SG specialists (22.7%). The multinomial model was more sensitive than indicator value analysis or abundance-based phi coefficient indices in detecting habitat specialists and also detects generalists statistically. Classification of specialists and generalists based on rarefied subsamples was highly consistent with classification based on the full sample, even for sampling percentages as low as 20%. Major advantages of the new method are (1) its ability to distinguish habitat generalists (species with no significant habitat affinity) from species that are simply too rare to classify and (2) applicability to a single representative sample or a single pooled set of representative samples from each of two habitat types. The method as currently developed can be applied to no more than two habitats at a time.     

Erprobung und Erfolgskontrolle eines Phytoremediations-verfahrens zur Sanierung Sprengstoff-kontaminierter Böden

Results of a field experiment (please refer to Warrelmann et al. 2000a) document TNT-degradation and regeneration after the establishment of a phytoremediation. Various approaches are included in a complex monitoring system, which are conducted in the field (nitroaromatics in soil, seepage water, and plants; soil fauna; soil fungi; decomposition) as well as in the lab (a battery of 6 biotests). The data document a high heterogeneity of the TNT-contamination, a rapid initial decrease and a mediocre displacement of nitroaromatics in plants and seepage water. The initial rapid transformation of TNT to aminodinitrotoluenes ceases significantly after 3 months. Results of the biotest battery allow a sophisticated assessment of soil toxicity; the results from the soil ecological field investigations make the long-term effects of TNT-contamination likely. In a third part of the series of articles, a synopsis and judgement of the project will be delivered.     

Evaluation of Public Housing Lead Risk Assessment Data

A unique data set from lead risk assessments performed on 67 public housing developments from across the United States was made available for analyzes. The data set includes results of lead analysis from 5906 dust wipes and from 1222 soil samples. A total of 487 dwelling units in these developments, as well as associated common areas, were sampled, all by the same team of inspectors. The number of dwelling units within a development that were sampled reflected the guidelines then in force, the 1990 Interim HUD Guidelines, rather than those specified in the 1995 Guidelines. Median dust lead loadings for floors, 151gm^–2 (14gft^–2), and window sills, 936gm^–2 (87gft^–2), were much less than former HUD limits of 1076gm^–2 (100gft^–2) and 5380gm^–2 (500gft^–2), respectively and are only about one-third of the recently established limits of 431gm^–2 (40gft^–2) and 2690gm^–2 (250gft^–2). In contrast, the median lead loading for window troughs, 8560gm^–2 (795gft^–2), was almost identical to the HUD clearance limit of 8610gm^–2 (800gft^–2). There was a strong positive correlation between floor and window trough lead loading values for samples from the same dwelling units and those from common areas of the housing developments. Door threshold samples, which may reflect conditions exterior to the dwelling unit, were collected from 53 dwelling units. Median lead loading levels of these samples were more than ten times higher than those in floor samples from the same dwelling units, were about the same as window sill samples and about one-half of levels in window trough samples. Composite sample results, simulated by averaging results from four samples within a dwelling unit, revealed that in order to have the same rate of excedence of standards, the composite standards would have to be reduced, for example, from the single sample value of 1076gm^–2 (100gft^–2) to 527gm^–2 (49gft^–2) for floor samples and from the single sample value of 8610gm^–2 (800gft^–2) to 5160gm^–2 (479gft^–2) for window troughs. For this public housing data set, the portion of the units in developments containing more than 225 units which exceeded the established limit for window samples was the same when using either the full data set or a random one-half of the data set. This suggests that, for this data set, the number of dwelling units sampled was excessive . Thus, the required increase in the number of dwelling units to be sampled specified in the 1995 Guidelines for developments with more than 225 dwelling units, may not have been necessary if this data set is representative of public housing developments in the United States.