Experimental and Ecological Implications of Evening Bird Surveys in Stream-Riparian Ecosystems

Stream-riparian ecosystems are dynamic and complex entities that can support high levels of bird assemblage abundance and diversity. The myriad patches (e.g., aquatic, floodplain, riparian) found in the riverscape habitat mosaic attract a unique mixture of aquatic, semiaquatic, riparian, and upland birds, each uniquely utilizing the river corridor. Whereas standard morning bird surveys are widely used across ecosystems, the variety of bird guilds and the temporal habitat partitioning that likely occur in stream-riparian ecosystems argue for the inclusion of evening surveys. At 41 stream reaches in Vermont and Idaho, USA, we surveyed bird assemblages using a combination of morning and evening fixed-width transect counts. Student’s paired t-tests showed that while bird abundance was not significantly different between morning and evening surveys, bird assemblage diversity (as measured by species richness, Shannon-Weiner’s index, and Simpson’s index) was significantly higher in the morning than in the evening. NMS ordinations of bird species and time (i.e., morning, evening) indicated that the structure of morning bird assemblages was different from that of evening assemblages. NMS further showed that a set of species was only found in evening surveys. The inclusion of evening counts in surveying bird assemblages in stream-riparian ecosystems has important experimental and ecological implications. Experimentally, the sole use of morning bird surveys may significantly underestimate the diversity and misrepresent the community composition of bird assemblages in these ecosystems. Ecologically, many of the birds detected in evening surveys were water-associated species that occupy high trophic levels and aerial insectivores that represent unique aquatic-terrestrial energy transfers.     

Environmental and Biological Assessment of Exposure to Benzene in Petroleum Workers

Occupational exposure to benzene was measured in two gasoline marketing terminals and five major refineries in Singapore. A total of 280 workers were monitored over two years. This assessment was carried out with two primary objectives: (1) To find out the extent of occupational exposure to benzene in the petroleum industry in Singapore, (2) To identify suitable biomarkers for monitoring of low levels of benzene exposure. The exposure was measured in five different categories of petroleum and petrochemical workers, i.e., truck drivers, despatch assistant, process operators, oil movements operators and laboratory technicians. The results revealed wide variations in exposure, from 0.01 to 13.6 ppm for personal time weighted average (TWA) exposure over the whole workshift. The exposure of truck drivers appeared to be the highest, with geometric mean (GM) of 1.98 ppm (ranged from 0.25 to 13.6 ppm). The average benzene exposure for process operators was relative low with a GM of 0.04 ppm. Lowest benzene exposure was found in the laboratory technicians, with a GM of 0.02 ppm. As cigarette smoking is known to affect metabolism of benzene, data analyses on the relationships with environmental exposure were conducted only on the 190 nonsmokers. The results showed that urinary trans, trans-muconic acid (ttMA), unmetabolized benzene in urine (UBZ) and benzene in blood (BBZ) were better biomarkers for low level benzene exposure as compared to urinary phenolic metabolites in urine, such as hydroquinone, phenol and catechol.     

Assessing the status and trend of bat populations across broad geographic regions with dynamic distribution models

Bats face unprecedented threats from habitat loss, climate change, disease, and wind power development, and populations of many species are in decline. A better ability to quantify bat population status and trend is urgently needed in order to develop effective conservation strategies. We used a Bayesian autoregressive approach to develop dynamic distribution models for Myotis lucifugus, the little brown bat, across a large portion of northwestern USA, using a four-year detection history matrix obtained from a regional monitoring program. This widespread and abundant species has experienced precipitous local population declines in northeastern USA resulting from the novel disease white-nose syndrome, and is facing likely range-wide declines. Our models were temporally dynamic and accounted for imperfect detection. Drawing on species-energy theory, we included measures of net primary productivity (NPP) and forest cover in models, predicting that M. lucifugus occurrence probabilities would covary positively along those gradients. Despite its common status, M. lucifugus was only detected during -50% of the surveys in occupied sample units. The overall naive estimate for the proportion of the study region occupied by the species was 0.69, but after accounting for imperfect detection, this increased to -0.90. Our models provide evidence of an association between NPP and forest cover and M. lucifugus distribution, with implications for the projected effects of accelerated climate change in the region, which include net aridification as snowpack and stream flows decline. Annual turnover, the probability that an occupied sample unit was a newly occupied one, was estimated to be low (-0.04-0.14), resulting in flat trend estimated with relatively high precision (SD = 0.04). We mapped the variation in predicted occurrence probabilities and corresponding prediction uncertainty along the productivity gradient. Our results provide a much needed baseline against which future anticipated declines in M. lucifugus occurrence can be measured. The dynamic distribution modeling approach has broad applicability to regional bat monitoring efforts now underway in several countries and we suggest ways to improve and expand our grid-based monitoring program to gain robust insights into bat population status and trend across large portions of North America.     

C-Lock (Patent Pending): A System for Estimating and Certifying Carbon Emission Reduction Credits for the Sequestration of Soil Carbon on Agricultural Land

The C-Lock system was developed to address the need for an improved method of quantifying and certifying project-level carbon emission reduction credits (CERC). It was designed to enable individual landowners to efficiently quantify, certify, pool, market and trade CERCs generated by agricultural management practices. We provide a general overview of the C-Lock system as it has been implemented for the USA State of South Dakota. C-Lock is comprised of four linked components: a web interface, a client database, a Geographic Information System (GIS) database of soil, climate and generalized land use history parameters, and the CENTURY soil carbon model. The user-friendly interface elicits generalized land-use and crop history information from the client from 1900 through 1989, then explicit annual information from 1990 onward. A climate-zone level landuse and crop management database is used to fill in gaps in the client-provided data. These data are used to drive the CENTURY model, which estimates annual changes in soil carbon stocks. Monte Carlo simulation is used to estimate uncertainty bounds, and these are applied to the CENTURY outputs in order to provide probabilistic estimates of accrued CERCs in a manner that is transparent and verifiable. In a demonstration application, CERCs are estimated for three different land-use scenarios on a representative field in eastern South Dakota: reduced tillage or conservation (no-till) management of a corn (maize)/wheat/soybean rotation, and enrollment in the Conservation Reserve Program, which entails establishing permanent grass cover. The credits are based on a business-asusual scenario of conventional tillage.     

Biogenic volatile organic compound emissions (BVOCs). I. Identifications from three continental sites in the U.S

Vegetation composition and biomass were surveyed for three specific sites in Atlanta, GA; near Rhinelander, WI; and near Hayden, CO. At each research site emissions of biogenic volatile organic compounds (BVOCs) from the dominant vegetation species were sampled by enclosing branches in bag enclosure systems and sampling the equilibrium head space onto multi-stage solid adsorbent cartridges. Analysis was performed using a thermal desorption technique with gas chromatography (GC) separation and mass spectrometry (MS) detection. Identification of BVOCs covering the GC retention index range (stationary phase DB-1) from approximately 400 to 1400 was achieved (volatilities C4-C14).     

The Trifinio Region: a case study of transboundary forest change in Central America

Strategic planning to increase forest cover in Central American transboundary areas of the Mesoamerican Biological Corridor requires understanding land-cover and land-use change trends and drivers. We estimated forest cover change from remotely sensed land-cover and land-use classifications from 1986, 2001, and 2010, in the tri-national Trifinio Region, bordering El Salvador, Guatemala, and Honduras. Our analysis spanned subnational, national, regional, and protected border areas. We determined correlations with direct drivers of deforestation, developing a multilevel linear regression model. Higher population density significantly correlated with deforestation; coffee, agroforestry, and pasture replaced forests. The tri-national park retained forests compared to neighboring areas, but additionality requires more research. The literature on drivers suggests similar processes and factors in other tropical regions. Forest cover governance efficacy is highly variable. Results indicate relationship between governance and forest cover though more comprehensive understanding of this complex region is needed to determine their causality.