Editorial board page for “Chemistry and Ecology”, Volume 14, Number 2

This is a scanned image of the original Editorial Board page(s) for this issue.     

Editorial board page for “Chemistry and Ecology”, Volume 15, Number 4

This is a scanned image of the original Editorial Board page(s) for this issue.     

Editorial board page for “Chemistry and Ecology”, Volume 17, Number 4

This is a scanned image of the original Editorial Board page(s) for this issue.     

Probabilistic movement model with emigration simulates movements of deer in Nebraska, 1990–2006

Movements of deer can affect population dynamics, spatial redistribution, and transmission and spread of diseases. Our goal was to model the movement of deer in Nebraska in an attempt to predict the potential for spread of chronic wasting disease (CWD) into eastern Nebraska. We collared and radio-tracked >600 white-tailed deer (Odocoileus virginianus) and mule deer (Odocoileus hemionus) in Nebraska during 1990–2006. We observed large displacements (>10 km) for both species and sexes of deer, including migrations up to 100 km and dispersals up to 50 km. Average distance traveled between successive daily locations was 166 m for male and 173 for female deer in eastern Nebraska, and 427 m for male and 459 for female deer in western Nebraska. Average daily displacement from initial capture point was 10 m for male and 14 m for female deer in eastern Nebraska, and 27 m for male and 28 m for female deer in western Nebraska. We used these data on naturally occurring movements to create and test 6 individual-based models of movement for white-tailed deer and mule deer in Nebraska, including models that incorporated sampling from empirical distributions of movement lengths and turn angles (DIST), correlated random walks (CRW), home point fidelity (FOCUS), shifting home point (SHIFT), probabilistic movement acceptance (MOVE), and probabilistic movement with emigration (MOVEwEMI). We created models in sequence in an attempt to account for the shortcomings of the previous model(s). We used the Kolmogrov–Smirnov goodness-of-fit test to verify improvement of simulated annual displacement distributions to empirical displacement distributions. The best-fit model (D = 0.07 and 0.08 for eastern and western Nebraska, respectively) included a probabilistic movement chance with emigration (MOVEwEMI) and resulted in an optimal daily movement length of 350 m (maximum daily movement length of 2800 m for emigrators) for eastern Nebraska and 370 m (maximum of 2960 m) for western Nebraska. The proportion of deer that moved as emigrators was 0.10 and 0.13 for eastern and western Nebraska, respectively. We propose that the observed spread of CWD may be driven by large movements of a small proportion of deer that help to establish a low prevalence of the disease in areas east of the current endemic area. Our movement models will be used in a larger individual-based simulation of movement, survival, and transmission of CWD to help determine future surveillance and management actions.     

Total and “Selective” Extraction Methods for Trace Metals in Marine Sediment Reference Samples (Mess-1, NBS 1646)

Trace metals (Cd, Cr, Cu, Mn, Pb and Zn) and Fe were analyzed in two sediment reference samples (NBS 1646, MESS-1) with AAS fitted with graphite furnace, using five different (three total and two selective) extraction procedures.

The comparison of the total digestion methods (hot strong acids) with reference materials gave good results for almost all of them, and the “teflon bomb” was preferred for its rapidity and ease of operation. Some non-referenced data on total and organic carbon, total nitrogen and minerals are given. The two selective leaching extractions (nitric and cold hydrochloric) were also compared with reference values.

The use of certified reference materials (CRM) for environmental chemistry is recommended, together with the determination of organic matter and fundamental mineralogical composition.     

Projected Climate‐Induced Habitat Loss for Salmonids in the John Day River Network,Oregon, U.S.A.

Abstract: Climate change will likely have profound effects on cold‐water species of freshwater fishes. As temperatures rise, cold‐water fish distributions may shift and contract in response. Predicting the effects of projected stream warming in stream networks is complicated by the generally poor correlation between water temperature and air temperature. Spatial dependencies in stream networks are complex because the geography of stream processes is governed by dimensions of flow direction and network structure. Therefore, forecasting climate‐driven range shifts of stream biota has lagged behind similar terrestrial modeling efforts. We predicted climate‐induced changes in summer thermal habitat for 3 cold‐water fish species—juvenile Chinook salmon, rainbow trout, and bull trout (Oncorhynchus tshawytscha, O. mykiss, and Salvelinus confluentus, respectively)—in the John Day River basin, northwestern United States. We used a spatially explicit statistical model designed to predict water temperature in stream networks on the basis of flow and spatial connectivity. The spatial distribution of stream temperature extremes during summers from 1993 through 2009 was largely governed by solar radiation and interannual extremes of air temperature. For a moderate climate change scenario, estimated declines by 2100 in the volume of habitat for Chinook salmon, rainbow trout, and bull trout were 69–95%, 51–87%, and 86–100%, respectively. Although some restoration strategies may be able to offset these projected effects, such forecasts point to how and where restoration and management efforts might focus.     

PM<Subscript>2.5</Subscript> pollution from household solid fuel burning practices in Central India: 2. Application of receptor models for source apportionment

USEPA’s UNMIX, positive matrix factorization (PMF) and effective variance-chemical mass balance (EV-CMB) receptor models were applied to chemically speciated profiles of 125 indoor PM_2.5 measurements, sampled longitudinally during 2012–2013 in low-income group households of Central India which uses solid fuels for cooking practices. Three step source apportionment studies were carried out to generate more confident source characterization. Firstly, UNMIX6.0 extracted initial number of source factors, which were used to execute PMF5.0 to extract source-factor profiles in second step. Finally, factor analog locally derived source profiles were supplemented to EV-CMB8.2 with indoor receptor PM_2.5 chemical profile to evaluate source contribution estimates (SCEs). The results of combined use of three receptor models clearly describe that UNMIX and PMF are useful tool to extract types of source categories within small receptor dataset and EV-CMB can pick those locally derived source profiles for source apportionment which are analog to PMF-extracted source categories. The source apportionment results have also shown three fold higher relative contribution of solid fuel burning emissions to indoor PM_2.5 compared to those measurements reported for normal households with LPG stoves. The previously reported influential source marker species were found to be comparatively similar to those extracted from PMF fingerprint plots. The comparison between PMF and CMB SCEs results were also found to be qualitatively similar. The performance fit measures of all three receptor models were cross-verified and validated and support each other to gain confidence in source apportionment results.