Occurrence of organochlorine compounds in Euphausia superba and unhatched eggs of Pygoscelis genus penguins from Admiralty Bay (King George Island, Antarctica) and estimation of biomagnification factors

Polychlorinated biphenyls (PCBs) and organochlorine pesticides are compounds that do not occur naturally in the environment and are not easily degraded by chemical or microbiological action. In the present work, those compounds were analysed in unhatched penguin eggs and whole krill collected in Admiralty Bay, King George Island, Antarctica in the austral summers of 2004-2005 and 2005-2006. The compounds found in higher levels (in a wet weight basis) were, in most of the egg samples, the PCBs (2.53-78.7 ng g(-1)), DDTs (2.07-38.0 ng g(-1)) and HCB (4.99-39.1 ng g(-1)) and after Kruskal-Wallis ANOVA, the occurrence seemed to be species-specific for the Pygoscelis genus. In all of the cases, the levels found were not higher than the ones in Arctic birds in a similar trophic level. The krill samples analysis allowed estimating the biomagnification factors (which resulted in up to 363 for HCB, one order of magnitude higher than DDTs and chlordanes and two orders of magnitude higher than the other groups) of the compounds found in eggs, whose only source of contamination is the female-offspring transfer. (C) 2009 Elsevier Ltd. All rights reserved.

Calibration of a Species-Specific Spectral Vegetation Index for Leaf Area Index (LAI) Monitoring: Example with MODIS Reflectance Time-Series on Eucalyptus Plantations

The leaf area index (LAI) is a key characteristic of forest ecosystems. Estimations of LAI from satellite images generally rely on spectral vegetation indices (SVIs) or radiative transfer model (RTM) inversions. We have developed a new and precise method suitable for practical application, consisting of building a species-specific SVI that is best-suited to both sensor and vegetation characteristics. Such an SVI requires calibration on a large number of representative vegetation conditions. We developed a two-step approach: (1) estimation of LAI on a subset of satellite data through RTM inversion; and (2) the calibration of a vegetation index on these estimated LAI. We applied this methodology to Eucalyptus plantations which have highly variable LAI in time and space. Previous results showed that an RTM inversion of Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared and red reflectance allowed good retrieval performance (R-2 = 0.80, RMSE = 0.41), but was computationally difficult. Here, the RTM results were used to calibrate a dedicated vegetation index (called "EucVI") which gave similar LAI retrieval results but in a simpler way. The R-2 of the regression between measured and EucVI-simulated LAI values on a validation dataset was 0.68, and the RMSE was 0.49. The additional use of stand age and day of year in the SVI equation slightly increased the performance of the index (R-2 = 0.77 and RMSE = 0.41). This simple index opens the way to an easily applicable retrieval of Eucalyptus LAI from MODIS data, which could be used in an operational way.

Suitability of distance metrics as indexes of home-range size in tropical rodent species

Estimators of home-range size require a large number of observations for estimation and sparse data typical of tropical studies often prohibit the use of such estimators. An alternative may be use of distance metrics as indexes of home range. However, tests of correlation between distance metrics and home-range estimators only exist for North American rodents. We evaluated the suitability of 3 distance metrics (mean distance between successive captures [SD], observed range length [ORL], and mean distance between all capture points [AD]) as indexes for home range for 2 Brazilian Atlantic forest rodents, Akodon montensis (montane grass mouse) and Delomys sublineatus (pallid Atlantic forest rat). Further, we investigated the robustness of distance metrics to low numbers of individuals and captures per individual. We observed a strong correlation between distance metrics and the home-range estimator. None of the metrics was influenced by the number of individuals. ORL presented a strong dependence on the number of captures per individual. Accuracy of SD and AD was not dependent on number of captures per individual, but precision of both metrics was low with numbers of captures below 10. We recommend the use of SD and AD instead of ORL and use of caution in interpretation of results based on trapping data with low captures per individual.