Detection histories for eight species of Amazona parrots in Venezuela during the NeoMaps bird surveys in 2010

Documenting changes in distribution is necessary for understanding species' response to environmental changes, but data on species distributions are heterogeneous in accuracy and resolution. Combining different data sources and methodological approaches can fill gaps in knowledge about the dynamic processes driving changes in species-rich, but data-poor regions. We combined recent bird survey data from the Neotropical Biodiversity Mapping Initiative (NeoMaps) with historical distribution records to estimate potential changes in the distribution of eight species of Amazon parrots in Venezuela. Using environmental covariates and presence-only data from museum collections and the literature, we first used maximum likelihood to fit a species distribution model (SDM) estimating a historical maximum probability of occurrence for each species. We then used recent, NeoMaps survey data to build single-season occupancy models (OM) with the same environmental covariates, as well as with time- and effort-dependent detectability, resulting in estimates of the current probability of occurrence. We finally calculated the disagreement between predictions as a matrix of probability of change in the state of occurrence. Our results suggested negative changes for the only restricted, threatened species, Amazona barbadensis, which has been independently confirmed with field studies. Two of the three remaining widespread species that were detected, Amazona amazonica, Amazona ochrocephala, also had a high probability of negative changes in northern Venezuela, but results were not conclusive for Amazona farinosa. The four remaining species were undetected in recent field surveys; three of these were most probably absent from the survey locations (Amazona autumnalis, Amazona mercenaria and Amazona festiva), while a fourth (Amazona dufresniana) requires more intensive targeted sampling to estimate its current status. Our approach is unique in taking full advantage of available, but limited data, and in detecting a high probability of change even for rare and patchily-distributed species. However, it is presently limited to species meeting the strong assumptions required for maximum-likelihood estimation with presence-only data, including very high detectability and representative sampling of its historical distribution.

Icebergs in the Amundsen Sea (Antarctica) determined from ENVISAT ASAR WSM data using object-based image analysis approach, link to ESRI GDB files

ENVISAT ASAR WSM images with pixel size 150 × 150 m, acquired in different meteorological, oceanographic and sea ice conditions were used to determined icebergs in the Amundsen Sea (Antarctica). An object-based method for automatic iceberg detection from SAR data has been developed and applied. The object identification is based on spectral and spatial parameters on 5 scale levels, and was verified with manual classification in four polygon areas, chosen to represent varying environmental conditions. The algorithm works comparatively well in freezing temperatures and strong wind conditions, prevailing in the Amundsen Sea during the year. The detection rate was 96% which corresponds to 94% of the area (counting icebergs larger than 0.03 km**2), for all seasons. The presented algorithm tends to generate errors in the form of false alarms, mainly caused by the presence of ice floes, rather than misses. This affects the reliability since false alarms were manually corrected post analysis.

Study on the effects of near-future ocean acidification on marine yeasts: a microcosm approach

Marine yeasts play an important role in biodegradation and nutrient cycling and are often associated with marine flora and fauna. They show maximum growth at pH levels lower than present-day seawater pH. Thus, contrary to many other marine organisms, they may actually profit from ocean acidification. Hence, we conducted a microcosm study, incubating natural seawater from the North Sea at present-day pH (8.10) and two near-future pH levels (7.81 and 7.67). Yeasts were isolated from the initial seawater sample and after 2 and 4 weeks of incubation. Isolates were classified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and representative isolates were identified by partial sequencing of the large subunit rRNA gene. From the initial seawater sample, we predominantly isolated a yeast-like filamentous fungus related to Aureobasidium pullulans, Cryptococcus sp., Candida sake, and various cold-adapted yeasts. After incubation, we found more different yeast species at near-future pH levels than at present-day pH. Yeasts reacting to low pH were related to Leucosporidium scottii, Rhodotorula mucilaginosa, Cryptococcus sp., and Debaryomyces hansenii. Our results suggest that these yeasts will benefit from seawater pH reductions and give a first indication that the importance of yeasts will increase in a more acidic ocean.

Metabarcoding approach for non-indigenous species surveillance in marine coastal waters

In this study, high-throughput sequencing (HTS) metabarcoding was applied for the surveillance of plankton communities within the southeastern (SE) Baltic Sea coastal zone. These results were compared with those from routine monitoring survey and morphological analyses. Four of five nonindigenous species found in the samples were identified exclusively by metabarcoding. All of them are considered as invasive in the Baltic Sea with reported impact on the ecosystem and biodiversity. This study indicates that, despite some current limitations, HTS metabarcoding can provide information on the presence of exotic species and advantageously complement conventional approaches, only requiring the same monitoring effort as before. Even in the currently immature status of HTS, this combination of HTS metabarcoding and observational records is recommended in the early detection of marine pests and delivery of the environmental status metrics of nonindigenous species.