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.

Spatially explicit estimates of length and biomass of Micromesistius poutassou (Blue Whiting) from acoustic and trawl surveys in the North-East Atlantic in May 2009

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Clupea harengus (Norwegian spring spawning herring) from acoustic and trawl surveys in the North-East Atlantic in May 2010

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Clupea harengus (Norwegian spring spawning herring) from acoustic and trawl surveys in the North-East Atlantic in May 2012

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Micromesistius poutassou (Blue Whiting) from acoustic and trawl surveys in the North-East Atlantic in May 2004

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Sea ice thickness from airborne electromagnetic (EM) induction sounding acquired from fixed-wing aircraft POLAR 5 surveys from several airports in Spitsbergen, Greenland, Canada and Alaska

While summer Arctic sea-ice extent has decreased over the past three decades, it is subject to large interannual and regional variations. Methodological challenges in measuring ice thickness continue to hamper our understanding of the response of the ice-thickness distribution to recent change, limiting the ability to forecast sea-ice change over the next decade. We present results from a 2400 km long pan-Arctic airborne electromagnetic (EM) ice thickness survey in April 2009, the first-ever large-scale EM thickness dataset obtained by fixed-wing aircraft over key regions of old ice in the Arctic Ocean between Svalbard and Alaska. The data provide detailed insight into ice thickness distributions characteristic for the different regions. Comparison with previous EM surveys shows that modal thicknesses of old ice had changed little since 2007, and remained within the expected range of natural variability.

Habitat Maps derived from Point Lookout Ecological Surveys (PLEA) of the Shag Rock, Manta Ray Bommie and Flat Rock dive sites at Point Lookout, North Stradbroke Island, Australia, in 2014, in ArcGIS (shapefile) format

In 2014, UniDive (The University of Queensland Underwater Club) conducted an ecological assessment of the Point Lookout Dive sites for comparison with similar surveys conducted in 2001 - the PLEA project. Involvement in the project was voluntary. Members of UniDive who were marine experts conducted training for other club members who had no, or limited, experience in identifying marine organisms and mapping habitats. Since the 2001 detailed baseline study, no similar seasonal survey has been conducted. The 2014 data is particularly important given that numerous changes have taken place in relation to the management of, and potential impacts on, these reef sites. In 2009, Moreton Bay Marine Park was re-zoned, and Flat Rock was converted to a marine national park zone (Green zone) with no fishing or anchoring. In 2012, four permanent moorings were installed at Flat Rock. Additionally, the entire area was exposed to the potential effects of the 2011 and 2013 Queensland floods, including flood plumes which carried large quantities of sediment into Moreton Bay and surrounding waters. The population of South East Queensland has increased from 2.49 million in 2001 to 3.18 million in 2011 (BITRE, 2013). This rapidly expanding coastal population has increased the frequency and intensity of both commercial and recreational activities around Point Lookout dive sites (EPA 2008). Habitats were mapped using a combination of towed GPS photo transects, aerial photography and expert knowledge. This data provides georeferenced information regarding the major features of each of the Point Lookout Dive Sites.

Parasound profiles and multibeam surveys in the Arctic Ocean

During the Pleistocene glaciations, Arctic ice sheets on western Eurasia, Greenland and North America terminated at their continental margins. In contrast, the exposed continental shelves in the Beringian region of Siberia are thought to have been covered by a tundra landscape. Evidence of grounded ice on seafloor ridges and plateaux off the coast of the Beringian margin, at depths of up to 1,000 m, have generally been attributed to ice shelves or giant icebergs that spread oceanwards during glacial maxima. Here we identify marine glaciogenic landforms visible in seismic profiles and detailed bathymetric maps along the East Siberian continental margin. We interpret these features, which occur in present water depths of up to 1,200 m, as traces from grounding events of ice sheets and ice shelves. We conclude that the Siberian Shelf edge and parts of the Arctic Ocean were covered by ice sheets of about 1 km in thickness during several Pleistocene glaciations before the most recent glacial period, which must have had a significant influence on albedo and oceanic and atmospheric circulation.