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.

Single beam survey data and links to sea-bottom video files off Helgoland

Kelp forests represent a major habitat type in coastal waters worldwide and their structure and distribution is predicted to change due to global warming. Despite their ecological and economical importance, there is still a lack of reliable spatial information on their abundance and distribution. In recent years, various hydroacoustic mapping techniques for sublittoral environments evolved. However, in turbid coastal waters, such as off the island of Helgoland (Germany, North Sea), the kelp vegetation is present in shallow water depths normally excluded from hydroacoustic surveys. In this study, single beam survey data consisting of the two seafloor parameters roughness and hardness were obtained with RoxAnn from water depth between 2 and 18 m. Our primary aim was to reliably detect the kelp forest habitat with different densities and distinguish it from other vegetated zones. Five habitat classes were identified using underwater-video and were applied for classification of acoustic signatures. Subsequently, spatial prediction maps were produced via two classification approaches: Linear discriminant analysis (LDA) and manual classification routine (MC). LDA was able to distinguish dense kelp forest from other habitats (i.e. mixed seaweed vegetation, sand, and barren bedrock), but no variances in kelp density. In contrast, MC also provided information on medium dense kelp distribution which is characterized by intermediate roughness and hardness values evoked by reduced kelp abundances. The prediction maps reach accordance levels of 62% (LDA) and 68% (MC). The presence of vegetation (kelp and mixed seaweed vegetation) was determined with higher prediction abilities of 75% (LDA) and 76% (MC). Since the different habitat classes reveal acoustic signatures that strongly overlap, the manual classification method was more appropriate for separating different kelp forest densities and low-lying vegetation. It became evident that the occurrence of kelp in this area is not simply linked to water depth. Moreover, this study shows that the two seafloor parameters collected with RoxAnn are suitable indicators for the discrimination of different densely vegetated seafloor habitats in shallow environments.

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.

Seismic data from Colle Gnifetti glacie saddle around borehole KCI, Swiss Alps

Two seismic surveys were carried out on the high-altitude glacier saddle, Colle Gnifetti, Monte Rosa, Italy/Switzerland. Explosive and vibroseismic sources were tested to explore the best way to generate seismic waves to deduce shallow and intermediate properties (<100 m) of firn and ice. The explosive source (SISSY) excites strong surface and diving waves, degrading data quality for processing; no englacial reflections besides the noisy bed reflector are visible. However, the strong diving waves are analyzed to derive the density distribution of the firn pack, yielding results similar to a nearby ice core. The vibrator source (ElViS), used in both P- and SH-wave modes, produces detectable laterally coherent reflections within the firn and ice column. We compare these with ice-core and radar data. The SH-wave data are particularly useful in providing detailed, high-resolution information on firn and ice stratigraphy. Our analyses demonstrate the potential of seismic methods to determine physical properties of firn and ice, particularly density and potentially also crystal-orientation fabric.

Table 1 : AMS-14C data used for the reconstruction of the Wismar Bay shoreline displacement curve

The Baltic coast of Mecklenburg-Vorpommern is located in the transition Zone between the region of Fennoscandian Uplift and the Central European Depression. In relation to the eustatic sea-level rise, the northeast coast shows a slower inundation, while for the southwestern area a faster transgression is indicated, which can be attributed to crustal movements. To determine the spatial and temporal differences since the onset of the Littorina Transgression, three relative sea-level curves have been established along a transect parallel to the gradient of upliftlsubsidence. The Wismar Bay area is one endpoint of the transect demonstrating today 10 Abb., 2 Tab. a relative sea-level rise of 1.4 mm/a. To determine the relative sea-level curve for the Wismar Bay, two sites were investigated on Rustwerder Spit (Poel) and Redentin. They provided reliable depth-age data, while the stratigraphy was additionally supported by lithological/geochemical, pollen, diatom and macrofossil data. Additional evidence was provided by archaeological submarine surveys and excavations. Comparing the new relative sea-level curve with a curve from the Vorpommern coast, it can be shown that for the period from 4000 cal BC until present, the differences between the two curves are caused by a constant neotectonic movement, while for the older periods an increasing isostatic component must be taken into account.

Classified Adélie penguin colonies from Landsat data

Breeding distribution of the Adelie penguin, Pygoscelis adeliae, was surveyed with Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data along the coastline of Antarctica, an area covering approximately 330° of longitude. An algorithm was designed to minimize the radiometric contribution from exogenous sources and to retrieve Adelie penguin colony location and spatial extent from the ETM+ data. In all, 9143 individual pixels were classified as belonging to an Adelie penguin colony class out of the entire dataset of 195 ETM+ scenes, where the dimension of each pixel is 30 m by 30 m, and each scene is approximately 180 km by 180 km. Pixel clustering identified a total of 187 individual Adelie penguin colonies, ranging in size from a single pixel (900 m**2) to a maximum of 875 pixels (0.788 km**2). Colony retrievals have a very low error of commission, on the order of 1 percent or less, and the error of omission was estimated to be 2.9 percent by population based on comparisons with direct observations from surveys across east Antarctica. Thus, the Landsat retrievals can successfully locate Adelie penguin colonies that account for ~97 percent of a regional population. Geographic coordinates and the spatial extent of each colony retrieved from the Landsat data are available publically. Regional analysis found several areas where the Landsat retrievals suggest populations that are significantly larger than published estimates. Six Adelie penguin colonies were found that are believed to be unreported in the literature.

Ecological assessment data of the marine flora and fauna of Point Lookout in 2014 (PLEA)

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. 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). Methodology used for the PLEA project was based on the 2001 survey protocols, Reef Check Australia protocols and Coral Watch methods. This hybrid methodology was used to monitor substrate and benthos, invertebrates, fish, and reef health impacts. Additional analyses were conducted with georeferenced photo transects. The PLEA marine surveys were conducted over six weekends in 2014 totaling 535 dives and 376 hours underwater. Two training weekends (February and March) were attended by 44 divers, whilst biological surveys were conducted on seasonal weekends (February, May, July and October). Three reefs were surveyed, with two semi-permanent transects at Flat Rock, two at Shag Rock, and one at Manta Ray Bommie. Each transect was sampled once every survey weekend, with the transect tapes deployed at a depth of 10 m below chart datum. Fish populations were assessed using a visual census along 3 x 20 m transects. Each transect was 5 m wide (2.5 m either side of the transect tape), 5 m high and 20 m in length. Fish families and species were chosen that are commonly targeted by recreational or commercial fishers, or targeted by aquarium collectors, and that were easily identified by their body shape. Rare or otherwise unusual species were also recorded. Target invertebrate populations were assessed using visual census along 3 x 20 m transects. Each transect was 5 m wide (2.5 m either side of the transect tape) and 20 m in length. The diver surveying invertebrates conducted a 'U-shaped' search pattern, covering 2.5 m on either side of the transect tape. Target impacts were assessed using a visual census along the 3 x 20 m transects. Each transect was 5 m wide (2.5 m either side of the transect tape) and 20 m in length. The transect was surveyed via a 'U-shaped' search pattern, covering 2.5 m on either side of the transect tape. Substrate surveys were conducted using the point sampling method, enabling percentage cover of substrate types and benthic organisms to be calculated. The substrate or benthos under the transect line was identified at 0.5m intervals, with a 5m gap between each of the three 20m segments. Categories recorded included various growth forms of hard and soft coral, key species/growth forms of algae, other living organisms (i.e. sponges), recently killed coral, and, non-living substrate types (i.e. bare rock, sand, rubble, silt/clay).