The bird GPS - long-range navigation in migrants

Nowadays few people consider finding their way in unfamiliar areas a problem as a GPS (Global Positioning System) combined with some simple map software can easily tell you how to get from A to B. Although this opportunity has only become available during the last decade, recent experiments show that long-distance migrating animals had already solved this problem. Even after displacement over thousands of kilometres to previously unknown areas, experienced but not first time migrant birds quickly adjust their course toward their destination, proving the existence of an experience-based GPS in these birds. Determining latitude is a relatively simple task, even for humans, whereas longitude poses much larger problems. Birds and other animals however have found a way to achieve this, although we do not yet know how. Possible ways of determining longitude includes using celestial cues in combination with an internal clock, geomagnetic cues such as magnetic intensity or perhaps even olfactory cues. Presently, there is not enough evidence to rule out any of these, and years of studying birds in a laboratory setting have yielded partly contradictory results. We suggest that a concerted effort, where the study of animals in a natural setting goes hand-in-hand with lab-based study, may be necessary to fully understand the mechanism underlying the long-distance navigation system of birds. As such, researchers must remain receptive to alternative interpretations and bear in mind that animal navigation may not necessarily be similar to the human system, and that we know from many years of investigation of long-distance navigation in birds that at least some birds do have a GPS-but we are uncertain how it works.

Large-scale spatial patterns in the distribution of Collembola (Hexapoda) species in Antarctic terrestrial ecosystems

We tested whether the distribution of three common springtail species (Gressittacantha terranova, Gomphiocephalus hodgsoni and Friesea grisea) in Victoria Land (Antarctica) could be modelled as a function of latitude, longitude, altitude and distance from the sea.

Victoria Land, Ross Dependency, Antarctica.

Generalized linear models were constructed using species presence/absence data relative to geographical features (latitude, longitude, altitude, distance from sea) across the species' entire ranges. Model results were then integrated with the known phylogeography of each species and hypotheses were generated on the role of climate as a major driver of Antarctic springtail distribution.

Based on model selection using Akaike's information criterion, the species' distributions were: hump-shaped relative to longitude and monotonic with altitude for Gressittacantha terranova; hump-shaped relative to latitude and monotonic with altitude for Gomphiocephalus hodgsoni; and hump-shaped relative to longitude and monotonic with latitude, altitude and distance from the sea for Friesea grisea.

No single distributional pattern was shared by the three species. While distributions were partially a response to climatic spatial clines, the patterns observed strongly suggest that past geological events have influenced the observed distributions. Accordingly, present-day spatial patterns are likely to have arisen from the interaction of historical and environmental drivers. Future studies will need to integrate a range of spatial and temporal scales to further quantify their respective roles.

Modelling the fate of persistent organic pollutants (POPs) in the North Sea system

The environmental fate of selected persistent organic pollutants (POPs) in the North Sea system is modelled with a high resolution Fate and Transport Ocean Model (FANTOM) that uses hydrodynamic model output from the Hamburg Shelf Ocean Model (HAMSOM). Large amounts of POPs enter the North Sea from the surrounding highly populated, industrialised and agricultural countries. Major pathways to the North Sea are atmospheric deposition and river inputs, with additional contributions coming from bottom sediments and adjacent seas. The model domain covers the entire North Sea region, extending northward as far as the Shetland Islands, and includes adjacent basins such as the Skagerrak, Kattegat, and the westernmost part of the Baltic Sea. Model resolution (for both models) is 1.5’ latitude x 2.5’ longitude (approximately 3 km horizontal resolution) with 30 vertical levels. The POP model also has 20 sediment layers. Important model processes controlling the fate of POPs in the North Sea system are discussed. Results focus on Lindane gamma- HCH or gamma-hexachlorocyclohexane) and PCB 153.

Shape and Rotation Modeling and Thermophysical Analysis of Near-Earth Asteroid (1917) Cuyo

We are conducting an ESO Large Program that includes optical photometry, thermal-IR observations, and optical-NIR spectroscopy of selected NEAs. Among the principal goals of the program are shape and spin-state modeling, and searching for YORP-induced changes in rotation periods. One of our targets is asteroid (1917) Cuyo, a near-Earth asteroid from the Amor group. We carried out an extensive observing campaign on Cuyo between April 2010 and April 2013, operating primarily at the ESO 3.6m NTT for optical photometry, and the 8.2m VLT at Paranal for thermal-IR imaging. Further optical observations were acquired at the ESO 2.2m telescope, the Palomar 200" Hale telescope (California), JPL’s Table Mountain Observatory (California) and the Faulkes Telescope South (Australia). We obtained optical imaging data for rotational lightcurves throughout this period, as the asteroid passed through a wide range of observational geometries, conducive to producing a good shape model and spin state solution. The preliminary shape and spin state model indicates a nearly spherical shape and a rotation pole at ecliptic longitude λ = 53° ± 20° and latitude β = -37° ± 10° (1-sigma error bars are approximate). The sidereal rotation period was measured to be 2.6899522 ± (3 × 10^-7) hours. Linkage with earlier lightcurve data shows possible evidence of a small change in rotation rate during the period 1989-2013. We applied the NEATM thermal model (Harris A., Icarus 131, 291, 1998) to our VLT thermal-IR measurements (8-19.6 μm), obtained in September and December 2011. The derived effective diameter ranges from 3.4 to 4.2 km, and the geometric albedo is 0.16 (+0.07, -0.04). Using the shape model and thermal fluxes we will perform a detailed thermophysical analysis using the new Advanced Thermophysical Model (Rozitis, B. & Green, S.F., MNRAS 415, 2042, 2011; Rozitis, B. & Green, S.F., MNRAS 423, 367, 2012). This work was performed in part at the Jet Propulsion Laboratory under a contract with NASA.

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

PREDICTING THE SPATIAL-DISTRIBUTION OF CLIMATE - TEMPERATURE IN GREAT-BRITAIN

1. The prediction and mapping of climate in areas between climate stations is of increasing importance in ecology.

2. Four categories of model, simple interpolation, thin plate splines, multiple linear regression and mixed spline-regression, were tested for their ability to predict the spatial distribution of temperature on the British mainland. The models were tested by external cross-verification.

3. The British distribution of mean daily temperature was predicted with the greatest accuracy by using a mixed model: a thin plate spline fitted to the surface of the country, after correction of the data by a selection from 16 independent topographical variables (such as altitude, distance from the sea, slope and topographic roughness), chosen by multiple regression from a digital terrain model (DTM) of the country.

4. The next most accurate method was a pure multiple regression model using the DTM. Both regression and thin plate spline models based on a few variables (latitude, longitude and altitude) only were comparatively unsatisfactory, but some rather simple methods of surface interpolation (such as bilinear interpolation after correction to sea level) gave moderately satisfactory results. Differences between the methods seemed to be dependent largely on their ability to model the effect of the sea on land temperatures.

5. Prediction of temperature by the best methods was greater than 95% accurate in all months of the year, as shown by the correlation between the predicted and actual values. The predicted temperatures were calculated at real altitudes, not subject to sea-level correction.

6. A minimum of just over 30 temperature recording stations would generate a satisfactory surface, provided the stations were well spaced.

7. Maps of mean daily temperature, using the best overall methods are provided; further important variables, such as continentality and length of growing season, were also mapped. Many of these are believed to be the first detailed representations at real altitude.

8. The interpolated monthly temperature surfaces are available on disk.

Aristarchos RISE2: A Wide-Field Fast Imager for Exoplanet Transit Timing

The detection of exoplanets is currently of great topical interest in astronomy. The Rapid Imager for Surveys of Exoplanets 2 (RISE2) camera will be built for exoplanet studies and in particular for detection of transit timing variations (TTV) induced by the presence of a third body in the system. It will be identical to RISE which has been running successfully on the 2m Liverpool Telescope since 2008 but modified for the 2.3m ARISTARCHOS telescope. For TTV work the RISE/LT combination is regularly producing timings with accuracy <10 seconds making it the best suited instrument for this work. Furthermore, RISE2/AT has the added benefit of being located at a significantly different longitude to the LT/RISE on La Palma, hence extending the transit coverage.