A statistical approach to evaluating distance metrics and analog assignments for pollen records

The modern analog technique typically uses a distance metric to determine the dissimilarity between fossil and modern biological assemblages. Despite this quantitative approach, interpretation of distance metrics is usually qualitative and rules for selection of analogs tend to be ad hoc. We present a statistical tool, the receiver operating characteristic (ROC) curve, which provides a framework for identifying analogs from distance metrics. If modern assemblages are placed into groups (e.g., biomes), this method can (1) evaluate the ability of different distance metrics to distinguish among groups, (2) objectively identify thresholds of the distance metric for determining analogs, and (3) compute a likelihood ratio and a Bayesian probability that a modern group is an analog for an unknown (fossil) assemblage. Applied to a set of 1689 modern pollen assemblages from eastern North America classified into eight biomes, ROC analysis confirmed that the squared-chord distance (SCD) outperforms most other distance metrics. The optimal threshold increased when more dissimilar biomes were compared. The probability of an analog vs no-analog result (a likelihood ratio) increased sharply when SCD decreased below the optimal threshold, indicating a nonlinear relationship between SCD and the probability of analog. Probabilities of analog computed for a postglacial pollen record at Tannersville Bog (Pennsylvania, USA) identified transitions between biomes and periods of no analog.     

Detecting lateral resistivity inhomogeneities with the Schlumberger array

A method of detecting lateral resistivity inhomogeneities with a multi-electrode cable for the Schlumberger array is presented. Using such a cable system, two dipole-dipole soundings are gathered in addition to the Schlumberger sounding at each point. Offset differences calculated from the dipole-dipole data give qualitative information about lateral resistivity inhomogeneities. Results from 1D modelling can give quantitative information about the 2D resistivity distribution since the dipole-dipole soundings are made to either side of the Schlumberger sounding point. Examples from two different locations in Norway are shown. At Reinøya, northern Norway, a dipping layer, confirmed by refraction seismic data, was identified. In a sedimentary basin with a contamination plume at Haslemoen, southern Norway, the method has revealed lateral variations in resistivity. In both cases, the Schlumberger soundings could be fitted with a 1D resistivity model.     

Aliens like us?

Simon Conway Morris explains that as and when (and indeed if) we meet the extraterrestrials, they will look eerily like us.     

A three-dimensional approach to fault seal analysis: fault-block juxtaposition & argillaceous smear modelling

Three‐dimensional (3D) numerical modelling of fault displacement enables the building of geological models to represent the complex 3D geometry and geological properties of faulted sedimentary basins. Using these models, cross‐fault juxtaposition relationships are predicted in 3D space and through time, based on the geometries of strata that are cut by faults. Forward modelling of fault development allows a 3D prediction of fault‐zone argillaceous smear using a 3D application of the Shale Gouge Ratio. Numerical models of the Artemis Field, Southern North Sea, UK and the Moab Fault, Utah, USA are used to demonstrate the developed techniques and compare them to traditional one‐ and two‐dimensional solutions. These examples demonstrate that a 3D analysis leads to significant improvements in the prediction of fault seal, the analysis of the interaction of the sealing properties of multiple faults, and the interpretation of fault seal within the context of sedimentary basin geometry.     

Detecting trends in seagrass abundance using aerial photograph interpretation: Problems arising with the evolution of mapping methods

In the late 1970s and early 1980s an inventory of seagrass distribution in southeast Australia was made from multiscale aerial photographs using a simple analog mapping method, referred to here as theCamera lucida technique. Four priority estuaries were remapped in 2003, also using multi-scale aerial photographs, but using a geographic information system (GIS)-based analog mapping technique. We quantified the degree of error that arose when estimates of seagrass distribution were compared for the two techniques be ressessing. with GIS, the same aerial photographs used earlier for four estuaries: Port Hacking, Bermagui River, Merimbula Lake, and St Georges Basin.Camera lucida produced a larger estimate of total seagrass area than GIS in all four locations: 8% of total seagrass area or 12.9 ha in Port Hacking, 15% or 5 ha in Bermagui River, 20% or 38.0 ha in Merimbula Lake, and 243% or 502.2 ha in St Georges Basin. The consistent and moderate discrepancy in Port Hacking, Bermagui River, and Merimbula Lake was attributed mainly to differences betweenCamera lucida and GIS, such as the tendency ofCamera lucida to overestimate seagrass area by amalgamating disjunct seagrass patches into continuous meadows. The large discrepancy at St Georges Basin was attributed to interoperator error; i.e., the operators who mapped photographs taken in 1979 had widely differing interpretations of the aerial photographs. A 20-yr trend was calculated for each estuary using either theCamera lucida or revised GIS value as the start point. Perceived management status of the seagrass depended greatly on the initial data used. In St Georges Basin, a catastrophic decline (65%, 553.9 ha) occurred based on theCamera lucida-GIS comparison but only a small decline (14.7%, 51.8 ha) when all photos were GIS assessed, In view of the technical developments in habitat mapping, evaluation of change in seagrass abundance using remote sensing needs an assessment of intra-operator error, inter-operator error, and systemic error before maps produced by superseded technology are considered useful baselines.     

The spectacular fossils of the ‘water margin’: the Cambrian biota of Chengjiang,Yunnan, China

Shi Nai'an's fourteenth century Chinese epic ‘Water Margin’ tells of the release of 36 heavenly spirits and 72 baleful stars from their captivity beneath a tablet of stone at Mount Longhu in Jiangxi Province. They are reincarnated as the 108 heroes of the Liangshan marsh in Shandong Province, who rise against an unjust world. The virtuous exploits of the ‘108’ were brought to life through the cathode‐ray screens of 1970s television sets, as the TV series The Water Margin introduced heroes like Lin Chong battling his evil nemesis Gao Qiu. Far to the west of Jiangxi Province and several hundred years after the Water Margin during the summer of 1984, a young scientist from Nanjing was working amongst the hills and lakes of southern Yunnan Province. He too overturned a stone slab, releasing from their half‐billion year captivity a cornucopia of new Chinese legends. His name was Xianguang Hou and he had made one of the most momentous fossil discoveries in history, uncovering the exceptionally preserved marine fossils of the Chengjiang biota from the ancient water margin of Cambrian seas.     

Understory and small trees contribute importantly to stemflow of a lower montane cloud forest

Stemflow (Sf) measurements in tropical rain and montane forests dominated by large trees rarely include the understory and small trees. In this study, contributions of lower (1‐ to 2‐m height) and upper (>2‐m height and <5‐cm diameter at breast height [DBH]) woody understory, small trees (5 < DBH < 10 cm), and canopy trees (>10‐cm DBH) to Sf per unit ground area (Sf_a) of a Mexican lower montane cloud forest were quantified for 32 days with rainfall (P) during the 2014 wet season. Rainfall, stemflow yield (Sf_y), vegetation height, density, and basal area were measured. Subsequently, stemflow funneling ratios (SFRs) were calculated, and three common methods to scale up Sf_y from individual trees to the stand level (tree‐Sf_y correlation, P‐Sf_y correlation, and mean‐Sf_y extrapolation) were used to calculate Sf_a. Understory woody plants, small trees, and upper canopy trees represented 96%, 2%, and 2%, respectively, of the total density. Upper canopy trees had the lowest SFRs (1.6 ± 0.5 Standard Error (SE) on average), although the lower understory had the highest (36.1 ± 6.4). Small trees and upper understory presented similar SFRs (22.9 ± 5.4 and 20.2 ± 3.9, respectively). Different Sf scaling methods generally yielded similar results. Overall Sf_a during the study period was 22.7 mm (4.5% of rainfall), to which the understory contributed 70.1% (15.9 mm), small trees 10.6% (2.4 mm), and upper canopy trees 19.3% (4.4 mm). Our results strongly suggest that for humid tropical forests with dense understory of woody plants and small trees, Sf of these groups should be measured to avoid an underestimation of overall Sf at the stand level.