Mid and late Holocene forest fires and deforestation in the subalpine belt of the Iberian range,northern Spain

The conversion of subalpine forests into grasslands for pastoral use is a well-knownphenomenon, although for most mountain areas the timing of deforestation has not been determined. The presence of charcoal fragments in soil profiles affected by shallow landsliding enabled us to date the occurrence of fires and the periods of conversion ofsubalpine forest into grasslands in the Urbión Mountains, Iberian Range, Spain. We found that the treeline in the highest parts of the northwestern massifs of the Iberian Range(the Urbión, Demanda, Neila, and Cebollera massifs) is currently between 1500 and 1600 m a.s.l., probably because of pastoral use of the subalpine belt, whereas in the past it would have reached almost the highest divides(at approximately 2100–2200 m a.s.l.). The radiocarbon dates obtained indicate that the transformation of the subalpine belt occurred during the Late Neolithic, Chalcolithic, Bronze Age, Iron Age, and Middle Ages. Forest clearing was probably moderate during fires prior to the Middle Ages, as the small size of the sheep herds and the local character of the markets only required small clearings, and therefore more limited fires. Thus, it is likely that the forest recovered burnt areas in a few decades; this suggests the management of the forest and grasslands following a slash-andburn system. During the Middle and Modern Ages deforestation and grassland expansion affected most of the subalpine belt and coincided with the increasing prevalence of transhumance, as occurred in other mountains in the Iberian Peninsula(particularly the Pyrenees). Although the occurrence of shallow landslides following deforestation between the Neolithic and the Roman Period cannot be ruled out, the most extensive shallow landsliding processes would have occurred from the Middle Ages until recent times.     

Statistical analyses of Global U-Pb Database 2017

The method of obtaining zircon samples affects estimation of the global U-Pb age distribution.Researchers typically collect zircons via convenience sampling and cluster sampling.When using these techniques,weight adjustments proportional to the areas of the sampled regions improve upon unweighted estimates.Here,grid-area and modern sediment methods are used to weight the samples from a new database of 418,967 U-Pb ages.Preliminary tests involve two age models.Model-1 uses the most precise U-Pb ages as the best ages.Model-2 uses the~(206)Pb/~(238)U age as the best age if it is less than a1000 Ma cutoff,otherwise it uses the~(207)Pb/~(206)Pb age as the best age.A correlation analysis between the Model-1 and Model-2 ages indicates nearly identical distributions for both models.However,after applying acceptance criteria to include only the most precise analyses with minimal discordance,a histogram of the rejected samples shows excessive rejection of the Model-2 analyses around the1000 Ma cutoff point.Because of the excessive rejection rate for Model-2,we select Model-1 as the preferred model.After eliminating all rejected samples,the remaining analyses use only Model-1 ages for five rock-type subsets of the database:igneous,meta-igneous,sedimentary,meta-sedimentary,and modern sediments.Next,time-series plots,cross-correlation analyses,and spectral analyses determine the degree of alignment among the time-series and their periodicity.For all rock types,the U-Pb age distributions are similar for ages older than 500 Ma,but align poorly for ages younger than 500 Ma.The similarities(500 Ma)and differences(500 Ma)highlight how reductionism from a detailed database enhances understanding of time-dependent sequences,such as erosion,detrital transport mechanisms,lithification,and metamorphism.Time-series analyses and spectral analyses of the age distributions predominantly indicate a synchronous period-tripling sequence of~91-Myr,~273-Myr,and~819-Myr among the various rock types.     

Gravimetric and magnetic fabric study of the Sintra Igneous complex: laccolith-plug emplacement in the Western Iberian passive margin

International Journal of Earth Sciences - The geometry and emplacement of the ~ 96 km2, Late Cretaceous Sintra Igneous complex (SIC, ca. 80 Ma) into the West Iberian passive...     

A Cambrian mixed carbonate–siliciclastic platform in SW Gondwana: evidence from the Western Sierras Pampeanas (Argentina) and implications for the early Paleozoic paleogeography of the proto-Andean margin

International Journal of Earth Sciences - The Western Sierras Pampeanas (WSP) of Argentina record a protracted geological history from the Mesoproterozoic assembly of the Rodinia supercontinent to...     

Increase in Dissolved Silica of Rivers Due to a Volcanic Eruption in an Estuarine Bay (Sorsogon Bay,Philippines)

Mount Bulusan, the Philippines’ fourth most active volcano, erupted in February 21, 2011, sending volcanic ash and pyroclastic materials to its surrounding rivers. The waters drained into the estuary of harmful algal blooms plagued Sorsogon Bay. We aim to determine the impact of the 2011 volcanic eruption and the preceding volcanic ash emissions to the dissolved silica concentration of rivers draining the flanks of Mt. Bulusan and its possible implications to the phytoplankton assemblage of the bay. Six river water sampling periods from August 2010 to October 2012 overlapped with Mt. Bulusan’s active phase of volcanism. Our data shows that mean river silica from pre-eruption levels of ~?500 μM increased by more than 200% during and post-eruption. Highest Si concentration of 2270 μM was measured from Cadacan River in August 2011. Here, we argue that the sustained general increase of dissolved silica is due to the silica-containing materials from Mt. Bulusan’s eruption and that their concentration in river waters is also a function of watershed lithology and precipitation. Increase in dissolved silica and other nutrients caused a shift to diatom domination and, possibly, termination of Pyrodinium bahamense var. compressum blooms. Silica load increase in embayments is a natural process that controls the dominance of algae. Our study also highlights the importance of Philippine rivers to the global ocean silica budget as a function of high precipitation, tectonics in general, and volcanism in particular.     

Potential Salinity and Temperature Futures for the Chesapeake Bay Using a Statistical Downscaling Spatial Disaggregation Framework

Estuaries are productive and ecologically important ecosystems, incorporating environmental drivers from watersheds, rivers, and the coastal ocean. Climate change has potential to modify the physical properties of estuaries, with impacts on resident organisms. However, projections from general circulation models (GCMs) are generally too coarse to resolve important estuarine processes. Here, we statistically downscaled near-surface air temperature and precipitation projections to the scale of the Chesapeake Bay watershed and estuary. These variables were linked to Susquehanna River streamflow using a water balance model and finally to spatially resolved Chesapeake Bay surface temperature and salinity using statistical model trees. The low computational cost of this approach allowed rapid assessment of projected changes from four GCMs spanning a range of potential futures under a high CO₂ emission scenario, for four different downscaling methods. Choice of GCM contributed strongly to the spread in projections, but choice of downscaling method was also influential in the warmest models. Models projected a ~2–5.5 °C increase in surface water temperatures in the Chesapeake Bay by the end of the century. Projections of salinity were more uncertain and spatially complex. Models showing increases in winter-spring streamflow generated freshening in the Upper Bay and tributaries, while models with decreased streamflow produced salinity increases. Changes to the Chesapeake Bay environment have implications for fish and invertebrate habitats, as well as migration, spawning phenology, recruitment, and occurrence of pathogens. Our results underline a potentially expanded role of statistical downscaling to complement dynamical approaches in assessing climate change impacts in dynamically challenging estuaries.     

Implementing landslide path dependency in landslide susceptibility modelling

Landslide susceptibility modelling—a crucial step towards the assessment of landslide hazard and risk—has hitherto not included the local, transient effects of previous landslides on susceptibility. In this contribution, we implement such transient effects, which we term “landslide path dependency”, for the first time. Two landslide path dependency variables are used to characterise transient effects: a variable reflecting how likely it is that an earlier landslide will have a follow-up landslide and a variable reflecting the decay of transient effects over time. These two landslide path dependency variables are considered in addition to a large set of conditioning attributes conventionally used in landslide susceptibility. Three logistic regression models were trained and tested fitted to landslide occurrence data from a multi-temporal landslide inventory: (1) a model with only conventional variables, (2) a model with conventional plus landslide path dependency variables, and (3) a model with only landslide path dependency variables. We compare the model performances, differences in the number, coefficient and significance of the selected variables, and the differences in the resulting susceptibility maps. Although the landslide path dependency variables are highly significant and have impacts on the importance of other variables, the performance of the models and the susceptibility maps do not substantially differ between conventional and conventional plus path dependent models. The path dependent landslide susceptibility model, with only two explanatory variables, has lower model performance, and differently patterned susceptibility map than the two other models. A simple landslide susceptibility model using only DEM-derived variables and landslide path dependency variables performs better than the path dependent landslide susceptibility model, and almost as well as the model with conventional plus landslide path dependency variables—while avoiding the need for hard-to-measure variables such as land use or lithology. Although the predictive power of landslide path dependency variables is lower than those of the most important conventional variables, our findings provide a clear incentive to further explore landslide path dependency effects and their potential role in landslide susceptibility modelling.