The necessity for intertidal foraging by estuarine populations of subadult dungeness crab,Cancer magister: Evidence from a bioenergetics model

Complex intertidal habitats characteristic of northeastern Pacific coastal estuaries provide critical nursery environments for young-of-the-year Dungeness crab,Cancer magister, yet their role in supporting subsequent year classes remains unclear. SubadultC. magister (40–130 mm; 1+ and >1+ year classes), which reach densities as high as 4,300 crabs ha^?1 in subtidal channels during low tides, migrate during flood tides from subtidal refuges into intertidal habitats to forage. As with other brachyuran species that undertake extensive tidally-driven migrations, intertidal foraging may contribute significantly to the energy budget of subadultC. magister. In order to explore the energetic incentive for intertidal migrations by subadult crabs, we developed an ontogenetically-based bioenergetics model for crabs within Willapa Bay, Washington. The model showed that energetic demand varied spatially across the bay, with the highest average energetic demand of a population of subadult crabs (2.13×10⁶ kJ ha^?1) occurring in a habitat stratum termed lower side channel (LSC) and characterized by relatively little subtidal area and extensive intertidal flats. Comparison of model results with subtidal prey production revealed that the latter could not satisfy subadultC. magister energetic demands, especially in LSC where modeled crab predation depleted subtidal prey biomass within 17 simulation days. We estimate that 1 ha of subtidal crabs from LSC would minimally require an additional 1.6 ha of intertidal area to satisfy energetic demands without depleting prey biomass. Our model results support the assertion thatC. magister make regular migrations to forage on productive intertidal flats, and suggest that intertidal foraging may contribute significantly to the diet of subadult crabs in coastal estuaries.     

Modelling spatial,altitudinal and temporal variability of annual precipitation in mountainous regions: The case of the Middle Zagros,Iran

Relationships between precipitation and elevation are difficult to model for mountainous regions, due to complexities in topography and moisture sources. Attempts to model these relationships need to be tested against long-term location specific meteorological data, and hence require a case-study approach. This study uses artificial neural networks to model these relationships for the Middle of Zagros region, in semi-arid western Iran. Precipitation data for the region were collected for 1995–2007. Annual precipitation was designated as the target variable for the network, which additionally included variables significantly related to precipitation for the region, including longitude, latitude, elevation, slope, distance from the ridge, and relative distance from moisture. Long-term changes in annual precipitation for the region are investigated for 1961–2010. The artificial neural network (ANN) model explains 76% of the spatial variability of precipitation in the Middle Zagros. Precipitation predominantly increases with elevation on the windward slope, to a maximum height of 2500 m.asl, and thereafter either remains constant or decreases slowly to the ridge. Precipitation in the region has decreased significantly over the study period, with fluctuations driven by AO, NAO, ENSO and variability in the strength of pressure centers. Spectral analysis reveals significant oscillations of 2–4 and 5 yr periods, which correspond temporally with cycles in macro-scale circulation, ENSO and the Mediterranean Low pressure.     

Regional attributes of hurricane surge response functions for hazard assessment

Accurate quantification of hurricane surge probabilities is critically important for coastal planning and design. Recently, the joint probability method has been shown to yield statistically reliable surge probabilities and has quickly become the method of choice for extreme-value surge analysis in the United States. A main disadvantage of the joint probability method is the requirement to have accurate computational surge simulations for a large array of hurricane conditions. Recently, this shortcoming has been overcome by using a variety of interpolation schemes to reduce the number of surge simulations required to an optimal sample for joint probability analysis. One interpolation scheme uses response functions, or physically based dimensionless scaling laws, that consider the relative impact of hurricane landfall position, central pressure, and storm size on surge magnitude at the location of interest. Here, the influence of regional changes in bathymetry on the physically based response function form is investigated. It will be shown that the influence of continental shelf width on surge generation along a continuous coast is coupled with the influence of storm size and that this coupled physical effect can be treated within the response functions via dimensionless scaling. The surge response function model presented here has an algebraic form for rapid calculation. This model performs well for the entire 600-km Texas coast, yielding accurate surge estimates (root-mean-square errors less than 0.22?m and R ² correlations better than 0.97) with virtually no bias (mean error magnitudes less than 0.03?m).     

The Exxon and BP oil spills: a comparison of psychosocial impacts

We address the research question: ‘Did the 2010 BP Deepwater Horizon oil spill have similar psychosocial impacts as the 1989 Exxon Valdez oil spill?’ We answer this question by comparing survey results from a random sample of Cordova, Alaska, residents collected 18 months after the Exxon spill with a random sample of residents in the Alabama coastal counties of Baldwin and south Mobile 1 year after the BP disaster. Analysis revealed similarly high levels of psychological stress for survivors of both disasters. For residents of coastal Alabama, the strongest predictors of psychosocial stress were exposure to oil, ties to renewable resources, concerns about their economic future, worries about air quality, and safety issues regarding seafood harvests in oiled areas. Differences between south Mobile and Baldwin counties were related to the former community’s economic ties to renewable resources and Baldwin County’s dependence on tourism for economic sustainability.     

Adaptive Governance and Climate Change in the Tropical Highlands of Western South America

Climate changes occurring during the past several decades in the high elevations of the tropical Andes Mountains have implications for the native plant and animal species, for the ecological integrity of the affected land cover, and for the human-biophysical systems involved. Consequences are also probable for rural inhabitants and their livelihoods, especially for farmers and pastoralists. Biophysical factors have always changed in these mountainous zones; the extent and degree of alteration acting on native and agricultural biodiversity is the concern. Addressing these climate changes is probably within the adaptive capacity of many local land-use systems, unless external socioeconomic or political forces are unsupportive or antagonistic. Suitable programs to provide information, subsidies, or alternatives could be designed. We highlight some of the inherent resiliencies of natural and cultural systems in the Andes and suggest that these systems contain lessons that could be useful elsewhere, in terms of the traits that allow for the sustainable utilization of dynamic and heterogeneous landscapes.     

Throughfall and its spatial variability in a temperate rainforest of simple structure

There are few throughfall data from southern hemisphere closed-forest, and none from Tasmanian callidendrous cool-temperate rainforest, which has a simpler structure than most primary rainforests. We determined throughfall, measured its local spatial variation, and tested its relationships with rainfall, rainfall intensity, wind speed, canopy dryness, canopy cover, and other structural variables in a cool-temperate callidendrous rainforest in Tasmania. Eighty-two percent of the precipitation was measured as throughfall, which occurred after 2.3 mm of rain fell on dry canopies. The cumulative rainfall in 25 randomly located funnel rain gauges on the forest floor varied from 160 to 567 mm. Canopy cover and other structural variables did not predict the spatial pattern of throughfall. While throughfall in rainfall events was related to rainfall amount and intensity, wind speed did not affect throughfall as a percentage of rainfall. Percentages of throughfall to rainfall over 100 for many low rainfall events may indicate a contribution of fog drip to precipitation on the forest floor. The high local spatial variability in throughfall indicates the mean moisture conditions on the forest floor may not be a good indicator of the potential for localised fire damage.     

Regional- and district-level drivers of timber harvesting in European Russia after the collapse of the Soviet Union

After the collapse of the Soviet Union, the forestry sector in Russia underwent substantial changes: the state forestry sector was decentralized, the timber industry was privatized, and timber use rights were allocated through short- and long-term leases. To date, there has been no quantitative assessment of the drivers of timber harvesting in European Russia following these changes. In this paper we estimate an econometric model of timber harvesting using remote sensing estimations of forest disturbance from 1990–2000 to 2000–2005 as our dependent variable. We aggregate forest disturbance to administrative districts – equivalent to counties in the United States – and test the impact of several biophysical and economic factors on timber harvesting. Additionally, we examine the impact that regions – equivalent to states in the United States and the main level of decentralized governance in Russia – have on timber harvesting by estimating the influence of regional-level effects on forest disturbance in our econometric model. Russian regions diverged considerably in political and economic conditions after the collapse of the Soviet Union, and the question is if these variations impacted timber harvesting after controlling for district-level biophysical and economic drivers. We find that the most important drivers of timber harvesting at the district level are road density, the percent of evergreen forest, and the total area of forest. The influence of these variables on timber harvesting changed over time and there was more harvesting closer to urban areas in 2000–2005. Even though district-level variables explain more than 70 percent of the variation in forest disturbance in our econometric model, we find that regional-level effects remain statistically significant. While we cannot identify the exact mechanism through which regional-level effects impact timber harvesting, our results suggest that sub-national differences can have a large and statistically significant impact on land-use outcomes and should be considered in policy design and evaluation.     

Long memory in temperature reconstructions

Ever since H. E. Hurst brought the concept of long memory time series to prominence in his study of river flows the origins of the so-called Hurst phenomena have remained elusive. Two sets of competing models have been proposed. The fractional Gaussian noises and their discrete time counter-part, the fractionally integrated processes, possess genuine long memory in the sense that the present state of a system has a temporal dependence on all past states. The alternative to these genuine long memory models are models which are non-stationary in the mean but for physical reasons are constrained to lie in a bounded range, hence on visual inspection appear to be stationary. In these models the long memory is merely an artifact of the method of analysis. There are now a growing number of millenial scale temperature reconstructions available. In this paper we present a new way of looking at long memory in these reconstructions and proxies, which gives support to them being described by the non-stationary models. The implications for climatic change are that the temperature time series are not mean reverting. There is no evidence to support the idea that the observed rise in global temperatures are a natural fluctuation which will reverse in the near future.