Variation in the emission rate of sounds in a captive group of false killer whales <Emphasis Type="Italic">Pseudorca crassidens</Emphasis> during feedings: possible food anticipatory vocal activity?

This study examines whether a group of captive false killer whales (Pseudorca crassidens ) showed variations in the vocal rate around feeding times. The high level of motivation to express appetitive behaviors in captive animals may lead them to respond with changes of the behavioral activities during the time prior to food deliveries which are referred to as food anticipatory activity. False killer whales at Qingdao Polar Ocean World (Qingdao, China) showed significant variations of the rates of both the total sounds and sound classes (whistles, clicks, and burst pulses) around feedings. Precisely, from the Transition interval that recorded the lowest vocalization rate (3.40 s/m/d), the whales increased their acoustic emissions upon trainers’ arrival (13.08 s/m/d). The high rate was maintained or intensified throughout the food delivery (25.12 s/m/d), and then reduced immediately after the animals were fed (9.91 s/m/d). These changes in the false killer whales sound production rates around feeding times supports the hypothesis of the presence of a food anticipatory vocal activity. Although sound rates may not give detailed information regarding referential aspects of the animal communication it might still shed light about the arousal levels of the individuals during different social or environmental conditions. Further experiments should be performed to assess if variations of the time of feeding routines may affect the vocal activity of cetaceans in captivity as well as their welfare.     

Explaining low yields and low food production in Cameroon: a farmers’ perspective

While a bottom-up approach to identifying problems affecting food production among small-scale farmers has been urged as an appropriate means of finding sustainable solutions, few studies have determined the practical process of doing so and measured the outcomes of such an approach. This paper uses information gained mainly through focus groups with small-scale farmers and semi-structured interviews, to identify farmers’ perceptions of reasons behind low yields and low agricultural production in three communities of Cameroon’s North West region. Three biophysical factors are identified as the main reasons of low production: the long and more frequent dry spells and late start of the start of the rainy season. Three socio-economic reasons are identified as most important: land scarcity, money to invest in agriculture and labour scarcity. Farmers rank their reasons based on the importance to their agricultural production in their local area. Some of the important claims made by farmers are tested using field data and statistical analysis. These include the claims that: (1) the rainy season is increasingly starting later than it used to; and (2) the length of dry spells are increasingly longer than they used to be. The results of these statistical tests are significant, showing that farmers’ knowledge of some of the local problems affecting their activities can serve as an important input into formal research and policy design. Peoples’ understanding of a problem affects the way they will act on it—in terms of searching for solutions and implementing change. Farmers can therefore provide useful insights on why they think there are large yield gaps within their local production environments. The current agricultural development policy of the Cameroon government is advocating greater public–private engagement and can benefit from farmers’ inputs and opinions in the design of relevant policies. In the same light, nonlocal based researchers and research institutions can draw on farmers’ knowledge to create and accumulate knowledge on sustainable solutions to problems of low yields and low food production in Cameroon.     

Terrestrial and Submerged Aquatic Vegetation Mapping in Fire Island National Seashore Using High Spatial Resolution Remote Sensing Data

The vegetation communities and spatial patterns on the Fire Island National Seashore are dynamic as the result of interactions with driving forces such as sand deposition, storm-driven over wash, salt spray, surface water, as well as with human disturbances. We used high spatial resolution QuickBird-2 satellite remote sensing data to map both terrestrial and submerged aquatic vegetation communities of the National Seashore. We adopted a stratified classification and unsupervised classification approach for mapping terrestrial vegetation types. Our classification scheme included detailed terrestrial vegetation types identified by previous vegetation mapping efforts of the National Park Service and three generalized categories of high-density seagrass, low-density seagrass coverages, and unvegetated bottom to map the submerged aquatic vegetation habitats. We used underwater videography, GPS-guided field reference photography, and bathymetric data to support remote sensing image classification and information extraction. This study achieved approximately 82% and 75% overall classification accuracy for the terrestrial and submnerged aquatic vegetations, respectively, and provided an updated vegetation inventory and change analysis for the Northeast Coastal and Barrier Network of the National Park Service.     

High order normal form construction near the elliptic orbit of the Sitnikov problem

We consider the Sitnikov problem; from the equations of motion we derive the approximate Hamiltonian flow. Then, we introduce suitable action–angle variables in order to construct a high order normal form of the Hamiltonian. We introduce Birkhoff Cartesian coordinates near the elliptic orbit and we analyze the behavior of the remainder of the normal form. Finally, we derive a kind of local stability estimate in the vicinity of the periodic orbit for exponentially long times using the normal form up to 40th order in Cartesian coordinates.     

Kriging Prediction Intervals Based on Semiparametric Bootstrap

Kriging is a widely used method for prediction, which, given observations of a (spatial) process, yields the best linear unbiased predictor of the process at a new location. The construction of corresponding prediction intervals typically relies on Gaussian assumptions. Here we show that the distribution of kriging predictors for non-Gaussian processes may be far from Gaussian, even asymptotically. This emphasizes the need for other ways to construct prediction intervals. We propose a semiparametric bootstrap method with focus on the ordinary kriging predictor. No distributional assumptions about the data generating process are needed. A simulation study for Gaussian as well as lognormal processes shows that the semiparametric bootstrap method works well. For the lognormal process we see significant improvement in coverage probability compared to traditional methods relying on Gaussian assumptions.     

Establishing robustness of a spatial dataset in a tolerance‐based vector model

Spatial data are usually described through a vector model in which geometries are represented by a set of coordinates embedded into an Euclidean space. The use of a finite representation, instead of the real numbers theoretically required, causes many robustness problems which are well known in the literature. Such problems are made even worse in a distributed context, where data is exchanged between different systems and several perturbations can be introduced in the data representation. In order to discuss the robustness of a spatial dataset, two implementation models have to be distinguished: the identity and the tolerance model. The robustness of a dataset in the identity model has been widely discussed in the literature and some algorithms of the Snap Rounding (SR) family can be successfully applied in such contexts. Conversely, this problem has been less explored in the tolerance model. The aim of this article is to propose an algorithm inspired by those of the SR family for establishing or restoring the robustness of a vector dataset in the tolerance model. The main ideas are to introduce an additional operation which spreads instead of snapping geometries, in order to preserve the original relation between them, and to use a tolerance region for such an operation instead of a single snapping location. Finally, some experiments on real‐world datasets are presented, confirming how the proposed algorithm can establish the robustness of a dataset.     

Response of aquatic plants to abiotic factors: a review

This review aims to determine how environmental characteristics of aquatic habitats rule species occurrence, life-history traits and community dynamics among aquatic plants, and if these particular adaptations and responses fit in with general predictions relating to abiotic factors and plant communities. The way key abiotic factors in aquatic habitats affect (1) plant life (recruitment, growth, and reproduction) and dispersal, and (2) the dynamics of plant communities is discussed. Many factors related to plant nutrition are rather similar in both aquatic and terrestrial habitats (e.g. light, temperature, substrate nutrient content, CO₂ availability) or differ markedly in intensity (e.g. light), variations (e.g. temperature) or in their effective importance for plant growth (e.g. nutrient content in substrate and water). Water movements (water-table fluctuations or flow velocity) have particularly drastic consequences on plants because of the density of water leading to strong mechanical strains on plant tissues, and because dewatering leads to catastrophic habitat modifications for aquatic plants devoid of cuticle and support tissues. Several abiotic factors that affect aquatic plants, such as substrate anoxia, inorganic carbon availability or temperature, may be modified by global change. This in turn may amplify competitive processes, and lead ultimately to the dominance of phytoplankton and floating species. Conserving the diversity of aquatic plants will rely on their ability to adapt to new ecological conditions or escape through migration.     

Trace element distribution in annual stalagmite laminae mapped by micrometer-resolution X-ray fluorescence: Implications for incorporation of environmentally significant species

Stalagmite ER78, from Grotta di Ernesto cave in NE Italy displays clear annual lamination consistent with its shallow depth below a forest ecosystem subject to autumnal peaking of water infiltration. Synchrotron radiation scanning micro-X-ray fluorescence analyses of heavy elements at European Synchrotron Radiation Facility beamline ID22, and light elements at ID21, with 1-3 μm resolution has been combined with data from ion microprobe analyses to reveal chemical variability across the visible layers of these annual laminae. A series of elements display a symmetrical peak, centered around the thin, dark layer at the top of each lamina. The peak concentration is ordered Y > Zn, Cu and Pb > P and Br. This hierarchy is thought to reflect the selectivity of transport of these elements, possibly by organic colloids flushed from the soil zone during autumn infiltration. Ion microprobe analysis indicates Na and F also increase, as does H, the latter reflecting increased microporosity. Sr displays a trough around the dark and thin autumn layer implying that its incorporation may be limited by competition with other elements. Mg and S show a different pattern of annual variation and Fe displays none. The trace metals, Br and Y display peak abundance in the early 20th century, which appears to reflect a period of tree-felling rather than a climatic anomaly. The results demonstrate the power of the high spatial resolution and low detection limits of the synchrotron technique, and its ability to produce quantitative maps that allow distinction of layered structure from that of isolated particles, or irregular inhomogeneities.