Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data

In even-aged, single species conifer plantations LiDAR height data can be modelled to provide accurate estimates of tree height and volume. However, it is apparent that growth models developed for single species stands are not directly transferable to a more general situation of mixed species plantations. This paper evaluates the ability of small footprint, dual-return, pulsed airborne LiDAR data to estimate the proportion of the productive species when mixed with a nurse crop in closed canopy plantations. A study area located in Galloway Forest District in Scotland is used as an example of Lodgepole pine and Sitka spruce mixed plantation; this area contains good examples of a wide range of pure and mixed species plantation types. Three species groups are studied: areas of pure Sitka spruce, areas of pure Lodgepole pine and areas where the two species have been planted together. Two approaches are assessed for detection of plantation mixtures: the first uses LiDAR intensity data to separate spruce and pine species and the second uses LiDAR-derived canopy density measures, coefficient of variation, skewness, percent of ground returns (which provides a measure of canopy openness) and the mean canopy height, which enables areas with height variations to be identified. From analysis of LiDAR data extracted from 54 study plots using logistic regression, the coefficient of variation and LiDAR intensity data provide the most useful predictors of the proportion of spruce in a pine/spruce mixture with coefficients of determination (R²) of 0.914 and 0.930 respectively. The method could be developed as a mapping tool, which in combination with existing inventory data should help to improve timber volume forecasting for mixed species even-aged plantations.     

A single amino acid change humanizes long-chain fatty acid binding and activation of mouse peroxisome proliferator-activated receptor α

Peroxisome proliferator-activated receptor α (PPARα) is an important regulator of hepatic lipid metabolism which functions through ligand binding. Despite high amino acid sequence identity (>90%), marked differences in PPARα ligand binding, activation and gene regulation have been noted across species. Similar to previous observations with synthetic agonists, we have recently reported differences in ligand affinities and extent of activation between human PPARα (hPPARα) and mouse PPARα (mPPARα) in response to long chain fatty acids (LCFA). The present study was aimed to determine if structural alterations could account for these differences. The binding of PPARα to LCFA was examined through in silico molecular modeling and docking simulations. Modeling suggested that variances at amino acid position 272 are likely to be responsible for differences in saturated LCFA binding to hPPARα and mPPARα. To confirm these results experimentally, LCFA binding, circular dichroism, and transactivation studies were performed using a F272I mutant form of mPPARα. Experimental data correlated with in silico docking simulations, further confirming the importance of amino acid 272 in LCFA binding. Although the driving force for evolution of species differences at this position are yet unidentified, this study enhances our understanding of ligand-induced regulation by PPARα and demonstrates the efficacy of molecular modeling and docking simulations.     

Mapping seagrass species, cover and biomass in shallow waters: An assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia)

Understanding, monitoring and modelling attributes of seagrass biodiversity, such as species composition, richness, abundance, spatial patterns, and disturbance dynamics, requires spatial information. This work assessed the accuracy of commonly available airborne hyper-spectral and satellite multi-spectral image data sets for mapping seagrass species composition, horizontal horizontal-projected foliage cover and above-ground dry-weight biomass. The work was carried out on the Eastern Banks in Moreton Bay, Australia, an area of shallow and clear coastal waters, containing a range of seagrass species, cover and biomass levels. Two types of satellite image data were used: Quickbird-2 multi-spectral and Landsat-5 Thematic Mapper multi-spectral. Airborne hyper-spectral image data were acquired from a CASI-2 sensor using a pixel size of 4.0 m. The mapping was constrained to depths shallower than 3.0 m, based on past modelling of the separability of seagrass reflectance signatures at increasing water depths. Our results demonstrated that mapping of seagrass cover, species and biomass to high accuracy levels (> 80%) was not possible across all image types. For each parameter mapped, airborne hyper-spectral data produced the highest overall accuracies (46%), followed by Quickbird-2 and then Landsat-5 Thematic Mapper. The low accuracy levels were attributed to the mapping methods and difficulties in matching locations on image and field data sets. Accurate mapping of seagrass cover, species composition and biomass, using simple approaches, requires further work using high-spatial resolution (< 5 m) and/or hyper-spectral image data. Further work is required to determine if and how the seagrass maps produced in this work are suitable for measuring attributes of seagrass biodiversity, and using these data for modelling floral and fauna biodiversity properties of seagrass environments, and for scaling-up seagrass ecosystem models.     

A bacterial colony growth algorithm for mobile robot localization

Achieving robot autonomy is a fundamental objective in Mobile Robotics. However in order to realize this goal, a robot must be aware of its location within an environment. Therefore, the localization problem (i.e.,the problem of determining robot pose relative to a map of its environment) must be addressed. This paper proposes a new biology-inspired approach to this problem. It takes advantage of models of species reproduction to provide a suitable framework for maintaining the multi-hypothesis. In addition, various strategies to track robot pose are proposed and investigated through statistical comparisons. The Bacterial Colony Growth Algorithm (BCGA) provides two different levels of modeling: a background level that carries on the multi-hypothesis and a foreground level that identifies the best hypotheses according to an exchangeable strategy. Experiments, carried out on the robot ATRV-Jr manufactured by iRobot, show the effectiveness of the proposed BCGA.

Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada

To effectively manage forested ecosystems an accurate characterization of species distribution is required. In this study we assess the utility of hyperspectral Airborne Imaging Spectrometer for Applications (AISA) imagery and small footprint discrete return Light Detection and Ranging (LiDAR) data for mapping 11 tree species in and around the Gulf Islands National Park Reserve, in coastal South-western Canada. Using hyperspectral imagery yielded producer's and user's accuracies for most species ranging from > 52-95.4 and > 63-87.8%, respectively. For species dominated by definable growth stages, pixel-level fusion of hyperspectral imagery with LiDAR-derived height and volumetric canopy profile data increased both producer's (+ 5.1-11.6%) and user's (+ 8.4-18.8%) accuracies. McNemar's tests confirmed that improvements in overall accuracies associated with the inclusion of LiDAR-derived structural information were statistically significant (p < 0.05). This methodology establishes a specific framework for mapping key species with greater detail and accuracy then is possible using conventional approaches (i.e., aerial photograph interpretation), or either technology on its own. Furthermore, in the study area, acquisition and processing costs were lower than a conventional aerial photograph interpretation campaign, making hyperspectral/LiDAR fusion a viable replacement technology.     

Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data

The objective of this study was to identify candidate features derived from airborne laser scanner (ALS) data suitable to discriminate between coniferous and deciduous tree species. Both features related to structure and intensity were considered. The study was conducted on 197 Norway spruce and 180 birch trees (leaves on conditions) in a boreal forest reserve in Norway. The ALS sensor used was capable of recording multiple echoes. The point density was 6.6 m^− 2. Laser echoes located within the vertical projection of the tree crowns, which were assumed to be circular and defined according to field measurements, were attributed to three categories: “first echoes of many”, “single echoes”, or “last echoes of many echoes”. They were denoted FIRST, SINGLE, and LAST, respectively. In tree species classification using ALS data features should be independent of tree heights. We found that many features were dependent on tree height and that this dependency influenced selection of candidate features. When we accounted for this dependency, it was revealed that FIRST and SINGLE echoes were located higher and LAST echoes lower in the birch crowns than in spruce crowns. The intensity features of the FIRST echoes differed more between species than corresponding features of the other echo categories. For the FIRST echoes the intensity values tended to be higher for birch than spruce. When using the various features for species classification, maximum overall classification accuracies of 77% and 73% were obtained for structural and intensity features, respectively. Combining candidate features related to structure and intensity resulted in an overall classification accuracy of 88%.     

A/O系统中活性污泥组成细菌的分离与鉴定

通过菌胶团破碎、混合细菌分离纯化、单一菌种生理生化特性研究等工作，从厌氧／好氧(Anoxic／Oxic，简称A／O)工艺活性污泥系统中分离出8种细菌并分别鉴定出其属别，其中普通细菌3种、亚硝化细菌4种和硝化细菌1种。在A／O工艺进水水质出现波动和需要调节出水质量的情况下，参照各种细菌的固有特性，调节和控制生产工艺参数，可迅速而有效地保证和提高A／O工艺处理化工生产废水的效果。     

Temporal shifts in the biodiversity of nearshore small fishes in Lake Simcoe

Nearshore small fish species represent a large proportion of fish biodiversity in Lake Simcoe, a large inland lake in southern Ontario, Canada. Over the past 30 years, Lake Simcoe has experienced several changes to its aquatic habitat, benthic invertebrate communities and predatory fish populations. This study compared samples of the nearshore small fish community in three geographic areas of Lake Simcoe. Fish community data were grouped into two time periods: a contemporary period (2007–2009) and a historical period (1982–1995). The fish community was compared across time periods for each area to assess if observed ecological changes had an impact on the small fish community. Species richness significantly declined between time periods in two areas (Cook's Bay and the southeast shoreline), the number of individuals captured declined between time periods in one area of the lake (Kempenfelt Bay) and Simpson's diversity index declined between time periods in one area of the lake (southeast shoreline). There were no significant differences in the Shannon–Weiner evenness index between time periods in any of the study areas. Additional analyses of intra- and inter-annual variation in fish sampling results generally supported the findings that shifts in the fish community occurred between time periods. Overall, this study suggests that the nearshore small fish biodiversity of Lake Simcoe has shifted over time but these shifts are not clearly related to recent increases in water clarity, macrophyte growth and nearshore benthic invertebrate densities.     

Italian market fish species identification and commercial frauds revealing by DNA sequencing

To guarantee consumers about the security of what they eat is the main goal of all the institutions working to assure food safety. In seafood field it appears of prime importance to be able to identify fish species throughout the production cycle also when the product has been already processed (breaded fish, fillets etc). In this context the latest system proposed for species identification is the so called “DNA-Barcoding”, made by carrying out the DNA sequencing of a standard barcode region. In the present study we carried out bi-directional sequence analysis of mitochondrial cytochrome b gene (Cytb) and cytochrome oxidase subunit I gene (COI). The main goal of the research was to unambiguously classify the most common fishes traded in Italy, being able to ascertain possible labeling frauds made substituting value species with less precious ones. With both genes we could correctly identify almost all the samples in study: respectively 58/58 with COI and 56/58 with Cytb. This study confirms mitochondrial genes Cytb and COI as good candidates for fish species identification by DNA sequencing. This method appears particularly suitable especially when morphological characterization is difficult, (for very close species), or impossible (for transformed foodstuffs) to carry out. What's more we could efficiently identify many fishes belonging to the Mediterranean fauna, which, to our knowledge, underwent for the first time this kind of analysis. Further development will concern the extension of the analysis to a largest number of local species.     

Modeling zero-inflated explanatory variables in hybrid Bayesian network classifiers for species occurrence prediction

Datasets with an excessive number of zeros are fairly common in several disciplines. The aim of this paper is to improve the predictive power of hybrid Bayesian network classifiers when some of the explanatory variables show a high concentration of values at zero. We develop a new hybrid Bayesian network classifier called zero-inflated tree augmented naive Bayes (Zi-TAN) and compare it with the already known tree augmented naive bayes (TAN) model. The comparison is carried out through a case study involving the prediction of the probability of presence of two species, the fire salamander (Salamandra salamandra) and the Spanish Imperial Eagle (Aquila adalberti), in Andalusia, Spain. The experimental results suggest that modeling the explanatory variables containing many zeros following our proposal boosts the performance of the classifier, as far as species distribution modeling is concerned.