Modelling Climate Change Impacts on Tropical Dry Forest Fauna

Tropical dry forests are among the most threatened ecosystems in the world, and those occurring in the insular Caribbean are particularly vulnerable. Climate change represents a significant threat for the Caribbean region and for small islands like Jamaica. Using the Hellshire Hills protected area in Jamaica, a simple model was developed to project future abundance of arthropods and lizards based on current sensitivities to climate variables derived from rainfall and temperature records. The abundances of 20 modelled taxa were predicted more often by rainfall variables than temperature, but both were found to have strong impacts on arthropod and lizard abundance. Most taxa were projected to decrease in abundance by the end of the century under drier and warmer conditions. Where an increase in abundance was projected under a low emissions scenario, this change was reduced or reversed under a high emissions climate change scenario. The validation process showed that, even for a small population, there was reasonable skill in predicting its annual variability. Results of this study show that this simple model can be used to identify the vulnerability of similar sites to the effects of shifting climate and, by extension, their conservation needs

Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453

High levels of population genetic differentiation in the American crocodile (Crocodylus acutus)

The American crocodile (Crocodylus acutus) is a widely distributed species across coastal and brackish areas of the Neotropical region of the Americas and the Greater Antilles. Available information on patterns of genetic differentiation in C. acutus shows a complex structuring influenced by interspecific interactions (mainly hybridization) and anthropogenic actions (mostly historical hunting, recent poaching, habitat loss and fragmentation, and unintentional translocation of individuals). In this study, we used data on mitochondrial DNA control region and 11 nuclear polymorphic microsatellite loci to assess the degree of population structure of C. acutus in South America, North America, Central America and the Greater Antilles. We used traditional genetic differentiation indices, Bayesian clustering and multivariate methods to create a more comprehensive picture of the genetic relationships within the species across its range. Analyses of mtDNA and microsatellite loci show evidence of a strong population genetic structure in the American crocodile, with unique populations in each sampling locality. Our results support previous findings showing large degrees of genetic differentiation between the continental and the Greater Antillean C. acutus. We report three new haplotypes unique to Venezuela, which are considerably less distant from the Central and North American haplotypes than to the Greater Antillean ones. Our findings reveal genetic population differentiation between Cuban and Jamaican C. acutus and offer the first evidence of strong genetic differentiation among the populations of Greater Antillean C. acutus

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Plant diversity patterns in neotropical dry forests and their conservation implications

This is the author accepted manuscript. The final version is available from American Association for the Advancement of Science via the DOI in this record.Seasonally dry tropical forests are distributed across Latin America and the Caribbean and are highly threatened, with less than 10% of their original extent remaining in many countries. Using 835 inventories covering 4660 species of woody plants, we show marked floristic turnover among inventories and regions, which may be higher than in other neotropical biomes, such as savanna. Such high floristic turnover indicates that numerous conservation areas across many countries will be needed to protect the full diversity of tropical dry forests. Our results provide a scientific framework within which national decision-makers can contextualize the floristic significance of their dry forest at a regional and continental scale.This paper is the result of the Latin American and Caribbean Seasonally Dry Tropical Forest Floristic Network (DRYFLOR), which has been supported at the Royal Botanic Garden Edinburgh by a Leverhulme Trust International Network Grant (IN-074). This work was also supported by the U.K. Natural Environment Research Council grant NE/I028122/1; Colciencias Ph.D. scholarship 529; Synthesys Programme GBTAF-2824; the NSF (NSF 1118340 and 1118369); the Instituto Humboldt (IAvH)–Red colombiana de investigación y monitoreo en bosque seco; the Inter-American Institute for Global Change Research (IAI; Tropi-Dry, CRN2-021, funded by NSF GEO 0452325); Universidad Nacional de Rosario (UNR); and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). The data reported in this paper are available at www.dryflor.info. R.T.P. conceived the study. M.P., A.O.-F., K.B.-R., R.T.P., and J.W. designed the DRYFLOR database system. K.B.-R. and K.G.D. carried out most analyses. K.B.-R. R.T.P., and K.G.D. wrote the manuscript with substantial input from A.D.-S., R.L.-P., A.O.-F., D.P., C.Q., and R.R. All the authors contributed data, discussed further analyses, and commented on various versions of the manuscript. K.B.-R. thanks G. Galeano who introduced her to dry forest research. We thank J. L. Marcelo, I. Huamantupa, C. Reynel, S. Palacios, and A. Daza for help with fieldwork and data entry in Peru

Plant Species Discrimination in a Tropical Wetland Using In Situ Hyperspectral Data

We investigated the use of full-range (400–2,500 nm) hyperspectral data obtained by sampling foliar reflectances to discriminate 46 plant species in a tropical wetland in Jamaica. A total of 47 spectral variables, including derivative spectra, spectral vegetation indices, spectral position variables, normalized spectra and spectral absorption features, were used for classifying the 46 species. The Mann–Whitney U-test, paired one-way ANOVA, principal component analysis (PCA), random forest (RF) and a wrapper approach with a support vector machine were used as feature selection methods. Linear discriminant analysis (LDA), an artificial neural network (ANN) and a generalized linear model fitted with elastic net penalties (GLMnet) were then used for species separation. For comparison, the RF classifier (denoted as RFa) was also used to separate the species by using all reflectance spectra and spectral indices, respectively, without applying any feature selection. The RFa classifier was able to achieve 91.8% and 84.8% accuracy with importance-ranked spectral indices and reflectance spectra, respectively. The GLMnet classifier produced the lowest overall accuracies for feature-selected reflectance spectra data (52–77%) when compared with the LDA and ANN methods. However, when feature-selected spectral indices were used, the GLMnet produced overall accuracies ranging from 79 to 88%, which were the highest among the three classifiers that used feature-selected data. A total of 12 species recorded a 100% producer accuracy, but with spectral indices, and an additional 8 species had perfect producer accuracies, regardless of the input features. The results of this study suggest that the GLMnet classifier can be used, particularly on feature-selected spectral indices, to discern vegetation in wetlands. However, it might be more efficient to use RFa without feature-selected variables, especially for spectral indices

Using the random forest algorithm to integrate hydroacoustic data with satellite images to improve the mapping of shallow nearshore benthic features in a marine protected area in Jamaica

Hydroacoustic and optical remote sensing have been commonly used to map shallow nearshore benthic features. However, the number, type, scale, and accuracy of the mapping products that can be obtained from the two sensors differ; as such, there can be limited agreement between their mapping products. These differences can be further accentuated if the hydroacoustic data are interpolated to produce a map. Interpolation introduces spatial uncertainty and reduces map accuracy. Consequently, maps generated from the two sensors may provide dissimilar spatial and temporal representations of the same benthic features. We therefore compared the performance of a random forest regression (RFr) and a universal kriging (UK) interpolation method and a post-classification enhancement that can be used to increase the accuracy and complementarity of benthic habitat maps derived from hydroacoustic data. First, we used single beam echosounder (SBES) survey bathymetry data from the Bluefields Bay marine protected area (MPA) in western Jamaica (13.82 km2 in size), to create a bathymetric surface model (BSM), from which rugosity and bathymetric position index (BPI) maps were generated. Next, the RFr was used to create submerged aquatic vegetation (SAV) percentage cover maps from the SBES SAV cover data by predicting cover at un-sampled locations. Predictors included auxiliary data such as depth, BPI, survey points coordinates and radiometrically corrected, deglinted and water column corrected image reflectance index values from each of the following: WorldView-2, Geoeye-1 and Landsat 8. Additionally, a SAV map was created using the UK. The most accurate SAV cover thresholds were identified and were used to create binary maps from the RFr and UK maps. A rugosity derived coral reef map was then added to the binary maps. The resulting benthic habitat maps had comparable accuracies and class coverage to benthic maps classified from GeoEye-1 and WorldView-2 images using pixel and object-based classifiers. However, map accuracies were calculated using a suboptimal number of reference points (<50) for two of the benthic map classes (SAV absent and coral reef). This was not considered to be problematic as the addition of the coral reef class to the binary maps resulted in a significant decrease in uncertainty (standard error and confidence interval width of the overall accuracy) and a significant increase in the user’s accuracy of the SAV absent map class. Also, the difference in uncertainty and accuracy between the map classes did not change. The methods used in this study can therefore be used to increase the accuracy (and to decrease the uncertainty) and the complementarity of maps derived from hydroacoustic data