Frequency tuning curves of the dolphin's hearing: envelope-following response study

Simultaneous tone-tone masking in conjunction with the envelope-following response (EFR) recording was used to obtain tuning curves in dolphins (Turslops truncatus). The EFR was evoked by amplitude-modulated probes of various frequencies. A modulation rate of 600 Hz was found to fit the requirement to have a narrow spectrum and evoke EFR of large amplitude. Tuning curves were obtained within the frequency range from 11.2 to 110 kHz. The Q₁₀ values of the obtained tuning curves varied from 12–14 at the 11.2 kHz center frequency to 17–20 at the 64–90 kHz frequencies.Abbreviations ABR auditory brainstem response - EFR envelope following response - ERB equivalent rectangular bandwidth     

Wolverines and declining snowpack: response to comments

The critiques by DeVink et al. (2010) and McKelvey et al. (2010) are flawed for several reasons. We show here that, contrary to what DeVink et al. claim, the influence of annual pelt price on wolverine harvest returns is essentially negligible. DeVink et al. also suggest that our results show the influence of snowpack on trapper success, rather than on actual wolverine population dynamics. This is unlikely, since most of the snowpack terms in our models are at 1- or 2-year time lags, whereas the impact of snow conditions on trapper success can only manifest in the current year. Both DeVink et al. and McKelvey et al. claim that wolverine populations across Canada are actually increasing, but provide no quantitative data to support this claim. Both sets of authors present alternative explanations for the declines in harvest returns, but none of those explanations are mutually exclusive with our own, and none can explain the significance of time-lagged snowpack on annual harvest returns. McKelvey et al.’s claim that our results represent a spurious correlation, as well as other points that they raise, suggests either a superficial understanding or deliberate misrepresentation of our methods and can simply reflect their underlying philosophical biases.     

ECMIS: computational approach for the identification of hotspots at protein-protein interfaces

Background

Various methods have been developed to computationally predict hotspot residues at novel protein-protein interfaces. However, there are various challenges in obtaining accurate prediction. We have developed a novel method which uses different aspects of protein structure and sequence space at residue level to highlight interface residues crucial for the protein-protein complex formation.

Results

ECMIS (Energetic Conservation Mass Index and Spatial Clustering) algorithm was able to outperform existing hotspot identification methods. It was able to achieve around 80% accuracy with incredible increase in sensitivity and outperforms other existing methods. This method is even sensitive towards the hotspot residues contributing only small-scale hydrophobic interactions.

Conclusion

Combination of diverse features of the protein viz. energy contribution, extent of conservation, location and surrounding environment, along with optimized weightage for each feature, was the key for the success of the algorithm. The academic version of the algorithm is available at http://caps.ncbs.res.in/download/ECMIS/ECMIS.zip.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-303) contains supplementary material, which is available to authorized users.     

On the identification of mortality hotspots in linear infrastructures

One of the main tasks when dealing with the impacts of infrastructures on wildlife is to identify hotspots of high mortality so one can devise and implement mitigation measures. A common strategy to identify hotspots is to divide an infrastructure into several segments and determine when the number of collisions in a segment is above a given threshold, reflecting a desired significance level that is obtained assuming a probability distribution for the number of collisions, which is often the Poisson distribution. The problem with this approach, when applied to each segment individually, is that the probability of identifying false hotspots (Type I error) is potentially high. The way to solve this problem is to recognize that it requires multiple testing corrections or a Bayesian approach. Here, we apply three different methods that implement the required corrections to the identification of hotspots: (i) the familywise error rate correction, (ii) the false discovery rate, and (iii) a Bayesian hierarchical procedure. We illustrate the application of these methods with data on two bird species collected on a road in Brazil. The proposed methods provide practitioners with procedures that are reliable and simple to use in real situations and, in addition, can reflect a practitioner’s concerns towards identifying false positive or missing true hotspots. Although one may argue that an overly cautionary approach (reducing the probability of type I error) may be beneficial from a biological conservation perspective, it may lead to a waste of resources and, probably worse, it may raise doubts about the methodology adopted and the credibility of those suggesting it.     

Seed dispersal curves: Behavior of the tail of the distribution

Summary Seed dispersal is important both to plant fitness and to plant population structure. We suggest that the tail of the seed dispersal curve is at least as important biologically as the modal portion of the curve, and we present a relatively simple, four-parameter model, based on diffusion principles, for the tail of the seed distribution. This model includes two types of qualitative behavior: algebraic tails (which tend to be longer and have greater reach) and exponential tails (which are shorter and have less reach). We have selected 68 data sets from the literature, each giving a seed shadow that could be categorized statistically as (1) exponential, (2) algebraic, (3) neither, or (4) both models fit adequately. Algebraic shapes for seed-shadow tails were common in this sample, and tail behavior was not generally specific to a particular dispersal mode. This result may suggest that algebraic tails are generally favored by selection and can be achieved by several means, but limitations of existing data sets and of statistical methodology preclude final judgement. Smaller complete samples of seed distances would provide a better basis for the analysis of tails than do the present form of data sets (consisting of counts of seeds in discrete distance categories).     

Conclusions from a dynamic population model for tsetse: response to comments

The critique by Hargrove et al. (Popul Ecol, 2011) of our recently published paper on a tsetse population model (Barclay and Vreysen in Popul Ecol 53:89–110, 2011) has made some good points but has also misinterpreted the intent of some of our results as we presented them. Hargrove et al. rightly say that there is a mismatch between the size of the unit cells in the model (1 ha) and the iteration rate of the model (every 5 days), yielding too low a dispersal rate to simulate reality. However, they have misconstrued several of our results that we presented as examples to imply that those results were a necessary condition for control of tsetse, especially using traps and targets.     

Diving into the unknown: identification of antimicrobial resistance hotspots in a tropical urban estuary

Antimicrobial resistance is widely studied and well-characterized from a clinical perspective. However, considerably less information is available regarding resistance in environmental settings, especially in aquatic habitats. This study presents data regarding the occurrence, distribution and the antimicrobial susceptibility profile of bacteria isolated from Guanabara Bay (GB), a heavily polluted tropical urban estuary and an important tourist attraction in Rio de Janeiro, Brazil. Water samples from sites characterized by growing degrees of pollution were analysed by culture-dependent methods, revealing the presence of multidrug-resistant bacteria and clinically relevant indicators of antimicrobial resistance, such as extended-spectrum beta-lactamases. Isolates were identified by mass spectrometry, which indicated the presence of potential human pathogens such as Aeromonas spp. and Vibrio spp. Bacteria harbouring beta-lactam resistance genes were also detected. Although GB is widely used as a recreational and fishing area, there is a substantial knowledge gap regarding the monitoring of antimicrobial resistance and the risk that exposure to these waters poses to public health. Thus, this study reveals new information that calls for better comprehension of antimicrobial resistance in aquatic environments, especially those used for recreational purposes.     

Recruitment hotspots and bottlenecks mediate the distribution of corals on a Caribbean reef

Recruitment hotspots are locations where organisms are added to populations at high rates. On tropical reefs where coral abundance has declined, recruitment hotspots are important because they have the potential to promote population recovery. Around St. John, US Virgin Islands, coral recruitment at five sites revealed a hotspot that has persistent for 14 years. Recruitment created a hotspot in density of juvenile corals that was 600 m southeast of the recruitment hotspot. Neither hotspot led to increased coral cover, thus revealing the stringency of the demographic bottleneck impeding progression of recruits to adult sizes and preventing population growth. Recruitment hotspots in low-density coral populations are valuable targets for conservation and sources of corals for restoration.     

Construction of a genetic map for Theileria parva: identification of hotspots of recombination

The tick-borne protozoan parasite Theileria parva is the causal agent of East Coast Fever (ECF), a severe lymphoproliferative disease of cattle in eastern, central and southern Africa. The life cycle of T. parva is predominantly haploid, with a brief diploid stage occurring in the tick vector that involves meiotic recombination. Resolved genetic studies of T. parva are currently constrained by the lack of a genome-wide high-definition genetic map of the parasite. We undertook a genetic cross of two cloned isolates of T. parva to construct such a map from 35 recombinant progeny, using a genome-wide panel of 79 variable number of tandem repeat markers. Progeny were established by in vitro cloning of cattle lymphocytes after infection with sporozoites prepared from Rhipicephalus appendiculatus ticks fed on a calf undergoing a dual infection with the two clonal parental stocks. The genetic map was determined by assigning individual markers to the four chromosome genome, whose physical length is approximately 8309 kilobasepairs (Kb). Segregation analysis of the markers among the progeny revealed a total genetic size of 1683.8 centiMorgans (cM), covering a physical distance of 7737.62 Kb (∼93% of the genome). The average genome-wide recombination rate observed for T. parva was relatively high, at 0.22 cM Kb⁻¹ per meiotic generation. Recombination hot-spots and cold-spots were identified for each of the chromosomes. A panel of 27 loci encoding determinants previously identified as immunorelevant or likely to be under selection were positioned on the linkage map. We believe this to be the first genetic linkage map for T. parva. This resource, with the availability of the genome sequence of T. parva, will promote improved understanding of the pathogen by facilitating the use of genetic analysis for identification of loci responsible for variable phenotypic traits exhibited by individual parasite stocks.     

Chloroplast genome of Hibiscus rosa-sinensis (Malvaceae): Comparative analyses and identification of mutational hotspots

Previous studies to resolve phylogenetic and taxonomic discrepancies of Hibiscus remained inconclusive. Here, we report chloroplast genome sequence of Hibiscus rosa-sinensis. Hibiscus rosa-sinensis chloroplast genome was 160,951 bp, comprising of large single copy (89,509 bp) and small single copy (20,246 bp) regions, separated by IRa and IRb (25,598 bp each). The genome contained 130 genes including 85 protein-coding genes, 37 transfer RNAs and 8 ribosomal RNAs. Comparative analyses of chloroplast genomes revealed similar structure among 12 species within family Malvaceae. Evolutionary rates of 77 protein-coding genes showed 95% similarities. Analyses of codon usage, amino acid frequency, putative RNA editing sites, and repeats showed a great extent of similarities between Hibiscus rosa-sinensis and Hibiscus syriacus. We identified 30 mutational hotpots including psbZ-trnG, trnK-rps16, trnD-trnY, trnW-trnP, rpl33-rps18, petG-trnW, trnS-trnG, trnH-psbA, atpB-rbcL, and rpl32-trnL that might be used as polymorphic and robust markers to resolve phylogenetic discrepancies in genus Hibiscus.     

A framework for the identification of hotspots of climate change risk for mammals

As rates of global warming increase rapidly, identifying species at risk of decline due to climate impacts and the factors affecting this risk have become key challenges in ecology and conservation biology. Here, we present a framework for assessing three components of climate‐related risk for species: vulnerability, exposure and hazard. We used the relationship between the observed response of species to climate change and a set of intrinsic traits (e.g. weaning age) and extrinsic factors (e.g. precipitation seasonality within a species geographic range) to predict, respectively, the vulnerability and exposure of all data‐sufficient terrestrial non‐volant mammals (3,953 species). Combining this information with hazard (the magnitude of projected climate change within a species geographic range), we identified global hotspots of species at risk from climate change that includes the western Amazon basin, south‐western Kenya, north‐eastern Tanzania, north‐eastern South Africa, Yunnan province in China, and mountain chains in Papua‐New Guinea. Our framework identifies priority areas for monitoring climate change effects on species and directing climate mitigation actions for biodiversity.     

Applications of binary segmentation to the estimation of quantal response curves and spatial intensity

This paper explores the use of binary segmentation procedures in two applications. The first application is concerned with the estimation of nonparametric quantal response curves. With Bernoulli data and an assumed monotone increasing curve, this gives rise a change-point model where the change points are determined using a sequence of nested hypothesis tests of whether a change point exists. The second application concerns cluster identification and inference for spatial data where the shape of the clusters and the number of clusters is unknown. The procedure involves a sequence of nested hypothesis tests of a single cluster versus a pair of distinct clusters. Examples of both applications are provided.     

Comments on Bartolino et al. (2011): limits of cumulative relative frequency distribution curves for hotspot identification

The recent paper by Bartolino et al. (Popul Ecol 53:351–359, 2011) presents a new method to objectively select hotspots using cumulative relative frequency distribution (CRFD) curves. This method is presented as being independent from the selection of any threshold and, therefore, less arbitrary than traditional approaches. We argue that this method, albeit mathematically sound, is based on likewise arbitrary decisions regarding threshold selection. Specifically, the use of the CRFD curve approach requires the occurrence of two criteria for the method to be applied correctly: the selection of a 45° tangent to the curve, and the need to consider the highest relative value of the study parameter corresponding to a 45° slope tangent to the curve. Using two case studies (dealing with species richness and abundance of a particular species), we demonstrate that these two criteria are really unrelated to the underlying causes that shape the spatial pattern of the phenomena under study, but rather related to sampling design and spatial scale; hence, one could likewise use different but valid criteria. Consequently, the CRFD curve approach is based on the selection of a pre-defined threshold that has little, if any, ecological justification, and that heavily influences the final hotspot selection. Therefore, we conclude that the CRFD curve approach itself is not necessarily better and more objective than any of the global methods typically used for hotspot identification. Indeed, mathematical and/or statistical approaches should not be viewed as a panacea to solve conservation problems, but rather used in combination with biological, practical, economic and social considerations.