The discovery of two spotted leopards (Panthera pardus) in Peninsular Malaysia

We discovered the presence of two individual spotted leopards Panthera pardus in Ulu Muda Forest Reserve, a previously under-researched selectively logged rainforest of Peninsular Malaysia. These findings are unexpected, because only two other studies have detected the spotted morph amongst many other melanistic leopards caught on camera traps in Peninsular Malaysia. We discuss the implications of our findings with respect to the prevalence of melanism among leopards in the region

Edge states in Open Antiferromagnetic Heisenberg Chains

In this letter we report our results in investigating edge effects of open antiferromagnetic Heisenberg spin chains with spin magnitudes $S=1/2, 1,3/2,2$ using the density-matrix renormalization group (DMRG) method initiated by White. For integer spin chains, we find that edge states with spin magnitude $S_{edge}=S/2$ exist, in agreement with Valence-Bond-Solid model picture. For half-integer spin chains, we find that no edge states exist for $S=1/2$ spin chain, but edge state exists in $S=3/2$ spin chain with $S_{edge}=1/2$, in agreement with previous conjecture by Ng. Strong finite size effects associated with spin dimmerization in half-integer spin chains will also be discussed.Comment: 4 pages, RevTeX 3.0, 5 figures in a separate uuencoded postscript file. Replaced once to enlarge the acknowlegement

Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids

Species Distribution Models (SDMs) are a powerful tool to derive habitat suitability predictions relating species occurrence data with habitat features. Two of the most frequently applied algorithms to model species-habitat relationships are Generalised Linear Models (GLM) and Random Forest (RF). The former is a parametric regression model providing functional models with direct interpretability. The latter is a machine learning non-parametric algorithm, more tolerant than other approaches in its assumptions, which has often been shown to outperform parametric algorithms. Other approaches have been developed to produce robust SDMs, like training data bootstrapping and spatial scale optimisation. Using felid presence-absence data from three study regions in Southeast Asia (mainland, Borneo and Sumatra), we tested the performances of SDMs by implementing four modelling frameworks: GLM and RF with bootstrapped and non-bootstrapped training data. With Mantel and ANOVA tests we explored how the four combinations of algorithms and bootstrapping influenced SDMs and their predictive performances. Additionally, we tested how scale-optimisation responded to species' size, taxonomic associations (species and genus), study area and algorithm. We found that choice of algorithm had strong effect in determining the differences between SDMs' spatial predictions, while bootstrapping had no effect. Additionally, algorithm followed by study area and species, were the main factors driving differences in the spatial scales identified. SDMs trained with GLM showed higher predictive performance, however, ANOVA tests revealed that algorithm had significant effect only in explaining the variance observed in sensitivity and specificity and, when interacting with bootstrapping, in Percent Correctly Classified (PCC). Bootstrapping significantly explained the variance in specificity, PCC and True Skills Statistics (TSS). Our results suggest that there are systematic differences in the scales identified and in the predictions produced by GLM vs. RF, but that neither approach was consistently better than the other. The divergent predictions and inconsistent predictive abilities suggest that analysts should not assume machine learning is inherently superior and should test multiple methods. Our results have strong implications for SDM development, revealing the inconsistencies introduced by the choice of algorithm on scale optimisation, with GLM selecting broader scales than RF

Wild dogs at stake: deforestation threatens the only Amazon endemic canid, the short-eared dog (Atelocynus microtis)

The persistent high deforestation rate and fragmentation of the Amazon forests are the main threats to their biodiversity. To anticipate and mitigate these threats, it is important to understand and predict how species respond to the rapidly changing landscape. The short-eared dog Atelocynus microtis is the only Amazon-endemic canid and one of the most understudied wild dogs worldwide. We investigated short-eared dog habitat associations on two spatial scales. First, we used the largest record database ever compiled for short-eared dogs in combination with species distribution models to map species habitat suitability, estimate its distribution range and predict shifts in species distribution in response to predicted deforestation across the entire Amazon (regional scale). Second, we used systematic camera trap surveys and occupancy models to investigate how forest cover and forest fragmentation affect the space use of this species in the Southern Brazilian Amazon (local scale). Species distribution models suggested that the short-eared dog potentially occurs over an extensive and continuous area, through most of the Amazon region south of the Amazon River. However, approximately 30% of the short-eared dog's current distribution is expected to be lost or suffer sharp declines in habitat suitability by 2027 (within three generations) due to forest loss. This proportion might reach 40% of the species distribution in unprotected areas and exceed 60% in some interfluves (i.e. portions of land separated by large rivers) of the Amazon basin. Our local-scale analysis indicated that the presence of forest positively affected short-eared dog space use, while the density of forest edges had a negative effect. Beyond shedding light on the ecology of the short-eared dog and refining its distribution range, our results stress that forest loss poses a serious threat to the conservation of the species in a short time frame. Hence, we propose a re-assessment of the short-eared dog's current IUCN Red List status (Near Threatened) based on findings presented here. Our study exemplifies how data can be integrated across sources and modelling procedures to improve our knowledge of relatively understudied species

Habitat use of the ocelot (Leopardus pardalis) in Brazilian Amazon

Amazonia forest plays a major role in providing ecosystem services for human and sanctuaries for wildlife. However, ongoing deforestation and habitat fragmentation in the Brazilian Amazon has threatened both. The ocelot is an ecologically important mesopredator and a potential conservation ambassador species, yet there are no previous studies on its habitat preference and spatial patterns in this biome. From 2010 to 2017, twelve sites were surveyed, totaling 899 camera trap stations, the largest known dataset for this species. Using occupancy modeling incorporating spatial autocorrelation, we assessed habitat use for ocelot populations across the Brazilian Amazon. Our results revealed a positive sigmoidal correlation between remote-sensing derived metrics of forest cover, disjunct core area density, elevation, distance to roads, distance to settlements and habitat use, and that habitat use by ocelots was negatively associated with slope and distance to river/lake. These findings shed light on the regional scale habitat use of ocelots and indicate important species–habitat relationships, thus providing valuable information for conservation management and land-use planning

Metabolic variability in seafloor brines revealed by carbon and sulphur dynamics

Brine fluids that upwell from deep, hot reservoirs below the sea bed supply the sea floor with energy-rich substrates and nutrients that are used by diverse microbial ecosystems. Contemporary hypersaline environments formed by brine seeps may provide insights into the metabolism and distribution of microorganisms on the early Earth or on extraterrestrial bodies. Here we use geochemical and genetic analyses to characterize microbial community composition and metabolism in two seafloor brines in the Gulf of Mexico: an active mud volcano and a quiescent brine pool. Both brine environments are anoxic and hypersaline. However, rates of sulphate reduction and acetate production are much higher in the brine pool, whereas the mud volcano supports much higher rates of methane production. We find no evidence of anaerobic oxidation of methane, despite high methane fluxes at both sites. We conclude that the contrasting microbial community compositions and metabolisms are linked to differences in dissolved-organic-matter input from the deep subsurface and different fluid advection rates between the two sites. DOI: 10.1038/NGEO47

Understanding childhood asthma in focus groups: perspectives from mothers of different ethnic backgrounds

BACKGROUND: Diagnosing childhood asthma is dependent upon parental symptom reporting but there are problems in the use of words and terms. The purpose of this study was to describe and compare understandings of childhood 'asthma' by mothers from three different ethnic backgrounds who have no personal experience of diagnosing asthma. A better understanding of parents' perceptions of an illness by clinicians should improve communication and management of the illness. METHOD: Sixty-six mothers living in east London describing their ethnic backgrounds as Bangladeshi, white English and black Caribbean were recruited to 9 focus groups. Discussion was semi-structured. Three sessions were conducted with each ethnic group. Mothers were shown a video clip of a boy with audible wheeze and cough and then addressed 6 questions. Sessions were recorded and transcribed verbatim. Responses were compared within and between ethnic groups. RESULTS: Each session, and ethnic group overall, developed a particular orientation to the discussion. Some mothers described the problem using single signs, while others imitated the sound or made comparisons to other illnesses. Hereditary factors were recognised by some, although all groups were concerned with environmental triggers. Responses about what to do included 'normal illness' strategies, use of health services and calls for complementary treatment. All groups were concerned about using medication every day. Expectations about the quality of life were varied, with recognition that restrictions may be based on parental beliefs about asthma, rather than asthma itself. CONCLUSION: Information from these focus groups suggests mothers know a great deal about childhood asthma even though they have no personal experience of it. Knowledge of how mothers from these ethnic backgrounds perceive asthma may facilitate doctor – patient communication with parents of children experiencing breathing difficulties

High resolution time-course mapping of early transcriptomic, molecular and cellular phenotypes in Huntington\u27s disease CAG knock-in mice across multiple genetic backgrounds.

Huntington\u27s disease is a dominantly inherited neurodegenerative disease caused by the expansion of a CAG repeat in the HTT gene. In addition to the length of the CAG expansion, factors such as genetic background have been shown to contribute to the age at onset of neurological symptoms. A central challenge in understanding the disease progression that leads from the HD mutation to massive cell death in the striatum is the ability to characterize the subtle and early functional consequences of the CAG expansion longitudinally. We used dense time course sampling between 4 and 20 postnatal weeks to characterize early transcriptomic, molecular and cellular phenotypes in the striatum of six distinct knock-in mouse models of the HD mutation. We studied the effects of the HttQ111 allele on the C57BL/6J, CD-1, FVB/NCr1, and 129S2/SvPasCrl genetic backgrounds, and of two additional alleles, HttQ92 and HttQ50, on the C57BL/6J background. We describe the emergence of a transcriptomic signature in HttQ111/+  mice involving hundreds of differentially expressed genes and changes in diverse molecular pathways. We also show that this time course spanned the onset of mutant huntingtin nuclear localization phenotypes and somatic CAG-length instability in the striatum. Genetic background strongly influenced the magnitude and age at onset of these effects. This work provides a foundation for understanding the earliest transcriptional and molecular changes contributing to HD pathogenesis