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

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

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

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

Predicting biodiversity richness in rapidly changing landscapes: climate, low human pressure or protection as salvation?

Rates of biodiversity loss in Southeast Asia are among the highest in the world, and the Indo-Burma and South-Central China Biodiversity Hotspots rank among the world’s most threatened. Developing robust multi-species conservation models is critical for stemming biodiversity loss both here and globally. We used a large and geographically extensive remote-camera survey and multi-scale, multivariate optimization species distribution modelling to investigate the factors driving biodiversity across these two adjoining biodiversity hotspots. Four major findings emerged from the work. (i) We identified clear spatial patterns of species richness, with two main biodiverse centres in the Thai-Malay Peninsula and in the mountainous region of Southwest China. (ii) Carnivores in particular, and large ungulates to a lesser degree, were the strongest indicators of species richness. (iii) Climate had the largest effect on biodiversity, followed by protected status and human footprint. (iv) Gap analysis between the biodiversity model and the current system of protected areas revealed that the majority of areas supporting the highest predicted biodiversity are not protected. Our results highlighted several key locations that should be prioritized for expanding the protected area network to maximize conservation effectiveness. We demonstrated the importance of switching from single-species to multi-species approaches to highlight areas of high priority for biodiversity conservation. In addition, since these areas mostly occur over multiple countries, we also advocate for a paradigmatic focus on transboundary conservation planning.The majority of the team, as well as the data, were part of the core WildCRU effort supported principally by a Robertson Foundation grant to DWM

Effects of human-induced habitat changes on site-use patterns in large Amazonian Forest mammals

The Amazon is one of the most diverse biomes around the globe, currently threatened by economic and industrial development and climate change. Large mammals are keystone species, playing an important role in ecosystem structure and function as ecological engineers, while being highly susceptible to deforestation, habitat degradation, and human exploitation. Using a unifying hierarchical Bayesian spatial approach, we examine the site-use patterns of four large Amazonian Forest mammals and their relationships to anthropogenic factors at a biome-wide scale. Our results showed that species’ patterns of site use are correlated with human induced habitat changes, and that this correlation is species-specific. The white-lipped peccary shows highest site-use estimates within strict protected areas, affected by proximity to urban areas and benefiting from indigenous territories, the tapir responding slightly to proximity to burned forested areas, while the giant armadillo and the jaguar were primarily affected by vegetation cover loss; disturbances related to the colonization of the Amazon. Our findings contribute to the understanding of how human-induced environmental changes influence the site-use patterns of these four large mammals, and inform future conservation and land use planning. Transboundary conservation efforts, empowering and integrating native (indigenous and non-indigenous) communities in land governance schemes, involving the private sector and securing the commitment of developed countries are important paths for the protection and sustainability of the globally-crucial Amazon rainforest

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

The effect of using games in teaching conservation

Examining the effects of using games in teaching conservatio

Data from: The contrasting role of male relatedness in different mechanisms of sexual selection in red junglefowl

In structured populations, competition for reproductive opportunities should be relaxed among related males. The few tests of this prediction often neglect the fact that sexual selection acts through multiple mechanisms, both before and after mating. We performed experiments to study the role of within-group male relatedness across pre- and postcopulatory mechanisms of sexual selection in social groups of red junglefowl, Gallus gallus, in which two related males and one unrelated male competed over females unrelated to all the males. We confirm theoretical expectations that, after controlling for male social status, competition over mating was reduced among related males. However, this effect was contrasted by other sexual selection mechanisms. First, females biased male mating in favor of the unrelated male, and might also favor his inseminations after mating. Second, males invested more -rather than fewer- sperm in postcopulatory competition with relatives. A number of factors may contribute to explain this counterintuitive pattern of sperm allocation, including trade-offs between male investment in pre- vs postcopulatory competition, differences in the relative relatedness of pre- vs. postcopulatory competitors, and female bias in sperm utilization in response to male relatedness. Collectively, these results reveal that within-group male relatedness may have contrasting effects in different mechanisms of sexual selection

Salmonella renal abscess in an immunocompetent child: case report and literature review

We describe a case of a 10-year-old immunocompetent girl with a left renal abscess due to Group C Salmonella (Salmonella serovar Oranienburg). Percutaneous drainage of the abscess was done. She also received 2 weeks of intravenous ceftriaxone, followed by 4 weeks of oral co-trimoxazole with resolution seen on ultrasound. A review of pediatric Salmonella renal abscesses is also presented.Published versio