Testing predictions of beetle community patterns derived empirically using remote sensing

Patterns generated from ecological surveys are rarely tested in similar habitats to assess the accuracy of predictions. Testing empirically derived predictions provides a strong tool for establishing the consistency of general patterns in ecology. We test the consistency of beetle community associations with habitat complexity in open canopy forests and make both community and morphospecies-level comparisons with results from a previous study. We use Normalized Difference Vegetation Indices (NDVIs) from remote sensing as a surrogate for habitat complexity. The positive relationships between NDVIs and site-based beetle species richness and abundance were consistent in open canopy forests both south and north of Sydney, Australia. NDVIs were also useful for predicting differences in beetle composition in open canopy forests. Taxon-specific responses to NDVI differences in 'southern forests' were very similar to responses in 'northern forests', most likely reflecting beetle trophic roles. This study shows that NDVIs can be used as rapid biodiversity indicators, when integrated with identified faunal responses to vegetation structure, provided that the lower vegetation strata may be measured by remote sensing.     

Relationship of Aerial Broad Band Reflectance to Meloidogyne incognita Density in Cotton

Aerial images were obtained on 22 July 1999 and 4 August 2000 from five cotton sites infested with Meloidogyne incognita. Images contained three broad bands representing the green (500-600 nm), red (600-700 nm), and near-infrared (700-900 nm) spectrum. Soil samples were collected and assayed for nematodes in the fall at these sites. Sampling locations were identified from images, by locating the coordinates of a wide range of light intensity (measured as a digital number) for each single band, and combinations of bands. There was no single band or band combination in which reflectance consistently predicted M. incognita density. In all 10 site-year combinations, the minimum number of samples necessary to estimate M. incognita density within 25% of the population mean was greater when sampling by reflectance-based classes (3 to 4 per site) than sampling based on the entire site as one unit. Two sites were sampled at multiple times during the growing season. At these sites, there was no single time during the growing season optimal to take images for nematode sampling. Aerial infrared photography conducted during the growing season could not be used to accurately determine fall population densities of M. incognita.     

Anti-biofilm and anti-adherence properties of novel cyclic dipeptides against oral pathogens

Microorganisms embedded in a biofilm are significantly more resistant to antimicrobial agents and the defences of the human immune system, than their planktonic counterpart. Consequently, compounds that can inhibit biofilm formation are of great interest for novel therapeutics. In this study, a screening approach was used to identify novel cyclic dipeptides that have anti-biofilm activity against oral pathogens. Five new active compounds were identified that prevent biofilm formation by the cariogenic bacterium Streptococcus mutans and the pathogenic fungus Candida albicans. These compounds also inhibit the adherence of microorganisms to a hydroxylapatite surface. Further investigations were conducted on these compounds to establish the structure–activity relationship, and it was deduced that the common cleft pattern is required for these molecules to act effectively against biofilms.     

Long-term trend of Ulva prolifera blooms in the western Yellow Sea

Blooms of the green macroalga Ulva prolifera in the western Yellow Sea occurred every year since 2008, and they have been reported and studied extensively using a variety of means including remote sensing. However, to date, long-term bloom patterns have not been reported except for a few case studies showing examples in different years. Here, using MODIS observations and an objective method to perform statistical analysis, mean Ulva coverage in the western Yellow Sea has been derived and analyzed between 2007 and 2015 at both monthly and annual scales. On annual scale, mean Ulva coverage decreased after 2008, but increased rapidly after 2012 from 8 km² in 2012 to 116 km² in 2015 (the largest ever reported in history for this region). In the month of June the mean coverage increased from 18 km² in 2012 to 363 km² in 2015. Other than 2009 and 2010, the month of June showed maximum Ulva coverage in every year. These coverage estimates are significantly lower than previously reported values as they represent “pure” algae coverage after taking into account of partial pixel coverage. Several environmental factors were examined in an attempt to determine the reasons behind such long-term changes, yet the results are inconclusive, suggesting a strong necessity of further coordinated and multi-disciplinary researches.     

Monitoring an invasive perennial at the landscape scale with remote sensing

Summary   Worldwide, invasive weeds threaten agricultural, natural and urban ecosystems. In Australia's agricultural and grazing regions, invasive species often establish across extensive areas where weed management is hampered by an inability to detect the location and timing of an outbreak. In these vast landscapes, an effective detection and monitoring system is required to delineate the extent of the invasion and identify spatial and temporal factors associated with weed establishment and thickening. In this study, we utilize a time series of remote sensing imagery to detect the spatial and temporal patterns of Prickly Acacia ( Acacia nilotica ) invasion in the Mitchell grass plains of North Queensland. We develop a spectral index from Landsat images which is applied to images from 1989 to 2004, in combination with a classification mask, to identify locations and monitor changes in Prickly Acacia density across 29 000 km² of Mitchell grass plains. The approach identified spectral and temporal signatures consistent with Prickly Acacia infestation on 1.9% of this landscape. Field checking of results confirmed presence of the weed in previously unrecorded locations. The approach may be used to evaluate future spread, or outcomes of management strategies for Prickly Acacia in this landscape and could be employed to detect and monitor invasions in other extensive landscapes.     

Extending the Horizon of Homology Detection with Coevolution-based Structure Prediction

Traditional sequence analysis algorithms fail to identify distant homologies when they lie beyond a detection horizon. In this review, we discuss how co-evolution-based contact and distance prediction methods are pushing back this homology detection horizon, thereby yielding new functional insights and experimentally testable hypotheses. Based on correlated substitutions, these methods divine three-dimensional constraints among amino acids in protein sequences that were previously devoid of all annotated domains and repeats. The new algorithms discern hidden structure in an otherwise featureless sequence landscape. Their revelatory impact promises to be as profound as the use, by archaeologists, of ground-penetrating radar to discern long-hidden, subterranean structures. As examples of this, we describe how triplicated structures reflecting longin domains in MON1A-like proteins, or UVR-like repeats in DISC1, emerge from their predicted contact and distance maps. These methods also help to resolve structures that do not conform to a “beads-on-a-string” model of protein domains. In one such example, we describe CFAP298 whose ubiquitin-like domain was previously challenging to perceive owing to a large sequence insertion within it. More generally, the new algorithms permit an easier appreciation of domain families and folds whose evolution involved structural insertion or rearrangement. As we exemplify with α1-antitrypsin, coevolution-based predicted contacts may also yield insights into protein dynamics and conformational change. This new combination of structure prediction (using innovative co-evolution based methods) and homology inference (using more traditional sequence analysis approaches) shows great promise for bringing into view a sea of evolutionary relationships that had hitherto lain far beyond the horizon of homology detection.     

Use of autonomous audio recordings for the rapid inventory of birds in the white‐sand forests of the Peruvian Amazon

White‐sand forests are patchily distributed ecosystems covering just 5% of Amazonia that host many specialist species of birds not found elsewhere, and these forests are threatened due to their small size and human exploitation of sand for construction projects. As a result, many species of birds that are white‐sand specialists are at risk of extinction, and immediate conservation action is paramount for their survival. Our objective was to evaluate current survey methods and determine the relative effect of the size of patches of these forests on the presence or absence of white‐sand specialists. Using point counts and autonomous recorders, we surveyed avian assemblages occupying patches of white‐sand forest in the Peruvian Amazon in April 2018. Overall, we detected 126 species, including 21 white‐sand forest specialists. We detected significantly more species of birds per survey point with autonomous recorders than point counts. We also found a negative relationship between avian species richness and distance from the edge of patches of white‐sand forest, but a significant, positive relationship when only counting white‐sand specialists. Although we detected more species with autonomous recorders, point counts were more effective for detecting canopy‐dwelling passerines. Therefore, we recommend that investigators conducting surveys for rare and patchily distributed species in the tropics use a mixed‐method approach that incorporates both autonomous recorders and visual observation. Finally, our results suggest that conserving large, continuous patches of white‐sand forest may increase the likelihood of survival of species of birds that are white‐sand specialists.