Cover Image Gallery
Macrozoobenthic organisms are widely used to assess stream ecosystem health. Traditionally, this was done by morphological determination. However, the mostly larval specimens can often not be reliably identiﬁed to species level. DNA metabarcoding provides reliable solutions here. Rather than studying individual specimens, the complete invertebrate sample is thoroughly homogenised using liquid nitrogen for DNA extraction of the whole community, as shown on the picture. Then a speciﬁc “barcoding” gene fragment is ampliﬁed using PCR, sequenced on a high-throughput sequencer and compared against available reference databases for taxonomic assignment. As benthic communities are usually very diverse, the PCR primers have to be designed to work on all the targeted groups like may- stone- and caddisﬂies, aquatic beetles and molluscs. Here the R package PrimerMiner assists in primer development by automated batch downloading and processing of sequence data from public databases, as well as visualising sequence diversity for each group to ﬁnd potential universal primer binding sites.
Photo © Vasco Elbrecht
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PrimerMiner: an r package for development and in silico validation of DNA metabarcoding primers
Vasco Elbrecht and Florian Leese
This month’s cover image was used as the banner for the Eco-Stats’ 15 Conference “Technological advances between Ecology and Statistics” held at the University of New South Wales Sydney, Australia, in December 2015. The outcomes of the conference are the subject of a Special Feature in this issue. The image shows a ﬂock of sulphur-crested cockatoos (Cacatua galerita) morphing into a residual plot, to symbolise the merging of ideas between ecology and statistics. It was conceptualised by David Warton and realised by Susannah Waters (UNSW Sydney, Australia) The Special Feature consists of ﬁve papers showcasing interdisciplinary collaboration, centred around problems estimating biodiversity and how it changes over space and time. It highlights the potential of interdisciplinary research, and of meetings designed to bring together researchers across disciplines, as a vehicle for scientiﬁc advances.
Photo © Susannah Waters (UNSW Sydney, Australia)
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Technical advances at the interface between ecology and statistics: improving the biodiversity knowledge generation workflow
David I. Warton and Melodie A. McGeoch
This issue’s cover image shows an endangered green turtle (Chelonia mydas) recovering at the Turtle Clinic in Moorea Island, French Polynesia, after being injured by a spear gun. Turtles continue to be exposed to intense fishing effort in French Polynesia despite their protected status. This highlights the importance of regular monitoring, and proper assessment of conservation interventions in general, to derive reliable conclusions and information to managers and decision-makers. However, it is often challenging to reliably estimate the true effect of an intervention, owing to the diverse sources of spatial and temporal variability in the studied ecosystem.
Thiault et al. developed a new statistical approach – called Progressive-Change BACIPS (Before-After Control-Impact Paired-Series) – that extends and generalizes the scope of BACIPS analyses to time-dependent effects. After quantifying the statistical power and accuracy of the method with simulated datasets, they used marine and terrestrial case studies to illustrate and validate their approach. They found that the Progressive-Change BACIPS leads to better estimates of the effects of environmental impacts and the time-scales over which they operate.
Photo © Lauric Thiault
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Progressive-Change BACIPS: a flexible approach for environmental impact assessment
Lauric Thiault, Laëtitia Kernaléguen, Craig W. Osenberg and Joachim Claudet
This month’s cover image looks into the eye of a Verreaux's eagle (Aquila verreauxii). This species is found in mountainous regions of sub-Saharan Africa, where cliffs provide suitable nesting habitat. The eagle pictured here is equipped with a tracking device from the University of Amsterdam Bird Tracking System (www.UvA-BiTS.nl). In South Africa concerns over the impacts of land use change and the development of wind farms have led to the implementation of tracking studies to better understand movement patterns of this majestic bird. Such studies have provided a wealth of high-resolution data and opportunities to explore sophisticated statistical methods for analysis of animal behaviour.
Leos-Barajas et al use accelerometer data from aerial (Verreaux’s eagle) and marine (blacktip reef shark) systems to demonstrate the use of hidden Markov models (HMMs) in providing quantitative measures of behaviour. HMMs are well suited to analysing animal accelerometer data because they account for serial autocorrelation in data and importantly they allow for inferences to be made about relative activity and behaviour when animals cannot be directly observed. In addition, HMMs provide data-driven estimates of the underlying distributions of the acceleration metrics, and the probability of switching between states, possibly as a function of covariates. The framework provided in the author’s paper “Analysis of animal accelerometer data using hidden Markov models” can be applied to a wide range of activity data, thereby providing exciting opportunities for understanding drivers of individual animal behaviour.
Photo © Andrew Jenkins
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Analysis of animal accelerometer data using hidden Markov models
Vianey Leos-Barajas, Theoni Photopoulou, Roland Langrock, Toby A. Patterson, Yuuki Y. Watanabe, Megan Murgatroyd and Yannis P. Papastamatiou
This cover image shows the Trupchun Valley, located in the Swiss National Park (SNP). Studying the development of nature in the absence of human interference has been a key objective since the SNP was established in 1914. Assessing dynamic vegetation changes has played an important role in the SNP’s research tradition, with the establishment of ? rst long-term observation plots by Josias Braun-Blanquet already in 1917. Comparing vegetation maps produced for nearly 100 years motivated our research on “How to predict plant functional types using imaging spectroscopy: Linking vegetation community traits, plant functional types and spectral response”. Despite these maps being elaborate, they either lack the spatial coverage or detail to allow us to understand how inter- and intraspeci? c plant trait variability and diversity patterns are in? uenced by topography, microclimate, herbivory and former land use. We were thus excited to ? nd strong relationships between plant life/growth forms, strategy types and indicators, and biochemical and structural vegetation traits which determine the spectral response in optical remote sensing instruments. Linking vegetation community’s functional signatures to spectral signatures allows us to accurately predict plant functional types using airborne imaging spectroscopy, substantially advancing our understanding of ecosystem processes in space and time.
Photo © Christian Schmid
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How to predict plant functional types using imaging spectroscopy: linking vegetation community traits, plant functional types and spectral response
Anna K. Schweiger, Martin Schütz, Anita C. Risch, Mathias Kneubühler, Rudolf Haller and Michael E. Schaepman
This month’s cover image shows a beautiful, brilliantly coloured fairy pitta (Pitta nympha) perched on a bamboo branch. The migratory fairy pitta breeds in Northeast Asia (Japan, South Korea, east China and Taiwan) from late April to September and winters mainly in Borneo from October to March. In Taiwan, the fairy pitta is also called the “eight colored bird” (as there are eight colors in its plumage: beige, yellow, green, brown, black, white, red on the vent area, and shiny blue on its wings) or the “little forest fairy” (as its body length is around 16–19 cm). The fairy pitta is rare and elusive, and is classi? ed as Vulnerable on the IUCN Red List, mainly due to the destruction of its primary habitats.
The majestic beauty of this fairy has provided the authors of ‘iNEXT: An R package for rarefaction and extrapolation of species diversity (Hill numbers)’ with a wealth of inspiration in formulating their methodology and relevant software to compute and plot the seamless sample-size- and sample-coverage-based rarefaction and extrapolation sampling curves for species diversity. Hsieh, Ma and Chao developed the iNEXT (iNterpolation and EXTrapolation) R package, which features an easy-to-use interface and ef? ciently uses all data to not only make robust and detailed inferences about the sampled assemblages, but also to make objective comparisons of species diversity among multiple assemblages.
Photo © Jia Hong Chen
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iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers)
T. C. Hsieh, K. H. Ma and Anne Chao
The Oostvaardersplassen nature reserve, Netherlands, was established on polder land reclaimed from Lake IJsselmeer in 1968. Re-wilding was initiated at this site from 1983 with the introduction of Heck cattle (Bos taurus), Konik horses (Equus ferus caballus) and red deer (Cervus elaphus). Moreover, a multitude of ponds were created throughout the reserve between 1985 and 2000 for avian biodiversity. The site is managed with a policy of minimal intervention, i.e. the population size of freely roaming large herbivores is not controlled by culling, no supplementary feeding is given during winter and vegetation is not managed. The only intervention is aimed to avoid unnecessary suffering and consist in shooting animals identi?ed as too weak to survive winter.
Most of the research examining the relationship between large herbivores and their impact on landscapes has used extant studies. An alternative approach is to estimate the impact of variations in herbivore populations through time using fossil dung fungal spores and pollen in sedimentary sequences. The ponds at Oostvaardersplassen provided the ideal settings for Baker et al. to develop further the dung fungal spore method and determine the relationship between spore abundance in sediments and herbivore biomass densities. Their results indicate that this method provides a robust quantitative measure of herbivore population size over time.
Photo © Henk Hupkes, Staatsbosbeheer
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Quantification of population sizes of large herbivores and their long-term functional role in ecosystems using dung fungal spores
Ambroise G. Baker, Perry Cornelissen, Shonil A. Bhagwat, Fransciscus W. M. Vera and Katherine J. Willis
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