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in category ecology

6,184 results found. For more information, click each entry to expand.

1: Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak
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Posted 24 Jan 2020

Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak
23,339 downloads bioRxiv ecology

Shi Zhao, Qianyin Lin, Jinjun Ran, Salihu S Musa, Guangpu Yang, Weiming Wang, Yijun Lou, Daozhou Gao, Lin Yang, Daihai He, Maggie H Wang

Backgrounds An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia hit a major city of China, Wuhan, December 2019 and subsequently reached other provinces/regions of China and countries. We present estimates of the basic reproduction number, R , of 2019-nCoV in the early phase of the outbreak. Methods Accounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate ( γ ), we estimated R by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI. Findings The early outbreak data largely follows the exponential growth. We estimated that the mean R ranges from 2.24 (95%CI: 1.96-2.55) to 3.58 (95%CI: 2.89-4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates of R . Conclusion The mean estimate of R for the 2019-nCoV ranges from 2.24 to 3.58, and significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.

2: Psychoactive plant- and mushroom-associated alkaloids from two behavior modifying cicada pathogens
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Posted 24 Jul 2018

Psychoactive plant- and mushroom-associated alkaloids from two behavior modifying cicada pathogens
17,727 downloads bioRxiv ecology

Greg Boyce, Emile Gluck-Thaler, Jason C. Slot, Jason E. Stajich, William J. Davis, Tim Y. James, John R. Cooley, Daniel G. Panaccione, Jørgen Eilenberg, Henrik Hjarvard De Fine Licht, Angie M. Macias, Matthew C. Berger, Kristen L. Wickert, Cameron M. Stauder, Ellie J. Spahr, Matthew D. Maust, Amy M. Metheny, Chris Simon, Gene Kritsky, Kathie T. Hodge, Richard A. Humber, Terry Gullion, Dylan P. G. Short, Teiya Kijimoto, Dan Mozgai, Nidia Arguedas, Matt T. Kasson

Entomopathogenic fungi routinely kill their hosts before releasing infectious spores, but select species keep insects alive while sporulating, which enhances dispersal. Transcriptomics and metabolomics studies of entomopathogens with post-mortem dissemination from their parasitized hosts have unraveled infection processes and host responses, yet mechanisms underlying active spore transmission by Entomophthoralean fungi in living insects remain elusive. Here we report the discovery, through metabolomics, of the plant-associated amphetamine, cathinone, in four Massospora cicadina-infected periodical cicada populations, and the mushroom-associated tryptamine, psilocybin, in annual cicadas infected with Massospora platypediae or Massospora levispora, which appear to represent a single fungal species. The absence of some fungal enzymes necessary for cathinone and psilocybin biosynthesis along with the inability to detect intermediate metabolites or gene orthologs are consistent with possibly novel biosynthesis pathways in Massospora. The neurogenic activities of these compounds suggest the extended phenotype of Massospora that modifies cicada behavior to maximize dissemination is chemically-induced.

3: Modeling Zero-Inflated Count Data With glmmTMB
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Posted 01 May 2017

Modeling Zero-Inflated Count Data With glmmTMB
14,742 downloads bioRxiv ecology

Mollie E Brooks, Kasper Kristensen, Koen J. van Benthem, Arni Magnusson, Casper W. Berg, Anders Nielsen, Hans J. Skaug, Martin Mächler, Benjamin M. Bolker

Ecological phenomena are often measured in the form of count data. These data can be analyzed using generalized linear mixed models (GLMMs) when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the standard error distributions used in GLMMs, e.g., parasite counts may be exactly zero for hosts with effective immune defenses but vary according to a negative binomial distribution for non-resistant hosts. We present a new R package, glmmTMB, that increases the range of models that can easily be fitted to count data using maximum likelihood estimation. The interface was developed to be familiar to users of the lme4 R package, a common tool for fitting GLMMs. To maximize speed and flexibility, estimation is done using Template Model Builder (TMB), utilizing automatic differentiation to estimate model gradients and the Laplace approximation for handling random effects. We demonstrate glmmTMB and compare it to other available methods using two ecological case studies. In general, glmmTMB is more flexible than other packages available for estimating zero-inflated models via maximum likelihood estimation and is faster than packages that use Markov chain Monte Carlo sampling for estimation; it is also more flexible for zero-inflated modelling than INLA, but speed comparisons vary with model and data structure. Our package can be used to fit GLMs and GLMMs with or without zero-inflation as well as hurdle models. By allowing ecologists to quickly estimate a wide variety of models using a single package, glmmTMB makes it easier to find appropriate models and test hypotheses to describe ecological processes.

4: Behavioural effects of temperature on ectothermic animals: unifying thermal physiology and behavioural plasticity
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Posted 09 Jun 2016

Behavioural effects of temperature on ectothermic animals: unifying thermal physiology and behavioural plasticity
13,537 downloads bioRxiv ecology

Paul K Abram, Guy Boivin, Joffrey Moiroux, Jacques Brodeur

Temperature imposes significant constraints on ectothermic animals, and these organisms have evolved numerous adaptations to respond to these constraints. While the impacts of temperature on the physiology of ectotherms have been extensively studied, there are currently no frameworks available that outline the multiple and often simultaneous pathways by which temperature can affect behaviour. Drawing from the literature on insects, we propose a unified framework that should apply to all ectothermic animals, generalizing temperature's behavioural effects into (1) Kinetic effects, resulting from temperature's bottom-up constraining influence on metabolism and neurophysiology over a range of timescales (from short- to long-term), and (2) Integrated effects, where the top-down integration of thermal information intentionally initiates or modifies a behaviour (behavioural thermoregulation, thermal orientation, thermosensory behavioural adjustments). We discuss the difficulty in distinguishing adaptive behavioural changes due to temperature from behavioural changes that are the products of constraints, and propose two complementary approaches to help make this distinction and class behaviours according to our framework: (i) behavioural kinetic null modeling and (ii) behavioural ecology experiments using temperature-insensitive mutants. Our framework should help to guide future research on the complex relationship between temperature and behaviour in ectothermic animals.

5: Model-based projections of Zika virus infections in childbearing women in the Americas
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Posted 12 Feb 2016

Model-based projections of Zika virus infections in childbearing women in the Americas
13,118 downloads bioRxiv ecology

T. Alex Perkins, Amir Siraj, Corrine Warren Ruktanonchai, Moritz UG Kraemer, Andrew J Tatem

Zika virus is a mosquito-borne pathogen that is rapidly spreading across the Americas. Due to associations between Zika virus infection and a range of fetal maladies, the epidemic trajectory of this viral infection poses a significant concern for the nearly 15 million children born in the Americas each year. Ascertaining the portion of this population that is truly at risk is an important priority. One recent estimate suggested that 5.42 million childbearing women live in areas of the Americas that are suitable for Zika occurrence. To improve on that estimate, which did not take into account the protective effects of herd immunity, we developed a new approach that combines classic results from epidemiological theory with seroprevalence data and highly spatially resolved data about drivers of transmission to make location-specific projections of epidemic attack rates. Our results suggest that 1.65 (1.45-2.06) million childbearing women and 93.4 (81.6-117.1) million people in total could become infected before the first wave of the epidemic concludes. Based on current estimates of rates of adverse fetal outcomes among infected women, these results suggest that tens of thousands of pregnancies could be negatively impacted by the first wave of the epidemic. These projections constitute a revised upper limit of populations at risk in the current Zika epidemic, and our approach offers a new way to make rapid assessments of the threat posed by emerging infectious diseases more generally.

6: Distance Sampling in R
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Posted 14 Jul 2016

Distance Sampling in R
12,317 downloads bioRxiv ecology

David L Miller, Eric Rexstad, Len Thomas, Laura Marshall, Jeffrey L Laake

Estimating the abundance and spatial distribution of animal and plant populations is essential for conservation and management. We introduce the R package Distance that implements distance sampling methods to estimate abundance. We describe how users can obtain estimates of abundance (and density) using the package as well documenting the links it provides with other more specialized R packages. We also demonstrate how Distance provides a migration pathway from previous software, thereby allowing us to deliver cutting-edge methods to the users more quickly.

7: Flowers respond to pollinator sound within minutes by increasing nectar sugar concentration.
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Posted 28 Dec 2018

Flowers respond to pollinator sound within minutes by increasing nectar sugar concentration.
12,239 downloads bioRxiv ecology

Marine Veits, Itzhak Khait, Uri Obolski, Eyal Zinger, Arjan Boonman, Aya Goldshtein, Kfir Saban, Udi Ben-Dor, Paz Estlein, Areej Kabat, Dor Peretz, Ittai Ratzersdorfer, Slava Krylov, Daniel Chamovitz, Yuval Sapir, Yossi Yovel, Lilach Hadany

Can plants hear? That is, can they sense airborne sounds and respond to them? Here we show that Oenothera drummondii flowers, exposed to the playback sound of a flying bee or to synthetic sound-signals at similar frequencies, produced sweeter nectar within 3 minutes, potentially increasing the chances of cross pollination. We found that the flowers vibrated mechanically in response to these sounds, suggesting a plausible mechanism where the flower serves as the plant's auditory sensory organ. Both the vibration and the nectar response were frequency-specific: the flowers responded to pollinator sounds, but not to higher frequency sound. Our results document for the first time that plants can rapidly respond to pollinator sounds in an ecologically relevant way. Sensitivity of plants to pollinator sound can affect plant-pollinator interactions in a wide range of ways: Plants could allocate their resources more adequately, focusing on the time of pollinator activity; pollinators would then be better rewarded per time unit; flower shape may be selected for its effect on hearing ability, and not only on signaling; and pollinators may evolve to make sounds that the flowers can hear. Finally, our results suggest that plants may be affected by other sounds as well, including antropogenic ones.

8: Glacier ice archives fifteen-thousand-year-old viruses
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Posted 07 Jan 2020

Glacier ice archives fifteen-thousand-year-old viruses
11,281 downloads bioRxiv ecology

Zhi-Ping Zhong, Natalie E. Solonenko, Yueh-Fen Li, Maria C. Gazitúa, Simon Roux, Mary E. Davis, James L Van Etten, Ellen Mosley-Thompson, Virginia I. Rich, Matthew B. Sullivan, Lonnie G. Thompson

While glacier ice cores provide climate information over tens to hundreds of thousands of years, study of microbes is challenged by ultra-low-biomass conditions, and virtually nothing is known about co-occurring viruses. Here we establish ultra-clean microbial and viral sampling procedures and apply them to two ice cores from the Guliya ice cap (northwestern Tibetan Plateau, China) to study these archived communities. This method reduced intentionally contaminating bacterial, viral, and free DNA to background levels in artificial-ice-core control experiments, and was then applied to two authentic ice cores to profile their microbes and viruses. The microbes differed significantly across the two ice cores, presumably representing the very different climate conditions at the time of deposition that is similar to findings in other cores. Separately, viral particle enrichment and ultra-low-input quantitative viral metagenomic sequencing from ~520 and ~15,000 years old ice revealed 33 viral populations (i.e., species-level designations) that represented four known genera and likely 28 novel viral genera (assessed by gene-sharing networks). In silico host predictions linked 18 of the 33 viral populations to co-occurring abundant bacteria, including Methylobacterium, Sphingomonas, and Janthinobacterium, indicating that viruses infected several abundant microbial groups. Depth-specific viral communities were observed, presumably reflecting differences in the environmental conditions among the ice samples at the time of deposition. Together, these experiments establish a clean procedure for studying microbial and viral communities in low-biomass glacier ice and provide baseline information for glacier viruses, some of which appear to be associated with the dominant microbes in these ecosystems.

9: Mapping migration in a songbird using high-resolution genetic markers
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Posted 08 Aug 2014

Mapping migration in a songbird using high-resolution genetic markers
8,952 downloads bioRxiv ecology

Kristen Ruegg, Eric C. Anderson, Kristina L Paxton, Vanessa Apkenas, Sirena Lao, Rodney B. Siegel, David F. DeSante, Frank Moore, Thomas B Smith

Neotropical migratory birds are declining across the Western Hemisphere, but conservation efforts have been hampered by the inability to assess where migrants are most limited–the breeding grounds, migratory stopover sites, or wintering areas. A major challenge has been the lack of an efficient, reliable, and broadly applicable method for connecting populations across the annual cycle. Here we show how high-resolution genetic markers can be used to identify populations of a migratory bird, the Wilson's warbler (Cardellina pusilla), at fine enough spatial scales to facilitate assessing regional drivers of demographic trends. By screening 1626 samples using 96 single nucleotide polymorphisms (SNPs) selected from a large pool of candidates (~450,000), we identify novel region-specific migratory routes and timetables of migration along the Pacific Flyway. Our results illustrate that high-resolution genetic markers are more reliable, accurate, and amenable to high throughput screening than previously described tracking techniques, making them broadly applicable to large-scale monitoring and conservation of migratory organisms.

10: Analyzing ecological networks of species interactions
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Posted 28 Feb 2017

Analyzing ecological networks of species interactions
6,561 downloads bioRxiv ecology

Eva Delmas, Mathilde Besson, Marie-Hélène Brice, Laura A. Burkle, Giulio V Dalla Riva, Marie-Josée Fortin, Dominique Gravel, Paulo R. Guimarães, David Hembry, Erica Newman, Jens M Olesen, Mathias M Pires, Justin D. Yeakel, Timothée Poisot

Networks provide one of the best representation for ecological communities, composed of many speecies with dense connections between them. Yet the methodological literature allowing one to analyse and extract meaning from ecological networks is dense, fragmented, and unwelcoming. We provide a general overview to the field, outlining both the intent of the different measures, their assumptions, and the contexts in which they can be used. We anchor this discussion in examples from empirical studies, and conclude by highlighting what we think should be the future developments in the field.

11: Relic DNA is abundant in soil and obscures estimates of soil microbial diversity
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Posted 16 Mar 2016

Relic DNA is abundant in soil and obscures estimates of soil microbial diversity
5,980 downloads bioRxiv ecology

Paul Carini, Patrick J Marsden, Jonathan W Leff, Emily E Morgan, Michael S Strickland, Noah Fierer

It is implicitly assumed that the microbial DNA recovered from soil originates from living cells. However, because relic DNA (DNA from dead cells) can persist in soil for weeks to years, it could impact DNA-based analyses of microbial diversity. We examined a wide range of soils and found that, on average, 40% of prokaryotic and fungal DNA was derived from the relic DNA pool. Relic DNA inflated the observed prokaryotic and fungal diversity by as much as 55%, and caused misestimation of taxon abundances, including taxa integral to key ecosystem processes. These findings imply that relic DNA can obscure treatment effects, spatiotemporal patterns, and relationships between taxa and environmental conditions. Moreover, relic DNA may represent a historical record of microbes formerly living in soil.

12: Applications for deep learning in ecology
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Posted 30 May 2018

Applications for deep learning in ecology
5,693 downloads bioRxiv ecology

Sylvain Christin, Éric Hervet, Nicolas Lecomte

A lot of hype has recently been generated around deep learning, a group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning revolutionized several research fields such as bioinformatics or medicine. Yet such a surge of tools and knowledge is still in its infancy in ecology despite the ever-growing size and the complexity of ecological datasets. Here we performed a literature review of deep learning implementations in ecology to identify its benefits in most ecological disciplines, even in applied ecology, up to decision makers and conservationists alike. We also provide guidelines on useful resources and recommendations for ecologists to start adding deep learning to their toolkit. At a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be processed by humans anymore, deep learning could become a necessity in ecology.

13: Analytical guidelines to increase the value of citizen science data: using eBird data to estimate species occurrence
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Posted 12 Mar 2019

Analytical guidelines to increase the value of citizen science data: using eBird data to estimate species occurrence
5,558 downloads bioRxiv ecology

A Johnston, WM Hochachka, ME Strimas-Mackey, V Ruiz Gutierrez, OJ Robinson, ET Miller, T. Auer, ST Kelling, D. Fink

Citizen science data are valuable for addressing a wide range of ecological research questions, and there has been a rapid increase in the scope and volume of data available. However, data from large-scale citizen science projects typically present a number of challenges that can inhibit robust ecological inferences. These challenges include: species bias, spatial bias, and variation in effort. To demonstrate addressing key challenges in analysing citizen science data, we use the example of estimating species distributions with data from eBird, a large semi-structured citizen science project. We estimate two widely applied metrics of species distributions: encounter rate and occupancy probability. For each metric, we assess the impact of data processing steps that either degrade or refine the data used in the analyses. We also test whether differences in model performance are maintained at different sample sizes. Model performance improved when data processing and analytical methods addressed the challenges arising from citizen science data. The largest gains in model performance were achieved with: 1) the use of complete checklists (where observers report all the species they detect and identify); and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists and effort variables. Improvements in model performance with data refinement were more evident with larger sample sizes. Here, we describe processes to refine semi-structured citizen science data to estimate species distributions. We demonstrate the value of complete checklists, which can inform the design and adaptation of citizen science projects. We also demonstrate the value of information on effort. The methods we have outlined are also likely to improve other forms of inference, and will enable researchers to conduct robust analyses and harness the vast ecological knowledge that exists within citizen science data. ### Competing Interest Statement The authors have declared no competing interest.

14: 2019-20 Wuhan coronavirus outbreak: Intense surveillance is vital for preventing sustained transmission in new locations
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Posted 25 Jan 2020

2019-20 Wuhan coronavirus outbreak: Intense surveillance is vital for preventing sustained transmission in new locations
5,302 downloads bioRxiv ecology

Robin N Thompson

The outbreak of pneumonia originating in Wuhan, China, has generated 830 confirmed cases, including 26 deaths, as of 24 January 2020. The virus (2019-nCoV) has spread elsewhere in China and to other countries, including South Korea, Thailand, Japan and USA. Fortunately, there has not yet been evidence of sustained human-to-human transmission outside of China. Here we assess the risk of sustained transmission whenever the coronavirus arrives in other countries. Data describing the times from symptom onset to hospitalisation for 47 patients infected in the current outbreak are used to generate an estimate for the probability that an imported case is followed by sustained human-to-human transmission. Under the assumptions that the imported case is representative of the patients in China, and that the 2019-nCoV is similarly transmissible to the SARS coronavirus, the probability that an imported case is followed by sustained human-to-human transmission is 0.37. However, if the mean time from symptom onset to hospitalisation can be halved by intense surveillance, then the probability that an imported case leads to sustained transmission is only 0.005. This emphasises the importance of current surveillance efforts in countries around the world, to ensure that the ongoing outbreak will not become a large global epidemic.

15: A systematic review and meta-analysis of the human blood index of the major African malaria vectors
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Posted 23 Sep 2018

A systematic review and meta-analysis of the human blood index of the major African malaria vectors
5,180 downloads bioRxiv ecology

James Orsborne, Luis Furuya-Kanamori, Claire L Jeffries, Mojca Kristan, Abdul Rahim Mohammed, Yaw A Afrane, Kathleen O’Reilly, Eduardo Massad, Chris Drakeley, Thomas Walker, Laith Yakob

The proportion of mosquito blood-meals that are of human origin, referred to as the 'human blood index' or HBI, is a key determinant of malaria transmission. We conducted a systematic review of the HBI for the major African malaria vectors. Evidence is presented for higher HBI among Anopheles gambiae (M/S forms and An. coluzzii/An. gambiae s.s. are not distinguished for most studies and therefore combined) as well as An. funestus when compared with An. arabiensis (prevalence odds ratio adjusted for whether mosquitoes were caught indoors and outdoors: 1.61 95%CI 1.08-2.39; 1.84 95%CI 1.35-2.52, respectively). This finding is in keeping with the entomological literature which describes An. arabiensis to be more zoophagic than the other major African vectors. However, meta-analysis also revealed that HBI was more associated with location of mosquito captures (indoors versus outdoors, R2=0.3) than with mosquito (sibling/) species (R2=0.11), and that this difference was significant (p<0.01). Some of the more important consequences of mosquito biting behaviour are discussed in the context of new theoretical and empirical developments that may improve both HBI assessment and how this metric can inform malaria control optimisation.

16: pcadapt: an R package to perform genome scans for selection based on principal component analysis
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Posted 30 May 2016

pcadapt: an R package to perform genome scans for selection based on principal component analysis
4,847 downloads bioRxiv ecology

Keurcien Luu, Eric Bazin, Michael G.B. Blum

The R package pcadapt performs genome scans to detect genes under selection based on population genomic data. It assumes that candidate markers are outliers with respect to how they are related to population structure. Because population structure is ascertained with principal component analysis, the package is fast and works with large-scale data. It can handle missing data and pooled sequencing data. By contrast to population-based approaches, the package handle admixed individuals and does not require grouping individuals into populations. Since its first release, pcadapt has evolved both in terms of statistical approach and software implementation. We present results obtained with robust Mahalanobis distance, which is a new statistic for genome scans available in the 2.0 and later versions of the package. When hierarchical population structure occurs, Mahalanobis distance is more powerful than the communality statistic that was implemented in the first version of the package. Using simulated data, we compare pcadapt to other software for genome scans (BayeScan, hapflk, OutFLANK, sNMF). We find that the proportion of false discoveries is around a nominal false discovery rate set at 10% with the exception of BayeScan that generates 40% of false discoveries. We also find that the power of BayeScan is severely impacted by the presence of admixed individuals whereas pcadapt is not impacted. Last, we find that pcadapt and hapflk are the most powerful software in scenarios of population divergence and range expansion. Because pcadapt handles next-generation sequencing data, it is a valuable tool for data analysis in molecular ecology.

17: The dynamic core microbiome: Structure, dynamics and stability
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Posted 14 May 2017

The dynamic core microbiome: Structure, dynamics and stability
4,727 downloads bioRxiv ecology

Johannes R. Bjork, Robert B. O’Hara, Marta Ribes, Rafel Coma, José M. Montoya

The long-term stability of microbiomes is crucial as the persistent occurrence of beneficial microbes and their associated functions ensure host health and well-being. Microbiomes are highly diverse and dynamic, making them challenging to understand. Because many natural systems work as temporal networks, we present an approach that allows identifying meaningful ecological patterns within complex microbiomes: the dynamic core microbiome. On the basis of six marine sponge species sampled monthly over three years, we study the structure, dynamics and stability of their microbiomes. What emerge for each microbiome is a negative relationship between temporal variability and mean abundance. The notion of the dynamic core microbiome allowed us to determine a relevant functional attribute of the microbiome: temporal stability is not determined by the diversity of a host's microbial assemblages, but rather by the density of those microbes that conform its core microbiome. The core microbial interaction network consisted of complementary members interacting weakly with dominance of comensal and amensal interactions that suggests self-regulation as a key determinant of the temporal stability of the microbiome. These interactions have likely coevolved to maintain host functionality and fitness over ecological, and even evolutionary time scales.

18: Modelling palaeoecological time series using generalized additive models
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Posted 15 May 2018

Modelling palaeoecological time series using generalized additive models
4,192 downloads bioRxiv ecology

Gavin L. Simpson

In the absence of annual laminations, time series generated from lake sediments or other similar stratigraphic sequences are irregularly spaced in time, which complicates formal analysis using classical statistical time series models. In lieu, statistical analyses of trends in palaeoenvironmental time series, if done at all, have typically used simpler linear regressions or (non-) parametric correlations with little regard for the violation of assumptions that almost surely occurs due to temporal dependencies in the data or that correlations do not provide estimates of the magnitude of change, just whether or not there is a linear or monotonic trend. Alternative approaches have used LOESS-estimated trends to justify data interpretations or test hypotheses as to the causal factors without considering the inherent subjectivity of the choice of parameters used to achieve the LOESS fit (e.g. span width, degree of polynomial). Generalized additive models (GAMs) are statistical models that can be used to estimate trends as smooth functions of time. Unlike LOESS, GAMs use automatic smoothness selection methods to objectively determine the complexity of the fitted trend, and as formal statistical models, GAMs, allow for potentially complex, non-linear trends, a proper accounting of model uncertainty, and the identification of periods of significant temporal change. Here, I present a consistent and modern approach to the estimation of trends in palaeoenvironmental time series using GAMs, illustrating features of the methodology with two example time series of contrasting complexity; a 150-year bulk organic matter δ15N time series from Small Water, UK, and a 3000-year alkenone record from Braya-Sø, Greenland. I discuss the underlying mechanics of GAMs that allow them to learn the shape of the trend from the data themselves and how simultaneous confidence intervals and the first derivatives of the trend are used to properly account for model uncertainty and identify periods of change. It is hoped that by using GAMs greater attention is paid to the statistical estimation of trends in palaeoenvironmental time series leading to more a robust and reproducible palaeoscience.

19: Climate change will drive novel cross-species viral transmission
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Posted 25 Jan 2020

Climate change will drive novel cross-species viral transmission
4,181 downloads bioRxiv ecology

Colin J Carlson, Gregory F Albery, Cory Merow, Christopher H. Trisos, Casey M. Zipfel, Evan A Eskew, Kevin J. Olival, Noam Ross, Shweta Bansal

At least 10,000 species of mammal virus are estimated to have the potential to spread in human populations, but the vast majority are currently circulating in wildlife, largely undescribed and undetected by disease outbreak surveillance. In addition, changing climate and land use are already driving geographic range shifts in wildlife, producing novel species assemblages and opportunities for viral sharing between previously isolated species. In some cases, this will inevitably facilitate spillover into humans - a possible mechanistic link between global environmental change and emerging zoonotic disease. Here, we map potential hotspots of viral sharing, using a phylogeographic model of the mammal-virus network, and projections of geographic range shifts for 3,870 mammal species under climate change and land use scenarios for the year 2070. Range-shifting mammal species are predicted to aggregate at high elevations, in biodiversity hotspots, and in areas of high human population density in Asia and Africa, driving the cross-species transmission of novel viruses at least 4,000 times. Counter to expectations, holding warming under 2 degrees C within the century does not reduce new viral sharing, due to greater range expansions - highlighting the need to invest in surveillance even in a low-warming future. Most projected viral sharing is driven by diverse hyperreservoirs (rodents and bats) and large-bodied predators (carnivores). Because of their unique dispersal capacity, bats account for the majority of novel viral sharing, and are likely to share viruses along evolutionary pathways that could facilitate future emergence in humans. Our findings highlight the urgent need to pair viral surveillance and discovery efforts with biodiversity surveys tracking range shifts, especially in tropical countries that harbor the most emerging zoonoses.

20: Diversity of entomopathogens Fungi: Which groups conquered the insect body?
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Posted 04 Apr 2014

Diversity of entomopathogens Fungi: Which groups conquered the insect body?
4,156 downloads bioRxiv ecology

João P. M. Araújo, David P. Hughes

The entomopathogenic Fungi comprise a wide range of ecologically diverse species. This group of parasites can be found distributed among all fungal phyla and as well as among the ecologically similar but phylogenetically distinct Oomycetes or water molds, that belong to a different kingdom (Stramenopila). As a group, the entomopathogenic fungi and water molds parasitize a wide range of insect hosts from aquatic larvae in streams to adult insects of high canopy tropical forests. Their hosts are spread among 18 orders of insects, in all developmental stages such as: eggs, larvae, pupae, nymphs and adults exhibiting completely different ecologies. Such assortment of niches has resulted in these parasites evolving a considerable morphological diversity, resulting in enormous biodiversity, much of which remains unknown. Here we gather together a huge amount of records of these entomopathogens to comparing and describe both their morphologies and ecological traits. These findings highlight a wide range of adaptations that evolved following the evolutionary transition to infecting the most diverse and widespread animals on Earth, the insects.

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