Winter Conditions Influence Biological Responses of Migrating Hummingbirds

Conserving biological diversity given ongoing environmental changes requires the knowledge of how organisms respond biologically to these changes; however, we rarely have this information. This data deficiency can be addressed with coordinated monitoring programs that provide field data across temporal and spatial scales and with process-based models, which provide a method for predicting how species, in particular migrating species that face different conditions across their range, will respond to climate change. We evaluate whether environmental conditions in the wintering grounds of broad-tailed hummingbirds influence physiological and behavioral attributes of their migration. To quantify winter ground conditions, we used operative temperature as a proxy for physiological constraint, and precipitation and the normalized difference vegetation index (NDVI) as surrogates of resource availability. We measured four biological response variables: molt stage, timing of arrival at stopover sites, body mass, and fat. Consistent with our predictions, we found that birds migrating north were in earlier stages of molt and arrived at stopover sites later when NDVI was low. These results indicate that wintering conditions impact the timing and condition of birds as they migrate north. In addition, our results suggest that biologically informed environmental surrogates provide a valuable tool for predicting how climate variability across years influences the animal populations

Graphics for uncertainty

Graphical methods such as colour shading and animation, which are widely available, can be very effective in communicating uncertainty. In particular, the idea of a ‘density strip’ provides a conceptually simple representation of a distribution and this is explored in a variety of settings, including a comparison of means, regression and models for contingency tables. Animation is also a very useful device for exploring uncertainty and this is explored particularly in the context of flexible models, expressed in curves and surfaces whose structure is of particular interest. Animation can further provide a helpful mechanism for exploring data in several dimensions. This is explored in the simple but very important setting of spatiotemporal data

Directional genetic differentiation and asymmetric migration

Understanding the population structure and patterns of gene flow within species is of fundamental importance to the study of evolution. In the fields of population and evolutionary genetics, measures of genetic differentiation are commonly used to gather this information. One potential caveat is that these measures assume gene flow to be symmetric. However, asymmetric gene flow is common in nature, especially in systems driven by physical processes such as wind or water currents. Since information about levels of asymmetric gene flow among populations is essential for the correct interpretation of the distribution of contemporary genetic diversity within species, this should not be overlooked. To obtain information on asymmetric migration patterns from genetic data, complex models based on maximum likelihood or Bayesian approaches generally need to be employed, often at great computational cost. Here, a new simpler and more efficient approach for understanding gene flow patterns is presented. This approach allows the estimation of directional components of genetic divergence between pairs of populations at low computational effort, using any of the classical or modern measures of genetic differentiation. These directional measures of genetic differentiation can further be used to calculate directional relative migration and to detect asymmetries in gene flow patterns. This can be done in a user-friendly web application called divMigrate-online introduced in this paper. Using simulated data sets with known gene flow regimes, we demonstrate that the method is capable of resolving complex migration patterns under a range of study designs.Comment: 25 pages, 8 (+3) figures, 1 tabl

"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts

Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained "dialogue acts" frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real-time. We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes, and present actionable rules based on our findings. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.Comment: 13 pages, 6 figures, IUI 201

Reduced tillage, but not organic matter input, increased nematode diversity and food web stability in European long‐term field experiments

Soil nematode communities and food web indices can inform about the complexity, nutrient flows and decomposition pathways of soil food webs, reflecting soil quality. Relative abundance of nematode feeding and life‐history groups are used for calculating food web indices, i.e., maturity index (MI), enrichment index (EI), structure index (SI) and channel index (CI). Molecular methods to study nematode communities potentially offer advantages compared to traditional methods in terms of resolution, throughput, cost and time. In spite of such advantages, molecular data have not often been adopted so far to assess the effects of soil management on nematode communities and to calculate these food web indices. Here, we used high‐throughput amplicon sequencing to investigate the effects of tillage (conventional vs. reduced) and organic matter addition (low vs. high) on nematode communities and food web indices in 10 European long‐term field experiments and we assessed the relationship between nematode communities and soil parameters. We found that nematode communities were more strongly affected by tillage than by organic matter addition. Compared to conventional tillage, reduced tillage increased nematode diversity (23% higher Shannon diversity index), nematode community stability (12% higher MI), structure (24% higher SI), and the fungal decomposition channel (59% higher CI), and also the number of herbivorous nematodes (70% higher). Total and labile organic carbon, available K and microbial parameters explained nematode community structure. Our findings show that nematode communities are sensitive indicators of soil quality and that molecular profiling of nematode communities has the potential to reveal the effects of soil management on soil quality

Parasite infections in a social carnivore: Evidence of their fitness consequences and factors modulating infection load

There are substantial individual differences in parasite composition and infection load in wildlife populations. Few studies have investigated the factors shaping this heterogeneity in large wild mammals or the impact of parasite infections on Darwinian fitness, particularly in juveniles. A host's parasite composition and infection load can be shaped by factors that determine contact with infective parasite stages and those that determine the host's resistance to infection, such as abiotic and social environmental factors, and age. Host–parasite interactions and synergies between coinfecting parasites may also be important. We test predictions derived from these different processes to investigate factors shaping infection loads (fecal egg/oocyte load) of two energetically costly gastrointestinal parasites: the hookworm Ancylostoma and the intracellular Cystoisospora, in juvenile spotted hyenas (Crocuta crocuta) in the Serengeti National Park, in Tanzania. We also assess whether parasite infections curtail survival to adulthood and longevity. Ancylostoma and Cystoisospora infection loads declined as the number of adult clan members increased, a result consistent with an encounter‐reduction effect whereby adults reduced encounters between juveniles and infective larvae, but were not affected by the number of juveniles in a clan. Infection loads decreased with age, possibly because active immune responses to infection improved with age. Differences in parasite load between clans possibly indicate variation in abiotic environmental factors between clan den sites. The survival of juveniles (<365 days old) to adulthood decreased with Ancylostoma load, increased with age, and was modulated by maternal social status. High‐ranking individuals with low Ancylostoma loads had a higher survivorship during the first 4 years of life than high‐ranking individuals with high Ancylostoma loads. These findings suggest that high infection loads with energetically costly parasites such as hookworms during early life can have negative fitness consequences

Dry-season length and runoff control annual variability in stream DOC dynamics in a small, shallowgroundwater-dominated agricultural watershed

International audienceAs a phenomenon integrating climate conditions and hydrological control of the connection betweenstreams and terrestrial dissolved organic carbon (DOC) sources, groundwater dynamics controlpatterns of stream DOC characteristics (concentrations and fluxes). Influence of intra-annualvariations in groundwater level, discharge and climatic factors on DOC concentrations and fluxeswere assessed over 13 years at the headwater watershed of Kervidy-Naizin (5 km²) in westernFrance. Four seasonal periods were delineated within each year according to groundwaterfluctuations (A: rewetting, B: high flow, C: recession, and D: drought). Annual and seasonal baseflow vs stormflow DOC concentrations were defined based on daily hydrograph readings. Highinter-annual variability of annual DOC fluxes (5.4-39.5 kg.ha-1.yr-1) indicates that several years ofdata are required to encompass variations in water flux to evaluate the actual DOC export capacity ofa watershed. Inter-annual variability of mean annual DOC concentrations was much lower (4.9-7.5mg C.l-1), with concentrations decreasing within each year from ca. 9.2 mg C.l-1 in A to ca. 3.0 mgC.l-1 in C. This indicates an intra-annual pattern of stream DOC concentrations controlled by DOCsource characteristics and groundwater dynamics very similar across years. Partial least squareregressions combined with multiple linear regressions showed that the dry season characteristics(length and drawdown) determine the mean annual DOC concentration while annual runoffdetermines the annual flux. Antagonistic mechanisms of production-accumulation and dilution depletioncombined with an unlimited DOC supply from riparian wetland soils can mitigate theresponse of stream concentrations to global changes and climatic variations

Binary Models for Marginal Independence

Log-linear models are a classical tool for the analysis of contingency tables. In particular, the subclass of graphical log-linear models provides a general framework for modelling conditional independences. However, with the exception of special structures, marginal independence hypotheses cannot be accommodated by these traditional models. Focusing on binary variables, we present a model class that provides a framework for modelling marginal independences in contingency tables. The approach taken is graphical and draws on analogies to multivariate Gaussian models for marginal independence. For the graphical model representation we use bi-directed graphs, which are in the tradition of path diagrams. We show how the models can be parameterized in a simple fashion, and how maximum likelihood estimation can be performed using a version of the Iterated Conditional Fitting algorithm. Finally we consider combining these models with symmetry restrictions

Understanding uncertainty in temperature effects on vector-borne disease: A Bayesian approach

Extrinsic environmental factors influence the distribution and population dynamics of many organisms, including insects that are of concern for human health and agriculture. This is particularly true for vector-borne infectious diseases, like malaria, which is a major source of morbidity and mortality in humans. Understanding the mechanistic links between environment and population processes for these diseases is key to predicting the consequences of climate change on transmission and for developing effective interventions. An important measure of the intensity of disease transmission is the reproductive number $R_0$. However, understanding the mechanisms linking $R_0$ and temperature, an environmental factor driving disease risk, can be challenging because the data available for parameterization are often poor. To address this we show how a Bayesian approach can help identify critical uncertainties in components of $R_0$ and how this uncertainty is propagated into the estimate of $R_0$. Most notably, we find that different parameters dominate the uncertainty at different temperature regimes: bite rate from 15-25$^\circ$ C; fecundity across all temperatures, but especially $\sim$25-32$^\circ$ C; mortality from 20-30$^\circ$ C; parasite development rate at $\sim$15-16$^\circ$C and again at $\sim$33-35$^\circ$C. Focusing empirical studies on these parameters and corresponding temperature ranges would be the most efficient way to improve estimates of $R_0$. While we focus on malaria, our methods apply to improving process-based models more generally, including epidemiological, physiological niche, and species distribution models.Comment: 27 pages, including 1 table and 3 figure

Evidence of strong stabilizing effects on the evolution of boreoeutherian (Mammalia) dental proportions.

The dentition is an extremely important organ in mammals with variation in timing and sequence of eruption, crown morphology, and tooth size enabling a range of behavioral, dietary, and functional adaptations across the class. Within this suite of variable mammalian dental phenotypes, relative sizes of teeth reflect variation in the underlying genetic and developmental mechanisms. Two ratios of postcanine tooth lengths capture the relative size of premolars to molars (premolar-molar module, PMM), and among the three molars (molar module component, MMC), and are known to be heritable, independent of body size, and to vary significantly across primates. Here, we explore how these dental traits vary across mammals more broadly, focusing on terrestrial taxa in the clade of Boreoeutheria (Euarchontoglires and Laurasiatheria). We measured the postcanine teeth of N = 1,523 boreoeutherian mammals spanning six orders, 14 families, 36 genera, and 49 species to test hypotheses about associations between dental proportions and phylogenetic relatedness, diet, and life history in mammals. Boreoeutherian postcanine dental proportions sampled in this study carry conserved phylogenetic signal and are not associated with variation in diet. The incorporation of paleontological data provides further evidence that dental proportions may be slower to change than is dietary specialization. These results have implications for our understanding of dental variation and dietary adaptation in mammals