Exclosure fences around nests of imperiled Florida Grasshopper Sparrows reduce rates of predation by mammals

Increasing nest survival by excluding predators is a goal of many bird conservation programs. However, new exclosure projects should be carefully evaluated to assess the potential risks of disturbance. We tested the effectiveness of predator exclosure fences (hereafter, fences) for nests of critically endangered Florida Grasshopper Sparrows (Ammodramus savannarum floridanus) at a dry prairie site (Three Lakes; 2015–2018) and a pasture site (the Ranch; 2015–2016) in Osceola County, Florida, USA. We installed fences at nests an average of 8 days after the start of incubation, and nest abandonment after fence installation was rare (2 of 149 installations). Predation was the leading cause of failure for unfenced nests at both sites (48–73%). At Three Lakes, nest cameras revealed that mammals and snakes were responsible for 61.5% and 38.5% of predation events, respectively, at unfenced nests. Fences reduced the daily probability of predation (0.016 for fenced nests vs. 0.074 for unfenced nests). The probability that a fenced nest would survive from discovery to fledging was more than double that of unfenced nests (60.4% vs. 27.7%). However, we found no difference in daily nest survival at the Ranch between the year before nests were fenced (2015; 0.874) and the year when all but one nest were fenced (2016; 0.867) because red imported fire ants (Solenopsis invicta) were responsible for 86% of predation events at fenced nests at the Ranch. The use of cameras at fenced nests revealed that site‐specific differences in nest predators explained variation in fence efficiency between sites. Our fence design may be useful for other species of grassland birds, but site‐specific predator communities and species‐specific response of target bird species to fences should be assessed before installing fences at other sites.     

Priors for the Bayesian star paradox

We show that the Bayesian star paradox, first proved mathematically by Steel and Matsen for a specific class of prior distributions, occurs in a wider context including less regular, possibly discontinuous, prior distributions.     

Spatial and temporal factors associated with nest survival of Gray Flycatchers in managed ponderosa pine forests

Gray Flycatchers (Empidonax wrightii) breed in a variety of habitats in the arid and semi‐arid regions of the western United States, but little is known about their breeding biology, especially in the northern portion of their range where they nest in ponderosa pine (Pinus ponderosa) forests. From May to July 2014 and 2015, we conducted surveys for singing male Gray Flycatchers along the eastern slope of the Cascade Range in Washington, U.S.A, monitored flycatcher nests, and quantified nest‐site vegetation. We used a logistic‐exposure model fit within a Bayesian framework to model the daily survival probability of flycatcher nests. During the 2 yr of our study, we monitored 141 nests, with 93% in ponderosa pines. Mean clutch size was 3.6 eggs and the mean number of young fledged per nest was 3.2. Predation accounted for 90% of failed nests. We found a positive association between daily nest survival and both nest height and distance of nest substrates from the nearest tree. Flycatchers that locate their nests higher above the ground and further from adjacent trees may be choosing the safest alternative because higher nests may be less exposed to terrestrial predators and nests in trees that are farther from other trees may be less exposed to arboreal predators such as jays (Corvidae) that may forage in patches with connected canopies. Nests in trees farther from other trees may also allow earlier detection of approaching predators and thus aid in nest defense.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Bayesian Quantile Regression for Longitudinal Studies with Nonignorable Missing Data

Summary .  We study quantile regression (QR) for longitudinal measurements with nonignorable intermittent missing data and dropout. Compared to conventional mean regression, quantile regression can characterize the entire conditional distribution of the outcome variable, and is more robust to outliers and misspecification of the error distribution. We account for the within-subject correlation by introducing a   ℓ₂   penalty in the usual QR check function to shrink the subject-specific intercepts and slopes toward the common population values. The informative missing data are assumed to be related to the longitudinal outcome process through the shared latent random effects. We assess the performance of the proposed method using simulation studies, and illustrate it with data from a pediatric AIDS clinical trial.     

ABC as a flexible framework to estimate demography over space and time: some cons,many pros

The analysis of genetic variation to estimate demographic and historical parameters and to quantitatively compare alternative scenarios recently gained a powerful and flexible approach: the Approximate Bayesian Computation (ABC). The likelihood functions does not need to be theoretically specified, but posterior distributions can be approximated by simulation even assuming very complex population models including both natural and human‐induced processes. Prior information can be easily incorporated and the quality of the results can be analysed with rather limited additional effort. ABC is not a statistical analysis per se, but rather a statistical framework and any specific application is a sort of hybrid between a simulation and a data‐analysis study. Complete software packages performing the necessary steps under a set of models and for specific genetic markers are already available, but the flexibility of the method is better exploited combining different programs. Many questions relevant in ecology can be addressed using ABC, but adequate amount of time should be dedicated to decide among alternative options and to evaluate the results. In this paper we will describe and critically comment on the different steps of an ABC analysis, analyse some of the published applications of ABC and provide user guidelines.     

Uncertainty associated with river health assessment in a varying environment: The case of a predictive fish-based index in France

Sensitive biological measures of river ecosystem quality are needed to assess, maintain or restore ecological conditions of water bodies. Since our understanding of these complex systems is imperfect, decision-making requires recognizing uncertainty. In this study, a new predictive multi-metric index based on fish functional traits was developed to assess French rivers. Information on fish assemblage structure, local environment and human-induced disturbances of 1654 French river sites was compiled. A Bayesian framework was used to predict theoretical metric values in absence of human pressure and to estimate the uncertainty associated with these predictions. The uncertainty associated with the index score gives the confidence associated with the evaluation of site ecological conditions.Among the 228 potential metrics tested, only 11 were retained for the index computation. The final index is independent from natural variability and sensitive to human-induced disturbances. In particular, it is affected by the accumulation of different degradations and specific degradations including hydrological perturbations. Predictive uncertainty is globally lower for IPR+ than for underlying metrics.This new methodology seems appropriate to develop bio-indication tools accounting for uncertainty related to reference condition definition and could be extended to other biological groups and areas. Our results support the use of multi-metric indexes to assess rivers and strengthen the idea that examination of uncertainty could contribute greatly to the improvement of the assessment power of bio-indicators.     

Stable isotope-based statistical tools as ecological indicator of pollution sources in Mediterranean transitional water ecosystems

N-stable isotope analysis of macroalgae has become a popular method for the monitoring of nitrogen pollution in aquatic ecosystems. Basing on changes in their δ¹⁵N, macroalgae have been successfully used as biological traps to intercept nitrogen inputs. As different nitrogen sources differ in their isotopic signature, this technique provides useful information on the origin of pollutants and their extension in the water body. However, isotopic fractionation potentially resulting from microbial nitrogen processing, and indirect isotopic variations due to effects of physicochemical conditions on algal nutrient uptake and metabolism, may affect anthropogenic N isotopic values during transportation and assimilation. This in turn can affect the observed isotopic signature in the algal tissue, inducing isotopic variations not related to the origin of assimilated nitrogen, representing a “background noise” in isotope-based water pollution studies.In this study, we focused on three neighbouring coastal lakes (Caprolace, Fogliano and Sabaudia lakes) located south of Rome (Italy). Lakes were characterized by differences in terms of anthropogenic pressure (i.e. urbanization, cultivated crops, livestock grazing) and potential “background noise” levels (i.e. nutrient concentration, pH, microbial concentration). Our aim was to assess nitrogen isotopic variations in fragments of Ulva lactuca specimens after 48 h of submersion to identify and locate the origins of nitrogen pollutants affecting each lake. δ¹⁵N were obtained for replicated specimens of U. lactuca spatially distributed to cover the entire surface of each lake, previously collected from a benchmark, unpolluted site. In order to reduce the environmental background noise on isotopic observations, a Bayesian hierarchical model relating isotopic variation to environmental covariates and random spatial effects was used to describe and understand the distribution of isotopic signals in each lake.Our procedure (i) allowed to remove background noise and confounding effects from the observed isotopic signals; (ii) allowed to detect “hidden” pollution sources that would not be detected when not accounting for the confounding effect of environmental background noise; (iii) produced maps of the three lakes providing a clear representation of the isotopic signal variation even where background noise was high. Maps were useful to locate nitrogen pollution sources, identify the origin of the dissolved nitrogen and quantify the extent of pollutants, showing localized organic pollution impacting Sabaudia and Fogliano, but not Caprolace. This method provided a clear characterization of both intra- and inter-lake anthropogenic pressure gradients, representing a powerful approach to the ecological indication and nitrogen pollution management in complex systems, as transitional waterbodies are.     

Leveraging whole genome sequencing data for demographic inference with approximate Bayesian computation

Accounting for historical demographic features, such as the strength and timing of gene flow and divergence times between closely related lineages, is vital for many inferences in evolutionary biology. Approximate Bayesian computation (ABC) is one method commonly used to estimate demographic parameters. However, the DNA sequences used as input for this method, often microsatellites or RADseq loci, usually represent a small fraction of the genome. Whole genome sequencing (WGS) data, on the other hand, have been used less often with ABC, and questions remain about the potential benefit of, and how to best implement, this type of data; we used pseudo‐observed data sets to explore such questions. Specifically, we addressed the potential improvements in parameter estimation accuracy that could be associated with WGS data in multiple contexts; namely, we quantified the effects of (a) more data, (b) haplotype‐based summary statistics, and (c) locus length. Compared with a hypothetical RADseq data set with 2.5 Mbp of data, using a 1 Gbp data set consisting of 100 Kbp sequences led to substantial gains in the accuracy of parameter estimates, which was mostly due to haplotype statistics and increased data. We also quantified the effects of including (a) locus‐specific recombination rates, and (b) background selection information in ABC analyses. Importantly, assuming uniform recombination or ignoring background selection had a negative effect on accuracy in many cases. Software and results from this method validation study should be useful for future demographic history analyses.