Identifying historical and future global change drivers that place species recovery at risk

Ecosystem management in the face of global change requires understanding how co-occurring threats affect species and communities. Such an understanding allows for effective management strategies to be identified and implemented. An important component of this is differentiating between factors that are within (e.g. invasive predators) or outside (e.g. drought, large wildfires) of a local manager's control. In the global biodiversity hotspot of south-western Australia, small- and medium-sized mammal species are severely affected by anthropogenic threats and environmental disturbances, including invasive predators, fire, and declining rainfall. However, the relative importance of different drivers has not been quantified. We used data from a long-term monitoring program to fit Bayesian state-space models that estimated spatial and temporal changes in the relative abundance of four threatened mammal species: the woylie (Bettongia penicillata), chuditch (Dasyurus geoffroii), koomal (Trichosurus vulpecula) and quenda (Isoodon fusciventor). We then use Bayesian structural equation modelling to identify the direct and indirect drivers of population changes, and scenario analysis to forecast population responses to future environmental change. We found that habitat loss or conversion and reduced primary productivity (caused by rainfall declines) had greater effects on species' spatial and temporal population change than the range of fire and invasive predator (the red fox Vulpes vulpes) management actions observed in the study area. Scenario analysis revealed that a greater extent of severe fire and further rainfall declines predicted under climate change, operating in concert are likely to further reduce the abundance of these species, but may be mitigated partially by invasive predator control. Considering both historical and future drivers of population change is necessary to identify the factors that risk species recovery. Given that both anthropogenic pressures and environmental disturbances can undermine conservation efforts, managers must consider how the relative benefit of conservation actions will be shaped by ongoing global change.     

Evolutionary rescue under demographic and environmental stochasticity

Populations suffer two types of stochasticity: demographic stochasticity, from sampling error in offspring number, and environmental stochasticity, from temporal variation in the growth rate. By modelling evolution through phenotypic selection following an abrupt environmental change, we investigate how genetic and demographic dynamics, as well as effects on population survival of the genetic variance and of the strength of stabilizing selection, differ under the two types of stochasticity. We show that population survival probability declines sharply with stronger stabilizing selection under demographic stochasticity, but declines more continuously when environmental stochasticity is strengthened. However, the genetic variance that confers the highest population survival probability differs little under demographic and environmental stochasticity. Since the influence of demographic stochasticity is stronger when population size is smaller, a slow initial decline of genetic variance, which allows quicker evolution, is important for population persistence. In contrast, the influence of environmental stochasticity is population-size-independent, so higher initial fitness becomes important for survival under strong environmental stochasticity. The two types of stochasticity interact in a more than multiplicative way in reducing the population survival probability. Our work suggests the importance of explicitly distinguishing and measuring the forms of stochasticity during evolutionary rescue.     

Defining null expectations for animal site fidelity

Site fidelity—the tendency to return to previously visited locations—is widespread across taxa. Returns may be driven by several mechanisms, including memory, habitat selection, or chance; however, pattern-based definitions group different generating mechanisms under the same label of ‘site fidelity’, often assuming memory as the main driver. We propose an operational definition of site fidelity as patterns of return that deviate from a null expectation derived from a memory-free movement model. First, using agent-based simulations, we show that without memory, intrinsic movement characteristics and extrinsic landscape characteristics are key determinants of return patterns and that even random movements may generate substantial probabilities of return. Second, we illustrate how to implement our framework empirically to establish ecologically meaningful, system-specific null expectations for site fidelity. Our approach provides a conceptual and operational framework to test hypotheses on site fidelity across systems and scales.     

Fast backward steps and detachment of single kinesin molecules measured under a wide range of loads

To understand force generation under a wide range of loads, the stepping of single kinesin molecules was measured at loads from −20 to 42 pN by optical tweezers with high temporal resolution. The optical trap has been improved to halve positional noise and increase bandwidth by using 200-nm beads. The step size of the forward and backward steps was 8.2 nm even over a wide range of loads. Histograms of the dwell times of backward steps and detachment fit well to two independent exponential equations with fast (~0.4 ms) and slow (>3 ms) time constants, indicating the existence of a fast step in addition to the conventional slow step. The dwell times of the fast steps were almost independent of the load and ATP concentration, while those of the slow backward steps and detachment depended on those. We constructed the kinetic model to explain the fast and slow steps under a wide range of loads.     

Genetic disruption of mammalian endoplasmic reticulum-associated protein degradation: Human phenotypes and animal and cellular disease models

Endoplasmic reticulum-associated protein degradation (ERAD) is a stringent quality control mechanism through which misfolded, unassembled and some native proteins are targeted for degradation to maintain appropriate cellular and organelle homeostasis. Several in vitro and in vivo ERAD-related studies have provided mechanistic insights into ERAD pathway activation and its consequent events; however, a majority of these have investigated the effect of ERAD substrates and their consequent diseases affecting the degradation process. In this review, we present all reported human single-gene disorders caused by genetic variation in genes that encode ERAD components rather than their substrates. Additionally, after extensive literature survey, we present various genetically manipulated higher cellular and mammalian animal models that lack specific components involved in various stages of the ERAD pathway.     

Coevolution of species colonisation rates controls food-chain length in spatially structured food webs

How the complexity of food webs depends on environmental variables is a long-standing ecological question. It is unclear though how food-chain length should vary with adaptive evolution of the constitutive species. Here we model the evolution of species colonisation rates and its consequences on occupancies and food-chain length in metacommunities. When colonisation rates can evolve, longer food-chains can persist. Extinction, perturbation and habitat loss all affect evolutionarily stable colonisation rates, but the strength of the competition-colonisation trade-off has a major role: weaker trade-offs yield longer chains. Although such eco-evo dynamics partly alleviates the spatial constraint on food-chain length, it is no magic bullet: the highest, most vulnerable, trophic levels are also those that least benefit from evolution. We provide qualitative predictions regarding how trait evolution affects the response of communities to disturbance and habitat loss. This highlights the importance of eco-evolutionary dynamics at metacommunity level in determining food-chain length.     

Horizontal biosedimentary gradients across the Sado estuary,W. Portugal

The topography of the Sado estuary, the second largest of Portugal, comprises the outer estuary inside the entrance channel and the inner estuary, on the inward side of which begins the tidal mudflats. The outer estuary subtidal area covers approximately 70 km² and presents a series of longitudinal intertidal sandbanks, separating a northern and a southern channel. A benthic survey was undertaken in the outer estuary during June 1986, in which superficial sediments and macrofauna were sampled at 133 locations. The environmental variables measured in the superficial sediments were the temperature, the granulometric structure, the silt, sand and the gravel content, and the total organic matter content. The primary macrofauna biological variables studied were the species composition, abundance and biomass, calculated on wet, dry and ash-free dry weight. The granulometry and the organic content of superficial sediments agreed with the transient and the residual currents velocity field, simulated in a 2-D hydrodynamic model previously elaborated for the outer estuary. The northern channel superficial sediments showed higher silt and total organic matter content, while the model also suggested lower transient and residual velocities, water flow and shear stress in this channel. The distribution patterns of the subtidal macrofauna were separated into two main groups of species, one comprising taxa essentially settled near the estuarine mouth and the other inwards. Biological primary variables also showed consistent patterns, comparable to other Portuguese estuaries. The major subtidal benthic biotopes were obtained through classification analysis and related to the prevailing hydrophysical and sedimentary conditions in the outer estuary.     

Design considerations for two-stage enrichment clinical trials

When there is a predictive biomarker, enrichment can focus the clinical trial on a benefiting subpopulation. We describe a two-stage enrichment design, in which the first stage is designed to efficiently estimate a threshold and the second stage is a “phase III-like” trial on the enriched population. The goal of this paper is to explore design issues: sample size in Stages 1 and 2, and re-estimation of the Stage 2 sample size following Stage 1. By treating these as separate trials, we can gain insight into how the predictive nature of the biomarker specifically impacts the sample size. We also show that failure to adequately estimate the threshold can have disastrous consequences in the second stage. While any bivariate model could be used, we assume a continuous outcome and continuous biomarker, described by a bivariate normal model. The correlation coefficient between the outcome and biomarker is the key to understanding the behavior of the design, both for predictive and prognostic biomarkers. Through a series of simulations we illustrate the impact of model misspecification, consequences of poor threshold estimation, and requisite sample sizes that depend on the predictive nature of the biomarker. Such insight should be helpful in understanding and designing enrichment trials.     

Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity

Leveraging information in aggregate data from external sources to improve estimation efficiency and prediction accuracy with smaller scale studies has drawn a great deal of attention in recent years. Yet, conventional methods often either ignore uncertainty in the external information or fail to account for the heterogeneity between internal and external studies. This article proposes an empirical likelihood-based framework to improve the estimation of the semiparametric transformation models by incorporating information about the t-year subgroup survival probability from external sources. The proposed estimation procedure incorporates an additional likelihood component to account for uncertainty in the external information and employs a density ratio model to characterize population heterogeneity. We establish the consistency and asymptotic normality of the proposed estimator and show that it is more efficient than the conventional pseudopartial likelihood estimator without combining information. Simulation studies show that the proposed estimator yields little bias and outperforms the conventional approach even in the presence of information uncertainty and heterogeneity. The proposed methodologies are illustrated with an analysis of a pancreatic cancer study.     

Bayesian estimation of two-part joint models for a longitudinal semicontinuous biomarker and a terminal event with INLA: Interests for cancer clinical trial evaluation

Two-part joint models for a longitudinal semicontinuous biomarker and a terminal event have been recently introduced based on frequentist estimation. The biomarker distribution is decomposed into a probability of positive value and the expected value among positive values. Shared random effects can represent the association structure between the biomarker and the terminal event. The computational burden increases compared to standard joint models with a single regression model for the biomarker. In this context, the frequentist estimation implemented in the R package frailtypack can be challenging for complex models (i.e., a large number of parameters and dimension of the random effects). As an alternative, we propose a Bayesian estimation of two-part joint models based on the Integrated Nested Laplace Approximation (INLA) algorithm to alleviate the computational burden and fit more complex models. Our simulation studies confirm that INLA provides accurate approximation of posterior estimates and to reduced computation time and variability of estimates compared to frailtypack in the situations considered. We contrast the Bayesian and frequentist approaches in the analysis of two randomized cancer clinical trials (GERCOR and PRIME studies), where INLA has a reduced variability for the association between the biomarker and the risk of event. Moreover, the Bayesian approach was able to characterize subgroups of patients associated with different responses to treatment in the PRIME study. Our study suggests that the Bayesian approach using the INLA algorithm enables to fit complex joint models that might be of interest in a wide range of clinical applications.