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Interspecific interactions can influence species' activity and movement patterns. In particular, species may avoid or attract each other through reactive responses in space and/or time. However, data and methods to study such reactive interactions have remained scarce and were generally limited to two interacting species. At this time, the deployment of camera traps opens new opportunities but adapted statistical techniques are still required to analyze interaction patterns with such data. We present the multivariate Hawkes process (MHP) and show how it can be used to analyze interactions between several species using camera trap data. Hawkes processes use flexible pairwise interaction functions, allowing us to consider asymmetries and variations over time when depicting reactive temporal interactions. After describing the theoretical foundations of the MHP, we outline how its framework can be used to study interspecific interactions with camera trap data. We design a simulation study to evaluate the performance of the MHP and of another existing method to infer interactions from camera trap‐like data. We also use the MHP to infer reactive interactions from real camera trap data for five species from South African savannas (impala Aepyceros melampus, greater kudu Tragelaphus strepsiceros, lion Panthera leo, blue wildebeest Connochaetes taurinus and Burchell's zebra Equus quagga burchelli). The simulation study shows that the MHP can be used as a tool to benchmark other methods of interspecific interaction inference and that this model can reliably infer interactions when enough data are considered. The analysis of real data highlights evidence of predator avoidance by prey and herbivore–herbivore attraction. Lastly, we present the advantages and limits of the MHP and discuss how it can be improved to infer attraction/avoidance patterns more reliably. As camera traps are increasingly used, the multivariate Hawkes process provides a promising framework to decipher the complexity of interactions structuring ecological communities. [ABSTRACT FROM AUTHOR]

Within communities, species are wrapped in a set of feedbacks with each other and with their environment. When such feedbacks are strong enough they can generate alternative stable states. So far, research on alternative stable states has mostly focused on systems with a small number of species and a limited diversity of interaction types. Here, we analyze a spatial model of plant community dynamics in stressed ecosystems such as drylands, where each species is characterized by a strategy, and the different species interact through facilitation and competition for space and resources, such as water. We identify three different types of multistability emerging from the interplay of competition and facilitation. Under low‐stress levels, plant communities organize in small groups of coexisting species, maintained by space, competition and facilitation (“cliques”). Under higher stress levels, positive feedback from facilitation lead to the dominance of a single facilitating species (“mutual exclusion states”). At the highest stress levels, the single facilitating species left in the system coexists with the desert state. By linking community ecology and alternative stable states theory using a spatial plant community model for stressed ecosystems, our study contributes to highlight the importance of positive feedback loops for the stability of ecological communities. [ABSTRACT FROM AUTHOR]

A critical question in tapping behavior is to understand whether the temporal control is exerted on the duration and trajectory of the downward-upward hand movement or on the pause between hand movements. In the present study, we determined the duration of both the movement execution and pauses of monkeys performing a synchronization-continuation task (SCT), using the speed profile of their tapping behavior. We found a linear increase in the variance of pause-duration as a function of interval, while the variance of the motor implementation was relatively constant across intervals. In fact, 96% of the variability of the duration of a complete tapping cycle (pause + movement) was due to the variability of the pause duration. In addition, we performed a Bayesian model selection to determine the effect of interval duration (450 -1,000 ms), serial-order (1-6 produced intervals), task phase (sensory cued or internally driven), and marker modality (auditory or visual) on the duration of the movement-pause and tapping movement. The results showed that the most important parameter used to successfully perform the SCT was the control of the pause duration. We also found that the kinematics of the tapping movements was concordant with a stereotyped ballistic control of the hand pressing the push-button. The present findings support the idea that monkeys used an explicit timing strategy to perform the SCT, where a dedicated timing mechanism controlled the duration of the pauses of movement, while also triggered the execution of fixed movements across each interval of the rhythmic sequence. [ABSTRACT FROM AUTHOR]

Abstract: This paper is a survey of existing estimation methods for pharmacokinetic/pharmacodynamic (PK/PD) models based on stochastic differential equations (SDEs). Most parametric estimation methods proposed for SDEs require high frequency data and are often poorly suited for PK/PD data which are usually sparse. Moreover, PK/PD experiments generally include not a single individual but a group of subjects, leading to a population estimation approach. This review concentrates on estimation methods which have been applied to PK/PD data, for SDEs observed with and without measurement noise, with a standard or a population approach. Besides, the adopted methodologies highly differ depending on the existence or not of an explicit transition density of the SDE solution. [Copyright &y& Elsevier]

Growth curve data consist of repeated measurements of a continuous growth process over time in a population of individuals. These data are classically analyzed by nonlinear mixed models. However, the standard growth functions used in this context prescribe monotone increasing growth and can fail to model unexpected changes in growth rates. We propose to model these variations using stochastic differential equations (SDEs) that are deduced from the standard deterministic growth function by adding random variations to the growth dynamics. A Bayesian inference of the parameters of these SDE mixed models is developed. In the case when the SDE has an explicit solution, we describe an easily implemented Gibbs algorithm. When the conditional distribution of the diffusion process has no explicit form, we propose to approximate it using the Euler–Maruyama scheme. Finally, we suggest validating the SDE approach via criteria based on the predictive posterior distribution. We illustrate the efficiency of our method using the Gompertz function to model data on chicken growth, the modeling being improved by the SDE approach. [ABSTRACT FROM AUTHOR]

Abstract: Parametric incomplete data models defined by ordinary differential equations (ODEs) are widely used in biostatistics to describe biological processes accurately. Their parameters are estimated on approximate models, whose regression functions are evaluated by a numerical integration method. Accurate and efficient estimations of these parameters are critical issues. This paper proposes parameter estimation methods involving either a stochastic approximation EM algorithm (SAEM) in the maximum likelihood estimation, or a Gibbs sampler in the Bayesian approach. Both algorithms involve the simulation of non-observed data with conditional distributions using Hastings–Metropolis (H–M) algorithms. A modified H–M algorithm, including an original local linearization scheme to solve the ODEs, is proposed to reduce the computational time significantly. The convergence on the approximate model of all these algorithms is proved. The errors induced by the numerical solving method on the conditional distribution, the likelihood and the posterior distribution are bounded. The Bayesian and maximum likelihood estimation methods are illustrated on a simulated pharmacokinetic nonlinear mixed-effects model defined by an ODE. Simulation results illustrate the ability of these algorithms to provide accurate estimates. [Copyright &y& Elsevier]

Abstract: An accurate estimation of the hemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is crucial for a precise spatial and temporal estimate of the underlying neuronal processes. Recent works have proposed non-parametric estimation of the HRF under the hypotheses of linearity and stationarity in time. Biological literature suggests, however, that response magnitude may vary with attention or ongoing activity. We therefore test a more flexible model that allows for the variation of the magnitude of the HRF with time in a maximum likelihood framework. Under this model, the magnitude of the HRF evoked by a single event may vary across occurrences of the same type of event. This model is tested against a simpler model with a fixed magnitude using information theory. We develop a standard EM algorithm to identify the event magnitudes and the HRF. We test this hypothesis on a series of 32 regions (4 ROIS on eight subjects) of interest and find that the more flexible model is better than the usual model in most cases. The important implications for the analysis of fMRI time series for event-related neuroimaging experiments are discussed. [Copyright &y& Elsevier]

Generalized multipartite networks consist in the joint observation of several networks implying some common pre-specified groups of individuals. Such complex networks arise commonly in social sciences, biology, ecology, etc. We propose a flexible probabilistic model named Multipartite Block Model (MBM) able to unravel the topology of multipartite networks by identifying clusters (blocks) of nodes sharing the same patterns of connectivity across the collection of networks they are involved in. The model parameters are estimated through a variational version of the Expectation-Maximization algorithm. The numbers of blocks are chosen using an Integrated Completed Likelihood criterion specifically designed for our model. A simulation study illustrates the robustness of the inference strategy. Finally, two datasets respectively issued from ecology and ethnobiology are analyzed with the MBM in order to illustrate its flexibility and its relevance for the analysis of real datasets. [ABSTRACT FROM AUTHOR]

Small farms rely on a range of nature's contributions to people (NCPs) provided by crop diversity, covering both material and immaterial dimensions that are crucial for livelihoods and well-being. The maintenance of these NCPs over time, despite perturbations, is a key component of small farms' resilience. However, the processes involved in farmers accessing the different NCPs provided by crops are largely unknown. Such knowledge would be instrumental for evaluating the vulnerability or resilience of farmers to potential disruptions that affect these distribution channels. In this study, we analyzed how the seed provisioning networks used by farmers to access crops relate to the different NCPs they receive from these crops, through a case study in Sahelian Senegal. Field surveys were conducted with 85 farmers, half men and half women, from two villages. The surveys documented which varieties of three important staple crop species (pearl millet, cowpea, peanut) farmers grew. Farmers were asked to cite their motivations for cultivating each variety as a proxy for NCPs, and to explain from where they obtained the seeds of each variety of these three species. We mobilized recent developments in Social-Ecological Network research, representing the relationships between social entities (i.e., farmers and seed sources), ecological entities (i.e., crops), and NCPs (i.e., motivations) as networks. We applied a block model clustering approach to analyze these relationships by testing if particular seed sources were associated with particular motivations, and if differences existed between men and women. We also analyzed households' profiles according to the motivations they cited and the seed sources they were connected to. We found that some crops contributions were related to different seed sources, for instance crops associated to food provision were sourced through markets, peers, and legacy, while other contributions were related to one seed source type, for instance crops associated to attachment were sourced exclusively through legacy. Women relied on a more limited pool of seed sources than men, and they preferentially source seeds from peers. Last, two groups of households were differentiated based on the number of crops contributions and of seed sources they mentioned. Our study brings insights on how the observed social-ecological network patterns affect the access of men and women farmers to NCPs, and the consequences for the maintenance of NCP provision in the face of perturbations. It contributes to unraveling the processes involved in the resilience of small farms that rely on crop diversity for their livelihoods. [Display omitted] • Farmers mobilize a mix of seed sources for key livelihood and cultural benefits including food production and attachment. • Some contributions are exclusively associated with one seed source type, e.g. attachment relates only to legacy. • Women relied on a more limited pool of sources for seeds than men, and mentioned the market and legacy less frequently. • Two groups of households were identified based on differences in contributions and the number of seed sources mentioned. [ABSTRACT FROM AUTHOR]

• Expert opinions were elicited as priors for covariate effects in survival models. • A guideline for expert elicitation of survival data in other contexts is provided. • Multiple experts were combined by 2 methods: pooling and averaging. • Survival of Ixodes ricinus ticks was highly sensitive to desiccation conditions. • Humidity is important to assess climate change impacts on I. ricinus populations. The survival of ectothermic species is heavily dependent on environmental conditions, such as temperature and water balance. Understanding their survival responses to abiotic factors could help predict impacts of climate change on their population dynamics and human health. However, making a statistical inference and formulating a predictive model for mortality rates can be challenging when the observation numbers are limited. This study proposed an expert opinion elicitation framework that integrates expert opinions as prior distributions for the effects of continuous explanatory variables, through a Bayesian Parametric Survival Model (B-PSM). A historical survival dataset of female Ixodes ricinus ticks (Acari: Ixodidae) with small sample size was used. A total of 6 acarologists were recruited as experts for interactive online interview sessions to provide their opinions on average survival time under 4 different temperature and humidity scenarios. Most experts shared similar opinions on the effects of abiotic variables, and none of the experts was confident in the interaction effect. The variation of the opinions across multiple experts was handled by two approaches: 1) pooling and 2) averaging methods. The results showed that the pooling approach retains the variations of expert opinions, it may also disregard some irrelevant opinions to the observed data. While the averaging approach forms a numerical consensus across all the experts, but it may be less informative when the opinions distinctly diverge. The survival time of I. ricinus was found to be best described by the Weibull distribution, suggesting the mortality rate of ticks increases over time (aging effects). Also, the posterior predictions revealed that I. ricinus ticks were susceptible to desiccation conditions, with an interaction effect with the temperature. Therefore, our results suggested that relative humidity is an important factor in the survival of I. ricinus that should not be disregarded when evaluating the impacts of climate change on their population dynamics. Finally, this study provided a guideline for implementing the B-PSM framework to incorporate expert opinions and develop predictive survival models that can be applied in other ecological contexts. [ABSTRACT FROM AUTHOR]

Biomonitoring ecosystems is necessary in order to evaluate risks and to efficiently manage ecosystems and their associated services. Agrosystems are the target of multiple stressors that can affect many species through effects cascading along food webs. However, classic biomonitoring, focused on species diversity or indicator species, might be a poor predictor of the risk of such whole-ecosystem perturbations. Thanks to high-throughput sequencing methods, however, it might be possible to obtain sufficient information about entire ecological communities to infer the functioning of their associated interaction networks, and thus monitor more closely the risk of the collapse of entire food webs due to external stressors. In the course of the 'next-generation biomonitoring' project, we collectively sought to experiment with this idea of inferring ecological networks on the basis of metabarcoding information gathered on different systems. We here give an overview of issues and preliminary results associated with this endeavour and highlight the main difficulties that such next-generation biomonitoring is still facing. Going from sampling protocols up to methods for comparing inferred networks, through biomolecular, bioinformatic, and network inference, we review all steps of the process, with a view towards generality and transferability towards other systems. [ABSTRACT FROM AUTHOR]