Search results for "Statistics & Probability"
showing 10 items of 436 documents
Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea)
2021
This work presents a computational methodology able to automatically classify the echoes of two krill species recorded in the Ross sea employing scientific echo-sounder at three different frequencies (38, 120 and 200 kHz). The goal of classifying the gregarious species represents a time-consuming task and is accomplished by using differences and/or thresholds estimated on the energy features of the insonified targets. Conversely, our methodology takes into account energy, morphological and depth features of echo data, acquired at different frequencies. Internal validation indices of clustering were used to verify the ability of the clustering in recognizing the correct number of species. Th…
Assessing the spatiotemporal persistence of fish distributions: a case study on two red mullet species (Mullus surmuletus and M. barbatus) in the wes…
2020
Understanding the spatiotemporal persistence of fish distributions is key to defining fish hotspots and effective fisheries-restricted areas (FRAs). Hierarchical Bayesian spatiotemporal models provide an excellent framework to understand these distributions, as they can accommodate different spatiotemporal behaviour in the data, primarily due to their flexibility. The aim of this research was to characterize the fundamental behavioural patterns of fish as persistent, opportunistic or progressive by comparing different spatiotemporal model structures in order to provide better information for marine spatial planning. To illustrate this method, the spatiotemporal distributions of 2 sympatric …
Efficient estimation of generalized linear latent variable models.
2019
Generalized linear latent variable models (GLLVM) are popular tools for modeling multivariate, correlated responses. Such data are often encountered, for instance, in ecological studies, where presence-absences, counts, or biomass of interacting species are collected from a set of sites. Until very recently, the main challenge in fitting GLLVMs has been the lack of computationally efficient estimation methods. For likelihood based estimation, several closed form approximations for the marginal likelihood of GLLVMs have been proposed, but their efficient implementations have been lacking in the literature. To fill this gap, we show in this paper how to obtain computationally convenient estim…
Testing hypotheses in evolutionary ecology with imperfect detection: capture-recapture structural equation modeling.
2012
8 pages; International audience; Studying evolutionary mechanisms in natural populations often requires testing multifactorial scenarios of causality involving direct and indirect relationships among individual and environmental variables. It is also essential to account for the imperfect detection of individuals to provide unbiased demographic parameter estimates. To cope with these issues, we developed a new approach combining structural equation models with capture-recapture models (CR-SEM) that allows the investigation of competing hypotheses about individual and environmental variability observed in demographic parameters. We employ Markov chain Monte Carlo sampling in a Bayesian frame…
Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
2020
Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental da…
Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests
2021
We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points x affects another set of points y but not vice versa. We use the model to investigate the effect of large trees to the locations of seedlings. In the model, every point in x has a parametric influence kernel or signal, which together form an influence field. Conditionally on the parameters, the influence field acts as a spatial covariate in the intensity of the model, and the intensity itself is a non-linear function of the parameters. Points outside the observation window may affect the influence field inside the window. We propose an edge correction to account for this missing data. The par…
Variational Approximations for Generalized Linear Latent Variable Models
2017
Generalized linear latent variable models (GLLVMs) are a powerful class of models for understanding the relationships among multiple, correlated responses. Estimation, however, presents a major challenge, as the marginal likelihood does not possess a closed form for nonnormal responses. We propose a variational approximation (VA) method for estimating GLLVMs. For the common cases of binary, ordinal, and overdispersed count data, we derive fully closed-form approximations to the marginal log-likelihood function in each case. Compared to other methods such as the expectation-maximization algorithm, estimation using VA is fast and straightforward to implement. Predictions of the latent variabl…
Spatio-Temporal model structures with shared components for semi-continuous species distribution modelling
2017
Abstract Understanding the spatio-temporal dynamism and environmental relationships of species is essential for the conservation of natural resources. Many spatio-temporally sampled processes result in continuous positive [ 0 , ∞ ) abundance datasets that have many zero values observed in areas that lie outside their optimum niche. In such cases the most common option is to use two-part or hurdle models, which fit independent models and consequently independent environmental effects to occurrence and conditional-to-presence abundance. This may be correct in some cases, but not as much in others where the detection probability is related to the abundance. The aim of this work is to infer the…
An Algebraic Derivation of Chao’s Estimator of the Number of Species in a Community Highlights the Condition Allowing Chao to Deliver Centered Estima…
2014
Anne Chao proposed a very popular, nonparametric estimator of the species richness of a community, on the basis of a limited size sampling of this community. This expression was originally derived on a statistical basis as a lower-bound estimate of the number of missing species in the sample and provides accordingly a minimal threshold for the estimation of the total species richness of the community. Hereafter, we propose an alternative, algebraic derivation of Chao’s estimator, demonstrating thereby that Chao’s formulation may also provide centered estimates (and not only a lower bound threshold), provided that the sampled communities satisfy a specific type of SAD (species abundance dist…
Estimation of local extinction rates when species detectability covaries with extinction probability: is it a problem ?
2006
Estimating the rate of change of the composition of communities is of direct interest to address many fundamental and applied questions in ecology. One methodological problem is that it is hard to detect all the species present in a community. Nichols et al. presented an estimator of the local extinction rate that takes into account species probability of detection, but little information is available on its performance. However, they predicted that if a covariance between species detection probability and local extinction rate exists in a community, the estimator of local extinction rate complement would be positively biased. Here, we show, using simulations over a wide range of parameters…