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AbstractContinuous processes in most applications are measured discretely with error. This complicates the task of detecting intersections and the number of intersections between two continuous processes (i.e., when the processes have the same value). Intersections of continuous processes are scientifically important, but challenging to estimate from data. For example, in the field of animal ecology, intersections of the paths of moving animals tracked with satellite technologies can be used to understand disease transmission. We illustrate how to quantify contact between animals using telemetry data (i.e., the recorded locations of an animal over time). We introduce our method to quantify contact time with accessible concepts from introductory stochastic process literature, such as Brownian motion. Then, we provide two data examples using white-tailed deer (Odocoileus virginianus) and mule deer (Odocoileus hemionus) telemetry data in a region with high prevalence of chronic wasting disease. Our work provides a needed connection between existing model-based literature for animal movement and rule-based literature for animal interaction. Further, our work illustrates a unique statistical problem receiving minimal attention with broad applicability in human and livestock tracking. [ABSTRACT FROM AUTHOR]

Species' responses to broad‐scale environmental or spatial gradients are typically unimodal. Current models of species' responses along gradients tend to be overly simplistic (e.g., linear, quadratic or Gaussian GLMs), or are suitably flexible (e.g., splines, GAMs) but lack direct ecologically interpretable parameters. We describe a parametric framework for species‐environment non‐linear modelling ('senlm'). The framework has two components: (i) a non‐linear parametric mathematical function to model the mean species response along a gradient that allows asymmetry, flattening/peakedness or bimodality; and (ii) a statistical error distribution tailored for ecological data types, allowing intrinsic mean–variance relationships and zero‐inflation. We demonstrate the utility of this model framework, highlighting the flexibility of a range of possible mean functions and a broad range of potential error distributions, in analyses of fish species' abundances along a depth gradient, and how they change over time and at different latitudes. [ABSTRACT FROM AUTHOR]

Exposure and susceptibility underlie every organism's infection status, and an untold diversity of factors can drive variation in both. Often, both exposure and susceptibility change in response to a given factor, and they can interact, such that their relative contributions to observed disease dynamics are obscured. These independent and interlinked changes often complicate empirical inference in disease ecology and ecoimmunology.Although many disease ecology studies address this problem, it is often implicit rather than explicit and requires a specific set of tools to tackle. Moreover, as yet, there is no established conceptual framework for disentangling susceptibility and exposure processes.Here, we consolidate previous theory and empirical understanding regarding the entwined effects of susceptibility and exposure, which we refer to as 'the Twin Pillar Problem'. We provide a framework for conceptualising exposure–susceptibility interactions, where they obscure, confound, induce or counteract one another, providing some well‐known examples for each complicating mechanism.We synthesise guidelines for anticipating and controlling for covariance between exposure and susceptibility, and we detail statistical and operational methodology that researchers have employed to deal with them. Finally, we discuss novel emerging frontiers in their study in ecology, and their potential for further integration in the fields of wildlife and human health. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Annual vegetation aboveground net primary productivity (ANPP) exhibits a non‐linear dependence on annual precipitation. A common pattern of non‐linearity, called asymmetry, arises when productivity responses in wet years are larger than declines in dry years. To date, ANPP asymmetry has been attributed primarily to vegetation water stress, an internal ecosystem response to precipitation and soil water availability. However, when quantified via the asymmetry index (AI) estimated from productivity measurements, the asymmetry can be a sampling artefact that arises from a positively skewed annual precipitation distribution. In this paper, we aimed to separate the sampling effect (from external precipitation variability) from the non‐linear response of the system (the internal ecosystem dynamics). We constructed a probabilistic model that integrates the precipitation distribution with the precipitation‐productivity response curve (PPT‐ANPP curve), derived using empirical formulae and a process‐based soil water balance model. The model was used to derive the probability density function of AI and to attribute its shape to the PPT distribution and the PPT‐ANPP response curve. The models were compared to data from 47 grasslands. Results demonstrated that positively skewed precipitation produces a positive AI as a statistical artefact. The non‐linear ecosystem PPT‐ANPP dependence can further enhance or dampen this statistical artefact. In all sites, the precipitation skew highly affected the probability of correctly identifying asymmetry using AI. Observed negative asymmetry arises from a larger soil water holding capacity and positive asymmetry from plant water stress. More robust statistical indicators of non‐linear ecological responses to climate variability are needed to improve ecosystem forecasts. [ABSTRACT FROM AUTHOR]

Abstract Species distribution modeling, which allows users to predict the spatial distribution of species with the use of environmental covariates, has become increasingly popular, with many software platforms providing tools to fit such models. However, the species observations used can have varying levels of quality and can have incomplete information, such as uncertain or unknown species identity. In this paper, we develop two algorithms to classify observations with unknown species identities which simultaneously predict several species distributions using spatial point processes. Through simulations, we compare the performance of these algorithms using 7 different initializations to the performance of models fitted using only the observations with known species identity. We show that performance varies with differences in correlation among species distributions, species abundance, and the proportion of observations with unknown species identities. Additionally, some of the methods developed here outperformed the models that did not use the misspecified data. We applied the best‐performing methods to a dataset of three frog species (Mixophyes). These models represent a helpful and promising tool for opportunistic surveys where misidentification is possible or for the distribution of species newly separated in their taxonomy.

The analysis of spatial point patterns has greatly advanced our understanding of ecological processes. However, the methods currently available for analyzing replicated spatial point patterns (RSPPs) are rarely used by ecologists. One barrier to the use of RSPP analyses is a lack of software to implement the approaches that have been developed in the statistical literature. Here, we provide a practical guide to RSPP analysis and introduce the RSPPlme4 R package that implements the approaches we discuss. The methods we outline use a linear modeling framework to link variation in the spatial structure of point patterns to discrete and continuous explanatory covariates. We describe methods for linear models and mixed-effects models of RSPPs, including approaches to estimating confidence intervals via semi-parametric bootstrapping. The syntax for model fitting is similar to that used in linear and linear mixed-effects modeling packages in R. The RSPPlme4 package also allows users to easily plot the results of model fits. We hope that this tutorial will make methods for RSPP analysis accessible to a wide range of ecologists and open new avenues for gaining insight into ecological processes from spatial data.

Species distribution modeling, which allows users to predict the spatial distribution of species with the use of environmental covariates, has become increasingly popular, with many software platforms providing tools to fit such models. However, the species observations used can have varying levels of quality and can have incomplete information, such as uncertain or unknown species identity.In this paper, we develop two algorithms to classify observations with unknown species identities which simultaneously predict several species distributions using spatial point processes. Through simulations, we compare the performance of these algorithms using 7 different initializations to the performance of models fitted using only the observations with known species identity.We show that performance varies with differences in correlation among species distributions, species abundance, and the proportion of observations with unknown species identities. Additionally, some of the methods developed here outperformed the models that did not use the misspecified data. We applied the best‐performing methods to a dataset of three frog species (Mixophyes).These models represent a helpful and promising tool for opportunistic surveys where misidentification is possible or for the distribution of species newly separated in their taxonomy. [ABSTRACT FROM AUTHOR]

Aim: The General Dynamic Model of island biogeography (GDM) explains patterns of species richness on volcanic hotspot archipelagos with respect to island age (T) and area (A), most commonly employing what we here call semi‐log ATT2 and LogATT2 models. These can be considered direct extensions of semi‐log and power law species‐area relationships (SARs), with a hump‐shaped function of T modelling drivers of biodiversity over and above area per se. We compare these and related models and investigate their conceptual and mathematical connections. Location: The Azores, Galapagos, Hawaii, Marquesas, Society Islands, Cape Verde Islands, Canary Islands and Aeolian archipelagos. Taxa: Vascular plants, birds, bryophytes, arthropods and molluscs. Methods: We gathered species richness data from databases and published sources, resulting in 34 datasets for various taxa. We used the Akaike information criterion corrected for small sample size (AICc) to compare the relative performance of models. We analysed the data at archipelago level and across archipelagos by using mixed effect models. We also calculated the relative time of expected peak diversity (Tpeak) from parameter estimates. Results: In almost three‐fourth of the cases the best model overall was one that contained only A or T rather than both predictors. There was greater variance between taxa than between archipelagos. Better dispersed taxa tended to reach peak diversity earlier. The most generally applicable of the models including T were the LogATT2 and LogTT2 models. These have important, previously unrecognized mathematical properties. Main Conclusions: SARs and species–time relationships should always be considered as potentially more parsimonious options. Considering the LogATT2 model as a modification of the power law S = cAz, where a lognormal function in T replaces the constant, c allows the construction of an extended framework for the GDM with incorporation of additional predictors such as measures of environmental heterogeneity and application beyond hotspot archipelagos. [ABSTRACT FROM AUTHOR]

Areas that contain ecologically distinct biological content, called bioregions, are a central component to spatial and ecosystem‐based management. We review and describe a variety of commonly used and newly developed statistical approaches for quantitatively determining bioregions.Statistical approaches to bioregionalization can broadly be classified as two‐stage approaches that either 'Group First, then Predict' or 'Predict First, then Group', or a newer class of one‐stage approaches that simultaneously analyse biological data with reference to environmental data to generate bioregions. We demonstrate these approaches using a selection of methods applied to simulated data and real data on demersal fish. The methods are assessed against their ability to answer several common scientific or management questions.The true number of simulated bioregions was only identified by both of the one‐stage methods and one two‐stage method. When the number of bioregions was known, many of the methods, but not all, could adequately infer the species, environmental and spatial characteristics of bioregions. One‐stage approaches, however, do so directly via a single model without the need for separate post‐hoc analyses and additionally provide an appropriate characterization of uncertainty.One‐stage approaches provide a comprehensive and consistent method for objectively identifying and characterizing bioregions using both biological and environmental data. Potential avenues of future development in one‐stage methods include incorporating presence‐only and multiple data types as well as considering functional aspects of bioregions. [ABSTRACT FROM AUTHOR]

Figure-ground organization refers to the visual perception that a contour separating two regions belongs to one of the regions. Recent studies have found neural correlates of figure-ground assignment in V2 as early as 10-25 ms after response onset, providing strong support for the role of local bottom-up processing. How much information about figure-ground assignment is available from locally computed cues? Using a large collection of natural images, in which neighboring regions were assigned a figure-ground relation by human observers, we quantified the extent to which figural regions locally tend to be smaller, more convex, and lie below ground regions. Our results suggest that these Gestalt cues are ecologically valid, and we quantify their relative power. We have also developed a simple bottom-up computational model of figure-ground assignment that takes image contours as input. Using parameters fit to natural image statistics, the model is capable of matching human-level performance when scene context limited.

ABSTRACT Here, we report our educational approach and learner evaluations of the first 5 years of the Explorations in Data Analysis for Metagenomic Advances in Microbial Ecology (EDAMAME) workshop, held annually at Michigan State University’s Kellogg Biological Station from 2014 to 2018. We hope this information will be useful for others who want to organize computing-intensive workshops and will encourage quantitative skill development among microbiologists. IMPORTANCE High-throughput sequencing and related statistical and bioinformatic analyses have become routine in microbiology in the past decade, but there are few formal training opportunities to develop these skills. A weeklong workshop can offer sufficient time for novices to become introduced to best computing practices and common workflows in sequence analysis. We report our experiences in executing such a workshop targeted to professional learners (graduate students, postdoctoral scientists, faculty, and research staff).

Globularia is a genus of small evergreen and perennial shrubs that are widespread in Europe. Globularia section Empetron includes a group of three species, G. cordifolia, G. meridionalis, and G. neapolitana, that have been taxonomically disputed for more than 150 years. Many morphological features have been proposed to discriminate these species. Nevertheless, evidence from both past and recent literature suggest that these differences among species are not consistent. In order to shed new light in this long-disputed group, we investigated 10 populations of the G. cordifolia species complex with both classical and geometric morphometrics and used environmental predictors in multivariate regression to explain patterns of variation. Our results showed that bract area and calyx teeth length are correlated with solar radiation and annual precipitation, whereas leaf dry mass per unit area can be explained by temperature seasonality. Leaf shape can be explained by temperature seasonality as well, although with a lower amount of explanatory and predictive power. Despite a comparatively low sample size in terms of populations, our results were based on a large number of individuals and were supported by a robust statistical approach. We can conclude that differences among the three species of Globularia could be related to the combined effects of several ecological variables and might not have taxonomical value. Our novel approach provided an ecological interpretation on a species complex that makes up a continuum of forms within the environmental framework of the Mediterranean basin.

With an unprecedented growth in the biomedical literature, keeping up to date with the new developments presents an immense challenge. Publications are often studied in isolation of the established literature, with interpretation being subjective and often introducing human bias. With ontology-driven annotation of biomedical data gaining popularity in recent years and online databases offering metatags with rich textual information, it is now possible to automatically text-mine ontological terms and complement the laborious task of manual management, interpretation, and analysis of the accumulated literature with downstream statistical analysis. In this paper, we have formulated an automated workflow through which we have identified ontological information, including nutrition-related terms in PubMed abstracts (from 1991 to 2016) for two main types of Inflammatory Bowel Diseases: Crohn’s Disease and Ulcerative Colitis; and two other gastrointestinal (GI) diseases, namely, Coeliac Disease and Irritable Bowel Syndrome. Our analysis reveals unique clustering patterns as well as spatial and temporal trends inherent to the considered GI diseases in terms of literature that has been accumulated so far. Although automated interpretation cannot replace human judgement, the developed workflow shows promising results and can be a useful tool in systematic literature reviews. The workflow is available at https://github.com/KociOrges/pytag.

With an unprecedented growth in the biomedical literature, keeping up to date with the new developments presents an immense challenge. Publications are often studied in isolation of the established literature, with interpretation being subjective and often introducing human bias. With ontology-driven annotation of biomedical data gaining popularity in recent years and online databases offering metatags with rich textual information, it is now possible to automatically text-mine ontological terms and complement the laborious task of manual management, interpretation, and analysis of the accumulated literature with downstream statistical analysis. In this paper, we have formulated an automated workflow through which we have identified ontological information, including nutrition-related terms in PubMed abstracts (from 1991 to 2016) for two main types of Inflammatory Bowel Diseases: Crohn's Disease and Ulcerative Colitis; and two other gastrointestinal (GI) diseases, namely, Coeliac Disease and Irritable Bowel Syndrome. Our analysis reveals unique clustering patterns as well as spatial and temporal trends inherent to the considered GI diseases in terms of literature that has been accumulated so far. Although automated interpretation cannot replace human judgement, the developed workflow shows promising results and can be a useful tool in systematic literature reviews. The workflow is available at https://github.com/KociOrges/pytag. [ABSTRACT FROM AUTHOR]

The area under the receiver operating characteristic (ROC) curve, known as the AUC, is currently considered to be the standard method to assess the accuracy of predictive distribution models. It avoids the supposed subjectivity in the threshold selection process, when continuous probability derived scores are converted to a binary presence–absence variable, by summarizing overall model performance over all possible thresholds. In this manuscript we review some of the features of this measure and bring into question its reliability as a comparative measure of accuracy between model results. We do not recommend using AUC for five reasons: (1) it ignores the predicted probability values and the goodness-of-fit of the model; (2) it summarises the test performance over regions of the ROC space in which one would rarely operate; (3) it weights omission and commission errors equally; (4) it does not give information about the spatial distribution of model errors; and, most importantly, (5) the total extent to which models are carried out highly influences the rate of well-predicted absences and the AUC scores. [ABSTRACT FROM AUTHOR]

Abstract: This study considers advanced statistical approaches for sequential data assimilation. These are explored in the context of nowcasting and forecasting using nonlinear differential equation based marine ecosystem models assimilating sparse and noisy non-Gaussian multivariate observations. The statistical framework uses a state space model with the goal of estimating the time evolving probability distribution of the ecosystem state. Assimilation of observations relies on stochastic dynamic prediction and Bayesian principles. In this study, a new sequential data assimilation approach is introduced based on Markov Chain Monte Carlo (MCMC). The ecosystem state is represented by an ensemble, or sample, from which distributional properties, or summary statistical measures, can be derived. The Metropolis-Hastings based MCMC approach is compared and contrasted with two other sequential data assimilation approaches: sequential importance resampling, and the (approximate) ensemble Kalman filter (including computational comparisons). A simple illustrative application is provided based on a 0-D nonlinear plankton ecosystem model with multivariate non-Gaussian observations of the ecosystem state from a coastal ocean observatory. The MCMC approach is shown to be straightforward to implement and to effectively characterize the non-Gaussian ecosystem state in both nowcast and forecast experiments. Results are reported which illustrate how non-Gaussian information originates, and how it can be used to characterize ecosystem properties. [Copyright &y& Elsevier]

This article studies biodiversity and modernization in four coffee-producing villages of Mexico. The four villages share many social and cultural traits of Indian communities in Mexico, which translate into a strong sense of both ethnic and community identity, but they differ in agrarian structure, as a result of diverse historical circumstances. Fieldwork was carried out in the four villages in 1998. All of the villages are situated in a tropical, mountainous environment, but they differ in the prevalent precipitation regime. Morpho-species were collected and named using local taxonomy. Researchers worked with a key informant, always the same in a given village, who helped with sampling and identified plants with native names.

The article discusses the use of distance-based multivariate control chart method for ecological and environmental monitoring which has become important with increasing threats from human disturbances. Some examples of the use of the method in monitoring coral reef fish assemblages of the Great Barrier Reef, Australia is outlined. The design of the method to identify impacts at individual sites as quickly as possible is also explored.

Seedling mortality in tree populations limits population growth rates and controls the diversity of forests. To learn about seedling mortality, ecologists use repeated censuses of forest quadrats to determine the number of tree seedlings that have survived from the previous census and to find new ones. Typically, newly found seedlings are marked with flags. But flagging is labor intensive and limits the spatial and temporal coverage of such studies. The alternative of not flagging has the advantage of ease but suffers from two main disadvantages. It complicates the analysis and loses information. The contributions of this article are (i) to introduce a method for using unflagged census data to learn about seedling mortality and (ii) to quantify the information loss so ecologists can make informed decisions about whether to flag. Based on presented results, we believe that not flagging is often the preferred alternative. The labor saved by not flagging can be used to better advantage in extending the coverage of the study. [ABSTRACT FROM AUTHOR]

The article proposes a statistical method for selecting among competing cluster models to use in the identification of assemblages for the protection of whole assemblages of species. An objective way to discriminate among competing models is provided by the method to determine the model that corresponds to the available data. The potential of the method to be used as a tool for the resolution of environmental disputes regarding the presence of community at a particular place and time is noted.

Coffee cultivation in Mexico is important both to people's livelihood and to the conservation of plant species richness. Management ranges from traditional shaded coffee garden to "modern" unshaded monoculture. Recognizing the importance of both livelihoods and biodiversity, we examined how the plant diversity of coffee gardens was affected by a certain form of "modernization," which, from 1974 to 1988, was strongly promoted through the intervention of the Mexican State and international development agencies: that is, planting high-yield varieties (HYV) with little or no shade and using chemical fertilizers and herbicides. Our research also sought to use ecological statistical approaches to understand modifications in a traditional human ecosystem. It centered on two questions: (1) How do differences in coffee production, along a gradient of modernization, affect plant species richness? (2) What is the relative importance of ecological vs. socioeconomic factors in explaining plant diversity in individual coffee gardens? To answer these questions, we compared floristic diversity of four coffee-growing villages differing in ecological context and in the degree to which they were modernized. The two poles of our traditional/modern gradient are the villages of Tierra Colorada, where the traditional Mexican variety of coffee bushes, called café criollo ("creole coffee") is not grown anymore, and San Lorenzo, where all the farms that we studied only grew criollo coffee. Discriminant analysis suggests that modernization can be viewed as a syndrome of traits, among which farmer's education and household size are important components. Overall, the small coffee gardens studied harbor high plant species richness. Our results show a significant negative effect of modernization on plant species richness in San Miguel, one of the four villages studied. Although ecological characteristics were prevalent in explaining species richness, the redundancy analyses (RDA) emphasized the complementary importance of socioeconomic factors in explaining variation in plant species richness in coffee gardens within a given community. Apparently, this importance increases as modernization sets in. In order words, when coffee gardens are managed in a traditional way, as in San Lorenzo, ecological factors are sufficient to explain most variation in species richness. However, in villages where cultivation includes modern practices, these practices exert a direct impact on species richness. The key loading factors for the socioeconomic RDAs were coffee variety, fertilizer, and age of coffee bushes. Our successful methodological approach suggests that numerical ecology offers promising tools for the analysis of human impacted ecosystems.

Globularia is a genus of small evergreen and perennial shrubs that are widespread in Europe. Globularia section Empetron includes a group of three species, G. cordifolia, G. meridionalis, and G. neapolitana, that have been taxonomically disputed for more than 150 years. Many morphological features have been proposed to discriminate these species. Nevertheless, evidence from both past and recent literature suggest that these differences among species are not consistent. In order to shed new light in this long-disputed group, we investigated 10 populations of the G. cordifolia species complex with both classical and geometric morphometrics and used environmental predictors in multivariate regression to explain patterns of variation. Our results showed that bract area and calyx teeth length are correlated with solar radiation and annual precipitation, whereas leaf dry mass per unit area can be explained by temperature seasonality. Leaf shape can be explained by temperature seasonality as well, although with a lower amount of explanatory and predictive power. Despite a comparatively low sample size in terms of populations, our results were based on a large number of individuals and were supported by a robust statistical approach. We can conclude that differences among the three species of Globularia could be related to the combined effects of several ecological variables and might not have taxonomical value. Our novel approach provided an ecological interpretation on a species complex that makes up a continuum of forms within the environmental framework of the Mediterranean basin. [ABSTRACT FROM AUTHOR]

Here, we report our educational approach and learner evaluations of the first 5 years of the Explorations in Data Analysis for Metagenomic Advances in Microbial Ecology (EDAMAME) workshop, held annually at Michigan State University's Kellogg Biological Station from 2014 to 2018. We hope this information will be useful for others who want to organize computing-intensive workshops and will encourage quantitative skill development among microbiologists. IMPORTANCE High-throughput sequencing and related statistical and bioinformatic analyses have become routine in microbiology in the past decade, but there are few formal training opportunities to develop these skills. A weeklong workshop can offer sufficient time for novices to become introduced to best computing practices and common workflows in sequence analysis. We report our experiences in executing such a workshop targeted to professional learners (graduate students, postdoctoral scientists, faculty, and research staff)., (Copyright © 2019 Shade et al.)