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Ecologists have long sought to understand the dynamics of populations and communities by deriving mathematical theory from first principles. Theoretical models often take the form of dynamical equations that comprise the ecological processes (e.g. competition, predation) believed to govern system dynamics. The inverse of this approach-inferring which processes and ecological interactions drive observed dynamics-remains an open problem in ecology. Here, we propose a way to attack this problem using a machine learning method known as symbolic regression, which seeks to discover relationships in time-series data and to express those relationships using dynamical equations. We found that this method could rapidly discover models that explained most of the variance in three classic demographic time series. More importantly, it reverse-engineered the models previously proposed by theoretical ecologists to describe these time series, capturing the core ecological processes these models describe and their functional forms. Our findings suggest a potentially powerful new way to merge theory development and data analysis., (© 2018 The Author(s).)

In a (2016) paper in this journal, I defuse allegations that theoretical ecological research is problematic because it relies on teleological metaphysical assumptions. Mark Sagoff offers a formal reply. In it, he concedes that I succeeded in establishing that ecologists abandoned robust teleological views long ago and that they use teleological characterizations as metaphors that aid in developing mechanistic explanations of ecological phenomena. Yet, he contends that I did not give enduring criticisms of theoretical ecology a fair shake in my paper. He says this is because enduring criticisms center on concerns about the nature of ecological networks and forces, the instrumentality of ecological laws and theoretical models, and the relation between theoretical and empirical methods in ecology that that paper does not broach. Below I set apart the distinct criticisms Sagoff presents in his commentary and respond to each in turn., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The world's leading environmental advisory institutions look to ecological theory and research as an objective guide for policy and resource management decision-making. In addition to the theoretical and broadly philosophical merits of doing so, it is therefore practically significant to clear up confusions about ecology's conceptual foundations and to clarify the basic workings of inferential methods used in the science. Through discussion of key moments in the genesis of the theoretical branch of ecology, this essay elucidates a general heuristic role of teleological metaphors in ecological research and defuses certain enduring confusions about work in ecology., (Copyright © 2016. Published by Elsevier Ltd.)

In the Anthropocene, intensifying ecological disturbances pose significant challenges to our predictive capabilities for ecosystem responses. Macroecology-which focuses on emergent statistical patterns in ecological systems-unveils consistent regularities in the organization of biodiversity and ecosystems. These regularities appear in terms of abundance, body size, geographical range, species interaction networks, or the flux of matter and energy. This paper argues for moving beyond qualitative resilience metaphors, such as the 'ball and cup', towards a more quantitative macroecological framework. We suggest a conceptual and theoretical basis for ecological resilience that integrates macroecology with a stochastic diffusion approximation constrained by principles of biological symmetry. This approach provides an alternative novel framework for studying ecological resilience in the Anthropocene. We demonstrate how our framework can effectively quantify the impacts of major disturbances and their extensive ecological ramifications. We further show how biological scaling insights can help quantify the consequences of major disturbances, emphasizing their cascading ecological impacts. The nature of these impacts prompts a re-evaluation of our understanding of resilience. Emphasis on regularities of ecological assemblages can help illuminate resilience dynamics and offer a novel basis to predict and manage the impacts of disturbance in the Anthropocene more efficiently. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Ecologists have long sought to understand the dynamics of populations and communities by deriving mathematical theory from first principles. Theoretical models often take the form of dynamical equations that comprise the ecological processes (e.g. competition, predation) believed to govern system dynamics. The inverse of this approach-inferring which processes and ecological interactions drive observed dynamics-remains an open problem in ecology. Here, we propose a way to attack this problem using a machine learning method known as symbolic regression, which seeks to discover relationships in time-series data and to express those relationships using dynamical equations. We found that this method could rapidly discover models that explained most of the variance in three classic demographic time series. More importantly, it reverse-engineered the models previously proposed by theoretical ecologists to describe these time series, capturing the core ecological processes these models describe and their functional forms. Our findings suggest a potentially powerful new way to merge theory development and data analysis.

Abstract Ecosystems are subject to a multitude of anthropogenic environmental changes. Experimental research in the field of multiple stressors has typically involved varying the number of stressors, here termed stressor richness, but without controlling for total stressor intensity. Mistaking stressor intensity effects for stressor richness effects can misinform management decisions when there is a trade‐off between mitigating these two factors. We incorporate multiple stressors into three community models and show that, at a fixed total stressor intensity, increasing stressor richness aggravates joint stressor effects on ecosystem functioning, but reduces effects on species persistence and composition. In addition, stressor richness weakens the positive selection and negative complementarity effects on ecosystem function. We identify the among‐species variation of stressor effects on traits as a key determinant of the resulting community‐level stressor effects. Taken together, our results unravel the mechanisms linking multiple environmental changes to biodiversity and ecosystem function.

In nature, the interaction between pathogens and their hosts is only one of a handful of interaction relationships between species, including parasitism, predation, competition, symbiosis, commensalism, and among others. From a non-anthropocentric view, parasitism has relatively fewer essential differences from the other relationships; but from an anthropocentric view, parasitism and predation against humans and their well-beings and belongings are frequently related to heinous diseases. Specifically, treating (managing) diseases of humans, crops and forests, pets, livestock, and wildlife constitute the so-termed medical enterprises (sciences and technologies) humans endeavor in biomedicine and clinical medicine, veterinary, plant protection, and wildlife conservation. In recent years, the significance of ecological science to medicines has received rising attentions, and the emergence and pandemic of COVID-19 appear accelerating the trend. The facts that diseases are simply one of the fundamental ecological relationships in nature, and the study of the relationships between species and their environment is a core mission of ecology highlight the critical importance of ecological science. Nevertheless, current studies on the ecology of medical enterprises are highly fragmented. Here, we (i) conceptually overview the fields of disease ecology of wildlife, cancer ecology and evolution, medical ecology of human microbiome-associated diseases and infectious diseases, and integrated pest management of crops and forests, across major medical enterprises. (ii) Explore the necessity and feasibility for a unified medical ecology that spans biomedicine, clinical medicine, veterinary, crop (forest and wildlife) protection, and biodiversity conservation. (iii) Suggest that a unified medical ecology of human diseases is both necessary and feasible, but laissez-faire terminologies in other human medical enterprises may be preferred. (iv) Suggest that the evo-eco paradigm for cancer research can play a similar role of evo-devo in evolutionary developmental biology. (v) Summarized 40 key ecological principles/theories in current disease-, cancer-, and medical-ecology literatures. (vi) Identified key cross-disciplinary discovery fields for medical/disease ecology in coming decade including bioinformatics and computational ecology, single cell ecology, theoretical ecology, complexity science, and the integrated studies of ecology and evolution. Finally, deep understanding of medical ecology is of obvious importance for the safety of human beings and perhaps for all living things on the planet.

The grand ambition of theorists studying ecology and evolution is to discover the logical and mathematical rules driving the world's biodiversity at every level from genetic diversity within species to differences between populations, communities, and ecosystems. This ambition has been difficult to realize in great part because of the complexity of biodiversity. Theoretical work has led to a complex web of theories, each having non-obvious consequences for other theories. Case in point, the recent realization that genetic diversity involves a great deal of temporal and spatial stochasticity forces theoretical population genetics to consider abiotic and biotic factors generally reserved to ecosystem ecology. This interconnectedness may require theoretical scientists to adopt new techniques adapted to reason about large sets of theories. Mathematicians have solved this problem by using formal languages based on logic to manage theorems. However, theories in ecology and evolution are not mathematical theorems, they involve uncertainty. Recent work in Artificial Intelligence in bridging logic and probability theory offers the opportunity to build rich knowledge bases that combine logic's ability to represent complex mathematical ideas with probability theory's ability to model uncertainty. We describe these hybrid languages and explore how they could be used to build a unified knowledge base of theories for ecology and evolution.

Most models of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations to account for observed slowing of growth rate only at higher densities due to limited resources such as space and nutrients. However, recent preclinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale positively with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models, however, tumor growth data are limited by the lower limit of detection (i.e., a measurable lesion) and confounding variables, such as tumor microenvironment, and immune responses may cause and mask deviations from exponential growth models. In this work, we present alternative growth models to investigate the presence of an Allee effect in cancer cells seeded at low cell densities in a controlled in vitro setting. We propose a stochastic modeling framework to disentangle expected deviations due to small population size stochastic effects from cooperative growth and use the moment approach for stochastic parameter estimation to calibrate the observed growth trajectories. We validate the framework on simulated data and apply this approach to longitudinal cell proliferation data of BT-474 luminal B breast cancer cells. We find that cell population growth kinetics are best described by a model structure that considers the Allee effect, in that the birth rate of tumor cells increases with cell number in the regime of small population size. This indicates a potentially critical role of cooperative behavior among tumor cells at low cell densities with relevance to early stage growth patterns of emerging and relapsed tumors. [ABSTRACT FROM AUTHOR]

Whether a species can be defined as 'endemic' or not is very controversial in theoretical terms, because the concepts of 'endemism' and 'area of endemism' remain much debated among scientists. We propose that it is necessary to consider the body size scale of an organism and the breadth of its distributional range if we want to define a given species as 'endemic' or not. Thus, for instance, Madagascar can be an appropriate area of endemism for animals as large and vagile as the fossa (Cryptoprocta ferox), but it is much too large to be considered an ecologically appropriate 'area of endemism' for small-sized frogs with limited dispersal abilities. Instead, for these small species it is ecologically more appropriate to consider given forest regions within Madagascar as 'areas of endemism'. Therefore, we propose a five-step approach in order to define whether a given species can be considered endemic or not within a set of potential candidate species, and we offer a suite of practical examples (African squirrels, African Artiodactyla and a family of freshwater turtles from the Americas) to elucidate the designated concept. We define the new concept presented herein as 'scale-dependent functional endemism'. The new concept has the benefits of including the 'ecological characteristics' of the target species in the concept of 'endemism' and being easily repeated, because it is based on rather objective criteria. [ABSTRACT FROM AUTHOR]

Mathematical models have been used successfully at diverse scales of biological organization, ranging from ecology and population dynamics to stochastic reaction events occurring between individual molecules in single cells. Generally, many biological processes unfold across multiple scales, with mutations being the best studied example of how stochasticity at the molecular scale can influence outcomes at the population scale. In many other contexts, however, an analogous link between micro- and macro-scale remains elusive, primarily due to the challenges involved in setting up and analyzing multi-scale models. Here, we employ such a model to investigate how stochasticity propagates from individual biochemical reaction events in the bacterial innate immune system to the ecology of bacteria and bacterial viruses. We show analytically how the dynamics of bacterial populations are shaped by the activities of immunity-conferring enzymes in single cells and how the ecological consequences imply optimal bacterial defense strategies against viruses. Our results suggest that bacterial populations in the presence of viruses can either optimize their initial growth rate or their population size, with the first strategy favoring simple immunity featuring a single restriction modification system and the second strategy favoring complex bacterial innate immunity featuring several simultaneously active restriction modification systems. [ABSTRACT FROM AUTHOR]

Water point mapping databases, generated through surveys of water sources such as wells and boreholes, are now available in many low and middle income countries, but often suffer from incomplete coverage. To address the partial coverage in such databases and gain insights into spatial patterns of water resource use, this study investigated the use of a maximum entropy (MaxEnt) approach to predict the geospatial distribution of drinking-water sources, using two types of unimproved sources in Kenya as illustration. Geographic locations of unprotected dug wells and surface water sources derived from the Water Point Data Exchange (WPDx) database were used as inputs to the MaxEnt model alongside geological/hydrogeological and socio-economic covariates. Predictive performance of the MaxEnt models was high (all > 0.9) based on Area Under the Receiver Operator Curve (AUC), and the predicted spatial distribution of water point was broadly consistent with household use of these unimproved drinking-water sources reported in household survey and census data. In developing countries where geospatial datasets concerning drinking-water sources often have necessarily limited resolution or incomplete spatial coverage, the modelled surface can provide an initial indication of the geography of unimproved drinking-water sources to target unserved populations and assess water source vulnerability to contamination and hazards. [ABSTRACT FROM AUTHOR]

Researchers and managers are often interested in monitoring the underlying state of a population (e.g., abundance), yet error in the observation process might mask underlying changes due to imperfect detection and availability for sampling. Additional heterogeneity can be introduced into a monitoring program when male-based surveys are used as an index for the total population. Often, male-based surveys are used for avian species, as males are conspicuous and more easily monitored than females. To determine if male-based lek surveys capture changes or trends in population abundance based on female survival and reproduction, we developed a virtual ecologist approach using the lesser prairie-chicken (Tympanuchus pallidicinctus) as an example. Our approach used an individual-based model to simulate lek counts based on female vital rate data, included models where detection and lek attendance probabilities were <1, and was analyzed using both unadjusted counts and an N-mixture model to compare estimates of population abundance and growth rates. Using lek counts to estimate population growth rates without accounting for detection probability or density-based lek attendance consistently biased population growth rates and abundance estimates. Our results therefore suggest that lek-based surveys used without accounting for lek attendance and detection probability may miss important trends in population changes. Rather than population-level inference, lek-based surveys not accounting for lek attendance and detection probability may instead be better for inferring broad-scale range shifts of lesser prairie-chicken populations in a presence/absence framework. [ABSTRACT FROM AUTHOR]

Research in applied ecology provides scientific evidence to guide conservation policy and management. Applied ecology is becoming increasingly quantitative and model selection via information criteria has become a common statistical modeling approach. Unfortunately, parameters that contain little to no useful information are commonly presented and interpreted as important in applied ecology. I review the concept of an uninformative parameter in model selection using information criteria and perform a literature review to measure the prevalence of uninformative parameters in model selection studies applying Akaike’s Information Criterion (AIC) in 2014 in four of the top journals in applied ecology (Biological Conservation, Conservation Biology, Ecological Applications, Journal of Applied Ecology). Twenty-one percent of studies I reviewed applied AIC metrics. Many (31.5%) of the studies applying AIC metrics in the four applied ecology journals I reviewed had or were very likely to have uninformative parameters in a model set. In addition, more than 40% of studies reviewed had insufficient information to assess the presence or absence of uninformative parameters in a model set. Given the prevalence of studies likely to have uninformative parameters or with insufficient information to assess parameter status (71.5%), I surmise that much of the policy recommendations based on applied ecology research may not be supported by the data analysis. I provide four warning signals and a decision tree to assist authors, reviewers, and editors to screen for uninformative parameters in studies applying model selection with information criteria. In the end, careful thinking at every step of the scientific process and greater reporting standards are required to detect uninformative parameters in studies adopting an information criteria approach. [ABSTRACT FROM AUTHOR]

This work aims to discuss a predator-prey system with distributed delay. Various conditions are presented to ensure the existence and global asymptotic stability of positive periodic solution of the involved model. The method is based on coincidence degree theory and the idea of Lyapunov function. At last, simulation results are presented to show the correctness of theoretical findings. [ABSTRACT FROM AUTHOR]

Interest in host-associated microbiomes has skyrocketed recently, yet our ability to explain microbiome variation has remained stubbornly low. Considering scales of interaction beyond the level of the individual host could lead to new insights. Metacommunity theory has many of the tools necessary for modeling multiscale processes and has been successfully applied to host microbiomes. However, the biotic nature of the host requires an expansion of theory to incorporate feedback between the habitat patch (host) and their local (microbial) community. This feedback can have unexpected effects, is predicted to be common, and can arise through a variety of mechanisms, including developmental, ecological, and evolutionary processes. We propose a new way forward for both metacommunity theory and host microbiome research that incorporates this feedback. Highlights Identifying the causes of variation among host microbiomes is an important unanswered question in human biology, and it is essentially an ecological question. Incorporating microbial transmission among hosts has the potential to increase our understanding of host microbiome variation. Existing metacommunity theory provides a framework for including transmission and other scale-related issues in microbiome science. Including behavioral, developmental, and evolutionary feedback between the host and the microbiome into metacommunity theory is likely necessary to fully understand microbiome variation. The feedback between the hosts and other potential reservoirs of microbial species, such as the abiotic environment or hosts of other species, can alter metacommunity predictions. [ABSTRACT FROM AUTHOR]

Predator–prey relationships are a central component of community dynamics. Classic approaches have tried to understand and predict these relationships in terms of consumptive interactions between predator and prey species, but characterizing the interaction this way is insufficient to predict the complexity and context dependency inherent in predator–prey relationships. Recent approaches have begun to explore predator–prey relationships in terms of an evolutionary-ecological game in which predator and prey adapt to each other through reciprocal interactions involving context-dependent expression of functional traits that influence their biomechanics. Functional traits are defined as any morphological, behavioral, or physiological trait of an organism associated with a biotic interaction. Such traits include predator and prey body size, predator and prey personality, predator hunting mode, prey mobility, prey anti-predator behavior, and prey physiological stress. Here, I discuss recent advances in this functional trait approach. Evidence shows that the nature and strength of many interactions are dependent upon the relative magnitude of predator and prey functional traits. Moreover, trait responses can be triggered by non-consumptive predator–prey interactions elicited by responses of prey to risk of predation. These interactions in turn can have dynamic feedbacks that can change the context of the predator–prey interaction, causing predator and prey to adapt their traits—through phenotypically plastic or rapid evolutionary responses—and the nature of their interaction. Research shows that examining predator–prey interactions through the lens of an adaptive evolutionary-ecological game offers a foundation to explain variety in the nature and strength of predator–prey interactions observed in different ecological contexts.

In the last few million years, the hominin brain more than tripled in size. Comparisons across evolutionary lineages suggest that this expansion may be part of a broader trend toward larger, more complex brains in many taxa. Efforts to understand the evolutionary forces driving brain expansion have focused on climatic, ecological, and social factors. Here, building on existing research on learning, we analytically and computationally model the predictions of two closely related hypotheses: The Cultural Brain Hypothesis and the Cumulative Cultural Brain Hypothesis. The Cultural Brain Hypothesis posits that brains have been selected for their ability to store and manage information, acquired through asocial or social learning. The model of the Cultural Brain Hypothesis reveals relationships between brain size, group size, innovation, social learning, mating structures, and the length of the juvenile period that are supported by the existing empirical literature. From this model, we derive a set of predictions—the Cumulative Cultural Brain Hypothesis—for the conditions that favor an autocatalytic take-off characteristic of human evolution. This narrow evolutionary pathway, created by cumulative cultural evolution, may help explain the rapid expansion of human brains and other aspects of our species’ life history and psychology. [ABSTRACT FROM AUTHOR]

McPherson’s Blau space and affiliation ecology model is a powerful tool for analyzing the ecological competition among social entities, such as organizations, along a combination of sociodemographic characteristics of their members. In this paper we introduce the R-based Graphical User Interface (GUI) package Blaunet, an integrated set of tools to calculate, visualize, and analyze the statuses of individuals and social entities in Blau space, parameterized by multiple sociodemographic traits as dimensions. The package is able to calculate the Blau statuses at the nodal, dyadic, and meso levels based on three types of information: sociodemographic characteristics, group affiliations (e.g., membership in groups/organizations), and network ties. To facilitate this, Blaunet has the following five main capabilities, it can: 1) identify a list of possible salient dimensions; 2) calculate, plot, and analyze niches for social entities by measuring the social distance along the salient dimensions between individuals affiliated with them; 3) generate Blau bubbles for individuals, thereby allowing the study of interpersonal influence of similar others even with limited or no network information; 4) capture niche dynamics cross-sectionally by calculating the intensity of exploitation from the carrying capacity and the membership rate; and 5) analyze the niche movement longitudinally by estimating the predicted niche movement equations. We illustrate these capabilities of Blaunet with example datasets. [ABSTRACT FROM AUTHOR]

Promoting health equity is a fundamental public health objective, yet health disparities remain largely overlooked in studies of vectorborne diseases, especially those transmitted by ticks. We sought to identify health disparities associated with Lyme disease and human monocytic ehrlichiosis, two of the most pervasive tickborne diseases within the United States. We used general linear mixed models to measure associations between county-level disease incidence and six variables representing racial/ethnic and socioeconomic characteristics of counties (percent white non-Hispanic; percent with a bachelors degree or higher; percent living below the poverty line; percent unemployed; percent of housing units vacant; per capita number of property crimes). Two ecological variables important to tick demography (percent forest cover; density of white-tailed deer) were included in secondary analyses to contextualize findings. Analyses included data from 2,695 counties in 37 states and the District of Columbia during 2007–2013. Each of the six variables was significantly associated with the incidence of one or both diseases, but the direction and magnitude of associations varied by disease. Results suggested that the incidence of Lyme disease was highest in counties with relatively higher proportions of white and more educated persons and lower poverty and crime rates; the incidence of human monocytic ehrlichiosis was highest in counties with relatively higher proportions of white and less educated persons, higher unemployment rates and lower crime rates. The percentage of housing units vacant was a strong positive predictor for both diseases with a magnitude of association comparable to those between incidence and the ecological variables. Our findings indicate that racial/ethnic and socioeconomic disparities in disease incidence appear to be epidemiologically important features of Lyme disease and human monocytic ehrlichiosis in the United States. Steps to mitigate encroachment of wild flora and fauna into areas with vacant housing might be warranted to reduce disease risk. [ABSTRACT FROM AUTHOR]

Over the last few years, several of the world’s national park networks have implemented ecological integrity monitoring programs. These programs are based on a series of indicators to detect changes in ecosystem integrity. There are many scientific and logistical challenges in developing these programs due to limits in both our knowledge of ecosystems functioning and the resources for implementing such programs. Thus, the relatively quick and simple implementation of many monitoring programs has been to the detriment of their scientific validity. Few studies have focused on evaluating an entire monitoring program. This project presents an approach to evaluate the ecological and statistical relevance of ecosystem integrity indicators measured within a program with the goal of iterative optimization. The approach is based on three complementary elements: (1) spatial characterization of park ecosystems based on the classification of satellite imagery, (2) ecological validation of indicators based on ecosystem conceptual models and (3) statistical validation of indicators based on analyses of statistical power. This innovative approach allows a systematic, quantified, reproducible and generalizable review of the indicators of an ecological integrity monitoring program. It provides managers with an overview of the spatial representativeness of indicators, their ecological and statistical relevance according to different parameters such as the period monitored, the amount of change to be detected, and the degree of significance. Thus, the approach identifies monitoring gaps and offers various alternatives for improving sampling. The approach was developed and tested in the network of Quebec national parks, more specifically in the Frontenac, Jacques-Cartier and Bic national parks. The results clearly identify the strengths and weaknesses of the current program in place and possible improvements are proposed for these parks. This approach is a relevant tool for park networks, particularly for those that have limited resources for monitoring ecological integrity. [ABSTRACT FROM AUTHOR]

Coral reef systems can undergo rapid transitions from coral-dominated to macroalgae-dominated states following disturbances, and models indicate that these may sometimes represent shifts between alternative stable states. While several mechanisms may lead to alternate stable states on coral reefs, only a few have been investigated theoretically. We explore a model that illustrates that reduced vulnerability of macroalgae to herbivory as macroalgae grow and mature could be an important mechanism: when macroalgae are palatable to herbivores as juveniles, but resistant as adults, coral-dominated and algae-dominated states are bistable across a wide range of parameter space. We compare two approaches to global sensitivity analysis to rank the relative importance of the model parameters in determining the presence and magnitude of alternative stable states, and find that the two most influential parameters are the death rate of coral and the rate of maturation of algae out of the vulnerable stage. The Random Forest approach for global sensitivity analysis, recently adopted by ecologists, provides a more efficient method for ranking the relative importance of parameters than a variance-based approach that has been used frequently by computer scientists and engineers. Our results suggest that managing reefs to reduce chronic stressors that cause coral mortality and/or enhance the growth rates of algae can help prevent reefs from becoming locked in a macroalgae-dominated state. [ABSTRACT FROM AUTHOR]

The increasing use of species distribution modeling (SDM) has raised new concerns regarding the inaccuracies, misunderstanding, and misuses of this important tool. One of those possible pitfalls − collinearity among environmental predictors − is assumed as an important source of model uncertainty, although it has not been subjected to a detailed evaluation in recent SDM studies. It is expected that collinearity will increase uncertainty in model parameters and decrease statistical power. Here we use a virtual species approach to compare models built using subsets of PCA-derived variables with models based on the original highly correlated climate variables. Moreover, we evaluated whether modelling algorithms and species data characteristics generate models with varying sensitivity to collinearity. As expected, collinearity among predictors decreases the efficiency and increases the uncertainty of species distribution models. Nevertheless, the intensity of the effect varied according to the algorithm properties: more complex procedures behaved better than simple envelope models. This may support the claim that complex models such as Maxent take advantage of existing collinearity in finding the best set of parameters. The interaction of the different factors with species characteristics (centroid and tolerance in environmental space) highlighted the importance of the so-called “idiosyncrasy in species responses” to model efficiency, but differences in prevalence may represent a better explanation. However, even models with low accuracy to predict suitability of individual cells may provide meaningful information on the estimation of range-size, a key species-trait for macroecological studies. We concluded that the use of PCA-derived variables is advised both to control the negative effects of collinearity and as a more objective solution for the problem of variable selection in studies dealing with large number of species with heterogeneous responses to environmental variables. [ABSTRACT FROM AUTHOR]

Previous studies have reported that bright ceilings appear higher than dark ceilings, irrespective of the other colorimetric properties of the ceiling color (hue, saturation) and irrespective of the luminance of the remaining room surfaces (walls, floor). In the present study, we expand these findings to width and depth estimates. We presented stereoscopic full-scale room simulations on a head-mounted display and varied the luminance of the side walls, rear wall, and ceiling independently of each other. Participants judged the width and depth of the simulated rooms. Our results show that the perceived spatial layout of a given room is significantly influenced by the luminance of the direct bounding surfaces (e.g., the side walls when judging perceived width) but less affected by the luminance of the other surfaces. In the discussion, we provide an overall picture of effects of surface luminance on the perceived layout of interior spaces and discuss the conclusions in the context of existing interior-design guidelines. [ABSTRACT FROM AUTHOR]

Because overall cranial morphology-biomechanics linkage in carnivorans is significantly influenced by both feeding and non-feeding ecological variables, whole-skull mechanical performance measures may be less sensitive to feeding ecology than regional characteristics within the skull. The temporomandibular joint could be one regional characteristic that is highly sensitive to feeding ecology considering that this joint is used in prey capture, food processing, and experiences compressive loading during mastication. Through 3D model construction, 3D printing, and compression tests, morphological and mechanical performance measures were determined for the temporomandibular joint trabecular bone structure of 40 species representative of the phylogenetic and ecology diversity of Carnivora. Remarkably, the results indicate that relative fill volume, relative structural complexity, elastic modulus, and relative maximum compressive strength of trabecular bone structure are not significantly related to phylogeny or ecology. The results reveal that morphological and mechanical performance attributes of trabecular bone structure are primarily influenced by body size, and that positive centroid size allometry and positive body mass allometry are present for structural complexity. The lack of feeding ecological signal in dorso-ventral compressive loading of temporomandibular joint models indicates that carnivoran temporomandibular joint trabecular structures may not undergo significant differential remodeling as an evolutionary response to different mechanically demanding feeding tasks. [ABSTRACT FROM AUTHOR]

Logistic regression (LR) models are among the most frequently used statistical tools in ecology. With LR one can infer if a species’ habitat use is related to environmental factors and estimate the probability of species occurrence based on the values of these factors. However, studies often use inadequate sampling with regards to the arbitrarily chosen ratio between occupied and unoccupied (or available) locations, and this has a profound effect on the inference and predictive power of LR models. To demonstrate the effect of various sampling strategies/efforts on the quality of LR models, we used a unique census dataset containing all the used roosting cavities of the tree-dwelling bat Nyctalus leisleri and all cavities where the species was absent. We compared models constructed from randomly selected data subsets with varying ratios of occupied and unoccupied cavities (1:1, 1:5, 1:10) with a full dataset model (ratio 1:31). These comparisons revealed that the power of LR models was low when the sampling did not reflect the population ratio of occupied and unoccupied cavities. The use of weights improved the subsampled models. Thus, this study warns against inadequate data sampling and highly encourages a randomized sampling procedure to estimate the true ratio of occupied:unoccupied locations, which can then be used to optimize a manageable sampling effort and apply weights to improve the LR model. Such an approach may provide robust and reliable models suitable for both inference and prediction. [ABSTRACT FROM AUTHOR]

This paper analyzes the trophic role of Pacific herring, the potential consequences of its depletion, and the impacts of alternative herring fishing strategies on a Northeast Pacific food web in relation to precautionary, ecosystem-based management. We used an Ecopath with Ecosim ecosystem model parameterized for northern British Columbia (Canada), employing Ecosim to simulate ecosystem effects of herring stock collapse. The ecological impacts of various herring fishing strategies were investigated with a Management Strategy Evaluation algorithm within Ecosim, accounting for variability in climatic drivers and stock assessment errors. Ecosim results suggest that herring stock collapse would have cascading impacts on much of the pelagic food web. Management Strategy Evaluation results indicate that herring and their predators suffer moderate impacts from the existing British Columbia harvest control rule, although more precautionary management strategies could substantially reduce these impacts. The non-capture spawn-on-kelp fishery, traditionally practiced by many British Columbia and Alaska indigenous peoples, apparently has extremely limited ecological impacts. Our simulations also suggest that adopting a maximum sustainable yield management strategy in Northeast Pacific herring fisheries could generate strong, cascading food web effects. Furthermore, climate shifts, especially when combined with herring stock assessment errors, could strongly reduce the biomasses and resilience of herring and its predators. By clarifying the trophic role of Pacific herring, this study aims to facilitate precautionary fisheries management via evaluation of alternative fishing strategies, and thereby to inform policy tradeoffs among multiple ecological and socioeconomic factors. [ABSTRACT FROM AUTHOR]

Our current understanding of biology is heavily based on a small number of genetically tractable model organisms. Most eukaryotic phyla lack such experimental models, and this limits our ability to explore the molecular mechanisms that ultimately define their biology, ecology, and diversity. In particular, marine protists suffer from a paucity of model organisms despite playing critical roles in global nutrient cycles, food webs, and climate. To address this deficit, an initiative was launched in 2015 to foster the development of ecologically and taxonomically diverse marine protist genetic models. The development of new models faces many barriers, some technical and others institutional, and this often discourages the risky, long-term effort that may be required. To lower these barriers and tackle the complexity of this effort, a highly collaborative community-based approach was taken. Herein, we describe this approach, the advances achieved, and the lessons learned by participants in this novel community-based model for research. [ABSTRACT FROM AUTHOR]

There are numerous reasons to conduct scientific research within protected areas, but research activities may also negatively impact organisms and habitats, and thus conflict with a protected area’s conservation goals. We developed a quantitative ecological decision-support framework that estimates these potential impacts so managers can weigh costs and benefits of proposed research projects and make informed permitting decisions. The framework generates quantitative estimates of the ecological impacts of the project and the cumulative impacts of the proposed project and all other projects in the protected area, and then compares the estimated cumulative impacts of all projects with policy-based acceptable impact thresholds. We use a series of simplified equations (models) to assess the impacts of proposed research to: a) the population of any targeted species, b) the major ecological assemblages that make up the community, and c) the physical habitat that supports protected area biota. These models consider both targeted and incidental impacts to the ecosystem and include consideration of the vulnerability of targeted species, assemblages, and habitats, based on their recovery time and ecological role. We parameterized the models for a wide variety of potential research activities that regularly occur in the study area using a combination of literature review and expert judgment with a precautionary approach to uncertainty. We also conducted sensitivity analyses to examine the relationships between model input parameters and estimated impacts to understand the dominant drivers of the ecological impact estimates. Although the decision-support framework was designed for and adopted by the California Department of Fish and Wildlife for permitting scientific studies in the state-wide network of marine protected areas (MPAs), the framework can readily be adapted for terrestrial and freshwater protected areas. [ABSTRACT FROM AUTHOR]

Spatial capture-recapture modelling (SCR) is a powerful analytical tool to estimate density and derive information on space use and behaviour of elusive animals. Yet, SCR has been seldom applied to the study of ecologically keystone small mammals. Here we highlight its potential and requirements with a case study on common voles (Microtus arvalis). First, we address mortality associated with live-trapping, which can be high in small mammals, and must be kept minimal. We designed and tested a nest box coupled with a classic Sherman trap and show that it allows a 5-fold reduction of mortality in traps. Second, we address the need to adjust the trapping grid to the individual home range to maximize spatial recaptures. In May-June 2016, we captured and tagged with transponders 227 voles in a 1.2-ha area during two monthly sessions. Using a Bayesian SCR with a multinomial approach, we estimated: (1) the baseline detection rate and investigated variation according to sex, time or behaviour (aversion/attraction after a previous capture); (2) the parameter sigma that describes how detection probability declines as a function of the distance to an individual´s activity centre, and investigated variation according to sex; and (3) density and population sex-ratio. We show that reducing the maximum distance between traps from 12 to 9.6m doubled spatial recaptures and improved model predictions. Baseline detection rate increased over time (after overcoming a likely aversion to entering new odourless traps) and was greater for females than males in June. The sigma parameter of males was twice that of females, indicating larger home ranges. Density estimates were of 142.92±38.50 and 168.25±15.79 voles/ha in May and June, respectively, with 2–3 times more females than males. We highlight the potential and broad applicability that SCR offers and provide specific recommendations for using it to study small mammals like voles. [ABSTRACT FROM AUTHOR]

We theoretically study the dynamics of two interacting microbial species in the chemostat. These species are competitors for a common resource, as well as mutualists due to cross-feeding. In line with previous studies (Assaneo, et al., 2013; Holland, et al., 2010; Iwata, et al., 2011), we demonstrate that this system has a rich repertoire of dynamical behavior, including bistability. Standard Lotka-Volterra equations are not capable to describe this particular system, as these account for only one type of interaction (mutualistic or competitive). We show here that the different steady state solutions can be well captured by an extended Lotka-Volterra model, which better describe the density-dependent interaction (mutualism at low density and competition at high density). This two-variable model provides a more intuitive description of the dynamical behavior than the chemostat equations. [ABSTRACT FROM AUTHOR]

Niche construction theory states that not only does the environment act on populations to generate Darwinian selection, but organisms reciprocally modify the environment and the sources of natural selection. Cancer cells participate in niche construction as they alter their microenvironments and create pre-metastatic niches; in fact, metastasis is a product of niche construction. Here, we present a mathematical model of niche construction and metastasis. Our model contains producers, which pay a cost to contribute to niche construction that benefits all tumor cells, and cheaters, which reap the benefits without paying the cost. We derive expressions for the conditions necessary for metastasis, showing that the establishment of a mutant lineage that promotes metastasis depends on niche construction specificity and strength of interclonal competition. We identify a tension between the arrival and invasion of metastasis-promoting mutants, where tumors composed only of cheaters remain small but are susceptible to invasion whereas larger tumors containing producers may be unable to facilitate metastasis depending on the level of niche construction specificity. Our results indicate that even if metastatic subclones arise through mutation, metastasis may be hindered by interclonal competition, providing a potential explanation for recent surprising findings that most metastases are derived from early mutants in primary tumors. [ABSTRACT FROM AUTHOR]

Statistical inference is a widely-used, powerful tool for learning about natural processes in diverse fields. The statistical software platforms AD Model Builder (ADMB) and Template Model Builder (TMB) are particularly popular in the ecological literature, where they are typically used to perform frequentist inference of complex models. However, both lack capabilities for flexible and efficient Markov chain Monte Carlo (MCMC) integration. Recently, the no-U-turn sampler (NUTS) MCMC algorithm has gained popularity for Bayesian inference through the software Stan because it is efficient for high dimensional, complex hierarchical models. Here, we introduce the R packages and , which provide NUTS sampling in parallel and interactive diagnostics with ShinyStan. The ADMB source code was modified to provide NUTS, while TMB models are linked directly into Stan. We describe the packages, provide case studies demonstrating their use, and contrast performance against Stan. For TMB models, we show how to test the accuracy of the Laplace approximation using NUTS. For complex models, the performance of ADMB and TMB was typically within +/- 50% the speed of Stan. In one TMB case study we found inaccuracies in the Laplace approximation, potentially leading to biased inference. provides a new method for estimating hierarchical ADMB models which previously were infeasible. TMB users can fit the same model in both frequentist and Bayesian paradigms, including using NUTS to test the validity of the Laplace approximation of the marginal likelihood for arbitrary subsets of parameters. These software developments extend the available statistical methods of the ADMB and TMB user base with no additional effort by the user. [ABSTRACT FROM AUTHOR]

In this paper, we develop an ecological, multi-level model that can be used to study the evolution of emerging technology. More specifically, by defining technology as a system composed of a set of interacting components, we can build upon the argument of multi-level density dependence from organizational ecology to develop a distribution-independent model of technological evolution. This allows us to distinguish between different stages of component development, which provides more insight into the emergence of stable component configurations, or dominant designs. We validate our hypotheses in the biotechnology industry by using patent data from the USPTO from 1976 to 2003. [ABSTRACT FROM AUTHOR]

Ecological models of mating systems provide a theoretical framework to predict the effect of the defendability of both breeding resources and mating partners on mating patterns. In resource-based mating systems, male control over breeding resources is tightly linked to female mating preference. To date, few field studies have experimentally investigated the relationship between male resource control and female mating preference in mammals due to difficulties in manipulating ecological factors (e.g., food contestability). We tested the within-group male resource defense hypothesis experimentally in a wild population of black capuchin monkeys (Sapajus nigritus) in Iguazú National Park, Argentina. Sapajus spp. represent an ideal study model as, in contrast to most primates, they have been previously argued to be characterized by female mate choice and a resource-based mating system in which within-group resource monopolization by high-ranking males drives female mating preference for those males. Here, we examined whether females (N = 12) showed a weaker preference for alpha males during mating seasons in which food distribution was experimentally manipulated to be less defendable relative to those in which it was highly defendable. Results did not support the within-group male resource defense hypothesis, as female sexual preferences for alpha males did not vary based on food defendability. We discuss possible reasons for our results, including the possibility of other direct and indirect benefits females receive in exercising mate choice, the potential lack of tolerance over food directed towards females by alpha males, and phylogenetic constraints. [ABSTRACT FROM AUTHOR]

The R-package allows to model and compare fluctuations of Tree-ring Width (TRW) and climate, or dendroclimatic fluctuations, while accounting for source variability. The package eases multilevel modeling and multivariate comparison in dendroclimatic analysis using the and packages, respectively. For implementing such libraries, the in-package algorithms transform the dendroclimatic fluctuations into Multilevel Dendroclimatic Data Series and maintain categorical variables and time units in the outputs. The dendroclimatic modeling is developed with two functions: and . The first function binds core-level cumulative TRWs to the processed data sets and subtracts trends in TRWs by fitting multilevel log-linear growth formulas or multilevel linear formulas. can also model within-group fluctuations in dendroclimatic variables other than tree-radial increments such as aridity indices or allometric components of tree growth: e.g. diameters at breast height over bark, tree basal areas, total tree biomass, among other. The second function compares fluctuations in objects that share outermost categorical variable and annual records. Here, we use BIOdry to model dendroclimatic relationships in northern and east-central Spain to illustrate future users in the implementation of the package for modeling ecological relationships in space and time. [ABSTRACT FROM AUTHOR]

The Environmental Attitudes Inventory (EAI) was developed to evaluate the multidimensional nature of environmental attitudes; however, it is based on a dataset from outside the Arab context. This study reinvestigated the construct validity of the EAI with a new dataset and confirmed the feasibility of applying it in the Arab context. One hundred and forty-eight subjects in Study 1 and 130 in Study 2 provided valid responses. An exploratory factor analysis (EFA) was used to extract a new factor structure in Study 1, and confirmatory factor analysis (CFA) was performed in Study 2. Both studies generated a seven-factor model, and the model fit was discussed for both the studies. Study 2 exhibited satisfactory model fit indices compared to Study 1. Factor loading values of a few items in Study 1 affected the reliability values and average variance extracted values, which demonstrated low discriminant validity. Based on the results of the EFA and CFA, this study showed sufficient model fit and suggested the feasibility of applying the EAI in the Arab context with a good construct validity and internal consistency. [ABSTRACT FROM AUTHOR]

Successfully applying theoretical models to natural communities and predicting ecosystem behavior under changing conditions is the backbone of predictive ecology. However, the experiments required to test these models are dictated by practical constraints, and models are often opportunistically validated against data for which they were never intended. Alternatively, we can inform and improve experimental design by an in-depth pre-experimental analysis of the model, generating experiments better targeted at testing the validity of a theory. Here, we describe this process for a specific experiment. Starting from food web ecological theory, we formulate a model and design an experiment to optimally test the validity of the theory, supplementing traditional design considerations with model analysis. The experiment itself will be run and described in a separate paper. The theory we test is that trophic population dynamics are dictated by species traits, and we study this in a community of terrestrial arthropods. We depart from the Allometric Trophic Network (ATN) model and hypothesize that including habitat use, in addition to body mass, is necessary to better model trophic interactions. We therefore formulate new terms which account for micro-habitat use as well as intra- and interspecific interference in the ATN model. We design an experiment and an effective sampling regime to test this model and the underlying assumptions about the traits dominating trophic interactions. We arrive at a detailed sampling protocol to maximize information content in the empirical data obtained from the experiment and, relying on theoretical analysis of the proposed model, explore potential shortcomings of our design. Consequently, since this is a “pre-experimental” exercise aimed at improving the links between hypothesis formulation, model construction, experimental design and data collection, we hasten to publish our findings before analyzing data from the actual experiment, thus setting the stage for strong inference. [ABSTRACT FROM AUTHOR]

Humans are remarkably adept at generalizing knowledge between experiences in a way that can be difficult for computers. Often, this entails generalizing constituent pieces of experiences that do not fully overlap, but nonetheless share useful similarities with, previously acquired knowledge. However, it is often unclear how knowledge gained in one context should generalize to another. Previous computational models and data suggest that rather than learning about each individual context, humans build latent abstract structures and learn to link these structures to arbitrary contexts, facilitating generalization. In these models, task structures that are more popular across contexts are more likely to be revisited in new contexts. However, these models can only re-use policies as a whole and are unable to transfer knowledge about the transition structure of the environment even if only the goal has changed (or vice-versa). This contrasts with ecological settings, where some aspects of task structure, such as the transition function, will be shared between context separately from other aspects, such as the reward function. Here, we develop a novel non-parametric Bayesian agent that forms independent latent clusters for transition and reward functions, affording separable transfer of their constituent parts across contexts. We show that the relative performance of this agent compared to an agent that jointly clusters reward and transition functions depends environmental task statistics: the mutual information between transition and reward functions and the stochasticity of the observations. We formalize our analysis through an information theoretic account of the priors, and propose a meta learning agent that dynamically arbitrates between strategies across task domains to optimize a statistical tradeoff. [ABSTRACT FROM AUTHOR]

Managing landscape connectivity is a widely recognized overarching strategy for conserving biodiversity in human-impacted landscapes. However, planning the conservation and management of landscape connectivity of multiple and ecologically distinct species is still challenging. Here we provide a spatially-explicit framework which identifies and prioritizes connectivity conservation and restoration actions for species with distinct habitat affinities. Specifically, our study system comprised three groups of common bird species, forest-specialists, farmland-specialists, and generalists, populating a highly heterogeneous agricultural countryside in the southwestern Iberian Peninsula. We first performed a comprehensive analysis of the environmental variables underlying the distributional patterns of each bird species to reveal generalities in their guild-specific responses to landscape structure. Then, we identified sites which could be considered pivotal in maintaining current levels of landscape connectivity for the three bird guilds simultaneously, as well as the number and location of sites that need to be restored to maximize connectivity levels. Interestingly, we found that a small number of sites defined the shortest connectivity paths for the three bird guilds simultaneously, and were therefore considered key for conservation. Moreover, an even smaller number of sites were identified as critical to expand the landscape connectivity at maximum for the regional bird assemblage as a whole. Our spatially-explicit framework can provide valuable decision-making support to conservation practitioners aiming to identify key connectivity and restoration sites, a particularly urgent task in rapidly changing landscapes such as agroecosystems. [ABSTRACT FROM AUTHOR]

With over 1 million species on earth, each biologically unique, do we have any hope of understanding whether species will persist in a warming world? We might, because it turns out that there is surprising regularity in how warming accelerates the major metabolic processes that power life. A persistent challenge has been to understand ecological effects of temperature in the context of species interactions, especially when individuals not only experience temperature but also mortality due to parasitism or predation. Kirk et al. have shown how the effects of parasites vary with warming in a manner entirely consistent with general temperature dependence of host and parasite metabolism. [ABSTRACT FROM AUTHOR]

Land managers increasingly rely upon landscape assessments to understand the status of natural resources and identify conservation priorities. Many of these landscape planning efforts rely on geospatial models that characterize the ecological integrity of the landscape. These general models utilize measures of habitat disturbance and human activity to map indices of ecological integrity. We built upon these modeling frameworks by developing a Landscape Integrity Index (LII) model using geospatial datasets of the human footprint, as well as incorporation of other indicators of ecological integrity such as biodiversity and vegetation departure. Our LII model serves as a general indicator of ecological integrity in a regional context of human activity, biodiversity, and change in habitat composition. We also discuss the application of the LII framework in two related coarse-filter landscape conservation approaches to expand the size and connectedness of protected areas as regional mitigation for anticipated land-use changes. [ABSTRACT FROM AUTHOR]