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bioRxiv2016

Genetic Allee effects and their interaction with ecological Allee effects

SummaryO_LIIt is now widely accepted that genetic processes such as inbreeding depression and loss of genetic variation can increase the extinction risk of small populations. However, it is generally unclear whether extinction risk from genetic causes gradually increases with decreasing population size or whether there is a sharp transition around a specific threshold population size. In the ecological literature, such threshold phenomena are called \"strong Allee effects\" and they can arise for example from mate limitation in small populations.\nC_LIO_LIIn this study, we aim to a) develop a meaningful notion of a \"strong genetic Allee effect\", b) explore whether and under what conditions such an effect can arise from inbreeding depression due to recessive deleterious mutations, and c) quantify the interaction of potential genetic Allee effects with the well-known mate-finding Allee effect.\nC_LIO_LIWe define a strong genetic Allee effect as a genetic process that causes a populations survival probability to be a sigmoid function of its initial size. The inflection point of this function defines the critical population size. To characterize survival-probability curves, we develop and analyze simple stochastic models for the ecology and genetics of small populations.\nC_LIO_LIOur results indicate that inbreeding depression can indeed cause a strong genetic Allee effect, but only if individuals carry sufficiently many deleterious mutations (lethal equivalents) on average and if these mutations are spread across sufficiently many loci. Populations suffering from a genetic Allee effect often first grow, then decline as inbreeding depression sets in, and then potentially recover as deleterious mutations are purged. Critical population sizes of ecological and genetic Allee effects appear to be often additive, but even superadditive interactions are possible.\nC_LIO_LIMany published estimates for the number of lethal equivalents in birds and mammals fall in the parameter range where strong genetic Allee effects are expected. Unfortunately, extinction risk due to genetic Allee effects can easily be underestimated as populations with genetic problems often grow initially, but then crash later. Also interactions between ecological and genetic Allee effects can be strong and should not be neglected when assessing the viability of endangered or introduced populations.\nC_LI

Ecology↗

bioRxiv2016

Liberation from equations: An equation-free method reveals the ecological interaction networks within complex microbial ecosystems

O_LIMapping the network of ecological interactions is key to understanding the composition, stability, function and dynamics of microbial communities. In recent years various approaches have been used to reveal microbial interaction networks from metagenomic sequencing data, such as time-series analysis, machine learning and statistical techniques. Despite these efforts it is still not possible to capture details of the ecological interactions behind complex microbial dynamics.\nC_LIO_LIWe developed the sparse S-map method (SSM), which generates a sparse interaction network from a multivariate ecological time-series without presuming any mathematical formulation for the underlying microbial processes. The advantage of the SSM over alternative methodologies is that it fully utilizes the observed data using a framework of empirical dynamic modelling. This makes the SSM robust to non-equilibrium dynamics and underlying complexity (nonlinearity) in microbial processes.\nC_LIO_LIWe showed that an increase in dataset size or a decrease in observational error improved the accuracy of SSM whereas, the accuracy of a comparative equation-based method was almost unchanged for both cases and equivalent to the SSM at best. Hence, the SSM outperformed a comparative equation-based method when datasets were large and the magnitude of observational errors were small. The results were robust to the magnitude of process noise and the functional forms of inter-specific interactions that we tested. We applied the method to a microbiome data of six mice and found that there were different microbial interaction regimes between young to middle age (4-40 week-old) and middle to old age (36-72 week-old) mice.\nC_LIO_LIThe complexity of microbial relationships impedes detailed equation-based modeling. Our method provides a powerful alternative framework to infer ecological interaction networks of microbial communities in various environments and will be improved by further developments in metagenomics sequencing technologies leading to increased dataset size and improved accuracy and precision.\nC_LI

ecology↗

bioRxiv2017

Interactions between ecological, evolutionary, and environmental processes unveil complex dynamics of island biodiversity

AimsUnderstanding how biodiversity emerges and varies in space and time is central to ecology and biogeography. Multiple processes affect biodiversity at different scales and organizational levels, hence progress in understanding biodiversity dynamics requires the integration of these underlying processes. Here we present BioGEEM (BioGeographical Eco-Evolutionary Model), a spatially-explicit, process-based model that integrates all processes hypothesized to be relevant for biodiversity dynamics and that can be used to evaluate their relative roles.\n\nLocationHypothetical oceanic islands\n\nMethodsThe model is stochastic, grid-based, and integrates ecological (metabolic constraints, demography, dispersal, and competition), evolutionary (mutation and speciation), and environmental (geo-climatic dynamics) processes. Plants on oceanic islands served as model system. We used the full model to test hypotheses about emergent patterns at different spatio-temporal scales and organizational levels (populations, species, communities, and assemblages), switching off processes to assess the importance 1) of competition for realistic population and range dynamics; 2) metabolic constraints for endemism and community composition; 3) environmental dynamics and 4) speciation for biogeographical patterns.\n\nResultsThe full model generated multiple patterns matching empirical and theoretical expectations. For example, populations were largest on young, species-poor islands. Species, particularly endemics, were better able to fill their potential range on small, species-poor islands. Richness gradients peaked at mid-elevations. The proportion of endemics was highest on old, large, and isolated environments within the islands. Species and trait richness showed unimodal temporal trends. Switching off selected processes affected these patterns, and we found most of our hypotheses supported.\n\nMain conclusionsIntegrating ecological, evolutionary, and environmental processes is essential to simultaneously generate realistic spatio-temporal dynamics at population, species, community, and assemblage level. Finally, large-scale biodiversity dynamics emerged directly from biological processes which make this mechanistic model a valuable virtual long-term field station to study the linkages between biogeography and ecology.

ecology↗

bioRxiv2017

Analyzing ecological networks of species interactions

Networks provide one of the best representations for ecological communities, composed of many species with sometimes complex connections between them. Yet the methodological literature allowing one to analyze and extract meaning from ecological networks is dense, fragmented, and unwelcoming. We provide a general overview to the field of using networks in community ecology, outlining both the intent of the different measures, their assumptions, and the contexts in which they can be used. When methodologically justified, we suggest good practices to use in the analysis of ecological networks. We anchor this synopsis with examples from empirical studies, and conclude by highlighting what identified as needed future developments in the field.

ecology↗

bioRxiv2017

A Theory For Ecological Survey Methods To Map Individual Distributions

Spatially-explicit approaches are widely recommended for ecosystem management. The quality of the data, such as presence/absence or habitat maps, affects the management actions recommended, and is, therefore, key to management success. However, available data are often biased and incomplete. Previous studies have advanced ways to resolve data bias and missing data, but questions remain about how we design ecological surveys to develop a dataset through field surveys. Ecological surveys may have multiple spatial scales, including the spatial extent of the target ecosystem (observation window), the resolution for mapping individual distributions (mapping unit), and the survey area within each mapping unit (sampling unit). We developed an ecological survey method for mapping individual distributions by applying spatially-explicit stochastic models. We used spatial point processes to describe individual spatial placements using either random or clustering processes. We then designed ecological surveys with different spatial scales and individual detectability. We found that the choice of mapping unit affected the presence mapped fraction, and the fraction of the total individuals covered by the presence mapped patches. Tradeoffs were found between these quantities and the map resolution, associated with equivalent asymptotic behaviors for both metrics at sufficiently small and large mapping unit scales. Our approach enabled consideration of the effect of multiple spatial scales in surveys, and estimation of the survey outcomes such as the presence mapped fraction and the number of individuals situated in the presence detected units. The developed theory may facilitate management decision-making and inform the design of monitoring and data gathering.

ecology↗

bioRxiv2017

Data-based, synthesis-driven: setting the agenda for computational ecology

Computational thinking is the integration of algorithms, software, and data, to solve general questions in a field. Computation ecology has the potential to transform the way ecologists think about the integration of data and models. As the practice is gaining prominence as a way to conduct ecological research, it is important to reflect on what its agenda could be, and how it fits within the broader landscape of ecological research. In this contribution, we suggest areas in which empirical ecologists, modellers, and the emerging community of computational ecologists could engage in a constructive dialogue to build on one anothers expertise; specifically, about the need to make predictions from models actionable, about the best standards to represent ecological data, and about the proper ways to credit data collection and data reuse. We discuss how training can be amended to improve computational literacy.

ecology↗

bioRxiv2017

The architecture and design of ecological null models

Many questions in ecology are best addressed using observational data because they concern spatial or temporal scales where experimentation is impractical. Null models, which make predictions in the absence of a particular ecological mechanism, are instrumental for making inferences in these situations, but which null models to use or how to best test them is often unclear; this ambiguity is problematic because different null models and tests can yield different results, suggesting contradictory ecological mechanisms. To address these challenges, this paper presents an overar ching framework for the development and testing of null models, in which desirable models and tests are obtained as solutions to mathematical optimization problems. As an example of how the framework can be applied, this paper shows how it can be used to design null model tests to check for effects of interspecific interactions on species co-occurrence patterns. A minimal sufficient statistic (metric) for effects of interspecific interactions is derived, which achieves the maximal level of data compression without losing information present in the data about interspecific in teractions. Existing, intuitive statistics are shown to lack this property. The paper then derives a statistical hypothesis test that has the greatest possible power (sen sitivity) for detecting effects of competition and facilitation given a controlled false positive rate. This test is shown numerically to improve greatly over existing tests. The optimization paradigm allows the most accurate inferences possible, and should be applicable throughout ecology where null models are used to make inferences.

ecology↗

bioRxiv2020

Environmental heterogeneity ruling the action of ecological processes over the bacterial metacommunity assembly

Determining which processes take place in the spatial distribution of bacterioplankton metacommunities has been a central goal of microbial ecology. In freshwater ecosystems, selection has been spotted as the main driver shaping bacterial community. However, its relative importance compared with others processes (dispersal, drift, diversification) may depend on spatial heterogeneity and dispersal rates within a metacommunity. Still, a decrease in the role of selection is expected with increasing dispersal homogenization. Here, we investigate the main ecological processes modulating the bacterial assembly in contrasting scenarios of environmental heterogeneity. We carried out a spatiotemporal survey in the floodplain system of Parana River. The bacterioplankton metacommunity was studied by a step-forward analysis combining different statistical inferences based on the phylogenetic and taxa turnover as well as co-occurrence networks. We found that selection was the main process even at both extremes of environmental heterogeneity and homogeneity, challenging the general view that the strength of selection is weakened due to dispersal homogenization. The ecological processes acting on the community also determined the complexity and randomness of bacterial networks. The heterogeneous selection promoted greater network complexity increasing the {beta}-diversity, and bacterial associations were more random with the increasing influence of stochasticity. Finally, the spatiotemporal heterogeneity was an important factor determining the number and identity of keystone taxa in the system. Integrating all these empirical evidences we propose a new conceptual model that elucidates how the environmental heterogeneity guides the action of ecological processes shaping the bacterial metacommunity.

ecology↗

bioRxiv2020

Behavioural traits and feeding ecology of Mediterranean lionfish and native species naivete to lionfish predation

The detrimental effects of invasion by Indo-Pacific lionfish (Pterois volitans/miles) on western Atlantic fishes have spurred concerns for Mediterranean fish biodiversity, where a Lessepsian invasion of lionfish has recently begun. In order to assess the potential impact on biodiversity, we examine key behavioural and ecological traits of lionfish, and the resident fish community in the Mediterranean, that may contribute to lionfish invasion success. We focus on Cyprus, where lionfish populations were first sighted in 2012 and have now established abundant and stable populations. Using field observations, we examine lionfish predatory behaviour and feeding ecology, and resident fish species naivete to hunting lionfish. Our findings suggest that lionfish in the Mediterranean are crepuscular generalist predators, with prey targeted dominated by small-bodied benthic or bentho-pelagic associated species. Such prey are more likely to be native than introduced (Lessepsian) fishes, with native prey fishes showing greater naivete towards lionfish than Lessepsian prey species. Notably, one of the Mediterraneans key ecological fish species (the native damselfish Chromis chromis), showed the highest level of naivete and was the most heavily targeted prey. Overall, lionfish in the Mediterranean show similar predatory behaviour and ecology to their western Atlantic counterparts. Although the Mediterranean invasion is still relatively recent, it may result in a similar disruption to reef fish biomass to that recorded in the Atlantic, with impact to the structure and biodiversity of reef fish communities and the services they provide.

ecology↗

bioRxiv2020

Ecological theory applied to environmental metabolomes reveals compositional divergence despite conserved molecular properties

Stream and river systems transport and process substantial amounts of dissolved organic matter (DOM) from terrestrial and aquatic sources to the ocean, with global biogeochemical implications. However, the underlying mechanisms affecting the spatiotemporal organization of DOM composition are under-investigated. To understand the principles governing DOM composition, we leverage the recently proposed synthesis of metacommunity ecology and metabolomics, termed meta-metabolome ecology. Applying this novel approach to a freshwater ecosystem, we demonstrated that despite similar molecular properties across metabolomes, metabolite identity significantly diverged due to environmental filtering. We refer to this phenomenon as thermodynamic redundancy, which is analogous to the ecological concept of functional redundancy. We suggest that under thermodynamic redundancy, divergent metabolomes can support equivalent biogeochemical function just as divergent ecological communities can support equivalent ecosystem function. As these analyses are performed in additional ecosystems, potentially generalizable principles, like thermodynamic redundancy, can be revealed and provide insight into DOM dynamics.

ecology↗

bioRxiv2020

phyr: An R package for phylogenetic species-distribution modelling in ecological communities

O_LIModel-based approaches are increasingly popular in ecological studies. A good example of this trend is the use of joint species distribution models to ask questions about ecological communities. However, most current applications of model-based methods do not include phylogenies despite the well-known importance of phylogenetic relationships in shaping species distributions and community composition. In part, this is due to lack of accessible tools allowing ecologists to fit phylogenetic species distribution models easily. C_LIO_LITo fill this gap, the R package phyr (pronounced fire) implements a suite of metrics, comparative methods and mixed models that use phylogenies to understand and predict community composition and other ecological and evolutionary phenomena. The phyr workhorse functions are implemented in C++ making all calculations and model estimations fast. C_LIO_LIphyr can fit a variety of models such as phylogenetic joint-species distribution models, spatiotemporal-phylogenetic autocorrelation models, and phylogenetic trait-based bipartite network models. phyr also estimates phylogenetically independent trait correlations with measurement error to test for adaptive syndromes and performs fast calculations of common alpha and beta phylogenetic diversity metrics. All phyr methods are united under Brownian motion or Ornstein-Uhlenbeck models of evolution and phylogenetic terms are modelled as phylogenetic covariance matrices. C_LIO_LIThe functions and model formula syntax we propose in phyr serves as a simple and unified framework that ignites the use of phylogenies to address a variety of ecological questions. C_LI

ecology↗

bioRxiv2020

Ecological stoichiometric characteristics of Carbon (C), Nitrogen (N) and Phosphorus (P) in leaf, root, stem, and soil in four wetland plants communities in Shengjin Lake, China,

Ecological stoichiometric should be incorporated into management and nutrient impacted ecosystems dynamic to understand the status of ecosystems and ecological interaction. The present study focused on ecological stoichiometric characteristics of different macrophyte plants soil, leave, stem and root after the removal of seine fishing since 2000 from Shengjin Lake. For C, N and P analysis from leaves, stems, roots and soil to explore their stoichiometric ratio and deriving environmental forces here four dominant plant communities (Zizania caduciflora, Vallisineria natans, Trapa quadrispinosa and Carex schmidtii) were collected. C, N, P and C: N: P ratio in leafs, stems, roots and soil among the plant communities vary and the studied plant communities had significant effect on the measured variables. There was high C: N in C.schmidtii soil (7.08{+/-}1.504) but not vary significantly (P >0.05), and N: P ratio measured high in V. natans (13.7{+/-}4.05) and C: P in T.quadrispinosa soil (81.14{+/-}43.88) and showed significant variation (P<0.05) respectively. High leaf C: N and N: P ratio was measured in C. schmidtii and V. natans respectively. Nevertheless, high leaf C: P ratio was measured in Z.caduciflora. From the three studied organs leafs C: N, N: P ratio showed high values compared to root and stems. The correlation analysis result showed that, at 0-10cm depth ranges SOC correlated negatively with stem total phosphorus (STP), and RTN (P<0.05) but positively strongly with LTP and LTN (P<0.01) respectively. Soil total nitrogen at 0-10cm strongly positively correlated with LTP (P<0.01) and positively with RN: P and LTC (P<0.05). Soil basic properties such as SMC.BD and pH positively correlated with soil ecological stoichiometric characteristics. Redundancy analysis (RDA) result showed pH and available phosphorus were the potential determinant of soil stoichiometry.

ecology↗

bioRxiv2020

modleR: a modular workflow to perform ecological niche modeling in R

Ecological niche models (ENM) use the environmental variables associated with the currently known distribution of a species to model its ecological niche and project it into the geographic space. Widely used and misused, ENM has become a common tool for ecologists and decision-makers. Many ENM platforms have been developed over the years, first as standalone programs, later as packages within script-based programming languages and environments. The democratization of these programming tools and the advent of Open Science brought a growing concern regarding the reproducibility, transparency, robustness, portability, and interoperability in ENM workflows. ENM workflows have some core components that are replicated between projects. However, they have a large internal variation due to the variety of research questions and applications. Any ecological niche modeling platform should take into account this trade-off between stability and reproducibility on one hand, and flexibility and decision-making on the other. Here, we present modleR, a four-step workflow that wraps some of the common phases executed during an ecological niche model procedure. We have divided the process into (1) data setup, (2) model fitting and projection, (3) partition joining and (4) ensemble modeling (consensus between algorithms). modleR is highly adaptable and replicable depending on the users needs and is open to deeper internal parametrization. It can be used as a testing platform due to its consistent folder structure and its capacity to control some sources of variation while changing others. It can be run in interactive local sessions and in high-performance or high-throughput computational (HPC/HTC) platforms and parallelized by species or algorithms. It can also communicate with other tools in the field, allowing the user to enter and exit the workflow at any phase, and execute complementary routines outside the package. Finally, it records metadata and session information at each step, ensuring reproducibility beyond the use of script-based applications.

ecology↗

bioRxiv2020

Ecological processes underlying the emergence of novel enzootic cycles--arboviruses in the neotropics as a case study

Pathogens originating from wildlife (zoonoses) pose a significant public health burden, comprising the majority of emerging infectious diseases. Efforts to control and prevent zoonotic disease have traditionally focused on animal-to-human transmission, or "spillover". However, in the modern era, increasing international mobility and commerce facilitate the spread of infected humans, non-human animals (hereafter animals), and their products worldwide, thereby increasing the risk that zoonoses will be introduced to new geographic areas. Imported zoonoses can potentially spill back to infect local wildlife--a danger magnified by urbanization and other anthropogenic pressures that increase contacts between human and wildlife populations. In this way, humans can function as vectors, dispersing zoonoses from their ancestral enzootic systems to establish reservoirs elsewhere in novel animal host populations. Once established, these enzootic cycles are largely unassailable by standard control measures and have the potential to feed human epidemics. Understanding when and why translocated zoonoses establish novel enzootic cycles requires disentangling ecologically complex and stochastic interactions between the zoonosis, the human population, and the natural ecosystem. We address this challenge by delineating potential ecological mechanisms affecting each stage of enzootic establishment--wildlife exposure, enzootic infection, and persistence--applying existing ecological concepts from epidemiology, invasion biology, and population ecology. We ground our study in the neotropics, where four arthropod-borne viruses (arboviruses) of zoonotic origin--yellow fever, dengue, chikungunya, and Zika viruses--have separately been introduced into the human population. This paper is a step towards developing a framework for predicting and preventing novel enzootic cycles in the face of zoonotic translocations.

ecology↗

bioRxiv2020

Dynamical Ising model of spatially-coupled ecological oscillators

Long-range synchrony from short-range interactions is a familiar pattern in biological and physical systems, many of which share a common set of "universal" properties at the point of synchronization. Common biological systems of coupled oscillators have been shown to be members of the Ising universality class, meaning that the very simple Ising model replicates certain spatial statistics of these systems at stationarity. This observation is useful because it reveals which aspects of spatial pattern arise independently of the details governing local dynamics, resulting in both deeper understanding of and a simpler baseline model for biological synchrony. However, in many situations a systems dynamics are of greater interest than their static spatial properties. Here, we ask whether a dynamical Ising model can replicate universal and non-universal features of ecological systems, using noisy coupled metapopulation models with two-cycle dynamics as a case study. The standard Ising model makes unrealistic dynamical predictions, but the Ising model with memory corrects this by using an additional parameter to reflect the tendency for local dynamics to maintain their phase of oscillation. By fitting the two parameters of the Ising model with memory to simulated ecological dynamics, we assess the correspondence between the Ising and ecological models in several of their features (location of the critical boundary in parameter space between synchronous and asynchronous dynamics, probability of local phase changes, and ability to predict future dynamics). We find that the Ising model with memory is reasonably good at representing these properties of ecological metapopulations. The correspondence between these models creates the potential for the simple and well-known Ising class of models to become a valuable tool for understanding complex biological systems.

ecology↗

bioRxiv2020

Ecological memory mitigates negative impacts of disturbance on biomass production in benthic diatom metacommunities

Disturbance events to coastal habitats such as extreme heat events, storms, or floods have increased in magnitude and frequency in recent years due to anthropogenic climate change and the destruction of habitats. These events constitute a major threat to many ecological communities and global biodiversity. Disturbance history influences ecosystem response to novel disturbances such that communities that have previously been exposed to disturbances should be more resilient to new disturbances compared to previously sheltered communities. This principle is defined as ecological memory. Resilience should also increase with access to a larger species pool, because a larger species pool increases species and response diversity of a community. One possibility of increasing the local species pool is connectivity via adequate dispersal between habitat patches with different species compositions in metacommunities. In a laboratory experiment, we exposed benthic diatom communities of different origin to a mechanical disturbance, simulated dispersal in half of the communities, and measured their chlorophyll a concentration over time. The local diatom communities originated from different locations on an intertidal flat that varied in hydrodynamic exposure history. Hydrodynamic exposure disturbs the sediment, and thereby determines sediment properties and the composition of intertidal diatom communities. In the experiment, disturbance negatively affected chlorophyll a concentration across all treatments. However, the response to disturbance depended on the ecological memory of the communities; the more exposed areas the communities originated from, the less negative was the effect of the mechanical disturbance. Interestingly, dispersal did not mitigate the negative impacts of disturbance in any of the communities. Our results highlight the importance of ecological memory for ecosystem functioning and demonstrate the limitations of patch connectivity to alleviate the impacts of disturbance events in metacommunities.

ecology↗

bioRxiv2020

Including intraspecific trait variability to avoid distortion of functional diversity and ecological inference: lessons from natural assemblages

O_LIFunctional diversity assessments are crucial and increasingly used for understanding ecological processes and managing ecosystems. The functional diversity of a community is assessed by sampling traits at one or more scales (individuals, populations, species) and calculating a summary index of the variation in trait values. However, it remains unclear how the scale at which traits are sampled and the indices used to estimate functional diversity may alter the patterns observed and inferences about ecological processes. C_LIO_LIFor 40 plant and 61 ant communities, we assess functional diversity using six methods - encompassing various mean-based and probabilistic methods - chosen to reflect common scenarios where different levels of detail are available in trait data. We test whether including trait variability at different scales (from individuals to species) alter functional diversity values calculated using volume-based and dissimilarity-based indices, Functional Richness (FRic) and Rao, respectively. We further test whether such effects alter the functional diversity patterns observed across communities and their relationships with environmental drivers such as abiotic gradients and occurrences of invasive species. C_LIO_LIIntraspecific trait variability strongly determined FRic and Rao. Methods using only species mean trait values to calculate FRic (convex hulls) and Rao (Gower-based dissimilarity) distorted the patterns observed when intraspecific trait variability was considered. These distortions generated Type I and Type II errors for the effects of environmental factors structuring the plant and ant communities. C_LIO_LIThe high sensitivity of FRic to individuals with extreme trait values was revealed in comparisons of different probabilistic methods including among-individual and among-population trait variability in functional diversity. By contrast, values and ecological patterns in Rao were consistent among methods including different scales of intraspecific trait variability. C_LIO_LIDecisions about where traits are sampled and how trait variability is included in functional diversity can drastically change the patterns observed and conclusions about ecological processes. We recommend sampling the traits of multiple individuals per species and capturing their intraspecific trait variability using probabilistic methods. We discuss how intraspecific trait variability can be reasonably estimated and included in functional diversity in the common circumstance where only limited trait data are available. C_LI

ecology↗

bioRxiv2020

Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution

Bayesian data analysis is increasingly used in ecology, but prior specification remains focused on choosing non-informative priors (e.g., flat or vague priors). One barrier to choosing more informative priors is that priors must be specified on model parameters (e.g., intercepts, slopes, sigmas), but prior knowledge often exists on the level of the response variable. This is particularly true for common models in ecology, like generalized linear mixed models, which may have a link function and dozens of parameters, each of which needs a prior distribution. We suggest that this difficulty can be overcome by simulating from the prior predictive distribution and visualizing the results on the scale of the response variable. In doing so, some common choices for non-informative priors on parameters can easily be seen to produce biologically impossible values of response variables. Such implications of prior choices are difficult to foresee without visualization. We demonstrate a workflow for prior selection using simulation and visualization with two ecological examples (predator-prey body sizes and spider responses to food competition). This approach is not new, but its adoption by ecologists will help to better incorporate prior information in ecological models, thereby maximizing one of the benefits of Bayesian data analysis.

ecology↗