scholarly journals Community dynamics and sensitivity to model structure: towards a probabilistic view of process-based model predictions

2018 ◽  
Vol 15 (149) ◽  
pp. 20180741 ◽  
Author(s):  
Clement Aldebert ◽  
Daniel B. Stouffer
Keyword(s):  
Biological Sciences ◽  
Community Dynamics ◽  
Parametric Uncertainty ◽  
Proof Of Concept ◽  
Predator Prey ◽  
Predictive Uncertainty ◽  
Structural Sensitivity ◽  
Model Predictions ◽  
Complex Models ◽  
Process Based Models

Statistical inference and mechanistic, process-based modelling represent two philosophically different streams of research whose primary goal is to make predictions. Here, we merge elements from both approaches to keep the theoretical power of process-based models while also considering their predictive uncertainty using Bayesian statistics. In environmental and biological sciences, the predictive uncertainty of process-based models is usually reduced to parametric uncertainty. Here, we propose a practical approach to tackle the added issue of structural sensitivity, the sensitivity of predictions to the choice between quantitatively close and biologically plausible models. In contrast to earlier studies that presented alternative predictions based on alternative models, we propose a probabilistic view of these predictions that include the uncertainty in model construction and the parametric uncertainty of each model. As a proof of concept, we apply this approach to a predator–prey system described by the classical Rosenzweig–MacArthur model, and we observe that parametric sensitivity is regularly overcome by structural sensitivity. In addition to tackling theoretical questions about model sensitivity, the proposed approach can also be extended to make probabilistic predictions based on more complex models in an operational context. Both perspectives represent important steps towards providing better model predictions in biology, and beyond.

Effects of parametric uncertainty on multi-scale model predictions of shock response of a pressed energetic material

2019 ◽  
Vol 125 (23) ◽  
pp. 235104 ◽  
Author(s):  
Sangyup Lee ◽  
Oishik Sen ◽  
Nirmal Kumar Rai ◽  
Nicholas J. Gaul ◽  
K. K. Choi ◽  
...  
Keyword(s):  
Energetic Material ◽  
Parametric Uncertainty ◽  
Scale Model ◽  
Multi Scale ◽  
Model Predictions ◽  
Shock Response

scholarly journals A simple standardized protocol to evaluate the reliability of seed rain estimates

2020 ◽  
pp. 1-6
Author(s):  
André J. Arruda ◽  
Fernando A.O. Silveira ◽  
Elise Buisson
Keyword(s):  
Restoration Ecology ◽  
Community Dynamics ◽  
Size Dependence ◽  
Seed Rain ◽  
Proof Of Concept ◽  
Sticky Traps ◽  
Standardized Protocol ◽  
Funnel Trap ◽  
Seed Trap ◽  
Cross Site

Abstract Seed dispersal has key implications for community dynamics and restoration ecology. However, estimating seed rain (the number and diversity of seeds arriving in a given area) is challenging, and the lack of standardization in measurement prevents cross-site comparisons. Seed trap effectiveness and accuracy of seed sorting methods are key components of seed rain estimates in need of standardization. We propose and describe a standardized protocol for evaluating the effectiveness of two seed trap types (sticky and funnel traps) and the accuracy of a seed sorting method. We used widely available seeds (arugula, quinoa, sesame and sunflower) to produce a gradient of seed size, weight and colour. Proof-of-concept was tested in a tropical grassland, where traps were set for 30 days. Our results suggest that we underestimate dispersal of seeds with less than 2 mm width that can be easily mistaken for debris and soil particles or that fail to adhere to sticky traps. Seeds on sticky traps may be more vulnerable to removal by wind and rain, whereas seeds in funnel traps are more susceptible to decay. We found no evidence of observer bias on seed sorting for funnel trap samples. However, accuracy on seed sorting for funnel trap samples tended to decline for seeds with less than 2 mm width, suggesting a size-dependence in seed retrieval success. Our standardized protocol addressing trap effectiveness and seed sorting methods will increase the reliability of data obtained in seed rain studies and allow more reliable comparisons between datasets.

scholarly journals Macroecological analyses support an overkill scenario for late Pleistocene extinctions

2004 ◽  
Vol 64 (3a) ◽  
pp. 407-414 ◽  
Author(s):  
J. A. F. Diniz-Filho
Keyword(s):  
Body Mass ◽  
Population Dynamic ◽  
High Selectivity ◽  
Environmental Changes ◽  
Dynamic Parameters ◽  
Allometric Relationships ◽  
Ecological Data ◽  
Predator Prey ◽  
A Value ◽  
Complex Models

The extinction of megafauna at the end of Pleistocene has been traditionally explained by environmental changes or overexploitation by human hunting (overkill). Despite difficulties in choosing between these alternative (and not mutually exclusive) scenarios, the plausibility of the overkill hypothesis can be established by ecological models of predator-prey interactions. In this paper, I have developed a macroecological model for the overkill hypothesis, in which prey population dynamic parameters, including abundance, geographic extent, and food supply for hunters, were derived from empirical allometric relationships with body mass. The last output correctly predicts the final destiny (survival or extinction) for 73% of the species considered, a value only slightly smaller than those obtained by more complex models based on detailed archaeological and ecological data for each species. This illustrates the high selectivity of Pleistocene extinction in relation to body mass and confers more plausibility on the overkill scenario.

scholarly journals Technical Note: Approximate Bayesian parameterization of a complex tropical forest model

2013 ◽  
Vol 10 (8) ◽  
pp. 13097-13128 ◽  
Author(s):  
F. Hartig ◽  
C. Dislich ◽  
T. Wiegand ◽  
A. Huth
Keyword(s):  
Parameter Estimation ◽  
Tropical Forest ◽  
Field Data ◽  
Complex Data ◽  
Forest Model ◽  
Likelihood Approximation ◽  
Simulation Based ◽  
Model Predictions ◽  
Approximate Bayesian ◽  
Process Based Models

Abstract. Inverse parameter estimation of process-based models is a long-standing problem in ecology and evolution. A key problem of inverse parameter estimation is to define a metric that quantifies how well model predictions fit to the data. Such a metric can be expressed by general cost or objective functions, but statistical inversion approaches are based on a particular metric, the probability of observing the data given the model, known as the likelihood. Deriving likelihoods for dynamic models requires making assumptions about the probability for observations to deviate from mean model predictions. For technical reasons, these assumptions are usually derived without explicit consideration of the processes in the simulation. Only in recent years have new methods become available that allow generating likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics. We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional MCMC, performs well in retrieving known parameter values from virtual field data generated by the forest model. We analyze the results of the parameter estimation, examine the sensitivity towards the choice and aggregation of model outputs and observed data (summary statistics), and show results from using this method to fit the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss differences of this approach to Approximate Bayesian Computing (ABC), another commonly used method to generate simulation-based likelihood approximations. Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can successfully be applied to process-based models of high complexity. The methodology is particularly suited to heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models in ecology and evolution.

scholarly journals What drives interaction strengths in complex food webs? A test with feeding rates of a generalist stream predator

2018 ◽  
Author(s):  
Daniel L. Preston ◽  
Jeremy S. Henderson ◽  
Landon P. Falke ◽  
Leah M. Segui ◽  
Tamara J. Layden ◽  
...  
Keyword(s):  
Food Webs ◽  
Body Mass ◽  
Prey Density ◽  
Scaling Laws ◽  
Community Dynamics ◽  
Abiotic Factors ◽  
Interaction Strength ◽  
Feeding Rates ◽  
Predator Prey ◽  
Mass Ratios

AbstractDescribing the mechanisms that drive variation in species interaction strengths is central to understanding, predicting, and managing community dynamics. Multiple factors have been linked to trophic interaction strength variation, including species densities, species traits, and abiotic factors. Yet most empirical tests of the relative roles of multiple mechanisms that drive variation have been limited to simplified experiments that may diverge from the dynamics of natural food webs. Here, we used a field-based observational approach to quantify the roles of prey density, predator density, predator-prey body-mass ratios, prey identity, and abiotic factors in driving variation in feeding rates of reticulate sculpin (Cottus perplexus). We combined data on over 6,000 predator-prey observations with prey identification time functions to estimate 289 prey-specific feeding rates at nine stream sites in Oregon. Feeding rates on 57 prey types showed an approximately log-normal distribution, with few strong and many weak interactions. Model selection indicated that prey density, followed by prey identity, were the two most important predictors of prey-specific sculpin feeding rates. Feeding rates showed a positive, accelerating relationship with prey density that was inconsistent with predator saturation predicted by current functional response models. Feeding rates also exhibited four orders-of-magnitude in variation across prey taxonomic orders, with the lowest feeding rates observed on prey with significant anti-predator defenses. Body-mass ratios were the third most important predictor variable, showing a hump-shaped relationship with the highest feeding rates at intermediate ratios. Sculpin density was negatively correlated with feeding rates, consistent with the presence of intraspecific predator interference. Our results highlight how multiple co-occurring drivers shape trophic interactions in nature and underscore ways in which simplified experiments or reliance on scaling laws alone may lead to biased inferences about the structure and dynamics of species-rich food webs.

The Application of Swarm Intelligence to Collective Robots

2006 ◽  
pp. 157-185 ◽  
Author(s):  
Amanda J.C. Sharkey ◽  
Noel Sharkey
Keyword(s):  
Swarm Intelligence ◽  
Proof Of Concept ◽  
Practical Application ◽  
Predator Prey ◽  
Expected Benefits ◽  
Intelligent Approach ◽  
Intelligence Approach ◽  
Robotic Applications ◽  
Study Application

This chapter considers the application of swarm intelligence principles to collective robotics. Our aim is to identify the reasons for the growing interest in the intersection of these two areas, and to evaluate the progress that has been made to date. In the course of this chapter, we will discuss the implications of taking a swarm intelligent approach, and review recent research and applications. The area of “swarm robotics” offers considerable promise for practical application, although it is still in its infancy, and many of the tasks that have been achieved are better described as “proof-of-concept” examples, rather than full-blown applications. In the first part of the chapter, we will examine what taking a swarm intelligence approach to robotics implies, and outline its expected benefits. We shall then proceed to review recent swarm robotic applications, before concluding with a case study application of predator-prey robotics that illustrates some of the potential of the approach.

scholarly journals Is the scaling of swim speed in sharks driven by metabolism?

2015 ◽  
Vol 11 (12) ◽  
pp. 20150781 ◽  
Author(s):  
David M. P. Jacoby ◽  
Penthai Siriwat ◽  
Robin Freeman ◽  
Chris Carbone
Keyword(s):  
Body Size ◽  
Ecosystem Functioning ◽  
Foraging Ecology ◽  
Geometric Model ◽  
Predator Prey ◽  
Marine Systems ◽  
Movement Rates ◽  
Scaling Relationship ◽  
Model Predictions ◽  
Ecological Parameters

The movement rates of sharks are intrinsically linked to foraging ecology, predator–prey dynamics and wider ecosystem functioning in marine systems. During ram ventilation, however, shark movement rates are linked not only to ecological parameters, but also to physiology, as minimum speeds are required to provide sufficient water flow across the gills to maintain metabolism. We develop a geometric model predicting a positive scaling relationship between swim speeds in relation to body size and ultimately shark metabolism, taking into account estimates for the scaling of gill dimensions. Empirical data from 64 studies (26 species) were compiled to test our model while controlling for the influence of phylogenetic similarity between related species. Our model predictions were found to closely resemble the observed relationships from tracked sharks, providing a means to infer mobility in particularly intractable species.

scholarly journals Space race functional responses

2015 ◽  
Vol 282 (1801) ◽  
pp. 20142121 ◽  
Author(s):  
Henrik Sjödin ◽  
Åke Brännström ◽  
Göran Englund
Keyword(s):  
Functional Response ◽  
Community Dynamics ◽  
Simple Structure ◽  
Functional Responses ◽  
Response Models ◽  
Predator Prey ◽  
Prey System ◽  
Space Race ◽  
Functional Response Models ◽  
The Relationship

We derive functional responses under the assumption that predators and prey are engaged in a space race in which prey avoid patches with many predators and predators avoid patches with few or no prey. The resulting functional response models have a simple structure and include functions describing how the emigration of prey and predators depend on interspecific densities. As such, they provide a link between dispersal behaviours and community dynamics. The derived functional response is general but is here modelled in accordance with empirically documented emigration responses. We find that the prey emigration response to predators has stabilizing effects similar to that of the DeAngelis–Beddington functional response, and that the predator emigration response to prey has destabilizing effects similar to that of the Holling type II response. A stability criterion describing the net effect of the two emigration responses on a Lotka–Volterra predator–prey system is presented. The winner of the space race (i.e. whether predators or prey are favoured) is determined by the relationship between the slopes of the species' emigration responses. It is predicted that predators win the space race in poor habitats, where predator and prey densities are low, and that prey are more successful in richer habitats.

scholarly journals Speciation through the lens of biomechanics: locomotion, prey capture and reproductive isolation

2016 ◽  
Vol 283 (1838) ◽  
pp. 20161294 ◽  
Author(s):  
Timothy E. Higham ◽  
Sean M. Rogers ◽  
R. Brian Langerhans ◽  
Heather A. Jamniczky ◽  
George V. Lauder ◽  
...  
Keyword(s):  
Biological Sciences ◽  
Reproductive Isolation ◽  
Prey Capture ◽  
Dynamic Interactions ◽  
Fish Predator ◽  
Local Conditions ◽  
Predator Prey ◽  
Primary Driver ◽  
Abiotic And Biotic Factors ◽  
Specific Species

Speciation is a multifaceted process that involves numerous aspects of the biological sciences and occurs for multiple reasons. Ecology plays a major role, including both abiotic and biotic factors. Whether populations experience similar or divergent ecological environments, they often adapt to local conditions through divergence in biomechanical traits. We investigate the role of biomechanics in speciation using fish predator–prey interactions, a primary driver of fitness for both predators and prey. We highlight specific groups of fishes, or specific species, that have been particularly valuable for understanding these dynamic interactions and offer the best opportunities for future studies that link genetic architecture to biomechanics and reproductive isolation (RI). In addition to emphasizing the key biomechanical techniques that will be instrumental, we also propose that the movement towards linking biomechanics and speciation will include (i) establishing the genetic basis of biomechanical traits, (ii) testing whether similar and divergent selection lead to biomechanical divergence, and (iii) testing whether/how biomechanical traits affect RI. Future investigations that examine speciation through the lens of biomechanics will propel our understanding of this key process.

scholarly journals System Biology Modeling with Compositional Microbiome Data Reveals Personalized Gut Microbial Dynamics and Keystone Species

2018 ◽  
Author(s):  
Chenhao Li ◽  
Lisa Tucker-Kellogg ◽  
Niranjan Nagarajan
Keyword(s):  
Microbial Communities ◽  
High Throughput Sequencing ◽  
Community Dynamics ◽  
Keystone Species ◽  
Microbial Interactions ◽  
Natural Environments ◽  
Sequencing Data ◽  
Predator Prey ◽  
Public Datasets ◽  
Microbiome Data

AbstractA growing body of literature points to the important roles that different microbial communities play in diverse natural environments and the human body. The dynamics of these communities is driven by a range of microbial interactions from symbiosis to predator-prey relationships, the majority of which are poorly understood, making it hard to predict the response of the community to different perturbations. With the increasing availability of high-throughput sequencing based community composition data, it is now conceivable to directly learn models that explicitly define microbial interactions and explain community dynamics. The applicability of these approaches is however affected by several experimental limitations, particularly the compositional nature of sequencing data. We present a new computational approach (BEEM) that addresses this key limitation in the inference of generalised Lotka-Volterra models (gLVMs) by coupling biomass estimation and model inference in an expectation maximization like algorithm (BEEM). Surprisingly, BEEM outperforms state-of-the-art methods for inferring gLVMs, while simultaneously eliminating the need for additional experimental biomass data as input. BEEM’s application to previously inaccessible public datasets (due to the lack of biomass data) allowed us for the first time to analyse microbial communities in the human gut on a per individual basis, revealing personalised dynamics and keystone species.

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