On the sympatric evolution and evolutionary stability of coexistence by relative nonlinearity of competition

If two species exhibit different nonlinear responses to a single shared resource, and if each species modifies the resource dynamics such that this favors its competitor, they may stably coexist. This coexistence mechanism, known as relative nonlinearity of competition, is well understood theoretically, but less is known about its evolutionary properties and its prevalence in real communities. We address this challenge by using adaptive dynamics theory and individual-based simulations to compare community stabilization and evolutionary stability of species that coexist by relative nonlinearity. In our analysis, evolution operates on the species' density-compensation strategies, and we consider a trade-off between population growth rates at high and low resource availability. We confirm previous findings that, irrespective of the particular model of density dependence, there are many combinations of overcompensating and undercompensating density-compensation strategies that allow stable coexistence by relative nonlinearity. However, our analysis also shows that most of these strategy combinations are not evolutionarily stable and will be outcompeted by an intermediate density-compensation strategy. Only very specific trade-offs lead to evolutionarily stable coexistence by relative nonlinearity. As we find no reason why these particular trade-offs should be common in nature, we conclude that the sympatric evolution and evolutionary stability of relative nonlinearity, while possible in principle, seems rather unlikely. We speculate that this may, at least in part, explain why empirical demonstrations of this coexistence mechanism are rare, noting, however, that the difficulty to detect relative nonlinearity in the field [...]Comment: PLOS ONE, in pres

A new method for faster and more accurate inference of species associations from big community data

1. Joint Species Distribution models (JSDMs) explain spatial variation in community composition by contributions of the environment, biotic associations, and possibly spatially structured residual covariance. They show great promise as a general analytical framework for community ecology and macroecology, but current JSDMs, even when approximated by latent variables, scale poorly on large datasets, limiting their usefulness for currently emerging big (e.g., metabarcoding and metagenomics) community datasets. 2. Here, we present a novel, more scalable JSDM (sjSDM) that circumvents the need to use latent variables by using a Monte-Carlo integration of the joint JSDM likelihood and allows flexible elastic net regularization on all model components. We implemented sjSDM in PyTorch, a modern machine learning framework that can make use of CPU and GPU calculations. Using simulated communities with known species-species associations and different number of species and sites, we compare sjSDM with state-of-the-art JSDM implementations to determine computational runtimes and accuracy of the inferred species-species and species-environmental associations. 3. We find that sjSDM is orders of magnitude faster than existing JSDM algorithms (even when run on the CPU) and can be scaled to very large datasets. Despite the dramatically improved speed, sjSDM produces more accurate estimates of species association structures than alternative JSDM implementations. We demonstrate the applicability of sjSDM to big community data using eDNA case study with thousands of fungi operational taxonomic units (OTU). 4. Our sjSDM approach makes the analysis of JSDMs to large community datasets with hundreds or thousands of species possible, substantially extending the applicability of JSDMs in ecology. We provide our method in an R package to facilitate its applicability for practical data analysis.Comment: 65 pages, 5 figure

An Extended Empirical Saddlepoint Approximation for Intractable Likelihoods

The challenges posed by complex stochastic models used in computational ecology, biology and genetics have stimulated the development of approximate approaches to statistical inference. Here we focus on Synthetic Likelihood (SL), a procedure that reduces the observed and simulated data to a set of summary statistics, and quantifies the discrepancy between them through a synthetic likelihood function. SL requires little tuning, but it relies on the approximate normality of the summary statistics. We relax this assumption by proposing a novel, more flexible, density estimator: the Extended Empirical Saddlepoint approximation. In addition to proving the consistency of SL, under either the new or the Gaussian density estimator, we illustrate the method using two examples. One of these is a complex individual-based forest model for which SL offers one of the few practical possibilities for statistical inference. The examples show that the new density estimator is able to capture large departures from normality, while being scalable to high dimensions, and this in turn leads to more accurate parameter estimates, relative to the Gaussian alternative. The new density estimator is implemented by the esaddle R package, which can be found on the Comprehensive R Archive Network (CRAN)

cito: An R package for training neural networks using torch

Deep Neural Networks (DNN) have become a central method for regression and classification tasks. Some packages exist that allow to fit DNN directly in R, but those are rather limited in their functionality. Most current deep learning applications rely on one of the major deep learning frameworks, in particular PyTorch or TensorFlow, to build and train DNNs. Using these frameworks, however, requires substantially more training and time than typical regression or machine learning functions in the R environment. Here, we present 'cito', a user-friendly R package for deep learning that allows to specify deep neural networks in the familiar formula syntax used in many R packages. To fit the models, 'cito' uses 'torch', taking advantage of the numerically optimized torch library, including the ability to switch between training models on CPUs or GPUs. Moreover, 'cito' includes many user-friendly functions for model plotting and analysis, including optional confidence intervals (CIs) based on bootstraps on predictions as well as explainable AI (xAI) metrics for effect sizes and variable importance with CIs and p-values. To showcase a typical analysis pipeline using 'cito', including its built-in xAI features to explore the trained DNN, we build a species distribution model of the African elephant. We hope that by providing a user-friendly R framework to specify, deploy and interpret deep neural networks, 'cito' will make this interesting model class more accessible to ecological data analysis. A stable version of 'cito' can be installed from the comprehensive R archive network (CRAN).Comment: 15 pages, 4 figures, 2 table

Species and genetic diversity patterns show different responses to land use intensity in central European grasslands

Aim: Empirical studies have often reported parallel patterns of genetic and species diversity, but the strength and generality of this association, as well as its origin, are still debated. Particularly in human‐dominated landscapes with complex histories of land use histories, more complicated and partly diverging patterns have been observed. In this study, we examine whether species and genetic diversity correlate across grasslands with different levels of land use pressure and spatial differentiation in habitat quality and heterogeneity. Location: We selected eight extensively used (grazed, unfertilized) dry grasslands and eight intensively used (mown, fertilized) hay meadows in southeastern Germany. Methods: We used vegetation surveys and molecular markers of six widespread dry grassland and six hay meadow plant species to compare species and genetic alpha and beta diversity between the two grassland types. Results: Species diversity patterns expectedly showed higher alpha diversity, stronger spatial structure and less turnover in dry grasslands than in hay meadows. Neither of the corresponding genetic diversity patterns showed the same significant trends. Main conclusion: Our results question the idea that species and genetic diversity patterns will always show similar patterns. Likely, genetic and species diversity emerge partly from shared, partly from different processes, including the regional species pool, environmental heterogeneity, fragmentation and land use history. The practical conservation implication is that species and genetic diversity are not generally interchangeable. Looking at species and genetic patterns together, however, may eventually lead to a better understanding of the complex processes that shape the structure and dynamics of ecological communities

Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks

Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait‐matching for determining species interactions, however, vary significantly among different types of ecological networks. Here, we show that ambiguity among empirical trait‐matching studies may have arisen at least in parts from using overly simple statistical models. Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, naïve Bayes, and k‐Nearest‐Neighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions. We found that the best ML models can successfully predict species interactions in plant–pollinator networks, outperforming GLMs by a substantial margin. Our results also demonstrate that ML models can better identify the causally responsible trait‐matching combinations than GLMs. In two case studies, the best ML models successfully predicted species interactions in a global plant–pollinator database and inferred ecologically plausible trait‐matching rules for a plant–hummingbird network from Costa Rica, without any prior assumptions about the system. We conclude that flexible ML models offer many advantages over traditional regression models for understanding interaction networks. We anticipate that these results extrapolate to other ecological network types. More generally, our results highlight the potential of machine learning and artificial intelligence for inference in ecology, beyond standard tasks such as image or pattern recognition

Supporting the Development of Cyber-Physical Systems with Natural Language Processing: A Report

Software has become the driving force for innovations in any technical system that observes the environment with different sensors and influence it by controlling a number of actuators; nowadays called Cyber-Physical System (CPS). The development of such systems is inherently inter-disciplinary and often contains a number of independent subsystems. Due to this diversity, the majority of development information is expressed in natural language artifacts of all kinds. In this paper, we report on recent results that our group has developed to support engineers of CPSs in working with the large amount of information expressed in natural language. We cover the topics of automatic knowledge extraction, expert systems, and automatic requirements classification. Furthermore, we envision that natural language processing will be a key component to connect requirements with simulation models and to explain tool-based decisions. We see both areas as promising for supporting engineers of CPSs in the future