Biotic and abiotic drivers of intraspecific trait variation within plant populations of three herbaceous plant species along a latitudinal gradient

Background: The importance of intraspecific trait variation (ITV) is increasingly acknowledged among plant ecologists. However, our understanding of what drives ITV between individual plants (ITVBI) at the population level is still limited. Contrasting theoretical hypotheses state that ITVBI can be either suppressed (stress-reduced plasticity hypothesis) or enhanced (stress-induced variability hypothesis) under high abiotic stress. Similarly, other hypotheses predict either suppressed (niche packing hypothesis) or enhanced ITVBI (individual variation hypothesis) under high niche packing in species rich communities. In this study we assess the relative effects of both abiotic and biotic niche effects on ITVBI of four functional traits (leaf area, specific leaf area, plant height and seed mass), for three herbaceous plant species across a 2300 km long gradient in Europe. The study species were the slow colonizing Anemone nemorosa, a species with intermediate colonization rates, Milium effusum, and the fast colonizing, non-native Impatiens glandulifera. Results: Climatic stress consistently increased ITVBI across species and traits. Soil nutrient stress, on the other hand, reduced ITVBI for A. nemorosa and I. glandulifera, but had a reversed effect for M. effusum. We furthermore observed a reversed effect of high niche packing on ITVBI for the fast colonizing non-native I. glandulifera (increased ITVBI), as compared to the slow colonizing native A. nemorosa and M. effusum (reduced ITVBI). Additionally, ITVBI in the fast colonizing species tended to be highest for the vegetative traits plant height and leaf area, but lowest for the measured generative trait seed mass. Conclusions: This study shows that stress can both reduce and increase ITVBI, seemingly supporting both the stress-reduced plasticity and stress-induced variability hypotheses. Similarly, niche packing effects on ITVBI supported both the niche packing hypothesis and the individual variation hypothesis. These results clearly illustrates the importance of simultaneously evaluating both abiotic and biotic factors on ITVBI. This study adds to the growing realization that within-population trait variation should not be ignored and can provide valuable ecological insights

The Population Genetic Signature of Polygenic Local Adaptation

Adaptation in response to selection on polygenic phenotypes may occur via subtle allele frequencies shifts at many loci. Current population genomic techniques are not well posed to identify such signals. In the past decade, detailed knowledge about the specific loci underlying polygenic traits has begun to emerge from genome-wide association studies (GWAS). Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that may have been influenced by local adaptation. We exploit the fact that GWAS provide an estimate of the additive effect size of many loci to estimate the mean additive genetic value for a given phenotype across many populations as simple weighted sums of allele frequencies. We first describe a general model of neutral genetic value drift for an arbitrary number of populations with an arbitrary relatedness structure. Based on this model we develop methods for detecting unusually strong correlations between genetic values and specific environmental variables, as well as a generalization of $Q_{ST}/F_{ST}$ comparisons to test for over-dispersion of genetic values among populations. Finally we lay out a framework to identify the individual populations or groups of populations that contribute to the signal of overdispersion. These tests have considerably greater power than their single locus equivalents due to the fact that they look for positive covariance between like effect alleles, and also significantly outperform methods that do not account for population structure. We apply our tests to the Human Genome Diversity Panel (HGDP) dataset using GWAS data for height, skin pigmentation, type 2 diabetes, body mass index, and two inflammatory bowel disease datasets. This analysis uncovers a number of putative signals of local adaptation, and we discuss the biological interpretation and caveats of these results.Comment: 42 pages including 8 figures and 3 tables; supplementary figures and tables not included on this upload, but are mostly unchanged from v

Spatially-constrained clustering of ecological networks

Spatial ecological networks are widely used to model interactions between georeferenced biological entities (e.g., populations or communities). The analysis of such data often leads to a two-step approach where groups containing similar biological entities are firstly identified and the spatial information is used afterwards to improve the ecological interpretation. We develop an integrative approach to retrieve groups of nodes that are geographically close and ecologically similar. Our model-based spatially-constrained method embeds the geographical information within a regularization framework by adding some constraints to the maximum likelihood estimation of parameters. A simulation study and the analysis of real data demonstrate that our approach is able to detect complex spatial patterns that are ecologically meaningful. The model-based framework allows us to consider external information (e.g., geographic proximities, covariates) in the analysis of ecological networks and appears to be an appealing alternative to consider such data

Spatial Smoothing Techniques for the Assessment of Habitat Suitability

Precise knowledge about factors influencing the habitat suitability of a certain species forms the basis for the implementation of effective programs to conserve biological diversity. Such knowledge is frequently gathered from studies relating abundance data to a set of influential variables in a regression setup. In particular, generalised linear models are used to analyse binary presence/absence data or counts of a certain species at locations within an observation area. However, one of the key assumptions of generalised linear models, the independence of the observations is often violated in practice since the points at which the observations are collected are spatially aligned. While several approaches have been developed to analyse and account for spatial correlation in regression models with normally distributed responses, far less work has been done in the context of generalised linear models. In this paper, we describe a general framework for semiparametric spatial generalised linear models that allows for the routine analysis of non-normal spatially aligned regression data. The approach is utilised for the analysis of a data set of synthetic bird species in beech forests, revealing that ignorance of spatial dependence actually may lead to false conclusions in a number of situations

Bayesian Learning and Predictability in a Stochastic Nonlinear Dynamical Model

Bayesian inference methods are applied within a Bayesian hierarchical modelling framework to the problems of joint state and parameter estimation, and of state forecasting. We explore and demonstrate the ideas in the context of a simple nonlinear marine biogeochemical model. A novel approach is proposed to the formulation of the stochastic process model, in which ecophysiological properties of plankton communities are represented by autoregressive stochastic processes. This approach captures the effects of changes in plankton communities over time, and it allows the incorporation of literature metadata on individual species into prior distributions for process model parameters. The approach is applied to a case study at Ocean Station Papa, using Particle Markov chain Monte Carlo computational techniques. The results suggest that, by drawing on objective prior information, it is possible to extract useful information about model state and a subset of parameters, and even to make useful long-term forecasts, based on sparse and noisy observations

Destination Choice Models for Rock Climbing in the Northeast Alps: A Latent-Class Approach Based on Intensity of Participation

Practitioners of outdoor sports, such as rock-climbers, are likely to exhibit preference heterogeneity that depends on the ‘keenness’ with which such sports are practiced. Such an intuition is born out in at least one study using latent class discrete choice modelling (Provencher et al. 2002). Preference heterogeneity has a reflection on the population’s structure of recreational values assigned to rock-climbing destinations, to their attributes and ultimately to land management policies addressing such attributes. In this study such hypothesis is tested on a panel of destination choices by a sample of rock-climbers members of the Veneto Chapter of the Italian Alpine Club. Preliminary estimates of latent-class (finite-mixing) specifications provided evidence that intensity of participation explained heterogeneity in taste. This motivated our splitting of the sample in a ‘high’ and a ‘low’ intensity of participation sub-samples, each of which is in turn analysed for the presence of endogenous preference classes using latent-class random utility based approaches. We find evidence in support of the hypothesis that there are at least four statistically well-defined classes in each sub-sample, thereby revealing a considerable richness in the structure of preference, which would otherwise be unobservable in more conventional approaches. From the model estimates, we first focus on the derivation of posterior individual specific welfare measures for some key destination attributes, and then for a welfare neutral land management policy. One emerging feature is the strong evidence of multi-modal distribution of values, a feature that is more difficult to capture when preference heterogeneity is modelled by other means. The results also show how the proposed policy is progressive in terms of benefit distribution in the sample, and that the distribution of individual welfare changes shows markedly different patterns between high and low demand sub-samples.Travel cost model, Preference heterogeneity, Non-market valuation, Random utility model, Latent class analysis, Rock-climbing, Destination choice modelling

Disentangling the information in species interaction networks

Shannon’s entropy measure is a popular means for quantifying ecological diversity. We explore how one can use information-theoretic measures (that are often called indices in ecology) on joint ensembles to study the diversity of species interaction networks. We leverage the little-known balance equation to decompose the network information into three components describing the species abundance, specificity, and redundancy. This balance reveals that there exists a fundamental trade-off between these components. The decomposition can be straightforwardly extended to analyse networks through time as well as space, leading to the corresponding notions for alpha, beta, and gamma diversity. Our work aims to provide an accessible introduction for ecologists. To this end, we illustrate the interpretation of the components on numerous real networks. The corresponding code is made available to the community in the specialised Julia package EcologicalNetworks.jl