Andrea Giometto

Complex Interaction of Dendritic Connectivity and Hierarchical Patch Size on Biodiversity in River-Like Landscapes

Habitat fragmentation and land use changes are causing major biodiversity losses. Connectivity of the landscape or environmental conditions alone can shape biodiversity patterns. In nature, however, local habitat characteristics are often intrinsically linked to a specific connectivity. Such a link is evident in riverine ecosystems, where hierarchical dendritic structures command related scaling on habitat capacity. We experimentally disentangled the effect of local habitat capacity (i.e., the patch size) and dendritic connectivity on biodiversity in aquatic microcosm metacommunities by suitably arranging patch sizes within river-like networks. Overall, more connected communities that occupy a central position in the network exhibited higher species richness, irrespective of patch size arrangement. High regional evenness in community composition was found only in landscapes preserving geomorphological scaling properties of patch sizes. In these landscapes, some of the rarer species sustained regionally more abundant populations better tracking their own niche requirements compared to landscapes with homogeneous patch size or landscapes with spatially uncorrelated patch size. Our analysis suggests that altering the natural link between dendritic connectivity and patch size strongly affects community composition and population persistence at multiple scales. The experimental results are demonstrating a principle that can be tested in theoretical metacommunity models and eventually be projected to real riverine ecosystems.

Big answers from small worlds: a user's guide for protist microcosms as a model system in ecology and evolution

Laboratory microcosm experiments using protists as model organisms have a long tradition and are widely used to investigate general concepts in population biology, community ecology and evolutionary biology. Many variables of interest are measured in order to study processes and patterns at different spatiotemporal scales and across all levels of biological organization. This includes measurements of body size, mobility or abundance, in order to understand population dynamics, dispersal behaviour and ecosystem processes. Also, a variety of manipulations are employed, such as temperature changes or varying connectivity in spatial microcosm networks. Past studies, however, have used varying methods for maintenance, measurement, and manipulation, which hinders across-study comparisons and meta-analyses, and the added value they bring. Furthermore, application of techniques such as flow cytometry, image and video analyses, and in situ environmental probes provide novel and improved opportunities to quantify variables of interest at unprecedented precision and temporal resolution. Here, we take the first step towards a standardization of well-established and novel methods and techniques within the field of protist microcosm experiments. We provide a comprehensive overview of maintenance, measurement and manipulation methods. An extensive supplement contains detailed protocols of all methods, and these protocols also exist in a community updateable online repository. We envision that such a synthesis and standardization of methods will overcome shortcomings and challenges faced by past studies and also promote activities such as meta-analyses and distributed experiments conducted simultaneously across many different laboratories at a global scale.

Scaling body size fluctuations

The size of an organism matters for its metabolic, growth, mortality, and other vital rates. Scale-free community size spectra (i.e., size distributions regardless of species) are routinely observed in natural ecosystems and are the product of intra- and interspecies regulation of the relative abundance of organisms of different sizes. Intra- and interspecies distributions of body sizes are thus major determinants of ecosystems’ structure and function. We show experimentally that single-species mass distributions of unicellular eukaryotes covering different phyla exhibit both characteristic sizes and universal features over more than four orders of magnitude in mass. Remarkably, we find that the mean size of a species is sufficient to characterize its size distribution fully and that the latter has a universal form across all species. We show that an analytical physiological model accounts for the observed universality, which can be synthesized in a log-normal form for the intraspecies size distributions. We also propose how ecological and physiological processes should interact to produce scale-invariant community size spectra and discuss the implications of our results on allometric scaling laws involving body mass.

Emerging predictable features of replicated biological invasion fronts

Significance Biological dispersal is a key driver of several fundamental processes in nature, crucially controlling the distribution of species and affecting their coexistence. Despite its relevance for important ecological processes, however, the subject suffers an acknowledged lack of experimentation, and current assessments point at inherent limitation to predictability even in the simplest ecological settings. We show, by combining replicated experimentation on the spread of the ciliate Tetrahymena sp. with a theoretical approach based on stochastic differential equations, that information on local unconstrained movement and reproduction of organisms (including demographic stochasticity) allows reliable prediction of both the propagation speed and range of variability of invasion fronts over multiple generations. Biological dispersal shapes species’ distribution and affects their coexistence. The spread of organisms governs the dynamics of invasive species, the spread of pathogens, and the shifts in species ranges due to climate or environmental change. Despite its relevance for fundamental ecological processes, however, replicated experimentation on biological dispersal is lacking, and current assessments point at inherent limitations to predictability, even in the simplest ecological settings. In contrast, we show, by replicated experimentation on the spread of the ciliate Tetrahymena sp. in linear landscapes, that information on local unconstrained movement and reproduction allows us to predict reliably the existence and speed of traveling waves of invasion at the macroscopic scale. Furthermore, a theoretical approach introducing demographic stochasticity in the Fisher–Kolmogorov framework of reaction–diffusion processes captures the observed fluctuations in range expansions. Therefore, predictability of the key features of biological dispersal overcomes the inherent biological stochasticity. Our results establish a causal link from the short-term individual level to the long-term, broad-scale population patterns and may be generalized, possibly providing a general predictive framework for biological invasions in natural environments.

Sample and population exponents of generalized Taylor’s law

Significance Taylor’s law (TL) has been verified very widely in the natural sciences, information technology, and finance. The widespread observation of TL suggests that a context-independent mechanism may be at work and stimulated the search for processes affecting the scaling of population fluctuations with population abundance. We show that limited sampling may explain why TL is often observed to have exponent b=2. Abrupt transitions in the TL exponent associated with smooth changes in the environment were recently discovered theoretically and comparable real-world transitions could harm fish populations, forests, and public health. Our study shows that limited sampling hinders the anticipation of such transitions and provides estimates for the number of samples required to reveal early warning signals of abrupt biotic change. Taylor’s law (TL) states that the variance V of a nonnegative random variable is a power function of its mean M; i.e., V=aMb. TL has been verified extensively in ecology, where it applies to population abundance, physics, and other natural sciences. Its ubiquitous empirical verification suggests a context-independent mechanism. Sample exponents b measured empirically via the scaling of sample mean and variance typically cluster around the value b=2. Some theoretical models of population growth, however, predict a broad range of values for the population exponent b pertaining to the mean and variance of population density, depending on details of the growth process. Is the widely reported sample exponent b≃2 the result of ecological processes or could it be a statistical artifact? Here, we apply large deviations theory and finite-sample arguments to show exactly that in a broad class of growth models the sample exponent is b≃2 regardless of the underlying population exponent. We derive a generalized TL in terms of sample and population exponents bjk for the scaling of the kth vs. the jth cumulants. The sample exponent bjk depends predictably on the number of samples and for finite samples we obtain bjk≃k/j asymptotically in time, a prediction that we verify in two empirical examples. Thus, the sample exponent b≃2 may indeed be a statistical artifact and not dependent on population dynamics under conditions that we specify exactly. Given the broad class of models investigated, our results apply to many fields where TL is used although inadequately understood.

Experimental evidence for strong stabilizing forces at high functional diversity of aquatic microbial communities

Unveiling the mechanisms that promote coexistence in biological communities is a fundamental problem in ecology. Stable coexistence of many species is commonly observed in natural communities. Most of these natural communities, however, are composed of species from multiple trophic and functional groups, while theory and experiments on coexistence have been focusing on functionally similar species. Here, we investigated how functional diversity affects the stability of species coexistence and productivity in multispecies communities by characterizing experimentally all pairwise species interactions in a pool of 11 species of eukaryotes (10 protists and one rotifer) belonging to three different functional groups. Species within the same functional group showed stronger competitive interactions compared to among-functional group interactions. This often led to competitive exclusion between species that had higher functional relatedness, but only at low levels of species richness. Communities with higher functional diversity resulted in increased species coexistence and community biomass production. Our experimental findings and the results of a stochastic model tailored to the experimental interaction matrix suggest the emergence of strong stabilizing forces when species from different functional groups interact in a homogeneous environment. By combining theoretical analysis with experiments we could also disentangle the relationship between species richness and functional diversity, showing that functional diversity per se is a crucial driver of productivity and stability in multispecies community.

Inferring species interactions in ecological communities: a comparison of methods at different levels of complexity

1. Natural communities commonly contain many different species and functional groups, and multiple types of species interactions act simultaneously, such as competition, predation, commensalism or mutualism. However, experimental and theoretical investigations have generally been limited by focusing on one type of interaction at a time or by a lack of a common methodological and conceptual approach to measure species interactions. 2. We compared four methods to measure and express species interactions. These approaches are, with increasing degree of model complexity, an extinction-based model, a relative yield model and two generalized Lotka-Volterra (LV) models. All four approaches have been individually applied in different fields of community ecology, but rarely integrated. We provide an overview of the definitions, assumptions and data needed for the specific methods and apply them to empirical data by experimentally deriving the interaction matrices among 11 protist and rotifer species, belonging to three functional groups. Furthermore, we compare their advantages and limitations to predict multispecies community dynamics and ecosystem functioning. 3. The relative yield method is, in terms of final biomass production, the best method in predicting the 11-species community dynamics from the pairwise competition experiments. The LV model, which is considering equilibrium among the species, suffers from experimental constraints given the strict equilibrium assumption, and this may be rarely satisfied in ecological communities. 4. We show how simulations of a LV stochastic community model, derived from an empirical interaction matrix, can be used to predict multispecies community dynamics across multiple functional groups. 5.Our work unites available tools to measure species interactions under one framework. This improves our ability to make management-oriented predictions of species coexistence/extinction and to compare ecosystem processes across study systems.

Generalized receptor law governs phototaxis in the phytoplankton Euglena gracilis

Significance Many phytoplankton species sense light and move toward or away from it. Such directed movement, called phototaxis, has major ecological implications because it contributes to the largest biomass migration on Earth, diel vertical migration of organisms responsible for roughly one-half of the global photosynthesis. We experimentally studied phototaxis for the flagellate alga Euglena gracilis by tracking algal populations over time in accurately controlled light fields. Observations coupled with formal model comparison lead us to propose a generalized receptor law governing phototaxis of phytoplankton. Such a model accurately reproduces experimental patterns resulting from accumulation and dispersion dynamics. Direct applications concern phytoplankton migrations and vertical distribution, bioreactor optimization, and the experimental study of biological invasions in heterogeneous environments. Phototaxis, the process through which motile organisms direct their swimming toward or away from light, is implicated in key ecological phenomena (including algal blooms and diel vertical migration) that shape the distribution, diversity, and productivity of phytoplankton and thus energy transfer to higher trophic levels in aquatic ecosystems. Phototaxis also finds important applications in biofuel reactors and microbiopropellers and is argued to serve as a benchmark for the study of biological invasions in heterogeneous environments owing to the ease of generating stochastic light fields. Despite its ecological and technological relevance, an experimentally tested, general theoretical model of phototaxis seems unavailable to date. Here, we present accurate measurements of the behavior of the alga Euglena gracilis when exposed to controlled light fields. Analysis of E. gracilis’ phototactic accumulation dynamics over a broad range of light intensities proves that the classic Keller–Segel mathematical framework for taxis provides an accurate description of both positive and negative phototaxis only when phototactic sensitivity is modeled by a generalized “receptor law,” a specific nonlinear response function to light intensity that drives algae toward beneficial light conditions and away from harmful ones. The proposed phototactic model captures the temporal dynamics of both cells’ accumulation toward light sources and their dispersion upon light cessation. The model could thus be of use in integrating models of vertical phytoplankton migrations in marine and freshwater ecosystems, and in the design of bioreactors.

Covariations in ecological scaling laws fostered by community dynamics

Significance Empirical laws portraying patterns in ecology are routinely observed in marine and terrestrial environments. Such patterns are recurrent but also show features that are distinctive of each ecosystem. For example, the number of species in an ecosystem increases with its area according to a well-defined mathematical law, but the rate of increase may vary across different ecosystem types. We show that different ecological patterns are linked to each other in a way that if one is changed, the others are affected as well. We verify our predictions on available empirical datasets and unravel yet unknown features of natural ecosystems, suggesting directions for empirical research. Scaling laws in ecology, intended both as functional relationships among ecologically relevant quantities and the probability distributions that characterize their occurrence, have long attracted the interest of empiricists and theoreticians. Empirical evidence exists of power laws associated with the number of species inhabiting an ecosystem, their abundances, and traits. Although their functional form appears to be ubiquitous, empirical scaling exponents vary with ecosystem type and resource supply rate. The idea that ecological scaling laws are linked has been entertained before, but the full extent of macroecological pattern covariations, the role of the constraints imposed by finite resource supply, and a comprehensive empirical verification are still unexplored. Here, we propose a theoretical scaling framework that predicts the linkages of several macroecological patterns related to species’ abundances and body sizes. We show that such a framework is consistent with the stationary-state statistics of a broad class of resource-limited community dynamics models, regardless of parameterization and model assumptions. We verify predicted theoretical covariations by contrasting empirical data and provide testable hypotheses for yet unexplored patterns. We thus place the observed variability of ecological scaling exponents into a coherent statistical framework where patterns in ecology embed constrained fluctuations.

The scaling structure of the global road network

Because of increasing global urbanization and its immediate consequences, including changes in patterns of food demand, circulation and land use, the next century will witness a major increase in the extent of paved roads built worldwide. To model the effects of this increase, it is crucial to understand whether possible self-organized patterns are inherent in the global road network structure. Here, we use the largest updated database comprising all major roads on the Earth, together with global urban and cropland inventories, to suggest that road length distributions within croplands are indistinguishable from urban ones, once rescaled to account for the difference in mean road length. Such similarity extends to road length distributions within urban or agricultural domains of a given area. We find two distinct regimes for the scaling of the mean road length with the associated area, holding in general at small and at large values of the latter. In suitably large urban and cropland domains, we find that mean and total road lengths increase linearly with their domain area, differently from earlier suggestions. Scaling regimes suggest that simple and universal mechanisms regulate urban and cropland road expansion at the global scale. As such, our findings bear implications for global road infrastructure growth based on land-use change and for planning policies sustaining urban expansions.