Biology subjects

Godoy, O.

Publications and source records attributed to Godoy, O..

29 records · Page 2Linked to original sources

bioRxiv2021

The dimensionality of plant-plant competition

To avoid extinction, every species must be able to exploit available resources at least as well as the other species in its community. All else being equal, theory predicts that the more distinct the niches of such co-occurring and competing species, the more species that can persist in the long run. However, both theoretical and experimental studies define a priori the nature and number of resources over which species compete. It therefore remains unclear whether or not species in empirically realistic contexts are actually exploiting all or some of the niches available to them. Here we provide a mathematical solution to this long-standing problem. Specifically, we show how to use the interactions between sets of co-occurring plant species to quantify their implied "niche dimensionality": the effective number of resources over which those species appear to be competing. We then apply this approach to quantify the niche dimensionality of 12 plant assemblages distributed across the globe. Contrary to conventional wisdom, we found that the niche dimensionality in these systems was much lower than the number of competing species. However, two high-resolution experiments also show that changes in the local environment induce a reshuffling of plants competitive roles and hence act to increase the assemblages effective niche dimensionality. Our results therefore indicate that homogeneous environments are unlikely to be able to maintain high diversity and also shows how environmental variation impacts species niches and hence their opportunities for long-term survival.

ecology↗

bioRxiv2021

Order of arrival promotes coexistence via spatial niche preemption by the weak competitor

Historical contingency, such as the order of species arrival, can modify competitive outcomes via niche modification or preemption. However, how these mechanisms ultimately modify stabilising niche and average fitness differences remains largely unknown. By experimentally assembling two congeneric spider mite species feeding on tomato plants during two generations, we show that order of arrival affects species competitive ability and changes the outcome of competition. Contrary to expectations, order of arrival did not cause positive frequency dependent priority effects. Instead, coexistence was predicted when the inferior competitor (Tetranychus urticae) arrived first. In that case, T. urticae colonised the preferred feeding stratum (leaves) of T. evansi leading to spatial niche preemption, which equalised fitness and reduced niche differences, driving community assembly to a close-to-neutrality scenario. Our study demonstrates how the order of species arrival and the spatial context of competitive interactions can jointly determine whether species can coexist.

ecology↗

bioRxiv2021

Towards a system-level causative knowledge of pollinator communities

Pollination plays a central role both in the maintenance of biodiversity and in crop production. However, habitat loss, pesticides, invasive species, and larger environmental fluctuations are contributing to a dramatic decline of numerous pollinators world-wide. This has increased the need for interventions to protect the composition, functioning, and dynamics of pollinator communities. Yet, how to make these interventions successful at the system level remains extremely challenging due to the complex nature of species interactions and the various unknown or unmeasured confounding ecological factors. Here, we propose that this knowledge can be derived by following a probabilistic causal analysis of pollinator communities. This analysis implies the inference of interventional expectations from the integration of observational and synthetic data. We propose that such synthetic data can be generated using theoretical models that can enable the tractability and scalability of unseen confounding ecological factors affecting the behavior of pollinator communities. We discuss a road map for how this probabilistic causal analysis can be accomplished to increase our system-level causative knowledge of natural communities.

ecology↗

bioRxiv2021

Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modeling approach

1Modeling species interactions in diverse communities traditionally requires a prohibitively large number of species-interaction coefficients, especially when considering environmental dependence of parameters. We implemented Bayesian variable selection via sparsity-inducing priors on non-linear species abundance models to determine which species-interactions should be retained and which can be represented as an average heterospecific interaction term, reducing the number of model parameters. We evaluated model performance using simulated communities, computing out-of-sample predictive accuracy and parameter recovery across different input sample sizes. We applied our method to a diverse empirical community, allowing us to disentangle the direct role of environmental gradients on species intrinsic growth rates from indirect effects via competitive interactions. We also identified a few neighboring species from the diverse community that had non-generic interactions with our focal species. This sparse modeling approach facilitates exploration of species-interactions in diverse communities while maintaining a manageable number of parameters.

ecology↗

bioRxiv2021

Community level individual-based plant-pollinator networks unveil pollen flow dynamics and plant reproductive success

Interaction networks are a widely used tool to understand the dynamics of plant-pollinator ecological communities. However, while most mutualistic networks have been defined at the species level, ecological processes such as pollination take place at different scales, including the individual or patch levels. Yet, current approaches studying fine-grain sub-specific plant-pollinator networks only account for interactions among nodes belonging to a single plant species due to the conceptual and mathematical limitations of modeling simultaneously several plant species each composed of several nodes. Here, we introduce a multilayer diffusion network framework that allows modeling simple diffusion processes between nodes pertaining to the same or different layers (i.e. species). It is designed to depict from the network structure the potential conspecific and heterospecific pollen flows among plant individuals or patches. This potential pollen flow is modeled as a transport-like system, in which pollen grain movements are represented as random-walkers that diffuse on an ensemble of bipartite layers of conspecific plants and their shared pollinators. We exemplify this physical conceptualization using a dataset of nine fine-grain sub-specific plant-pollinator networks from a Mediterranean grassland of annual plants, where plant nodes represent groups of conspecifics within patches of 1m2. The diffusion networks show pollinators effectively connecting sets of patches of the same and different plant species, forming a modular structure. Interestingly, different properties of the network structure, such as the conspecific pollen arrival probability and the number of conspecific subgraphs in which plants are embedded, are critical for the seed production of different plant species. We provide a simple but robust set of metrics to calculate potential pollen flow and scale down network ecology to functioning properties at the individual or patch level, where most ecological processes take place, hence moving forward the description and interpretation of species-rich communities across scales.

ecology↗

bioRxiv2021

Species-area relationships emerge from multiple coexistence mechanisms

The increase of species richness with area is a universal phenomenon on Earth. How-ever, this observation contrasts with our poor understanding of how these species-area relationships (SARs) emerge from the collective effects of area, spatial heterogeneity, and local interactions. By combining a structuralist approach with five years of empirical observations in a highly-diverse Mediterranean grassland, we show that spatial hetero-geneity plays a little role in the accumulation of species richness with area in our system. Instead, as we increase the sampled area more species combinations are realized, and they coexist mainly due to direct pairwise interactions rather than by changes in single-species dominance or by indirect interactions. We also identify a small set of transient species with small population sizes that are consistently found across spatial scales. These findings empirically support the importance of the architecture of species interactions together with stochastic events for driving coexistence- and species-area relationships.

ecology↗

bioRxiv2021

Fine scale prediction of ecological community composition using a two-step sequential machine learning ensemble

Prediction is one the last frontiers in ecology. Indeed, predicting fine scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models informed by ecological deterministic processes to predict species abundances using reasonably easy to obtain data. To overcome the classical procedure in ecology of parameterizing complex population models of multiple species interactions and poor predictive power, we followed instead a sequential data-driven modeling approach. We use this framework to predict species abundances over 5 years in a highly diverse annual plant community. Our models show a surprisingly high spatial predictive accuracy (RSE ~ 0.13) using only easy to measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggest that predicting the temporal dimension of our system requires longer time series data. Such data would likely capture additional sources of variability that determine temporal patterns of species abundances. In addition, we show that these data-driven models can also inform back mechanistic models of important missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Being able to gain predictive power at fine-scale species composition while maintaining a mechanistic understanding of the underlying processes can be a pivotal tool for conservation, specially given the human induced rapid environmental changes we are experiencing. Here, we document how this objective can be achieved by promoting the interplay between classic modelling approaches in ecology and recently developed data-driven models. Author summaryPrediction is challenging but recently developed machine learning techniques allow to dramatically improve prediction accuracy in several domains. However, these tools are often of little application in ecology due to the complexity of gathering information on the needed explanatory variables, which often comprise not only physical variables such as temperature or soil nutrients, but also information about the complex network of species interactions regulating species abundances. Here we present a two-step sequential modelling framework that overcomes these constraints. We first infer potential species abundances training models just with easily obtained abiotic variables, and then use this outcome to fine-tune the prediction of the realized species abundances when taking into account the rest of the predicted species in the community. Overall, our results show a promising way forward for fine scale prediction in ecology.

ecology↗

bioRxiv2019

A mechanistic path to maximize biomass productivity while maintaining diversity.

With ongoing biodiversity loss, it is important to understand how the mechanisms that promote coexistence relate to those that increase functioning in diverse communities. Both coexistence and biodiversity functioning research have unified their mechanisms into two classes. However, despite seeming similarities, theory suggests that coexistence and biodiversity mechanisms do not necessarily map onto each other, yet direct empirical evidence for this prediction is lacking. We coupled field-parameterized models of competition between 10 plants with a biodiversity-functioning experiment measuring biomass production, litter decomposition, and soil nutrient content under contrasting environmental conditions. We related biodiversity mechanisms (complementarity and selection effects), to coexistence mechanisms (niche and fitness differences). As predicted by theory, complementarity effects were positively correlated with niche differences and differences in selection effects were correlated with fitness differences. However, we also found that niche differences contributed to selection effects and fitness differences to complementarity effects. Despite this complexity more stably coexisting communities (i.e. those in which niche differences offset fitness differences) produced more biomass, particularly under drought. This relationship was weaker for litter decomposition rates and soil nutrient acquisition, showing that the mechanisms promoting plant coexistence may differ from those promoting high levels of functions that are less directly related to plant performance. We provide the first empirical evidence that the mechanisms promoting stable coexistence correlate with those driving high biomass production. These findings establish a link between stable coexistence and functioning, which could allow better predictions of how diversity loss induced by global change translates to changes in ecosystem function.

ecology↗

bioRxiv2019

Functional traits and phenotypic plasticity modulate species coexistence across contrasting environments

Functional traits are expected to modulate plant competitive dynamics. However, how traits and their plasticity in response to contrasting environments connect with the mechanisms determining species coexistence remains poorly understood. Here, we coupled field experiments under two contrasted climatic conditions to a plant population model describing competitive dynamics between 10 annual plant species in order to evaluate the associations of niche and fitness differences with 19 functional traits covering physiological, morphological and reproductive characteristics. We found a rich diversity of univariate and multidimensional relationships, in which outstand the primary role of traits related to water- and light-use-efficiency in modulating the determinants of competitive outcomes. Importantly, such traits and their plasticity promoted species coexistence across climatic conditions by enhancing stabilizing niche differences and by generating competitive trade-offs between species. Our study represents a significant advance showing how leading dimensions of plant form and function connect to the mechanisms determining the maintenance of biodiversity.

ecology↗

bioRxiv2018

Does deterministic coexistence theory matter in a finite world? Insights from serpentine annual plants

Contemporary studies of species coexistence are underpinned by deterministic models that assume that competing species have continuous (i.e. non-integer) densities, live in infinitely large landscapes, and coexist over infinite time horizons. By contrast, in nature species are composed of discrete individuals subject to demographic stochasticity, and occur in habitats of finite size where extinctions occur in finite time. One consequence of these discrepancies is that metrics of species coexistence derived from deterministic theory may be unreliable predictors of the duration of species coexistence in nature. These coexistence metrics include invasion growth rates and niche and fitness differences, which are now commonly applied in theoretical and empirical studies of species coexistence. Here we test the efficacy of deterministic coexistence metrics on the duration of species coexistence in a finite world. We introduce new theoretical and computational methods to estimate coexistence times in stochastic counterparts of classic deterministic models of competition. Importantly, we parameterized this model using experimental field data for 90 pairwise combinations of 18 species of annual plants, allowing us to derive biologically-informed estimates of coexistence times for a natural system. Strikingly, we find that for species expected to deterministically coexist, habitat sizes containing only tens of individuals have predicted coexistence times of greater than 1, 000 years. We also find that invasion growth rates explain 60% of the variation in intrinsic coexistence times, reinforcing their general usefulness in studies of coexistence. However, only by integrating information on both invasion growth rates and species equilibrium population sizes could most (> 99%) of the variation in species coexistence times be explained. This integration is achieved with demographically uncoupled single species models solely determined by the invasion growth rates and equilibrium population sizes. Moreover, because of a complex relationship between niche overlap/fitness differences and equilibrium population sizes, increasing niche overlap and increasing fitness differences did not always result in decreasing coexistence times as deterministic theory would predict. Nevertheless, our results tend to support the informed use of deterministic theory for understanding the duration of species coexistence, while highlighting the need to incorporate information on species equilibrium population sizes in addition to invasion growth rates.

ecology↗

bioRxiv2017

Opposing effects of floral visitors and soil conditions on the determinants of competitive outcomes maintain species diversity in heterogeneous landscapes

Theory argues that both soil conditions and aboveground trophic interactions are equally important for determining plant species diversity. However, it remains unexplored how they modify the niche differences that stabilise species coexistence and the average fitness differences driving competitive dominance. We conducted a field study in Mediterranean annual grasslands to parameterise population models of six competing plant species. Spatially explicit floral visitor assemblages and soil salinity variation were characterized for each species. Both floral visitors and soil salinity modified species population dynamics via direct changes in seed production and indirect changes in competitive responses. Although the magnitude and sign of these changes were species specific, floral visitors promoted coexistence at neighbourhood scales while soil salinity did so over larger scales by changing the superior competitor's identity. Our results show how below and aboveground interactions maintain diversity in heterogeneous landscapes through their opposing effects on the determinants of competitive outcomes.

ecology↗