Biology subjects

Garcia-Callejas, D.

Publications and source records attributed to Garcia-Callejas, D..

10 recordsLinked to original sources

bioRxiv2024

Beyond community-weighted means: quantifying trait distributions for detecting community assembly patterns

The distributions of ecological traits are commonly used to infer the processess structuring ecological communities, as these processes (either deterministic or stochastic) select and filter species, leading to distint trait patterns across communities. Trait distributions are frequently characterised by their average, the community-weighted mean, but increasing evidence exists for non-gaussian trait distributions in empirical communities. In such situations, community-weighted means are insufficient to capture the patterns of community traits and to infer the implied ecological processes. Here we analyse the empirical distributions of 6 functional traits of butterflies from a natural community and a filtered community from a urban area across a period of six years. First, we show that to adequately describe trait distributions, statistical descriptors beyond community-weighted means are needed, as distributions were in all cases clearly non-gaussian. In particular, besides distribution averages and standard deviations, we compute the skewness, kurtosis, the range of trait values, and a multimodality index. Second, we compare this set of descriptors between our natural and filtered communities. We find clear differences between communities, detected in particular by combining the relationships between the skewness, kurtosis, and range of the distributions. These analyses allow us to infer that the filtered community is mostly shaped by deterministic filtering processes, through a mixture of directional and stabilizing assembly filters. These patterns are furthermore consistent across the six years of data, providing further evidence of deterministic processes shaping the assembly of our urban butterfly community. Overall, we provide evidence of the ubiquity of non-gaussian trait distributions across natural and filtered communities, propose key descriptors for understanding and comparing such distributions, and identify filtering process structuring these communities.

ecology↗

bioRxiv2024

Ecological networks across interaction types are modular and highly driven by sampling intensity at biogeographical scales

Understanding how the structure of ecological communities varies across biotic and abiotic dimensions is a fundamental goal in ecology. This challenge is now approachable due to the increasing availability of data on community structure across the globe. Ecological communities are often defined with respect to the guilds considered and the interactions they engage in, but it is unclear whether interactions of different types respond similarly to large-scale environmental gradients. Therefore, we lack a deeper understanding of how the emergent structure of interaction networks varies across biogeographical gradients, and how this effect may change depending on their constituent interaction types. Here, using a unique dataset of 952 networks across the globe, we provide a first comparison of network structural metrics and their large-scale variability for five overarching interaction types (feeding, frugivory, herbivory, parasitism and pollination). We show that degree distribution, but not connectance alone, helps us understand the observed network structures, and this pattern is maintained across interaction types (with the partial exception of food webs). Moreover, degree distribution descriptors are generally explained by differences across studies, which represent a proxy for variability in sampling and network construction methods. Environ-mental factors show weaker but robust effects on network degree distribution, and food webs are generally more sensitive to changes in environmental factors than networks of other interaction types. By analysing common descriptors of the degree distributions of ecological networks, this study underscores for the first time generalities and differences across networks of different interaction types and their response to environmental and anthropogenic factors.

ecology↗

bioRxiv2023

Species traits and community structure can drive large-scale spatial propagation of effects in ecosystems

Species can directly and indirectly affect others across communities and habitats, yet the spatial scale over which such effects spread remains unclear. This uncertainty arises partly because the species traits and landscape structures allowing indirect effects to propagate may differ across scales. Here, we use a topological network metric, communicability, to explore the spatial propagation of effects in a large-scale plant-frugivore network projected across the territory of Aotearoa New Zealand. We show that generalism and species prevalence, and complementary morphological traits such as fruit and body size, are important predictors of species capacity to propagate effects, but their importance differed across scales. Further-more, native bird species (but not exotics) showed a positive relationship between body size and their potential to propagate effects. Habitat composition was the most important land-scape factor in our study, generating hotspots of effect propagation around forested areas, whereas landscapes containing a variety of habitats acted as a buffer against propagation. Overall, our results indicate that species displaying specific sets of traits, including ubiquity, interaction generalism, and a combination of large body size and native status, are the most likely to propagate large-scale ecological impacts in the plant-frugivore communities studied, yet landscape properties may moderate this spread.

ecology↗

bioRxiv2023

Structural asymmetry in biotic interactions as a tool to understand and forecast ecological persistence

A universal feature of ecological systems is that species do not interact with others with the same sign and strength. Yet, the consequences of this asymmetry in biotic interactions for the short- and long-term persistence of individual species and entire communities remains unclear. Here, we develop a set of metrics to evaluate how asymmetric interactions among species translate to asymmetries in their individual vulnerability to extinction under changing environmental conditions. These metrics, which solve previous limitations of how to independently quantify the size from the shape of the so-called feasibility domain, provide rigorous advances to understand simultaneously why some species and communities present more opportunities to persist than others. We further demonstrate that our shape-related metrics are useful to predict short-term changes in species relative abundances during seven years in a Mediterranean grassland. Our approach is designed to be applied to any ecological system regardless of the number of species and type of interactions. With it, we show that is possible to obtain both mechanistic and predictive information on ecological persistence for individual species and entire communities, paving the way for a stronger integration of theoretical and empirical research.

ecology↗

bioRxiv2021

The non-random structure of multi-trophic ecological interactions maximizes species coexistence within ecologically realistic constraints

Theory posits that the persistence of species in ecological communities is shaped by their interactions within and across trophic guilds. However, we lack empirical evaluations of how the structure, strength, and sign of biotic interactions drive the potential to coexist in diverse multi-trophic communities. Here we model community feasibility domains, a theoretically-informed measure of multi-species coexistence probability, from grassland communities comprising more than 45 species on average from three trophic guilds (plants, pollinators, and herbivores). Contrary to our hypothesis, increasing community complexity, measured either as the number of guilds or community richness, did not decrease community feasibility. Rather, we observed that high degrees of species self-regulation and niche partitioning allow maintaining larger levels of community feasibility and higher species persistence in more diverse communities. Our results show that biotic interactions within and across guilds are not random in nature and both structures significantly contribute to maintaining multi-trophic diversity.

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↗

bioRxiv2018

Spatial trophic cascades in communities connected by dispersal and foraging

Pairwise interactions between species have indirect consequences that reverberate throughout the whole ecosystem. In particular, interaction effects may propagate in a spatial dimension, to localities connected by organismal movement. Here we study the propagation of interactions with a spatially explicit metacommunity model, where local sites are connected by dispersal, foraging, or by both types of movement. We show that direct and net effects of pairwise interactions may differ in sign when foraging across localities is prevalent. Further, the effect of a species over another in the local community does not necessarily correspond to its effect at the metacommunity scale; this correspondence is again mediated by the type of movement across localities. Networks of net effects are fully connected, indicating that every species in the metacommunity has a non-zero influence on every other species. Lastly, the magnitude of net effects between any two species strongly decays with the distance between them. These theoretical results strengthen the importance of considering indirect effects across species at both the local and regional scale, point to the differences between types of movement across locations, and thus open novel avenues for the study of interaction effects in spatially explicit settings.

ecology↗

bioRxiv2018

On the variability of Species Abundance Distributions with trophic guild and community structure

Species Abundance Distributions (SADs) are one of the most studied properties of ecological communities, and their variability has been studied mostly in the context of horizontal communities, i.e. sets of species from a particular trophic guild. However, virtually all ecological communities encompass several trophic guilds, and the trophic interactions between them are key for explaining the persistence and abundance of individual species. Here I ask whether trophic interactions are also important in shaping Species Abundance Distributions of the different guilds of a community. I analyze the variation in SAD shape across trophic guilds in model and empirical communities. For that, I use a theoretical model that allows tracking the variations in abundances across trophic levels. The relationship between SAD shape and (1) trophic level, and (2) degree of predator specialization is analyzed using mixed-effect models. I combine this approach with an analysis of 4676 empirical datasets spanning terrestrial, marine and freshwater habitats, for which the variation in SAD shape is related to (1) trophic guild, and (2) habitat type. The evenness of model SADs is positively correlated to the trophic level of the guild considered, and also to the number of prey species per predator. These findings are confirmed by the empirical data: there is a significant relationship between SAD evenness and trophic guild, whereby primary producers display the most uneven SADs and pure carnivores the most even ones. Furthermore, regardless of trophic guild, SADs from marine habitats are the most even ones, with terrestrial SADs being the most uneven.

ecology↗