Biology subjects

Sarmento Cabral, J.

Publications and source records attributed to Sarmento Cabral, J..

9 recordsLinked to original sources

bioRxiv2024

metaRange: A framework to build mechanistic range models

1O_LIMechanistic or process-based models offer great insights into the range dynamics of species facing non-equilibrium conditions, such as climate and land-use changes or invasive species. Their consideration of underlying mechanisms relaxes the species-environment equilibrium assumed by correlative approaches, while also generating conservation-relevant indicators, such as range-wide abundance time-series and migration rates if demographically explicit. However, the computational complexity of mechanistic models limits their development and applicability to large spatiotemporal extents. C_LIO_LIWe developed the R package metaRange that is a modular framework to build population-based and metabolically constrained range models. It provides a function catalogue for users to calculate niche-based suitability, metabolic scaling, population dynamics, biotic interactions, and kernel-based dispersal, which may include directed movement. The frameworks modularity enables the user to combine, extend, or replace these functions, making it possible to customize the model to the ecology of the study system. The package supports an unlimited number of static or dynamic environmental factors as input, including climate and land use. C_LIO_LIAs an example, we simulated 100 virtual species in Germany on a 1 km2 resolution over 110 years under realistic environmental fluctuations in three scenarios: without competition, with competition, and with competition and a generalist-specialist trade-off. The results are in accordance with theoretical expectations. Due to the population-level, the package can execute such extensive simulation experiments on regular enduser hardware in a short amount of time. We provide detailed technical documentation, both for the individual functions in the package as well as instructions on how to set up different types of model structures and experimental designs. C_LIO_LIThe metaRange framework enables process-based simulations of range dynamic of multiple interacting species on a high resolution and low computational demand. We believe that it allows for theoretical insights and hypotheses testing about future range dynamics of real-world species, which may better support conservation policies targeting biodiversity loss mitigation. C_LI

ecology↗

bioRxiv2021

gen3sis: the general engine for eco-evolutionary simulations on the origins of biodiversity

Understanding the origins of biodiversity has been an aspiration since the days of early naturalists. The immense complexity of ecological, evolutionary and spatial processes, however, has made this goal elusive to this day. Computer models serve progress in many scientific fields, but in the fields of macroecology and macroevolution, eco-evolutionary models are comparatively less developed. We present a general, spatially-explicit, eco-evolutionary engine with a modular implementation that enables the modelling of multiple macroecological and macroevolutionary processes and feedbacks across representative spatio-temporally dynamic landscapes. Modelled processes can include environmental filtering, biotic interactions, dispersal, speciation and evolution of ecological traits. Commonly observed biodiversity patterns, such as , {beta} and {gamma} diversity, species ranges, ecological traits and phylogenies, emerge as simulations proceed. As a case study, we examined alternative hypotheses expected to have shaped the latitudinal diversity gradient (LDG) during the Earths Cenozoic era. We found that a carrying capacity linked with energy was the only model variant that could simultaneously produce a realistic LDG, species range size frequencies, and phylogenetic tree balance. The model engine is open source and available as an R-package, enabling future exploration of various landscapes and biological processes, while outputs can be linked with a variety of empirical biodiversity patterns. This work represents a step towards a numeric and mechanistic understanding of the physical and biological processes that shape Earths biodiversity.

evolutionary biology↗

bioRxiv2021

Accuracy of the pneumatic method for estimating xylem vulnerability to embolism in temperate diffuse-porous tree species

O_LIThe increasing frequency of global change-type droughts has created a need for fast, accurate and widely applicable techniques for estimating xylem embolism resistance to improve forecasts of future forest changes. C_LIO_LIWe used data from 12 diffuse-porous temperate tree species covering a wide range of xylem safety to compare the pneumatic and flow-centrifuge method for constructing xylem vulnerability curves. We evaluated the agreement between parameters estimated with both methods and the sensitivity of pneumatic measurements to the measurement duration. C_LIO_LIThe agreement between xylem water potentials at 50% air discharged (PAD) estimated with the Pneumatron and 50% loss of hydraulic conductivity (PLC) estimated with the flow-centrifuge method was high (mean signed deviation: 0.12 MPa, Pearson correlation: 0.96 after 15 sec of gas extraction). However, the relation between the estimated slopes was more variable, resulting in lower agreement in xylem water potential at 12% and 88% PAD/PLC. All parameters were sensitive to the duration of the pneumatic measurement, with highest overall agreement between methods after 16 sec. C_LIO_LIWe conclude that, if applied correctly, the pneumatic method enables fast and inexpensive estimations of embolism resistance for a wide range of temperate, diffuse-porous species, which makes it attractive for predicting plant performance under climate change. C_LI

plant biology↗

bioRxiv2020

Modeling the long-term dynamics of tropical forests: from leaf traits to whole-tree growth patterns

Tropical forests are the most diverse terrestrial ecosystems and home to numerous tree species with diverse ecological strategies competing for resources in space and time. Functional traits influence the ecophysiological performance of tree species, yet the relationship between traits and emergent long-term growth pattern is poorly understood. Here, we present a novel 3D forest stand model in which growth patterns of individual trees and forest stands are emergent properties of leaf traits. Individual trees are simulated as 3D functional-structural tree models (FSTMs), considering branches up to the second order and leaf dynamics at a resolution of one m3. Each species is characterized by a set of leaf traits that corresponds to a specific position on the leaf economic spectrum and determines light-driven carbon assimilation, respiration and mortality rates. Applying principles of the pipe model theory, these leaf scale-processes are coupled with within-tree carbon allocation, i.e., 3D tree growth emerges from low-level processes. By integrating these FSTMs into a dynamic forest stand model, we go beyond modern stand models to integrate structurally-detailed internal physiological processes with interspecific competition, and interactions with the environment in diverse tree communities. For model calibration and validation, we simultaneously compared a large number of emergent patterns at both the tree and forest levels in a pattern-oriented modeling framework. At the tree level, varying specific leaf area and correlated leaf traits determined the maximum height and age of a tree, as well as its size-dependent growth rate and shade tolerance. Trait variations along the leaf economic spectrum led to a continuous transition from fast-growing, short-lived and shade-intolerant to slow-growing, long-lived and shade-tolerant trees. These emerging patterns resembled well-known functional tree types, indicating a fundamental impact of leaf traits on long-term growth patterns. At the forest level, a large number of patterns taken from lowland Neotropical forests were reproduced, indicating that our forest model simulates structurally realistic forests over long time spans. Our ecophysiological approach improves the understanding of how leaf level processes scale up to the tree and the stand level, and facilitates the development of next-generation forest models for species-rich forests in which tree performance emerges directly from functional traits.

ecology↗

bioRxiv2020

Effects of species traits and abiotic factors during the stages of plant invasions

O_LIThe success of species invasions depends on multiple factors acting over the four invasion stages transport, colonisation, establishment, and landscape spread. Each of these stages is influenced simultaneously by particular species traits and abiotic factors. While the importance of many of these determinants has already been investigated in relative isolation, they are rarely studied in combination and even then mostly ignore the final phase, i.e., landscape spread. C_LIO_LIHere we address this shortcoming by exploring the effect of both species traits and abiotic factors on the success of invasions using an individual-based mechanistic model, and relate those factors to the stages of invasion. This approach enables us to explicitly control abiotic factors (temperature as surrogate for productivity, disturbance and propagule pressure) as well as to monitor whole-community trait distributions of environmental adaptation, mass and dispersal abilities. We simulated introductions of plant individuals to an oceanic island to assess which abiotic factors and species traits contribute to invasion success. C_LIO_LIWe found that the most influential factors were higher propagule pressure and a particular set of traits. This invasion trait syndrome was characterized by a relative similarity in functional traits of invasive species to natives, while invasives had on average higher environmental tolerances, higher body mass and increased dispersal abilities, i.e., were more generalist and dispersive. C_LIO_LIOur results highlight the importance in management practice of reducing the import of alien species, especially from similar habitats. C_LI

ecology↗

bioRxiv2020

Temporal environmental variation imposes differential selection on genomic and ecological traits of virtual plant communities

The reaction of species to changing conditions determines how community composition will change functionally -- not only by (temporal) species turnover, but also by trait shifts within species. For the latter, selection from standing variation has been suggested to be more efficient than acquiring new mutations. Yet, studies on community trait composition and trait selection largely focus on phenotypic variation in ecological traits, whereas the underlying genomic traits remain relatively understudied despite evidence of their role to standing variation. Using a genome-explicit, niche- and individual-based model, we address the potential interactions between genomic and ecological traits shaping communities under an environmental selective forcing, namely temporal variation. In this model, all ecological traits are explicitly coded by the genome. For our experiments, we initialized 90 replicate communities, each with ca. 350 initial species, characterized by random genomic and ecological trait combinations, on a 2D spatially-explicit landscape with two orthogonal gradients (temperature and resource use). We exposed each community to two contrasting scenarios: without (i.e. static environments) and with temporal variation. We then analyzed emerging compositions of both genomic and ecological traits at the community, population and genomic levels. Communities in variable environments were species poorer than in static environments, populations more abundant and genomes had a higher numbers of genes. The surviving genomes (i.e. those selected by variable environments) coded for enhanced environmental tolerance and smaller biomass, which resulted in faster life cycles and thus also in increased potential for evolutionary rescue. Even under the constant environmental filtering presented by temporal environmental variation, larger, more linked genomes allowed selection of increased variation in dispersal abilities. Our results provide clues to how sexually-reproducing diploid plant communities might react to increased environmental variation and highlights the importance of genomic traits and their interaction with ecological traits for eco-evolutionary responses to changing climates.

ecology↗

bioRxiv2020

Modeling How Community Assembly Alters the Functioning of Ecosystems

Although the effects of species richness on ecosystem functioning have been extensively studied, there is increased interest in understanding how community assembly in general might alter the functioning of ecosystems. We focus on two complementary approaches for evaluating how community assembly influences ecosystem function (here, productivity). The first quantifies the relative importance of complementarity and selection by contrasting monocultures with polycultures. The second identifies the effects of species losses and/or gains relative to the baseline polyculture, as well as the indirect effects on other species productivity. We evaluate and contrast these two approaches, using simulated communities structured by different, known competition mechanisms, where species compete for different resources and experience varying levels of environmental heterogeneity. We find that the metrics provided by these approaches can jointly discriminate the mechanisms of competition driving productivity. We then apply our methods to data from a long-term biodiversity-ecosystem experiment (the Jena Experiment) and find that the data do not correspond to any of the competition scenarios we modeled. We address two additional possible complications: facilitation by nitrogen fixing plants, and non-equilibrium behavior during community assembly, and find that a combination of resource competition and facilitation by nitrogen fixing plants is the more likely explanation for the results obtained at Jena.

ecology↗

bioRxiv2017

Interactions between ecological, evolutionary, and environmental processes unveil complex dynamics of island biodiversity

AimsUnderstanding how biodiversity emerges and varies in space and time is central to ecology and biogeography. Multiple processes affect biodiversity at different scales and organizational levels, hence progress in understanding biodiversity dynamics requires the integration of these underlying processes. Here we present BioGEEM (BioGeographical Eco-Evolutionary Model), a spatially-explicit, process-based model that integrates all processes hypothesized to be relevant for biodiversity dynamics and that can be used to evaluate their relative roles.\n\nLocationHypothetical oceanic islands\n\nMethodsThe model is stochastic, grid-based, and integrates ecological (metabolic constraints, demography, dispersal, and competition), evolutionary (mutation and speciation), and environmental (geo-climatic dynamics) processes. Plants on oceanic islands served as model system. We used the full model to test hypotheses about emergent patterns at different spatio-temporal scales and organizational levels (populations, species, communities, and assemblages), switching off processes to assess the importance 1) of competition for realistic population and range dynamics; 2) metabolic constraints for endemism and community composition; 3) environmental dynamics and 4) speciation for biogeographical patterns.\n\nResultsThe full model generated multiple patterns matching empirical and theoretical expectations. For example, populations were largest on young, species-poor islands. Species, particularly endemics, were better able to fill their potential range on small, species-poor islands. Richness gradients peaked at mid-elevations. The proportion of endemics was highest on old, large, and isolated environments within the islands. Species and trait richness showed unimodal temporal trends. Switching off selected processes affected these patterns, and we found most of our hypotheses supported.\n\nMain conclusionsIntegrating ecological, evolutionary, and environmental processes is essential to simultaneously generate realistic spatio-temporal dynamics at population, species, community, and assemblage level. Finally, large-scale biodiversity dynamics emerged directly from biological processes which make this mechanistic model a valuable virtual long-term field station to study the linkages between biogeography and ecology.

ecology↗

bioRxiv2017

Effects of time and isolation on plant diversity: testing island biogeography theory with an eco-evolutionary model

AimsThe General Dynamic Model of oceanic island biogeography (GDM) predicts how biogeographical rates, species richness, and endemism vary depending on island age, area, and isolation, based on the interplay of colonization, extinction, and speciation. Here, we used a simulation model to test whether GDM predictions may arise from individual- and population-level processes.\n\nLocationHypothetical hotspot islands.\n\nMethodsOur model (i) considers an idealized island ontogeny, (ii) metabolic constraints, and (iii) stochastic, spatially-explicit, and niche-based processes at the level of individuals and populations (plant demography, dispersal, competition, mutation, and speciation). Isolation scenarios involved varying dispersal ability and distances to mainland.\n\nResultsHumped temporal trends were obtained for species richness, endemic richness, proportion of cladogenetic endemic species, number of radiating lineages, number of species per radiating lineage, and biogeographical rates. The proportion of anagenetic endemics and of all endemics steadily increased over time. Extinction rates of endemic species peaked later than for non-endemic species. Species richness and the number of anagenetic endemics decreased with isolation as did rates of colonization, anagenesis, and extinction. The proportion of all endemics and of cladogenetic endemics, the number of cladogenetic endemics, of radiating lineages, and of species per radiating lineage, and the cladogenesis rate all increased with isolation.\n\nMain conclusionsThe results confirm most GDM predictions related to island ontogeny and isolation, but predict an increasing proportion of endemics throughout the experiment: a difference attributable to diverging assumptions on late island ontogeny. New insights regarding the extinction trends of endemics further demonstrate how simulation models focusing on low ecological levels provide tools to test biogeographical-scale predictions and to develop more detailed predictions for further empirical tests.

ecology↗