Biology subjects

Laughlin, D. C.

Publications and source records attributed to Laughlin, D. C..

7 recordsLinked to original sources

bioRxiv2026

Still standing: persistence traits capture belowground plant functions beyond resource exploration and acquisition

Belowground plant trait research has predominantly focused on trade-offs in fine root traits via the root economics space. Yet, this fine root framework captures only a fraction of the functional strategies plants employ beneath the soil surface. Here, we broaden the perspective on belowground plant functioning by integrating traits related to root system extent, clonality and bud banks, using data from the new UNDERPLOT database. This integration links measurable traits to key belowground functions: resource acquisition, spatial exploration, and persistence. Our analysis shows that the fine root economics space explains less than 5% of the variation in traits related to root system extent, clonality, and bud banks. Instead, an expanded trait analysis reveals three significant dimensions, explaining 62% of total trait variation. The third dimension, represents an independent, persistence-related gradient, not captured by existing root economics frameworks. We propose that understanding belowground plant strategies requires embracing additional functional gradients. The strategy of persistence, in particular, varies significantly across growth forms and is a critical dimension of plant response to resource limitation and stress, becoming increasingly important as global change shifts disturbance regimes.

plant biology↗

bioRxiv2024

Interpretable and predictive models to harness the life science data revolution

The proliferation of high-dimensional data in ecology and evolutionary biology raises the promise of statistical and machine learning models that are highly predictive and interpretable. However, high-dimensional data are commonly burdened with an inherent trade-off: in-sample prediction of outcomes will improve as additional variables are included in the model, but this may come at the cost of poor predictive accuracy and limited generalizability for future or unsampled observations (out-of-sample prediction). To confront this problem of overfitting, sparse models can focus on key variables by correctly placing low weight on unimportant variables. We competed nine methods to quantify their performance in variable selection and prediction using simulated data with different sample sizes, numbers of variables, and strengths of effects. Overfitting was typical for many methods and simulation scenarios. Despite this, in-sample and out-of-sample prediction converged on the true predictive target for simulations with more observations, larger causal effects, and fewer variables. Accurate variable selection to support process-based understanding will be unattainable for many realistic sampling schemes in ecology and evolution. We use our analyses to characterize data attributes for which statistical learning is possible, and illustrate how some sparse methods can achieve predictive accuracy while mitigating and learning the extent of overfitting.

genomics↗

bioRxiv2023

You shall know a species by the company it keeps: leveraging co-occurrence data to improve ecological prediction

O_LIMaking predictions about species, including how they respond to environmental change, is a central challenge for ecologists. Due to the huge number of species, ecologists seek generalizations based on species traits and phylogenetic relationships, but the predictive power of trait-based and phylogenetic models is often low. Species co-occurrence patterns may contain additional information about species ecological attributes not captured by traits or phylogenies. C_LIO_LIWe propose using ordination to encode the information contained in species co-occurrence data in low-dimensional vectors that can be used to represent species in ecological prediction. We present an efficient method to derive species vectors from co-occurrence data using GloVe, an unsupervised learning algorithm originally designed for language modeling. To demonstrate the method, we used GloVe to generate vectors for nearly 40,000 plant species using co-occurrence statistics derived from a global vegetation dataset and tested their ability to predict elevational range shifts in European montane plant species. C_LIO_LICo-occurrence-based species vectors were weakly correlated with traits or phylogeny, indicating that they encode unique information about species. Models including co-occurrence-based vectors explained twice as much variation in species range shifts as models including only traits or phylogenetic information. C_LIO_LIGiven the widespread availability of species occurrence data, species vectors learned from co-occurrence patterns are a widely applicable and powerful tool for encoding ecological information about species, with many potential applications for describing and predicting the ecology of species, communities, and ecosystems. C_LI

ecology↗

bioRxiv2022

Trait-based population dynamics shape the contours of fitness landscapes

O_LIFunctional traits affect the demographic performance of individuals in their environment, leading to fitness differences that scale up to drive population dynamics and community assembly. Understanding the links between traits and fitness is therefore critical for predicting how populations and communities respond to environmental change. However, the net effects of traits on species fitness are largely unknown because we have lacked a framework for estimating fitness across multiple species and environments. C_LIO_LIWe present a modeling framework that integrates trait effects on demographic performance over the life cycles of individuals to estimate the net effect of traits on species fitness. This approach involves 1) modeling trait effects on individual demographic rates (growth, survival, and recruitment) as multidimensional performance surfaces that vary with individual size and environment and 2) integrating these effects into a population model to project population growth rates (i.e., fitness) as a function of traits and environment. We illustrate our approach by estimating performance surfaces and fitness landscapes for trees across a temperature gradient in the eastern United States. C_LIO_LIFunctional traits (wood density, specific leaf area, and maximum height) interacted with individual size and temperature to influence tree growth, survival, and recruitment rates, generating demographic trade-offs and shaping the contours of fitness landscapes. Tall tree species had high survival, growth, and fitness across the temperature gradient. Wood density and specific leaf area had interactive effects on demographic performance, resulting in fitness landscapes with multiple peaks. C_LIO_LIWith this approach it is now possible to empirically estimate the net effect of traits on fitness, leading to improved understanding of the selective forces that drive community assembly and permitting generalizable predictions of population and community dynamics in changing environments. C_LI

ecology↗

bioRxiv2021

Predictions of biodiversity are improved by integrating trait-based competition with abiotic filtering

All organisms must simultaneously tolerate the environment and access limiting resources if they are to persist. Otherwise they go extinct. Approaches to understanding environmental tolerance and resource competition have generally been developed independently. Consequently, integrating the factors that determine abiotic tolerance with those that affect competitive interactions to model species abundances and community structure remains an unresolved challenge. This is likely the reason why current models of community assembly do not accurately predict species abundances and dynamics. Here, we introduce a new synthetic framework that models both abiotic tolerance and biotic competition by using functional traits, which are phenotypic attributes that influence organism fitness. First, our framework estimates species carrying capacities that vary along abiotic gradients based on whether the phenotype tolerates the local environment. Second, it estimates pairwise competitive interactions as a function of multidimensional trait differences between species and determines which trait combinations produce the most competitive phenotypes. We demonstrate that our combined approach more than doubles the explained variance of species covers in a wetland community compared to the model of abiotic tolerances alone. Trait-based integration of competitive interactions and abiotic filtering improves our ability to predict species abundances across space, bringing us closer to more accurate predictions of biodiversity structure in a changing world.

ecology↗

bioRxiv2020

Global Root Traits (GRooT) Database

MotivationTrait data are fundamental to quantitatively describe plant form and function. Although root traits capture key dimensions related to plant responses to changing environmental conditions and effects on ecosystem processes, they have rarely been included in large-scale comparative studies and global models. For instance, root traits remain absent from nearly all studies that define the global spectrum of plant form and function. Thus, to overcome conceptual and methodological roadblocks preventing a widespread integration of root trait data into large-scale analyses we created the Global Root Trait (GRooT) Database. GRooT provides ready-to-use data by combining the expertise of root ecologists with data mobilization and curation. Specifically, we (i) determined a set of core root traits relevant to the description of plant form and function based on an assessment by experts, (ii) maximized species coverage through data standardization within and among traits, and (iii) implemented data quality checks. Main types of variables containedGRooT contains 114,222 trait records on 38 continuous root traits. Spatial location and grainGlobal coverage with data from arid, continental, polar, temperate, and tropical biomes. Data on root traits derived from experimental studies and field studies. Time period and grainData recorded between 1911 and 2019 Major taxa and level of measurementGRooT includes root trait data for which taxonomic information is available. Trait records vary in their taxonomic resolution, with sub-species or varieties being the highest and genera the lowest taxonomic resolution available. It contains information for 184 sub-species or varieties, 6,214 species, 1,967 genera and 254 families. Due to variation in data sources, trait records in the database include both individual observations and mean values. Software formatGRooT includes two csv file. A GitHub repository contains the csv files and a script in R to query the database.

ecology↗

bioRxiv2020

The fungal collaboration gradient dominates the root economics space in plants

Plant economics run on carbon and nutrients instead of money. Leaf strategies aboveground span an economic spectrum from live fast and die young to slow and steady, but the economy defined by root strategies belowground remains unclear. Here we take a holistic view of the belowground economy, and show that root-mycorrhizal collaboration can short circuit a one-dimensional economic spectrum, providing an entire space of economic possibilities. Root trait data from 1,781 species across the globe confirm a classical fast-slow conservation gradient but show that most variation is explained by an orthogonal collaboration gradient, ranging from do-it-yourself resource uptake to outsourcing of resource uptake to mycorrhizal fungi. This broadened root economics space provides a solid foundation for predictive understanding of belowground responses to changing environmental conditions. One Sentence SummaryCollaboration broadens the root economics space ranging from do-it-yourself resource acquisition to outsourcing to mycorrhizal partners.

ecology↗