Biology subjects

Schneider, F. D.

Publications and source records attributed to Schneider, F. D..

5 recordsLinked to original sources

bioRxiv2023

A fast-slow trait continuum at the level of entire communities

Across the tree of life, organismal functional strategies form a continuum from slow-to fast-growing organisms, in response to common drivers such as resource availability and disturbance. However, the synchronization of these strategies at the entire community level is untested. We combine trait data for >2800 above-and belowground taxa from 14 trophic guilds spanning a disturbance and resource availability gradient in German grasslands. Most guilds consistently respond to these drivers through both direct and trophically-mediated effects, resulting in a slow-fast axis at the level of the entire community. Fast trait communities were also associated with faster rates of whole ecosystem functioning. These findings demonstrate that slow and fast strategies can be manifested at the level of whole ecosystems, opening new avenues of ecosystem-level functional classification.

ecology↗

bioRxiv2022

Remote sensing-based forest modeling reveals positive effects of functional diversity on productivity at local spatial scale

O_LIForest biodiversity is critical for many ecosystem functions and services at plot scale, but it is uncertain how biodiversity influences ecosystem functioning across environmental gradients and contiguous larger areas. We used remote sensing and process-based terrestrial biosphere modeling to explore functional diversity-productivity relationships at multiple scales for a heterogeneous forest site in Switzerland. C_LIO_LIWe ran the biosphere model with empirical data about forest structure and composition derived from ground-based surveys, airborne laser scanning and imaging spectroscopy for the years 2006-2015 at 10x10-m spatial resolution. We then related the model outputs forest productivity to functional diversity under observed and experimental model conditions. C_LIO_LIFunctional diversity increased productivity significantly (p < 0.001) across all simulations at 20x20-m to 30x30-m scale, but at 100x100-m scale positive relationships disappeared under homogeneous soil conditions. C_LIO_LIWhereas local functional diversity was an important driver of productivity, environmental context (especially soil depth, texture and water availability) underpinned the variation of productivity (and functional diversity) at larger spatial scales. Integration of remotely-sensed information on canopy composition and structure into terrestrial biosphere models helps fill the knowledge gap about how plant biodiversity affects carbon cycling and biosphere feedbacks onto climate over large contiguous areas. C_LI

ecology↗

bioRxiv2021

From zero to infinity: minimum to maximum diversity of the planet by spatio-parametric Rao's quadratic entropy

AimThe majority of work done to gather information on Earth diversity has been carried out by in-situ data, with known issues related to epistemology (e.g., species determination and taxonomy), spatial uncertainty, logistics (time and costs), among others. An alternative way to gather information about spatial ecosystem variability is the use of satellite remote sensing. It works as a powerful tool for attaining rapid and standardized information. Several metrics used to calculate remotely sensed diversity of ecosystems are based on Shannons Information Theory, namely on the differences in relative abundance of pixel reflectances in a certain area. Additional metrics like the Raos quadratic entropy allow the use of spectral distance beside abundance, but they are point descriptors of diversity, namely they can account only for a part of the whole diversity continuum. The aim of this paper is thus to generalize the Raos quadratic entropy by proposing its parameterization for the first time. InnovationThe parametric Raos quadratic entropy, coded in R, i) allows to represent the whole continuum of potential diversity indices in one formula, and ii) starting from the Raos quadratic entropy, allows to explicitly make use of distances among pixel reflectance values, together with relative abundances. Main conclusionsThe proposed unifying measure is an integration between abundance- and distance-based algorithms to map the continuum of diversity given a satellite image at any spatial scale.

ecology↗

bioRxiv2020

A Biodiversity Composition Map of California Derived from Environmental DNA Metabarcoding and Earth Observation

Unique ecosystems globally are under threat from ongoing anthropogenic environmental change. Effective conservation management requires more thorough biodiversity surveys that can reveal system-level patterns and that can be applied rapidly across space and time. We offer a way to use environmental DNA, community science and remote sensing together as methods to reduce the discrepancy between the magnitude of change and historical approaches to measure it. Taking advantages of modern ecological models, we integrate environmental DNA and Earth observations to evaluate regional biodiversity patterns for a snapshot of time, and provide critical community-level characterization. We collected 278 samples in Spring 2017 from coastal, shrub and lowland forest sites in California, a large-scale biodiversity hotspot. We applied gradient forest to model 915 family occurrences and community composition together with environmental variables and multi-scalar habitat classifications to produce a statewide biodiversity-based map. 16,118 taxonomic entries recovered were associated with environmental variables to test their predictive strength on alpha, beta, and zeta diversity. Local habitat classification was diagnostic of community composition, illuminating a characteristic of biodiversity hotspots. Using gradient forest models, environmental variables predicted 35% of the variance in eDNA patterns at the family level, with elevation, sand percentage, and greenness (NDVI32) as the top predictors. This predictive power was higher than we found in published literature at global scale. In addition to this indication of substantial environmental filtering, we also found a positive relationship between environmentally predicted families and their numbers of biotic interactions. In aggregate, these analyses showed that strong eDNA community-environment correlation is a general characteristic of temperate ecosystems, and may explain why communities easily destabilize under disturbances. Our study provides the first example of integrating citizen science based eDNA with biodiversity mapping across the tree of life, with promises to produce large scale, high resolution assessments that promote a more comprehensive and predictive understanding of the factors that influence biodiversity and enhance its maintenance.

ecology↗

bioRxiv2018

Towards an Ecological Trait-data Standard

O_LITrait-based approaches are widespread throughout ecological research, offering great potential for trait data to deliver general and mechanistic conclusions. Accordingly,a wealth of trait data is available for many organism groups, but, due to a lack of standardisation, these data come in heterogeneous formats.\nC_LIO_LIWe review current initiatives and infrastructures for standardising trait data and discuss the importance of standardisation for trait data hosted in distributed open-access repositories.\nC_LIO_LIIn order to facilitate the standardisation and harmonisation of distributed trait datasets, we propose a general and simple vocabulary as well as a simple data structure for storing and sharing ecological trait data.\nC_LIO_LIAdditionally, we provide an R-package that enables the transformation of any tabular dataset into the proposed format. This also allows trait datasets from heterogeneous sources to be harmonised and merged, thus facilitating data compilation for any particular research focus.\nC_LIO_LIWith these decentralised tools for trait-data harmonisation, we intend to facilitate the exchange and analysis of trait data within ecological research and enable global syntheses of traits across a wide range of taxa and ecosystems.\nC_LI

ecology↗