Biology subjects

Wittmann, F.

Publications and source records attributed to Wittmann, F..

3 recordsLinked to original sources

bioRxiv2025

Scaling up tree diversity inventories across Amazonian ecosystems using field spectroscopy

O_LISpecies identification in Amazonian forest inventories is challenging due to a shortage of taxonomists, high biodiversity, and morphological similarities leading to taxonomic errors. Near-infrared spectroscopy (NIRS) is a promising tool for improving species identification efficiency and reliability. C_LIO_LIThis study assessed the effectiveness of NIRS in discriminating against 26 abundant tree species across three Amazonian ecosystems: upland forest, white- sand ecosystems, and floodplain forest, using spectral data from different tree tissues--outer bark, inner bark, and fresh leaves. Each tissue was tested using Linear Discriminant Analysis (LDA) spectral models with two cross-validation methods: leave-one-out and 70/30 hold-out. C_LIO_LIResults showed high discrimination accuracy for all tissues and ecosystems. The general models achieved 86% accuracy for outer bark, 97% for inner bark and 98% for fresh leaves. The most informative spectral bands varied by tissue type: SWIR I (1300-1900 nm) for outer bark, and VIS (400-700 nm) + SWIR I (1300- 1900 nm) for inner bark and fresh leaves. A general model integrating species across ecosystems confirmed NIRS as an effective tool for in-field tree identification. These findings highlight the potential of VIS-NIR spectroscopy to Amazonian biodiversity inventories, contributing to more accurate species identification, refining forest management and conservation efforts. C_LI

ecology↗

bioRxiv2024

Plant recruitment six years after the Samarcos tailings-dam disaster: Impacts on species richness and plant growth

One of the greatest tragedies in Brazilian mining history occurred in November 2015 in Mariana, Minas Gerais state, when a dam from the mining company Samarco was breached. Millions of mine tailings from this upstream embankment were dumped over the Doce River basin, impacting an area of approximately 1469 ha of riparian vegetation. Our objective was to experimentally investigate whether plant recruitment and establishment are impaired in areas affected by tailings six years after the deposition. To achieve this goal, in 2021 we compared soil chemical properties between affected and unaffected areas, performed a soil seed bank experiment in controlled conditions, and conducted a greenhouse growth experiment using the two most abundant plant species. Affected soils presented lower fertility and organic matter content. At the same time, the mean abundance and richness of emerging plants did not differ between soils. Still, affected areas exhibited approximately 35% lower accumulated species richness (gamma diversity) than unaffected ones. The three most abundant species in both areas represented 34% of the individuals, being Marsypianthes chamaedrys (Vahl) Kuntze, Ludwigia octovalvis (Jacq.) P.H. Raven and Ageratum conyzoides L. In the growth experiment, plants growing in affected soils presented reduced height and stem diameter increment (L. octovalvis) or allocated fewer resources to root production than aerial parts (M. chamaedrys), potentially in response to soil infertility and density. Even after six years, our results showed that tailings- affected areas continue to experience negative impacts on plant recruitment, highlighting its adverse effects on ecosystem functions and services.

ecology↗

bioRxiv2021

The Maximum Entropy Formalism of statistical mechanics in a biological application: a quantitative analysis of tropical forest ecology

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics.

ecology↗