Christine I. B. Wallis- PhD
- PostDoc Position at Technische Universität Berlin
Christine I. B. Wallis
- PhD
- PostDoc Position at Technische Universität Berlin
About
12
Publications
14,830
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287
Citations
Introduction
Current institution
Additional affiliations
August 2020 - August 2023
December 2018 - December 2019
Publications
Publications (12)
Insect and pathogen outbreaks have a major impact on northern forest ecosystems. Even for pathogens that have been present in a region for decades, such as beech bark disease (BBD), new waves of mortality are expected in host populations. Hence, there is a need for innovative approaches to monitor their advancement extensively in real-time. Here we...
Airborne hyperspectral imaging holds great promise for estimating plant diversity and composition, given its unprecedented combination of aerial coverage, spatial resolution, and spectral detail. Recently, there has been renewed attention toward the spectral variation hypothesis (SVH), which predicts that higher spectral variation is correlated wit...
Imaging spectroscopy is emerging as a leading remote sensing method for quantifying plant biodiversity. The spectral variation hypothesis predicts that variation in plant hyperspectral reflectance is related to variation in taxonomic and functional identity. While most studies report some correlation between spectral and field‐based (i.e., taxonomi...
The advent of new spaceborne imaging spectrometers offers new opportunities for ecologists to map vegetation traits at global scales. However, to date most imaging spectroscopy studies exploiting satellite spectrometers have been constrained to the landscape scale. In this paper we present a new method to map vegetation traits at the landscape scal...
Optical remote sensing permits modeling of variables related to forest biomass, which is a critical determinant of carbon (C) stocks and fluxes. Plant functional characteristics can be captured by (hyper)spectral data, but it remains unclear whether the links between spectral information and C content are driven largely by tree composition, tree di...
Biodiversity and ecosystem functions are highly threatened by global change. It has been proposed that geodiversity can be used as an easy-to-measure surrogate of biodiversity to guide conservation management. However, so far, there is mixed evidence to what extent geodiversity can predict biodiversity and ecosystem functions at the regional scale...
Tropical montane forests, particularly Andean rainforest, are important ecosystems for regional carbon and water cycles as well as for biological diversity and speciation. Owing to their remoteness, however, ecological key-processes are less understood as in the tropical lowlands. Remote sensing allows modeling of variables related to spatial patte...
Texture information from passive remote sensing images provides surrogates for habitat structure, which is relevant for modeling biodiversity across space and time and for developing effective ecological indicators. However, the applicability of this information might differ among taxa and diversity measures. We compared the ability of indicators d...
Environmental stressors and changes in land use have led to rapid and dramatic species losses. As such, we need effective monitoring programs that alert us not only to biodiversity losses, but also to functional changes in species assemblages and associated ecosystem processes. Ants are important components of terrestrial food webs and a key group...
Corrigendum to “Contrasting performance of Lidar and optical texture models in predicting avian diversity in a tropical mountain forest” (vol 174, pg 223, 2016, Figure 2)
Questions
Question (1)
I want to assess the mean phylogenetic distance of birds observed in southern Ecuador using the birdtree.org data. The authors provide phylogenies of species subsets with 100 to 10000 phylogenetic trees. However, I do not know whether it is better to built a consensus tree or whether I should use a loop to measure MPD for all 10k trees and test later which estimates are the most likely.
Any help on extracting the 'best' phylogenetic tree in R would be appreciated.
