Gap Filling in the Plant Kingdom---Trait Prediction Using Hierarchical Probabilistic Matrix Factorization

Plant traits are a key to understanding and predicting the adaptation of ecosystems to environmental changes, which motivates the TRY project aiming at constructing a global database for plant traits and becoming a standard resource for the ecological community. Despite its unprecedented coverage, a large percentage of missing data substantially constrains joint trait analysis. Meanwhile, the trait data is characterized by the hierarchical phylogenetic structure of the plant kingdom. While factorization based matrix completion techniques have been widely used to address the missing data problem, traditional matrix factorization methods are unable to leverage the phylogenetic structure. We propose hierarchical probabilistic matrix factorization (HPMF), which effectively uses hierarchical phylogenetic information for trait prediction. We demonstrate HPMF's high accuracy, effectiveness of incorporating hierarchical structure and ability to capture trait correlation through experiments.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012

Imputing missing data in plant traits: A guide to improve gap‐filling

Aim: Globally distributed plant trait data are increasingly used to understand relationships between biodiversity and ecosystem processes. However, global trait databases are sparse because they are compiled from many, mostly small databases. This sparsity in both trait space completeness and geographical distribution limits the potential for both multivariate and global analyses. Thus, ‘gap-filling’ approaches are often used to impute missing trait data. Recent methods, like Bayesian hierarchical probabilistic matrix factorization (BHPMF), can impute large and sparse data sets using side information. We investigate whether BHPMF imputation leads to biases in trait space and identify aspects influencing bias to provide guidance for its usage. Innovation: We use a fully observed trait data set from which entries are randomly removed, along with extensive but sparse additional data. We use BHPMF for imputation and evaluate bias by: (1) accuracy (residuals, RMSE, trait means), (2) correlations (bi-and multivariate) and (3) taxonomic and functional clustering (valuewise, uni-and multivariate). BHPMF preserves general patterns of trait distributions but induces taxonomic clustering. Data set–external trait data had little effect on induced taxonomic clustering and stabilized trait–trait correlations. Main Conclusions: Our study extends the criteria for the evaluation of gap-filling beyond RMSE, providing insight into statistical data structure and allowing better informed use of imputed trait data, with improved practice for imputation. We expect our findings to be valuable beyond applications in plant ecology, for any study using hierarchical side information for imputation

Ecosystem Restoration: What, Why, How, and Where?

Our world contains many ecosystems, from tropical forests to coral reefs to urban parks. Ecosystems help us in important ways, including cleaning our air and water, storing carbon, and producing food. People have been shaping most ecosystems for at least 12,000 years. Human impact has become so intense that many ecosystems are now threatened. That is why the United Nations has decided that the next 10 years are the Decade on Ecosystem Restoration. But what is ecosystem restoration and how do we do it? In this article, we will tell you why ecosystem restoration is important and why it can be difficult. We will explain how it can be done well, and give examples from a range of projects. Successful restoration must include local people and requires lots of data. Restoration should not always return ecosystems back to what they were like once before

Imputing missing data in plant traits: A guide to improve gap‐filling

Aim: Globally distributed plant trait data are increasingly used to understand relationships between biodiversity and ecosystem processes. However, global trait databases are sparse because they are compiled from many, mostly small databases. This sparsity in both trait space completeness and geographical distribution limits the potential for both multivariate and global analyses. Thus, ‘gap‐filling’ approaches are often used to impute missing trait data. Recent methods, like Bayesian hierarchical probabilistic matrix factorization (BHPMF), can impute large and sparse data sets using side information. We investigate whether BHPMF imputation leads to biases in trait space and identify aspects influencing bias to provide guidance for its usage. Innovation: We use a fully observed trait data set from which entries are randomly removed, along with extensive but sparse additional data. We use BHPMF for imputation and evaluate bias by: (1) accuracy (residuals, RMSE, trait means), (2) correlations (bi‐ and multivariate) and (3) taxonomic and functional clustering (valuewise, uni‐ and multivariate). BHPMF preserves general patterns of trait distributions but induces taxonomic clustering. Data set–external trait data had little effect on induced taxonomic clustering and stabilized trait–trait correlations. Main Conclusions: Our study extends the criteria for the evaluation of gap‐filling beyond RMSE, providing insight into statistical data structure and allowing better informed use of imputed trait data, with improved practice for imputation. We expect our findings to be valuable beyond applications in plant ecology, for any study using hierarchical side information for imputation

Leaf reflectance spectra capture the evolutionary history of seed plants

Leaf reflection spectra have been increasingly used to assess plant diversity. However, we do not yet understand how spectra vary across the tree of life or how the evolution of leaf traits affects the differentiation of spectra among species and lineages. Here we describe a framework that integrates spectra with phylogenies and apply it to aglobal dataset of over 16 000 leaf-level spectra (400–2400 nm) for 544 seed plant species. We test for phylogenetic signal in spectra, evaluate their ability to classify lineages, and characterize their evolutionary dynamics. We show that phylogenetic signal is present in leaf spectra but that the spectral regions most strongly associated with the phylogeny vary among lineages. Despite among-lineage heterogeneity, broad plant groups, orders, and families can be identified from reflectance spectra. Evolutionary models also reveal that different spectral regions evolve at different rates and under different constraint levels, mirroring the evolution of their underlying traits. Leaf spectra capture the phylogenetic history of seed plants and the evolutionary dynamics of leaf chemistry and structure. Consequently, spectra have the potential to provide breakthrough assessments of leaf evolution and plant phylogenetic diversity at global scales

Monitoring plant functional diversity from space

The world’s ecosystems are losing biodiversity fast. A satellite mission designed to track changes in plant functional diversity around the globe could deepen our understanding of the pace and consequences of this change and how to manage it

Effects of climate change on the distribution of plant species and plant functional strategies on the Canary Islands

Aim Oceanic islands possess unique floras with high proportions of endemic species. Island floras are expected to be severely affected by changing climatic conditions as species on islands have limited distribution ranges and small population sizes and face the constraints of insularity to track their climatic niches. We aimed to assess how ongoing climate change affects the range sizes of oceanic island plants, identifying species of particular conservation concern. Location Canary Islands, Spain. Methods We combined species occurrence data from single-island endemic, archipelago endemic and nonendemic native plant species of the Canary Islands with data on current and future climatic conditions. Bayesian Additive Regression Trees were used to assess the effect of climate change on species distributions; 71% (n = 502 species) of the native Canary Island species had models deemed good enough. To further assess how climate change affects plant functional strategies, we collected data on woodiness and succulence. Results Single-island endemic species were projected to lose a greater proportion of their climatically suitable area (x ̃ = −0.36) than archipelago endemics (x ̃ = −0.28) or nonendemic native species (x ̃ = −0.26), especially on Lanzarote and Fuerteventura, which are expected to experience less annual precipitation in the future. Moreover, herbaceous single-island endemics were projected to gain less and lose more climatically suitable area than insular woody single-island endemics. By contrast, we found that succulent single-island endemics and nonendemic natives gain more and lose less climatically suitable area. Main Conclusions While all native species are of conservation importance, we emphasise single-island endemic species not characterised by functional strategies associated with water use efficiency. Our results are particularly critical for other oceanic island floras that are not constituted by such a vast diversity of insular woody species as the Canary Islands

A possible role for river restoration enhancing biodiversity through interaction with wildfire

BackgroundHistorically, wildfire regimes produced important landscape-scale disturbances in many regions globally. The “pyrodiversity begets biodiversity” hypothesis suggests that wildfires that generate temporally and spatially heterogeneous mosaics of wildfire severity and post-burn recovery enhance biodiversity at landscape scales. However, river management has often led to channel incision that disconnects rivers from their floodplains, desiccating floodplain habitats and depleting groundwater. In conjunction with predicted increases in frequency, intensity and extent of wildfires under climate change, this increases the likelihood of deep, uniform burns that reduce biodiversity.Predicted synergy of river restoration and biodiversity increaseRecent focus on floodplain re-wetting and restoration of successional floodplain habitat mosaics, developed for river management and flood prevention, could reduce wildfire intensity in restored floodplains and make the burns less uniform, increasing climate-change resilience; an important synergy. According to theory, this would also enhance biodiversity. However, this possibility is yet to be tested empirically. We suggest potential research avenues.Illustration and future directionsWe illustrate the interaction between wildfire and river restoration using a restoration project in Oregon, USA. A project to reconnect the South Fork McKenzie River and its floodplain suffered a major burn (“Holiday Farm” wildfire, 2020), offering a rare opportunity to study the interaction between this type of river restoration and wildfire; specifically, the predicted increases in pyrodiversity and biodiversity. Given the importance of river and wetland ecosystems for biodiversity globally, a research priority should be to increase our understanding of potential mechanisms for a “triple win” of flood reduction, wildfire alleviation and biodiversity promotion

The influence of C3 and C4 vegetation on soil organic matter dynamics in contrasting semi-natural tropical ecosystems

Variations in the carbon isotopic composition of soil organic matter (SOM) in bulk and fractionated samples were used to assess the influence of C3 and C4 vegetation on SOM dynamics in semi-natural tropical ecosystems sampled along a precipitation gradient in West Africa. Differential patterns in SOM dynamics in C3/C4 mixed ecosystems occurred at various spatial scales. Relative changes in C / N ratios between two contrasting SOM fractions were used to evaluate potential site-scale differences in SOM dynamics between C3- and C4-dominated locations. These differences were strongly controlled by soil texture across the precipitation gradient, with a function driven by bulk δ13C and sand content explaining 0.63 of the observed variability. The variation of δ13C with soil depth indicated a greater accumulation of C3-derived carbon with increasing precipitation, with this trend also being strongly dependant on soil characteristics. The influence of vegetation thickening on SOM dynamics was also assessed in two adjacent, but structurally contrasting, transitional ecosystems occurring on comparable soils to minimise the confounding effects posed by climatic and edaphic factors. Radiocarbon analyses of sand-size aggregates yielded relatively short mean residence times (τ) even in deep soil layers, while the most stable SOM fraction associated with silt and clay exhibited shorter τ in the savanna woodland than in the neighbouring forest stand. These results, together with the vertical variation observed in δ13C values, strongly suggest that both ecosystems are undergoing a rapid transition towards denser closed canopy formations. However, vegetation thickening varied in intensity at each site and exerted contrasting effects on SOM dynamics. This study shows that the interdependence between biotic and abiotic factors ultimately determine whether SOM dynamics of C3- and C4-derived vegetation are at variance in ecosystems where both vegetation types coexist. The results highlight the far-reaching implications that vegetation thickening may have for the stability of deep SOM. Â © Author(s) 2015

Remote sensing of geomorphodiversity linked to biodiversity — part III: traits, processes and remote sensing characteristics

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed