Discriminating woody species assemblages from National Forest Inventory data based on phylogeny in Georgia.

Classifications of forest vegetation types and characterization of related species assemblages are important analytical tools for mapping and diversity monitoring of forest communities. The discrimination of forest communities is often based on ß-diversity, which can be quantified via numerous indices to derive compositional dissimilarity between samples. This study aims to evaluate the applicability of unsupervised classification for National Forest Inventory data from Georgia by comparing two cluster hierarchies. We calculated the mean basal area per hectare for each woody species across 1059 plot observations and quantified interspecies distances for all 87 species. Following an unspuervised cluster analysis, we compared the results derived from the species-neutral dissimilarity (Bray-Curtis) with those based on the Discriminating Avalanche dissimilarity, which incorporates interspecies phylogenetic variation. Incorporating genetic variation in the dissimilarity quantification resulted in a more nuanced discrimination of woody species assemblages and increased cluster coherence. Favorable statistics include the total number of clusters (23 vs. 20), mean distance within clusters (0.773 vs. 0.343), and within sum of squares (344.13 vs. 112.92). Clusters derived from dissimilarities that account for genetic variation showed a more robust alignment with biogeographical units, such as elevation and known habitats. We demonstrate that the applicability of unsupervised classification of species assemblages to large-scale forest inventory data strongly depends on the underlying quantification of dissimilarity. Our results indicate that by incorporating phylogenetic variation, a more precise classification aligned with biogeographic units is attained. This supports the concept that the genetic signal of species assemblages reflects biogeographical patterns and facilitates more precise analyses for mapping, monitoring, and management of forest diversity.

The Spectral Species Concept in Living Color.

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

Deep learning and citizen science enable automated plant trait predictions from photographs.

Plant functional traits ('traits') are essential for assessing biodiversity and ecosystem processes, but cumbersome to measure. To facilitate trait measurements, we test if traits can be predicted through visible morphological features by coupling heterogeneous photographs from citizen science (iNaturalist) with trait observations (TRY database) through Convolutional Neural Networks (CNN). Our results show that image features suffice to predict several traits representing the main axes of plant functioning. The accuracy is enhanced when using CNN ensembles and incorporating prior knowledge on trait plasticity and climate. Our results suggest that these models generalise across growth forms, taxa and biomes around the globe. We highlight the applicability of this approach by producing global trait maps that reflect known macroecological patterns. These findings demonstrate the potential of Big Data derived from professional and citizen science in concert with CNN as powerful tools for an efficient and automated assessment of Earth's plant functional diversity.

Radiative transfer modelling reveals why canopy reflectance follows function.

Optical remote sensing is potentially highly informative to track Earth's plant functional diversity. Yet, causal explanations of how and why plant functioning is expressed in canopy reflectance remain limited. Variation in canopy reflectance can be described by radiative transfer models (here PROSAIL) that incorporate plant traits affecting light transmission in canopies. To establish causal links between canopy reflectance and plant functioning, we investigate how two plant functional schemes, i.e. the Leaf Economic Spectrum (LES) and CSR plant strategies, are related to traits with relevance to reflectance. These traits indeed related to both functional schemes, whereas only traits describing leaf properties correlated with the LES. In contrast, traits related to canopy structure showed no correlation to the LES, but to CSR strategies, as the latter integrates both plant economics and size traits, rather than solely leaf economics. Multiple optically relevant traits featured comparable or higher correspondence to the CSR space than those traits originally used to allocate CSR scores. This evidences that plant functions and strategies are directly expressed in reflectance and entails that canopy 'reflectance follows function'. This opens up new possibilities to understand differences in plant functioning and to harness optical remote sensing data for monitoring Earth´s functional diversity.

Invasion by the Alien Tree Prunus serotina Alters Ecosystem Functions in a Temperate Deciduous Forest.

Alien invasive species can affect large areas, often with wide-ranging impacts on ecosystem structure, function, and services. Prunus serotina is a widespread invader of European temperate forests, where it tends to form homogeneous stands and limits recruitment of indigenous trees. We hypotesized that invasion by P. serotina would be reflected in the nutrient contents of the native species' leaves and in the respiration of invaded plots as efficient resource uptake and changes in nutrient cycling by P. serotina probably underly its aggressive invasiveness. We combined data from 48 field plots in the forest of Compiègne, France, and data from an experiment using 96 microcosms derived from those field plots. We used general linear models to separate effects of invasion by P. serotina on heterotrophic soil and litter respiration rates and on canopy foliar nutrient content from effects of soil chemical properties, litter quantity, litter species composition, and tree species composition. In invaded stands, average respiration rates were 5.6% higher for soil (without litter) and 32% higher for soil and litter combined. Compared to indigenous tree species, P. serotina exhibited higher foliar N (+24.0%), foliar P (+50.7%), and lower foliar C:N (-22.4%) and N:P (-10.1%) ratios. P. serotina affected foliar nutrient contents of co-occuring indigenous tree species leading to decreased foliar N (-8.7 %) and increased C:N ratio (+9.5%) in Fagus sylvatica, decreased foliar N:P ratio in Carpinus betulus (-13.5%) and F. sylvatica (-11.8%), and increased foliar P in Pinus sylvestris (+12.3%) in invaded vs. uninvaded stands. Our results suggest that P. serotina is changing nitrogen, phosphorus, and carbon cycles to its own advantage, hereby increasing carbon turnover via labile litter, affecting the relative nutrient contents in the overstory leaves, and potentially altering the photosynthetic capacity of the long-lived indigenous broadleaved species. Uncontrolled invasion of European temperate forests by P. serotina may affect the climate change mitigation potential of these forests in the long term, through additive effects on local nutrient cycles.

Alien invasive slider turtle in unpredicted habitat: a matter of niche shift or of predictors studied?

BACKGROUND: Species Distribution Models (SDMs) aim on the characterization of a species' ecological niche and project it into geographic space. The result is a map of the species' potential distribution, which is, for instance, helpful to predict the capability of alien invasive species. With regard to alien invasive species, recently several authors observed a mismatch between potential distributions of native and invasive ranges derived from SDMs and, as an explanation, ecological niche shift during biological invasion has been suggested. We studied the physiologically well known Slider turtle from North America which today is widely distributed over the globe and address the issue of ecological niche shift versus choice of ecological predictors used for model building, i.e., by deriving SDMs using multiple sets of climatic predictor. PRINCIPAL FINDINGS: In one SDM, predictors were used aiming to mirror the physiological limits of the Slider turtle. It was compared to numerous other models based on various sets of ecological predictors or predictors aiming at comprehensiveness. The SDM focusing on the study species' physiological limits depicts the target species' worldwide potential distribution better than any of the other approaches. CONCLUSION: These results suggest that a natural history-driven understanding is crucial in developing statistical models of ecological niches (as SDMs) while "comprehensive" or "standard" sets of ecological predictors may be of limited use.

The link between rapid enigmatic amphibian decline and the globally emerging chytrid fungus.

Amphibians are globally declining and approximately one-third of all species are threatened with extinction. Some of the most severe declines have occurred suddenly and for unknown reasons in apparently pristine habitats. It has been hypothesized that these "rapid enigmatic declines" are the result of a panzootic of the disease chytridiomycosis caused by globally emerging amphibian chytrid fungus. In a Species Distribution Model, we identified the potential distribution of this pathogen. Areas and species from which rapid enigmatic decline are known significantly overlap with those of highest environmental suitability to the chytrid fungus. We confirm the plausibility of a link between rapid enigmatic decline in worldwide amphibian species and epizootic chytridiomycosis.