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.

Throwing light on dark diversity of vascular plants in China: predicting the distribution of dark and threatened species under global climate change.

BACKGROUND: As global climate change accelerates, ecologists and conservationists are increasingly investigating changes in biodiversity and predicting species distribution based on species observed at sites, but rarely consider those plant species that could potentially inhabit but are absent from these areas (i.e., the dark diversity and its distribution). Here, we estimated the dark diversity of vascular plants in China and picked up threatened dark species from the result, and applied maximum entropy (MaxEnt) model to project current and future distributions of those dark species in their potential regions (those regions that have these dark species). METHODS: We used the Beals probability index to estimate dark diversity in China based on available species distribution information and explored which environmental variables had significant impacts on dark diversity by incorporating bioclimatic data into the random forest (RF) model. We collected occurrence data of threatened dark species (Eucommia ulmoides, Liriodendron chinense, Phoebe bournei, Fagus longipetiolata, Amentotaxus argotaenia, and Cathaya argyrophylla) and related bioclimatic information that can be used to predict their distributions. In addition, we used MaxEnt modeling to project their distributions in suitable areas under future (2050 and 2070) climate change scenarios. RESULTS: We found that every study region's dark diversity was lower than its observed species richness. In these areas, their numbers of dark species are ranging from 0 to 215, with a generally increasing trend from western regions to the east. RF results showed that temperature variables had a more significant effect on dark diversity than those associated with precipitation. The results of MaxEnt modeling showed that most threatened dark species were climatically suitable in their potential regions from current to 2070. DISCUSSIONS: The results of this study provide the first ever dark diversity patterns concentrated in China, even though it was estimated at the provincial scale. A combination of dark diversity and MaxEnt modeling is an effective way to shed light on the species that make up the dark diversity, such as projecting the distribution of specific dark species under global climate change. Besides, the combination of dark diversity and species distribution models (SDMs) may also be of value for ex situ conservation, ecological restoration, and species invasion prevention in the future.

Anticipating species distributions: Handling sampling effort bias under a Bayesian framework.

Anticipating species distributions in space and time is necessary for effective biodiversity conservation and for prioritising management interventions. This is especially true when considering invasive species. In such a case, anticipating their spread is important to effectively plan management actions. However, considering uncertainty in the output of species distribution models is critical for correctly interpreting results and avoiding inappropriate decision-making. In particular, when dealing with species inventories, the bias resulting from sampling effort may lead to an over- or under-estimation of the local density of occurrences of a species. In this paper we propose an innovative method to i) map sampling effort bias using cartogram models and ii) explicitly consider such uncertainty in the modeling procedure under a Bayesian framework, which allows the integration of multilevel input data with prior information to improve the anticipation species distributions.

Specialist Insect Herbivore and Light Availability Do Not Interact in the Evolution of an Invasive Plant.

Release from specialist insect herbivores may allow invasive plants to evolve traits associated with decreased resistance and increased competitive ability. Given that there may be genetic trade-off between resistance and tolerance, invasive plants could also become more tolerant to herbivores. Although it is widely acknowledged that light availability affects tolerance to herbivores, little information is available for whether the effect of light availability on tolerance differ between the introduced and native populations. We conducted a common garden experiment in the introduced range of Alternanthera philoxeroides using ten invasive US and ten native Argentinean populations at two levels of light availability and in the presence or absence of a specialist stem-boring insect Agasicles hygrophila. Plant biomass (total and storage root biomass), two allocation traits (root/shoot ratio and branch intensity, branches biomass/main stem biomass) and two functional traits (specific stem length and specific leaf area), which are potentially associated with herbivore resistance and light capture, were measured. Overall, we found that A. philoxeroides from introduced ranges had comparable biomass and tolerance to specialist herbivores, lower branch intensity, lower specific stem length and specific leaf area. Moreover, introduced populations displayed higher shade tolerance of storage root biomass and lower plastic response to shading in specific stem length. Finally, light availability had no significant effect on evolution of tolerance to specialist herbivores of A. philoxeroides. Our results suggest that post-introduction evolution might have occurred in A. philoxeroides. While light availability did not influence the evolution of tolerance to specialist herbivores, increased shade tolerance and release from specialist insects might have contributed to the successful invasion of A. philoxeroides.

Spectral variation versus species beta-diversity at different spatial scales: a test in African highland savannas.

Few studies exist that explicitly analyse the effect of grain, i.e. the sampling unit dimension, on vascular plant species turnover (beta-diversity) among sites. While high beta-diversity is often a result of high environmental heterogeneity, remotely sensed spectral distances among sampling units may be used as a proxy of environmental gradients which spatially shape the patterns of species turnover. In this communication, we aimed to (i) test the potential relation between spectral variation and species beta-diversity in a savanna environment and to (ii) investigate the effect of grain on the achieved patterns. Field data gathered by the BIOTA Southern Africa biodiversity monitoring programme were used to model the relation between spectral variation and species turnover at different spatial grains (10 m x 10 m and 20 m x 50 m). Our results indicate that the overall fit was greater at the larger grain size, confirming the theoretical assumption that using a lower grain size would generally lead to a higher noise in the calculation of species turnover. This communication represents one of the first attempts at relating beta-diversity to spectral variation, while incorporating the effects of grain size in the study. The results of this study could have significant implications for biodiversity research and conservation planning at a regional or even larger spatial scale.