Deciphering the enigma of undetected species, phylogenetic, and functional diversity based on Good-Turing theory

Estimating the species, phylogenetic, and functional diversity of a community is challenging because rare species are often undetected, even with intensive sampling. The Good-Turing frequency formula, originally developed for cryptography, estimates in an ecological context the true frequencies of rare species in a single assemblage based on an incomplete sample of individuals. Until now, this formula has never been used to estimate undetected species, phylogenetic, and functional diversity. Here, we first generalize the Good-Turing formula to incomplete sampling of two assemblages. The original formula and its two-assemblage generalization provide a novel and unified approach to notation, terminology, and estimation of undetected biological diversity. For species richness, the Good-Turing framework offers an intuitive way to derive the non-parametric estimators of the undetected species richness in a single assemblage, and of the undetected species shared between two assemblages. For phylogenetic diversity, the unified approach leads to an estimator of the undetected Faith\u27s phylogenetic diversity (PD, the total length of undetected branches of a phylogenetic tree connecting all species), as well as a new estimator of undetected PD shared between two phylogenetic trees. For functional diversity based on species traits, the unified approach yields a new estimator of undetected Walker et al.\u27s functional attribute diversity (FAD, the total species-pairwise functional distance) in a single assemblage, as well as a new estimator of undetected FAD shared between two assemblages. Although some of the resulting estimators have been previously published (but derived with traditional mathematical inequalities), all taxonomic, phylogenetic, and functional diversity estimators are now derived under the same framework. All the derived estimators are theoretically lower bounds of the corresponding undetected diversities; our approach reveals the sufficient conditions under which the estimators are nearly unbiased, thus offering new insights. Simulation results are reported to numerically verify the performance of the derived estimators. We illustrate all estimators and assess their sampling uncertainty with an empirical dataset for Brazilian rain forest trees. These estimators should be widely applicable to many current problems in ecology, such as the effects of climate change on spatial and temporal beta diversity and the contribution of trait diversity to ecosystem multi-functionality

Evaluating the potential of full-waveform lidar for mapping pan-tropical tree species richness

AIM: Mapping tree species richness across the tropics is of great interest for effective conservation and biodiversity management. In this study, we evaluated the potential of full‐waveform lidar data for mapping tree species richness across the tropics by relating measurements of vertical canopy structure, as a proxy for the occupation of vertical niche space, to tree species richness. LOCATION: Tropics. TIME PERIOD: Present. MAJOR TAXA STUDIED: Trees. METHODS: First, we evaluated the characteristics of vertical canopy structure across 15 study sites using (simulated) large‐footprint full‐waveform lidar data (22 m diameter) and related these findings to in‐situ tree species information. Then, we developed structure–richness models at the local (within 25–50 ha plots), regional (biogeographical regions) and pan‐tropical scale at three spatial resolutions (1.0, 0.25 and 0.0625 ha) using Poisson regression. RESULTS: The results showed a weak structure–richness relationship at the local scale. At the regional scale (within a biogeographical region) a stronger relationship between canopy structure and tree species richness across different tropical forest types was found, for example across Central Africa and in South America [R^{2} ranging from .44–.56, root mean squared difference as a percentage of the mean (RMSD%) ranging between 23–61%]. Modelling the relationship pan‐tropically, across four continents, 39% of the variation in tree species richness could be explained with canopy structure alone (R^{2} = .39 and RMSD% = 43%, 0.25‐ha resolution). MAIN CONCLUSIONS: Our results may serve as a basis for the future development of a set of structure–richness models to map high resolution tree species richness using vertical canopy structure information from the Global Ecosystem Dynamics Investigation (GEDI). The value of this effort would be enhanced by access to a larger set of field reference data for all tropical regions. Future research could also support the use of GEDI data in frameworks using environmental and spectral information for modelling tree species richness across the tropics

Fifteen essential science advances needed for effective restoration of the world's forest landscapes

There has never been a more pressing and opportune time for science and practice to collaborate towards restoration of the world's forests. Multiple uncertainties remain for achieving successful, long-term forest landscape restoration (FLR). In this article, we use expert knowledge and literature review to identify knowledge gaps that need closing to advance restoration practice, as an introduction to a landmark theme issue on FLR and the UN Decade on Ecosystem Restoration. Aligned with an Adaptive Management Cycle for FLR, we identify 15 essential science advances required to facilitate FLR success for nature and people. They highlight that the greatest science challenges lie in the conceptualization, planning and assessment stages of restoration, which require an evidence base for why, where and how to restore, at realistic scales. FLR and underlying sciences are complex, requiring spatially explicit approaches across disciplines and sectors, considering multiple objectives, drivers and trade-offs critical for decision-making and financing. The developing tropics are a priority region, where scientists must work with stakeholders across the Adaptive Management Cycle. Clearly communicated scientific evidence for action at the outset of restoration planning will enable donors, decision makers and implementers to develop informed objectives, realistic targets and processes for accountability. This article paves the way for 19 further articles in this theme issue, with author contributions from across the world. This introduction article is part of the theme issue ‘Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration’

Do differences in understory light contribute to species distributions along a tropical rainfall gradient?

In tropical forests, regional differences in annual rainfall correlate with differences in plant species composition. Although water availability is clearly one factor determining species distribution, other environmental variables that covary with rainfall may contribute to distributions. One such variable is light availability in the understory, which decreases towards wetter forests due to differences in canopy density and phenology. We established common garden experiments in three sites along a rainfall gradient across the Isthmus of Panama in order to measure the differences in understory light availability, and to evaluate their influence on the performance of 24 shade-tolerant species with contrasting distributions. Within sites, the effect of understory light availability on species performance depended strongly on water availability. When water was not limiting, either naturally in the wetter site or through water supplementation in drier sites, seedling performance improved at higher light. In contrast, when water was limiting at the drier sites, seedling performance was reduced at higher light, presumably due to an increase in water stress that affected mostly wet-distribution species. Although wetter forest understories were on average darker, wet-distribution species were not more shade-tolerant than dry-distribution species. Instead, wet-distribution species had higher absolute growth rates and, when water was not limiting, were better able to take advantage of small increases in light than dry-distribution species. Our results suggest that in wet forests the ability to grow fast during temporary increases in light may be a key trait for successful recruitment. The slower growth rates of the dry-distribution species, possibly due to trade-offs associated with greater drought tolerance, may exclude these species from wetter forests

Aboveground Biomass Accumulation in a Tropical Wet Forest in Nicaragua Following a Catastrophic Hurricane Disturbance 1

Among their effects on forest structure and carbon dynamics, hurricanes frequently create large-scale canopy gaps that promote secondary growth. To measure the accumulation of aboveground biomass (AGBM) in a hurricane damaged forest, we established permanent plots 4 mo after the landfall of Hurricane Joan on the Atlantic coast of Nicaragua in October 1988. We quantified AGBM accumulation in these plots by correlating diameter measurements to AGBM values using a published allometric regression equation for tropical wet forests. In the first measurement year following the storm, AGBM in hurricane-affected plots was quite variable, ranging from 26 to 153 Mg/ha, with a mean of 78 (±15) Mg/ha. AGBM was substantially lower than in two control plots several kilometers outside the hurricane's path (331 ±15 Mg/ha). Biomass accumulation was slow (5.36 ± 0.74 Mg/ha/yr), relative to previous studies of forest regeneration following another hurricane (Hugo) and agricultural activity. We suggest that large-scale, homogenous canopy damage caused by Hurricane Joan impeded the dispersal and establishment of pioneer trees and led to a secondary forest dominated by late successional species that resprouted and survived the disturbance. With the relatively slow rate of biomass accumulation, any tightening in disturbance interval could reduce the maximum capacity of the living biomass to store carbon.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73646/1/j.1744-7429.2005.00077.x.pd