Evaluating the Sensitivity of Forest Structural Diversity Characterization to LiDAR Point Density

Recent expansion in data sharing has created unprecedented opportunities to explore structure-function linkages in ecosystems across spatial and temporal scales. However, characteristics of the same data product, such as resolution, can change over time or spatial locations, as protocols are adapted to new technology or conditions, which may impact the data’s potential utility and accuracy for addressing end user scientific questions. The National Ecological Observatory Network (NEON) provides data products for users from 81 sites and over a planned 30-year time frame, including discrete return Light Detection and Range (LiDAR) from an airborne observatory platform. LiDAR is a well-established and increasingly available remote sensing technology for measuring three-dimensional (3D) characteristics of ecosystem and landscape structure, including forest structural diversity. The LiDAR product that NEON provides can vary in point density from 2 – 25+ points/m2 depending on instrument and acquisition date. We used NEON LiDAR from five forested sites to (1) identify the minimum point density at which structural diversity metrics can be robustly estimated across forested sites from different ecoclimatic zones in the USA and (2) to test the effects of variable point density on the estimation of a suite of structural diversity metrics and multivariate structural complexity types within and across forested sites. Twelve out of sixteen structural diversity metrics were sensitive to LiDAR point density in at least one of the five NEON forested sites. The minimum point density to reliably estimate the metrics ranged from 2.0 to 7.5 pt/m2, but our results indicate that point densities above 7-8 pt/m2 should provide robust measurements of structural diversity in forests for temporal or spatial comparisons. The delineation of multivariate structural complexity types from a suite of 16 structural diversity metrics was robust within sites and across forest types for a LiDAR point density of 4 pt/m2 and above. This study shows that different metrics of structural diversity can vary in their sensitivity to the resolution of LiDAR data and users of these open-source data products should consider the point density of their data and use caution in metric selection when making spatial or temporal comparisons from these datasets.

Inconsistent Patterns of Microbial Diversity and Composition Between Highly Similar Sequencing Protocols: A Case Study With Reef-Building Corals

16S rRNA gene profiling (amplicon sequencing) is a popular technique for understanding host-associated and environmental microbial communities. Most protocols for sequencing amplicon libraries follow a standardized pipeline that can differ slightly depending on laboratory facility and user. Given that the same variable region of the 16S gene is targeted, it is generally accepted that sequencing output from differing protocols are comparable and this assumption underlies our ability to identify universal patterns in microbial dynamics through meta-analyses. However, discrepant results from a combined 16S rRNA gene dataset prepared by two labs whose protocols differed only in DNA polymerase and sequencing platform led us to scrutinize the outputs and challenge the idea of confidently combining them for standard microbiome analysis. Using technical replicates of reef-building coral samples from two species, Montipora aequituberculata and Porites lobata, we evaluated the consistency of alpha and beta diversity metrics between data resulting from these highly similar protocols. While we found minimal variation in alpha diversity between platform, significant differences were revealed with most beta diversity metrics, dependent on host species. These inconsistencies persisted following removal of low abundance taxa and when comparing across higher taxonomic levels, suggesting that bacterial community differences associated with sequencing protocol are likely to be context dependent and difficult to correct without extensive validation work. The results of this study encourage caution in the statistical comparison and interpretation of studies that combine rRNA gene sequence data from distinct protocols and point to a need for further work identifying mechanistic causes of these observed differences.

The stochastic logistic model with correlated carrying capacities reproduces beta-diversity metrics of microbial

The large taxonomic variability of microbial community composition is a consequence of the combination of environmental variability, mediated through ecological interactions, and stochasticity. Most of the analysis aiming to infer the biological factors determining this difference in community structure start by quantifying how much communities are similar in their composition, trough beta-diversity metrics. The central role that these metrics play in microbial ecology does not parallel with a quantitative understanding of their relationships and statistical properties. In particular, we lack a framework that reproduces the empirical statistical properties of beta-diversity metrics. Here we take a macroecological approach and introduce a model to reproduce the statistical properties of community similarity. The model is based on the statistical properties of individual communities and on a single tunable parameter, the correlation of species' carrying capacities across communities, which sets the difference of two communities. The model reproduces quantitatively the empirical values of several commonly-used beta-diversity metrics, as well as the relationships between them. In particular, this modeling framework naturally reproduces the negative correlation between overlap and dissimilarity, which has been observed in both empirical and experimental communities and previously related to the existence of universal features of community dynamics. In this framework, such correlation naturally emerges due to the effect of random sampling.

Low impact of forest conversion on biodiversity: evidence from small mammals in contrasting forests of Mt. Liangshan, China

Secondary and plantation forests are the main alternative forests remaining after the deforestation of primary forests. Understanding the conservation value of secondary and plantation forests is important for resource utilization. To explore the impact of forest conversion on biodiversity, we compared multiple diversity metrics (taxonomic, phylogenetic and functional diversity) and community structures of small mammals in the primary, secondary and plantation forests on Mt. Liangshan, Sichuan Province, China. Seven field surveys were conducted to survey local small mammal assemblages between 2016 and 2020. We found that the taxonomic, phylogenetic and functional diversity metrics of small mammals in the three forest types were similar at the landscape scale, while all diversity metrics were lowest in the plantation forest and highest in the primary forests at the site scale. The community structure analysis showed that random processes were dominant across the three forest types, and there was no difference in small mammal community structures among the three forest types. Our results indicated that secondary and plantation forests in the nature reserves, adjacent to the primary forest and exposed to little human disturbance, also can provide important habitats for small mammals.

The power of microbiome studies: some considerations on which alpha and beta metrics to use and how to report analysis the results

Since sequencing techniques become less expensive, larger sample sizes are applicable for microbiota studies. The aim of this study is to show how, and to what extent, different diversity metrics and different compositions of the microbiota influence the needed sample size to observed dissimilar groups. Empirical 16S rRNA amplicon sequence data obtained from animal experiments, observational human data, and simulated data was used to perform retrospective power calculations. A wide variation of alpha diversity and beta diversity metrics were used to compare the different microbiota data sets and the effect on the sample size. Our data showed that beta diversity metrics are most sensitive to observe differences compared to alpha diversity metrics. The structure of the data influenced which alpha metrics are most sensitive. Regarding beta diversity, the Bray-Curtis metric is in general most sensitive to observe differences between groups, resulting in lower sample size and potential publication bias. We recommend to perform power calculations and to use multiple diversity metrics as an outcome measure. To improve microbiota studies awareness needs to be raised on the sensitivity and bias for microbiota research outcomes created by the used metrics rather than biological differences. We have seen that different alpha and beta diversity metrics lead to different study power: on the basis of this observation, one could be naturally tempted to try all possible metrics until one or more are found that give a statistically significant test result, i.e. p-value < α. This way of proceeding is one of the many forms of the so-called p-value hacking. To this end, in our opinion, the only way to protect ourselves from (the temptation of) p-hacking would be to publish, and we stress here the word publish, a statistical plan before experiments are initiated: this practice is customary for clinical trials where a statistical plan describing the endpoints and the corresponding statistical analyses must be disclosed before the start of the study.

Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery

Plant biodiversity is an important feature of grassland ecosystems, as it is related to the provision of many ecosystem services crucial for the human economy and well-being. Given the importance of grasslands, research has been carried out in recent years on the potential to monitor them with novel remote sensing techniques. In this study, the optical diversity (also called spectral diversity) approach was adopted to check the potential of using high-resolution hyperspectral images to estimate α-diversity in grassland ecosystems. In 2018 and 2019, grassland species composition was surveyed and canopy hyperspectral data were acquired at two grassland sites: Monte Bondone (IT-MBo; species-rich semi-natural grasslands) and an experimental farm of the University of Padova, Legnaro, Padua, Italy (IT-PD; artificially established grassland plots with a species-poor mixture). The relationship between biodiversity (species richness, Shannon’s, species evenness, and Simpson’s indices) and optical diversity metrics (coefficient of variation-CV and standard deviation-SD) was not consistent across the investigated grassland plant communities. Species richness could be estimated by optical diversity metrics with an R = 0.87 at the IT-PD species-poor site. In the more complex and species-rich grasslands at IT-MBo, the estimation of biodiversity indices was more difficult and the optical diversity metrics failed to estimate biodiversity as accurately as in IT-PD probably due to the higher number of species and the strong canopy spatial heterogeneity. Therefore, the results of the study confirmed the ability of spectral proxies to detect grassland α-diversity in man-made grassland ecosystems but highlighted the limitations of the spectral diversity approach to estimate biodiversity when natural grasslands are observed. Nevertheless, at IT-MBo, the optical diversity metric SD calculated from post-processed hyperspectral images and transformed spectra showed, in the red part of the spectrum, a significant correlation (up to R = 0.56, p = 0.004) with biodiversity indices. Spatial resampling highlighted that for the IT-PD sward the optimal optical pixel size was 1 cm, while for the IT-MBo natural grassland it was 1 mm. The random pixel extraction did not improve the performance of the optical diversity metrics at both study sites. Further research is needed to fully understand the links between α-diversity and spectral and biochemical heterogeneity in complex heterogeneous ecosystems, and to assess whether the optical diversity approach can be adopted at the spatial scale to detect β-diversity. Such insights will provide more robust information on the mechanisms linking grassland diversity and optical heterogeneity.

Diversity Metrics Are Robust to Differences in Sampling Location and Depth for Environmental DNA of Plants in Small Temperate Lakes

Environmental DNA (eDNA) analysis methods permit broad yet detailed biodiversity sampling to be performed with minimal field effort. However, considerable uncertainty remains regarding the spatial resolution necessary for effective sampling, especially in aquatic environments. Also, contemporary plant communities are under-investigated with eDNA methods relative to animals and microbes. We analyzed eDNA samples from six small temperate lakes to elucidate spatial patterns in the distributions of algae and aquatic and terrestrial plants, using metabarcoding of the Internal Transcribed Spacer-1 (ITS1) genomic region. Sampling locations were varied across horizontal and vertical space: sites in each lake included a mixture of nearshore and offshore positions, each of which was stratified into surface (shallow) and benthic (deep) samples. We detected the expected community variation (beta diversity) from lake to lake, but only small effects of offshore distance and sampling depth. Taxon richness (alpha diversity) was slightly higher in nearshore samples, but displayed no other significant spatial effects. These diversity metrics imply that plant eDNA is more evenly distributed than its generating organisms in these small lake environments. Read abundances were heavily weighted toward aquatic macrophytes, though taxon richness was greatest in the algae and other non-vascular plants. We also identified representatives of many phylogenetically and ecologically varied plant taxa, including terrestrial species from surrounding areas. We conclude that freshwater plant eDNA surveys successfully capture differences among lake communities, and that easily accessible, shore-based sampling may be a reliable technique for informing research and management in similar ecosystems.

Characterisation of Early Microbial Colonisers within the Spiral Colon of Pre- and Post-Natal Piglets

Initial enteric microbial colonisation influences animal health and disease, hence an understanding of the first microbial colonisers within the piglet is important. The spiral colon of piglets that were stillborn (n = 20), born-alive (n = 10), and born alive and had sucked (n = 9) were collected from 28 sows to investigate whether initial microbial colonisation occurs pre- or post-partum and how it develops during the first 24 h post-partum. To examine this, DNA was extracted and 16S rRNA amplicon analysis was performed to allow analysis of microbial communities. The results indicate that microbial colonisation of the spiral colon had occurred in stillborn pigs, suggesting microbial exposure prior to birth. Alpha diversity metrics indicated that the number of taxa and community richness were higher in piglets that sucked (p < 0.001) and community evenness was lower in stillborns in comparison to born-alive (p < 0.001) but was not affected by colostrum consumption (p < 0.001). Additionally, when compared with stillborn piglets, the bacteria colonising the spiral colon during the first 24 h post-partum included the potentially pathogenic bacteria Escherichia coli, Clostridium perfringens and Clostridium celatum, and potentially beneficial bacteria Lactobacillus reutueri and Faecalibacterium prausnitzii. The relative presence of Archaea was high in stillborn piglets but decreased with post-natal environmental exposure. It is evident that stillborn piglets have bacteria present within their spiral colon, however further studies are needed in order to determine the time at which colonisation is initiated and the mechanisms determining how colonisation occurs. Additionally, as expected, the immediate post-natal environment largely influences the microorganisms colonising, while colostrum consumption further contributes to the microbial community enrichment.

Population genomics and conservation management of a declining tropical rodent

Conservation management is improved by incorporating information about the spatial distribution of population genetic diversity into planning strategies. Northern Australia is the location of some of the world’s most severe ongoing declines of endemic mammal species, yet we have little genetic information from this regional mammal assemblage to inform a genetic perspective on conservation assessment and planning. We used next-generation sequencing data from remnant populations of the threatened brush-tailed rabbit-rat (Conilurus penicillatus) to compare patterns of genomic diversity and differentiation across the landscape and investigate standardised hierarchical genomic diversity metrics to better understand brush-tailed rabbit-rat population genomic structure. We found strong population structuring, with high levels of differentiation between populations (FST = 0.21–0.78). Two distinct genomic lineages between the Tiwi Islands and mainland are also present. Prioritisation analysis showed that one population in both lineages would need to be conserved to retain at least ~80% of alleles for the species. Analysis of standardised genomic diversity metrics showed that approximately half of the total diversity occurs among lineages (δ = 0.091 from grand total γ = 0.184). We suggest that a focus on conserving remnant island populations may not be appropriate for the preservation of species-level genomic diversity and adaptive potential, as these populations represent a small component of the total diversity and a narrow subset of the environmental conditions in which the species occurs. We also highlight the importance of considering both genomic and ecological differentiation between source and receiving populations when considering translocations for conservation purposes.