Semi-quantitative metabarcoding reveals how climate shapes arthropod community assembly along elevation gradients on Hawaii Island

Spatial variation in climatic conditions along elevation gradients provides an important backdrop by which communities assemble and diversify. Lowland habitats tend to be connected through time, whereas highlands can be continuously or periodically isolated, conditions that have been hypothesized to promote high levels of species endemism. This tendency is expected to be accentuated among taxa that show niche conservatism within a given climatic envelope. While species distribution modeling approaches have allowed extensive exploration of niche conservatism among target taxa, a broad understanding of the phenomenon requires sampling of entire communities. Species-rich groups such as arthropods are ideal case studies for understanding ecological and biodiversity dynamics along elevational gradients given their important functional role in many ecosystems, but community-level studies have been limited due to their tremendous diversity. Here, we develop a novel semi-quantitative metabarcoding approach that combines specimen counts and size-sorting to characterize arthropod community-level diversity patterns along elevational transects on two different volcanoes of the island of Hawai'i. We found that arthropod communities between the two transects became increasingly distinct compositionally at higher elevations. Resistance surface approaches suggest that climatic differences between sampling localities are an important driver in shaping beta-diversity patterns, though the relative importance of climate varies across taxonomic groups. Nevertheless, the climatic niche position of OTUs between transects was highly correlated, suggesting that climatic filters shape the colonization between adjacent volcanoes. Taken together, our results highlight climatic niche conservatism as an important factor shaping ecological assembly along elevational gradients and suggest topographic complexity as an important driver of diversification.

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.

Hyperspectral and LiDAR Data Fusion Classification Using Superpixel Segmentation-Based Local Pixel Neighborhood Preserving Embedding

A new method of superpixel segmentation-based local pixel neighborhood preserving embedding (SSLPNPE) is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR) data based on the extinction profiles (EPs), superpixel segmentation and local pixel neighborhood preserving embedding (LPNPE). A new workflow is proposed to calibrate the Goddard’s LiDAR, hyperspectral and thermal (G-LiHT) data, which allows our method to be applied to actual data. Specifically, EP features are extracted from both sources. Then, the derived features of each source are fused by the SSLPNPE. Using the labeled samples, the final label assignment is produced by a classifier. For the open standard experimental data and the actual data, experimental results prove that the proposed method is fast and effective in hyperspectral and LiDAR data fusion.

Episodic Canopy Structural Transformations and Biological Invasion in a Hawaiian Forest

The remaining native forests on the Hawaiian Islands have been recognized as threatened by changing climate, increasing insect outbreak, new deadly pathogens, and growing populations of canopy structure-altering invasive species. The objective of this study was to assess long-term, net changes to upper canopy structure in sub-montane forests on the eastern slope of Mauna Kea volcano, Hawai'i, in the context of continuing climate events, insect outbreaks, and biological invasion. We used high-resolution multi-temporal Light Detection and Ranging (LiDAR) data to quantify near-decadal net changes in forest canopy height and gap distributions at a critical transition between alien invaded lowland and native sub-montane forest at the end of a recent drought and host-specific insect (Scotorythra paludicola) outbreak. We found that sub-montane forests have experienced a net loss in average canopy height, and therefore structure and aboveground carbon stock. Additionally, where invasive alien tree species co-dominate with native trees, the upper canopy structure became more homogeneous. Tracking the loss of forest canopy height and spatial variation with airborne LiDAR is a cost-effective way to monitor forest canopy health, and to track and quantify ecological impacts of invasive species through space and time.