A document by Chenyang Wei. Click on the document to view its contents.

Biologists increasingly rely on computer code to collect and analyze their data, reinforcing the importance of published code for transparency, reproducibility, training, and a basis for further work. Here we conduct a literature review examining temporal trends in code sharing in ecology and evolution publications since 2010, and test for an influence of code sharing on citation rate. We find that there is wide room for improvement in sharing code, as scientists are overwhelmingly (95%) failing to publish their code and that there has been no significant improvement over time. We also determined that there is a significant incentive to share, as we additionally find that code sharing can considerably improve citations, particularly when combined with open-access publication.

The 3-dimensional structure of habitats is a critical component of species' niches driving coexistence in species-rich ecosystems. However, its influence on structuring and partitioning recruitment niches has not been widely addressed. We developed a new method to combine Species Distribution Modeling and Structure from Motion and characterized three-dimensional recruitment niches of two ecosystem engineers on Caribbean coral reefs, scleractinian corals and gorgonians. Fine-scale roughness was the most important predictor of suitable habitat for both taxa, and their niches largely overlapped, primarily due to scleractinians broader niche breadth. Crevices and holes at mm-scales on calcareous rock with low coral cover were more suitable for octocorals than for scleractinian recruits, suggesting the decline of scleractinian corals is facilitating the recruitment of octocorals on contemporary Caribbean reefs. However, the relative abundances of the taxa were independent of the amount of suitable habitat on the reef, emphasizing niche-processes solely do not predict recruitment rates.

The Greater Cape Floristic Region (GCFR) of South Africa includes marine and terrestrial biomes with species diversity rivaling mega-diverse tropical rainforests in a compact area (300x700km). Extinction risk studies suggest that GCFR species are among the most vulnerable to climate change over the next 50 years. I present a scoping proposal commissioned by NASA to develop a field campaign to measure and monitor the distribution and abundance of biodiversity with new remotely-sensed data and the rich historical data in this region. I will summarize the central questions to be addressed by this field campaign and lay out the proposed study design to integrate satellite, airborne, and in situ data collection. Our plan centers around the collection of new hyperspectral imagery from AVIRIS-NG, PRISM, and HyTES spectrometers combined with the LVIS laser altimeter. These data will be collected at approximately 20 m spatial resolution across much of the GCFR and nearby aquatic and marine ecosystems. These data will then be combined with existing and new observations of the spatial distribution of community composition and functional traits to enable high resolution mapping and modeling of several essential biodiversity variables (EBVs) including species distributions, functional traits (including leaf properties), and three-dimensional canopy structure. Given the wealth of available independent in situ data available that can be brought to bear, the GCFR is an ideal system to fully evaluate the capabilities of remote-sensing technology to characterize biodiversity patterns across diverse landscapes in a relatively compact geographic area. In combination with the rich historical data and well-developed ecological understanding in this region, these new observations will enable detailed exploration into the drivers and mechanisms of change including the feedbacks from changing biodiversity to regional climate, disturbance, post-fire recovery, freshwater provisioning, and other ecosystem services.

Human-mediated climate change over the past century has made significant impacts on global ecosystems and biodiversity including accelerating redistribution of the geographic ranges of species. In mountainous regions, the transition zone from continuous closed-canopy subalpine forests to treeless alpine tundra areas at higher elevations is commonly referred to as ‘Alpine Treeline Ecotone’ (ATE). Globally, warming climate is expected to drive the ATE upslope, which could lead to negative impacts on local biodiversity and modify ecosystem function. However, existing studies rely primarily on field-based data which are difficult and time consuming to collect. In this research, we define three critical characteristics of the ATE including 1) an abrupt spatial shift in vegetative activity as elevation varies, 2) reduction in vegetative activity as elevation increases, and 3) vegetative activity is at an intermediate level. Using the geospatial tools provided by Google Earth Engine, we construct an index (ATEI) to identify areas with the three ATE features based on the image gradients of vegetative activity and elevation datasets. Based on the ATEI and Google Earth imagery in 115 Landsat pixels, we establish a Logistic regression model to estimate the probability of whether or not a sampled pixel is located within the ATE. The prediction accuracy is approximately 80%. Furthermore, the ATEI-estimated ATE elevation is strongly correlated (r = 0.96) with a set of field-based data at 20 sampling sites from across the region. Based on the average annual ATEIs from 2009 to 2011, we estimate the average ATE elevation for each mountain range in the western U.S. The result varies from 1,183 m to 3,584 m. The detection metric developed in this study facilitates monitoring the geographic location and potential shifts of ATEs as well as the general impact of climate change in mountainous regions during recent decades. We also expect this approach to be useful in monitoring other ecosystem boundaries.