Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches

A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30-yr horizon of the network. In contrast to NEON’s extensive use of automated instruments to collect environmental data, NEON’s biodiversity surveys are almost entirely conducted using traditional human-centric field methods. We believe that the combination of instrumentation for remote data collection and machine learning models to process such data represents an important opportunity for NEON to expand the scope, scale, and usability of its biodiversity data collection while potentially reducing long-term costs. In this manuscript, we first review the current status of instrument-based biodiversity surveys within the NEON project and previous research at the intersection of biodiversity, instrumentation, and machine learning at NEON sites. We then survey methods that have been developed at other locations but could potentially be employed at NEON sites in future. Finally, we expand on these ideas in five case studies that we believe suggest particularly fruitful future paths for automated biodiversity measurement at NEON sites: acoustic recorders for sound-producing taxa, camera traps for medium and large mammals, hydroacoustic and remote imagery for aquatic diversity, expanded remote and ground-based measurements for plant biodiversity, and laboratory-based imaging for physical specimens and samples in the NEON biorepository. Through its data science-literate staff and user community, NEON has a unique role to play in supporting the growth of such automated biodiversity survey methods, as well as demonstrating their ability to help answer key ecological questions that cannot be answered at the more limited spatiotemporal scales of human-driven surveys

Incorporating canopy structure from simulated GEDI lidar into bird species distribution models

The Global Ecosystem Dynamics Investigation (GEDI) lidar began data acquisition from the International Space Station in March 2019 and is expected to make over 10 billion measurements of canopy structure and topography over two years. Previously, airborne lidar data with limited spatial coverage have been used to examine relationships between forest canopy structure and faunal diversity, most commonly bird species. GEDI’s latitudinal coverage will permit these types of analyses at larger spatial extents, over the majority of the Earth’s forests, and most importantly in areas where canopy structure is complex and/or poorly understood. In this regional study, we examined the impact that GEDI-derived Canopy Structure variables have on the performance of bird species distribution models (SDMs) in Sonoma County, California. We simulated GEDI waveforms for a two-year period and then interpolated derived Canopy Structure variables to three grid sizes of analysis. In addition to these variables, we also included Phenology, Climate, and other Auxiliary variables to predict the probability of occurrence of 25 common bird species. We used a weighted average ensemble of seven individual machine learning models to make predictions for each species and calculated variable importance. We found that Canopy Structure variables were, on average at our finest resolution of 250 m, the second most important group (32.5%) of predictor variables after Climate variables (35.3%). Canopy Structure variables were most important for predicting probability of occurrence of birds associated with Conifer forest habitat. Regarding spatial analysis scale, we found that finer-scale models more frequently performed better than coarser-scale models, and the importance of Canopy Structure variables was greater at finer spatial resolutions. Overall, GEDI Canopy Structure variables improved SDM performance for at least one spatial resolution for 19 of 25 species and thus show promise for improving models of bird species occurrence and mapping potential habitat

Afromontane forest ecosystem studies with multi-source satellite data

The Afromontane Forest of north Eastern Nigeria is an important ecological ecosystem endowed with flora and fauna species. The main goals of this thesis were to explore the potential of multi-source satellite remote sensing for the assessment of the biodiversity-rich Afromontane Forest ecosystem using different methods and algorithms to retrieve two major remote sensing -essential biodiversity variables (RS-EBV) which are related and are also the major determinants of biological and ecosystem stability

Mapping Migratory Bird Prevalence Using Remote Sensing Data Fusion

This is the publisher’s final pdf. The published article is copyrighted by the Public Library of Science and can be found at: http://www.plosone.org/home.action.Background: Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. \ud \ud Methodology and Principal Findings: A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy ("fusion") models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. \ud \ud Conclusion and Significance: Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level

Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward

Ecosystem structure, especially vertical vegetation structure, is one of the six essential biodiversity variable classes and is an important aspect of habitat heterogeneity, affecting species distributions and diversity by providing shelter, foraging, and nesting sites. Point clouds from airborne laser scanning (ALS) can be used to derive such detailed information on vegetation structure. However, public agencies usually only provide digital elevation models, which do not provide information on vertical vegetation structure. Calculating vertical structure variables from ALS point clouds requires extensive data processing and remote sensing skills that most ecologists do not have. However, such information on vegetation structure is extremely valuable for many analyses of habitat use and species distribution. We here propose 10 variables that should be easily accessible to researchers and stakeholders through national data portals. In addition, we argue for a consistent selection of variables and their systematic testing, which would allow for continuous improvement of such a list to keep it up-to-date with the latest evidence. This initiative is particularly needed not only to advance ecological and biodiversity research by providing valuable open datasets but also to guide potential users in the face of increasing availability of global vegetation structure products

Gather Round the Tree: Woody Aboveground Biomass Increases Animal Presence and Species Richness in a Tropical Forest-Savanna Ecotone

Boundaries between ecosystems are often biodiversity hotspots with relatively high vulnerability to global change. The boundary between tropical rainforest and savanna ecosystems in the Amazon presents an ecotone that is undergoing a shift in ecosystem structure, as a warming climate promotes the expansion of grassland. How animal communities in the Amazon will respond to changes in ecosystem structure is a crucial unanswered question with implications for the many ecosystem services that animals provide, from a food source for Indigenous people to seed dispersal for vulnerable tree populations. Recent modeling work has forecasted that faunal savannization will occur in the Amazon, as savanna-dwelling animals replace forest specialists. However, empirical data to test these forecasts has remained scarce, due to the need for large-scale data across local and regional forest-savanna gradients. To overcome this difficulty, we quantified associations between terrestrial vertebrates and ecosystem structure using replicated camera traps across a forest-savanna ecotone in central Guyana. To capture continuous gradients in woody biomass across the ecotone, we paired radar-derived measurements of aboveground biomass from Phased Array-type L-band Synthetic Aperture Radar (PALSAR) with animal species presence at camera trap sites, including \u3e54,000 individual photos. We hypothesized that different animal species communities would emerge in sites with different levels of aboveground biomass, representing forest and savanna specialists. We tested this hypothesis with hierarchical Bayesian models for animal species detection and species richness across our study landscapes. Our results did not support the hypothesis that there is a guild of savanna specialists with increased presence in sites with low aboveground biomass. Instead, nearly all (54 out of 56) species showed increased probability of detection in sites with higher aboveground biomass. Consequently, overall species richness was significantly related to aboveground biomass, including a median proportional increase in species richness of 90.0% (CI: 21.57 to 200.0%) for every kiloton of biomass at a site. These results suggest that woody structure plays a critical role in supporting animal species richness at the Amazonian forest-savanna ecotone, including non-forest tree cover such as bush islands, gallery forest, and isolated trees. Ongoing declines in tree cover will likely have detrimental impacts across most groups of animal species. Without landscape conservation strategies to maintain tree cover at the forest-savanna boundary, climate change could have severe consequences for Amazonian animal populations

Integrating forest structural diversity measurement into ecological research

The measurement of forest structure has evolved steadily due to advances in technology, methodology, and theory. Such advances have greatly increased our capacity to describe key forest structural elements and resulted in a range of measurement approaches from traditional analog tools such as measurement tapes to highly derived and computationally intensive methods such as advanced remote sensing tools (e.g., lidar, radar). This assortment of measurement approaches results in structural metrics unique to each method, with the caveat that metrics may be biased or constrained by the measurement approach taken. While forest structural diversity (FSD) metrics foster novel research opportunities, understanding how they are measured or derived, limitations of the measurement approach taken, as well as their biological interpretation is crucial for proper application. We review the measurement of forest structure and structural diversity—an umbrella term that includes quantification of the distribution of functional and biotic components of forests. We consider how and where these approaches can be used, the role of technology in measuring structure, how measurement impacts extend beyond research, and current limitations and potential opportunities for future research

Remote Sensing for Management of Invasive Plants in Great Lakes Coastal Wetlands

Great Lakes coastal wetlands are some of the most crucial ecosystems to biodiversity in the Great Lakes Basin, yet suffer increasing degradation due to invasive plants. Wetland plant invasions can be controlled in their initial stages, but early detection of invasive plants using field surveys are often untenable due to budget constraints. Remote sensing techniques offer solutions to management objectives during the early stages of invasion on a landscape scale due to their ability to cheaply create spatially explicit information about plant distributions. Some invasive plants, such as Typha x. glauca, are conspicuous on a large scale, and can be mapped to their current extent using new satellite and modeling techniques. Inconspicuous invasive plants however, such as Hydrocharis morsus-ranae, may be undetectable by remote sensing sources and require predictive strategies. In this thesis I explored the use of remote sensing in the management of a conspicuous and inconspicuous invasive wetland plants in the St. Mary’s River, MI. I successfully classified the current extent of conspicuous Typha x. glauca and other wetland vegetation types to provide spatially explicit maps for early detection and management and examined methods that can be adapted for use in emergent wetlands worldwide. The habitat suitability of inconspicuous Hydrocharis morsus-ranae was also determined using novel fine-scale habitat covariates determined from lidar and radar. Habitat covariates derived from these sources should see wider use in species distribution modeling, particularly with wetland plants, to create better predictions of invasive plant expansions. Implementation of new and upcoming remote sensing data sources and methods will allow for better invasive wetland plant management at greater spatial and temporal scales than field studies alone