Geology topics

Rachel Sprague

Publications and source records attributed to Rachel Sprague.

3 recordsLinked to original sources

USGS2026

Mortality in the Hawaiian Short-eared Owl (Pueo, Asio flammeus sandwichensis): Causes and spatial trends

Trends in wildlife mortality can be particularly challenging to identify in low-density species where mortalities are less likely to be detected or documented. The Pueo, or Hawaiian Short-eared Owl ( Asio flammeus sandwichensis ), is the only native raptor that breeds on all the main Hawaiian Islands. The Pueo is state-listed as endangered on the island of Oʻahu, but drivers of population dynamics are not well known, including causes of mortality. In this study, existing records from organizations across the Hawaiian Islands were compiled to evaluate the causes of mortality in Pueo. Here, 242 unique records spanning the years 1993 to 2024 from state, federal, and private organizations were evaluated for causes of Pueo death. Trauma due to collisions with vehicles, wind turbines, fences, and other human-made objects was the leading detected cause of death (62%) followed by emaciation (13%) and disease (6%), with 19% of deaths due to unknown causes. The islands of Hawaiʻi and Kauaʻi, both with relatively low human populations, had the highest total number of reported mortalities (n = 77, 32%). The highest number of recorded mortalities was in the month of July (16%), and mortalities were generally higher from June through August. Although our study has detection biases, our results are consistent with global studies suggesting that blunt force trauma from vehicles, wind turbines, and other infrastructure are potentially important sources of mortality in raptors. Similar to other studies, rodenticide effects and infectious disease were potentially underestimated as sources of mortality, because most birds did not undergo a full necropsy. Further, there may be confounding variables due to ad hoc data collection. Development of a state-wide system for reporting and recording wildlife mortality along with future studies that include necropsies may improve our ability to discern which sources of mortality may be mitigated.

Hawaii↗

USGS2024

Using systematic conservation planning to recover climate resilient habitat for threatened and endangered species while retaining areas of cultural importance

The effective management of at-risk species often requires fine-scale actions by natural resource managers. However, balancing these actions with concurrent land uses is challenging, particularly when compounded by the interplay of climate shifts, and escalating wildland–urban interface conflicts. We used spatial prioritization tools designed for biodiversity conservation to help resource managers on the Island of Lānaʻi prioritize mutually exclusive land use objectives: endangered species recovery and subsistence and recreational hunting. We weighed the current and anticipated future distributions of threatened and endangered plant species against the distribution of non-native game mammals to plan for species recovery more effectively. Prioritization results identified multiple footprints that could support recovery of all endangered species targets in climate resilient areas while retaining the majority of existing hunting areas. However, very little native vegetation was retained in conservation footprints without deliberate inclusion, which increased footprint area by 268%. Scenarios which prioritized contiguous conservation areas also dramatically increased conservation footprint area, although these scenarios may reduce associated fencing costs. This work demonstrates how spatial prioritization may guide localized species recovery efforts by supporting long-term conservation planning that addresses anticipated climate-driven increases in conflict between conservation and other land uses, with clear applicability beyond Lānaʻi.

Conservation↗

USGS2024

Fine-resolution land cover mapping over large and mountainous areas for Lāna‘i, Hawaii using posterior probabilities, and expert knowledge

The task of accurately mapping species-specific vegetation cover in remote and topographically complex regions like those found in Hawaiʻi presents unique challenges. This study leverages a machine learning approach to accurately classify vegetation into fine species-specific classes across the island of Lāna‘i, Hawaii, offering a novel methodology for tackling such challenges. Utilizing high-resolution WordView-2 satellite imagery, a neural network classifier and a custom lidar-based geometric correction, we introduced two new approaches to refine our high-resolution land cover classifications. This included the implementation of prior-based adjustments to class posterior probabilities to enhance land cover classification accuracy. Moreover, we developed mixed hierarchical classification maps that use class posterior probabilities to identify, at the pixel level, the finest land cover class that meets a user-defined confidence threshold. The resulting high-resolution land cover map for Lāna‘i captures the rich diversity and distribution of native and invasive plant species with high overall accuracy, generally exceeding 95%, based on independent ground control data. The capacity to produce wall-to-wall species-level vegetation maps provides a new window into monitoring vegetation dynamics on Lāna‘i and similarly remote and topographically complex regions, and contributes to our broader understanding of ecosystem responses to invasive species, climatic changes, and land management practices such as erosion and sediment control planning. Our approach offers a blueprint for similar efforts in other complex and remote ecosystems.

Hawaii↗