Unmanned aerial vehicles (UAVs) equipped with multispectral sensors present an opportunity to monitor vegetation with on-demand high spatial and temporal resolution. In this study we use multispectral imagery from quadcopter UAVs to monitor the progression of a water manipulation experiment on a common shrub, Baccharis pilularis (coyote brush) at the Blue Oak Ranch Reserve (BORR) ~20 km east of San Jose, California. We recorded multispectral imagery at several altitudes with nearly hourly intervals to explore the relationship between two common spectral indices, NDVI (normalized difference vegetation index) and NDRE (normalized difference red edge index), leaf water content and water potential as physiological metrics of plant water status, across a gradient of water deficit. An examination of the spatial and temporal thresholds at which water limitations were most detectable revealed that the best separation between levels of water deficit were at higher resolution (lower flying height), and in the morning (NDVI) and early morning (NDRE). We found that both measures were able to identify moisture deficit across treatments; however, NDVI was better able to distinguish between treatments than NDRE and was more positively correlated with field measurements of leaf water content. Finally, we explored how relationships between spectral indices and water status changed when the imagery was scaled to courser resolutions provided by satellite-based imagery (PlanetScope).We found that PlanetScope data was able to capture the overall trend in treatments but unable to capture subtle changes in water content. These kinds of experiments that evaluate the relationship between direct field measurements and UAV camera sensitivity are needed to enable translation of field-based physiology measurements to landscape or regional scales.

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors present an opportunity to monitor vegetation with on-demand high spatial and temporal resolution. In this study, we use multispectral imagery from quadcopter UAVs to monitor the progression of a water manipulation experiment on a common shrub, Baccharis pilularis (coyote brush), at the Blue Oak Ranch Reserve (BORR) near San Jose, California. We recorded multispectral data from the plants at several altitudes with nearly hourly intervals to explore the relationship between two common spectral indices, NDVI and NDRE, and plant water content and water potential, as physiological metrics of plant water status, across a gradient of water deficit. An examination of the spatial and temporal thresholds at which water limitations were most detectable revealed that the best separation between levels of water deficit were at higher resolution (lower flying height), and in the morning (NDVI) and early morning (NDRE). We found that both measures were able to identify moisture deficit in plants and distinguish them from control and watered plants; however, NDVI was better able to distinguish between treatments than NDRE and was more positively correlated with field measurements of plant water content than NDRE. Finally, we explored how relationships between spectral indices and water status changed when the imagery was scaled to courser resolutions provided by satellite-based imagery (PlanetScope) and found that PlanetScope data was able to capture the overall trend in treatments but was not able to capture subtle changes in water content. These kinds of experiments that evaluate the relationship between direct field measurements and UAV camera sensitivity are needed to enable translation of field-based physiology measurements to landscape or regional scales.

Historical data play an important role in our understanding of environmental change and ecosystem dynamics. By lengthening the temporal scale of scientific inquiry, historical data reveal insights into the dynamic nature of ecosystems. However, most historical data has yet to make a full contribution, remaining “dark” and out of reach to the broader scientific community. This article responds to several calls stressing the importance of empirical historical materials and urges their preservation and accessibility. Despite the importance of historical data collections, few standards have emerged to integrate historical dark data into the larger digital data landscape. To encourage greater use of historical data across scientific disciplines it is vital to make data findable, accessible, interoperable, and reusable (e.g., the FAIR principles). In this paper we discuss the potential of historical dark data to contribute to the modern digital ecological data landscape. We do this by focusing on three cases from the University of California field and research stations and the groups that have worked to make historical dark data discoverable. Despite the common goal of maximizing the potential use of these data collections, each case and the methods employed are unique, and showcase varying levels of success in achieving the FAIR principles and shepherding historical data into the twenty-first century.

The increasing occurrence of catastrophic wildfire across the globe threatens public health, community safety, ecosystem functioning, and biodiversity resilience. Wildfire risk is closely connected to shifting climatic trends and their impacts on fuel availability and flammability. Although previous research has explored the connection between meteorological conditions and wildfire probabilities, there remains a substantial gap in understanding the influence of hydrologic drivers, such as groundwater recharge, on wildfire dynamics. Both short- and long-term variations in these variables are crucial in shaping fuel conditions, and significant changes can create environments more prone to severe wildfires. This study focuses on Santa Barbara County to examine the connection between wildfire probability and various environmental factors, including meteorological and hydrological data from 1994 to 2021, topography, vegetation, and proximity to road. Using a random forest (RF) machine learning model and fine-scale data (270 m resolution) we achieved high predictive accuracy in identifying wildfire probability. Our findings confirm the important roles of short-term meteorological conditions, such as mean precipitation 12 months and relative humidity 1 month before a wildfire event, in predicting wildfire occurrence. In addition, our results emphasize the critical contribution of long-term hydrological components, such as mean deviation from the historical normal in actual evapotranspiration and recharge in the years preceding the fire, in influencing wildfire probability. Partial dependence plots from our RF model revealed that both positive and negative deviations of these hydrological variables can increase the likelihood of wildfire by controlling fuel water availability and productivity. These findings are particularly relevant given the increasing extreme weather patterns in southern California, significantly affecting water availability and fuel conditions. This study provides valuable insights into the complex interactions between wildfire occurrence and hydrometeorological conditions. Additionally, the resulting wildfire probability map, can aid in identifying high-risk areas, contributing to enhanced mitigation planning and prevention strategies.

Oak trees are emblematic of California landscapes, they serve as keystone cultural and ecological species and as indicators of natural biological diversity. As historically undeveloped landscapes are increasingly converted to urban environments, endemic oak woodland extent is reduced, which underscores the importance of strategic placement and reintroduction of oaks and woodland landscape for the maintenance of biodiversity and reduction of habitat fragmentation. This paper investigated the effects of human urban development on oak species in California by first modeling historical patterns of richness for eight oak tree species using historical map and plot data from the California Vegetation Type Mapping (VTM) collection. We then examined spatial intersections between hot spots of historical oak richness and modern urban and conservation lands and found that impacts from development and conservation vary by both species and richness. Our findings suggest that the impact of urban development on oaks has been small within the areas of highest oak richness but that areas of highest oak richness are also poorly conserved. Third, we argue that current policy measures are inadequate to conserve oak woodlands and suggest regions to prioritize acquisition of conservation lands as well as examine urban regions where historic centers of oak richness were lost as potential frontiers for oak reintroduction. We argue that urban planning could benefit from the adoption of historical data and modern species distribution modelling techniques primarily used in natural resources and conservation fields to better locate hot spots of species richness, understand where habitats and species have been lost historically and use this evidence as incentive to recover what was lost and preserve what still exists. This adoption of historical data and modern techniques would then serve as a paradigm shift in the way Urban Planners recognize, quantify, and use landscape history in modern built environments.

Rapid declines in biodiversity necessitate global action and spatial tools that can inform how to set and implement action for new conservation targets. GIScience (the science of geographic information systems, or GIS) has proven useful for conservation decision‐making, driving the development of new data, tools, and mapping solutions. Combined with urgent conservation needs, it is an ideal time to examine additional opportunities to advance a geographic approach to conservation. Here, we outline a hierarchy of needs that should be prioritized to ensure that GIScience and geography can support the new, and growing, wave of global conservation targets. Based on a participatory workshop involving conservation and GIS researchers and practitioners, we recommend six actions organized around three themes: platforms, personnel, and partnerships. Those six actions include: (1) build standard platforms for data sharing and maintenance; (2) design predictive models for decision support; (3) develop open educational materials that stress problem solving; (4) infuse co‐design into courses and learning materials; (5) support a GIS Conservation Corp and culture; and (6) sustain engagement across academia and practice. Implementing this agenda will lead to bridged knowledge systems, greater trust in spatial evidence, new funding pathways, and durable connections between spatial scientists, conservationists, and decision‐makers translating geospatial innovation into measurable biodiversity gains.