Drivers of plant community structure on San Clemente Island

Drivers of plant community structure on San Clemente Island

A third paper from our collaboration with Scott Loss and post-doc Shishir Paudel at Oklahoma State University has been published in Ecosphere.  This paper analyzes the vegetation data collected as Shishir searched for invasive earthworms on the island.

Determinants of native and non-native plant community structure on an oceanic island

Shishir Paudel, Juan C. Benavides, Beau MacDonald, Travis Longcore, Gail W. T. Wilson, and Scott R. Loss

Understanding the relative importance of environmental and anthropogenic factors in driving plant community structure, including relative dominance of native and non-native species, helps predict community responses to biological invasions. To assess factors influencing plant communities on San Clemente Island, USA, we conducted an islandwide vegetation survey in which we measured plant species richness and percent cover of native and non-native plants, as well as physical environmental variables, soil chemical properties, abundance of soil microbial functional groups (e.g., arbuscular mycorrhizal fungi [AMF]), and a human disturbance variable (distance to road). We found that total plant species richness decreased with increasing non-native plant cover, soil pH, and AMF abundance. Native plant cover increased with increasing distance to a major paved road and decreased with increasing soil moisture and pH. Non-native plant cover decreased with increasing distance to a major paved road and increased with increasing soil moisture, AMF abundance, and from southwest to northeast, a geographic/climatic gradient that represents increasing moisture. Nonmetric multidimensional scaling ordination further illustrated that trends in plant community composition were correlated with elevation, distance to a major paved road, and soil moisture, organic matter, and ammonium. These results suggest complex effects of physical environmental, soil chemical, and human-related factors on plant community structure on an oceanic island, and moreover, that different factors affect cover of native and non-native plants. Notably, our observation of apparent moisture limitation of non-native plants suggests that, in some contexts, drought conditions can limit plant invasions and may even represent an opportunity for efficient control or eradication of invasive plants. The apparent negative effect of non-native plants on native plant cover and overall plant species richness represents a conservation concern for native biodiversity on oceanic islands and suggests the potential for community reassembly as invasive species increasingly dominate due to anthropogenic disturbances.

Paudel, S., J. C. Benavides, B. MacDonald, T. Longcore, G. W. T. Wilson, and S. R. Loss. 2017. Determinants of native and non-native plant community structure on an oceanic island. Ecosphere 8(9):e01927. 10.1002/ecs2.1927

 
How bright the moon: correcting a propagated figure error in the literature

How bright the moon: correcting a propagated figure error in the literature

Last year, the National Park Service released our report, Artificial night lighting and protected lands: Ecological effects and management approaches (Longcore and Rich 2016), which had been in the works for quite a while. Our colleague Andrej Mohar, while enthusiastic about the report overall, pointed out that a figure that we had included of natural illumination under various conditions was wrong, and by quite a bit, when it came to the levels under a “quarter moon.”  This is the story of that error, where it came from, and its correction.

The figure we used in the 2016 NPS report was re-printed with very minor adjustments from Paul Beier’s chapter in our 2006 edited book, Ecological Consequences of Artificial Night Lighting (Island Press).

BeierOriginal

This figure was published in Ecological Consequences of Artificial Night Lighting and re-published with slight adjustment of the icons in our 2016 NPS report.

The “quarter moon” is shown as causing ground-based illumination of 0.1 lux or thereabouts.  This is obviously and very wrong, and in fact is contradicted in Chapter 1 of the book, which indicates 0.01-0.03 lux for a quarter moon. We should have caught it then, but did not.

Furthermore, a quarter moon, technically, is half of the disk of the moon illuminated, which is one quarter of the entire moon. The icon we used for the “quarter moons” in the figure, however, is that of a crescent moon, with closer to one quarter of the disk  illuminated. We had switched the icon for the “quarter moons” from a depiction of three quarters of the disk illuminated to one quarter of the disk illuminated to match this commonsense language.

Beier (2006) had adapted the graphic from McFarland et al. (1999), who cited their sources as Blaxter (1970) and Brown (1952).  It was McFarland et al. (1999) who introduced the error.  They correctly copied the illumination line from a moon with three-quarters of the face illuminated (a gibbous moon) and incorrectly labeled it as “quarter moons.” They  had three-quarters of the face illuminated in the icon, which was correct, but inconsistent with the label.

McFarland1999

 Illuminations produced by the sun and moon as reported by McFarland et al. (1999). Note that “quarter moons” identifies the same line as a three-quarter moon in Brown (1952) once the conversion from footcandles to lux is made.

Compare the figure from McFarland et al. (1999) with the figure produced by Brown for the Department of Navy in 1952. The Brown figure is in footcandles.

Brown1952.png

Illumination produced by the phases of the moon reported by Brown (1952). The text “1st and 3rd Quarters” indicates the “apparent half moon.”

The 1952 Brown report provides curves of illumination from the moon at four phases: full (phase angle 0º), three-quarters full (phase angle 60º), half full (phase angle 90º, which is technically a “quarter moon”) and one-quarter full (phase angle 120º). In the McFarland et al. (1999) diagram, two curves are given: one for the full moon and one for “quarter moons.” The full moon line is correct. The “quarter moons” line is the same as the three-quarters full moon in Brown (1952), meaning that the lunar disk is three-quarters illuminated (phase angle 60º) and not a “quarter moon” in the sense of one quarter of the disk being illuminated or even a quarter moon meaning one quarter of the entire moon visible and illuminated (half of the disk visible and illuminated).

Unfortunately, this error propagated forward to Beier (2006), our 2016 report (which is being reissued with a corrected figure), and Gaston et al. (2014), who changed the icon to a crescent moon (as we did in our report) and might have shifted the line down slightly, but not enough to be accurate, especially given the icon depiction of a crescent moon and textual description.

 

Gastonetal2014

Illumination from the sun and moon reported by Gaston et al. (2014). The curve for the “quarter moons” is shifted downward slightly but is an order of magnitude higher than a crescent moon (a quarter of the face illuminated) and higher than a quarter moon (half of the face illuminated).

I regret not catching the error when editing Beier’s chapter or when updating the figure in 2016. But Andrej did catch it and now the record can be set straight.

BeierCorrected.png

This corrected version of the figure shows maximum values for a full moon (0.3 lux) and quarter moons (0.03 lux) with the proper icon for quarter moon showing half of the face illuminated.

For the record, the approximate values for the maximum clear-sky illumination from the moon directly overhead at its phases are as follows.

 

Phase Angle

Brown (1952) (lux)

(Krisciunas & Schaefer 1991) (lux)

Full
100% illuminated

0.37

0.423

Gibbous
75% illuminated

60º

0.10

0.071

First and Last Quarter
50% illuminated

90º

0.043

0.028

Crescent
25% illuminated

120º

0.013

0.008

These values can vary based on the distance between the sun and the moon and whether the moon is waxing or waning because of the differing characteristics of the face of the moon. Illuminations this high are unlikely to occur under most circumstances, especially at temperate latitudes. A working estimate of illumination from the full moon is closer to 0.1 lux on the ground than the ~0.4 lux potential maximum illumination, a fact that has been recently discussed by Kyba et al. (2017).

Travis Longcore, Ph.D.
August 5, 2017

Literature Cited

Beier, P. 2006. Effects of artificial night lighting on terrestrial mammals. Pages 19–42 in C. Rich, and T. Longcore, editors. Ecological consequences of artificial night lighting. Island Press, Washington, D.C.

Blaxter, J. H. S. 1970. Light, Animals, Fishes. Pages 213–230 in O. Kinne, editor. Marine ecology: a comprehensive integrated treatise on life in oceans and coastal waters. Wiley-Interscience, London.

Brown, D. R. 1952. Natural illumination charts. Research and Development Project NS 714-100. Pages 1–11, 43 plates. Department of the Navy, Bureau of Ships, Washington, D.C.

Gaston, K. J., J. P. Duffy, S. Gaston, J. Bennie, and T. W. Davies. 2014. Human alteration of natural light cycles: causes and ecological consequences. Oecologia 176:917–931.

Krisciunas, K., and B. E. Schaefer. 1991. A model of the brightness of moonlight. Publications of the Astronomical Society of the Pacific 103:1033–1039.

Kyba, C., A. Mohar, and T. Posch. 2017. How bright is moonlight? Astronomy & Geophysics 58:1.31–31.32.

Longcore, T., and C. Rich. 2016. Artificial night lighting and protected lands: Ecological effects and management approaches. Natural Resource Report NPS/NRSS/NSNS/NRR—2016/1213. National Park Service, Fort Collins, Colorado.

McFarland, W., C. Wahl, T. Suchanek, and F. McAlary. 1999. The behavior of animals around twilight with emphasis on coral reef communities. Pages 583–628 in S. N. Archer, M. B. A. Djamgoz, E. R. Loew, J. C. Partridge, and S. Vallerga, editors. Adaptive mechanisms in the ecology of vision. Kluwer Academic Publishers, Dordrecht.

 

CubeSats to measure light pollution

CubeSats to measure light pollution

I had the fortune of being able to offer some examples of environmental applications in a paper by Dee Pack and Brian Hardy from Aerospace Corporation for the Small Satellite Conference this summer in Utah.  We (mostly they) show the feasibility of using small satellites to measure upward radiance from Earth at night, with examples ranging from the Middle East to Southern California.  I’m glad to have offered the concept of “darkest path” modeling for wildlife connectivity and perspectives on the usefulness of spectral information at this scale. The work we have been doing with the VIIRS Day-Night Band shows up for the identification of a very bright greenhouse on the Oxnard Plain.  Here is the citation, abstract, and download link.

Pack, D. W., B. S. Hardy, and T. Longcore. 2017. Studying Earth at night from CubeSats. Proceedings of the 31st Annual AIAA/USU Conference on Small Satellites, Leo Missions, SSC17-WK-35.

This paper presents examples of the latest imaging data of the Earth at night from multiple CubeSat platforms. Beginning in 2012, with AeroCube-4, The Aerospace Corporation has launched multiple CubeSat platforms in different orbits equipped with a common suite of CMOS sensors. Originally designed as utility cameras to assist with attitude control system studies and star sensor development, we have been using these simple camera sensors to image the Earth at night since 2014. Our initial work focused on observing nighttime urban lights and global gas flare signals at higher resolution than is possible with the VIIRS sensor. To achieve optimum sensitivity and resolution, orbital motion is compensated for via the use of on-board reaction wheels to perform point-and-stare experiments, often with multiple frame exposures as the sensor moves in orbit. Ground sample distances for these systems range from approximately 100 to 130 meters for the narrow-field-of-view cameras, to 500 meters for the medium-field-of-view cameras. In our initial work, we demonstrated that CMOS sensors flown on AeroCube satellites can achieve a nighttime light detection sensitivity on the order of 20 nW-cm-2-sr-1. This resolution and sensitivity allows for detection of urban lighting, road networks, major infrastructure illumination, natural gas flares, and other phenomena of interest. For wide-area surveys, we can also program our cameras to observe regions of interest and co-add pixels to reduce the data bandwidth. This allows for a greater number of frames to be collected and downloaded. These results may then be used to task later satellite passes. Here, we present new examples of our nighttime Earth observation studies using CubeSats. These include: 1) detecting urban growth and change via repeat imaging, 2) investigating the utility of color observations, 3) spotting major sources of light pollution, 4) studying urban-wildland interface regions where lighting may be important to understanding wildlife corridors, 5) imaging lightning and cloud cover at night using wide-area imaging, 6) observations of the very bright lights of fishing boats, and 7) observing other interesting natural phenomenon, including airglow emissions, and the streaking caused by proton strikes in the South Atlantic Anomaly. Our ongoing work includes utilizing a diversity in overpass times from multiple satellites to observe nighttime scenes, imaging high-latitude cities not optimally accessed by the international space station’s cameras, and building a catalogue of observations of rapidly developing megacities and global infrastructure nodes. Data from CMOS sensors flown in common on 5 different AeroCubes in 4 different orbits have been collected. Our results show that enhanced CubeSat sensors can improve mapping of the human footprint in targeted regions via nighttime lights and contribute to better monitoring of: urban growth, light pollution, energy usage, the urban-wildland interface, the improvement of electrical power grids in developing countries, light-induced fisheries, and oil industry flare activity. Future CubeSat sensors should be able to contribute to nightlights monitoring efforts by organizations such as NOAA, NASA, ESA, the World Bank and others, and offer low-cost options for nighttime studies.

Spatial and Temporal Analysis of Nighttime Lighting In and Around National Parks

Shanetal2017ParkLight

Yu Chuan Shan, Ben Banet, and I have been working the past couple of years on developing a monthly database of upward radiance from within and buffers around all of the National Park units in the United States.  They are presenting the research today at the USC undergraduate research symposium.  The results presented only scratch the surface of what we can do to analyze this high-resolution database over space and time.

Shan also put together a website to walk through the project.

The poster can be downloaded here. Please cite as:

Shan, Yu Chuan, Ben Banet, and Travis Longcore. 2017. Spatial and Temporal Analysis of Nighttime Lighting In and Around National Parks. USC Undergraduate Symposium for Scholarly and Creative Work (Los Angeles, April 12, 2017).

Healthy Urbanism in Polluted Cities

usc_parkspollutionobesity-e1478555797146

 

Colleagues at USC Environmental Health Centers are putting together a 1-day conference titled Parks, Pollution, and Obesity. I’ve been awarded a USC Architecture Graduate Research Scholar grant to work with this group to bridge between the public health researchers and landscape architects and urbanists. The premise is as follows.

Awareness among landscape architects, urban designers, and urbanists of the need for green spaces to promote physical activity and combat obesity is high and provision of recreational opportunities in park-poor communities is perceived as an unmitigated good. The adverse health consequences of breathing polluted air is likewise well known, but generally assumed to apply to respiratory diseases such as asthma and lung cancer. Recent research, however, has documented that exposure to air pollution can result directly in increased propensity for obesity, through exposure to particulates and chemicals that disrupt metabolism and promote fat accumulation (McConnell et al. 2016). Air pollution is also linked epidemiologically with diabetes and cardiovascular disease. Urbanists therefore face a challenge of balancing the benefits of a landscape that promotes activity and provides green space in park-poor neighborhoods, and the adverse impacts of exposure to air pollution, which is exacerbated during exercise. Planners and designers need high-quality information to guide location and attributes of green spaces if the benefits are to outweigh the harms.

MLA student Nina Mross will be working with us as a Graduate Research Scholar to review the efforts made within landscape architecture and urbanism to address air quality concerns, describe case studies of such efforts, and illustrate the best practices that arise from the April conference.

Reference

McConnell, R., F. Gilliland, M. Goran, H. Allayee, A. Hricko, and S. Mittelman. 2016. Does near-roadway air pollution contribute to childhood obesity? Pediatric Obesity 11:1–3.