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Maybe your Uncle Lester, a determined foe of garden pests, likes to tease about starting a “save the earwigs” campaign. Or a winking colleague proposes a new pressure group, People for the Ethical Treatment of Insects—the acronym is pronounced “petty.” Invertebrate conservation biology is a subject made to order for gibes. Homo sapiens is for the most part a vertebrocentric species, prejudging the spineless classes as near invisible and boring at best, and as ugly, small, mean, indestructible, overfecund disease vectors at worst.
Darwin's finches are well known for their remarkable diversity in beak form and function. Field studies have shown that beaks evolve by natural selection in response to variation in local ecological conditions. We posit a new hypothesis: As a consequence of beak evolution, there have been changes in the structure of finch vocal signals. We base this hypothesis on the discovery that beaks play a functional role in song production in songbirds. Recent field studies provide support for a link between beak morphology and song structure in Darwin's finches, although much remains to be learned. Because song plays a significant role in finch mating dynamics, we suggest that the functional link between beaks and song may have contributed to the process of speciation and adaptive radiation in these birds.
Characterization of ecosystem structure, diversity, and function is increasingly desired at finer spatial and temporal scales than have been derived in the past. Many ecological applications require detailed data representing large spatial extents, but these data are often unavailable or are impractical to gather using field-based techniques. Remote sensing offers an option for collecting data that can represent broad spatial extents with detailed attribute characterizations. Remotely sensed data are also appropriate for use in studies across spatial scales, in conjunction with field-collected data. This article presents the pertinent technical aspects of remote sensing for images at high spatial resolution (i.e., with a pixel size of 16 square meters or less), existing and future options for the processing and analysis of remotely sensed data, and attributes that can be estimated with these data for forest ecosystems.
Remote sensing data provide essential input for today's climate and ecosystem models. It is generally agreed that many model processes are not accurately depicted by current remotely sensed indices of vegetation and that new observational capabilities are needed at different spatial and spectral scales to reduce uncertainty. Recent advances in materials and optics have allowed the development of smaller, more stable, accurately calibrated imaging spectrometers that can quantify biophysical properties on the basis of the spectral absorbing and scattering characteristics of the land surface. Airborne and spaceborne imaging spectrometers, which measure large numbers (hundreds) of narrow spectral bands, are becoming more widely available from government and commercial sources; thus, it is increasingly feasible to use data from imaging spectroscopy for environmental research. In contrast to multispectral sensors, imaging spectroscopy produces quantitative estimates of biophysical absorptions, which can be used to improve scientific understanding of ecosystem functioning and properties. We present the recent advances in imaging spectroscopy and new capabilities for using it to quantify a range of ecological variables.
Remote sensing, geographic information systems, and modeling have combined to produce a virtual explosion of growth in ecological investigations and applications that are explicitly spatial and temporal. Of all remotely sensed data, those acquired by Landsat sensors have played the most pivotal role in spatial and temporal scaling. Modern terrestrial ecology relies on remote sensing for modeling biogeochemical cycles and for characterizing land cover, vegetation biophysical attributes, forest structure, and fragmentation in relation to biodiversity. Given the more than 30-year record of Landsat data, mapping land and vegetation cover change and using the derived surfaces in ecological models is becoming commonplace. In this article, we summarize this large body of work, highlighting the unique role of Landsat.
Until recently, continuous monitoring of global vegetation productivity has not been possible because of technological limitations. This article introduces a new satellite-driven monitor of the global biosphere that regularly computes daily gross primary production (GPP) and annual net primary production (NPP) at 1-kilometer (km) resolution over 109,782,756 km2 of vegetated land surface. We summarize the history of global NPP science, as well as the derivation of this calculation, and current data production activity. The first data on NPP from the EOS (Earth Observing System) MODIS (Moderate Resolution Imaging Spectroradiometer) sensor are presented with different types of validation. We offer examples of how this new type of data set can serve ecological science, land management, and environmental policy. To enhance the use of these data by nonspecialists, we are now producing monthly anomaly maps for GPP and annual NPP that compare the current value with an 18-year average value for each pixel, clearly identifying regions where vegetation growth is higher or lower than normal.
The possibility of global, three-dimensional remote sensing of forest structure with interferometric synthetic aperture radar (InSAR) bears on important forest ecological processes, particularly the carbon cycle. InSAR supplements two-dimensional remote sensing with information in the vertical dimension. Its strengths in potential for global coverage complement those of lidar (light detecting and ranging), which has the potential for high-accuracy vertical profiles over small areas. InSAR derives its sensitivity to forest vertical structure from the differences in signals received by two, spatially separate radar receivers. Estimation of parameters describing vertical structure requires multiple-polarization, multiple-frequency, or multiple-baseline InSAR. Combining InSAR with complementary remote sensing techniques, such as hyperspectral optical imaging and lidar, can enhance vertical-structure estimates and consequent biophysical quantities of importance to ecologists, such as biomass. Future InSAR experiments will supplement recent airborne and spaceborne demonstrations, and together with inputs from ecologists regarding structure, they will suggest designs for future spaceborne strategies for measuring global vegetation structure.
A growing body of research has demonstrated the complementary nature of remote sensing and ecosystem modeling in studies of terrestrial carbon cycling. Whereas remote sensing instruments are designed to capture spatially continuous information on the reflectance properties of landscape and vegetation, models focus on the underlying biogeochemical processes that regulate carbon transformation, often over longer temporal scales. Remote sensing capabilities, developed over the past several decades, now provide regular, high-resolution (10-meter to 1-kilometer) mapping and monitoring of land surface characteristics relevant to modeling, including vegetation type, biomass, stand age class, phenology, leaf area index, and tree height. Integration of these data sets with ecosystem process models and distributed climate data provides a means for regional assessment of carbon fluxes and analysis of the underlying processes affecting them. Applications include monitoring of carbon pools and flux in response to the United Nations Framework Convention on Climate Change.
To date, research on the effects of urbanization, which include reduced biodiversity, has focused on changes at particular sites or along gradients of urbanization. Comparatively little work has investigated changes in biodiversity at any citywide—much less global—scale, and no attempt has been made to quantify such changes in human terms. We have developed a novel data set that reveals a systematic pattern of biodiversity: Within cities worldwide, most residents are concentrated in neighborhoods of impoverished biodiversity. This pattern exists despite substantial biodiversity present in cities overall, and becomes more severe when only native species are considered. As humanity becomes increasingly urban, these findings have a tragic and seldom-considered consequence: Billions of people may lose the opportunity to benefit from or develop an appreciation of nature. Because nearby surroundings shape people's baselines of ecological health, our findings suggest adverse consequences for conservation in general as well as for humans' quality of life if the problem remains uncorrected.