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Premise of the study: The function of most ecosystems has been altered by human activities. To asses the recovery of plant communities, we must evaluate the recovery of plant functional traits. The seasonally dry tropical forest (SDTF), a highly threatened ecosystem, is assumed to recover relatively quickly from disturbance, but an integrated evaluation of recovery in floristic, structural, and functional terms has not been performed. In this study we aimed to (a) compare SDTF plant functional, floristic, and structural change along succession: (b) identify tree functional groups; and (c) explore the spectral properties of different successional stages. Methods: Across a SDTF successional gradient, we evaluated the change of species composition, vegetation structure, and leaf spectral reflectance and functional traits (related to water use, light acquisition, nutrient conservation, and CO2 acquisition) of 25 abundant tree species. Key results: A complete recovery of SDTF takes longer than the time period inferred from floristic or structural data. Plant functional traits changed along succession from those that maximize photoprotection and heat dissipation in early succession, where temperature is an environmental constraint, to those that enhance light acquisition in late succession, where light may be limiting. A spectral indicator of plant photosynthetic performance (photochemical reflectance index) discriminated between early and late succession. This constitutes a foundation for further exploration of remote sensing technologies for studying tropical succession. Conclusions: A functional approach should be incorporated as a regular descriptor of forest succession because it provides a richer understanding of vegetation dynamics than is offered by either the floristic or structural approach alone. [ABSTRACT FROM AUTHOR]

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Copyright of Biotropica is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, we investigated the use of Bayesian networks for inferring tropical dry forest leaf area index (LAI) from satellite imagery in dry and wet seasons. LAI was chosen as the variable of interest because leaf area is the exchange surface between the photosynthetically active component of the canopy and the atmosphere. Initial network estimates were obtained from ground truth plot data with known forest structure, LAI, and satellite reflectance in the red and near-infrared bands (as observed by the Landsat 7 Enhanced Thematic Mapper Plus sensor). We tested the performance of the Bayesian networks with scoring rules and also with confidence and surprise scores. We evaluated the networks on a per-pixel basis and created both LAI maps of the study area as well predicted the probability maps for the highest LAI states. Results not only demonstrate the predictive power of a Bayesian network but also its explanatory power which is far beyond what is typically available with current pixel classifier approaches such as spectral vegetation indices or other approaches such as neural networks. [ABSTRACT FROM AUTHOR]

The article focuses on the need for integrated research for a sustainable future in tropical dry forests. Tropical rain forests have received little or no attention compared with the tropical rainforests. The lack of conservation effort in tropical dry forests is reflected in the fact that few national parks and biological reserves protect and preserve their natural richness. There are only a handful of real biological research stations with a mandate to bridge the gap between ecology and conservation biology in these ecosystems.