Geographical ecology of dry forest tree communities in the West Indies

© 2018 The Authors. Journal of Biogeography Published by John Wiley & Sons Ltd Aim: Seasonally dry tropical forest (SDTF) of the Caribbean Islands (primarily West Indies) is floristically distinct from Neotropical SDTF in Central and South America. We evaluate whether tree species composition was associated with climatic gradients or geographical distance. Turnover (dissimilarity) in species composition of different islands or among more distant sites would suggest communities structured by speciation and dispersal limitations. A nested pattern would be consistent with a steep resource gradient. Correlation of species composition with climatic variation would suggest communities structured by broad-scale environmental filtering. Location: The West Indies (The Bahamas, Cuba, Hispaniola, Jamaica, Puerto Rico, US Virgin Islands, Guadeloupe, Martinique, St. Lucia), Providencia (Colombia), south Florida (USA) and Florida Keys (USA). Taxon: Seed plants—woody taxa (primarily trees). Methods: We compiled 572 plots from 23 surveys conducted between 1969 and 2016. Hierarchical clustering of species in plots, and indicator species analysis for the resulting groups of sites, identified geographical patterns of turnover in species composition. Nonparametric analysis of variance, applied to principal components of bioclimatic variables, determined the degree of covariation in climate with location. Nestedness versus turnover in species composition was evaluated using beta diversity partitioning. Generalized dissimilarity modelling partitioned the effect of climate versus geographical distance on species composition. Results: Despite a set of commonly occurring species, SDTF tree community composition was distinct among islands and was characterized by spatial turnover on climatic gradients that covaried with geographical gradients. Greater Antillean islands were characterized by endemic indicator species. Northern subtropical areas supported distinct, rather than nested, SDTF communities in spite of low levels of endemism. Main conclusions: The SDTF species composition was correlated with climatic variation. SDTF on large Greater Antillean islands (Hispaniola, Jamaica and Cuba) was characterized by endemic species, consistent with their geological history and the biogeography of plant lineages. These results suggest that both environmental filtering and speciation shape Caribbean SDTF tree communities

Plant diversity patterns in neotropical dry forests and their conservation implications

This is the author accepted manuscript. The final version is available from American Association for the Advancement of Science via the DOI in this record.Seasonally dry tropical forests are distributed across Latin America and the Caribbean and are highly threatened, with less than 10% of their original extent remaining in many countries. Using 835 inventories covering 4660 species of woody plants, we show marked floristic turnover among inventories and regions, which may be higher than in other neotropical biomes, such as savanna. Such high floristic turnover indicates that numerous conservation areas across many countries will be needed to protect the full diversity of tropical dry forests. Our results provide a scientific framework within which national decision-makers can contextualize the floristic significance of their dry forest at a regional and continental scale.This paper is the result of the Latin American and Caribbean Seasonally Dry Tropical Forest Floristic Network (DRYFLOR), which has been supported at the Royal Botanic Garden Edinburgh by a Leverhulme Trust International Network Grant (IN-074). This work was also supported by the U.K. Natural Environment Research Council grant NE/I028122/1; Colciencias Ph.D. scholarship 529; Synthesys Programme GBTAF-2824; the NSF (NSF 1118340 and 1118369); the Instituto Humboldt (IAvH)–Red colombiana de investigación y monitoreo en bosque seco; the Inter-American Institute for Global Change Research (IAI; Tropi-Dry, CRN2-021, funded by NSF GEO 0452325); Universidad Nacional de Rosario (UNR); and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). The data reported in this paper are available at www.dryflor.info. R.T.P. conceived the study. M.P., A.O.-F., K.B.-R., R.T.P., and J.W. designed the DRYFLOR database system. K.B.-R. and K.G.D. carried out most analyses. K.B.-R. R.T.P., and K.G.D. wrote the manuscript with substantial input from A.D.-S., R.L.-P., A.O.-F., D.P., C.Q., and R.R. All the authors contributed data, discussed further analyses, and commented on various versions of the manuscript. K.B.-R. thanks G. Galeano who introduced her to dry forest research. We thank J. L. Marcelo, I. Huamantupa, C. Reynel, S. Palacios, and A. Daza for help with fieldwork and data entry in Peru

Predictions of Tropical Forest Biomass and Biomass Growth Based on Stand Height or Canopy Area Are Improved by Landsat-Scale Phenology across Puerto Rico and the U.S. Virgin Islands

Remotely-sensed estimates of forest biomass are usually based on various measurements of canopy height, area, volume or texture, as derived from LiDAR, radar or fine spatial resolution imagery. These measurements are then calibrated to estimates of stand biomass that are primarily based on tree stem diameters. Although humid tropical forest seasonality can have low amplitudes compared with temperate regions, seasonal variations in growth-related factors like temperature, humidity, rainfall, wind speed and day length affect both tropical forest deciduousness and tree height-diameter relationships. Consequently, seasonal patterns in spectral measures of canopy greenness derived from satellite imagery should be related to tree height-diameter relationships and hence to estimates of forest biomass or biomass growth that are based on stand height or canopy area. In this study, we tested whether satellite image-based measures of tropical forest phenology, as estimated by indices of seasonal patterns in canopy greenness constructed from Landsat satellite images, can explain the variability in forest deciduousness, forest biomass and net biomass growth after already accounting for stand height or canopy area. Models of forest biomass that added phenology variables to structural variables similar to those that can be estimated by LiDAR or very high-spatial resolution imagery, like canopy height and crown area, explained up to 12% more variation in biomass. Adding phenology to structural variables explained up to 25% more variation in Net Biomass Growth (NBG). In all of the models, phenology contributed more as interaction terms than as single-effect terms. In addition, models run on only fully-forested plots performed better than models that included partially-forested plots. For forest NBG, the models produced better results when only those plots with a positive growth, from Inventory Cycle 1 to Inventory Cycle 2, were analyzed, as compared to models that included all plot

Data from: Regional variation in Caribbean dry forest tree species composition

How does tree species composition vary in relation to geographical and environmental gradients in a globally rare tropical/subtropical broadleaf dry forest community in the Caribbean? We analyzed data from 153 Forest Inventory and Analysis (FIA) plots from Puerto Rico and the U.S. Virgin Islands (USVI), along with 42 plots that we sampled in the Bahamian Archipelago (on Abaco and Eleuthera Islands). FIA data were collected using published protocols. In the Bahamian Archipelago, we recorded terrain and landscape variables, and identified to species and measured the diameter of all stems ≥5 cm at 1.3 m height in 10 m radius plots. All data were analyzed using clustering, ordination, and indicator species analysis at regional and local scales. Regionally, the largest cluster group included over half of all plots and comprised plots from all three island groups. Indicator species were native Bursera simaruba (Burseraceae) and Metopium toxiferum (Anacardiaceae). Species composition was similar to dry forests throughout the region based on published studies. Other groups we identified at the regional scale consisted of many Puerto Rico and USVI plots that were dominated by non-native species, documenting the widespread nature of novel ecosystems. At the local scale the Bahamian data clustered into two main groups corresponding largely to the two islands sampled, a pattern consistent with the latitudinal aridity gradient. Bahamian dry forests share previously undocumented compositional similarity with native-dominated dry forests found throughout the Caribbean, but they lack extensive post-disturbance novel dry forests dominated by non-native trees found in the Greater Antilles

Characterization of dry-season phenology in tropical forests by reconstructing cloud-free Landsat time series

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Multiscale predictors of small tree survival across a heterogeneous tropical landscape

Uncertainties about controls on tree mortality make forest responses to land-use and climate change difficult to predict. We tracked biomass of tree functional groups in tropical forest inventories across Puerto Rico and the U.S. Virgin Islands, and with random forests we ranked 86 potential predictors of small tree survival (young or mature stems 2.5–12.6 cm diameter at breast height). Forests span dry to cloud forests, range in age, geology and past land use and experienced severe drought and storms. When excluding species as a predictor, top predictors are tree crown ratio and height, two to three species traits and stand to regional factors reflecting local disturbance and the system state (widespread recovery, drought, hurricanes). Native species, and species with denser wood, taller maximum height, or medium typical height survive longer, but short trees and species survive hurricanes better. Trees survive longer in older stands and with less disturbed canopies, harsher geoclimates (dry, edaphically dry, e.g., serpentine substrates, and highest-elevation cloud forest), or in intervals removed from hurricanes. Satellite image phenology and bands, even from past decades, are top predictors, being sensitive to vegetation type and disturbance. Covariation between stand-level species traits and geoclimate, disturbance and neighboring species types may explain why most neighbor variables, including introduced vs. native species, had low or no importance, despite univariate correlations with survival. As forests recovered from a hurricane in 1998 and earlier deforestation, small trees of introduced species, which on average have lighter wood, died at twice the rate of natives. After hurricanes in 2017, the total biomass of trees ≥12.7 cm dbh of the introduced species Spathodea campanulata spiked, suggesting that more frequent hurricanes might perpetuate this light-wooded species commonness. If hurricane recovery favors light-wooded species while drought favors others, climate change influences on forest composition and ecosystem services may depend on the frequency and severity of extreme climate events

Characterization of Dry-Season Phenology in Tropical Forests by Reconstructing Cloud-Free Landsat Time Series

Fine-resolution satellite imagery is needed for characterizing dry-season phenology in tropical forests since many tropical forests are very spatially heterogeneous due to their diverse species and environmental background. However, fine-resolution satellite imagery, such as Landsat, has a 16-day revisit cycle that makes it hard to obtain a high-quality vegetation index time series due to persistent clouds in tropical regions. To solve this challenge, this study explored the feasibility of employing a series of advanced technologies for reconstructing a high-quality Landsat time series from 2005 to 2009 for detecting dry-season phenology in tropical forests; Puerto Rico was selected as a testbed. We combined bidirectional reflectance distribution function (BRDF) correction, cloud and shadow screening, and contaminated pixel interpolation to process the raw Landsat time series and developed a thresholding method to extract 15 phenology metrics. The cloud-masked and gap-filled reconstructed images were tested with simulated clouds. In addition, the derived phenology metrics for grassland and forest in the tropical dry forest zone of Puerto Rico were evaluated with ground observations from PhenoCam data and field plots. Results show that clouds and cloud shadows are more accurately detected than the Landsat cloud quality assessment (QA) band, and that data gaps resulting from those clouds and shadows can be accurately reconstructed (R2 = 0.89). In the tropical dry forest zone, the detected phenology dates (such as greenup, browndown, and dry-season length) generally agree with the PhenoCam observations (R2 = 0.69), and Landsat-based phenology is better than MODIS-based phenology for modeling aboveground biomass and leaf area index collected in field plots (plot size is roughly equivalent to a 3 × 3 Landsat pixels). This study suggests that the Landsat time series can be used to characterize the dry-season phenology of tropical forests after careful processing, which will help to improve our understanding of vegetation–climate interactions at fine scales in tropical forests

Characterization of Dry-Season Phenology in Tropical Forests by Reconstructing Cloud-Free Landsat Time Series

Fine-resolution satellite imagery is needed for characterizing dry-season phenology in tropical forests since many tropical forests are very spatially heterogeneous due to their diverse species and environmental background. However, fine-resolution satellite imagery, such as Landsat, has a 16-day revisit cycle that makes it hard to obtain a high-quality vegetation index time series due to persistent clouds in tropical regions. To solve this challenge, this study explored the feasibility of employing a series of advanced technologies for reconstructing a high-quality Landsat time series from 2005 to 2009 for detecting dry-season phenology in tropical forests; Puerto Rico was selected as a testbed. We combined bidirectional reflectance distribution function (BRDF) correction, cloud and shadow screening, and contaminated pixel interpolation to process the raw Landsat time series and developed a thresholding method to extract 15 phenology metrics. The cloud-masked and gap-filled reconstructed images were tested with simulated clouds. In addition, the derived phenology metrics for grassland and forest in the tropical dry forest zone of Puerto Rico were evaluated with ground observations from PhenoCam data and field plots. Results show that clouds and cloud shadows are more accurately detected than the Landsat cloud quality assessment (QA) band, and that data gaps resulting from those clouds and shadows can be accurately reconstructed (R2 = 0.89). In the tropical dry forest zone, the detected phenology dates (such as greenup, browndown, and dry-season length) generally agree with the PhenoCam observations (R2 = 0.69), and Landsat-based phenology is better than MODIS-based phenology for modeling aboveground biomass and leaf area index collected in field plots (plot size is roughly equivalent to a 3 × 3 Landsat pixels). This study suggests that the Landsat time series can be used to characterize the dry-season phenology of tropical forests after careful processing, which will help to improve our understanding of vegetation–climate interactions at fine scales in tropical forests