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There has been an increasing interest in urban forests and the levels of biodiversity they contain. Currently there are no spatially explicit maps of tree species richness in urban areas. This research tests and identifies GIS and remote sensing metrics (climate, area, productivity, three-dimensional structure) hypothesized to be associated with species richness in native forests and identifies methods that can be applied to predict and map tree species richness in cities. We quantified tree species richness, floristic composition, and structure in 28 1-ha plots in the city of Los Angeles. Climate and remote sensing metrics from high-resolution aerial imagery (10 cm), QuickBird (60 cm), Landsat (30 m), MODIS (250 m), and airborne lidar (2 m) were collected for each plot. There were 1208 individual stems and 108 trees identified to species. Species richness ranged from 2 to 31 species per ha and averaged 17 species per ha. Tree canopy cover from QuickBird explained the highest portion of variance (54%) in tree species richness followed by NDVI from Landsat (42%). Tree species richness can be higher in residential urban forests than native forests in the United States. Spatially explicit species richness maps at 1 ha can be created and tested for cities in order to identify both hotspots and coldspots of tree species richness and changes in species richness over time.

Aim To determine if habitat diversity, as estimated by climatic and topographic variables, can predict patterns of orchid diversity on different islands and archipelagos with similar explanatory power to biogeographical variables, such as area, isolation and age of an island. Location Sixty-three islands on eight archipelagos (Solomon Islands, Vanuatu, New Caledonia, Fiji, Samoa, Tonga, Niue and the Cook Islands) in the south-west Pacific. Methods For each island, we determined the orchid species present, age, area, isolation, and indicators of topographic heterogeneity and climatic variability. We then determined the power of various biogeographical, climatic and topographic variables to predict the number of indigenous and endemic species on archipelagos, and on islands within archipelagos, using generalized linear models ( GLMs) and generalized linear mixed models ( GLMMs) respectively. Results We identified a total of 552 species in 110 genera. Area was the only significant biogeographical variable for predicting patterns of orchid species diversity on archipelagos and islands. However, climatic and topographic predictors of habitat diversity performed similarly well. The range in curvature was the best indicator of species richness from the topographic variables, while the range of temperature was the best climatic predictor. These key variables were often strongly correlated with area. Main conclusions Climatic and topographic variables are useful indicators of habitat diversity. The high explanatory power of area and climatic and topographic predictors, and the strong correlation among these variables, suggests that increasing habitat diversity with increasing area may be the major driver of the species-area relationship. Using climatic and topographic variables as predictors of species richness therefore allows determining the key environmental factors and processes driving species diversity. [ABSTRACT FROM AUTHOR]

To identify forests of high priority for conservation in tropical dry forests of New Caledonia, Fiji, the Marquesas and Hawaii, we examined patterns of woody plant species richness (total, native and endemic) and threatened species (IUCN categorization and density) at the stand level, using Gentry's transect method. There were associations between total, native and endemic plant species richness in all four Pacific dry forest regions but we found no significant association with the presence or density of species listed on the IUCN Red List. Dry forests in New Caledonia and Hawaii merit the highest conservation priority in the Pacific, based on level of endemism and number of threatened species. The study sites that merit high conservation priority are Metzdorf, Nekoro and Pindai, in New Caledonia, Kokee and Kaupulehu, in Hawaii, and Vatia, in Fiji. New Caledonia and Fiji have a small dry forest extent and protected area extent compared with other dry forests in biodiversity hotspots. Although we identified priority areas for dry forest conservation, more comparative plot data, presence/absence data in fragments and regional geographical data are needed to adequately manage and protect dry forests in the Pacific. [ABSTRACT FROM AUTHOR]

Aim We examine variation in woody plant species richness and endemism within tropical dry forest on oceanic islands and determine what climatic and biogeographic metrics best explain native species richness and endemism across archipelagos, islands and plots. Location Oceanic islands in the Pacific. Methods Stand-level sampling (0.1 ha) at 35 different dry forest sites across 16 islands, and five archipelagos (New Caledonia, Fiji, Marquesas, Marianas and Hawaii). Descriptors of native species richness and endemism were calculated at the plot, island and archipelago level. Biogeographic and climate metrics at the archipelago, island and plot level were drawn from the literature and computer databases. The effects of biogeographic and climate metrics were investigated using linear mixed-effects models. Results Dry forests of New Caledonia and Fiji had the highest native species richness, while New Caledonia and Hawaii had the highest endemism. Native species richness and endemism within tropical dry forests on oceanic islands are primarily influenced by biogeographic metrics, especially isolation of the archipelago, and not climatic metrics. Most variance in native species richness and endemism (60% and 64%) is at the archipelago level compared with the island (8%, 16%) and plot (32%, 15%) level. At the island level, species richness in tropical dry forest is affected by precipitation, while island area significantly affects endemism. The area of forest fragments is an important predictor of native species richness and endemism in plots. Main conclusions Although dry forests in the Pacific have been exceptionally deforested and degraded, high native species richness and endemism remains in a number of forest fragments. Biogeographic metrics explain most of the variance in native species richness and endemism across scales, while climatic metrics are important at the island level. First-order assessments of native richness and endemism at the archipelago, island and stand-level are possible for forest types on oceanic archipelagos. [ABSTRACT FROM AUTHOR]

Most plant species are non-randomly distributed across environmental gradients in light, water, and nutrients. In tropical forests, these gradients result from biophysical processes related to the structure of the canopy and terrain, but how does species richness in tropical forests vary over such gradients, and can remote sensing capture this variation? Using airborne lidar, we tested the extent to which variation in tree species richness is statistically explained by lidar-measured structural variation in canopy height and terrain in the extensively studied, stem-mapped 50-ha plot on Barro Colorado Island (BCI), Panama. We detected differences in species richness associated with variation in canopy height and topography across spatial scales ranging from 0.01-ha to 1.0-ha. However, species richness was most strongly associated with structural variation at the 1.0-ha scale. We developed a predictive generalized least squares model of species richness at the 1.0-ha scale (R2 = 0.479, RMSE = 8.3 species) using the mean and standard deviation of canopy height, mean elevation, and terrain curvature. The model demonstrates that lidar-derived measures of forest and terrain structure can capture a significant fraction of observed variation in tree species richness in tropical forests on local-scales. [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) derived from remote sensing is a common explanatory variable inputted in correlative biodiversity models in the form of descriptive statistics summarizing complex time series. Here, we hypothesized that a single meaningful remotely-sensed scene can provide better prediction of species richness than any usual multi-scene statistics. We tested this idea using a 15-year time series of six-day composite MODIS NDVI data combined with field measurements of tree species richness in the tropical biodiversity hotspot of New Caledonia. Although some overall, seasonal, annual and monthly statistics appeared to successfully correlate with tree species richness in New Caledonia, a range of individual scenes were found to provide significantly better predictions of both the overall tree species richness (|r| = 0.68) and the richness of large trees (|r| = 0.91). A preliminary screening of the NDVI-species richness relationship within each time step can therefore be an effective and straightforward way to maximize the accuracy of NDVI-based correlative biodiversity models. [ABSTRACT FROM AUTHOR]