Biogeographical analyses to facilitate targeted conservation of orchid diversity hotspots in Costa Rica

Aim: We conduct a biogeographical assessment of orchids in a global biodiversity hotspot to explore their distribution and occurrences of local hotspots while identifying geographic attributes underpinning diversity patterns. We evaluate habitat characteristics associated with orchid diversity hotspots and make comparisons to other centres of orchid diversity to test for global trends. The ultimate goal was to identify an overall set of parameters that effectively characterize critical habitats to target in local and global orchid conservation efforts. Location: Costa Rica; Mesoamerica. Taxon: Orchidaceae. Methods: Data from an extensive set of herbarium records were used to map orchid distributions and to identify diversity hotspots. Hotspot data were combined with geographic attribute data, including environmental and geopolitical variables, and a random forest regression model was utilized to assess the importance of each variable for explaining the distribution of orchid hotspots. A likelihood model was created based on variable importance to identify locations where suitable habitats and unidentified orchid hotspots might occur. Results: Orchids were widely distributed and hotspots occurred primarily in mountainous regions, but occasionally at lower elevations. Precipitation and vegetation cover were the most important predictive variables associated with orchid hotspots. Variable values underpinning Costa Rican orchid hotspots were similar to those reported at other sites worldwide. Models also identified suitable habitats for sustaining orchid diversity that occurred outside of known hotspots and protected areas. Main conclusions: Several orchid diversity hotspots and potentially suitable habitats occur outside of known distributions and/or protected areas. Recognition of these sites and their associated geographic attributes provides clear targets for optimizing orchid conservation efforts in Costa Rica, although certain caveats warrant consideration. Habitats linked with orchid hotspots in Costa Rica were similar to those documented elsewhere, suggesting the existence of a common biogeographical trend regarding critical habitats for orchid conservation in disparate tropical regions.Universidad de Puerto Rico/[]/UPR/Puerto RicoUniversidad de Costa Rica/[]/UCR/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Agroalimentarias::Jardín Botánico Lankester (JBL

Predicting invasions of North American basses in Japan using native range data and a genetic algorithm

Largemouth bass Micropterus salmoides and smallmouth bass M. dolomieu have been introduced into freshwater habitats in Japan, with potentially serious consequences for native fish populations. In this paper we apply the technique of ecological niche modeling using the genetic algorithm for rule-set prediction (GARP) to predict the potential distributions of these two species in Japan. This algorithm constructs a niche model based on point occurrence records and ecological coverages. The model can be visualized in geographic space, yielding a prediction of potential geographic range. The model can then be tested by determining how well independent point occurrence data are predicted according to the criteria of sensitivity and specificity provided by receiver–operator curve analysis. We ground-truthed GARP’s ability to forecast the geographic occurrence of each species in its native range. The predictions were statistically significant for both species (P , 0.001). We projected the niche models onto the Japanese landscape to visualize the potential geographic ranges of both species in Japan. We tested these predictions using known occurrences from introduced populations of largemouth bass, both in the aggregate and by habitat type. All analyses robustly predicted known Japanese occurrences (P , 0.001). The number of smallmouth bass in Japan was too small for statistical tests, but the 10 known occurrences were predicted by the majority of models

Neighborhood disparities in stroke and myocardial infarction mortality: a GIS and spatial scan statistics approach

<p>Abstract</p> <p>Background</p> <p>Stroke and myocardial infarction (MI) are serious public health burdens in the US. These burdens vary by geographic location with the highest mortality risks reported in the southeastern US. While these disparities have been investigated at state and county levels, little is known regarding disparities in risk at lower levels of geography, such as neighborhoods. Therefore, the objective of this study was to investigate spatial patterns of stroke and MI mortality risks in the East Tennessee Appalachian Region so as to identify neighborhoods with the highest risks.</p> <p>Methods</p> <p>Stroke and MI mortality data for the period 1999-2007, obtained free of charge upon request from the Tennessee Department of Health, were aggregated to the census tract (neighborhood) level. Mortality risks were age-standardized by the direct method. To adjust for spatial autocorrelation, population heterogeneity, and variance instability, standardized risks were smoothed using Spatial Empirical Bayesian technique. Spatial clusters of high risks were identified using spatial scan statistics, with a discrete Poisson model adjusted for age and using a 5% scanning window. Significance testing was performed using 999 Monte Carlo permutations. Logistic models were used to investigate neighborhood level socioeconomic and demographic predictors of the identified spatial clusters.</p> <p>Results</p> <p>There were 3,824 stroke deaths and 5,018 MI deaths. Neighborhoods with significantly high mortality risks were identified. Annual stroke mortality risks ranged from 0 to 182 per 100,000 population (median: 55.6), while annual MI mortality risks ranged from 0 to 243 per 100,000 population (median: 65.5). Stroke and MI mortality risks exceeded the state risks of 67.5 and 85.5 in 28% and 32% of the neighborhoods, respectively. Six and ten significant (p < 0.001) spatial clusters of high risk of stroke and MI mortality were identified, respectively. Neighborhoods belonging to high risk clusters of stroke and MI mortality tended to have high proportions of the population with low education attainment.</p> <p>Conclusions</p> <p>These methods for identifying disparities in mortality risks across neighborhoods are useful for identifying high risk communities and for guiding population health programs aimed at addressing health disparities and improving population health.</p