This paper synthesizes the extent and nature of scientific information about how dredging activities potentially affect habitats and key ecological functions supporting recruitment and sustainability of estuarine and ...This paper synthesizes the extent and nature of scientific information about how dredging activities potentially affect habitats and key ecological functions supporting recruitment and sustainability of estuarine and marine environment. Fourteen samples were collected after dredging （2008） from fixed sampling stations. The impact on community was estimated at species level （Foraminifera, Protozoan, using statistical analysis）. The maximum negative effect on benthic foraminifera was reduction by 60%, for species richness and by 50% for diversity. This data were compared with the data obtained before dredging （2006） in a time services spanning 2 years. Its revealed that reestablishment of directly with in less than 3 months of the end of dredging, although affected foraminifera and of physico-chemical substrate characteristic 2 years later there was a considerable improvement of whole faunal community. Statistical treatment was given to the data sets to know the relation among parameters. Before, this type of activity is undertaken, each case should be studied regarding viability, the environmental medium where it will take place, the best time of year, and the type of dredging to be used. Small-patch dredging operations are proposed when ever possible, since they allow a quick readjustment of the initial sediment structure and benthic foraminifera. These findings will help to underpin improved planning of management strategies for dredging operations in India and other countries.展开更多
We tested the prediction that at coarse spatial scales, variables associated with climate, energy, and productivity hy- potheses should be better predictor（s） of bat species richness than those associated with envir...We tested the prediction that at coarse spatial scales, variables associated with climate, energy, and productivity hy- potheses should be better predictor（s） of bat species richness than those associated with environmental heterogeneity. Distribution ranges of 64 bat species were estimated with niche-based models informed by 3629 verified museum specimens. The influence of environmental correlates on bat richness was assessed using ordinary least squares regression （OLS）, simultaneous autoregressive models （SAR）, conditional autoregressive models （CAR）, spatial eigenvector-based filtering models （SEVM）, and Classification and Regression Trees （CART）. To test the assumption of stationarity, Geographically Weighted Regression （GWR） was used. Bat species richness was highest in the eastern parts of southern Africa, particularly in central Zimbabwe and along the western border of Mozambique. We found support for the predictions of both the habitat heterogeneity and climate/productivity/energy hypothe- ses, and as we expected, support varied among bat families and model selection. Richness patterns and predictors of Miniopteridae and Pteropodidae clearly differed from those of other bat families. Altitude range was the only independent variable that was sig- nificant in all models and it was most often the best predictor of bat richness. Standard coefficients of SAR and CAR models were similar to those of OLS models, while those of SEVM models differed. Although GWR indicated that the assumption of stationa- rity was violated, the CART analysis corroborated the findings of the curve-fitting models. Our results identify where additional data on current species ranges, and future conservation action and ecological work are needed.展开更多
文摘This paper synthesizes the extent and nature of scientific information about how dredging activities potentially affect habitats and key ecological functions supporting recruitment and sustainability of estuarine and marine environment. Fourteen samples were collected after dredging （2008） from fixed sampling stations. The impact on community was estimated at species level （Foraminifera, Protozoan, using statistical analysis）. The maximum negative effect on benthic foraminifera was reduction by 60%, for species richness and by 50% for diversity. This data were compared with the data obtained before dredging （2006） in a time services spanning 2 years. Its revealed that reestablishment of directly with in less than 3 months of the end of dredging, although affected foraminifera and of physico-chemical substrate characteristic 2 years later there was a considerable improvement of whole faunal community. Statistical treatment was given to the data sets to know the relation among parameters. Before, this type of activity is undertaken, each case should be studied regarding viability, the environmental medium where it will take place, the best time of year, and the type of dredging to be used. Small-patch dredging operations are proposed when ever possible, since they allow a quick readjustment of the initial sediment structure and benthic foraminifera. These findings will help to underpin improved planning of management strategies for dredging operations in India and other countries.
文摘We tested the prediction that at coarse spatial scales, variables associated with climate, energy, and productivity hy- potheses should be better predictor（s） of bat species richness than those associated with environmental heterogeneity. Distribution ranges of 64 bat species were estimated with niche-based models informed by 3629 verified museum specimens. The influence of environmental correlates on bat richness was assessed using ordinary least squares regression （OLS）, simultaneous autoregressive models （SAR）, conditional autoregressive models （CAR）, spatial eigenvector-based filtering models （SEVM）, and Classification and Regression Trees （CART）. To test the assumption of stationarity, Geographically Weighted Regression （GWR） was used. Bat species richness was highest in the eastern parts of southern Africa, particularly in central Zimbabwe and along the western border of Mozambique. We found support for the predictions of both the habitat heterogeneity and climate/productivity/energy hypothe- ses, and as we expected, support varied among bat families and model selection. Richness patterns and predictors of Miniopteridae and Pteropodidae clearly differed from those of other bat families. Altitude range was the only independent variable that was sig- nificant in all models and it was most often the best predictor of bat richness. Standard coefficients of SAR and CAR models were similar to those of OLS models, while those of SEVM models differed. Although GWR indicated that the assumption of stationa- rity was violated, the CART analysis corroborated the findings of the curve-fitting models. Our results identify where additional data on current species ranges, and future conservation action and ecological work are needed.
