A probability-based multiscale model to quantify the impact of hydropeaking on mesohabitat suitability in rivers

One of the negative effects of hydropower on river environment includes rapid changes in flow and habitat conditions. Any sudden flow change could force fish to move towards a refuge area in a short period of time, causing serious disturbances in the life cycle of the fish. A probability-based multiscale model was developed to quantify the impact of hydropeaking on habitat suitability for two fish species. The model used habitat preference curves, river flow and depth to develop the suitability maps. The suitability index maps reveal that habitat suitability deteriorates as flow increases in this part of the river. The probability model showed that, on average, suitability indices are higher for adult grayling than juvenile trout in hydropeaking events in the studied area. In addition, the life stages of fish determine their response to the sudden flow change. The method developed shows the potential to be used in river management and the evaluation of hydropeaking impacts in river systems affected by hydropower.

Habitat Suitability, Population Structure and Conservation Status of Pinanga arinasae (Arecaceae), an Endemic Palm in Bali Island, Indonesia

The conservation of species should be based on knowledge of habitat requirements, population structure and conservation status. This knowledge is quite important to design conservation areas for species and to promote long-term persistence. In this study, we investigated habitat suitability, population size structure and conservation status of Pinanga arinasae in Bali. Plots with palms and adjacent areas with no palms were sampled to characterize key habitat variables. Habitat suitability was modeled using Artificial Neural Network (ANN) and Random Forest (RF) methods. The population size structure was characterized by counting and measuring the height and reproductive status of the individuals found in plots. Furthermore, we assessed the extinction risk of the species using the IUCN Red List Criteria. The ANN variables that best explained occurrence were litter depth, elevation, canopy openness and slope. The RF variables that best explained the data were elevation, litter depth, slope, and aspect. Both ANN and RF are robust models that can be used to predict the occurrence of P. arinasae. The population size structure included many seedlings, but juvenile and mature individuals were found in relatively small numbers. Based on the findings, we proposed Endangered B1+B2ab(i,ii,iii,v); D as the conservation status of P. arinasae.