Fish species richness is associated with the availability of landscape components across seasons in the Amazonian floodplain.

Understanding environmental biodiversity drivers in freshwater systems continues to be a fundamental challenge in studies of their fish assemblages. The present study seeks to determine the degree to which landscape variables of Amazonian floodplain lakes influences fish assemblages in these environments. Fish species richness was estimated in 15 Amazonian floodplain lakes during the high and low-water phases and correlated with the areas of four inundated wetland classes: (i) open water, (ii) flooded herbaceous, (iii) flooded shrubs and (iv) flooded forest estimated in different radius circular areas around each sampling site. Data were analyzed using generalized linear models with fish species richness, total and guilds as the dependent variable and estimates of buffered landscape areas as explanatory variables. Our analysis identified the significance of landscape variables in determining the diversity of fish assemblages in Amazonian floodplain lakes. Spatial scale was also identified as a significant determinant of fish diversity as landscape effects were more evident at larger spatial scales. In particular, (1) total species richness was more sensitive to variations in the landscape areas than number of species within guilds and (2) the spatial extent of the wetland class of shrubs was consistently the more influential on fish species diversity.

Organochlorine and trace metal residues in adult southern bent-wing bat (Miniopterus schreibersii bassanii) in southeastern Australia.

In recent times, the apparent population decline of the southern bent-wing bat (Miniopterus schreibersii bassanii) at Bat Cave, Naracoorte has been ascribed to pesticide use in the region, following the finding of organochlorine and orgaonophosphate insecticide residues in bat guano. Adult southern bent-wing bats were collected from Bat Cave and Starlight Cave in 2003. Organochlorine contaminants were detected in all carcass samples: p,p'-DDE was by far the most dominant contaminant with concentrations ranging from 11,000 to 59,000ngg(-1), followed by p,p'-DDT (110-1600ngg(-1)), p,p'-DDD (35-620ngg(-1)), summation operatorPCBs (33-490ngg(-1)), summation operatorchlordane and related compounds (7.9-270ngg(-1)), HCB (1.6-120ngg(-1)), HP epox. (3.1-230ngg(-1)), TCPMOH (3.8-38ngg(-1)), summation operatorHCHs (1.4-9.6ngg(-1)), and TCPMe (0.1-4.2ngg(-1)) (all values on lipid-weight basis). No significant difference in DDE, DDD, DDT, summation operatorDDT, summation operatorPCB, trans-chlordane, heptachlor epoxide, trans-nonachlor, alpha-HCH, beta-HCH, gamma-HCH, TCPMOH or TCPMe concentrations were observed either between sexes within sites, or between sites (p>0.05). However, there were significant differences in HCB and oxychlordane concentrations between sexes and between sites (p<0.05), between site differences in cis-nonachlor concentrations in male bats (p<0.05), and cis-chlordane concentrations between sexes at Starlight Cave, and between males of each site (p<0.05). There were also significant differences in the liver concentrations of some metals between sexes within sites (Ag, Cd, Co, Cu, Pb, Se, Zn), and between sites (Ag, Cd, Co, Cu, Hg, Pb, Se, V, Zn). Clustering or grouping of sites was observed when the OC data was expressed on a lipid-weight basis. These inter-site differences in OC concentrations reflect local exposure over a period of time, and do not unambiguously support any suggestion that we are witnessing incipient speciation. However, for conservation purposes, it may be prudent to assume that there are two sub-populations of M. s. bassani feeding in different locations in this region of southern Australia, rather than the single homogeneous population suggested by genetic studies.

Integrating multibeam backscatter angular response, mosaic and bathymetry data for benthic habitat mapping.

Multibeam echosounders (MBES) are increasingly becoming the tool of choice for marine habitat mapping applications. In turn, the rapid expansion of habitat mapping studies has resulted in a need for automated classification techniques to efficiently map benthic habitats, assess confidence in model outputs, and evaluate the importance of variables driving the patterns observed. The benthic habitat characterisation process often involves the analysis of MBES bathymetry, backscatter mosaic or angular response with observation data providing ground truth. However, studies that make use of the full range of MBES outputs within a single classification process are limited. We present an approach that integrates backscatter angular response with MBES bathymetry, backscatter mosaic and their derivatives in a classification process using a Random Forests (RF) machine-learning algorithm to predict the distribution of benthic biological habitats. This approach includes a method of deriving statistical features from backscatter angular response curves created from MBES data collated within homogeneous regions of a backscatter mosaic. Using the RF algorithm we assess the relative importance of each variable in order to optimise the classification process and simplify models applied. The results showed that the inclusion of the angular response features in the classification process improved the accuracy of the final habitat maps from 88.5% to 93.6%. The RF algorithm identified bathymetry and the angular response mean as the two most important predictors. However, the highest classification rates were only obtained after incorporating additional features derived from bathymetry and the backscatter mosaic. The angular response features were found to be more important to the classification process compared to the backscatter mosaic features. This analysis indicates that integrating angular response information with bathymetry and the backscatter mosaic, along with their derivatives, constitutes an important improvement for studying the distribution of benthic habitats, which is necessary for effective marine spatial planning and resource management.

Hydrologic landscape regionalisation using deductive classification and random forests.

Landscape classification and hydrological regionalisation studies are being increasingly used in ecohydrology to aid in the management and research of aquatic resources. We present a methodology for classifying hydrologic landscapes based on spatial environmental variables by employing non-parametric statistics and hybrid image classification. Our approach differed from previous classifications which have required the use of an a priori spatial unit (e.g. a catchment) which necessarily results in the loss of variability that is known to exist within those units. The use of a simple statistical approach to identify an appropriate number of classes eliminated the need for large amounts of post-hoc testing with different number of groups, or the selection and justification of an arbitrary number. Using statistical clustering, we identified 23 distinct groups within our training dataset. The use of a hybrid classification employing random forests extended this statistical clustering to an area of approximately 228,000 km2 of south-eastern Australia without the need to rely on catchments, landscape units or stream sections. This extension resulted in a highly accurate regionalisation at both 30-m and 2.5-km resolution, and a less-accurate 10-km classification that would be more appropriate for use at a continental scale. A smaller case study, of an area covering 27,000 km2, demonstrated that the method preserved the intra- and inter-catchment variability that is known to exist in local hydrology, based on previous research. Preliminary analysis linking the regionalisation to streamflow indices is promising suggesting that the method could be used to predict streamflow behaviour in ungauged catchments. Our work therefore simplifies current classification frameworks that are becoming more popular in ecohydrology, while better retaining small-scale variability in hydrology, thus enabling future attempts to explain and visualise broad-scale hydrologic trends at the scale of catchments and continents.

The consequences of land use change on nutrient exports: a regional scale assessment in south-west Victoria, Australia.

Estimation of nutrient load production based on multi-temporal remotely sensed land use data for the Glenelg-Hopkins region in south-west Victoria, Australia, is discussed. Changes in land use were mapped using archived Landsat data and computerised classification techniques. Land use change has been rapid in recent history with 16% of the region transformed in the last 22 years. Total nitrogen and phosphorus loads were estimated using an export coefficient model. The analysis demonstrates an increase in modelled nitrogen and phosphorus loadings from 1980 to 2002. Whilst such increases were suspected from past anecdotal and ad-hoc evidence, our modelling estimated the magnitude of such increases and thus demonstrated the enormous potential of using remote sensing and GIS for monitoring regional scale environmental processes.