Challenges Using Extrapolated Family-level Macroinvertebrate Metrics in Moderately Disturbed Tropical Streams: a Case-study From Belize

Family-level biotic metrics were originally designed to rapidly assess gross organic pollution effects, but came to be regarded as general measures of stream degradation. Improvements in water quality in developed countries have reignited debate about the limitations of family-level taxonomy to detect subtle change, and is resulting in a shift back towards generic and species-level analysis to assess smaller effects. Although the scale of pollution characterizing past condition of streams in developed countries persists in many developing regions, some areas are still considered to be only moderately disturbed. We sampled streams in Belize to investigate the ability of family-level macroinvertebrate metrics to detect change in stream catchments where less than 30% of forest had been cleared. Where disturbance did not co-vary with natural gradients of change, and in areas characterized by low intensity activities, none of the metrics tested detected significant change, despite evidence of environmental impacts. We highlight the need for further research to clarify the response of metrics to disturbance over a broader study area that allows replication for confounding sources of natural variation. We also recommend research to develop more detailed understanding of the taxonomy and ecology of Neotropical macroinvertebrates to improve the robustness of metric use

Big data opportunities and challenges for assessing multiple stressors across scales in aquatic ecosystems

Aquatic ecosystems are under threat from multiple stressors, which vary in distribution and intensity across temporal and spatial scales. Monitoring and assessment of these ecosystems have historically focussed on collection of physical and chemical information and increasingly include associated observations on biological condition. However, ecosystem assessment is often lacking because the scale and quality of biological observations frequently fail to match those available from physical and chemical measurements. The advent of high-performance computing, coupled with new earth observation platforms, has accelerated the adoption of molecular and remote sensing tools in ecosystem assessment. To assess how emerging science and tools can be applied to study multiple stressors on a large (ecosystem) scale and to facilitate greater integration of approaches among different scientific disciplines, a workshop was held on 10-12 September 2014 at the Sydney Institute of Marine Sciences, Australia. Here we introduce a conceptual framework for assessing multiple stressors across ecosystems using emerging sources of big data and critique a range of available big-data types that could support models for multiple stressors. We define big data as any set or series of data, which is either so large or complex, it becomes difficult to analyse using traditional data analysis methods

Do laboratory salinity tolerances of freshwater animals correspond with their field salinity?

In this paper we compare laboratory-derived acute salinity tolerance (LC50 values) of freshwater macroinvertebrates (range 5.5-76 mS/cm) and fish (range 2.7-82 mS/cm) from southeastern Australia with the salinity from which they have been collected in the field. Only 4% of the macroinvertebrates were collected at salinity levels substantially higher than their 72-h LC50 obtained from directly transferring animals from low salinity water to the water they were tested (direct transfer LC50). This LC50 value was correlated with the maximum salinity at which a species had been collected. For common macroinvertebrates, the maximum field salinity was approximated by the direct transfer 72-h LC50. For adult freshwater fish, 21% of species were collected at salinities substantially greater than their acute direct transfer LC50 and there was a weak relationship between these two variables. Although there was a weak correlation between the direct transfer LC50 of early life stages of freshwater fish and the maximum field salinity, 58% of the field distribution were in higher than their LC50 values. In contrast, LC50 determined from experiments that acclimated adult fish to higher salinity (slow acclimation) provided a better indication of the field distribution: with only one fish species (7%) being in conflict with their maximum field salinity and a strong positive relationship between these variables. This study shows that laboratory measures of acute salinity tolerance can reflect the maximum salinity that macroinvertebrate and fish species inhabit and are consistent with some anecdotal observations from other studies

Patterning, predicting stream macroinvertebrate assemblages in Victoria (Australia) using artificial neural networks and genetic algorithms

Macroinvertebrate assemblages are widely used for biomonitoring of stream ecosystems. Several modern assessment concepts and approaches have been desribed. The so-called referential approach (Parsons and Norris 1996, Marchant et al. 1999, Smith et al. 1999) is based on the comparison of macroinvertebrate communities between potentially impacted sites and reference sites considered to be pristine. Knowing the relationships between environmental variables and macroinvertebrate occurrence at reference sites, it is possible to predict species or taxa, which should occur at the remaining sites in the absence of anthropogenic stress. The ratio of observed/expected (O/E) families is used as a measure for sitespecific ecological conditions. Statistical and computational techniques have been successfully integrated into the referential approach facilitating stream site classification and prediction of macroinvertebrate assemblages. Classification or grouping of macroinvertebrates into assemblages is sometimes criticized as an arbitrary procedure as they are usually distributed in continuous gradients rather than well defined separate groups (Chessman 1999). However in order to deal with large numbers of macroinvertebrate taxa it is often crucial to consider groups instead of individual taxa provided appropriate classification techniques are available. Widely used statistical methods for data classification and ordination are cluster and principal component analysis. Both methods have shortcomings in coping with heterogeneous and nonlinear data, and results can be confounded by outliers and missing data. Artificial neural network (ANN) based classification techniques such as Kohonen or Self- Organizing Maps (SOM) may help to overcome these shortcomings. A number of ecological case studies have shown that SOM are an efficient classification tool (Chon et al. 1996, 2003, Cereghino et al. 2001, Park et al. 2001a, 2003a, Brosse et al. 2001, Giraudel and Lek 2001). ANN as well as genetic algorithms (GA) prove to be appropriate for the prediction of macroinvertebrate and fish assemblages in streams. Multi-layer perceptron ANN were successfully applied to predict the occurrence of stream macroinvertebrates from environmental variables (Walley and Fontama 1998, Schleiter et al. 1999, Pudmenzky et al. 1998, Hoang et al. 2001). GA were used to predict fish distribution from physical characteristics of streams (d'Angelo et al. 1995) and to select input variables of classification tree models predicting benthic macroinvertebrate communities in Belgian watercourses (Goethals et al. 2003). Even though ANN have clearly demonstrated their potential for ecological applications in terms of classification and prediction they store learned models in a highly distributed manner by means of connection weights, which bear little resemblance to human understanding of rules or concepts. By contrast, GA can be used for knowledge discovery by deriving predictive models or rule sets, which can easily be understood (Recknagel 2001). Recknagel et al. (2002) compared applications of ANN and GA in terms of forecasting and understanding of algal blooms in Lake Kasumigaura (Japan). It was demonstrated that models explicitly synthesized by GA not only performed better in seven-days-ahead predictions of algal blooms than ANN models, but provided more transparency for explanation as well. The present paper demonstrates the use of both ANN and GA for the classification and prediction of macroinvertebrate spatial assemblages in the stream system of Victoria (Australia). The stream database contains abundances of macroinvertebrates in conjunction with environmental and stream habitat characteristics. Both ANN and GA are applied in order to best compromise: (i) the discovery and explanation of patterns of macroinvertebrate occurrence within the Victorian landscape, and (ii) the prediction of these patterns from environmental variables. The predictive and explanatory performance of both ANN and GA will also be compared. © Springer-Verlag Berlin Heidelberg 2005