David G. Armanini

Incorporating traits in aquatic biomonitoring to enhance causal diagnosis and prediction

The linkage of trait responses to stressor gradients has potential to expand biomonitoring approaches beyond traditional taxonomically based assessments that identify ecological effect to provide a causal diagnosis. Traits-based information may have several advantages over taxonomically based methods. These include providing mechanistic linkages of biotic responses to environmental conditions, consistent descriptors or metrics across broad spatial scales, more seasonal stability compared with taxonomic measures, and seamless integration of traits-based analysis into assessment programs. A traits-based biomonitoring approach does not require a new biomonitoring framework, because contemporary biomonitoring programs gather the basic site-by-species composition matrices required to link community data to the traits database. Impediments to the adoption of traits-based biomonitoring relate to the availability, consistency, and applicability of existing trait data. For example, traits generalizations among taxa across biogeographical regions are rare, and no consensus exists relative to the required taxonomic resolution and methodology for traits assessment. Similarly, we must determine if traits form suites that are related to particular stressor effects, and whether significant variation of traits occurs among allopatric populations. Finally, to realize the potential of traits-based approaches in biomonitoring, a concerted effort to standardize terminology is required, along with the establishment of protocols to ease the sharing and merging of broad, geographical trait information.

GenGIS 2: geospatial analysis of traditional and genetic biodiversity, with new gradient algorithms and an extensible plugin framework.

GenGIS is free and open source software designed to integrate biodiversity data with a digital map and information about geography and habitat. While originally developed with microbial community analyses and phylogeography in mind, GenGIS has been applied to a wide range of datasets. A key feature of GenGIS is the ability to test geographic axes that can correspond to routes of migration or gradients that influence community similarity. Here we introduce GenGIS version 2, which extends the linear gradient tests introduced in the first version to allow comprehensive testing of all possible linear geographic axes. GenGIS v2 also includes a new plugin framework that supports the development and use of graphically driven analysis packages: initial plugins include implementations of linear regression and the Mantel test, calculations of alpha-diversity (e.g., Shannon Index) for all samples, and geographic visualizations of dissimilarity matrices. We have also implemented a recently published method for biomonitoring reference condition analysis (RCA), which compares observed species richness and diversity to predicted values to determine whether a given site has been impacted. The newest version of GenGIS supports vector data in addition to raster files. We demonstrate the new features of GenGIS by performing a full gradient analysis of an Australian kangaroo apple data set, by using plugins and embedded statistical commands to analyze human microbiome sample data, and by applying RCA to a set of samples from Atlantic Canada. GenGIS release versions, tutorials and documentation are freely available at http://kiwi.cs.dal.ca/GenGIS, and source code is available at https://github.com/beiko-lab/gengis.

The lentic-lotic character of Mediterranean rivers and its importance to aquatic invertebrate communities

Hydromorphological features are crucial in structuring habitats for freshwater organisms. The quantification of these variables is often performed through accurate measuring or detailed estimation, but their assessment is not always feasible for river management purposes. Economic and time constraints often lead to difficulty in creating simple summaries of collected data for practical use. The Lentic–lotic River Descriptor (LRD) was developed to identify the character of a river site in terms of local hydraulic conditions. Information about the presence of flow types, channel substrates, in-stream vegetation, organic debris and artificial features is included in its calculation. The main aim of this paper is to investigate whether the lentic–lotic character of a river site, as summarized with the LRD descriptor, is relevant to aquatic invertebrate communities in nearly natural river sites. Invertebrate data were collected with multi-habitat, proportional sampling and hydromorphological information was gained by applying the CARAVAGGIO method (river habitat survey technique) in the field. The dataset was generated from High or Good ecological status river sites located in Mediterranean areas of Italy. Correspondence Analysis was performed to relate the invertebrate community structure to a set of catchment-scale, reach-scale and chemical environmental variables. The results of the multivariate analysis indicate that LRD provides a persuasive explanation of the most important axis of variation in benthic data. This paper also presents the optimal LRD range for a set of invertebrate taxa, accompanied by a short discussion of their potential use in conservation issues.

Establishing Standards and Assessment Criteria for Ecological Instream Flow Needs in Agricultural Regions of Canada

Agricultural land use can place heavy demands on regional water resources, strongly influencing the quantity and timing of water flows needed to sustain natural ecosystems. The effects of agricultural practices on streamflow conditions are multifaceted, as they also contribute to the severity of impacts arising from other stressors within the river ecosystem. Thus, river scientists need to determine the quantity of water required to sustain important aquatic ecosystem components and ecological services, to support wise apportionment of water for agricultural use. It is now apparent that arbitrarily defined minimum flows are inadequate for this task because the complex habitat requirements of the biota, which underpin the structure and function of a river ecosystem, are strongly influenced by predictable temporal variations in flow. We present an alternative framework for establishing a first-level, regional ecological instream flow needs standard based on adoption of the Indicators of Hydrologic Alteration/Range of Variability Approach as a broadly applicable hydrological assessment tool, coupling this to the Canadian Ecological Flow Index which assesses ecological responses to hydrological alteration. By explicitly incorporating a new field-based ecological assessment tool for small agricultural streams, we provide a necessary verification of altered hydrology that is broadly applicable within Canada and essential to ensure the continuous feedback between the application of flow management criteria and ecological condition.

Towards generalised reference condition models for environmental assessment: a case study on rivers in Atlantic Canada

Evaluation of the ecological status of river sites in Canada is supported by building models using the reference condition approach. However, geography, data scarcity and inter-operability constraints have frustrated attempts to monitor national-scale status and trends. This issue is particularly true in Atlantic Canada, where no ecological assessment system is currently available. Here, we present a reference condition model based on the River Invertebrate Prediction and Classification System approach with regional-scale applicability. To achieve this, we used biological monitoring data collected from wadeable streams across Atlantic Canada together with freely available, nationally consistent geographic information system (GIS) environmental data layers. For the first time, we demonstrated that it is possible to use data generated from different studies, even when collected using different sampling methods, to generate a robust predictive model. This model was successfully generated and tested using GIS-based rather than local habitat variables and showed improved performance when compared to a null model. In addition, ecological quality ratio data derived from the model responded to observed stressors in a test dataset. Implications for future large-scale implementation of river biomonitoring using a standardised approach with global application are presented.