Climate change and glacier retreat in the French Pyrénées: implications for alpine river ecosystems

Climate change disproportionately threatens alpine river ecosystems due to the strong connections between cryosphere, hydrology and physicochemical habitat. Our general understanding of how these systems will respond to warming is, however, based on conceptual models derived from studies undertaken at relatively small spatial scales. This research utilizes: (i) field data collected from five glacierized river basins in the French Pyrénées; (ii) field based experimentation; and (iii) climate/hydrological modelling, to improve understanding of alpine river ecosystem change. Despite a linear, harsh-begin, physicochemical habitat gradient running from high to low meltwater (snow and ice) contribution, observed benthic macroinvertebrate community level metrics were unimodal (i.e. mid-meltwater peak). Community assembly processes shifted from niche filtering/stochastic (trait convergence) at high meltwater sites, to limiting similarity/stochastic (trait divergence) at low meltwater sites. Benthic macroinvertebrate community structure, feeding interactions and body size spectra were altered when invertebrate predator range expansion was experimentally simulated. Empirical observation (space for time substitution) and statistical modelling both suggest an increase in reach scale diversity (alpha) is likely as glacier cover is lost. However, a reduction in habitat heterogeneity is likely to lead to biotic homogenization (reduced beta diversity) as a specialist high meltwater community is replaced by a more generalist community. The need to consolidate monitoring strategies is highlighted and functional trait profiles are suggested as useful bio-monitoring tools for detecting future change

Low-cost environmental sensor networks:recent advances and future directions

The use of low-cost sensor networks (LCSNs) is becoming increasingly popular in the environmental sciences and the unprecedented monitoring data generated enable research across a wide spectrum of disciplines and applications. However, in particular, non-technical challenges still hinder the broader development and application of LCSNs. This paper reviews the development of LCSNs over the last 15 years, highlighting trends and future opportunities for a diverse range of environmental applications. We found air quality, meteorological and water-related networks were particularly well represented with few studies focusing on sensor networks for ecological systems. Furthermore, we identified bias toward studies that have direct links to human health, safety and livelihoods. These studies were more likely to involve downstream data analytics, visualizations, and multi-stakeholder participation through citizen science initiatives. However, there was a paucity of studies that considered sustainability factors for the development and implementation of LCSNs. Existing LCSNs are largely focused on detecting and mitigating events which have a direct impact on humans such as flooding, air pollution or geo-hazards, while these applications are important there is a need for future development of LCSNs for monitoring ecosystem structure and function. Our findings highlight three distinct opportunities for future research to unleash the full potential of LCSNs: (1) improvement of links between data collection and downstream activities; (2) the potential to broaden the scope of application systems and fields; and (3) to better integrate stakeholder engagement and sustainable operation to enable longer and greater societal impacts

The Autobot-WQ: A portable, low-cost autosampler to provide new insight into urban spatio-temporal water quality dynamics

Urbanization and the increase in urban land cover are growing concerns associated with numerous negative impacts on surface water quality. Currently, many emerging contaminants are difficult to measure with no field deployable sensors currently available. Hence, discrete grab samples are required for subsequent laboratory analysis. To capture the spatiotemporal variability in pollution pulses, autosamplers can be used, but commercial offerings are both expensive and have a large footprint. This can be problematic in urban environments where there is a high density of point source inputs and risk of vandalism or theft. Here, we present a small and robust low-cost autosampler that is ideally suited for deployment in urban environments. The design is based on “off the shelf” open-source hardware components and software and requires no prior engineering, electronics, or computer programming experience to build. The autosampler uses a small peristaltic pump to enable collection of 14 small volume samples (50 mL) and is housed in a small footprint camera case. To illustrate the technology, we present two use cases for rapid sampling of stormwater pulses of: 1) an urban river channel and 2) green roof runoff. When compared with a commercial autosampler, our device showed comparable results and enabled us to capture temporal dynamics in key water quality parameters (e.g., dissolved organic matter) following rain events in an urban stream. Water quality differences associated with differing green roof design/maintenance regimes (managed and unmanaged vegetation) were captured using the autosampler, highlighting how unmanaged vegetation has a greater potential for mitigating the rapid runoff and peaked pollutant inputs associated with impervious surfaces. These two case studies show that our portable autosampler provides capacity to improve understanding of the impact of urban design and infrastructure on water quality and can lead to the development of more effective mitigation solutions. Finally, we discuss opportunities for further technical refinement of our autosampler and applications to improve environmental monitoring. We propose a holistic monitoring approach to address some of the outstanding challenges in urban areas and enable monitoring to shift from discrete point sources towards characterization of catchment or network scale dynamics