Ian Thornhill

Local and landscape scale determinants of macroinvertebrate assemblages and their conservation value in ponds across an urban land-use gradient

Urbanisation represents a growing threat to natural communities across the globe. Small aquatic habitats such as ponds are especially vulnerable and are often poorly protected by legislation. Many ponds are threatened by development and pollution from the surrounding landscape, yet their biodiversity and conservation value remain poorly described. Here we report the results of a survey of 30 ponds along an urban land-use gradient in the West Midlands, UK. We outline the environmental conditions of these urban ponds to identify which local and landscape scale environmental variables determine the biodiversity and conservation value of the macroinvertebrate assemblages in the ponds. Cluster analysis identified four groups of ponds with contrasting macroinvertebrate assemblages reflecting differences in macrophyte cover, nutrient status, riparian shading, the nature of the pond edge, surrounding land-use and the availability of other wetland habitats. Pond conservation status varied markedly across the sites. The richest macroinvertebrate assemblages with high conservation value were found in ponds with complex macrophyte stands and floating vegetation with low nutrient concentrations and little surrounding urban land. The most impoverished assemblages were found in highly urban ponds with hard-engineered edges, heavy shading and nutrient rich waters. A random forest classification model revealed that local factors usually had primacy over landscape scale factors in determining pond conservation value, and constitute a priority focus for management.

The application of graph theory and percolation analysis for assessing change in the spatial configuration of pond networks

Pond networks support high levels of biodiversity when compared to other freshwater ecosystems such as rivers, lakes and streams. The persistence of species in these small, sometimes ephemeral, aquatic habitats depends on the dispersal of individuals among ponds in the landscape. However, the number of ponds across the landscape is at a historical low as urbanisation and intensified agricultural practices have led to a substantial loss of ponds (nodes in the pond network) over more than a century. Here, we examine the extent and drivers of pond loss in a heavily urbanised landscape (Birmingham, UK) over 105 years and determine how pond loss influences key structural properties of the pond network using graph theoretic approaches. Specifically, we calculated minimum spanning trees (MST) and performed percolation analyses to determine changes in both the spatial configuration and resilience of the pond network through time. Pond numbers declined by 82% between ca1904 and 2009, such that pond density decreased from 7.1 km-2 to 1.3 km-2. The MST analyses revealed increased distance between ponds in the network (i.e. edge length increased) by up to 49% over the 105-year period, indicating that ponds in the modern landscape (2009) were considerably more isolated, with fewer neighbours. This study demonstrates that graph theory has an excellent potential to inform the management of pond networks in order to support ecological communities that are less vulnerable to environmental change.

Prioritising local action for water quality improvement using citizen science; a study across three major metropolitan areas of China

Streams in urban areas are prone to degradation. While urbanization-induced poor water quality is a widely observed and well documented phenomenon, the mechanism to pinpoint local drivers of urban stream degradation, and their relative influence on water quality, is still lacking. Utilizing data from the citizen science project FreshWater Watch, we use a machine learning approach to identify key indicators, potential drivers, and potential controls to water quality across the metropolitan areas of Shanghai, Guangzhou and Hong Kong. Partial dependencies were examined to establish the direction of relationships between predictors and water quality. A random forest classification model indicated that predictors of stream water colour (drivers related to artificial land coverage and agricultural land use coverage) and potential controls related to the presence of bankside vegetation were found to be important in identifying basins with degraded water quality conditions, based on individual measurements of turbidity and nutrient (N-NO3 and P-PO4) concentrations.

Getting the full picture: Assessing the complementarity of citizen science and agency monitoring data

While the role of citizen science in engaging the public and providing large-scale datasets has been demonstrated, the nature of and potential for this science to supplement environmental monitoring efforts by government agencies has not yet been fully explored. To this end, the present study investigates the complementarity of a citizen science programme to agency monitoring of water quality. The Environment Agency (EA) is the governmental public body responsible for, among other duties, managing and monitoring water quality and water resources in England. FreshWater Watch (FWW) is a global citizen science project that supports community monitoring of freshwater quality. FWW and EA data were assessed for their spatio-temporal complementarity by comparing the geographical and seasonal coverage of nitrate (N-NO3) sampling across the River Thames catchment by the respective campaigns between spring 2013 and winter 2015. The analysis reveals that FWW citizen science-collected data complements EA data by filling in both gaps in the spatial and temporal coverage as well as gaps in waterbody type and size. In addition, partial spatio-temporal overlap in sampling efforts by the two actors is discovered, but EA sampling is found to be more consistent than FWW sampling. Statistical analyses indicate that regardless of broader geographical overlap in sampling effort, FWW sampling sites are associated with a lower stream order and water bodies of smaller surface areas than EA sampling sites. FWW also samples more still-water body sites than the EA. As a possible result of such differences in sampling tendencies, nitrate concentrations, a measure of water quality, are lower for FWW sites than EA sites. These findings strongly indicate that citizen science has clear potential to complement agency monitoring efforts by generating information on freshwater ecosystems that would otherwise be under reported.